This project utilizes machine learning to predict retail store sales, aiding businesses in inventory management, cost reduction, and profit optimization.
Retail businesses rely on accurate sales forecasting to manage their inventory and make data-driven decisions. This project demonstrates the power of machine learning in predicting store sales.
The primary objectives of this project are as follows:
- Sales Forecasting: Predict store sales accurately to help businesses plan their operations.
- Data Analysis: Explore the dataset to gain insights into sales trends.
- Data Cleaning: Ensure data quality by handling missing values.
- Model Building: Utilize Linear and Lasso Regression for sales prediction.
- Evaluation: Assess model performance using Mean Squared Error (MSE) and R-squared.
- Data Preprocessing: Prepare data for machine learning models.
This project has significant implications for retail businesses and data analysis in general:
- Sales Forecasting: Accurate predictions aid inventory management and profit optimization.
- Data Analysis: Demonstrates the importance of data analysis for decision-making.
- Machine Learning: Highlights the application of machine learning in real-world scenarios.
- Data Preprocessing: Emphasizes the need for data preparation in machine learning.
To run this project, you need to install the required Python libraries. Use the following command to install them using pip:
pip install pandas numpy matplotlib seaborn scikit-learn- Clone this repository to your local machine.
- Install the required libraries as mentioned in the Installation section.
- Run the Jupyter Notebook or Python script provided in the project.
This project was completed by Suraj R S, a student at RNS Institute of Technology, Bangalore. For more details and access to the code, visit GitHub: Surajrs812.