Predicts if a driver is fit to drive or not. Performance of Logistic Regression, Naive Bayes, and Random Forests using Scikit-Learn is compared.
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Updated
Aug 30, 2019 - Jupyter Notebook
Predicts if a driver is fit to drive or not. Performance of Logistic Regression, Naive Bayes, and Random Forests using Scikit-Learn is compared.
The ML-GYM repository showcases machine learning projects using **scikit-learn**, covering classification, regression, and clustering. It offers educational resources for beginners and practical examples for experienced users, complete with detailed instructions.
Random Forest Classifier
Used Supervised Classification Predictive Machine Learning models such as Decision Trees, KNN, Logistic Regression, Random Forests, and SVM
A 20m presentation showing the concepts behind oblique random survival forest and some of its recent applications.
Material for the Computational Statistics Project | Summer 2022 | University of Bonn
Supervised Machine Learning using SciKit and other tools to do PCA, SVM, random forests, etc. for facial recognition and predictive decision making.
Data Analytics and Machine Learning in R. Linear-regression, Logistic-regression, Hierarchical-clustering, Boosting, Bagging, Random-forests, K-means-clustering, K-nearest-neighbors (K-N-N), Tree-pruning, Subset-selection, LDA, QDA, Support Vector Machines (SVM)
This repo contains material for a workshop on Random Forests in phonetics/phonology research
Embankment dam land-cover segmentation based on multispectral remote sensing imagery.
RFA package for implementing random forest adjustment.
Data Science - Case Study with Classification Application in Python Using scikit-learn
Price Prediction using Random Forests
Develop a Lead Prediction System to enhance marketing efforts by accurately identifying prospective customers.
Exploratory data analysis and predicting diabetics using PySpark
Group project for the course Business Analytics Applications with Python for my MScBA in Business Analytics & Management.
Poetry Identification Code from my dissertation runs on zip files containing DJVUXML from the Internet Archive.
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
Decision_Trees_and_Random_Forests
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