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Name: suayipc
Type: User
Name: suayipc
Type: User
Following along with Udemy Course - People Analytics 101. The course uses R, I am using Python to do analysis of the data and build models to determine why people leave a company.
Your client is a large MNC and they have 9 broad verticals across the organisation. One of the problem your client is facing is around identifying the right people for promotion (only for manager position and below) and prepare them in time. Currently the process, they are following is: They first identify a set of employees based on recommendations/ past performance Selected employees go through the separate training and evaluation program for each vertical. These programs are based on the required skill of each vertical At the end of the program, based on various factors such as training performance, KPI completion (only employees with KPIs completed greater than 60% are considered) etc., employee gets promotion For above mentioned process, the final promotions are only announced after the evaluation and this leads to delay in transition to their new roles. Hence, company needs your help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle. They have provided multiple attributes around Employee's past and current performance along with demographics. Now, The task is to predict whether a potential promotee at checkpoint in the test set will be promoted or not after the evaluation process.
A demonstration of people analytics
Binary Classification Project
Analysis of the employee records for the HR department
The Requirement of a Business Problem is to develop a predictive model to Analyse and to Predict the WorkForce Analytics of Private Organization to improve the company's performance using Classification Analysis with Python.
Analyse HR data to predict which employees will leave the company next
Exploration of HR Analytic Data of a fictions small company
The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.
Course Website for "Data Scraping and Introduction to Text Analysis" Duke Data Science (MIDS)
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Materials for POLS 7012: Introduction to Political Methodology
introduction to text analytics in python training for odsc west 2018
This is the repository for the course Introduction to Data Science offered by the Department of Information Technologies, Åbo Akademi University, Finland
This repository contains codes from my course introduction to data science on youtube.
Code repository and shared information for a course on R programming. This material is designed to help teach new students the R language using R Studio and accompanying products.
Experimenting with Julia Language
Bu çalışma araştırmalar yaparken benzerlerine rastlayıp iyileştirerek derlemeye çalıştığım ve derin öğrenme (deep learning) konusunda kısa bir özet ve bolca kaynak yönlendirmesi olan (hatta sonunda koca bir liste var) hızlıca konuya giriş yapılabilinmesi için gereklilikleri özetlemektedir. Lütfen katkı vermekten çekinmeyin 👽
BTK Akademi -1 Milyon İstihdam Projesi için Merve Ayyüce Kızrak tarafından Hazırlanmıştır.
A list of selected resources, methods, and tools dedicated to Legal Text Analytics.
ME314 2019 Course Website
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