salary-expectation-based-experience.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Expectation Based on Experience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import library and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <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",
       "  </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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YearsExperience', 'Salary'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dimension dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <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",
       "</table>\n",
       "</div>"
      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values detection and cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "missing values : Null, Blank, NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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: 612.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    False\n",
       "Salary             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Years of Experience')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.plot(data['Salary'],data['YearsExperience'])\n",
    "\n",
    "plt.xlabel('Salary')\n",
    "plt.ylabel('Years of Experience')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,5))\n",
    "corr = data.corr('pearson')\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool_),cmap=sns.diverging_palette(220,10, as_cmap=True), square=True, ax=ax,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split dataset for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variable x,y and remove salary column for prediction\n",
    "x = data.drop('Salary',axis=1)\n",
    "y = data['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   YearsExperience\n",
      "0              1.1\n",
      "1              1.3\n",
      "2              1.5\n",
      "3              2.0\n",
      "4              2.2\n",
      "0    39343.0\n",
      "1    46205.0\n",
      "2    37731.0\n",
      "3    43525.0\n",
      "4    39891.0\n",
      "Name: Salary, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(x.head()) # YearExperience column\n",
    "print(y.head()) # Salary column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create test and train part\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create model regression and fit it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model for training\n",
    "\n",
    "L.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model prediction for testing\n",
    "\n",
    "y_pred = L.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values : 0.9024461774180497\n"
     ]
    }
   ],
   "source": [
    "# predicted values\n",
    "print('Predicted values :',L.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prediction : 90% accuracy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicted Salary based on experience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Actual salary vs Predicted salary***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual salary : 0      39343.0\n",
      "1      46205.0\n",
      "2      37731.0\n",
      "3      43525.0\n",
      "4      39891.0\n",
      "5      56642.0\n",
      "6      60150.0\n",
      "7      54445.0\n",
      "8      64445.0\n",
      "9      57189.0\n",
      "10     63218.0\n",
      "11     55794.0\n",
      "12     56957.0\n",
      "13     57081.0\n",
      "14     61111.0\n",
      "15     67938.0\n",
      "16     66029.0\n",
      "17     83088.0\n",
      "18     81363.0\n",
      "19     93940.0\n",
      "20     91738.0\n",
      "21     98273.0\n",
      "22    101302.0\n",
      "23    113812.0\n",
      "24    109431.0\n",
      "25    105582.0\n",
      "26    116969.0\n",
      "27    112635.0\n",
      "28    122391.0\n",
      "29    121872.0\n",
      "Name: Salary, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Actual salary :',y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted salary : [115790.21011287  71498.27809463 102596.86866063  75267.80422384\n",
      "  55477.79204548  60189.69970699]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted salary :',y_pred)"
   ]
  }
 ],
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