/Cab-fare-Prediction

Our machine learning project focuses on building and evaluating predictive models for cab fare prediction. We perform extensive data processing, cleaning, and feature extraction to prepare the dataset for model training. This project aims to predict cab fares accurately based on various input parameters such as location, no. of passengers and time.

Primary LanguageJupyter NotebookMIT LicenseMIT

Cab Fare Prediction - New York City Taxi Fare Prediction(kaggle)

Overview

Our machine learning project focuses on building and evaluating predictive models for cab fare prediction. We perform extensive data processing, cleaning, and feature extraction to prepare the dataset for model training. This project aims to predict cab fares accurately based on various input parameters such as distance, time, and location.

Table of Contents

  1. Features
  2. Installation
  3. Usage
  4. Machine Learning Models Used
  5. Kaggle Competition and Solution
  6. Contributing
  7. License
  8. Contact

Features

  • Comprehensive data processing and cleaning techniques.
  • Feature extraction to capture relevant information for cab fare prediction.
  • Visualization of data insights and model performance using plots.
  • Evaluation metrics including accuracy, RMSE (Root Mean Square Error), and RAE (Relative Absolute Error).

Installation

To run the machine learning project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/mridul0703/Cab-fare-Prediction.git

  2. Navigate to the project directory:

    cd cab-fare-prediction

  3. Open the project in your preferred Python environment.

Usage

Explore the following functionalities of the machine learning project:

  • Data Processing: Clean and preprocess the dataset for model training.
  • Feature Extraction: Extract relevant features from the dataset.
  • Model Training: Train various machine learning models on the prepared dataset.
  • Model Evaluation: Evaluate model performance using accuracy, RMSE, and RAE metrics.
  • Data Visualization: Visualize insights from the dataset and model performance using plots.

Explore the project in Google Colab by clicking the badge below:

Open In Colab

Machine Learning Models Used

The machine learning project utilizes the following models:

  1. Linear Regression
  2. Decision Trees
  3. Random Forest
  4. Gradient Boosting
  5. Support Vector Machines (SVM)
  6. Neural Networks
  7. k-Nearest Neighbors (k-NN)
  8. XGBoost
  9. LightGBM

Kaggle Competition and Solution

We participated in a Kaggle competition for cab fare prediction. You can find our competition entry and solution in the following links:

Contributing

We welcome contributions to enhance the features and usability of our machine learning project. To contribute, please follow the guidelines mentioned in the repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries or feedback, please contact us at email@example.com.