diff --git a/src/ai/predictive_analytics.py b/src/ai/predictive_analytics.py
new file mode 100644
index 000000000..bcc53a969
--- /dev/null
+++ b/src/ai/predictive_analytics.py
@@ -0,0 +1,98 @@
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+import joblib
+
+class PredictiveAnalytics:
+    def __init__(self):
+        """
+        Initialize the PredictiveAnalytics class.
+        """
+        self.model = LinearRegression()
+        self.is_trained = False
+
+    def train(self, data, target_column):
+        """
+        Train the predictive model using the provided data.
+
+        :param data: DataFrame containing the features for training.
+        :param target_column: The name of the target column to predict.
+        """
+        X = data.drop(columns=[target_column])
+        y = data[target_column]
+
+        # Split the data into training and testing sets
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # Train the model
+        self.model.fit(X_train, y_train)
+        self.is_trained = True
+
+        # Evaluate the model
+        predictions = self.model.predict(X_test)
+        mse = mean_squared_error(y_test, predictions)
+        r2 = r2_score(y_test, predictions)
+
+        print(f"Model trained successfully. MSE: {mse}, R^2: {r2}")
+
+    def predict(self, new_data):
+        """
+        Make predictions using the trained model.
+
+        :param new_data: DataFrame containing the features for prediction.
+        :return: Array of predictions.
+        """
+        if not self.is_trained:
+            raise Exception("Model is not trained yet. Please train the model before making predictions.")
+        
+        return self.model.predict(new_data)
+
+    def save_model(self, filename):
+        """
+        Save the trained model to a file.
+
+        :param filename: The name of the file to save the model.
+        """
+        joblib.dump(self.model, filename)
+        print(f"Model saved to {filename}")
+
+    def load_model(self, filename):
+        """
+        Load a trained model from a file.
+
+        :param filename: The name of the file to load the model from.
+        """
+        self.model = joblib.load(filename)
+        self.is_trained = True
+        print(f"Model loaded from {filename}")
+
+# Example usage
+if __name__ == "__main__":
+    # Sample data for demonstration
+    data = pd.DataFrame({
+        'feature1': [1, 2, 3, 4, 5],
+        'feature2': [2, 3, 4, 5, 6],
+        'target': [3, 5, 7, 9, 11]
+    })
+
+    # Initialize the predictive analytics model
+    analytics = PredictiveAnalytics()
+
+    # Train the model
+    analytics.train(data, target_column='target')
+
+    # Make predictions
+    new_data = pd.DataFrame({
+        'feature1': [6, 7],
+        'feature2': [7, 8]
+    })
+    predictions = analytics.predict(new_data)
+    print("Predictions:", predictions)
+
+    # Save the model
+    analytics.save_model("predictive_model.pkl")
+
+    # Load the model
+    analytics.load_model("predictive_model.pkl")