https://github.com/leabrodyheine/water-pump-status-prediction

This project implements machine learning models to predict the status of water pumps in Tanzania using data from DrivenData's competition. The project includes preprocessing steps, model evaluation using cross-validation, and hyperparameter optimization with Optuna.
https://github.com/leabrodyheine/water-pump-status-prediction

argparse cross-validation gradient-boosting-classifier logistic-regression machine-learning multilayer-perceptron numpy optuna pandas random-forest-classifier scikit-learn

Last synced: 5 days ago
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This project implements machine learning models to predict the status of water pumps in Tanzania using data from DrivenData's competition. The project includes preprocessing steps, model evaluation using cross-validation, and hyperparameter optimization with Optuna.

• Host: GitHub
• URL: https://github.com/leabrodyheine/water-pump-status-prediction
• Owner: leabrodyheine
• Created: 2024-05-19T03:02:49.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-19T03:18:41.000Z (8 months ago)
• Last Synced: 2024-11-12T21:41:34.316Z (2 months ago)
• Topics: argparse, cross-validation, gradient-boosting-classifier, logistic-regression, machine-learning, multilayer-perceptron, numpy, optuna, pandas, random-forest-classifier, scikit-learn
• Language: Python
• Homepage:
• Size: 9.77 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Water-Pump-Status-Prediction

This project implements machine learning models to predict the status of water pumps in Tanzania using data from DrivenData's competition. The project includes preprocessing steps, model evaluation using cross-validation, and hyperparameter optimization with Optuna.

## Libraries/Imports Used

- optuna

- scikitlearn

- pandas

- NumPy

- subprocess

- argparse

- os

## Project Structure

- **part1.py**: This script trains and evaluates various machine learning models with different preprocessing techniques using cross-validation.

- **part2.py**: This script performs hyperparameter optimization using Optuna.

- **TestAll.py**: This script automates the process of running `part1.py` with different combinations of preprocessing and model types.

## Usage

### Running Part 1

To evaluate the models with different preprocessing techniques and model types, you can manually run `part1.py` with the desired arguments. However, it's more efficient to use `TestAll.py` to automate this process.

**Example Command:**

python part1.py `      `

**Automate Part 1 with `TestAll.py`:**

The `TestAll.py` script loops through all the command-line argument options instead of manually writing the command in the terminal for each variation of a model’s training. Run this file in your IDE.

### Running Part 2

For hyperparameter optimization, run the `part2.py` script. It uses Optuna to find the best hyperparameters for different classifiers.

## Data

The dataset for this project is sourced from the DrivenData competition Pump it Up: Data Mining the Water Table. It consists of training data (input features and labels) and test data (input features only).

## Preprocessing

Various preprocessing steps are implemented, including handling categorical features, dealing with missing values, scaling numerical values, and dealing with datetime features. For categorical features, I use three types of encoding: O

- OneHotEncoder

- OrdinalEncoder

- TargetEncoder

For numerical features, I consider two options: no scaling and StandardScaler.

## Machine Learning Models

I evaluate the performance of five families of machine learning models:

- Logistic Regression

- Random Forest Classifier

- Gradient Boosting Classifier

- Histogram-based Gradient Boosting Classifier

- Multi-layer Perceptron Classifier

## Hyperparameter Optimization

The project uses Optuna for hyperparameter optimization. The optimization process includes defining the configuration space and evaluating the performance using cross-validation.