Project Aim: Predict taxi ride demand in a specific area using prediction algorithms.
Description: Meeting ride requests is challenging due to their unpredictable and spontaneous nature. Hence, it's crucial to implement a prediction algorithm to estimate the upcoming ride demand. This project focuses on forecasting ride-request demands in a specific area, identified by latitude and longitude values, over a defined duration in military hours.
Project Overview - This project predicts the hourly ride request demand for Ola using machine learning. The goal is to analyze how factors such as time, weather, season, and holidays affect the number of ride requests and use this insight to forecast demand peaks.
Data Cleaning & Preprocessing: Handled missing values, removed outliers, and extracted time-based features (hour, day, month, weekday). Created additional features like weekend/weekday, AM/PM indicator, and holiday flag using Python’s datetime and holidays libraries.
Exploratory Data Analysis (EDA): Visualized hourly and seasonal ride demand trends. Created correlation heatmaps, boxplots, and bar charts to identify key factors influencing demand.
Feature Engineering: Encoded categorical variables (season, weather) and scaled numerical features. Selected important predictors for model training.
Model Building & Evaluation: Trained multiple regression models – Linear Regression, Lasso, Ridge, and Random Forest. Evaluated models using MAE, RMSE, and R² Score. Random Forest showed the most reliable results for capturing non-linear demand patterns.
Visualization: Demand trend plots, feature importance charts, and correlation heatmaps for better interpretability.
Tech Stack: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Holidays, EDA
Key Insights: Ride demand peaks during morning and evening commute hours. Weather and seasonal changes significantly influence ride requests. Tree-based models (like Random Forest) provide strong predictive performance for real-world demand forecasting.