Cab Fare Prediction - Data Science Capstone Project

This project aims to apply analytics for fare prediction using historical data from a pilot project.

Files

• Train Data:

  • Variables:
    • pickup_datetime
    • pickup_longitude
    • pickup_latitude
    • dropoff_longitude
    • dropoff_latitude
    • passenger_count

• Test Data:

  • Test data requiring fare prediction

• Original Predictions:

  • Python Predictions: Original_with_fare&Dist_python.csv
  • R Predictions: Original_with_fare_amount&Dist_R.csv

• Project Reports:

  • Detailed project report: Project Report_Cab_Rental

• Code Files:

  • Python Code: Project_2.ipynb
  • R Code: Project_2_R

Project Overview

Missing Value Analysis

• Identified missing values in the dataset and addressed them.

Feature Engineering

• Derived new variables (Month, Year, Time (Hrs), Day, Day/Night) from the pickup_datetime variable.
• Calculated Distance_Km using geographical coordinates.

Outlier Analysis

• Filtered data based on reasonable thresholds for passenger_count, fare_amount, and Distance_Km.
• Conducted boxplot analysis for outliers in specific variables.

Converted Variables

• Converted specific variables (passenger_count, Month, Year, Day, Day/Night) into factor variables.

Dimensionality Reduction

• Removed variables (pickup_datetime, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude) based on heatmap analysis.

Dummy Variables (One Hot Encoding)

• Created dummy variables for better analysis of specific factor variables (Month, Year, Day, Day/Night, passenger_count).

Machine Learning Models

• Applied Linear Regression (LR) model in Python and Random Forest in R for fare prediction.

Usage

1. Clone the repository:

   git clone <repository_url>

2. Navigate to the project directory:

   cd Cab-Fare-Prediction--Data-Science-Capstone-project

3. Explore the project files and reports for a detailed understanding.

Feel free to refer to the Project Report_Cab_Rental for an in-depth explanation of the project.

Note: Adjust file paths and comments as needed for your project structure.