This project uses the real data from IBM to predict employee attrition by using supervised machine learning.
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Updated
Apr 15, 2022 - Jupyter Notebook
This project uses the real data from IBM to predict employee attrition by using supervised machine learning.
As a budding data analytics professional, reading official and unofficial documentation and producing accessible reports is par the course. As an intellectual exercise, I am creating a data visualization of my LinkedIn network using an article from Medium as my "documentation". Second to that, this exercise is also an opportunity for me to use a…
R code used in the article "Gender Pay Gap And People Analytics: A Practice With Open Data"
The following SQL queries count the headcount of current employees at a fictitious company by different segments (Performance Score, Employee Satisfaction, Compensation, Company Tenure, Department, and Employee Engagement).
Based on employee data recorded during 16 years of activity, a company seeks to reduce turnover rate, absences, and employee dissatisfaction; and increase performance.
The begining of a platform for Convivencia con Dios community
Hello people!
Counts number of people in frame and writes number into .txt file
This project analyzes fictional recruiting data through two main approaches - prediction and explanation. A multilayer perceptron is used for prediction and logistic regression for explanation.
Our team analyzed real data to help TFA optimize its corps matching process for placement acceptance and successful corps experiences. Finalists were selected in a multi-round process based on the degree to which they demonstrated.
This repository hosts a People Analytics project using SQL. The data was loaded from a csv file to MySQL RDBMS. The data was then cleaned and then analysed.
This project aims to investigate how bad an employee attrition is in a company and to characterize employees who left the company so the HR manager can better understand the issue and prevent the same issue to happen again.
People analytics project in R that implements predictive modeling to identify employees most likely to leave a company. Discussion around implications for the sample firm and proposed interventions draw on best practices in organizational development.
R code used in the article "Predicting Employee Attrition: R vs DMWay"
Used Microsoft SQL Server to write several queries to calculate the attrition rate for a fictitious company between 2014 to 2018.
Used linear and tree-based models, visualizations techniques to solve commonplace data science problems, including calculating conversion rate, analyzing A/B testing, churn/retention prediction, fraud detection, funnel analysis, pricing testing, marketing campaign optimization, clustering, user referral, loan default prediction, optimization, pe…
This notebook is in the area of People Analytics and includes the analysis of the IBM dataset to identify attrition. Besides attrition I also focus on diversity. I created strategies to reduce attrition.
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