Using classification trees to predict a hand written digit.
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Updated
Nov 29, 2018 - Jupyter Notebook
Using classification trees to predict a hand written digit.
Creating a classification decision tree that predicts heart disease. 79% Recall and 70% Precision
This repo contains an R programming project that implements and compares decision tree models including C5.0, C5.4 and CART.
Performed segmentation analysis and predictive modeling on insurance broker performance to conclude a random forest model (highest AUC of 73%) predicted whether 2020 Gross Written Premium will increase or decrease from 2019 with a misclassification rate of 35%. Four classification models (classification trees, logistic regression, random forests…
Build, train and deploy ML model to classify penguin types on tabular data
Using Logistics, Classification, and KNN modelling to predict if a credit card account will default.
A collection of mini projects in R that apply statistical and machine learning methods and tools to solve data-driven problems
BigData, Classification, Clustering, Graph analytics, Splunk, Spark, Neo4j, KNIME
Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees
Focused customer retention programs
ML projects, which I worked on utilising different machine learning algorithms.
A set of tools useful for doing experiments with fuzzy forests
Using the tree library to construct a classification tree
Classification of movie rankings
Analyze the ATM dataset and predict the monthly withdrawal and rating of an ATM based on the features in the dataset 🏧
Finding out whether it is possible to predict the quality of the wine using Random Forests, Neural Networks, Classification trees and other methods
Material for the Computational Statistics Project | Summer 2022 | University of Bonn
My Matlab Ph.D. thesis coding project: the enhanced version of Tree-like Divide to Simplify (T-DTS) ANN (AI/ML) structure-based tool used for classification tasks. The credits: the v.1.0 was developed by Dr. M. Rybnik under supervision of Prof. K. Madani
Code written for Google's ML Recipies Class
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