AlexIrungu/Tanzania-Wells-Classification-
Domain:
environment and energyRecord type:
project
Creator:
Ale
Host:
# Phase 3 Project
This repository contains the Phase 3 project for the data science course. The project involves analyzing water pump functionality data to predict the operational status of water pumps in Tanzania.
## Table of Contents
- Introduction
- Project Structure
- Installation
- Usage
- Required Libraries
- Results
- Contributing
## Introduction
This project was completed as part of the Phase 3 curriculum for the data science course. The main objective of this project is to develop a machine learning model that can predict the functionality status of water pumps in Tanzania based on various features such as water quality, quantity, location, and pump type.
### Final Project Submission Details
- **Student Name**: Alex Irungu
- **Group**: Group 1
- **Student Pace**: Part-Time
- **Scheduled Review Date/Time**: Phase 3
- **Instructor Name**: Samuel Karu
## Project Structure
The repository contains the following files and directories:
- `index.ipynb`: Jupyter Notebook containing the project code, analysis, and results.
- `README.md`: This README file.
- `requirements.txt`: List of Python packages required for this project.
- `data.csv`: Datasets used for this project.
## Installation
To run the code in this repository, you will need to have Python and Jupyter Notebook installed. You can install the necessary Python libraries by running:
```bash
pip install -r requirements.txt
```
## Usage
To view and run the project:
1. Clone this repository to your local machine.
2. Navigate to the project directory.
3. Launch Jupyter Notebook:
```bash
jupyter notebook
```
4. Open `index.ipynb` in the Jupyter Notebook interface.
5. Run the cells in the notebook to see the analysis and results.
## Required Libraries
The following Python libraries are used in this project:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selectio …