Marker Selection by matching manifolds and elastic net

Source for book "Feature Engineering A-Z"

Marker gene selection from scRNA-seq data

Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.

A beginner level Machine Learning pipeline covering all basic steps.

Webapps using Flask for implementation of machine learning sentiment prediction with feature selection PSO and GA with SVM as classifier

Investigate the reasons behind bankruptcy and attempt to identify early warning signs. Perform exploratory data analytics using pandas profiling and apply missing value treatments and oversampling

Collection of end-to-end regression problems (in-depth: linear regression, logistic regression, poisson regression) 📈

Machine Learning Classification on Unbalanced Real World Dataset

Zindi competition on predicting the likelihood of credit default of ecommerce clients

Detecção de Fraudes no Tráfego de Cliques em Propagandas de Aplicações Mobile

Sample Review & Feature Selection for Audio Datasets

Predicting the Likelihood of Diabetes Using Common Signs and Symptoms - About one-third of patients with diabetes do not know that they have diabetes according to the findings published by many diabetes institutes around the world. Detecting and treating diabetes patients at early stages is critical in order to keep them healthy and to ensure th…

The Shakespeare-Method repository contains the code we used to develop a new method to identify attributed and unattributed potential adverse events using the unstructured notes portion of electronic health records.

Creating Customer Segments

Statistical regularization

Exploratory Data Analysis (EDA), Data cleaning & Data preprocessing and Features Engineering,

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

Implementing all ML models and feature selection techniques that can be used.