https://github.com/walidalsafadi/store-sales-ts-forecasting

Use machine learning to predict grocery sales
https://github.com/walidalsafadi/store-sales-ts-forecasting

data-visualization eda feature-engineering model-optimization time-series-forecasting xgboost

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Use machine learning to predict grocery sales

• Host: GitHub
• URL: https://github.com/walidalsafadi/store-sales-ts-forecasting
• Owner: WalidAlsafadi
• License: apache-2.0
• Created: 2024-09-26T13:39:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-24T11:16:41.000Z (over 1 year ago)
• Last Synced: 2025-04-01T23:52:07.618Z (about 1 year ago)
• Topics: data-visualization, eda, feature-engineering, model-optimization, time-series-forecasting, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 833 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# **Sales Forecasting Using Time Series Analysis with XGBoost**

## **Project Overview** 🎯

This project aims to forecast sales for thousands of product families sold at Favorita stores using historical data. The analysis is performed using XGBoost, a powerful machine learning algorithm. Through feature engineering and model tuning, the project provides both short-term and long-term sales predictions, with a focus on generating a 30-day forecast.

## **Project Workflow** 📈

1. **Data Collection and Preprocessing**:

    - Used historical sales data provided by Favorita stores.

    - Processed the data to handle missing values and outliers.

    - Performed feature engineering to create lag features and rolling statistics.

    - Applied one-hot encoding for categorical variables and scaling for numerical variables.

2. **Exploratory Data Analysis (EDA)**:

    - Visualized sales trends over time.

    - Analyzed seasonal effects, promotions, and other external factors influencing sales.

3. **Feature Engineering**:

    - Created lag features and rolling windows to capture historical patterns.

    - Engineered calendar-related features such as day of the week, month, and promotions.

4. **Model Building**:

    - Implemented **XGBoost** for time series forecasting.

    - Fine-tuned the model using RMSE and RMSLE as evaluation metrics.

    - Generated a 30-day sales forecast.

5. **Model Evaluation**:

    - Used RMSE and RMSLE to assess the model’s accuracy.

    - Compared the model’s predictions against actual sales data for validation.

## **Key Features** 🛠️

- **XGBoost Model**: Applied an advanced machine learning approach for time series forecasting.

- **Feature Engineering**: Integrated lag and rolling statistics to enhance forecast accuracy.

- **Data Visualization**: Plots and charts to illustrate key trends and model performance.

- **30-Day Sales Forecast**: Focused on predicting future sales, with results analyzed for potential improvements.

## **Challenges & Improvements** 🔧

- **Current Challenge**: The 30-day forecast results show a linear pattern, lacking the nuanced shape of actual sales data.

- **Future Work**: Exploring advanced techniques such as model stacking, ensemble methods, and deeper time-series models (e.g., ARIMA, LSTM) to improve forecast accuracy.

## **Results** 📊

- Achieved a RMSLE of 0.409.

- Significant improvement in prediction accuracy through feature engineering.

### **Check it out on Kaggle 🔗**

You can also explore the project on Kaggle: https://www.kaggle.com/code/walidkw/store-sales-ts-forecasting-xgboost