The Laptop Price Predictor is a Python-based machine learning project that aims to predict laptop prices based on various features and specifications. This project involves a thorough data analysis and modeling pipeline, from data preprocessing to building a stacked ensemble model for accurate predictions.
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Data Cleaning and Preprocessing: Raw laptop data is cleaned, missing values are handled, and outliers are addressed to ensure high-quality data for analysis.
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Data Analysis: In-depth exploratory data analysis (EDA) is performed to gain insights into the relationships between different laptop features and their impact on prices.
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Feature Engineering: Relevant features are selected, and new features are engineered to enhance the predictive power of the models.
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Model Selection: Various machine learning models are evaluated, including linear regression, decision trees, random forests, and gradient boosting, to determine the best-performing models.
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Stacked Model: A stacked ensemble model is constructed, combining the strengths of multiple base models to improve predictive accuracy.
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Testing and Evaluation: The developed model is thoroughly tested using appropriate evaluation metrics such as mean absolute error (MAE) or root mean squared error (RMSE) to assess its performance.
Explore the Jupyter notebook
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License.