taitanic_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Titanic-Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>788</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. George Hugh</td>\n",
       "      <td>male</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hart, Miss. Eva Miriam</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>F.C.C. 13529</td>\n",
       "      <td>26.250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Bracken, Mr. James H</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220367</td>\n",
       "      <td>13.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>440</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Kvillner, Mr. Johan Henrik Johannesson</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 18723</td>\n",
       "      <td>10.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                    Name  \\\n",
       "787          788         0       3               Rice, Master. George Hugh   \n",
       "874          875         1       2   Abelson, Mrs. Samuel (Hannah Wizosky)   \n",
       "535          536         1       2                  Hart, Miss. Eva Miriam   \n",
       "221          222         0       2                    Bracken, Mr. James H   \n",
       "439          440         0       2  Kvillner, Mr. Johan Henrik Johannesson   \n",
       "\n",
       "        Sex   Age  SibSp  Parch        Ticket    Fare Cabin Embarked  \n",
       "787    male   8.0      4      1        382652  29.125   NaN        Q  \n",
       "874  female  28.0      1      0     P/PP 3381  24.000   NaN        C  \n",
       "535  female   7.0      0      2  F.C.C. 13529  26.250   NaN        S  \n",
       "221    male  27.0      0      0        220367  13.000   NaN        S  \n",
       "439    male  31.0      0      0    C.A. 18723  10.500   NaN        S  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>601</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>243847</td>\n",
       "      <td>27.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ahlin, Mrs. Johan (Johanna Persdotter Larsson)</td>\n",
       "      <td>female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Ellis Anna Maria</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "600          601         1       2   \n",
       "40            41         0       3   \n",
       "119          120         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "600  Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...  female  24.0      2   \n",
       "40      Ahlin, Mrs. Johan (Johanna Persdotter Larsson)  female  40.0      1   \n",
       "119                  Andersson, Miss. Ellis Anna Maria  female   2.0      4   \n",
       "\n",
       "     Parch  Ticket    Fare Cabin Embarked  \n",
       "600      1  243847  27.000   NaN        S  \n",
       "40       0    7546   9.475   NaN        S  \n",
       "119      2  347082  31.275   NaN        S  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debasish Das\\AppData\\Local\\Temp\\ipykernel_8276\\860536840.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"Age\"].fillna(df[\"Age\"].median(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df[\"Age\"].fillna(df[\"Age\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Histogram using Seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='Age', hue='Survived', multiple='stack', palette='coolwarm', kde=False)\n",
    "plt.title('Age Distribution of Passengers by Survival')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin=[0,10,20,40,60,90]\n",
    "lavel=[\"Child\",\"Teen\",\"Adult\",\"Middle_Age\",\"Old\"]\n",
    "df[\"Age group\"]=pd.cut(df[\"Age\"],bins=bin,labels=lavel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   PassengerId  891 non-null    int64   \n",
      " 1   Survived     891 non-null    int64   \n",
      " 2   Pclass       891 non-null    int64   \n",
      " 3   Name         891 non-null    object  \n",
      " 4   Sex          891 non-null    object  \n",
      " 5   Age          891 non-null    float64 \n",
      " 6   SibSp        891 non-null    int64   \n",
      " 7   Parch        891 non-null    int64   \n",
      " 8   Ticket       891 non-null    object  \n",
      " 9   Fare         891 non-null    float64 \n",
      " 10  Cabin        204 non-null    object  \n",
      " 11  Embarked     889 non-null    object  \n",
      " 12  Age group    891 non-null    category\n",
      "dtypes: category(1), float64(2), int64(5), object(5)\n",
      "memory usage: 84.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Age\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df,columns=[\"Age group\"]) # one hot encodeing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cabin column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Cabin\"].isnull().sum()\n",
    "# 687\n",
    "#We don't have enhough data so we are delete the row\n",
    "df.drop(columns=[\"Cabin\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age group_Child</th>\n",
       "      <th>Age group_Teen</th>\n",
       "      <th>Age group_Adult</th>\n",
       "      <th>Age group_Middle_Age</th>\n",
       "      <th>Age group_Old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Leyson, Mr. Robert William Norman</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 29566</td>\n",
       "      <td>10.5</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Coleridge, Mr. Reginald Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>W./C. 14263</td>\n",
       "      <td>10.5</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>527</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Ridsdale, Miss. Lucy</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>W./C. 14258</td>\n",
       "      <td>10.5</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "234          235         0       2  Leyson, Mr. Robert William Norman    male   \n",
       "242          243         0       2    Coleridge, Mr. Reginald Charles    male   \n",
       "526          527         1       2               Ridsdale, Miss. Lucy  female   \n",
       "\n",
       "     SibSp  Parch       Ticket  Fare Embarked  Age group_Child  \\\n",
       "234      0      0   C.A. 29566  10.5        S            False   \n",
       "242      0      0  W./C. 14263  10.5        S            False   \n",
       "526      0      0  W./C. 14258  10.5        S            False   \n",
       "\n",
       "     Age group_Teen  Age group_Adult  Age group_Middle_Age  Age group_Old  \n",
       "234           False             True                 False          False  \n",
       "242           False             True                 False          False  \n",
       "526           False            False                  True          False  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex and Pclass Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "male      577\n",
       "female    314\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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paWrYsKGaNm2qcePG6eeff7a2F+f9GxQUZPO8SpUqkmR9TfNfm3/+rvj6+lr75jty5IjWrVtXYOzw8PBCxy7sPfr000/L09NTt99+uxo0aKDY2Fibyxv/TYMGDWyeWywW1a9f3+Z93LdvX7m6ulrvp0xPT9fq1as1YMCAIq2yGBkZqfXr1ystLU3ffvutYmNj9ccff6h79+7F/nxwcnKy3st4ub59++rChQvWe9zOnTunL7/8Uvfff/9Va/Xz81OXLl1sLu9bunSpnJycbC5XzcvL05tvvqkGDRrI1dVVfn5+qlatmn7++Web9ycA+2HVPgAlbtOmTTp58qSWLFmiJUuWFGhftGiRIiIiTB0z/4b4J598UpGRkYX2udal1X/55Rc5OjpeNeg8/vjj6tGjhz777DOtX79e48eP15QpU7Rp0ya1aNGiSOO4u7tfU53/dKU/2oq60ENJsOeKg1dz5513Wlft+6ePP/5YgwcPVq9evTRu3Dj5+/tb7we7fEbE399fe/bs0fr167V27VqtXbtW8+fP16BBg7Rw4ULrOMeOHdP//d//6auvvtJ//vMfvfnmm5o9e7YefvjhYr1/r/SaGoZh+nXIy8vTXXfdpaeeeqrQ9vxwnq+w92iTJk106NAhrV69WuvWrdOnn36qWbNmacKECYUunlAcVapUUffu3bVo0SJNmDBBK1asUFZWVoGVNv+Nh4eH2rdvr/bt28vPz0+TJk3S2rVrFRMTY/r35fKZocu1adNGtWvX1rJly/TAAw/oiy++0IULF2xmla6kX79+GjJkiPbs2aPmzZtr2bJl6tKli8179ZVXXtH48eP10EMP6cUXX5Svr68cHBz0+OOP2ywQAsB+CFIAStyiRYvk7++vmTNnFmhbuXKlVq1apdmzZ8vd3V3BwcH65ptvdP78eZtZqaNHj9rsV7duXUmSs7Oz9V/RS1JiYqK2bNmisLCwK85I5atXr56eeOIJPfHEEzpy5IiaN2+uadOm6eOPP5Z05WBTHPkzGZcf8/Dhw5L+XslO+t9MRVpams3S4IXN5hS1tuDgYEl/rySWv9JdvkOHDlnbr9Xl4+T/jKW/FxhISEgolZ91vhUrVqhu3bpauXKlzetS2BL9Li4u6tGjh3r06KG8vDw9+uijev/99zV+/HhrAPL19dWQIUM0ZMgQnTt3TnfeeacmTpyohx9+uFTev/mv3dGjR23C/+nTpwvMBNarV0/nzp275rErVaqkvn37qm/fvsrOzlbv3r318ssvKy4u7l+Xz//nZY+GYejo0aNq1qyZzfZBgwapZ8+e2rlzpxYtWqQWLVro5ptvLnbNrVq1kiSdPHlSku3vy+WKM/vZp08fvfXWW8rIyNDSpUtVu3ZttWnT5l/369Wrl4YPH269vO/w4cOKi4uz6bNixQp16tRJc+fOtdmelpZ2xX8cAFC2uLQPQIm6cOGCVq5cqe7du+u+++4r8Bg5cqTOnj1rvRwmMjJSly5d0pw5c6zHyMvLKxDC/P391bFjR73//vvWP4guV9iSyUV15swZ9e/fX7m5uVddxe78+fMFll+uV6+eKleurKysLOu2SpUqFfgjrbj+/PNPmxXdMjIy9OGHH6p58+YKDAy01iDJ5j6XzMxM62zJ5YpaW6tWreTv76/Zs2fbnNvatWt18OBBRUVFFfeUbISHh8vFxUVvv/22zUzL3LlzlZ6eXmLjFCZ/xufycXfs2KH4+HibfqdPn7Z57uDgYP3jP/+1+WcfT09P1a9f39peGu/fLl26yMnJSe+9957N9vzlsy/Xp08fxcfHa/369QXa0tLSlJOT86/j/fMcXVxcFBISIsMwrKsrXs2HH35oc//hihUrdPLkSXXr1s2mX7du3eTn56fXXntNW7ZsKfJs1OWXDF8u/x6s/MtHg4OD5ejoWOC+sFmzZhVpnMv17dtXWVlZWrhwodatW6c+ffoUaT8fHx9FRkZq2bJlWrJkiVxcXNSrVy+bPo6OjgVmH5cvX37N94ICKDnMSAEoUZ9//rnOnj2re+65p9D2Nm3aqFq1alq0aJH69u2rXr166fbbb9cTTzyho0ePqnHjxvr888915swZSbYzKDNnzlS7du3UtGlTDRs2THXr1lVycrLi4+N14sSJIn2/yuHDh/Xxxx/LMAxlZGRo7969Wr58uc6dO6fp06era9euV923S5cu6tOnj0JCQuTk5KRVq1YpOTlZ/fr1s/YLDQ3Ve++9p5deekn169eXv79/gVmdomrYsKGGDh2qnTt3KiAgQPPmzVNycrLmz59v7RMREaGgoCANHTpU48aNk6Ojo+bNm6dq1apZl2U3W5uzs7Nee+01DRkyRB06dFD//v2VnJyst956S7Vr19aYMWOKdT7/VK1aNesS3l27dtU999yjQ4cOadasWbrttttMX9JlRvfu3bVy5Urde++9ioqKUkJCgmbPnq2QkBCbBQQefvhhnTlzRp07d1bNmjX1xx9/6J133lHz5s3VpEkTSVJISIg6duyo0NBQ+fr6ateuXVqxYoXNcuUl8f69XEBAgEaPHq1p06bpnnvuUdeuXbV3716tXbtWfn5+Nr8748aN0+eff67u3btr8ODBCg0NVWZmpvbt26cVK1bo999//9dZjoiICAUGBuqOO+5QQECADh48qHfffVdRUVH/Oosr/T1j165dOw0ZMkTJycmaMWOG6tevb7PQjPT3e69fv35699135ejoqP79+xfp9ejZs6fq1KmjHj16qF69esrMzNTXX3+tL774Qrfddpt69OghSfL29tb999+vd955RxaLRfXq1dPq1auLdQ9Vy5YtVb9+fT333HPKysoq0mV9+fr27asHH3xQs2bNUmRkZIEvmu7evbsmT56sIUOGqG3bttq3b58WLVpkM3MLwM7stVwggBtTjx49DDc3NyMzM/OKfQYPHmw4OzsbqamphmEYxqlTp4wHHnjAqFy5suHt7W0MHjzY2Lp1qyHJWLJkic2+x44dMwYNGmQEBgYazs7Oxk033WR0797dWLFixb/WJsn6cHBwMHx8fIwWLVoYo0ePNvbv31+g/z+XFU5NTTViY2ONxo0bG5UqVTK8vb2N1q1bG8uWLbPZLykpyYiKijIqV65sSLIufZy/rPXOnTsLjHWl5c+joqKM9evXG82aNTNcXV2Nxo0bG8uXLy+w/+7du43WrVsbLi4uRlBQkDF9+vRCj3ml2q60JPTSpUuNFi1aGK6uroavr68xYMAA48SJEzZ9YmJijEqVKhWo6UrLshfm3XffNRo3bmw4OzsbAQEBxogRI4y//vqr0OOZWf78an3z8vKMV155xQgODjZcXV2NFi1aGKtXrzZiYmKM4OBga78VK1YYERERhr+/v/X1HT58uHHy5Elrn5deesm4/fbbDR8fH8Pd3d1o3Lix8fLLLxvZ2dk2Yxbl/Xul90lhP6OcnBxj/PjxRmBgoOHu7m507tzZOHjwoFG1alXj//2//2ez/9mzZ424uDijfv36houLi+Hn52e0bdvWeOONN6x15r/nC1vi//333zfuvPNOo2rVqoarq6tRr149Y9y4cUZ6evqVfxCX1f3JJ58YcXFxhr+/v+Hu7m5ERUUZf/zxR6H7/PDDD4YkIyIi4qrHvtwnn3xi9OvXz6hXr57h7u5uuLm5GSEhIcZzzz1ns+y6Yfz9mRMdHW14eHgYVapUMYYPH2788ssvhS5/Xth7+3LPPfecIcmoX79+oe3/XP48X0ZGhuHu7m5IMj7++OMC7RcvXjSeeOIJo3r16oa7u7txxx13GPHx8QWOx/LngP1YDKMYd60CQCn77LPPdO+99+r777/XHXfcYe9ygOtGWlqaqlSpopdeeumql6qWZ3v37lXz5s314YcfauDAgfYuBwAKxT1SAOzuwoULNs9zc3P1zjvvyMvLSy1btrRTVUD598/fHUmaMWOGJKljx45lW0wJmjNnjjw9PW2WAweA8oZ7pADY3ahRo3ThwgWFhYUpKytLK1eu1LZt2/TKK6+U+FLhwI1k6dKlWrBgge6++255enrq+++/1yeffKKIiIjrcib3iy++0IEDB/TBBx9o5MiRqlSpkr1LAoAr4tI+AHa3ePFiTZs2TUePHtXFixdVv359jRgxwuZGfQAF/fjjj3rqqae0Z88eZWRkKCAgQNHR0XrppZfk6elp7/JMq127tpKTkxUZGamPPvqoSItYAIC9EKQAAAAAwCTukQIAAAAAkwhSAAAAAGASi01IysvL059//qnKlSvbfIEhAAAAgIrFMAydPXtWNWrUkIPDVead7PgdVoZhGMaJEyeMAQMGGL6+voabm5txyy232HwJYV5envXLBt3c3IwuXboYhw8ftjnG6dOnbb7M86GHHjLOnj1b5BqOHz9u80WdPHjw4MGDBw8ePHjwqNiP48ePXzVD2HVG6q+//tIdd9yhTp06ae3atapWrZqOHDmiKlWqWPtMnTpVb7/9thYuXKg6depo/PjxioyM1IEDB+Tm5iZJGjBggE6ePKkNGzbo0qVLGjJkiB555BEtXry4SHXkrwp0/PhxeXl5lfyJAgAAALguZGRkqFatWv+6cqhdV+175plntHXrVn333XeFthuGoRo1auiJJ57Qk08+KUlKT09XQECAFixYoH79+ungwYMKCQnRzp071apVK0nSunXrdPfdd+vEiROqUaPGv9aRkZEhb29vpaenE6QAAACACqyo2cCui018/vnnatWqle6//375+/urRYsWmjNnjrU9ISFBSUlJCg8Pt27z9vZW69atFR8fL0mKj4+Xj4+PNURJUnh4uBwcHLRjx45Cx83KylJGRobNAwAAAACKyq5B6rffftN7772nBg0aaP369RoxYoQee+wxLVy4UJKUlJQkSQoICLDZLyAgwNqWlJQkf39/m3YnJyf5+vpa+/zTlClT5O3tbX3UqlWrpE8NAAAAwA3MrkEqLy9PLVu21CuvvKIWLVrokUce0bBhwzR79uxSHTcuLk7p6enWx/Hjx0t1PAAAAAA3FrsuNlG9enWFhITYbGvSpIk+/fRTSVJgYKAkKTk5WdWrV7f2SU5OVvPmza19UlJSbI6Rk5OjM2fOWPf/J1dXV7m6upbUaQAAAADlUm5uri5dumTvMsoVR0dHOTk5XfPXHtk1SN1xxx06dOiQzbbDhw8rODhYklSnTh0FBgZq48aN1uCUkZGhHTt2aMSIEZKksLAwpaWlaffu3QoNDZUkbdq0SXl5eWrdunXZnQwAAABQjpw7d04nTpyQHdeWK7c8PDxUvXp1ubi4FPsYdg1SY8aMUdu2bfXKK6+oT58++uGHH/TBBx/ogw8+kCRZLBY9/vjjeumll9SgQQPr8uc1atRQr169JP09g9W1a1frJYGXLl3SyJEj1a9fvyKt2AcAAADcaHJzc3XixAl5eHioWrVq1zz7cqMwDEPZ2dk6deqUEhIS1KBBg6t/6e5V2DVI3XbbbVq1apXi4uI0efJk1alTRzNmzNCAAQOsfZ566illZmbqkUceUVpamtq1a6d169ZZv0NKkhYtWqSRI0eqS5cucnBwUHR0tN5++217nBIAAABgd5cuXZJhGKpWrZrc3d3tXU654u7uLmdnZ/3xxx/Kzs62yRVm2PV7pMoLvkcKAAAAN5KLFy8qISFBderUKXZQuJFd7fW5Lr5HCgAAAACuRwQpAAAAADCJIAUAAACgTGzevFkWi0VpaWmlOs7gwYOti9OVFoIUAAAAUMGcOnVKI0aMUFBQkFxdXRUYGKjIyEht3bq1VMdt27atTp48KW9v71IdpyzYddU+AAAAAGUvOjpa2dnZWrhwoerWravk5GRt3LhRp0+fLtbxDMNQbm6unJyuHi9cXFwUGBhYrDHKG2akAAAAgAokLS1N3333nV577TV16tRJwcHBuv322xUXF6d77rlHv//+uywWi/bs2WOzj8Vi0ebNmyX97xK9tWvXKjQ0VK6urpo3b54sFot+/fVXm/HefPNN1atXz2a/tLQ0ZWRkyN3dXWvXrrXpv2rVKlWuXFnnz5+XJB0/flx9+vSRj4+PfH191bNnT/3+++/W/rm5uRo7dqx8fHxUtWpVPfXUU2XyJcQEKQAAAKAC8fT0lKenpz777DNlZWVd07GeeeYZvfrqqzp48KDuu+8+tWrVSosWLbLps2jRIj3wwAMF9vXy8lL37t21ePHiAv179eolDw8PXbp0SZGRkapcubK+++47bd26VZ6enuratauys7MlSdOmTdOCBQs0b948ff/99zpz5oxWrVp1TedVFAQpAAAAoAJxcnLSggULtHDhQvn4+OiOO+7Qs88+q59//tn0sSZPnqy77rpL9erVk6+vrwYMGKBPPvnE2n748GHt3r1bAwYMKHT/AQMG6LPPPrPOPmVkZGjNmjXW/kuXLlVeXp7+85//qGnTpmrSpInmz5+vxMRE6+zYjBkzFBcXp969e6tJkyaaPXt2mdyDRZACAAAAKpjo6Gj9+eef+vzzz9W1a1dt3rxZLVu21IIFC0wdp1WrVjbP+/Xrp99//13bt2+X9PfsUsuWLdW4ceNC97/77rvl7Oyszz//XJL06aefysvLS+Hh4ZKkvXv36ujRo6pcubJ1Js3X11cXL17UsWPHlJ6erpMnT6p169bWYzo5ORWoqzQQpAAAAIAKyM3NTXfddZfGjx+vbdu2afDgwXrhhRfk4PB3RLj8PqNLly4VeoxKlSrZPA8MDFTnzp2tl+stXrz4irNR0t+LT9x33302/fv27WtdtOLcuXMKDQ3Vnj17bB6HDx8u9HLBssSqfeVQYmKiUlNT7V0GAFQ4fn5+CgoKsncZAGAXISEh+uyzz1StWjVJ0smTJ9WiRQtJsll44t8MGDBATz31lPr376/ffvtN/fr1+9f+d911l/bv369NmzbppZdesra1bNlSS5culb+/v7y8vArdv3r16tqxY4fuvPNOSVJOTo52796tli1bFrnm4iBIlTOJiYlq0qSJ9TpRAEDZ8fDw0MGDBwlTAG5op0+f1v3336+HHnpIzZo1U+XKlbVr1y5NnTpVPXv2lLu7u9q0aaNXX31VderUUUpKip5//vkiH793794aMWKERowYoU6dOqlGjRpX7X/nnXcqMDBQAwYMUJ06dWwu0xswYIBef/119ezZU5MnT1bNmjX1xx9/aOXKlXrqqadUs2ZNjR49Wq+++qoaNGigxo0ba/r06aX+hb8SQarcSU1N1fnz5/Xsax8quG4Te5cDABXGH78d1CtPD1JqaipBCsANzdPTU61bt9abb76pY8eO6dKlS6pVq5aGDRumZ599VpI0b948DR06VKGhoWrUqJGmTp2qiIiIIh2/cuXK6tGjh5YtW6Z58+b9a3+LxaL+/ftr6tSpmjBhgk2bh4eHvv32Wz399NPq3bu3zp49q5tuukldunSxzlA98cQTOnnypGJiYuTg4KCHHnpI9957r9LT002+MuZYjLJYZL2cy8jIkLe3t9LT0684ZVhWfvzxR4WGhur95TvVMKR0pyMBAP9z+MCPGn7/bWVyOQgAlLaLFy8qISFBderUkZubm73LKXeu9voUNRuw2AQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJCd7FwAAAADA/hITE5Wamlpm4/n5+SkoKKjMxitpBCkAAACggktMTFSTJk10/vz5MhvTw8NDBw8evG7DFEEKAAAAqOBSU1N1/vx5Pfvahwqu26TUx/vjt4N65elBSk1NNR2kZs6cqddff11JSUm69dZb9c477+j2228vpUqvjCAFAAAAQJIUXLeJGoa0tHcZV7R06VKNHTtWs2fPVuvWrTVjxgxFRkbq0KFD8vf3L9NaWGwCAAAAwHVh+vTpGjZsmIYMGaKQkBDNnj1bHh4emjdvXpnXQpACAAAAUO5lZ2dr9+7dCg8Pt25zcHBQeHi44uPjy7weghQAAACAci81NVW5ubkKCAiw2R4QEKCkpKQyr4cgBQAAAAAmEaQAAAAAlHt+fn5ydHRUcnKyzfbk5GQFBgaWeT0EKQAAAADlnouLi0JDQ7Vx40brtry8PG3cuFFhYWFlXg/LnwMAAACQ9Pf3O5XnccaOHauYmBi1atVKt99+u2bMmKHMzEwNGTKkhCv8dwQpAAAAoILz8/OTh4eHXnl6UJmN6eHhIT8/P1P79O3bV6dOndKECROUlJSk5s2ba926dQUWoCgLBCkAAACgggsKCtLBgweVmppaZmP6+fkpKCjI9H4jR47UyJEjS6EicwhSAAAAABQUFFSsYFNRsdgEAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAm8T1SAAAAAJSYmHhdfCFveUGQAgAAACq4xMRENWnSROfPny+zMT08PHTw4MHrNkwRpAAAAIAKLjU1VefPn9eCSU+qce1apT7er78f1+AX3lBqamqRg9S3336r119/Xbt379bJkye1atUq9erVq3QLvQqCFAAAAABJUuPatdSicX17l1GozMxM3XrrrXrooYfUu3dve5dDkAIAAABQ/nXr1k3dunWzdxlWrNoHAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASq/YBAAAAkPT39zuV13HOnTuno0ePWp8nJCRoz5498vX1tcuX+hKkAAAAgArOz89PHh4eGvzCG2U2poeHh/z8/Ircf9euXerUqZP1+dixYyVJMTExWrBgQUmX968IUgAAAEAFFxQUpIMHDyo1NbXMxvTz8zM1k9SxY0cZhlGKFZlDkAIAAACgoKAgu1wid71isQkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAHCDKk+LM5QnJfG6EKQAAACAG4yjo6MkKTs7286VlE/nz5+XJDk7Oxf7GKzaBwAAANxgnJyc5OHhoVOnTsnZ2VkODsyfSH/PRJ0/f14pKSny8fGxBs7iIEgBAAAANxiLxaLq1asrISFBf/zxh73LKXd8fHwUGBh4TccgSAEAAAA3IBcXFzVo0IDL+/7B2dn5mmai8hGkAAAAgBuUg4OD3Nzc7F3GDcmuF0tOnDhRFovF5tG4cWNr+8WLFxUbG6uqVavK09NT0dHRSk5OtjlGYmKioqKi5OHhIX9/f40bN045OTllfSoAAAAAKhC7z0jdfPPN+vrrr63PnZz+V9KYMWO0Zs0aLV++XN7e3ho5cqR69+6trVu3SpJyc3MVFRWlwMBAbdu2TSdPntSgQYPk7OysV155pczPBQAAAEDFYPcg5eTkVOiNXunp6Zo7d64WL16szp07S5Lmz5+vJk2aaPv27WrTpo2++uorHThwQF9//bUCAgLUvHlzvfjii3r66ac1ceJEubi4FDpmVlaWsrKyrM8zMjJK5+QAAAAA3JDsvg7ikSNHVKNGDdWtW1cDBgxQYmKiJGn37t26dOmSwsPDrX0bN26soKAgxcfHS5Li4+PVtGlTBQQEWPtERkYqIyND+/fvv+KYU6ZMkbe3t/VRq1atUjo7AAAAADciuwap1q1ba8GCBVq3bp3ee+89JSQkqH379jp79qySkpLk4uIiHx8fm30CAgKUlJQkSUpKSrIJUfnt+W1XEhcXp/T0dOvj+PHjJXtiAAAAAG5odr20r1u3btb/btasmVq3bq3g4GAtW7ZM7u7upTauq6urXF1dS+34AAAAAG5sdr+073I+Pj5q2LChjh49qsDAQGVnZystLc2mT3JysvWeqsDAwAKr+OU/v9Yv2AIAAACAKylXQercuXM6duyYqlevrtDQUDk7O2vjxo3W9kOHDikxMVFhYWGSpLCwMO3bt08pKSnWPhs2bJCXl5dCQkLKvH4AAAAAFYNdL+178skn1aNHDwUHB+vPP//UCy+8IEdHR/Xv31/e3t4aOnSoxo4dK19fX3l5eWnUqFEKCwtTmzZtJEkREREKCQnRwIEDNXXqVCUlJen5559XbGwsl+4BAAAAKDV2DVInTpxQ//79dfr0aVWrVk3t2rXT9u3bVa1aNUnSm2++KQcHB0VHRysrK0uRkZGaNWuWdX9HR0etXr1aI0aMUFhYmCpVqqSYmBhNnjzZXqcEAAAAoAKwa5BasmTJVdvd3Nw0c+ZMzZw584p9goOD9eWXX5Z0aQAAAABwReXqHikAAAAAuB4QpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJpWbIPXqq6/KYrHo8ccft267ePGiYmNjVbVqVXl6eio6OlrJyck2+yUmJioqKkoeHh7y9/fXuHHjlJOTU8bVAwAAAKhIykWQ2rlzp95//301a9bMZvuYMWP0xRdfaPny5dqyZYv+/PNP9e7d29qem5urqKgoZWdna9u2bVq4cKEWLFigCRMmlPUpAAAAAKhA7B6kzp07pwEDBmjOnDmqUqWKdXt6errmzp2r6dOnq3PnzgoNDdX8+fO1bds2bd++XZL01Vdf6cCBA/r444/VvHlzdevWTS+++KJmzpyp7Oxse50SAAAAgBuc3YNUbGysoqKiFB4ebrN99+7dunTpks32xo0bKygoSPHx8ZKk+Ph4NW3aVAEBAdY+kZGRysjI0P79+684ZlZWljIyMmweAAAAAFBUTvYcfMmSJfrxxx+1c+fOAm1JSUlycXGRj4+PzfaAgAAlJSVZ+1weovLb89uuZMqUKZo0adI1Vg8AAACgorLbjNTx48c1evRoLVq0SG5ubmU6dlxcnNLT062P48ePl+n4AAAAAK5vdgtSu3fvVkpKilq2bCknJyc5OTlpy5Ytevvtt+Xk5KSAgABlZ2crLS3NZr/k5GQFBgZKkgIDAwus4pf/PL9PYVxdXeXl5WXzAAAAAICisluQ6tKli/bt26c9e/ZYH61atdKAAQOs/+3s7KyNGzda9zl06JASExMVFhYmSQoLC9O+ffuUkpJi7bNhwwZ5eXkpJCSkzM8JAAAAQMVgt3ukKleurFtuucVmW6VKlVS1alXr9qFDh2rs2LHy9fWVl5eXRo0apbCwMLVp00aSFBERoZCQEA0cOFBTp05VUlKSnn/+ecXGxsrV1bXMzwkAAABAxWDXxSb+zZtvvikHBwdFR0crKytLkZGRmjVrlrXd0dFRq1ev1ogRIxQWFqZKlSopJiZGkydPtmPVAAAAAG505SpIbd682ea5m5ubZs6cqZkzZ15xn+DgYH355ZelXBkAAAAA/I/dv0cKAAAAAK43BCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJxQpSdevW1enTpwtsT0tLU926da+5KAAAAAAoz4oVpH7//Xfl5uYW2J6VlaX//ve/11wUAAAAAJRnTmY6f/7559b/Xr9+vby9va3Pc3NztXHjRtWuXbvEigMAAACA8shUkOrVq5ckyWKxKCYmxqbN2dlZtWvX1rRp00qsOAAAAAAoj0wFqby8PElSnTp1tHPnTvn5+ZVKUQAAAABQnpkKUvkSEhJKug4AAAAAuG4UK0hJ0saNG7Vx40alpKRYZ6ryzZs375oLAwAAAIDyqlhBatKkSZo8ebJatWql6tWry2KxlHRdAAAAAFBuFStIzZ49WwsWLNDAgQNLuh4AAOzq4MGD9i4BACocPz8/BQUF2bsMU4oVpLKzs9W2bduSrgUAALs5cypJFouDHnzwQXuXAgAVjoeHhw4ePHhdhaliBamHH35Yixcv1vjx40u6HgAA7OLc2TQZRp7GTvxAjW5uYe9yAKDC+OO3g3rl6UFKTU298YPUxYsX9cEHH+jrr79Ws2bN5OzsbNM+ffr0EikOAICyVqt2QzUMaWnvMgAA5VyxgtTPP/+s5s2bS5J++eUXmzYWngAAAABwoytWkPrmm29Kug4AAAAAuG442LsAAAAAALjeFGtGqlOnTle9hG/Tpk3FLggAAAAAyrtizUg1b95ct956q/UREhKi7Oxs/fjjj2ratGmRj/Pee++pWbNm8vLykpeXl8LCwrR27Vpr+8WLFxUbG6uqVavK09NT0dHRSk5OtjlGYmKioqKi5OHhIX9/f40bN045OTnFOS0AAAAAKJJizUi9+eabhW6fOHGizp07V+Tj1KxZU6+++qoaNGggwzC0cOFC9ezZUz/99JNuvvlmjRkzRmvWrNHy5cvl7e2tkSNHqnfv3tq6daskKTc3V1FRUQoMDNS2bdt08uRJDRo0SM7OznrllVeKc2oAAAAA8K9K9B6pBx98UPPmzSty/x49eujuu+9WgwYN1LBhQ7388svy9PTU9u3blZ6errlz52r69Onq3LmzQkNDNX/+fG3btk3bt2+XJH311Vc6cOCAPv74YzVv3lzdunXTiy++qJkzZyo7O7skTw0AAAAArEo0SMXHx8vNza1Y++bm5mrJkiXKzMxUWFiYdu/erUuXLik8PNzap3HjxgoKClJ8fLx1vKZNmyogIMDaJzIyUhkZGdq/f/8Vx8rKylJGRobNAwAAAACKqliX9vXu3dvmuWEYOnnypHbt2qXx48ebOta+ffsUFhamixcvytPTU6tWrVJISIj27NkjFxcX+fj42PQPCAhQUlKSJCkpKckmROW357ddyZQpUzRp0iRTdQIAAABAvmIFKW9vb5vnDg4OatSokSZPnqyIiAhTx2rUqJH27Nmj9PR0rVixQjExMdqyZUtxyiqyuLg4jR071vo8IyNDtWrVKtUxAQAAANw4ihWk5s+fX2IFuLi4qH79+pKk0NBQ7dy5U2+99Zb69u2r7OxspaWl2cxKJScnKzAwUJIUGBioH374weZ4+av65fcpjKurq1xdXUvsHAAAAABULNd0j9Tu3bv18ccf6+OPP9ZPP/1UIgXl5eUpKytLoaGhcnZ21saNG61thw4dUmJiosLCwiRJYWFh2rdvn1JSUqx9NmzYIC8vL4WEhJRIPQAAAADwT8WakUpJSVG/fv20efNm62xRWlqaOnXqpCVLlqhatWpFOk5cXJy6deumoKAgnT17VosXL9bmzZu1fv16eXt7a+jQoRo7dqx8fX3l5eWlUaNGKSwsTG3atJEkRUREKCQkRAMHDtTUqVOVlJSk559/XrGxscw4AQAAACg1xZqRGjVqlM6ePav9+/frzJkzOnPmjH755RdlZGToscceK/JxUlJSNGjQIDVq1EhdunTRzp07tX79et11112S/v6+qu7duys6Olp33nmnAgMDtXLlSuv+jo6OWr16tRwdHRUWFqYHH3xQgwYN0uTJk4tzWgAAAABQJMWakVq3bp2+/vprNWnSxLotJCREM2fONLXYxNy5c6/a7ubmppkzZ2rmzJlX7BMcHKwvv/yyyGMCAAAAwLUq1oxUXl6enJ2dC2x3dnZWXl7eNRcFAAAAAOVZsYJU586dNXr0aP3555/Wbf/97381ZswYdenSpcSKAwAAAIDyqFhB6t1331VGRoZq166tevXqqV69eqpTp44yMjL0zjvvlHSNAAAAAFCuFOseqVq1aunHH3/U119/rV9//VWS1KRJE4WHh5docQAAAABQHpmakdq0aZNCQkKUkZEhi8Wiu+66S6NGjdKoUaN022236eabb9Z3331XWrUCAAAAQLlgKkjNmDFDw4YNk5eXV4E2b29vDR8+XNOnTy+x4gAAAACgPDIVpPbu3auuXbtesT0iIkK7d+++5qIAAAAAoDwzFaSSk5MLXfY8n5OTk06dOnXNRQEAAABAeWYqSN1000365Zdfrtj+888/q3r16tdcFAAAAACUZ6aC1N13363x48fr4sWLBdouXLigF154Qd27dy+x4gAAAACgPDK1/Pnzzz+vlStXqmHDhho5cqQaNWokSfr11181c+ZM5ebm6rnnniuVQgEAAACgvDAVpAICArRt2zaNGDFCcXFxMgxDkmSxWBQZGamZM2cqICCgVAoFAAAAgPLC9BfyBgcH68svv9Rff/2lo0ePyjAMNWjQQFWqVCmN+gAAAACg3DEdpPJVqVJFt912W0nWAgAAAADXBVOLTQAAAAAACFIAAAAAYBpBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwycneBQAAUJ4cO/yLvUsAgArl+O+H7V1CsRCkAACQ5OLqLovFQe++MtLepQBAhWOxOCgrK8veZZhCkAIAQJKXj58MI0+P9L5bbW8NsXc5AFBh/P5nsia+/5FcXV3tXYopBCkAAC7T9tYQ9e/ayd5lAECF8dOvRzXx/Y/sXYZpLDYBAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkuwapKVOm6LbbblPlypXl7++vXr166dChQzZ9Ll68qNjYWFWtWlWenp6Kjo5WcnKyTZ/ExERFRUXJw8ND/v7+GjdunHJycsryVAAAAABUIHYNUlu2bFFsbKy2b9+uDRs26NKlS4qIiFBmZqa1z5gxY/TFF19o+fLl2rJli/7880/17t3b2p6bm6uoqChlZ2dr27ZtWrhwoRYsWKAJEybY45QAAAAAVABO9hx83bp1Ns8XLFggf39/7d69W3feeafS09M1d+5cLV68WJ07d5YkzZ8/X02aNNH27dvVpk0bffXVVzpw4IC+/vprBQQEqHnz5nrxxRf19NNPa+LEiXJxcbHHqQEAAAC4gZWre6TS09MlSb6+vpKk3bt369KlSwoPD7f2ady4sYKCghQfHy9Jio+PV9OmTRUQEGDtExkZqYyMDO3fv7/QcbKyspSRkWHzAAAAAICiKjdBKi8vT48//rjuuOMO3XLLLZKkpKQkubi4yMfHx6ZvQECAkpKSrH0uD1H57flthZkyZYq8vb2tj1q1apXw2QAAAAC4kZWbIBUbG6tffvlFS5YsKfWx4uLilJ6ebn0cP3681McEAAAAcOOw6z1S+UaOHKnVq1fr22+/Vc2aNa3bAwMDlZ2drbS0NJtZqeTkZAUGBlr7/PDDDzbHy1/VL7/PP7m6usrV1bWEzwIAAABARWHXGSnDMDRy5EitWrVKmzZtUp06dWzaQ0ND5ezsrI0bN1q3HTp0SImJiQoLC5MkhYWFad++fUpJSbH22bBhg7y8vBQSElI2JwIAAACgQrHrjFRsbKwWL16s//u//1PlypWt9zR5e3vL3d1d3t7eGjp0qMaOHStfX195eXlp1KhRCgsLU5s2bSRJERERCgkJ0cCBAzV16lQlJSXp+eefV2xsLLNOAAAAAEqFXYPUe++9J0nq2LGjzfb58+dr8ODBkqQ333xTDg4Oio6OVlZWliIjIzVr1ixrX0dHR61evVojRoxQWFiYKlWqpJiYGE2ePLmsTgMAAABABWPXIGUYxr/2cXNz08yZMzVz5swr9gkODtaXX35ZkqUBAAAAwBWVm1X7AAAAAOB6QZACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwycneBaBwh/b/pPOZmfYuAwAqjGOHf7F3CQCA6whBqpzJysqSxeKg6RMfsXcpAFDhWCwO8nB1sXcZAIDrAEGqnHF1dZVh5Gni8IGqXSPA3uUAQIWxbe8BfbDyS1X1rmzvUgAA1wGCVDnVtW0rtWhc395lAECF8sHKL+1dAgDgOmHXxSa+/fZb9ejRQzVq1JDFYtFnn31m024YhiZMmKDq1avL3d1d4eHhOnLkiE2fM2fOaMCAAfLy8pKPj4+GDh2qc+fOleFZAAAAAKho7BqkMjMzdeutt2rmzJmFtk+dOlVvv/22Zs+erR07dqhSpUqKjIzUxYsXrX0GDBig/fv3a8OGDVq9erW+/fZbPfII9xcBAAAAKD12vbSvW7du6tatW6FthmFoxowZev7559WzZ09J0ocffqiAgAB99tln6tevnw4ePKh169Zp586datWqlSTpnXfe0d1336033nhDNWrUKPTYWVlZysrKsj7PyMgo4TMDAAAAcCMrt98jlZCQoKSkJIWHh1u3eXt7q3Xr1oqPj5ckxcfHy8fHxxqiJCk8PFwODg7asWPHFY89ZcoUeXt7Wx+1atUqvRMBAAAAcMMpt0EqKSlJkhQQYLtyXUBAgLUtKSlJ/v7+Nu1OTk7y9fW19ilMXFyc0tPTrY/jx4+XcPUAAAAAbmQVctU+V1dXubq62rsMAAAAANepcjsjFRgYKElKTk622Z6cnGxtCwwMVEpKik17Tk6Ozpw5Y+0DAAAAACWt3AapOnXqKDAwUBs3brRuy8jI0I4dOxQWFiZJCgsLU1pamnbv3m3ts2nTJuXl5al169ZlXjMAAACAisGul/adO3dOR48etT5PSEjQnj175Ovrq6CgID3++ON66aWX1KBBA9WpU0fjx49XjRo11KtXL0lSkyZN1LVrVw0bNkyzZ8/WpUuXNHLkSPXr1++KK/YBAAAAwLWya5DatWuXOnXqZH0+duxYSVJMTIwWLFigp556SpmZmXrkkUeUlpamdu3aad26dXJzc7Pus2jRIo0cOVJdunSRg4ODoqOj9fbbb5f5uQAAAACoOOwapDp27CjDMK7YbrFYNHnyZE2ePPmKfXx9fbV48eLSKA8AAAAAClVu75ECAAAAgPKKIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMOmGCVIzZ85U7dq15ebmptatW+uHH36wd0kAAAAAblA3RJBaunSpxo4dqxdeeEE//vijbr31VkVGRiolJcXepQEAAAC4Ad0QQWr69OkaNmyYhgwZopCQEM2ePVseHh6aN2+evUsDAAAAcANysncB1yo7O1u7d+9WXFycdZuDg4PCw8MVHx9f6D5ZWVnKysqyPk9PT5ckZWRklG6xRXDu3DlJ0k+/HlXmhYt2rgYAKo5ffz8uSdp7JEGy3BD/zggA14XDf5yQ9PffweXh7/H8GgzDuGq/6z5IpaamKjc3VwEBATbbAwIC9Ouvvxa6z5QpUzRp0qQC22vVqlUqNRbHiCnv2LsEAKiQxk7/wN4lAECF1KFDB3uXYOPs2bPy9va+Yvt1H6SKIy4uTmPHjrU+z8vL05kzZ1S1alVZLBY7VgZc3zIyMlSrVi0dP35cXl5e9i4HACoMPn+BkmMYhs6ePasaNWpctd91H6T8/Pzk6Oio5ORkm+3JyckKDAwsdB9XV1e5urrabPPx8SmtEoEKx8vLi/+RA4Ad8PkLlIyrzUTlu+4vAndxcVFoaKg2btxo3ZaXl6eNGzcqLCzMjpUBAAAAuFFd9zNSkjR27FjFxMSoVatWuv322zVjxgxlZmZqyJAh9i4NAAAAwA3ohghSffv21alTpzRhwgQlJSWpefPmWrduXYEFKACULldXV73wwgsFLp0FAJQuPn+Bsmcx/m1dPwAAAACAjev+HikAAAAAKGsEKQAAAAAwiSAFAAAAACYRpACUusGDB6tXr172LgMAygXDMPTII4/I19dXFotFe/bssUsdv//+u13HB653N8SqfQAAANeLdevWacGCBdq8ebPq1q0rPz8/e5cEoBgIUgAAAGXo2LFjql69utq2bWvvUgBcAy7tA2CjY8eOGjVqlB5//HFVqVJFAQEBmjNnjvVLritXrqz69etr7dq1kqTc3FwNHTpUderUkbu7uxo1aqS33nrrqmPk5eVpypQp1n1uvfVWrVixoixODwDsavDgwRo1apQSExNlsVhUu3btf/1M3Lx5sywWi9avX68WLVrI3d1dnTt3VkpKitauXasmTZrIy8tLDzzwgM6fP2/db926dWrXrp18fHxUtWpVde/eXceOHbtqfb/88ou6desmT09PBQQEaODAgUpNTS211wO4nhGkABSwcOFC+fn56YcfftCoUaM0YsQI3X///Wrbtq1+/PFHRUREaODAgTp//rzy8vJUs2ZNLV++XAcOHNCECRP07LPPatmyZVc8/pQpU/Thhx9q9uzZ2r9/v8aMGaMHH3xQW7ZsKcOzBICy99Zbb2ny5MmqWbOmTp48qZ07dxb5M3HixIl69913tW3bNh0/flx9+vTRjBkztHjxYq1Zs0ZfffWV3nnnHWv/zMxMjR07Vrt27dLGjRvl4OCge++9V3l5eYXWlpaWps6dO6tFixbatWuX1q1bp+TkZPXp06dUXxPgumUAwGU6dOhgtGvXzvo8JyfHqFSpkjFw4EDrtpMnTxqSjPj4+EKPERsba0RHR1ufx8TEGD179jQMwzAuXrxoeHh4GNu2bbPZZ+jQoUb//v1L8EwAoHx68803jeDgYMMwivaZ+M033xiSjK+//traPmXKFEOScezYMeu24cOHG5GRkVcc99SpU4YkY9++fYZhGEZCQoIhyfjpp58MwzCMF1980YiIiLDZ5/jx44Yk49ChQ8U+X+BGxT1SAApo1qyZ9b8dHR1VtWpVNW3a1LotICBAkpSSkiJJmjlzpubNm6fExERduHBB2dnZat68eaHHPnr0qM6fP6+77rrLZnt2drZatGhRwmcCAOWbmc/Eyz+bAwIC5OHhobp169ps++GHH6zPjxw5ogkTJmjHjh1KTU21zkQlJibqlltuKVDL3r179c0338jT07NA27Fjx9SwYcPinSRwgyJIASjA2dnZ5rnFYrHZZrFYJP19r9OSJUv05JNPatq0aQoLC1PlypX1+uuva8eOHYUe+9y5c5KkNWvW6KabbrJpc3V1LcnTAIByz8xn4j8/hwv7rL78sr0ePXooODhYc+bMUY0aNZSXl6dbbrlF2dnZV6ylR48eeu211wq0Va9e3dyJARUAQQrANdm6davatm2rRx991Lrtajczh4SEyNXVVYmJierQoUNZlAgA5VZpfSaePn1ahw4d0pw5c9S+fXtJ0vfff3/VfVq2bKlPP/1UtWvXlpMTfyIC/4bfEgDXpEGDBvrwww+1fv161alTRx999JF27typOnXqFNq/cuXKevLJJzVmzBjl5eWpXbt2Sk9P19atW+Xl5aWYmJgyPgMAsJ/S+kysUqWKqlatqg8++EDVq1dXYmKinnnmmavuExsbqzlz5qh///566qmn5Ovrq6NHj2rJkiX6z3/+I0dHx2LVAtyoCFIArsnw4cP1008/qW/fvrJYLOrfv78effRR6/LohXnxxRdVrVo1TZkyRb/99pt8fHzUsmVLPfvss2VYOQCUD6Xxmejg4KAlS5boscce0y233KJGjRrp7bffVseOHa+4T40aNbR161Y9/fTTioiIUFZWloKDg9W1a1c5OLDQM/BPFsMwDHsXAQAAAADXE/55AQAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQBuePHx8XJ0dFRUVJS9SwEA3CAshmEY9i4CAIDS9PDDD8vT01Nz587VoUOHVKNGDXuXBAC4zjEjBQC4oZ07d05Lly7ViBEjFBUVpQULFti0f/7552rQoIHc3NzUqVMnLVy4UBaLRWlpadY+33//vdq3by93d3fVqlVLjz32mDIzM8v2RAAA5QpBCgBwQ1u2bJkaN26sRo0a6cEHH9S8efOUfzFGQkKC7rvvPvXq1Ut79+7V8OHD9dxzz9nsf+zYMXXt2lXR0dH6+eeftXTpUn3//fcaOXKkPU4HAFBOcGkfAOCGdscdd6hPnz4aPXq0cnJyVL16dS1fvlwdO3bUM888ozVr1mjfvn3W/s8//7xefvll/fXXX/Lx8dHDDz8sR0dHvf/++9Y+33//vTp06KDMzEy5ubnZ47QAAHbGjBQA4IZ16NAh/fDDD+rfv78kycnJSX379tXcuXOt7bfddpvNPrfffrvN871792rBggXy9PS0PiIjI5WXl6eEhISyOREAQLnjZO8CAAAoLXPnzlVOTo7N4hKGYcjV1VXvvvtukY5x7tw5DR8+XI899liBtqCgoBKrFQBwfSFIAQBuSDk5Ofrwww81bdo0RURE2LT16tVLn3zyiRo1aqQvv/zSpm3nzp02z1u2bKkDBw6ofv36pV4zAOD6wT1SAIAb0meffaa+ffsqJSVF3t7eNm1PP/20Nm3apGXLlqlRo0YaM2aMhg4dqj179uiJJ57QiRMnlJaWJm9vb/38889q06aNHnroIT388MOqVKmSDhw4oA0bNhR5VgsAcOPhHikAwA1p7ty5Cg8PLxCiJCk6Olq7du3S2bNntWLFCq1cuVLNmjXTe++9Z121z9XVVZLUrFkzbdmyRYcPH1b79u3VokULTZgwge+iAoAKjhkpAAAu8/LLL2v27Nk6fvy4vUsBAJRj3CMFAKjQZs2apdtuu01Vq1bV1q1b9frrr/MdUQCAf0WQAgBUaEeOHNFLL72kM2fOKCgoSE888YTi4uLsXRYAoJzj0j4AAAAAMInFJgAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAm/X+DmbXDGpOXmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='Sex', hue='Survived', multiple='stack', palette='coolwarm', kde=False)\n",
    "plt.title('Age Distribution of Passengers by Survival')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='Pclass', hue='Survived', multiple='stack', palette='coolwarm', kde=True)\n",
    "plt.title('Pclass Distribution of Passengers by Survival')\n",
    "plt.xlabel('Pclass')\n",
    "plt.ylabel('No of people')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name  SibSp  Parch  \\\n",
      "0                            Braund, Mr. Owen Harris      1      0   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      1      0   \n",
      "2                             Heikkinen, Miss. Laina      0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      1      0   \n",
      "4                           Allen, Mr. William Henry      0      0   \n",
      "\n",
      "             Ticket     Fare Embarked  Age group_Child  Age group_Teen  \\\n",
      "0         A/5 21171   7.2500        S            False           False   \n",
      "1          PC 17599  71.2833        C            False           False   \n",
      "2  STON/O2. 3101282   7.9250        S            False           False   \n",
      "3            113803  53.1000        S            False           False   \n",
      "4            373450   8.0500        S            False           False   \n",
      "\n",
      "   Age group_Adult  Age group_Middle_Age  Age group_Old  Sex_female  Sex_male  \n",
      "0             True                 False          False       False      True  \n",
      "1             True                 False          False        True     False  \n",
      "2             True                 False          False        True     False  \n",
      "3             True                 False          False        True     False  \n",
      "4             True                 False          False       False      True  \n"
     ]
    }
   ],
   "source": [
    "# One-hot encode the 'Sex' column\n",
    "df = pd.get_dummies(df, columns=['Sex'])\n",
    "\n",
    "# Display the first few rows of the modified dataset\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embarked Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Class\"]=df[\"Embarked\"].map({'C':1,'Q':2,'S':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)'"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].sample(2)\n",
    "\"\"\"Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "sns.histplot(data=df,x=\"Embarked\",hue=\"Survived\")\n",
    "plt.title(\"Embarked vs Survival\")\n",
    "plt.xlabel(\"Embarked\")\n",
    "plt.ylabel(\"Survival\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SipSp And Parch Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age group_Child</th>\n",
       "      <th>Age group_Teen</th>\n",
       "      <th>Age group_Adult</th>\n",
       "      <th>Age group_Middle_Age</th>\n",
       "      <th>Age group_Old</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>405</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Oreskovic, Miss. Marija</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315096</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>776</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Myhrman, Mr. Pehr Fabian Oliver Malkolm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347078</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Canavan, Miss. Mary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364846</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Smith, Mr. Thomas</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>384461</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>370</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Aubart, Mme. Leontine Pauline</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17477</td>\n",
       "      <td>69.3000</td>\n",
       "      <td>C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                     Name  \\\n",
       "404          405         0       3                  Oreskovic, Miss. Marija   \n",
       "775          776         0       3  Myhrman, Mr. Pehr Fabian Oliver Malkolm   \n",
       "501          502         0       3                      Canavan, Miss. Mary   \n",
       "260          261         0       3                        Smith, Mr. Thomas   \n",
       "369          370         1       1            Aubart, Mme. Leontine Pauline   \n",
       "\n",
       "     SibSp  Parch    Ticket     Fare Embarked  Age group_Child  \\\n",
       "404      0      0    315096   8.6625        S            False   \n",
       "775      0      0    347078   7.7500        S            False   \n",
       "501      0      0    364846   7.7500        Q            False   \n",
       "260      0      0    384461   7.7500        Q            False   \n",
       "369      0      0  PC 17477  69.3000        C            False   \n",
       "\n",
       "     Age group_Teen  Age group_Adult  Age group_Middle_Age  Age group_Old  \\\n",
       "404            True            False                 False          False   \n",
       "775            True            False                 False          False   \n",
       "501           False             True                 False          False   \n",
       "260           False             True                 False          False   \n",
       "369           False             True                 False          False   \n",
       "\n",
       "     Sex_female  Sex_male  Class  \n",
       "404        True     False    3.0  \n",
       "775       False      True    3.0  \n",
       "501        True     False    2.0  \n",
       "260       False      True    2.0  \n",
       "369        True     False    1.0  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean       0.523008\n",
       "std        1.102743\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        1.000000\n",
       "max        8.000000\n",
       "Name: SibSp, dtype: float64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SibSp\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean       0.381594\n",
       "std        0.806057\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        6.000000\n",
       "Name: Parch, dtype: float64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Parch\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Family_member\"]=df[\"Parch\"]+df[\"SibSp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age group_Child</th>\n",
       "      <th>Age group_Teen</th>\n",
       "      <th>Age group_Adult</th>\n",
       "      <th>Age group_Middle_Age</th>\n",
       "      <th>Age group_Old</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Class</th>\n",
       "      <th>Family_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Birkeland, Mr. Hans Martin Monsen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>312992</td>\n",
       "      <td>7.775</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name  SibSp  \\\n",
       "408          409         0       3  Birkeland, Mr. Hans Martin Monsen      0   \n",
       "\n",
       "     Parch  Ticket   Fare Embarked  Age group_Child  Age group_Teen  \\\n",
       "408      0  312992  7.775        S            False           False   \n",
       "\n",
       "     Age group_Adult  Age group_Middle_Age  Age group_Old  Sex_female  \\\n",
       "408             True                 False          False       False   \n",
       "\n",
       "     Sex_male  Class  Family_member  \n",
       "408      True    3.0              0  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Parch\",\"SibSp\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Embarked\"],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debasish Das\\AppData\\Local\\Temp\\ipykernel_8276\\2879774509.py:2: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  sns.histplot(data=df, x='Fare', multiple='stack', palette='coolwarm', kde=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='Fare', multiple='stack', palette='coolwarm', kde=False)\n",
    "plt.title('Fare Distribution of Passengers by Survival')\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debasish Das\\AppData\\Local\\Temp\\ipykernel_8276\\1450544794.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxenplot(data=df, y='Fare', palette='coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxenplot(data=df, y='Fare', palette='coolwarm')\n",
    "plt.title('Fare Distribution of Passengers by Survival')\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age group_Child</th>\n",
       "      <th>Age group_Teen</th>\n",
       "      <th>Age group_Adult</th>\n",
       "      <th>Age group_Middle_Age</th>\n",
       "      <th>Age group_Old</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Class</th>\n",
       "      <th>Family_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strandberg, Miss. Ida Sofia</td>\n",
       "      <td>7553</td>\n",
       "      <td>9.8375</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Slocovski, Mr. Selman Francis</td>\n",
       "      <td>SOTON/OQ 392086</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Meyer, Mr. Edgar Joseph</td>\n",
       "      <td>PC 17604</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>597</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Leitch, Miss. Jessie Wills</td>\n",
       "      <td>248727</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "862          863         1       1   \n",
       "474          475         0       3   \n",
       "87            88         0       3   \n",
       "34            35         0       1   \n",
       "596          597         1       2   \n",
       "\n",
       "                                                  Name           Ticket  \\\n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...            17466   \n",
       "474                        Strandberg, Miss. Ida Sofia             7553   \n",
       "87                       Slocovski, Mr. Selman Francis  SOTON/OQ 392086   \n",
       "34                             Meyer, Mr. Edgar Joseph         PC 17604   \n",
       "596                         Leitch, Miss. Jessie Wills           248727   \n",
       "\n",
       "        Fare  Age group_Child  Age group_Teen  Age group_Adult  \\\n",
       "862  25.9292            False           False            False   \n",
       "474   9.8375            False           False             True   \n",
       "87    8.0500            False           False             True   \n",
       "34   82.1708            False           False             True   \n",
       "596  33.0000            False           False             True   \n",
       "\n",
       "     Age group_Middle_Age  Age group_Old  Sex_female  Sex_male  Class  \\\n",
       "862                  True          False        True     False    3.0   \n",
       "474                 False          False        True     False    3.0   \n",
       "87                  False          False       False      True    3.0   \n",
       "34                  False          False       False      True    1.0   \n",
       "596                 False          False        True     False    3.0   \n",
       "\n",
       "     Family_member  \n",
       "862              0  \n",
       "474              0  \n",
       "87               0  \n",
       "34               1  \n",
       "596              0  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Title\"] = df[\"Name\"].apply(lambda x: x.split(\",\")[1].split(\".\")[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Mr              517\n",
       "Miss            182\n",
       "Mrs             125\n",
       "Master           40\n",
       "Dr                7\n",
       "Rev               6\n",
       "Mlle              2\n",
       "Major             2\n",
       "Col               2\n",
       "the Countess      1\n",
       "Capt              1\n",
       "Ms                1\n",
       "Sir               1\n",
       "Lady              1\n",
       "Mme               1\n",
       "Don               1\n",
       "Jonkheer          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debasish Das\\AppData\\Local\\Temp\\ipykernel_8276\\1431326651.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"Title\"].replace([\"Dr\",\"Col\",\"Rev\",\"Ms\",\"Dona\",\"Major\",\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Mr        517\n",
       "Miss      182\n",
       "Mrs       125\n",
       "Master     40\n",
       "Other      27\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].replace([\"Dr\",\"Col\",\"Rev\",\"Ms\",\"Dona\",\"Major\",\n",
    "\"Mlle\",\n",
    "\"Don\",\n",
    "\"Mme\",\n",
    "\"Lady\",\n",
    "\"Sir\",\n",
    "\"Capt\",\n",
    "\"the Countess\",\n",
    "\"Jonkheer\"],\"Other\",inplace=True)\n",
    "df[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Name\"],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"PassengerId\",\"Ticket\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age group_Child</th>\n",
       "      <th>Age group_Teen</th>\n",
       "      <th>Age group_Adult</th>\n",
       "      <th>Age group_Middle_Age</th>\n",
       "      <th>Age group_Old</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Class</th>\n",
       "      <th>Family_member</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Fare  Age group_Child  Age group_Teen  \\\n",
       "0           0       3   7.2500            False           False   \n",
       "1           1       1  71.2833            False           False   \n",
       "2           1       3   7.9250            False           False   \n",
       "3           1       1  53.1000            False           False   \n",
       "4           0       3   8.0500            False           False   \n",
       "..        ...     ...      ...              ...             ...   \n",
       "886         0       2  13.0000            False           False   \n",
       "887         1       1  30.0000            False            True   \n",
       "888         0       3  23.4500            False           False   \n",
       "889         1       1  30.0000            False           False   \n",
       "890         0       3   7.7500            False           False   \n",
       "\n",
       "     Age group_Adult  Age group_Middle_Age  Age group_Old  Sex_female  \\\n",
       "0               True                 False          False       False   \n",
       "1               True                 False          False        True   \n",
       "2               True                 False          False        True   \n",
       "3               True                 False          False        True   \n",
       "4               True                 False          False       False   \n",
       "..               ...                   ...            ...         ...   \n",
       "886             True                 False          False       False   \n",
       "887            False                 False          False        True   \n",
       "888             True                 False          False        True   \n",
       "889             True                 False          False       False   \n",
       "890             True                 False          False       False   \n",
       "\n",
       "     Sex_male  Class  Family_member  Title  \n",
       "0        True    3.0              1     Mr  \n",
       "1       False    1.0              1    Mrs  \n",
       "2       False    3.0              0   Miss  \n",
       "3       False    3.0              1    Mrs  \n",
       "4        True    3.0              0     Mr  \n",
       "..        ...    ...            ...    ...  \n",
       "886      True    3.0              0  Other  \n",
       "887     False    3.0              0   Miss  \n",
       "888     False    3.0              3   Miss  \n",
       "889      True    1.0              0     Mr  \n",
       "890      True    2.0              0     Mr  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debasish Das\\AppData\\Local\\Temp\\ipykernel_8276\\3550135331.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Class'].fillna(-1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['Class'].fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Class</th>\n",
       "      <th>Family_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>2.527497</td>\n",
       "      <td>0.904602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.808714</td>\n",
       "      <td>1.613459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass        Fare       Class  Family_member\n",
       "count  891.000000  891.000000  891.000000  891.000000     891.000000\n",
       "mean     0.383838    2.308642   32.204208    2.527497       0.904602\n",
       "std      0.486592    0.836071   49.693429    0.808714       1.613459\n",
       "min      0.000000    1.000000    0.000000   -1.000000       0.000000\n",
       "25%      0.000000    2.000000    7.910400    2.000000       0.000000\n",
       "50%      0.000000    3.000000   14.454200    3.000000       0.000000\n",
       "75%      1.000000    3.000000   31.000000    3.000000       1.000000\n",
       "max      1.000000    3.000000  512.329200    3.000000      10.000000"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Survived              891 non-null    int64  \n",
      " 1   Pclass                891 non-null    int64  \n",
      " 2   Fare                  891 non-null    float64\n",
      " 3   Age group_Child       891 non-null    bool   \n",
      " 4   Age group_Teen        891 non-null    bool   \n",
      " 5   Age group_Adult       891 non-null    bool   \n",
      " 6   Age group_Middle_Age  891 non-null    bool   \n",
      " 7   Age group_Old         891 non-null    bool   \n",
      " 8   Sex_female            891 non-null    bool   \n",
      " 9   Sex_male              891 non-null    bool   \n",
      " 10  Class                 891 non-null    float64\n",
      " 11  Family_member         891 non-null    int64  \n",
      " 12  Title                 891 non-null    object \n",
      "dtypes: bool(7), float64(2), int64(3), object(1)\n",
      "memory usage: 48.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "# Fit and transform the Title column\n",
    "df['Title'] = label_encoder.fit_transform(df['Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(columns=[\"Survived\"])\n",
    "y=df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8324022346368715\n"
     ]
    }
   ],
   "source": [
    "model=XGBClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)\n",
    "result=accuracy_score(y_test,y_pred)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7932960893854749\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Debasish Das\\Documents\\01_ML Project\\Ml Projects\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)\n",
    "result=accuracy_score(y_test,y_pred)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "model=GradientBoostingClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)\n",
    "result=accuracy_score(y_test,y_pred)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8324022346368715"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=RandomForestClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)\n",
    "result=accuracy_score(y_test,y_pred)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"class sklearn.ensemble.RandomForestClassifier\\n(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2,\\nmin_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None,\\nmin_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, \\nwarm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)[source]\\n\""
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"class sklearn.ensemble.RandomForestClassifier\n",
    "(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2,\n",
    "min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None,\n",
    "min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, \n",
    "warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)[source]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combination 1\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 2\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.75\n",
      "------------------------------\n",
      "Combination 3\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.75\n",
      "------------------------------\n",
      "Combination 4\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 5\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 6\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 7\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 8\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 9\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.74\n",
      "------------------------------\n",
      "Combination 10\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 11\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 12\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 13\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 14\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 15\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 16\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 17\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 18\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 19\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 20\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 21\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.78\n",
      "Test Accuracy : 0.76\n",
      "------------------------------\n",
      "Combination 22\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 23\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 24\n",
      "n_estimators: 5, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 25\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 26\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 27\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 28\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 29\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 30\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 31\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 32\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 33\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.74\n",
      "------------------------------\n",
      "Combination 34\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.76\n",
      "------------------------------\n",
      "Combination 35\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 36\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 37\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.76\n",
      "------------------------------\n",
      "Combination 38\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 39\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 40\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 41\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 42\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.76\n",
      "------------------------------\n",
      "Combination 43\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 44\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 45\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 46\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.74\n",
      "------------------------------\n",
      "Combination 47\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 48\n",
      "n_estimators: 5, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 49\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 50\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 51\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 52\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 53\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.75\n",
      "------------------------------\n",
      "Combination 54\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 55\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 56\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 57\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.76\n",
      "Test Accuracy : 0.76\n",
      "------------------------------\n",
      "Combination 58\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 59\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 60\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 61\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 62\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 63\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 64\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 65\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 66\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 67\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 68\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 69\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.78\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 70\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 71\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 72\n",
      "n_estimators: 5, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 73\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 74\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 75\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 76\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 77\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.76\n",
      "Test Accuracy : 0.68\n",
      "------------------------------\n",
      "Combination 78\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 79\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 80\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 81\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 82\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 83\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 84\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 85\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 86\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 87\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 88\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 89\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.75\n",
      "------------------------------\n",
      "Combination 90\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.74\n",
      "------------------------------\n",
      "Combination 91\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 92\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 93\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 94\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 95\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 96\n",
      "n_estimators: 5, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 97\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 98\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.73\n",
      "------------------------------\n",
      "Combination 99\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 100\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 101\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 102\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 103\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 104\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 105\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.75\n",
      "------------------------------\n",
      "Combination 106\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 107\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 108\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 109\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.79\n",
      "Test Accuracy : 0.74\n",
      "------------------------------\n",
      "Combination 110\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 111\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 112\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 113\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 114\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 115\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 116\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 117\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 118\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 119\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 120\n",
      "n_estimators: 5, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 121\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 122\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 123\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 124\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 125\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 126\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 127\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 128\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 129\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 130\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 131\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 132\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 133\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 134\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 135\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 136\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 137\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 138\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 139\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 140\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 141\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 142\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 143\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 144\n",
      "n_estimators: 10, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 145\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 146\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 147\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 148\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 149\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 150\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 151\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 152\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 153\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 154\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 155\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 156\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 157\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 158\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 159\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 160\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 161\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 162\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 163\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 164\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 165\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 166\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 167\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 168\n",
      "n_estimators: 10, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 169\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 170\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 171\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 172\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 173\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 174\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 175\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 176\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 177\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.78\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 178\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 179\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 180\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 181\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 182\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 183\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 184\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 185\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 186\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 187\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 188\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 189\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 190\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 191\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 192\n",
      "n_estimators: 10, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 193\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 194\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 195\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 196\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 197\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 198\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 199\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 200\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 201\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 202\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 203\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 204\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 205\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 206\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 207\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 208\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 209\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 210\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 211\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 212\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 213\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.80\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 214\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 215\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 216\n",
      "n_estimators: 10, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 217\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 218\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 219\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 220\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 221\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 222\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 223\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 224\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 225\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 226\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 227\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 228\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 229\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.75\n",
      "------------------------------\n",
      "Combination 230\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 231\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 232\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 233\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 234\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 235\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 236\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 237\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 238\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 239\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 240\n",
      "n_estimators: 10, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 241\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 242\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 243\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 244\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 245\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 246\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 247\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 248\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 249\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 250\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 251\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 252\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 253\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 254\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 255\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 256\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 257\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 258\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 259\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 260\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 261\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 262\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 263\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 264\n",
      "n_estimators: 30, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 265\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 266\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 267\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 268\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 269\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 270\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 271\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 272\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 273\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 274\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 275\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 276\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 277\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 278\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 279\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 280\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 281\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 282\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 283\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 284\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 285\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 286\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 287\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 288\n",
      "n_estimators: 30, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 289\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 290\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 291\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 292\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 293\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 294\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 295\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 296\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 297\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 298\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 299\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 300\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 301\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 302\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 303\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 304\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 305\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 306\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 307\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 308\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 309\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 310\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 311\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 312\n",
      "n_estimators: 30, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 313\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 314\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 315\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 316\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 317\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 318\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 319\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 320\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 321\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 322\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 323\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 324\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 325\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 326\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 327\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 328\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 329\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 330\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 331\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 332\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 333\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 334\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 335\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 336\n",
      "n_estimators: 30, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 337\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 338\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 339\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 340\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 341\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 342\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 343\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 344\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 345\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 346\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 347\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 348\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 349\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 350\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 351\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 352\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 353\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.77\n",
      "------------------------------\n",
      "Combination 354\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 355\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 356\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 357\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.81\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 358\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 359\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 360\n",
      "n_estimators: 30, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 361\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 362\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 363\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 364\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 365\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 366\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 367\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 368\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 369\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 370\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 371\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 372\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 373\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 374\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 375\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 376\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 377\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 378\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 379\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 380\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 381\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 382\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 383\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 384\n",
      "n_estimators: 50, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 385\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 386\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 387\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 388\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 389\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 390\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 391\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 392\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 393\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 394\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 395\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 396\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 397\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 398\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 399\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 400\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 401\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 402\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 403\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 404\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 405\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 406\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 407\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 408\n",
      "n_estimators: 50, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 409\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 410\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 411\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 412\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 413\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 414\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 415\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 416\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 417\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 418\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 419\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 420\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 421\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 422\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 423\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 424\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 425\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 426\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 427\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 428\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 429\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 430\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 431\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 432\n",
      "n_estimators: 50, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 433\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 434\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 435\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 436\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 437\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 438\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 439\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 440\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 441\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 442\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 443\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 444\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 445\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 446\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 447\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 448\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 449\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 450\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 451\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 452\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 453\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 454\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 455\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 456\n",
      "n_estimators: 50, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 457\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 458\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 459\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 460\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 461\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 462\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 463\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 464\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 465\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 466\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 467\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 468\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 469\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 470\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 471\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 472\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 473\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 474\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 475\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 476\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 477\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 478\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 479\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 480\n",
      "n_estimators: 50, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 481\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 482\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 483\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 484\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 485\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 486\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 487\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 488\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 489\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 490\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 491\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 492\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 493\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 494\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 495\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 496\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 497\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 498\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 499\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 500\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 501\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 502\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 503\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 504\n",
      "n_estimators: 70, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 505\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 506\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 507\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 508\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 509\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 510\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 511\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 512\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 513\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 514\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 515\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 516\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 517\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 518\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 519\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 520\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 521\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 522\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 523\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 524\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 525\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 526\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 527\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 528\n",
      "n_estimators: 70, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 529\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 530\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 531\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 532\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 533\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 534\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 535\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 536\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 537\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 538\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 539\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 540\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 541\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 542\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 543\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 544\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 545\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 546\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 547\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 548\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 549\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 550\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 551\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 552\n",
      "n_estimators: 70, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 553\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 554\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 555\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 556\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 557\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 558\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 559\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 560\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 561\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 562\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 563\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 564\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 565\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 566\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 567\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 568\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 569\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 570\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 571\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 572\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 573\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 574\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 575\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 576\n",
      "n_estimators: 70, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 577\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 578\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 579\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 580\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 581\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 582\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 583\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 584\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 585\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 586\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 587\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 588\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.84\n",
      "------------------------------\n",
      "Combination 589\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 590\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 591\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 592\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 593\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 594\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 595\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 596\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 597\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 598\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 599\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 600\n",
      "n_estimators: 70, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 601\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 602\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 603\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 604\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 605\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 606\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 607\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 608\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 609\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 610\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 611\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 612\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 613\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 614\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 615\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 616\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 617\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 618\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 619\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 620\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 621\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 622\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 623\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 624\n",
      "n_estimators: 100, max_depth: 5, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 625\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 626\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 627\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 628\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 629\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 630\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 631\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 632\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 633\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 634\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 635\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 636\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 637\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 638\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 639\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 640\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 641\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 642\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 643\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 644\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 645\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 646\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 647\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 648\n",
      "n_estimators: 100, max_depth: 7, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 649\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 650\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 651\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 652\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 653\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 654\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 655\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 656\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 657\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 658\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 659\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 660\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 661\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 662\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 663\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 664\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 665\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 666\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 667\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 668\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 669\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 670\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 671\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 672\n",
      "n_estimators: 100, max_depth: 9, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 673\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 674\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 675\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 676\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 677\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 678\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 679\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 680\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 681\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 682\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 683\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 684\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 685\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 686\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 687\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 688\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 689\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.78\n",
      "------------------------------\n",
      "Combination 690\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 691\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 692\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 693\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 694\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 695\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 696\n",
      "n_estimators: 100, max_depth: 11, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 697\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 698\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 699\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 700\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 701\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 702\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 703\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 704\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 2, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.89\n",
      "Test Accuracy : 0.83\n",
      "------------------------------\n",
      "Combination 705\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 706\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 707\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 708\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 709\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.82\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 710\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 711\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 712\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 3, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.88\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 713\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 50\n",
      "Train Accuracy: 0.83\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 714\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 100\n",
      "Train Accuracy: 0.85\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 715\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 150\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 716\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: sqrt, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.82\n",
      "------------------------------\n",
      "Combination 717\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 50\n",
      "Train Accuracy: 0.84\n",
      "Test Accuracy : 0.79\n",
      "------------------------------\n",
      "Combination 718\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 100\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n",
      "Combination 719\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 150\n",
      "Train Accuracy: 0.86\n",
      "Test Accuracy : 0.81\n",
      "------------------------------\n",
      "Combination 720\n",
      "n_estimators: 100, max_depth: 13, min_sample_split: 4, max_features: log2, max_sample: 200\n",
      "Train Accuracy: 0.87\n",
      "Test Accuracy : 0.80\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning for Random forest (Used in the session)\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "# # Define the hyperparameter grid\n",
    "param_grid = {\n",
    "   'n_estimators'    : [5,10,30,50,70,100],\n",
    "   'max_depth'       : [5,7,9,11,13],\n",
    "'min_sample_split'  : [2,3,4],\n",
    "'max_features'       :[\"sqrt\",\"log2\"],\n",
    "\"max_sample\"         : [50,100,150,200]\n",
    " }\n",
    "\n",
    "index = 0\n",
    "\n",
    "answers_grid = {\n",
    "     'combination'       :[],\n",
    "     'train_Accuracy'    :[],\n",
    "     'test_Accuracy'     :[],\n",
    "      'p_score'    :[],\n",
    "     'r_score'     :[],\n",
    "     'n_estimators'  :[],\n",
    "     'max_depth'     :[],\n",
    "     'min_sample_split'         :[],\n",
    "     'max_features'             :[],\n",
    "     'max_sample'      :[]\n",
    "\n",
    "     }\n",
    "\n",
    "\n",
    " # Loop through each combination of hyperparameters\n",
    "for n_estimators in param_grid['n_estimators']:\n",
    "   for max_depth in param_grid['max_depth']:\n",
    "     for min_sample_split in param_grid['min_sample_split']:\n",
    "       for max_features in param_grid['max_features']:\n",
    "           for max_sample in param_grid['max_sample']:\n",
    "\n",
    "               index = index + 1\n",
    "\n",
    "               # Define and train the XGBoost model\n",
    "               model = RandomForestClassifier(\n",
    "                                        n_estimators = n_estimators,\n",
    "                                        max_depth = max_depth,\n",
    "                                        min_samples_split = min_sample_split,\n",
    "                                        max_features = max_features,\n",
    "                                        max_samples = max_sample)\n",
    "\n",
    "\n",
    "\n",
    "               x=df.drop(columns=[\"Survived\"])\n",
    "               y=df['Survived']\n",
    "\n",
    "               x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "               model.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "\n",
    "               # Predict on training and testing sets\n",
    "               y_pred_train = model.predict(x_train)\n",
    "               y_pred_test = model.predict(x_test)\n",
    "\n",
    "\n",
    "               # Calculate train and test results\n",
    "\n",
    "               train_accuracy =  accuracy_score (y_train, y_pred_train)\n",
    "               test_accuracy  =  accuracy_score (y_test , y_pred_test)\n",
    "               p=precision_score(y_test, y_pred) \n",
    "               r=recall_score(y_test, y_pred) \n",
    "                # Include into the lists\n",
    "               answers_grid ['combination']   .append(index)\n",
    "               answers_grid ['train_Accuracy']    .append(train_accuracy)\n",
    "               answers_grid ['test_Accuracy']     .append(test_accuracy)\n",
    "               answers_grid ['p_score']    .append(p)\n",
    "               answers_grid ['r_score']     .append(r)\n",
    "               answers_grid ['n_estimators']   .append(n_estimators)\n",
    "               answers_grid ['max_depth']      .append(max_depth)\n",
    "               answers_grid ['min_sample_split']          .append(min_sample_split)\n",
    "               answers_grid ['max_features']              .append(max_features)\n",
    "               answers_grid ['max_sample']       .append(max_sample)\n",
    "\n",
    "\n",
    "               # Print results for this combination\n",
    "               print(f\"Combination {index}\")\n",
    "               print(f\"n_estimators: {n_estimators}, max_depth: {max_depth}, min_sample_split: {min_sample_split}, max_features: {max_features}, max_sample: {max_sample}\")\n",
    "               print(f\"Train Accuracy: {train_accuracy:.2f}\")\n",
    "               print(f\"Test Accuracy : {test_accuracy :.2f}\")\n",
    "               print(\"-\" * 30)\n",
    "result=pd.DataFrame(answers_grid)\n",
    "file=\"result4.xlsx\"\n",
    "result.to_excel(file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### best combination :-93,\ttrain_accuracy:- 0.865168539,\ttest_accuracy:-0.860335196,\tn_estimator:-10,\tmax_depth:- 11,\tsample:-3,\tmaxfeature:-sqrt,\tmax_sample:- 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8268156424581006\n",
      "0.7792207792207793\n",
      "0.8108108108108109\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "model=RandomForestClassifier( n_estimators=10,\tmax_depth= 11,\tmin_samples_split=2,\tmax_features=\"log2\",\tmax_samples=200)\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)\n",
    "result=accuracy_score(y_test,y_pred)\n",
    "p=precision_score(y_test, y_pred) \n",
    "r=recall_score(y_test, y_pred) \n",
    "print(result)\n",
    "print(p)\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Random.joblib']"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(model, 'Random.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('Random.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"import pickle\n",
    "# Save the model\n",
    "with open(\"model.pkl\",\"wb\") as file:\n",
    "    pickle.dump(model,file)\"\"\"\n"
   ]
  }
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