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

Turkish sentiment analysis

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

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

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

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

Introduction to Statistical Learning

Building a Logistic regression model for the prediction of Sales and implementing it in a web app based on Python 🐍

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.

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

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…

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

Nested Dichotomy Logistic Regression Models

Experiments in ML with tidymodels

MIT EDX Course

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

Analysis of Data Scientist Job Descriptions using Natural Language Processing