Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "df=pd.read_csv('placement.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   cgpa  package\n0  6.89     3.26\n1  5.12     1.98\n2  7.82     3.25\n3  7.42     3.67\n4  6.94     3.57",
      "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>cgpa</th>\n      <th>package</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6.89</td>\n      <td>3.26</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.12</td>\n      <td>1.98</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.82</td>\n      <td>3.25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7.42</td>\n      <td>3.67</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.94</td>\n      <td>3.57</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zuhai\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='cgpa', ylabel='package'>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df['cgpa']\n",
    "y=df['package']\n",
    "sns.scatterplot(x,y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "0      6.89\n1      5.12\n2      7.82\n3      7.42\n4      6.94\n       ... \n195    6.93\n196    5.89\n197    7.21\n198    7.63\n199    6.22\nName: cgpa, Length: 200, dtype: float64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(df.drop(columns=['package']),df['package'],test_size=0.2,random_state=2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "lr=LinearRegression()\n",
    "model=lr.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "     cgpa\n112  8.58\n29   7.15\n182  5.88\n199  6.22\n193  4.57\n85   4.79\n10   5.32\n54   6.86\n115  8.35\n35   6.87\n12   8.94\n92   7.90\n13   6.93\n126  5.91\n174  7.32\n2    7.82\n44   5.09\n3    7.42\n113  6.94\n14   7.73\n23   6.19\n25   7.28\n6    6.73\n134  7.20\n165  8.21\n173  6.75\n45   7.87\n65   7.60\n48   8.63\n122  5.12\n178  8.15\n64   7.36\n9    8.31\n57   6.60\n78   6.59\n71   7.47\n128  7.93\n176  6.29\n131  6.37\n53   6.47",
      "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>cgpa</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>112</th>\n      <td>8.58</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>7.15</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>5.88</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>6.22</td>\n    </tr>\n    <tr>\n      <th>193</th>\n      <td>4.57</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>4.79</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>5.32</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>6.86</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>8.35</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>6.87</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>8.94</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>7.90</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>6.93</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>5.91</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>7.32</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.82</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>5.09</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7.42</td>\n    </tr>\n    <tr>\n      <th>113</th>\n      <td>6.94</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>7.73</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>6.19</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>7.28</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6.73</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>7.20</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>8.21</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>6.75</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>7.87</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>7.60</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>8.63</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>8.15</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>7.36</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>8.31</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>6.60</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>6.59</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>7.47</td>\n    </tr>\n    <tr>\n      <th>128</th>\n      <td>7.93</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>6.29</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td>6.37</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>6.47</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "112    4.10\n29     3.49\n182    2.08\n199    2.33\n193    1.94\n85     1.48\n10     1.86\n54     3.09\n115    4.21\n35     2.87\n12     3.65\n92     4.00\n13     2.89\n126    2.60\n174    2.99\n2      3.25\n44     1.86\n3      3.67\n113    2.37\n14     3.42\n23     2.48\n25     3.65\n6      2.60\n134    2.83\n165    4.08\n173    2.56\n45     3.58\n65     3.81\n48     4.09\n122    2.01\n178    3.63\n64     2.92\n9      3.51\n57     1.94\n78     2.21\n71     3.34\n128    3.34\n176    3.23\n131    2.01\n53     2.61\nName: package, dtype: float64"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zuhai\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([3.89111601])"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# checking value for slope m"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "m = model.coef_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.55795197])"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "b = model.intercept_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "-0.8961119222429144"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# applying y=mx+b formula"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "y = m*8.58+b"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "array([3.89111601])"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Loss function and Accuracy function MAE, MSE, RMSE, R2, R2 adjusted"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error , r2_score"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "0.2884710931878175"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean absolute error\n",
    "mean_absolute_error(y_test,y_pred)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "0.12129235313495527"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean squared error\n",
    "mean_squared_error(y_test,y_pred)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "0.34827051717731616"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Root mean squared error\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "0.780730147510384"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r2 score\n",
    "r2_score(y_test,y_pred)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "(40, 1)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adjusted r2 score\n",
    "X_test.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "r2=r2_score(y_test,y_pred)\n",
    "adj_r2=1-((1-r2)*(40-1)/(40-1-1))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "0.7749598882343415"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_r2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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