Income Classification using Machine Learning

Welcome to the Income Classification project! This repository contains code and resources for building a machine learning model to classify individuals' income levels based on various features.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Model Architecture
• Evaluation
• Contributing
• License
• Contact

Introduction

The goal of this project is to predict whether an individual's income exceeds a certain threshold based on demographic and employment-related features. This can be useful for various applications, including targeted marketing, financial analysis, and social studies.

Features

• Data Preprocessing: Handling missing values, encoding categorical variables, and scaling numerical features.
• Model Training: Implementing various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.
• Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, and F1-score.
• Hyperparameter Tuning: Optimizing model parameters for better performance.
• Visualization: Plotting feature importance, confusion matrix, and ROC curves.

Installation

To get started with the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/Income-Classification-using-ML.git

2. Navigate to the project directory:

   cd Income-Classification-using-ML

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To run the project, use the following command:

python main.py

Model Architecture

The project explores various machine learning models, including:

• Logistic Regression: A simple yet effective linear model for binary classification.
• Decision Trees: A non-linear model that splits data based on feature values.
• Random Forests: An ensemble method that combines multiple decision trees for better performance.
• Gradient Boosting: An advanced ensemble method that builds models sequentially to correct errors of previous models.

Evaluation

The models are evaluated using the following metrics:

• Accuracy: The proportion of correctly classified instances.
• Precision: The proportion of true positive predictions among all positive predictions.
• Recall: The proportion of true positive predictions among all actual positives.
• F1-Score: The harmonic mean of precision and recall.

Contributing

We welcome contributions! If you'd like to contribute, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes
4. Commit your changes (git commit -m 'Add some feature')
5. Push to the branch (git push origin feature-branch)
6. Open a pull request

License

This project is licensed under the MIT License. See the LICENSE file for more details.