Decision Tree Implementation using Scikit Learn
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Updated
Nov 12, 2018 - Jupyter Notebook
Decision Tree Implementation using Scikit Learn
Udacity - Data Scientist Nanodegree Program - Supervised Learning
Machine learning examples tested on Google Colab in Python3 for learning and practice. Updated once a week.
Implementing an adaptive boosting algorithm (AdaBoost) using decision stumps learned using information gain as the weak learners to classify the notorious handwritten digits problem
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
Finding donors for charity using Machine Learning.
An ensemble of 3 models - AdaBoost, XgBoost and Random Forests to classify machine failures.
📚 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.
Coursework on Introduction to Machine Learning - CS M146
A Machine Learning Library aiming to provide reliable ensemble classifiers
Using sklearn to predict if an individual earns above or below $50k from census information
Graduation Project - Sentiment Mining
Implementation of an adaboost algorithm on the dataset HC_Body_Temperature
Python Notebook for Ensemble Learning on Wisconsin Breast Cancer Database
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