Code/Day3-MultipleLinearReg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dataset=pd.read_csv('../datasets/50_Startups.csv')\n",
    "X=dataset.iloc[:,:-1].values\n",
    "Y=dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   R&D Spend  Administration  Marketing Spend       State     Profit\n0  165349.20       136897.80        471784.10    New York  192261.83\n1  162597.70       151377.59        443898.53  California  191792.06\n2  153441.51       101145.55        407934.54     Florida  191050.39\n3  144372.41       118671.85        383199.62    New York  182901.99\n4  142107.34        91391.77        366168.42     Florida  166187.94\n5  131876.90        99814.71        362861.36    New York  156991.12\n6  134615.46       147198.87        127716.82  California  156122.51\n7  130298.13       145530.06        323876.68     Florida  155752.60\n8  120542.52       148718.95        311613.29    New York  152211.77\n9  123334.88       108679.17        304981.62  California  149759.96",
      "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>R&amp;D Spend</th>\n      <th>Administration</th>\n      <th>Marketing Spend</th>\n      <th>State</th>\n      <th>Profit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>165349.20</td>\n      <td>136897.80</td>\n      <td>471784.10</td>\n      <td>New York</td>\n      <td>192261.83</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>162597.70</td>\n      <td>151377.59</td>\n      <td>443898.53</td>\n      <td>California</td>\n      <td>191792.06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>153441.51</td>\n      <td>101145.55</td>\n      <td>407934.54</td>\n      <td>Florida</td>\n      <td>191050.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>144372.41</td>\n      <td>118671.85</td>\n      <td>383199.62</td>\n      <td>New York</td>\n      <td>182901.99</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>142107.34</td>\n      <td>91391.77</td>\n      <td>366168.42</td>\n      <td>Florida</td>\n      <td>166187.94</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>131876.90</td>\n      <td>99814.71</td>\n      <td>362861.36</td>\n      <td>New York</td>\n      <td>156991.12</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>134615.46</td>\n      <td>147198.87</td>\n      <td>127716.82</td>\n      <td>California</td>\n      <td>156122.51</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>130298.13</td>\n      <td>145530.06</td>\n      <td>323876.68</td>\n      <td>Florida</td>\n      <td>155752.60</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>120542.52</td>\n      <td>148718.95</td>\n      <td>311613.29</td>\n      <td>New York</td>\n      <td>152211.77</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>123334.88</td>\n      <td>108679.17</td>\n      <td>304981.62</td>\n      <td>California</td>\n      <td>149759.96</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "******************** Before Label Encoder ********************\n\n['New York' 'California' 'Florida' 'New York' 'Florida' 'New York'\n 'California' 'Florida' 'New York' 'California' 'Florida' 'California'\n 'Florida' 'California' 'Florida' 'New York' 'California' 'New York'\n 'Florida' 'New York' 'California' 'New York' 'Florida' 'Florida'\n 'New York' 'California' 'Florida' 'New York' 'Florida' 'New York'\n 'Florida' 'New York' 'California' 'Florida' 'California' 'New York'\n 'Florida' 'California' 'New York' 'California' 'California' 'Florida'\n 'California' 'New York' 'California' 'New York' 'Florida' 'California'\n 'New York' 'California']\n******************** After Label Encoder ********************\n\n[2 0 1 2 1 2 0 1 2 0 1 0 1 0 1 2 0 2 1 2 0 2 1 1 2 0 1 2 1 2 1 2 0 1 0 2 1\n 0 2 0 0 1 0 2 0 2 1 0 2 0]\n"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "print('*'* 20 +' Before Label Encoder '+'*'*20+'\\n')\n",
    "print(X[:,3])\n",
    "X[:,3]=labelencoder.fit_transform(X[:,3])\n",
    "print('*'* 20 +' After Label Encoder '+'*'*20+'\\n')\n",
    "print(X[:,3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "onehotencoder=OneHotEncoder(categories='auto')\n",
    "print('*'* 20 +' Before One hot Encoder '+'*'*20+'\\n')\n",
    "print(X)\n",
    "X=onehotencoder.fit_transform(X).toarray()\n",
    "print('*'* 20 +' After One hot Encoder '+'*'*20+'\\n')\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=X[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,Y_train,Y_test= train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "regression=LinearRegression()\n",
    "regression=regression.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1490\n10\n[[0. 0. 0. ... 0. 1. 0.]\n [0. 0. 0. ... 1. 0. 0.]\n [0. 0. 0. ... 0. 1. 0.]\n ...\n [0. 0. 0. ... 0. 0. 1.]\n [0. 0. 0. ... 0. 1. 0.]\n [0. 0. 0. ... 0. 1. 0.]] [110421.259933    99635.0731779  110421.259933   110421.259933\n 110421.259933   119829.39601805 119829.39601805 119829.39601805\n 110421.259933   110421.259933  ]\n"
    }
   ],
   "source": [
    "y_pred= regression.predict(X_test)\n",
    "print(X_test.size)\n",
    "print(Y_test.size)\n",
    "print(X_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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