Salary prediction using Lineaar Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "457f3a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e964d726",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=pd.read_csv(\"Salary_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e67fbb04",
   "metadata": {},
   "outputs": [
    {
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       "      <th>15</th>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>5.2</td>\n",
       "      <td>66030.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
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       "      <td>5.4</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81364.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>6.1</td>\n",
       "      <td>93941.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
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       "      <td>6.9</td>\n",
       "      <td>91739.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>7.2</td>\n",
       "      <td>98274.0</td>\n",
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       "      <td>8.0</td>\n",
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       "      <th>23</th>\n",
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      "text/plain": [
       "    Unnamed: 0  YearsExperience    Salary\n",
       "0            0              1.2   39344.0\n",
       "1            1              1.4   46206.0\n",
       "2            2              1.6   37732.0\n",
       "3            3              2.1   43526.0\n",
       "4            4              2.3   39892.0\n",
       "5            5              3.0   56643.0\n",
       "6            6              3.1   60151.0\n",
       "7            7              3.3   54446.0\n",
       "8            8              3.3   64446.0\n",
       "9            9              3.8   57190.0\n",
       "10          10              4.0   63219.0\n",
       "11          11              4.1   55795.0\n",
       "12          12              4.1   56958.0\n",
       "13          13              4.2   57082.0\n",
       "14          14              4.6   61112.0\n",
       "15          15              5.0   67939.0\n",
       "16          16              5.2   66030.0\n",
       "17          17              5.4   83089.0\n",
       "18          18              6.0   81364.0\n",
       "19          19              6.1   93941.0\n",
       "20          20              6.9   91739.0\n",
       "21          21              7.2   98274.0\n",
       "22          22              8.0  101303.0\n",
       "23          23              8.3  113813.0\n",
       "24          24              8.8  109432.0\n",
       "25          25              9.1  105583.0\n",
       "26          26              9.6  116970.0\n",
       "27          27              9.7  112636.0\n",
       "28          28             10.4  122392.0\n",
       "29          29             10.6  121873.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "ds.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60fb5948",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.4</td>\n",
       "      <td>83089.0</td>\n",
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       "      <th>18</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>6.1</td>\n",
       "      <td>93941.0</td>\n",
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       "      <th>20</th>\n",
       "      <td>6.9</td>\n",
       "      <td>91739.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>101303.0</td>\n",
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       "      <td>9.6</td>\n",
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      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.2   39344.0\n",
       "1               1.4   46206.0\n",
       "2               1.6   37732.0\n",
       "3               2.1   43526.0\n",
       "4               2.3   39892.0\n",
       "5               3.0   56643.0\n",
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       "12              4.1   56958.0\n",
       "13              4.2   57082.0\n",
       "14              4.6   61112.0\n",
       "15              5.0   67939.0\n",
       "16              5.2   66030.0\n",
       "17              5.4   83089.0\n",
       "18              6.0   81364.0\n",
       "19              6.1   93941.0\n",
       "20              6.9   91739.0\n",
       "21              7.2   98274.0\n",
       "22              8.0  101303.0\n",
       "23              8.3  113813.0\n",
       "24              8.8  109432.0\n",
       "25              9.1  105583.0\n",
       "26              9.6  116970.0\n",
       "27              9.7  112636.0\n",
       "28             10.4  122392.0\n",
       "29             10.6  121873.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#EDA\n",
    "\n",
    "ds=ds.drop(\"Unnamed: 0\",axis=1)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "757b2c37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0459454d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1e75022",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "  <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>5.413333</td>\n",
       "      <td>76004.000000</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>1.200000</td>\n",
       "      <td>37732.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>56721.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>65238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>100545.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.600000</td>\n",
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      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.413333   76004.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.200000   37732.000000\n",
       "25%           3.300000   56721.750000\n",
       "50%           4.800000   65238.000000\n",
       "75%           7.800000  100545.750000\n",
       "max          10.600000  122392.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "261187f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c698d_row0_col0, #T_c698d_row1_col1 {\n",
       "  background-color: #00ff80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c698d_row0_col1, #T_c698d_row1_col0 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c698d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c698d_level0_col0\" class=\"col_heading level0 col0\" >YearsExperience</th>\n",
       "      <th id=\"T_c698d_level0_col1\" class=\"col_heading level0 col1\" >Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c698d_level0_row0\" class=\"row_heading level0 row0\" >YearsExperience</th>\n",
       "      <td id=\"T_c698d_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_c698d_row0_col1\" class=\"data row0 col1\" >0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c698d_level0_row1\" class=\"row_heading level0 row1\" >Salary</th>\n",
       "      <td id=\"T_c698d_row1_col0\" class=\"data row1 col0\" >0.978242</td>\n",
       "      <td id=\"T_c698d_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24b7a2aeac0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor=ds.corr()\n",
    "cor.style.background_gradient(cmap='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44af65aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=ds[[\"YearsExperience\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e47a4a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=ds[\"Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ce2347f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train test Process\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5873878",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb91d7c3",
   "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>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience\n",
       "27              9.7\n",
       "11              4.1\n",
       "17              5.4\n",
       "22              8.0\n",
       "5               3.0\n",
       "16              5.2\n",
       "8               3.3\n",
       "14              4.6\n",
       "23              8.3\n",
       "20              6.9\n",
       "1               1.4\n",
       "29             10.6\n",
       "6               3.1\n",
       "4               2.3\n",
       "18              6.0\n",
       "19              6.1\n",
       "9               3.8\n",
       "7               3.3\n",
       "25              9.1\n",
       "3               2.1\n",
       "0               1.2\n",
       "21              7.2\n",
       "15              5.0\n",
       "12              4.1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dac26a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MACHINE LEARNING MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "991433fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e2e4f2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmodel=LinearRegression()\n",
    "rmodel.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e4532a47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 40749.96184072 122700.62295594  64962.65717022  63100.14214487\n",
      " 115250.56285456 107800.50275317]\n"
     ]
    }
   ],
   "source": [
    "y_pred=rmodel.predict(x_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa59ec13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9312.57512673]\n"
     ]
    }
   ],
   "source": [
    "print(rmodel.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3b5a6318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25849.84163795526\n"
     ]
    }
   ],
   "source": [
    "print(rmodel.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16328bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating R Squared Score\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1b82fd09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.8169515729126"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r=r2_score(y_test,y_pred)\n",
    "r*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d21be876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Salary'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=ds,x=y_test,y=y_pred,color='orange')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}