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