Machine Learning Classification on Unbalanced Real World Dataset
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Updated
Aug 23, 2017 - HTML
Machine Learning Classification on Unbalanced Real World Dataset
Creating Customer Segments
Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervise…
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.
Statistical regularization
Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.
Implementing all ML models and feature selection techniques that can be used.
Build an algorithm to best identify potential donors of CharityML
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
🇧🇩 Storing data from Bangladesh and will do analysis, visualizations etc.
business analytics course homework assignments
Basketball project for the Kaggle competition: "Kobe Bryant Shot Selection". While the competition is closed, my best submission currently places me in the 93rd percentile (top 7%) of the leaderboard for this competition.
Marker gene selection from scRNA-seq data
The code for the survey paper of feature extraction
Zindi competition on predicting the likelihood of credit default of ecommerce clients
Detecção de Fraudes no Tráfego de Cliques em Propagandas de Aplicações Mobile
This workshop is part of the "Machine Learning in R" graduate course held at University of Münster, School of Business and Economics (winter term 2020/21). 🎓
Sample Review & Feature Selection for Audio Datasets
The purpose of this paper is to analyze how and how much a film's attributes affect its rating, using several regression techniques.
A beginner level Machine Learning pipeline covering all basic steps.
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