https://github.com/iguptashubham/water-potability-prediction-using-lightgbm
Water Potability Prediction Using LightGBM
https://github.com/iguptashubham/water-potability-prediction-using-lightgbm
Last synced: 3 months ago
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Water Potability Prediction Using LightGBM
- Host: GitHub
- URL: https://github.com/iguptashubham/water-potability-prediction-using-lightgbm
- Owner: iguptashubham
- Created: 2024-12-26T18:01:45.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2024-12-26T18:37:30.000Z (5 months ago)
- Last Synced: 2024-12-26T19:24:37.594Z (5 months ago)
- Language: Jupyter Notebook
- Size: 507 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Water Potability Prediction Using LightGBM
## Overview
This project focuses on predicting the potability of water using the LightGBM algorithm. It is a classification problem where the goal is to determine whether a given water sample is potable (drinkable) or not, based on various features.
## Project Structure
- **Data**: The dataset includes various attributes such as pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity, which are used to predict water potability.
- **Model**: LightGBM, a gradient boosting framework, is employed to build and train the classification model.
- **Pipeline**: The project includes steps for data preprocessing, model training, and evaluation to ensure accurate predictions.
## DVC ML Pipeline
## Features
- **Gradient Boosting Decision Tree**: Leverages the power of ensemble learning to improve prediction accuracy.
- **Binary Classification**: Determines whether the water is potable (1) or not potable (0).
- **Evaluation Metrics**: Uses binary log loss as the evaluation metric to assess model performance.
## Installation
To get started, clone the repository and install the necessary dependencies using the provided requirements file.
```sh
git clone https://github.com/iguptashubham/water-potability-prediction-using-LightGBM.git
cd water-potability-prediction-using-LightGBM
pip install -r requirements.txt
```