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. # 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 nume …