Add the Machine learning code for performance tracking system

Machine_Learning/app.py

+import pandas as pd
+from flask import Flask, request, jsonify
+import pickle
+from flask_cors import CORS 
+import pymysql.cursors
+
+app = Flask(__name__)
+CORS(app)
+
+db_config = {
+    "host": "dcsrp-db-01.cvqssef8vzgx.ap-southeast-1.rds.amazonaws.com",
+    "port":3306,
+    "user": "dcsrp_pradeep",
+    "password": "v4Dw4swkpKco4QuDdLz7yeM3HHjiaPeg",
+    "db": "dcsrp_db",
+    "charset": "utf8mb4",
+    "cursorclass": pymysql.cursors.DictCursor,
+}
+
+conn = None 
+
+try:
+    conn = pymysql.connect(**db_config)
+    cursor = conn.cursor()
+    print("Connected to the MySQL database")
+except Exception as e:
+    print("Error:", str(e))
+finally:
+    if conn:
+        conn.close()
+
+with open('delivery.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+with open('churn.pkl', 'rb') as model_file:
+    churnModel = pickle.load(model_file)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        request_data = request.get_json()
+
+        skill_mapping = {"Good": 1, "Average": 2, "Bad": 3}
+        traffic_mapping = {"low": 3, "middle": 2, "high": 1}
+        
+        driver_skill = skill_mapping.get(request_data["driver_skill"], 2) 
+        traffic_condition = traffic_mapping.get(request_data["traffic_condition"], 2)  
+        data = {'skill': [driver_skill], 'traffic': [traffic_condition]}
+        df = pd.DataFrame(data)
+        proba = model.predict_proba(df)[0][1]  
+        proba_percentage = int(proba * 100) 
+          
+        result = {
+            "probability": proba_percentage
+        }
+        
+        return jsonify(result)
+    
+    except Exception as e:
+        return jsonify({"error": str(e)})
+    
+@app.route('/churnPredict', methods=['POST'])
+def churnpredict():
+    try:
+        request_data = request.get_json()
+        item_status_mapping = {"Damaged": 1, "Not-Damaged": 0}
+        item_feedback_mapping = {"Unsatisifed": 0, "Satisfied": 1}
+        item_status = item_status_mapping.get(request_data["status"],0) 
+        item_feedback = item_feedback_mapping.get(request_data["feedback"])  
+        item_onTime=request_data["ontime"]
+        data = {"item_status": [item_status], "on_time": [item_onTime], "feedback": [item_feedback]}
+        df = pd.DataFrame(data)
+        proba = churnModel.predict(df)  
+        proba_percentage = int(proba*10)
+        result = {
+            "probability": proba_percentage
+        }
+        
+        return jsonify(result)
+    
+    except Exception as e:
+        return jsonify({"error": str(e)})
+    
+
+@app.route("/savePred", methods=["PUT"])
+def update_data():
+    try:
+        conn = pymysql.connect(**db_config)
+        cursor = conn.cursor()
+
+        data = request.json
+        id=data["id"]
+        driver_skill = data["driver_skill"]
+        traffic_condition = data["traffic_condition"]
+        success = data["success"]
+
+        update_query = (
+            "UPDATE order_details SET driver_skill = %s, traffic_condition = %s, success = %s WHERE id = %s"
+        )
+
+        cursor.execute(update_query, (driver_skill, traffic_condition, success, id))
+        conn.commit()
+
+        cursor.close()
+        conn.close()
+
+        return jsonify({"message": "Data updated successfully"})
+
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+if __name__ == '__main__':
+    app.run()

Machine_Learning/churnTrain.py

+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+import pickle
+
+data = pd.read_csv('churn_data.csv')
+X = data[['item_status','on_time','feedback']]
+y = data['churn']
+
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+
+model.fit(X, y)
+
+with open('churn.pkl', 'wb') as model_file:
+    pickle.dump(model, model_file)

Machine_Learning/churn_data.csv

+item_status,on_time,feedback,churn
+1, 0, 0, 9
+1, 1, 0, 7
+0, 1, 0, 2
+1, 0, 1, 5
+0, 1, 0, 5
+1, 1, 1, 3
+0, 0, 0, 9
+1, 1, 0, 8
+0, 1, 1, 4
+1, 0, 1, 6
+0, 1, 0, 7
+1, 1, 0, 8
+0, 0, 1, 3
+1, 0, 1, 4
+0, 1, 1, 1
+1, 1, 0, 6
+0, 1, 0, 3
+1, 1, 1, 5
+0, 0, 0, 10
+1, 0, 1, 4
+0, 1, 1, 1
+1, 1, 0, 2
+0, 1, 0, 3
+1, 1, 0, 7
+0, 0, 1, 10
+1, 0, 1, 4
+0, 1, 0, 3
+1, 1, 1, 8
+0, 0, 0, 10
+1, 1, 0, 2

Machine_Learning/dataset.csv

+skill,traffic,pred
+1,1,1
+1,1,1
+1,1,0
+1,2,1
+1,3,1
+1,3,1
+2,1,0
+2,1,1
+2,2,1
+2,2,1
+2,2,0
+2,3,1
+3,1,0
+3,2,0
+3,2,1
+3,3,1
+

Machine_Learning/train.py

+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+import pickle
+
+data = pd.read_csv('dataset.csv')
+X = data[['skill', 'traffic']]
+y = data['pred']
+
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+
+model.fit(X, y)
+
+with open('delivery.pkl', 'wb') as model_file:
+    pickle.dump(model, model_file)