Food Demand Forecast Analysis Project

This project focuses on forecasting food demand using historical sales and external factors that impact customer purchasing behavior. By leveraging data analysis and machine learning models, the project aims to predict future food demand, helping optimize inventory management and minimize waste.

Table of Contents

• Food Demand Forecast Analysis Project
  • Table of Contents
  • Overview
  • Dataset
  • Exploratory Data Analysis (EDA)
  • Machine Learning Models
  • Results
  • Installation
  • Technologies Used
  • Usage
  • Contributing
  • License

Overview

This project aims to forecast food demand for a given set of outlets or regions by analyzing past sales data and external factors such as seasonality, promotions, and weather conditions. Accurate demand forecasting is crucial for reducing food waste and optimizing supply chains in the food industry.

Dataset

The dataset used includes:

• Sales Data: Historical records of food item sales.
• External Factors: Variables such as weather, holidays, and promotions that might affect food demand.
• Time-Series Information: Date-related information to identify seasonal patterns and trends.

Exploratory Data Analysis (EDA)

The project begins with a thorough exploratory data analysis to uncover trends, correlations, and patterns within the data. Key analyses include:

• Seasonal Trends: Identifying food demand peaks and troughs.
• Influence of Promotions: Understanding how promotional events affect demand.
• Geospatial Analysis: Analyzing demand by region or store location.

Machine Learning Models

Several machine learning models are implemented to forecast future food demand:

1. Linear Regression: A baseline model for demand prediction.
2. Random Forest: A more advanced model that captures non-linear relationships.
3. Gradient Boosting: A powerful ensemble technique for demand forecasting.
4. Time Series Models (ARIMA): Specifically used for capturing temporal patterns in the data.

Each model is evaluated using relevant performance metrics to determine its predictive power.

Results

The performance of each model is assessed using metrics such as:

• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)
• R² Score

The best-performing model is used to generate forecasts, which are then visualized in Tableau to assist in decision-making.

Installation

If you're looking to run the project locally:

1. Clone the repository:

   git clone https://github.com/your-username/food-demand-forecast.git

2. Install the necessary dependencies:

pip install -r requirements.txt

3. Open the Tableau workbook file (.twb) in Tableau Desktop to visualize the results.

Technologies Used

• Python: For data analysis and model building.
• Pandas: Data manipulation and processing.
• Scikit-learn: Machine learning models.
• Tableau: Data visualization.
• Matplotlib & Seaborn: For EDA and plotting.
• ARIMA: For time-series analysis.

Usage

• Open the Tableau workbook Food Demand Forcast Analysis Project.twb to view interactive visualizations and results.
• Use the Jupyter notebooks provided in the repository to explore the data analysis and modeling steps.

Contributing

Contributions to the project are welcome. If you have ideas for improvements, feel free to create a pull request or raise an issue.

1. Fork the repository.
2. Create a new branch:

git checkout -b feature-branch

3. Commit your changes:

git commit -m 'Add feature'

4. Push to the branch:

git push origin feature-branch

5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.