Which algorithms are used in this complaint categorization project?

The system implements and compares two classification algorithms: Random Forest and K-Nearest Neighbors (KNN). Random Forest achieved superior performance with 87.4% accuracy and 0.921 ROC-AUC.

What is the accuracy of the complaint categorization model?

The Random Forest model achieves 87.4% accuracy with an F1-score of 0.861 and ROC-AUC of 0.921, demonstrating excellent predictive capability for consumer dispute classification.

Who can benefit from this complaint categorization system?

Regulatory agencies (CFPB), financial institutions, consumer protection organizations, and compliance teams can use this system for automated complaint screening, dispute prediction, and regulatory oversight.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and imbalanced-learn for machine learning. NLP features use TF-IDF vectorization. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Consumer Complaint Categorization Using Machine Learning

The Consumer Complaint Categorization Using Machine Learning system is a comprehensive machine learning solution that predicts whether a consumer complaint will be disputed. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Random Forest and K-Nearest Neighbors with TF-IDF text processing and SMOTE for class imbalance, achieving 87.4% accuracy and 0.921 ROC-AUC.

The system utilizes the CFPB Consumer Complaint Database containing approximately 100,000 records with 18 features including complaint narratives, product categories, company information, and response details. It provides real-time prediction capabilities, comprehensive visual analytics, and automated reporting for regulatory compliance, consumer protection monitoring, and quality assurance in financial services.

Python 3.8+ Machine Learning Random Forest K-Nearest Neighbors NLP TF-IDF Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Complaint Classification
Random Forest (87.4% Accuracy)
K-Nearest Neighbors (84.2% Accuracy)
TF-IDF Text Vectorization
Leak-free SMOTE Oversampling
Interactive Visualizations
Real-time Dispute Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

🌲 Random Forest

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

🎯 Accuracy: 87.4%

📊 K-Nearest Neighbors

Instance-based learning with k=5, distance weighting, Euclidean metric

🎯 Accuracy: 84.2%

📝 TF-IDF Vectorization

Text feature extraction from complaint narratives with 5,000 features

📊 Top Feature: company_response

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV AUC: 0.918

Methodology & Workflow

1 Data Collection
100,000 consumer complaints from CFPB database

2 Data Preprocessing
Cleaning, encoding, text preprocessing, TF-IDF

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Random Forest and KNN with leak-free SMOTE

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

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

Model Performance Comparison

Metric	KNN	Random Forest	Best
Test Accuracy	84.2%	87.4%	Random Forest
Test Precision	83.1%	87.3%	Random Forest
Test Recall	84.2%	87.4%	Random Forest
Test F1-Score	82.0%	86.1%	Random Forest
Test ROC-AUC	89.7%	92.1%	Random Forest
CV Mean ROC-AUC	89.1%	91.8%	Random Forest

Project Package Includes:

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

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