This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achi…

A python machine learning library for advanced feature extraction and interpretation.

It contains the code and data for M5 Forecasting - Accuracy competition on Kaggle.

Exploring categorical features with various encodings and models

A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).

Qualcomm-Institute 14th Winter project @ UC San Diego

Automatic Feature Engineering & Selection for Kaggle Playground Competitions

Production-ready ML pipeline demonstrating feature engineering, hyperparameter tuning, stacking ensembles and pseudo-label learning.

This repository is a comprehensive guide to different Encoding techniques in Machine Learning, explaining when to use each method and best practices. You'll find practical examples, ready-to-use code, and comparisons between various techniques like Label Encoding, One-Hot Encoding, Target Encoding, and more!

A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python) (Done alongside Kaggle ML courses)

HackerEarth Machine Learning challenge: Of Genomes And Genetics

Creating a sophisticated web application for transaction analysis, incorporating ML, Bootstrap, Dash, and Plotly. Users can seamlessly upload credit card CSV files, exploring transactions interactively in both tabular and dashboard report formats.

TCD ML Comp. 2019/20 - Income Prediction (Ind.)

Life expectancy data processing

Materials from a paper/talk for Southeast SAS User Group Conference

Final project for "How to win a data science competition" Coursera course

This repo contains code for experimenting with categorical encoding - WoE, Catboost, Target encoder, and many more.

Прогнозирование рыночной стоимости автомобилей