🚖 Taxi Price Prediction Using Machine Learning

📌 Project Overview

This project focuses on building a machine learning pipeline to predict taxi trip prices based on multiple real-world factors such as distance, time, weather, traffic conditions, and fare structure. Through robust preprocessing and regression modeling, we aim to generate accurate trip cost predictions to assist ride-hailing services, pricing engines, and transportation analytics.

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🧠 Technical Highlights

• Dataset: Contains 1000 real-world taxi trips with 11 features such as:

  • Trip_Distance_km, Passenger_Count, Traffic_Conditions, Weather, Trip_Duration_Minutes, and various fare components.

• Preprocessing:

  • Imputed missing values using formula-based calculations and default statistical methods
  • Applied log transformation for skewed distributions
  • Encoded categorical variables using LabelEncoder

• Feature Selection:

  • Used correlation thresholding to select highly relevant features

• Models Applied:

  • Ridge Regression
  • XGBoost Regressor
  • Random Forest Regressor
  • AdaBoost Regressor
  • Gradient Boosting Regressor
  • Bagging Regressor

• Best Performance:

  • 📈 Gradient Boosting Regressor achieved R² score of 0.914

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🎯 Purpose and Applications

• 💸 Dynamic Fare Estimation for ride-hailing apps and taxi meters
• 📉 Cost prediction under varying traffic and weather scenarios
• 🧪 Educational reference for regression modeling, feature engineering, and data cleaning
• 🛠️ Basis for building real-time APIs and fare calculators

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⚙️ Installation

Clone the repository

git clone https://github.com/BhaveshBhakta/Taxi-Price-Prediction-Using-ML.git
cd axi-Price-Prediction-Using-ML

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🤝 Collaboration

Contributions are welcome! If you’d like to improve model performance, add new visualizations, or integrate the project with a web interface.