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📚 Project Overview
The Car Price Prediction project leverages machine learning to estimate the selling price of used cars based on factors like age, mileage, and fuel type. Designed as a data-driven application, it helps buyers and sellers make smarter, faster pricing decisions.

📊 Data Analysis & Preparation
Starting from real-world datasets, I performed data cleaning, feature engineering, and exploratory analysis to uncover correlations and patterns. This included handling missing data, encoding categorical variables, and normalizing numerical features to optimize model performance.

🤖 Model Building & Evaluation
Using Python’s scikit-learn, I trained multiple regression models to identify the best predictor of car prices. Metrics such as Mean Squared Error (MSE) and R² score guided model selection, ensuring accuracy and robustness. The pipeline covers data splitting, training, testing, and evaluation.

📈 Visualization & Insights
Visualization libraries like Matplotlib and Seaborn were used to explore data distribution, relationships, and residual errors. These visuals help explain model predictions, highlight important features, and validate assumptions.

⚙️ Key Features

• Predicts car prices based on real dataset features

• Multiple regression models with performance comparison

• Feature engineering to boost accuracy

• Data visualization for transparency and insights

• Clean, modular Python code with documentation

🧠 Learnings & Impact
Through this project, I deepened my skills in data preprocessing, regression modeling, and performance evaluation. It also enhanced my ability to translate raw data into actionable insights, while maintaining clean and maintainable code.

🛠 Tech Stack & Tools
Languages & Libraries: Python, scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
Version Control: Git & GitHub
Development Environment: Jupyter Notebook & VS Code

🔗 Project Repository
View on GitHub → Car Price Prediction