Parv Neema.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f7d7c63",
   "metadata": {},
   "source": [
    "# Salary Prediction Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda01e8b",
   "metadata": {},
   "source": [
    "## Name - Parv Neema \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1c6af5",
   "metadata": {},
   "source": [
    "## Enrollment No. - EN19CS301240"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f905f86e",
   "metadata": {},
   "source": [
    "## Concept Used - Supervised Machine Learning and Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7f06b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries required for our analysis\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5f13af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieving the dataset to perform further operations on our given data \n",
    "\n",
    "df=pd.read_csv(\"Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed597369",
   "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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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
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       "      <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",
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       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
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       "      <td>2.9</td>\n",
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       "      <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",
       "    </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",
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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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       "      <td>67938.0</td>\n",
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       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing our dataset \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "789c059b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the dataset \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6865026",
   "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>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",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
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       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
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       "    <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",
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       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the statistical results.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "175c19f3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
       "      <td>False</td>\n",
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       "      <th>7</th>\n",
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       "      <th>9</th>\n",
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       "      <td>False</td>\n",
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       "      <th>12</th>\n",
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       "      <td>False</td>\n",
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       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>14</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process to check if any missing value is present in our dataset or not\n",
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3baa2e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This gives us the inference that no missing values are present in the dataset so we can move ahead with further operations\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd1a4097",
   "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 scores by plotting the distribution\n",
    "\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": "markdown",
   "id": "94e771c8",
   "metadata": {},
   "source": [
    "## From the above graph we can see that there exists a linear relationship between Years of Experience and Salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3246c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing the data into features(input) and labels(output)\n",
    "X = df.iloc[:, :-1].values  \n",
    "Y = df.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7aad655a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and Test splitting \n",
    "\n",
    "from sklearn.model_selection import train_test_split  \n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f0d804",
   "metadata": {},
   "source": [
    "## Implementing Linear Regression Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58a2149c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27917679",
   "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 regression line\n",
    "line = regressor.coef_*X+regressor.intercept_       # y = mx + c\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, line, color = 'red');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f90ff202",
   "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"
     ]
    }
   ],
   "source": [
    "# Testing our Algorithm\n",
    "print(X_test) \n",
    "Y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4d4d1cd",
   "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>40748.961841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391.0</td>\n",
       "      <td>122699.622956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57081.0</td>\n",
       "      <td>64961.657170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63218.0</td>\n",
       "      <td>63099.142145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>115249.562855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431.0</td>\n",
       "      <td>107799.502753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual      Predicted\n",
       "0   37731.0   40748.961841\n",
       "1  122391.0  122699.622956\n",
       "2   57081.0   64961.657170\n",
       "3   63218.0   63099.142145\n",
       "4  116969.0  115249.562855\n",
       "5  109431.0  107799.502753"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe of actual and predicted values.\n",
    "data_frame = pd.DataFrame({'Actual': Y_test, 'Predicted': Y_pred})  \n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a0198d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9411949620562126\n",
      "Test Score: 0.988169515729126\n"
     ]
    }
   ],
   "source": [
    "# Estimating the training and test score\n",
    "print(\"Training Score:\", regressor.score(X_train, Y_train))\n",
    "print(\"Test Score:\", regressor.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b1de309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Salary for [[7.0]] years of experience is : 91968.1250377325\n"
     ]
    }
   ],
   "source": [
    "# Predicting score for 7.0 Years of experience\n",
    "\n",
    "years= [[7.0]]\n",
    "own_pred= regressor.predict(years)\n",
    "print(f\"Predicted Salary for {years} years of experience is :\",format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b634b17",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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