A collection of research papers on decision, classification and regression trees with implementations.

Learning to create Machine Learning Algorithms

📈 Click-Through Rate Prediction using Logistic Regression and Tree Algorithms

Collection of Artificial Intelligence Algorithms implemented on various problems

🐍 Data Science Boot-Camp : UC San DiegoX

Build serialisable flowchart-style decision trees with D3.

Abnormal Traffic Identification Classifier based on Machine Learning. My code for undergraduate graduation design.

IDS Alert Prioritization INSuRE Research Project

Simple implementation of CART algorithm to train decision trees

Flask based Web application for predicting the income of a person

Speech_Emotion_detection-CNN,SVM,RF,DT,MLP

Using Machine Learning to Identify Fraud in the Enron Corpus

Python implementation of basic machine learning algorithms

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

This project is on twitter sentimental analysis by combining lexicon based and machine learning approaches. A supervised lexicon-based approach for extracting sentiments from tweets was implemented. Various supervised machine learning approaches were tested using scikit-learn libraries in python and implemented Decision Trees and Naive Bayes techniques.

Predicting the default customers

Machine learning library for classification tasks

Python implementation of Decision trees using ID3 algorithm

Decision tree in Go based on @random-forests example

Analysis and Prediction of Poker Hand Strength

Predict conversion rate and generate ideas to improve conversion rate

Machine learning library for classification tasks

Quizzaro The Personality Quiz

Prediction whether a customer will Churn the Telecom Company or not

ID3 Decision Tree Classifier for Machine Learning along with Reduced Error Pruning and Random Forest to avoid overfitting