Simple Linear Regression - Assignment 1 and 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "74dc3cf7",
   "metadata": {},
   "source": [
    "Delivery_time -> Predict delivery time using sorting time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "78a89f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "650bfbcd",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
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       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
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       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
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       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
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       "      <td>29.00</td>\n",
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       "      <td>9.50</td>\n",
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       "      <td>10</td>\n",
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       "      <th>9</th>\n",
       "      <td>18.75</td>\n",
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       "      <th>10</th>\n",
       "      <td>19.83</td>\n",
       "      <td>8</td>\n",
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       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
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       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
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       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
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       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Delivery Time  Sorting Time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:/Users/Sathya/Desktop/Data science assignments/20 November/Simple Linear Regression  - Assignments/2. Simple Linear Regression/delivery_time.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b584e1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72cbbf99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10],\n",
       "       [ 4],\n",
       "       [ 6],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 3],\n",
       "       [10],\n",
       "       [ 9],\n",
       "       [ 8],\n",
       "       [ 4],\n",
       "       [ 7],\n",
       "       [ 3],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 2],\n",
       "       [ 7],\n",
       "       [ 5]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['Sorting Time'].values.reshape(-1,1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "33474fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21.  ],\n",
       "       [13.5 ],\n",
       "       [19.75],\n",
       "       [24.  ],\n",
       "       [29.  ],\n",
       "       [15.35],\n",
       "       [19.  ],\n",
       "       [ 9.5 ],\n",
       "       [17.9 ],\n",
       "       [18.75],\n",
       "       [19.83],\n",
       "       [10.75],\n",
       "       [16.68],\n",
       "       [11.5 ],\n",
       "       [12.03],\n",
       "       [14.88],\n",
       "       [13.75],\n",
       "       [18.11],\n",
       "       [ 8.  ],\n",
       "       [17.83],\n",
       "       [21.5 ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Delivery Time'].values.reshape(-1,1)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a1e1d237",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.linear_model as sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d68a793",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c17bd0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w=lin.fit(x,y)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d987ba2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.6490199]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2dfc85cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.58273397])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3dadaa6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6822714748417231"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fb3cbe1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the value of sortintime 9\n",
      "Enter the value of sortintime 22\n",
      "Enter the value of sortintime 2\n",
      "The expeted delivery time is [[21.42391304]] for 9.0 sorting time\n",
      "The expeted delivery time is [[42.8611717]] for 22.0 sorting time\n",
      "The expeted delivery time is [[9.88077377]] for 2.0 sorting time\n"
     ]
    }
   ],
   "source": [
    "s1 = float(input('Enter the value of sortintime '))\n",
    "s2 = float(input('Enter the value of sortintime '))\n",
    "s3 = float(input('Enter the value of sortintime '))\n",
    "pred_y1 = (w.coef_*s1) + w.intercept_\n",
    "pred_y2 = (w.coef_*s2) + w.intercept_\n",
    "pred_y3 = (w.coef_*s3) + w.intercept_\n",
    "print('The expeted delivery time is {} for {} sorting time'.format(pred_y1,s1))\n",
    "print('The expeted delivery time is {} for {} sorting time'.format(pred_y2,s2))\n",
    "print('The expeted delivery time is {} for {} sorting time'.format(pred_y3,s3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "804fcbbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23.07293294],\n",
       "       [13.17881356],\n",
       "       [16.47685335],\n",
       "       [21.42391304],\n",
       "       [23.07293294],\n",
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       "       [18.12587325],\n",
       "       [11.52979366],\n",
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       "       [18.12587325],\n",
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       "       [13.17881356],\n",
       "       [16.47685335],\n",
       "       [18.12587325],\n",
       "       [ 9.88077377],\n",
       "       [18.12587325],\n",
       "       [14.82783346]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "002c9357",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "83054f84",
   "metadata": {},
   "source": [
    "Salary_hike -> Build a prediction model for Salary_hike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ae6b7da",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>37731.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
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       "      <th>16</th>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = pd.read_csv(\"C:/Users/Sathya/Desktop/Data science assignments/20 November/Simple Linear Regression  - Assignments/2. Simple Linear Regression/Salary_Data.csv\")\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2c6ead08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = data2.copy()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8dcbafc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YearsExperience', 'Salary'], dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a2e0026",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df2['YearsExperience'].values.reshape(-1,1)\n",
    "y = df2['Salary'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "39cdddb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin2 = sk.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "78a0e69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = lin2.fit(x,y)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "516b8735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9449.96232146]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b697c1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25792.20019867])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8af8a6d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9569566641435086"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "98268794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 36187.15875227],\n",
       "       [ 38077.15121656],\n",
       "       [ 39967.14368085],\n",
       "       [ 44692.12484158],\n",
       "       [ 46582.11730587],\n",
       "       [ 53197.09093089],\n",
       "       [ 54142.08716303],\n",
       "       [ 56032.07962732],\n",
       "       [ 56032.07962732],\n",
       "       [ 60757.06078805],\n",
       "       [ 62647.05325234],\n",
       "       [ 63592.04948449],\n",
       "       [ 63592.04948449],\n",
       "       [ 64537.04571663],\n",
       "       [ 68317.03064522],\n",
       "       [ 72097.0155738 ],\n",
       "       [ 73987.00803809],\n",
       "       [ 75877.00050238],\n",
       "       [ 81546.97789525],\n",
       "       [ 82491.9741274 ],\n",
       "       [ 90051.94398456],\n",
       "       [ 92886.932681  ],\n",
       "       [100446.90253816],\n",
       "       [103281.8912346 ],\n",
       "       [108006.87239533],\n",
       "       [110841.86109176],\n",
       "       [115566.84225249],\n",
       "       [116511.83848464],\n",
       "       [123126.81210966],\n",
       "       [125016.80457395]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6783a5fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the value of YearExperience 2\n",
      "Enter the value of YearExperience 6\n",
      "Enter the value of YearExperience 11\n",
      "The expeted salary hike is [[44692.12484158]] for yearExperience 2.0\n",
      "The expeted salary hike is [[82491.9741274]] for yearExperience 6.0\n",
      "The expeted salary hike is [[129741.78573467]] for yearExperience 11.0\n"
     ]
    }
   ],
   "source": [
    "v1 = float(input('Enter the value of YearExperience '))\n",
    "v2 = float(input('Enter the value of YearExperience '))\n",
    "v3 = float(input('Enter the value of YearExperience '))\n",
    "h1 =( v.coef_*v1 )+ v.intercept_\n",
    "h2 =( v.coef_*v2 )+ v.intercept_\n",
    "h3 =( v.coef_*v3 )+ v.intercept_\n",
    "print('The expeted salary hike is {} for yearExperience {}'.format(h1,v1))\n",
    "print('The expeted salary hike is {} for yearExperience {}'.format(h2,v2))\n",
    "print('The expeted salary hike is {} for yearExperience {}'.format(h3,v3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "898aa911",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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