Upload 4 files

app.py

@@ -0,0 +1,69 @@
+from flask import Flask, request, jsonify
+import joblib
+import numpy as np
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+# Load the trained model
+model = joblib.load('random_forest_model.pkl')
+
+@app.route('/')
+def home():
+    return "Welcome to the Customer Churn Prediction API!"
+
+# Define the prediction endpoint
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get the JSON data from the request
+        data = request.get_json()
+
+        # Extract features from the input JSON
+        features = [
+            data.get("gender"),
+            data.get("SeniorCitizen"),
+            data.get("Partner"),
+            data.get("Dependents"),
+            data.get("tenure"),
+            data.get("PhoneService"),
+            data.get("MultipleLines"),
+            data.get("InternetService"),
+            data.get("OnlineSecurity"),
+            data.get("OnlineBackup"),
+            data.get("DeviceProtection"),
+            data.get("TechSupport"),
+            data.get("StreamingTV"),
+            data.get("StreamingMovies"),
+            data.get("Contract"),
+            data.get("PaperlessBilling"),
+            data.get("PaymentMethod"),
+            data.get("MonthlyCharges"),
+            data.get("TotalCharges")
+        ]
+
+        # Convert the features to a NumPy array for the model
+        features_array = np.array([features])
+
+        # Perform prediction
+        prediction = model.predict(features_array)
+        prediction_probability = model.predict_proba(features_array)
+
+        # Map prediction result to a human-readable label
+        churn_label = "Yes" if prediction[0] == 1 else "No"
+        response = {
+            "prediction": churn_label,
+            "probability": {
+                "No": prediction_probability[0][0],
+                "Yes": prediction_probability[0][1]
+            }
+        }
+
+        return jsonify(response)
+
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+# Run the app
+if __name__ == '__main__':
+    app.run(debug=True)

random_forest_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06f3641f78b28056a53da6018b310d32a6dc12a70a1390ceab9d3207ddbf8fe2
+size 15056825