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# Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize Flask app with a name reflective of the project
extraalearn_api = Flask("ExtLearn")
# Load the trained lead conversion model (ensure the file name matches your saved model)
model = joblib.load("extlearn_model.joblib")
# Define a route for the home page
@extraalearn_api.get('/')
def home():
return "Welcome to the ExtraaLearn Lead Conversion Prediction System"
# Define an endpoint to predict status (converted/not converted) for a lead
@extraalearn_api.post('/v1/predict')
def predict_conversion():
# Get JSON data from the request body
data = request.get_json()
# Extract relevant features based on the ExtraaLearn dataset
# These must match the exact feature names used during model training
sample = {
'age': data['age'],
'current_occupation': data['current_occupation'],
'first_interaction': data['first_interaction'],
'profile_completed': data['profile_completed'],
'website_visits': data['website_visits'],
'time_spent_on_website': data['time_spent_on_website'],
'page_views_per_visit': data['page_views_per_visit'],
'last_activity': data['last_activity'],
'print_media_type1': data['print_media_type1'],
'print_media_type2': data['print_media_type2'],
'digital_media': data['digital_media'],
'educational_channels': data['educational_channels'],
'referral': data['referral']
}
# Convert the extracted data into a DataFrame for the model pipeline
input_data = pd.DataFrame([sample])
# Calculate the engineered feature 'age_time_interaction'
input_data['age_time_interaction'] = input_data['age'] * input_data['time_spent_on_website']
# Make a prediction (1 for converted, 0 for not converted)
prediction = int(model.predict(input_data)[0])
# Optional: Get the probability of conversion
probability = model.predict_proba(input_data)[0][1]
# Return the prediction and probability as a JSON response
return jsonify({
'Status_Prediction': prediction,
'Conversion_Probability': round(float(probability), 4),
'Message': 'High Potential Lead' if prediction == 1 else 'Low Potential Lead'
})
# Run the Flask app
if __name__ == '__main__':
extraalearn_api.run(debug=True)
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