Income Classification using Logistic Regression

Project Brief

Objective: In this project, I will build a logistic regression model to classify income based on census data from the 1994 Census database. My goal is to predict whether a person earns more than $50,000 annually based on this data.
Data source: https://archive.ics.uci.edu/dataset/20/census+income

Tasks Overview

Here's a breakdown of the tasks I'll be performing using Python and various packages:

1. Exploratory Data Analysis (EDA) and Logistic Regression Assumptions: I'll start by checking if the dataset is balanced. Then, I'll create dummy variables for the predictor variables using libraries such as pandas, numpy, and seaborn.

2. Heatmap of Correlation Values: I'll visualize the correlation among predictor variables using the seaborn library.

3. Split Data and Fit Logistic Regression Model: I'll split the data into training and testing sets using train_test_split from sklearn and fit a logistic regression model on the training set using LogisticRegression from sklearn.

4. Model Parameters and Coefficients: I'll print model parameters, including the intercept and coefficients, using LogisticRegression from sklearn.

5. Evaluate Model Predictions: I'll evaluate the model's predictions on the test set by printing the confusion matrix and accuracy score using functions from sklearn.

6. Model Coefficients and Variable Names: I'll create a DataFrame of model coefficients and variable names, sorting them by coefficient values.

7. Barplot of Coefficients: I'll create a barplot of coefficients in ascending order using seaborn and matplotlib libraries.

8. ROC Curve and AUC: I'll plot the ROC curve and print the AUC (Area Under the Curve) value using roc_curve and roc_auc_score from sklearn.