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Clearanceprojectson Bigdataand Machine Learning Sale

Kenji Sato
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clearanceprojectson bigdataand machine learning sale

An advanced machine learning project aimed at predicting sales for a retail dataset using state-of-the-art regression algorithms. This project involves comprehensive data cleaning, feature engineering, and model optimization techniques to achieve high prediction accuracy. - Introduction - Dataset Overview - Data Preprocessing - Feature Engineering - Models and Techniques - Model Evaluation - Results - Conclusion - Contributing - License Accurate sales forecasting is crucial for retail businesses to manage inventory, optimize pricing strategies, and maximize profits.

This project builds a robust machine learning model to predict sales based on historical data and various product and store attributes. The dataset used is the Big Mart Sales dataset, which contains 8523 rows and 12 variables.

It includes information about products across multiple outlets, such as: - Item_Identifier: Unique product ID - Item_Weight: Weight of the product - Item_Fat_Content: Fat content of the product (Low Fat, Regular, etc.) - Item_Visibility: The percentage of total display area allocated to this product in a store - Item_Type: The category to which the product belongs - Item_MRP: Maximum Retail Price (list price) of the product - Outlet_Identifier: Unique store ID - Outlet_Establishment_Year: The year the store was established - Outlet_Size: The size of the store (Small, Medium, High) - Outlet_Location_Type: The type of city in which the store is located - Outlet_Type: Whether the outlet is just a grocery store or some sort of supermarket - Item_Outlet_Sales: Sales of the product in the particular store (Target variable) - Handling Missing Values: - Item_Weight: Imputed using KNNImputer based on nearest neighbors.

Outlet_Size: Predicted missing values using a RandomForestClassifier trained on other features. - Data Cleaning: - Replaced zero values in Item_Visibility with the mean visibility. - Standardized entries in Item_Fat_Content . - Replaced zero values in - Feature Encoding: - Applied One-Hot Encoding to categorical variables with multiple categories. - Used Label Encoding for Outlet_Identifier andItem_Type . - Feature Scaling: - Used StandardScaler to standardize numerical features. - New Features Created: - Years_Operational: Number of years the outlet has been operational. - Item_Type_Combined: Consolidated item types into broader categories.

Price_per_Unit_Weight: Price of the item per unit weight. - Item_MRP_Tier: Binned Item_MRP into tiers (Low, Medium, High, Very High). - Adjusted Features: - Modified Item_Fat_Content for non-consumable items. - Modified - Regression Models Used: - Linear Regression - Ridge Regression - Lasso Regression - Decision Tree Regressor - Random Forest Regressor - ExtraTrees Regressor - XGBoost Regressor - LightGBM Regressor - CatBoost Regressor - Hyperparameter Tuning: - Performed using GridSearchCV for CatBoostRegressor .

Performed using GridSearchCV for - Model Evaluation Metrics: - R2 Score - Root Mean Squared Error (RMSE) - Mean Absolute Error (MAE) - Best Model: CatBoostRegressor after hyperparameter tuning. - Performance Metrics: - Train R2 Score: 0.6041 - Test R2 Score: 0.6211 - Train RMSE: 1076.1006 - Test RMSE: 1040.7172 - Train MAE: 758.3063 - Test MAE: 747.7749 - Feature Importance: - Analyzed to understand the impact of each feature on the sales prediction.

Residual Analysis: - Performed to ensure that the residuals are normally distributed and to detect any patterns. - Visualization: - Correlation heatmaps, feature distributions, and residual plots were used to visualize data relationships and model performance. The project successfully built a predictive model for sales forecasting with a reasonable accuracy. Advanced data preprocessing and feature engineering significantly improved the model's performance. The CatBoostRegressor proved to be the most effective algorithm for this dataset. Contributions are welcome! Please follow these steps: - Fork the repository.

Create a new branch: git checkout -b feature/your-feature-name - Commit your changes: git commit -m "Add your message" - Push to the branch: git push origin feature/your-feature-name - Open a pull request. This project is licensed under the MIT License. See the LICENSE file for details. Feel free to customize this README and GitHub description to better suit your project's specifics or to add any additional information.

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