Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o4Mth1MXEx9d"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "cleaned_data = pd.read_csv('startups_data_cleaned_step1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "id": "bKQEvIcsFBDp",
    "outputId": "f459b34d-c03a-46f6-ed8b-e653e447810f"
   },
   "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>name</th>\n",
       "      <th>market</th>\n",
       "      <th>funding_total_usd</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>funding_rounds</th>\n",
       "      <th>founded_at</th>\n",
       "      <th>founded_month</th>\n",
       "      <th>...</th>\n",
       "      <th>round_C</th>\n",
       "      <th>round_D</th>\n",
       "      <th>round_E</th>\n",
       "      <th>round_F</th>\n",
       "      <th>round_G</th>\n",
       "      <th>round_H</th>\n",
       "      <th>category_count</th>\n",
       "      <th>clean_name</th>\n",
       "      <th>is_operating</th>\n",
       "      <th>category_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le HerbÈ</td>\n",
       "      <td>Medical Marijuana Patients</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>WA</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>le herbè</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kandid</td>\n",
       "      <td>Apps</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>kandid</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Manly Man Can</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>30.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>MI</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>Pinckney</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>the manly man can</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MyKidsCalendar</td>\n",
       "      <td>Software</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Newark</td>\n",
       "      <td>Long Valley</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-22</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>mykidscalendar</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peach</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-19</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>peach</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</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",
       "      <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>12301</th>\n",
       "      <td>eBay</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>6700000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>SF Bay Area</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>1</td>\n",
       "      <td>1995-09-01</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>ebay</td>\n",
       "      <td>1</td>\n",
       "      <td>Business Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12302</th>\n",
       "      <td>Arkansas Children's Hospital</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AR</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>arkansas children's hospital</td>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12303</th>\n",
       "      <td>Northcentral Technical College</td>\n",
       "      <td>Education</td>\n",
       "      <td>6200000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>WI</td>\n",
       "      <td>WI - Other</td>\n",
       "      <td>Antigo</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>northcentral technical college</td>\n",
       "      <td>1</td>\n",
       "      <td>Education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12304</th>\n",
       "      <td>EATON</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>eaton</td>\n",
       "      <td>0</td>\n",
       "      <td>Transport &amp; Automotive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12305</th>\n",
       "      <td>Since1910.com</td>\n",
       "      <td>Jewelry</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NY</td>\n",
       "      <td>Long Island</td>\n",
       "      <td>Garden City</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>since1910</td>\n",
       "      <td>1</td>\n",
       "      <td>Fashion &amp; Lifestyle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12306 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name                        market  \\\n",
       "0                            Le HerbÈ   Medical Marijuana Patients    \n",
       "1                              Kandid                         Apps    \n",
       "2                   The Manly Man Can                      Fashion    \n",
       "3                      MyKidsCalendar                     Software    \n",
       "4                               Peach                      Fashion    \n",
       "...                               ...                           ...   \n",
       "12301                            eBay            Business Services    \n",
       "12302    Arkansas Children's Hospital                Biotechnology    \n",
       "12303  Northcentral Technical College                    Education    \n",
       "12304                           EATON                   Automotive    \n",
       "12305                   Since1910.com                      Jewelry    \n",
       "\n",
       "       funding_total_usd country_code state_code       region           city  \\\n",
       "0               100000.0          USA         WA      Seattle        Seattle   \n",
       "1              1400000.0          USA         CA  SF Bay Area  San Francisco   \n",
       "2                   30.0          USA         MI      Detroit       Pinckney   \n",
       "3               110000.0          USA         NJ       Newark    Long Valley   \n",
       "4               500000.0          USA         CA  SF Bay Area       Berkeley   \n",
       "...                  ...          ...        ...          ...            ...   \n",
       "12301          6700000.0          USA         CA  SF Bay Area       San Jose   \n",
       "12302          3500000.0          USA         AR  Little Rock    Little Rock   \n",
       "12303          6200000.0          USA         WI   WI - Other         Antigo   \n",
       "12304          2400000.0          USA         OH    Cleveland      Cleveland   \n",
       "12305           100000.0          USA         NY  Long Island    Garden City   \n",
       "\n",
       "       funding_rounds  founded_at  founded_month  ...  round_C  round_D  \\\n",
       "0                   1  2014-12-01             12  ...        0        0   \n",
       "1                   1  2014-11-19             11  ...        0        0   \n",
       "2                   1  2014-10-26             10  ...        0        0   \n",
       "3                   1  2014-10-22             10  ...        0        0   \n",
       "4                   1  2014-10-19             10  ...        0        0   \n",
       "...               ...         ...            ...  ...      ...      ...   \n",
       "12301               1  1995-09-01              9  ...        0        0   \n",
       "12302               1  2012-01-01              1  ...        0        0   \n",
       "12303               1  2012-01-01              1  ...        0        0   \n",
       "12304               1  2011-01-01              1  ...        0        0   \n",
       "12305               1  2010-01-01              1  ...        0        0   \n",
       "\n",
       "      round_E round_F  round_G  round_H  category_count  \\\n",
       "0           0       0        0        0               3   \n",
       "1           0       0        0        0               1   \n",
       "2           0       0        0        0               1   \n",
       "3           0       0        0        0               1   \n",
       "4           0       0        0        0               3   \n",
       "...       ...     ...      ...      ...             ...   \n",
       "12301       0       0        0        0               6   \n",
       "12302       0       0        0        0               1   \n",
       "12303       0       0        0        0               1   \n",
       "12304       0       0        0        0               3   \n",
       "12305       0       0        0        0               3   \n",
       "\n",
       "                           clean_name  is_operating        category_updated  \n",
       "0                            le herbè             1              Healthcare  \n",
       "1                              kandid             1              Technology  \n",
       "2                   the manly man can             1     Fashion & Lifestyle  \n",
       "3                      mykidscalendar             1              Technology  \n",
       "4                               peach             1     Fashion & Lifestyle  \n",
       "...                               ...           ...                     ...  \n",
       "12301                            ebay             1       Business Services  \n",
       "12302    arkansas children's hospital             1              Healthcare  \n",
       "12303  northcentral technical college             1               Education  \n",
       "12304                           eaton             0  Transport & Automotive  \n",
       "12305                       since1910             1     Fashion & Lifestyle  \n",
       "\n",
       "[12306 rows x 39 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WNJsDfPjHW3F",
    "outputId": "a55a5b9c-7c81-4e75-8d19-4e500c5998c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'market', 'funding_total_usd', 'country_code', 'state_code',\n",
      "       'region', 'city', 'funding_rounds', 'founded_at', 'founded_month',\n",
      "       'founded_quarter', 'founded_year', 'first_funding_at',\n",
      "       'last_funding_at', 'seed', 'venture', 'equity_crowdfunding',\n",
      "       'undisclosed', 'convertible_note', 'debt_financing', 'angel', 'grant',\n",
      "       'private_equity', 'post_ipo_equity', 'post_ipo_debt',\n",
      "       'secondary_market', 'product_crowdfunding', 'round_A', 'round_B',\n",
      "       'round_C', 'round_D', 'round_E', 'round_F', 'round_G', 'round_H',\n",
      "       'category_count', 'clean_name', 'is_operating', 'category_updated'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(cleaned_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "zC24UKTnHb3t",
    "outputId": "713e404a-27cb-4e09-ab8c-952d00ef2886"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                     object\n",
       "market                   object\n",
       "funding_total_usd       float64\n",
       "country_code             object\n",
       "state_code               object\n",
       "region                   object\n",
       "city                     object\n",
       "funding_rounds            int64\n",
       "founded_at               object\n",
       "founded_month             int64\n",
       "founded_quarter           int64\n",
       "founded_year              int64\n",
       "first_funding_at         object\n",
       "last_funding_at          object\n",
       "seed                      int64\n",
       "venture                   int64\n",
       "equity_crowdfunding       int64\n",
       "undisclosed               int64\n",
       "convertible_note          int64\n",
       "debt_financing            int64\n",
       "angel                     int64\n",
       "grant                     int64\n",
       "private_equity            int64\n",
       "post_ipo_equity           int64\n",
       "post_ipo_debt             int64\n",
       "secondary_market          int64\n",
       "product_crowdfunding      int64\n",
       "round_A                   int64\n",
       "round_B                   int64\n",
       "round_C                   int64\n",
       "round_D                   int64\n",
       "round_E                   int64\n",
       "round_F                   int64\n",
       "round_G                   int64\n",
       "round_H                   int64\n",
       "category_count            int64\n",
       "clean_name               object\n",
       "is_operating              int64\n",
       "category_updated         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QjfPK5DQVo9f"
   },
   "outputs": [],
   "source": [
    "df_processed = cleaned_data.copy()\n",
    "\n",
    "# Convert date columns to datetime\n",
    "date_columns = ['founded_at', 'first_funding_at', 'last_funding_at']\n",
    "for col in date_columns:\n",
    "    df_processed[col] = pd.to_datetime(df_processed[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RdESdv-CVo9f"
   },
   "outputs": [],
   "source": [
    "#temporal features\n",
    "df_processed['days_to_first_funding'] = (df_processed['first_funding_at'] - df_processed['founded_at']).dt.days\n",
    "df_processed['funding_duration'] = (df_processed['last_funding_at'] - df_processed['first_funding_at']).dt.days\n",
    "df_processed['company_age'] = (pd.Timestamp('2024-01-01') - df_processed['founded_at']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7RWggpSKVo9g"
   },
   "outputs": [],
   "source": [
    "#NaN values\n",
    "df_processed['days_to_first_funding'] = df_processed['days_to_first_funding'].fillna(-1)\n",
    "df_processed['funding_duration'] = df_processed['funding_duration'].fillna(0)\n",
    "df_processed['company_age'] = df_processed['company_age'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cftl-7k8Vo9g"
   },
   "outputs": [],
   "source": [
    "# Group funding types\n",
    "df_processed['early_stage_funding'] = df_processed['seed'] + df_processed['angel']\n",
    "df_processed['growth_stage_funding'] = df_processed['round_A'] + df_processed['round_B']\n",
    "df_processed['late_stage_funding'] = df_processed['round_C'] + df_processed['round_D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cOKIaPNPVo9g",
    "outputId": "337a3477-0179-468d-cd60-eb3bb65f92a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows where 'round_E' is 0: 12229\n",
      "Number of rows where 'round_F' is 0: 12283\n",
      "Number of rows where 'round_G' is 0: 12229\n",
      "Number of rows where 'round_H' is 0: 12229\n"
     ]
    }
   ],
   "source": [
    "count_round_E = cleaned_data[cleaned_data['round_E'] == 0].shape[0]\n",
    "print(f\"Number of rows where 'round_E' is 0: {count_round_E}\")\n",
    "\n",
    "count_round_F = cleaned_data[cleaned_data['round_F'] == 0].shape[0]\n",
    "print(f\"Number of rows where 'round_F' is 0: {count_round_F}\")\n",
    "\n",
    "count_round_G = cleaned_data[cleaned_data['round_G'] == 0].shape[0]\n",
    "print(f\"Number of rows where 'round_G' is 0: {count_round_E}\")\n",
    "\n",
    "count_round_H = cleaned_data[cleaned_data['round_H'] == 0].shape[0]\n",
    "print(f\"Number of rows where 'round_H' is 0: {count_round_E}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4lC4yZ_3L4IP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#seleected features\n",
    "selected_features = [\n",
    "    'funding_total_usd',\n",
    "    'funding_rounds',\n",
    "    'days_to_first_funding',\n",
    "    'funding_duration',\n",
    "    'early_stage_funding',\n",
    "    'growth_stage_funding',\n",
    "    'late_stage_funding',\n",
    "    'category_count'\n",
    "]\n",
    "\n",
    "#encoding\n",
    "categorical_features = ['category_updated', 'state_code']\n",
    "le = LabelEncoder()\n",
    "for feature in categorical_features:\n",
    "    df_processed[feature + '_encoded'] = le.fit_transform(df_processed[feature])\n",
    "\n",
    "#combining\n",
    "feature_columns = selected_features + [f + '_encoded' for f in categorical_features]\n",
    "\n",
    "X = df_processed[feature_columns]\n",
    "y = df_processed['is_operating']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "#split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_scaled, y, test_size=0.2, random_state=42, stratify=y\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JWYjLE8PyhO9",
    "outputId": "ac8d458b-1739-40f4-838d-7d9dc5435faf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8235"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.info\n",
    "sum(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rf6OyDDXs0Sl"
   },
   "source": [
    "## First RF model with grid-search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TDgsRQCOs0Sl",
    "outputId": "f44aee5e-3f8f-40a2-8a35-989294e1f687"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning hyperparameters...\n",
      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=2, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=5, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=300; total time=   2.5s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=20, min_samples_split=10, n_estimators=300; total time=   2.5s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=2, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=200; total time=   1.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=5, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=300; total time=   2.5s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=300; total time=   2.5s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=30, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.5s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight={0: 5, 1: 1}, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.6s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=2, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.5s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.6s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.7s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=5, n_estimators=300; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.3s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=200; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=10, min_samples_split=10, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.3s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=200; total time=   2.7s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=100; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=100; total time=   1.5s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=300; total time=   3.6s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=300; total time=   3.5s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=300; total time=   3.6s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=300; total time=   4.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=2, n_estimators=300; total time=   4.1s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=200; total time=   2.5s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=200; total time=   2.5s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=200; total time=   2.4s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=200; total time=   2.4s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=200; total time=   2.5s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=300; total time=   3.8s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=300; total time=   3.3s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=300; total time=   3.1s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=5, n_estimators=300; total time=   3.5s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=200; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=300; total time=   3.3s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight=balanced, max_depth=20, min_samples_split=10, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=200; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=200; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=200; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=200; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=300; total time=   3.4s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=2, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=200; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=100; total time=   1.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=300; total time=   3.1s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=5, n_estimators=300; total time=   3.3s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=200; total time=   2.0s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=100; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=100; total time=   1.5s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=300; total time=   3.3s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=100; total time=   1.4s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=300; total time=   3.5s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=300; total time=   3.6s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=100; total time=   1.2s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=300; total time=   3.4s\n",
      "[CV] END class_weight=balanced, max_depth=30, min_samples_split=10, n_estimators=300; total time=   3.7s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=200; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=200; total time=   2.4s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=200; total time=   2.3s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=200; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=200; total time=   2.1s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.3s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.1s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.2s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.1s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=2, n_estimators=300; total time=   3.0s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=200; total time=   1.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=5, n_estimators=300; total time=   2.9s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=200; total time=   1.8s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.5s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.4s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.3s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.2s\n",
      "[CV] END class_weight=balanced, max_depth=None, min_samples_split=10, n_estimators=300; total time=   2.1s\n",
      "Best hyperparameters: {'class_weight': 'balanced', 'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "Training with the best parameters...\n",
      "Accuracy: 0.8347\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.47      0.07      0.12       403\n",
      "           1       0.84      0.98      0.91      2059\n",
      "\n",
      "    accuracy                           0.83      2462\n",
      "   macro avg       0.66      0.53      0.51      2462\n",
      "weighted avg       0.78      0.83      0.78      2462\n",
      "\n",
      "Confusion Matrix:\n",
      "[[  28  375]\n",
      " [  32 2027]]\n",
      "Feature Importances:\n",
      "                    Feature  Importance\n",
      "2     days_to_first_funding    0.213816\n",
      "0         funding_total_usd    0.181106\n",
      "3          funding_duration    0.101899\n",
      "9        state_code_encoded    0.093557\n",
      "8  category_updated_encoded    0.092756\n",
      "4       early_stage_funding    0.091211\n",
      "5      growth_stage_funding    0.087525\n",
      "7            category_count    0.078336\n",
      "1            funding_rounds    0.039191\n",
      "6        late_stage_funding    0.020602\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_recall_curve, auc\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#parameter grid for tuning\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [10, 20, 30, None],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'class_weight': [{0: 5, 1: 1}, 'balanced']\n",
    "}\n",
    "\n",
    "#GridSearchCV\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=RandomForestClassifier(random_state=42),\n",
    "    param_grid=param_grid,\n",
    "    cv=5,  # 5-fold cross-validation\n",
    "    scoring='accuracy',\n",
    "    n_jobs=-1,  # Use all available cores\n",
    "    verbose=2\n",
    ")\n",
    "\n",
    "#fitting\n",
    "print(\"Tuning hyperparameters...\")\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "#best model\n",
    "best_rf_model = grid_search.best_estimator_\n",
    "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
    "\n",
    "print(\"Training with the best parameters...\")\n",
    "\n",
    "y_pred = best_rf_model.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')\n",
    "\n",
    "print('Classification Report:')\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "importances = best_rf_model.feature_importances_\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_columns,\n",
    "    'Importance': importances\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print('Feature Importances:')\n",
    "print(feature_importance_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t0d-yveTs0Sm"
   },
   "source": [
    "The model achieves a high overall accuracy of 83.5% but shows significant class imbalance issues. For Class 0 (the minority class), the model only correctly identifies 7% of cases (recall of 0.07), and when it predicts Class 0, it's right less than half the time (precision of 0.47). This means out of 403 actual Class 0 cases, it only correctly identified 28 while misclassifying 375 as Class 1.\n",
    "In contrast, for Class 1 (the majority class), the model shows excellent performance with 98% recall and 84% precision. Out of 2059 actual Class 1 cases, it correctly identified 2027 while only misclassifying 32 as Class 0. This stark difference in performance (7% vs 98% recall) indicates a strong bias toward predicting the majority class, despite using balanced class weights in the model's configuration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mG0iVoGCs0Sm"
   },
   "source": [
    "## Using SMOTE to balanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GEgk86Bms0Sm"
   },
   "source": [
    "We try to use SMOTE to create sythetic samples of the miniority class and then performs hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2RQO4N4B6J0T",
    "outputId": "c1e8584c-40e4-4740-c0ff-7c5f7868c53d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "\n",
      "Best parameters: {'n_estimators': 300, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 15, 'class_weight': 'balanced'}\n",
      "\n",
      "Model Evaluation:\n",
      "-----------------\n",
      "Accuracy: 0.7510\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.36      0.32       403\n",
      "           1       0.87      0.83      0.85      2059\n",
      "\n",
      "    accuracy                           0.75      2462\n",
      "   macro avg       0.58      0.59      0.59      2462\n",
      "weighted avg       0.77      0.75      0.76      2462\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 146  257]\n",
      " [ 356 1703]]\n",
      "\n",
      "Top 10 Most Important Features:\n",
      "                    Feature  Importance       Std\n",
      "8  category_updated_encoded    0.170055  0.034791\n",
      "5      growth_stage_funding    0.138994  0.029938\n",
      "0         funding_total_usd    0.123970  0.019206\n",
      "2     days_to_first_funding    0.120475  0.012603\n",
      "7            category_count    0.098713  0.024386\n",
      "4       early_stage_funding    0.092163  0.013855\n",
      "1            funding_rounds    0.081620  0.016924\n",
      "9        state_code_encoded    0.080126  0.015144\n",
      "3          funding_duration    0.069495  0.011190\n",
      "6        late_stage_funding    0.024389  0.009821\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Probability Threshold Analysis:\n",
      "\n",
      "Threshold: 0.3\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.08      0.13       403\n",
      "           1       0.84      0.97      0.90      2059\n",
      "\n",
      "    accuracy                           0.82      2462\n",
      "   macro avg       0.58      0.52      0.51      2462\n",
      "weighted avg       0.76      0.82      0.77      2462\n",
      "\n",
      "\n",
      "Threshold: 0.4\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.23      0.27       403\n",
      "           1       0.86      0.91      0.88      2059\n",
      "\n",
      "    accuracy                           0.80      2462\n",
      "   macro avg       0.59      0.57      0.57      2462\n",
      "weighted avg       0.77      0.80      0.78      2462\n",
      "\n",
      "\n",
      "Threshold: 0.5\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.36      0.32       403\n",
      "           1       0.87      0.83      0.85      2059\n",
      "\n",
      "    accuracy                           0.75      2462\n",
      "   macro avg       0.58      0.59      0.59      2462\n",
      "weighted avg       0.77      0.75      0.76      2462\n",
      "\n",
      "\n",
      "Threshold: 0.6\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.27      0.53      0.35       403\n",
      "           1       0.89      0.71      0.79      2059\n",
      "\n",
      "    accuracy                           0.68      2462\n",
      "   macro avg       0.58      0.62      0.57      2462\n",
      "weighted avg       0.78      0.68      0.72      2462\n",
      "\n",
      "\n",
      "Threshold: 0.7\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.23      0.68      0.34       403\n",
      "           1       0.90      0.55      0.68      2059\n",
      "\n",
      "    accuracy                           0.57      2462\n",
      "   macro avg       0.56      0.61      0.51      2462\n",
      "weighted avg       0.79      0.57      0.62      2462\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import precision_recall_curve,auc\n",
    "\n",
    "#apply SMOTE to balance the training data\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "#broader hyperparameter grid\n",
    "param_dist = {\n",
    "    'n_estimators': [200, 300, 400],\n",
    "    'max_depth': [5, 7, 10, 15],\n",
    "    'min_samples_split': [5, 10, 15],\n",
    "    'min_samples_leaf': [4, 6, 8],\n",
    "    'max_features': ['sqrt', 'log2', None],\n",
    "    'class_weight': ['balanced', {0: 3, 1: 1}, {0: 5, 1: 1}]\n",
    "}\n",
    "\n",
    "rf_random = RandomizedSearchCV(\n",
    "    RandomForestClassifier(random_state=42),\n",
    "    param_distributions=param_dist,\n",
    "    n_iter=100,\n",
    "    cv=5,\n",
    "    scoring='f1',\n",
    "    n_jobs=-1,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "#model with balanced data\n",
    "print(\"Training model...\")\n",
    "rf_random.fit(X_train_balanced, y_train_balanced)\n",
    "\n",
    "best_rf = rf_random.best_estimator_\n",
    "print(\"\\nBest parameters:\", rf_random.best_params_)\n",
    "\n",
    "y_pred = best_rf.predict(X_test)\n",
    "y_prob = best_rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "print(\"\\nModel Evaluation:\")\n",
    "print(\"-----------------\")\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.4f}')\n",
    "\n",
    "print('\\nClassification Report:')\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print('\\nConfusion Matrix:')\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_prob)\n",
    "pr_auc = auc(recall, precision)\n",
    "\n",
    "importances = best_rf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in best_rf.estimators_], axis=0)\n",
    "\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_columns,\n",
    "    'Importance': importances,\n",
    "    'Std': std\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print('\\nTop 10 Most Important Features:')\n",
    "print(feature_importance_df.head(10))\n",
    "\n",
    "\n",
    "def plot_feature_importance(importance_df, top_n=10):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    importance_df = importance_df.head(top_n)\n",
    "    plt.bar(importance_df['Feature'], importance_df['Importance'])\n",
    "    plt.errorbar(x=range(len(importance_df)),\n",
    "                y=importance_df['Importance'],\n",
    "                yerr=importance_df['Std'],\n",
    "                fmt='none', color='black', capsize=5)\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.title(f'Top {top_n} Feature Importances with Standard Deviation')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_feature_importance(feature_importance_df)\n",
    "\n",
    "print(\"\\nProbability Threshold Analysis:\")\n",
    "thresholds = np.arange(0.3, 0.8, 0.1)\n",
    "for threshold in thresholds:\n",
    "    y_pred_threshold = (y_prob >= threshold).astype(int)\n",
    "    print(f\"\\nThreshold: {threshold:.1f}\")\n",
    "    print(classification_report(y_test, y_pred_threshold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7MqFjjd-yhO9",
    "outputId": "0e4a5307-5e6e-4352-90af-9e884d5b4a22"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Performance Metrics:\n",
      "------------------------------\n",
      "AUC Score: 0.6650\n",
      "Precision: 0.8689\n",
      "Recall: 0.8271\n",
      "F1 Score: 0.8475\n",
      "\n",
      "Confusion Matrix (Counts):\n",
      "[[ 146  257]\n",
      " [ 356 1703]]\n",
      "\n",
      "Confusion Matrix (Percentages):\n",
      "True Negatives: 36.23%\n",
      "False Positives: 63.77%\n",
      "False Negatives: 17.29%\n",
      "True Positives: 82.71%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, precision_score, recall_score, f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#ROC curve\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_prob)\n",
    "auc_score = auc(fpr_rf, tpr_rf)\n",
    "\n",
    "#plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fpr_rf, tpr_rf, color='darkblue', lw=2,\n",
    "         label=f'ROC curve (AUC = {auc_score:.4f})')\n",
    "plt.plot([0, 1], [0, 1], 'r--', label='Random classifier')\n",
    "plt.xlim([-0.01, 1.01])\n",
    "plt.ylim([-0.01, 1.01])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve with AUC Score')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "metrics_dict = {\n",
    "    'AUC Score': auc_score,\n",
    "    'Precision': precision_score(y_test, y_pred),\n",
    "    'Recall': recall_score(y_test, y_pred),\n",
    "    'F1 Score': f1_score(y_test, y_pred)\n",
    "}\n",
    "\n",
    "print(\"\\nModel Performance Metrics:\")\n",
    "print(\"-\" * 30)\n",
    "for metric, value in metrics_dict.items():\n",
    "    print(f\"{metric}: {value:.4f}\")\n",
    "\n",
    "# Calculate confusion matrix with percentages\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "conf_matrix_pct = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "print(\"\\nConfusion Matrix (Counts):\")\n",
    "print(conf_matrix)\n",
    "print(\"\\nConfusion Matrix (Percentages):\")\n",
    "print(\"True Negatives: {:.2f}%\".format(conf_matrix_pct[0][0] * 100))\n",
    "print(\"False Positives: {:.2f}%\".format(conf_matrix_pct[0][1] * 100))\n",
    "print(\"False Negatives: {:.2f}%\".format(conf_matrix_pct[1][0] * 100))\n",
    "print(\"True Positives: {:.2f}%\".format(conf_matrix_pct[1][1] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vngDTqmsyhO9",
    "outputId": "5b52f9f0-6bd8-45db-fcac-5a3a93fc0f26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "\n",
      "Best parameters: {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 6, 'max_features': 'sqrt', 'max_depth': 5, 'class_weight': {0: 10, 1: 1}}\n",
      "\n",
      "Initial Model Evaluation:\n",
      "-----------------\n",
      "Accuracy: 0.1751\n",
      "\n",
      "Initial Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.17      1.00      0.28       403\n",
      "           1       1.00      0.01      0.03      2059\n",
      "\n",
      "    accuracy                           0.18      2462\n",
      "   macro avg       0.58      0.51      0.16      2462\n",
      "weighted avg       0.86      0.18      0.07      2462\n",
      "\n",
      "\n",
      "Final Results (Threshold = 0.30):\n",
      "--------------------------------------------------\n",
      "\n",
      "Confusion Matrix (Counts):\n",
      "[[ 382   21]\n",
      " [1749  310]]\n",
      "\n",
      "Confusion Matrix (Percentages):\n",
      "True Negatives: 94.79%\n",
      "False Positives: 5.21%\n",
      "False Negatives: 84.94%\n",
      "True Positives: 15.06%\n",
      "\n",
      "Final Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.18      0.95      0.30       403\n",
      "           1       0.94      0.15      0.26      2059\n",
      "\n",
      "    accuracy                           0.28      2462\n",
      "   macro avg       0.56      0.55      0.28      2462\n",
      "weighted avg       0.81      0.28      0.27      2462\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_recall_curve, auc, roc_curve\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "#stronger class weights for class 0\n",
    "param_dist = {\n",
    "    'n_estimators': [200, 300, 400],\n",
    "    'max_depth': [5, 7, 10, 15],\n",
    "    'min_samples_split': [5, 10, 15],\n",
    "    'min_samples_leaf': [4, 6, 8],\n",
    "    'max_features': ['sqrt', 'log2', None],\n",
    "    'class_weight': ['balanced', {0: 5, 1: 1}, {0: 7, 1: 1}, {0: 10, 1: 1}]\n",
    "}\n",
    "\n",
    "rf_random = RandomizedSearchCV(\n",
    "    RandomForestClassifier(random_state=42),\n",
    "    param_distributions=param_dist,\n",
    "    n_iter=100,\n",
    "    cv=5,\n",
    "    scoring='precision',\n",
    "    n_jobs=-1,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "\n",
    "print(\"Training model...\")\n",
    "rf_random.fit(X_train_balanced, y_train_balanced)\n",
    "\n",
    "best_rf = rf_random.best_estimator_\n",
    "print(\"\\nBest parameters:\", rf_random.best_params_)\n",
    "\n",
    "y_pred = best_rf.predict(X_test)\n",
    "y_prob = best_rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "print(\"\\nInitial Model Evaluation:\")\n",
    "print(\"-----------------\")\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.4f}')\n",
    "print('\\nInitial Classification Report:')\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "thresholds = np.linspace(0.3, 0.9, 20)\n",
    "threshold_metrics = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "    y_pred_threshold = (y_prob >= threshold).astype(int)\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred_threshold)\n",
    "    conf_matrix_pct = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    threshold_metrics.append({\n",
    "        'threshold': threshold,\n",
    "        'precision': precision_score(y_test, y_pred_threshold),\n",
    "        'recall': recall_score(y_test, y_pred_threshold),\n",
    "        'f1': f1_score(y_test, y_pred_threshold),\n",
    "        'fp_rate': conf_matrix_pct[0][1] * 100,\n",
    "        'tp_rate': conf_matrix_pct[1][1] * 100\n",
    "    })\n",
    "\n",
    "metrics_df = pd.DataFrame(threshold_metrics)\n",
    "optimal_threshold = metrics_df[metrics_df['fp_rate'] < 40].iloc[0]['threshold']\n",
    "\n",
    "y_pred_final = (y_prob >= optimal_threshold).astype(int)\n",
    "\n",
    "conf_matrix_final = confusion_matrix(y_test, y_pred_final)\n",
    "conf_matrix_pct_final = conf_matrix_final.astype('float') / conf_matrix_final.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "print(f\"\\nFinal Results (Threshold = {optimal_threshold:.2f}):\")\n",
    "print(\"-\" * 50)\n",
    "print(\"\\nConfusion Matrix (Counts):\")\n",
    "print(conf_matrix_final)\n",
    "print(\"\\nConfusion Matrix (Percentages):\")\n",
    "print(f\"True Negatives: {conf_matrix_pct_final[0][0]*100:.2f}%\")\n",
    "print(f\"False Positives: {conf_matrix_pct_final[0][1]*100:.2f}%\")\n",
    "print(f\"False Negatives: {conf_matrix_pct_final[1][0]*100:.2f}%\")\n",
    "print(f\"True Positives: {conf_matrix_pct_final[1][1]*100:.2f}%\")\n",
    "\n",
    "print(\"\\nFinal Classification Report:\")\n",
    "print(classification_report(y_test, y_pred_final))\n",
    "\n",
    "# Plot metrics vs threshold\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(metrics_df['threshold'], metrics_df['fp_rate'], label='False Positive Rate')\n",
    "plt.plot(metrics_df['threshold'], metrics_df['recall'] * 100, label='Recall')\n",
    "plt.plot(metrics_df['threshold'], metrics_df['precision'] * 100, label='Precision')\n",
    "plt.axvline(x=optimal_threshold, color='r', linestyle='--', label='Optimal Threshold')\n",
    "plt.xlabel('Classification Threshold')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Metrics vs Classification Threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "importances = best_rf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in best_rf.estimators_], axis=0)\n",
    "\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_columns,\n",
    "    'Importance': importances,\n",
    "    'Std': std\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plot_feature_importance(feature_importance_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iS-0YWoXs0Sm",
    "outputId": "5884da56-f5e7-47bc-c243-8de0b178f69d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "\n",
      "Best parameters: {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 6, 'max_features': 'sqrt', 'max_depth': 5, 'class_weight': {0: 10, 1: 1}}\n",
      "\n",
      "Performing cross-validation on best model...\n",
      "\n",
      "Cross-Validation Results:\n",
      "--------------------------------------------------\n",
      "\n",
      "Accuracy:\n",
      "Training - Mean: 0.510 (±0.002)\n",
      "Validation - Mean: 0.509 (±0.006)\n",
      "\n",
      "Precision:\n",
      "Training - Mean: 1.000 (±0.000)\n",
      "Validation - Mean: 0.979 (±0.038)\n",
      "\n",
      "Recall:\n",
      "Training - Mean: 0.021 (±0.005)\n",
      "Validation - Mean: 0.019 (±0.012)\n",
      "\n",
      "F1:\n",
      "Training - Mean: 0.041 (±0.009)\n",
      "Validation - Mean: 0.037 (±0.024)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initial Model Evaluation:\n",
      "-----------------\n",
      "Accuracy: 0.1751\n",
      "\n",
      "Initial Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.17      1.00      0.28       403\n",
      "           1       1.00      0.01      0.03      2059\n",
      "\n",
      "    accuracy                           0.18      2462\n",
      "   macro avg       0.58      0.51      0.16      2462\n",
      "weighted avg       0.86      0.18      0.07      2462\n",
      "\n",
      "\n",
      "Final Results (Threshold = 0.30):\n",
      "--------------------------------------------------\n",
      "\n",
      "Confusion Matrix (Counts):\n",
      "[[ 382   21]\n",
      " [1749  310]]\n",
      "\n",
      "Confusion Matrix (Percentages):\n",
      "True Negatives: 94.79%\n",
      "False Positives: 5.21%\n",
      "False Negatives: 84.94%\n",
      "True Positives: 15.06%\n",
      "\n",
      "Final Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.18      0.95      0.30       403\n",
      "           1       0.94      0.15      0.26      2059\n",
      "\n",
      "    accuracy                           0.28      2462\n",
      "   macro avg       0.56      0.55      0.28      2462\n",
      "weighted avg       0.81      0.28      0.27      2462\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/xiaohan/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import (classification_report, confusion_matrix, accuracy_score,\n",
    "                           precision_recall_curve, auc, roc_curve, precision_score,\n",
    "                           recall_score, f1_score)\n",
    "from sklearn.model_selection import RandomizedSearchCV, cross_validate, StratifiedKFold\n",
    "\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "param_dist = {\n",
    "    'n_estimators': [200, 300, 400],\n",
    "    'max_depth': [5, 7, 10, 15],\n",
    "    'min_samples_split': [5, 10, 15],\n",
    "    'min_samples_leaf': [4, 6, 8],\n",
    "    'max_features': ['sqrt', 'log2', None],\n",
    "    'class_weight': ['balanced', {0: 5, 1: 1}, {0: 7, 1: 1}, {0: 10, 1: 1}]\n",
    "}\n",
    "\n",
    "rf_random = RandomizedSearchCV(\n",
    "    RandomForestClassifier(random_state=42),\n",
    "    param_distributions=param_dist,\n",
    "    n_iter=100,\n",
    "    cv=5,\n",
    "    scoring='precision',\n",
    "    n_jobs=-1,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "print(\"Training model...\")\n",
    "rf_random.fit(X_train_balanced, y_train_balanced)\n",
    "\n",
    "best_rf = rf_random.best_estimator_\n",
    "print(\"\\nBest parameters:\", rf_random.best_params_)\n",
    "\n",
    "print(\"\\nPerforming cross-validation on best model...\")\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "scoring = {\n",
    "    'accuracy': 'accuracy',\n",
    "    'precision': 'precision',\n",
    "    'recall': 'recall',\n",
    "    'f1': 'f1'\n",
    "}\n",
    "\n",
    "cv_results = cross_validate(\n",
    "    best_rf,\n",
    "    X_train_balanced,\n",
    "    y_train_balanced,\n",
    "    cv=skf,\n",
    "    scoring=scoring,\n",
    "    return_train_score=True,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "print(\"\\nCross-Validation Results:\")\n",
    "print(\"-\" * 50)\n",
    "for metric in scoring.keys():\n",
    "    train_scores = cv_results[f'train_{metric}']\n",
    "    val_scores = cv_results[f'test_{metric}']\n",
    "    print(f\"\\n{metric.capitalize()}:\")\n",
    "    print(f\"Training - Mean: {train_scores.mean():.3f} (±{train_scores.std()*2:.3f})\")\n",
    "    print(f\"Validation - Mean: {val_scores.mean():.3f} (±{val_scores.std()*2:.3f})\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "for metric in scoring.keys():\n",
    "    plt.plot(cv_results[f'test_{metric}'], label=f'Validation {metric}')\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Cross-Validation Scores Across Folds')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "y_pred = best_rf.predict(X_test)\n",
    "y_prob = best_rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "print(\"\\nInitial Model Evaluation:\")\n",
    "print(\"-----------------\")\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.4f}')\n",
    "print('\\nInitial Classification Report:')\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "thresholds = np.linspace(0.3, 0.9, 20)\n",
    "threshold_metrics = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "    y_pred_threshold = (y_prob >= threshold).astype(int)\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred_threshold)\n",
    "    conf_matrix_pct = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    threshold_metrics.append({\n",
    "        'threshold': threshold,\n",
    "        'precision': precision_score(y_test, y_pred_threshold),\n",
    "        'recall': recall_score(y_test, y_pred_threshold),\n",
    "        'f1': f1_score(y_test, y_pred_threshold),\n",
    "        'fp_rate': conf_matrix_pct[0][1] * 100,\n",
    "        'tp_rate': conf_matrix_pct[1][1] * 100\n",
    "    })\n",
    "\n",
    "metrics_df = pd.DataFrame(threshold_metrics)\n",
    "optimal_threshold = metrics_df[metrics_df['fp_rate'] < 40].iloc[0]['threshold']\n",
    "\n",
    "y_pred_final = (y_prob >= optimal_threshold).astype(int)\n",
    "\n",
    "conf_matrix_final = confusion_matrix(y_test, y_pred_final)\n",
    "conf_matrix_pct_final = conf_matrix_final.astype('float') / conf_matrix_final.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "print(f\"\\nFinal Results (Threshold = {optimal_threshold:.2f}):\")\n",
    "print(\"-\" * 50)\n",
    "print(\"\\nConfusion Matrix (Counts):\")\n",
    "print(conf_matrix_final)\n",
    "print(\"\\nConfusion Matrix (Percentages):\")\n",
    "print(f\"True Negatives: {conf_matrix_pct_final[0][0]*100:.2f}%\")\n",
    "print(f\"False Positives: {conf_matrix_pct_final[0][1]*100:.2f}%\")\n",
    "print(f\"False Negatives: {conf_matrix_pct_final[1][0]*100:.2f}%\")\n",
    "print(f\"True Positives: {conf_matrix_pct_final[1][1]*100:.2f}%\")\n",
    "\n",
    "print(\"\\nFinal Classification Report:\")\n",
    "print(classification_report(y_test, y_pred_final))\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(metrics_df['threshold'], metrics_df['fp_rate'], label='False Positive Rate')\n",
    "plt.plot(metrics_df['threshold'], metrics_df['recall'] * 100, label='Recall')\n",
    "plt.plot(metrics_df['threshold'], metrics_df['precision'] * 100, label='Precision')\n",
    "plt.axvline(x=optimal_threshold, color='r', linestyle='--', label='Optimal Threshold')\n",
    "plt.xlabel('Classification Threshold')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Metrics vs Classification Threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "importances = best_rf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in best_rf.estimators_], axis=0)\n",
    "\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_columns,\n",
    "    'Importance': importances,\n",
    "    'Std': std\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plot_feature_importance(feature_importance_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6CX8Ed3Qs0Sn"
   },
   "source": [
    "## Use RandomOverSampler and RandomUnderSampler from the imbalanced-learn (imblearn) library to create two variations of the training data alongside the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "J40VSO_cs0Sn",
    "outputId": "5731c129-b17d-4fd8-abb0-6e2b6c8b3397"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original training set distribution:\n",
      "is_operating\n",
      "1    8235\n",
      "0    1609\n",
      "Name: count, dtype: int64\n",
      "\n",
      "After oversampling distribution:\n",
      "is_operating\n",
      "1    8235\n",
      "0    8235\n",
      "Name: count, dtype: int64\n",
      "\n",
      "After undersampling distribution:\n",
      "is_operating\n",
      "0    1609\n",
      "1    1609\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Original Model Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.03      0.06       403\n",
      "           1       0.84      0.99      0.91      2059\n",
      "\n",
      "    accuracy                           0.83      2462\n",
      "   macro avg       0.63      0.51      0.48      2462\n",
      "weighted avg       0.77      0.83      0.77      2462\n",
      "\n",
      "\n",
      "Oversampling Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.39      0.35       403\n",
      "           1       0.88      0.84      0.86      2059\n",
      "\n",
      "    accuracy                           0.77      2462\n",
      "   macro avg       0.60      0.61      0.60      2462\n",
      "weighted avg       0.78      0.77      0.77      2462\n",
      "\n",
      "\n",
      "Undersampling Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.65      0.36       403\n",
      "           1       0.90      0.62      0.73      2059\n",
      "\n",
      "    accuracy                           0.62      2462\n",
      "   macro avg       0.57      0.63      0.55      2462\n",
      "weighted avg       0.79      0.62      0.67      2462\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Importances Comparison:\n",
      "                    Feature  Importance_Original  Importance_Oversampling  \\\n",
      "2     days_to_first_funding             0.201592                 0.197668   \n",
      "0         funding_total_usd             0.175394                 0.176755   \n",
      "3          funding_duration             0.120587                 0.105160   \n",
      "5      growth_stage_funding             0.112754                 0.101196   \n",
      "8  category_updated_encoded             0.082892                 0.105200   \n",
      "4       early_stage_funding             0.082329                 0.089315   \n",
      "9        state_code_encoded             0.080157                 0.090097   \n",
      "7            category_count             0.067822                 0.068120   \n",
      "1            funding_rounds             0.042310                 0.041257   \n",
      "6        late_stage_funding             0.034165                 0.025232   \n",
      "\n",
      "   Importance_Undersampling  \n",
      "2                  0.181360  \n",
      "0                  0.177986  \n",
      "3                  0.104140  \n",
      "5                  0.103905  \n",
      "8                  0.111486  \n",
      "4                  0.086704  \n",
      "9                  0.087356  \n",
      "7                  0.077431  \n",
      "1                  0.044202  \n",
      "6                  0.025429  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import (\n",
    "    classification_report,\n",
    "    confusion_matrix,\n",
    "    ConfusionMatrixDisplay,\n",
    "    roc_curve,\n",
    "    auc,\n",
    "    RocCurveDisplay\n",
    ")\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Use the same feature engineering as before\n",
    "X = df_processed[feature_columns]\n",
    "y = df_processed['is_operating']\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "# Print original distribution\n",
    "print(\"Original training set distribution:\")\n",
    "print(pd.Series(y_train).value_counts())\n",
    "\n",
    "# 1. Original Model (for comparison)\n",
    "rf_original = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_depth=15,\n",
    "    min_samples_split=5,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "rf_original.fit(X_train, y_train)\n",
    "y_pred_original = rf_original.predict(X_test)\n",
    "y_pred_proba_original = rf_original.predict_proba(X_test)\n",
    "\n",
    "# 2. Oversampling\n",
    "oversample = RandomOverSampler(random_state=42)\n",
    "X_over, y_over = oversample.fit_resample(X_train, y_train)\n",
    "print(\"\\nAfter oversampling distribution:\")\n",
    "print(pd.Series(y_over).value_counts())\n",
    "\n",
    "rf_over = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_depth=15,\n",
    "    min_samples_split=5,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "rf_over.fit(X_over, y_over)\n",
    "y_pred_over = rf_over.predict(X_test)\n",
    "y_pred_proba_over = rf_over.predict_proba(X_test)\n",
    "\n",
    "# 3. Undersampling\n",
    "undersample = RandomUnderSampler(random_state=42)\n",
    "X_under, y_under = undersample.fit_resample(X_train, y_train)\n",
    "print(\"\\nAfter undersampling distribution:\")\n",
    "print(pd.Series(y_under).value_counts())\n",
    "\n",
    "rf_under = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_depth=15,\n",
    "    min_samples_split=5,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "rf_under.fit(X_under, y_under)\n",
    "y_pred_under = rf_under.predict(X_test)\n",
    "y_pred_proba_under = rf_under.predict_proba(X_test)\n",
    "\n",
    "# Print classification reports\n",
    "print(\"\\nOriginal Model Results:\")\n",
    "print(classification_report(y_test, y_pred_original))\n",
    "print(\"\\nOversampling Results:\")\n",
    "print(classification_report(y_test, y_pred_over))\n",
    "print(\"\\nUndersampling Results:\")\n",
    "print(classification_report(y_test, y_pred_under))\n",
    "\n",
    "# Plot confusion matrices using ConfusionMatrixDisplay\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "titles = ['Original Model', 'Oversampling Model', 'Undersampling Model']\n",
    "predictions = [y_pred_original, y_pred_over, y_pred_under]\n",
    "\n",
    "for ax, y_pred, title in zip(axes, predictions, titles):\n",
    "    ConfusionMatrixDisplay.from_predictions(\n",
    "        y_test,\n",
    "        y_pred,\n",
    "        ax=ax,\n",
    "        cmap='Blues',\n",
    "        values_format='d'\n",
    "    )\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot ROC curves using RocCurveDisplay\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Plot ROC curves for all models\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_test,\n",
    "    y_pred_proba_original[:, 1],\n",
    "    name='Original Model'\n",
    ")\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_test,\n",
    "    y_pred_proba_over[:, 1],\n",
    "    name='Oversampling'\n",
    ")\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_test,\n",
    "    y_pred_proba_under[:, 1],\n",
    "    name='Undersampling'\n",
    ")\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve Comparison')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Compare feature importances\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_columns,\n",
    "    'Importance_Original': rf_original.feature_importances_,\n",
    "    'Importance_Oversampling': rf_over.feature_importances_,\n",
    "    'Importance_Undersampling': rf_under.feature_importances_\n",
    "})\n",
    "print(\"\\nFeature Importances Comparison:\")\n",
    "print(feature_importance_df.sort_values('Importance_Original', ascending=False))"
   ]
  }
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