PRITISH RAJPOOT (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Prediction Model\n",
    "\n",
    "# Pritish Rajpoot\n",
    "\n",
    "# EN19CS303035\n",
    "\n",
    "# Concept Used - Supervised Machine Learning and Linear Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing dataset\n",
    "url=\"https://raw.githubusercontent.com/CSCult/salary-prediction/main/Salary_Data.csv\"\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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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       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <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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       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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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       "    <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing our dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
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       "      <td>37731.0</td>\n",
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      "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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
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       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
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       "      <td>10.3</td>\n",
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       "      <td>10.5</td>\n",
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      "text/plain": [
       "    YearsExperience    Salary\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"
      ]
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     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking statistical information about the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(30, 2)"
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     "execution_count": 27,
     "metadata": {},
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   "source": [
    "# checking the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>15</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience  Salary\n",
       "0             False   False\n",
       "1             False   False\n",
       "2             False   False\n",
       "3             False   False\n",
       "4             False   False\n",
       "5             False   False\n",
       "6             False   False\n",
       "7             False   False\n",
       "8             False   False\n",
       "9             False   False\n",
       "10            False   False\n",
       "11            False   False\n",
       "12            False   False\n",
       "13            False   False\n",
       "14            False   False\n",
       "15            False   False\n",
       "16            False   False\n",
       "17            False   False\n",
       "18            False   False\n",
       "19            False   False\n",
       "20            False   False\n",
       "21            False   False\n",
       "22            False   False\n",
       "23            False   False\n",
       "24            False   False\n",
       "25            False   False\n",
       "26            False   False\n",
       "27            False   False\n",
       "28            False   False\n",
       "29            False   False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Analyzing the data by plotting the distribution\n",
    "df.plot(x='YearsExperience', y='Salary', style='o')\n",
    "plt.title('Years of Experience vs Salary')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data\n",
    "X=df.iloc[: , : -1].values\n",
    "Y=df.iloc[: , 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Training\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=0)\n",
    "reg= LinearRegression()\n",
    "reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plotting the line of regression\n",
    "line= reg.coef_*X + reg.intercept_  # y = mx + c\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, line, color='Red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.5]\n",
      " [10.3]\n",
      " [ 4.1]\n",
      " [ 3.9]\n",
      " [ 9.5]\n",
      " [ 8.7]\n",
      " [ 9.6]\n",
      " [ 4. ]]\n"
     ]
    }
   ],
   "source": [
    "# Testing Data\n",
    "print(X_test)\n",
    "y_pred= reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37731.0</td>\n",
       "      <td>41056.257055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391.0</td>\n",
       "      <td>123597.709384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57081.0</td>\n",
       "      <td>65443.504334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63218.0</td>\n",
       "      <td>63567.562235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>116093.940990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431.0</td>\n",
       "      <td>108590.172597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>112635.0</td>\n",
       "      <td>117031.912039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55794.0</td>\n",
       "      <td>64505.533285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual      Predicted\n",
       "0   37731.0   41056.257055\n",
       "1  122391.0  123597.709384\n",
       "2   57081.0   65443.504334\n",
       "3   63218.0   63567.562235\n",
       "4  116969.0  116093.940990\n",
       "5  109431.0  108590.172597\n",
       "6  112635.0  117031.912039\n",
       "7   55794.0   64505.533285"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing actual result to the predicted model result\n",
    "data_frame= pd.DataFrame({'Actual':Y_test, 'Predicted':y_pred})\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9395413526983522\n",
      "Test Score: 0.9779208335417602\n"
     ]
    }
   ],
   "source": [
    "# Estimating training training and test score\n",
    "print(\"Training Score:\", reg.score(X_train, Y_train))\n",
    "print(\"Test Score:\", reg.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Salary for the year you entered: 120783.7962361974\n"
     ]
    }
   ],
   "source": [
    "# Predicting score for 10.0 YearsExperience\n",
    "years= [[10.0]]\n",
    "own_pred= reg.predict(years)\n",
    "print(\"Predicted Salary for the year you entered: {}\".format(own_pred[0]))"
   ]
  }
 ],
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