https://github.com/gurramankit/censusproject_ml_randomforest

The objective of this project is to build a classification model using the Census Income dataset from the UCI Machine Learning Repository. The model predicts whether an individual's income exceeds $50,000 per year, based on their demographic and employment-related attributes.
https://github.com/gurramankit/censusproject_ml_randomforest

matplotlib numpy pandas python random-forest scikit-learn seaborn

Last synced: 8 days ago
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The objective of this project is to build a classification model using the Census Income dataset from the UCI Machine Learning Repository. The model predicts whether an individual's income exceeds $50,000 per year, based on their demographic and employment-related attributes.

• Host: GitHub
• URL: https://github.com/gurramankit/censusproject_ml_randomforest
• Owner: gurramankit
• Created: 2024-10-29T17:55:14.000Z (9 days ago)
• Default Branch: main
• Last Pushed: 2024-10-29T18:03:55.000Z (9 days ago)
• Last Synced: 2024-10-29T19:15:05.080Z (9 days ago)
• Topics: matplotlib, numpy, pandas, python, random-forest, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 690 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CensusProject_ML_RandomForest

The objective of this project is to build a classification model using the Census Income dataset from the UCI Machine Learning Repository. The model predicts whether an individual's income exceeds $50,000 per year, based on their demographic and employment-related attributes.

## Overview

This project aims to classify whether an individual earns more than $50,000 per year, using data from the 1994 US Census. This classification task leverages demographic and employment-related attributes to predict income, making it an essential tool for income prediction and social-economic analysis.

## Dataset

- **Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/census+income)

- **Size**: 48,842 instances, covering demographic, employment, and income data.

## Problem Statement

The goal is to predict if a person's income exceeds $50,000 per year based on their demographic attributes and work-related features.

## Project Tasks

### 1. Data Preprocessing

- **Handling Missing Values**: Managed missing entries to maintain data quality.

- **Encoding Categorical Variables**: Used encoding techniques to convert categorical data into numerical form.

- **Feature Scaling**: Applied scaling for algorithm compatibility and performance.

### 2. Exploratory Data Analysis (EDA)

Conducted EDA to explore feature relationships and visualize income distribution patterns. Key insights included trends based on age, education, occupation, and work hours.

### 3. Model Building and Evaluation

Developed multiple machine learning models for classification, with a focus on accuracy and robustness:

- **Best Model**: Random Forest Classifier, achieving **84% accuracy**.

- **Evaluation Metrics**: Accuracy, Precision, Recall, F1-score for comprehensive assessment.

## Results

- The Random Forest model achieved an accuracy of 84%, successfully predicting income categories with high performance.

## Installation and Usage

1. Clone this repository:

    ```bash

    git clone https://github.com/gurramankit/census-income-classification.git

    ```

2. Install the necessary libraries:

    ```bash

    pip install -r requirements.txt

    ```

3. Run the project:

    ```bash

    python main.py

    ```

## Project Structure

- `data/` - Contains the Census Income dataset.

- `notebooks/` - Jupyter notebooks for EDA, data preprocessing, and model development.

- `src/` - Python scripts for data processing and model implementation.

- `README.md` - Overview and instructions.

- `requirements.txt` - Dependencies for running the project.