Cyber-attack classification in the network traffic database using NSL-KDD dataset

To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.

SageMaker Experiments and DVC

Credit Risk Analysis with Machine Learning

Interpreting wealth distribution via poverty map inference using multimodal data

In this repo, I am doing data analysis in water potability and check each and every classification model's accuracy.

A python script for basic data cleaning/manipulation and modelling based on the open source House Sales Advanced Regression Techniques(Kaggle)

Utilizing Machine Learning for portfolio selection with the aim of out-performing benchmark indices

Application of Machine Learning models to predict Company Bankruptcy

Predict the winning probability of white player in a chess game on the basis of first move of white player and first move of black player. In the dataset all the set of moves are given but I choose to predict the white winner the first move

Using SHAP values to explain model features

Time Series Analysis and Forecasting for an online store using LSTM and CatBoost Algorithm.

Implemented various ML algorithms with and without library functions. Final Project-->Application of LGBM, XGBoost, Catboost and SVC

HealthCare Length of Stay predictions with Booster algorithms

Implemeting Modern Day Boosting algos like LightGBM, XgBoost and Catboost to predict yield of Wild Blueberry, done as part of a Kaggle Competition.

HackerEarth Machine Learning Challenge - Adopt a Pet Buddy, ----MultiClass and MultiLabel Classification using Catboost Classifier--

Rank 9/113 AnalyticsVidhya

Predict the likelihood of a genome sequence undergoing mutation.