Diabetes-Detection using Machine Learning

This classifier webapp basically developed using Streamlit (Python - framework) and it classifies the categories of "Diabetic" or "Not Diabetic" based on certain input parameters. In this app we can choose different classifiers like; SVM, RandomForest, GridSearch, Logistics Regression, for the classification. This app can also generate a visualized report with patient's data.

This webapp deployed on heroku. app-link : https://diabetes-detection-pima-app.herokuapp.com/

Technologies Used:

For Development

• Python3 (Programming Language)
• Streamlit (Python-Framework)
• Jupyter Notebook
• VS Code (IDE)
• Linux-Ubuntu:20.04 (OS)

For EDA

• NumPy
• Pandas
• Matplotlib
• Seaborn

For Model-training

• sklearn.model_selection - train_test_split
• sklearn.svm - SVC
• sklearn.ensemble - RandomForestClassifier
• sklearn.linear_model - LogisticRegression
• sklearn.model_selection - GridSearchCV

Deployment

• GitHub
• Heroku (PaaS)

Refernces

• For Dataset Kaggle
• For UI Streamlit