We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.

This project develops an advanced predictive model to identify thyroid disease recurrence using machine learning algorithms. We used a detailed dataset with demographic, medical, and clinical features, and implemented Logistic Regression, Decision Tree, Random Forest, and CatBoost Classifier. Rigorous preprocessing and EDA were performed.

Behavior-Based Malware Detection using GBDTs

This project predicts MBTI personality types from users' recent 50 posts using NLP and ML techniques.

Bank Churn Classification

Дипломная работа физического факультета СПбГУ

bank marketing prediction for a term deposit campaign, identify non-potential customers

A Domestic violence support system for the victims, that enables users to share their thought and provides knowledge about the particular type of abuse they are going through.

ML competition submission to classify anonymous age related condition

Ad huc solution for anomaly classification of HTTP requests between service and end-user based on limited data / Решение задачи поиска аномальных HTTP запросов (их классификации) к сервису.

Credit_Card_Approval_odinschool_project

The purpose is to train a predictive model that can determine if a given customer will subscribe to a term deposit based on these various features. By analyzing historical data on successful and unsuccessful subscription outcomes, patterns can be identified which help predict future subscription behavior.

Machine learning to predict which passengers survived the Titanic shipwreck

Uses letter frequency and catboost classifier model in synchronous for guessing letters in hangman game instance. The model performance is evaluated on both seen words in the dictionary and words out of the dictionary.

Kaggle competition on network intrusion detection. Train model, predict test set, submit as CSV (ID, Class). F1-score metric. Part of my 2023 master's program at the University of Ottawa in AI for Cyber Security.

Identify health insurance customers with interest in a vehicle insurance.

ML-solution of the case of the District hackathon Leaders of Digital 2023. The task was to predict accidents (accidents, pipe ruptures, fires) based on the weather forecast for each of the urban districts. Gradient boosting (macro f1), cross-validation, shap values.