Enhancements to commonly used pyspark functions for modelling
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Updated
Dec 22, 2021 - Python
Enhancements to commonly used pyspark functions for modelling
Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data
[Modeling] Project in 2022 - Simple Model of important factors in the incidence of heart disease and prediction model
Introduction to cross-valdation in Machine Learning, with coding examples for supervised classification.
Green AI Hackathon: Using AlphaEarth Foundations to Detect/Predict Tropical Deforestation
Machine Learning App in R for predicting whether a newly released movie will be a hit or a flop. Practically useful for streaming services and cinemas.
Mithilfe von Machine Learning und Open Data zu Unfällen in Berlin (2018-2021) beantworten wir folgende Frage: Was sind die wichtigen Faktoren/Einflüsse auf Unfallgefahr? Und wie gut lässt sich damit die Unfallschwere überhaupt vorhersagen?
Applying Convolutional Neural Networks (CNN) for recognizing manuscript digits.
Data-Sprint-61---Meteorite-Threat-Identification-challenge using StratifiedKFolds and Manual Hyperparameter tuning of the algorithm
Read in a mnist file and using a neural network, predict the classifcation of 0-9 throughout the mninst dataset.
Human Activity Recognition (HAR) has a wide range of applications due to the widespread usage of acquisition devices such as smartphones and its ability to capture human activity data.
This research processed a fake news dataset, using TF-IDF and Count Vectorizer for feature extraction and evaluating multiple ML models through stratified cross-validation. Logistic Regression with TF-IDF was selected as one of the best models and further explained using LIME for interpretability.
Machine learning prediction project, 2019.
Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service.
The goal of this project is to develop an automatic classification system for exotic fruits based on numerical features using the K-nearest neighbors (K-NN) algorithm
This project consists in using machine learning to analyze the factors that affect wine quality and in building a model for predicting it. The model was tested on unseen wines to evaluate its accuracy.
Object detection exercise for the Neural Networks for Computer Vision course. Using stratified KFold for data with multiple labels and instances, and self-implementation of mAP with multiple configurations.
Malicious URL detector built with deep exploration on feature engineering.
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