Village Infrastructure Prediction

This repository contains Jupyter notebooks and datasets for predicting village infrastructure using machine learning models.

Project Overview

The goal of this project is to analyze village infrastructure data and build predictive models using logistic regression and random forest classifiers.

Repository Structure

• dataset_creation.ipynb - Processes raw data and performs feature engineering.
• lr_trained_model.ipynb - Implements and evaluates a Logistic Regression model.
• rf_trained_model.ipynb - Implements and evaluates a Random Forest model.
• main.ipynb - Integrates dataset creation and model execution.
• Village_infras.csv - Contains village infrastructure data with 13 columns and 20000 rows.

Requirements

To run the notebooks, install the following dependencies:

pip install pandas scikit-learn numpy matplotlib seaborn

Usage

1. Run dataset_creation.ipynb to preprocess the data.
2. Execute lr_trained_model.ipynb or rf_trained_model.ipynb to train and evaluate models.
3. Use main.ipynb to integrate the entire workflow.

Results

• The logistic regression model provides a baseline performance.
• The random forest classifier improves predictive accuracy.