{ "cells": [ { "cell_type": "markdown", "id": "7dddb8de-6b45-4730-8dd3-66f94033984f", "metadata": {}, "source": [ "# Implementing Linear Regression with Categorical variable Using Sklearn\n", "\n", "https://medium.com/analytics-vidhya/implementing-linear-regression-using-sklearn-76264a3c073c" ] }, { "cell_type": "markdown", "id": "a1990816-9fab-4729-ad27-b9101f1cd4ea", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 52, "id": "ad6be8f5-d607-40db-b081-3cf283a824be", "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "d93023d0-b40e-44d4-abc9-ad45cc1a5017", "metadata": {}, "source": [ "## Datasets" ] }, { "cell_type": "code", "execution_count": 53, "id": "4435635a-4747-4e6b-ac4a-caeb2b5da3df", "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>Age</th>\n", " <th>YearsExperience</th>\n", " <th>Salary</th>\n", " <th>Gender</th>\n", " <th>Classification</th>\n", " <th>Job</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>22</td>\n", " <td>1.1</td>\n", " <td>39343</td>\n", " <td>Female</td>\n", " <td>Low</td>\n", " <td>Assistant</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>22</td>\n", " <td>1.3</td>\n", " <td>46205</td>\n", " <td>Male</td>\n", " <td>TOP</td>\n", " <td>Professor</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>23</td>\n", " <td>1.5</td>\n", " <td>37731</td>\n", " <td>Female</td>\n", " <td>TOP</td>\n", " <td>Administrative</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>24</td>\n", " <td>2.0</td>\n", " <td>43525</td>\n", " <td>Female</td>\n", " <td>Medium</td>\n", " <td>Assistant</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>25</td>\n", " <td>2.2</td>\n", " <td>39891</td>\n", " <td>Male</td>\n", " <td>Medium</td>\n", " <td>Professor</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age YearsExperience Salary Gender Classification Job\n", "0 22 1.1 39343 Female Low Assistant\n", "1 22 1.3 46205 Male TOP Professor\n", "2 23 1.5 37731 Female TOP Administrative\n", "3 24 2.0 43525 Female Medium Assistant\n", "4 25 2.2 39891 Male Medium Professor" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=df = pd.read_excel('./Data/Multiple_variable.xlsx',sheet_name='Sheet2')\n", "df.head()" ] }, { "cell_type": "markdown", "id": "fca7c1cc-eb5b-4598-8db4-9105ad4190d1", "metadata": {}, "source": [ "## Data Pre-Processing" ] }, { "cell_type": "code", "execution_count": 54, "id": "78fb1698-b636-4310-88b6-6026a2606cbf", "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>Age</th>\n", " <th>YearsExperience</th>\n", " <th>Salary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>36.000000</td>\n", " <td>36.000000</td>\n", " <td>36.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>34.472222</td>\n", " <td>6.008333</td>\n", " <td>82228.277778</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>6.942565</td>\n", " <td>3.031489</td>\n", " <td>28784.838078</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>22.000000</td>\n", " <td>1.100000</td>\n", " <td>37731.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>29.000000</td>\n", " <td>3.575000</td>\n", " <td>57050.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>37.000000</td>\n", " <td>5.600000</td>\n", " <td>82225.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>40.250000</td>\n", " <td>9.000000</td>\n", " <td>110232.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>49.000000</td>\n", " <td>10.500000</td>\n", " <td>122391.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age YearsExperience Salary\n", "count 36.000000 36.000000 36.000000\n", "mean 34.472222 6.008333 82228.277778\n", "std 6.942565 3.031489 28784.838078\n", "min 22.000000 1.100000 37731.000000\n", "25% 29.000000 3.575000 57050.000000\n", "50% 37.000000 5.600000 82225.500000\n", "75% 40.250000 9.000000 110232.000000\n", "max 49.000000 10.500000 122391.000000" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 55, "id": "de4076c4-a2ff-4528-8d40-08138a70dd6f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age int64\n", "YearsExperience float64\n", "Salary int64\n", "Gender object\n", "Classification object\n", "Job object\n", "dtype: object" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "id": "cb6a8a36-0962-4645-a57a-89cb103ee0a5", "metadata": {}, "source": [ "## Explanatory Data Analysis" ] }, { "cell_type": "code", "execution_count": 56, "id": "6b8b2132-3195-46b9-8699-54be5ac76b94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x2b8f3b597c0>" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 612.125x540 with 12 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(df, hue='Gender')" ] }, { "cell_type": "markdown", "id": "f4d578cc-bfba-4a65-9b01-65e073a1a6ed", "metadata": {}, "source": [ "Years of Experience is directly proportional to Salary and even age." ] }, { "cell_type": "code", "execution_count": 57, "id": "43f7385b-ea02-40af-ac8a-2a8a95879303", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x2b8f2b5d2e0>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432.125x180 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(df,x_vars=['Age','YearsExperience'],y_vars=['Salary'],hue='Gender')" ] }, { "cell_type": "markdown", "id": "8a585b63-98d7-46c4-9dd0-07866a8b85d0", "metadata": {}, "source": [ "Age and Years of Experience both are correlated with Salary" ] }, { "cell_type": "code", "execution_count": 58, "id": "d8e22b0a-7aa3-41e2-9ef2-30175d443541", "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>Age</th>\n", " <th>YearsExperience</th>\n", " <th>Salary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Age</th>\n", " <td>1.000000</td>\n", " <td>0.858866</td>\n", " <td>0.825977</td>\n", " </tr>\n", " <tr>\n", " <th>YearsExperience</th>\n", " <td>0.858866</td>\n", " <td>1.000000</td>\n", " <td>0.982536</td>\n", " </tr>\n", " <tr>\n", " <th>Salary</th>\n", " <td>0.825977</td>\n", " <td>0.982536</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age YearsExperience Salary\n", "Age 1.000000 0.858866 0.825977\n", "YearsExperience 0.858866 1.000000 0.982536\n", "Salary 0.825977 0.982536 1.000000" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr()" ] }, { "cell_type": "code", "execution_count": 59, "id": "f4955f09-72dc-4a38-adef-5dac84205cd8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(df.corr(),annot=True,lw=1)" ] }, { "cell_type": "code", "execution_count": 60, "id": "f32355eb-01d4-4019-8bbb-71e1110ca81b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='Gender', ylabel='Age'>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "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": [ "sns.boxplot(y='Age',x='Gender',data=df)" ] }, { "cell_type": "code", "execution_count": 61, "id": "6b3a49df-9315-4690-b9c4-10189aff940a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='Classification', ylabel='Salary'>" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "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": [ "sns.boxplot(y='Salary',x='Classification',data=df)" ] }, { "cell_type": "markdown", "id": "af03ae9c-6f85-4444-9d60-6c606a4124bb", "metadata": {}, "source": [ "## Dummy Variable\n", "Regression results are easiest to interpret when dummy variables are limited to two specific values, 1 or 0. Typically, 1 represents the presence of a qualitative attribute, and 0 represents the absence." ] }, { "cell_type": "code", "execution_count": 62, "id": "360bf1df-a5f2-4bc9-a5b5-e3979481bca9", "metadata": {}, "outputs": [], "source": [ "X = df[['Age', 'YearsExperience', 'Gender', 'Classification', 'Job']]" ] }, { "cell_type": "code", "execution_count": 63, "id": "152fb3ec-c531-4a97-ab8e-327f2096688b", "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>Age</th>\n", " <th>YearsExperience</th>\n", " <th>Gender_Male</th>\n", " <th>Classification_Medium</th>\n", " <th>Classification_TOP</th>\n", " <th>Job_Assistant</th>\n", " <th>Job_Manager</th>\n", " <th>Job_Professor</th>\n", " <th>Job_Senior Manager</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>22</td>\n", " <td>1.1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>22</td>\n", " <td>1.3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>23</td>\n", " <td>1.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>24</td>\n", " <td>2.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>25</td>\n", " <td>2.2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age YearsExperience Gender_Male Classification_Medium \\\n", "0 22 1.1 0 0 \n", "1 22 1.3 1 0 \n", "2 23 1.5 0 0 \n", "3 24 2.0 0 1 \n", "4 25 2.2 1 1 \n", "\n", " Classification_TOP Job_Assistant Job_Manager Job_Professor \\\n", "0 0 1 0 0 \n", "1 1 0 0 1 \n", "2 1 0 0 0 \n", "3 0 1 0 0 \n", "4 0 0 0 1 \n", "\n", " Job_Senior Manager \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = pd.get_dummies(data=X, drop_first=True)\n", "X.head()" ] }, { "cell_type": "code", "execution_count": 64, "id": "b96d1124-b7eb-48cd-a508-0a2ecb300565", "metadata": {}, "outputs": [], "source": [ "Y = df['Salary']" ] }, { "cell_type": "markdown", "id": "4970dda4-f556-4482-82a6-24922fa47579", "metadata": {}, "source": [ "## Create a train and test dataset" ] }, { "cell_type": "code", "execution_count": 65, "id": "9beb7b5a-9d05-4166-afa8-a9d282192012", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(21, 9)\n", "(15, 9)\n", "(21,)\n", "(15,)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=101)\n", "print(X_train.shape)\n", "print(X_test.shape)\n", "print(y_train.shape)\n", "print(y_test.shape)" ] }, { "cell_type": "markdown", "id": "7c0b060e-dc1b-44a5-98a2-0523373bdbd7", "metadata": {}, "source": [ "## SkLearn" ] }, { "cell_type": "code", "execution_count": 66, "id": "517fbb26-3ec8-4bf5-be39-890936de52e5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "model = LinearRegression()\n", "model.fit(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 67, "id": "584d7b5f-bf17-4bca-a108-7bac407bd609", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "40767.075290708104\n" ] } ], "source": [ "# print the intercept\n", "print(model.intercept_)" ] }, { "cell_type": "markdown", "id": "5ea699bb-667c-436e-9256-3ee83242530a", "metadata": {}, "source": [ "The sign of each coefficient indicates the direction of the relationship between a predictor variable and the response variable.\n", "\n", "* A positive sign indicates that as the predictor variable increases, the Target variable also increases.\n", "* A negative sign indicates that as the predictor variable increases, the Target variable decreases" ] }, { "cell_type": "code", "execution_count": 68, "id": "fd44923e-6d9a-420a-9ba0-c0a202d83107", "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>Coefficient</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Age</th>\n", " <td>-548.323776</td>\n", " </tr>\n", " <tr>\n", " <th>YearsExperience</th>\n", " <td>10743.731522</td>\n", " </tr>\n", " <tr>\n", " <th>Gender_Male</th>\n", " <td>-655.537127</td>\n", " </tr>\n", " <tr>\n", " <th>Classification_Medium</th>\n", " <td>-6061.914786</td>\n", " </tr>\n", " <tr>\n", " <th>Classification_TOP</th>\n", " <td>-1234.672994</td>\n", " </tr>\n", " <tr>\n", " <th>Job_Assistant</th>\n", " <td>-1114.042048</td>\n", " </tr>\n", " <tr>\n", " <th>Job_Manager</th>\n", " <td>2291.025846</td>\n", " </tr>\n", " <tr>\n", " <th>Job_Professor</th>\n", " <td>964.080429</td>\n", " </tr>\n", " <tr>\n", " <th>Job_Senior Manager</th>\n", " <td>-2141.064227</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Coefficient\n", "Age -548.323776\n", "YearsExperience 10743.731522\n", "Gender_Male -655.537127\n", "Classification_Medium -6061.914786\n", "Classification_TOP -1234.672994\n", "Job_Assistant -1114.042048\n", "Job_Manager 2291.025846\n", "Job_Professor 964.080429\n", "Job_Senior Manager -2141.064227" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coeff_parameter = pd.DataFrame(model.coef_,X.columns,columns=['Coefficient'])\n", "coeff_parameter" ] }, { "cell_type": "code", "execution_count": 69, "id": "ee4decf4-4cfa-453c-8b76-cbfaa7b47f5d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 80970.53158392, 54147.79230197, 84608.3284182 , 112838.31994826,\n", " 111603.646954 , 121986.73865586, 111208.49523541, 125909.94403861,\n", " 112148.27092338, 43036.55273237, 127901.81847906, 85930.91891418,\n", " 71270.93953831, 62820.7589416 , 41918.81087788])" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = model.predict(X_test)\n", "predictions" ] }, { "cell_type": "code", "execution_count": 70, "id": "b543b40b-31dd-42e7-bed9-f4918ba62cad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='Salary'>" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "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": [ "sns.regplot(y_test,predictions)" ] }, { "cell_type": "markdown", "id": "b8851655-d7c8-4eca-94e1-181c7984bac3", "metadata": {}, "source": [ "## StatsModels" ] }, { "cell_type": "code", "execution_count": 71, "id": "b08c30ce-eaf4-436f-a9a4-b9d41eaebd0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Salary R-squared: 0.971\n", "Model: OLS Adj. R-squared: 0.952\n", "Method: Least Squares F-statistic: 51.09\n", "Date: Sun, 09 Jul 2023 Prob (F-statistic): 4.20e-08\n", "Time: 22:21:20 Log-Likelihood: -207.81\n", "No. Observations: 21 AIC: 433.6\n", "Df Residuals: 12 BIC: 443.0\n", "Df Model: 8 \n", "Covariance Type: nonrobust \n", "=========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------------\n", "const 3.261e+04 9431.499 3.458 0.005 1.21e+04 5.32e+04\n", "Age -548.3238 427.836 -1.282 0.224 -1480.499 383.852\n", "YearsExperience 1.074e+04 1744.592 6.158 0.000 6942.592 1.45e+04\n", "Gender_Male -655.5371 3091.666 -0.212 0.836 -7391.698 6080.624\n", "Classification_Medium -6061.9148 4374.305 -1.386 0.191 -1.56e+04 3468.878\n", "Classification_TOP -1234.6730 4574.620 -0.270 0.792 -1.12e+04 8732.568\n", "Job_Assistant 7039.3730 3997.588 1.761 0.104 -1670.623 1.57e+04\n", "Job_Manager 1.044e+04 5193.314 2.011 0.067 -870.818 2.18e+04\n", "Job_Professor 9117.4955 4842.954 1.883 0.084 -1434.395 1.97e+04\n", "Job_Senior Manager 6012.3508 6454.697 0.931 0.370 -8051.225 2.01e+04\n", "==============================================================================\n", "Omnibus: 9.775 Durbin-Watson: 2.131\n", "Prob(Omnibus): 0.008 Jarque-Bera (JB): 2.092\n", "Skew: 0.115 Prob(JB): 0.351\n", "Kurtosis: 1.471 Cond. No. 4.70e+17\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The smallest eigenvalue is 1.18e-31. This might indicate that there are\n", "strong multicollinearity problems or that the design matrix is singular.\n" ] } ], "source": [ "import statsmodels.api as sm\n", "X_train_Sm= sm.add_constant(X_train)\n", "X_train_Sm= sm.add_constant(X_train)\n", "ls=sm.OLS(y_train,X_train_Sm).fit()\n", "print(ls.summary())" ] }, { "cell_type": "markdown", "id": "429a095c-8a66-483b-986f-12a2dcba9104", "metadata": {}, "source": [ "### What Are the Adjusted R-squared?\n", "We Use adjusted R-squared to compare the goodness-of-fit for regression models that contain different numbers of independent variables." ] }, { "cell_type": "code", "execution_count": null, "id": "6a457aff-04e4-49e6-b96e-80371c58f77c", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }