Which algorithms are used in this purchase intention prediction project?

The system implements and compares two algorithms: Decision Tree (interpretable) and Random Forest (ensemble). Random Forest achieved superior performance with 92.5% accuracy and 0.955 ROC-AUC.

What is the accuracy of the purchase intention prediction model?

The Random Forest model achieves 92.5% accuracy with an F1-score of 92.1% and ROC-AUC of 0.955, demonstrating excellent predictive capability for customer purchase behavior.

What features are used for purchase intention prediction?

The system uses features including Age, AnnualIncome, NumberOfPurchases, TimeSpentOnWebsite, LoyaltyProgram, DiscountsAvailed, and engineered features like Engagement_Score and Purchase_Potential.

Who can benefit from this purchase intention prediction system?

E-commerce companies, retailers, marketing teams, business analysts, and customer relationship managers can use this system for targeted marketing, customer segmentation, and revenue optimization.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and Matplotlib for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Watch Demo Video

Machine Learning

Customer Purchase Intention Prediction Using Machine Learning

The Customer Purchase Intention Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts customer purchase intention using demographic, behavioral, and transactional data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Decision Tree and Random Forest with SMOTE for class imbalance, achieving 92.5% accuracy and 0.955 ROC-AUC.

The system utilizes a customer dataset containing 1,000 records with 10 features including Age, AnnualIncome, NumberOfPurchases, TimeSpentOnWebsite, LoyaltyProgram, and DiscountsAvailed. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time purchase intention prediction, enabling businesses to optimize marketing strategies and maximize revenue.

Python 3.8+ Machine Learning Random Forest Decision Tree Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Purchase Prediction
Random Forest (92.5% Accuracy)
Decision Tree (88.3% Accuracy)
Feature Engineering (Engagement Score, Purchase Potential)
Interactive Visualizations
Real-time Purchase Predictions
Model Performance Comparison
Feature Importance Analysis
Customer Targeting Insights
Flask Web Application

Algorithms Used

🌳 Decision Tree

Interpretable tree-based classifier with max_depth=10, class_weight='balanced'

🎯 Accuracy: 88.3%

🌲 Random Forest

Ensemble learning with n_estimators=100, max_depth=10, class_weight='balanced'

🎯 Accuracy: 92.5%

🔧 Feature Engineering

Age_Group, Income_Group, Purchase_Intensity, Engagement_Score

📊 Top Feature: Engagement_Score

🔄 SMOTE Oversampling

Synthetic minority oversampling for class imbalance handling

📊 CV Mean: 91.8%

Methodology & Workflow

1 Data Collection
1,000 customer records with 10 features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Decision Tree and Random Forest with SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time purchase prediction

Model Performance Comparison

Metric	Decision Tree	Random Forest	Best
Test Accuracy	88.3%	92.5%	Random Forest
Test Precision	87.0%	93.0%	Random Forest
Test Recall	85.0%	91.2%	Random Forest
Test F1-Score	86.0%	92.1%	Random Forest
Test ROC-AUC	92.0%	95.5%	Random Forest
CV Mean Accuracy	87.5%	91.8%	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Customer Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

Check Payment Status

LIMITED TIME OFFER -70%

Complete Project Package Lifetime Access

Original Price

₹9,999

Today's Price ₹2,999 💎 Save ₹7,000

You Save ₹7,000 (70% OFF)

Complete Source Code

Documentation & PPT

Video Tutorial

24/7 Expert Support

Scan & Pay with UPI

SECURE

Payee Thirumalai Kumar

UPI ID 9600095045@icici

Amount ₹2,999

Submit Your Payment

100% SECURE

Full Name *

Email *

Phone Number *

College/University *

Degree

Passout Year

Department

Payment Details

Enter your UPI Transaction ID and upload payment screenshot for verification.

Transaction ID *

Payment Screenshot *

Click to upload screenshot JPG, PNG, GIF, PDF • Max 5MB

📚 Academic & Research Support Services

Need help with Thesis, Dissertation, Assignments, or PhD Research? We've got you covered!

📝

Thesis & Dissertation

Complete thesis writing, research guidance, and formatting support

Expert Help

📄

Assignment Help

Quality assignment writing, editing, and proofreading services

100% Original

🔬

PhD Research

Research proposal, literature review, data analysis & publication

PhD Level

📊

Project Guidance

Academic projects, mini projects, and final year project support

Hands-on

📞 Need custom support? Contact us directly!

Chat on WhatsApp → Email Us

Final Year Projects