TATA-Data-Science-Assignment-

📌 In Customer_Churn_TATA.ipynb file; Problem Statement 1: Data Science Task Deliverables are there.

✅ Data Preprocessing , Feature Engineering, Model Training and Selection, Model Evaluation points are covered.

✅ I have attached a ppt where Model Evaluation Plots and Model Interpretation and recommendations(Bonus) points are mentioned.

📌 For Problem Statement 2: MLOps Task; the files are mentioned below

✅ fastapiapp.py : Here the Api building code is there

✅ train.py : Building a Random Forest Based model and store the model.pkl file in models folder code is there

✅ Dockerfile : Building the docker image from Dockerfile and requirements.txt file has the requirements there for docker image

✅ .github\workflows : Inside this folder github action based docker-build-push.yaml file is there

✅ Screenshots : Has all the supporting screenshots which ensures that my Mlops code is running in my local

✅ deployment.yaml, service.yaml, ingress.yaml : Used for scaling the deployment the cloud K8S cluster(ex. AKS,GKS)

✅ Instructions to run locally : All the Commands are in the attached ppt file