Flight Analysis - Flight Delay

R Package to Perform Clustering of Three-way Count Data Using Mixtures of Matrix Variate Poisson-log Normal Model With Parameter Estimation via MCMC-EM, Variational Gaussian Approximations, or a Hybrid Approach Combining Both.

This repository explores and compares different regression models for predicting continuous outcomes. This repository includes implementations and evaluations of five key regression models. The primary goal is to demonstrate how each model works, evaluate their performance using R-squared values, and guide users in selecting the best model.

We have been given historical sales data for 45 stores situated in various regions. Each store comprises multiple departments, and our objective is to forecast sales for each department within these stores.

Open Machine Learning course at MIPT

A web application that employs machine learning models to provide accurate and instant car price estimations based on various features and specifications.

Multiple Disease Prediction System using Machine Learning.

Integrated robust and reliable ML Pipelines for Research and Production environment

End-to-end projects: customer churning prediction using the Random Forest Classifier Algorithm with 97% accuracy; performing pre-processing steps; EDA and Visulization fitting data into the algorithm; and hyper-parameter tuning to reduce TN and FN values to perform our model with new data. Finally, deploy the model using the Streamlit web app.

Model to predict fraudulent bank applications using a large Kaggle dataset

This is a Premiere Project done by Team Gitlab in Hamoye Data Science Program Dec'22. Out of 5 models used on the data, Random Forest Classifier was used to further improve the prediction of characters death. With parameter tuning and few cross validation, we were able to reduce the base error by 5.42% and increase accuracy by 2,42%.

This is about Treue Technologies Data science Internship tasks.

This project aims to predict the future stock prices of various companies using machine learning and deep learning techniques. By analyzing historical stock price data and incorporating relevant features, the goal is to build accurate and robust models that can forecast stock prices over different time horizons.

Check my projects related to ML feature engineering and modeling.

Amazon employee data to predict approval/ denial

Machine Learning project based on UCI mushroom dataset