Demand Forecasting for a Retail Store

Description

This project aims to develop a time series forecasting model to predict the demand for products in a retail store using historical sales data. The model utilizes Holt-Winters Exponential Smoothing to capture seasonality and trends in the sales data.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Statsmodels
• Scikit-learn

How to Use

1. Ensure you have Python installed on your system.
2. Install the required libraries by running pip install Pandas' ; 'pip install NumPy' ; 'pip install Matplotlib' ; pip install Statsmodels; pip install Scikit-learn.
3. Download the dataset file or replace it with your own dataset.
4. Run the code in a Python environment or Jupyter Notebook.

Code Explanation

• Load the sales data from dataset.

• Convert the date column to datetime type and set it as the index with explicit frequency.

• Visualize the sales data over time.

• Decompose the time series into trend, seasonality, and residuals using seasonal decomposition.

• Split the data into train and test sets.

• Build and train the forecasting model using Holt-Winters Exponential Smoothing.

• Forecast future demand and evaluate the model's performance using mean squared error (MSE).

• Visualize the forecasted demand along with the actual sales data.

• Results

The mean squared error (MSE) for the forecasting model is calculated.