Decision Tree Implementation using Scikit Learn

Udacity - Data Scientist Nanodegree Program - Supervised Learning

Using Classification Models with cross-validation and hyperparameter tuning to predict shoppers decision to make online purchase.

This project demonstrates building a classification model for imbalanced data. Feature engineering, feature selection and extensive EDA. Comparing of logistic regression, random forest and ADA Boost models are done before finalizing the best model.

Projects done for Machine Learning (including Academic Projects)

iris Dataset classification (pre-processing, Scaling, and plotting ) // AdaBoost and Random forest

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.

Finding donors for charity using Machine Learning.

2022 POSTECH OIBC CHALLENGE Duck Curve 팀 결과물 입니다.

Ada boost demo

📚 Assignments in the course IT3212 - Data Driven Software at NTNU. Our task is to classify whether a tweet is related to a disaster or not.

This project focuses on predicting the Myers-Briggs Personality Type Indicator (MBTI) using various machine learning techniques. MBTI is a type indicator that categorizes individuals into one of 16 personality types based on their preferences in four dimensions: Introversion/Extraversion, Sensing/Intuition, Thinking/Feeling, and Judging/Perceiving.

DVboost R package

Based on the powerful econometrics and statistical background and rich data science resources of School of Economics (SOE) and Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, WISER CLUB is a data science mutual aid learning organization jointly organized by SOE and WISE graduate students and undergraduate students.

Focused on math and applied the methods into programming.

Machine Learning 2 course - 2020

Minimal implementation of Adaboost classifier using weighted decision stumps without sklearn.

In this project I will employ several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census.