Group Stratified Shuffle Split cross validator for data science projects
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Updated
Mar 30, 2020 - Jupyter Notebook
Group Stratified Shuffle Split cross validator for data science projects
A theoretical analysis of possible approaches to an imbalanced dataset.
The project involves deciding on the mode of transport that the employees prefer while commuting to the office.
Classify people to predict their income class, either above 50K or below 50K based on their age, work class, education level, ... using ScikitLearn
Expresso Churn Prediction Challenge - dealing with imbalanced dataset
This particular notebook consist of all the Feature Engineering technique and Feature Transformation technique
Using a credit score data from Kaggle, determine clients to provide loans and are less likely to default.
Predict probability of default on credit
Fully connected neural network analyzing sentiments in reviews for Amazon's Alexa.
Data modeling for credit card fraud via Kaggle dataset
ML workflow designed for processing neurophysiological MEA data
Using R Markdown for Data Analysis, Machine Learning
Este repositório contém um código de Machine Learning que utiliza o algoritmo AllKNN do pacote imblearn para realizar o balanceamento de dados.
A machine learning web app which predicts whether an employee gets promoted or not
Pseudo Code or parts of code to speed up the process of data exploration and model building with little modifications.
A repository dedicated to classifying future customers into subscribers and non subscribers to a `term deposit`
This Project is all about building a DL pipeline to process the real world, user-supplied Images. Given an image of a dog the algorithm will identify an estimate of the canine’s breed. If supplied with an image of a human, the code will identify the resembling dog breed.
This dataset imbalance visualization toolkit will be the beginning of a fire-new branch in NILM studies. (the website is pending)
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