Data Preprocessing templet1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8f641957",
   "metadata": {},
   "source": [
    "# Data Preprocessing Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b178aab7",
   "metadata": {},
   "source": [
    "# Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc72de13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6081997",
   "metadata": {},
   "source": [
    "# Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19fdbe57",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f56dcad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "4  Germany  40.0      NaN       Yes\n",
       "5   France  35.0  58000.0       Yes\n",
       "6    Spain   NaN  52000.0        No\n",
       "7   France  48.0  79000.0       Yes\n",
       "8  Germany  50.0  83000.0        No\n",
       "9   France  37.0  67000.0       Yes"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e27aad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['France' 44.0 72000.0]\n",
      " ['Spain' 27.0 48000.0]\n",
      " ['Germany' 30.0 54000.0]\n",
      " ['Spain' 38.0 61000.0]\n",
      " ['Germany' 40.0 nan]\n",
      " ['France' 35.0 58000.0]\n",
      " ['Spain' nan 52000.0]\n",
      " ['France' 48.0 79000.0]\n",
      " ['Germany' 50.0 83000.0]\n",
      " ['France' 37.0 67000.0]]\n"
     ]
    }
   ],
   "source": [
    "X=df.iloc[:,:-1].values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0d0bc6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "Y=df.iloc[:,-1].values\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e69218",
   "metadata": {},
   "source": [
    "# Taking care of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1eb86c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imputer.fit(X[:,1:3])\n",
    "(X[:,1:3]) = imputer.transform(X[:,1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25ca5023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['France', 44.0, 72000.0],\n",
       "       ['Spain', 27.0, 48000.0],\n",
       "       ['Germany', 30.0, 54000.0],\n",
       "       ['Spain', 38.0, 61000.0],\n",
       "       ['Germany', 40.0, 63777.77777777778],\n",
       "       ['France', 35.0, 58000.0],\n",
       "       ['Spain', 38.77777777777778, 52000.0],\n",
       "       ['France', 48.0, 79000.0],\n",
       "       ['Germany', 50.0, 83000.0],\n",
       "       ['France', 37.0, 67000.0]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879fcad6",
   "metadata": {},
   "source": [
    "# Encoding categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca2dd58",
   "metadata": {},
   "source": [
    "# Encoding the Independent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62ca3139",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct=ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[0])], remainder='passthrough')\n",
    "X = np.array(ct.fit_transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "235849f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 0.0, 44.0, 72000.0],\n",
       "       [0.0, 0.0, 1.0, 27.0, 48000.0],\n",
       "       [0.0, 1.0, 0.0, 30.0, 54000.0],\n",
       "       [0.0, 0.0, 1.0, 38.0, 61000.0],\n",
       "       [0.0, 1.0, 0.0, 40.0, 63777.77777777778],\n",
       "       [1.0, 0.0, 0.0, 35.0, 58000.0],\n",
       "       [0.0, 0.0, 1.0, 38.77777777777778, 52000.0],\n",
       "       [1.0, 0.0, 0.0, 48.0, 79000.0],\n",
       "       [0.0, 1.0, 0.0, 50.0, 83000.0],\n",
       "       [1.0, 0.0, 0.0, 37.0, 67000.0]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7eac03a",
   "metadata": {},
   "source": [
    "### Encoding the Dependent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36cb4af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "Y=le.fit_transform(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99ab4a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28eaedc2",
   "metadata": {},
   "source": [
    "# Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "03d03c1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "24fc406f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0 0.0 1.0 38.77777777777778 52000.0]\n",
      " [0.0 1.0 0.0 40.0 63777.77777777778]\n",
      " [1.0 0.0 0.0 44.0 72000.0]\n",
      " [0.0 0.0 1.0 38.0 61000.0]\n",
      " [0.0 0.0 1.0 27.0 48000.0]\n",
      " [1.0 0.0 0.0 48.0 79000.0]\n",
      " [0.0 1.0 0.0 50.0 83000.0]\n",
      " [1.0 0.0 0.0 35.0 58000.0]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a3f76ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0 1.0 0.0 30.0 54000.0]\n",
      " [1.0 0.0 0.0 37.0 67000.0]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ece85554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f463942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "print(Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281dbc4c",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "92293789",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train[:, 3:] = sc.fit_transform(X_train[:, 3:])\n",
    "X_test[:, 3:] = sc.transform(X_test[:, 3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "63c7d0c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0 0.0 1.0 -0.19159184384578545 -1.0781259408412425]\n",
      " [0.0 1.0 0.0 -0.014117293757057777 -0.07013167641635372]\n",
      " [1.0 0.0 0.0 0.566708506533324 0.633562432710455]\n",
      " [0.0 0.0 1.0 -0.30453019390224867 -0.30786617274297867]\n",
      " [0.0 0.0 1.0 -1.9018011447007988 -1.420463615551582]\n",
      " [1.0 0.0 0.0 1.1475343068237058 1.232653363453549]\n",
      " [0.0 1.0 0.0 1.4379472069688968 1.5749910381638885]\n",
      " [1.0 0.0 0.0 -0.7401495441200351 -0.5646194287757332]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ea68a6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0 1.0 0.0 -1.4661817944830124 -0.9069571034860727]\n",
      " [1.0 0.0 0.0 -0.44973664397484414 0.2056403393225306]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dc05e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X_train)"
   ]
  }
 ],
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