The project involves building and evaluating different regression models to predict the stock amount for the different products which are sold in different stores.
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Data Preparation:
- The dataset contains features such as store, year, month, weekday, weekend, holidays, temperature, and humidity.
- Additional features may be derived from existing ones to gain more insights and improve model performance.
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Feature Engineering:
- Inclusion of extra features in the dataset to enhance the model's ability to capture relevant patterns.
- Handling holidays and other specific events that may affect sales.
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Model Selection and Training:
- Various regression models are evaluated, including Linear Regression, Gradient Boosting Regressor, Lasso, and Ridge.
- Models are trained on the training dataset and their performance is evaluated using mean absolute error (MAE) on both training and validation datasets.
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Evaluation and Comparison:
- The performance of each model is assessed to determine which model provides the lowest training and validation errors.
- Gradient Boosting Regressor is identified as the best performing model with the lowest errors, indicating its superior ability to capture the data's complexities.
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Visualization:
- The data distribution and relationships between features and sales are visualized using various plots (e.g., distribution plots, box plots, bar plots).
- These visualizations help in understanding the data and the impact of different features on sales.
The project successfully identifies the Gradient Boosting Regressor as the most effective model for predicting sales. The model outperforms others in terms of both training and validation errors. The project demonstrates the importance of feature engineering and model evaluation in building predictive models, providing a comprehensive approach to sales prediction using machine learning techniques.