Big Mart is a retail chain that operates numerous stores across various locations. Predicting sales can help in optimizing inventory, improving sales strategies, and enhancing overall profitability. This project aims to build a predictive model using machine learning techniques to forecast the sales of products in different stores.
Objective
The main objective of this project is to predict the sales of products in different Big Mart outlets based on historical sales data and product attributes.
Data Collection and Preprocessing
Data Description
The dataset used in this project consists of various attributes of products and historical sales data. The key features include:
Datasets Link : - kaggle
- 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)
Model Building
Model Selection
Several machine learning algorithms were considered, including:
- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
- Gradient Boosting Regressor
Model Training
The dataset was split into training and testing sets. Hyperparameter tuning was performed using GridSearchCV to find the best parameters for each model.
Model Evaluation
Models were evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) score.
Installation Steps :-
- Install Python 3.8
- Install all dependencies cmd -python -m pip install --user -r requirements.txt
- Finally run cmd - python app.py