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.

In this project I use classification models to predict potential donors given a set of demographic factors.

Machine learning model to predict the number of food-borne illnesses that might occur in a state in USA

It Works on Credit card fraud dataset, which is bias where we make it unbaised and We using Adaboost Classifier which give a greater Efficiency of classification .

Algorithms from scratch to know how the algorithms work.

Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.

Most popular machine learning algorithms written only with numpy