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Python Implementation of the Machine Learning Decision Tree Algorithm for Classification problems.

EDA and Machine Learning Models in R and Python (Regression, Classification, Clustering, SVM, Decision Tree, Random Forest, Time-Series Analysis, Recommender System, XGBoost)

A repository containing basic machine learning algorithms and examples. This project covers linear regression, decision trees, and clustering techniques using Python and scikit-learn.

Python package for analysing data using machine learning techniques. Provides implementations of different kinds of decision trees and random forests in order to solve classification problems.

This project develops a Depression Detection System using Machine Learning on Twitter data. It predicts depression by analyzing tweets with SVM, Logistic Regression, Decision Trees, and NLTK in Python.

✨Machine Learning & Data Analysis project predicting BMI, Stress Level, and clustering health-related features using Python. Implements Regression, Classification (Logistic, KNN, Decision Tree, Random Forest, SVM, Naive Bayes), K-Means & Hierarchical Clustering, with visualizations in Matplotlib ... Read more

:trident: Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.

Oblique Decision Tree in Python

A Fuzzy Decision Tree implementation for Python.

This repository basically covers Python implementation of different machine learning algorithms like Linear Regression, Logistic regression, Decision tree, PCA,SVM, kNN. It also has implementation of NLP and Recommender System.