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employee-attrition-analysis

feast

Feature Store for Machine Learning

loan-status-prediction

Problem Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.

machinelearning_coursera

These are exercise files I tried to solve while taking the Machine Learning Course from Andrew NG on Coursera

merlin

Kubernetes-friendly ML model management, deployment, and serving.

movie-recommender-system-

In this project we have tried to create Movier Recommender System using Python along with data cleaning and exploration. Some of the algorithm followed are: popularity based, Content based filtering, User based Collaborative filtering and Item based Collaborative filtering( Slope One Algorithm). The data is taken from Movie Lens Dataset.

nerd-fonts

:abcd: Iconic font aggregator, collection, and patcher. 40+ patched fonts, over 3,600 glyph/icons, includes popular collections such as Font Awesome & fonts such as Hack

nlp--predicting-gender

This repository contains the python code used to predict gender on the basis of user profile features like username, description and status. The evaluation criteria was both model accuracy and the AUC value.

odsc_2020_esn

Short demo of light weight ESN implementation for ODSC East 2020

predictingcrimesinchicago

The data was taken from Kaggle "https://www.kaggle.com/currie32/crimes-in-chicago" to analyze. SAS JMP is used to clean, organize and visualize the data and create the Classification Model to predict the probability of crimes in the Chicago city. Various models are evaluated and the report is created.

thinkstats2

Text and supporting code for Think Stats, 2nd Edition