Simple_Linear_Regression_Using_Scikit-Learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38caad8e",
   "metadata": {},
   "source": [
    "## In this Notebook we will implement simple linear regression using Scikit-learn Library\n",
    "\n",
    "\n",
    "### We will be working on employees salary data set to predict salary of an employee based on their years of experience."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd161e1",
   "metadata": {},
   "source": [
    "### 1. Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60acd65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fdeee0b",
   "metadata": {},
   "source": [
    "### 2. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fcafb4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv data file with pandas\n",
    "df = pd.read_csv(\"data/Salary_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfff52a5",
   "metadata": {},
   "source": [
    "### 3. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b40d83ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience  Salary\n",
       "0              1.1   39343\n",
       "1              1.3   46205\n",
       "2              1.5   37731\n",
       "3              2.0   43525\n",
       "4              2.2   39891"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check few initial rows of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0baa0c00",
   "metadata": {},
   "source": [
    "##### As we can see, there are two columns in our dataset: \n",
    "* In the first column, We have years of experience of the employees.\n",
    "* In second column, We have salaries of the employees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a514679",
   "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     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# Check data info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bfebef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check some general descriptive statistics (e.g., central tendency, dispersion etc.)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7fac7f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null or missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ce0b991",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for outliers: using box plot\n",
    "fig,ax = plt.subplots(1, 2, figsize=(12,5))\n",
    "\n",
    "sns.boxplot(ax=ax[0], data=df['YearsExperience'])\n",
    "sns.boxplot(ax=ax[1], data=df['Salary'])\n",
    "\n",
    "ax[0].set_title('Box Plot - Years of Experience')\n",
    "ax[1].set_title('Box Plot - Salary')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "691d87ee",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b46f18c",
   "metadata": {},
   "source": [
    "#### 1. Histogram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dbaf7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.hist(df['YearsExperience'], bins=12)\n",
    "plt.title('Histogram of Years of Experience')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fbe7207f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.hist(df['Salary'], bins=15)\n",
    "plt.title('Histogram of Salary')\n",
    "plt.xlabel('Salary')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefd2309",
   "metadata": {},
   "source": [
    "#### 2. Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5209e763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.scatter(df['YearsExperience'], df['Salary'])\n",
    "plt.title('Years of Experience vs. Salary')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b2a901",
   "metadata": {},
   "source": [
    "##### We can observe a sort of linear relationship between years of experience and salary of the employees."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcb804d",
   "metadata": {},
   "source": [
    " ### 4. Create train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1863eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "537e0c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d07ac0a",
   "metadata": {},
   "source": [
    "### 5. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "878c4196",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb9b27c",
   "metadata": {},
   "source": [
    "### 6. Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b09c758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1940789",
   "metadata": {},
   "source": [
    "### 7. Prediction on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24e844ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209b5880",
   "metadata": {},
   "source": [
    "### 8. Plot the fitted line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b09a9632",
   "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": [
    "# Training data\n",
    "plt.scatter(X_train,y_train,color=\"red\")\n",
    "plt.plot(X_train,lr.predict(X_train),color=\"blue\")\n",
    "plt.title(\"Salary vs Experience: Train set\")\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2091108",
   "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": [
    "# Test data\n",
    "plt.scatter(X_test,y_test,color=\"red\")\n",
    "plt.plot(X_test,y_pred,color=\"blue\")\n",
    "plt.title(\"Salary vs Experience: Test set\")\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "62f79020",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression coefficient: [9569.58688543] \n",
      "Intercept: 24393.16915194835\n"
     ]
    }
   ],
   "source": [
    "# Check the regression coefficient and intercept\n",
    "print(f'Regression coefficient: {lr.coef_} \\nIntercept: {lr.intercept_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0dc02b6",
   "metadata": {},
   "source": [
    "### 9. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1196f43c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error (MAE): 4078.2596424835124\n",
      "Mean squared error (MSE): 25922093.28794688\n",
      "Root Mean squared error (RMSE): 5091.37440068464\n",
      "R-squared (r2): 0.9654753572257188\n"
     ]
    }
   ],
   "source": [
    "# On training data\n",
    "y_pred_train = lr.predict(X_train)\n",
    "\n",
    "print('Mean absolute error (MAE):', mean_absolute_error(y_train, y_pred_train))\n",
    "print('Mean squared error (MSE):', mean_squared_error(y_train, y_pred_train))\n",
    "print('Root Mean squared error (RMSE):', np.sqrt(mean_squared_error(y_train, y_pred_train)))\n",
    "print('R-squared (r2):', r2_score(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "144dc7b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error (MAE): 6802.779572073898\n",
      "Mean squared error (MSE): 56137509.9978255\n",
      "Root Mean squared error (RMSE): 7492.496913434499\n",
      "R-squared (r2): 0.8886956733784565\n"
     ]
    }
   ],
   "source": [
    "# On test data\n",
    "print('Mean absolute error (MAE):', mean_absolute_error(y_test, y_pred))\n",
    "print('Mean squared error (MSE):', mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean squared error (RMSE):', np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('R-squared (r2):', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb4ad38",
   "metadata": {},
   "source": [
    "### 10. Save trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70bf2186",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "import joblib  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "daf55162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trained_models\\\\Simple_LR_sklearn.pkl']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model as a pickle in a file\n",
    "joblib.dump(lr, 'trained_models\\Simple_LR_sklearn.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76ae5aca",
   "metadata": {},
   "source": [
    "### 11. Load the saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ff669c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the model from the file\n",
    "model = joblib.load('trained_models\\Simple_LR_sklearn.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcfd756",
   "metadata": {},
   "source": [
    "### 12. Predict on a new data point using the saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4d99ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted salary for the employees with 2.4 years of experience is: 47360.17767698722\n"
     ]
    }
   ],
   "source": [
    "yr_exp = 2.4\n",
    "pred_salary = model.predict([[yr_exp]])\n",
    "print(f'Predicted salary for the employees with {yr_exp} years of experience is: {pred_salary[0]}')"
   ]
  }
 ],
 "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}