Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('50_Startups.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['R&D Spend', 'Administration', 'Marketing Spend']]\n",
    "y = df['Profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predictions')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predict)\n",
    "plt.xlabel('Y_test')\n",
    "plt.ylabel('Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9526871409741319"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.explained_variance_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.80018553, -0.08553751,  0.03032614])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>0.800186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Administration</td>\n",
       "      <td>-0.085538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Marketing Spend</td>\n",
       "      <td>0.030326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Coeff\n",
       "R&D Spend        0.800186\n",
       "Administration  -0.085538\n",
       "Marketing Spend  0.030326"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = pd.DataFrame(lm.coef_,x.columns,columns=['Coeff'])\n",
    "cf"
   ]
  }
 ],
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