I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.

All my Machine Learning Projects from A to Z in (Python & R)

Full machine learning practical with R.

Full machine learning practical with Python.

If you miss payments or you don't pay the right amount, your creditor may send you a default notice, also known as a notice of default. If the default is applied it'll be recorded in your credit file and can affect your credit rating. An account defaults when you break the terms of the credit agreement.

Assignments in Machine Learning class at Stockton University

This project demonstrates Decision Tree Classification using two pruning techniques: pre-pruning and post-pruning. Both approaches are implemented and compared to control overfitting and improve model generalization.

Build and evaluate classification model using PySpark 3.0.1 library.

Decision Tree and Artificial Neural Network for Cell of Cancer

No description provided.

Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables. Decision tree classification observes features of an object and trains a model in the structure of a tree to predict the class of the data.

Some of my Python Projects

ML model that creates a decision tree to classify recipes into cuisines based on their ingredients.

House Prices Prediction and Credit Default Risk Prediction competitions. Advanced decision tree-based regression and classification models are used.

MACHINE LEARNING ALGORITHMS

Third homework to the subject IZU.

Breast Cancer Prediction with Logistic Regression Classification gives an accuracy of 96.70%. apart from this Decision Tree Classification gives more accuracy along with LRC. Dataset can be available on UCI Machine Learning.

Prediction of students' dropout using classification models. Data visualisation, feature selection, dimensionality reduction, model selection and interpretation, parameters tuning.

Machine Learning Mastery is a comprehensive repository designed to teach machine learning with Python. It covers essential techniques from data preprocessing to advanced methods in classification, regression, and clustering, catering to beginners and advanced learners alike.

In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.

Sentiment Analysis of Movies Dataset

Implementation of Decision Tree algorithm in python, this is a basic implementation and will be helpful for beginners to start, understand and implement Decision Trees. This repository will help in understanding decision trees using Python. This also includes plotting ROC curve, confusion metrics etc.

This project is developed as part of Digital Skill Fair (DSF) 35.0 - Data Science by Dibimbing. I am using Wine Recognition Dataset from scikit-learn, which is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators.

This repository includes basic algorithms of machine learning.

Implementation of Decision Tree classification algorithm in Python using Pandas, NumPy and Scikit-Learn.

Fast, customizable terminal directory tree viewer with ignore patterns and depth control. See files and directories at a glance in any project. :octocat: