Regression Metrics .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a7920e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1ce087e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day48-simple-linear-regression/placement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7afa8d4e",
   "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>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>"
      ],
      "text/plain": [
       "   cgpa  package\n",
       "0  6.89     3.26\n",
       "1  5.12     1.98\n",
       "2  7.82     3.25\n",
       "3  7.42     3.67\n",
       "4  6.94     3.57"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19eec5c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Package(in lpa)')"
      ]
     },
     "execution_count": 4,
     "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(df['cgpa'],df['package'])\n",
    "plt.xlabel('CGPA')\n",
    "plt.ylabel('Package(in lpa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91fd1117",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:1]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c6dba4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3.26\n",
       "1      1.98\n",
       "2      3.25\n",
       "3      3.67\n",
       "4      3.57\n",
       "       ... \n",
       "195    2.46\n",
       "196    2.57\n",
       "197    3.24\n",
       "198    3.96\n",
       "199    2.33\n",
       "Name: package, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fe906c6",
   "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=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ba07a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49bcb72d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    4.10\n",
       "29     3.49\n",
       "182    2.08\n",
       "199    2.33\n",
       "193    1.94\n",
       "85     1.48\n",
       "10     1.86\n",
       "54     3.09\n",
       "115    4.21\n",
       "35     2.87\n",
       "12     3.65\n",
       "92     4.00\n",
       "13     2.89\n",
       "126    2.60\n",
       "174    2.99\n",
       "2      3.25\n",
       "44     1.86\n",
       "3      3.67\n",
       "113    2.37\n",
       "14     3.42\n",
       "23     2.48\n",
       "25     3.65\n",
       "6      2.60\n",
       "134    2.83\n",
       "165    4.08\n",
       "173    2.56\n",
       "45     3.58\n",
       "65     3.81\n",
       "48     4.09\n",
       "122    2.01\n",
       "178    3.63\n",
       "64     2.92\n",
       "9      3.51\n",
       "57     1.94\n",
       "78     2.21\n",
       "71     3.34\n",
       "128    3.34\n",
       "176    3.23\n",
       "131    2.01\n",
       "53     2.61\n",
       "Name: package, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adc72bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b13eb57",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09e943c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd2deaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.89111601])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X_test.iloc[0].values.reshape(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d46d01c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.780730147510384"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50af4105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Package(in lpa)')"
      ]
     },
     "execution_count": 15,
     "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(df['cgpa'],df['package'])\n",
    "plt.plot(df.cgpa,lr.predict(X),color=\"red\")\n",
    "plt.xlabel('CGPA')\n",
    "plt.ylabel('Package(in lpa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10fd77bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8961119222429144"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7067c600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55795197])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e37136cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae75209d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1440830d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.1 , 3.49, 2.08, 2.33, 1.94, 1.48, 1.86, 3.09, 4.21, 2.87, 3.65,\n",
       "       4.  , 2.89, 2.6 , 2.99, 3.25, 1.86, 3.67, 2.37, 3.42, 2.48, 3.65,\n",
       "       2.6 , 2.83, 4.08, 2.56, 3.58, 3.81, 4.09, 2.01, 3.63, 2.92, 3.51,\n",
       "       1.94, 2.21, 3.34, 3.34, 3.23, 2.01, 2.61])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "93b124cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2884710931878175"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "022e3f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12129235313495527"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cc2447cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34827051717731616"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0fcba937",
   "metadata": {},
   "outputs": [],
   "source": [
    "r2=r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44e768d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.780730147510384"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6ba72073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adjusted r2 score\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ac470833",
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_r2=1-((1-r2)*(40-1))/(40-1-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d8e9b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7749598882343415"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7b6bfdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df=df.copy()\n",
    "new_df[\"random_feature\"]=np.random.rand(200)\n",
    "new_df=new_df[[\"cgpa\",\"random_feature\",\"package\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "81283096",
   "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>cgpa</th>\n",
       "      <th>random_feature</th>\n",
       "      <th>package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.89</td>\n",
       "      <td>0.841718</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.12</td>\n",
       "      <td>0.989147</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "      <td>0.890318</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>0.141429</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.94</td>\n",
       "      <td>0.676672</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>6.93</td>\n",
       "      <td>0.159527</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5.89</td>\n",
       "      <td>0.494735</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>7.21</td>\n",
       "      <td>0.537039</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>7.63</td>\n",
       "      <td>0.240744</td>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6.22</td>\n",
       "      <td>0.443202</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  random_feature  package\n",
       "0    6.89        0.841718     3.26\n",
       "1    5.12        0.989147     1.98\n",
       "2    7.82        0.890318     3.25\n",
       "3    7.42        0.141429     3.67\n",
       "4    6.94        0.676672     3.57\n",
       "..    ...             ...      ...\n",
       "195  6.93        0.159527     2.46\n",
       "196  5.89        0.494735     2.57\n",
       "197  7.21        0.537039     3.24\n",
       "198  7.63        0.240744     3.96\n",
       "199  6.22        0.443202     2.33\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b371a169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Package(in lpa)')"
      ]
     },
     "execution_count": 49,
     "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(new_df['random_feature'],new_df['package'])\n",
    "plt.xlabel('random_feature')\n",
    "plt.ylabel('Package(in lpa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d582c680",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df.iloc[:,0:2]\n",
    "y = new_df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7bc7082c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1e9dff1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dfb7d480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e0b719ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f766d929",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7826110156432399\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"R2 score\",r2_score(y_test,y_pred))\n",
    "r2 = r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "709c0f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7708602597320636"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - ((1-r2)*(40-1)/(40-1-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "262c2f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df2 = df.copy()\n",
    "\n",
    "new_df2['iq'] = new_df2['package'] + (np.random.randint(-12,12,200)/10)\n",
    "\n",
    "new_df2 = new_df2[['cgpa','iq','package']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d0e1c1cd",
   "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>cgpa</th>\n",
       "      <th>iq</th>\n",
       "      <th>package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>7.13</td>\n",
       "      <td>3.53</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>7.28</td>\n",
       "      <td>3.22</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>7.89</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>8.93</td>\n",
       "      <td>4.71</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>6.60</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa    iq  package\n",
       "160  7.13  3.53     2.93\n",
       "183  7.28  3.22     2.72\n",
       "194  7.89  2.97     3.67\n",
       "163  8.93  4.71     3.91\n",
       "57   6.60  1.94     1.94"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df2.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "28f57632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Package(in lpa)')"
      ]
     },
     "execution_count": 61,
     "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(new_df2['iq'],new_df2['package'])\n",
    "plt.xlabel('iq')\n",
    "plt.ylabel('Package(in lpa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7206cec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(-100,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3adbf3e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df2.iloc[:,0:2]\n",
    "y = new_df2.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b0c70af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0387fa2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "792d7d58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8150664686415524\n"
     ]
    }
   ],
   "source": [
    "print(\"R2 score\",r2_score(y_test,y_pred))\n",
    "r2 = r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5ceb6b86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8050700615410957"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - ((1-r2)*(40-1)/(40-1-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9167e0ef",
   "metadata": {},
   "outputs": [],
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