https://github.com/rvats20/income-classification-using-ml
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
https://github.com/rvats20/income-classification-using-ml
classification machine-learning machine-learning-algorithms ml pandas-dataframe python scikit-learn
Last synced: about 1 month ago
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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
- Host: GitHub
- URL: https://github.com/rvats20/income-classification-using-ml
- Owner: rvats20
- License: mit
- Created: 2024-11-26T03:21:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-28T10:25:17.000Z (over 1 year ago)
- Last Synced: 2025-02-06T16:59:10.793Z (over 1 year ago)
- Topics: classification, machine-learning, machine-learning-algorithms, ml, pandas-dataframe, python, scikit-learn
- Language: Jupyter Notebook
- Homepage: https://rvats20.github.io/portfolio/
- Size: 791 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Model Architecture](#model-architecture)
- [Evaluation](#evaluation)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#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:**
```bash
git clone https://github.com/your-username/Income-Classification-using-ML.git
```
2. **Navigate to the project directory:**
```bash
cd Income-Classification-using-ML
```
3. **Install the required dependencies:**
```bash
pip install -r requirements.txt
```
## Usage
To run the project, use the following command:
```bash
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](LICENSE) file for more details.