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.

Exploring categorical features with various encodings and models

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

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

HackerEarth Machine Learning challenge: Of Genomes And Genetics

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)

Прогнозирование рыночной стоимости автомобилей

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.

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

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.

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.

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

Life expectancy data processing

Encode Categorical Features based on Target/Class

This repository contains pre-requisite notebooks of Feature Engineering Course from Kaggle for my internship as a Machine Learning Application Developer at Technocolabs.