Predicting sales for Big Mart outlets using machine learning algorithms to optimize inventory and marketing strategies.
This project aims to predict the sales of Big Mart outlets using various machine learning algorithms. By analyzing historical sales data, we can provide insights to optimize inventory management, marketing strategies, and improve overall sales performance.
The dataset used for this project is sourced from Kaggle. It contains information on various products and their sales across different Big Mart outlets. The key features include:
Item Identifier
Item Weight
Item Fat Content
Item Visibility
Item Type
Item MRP
Outlet Identifier
Outlet Establishment Year
Outlet Size
Outlet Location Type
Outlet Type
Item Outlet Sales (target variable)
Python: Programming language Streamlit: Framework for building the web app Scikit-learn: Machine learning library Pandas: Data manipulation library NumPy: Numerical computing library Matplotlib/Seaborn: Data visualization libraries
The EDA phase involves analyzing the dataset to uncover patterns, anomalies, and relationships between variables. Key steps include:
Data cleaning
Handling missing values
Feature engineering
Data visualization
We experiment with various machine learning algorithms to predict sales, including:
XGBoost
Decision Trees
Random Forest
The models are evaluated using metrics such as: R-squared = 0.5547040460048651
The graphical representation is also shown
The results section highlights the performance of different models and compares their accuracy. Visualizations and charts are used to illustrate the findings.
This project demonstrates the application of machine learning techniques to predict sales for Big Mart outlets. The insights gained can help in making informed business decisions, optimizing inventory, and improving sales strategies.
Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.
The application is deployed using Streamlit. You can access it here = https://ml-project-12-big-mart-sales-prediction-webapp-rfzqmbxkhuftgna.streamlit.app/