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).

A submission for HUAWEI - 2020 DIGIX GLOBAL AI CHALLENGE

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

HackerEarth Machine Learning challenge: Of Genomes And Genetics

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!

Qualcomm-Institute 14th Winter project @ UC San Diego

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.

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)

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

Life expectancy data processing

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.

Data preprocessing for machine learning modelling. Quantile transformation for the outliers removal, replacing NULLs with medians, using target encoder and Z-score standardisation for the numeric variables.

Прогнозирование рыночной стоимости автомобилей