midterm_project_ml_zoomcamp

Link for the dataset: https://www.kaggle.com/datasets/sooyoungher/smoking-drinking-dataset/data

My Kaggle Profile: https://www.kaggle.com/lokmanefe

My GitHub Profile: https://github.com/lokicik

My LinkedIn Profile: https://www.linkedin.com/in/lokmanefe/

Feel free to reach out! ✌

Project Presentation

I would like to introduce my most comprehensive project to date! I have completed the project "Drinking or Smoking Classification" which I developed as the Midterm Project during the 7th week of the Machine Learning Zoomcamp organized by DataTalksClub and presented by Alexey Grigorev. I had the opportunity to apply all the techniques I learned, and I gave my all to this project. I look forward to your feedback and hope for positive reviews!

Problem Description

Besides regular users of smoke and alcohol, passive smokers and non-regular drinkers are getting their body hurt aswell. This project can be used to determine if a passive smoker or a non-regular drinker doing how much damage to their body and know about if they are smoking or drinking as much as a regular users, so they can lower their usage of drinking or smoking. This project also can be used to determine if a child is smoking or drinking, his/her body is affected by the smokers or drinkers around him/her or not.

Problem Solution

I've used the dataset to develop 2 different models, one for Alcohol Drinking Prediction, and one for Smoking Prediction. The reason I did this is so that the user can input a single record and get the results from both models, drinking and smoking probability. The target variable for the drinking model is "DRK_YN", for the smoking model it's "SMK_stat_type_cd". I've extracted the target variables from the dataset and trained models separately. The Smoking Model is a multiclass -can be binary if predictions for 0 and 1 is summarized in prediction 0, which would mean not drinker, and 1 stays 1 which already stands for drinker- classification model which predicts 0/1/2 (never smoked/smoked but quit/smoker). The Drinking Model is a binary classification model which predicts 0/1(drinker/not drinker).

Dataset

This dataset is collected from National Health Insurance Service in Korea. All personal information and sensitive data were excluded. The purpose of this dataset is to:

• Analysis of body signal
• Classification of smoker or drinker

Details of dataset:

• Sex male, female

• age round up to 5 years

• height round up to 5 cm[cm]

• weight [kg]

• sight_left eyesight(left)

• sight_right eyesight(right)

• hear_left hearing left, 1(normal), 2(abnormal)

• hear_right hearing right, 1(normal), 2(abnormal)

• SBP Systolic blood pressure[mmHg]

• DBP Diastolic blood pressure[mmHg]

• BLDS BLDS or FSG(fasting blood glucose)[mg/dL]

• tot_chole total cholesterol[mg/dL]

• HDL_chole HDL cholesterol[mg/dL]

• LDL_chole LDL cholesterol[mg/dL]

• triglyceride triglyceride[mg/dL]

• hemoglobin hemoglobin[g/dL]

• urine_protein protein in urine, 1(-), 2(+/-), 3(+1), 4(+2), 5(+3), 6(+4) serum_creatinine serum(blood) creatinine[mg/dL]

• SGOT_AST SGOT(Glutamate-oxaloacetate transaminase) AST(Aspartate transaminase)[IU/L]

• SGOT_ALT ALT(Alanine transaminase)[IU/L]

• gamma_GTP y-glutamyl transpeptidase[IU/L]

• SMK_stat_type_cd Smoking state, 1(never), 2(used to smoke but quit), 3(still smoke)

• DRK_YN Drinker or Not

Midterm Project Requirements (Evaluation Criteria)

• Problem description
• EDA
• Model training
• Exporting notebook to script
• Model deployment
• Reproducibility
• Dependency and environment management
• Containerization
• Cloud deployment

Dependency and environment management guide

You can easily install dependencies from requirements.txt and use venv.

• pip install pipenv

• pip shell

• pip install -r requirements.txt

Containerization

1-)docker tag image_name YOUR_DOCKERHUB_NAME/image_name

2-)docker push YOUR_DOCKERHUB_NAME/image_name

Useful link https://www.biostars.org/p/9531985/#:~:text=As%20the%20error%20says%20requested,docker%20hub%20credentials%20are%20incorrect.

Deployment guide

To run it locally:

• Run python predict.py on a terminal

• Open a new terminal and run python predict_docker.py

To run it on docker:

• Open a terminal

• docker build -t midterm_project .

• docker run -it --rm -p 7860:7860 midterm_project

• Open a new terminal and run python predict_docker.py

To run it on cloud:

• Push your image to docker hub like in the Containerization section

• Open https://render.com/ and create a web service

• Select "Deploy an existing image from a registry"

• Enter the image url for your "YOUR_DOCKERHUB_NAME/image_name"

• Finalize the setup and run your web service

• Open up a new terminal and run python predict_cloud.py

• Here is the final result