Collection of stats, modeling, and data science tools in Python and R.

Turkish sentiment analysis

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

Rule-based Algorithmic Trading using a Genetic Algorithm and Machine Learning Signals for the Cryptocurrency Market.

E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing

Genetic Algorithm, Curve Fitting, Reinforcement Learning, Iteration Value, Iteration Policy, FrozenLake-v1 Environment, Q-Learning, Hidden Markov Models, ML, Linear Regression, Multiple Regression, Classification, Decision Tree, K-Nearest Neighbors, Logistic Regression, Optimization, Random Forest, Gradient Boosting, XGBoost Classifier

Modelling with Tidymodels and Parsnip - A Tidy Approach to a Classification Problem

Introduction to Statistical Learning

Personality Prediction based on Big 5 Model. Uses Multinomial Logistic Regression for Classification and Tailwind, Flask for web interface.

Application that predicts the number of stars that of a Yelp Review in realtime as a reviewer types it. Runs as a microservice-based application using Node.js, Python, and Docker. Displays results from Google Natural Language API and a custom trained classification models.

MIT EDX Course

Loan Application Prediction through machine learning moldes : Logistic Regression, Random Forest, DecisionTree,

Machine Learning model to predict if a client will subscribe to the product, given his/her demographic and marketing campaign related information

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, you will be working to understand the results of an A/B test run by an e-commerce website. Your goal is to work through this notebook to help the company understand if th…

Nested Dichotomy Logistic Regression Models

Experiments in ML with tidymodels

This repository commits to the application of biostatistics knowledge on clinical, randomized trials and observational studies.

An introduction to some generalized linear models using the likelihood approach and R. (scroll down for a menu)

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

Collection of end-to-end regression problems (in-depth: linear regression, logistic regression, poisson regression) 📈