Mustafa Sen's repositories
Attrition-Analysis-on-the-HR-Department
The rate of attrition or the inverse retention rate is the most commonly used metric while trying to analyze attrition. The attrition rate is typically calculated as the number of employees lost every year over the employee base. This employee base can be tricky however. Most firms just use a start of year employee count as the base. Some firms calculate it on a rolling 12 month basis to get a full year impact. This ratio becomes harder to use if your firm is growing its employee base. For example, let's say on Jan 1st of this year there were 1000 employees in the firm. Over the next 12 months we've lost 100 employees. Is it as straight forward as a 10% attrition rate. Where it gets fuzzy is how many of those 100 employees that were lost were in the seat on Jan 1st. Were all the 100 existing employees as of Jan 1st or were they new hires during the year that termed. Hence the attrition rate must be looked at in several views.
HR-Churn-Project
Human Resources Analytics: Predicting Employee Churn with Python: Pointing out all the factors which contributed most to employee turnover; make a model that can foresee if a specific employee will leave the organization or not. ML Algorithms: Random Forest; Logistic Regression; Ada Boost Other skills employed: Exploratory Data Analysis; Feature Engineering and Feature Scaling
HR_Analysis
HR Evaluation using predictive model (Python)
HR_Analytics_Practice
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.
Human-Resource-Data-Prediction-Analysis
Analysis of employee data in the dataset "HR_comma_sep.csv" to find out what contributes to employees leaving the company.
loan-prediction
Predict which loans will be foreclosed on.
Mustafa-Sen
Config files for my GitHub profile.
pandas-videos
Jupyter notebook and datasets from the pandas Q&A video series
pdsnd_github
GitHub project (Project 3) repository for PDSND
pycon-2019-tutorial
Data Science Best Practices with pandas
solutions
Solutions for projects.