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
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.
📚 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.
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
Implementation of an adaboost algorithm on the dataset HC_Body_Temperature
Naive, XGBoost, AdaBoost and other Regressors
Predict an individual’s belief in climate change based on historical tweet data
This Machine Learning project involves predicting the candidate a voter is likely to elect, given a set of predictors such as age, gender, ratings on various parameters and Euroscepticism. Algorithms used are: KNN, Logistic Regression, Naive Bayes, AdaBoost, Gradient Boost, Random Forest, LDA. The hight accuracy score is 84% (KNN). Using the Odd…
The creation of the churn prediction models of Telco Company.
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