Project Title

Big Mart Sales Prediction Using Machine Learning with Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Healthcare Analytics | Data Science | Predictive Analytics

Project Level

Beginner to Intermediate

Project Objective

Retail businesses generate large volumes of sales data every day. Accurate sales prediction helps retailers optimize inventory management, improve business planning, and maximize profits. The objective of this project is to build an intelligent Machine Learning model capable of predicting product sales in Big Mart stores based on product and outlet characteristics.

Students will learn the complete Machine Learning workflow, including data collection, preprocessing, exploratory data analysis, feature engineering, model training, testing, evaluation, and prediction. This project provides practical exposure to Artificial Intelligence, Retail Analytics, and Business Intelligence concepts used in real-world retail organizations.

Session Access Link

Project Training Session

Session Link:

Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand the fundamentals of Machine Learning.

• Learn how cost prediction systems work.

• Understand regression algorithms and predictive analytics.

• Perform data cleaning and preprocessing.

• Conduct exploratory data analysis (EDA).

• Work with healthcare and insurance datasets.

• Handle categorical and numerical data.

• Train and evaluate machine learning models.

• Measure model performance using different evaluation metrics.

• Build a real-world insurance cost prediction system.

• Improve analytical and problem-solving skills.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn

• NumPy

• Pandas

Data Visualization Libraries

• Matplotlib

• Seaborn

Development Platforms

• Google Colab

• Jupyter Notebook

• VS Code (Optional)

Data Storage

• CSV Files

• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Big Mart Sales Dataset containing:

• 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)

Dataset Format:

• CSV File

Phase 2: Data Preprocessing

Students will perform:

• Removal of duplicate records
• Handling missing values
• Data cleaning
• Encoding categorical variables
• Feature selection
• Data normalization/scaling
• Outlier detection and treatment

Purpose:

To improve data quality and model performance.

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Summary Statistics
• Correlation Analysis
• Histograms
• Count Plots
• Box Plots
• Scatter Plots
• Heatmaps

Benefits:

• Understand sales trends.
• Identify factors affecting product sales.
• Detect patterns and anomalies in retail data.

Phase 4: Feature Engineering

Students will prepare data for machine learning using:

• Label Encoding
• One-Hot Encoding
• Feature Scaling
• Feature Selection Techniques

Benefits:

• Improves model performance.
• Enhances prediction accuracy.
• Reduces irrelevant features.

Phase 5: Model Building

Students will train Machine Learning models such as:

Linear Regression

• Simple and effective regression model.
• Suitable for sales prediction.

Decision Tree Regressor

• Captures complex relationships.
• Easy to interpret.

Random Forest Regressor

• Ensemble learning method.
• Provides higher prediction accuracy.

Gradient Boosting Regressor

• Improves prediction performance.
• Handles complex datasets effectively.

XGBoost Regressor (Optional)

• Industry-standard boosting algorithm.
• Commonly used in forecasting applications.

Phase 6: Model Evaluation

Students will evaluate model performance using:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R-Squared Score (R²)

Expected Accuracy:

80% – 95% R² Score (depending on dataset quality and feature engineering)

Phase 7: Prediction System

Students will create a system where users can:

• Enter product and outlet details.
• Click Predict.
• Receive output:
  
  • Predicted Product Sales

Example:

Input:

• Item Type: Dairy Product
• Item MRP: ₹150
• Outlet Type: Supermarket

Output:

• Predicted Sales: ₹2,850

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)

• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

• Project Report (PDF)

Screenshots

• Dataset Import

• Data Cleaning

• Exploratory Data Analysis

• Model Training

• Evaluation Results

• Prediction Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Model (.pkl file)

• Output Results

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

BigMartSalesPrediction_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.