Retail Sales Forecasting
Project Description:
Use data science techniques to analyze and forecast retail sales data. This project will utilize statistical analysis and simple machine learning models to predict future sales based on historical data, promotional information, and economic factors.
Use the below dataset.
Retail_Sales_Forecasting_Dataset.csv (1.4 MB)
Tasks:
- Load the provided retail sales dataset.
- Conduct exploratory data analysis to understand key trends and patterns.
- Create new features that might influence sales, such as day of the week, month, or promotional flags.
- Split the data into training and test sets.
- Use a simple linear regression model to predict sales.
- Evaluate the model’s performance using metrics like MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error).
- Visualize the actual vs. predicted sales to assess the model’s effectiveness.
Expected Outcome:
A Jupyter notebook containing the exploratory data analysis, feature engineering, model training, evaluation, and visualizations that provide insights into the sales forecasting process.
This project emphasizes data handling, exploratory analysis, and basic predictive modeling, which are core skills in data science. It’s designed to be approachable for participants with a basic to intermediate understanding of data science principles.
Level:
Beginner