Credit Risk Analysis with Machine Learning

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

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

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

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

A ML project with EDA and model that helps in predicting next-day rain by training classification models on the target variable RainTomorrow.

This project is to support Allcorrect Games with creating a machine learning model to help make label predictions on text game reviews in regards to localization.

Modified fastapi microservice with scheduled A/B tests of base&enhanced catboost models (microservice based on final project of Start ML course at karpov.courses)

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

Source code of TFI's Kaggle Competition: Restaurant Revenue Prediction.

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

Binary Classification on Job Manual Usage

Deploying Flight Price Prediction via Microsoft Azure

this project leverages machine learning to predict customer churn, and we've built an interactive interface using Streamlit and containerized everything with Docker for easy deployment.

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--