feat: Add preprocessing pipeline and model script for Medical Appointment No-Shows

This PR adds the preprocessing pipeline and machine learning model script for predicting medical appointment no-shows using Random Forest classifier with proper feature scaling and encoding.

Author: Gokul45-45

projects/detection/Medical_Appointment_No_Shows/model.py

@@ -0,0 +1,98 @@
+"""Medical Appointment No-Shows Prediction Model
+This module implements a machine learning model to predict whether a patient
+will miss their medical appointment using preprocessing pipeline and model training.
+"""
+
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+
+
+class AppointmentPreprocessor:
+    """Preprocessing pipeline for medical appointment data."""
+    
+    def __init__(self):
+        self.scaler = StandardScaler()
+        self.label_encoders = {}
+    
+    def preprocess(self, data):
+        """Preprocess medical appointment data.
+        
+        Args:
+            data (pd.DataFrame): Raw appointment data
+            
+        Returns:
+            pd.DataFrame: Preprocessed data ready for model training
+        """
+        # Handle missing values
+        data = data.fillna(data.mean(numeric_only=True))
+        
+        # Encode categorical variables
+        categorical_cols = data.select_dtypes(include=['object']).columns
+        for col in categorical_cols:
+            if col not in self.label_encoders:
+                self.label_encoders[col] = LabelEncoder()
+            data[col] = self.label_encoders[col].fit_transform(data[col].astype(str))
+        
+        # Scale numerical features
+        numerical_cols = data.select_dtypes(include=['int64', 'float64']).columns
+        data[numerical_cols] = self.scaler.fit_transform(data[numerical_cols])
+        
+        return data
+
+
+class AppointmentNoShowModel:
+    """Machine Learning model for predicting appointment no-shows."""
+    
+    def __init__(self):
+        self.model = RandomForestClassifier(n_estimators=100, random_state=42)
+        self.preprocessor = AppointmentPreprocessor()
+    
+    def train(self, X_train, y_train):
+        """Train the model.
+        
+        Args:
+            X_train: Training features
+            y_train: Training labels
+        """
+        self.model.fit(X_train, y_train)
+    
+    def predict(self, X_test):
+        """Make predictions.
+        
+        Args:
+            X_test: Test features
+            
+        Returns:
+            predictions: Predicted labels
+        """
+        return self.model.predict(X_test)
+    
+    def evaluate(self, X_test, y_test):
+        """Evaluate model performance.
+        
+        Args:
+            X_test: Test features
+            y_test: Test labels
+            
+        Returns:
+            dict: Performance metrics
+        """
+        predictions = self.predict(X_test)
+        
+        metrics = {
+            'accuracy': accuracy_score(y_test, predictions),
+            'precision': precision_score(y_test, predictions, average='weighted'),
+            'recall': recall_score(y_test, predictions, average='weighted'),
+            'f1': f1_score(y_test, predictions, average='weighted')
+        }
+        
+        return metrics
+
+
+if __name__ == '__main__':
+    # Example usage
+    print('Medical Appointment No-Shows Prediction Model initialized.')