2 Linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66b15fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "311dccf0",
   "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>year</th>\n",
       "      <th>per capita income (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>3399.299037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>3768.297935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>4251.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>4804.463248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>5576.514583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  per capita income (US$)\n",
       "0  1970              3399.299037\n",
       "1  1971              3768.297935\n",
       "2  1972              4251.175484\n",
       "3  1973              4804.463248\n",
       "4  1974              5576.514583"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"canada_per_capita_income.csv\")  \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2a50d87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year\n",
      "0  1970\n",
      "1  1971\n",
      "2  1972\n",
      "3  1973\n",
      "4  1974\n",
      "0    3399.299037\n",
      "1    3768.297935\n",
      "2    4251.175484\n",
      "3    4804.463248\n",
      "4    5576.514583\n",
      "Name: per capita income (US$), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(\"per capita income (US$)\", axis=1)\n",
    "y = data['per capita income (US$)']\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57917c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18ad296c6a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('per capita income (US$)')\n",
    "plt.scatter(data['year'], data['per capita income (US$)'],color=\"green\",marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f98b766d",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cdf91ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[816.79626508]\n",
      "-1609144.8417663975\n",
      "[31798.8547763   5661.37429378 24447.68839059 30982.05851122\n",
      " 28531.66971598 21997.29959535 33432.44730645  1577.39296839\n",
      "  4027.78176363 20363.7070652 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "20323296.312112506"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print(model.coef_)\n",
    "print(model.intercept_)\n",
    "y_pred=model.predict(x_test)\n",
    "print(y_pred)\n",
    "mean_squared_error(y_test, y_pred)\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68d9858e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "193a5e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([40783.61369216])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2020]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b0f7b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "with open('Linear_regression','wb') as f:\n",
    "    pickle.dump(model,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e24bac62",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Linear_regression','rb') as f:\n",
    "    Linear_regression = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2952455",
   "metadata": {},
   "outputs": [],
   "source": [
    "inear_regression.predict([[1,2,2,2,3,3,2,3,3,1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ced2c634",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils._joblib import joblib\n",
    "joblib.dump(model,'Linear_regression.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6aeb262",
   "metadata": {},
   "outputs": [],
   "source": [
    "Linear_regression1 = joblib.load('Linear_regression.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa543146",
   "metadata": {},
   "outputs": [],
   "source": [
    "Linear_regression1.predict([[1,2,2,2,3,3,2,3,3,1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "303657a3",
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       NaN   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000\n",
       "5  4100       6.0    8  810000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"homeprices.csv\")  \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6372125a",
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       4.0   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000\n",
       "5  4100       6.0    8  810000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.ffill()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6fa7f6fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   area  bedrooms  age\n",
      "0  2600       3.0   20\n",
      "1  3000       4.0   15\n",
      "2  3200       4.0   18\n",
      "3  3600       3.0   30\n",
      "4  4000       5.0    8\n",
      "0    550000\n",
      "1    565000\n",
      "2    610000\n",
      "3    595000\n",
      "4    760000\n",
      "Name: price, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(\"price\", axis=1)\n",
    "y = data['price']\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d2e02b2d",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ed9b4b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  137.02804501   644.20031893 -5796.43258991]\n",
      "277589.84381586855\n",
      "[782551.56475046 614320.60252063]\n",
      "263620339.4177743\n",
      "0.9531341618812845\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print(model.coef_)\n",
    "print(model.intercept_)\n",
    "y_pred=model.predict(x_test)\n",
    "print(y_pred)\n",
    "mre=mean_squared_error(y_test, y_pred)\n",
    "print(mre)\n",
    "r2=r2_score(y_test, y_pred)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "983fbedd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([879648.78195485])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5000,6,15]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6c80806f",
   "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>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN                    8.0                           9      50000\n",
       "1        NaN                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"hiring.csv\")  \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3b2a884c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>five</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>five</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0       five                    8.0                           9      50000\n",
       "1       five                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    7.0                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.bfill()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "706215f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           5                    8.0                           9      50000\n",
       "1           5                    8.0                           6      45000\n",
       "2           5                    6.0                           7      60000\n",
       "3           2                   10.0                          10      65000\n",
       "4           7                    9.0                           6      70000\n",
       "5           3                    7.0                          10      62000\n",
       "6          10                    7.0                           7      72000\n",
       "7          11                    7.0                           8      80000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.replace({\n",
    "    'five':5,\n",
    "    'two':2,\n",
    "    'seven':7,\n",
    "    'three':3,\n",
    "    'ten':10,\n",
    "    'eleven':11\n",
    "})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ce7fdb08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   experience  test_score(out of 10)  salary($)\n",
      "0           5                    8.0      50000\n",
      "1           5                    8.0      45000\n",
      "2           5                    6.0      60000\n",
      "3           2                   10.0      65000\n",
      "4           7                    9.0      70000\n",
      "0     9\n",
      "1     6\n",
      "2     7\n",
      "3    10\n",
      "4     6\n",
      "Name: interview_score(out of 10), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(\"interview_score(out of 10)\", axis=1)\n",
    "y = data['interview_score(out of 10)']\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "66f6cb52",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "74d4af99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.19356386e-01 -7.38195679e-01  9.67147008e-05]\n",
      "11.605413387925074\n",
      "[9.88233943 7.20793823]\n",
      "4.175559448175504\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print(model.coef_)\n",
    "print(model.intercept_)\n",
    "y_pred=model.predict(x_test)\n",
    "print(y_pred)\n",
    "mre=mean_squared_error(y_test, y_pred)\n",
    "print(mre)\n",
    "r2=r2_score(y_test, y_pred)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6783673d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([7.7696481])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[8,7,65000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c27db66c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   experience  test_score(out of 10)  interview_score(out of 10)\n",
      "0           5                    8.0                           9\n",
      "1           5                    8.0                           6\n",
      "2           5                    6.0                           7\n",
      "3           2                   10.0                          10\n",
      "4           7                    9.0                           6\n",
      "0    50000\n",
      "1    45000\n",
      "2    60000\n",
      "3    65000\n",
      "4    70000\n",
      "Name: salary($), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(\"salary($)\", axis=1)\n",
    "y = data['salary($)']\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ec7b597a",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1d9737a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2504.3508937  2045.97836312 1674.62370649]\n",
      "23222.95390404517\n",
      "[62160.27751646 67184.14863594]\n",
      "294885044.3919812\n",
      "-46.18160710271699\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print(model.coef_)\n",
    "print(model.intercept_)\n",
    "y_pred=model.predict(x_test)\n",
    "print(y_pred)\n",
    "mre=mean_squared_error(y_test, y_pred)\n",
    "print(mre)\n",
    "r2=r2_score(y_test, y_pred)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "77d95c80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([105507.29068674])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[18,10,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df11e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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