🚕 Cab Ride Price Analysis and Prediction

This project focuses on analyzing cab ride data to uncover pricing patterns and build a basic price prediction model using Python. It demonstrates core data science and analytics skills, including data cleaning, exploratory data analysis (EDA), visualization, statistical testing, and machine learning.

The goal is to understand what factors influence cab ride prices and to showcase a complete data analysis workflow from raw data to insights and predictions.

📊 Project Overview

The project performs the following tasks:

Cleans and preprocesses raw cab ride data

Explores relationships between distance, time, and ride price

Visualizes trends and distributions using Python plotting libraries

Builds a Linear Regression model to predict cab ride prices

Simulates an A/B test to demonstrate statistical hypothesis testing

This project is ideal for showcasing entry-level to intermediate data science skills.

🛠️ Technologies Used

Python

Pandas – data cleaning and manipulation

NumPy – numerical computations

Matplotlib & Seaborn – data visualization

Scikit-learn – linear regression model

SciPy / Statistics concepts – A/B testing simulation

🔍 Key Features Data Cleaning & Preparation

Handling missing values

Correcting data types

Removing outliers and invalid entries

Exploratory Data Analysis (EDA)

Price distribution analysis

Distance vs. price relationship

Time-based pricing trends

Correlation analysis between variables

Data Visualization

Line charts and scatter plots

Histograms and box plots

Clear visual storytelling for insights

Price Prediction Model

Simple Linear Regression model

Train/test split for evaluation

Model performance evaluation using metrics such as R² and MSE

A/B Testing Simulation

Simulates two pricing strategies

Applies statistical testing to compare outcomes

Demonstrates hypothesis testing and decision-making

📈 Results & Insights

Ride distance is a strong predictor of price

Visualization highlights pricing variability across trips

Linear regression provides a baseline prediction model

A/B testing shows how statistical analysis can guide business decisions

📂 Project Structure Cab-Ride-Price-Analysis-and-Prediction/ │ ├── data/ # Dataset files ├── notebooks/ # Jupyter notebooks for analysis ├── visuals/ # Generated plots and charts ├── model/ # Regression model code ├── README.md # Project documentation └── requirements.txt # Python dependencies

🚀 Future Improvements

Use advanced models (Random Forest, XGBoost)

Include real-time or larger datasets

Add feature engineering (traffic, weather, time of day)

Deploy model as a web app using Flask or Streamlit

📌 Conclusion

This project demonstrates a complete data science pipeline and highlights practical skills in data analysis, visualization, machine learning, and statistics. It is suitable for learning purposes, portfolio building, and showcasing analytical thinking in real-world scenarios.