Regression Project (Store Sales -- Time Series Forecasting)

Introduction/Objective

Welcome to the Regression Project focused on Time Series Forecasting for Corporation Favorita. This project aims to develop a robust forecasting model to predict store sales, empowering Favorita with data-driven insights for optimized inventory management and business performance.

Project Structure

This project follows a systematic approach with the following structure:

Regression-Store-Sales
├── data
│   ├── train.csv
│   ├── test.csv
│   ├── transaction.csv
│   ├── sample_submission.csv
│   ├── stores.csv
│   ├── oil.csv
│   └── holidays_events.csv
├── notebooks
│   ├── main.ipynb
├── src
│   ├── data_preprocessing.py
│   ├── feature_engineering.py
│   ├── model.py
│   └── visualization.py
├── requirements.txt
├── README.md
└── .env

• data: Contains the dataset files.
• notebooks: Jupyter notebooks for EDA, trends analysis, hyperparameter tuning, and model evaluation.
• src: Python scripts for data preprocessing, feature engineering, model building, and visualization.
• requirements.txt: Lists the project dependencies.
• README.md: Documentation providing an overview, project structure, and usage instructions.

Technical Content

1. Exploratory Data Analysis (EDA)

Explore the dataset using main-1.ipynb. Uncover patterns, distributions, and relationships within the data.

2. Trends of Sales Over Time

Analyze trends and seasonality of sales over time with main-1.ipynb. Identify patterns crucial for decision-making.

3. Hyperparameter Tuning

Implement hyperparameter tuning for models using main-1.ipynb. Optimize model performance for accurate predictions.

4. Model Evaluation

Evaluate the forecasting models in main-1.ipynb. Assess the accuracy and reliability of predictions.

Conclusion/Recommendations

Summarize the insights gained from each step and provide recommendations for leveraging the predictive models to enhance Favorita's business strategies.

Usage Instructions

1. Set up a virtual environment and install dependencies using pip install -r requirements.txt.
2. Execute the notebooks in the specified order: EDA, Trends, Hyperparameter Tuning, Model Evaluation.
3. Utilize the Python scripts in the src folder for data processing, feature engineering, model building, and visualization.

References

Acknowledge the use of libraries, tools, and resources instrumental in the project.

Appreciation

Express gratitude to Azubi Africa for their transformative programs that equipped you with the skills for impactful data science projects.

Connect with me for further discussions and collaboration on this exciting project!