This project builds an advanced CatBoost-based loan payback prediction model using extensive feature engineering and class-balanced training. It implements 5-fold Stratified Cross-Validation, frequency encoding, engineered financial ratios, and optimized CatBoost hyperparameters.

🔴 Customer Churn Prediction (Bank Customers) 🔴 In this project, I analyzed bank customer data to predict who might leave the bank. I cleaned and prepared the dataset by handling missing values and encoding categorical features. I trained machine learning models to classify customers based on churn risk.

Unsupervised Learning project analyzing TradeAhead stock data using K-Means, Hierarchical Clustering, and PCA. Includes full EDA, preprocessing, cluster evaluation (Silhouette Score), interpretation, and actionable business insights.

Using Logistic regression algorithm for predicting whether the patient has heart disease or not.

Chapter 10: Data Preparation for Fraud Analytics

The primary goal of this project is to develop and evaluate a robust multiclass classification model.

🔍 Practical Implementation of Linear Regression using Python and Scikit-learn on a Salary Prediction Dataset. This project demonstrates the full workflow of a supervised machine learning task — from data preprocessing and model training to evaluation and visualization.

A predictive model for breast cancer detection using K-Nearest Neighbors, demonstrating the impact of feature scaling on model performance and recall.

Logistic Regression model built using a housing dataset to predict whether a house is high-priced or not based on features like area, number of bedrooms, bathrooms, and stories. The project includes a comparison between models trained on unscaled vs. scaled data, demonstrating the effect of feature scaling on model performance.

Practical implementation of classification techniques in both machine learning and statistical pattern recognition.

Built and validated a Logistic Regression model achieving 98% accuracy with high recall for malignant tumors — interpretable, lightweight, and production-ready for healthcare analytics use cases.

The provided model is a Tea_yield_prediction system which foretells the output of tea products from a tea garden annually with an accuracy of 99.99%. It was trained on natural data obtained through permission from local authorities

Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.

This Machine Learning model predicts price of houses using dataset of various house features

This project presents a machine learning-based solution for detecting fraudulent financial transactions. Using a dataset of over 6 million records with detailed transaction behavior, the goal is to build a predictive model that accurately identifies fraud in real time.

A complete machine learning project to detect fraudulent credit card transactions. It includes data preprocessing, feature scaling, model training (Logistic Regression), evaluation, and deployment using Streamlit. Built with modular, production-ready code and a simulated dataset for privacy-safe demonstrations.

This housing dataset aims to predict land prices according to user preference. The datasets consists of several variables which includes POSTED_BY, UNDER_CONSTRUCTION, RERA, BHK_NO., BHK_OR_RK, SQUARE_FT, READY_TO_MOVE, RESALE, ADDRESS, LONGITUDE, LATITUDE, TARGET(PRICE_IN_LACS).

Complete K-Nearest Neighbors (KNN) Classification on Iris Dataset. Includes full pipeline from data cleaning, EDA, feature scaling, model evaluation, and visual proofs of learning.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Machine learning project for classifying breast tumors as malignant or benign using the Breast Cancer Wisconsin dataset. Includes data preprocessing, model training (Naive Bayes, Logistic Regression, Decision Tree), evaluation, and visualizations.