Taxi Fare Prediction Project

This repository contains the code and documentation for a project aimed at predicting taxi fares using historical trip and fare data. The project involves data loading, preprocessing, exploratory data analysis, model training, and evaluation.

Getting Started

Prerequisites

• Python 3.6 or higher
• pandas
• scikit-learn
• jupyter (optional, for running Jupyter notebooks)

Installation

1. Clone the repository:

git clone https://github.com/NafisRayan/Data-Mining-Python

2. Navigate to the project directory:

cd taxi-fare-prediction

3. Install the required Python packages:

pip install pandas scikit-learn jupyter

Running the Project

• To run the project, start by loading the data. The project expects two CSV files: fare.csv and trips.csv. These files should be placed in the same directory as the script or notebook.
• The main script or notebook (main.py or main.ipynb) contains the code for data loading, preprocessing, model training, and evaluation. Run this script or notebook to execute the project.

Project Structure

• Contains the raw data files (fare.csv and trips.csv).
• Contains Jupyter notebooks for data exploration, preprocessing, and model development.
• Will contain the trained model files.
• main.py or main.ipynb: The main script or notebook that orchestrates the data loading, preprocessing, model training, and evaluation.
• README.md: This file, containing information about the project, how to set it up, and how to run it.

Contributing

Contributions are welcome! Please read the contributing guidelines for details on how to contribute to this project.