The "Car Price Prediction" project focuses on predicting the prices of cars using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Lasso regression, and Linear regression, this project provides a comprehensive solution for accurate price estimation.
The "Car Price Prediction" project aims to develop a model that can accurately predict the prices of cars based on various features. This prediction task is of great importance in the automotive industry, enabling buyers and sellers to make informed decisions. By employing machine learning algorithms and a curated dataset, this project offers a valuable tool for estimating car prices.
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Data Collection and Processing: The project involves collecting a dataset containing features related to cars, such as make, model, year, mileage, fuel type, and more. Using Pandas, the collected data is cleaned, preprocessed, and transformed to ensure it is suitable for analysis. The dataset is included in the repository for easy access.
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Data Visualization: The project utilizes data visualization techniques to gain insights into the dataset. Matplotlib and Seaborn are employed to create visualizations such as scatter plots, box plots, and correlation matrices. These visualizations provide a deeper understanding of the relationships between features and help identify patterns and outliers.
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Train-Test Split: To evaluate the performance of the regression models, the project employs the train-test split technique. The dataset is divided into training and testing subsets, ensuring that the models are trained on a portion of the data and evaluated on unseen data. This allows for an accurate assessment of the models' predictive capabilities.
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Regression Models (Lasso and Linear Regression): The project utilizes two regression models, Lasso and Linear Regression, to predict car prices. Lasso regression is a regularization technique that can handle high-dimensional data and perform feature selection. Linear regression is a classic regression algorithm that models the linear relationship between the features and the target variable. The Scikit-learn library provides implementations of these models that are utilized in this project.
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Model Evaluation: The project evaluates the performance of the regression models using evaluation metrics such as mean squared error (MSE) and mean absolute error (MAE). These metrics quantify the differences between the predicted and actual car prices, providing insights into the models' accuracy and precision. Additionally, visualizations such as scatter plots are created to compare the predicted prices against the actual prices.
To run this project locally, follow these steps:
- Clone the repository:
gh repo clone MYoussef885/Car_Price_Prediction - Install the required libraries:
If you're using Google Colab, you don't need to pip install. Just follow the importing the dependencies section. - Launch Google Colab:
https://colab.research.google.com/ - Open the
Car_Price_Prediction.ipynbfile and run the notebook cells sequentially.
The "Car Price Prediction" project offers a practical solution for estimating car prices based on various features. By leveraging data collection, preprocessing, visualization, Lasso and Linear regression modeling, and model evaluation, this project provides a comprehensive approach to addressing the price prediction task. The project also includes a curated dataset to facilitate seamless exploration and experimentation.
This project is licensed under the MIT license. See the LICENSE file for more information.
This project is made possible by the contributions of the open-source community and the powerful libraries it provides, including NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Lasso, and Linear regression. I extend my gratitude to the developers and maintainers of these libraries for their valuable work. In addition, the mentor Siddhardan, visit his channel here : https://www.youtube.com/@Siddhardhan