Big Mart Sales Prediction using Machine Learning Web App

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
Download Link
Demo Video

Detailed Project Description

Big Mart Sales Prediction using Machine Learning Web App applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- Machine Learning

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Machine Learning) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface