The Lifecycle to Build a Web Application for Prediction from Scratch

guest_blog 26 Aug, 2022 • 8 min read

Image for post

Problem Definition

Data Collection


Data Preparation and Exploration

Python Code:

The header of the heart disease datase

#Check null values
Image for post
     Null values in the dataset
web application - data distribution

People with and without heart disease in the dataset

Attributes Correlation

web application - correlation

web application - Scatterplot between age and heart rate

web application - chest pain

web application - angina

Image for post

web application - thalessemia

web application - correlation between opeak and slope

web application -

Data Modeling

# Initialize data and target
target = df[‘target’]
features = df.drop([‘target’], axis = 1)

– Data Splitting

# Split the data into training set and testing set
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.2, random_state = 0)

– Machine Learning Model

def fit_eval_model(model, train_features, y_train, test_features, y_test):    results = {}    # Train the model, y_train)    # Test the model
    train_predicted = model.predict(train_features)
    test_predicted = model.predict(test_features)    # Classification report and Confusion Matrix
    results[‘classification_report’] = classification_report(y_test,    test_predicted)
    results[‘confusion_matrix’] = confusion_matrix(y_test, test_predicted)    return results
# Initialize the models
sv = SVC(random_state = 1)
rf = RandomForestClassifier(random_state = 1)
ab = AdaBoostClassifier(random_state = 1)
gb = GradientBoostingClassifier(random_state = 1)# Fit and evaluate models
results = {}
for cls in [sv, rf, ab, gb]:
    cls_name = cls.__class__.__name__
    results[cls_name] = {}
    results[cls_name] = fit_eval_model(cls, X_train, y_train, X_test, y_test)
# Print classifiers results
for result in results:
    print (result)
    print()for i in results[result]:
    print (i, ‘:’)
    print (‘ — — -’)
Image for post

Gradient Boosting Result

– Save the Prediction Model

# Save the model as serialized object pickle
with open(‘model.pkl’, ‘wb’) as file:
pickle.dump(gb, file)


Model Deployment

├── model.pkl
├── templates/
 └── Heart Disease Classifier.html


Web App Python Code

import numpy as np
import pickle
from flask import Flask, request, render_template
# Create application
app = Flask(__name__)
# Load machine learning model
model = pickle.load(open(‘model.pkl’, ‘rb’))
# Bind home function to URL
def home():
    return render_template(‘Heart Disease Classifier.html’)
# Bind predict function to URL
@app.route(‘/predict’, methods =[‘POST’])
def predict():
    # Put all form entries values in a list 
    features = [float(i) for i in request.form.values()]
    # Convert features to array
    array_features = [np.array(features)]
    # Predict features
    prediction = model.predict(array_features)
    output = prediction
    # Check the output values and retrieve the result with html tag based on the value
 if output == 1:
    return render_template(‘Heart Disease Classifier.html’, 
 result = ‘The patient is not likely to have heart disease!’)
    return render_template(‘Heart Disease Classifier.html’, 
 result = ‘The patient is likely to have heart disease!’)
if __name__ == ‘__main__’:
#Run the application


HTML Template

Image for post

<form action = “{{url_for(‘predict’)}}” method =”POST” >
<strong style="color:red">{{result}}</strong>

Image for post


About the Author

Nada Alay

I am working in the data analytics field and passionate about data science, machine learning, and scientific research.

guest_blog 26 Aug 2022

BeginnerMachine LearningProgrammingPythonStructured Data

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Responses From Readers