Identify-Diabetic-Patient-Readmission

Classifying the hospital re-admission probability of a DIABETIC patient by using appropriate Data Science techniques. You can view the project demo on YouTube.

Table of contents

• General info
• Screenshots
• Demo
• Technologies and Tools
• Code Examples
• Status
• Contact

General info

Project was binary classification (Supervised Learning) problem. Major steps involved were as follow :

• STEP: 1 - Data Cleaning
• STEP: 2 - Feature Engineering and Feature Creation
• STEP: 3 - Transformation and Outlier Removal
• STEP: 4 - Exploratory Data Analysis and Sampling
• STEP: 5 - Model Building and Evaluation
• STEP: 6 - Best Model and Deployment
• STEP: 7 - Interpretations and Insights
• STEP: 8 - Improvements and Future Work

Demo

The entire demo of the project can be found on YouTube.

Screenshots

Technologies and Tools

• Python
• Flask
• Scikit-learn
• HTML

Code Examples

# Code for creating pickle file of model and transform  

import pickle

pickle.dump(scaler, open('tranform.pkl','wb'))
pickle.dump(rf_clf, open('model.pkl','wb'))

X_test=scaler.transform(X_test_unscaled[:1])

predictions=rf_clf.predict(X_test)
print("Predicted Result : ",predictions)

predictions = rf_clf.predict_proba(X_test)
print("Predicted Result probability : ",predictions)

# Flask code for using deployed pickle file and connecting to interface

import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle

app = Flask(__name__)
scaler = pickle.load(open('tranform.pkl','rb'))
model = pickle.load(open('model.pkl', 'rb'))

@app.route('/')
def home():
    return render_template('interface.html')

@app.route('/predict',methods=['POST'])
def predict():
    '''
    For rendering results on HTML GUI
    '''
    int_features = [int(x) for x in request.form.values()]
    
    final_features = [np.array(int_features)]
    
    final_features = np.pad(final_features, (0, 63), 'constant')
    
    final_features = scaler.transform(final_features)
    
    prediction = model.predict_proba(final_features)

    output = prediction[0]

    return render_template('interface.html', prediction_text='Readmission probability is {}'.format(output))

if __name__ == "__main__":
    app.run(debug=True)

Status

Project is: finished.

Contact

If you loved what you read here and feel like we can collaborate to produce some exciting stuff, or if you just want to shoot a question, please feel free to connect with me on email or LinkedIn