atnabon/car-price

🚗 Car Price Prediction Project 📈

Welcome to the Car Price Prediction project repository! In this project, I aim to predict car prices using various machine learning algorithms and techniques. I have gone through a rigorous process of data cleaning, exploratory data analysis (EDA), feature engineering, data preprocessing, model development, and evaluation to arrive at my best-performing model, the Linear Regression.

Project Overview 🌟

In this project, I've accomplished the following steps:

1. Data Cleaning: I started by cleaning and preparing the dataset to remove any inconsistencies and missing values.

2. EDA: Extensive Exploratory Data Analysis was conducted, including univariate, bivariate, statistical testing, and multivariate analysis to gain insights into the data.

3. Feature Engineering: I employed feature engineering techniques to create new features and enhance the predictive power of my models.

4. PCA (Principal Component Analysis): Dimensionality reduction through PCA was explored to reduce complexity and improve model performance.

5. Data Preprocessing: Encoding categorical variables and scaling numerical features was carried out to prepare the data for modeling.

6. Model Development: I developed several regression models, including Linear Regression, Decision Tree, AdaBoost, Gradient Boosting, and Random Forest Regressor.

7. Model Evaluation: Thorough model evaluation was performed, and various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared were used to compare and select the best-performing model.

Best Model 🏆

After evaluating all the models, the Linear Regression emerged as the top performer, demonstrating excellent generalization capabilities.

Repository Structure 📂

• 💾 data.csv: Contains the dataset used for this project.
• 📓 carprice.ipynb: Jupyter notebook with code for data cleaning, EDA, feature engineering, model development, and evaluation.
• 📄 requirements.txt: List of Python packages required to run the project code.
• 📄 README.md: You are currently reading it! The main project documentation.

How to Run 🏃‍♀️🏃‍♂️

1. Clone this repository to your local machine.
2. Create a virtual environment and install the required packages using pip install -r requirements.txt.
3. Navigate to the notebooks/ or scripts/ directory to run the code for various project stages.
4. Follow the step-by-step instructions in the Jupyter notebooks or Python scripts.

Contributions 🤝

Contributions are welcome! If you have ideas for improvement or find any issues, please feel free to open an issue or submit a pull request.

Credits and Acknowledgments 👏

I would like to acknowledge the open-source community and libraries like scikit-learn, pandas, and matplotlib for their invaluable contributions to this project.

Happy Coding! 🚀