Developed an end-to-end Car Price Prediction web application that estimates the market value of a car based on key specifications such as brand, fuel type, engine configuration, and technical features. This project was built as a learning-focused data science initiative to understand how pricing dynamics work in the automobile industry. The problem scenario is inspired by a business case where an automobile company plans to enter a new market and needs to identify the most influential factors affecting car prices. The solution combines exploratory data analysis, feature engineering, and machine learning to support data-driven decision making. I performed in-depth EDA to uncover pricing trends, relationships between technical attributes and price, and potential business insights. Based on these findings, a Random Forest Regressor was trained, with hyperparameter tuning using GridSearchCV to improve model performance and robustness. The trained model was serialized using Joblib for reuse and deployment. To make the solution interactive and user-friendly, I built a Streamlit web application that allows users to input car specifications through a structured form and receive real-time price predictions. Additional features include an input glossary for better interpretability and a clean, responsive UI layout for improved user experience. Tools & Technologies: Python (Pandas, NumPy, Scikit-learn), Random Forest, GridSearchCV, Streamlit, Matplotlib, Seaborn, Joblib Key Skills Demonstrated: Data Analysis & EDA, Machine Learning (Regression), Feature Understanding, Model Optimization, Model Deployment, Business-oriented problem solving