About Problem

At first, we work at a company and we have some employee who work with together.But some of them are not happy in our company and they might think of leaving in our company.So we need to predict people who want to leave or churn in our company.People who want to churn is 1.People who dont want to churn is 0.We want to identity between 0 and 1.If people is likely to churn, we will send some bonus like money, air ticket, etc.., to stop from churning.We need to be accurate because we dont want to lose money for people who arent going to churn and we dont want to accidentally miss people who are going to churn.Our problem is binary classification.So we want to use binary classification models like logistic, decision tree, etc.., to identity them.

About Dataset

In this project, we will use employee dataset from kaggle.

Context:

This dataset contains information about employees in a company, including their educational backgrounds, work history, demographics, and employment-related factors. It has been anonymized to protect privacy while still providing valuable insights into the workforce.

Columns:

Education: The educational qualifications of employees, including degree, institution, and field of study.

Joining Year: The year each employee joined the company, indicating their length of service.

City: The location or city where each employee is based or works.

Payment Tier: Categorization of employees into different salary tiers.

Age: The age of each employee, providing demographic insights.

Gender: Gender identity of employees, promoting diversity analysis.

Ever Benched: Indicates if an employee has ever been temporarily without assigned work.

Experience in Current Domain: The number of years of experience employees have in their current field.

Leave or Not: a target column

Usage:

This dataset can be used for various HR and workforce-related analyses, including employee retention, salary structure assessments, diversity and inclusion studies, and leave pattern analyses. Researchers, data analysts, and HR professionals can gain valuable insights from this dataset.

Deploy in local machine

Clone the project

  git clone https://github.com/anonyblank/mid-term.git

Go to the project directory

  cd mid-term/

Create Directory

  mkdir model/

Install dependencies

  pipenv install

Activate environment

  pipenv shell

Run train.py

  python train.py

Run predict.py

  python predict.py

Run request.py

  python request.py

Deploy with Docker

Clone the project

  git clone https://github.com/anonyblank/mid-term.git

Go to the project directory

  cd mid-term/

Build docker image

  docker build -t mid-term .

Run docker image

  docker run -it --rm -p 80:9696 mid-term

Deploy with Cloud