Skip to content

Latest commit

 

History

History
39 lines (34 loc) · 1.86 KB

README.md

File metadata and controls

39 lines (34 loc) · 1.86 KB
Raw

Employee-Retention-Prediction

  • Employee Retention Prediction using Machine Learning

Project Abstaction

  • Project's main is to predict that whether an employee will stay in the current organization or will left the organization by processing on given inputs.
  • RanmForest Classification and XGBoost these algorithms are use to train the model.
  • XGBoost gives the best model prediction from both algorithms.
  • Model is exposed using the REST API which is constructed in Flask and Python.
  • Project also process on old and failed data and it will create archive files on those datasets.
  • Prediction and Training logs can be visualized using the ELK + Filebeat.
  • Matrices from projects can be visualized through the Prometheus and Grafana.

Project's Tools and Technology

  • Programing Language: Python
  • Web Development Framework: Flask
  • Machine Learning Libraries: Scikit-Learn, Pandas, Numpy, Matplotlib, XGBoost
  • Machine Learning Algorithm: KMeans, Random Forest and XGBoost
  • Container Technology: Docker
  • Log Management System: ElasticSearch, Logstash, Kibana, Filebeats
  • Metrics Monitoring System: Prometheus, Grafana
  • Database: SQLite3

ELKStack Pipline in Brief

  • Filebeat will collect the prediction and training logs from project and will transfer the logs to logstash.
  • Logstash will process those logs and forward to ElasticSearch.
  • At ElasticSearch, I have created index name as logstash-* which will give us the logs.
  • Kibana is used to visualize logs from above index.

Running the Project

  • Create virtual environment
  • Install dependancies from requierments.txt pip install requirements.txt
  • Run python main.py inside App folder.
  • You can also run project as Docker Container by building docker image. docker build -t <image name as per your prefrence> .

Thank You!

  • D H R U V   P R A J A P A T I