diff --git a/Medical Insurance Cost Prediction/dataset/insurance.csv b/Medical Insurance Cost Prediction/dataset/insurance.csv
new file mode 100644
index 000000000..04bc36e41
--- /dev/null
+++ b/Medical Insurance Cost Prediction/dataset/insurance.csv	
@@ -0,0 +1,1339 @@
+age,sex,bmi,children,smoker,region,charges
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diff --git a/Medical Insurance Cost Prediction/model/README.md b/Medical Insurance Cost Prediction/model/README.md
new file mode 100644
index 000000000..665bf526c
--- /dev/null
+++ b/Medical Insurance Cost Prediction/model/README.md	
@@ -0,0 +1,97 @@
+Medical Insurance Cost Prediction using Machine Learning
+
+This repository contains a Python script (issue.py) that demonstrates the prediction of medical insurance costs using various machine learning regression models. The script analyzes the "insurance.csv" dataset (available on Kaggle), which includes information about individuals and their associated medical insurance charges.
+
+Goal:
+
+The goal of this project is to build and evaluate machine learning models capable of accurately predicting the cost of medical insurance based on various factors like age, BMI, smoking habits, and region.
+
+Dataset:
+
+The dataset used for this project can be found on Kaggle:https://www.kaggle.com/datasets/mirichoi0218/insurance
+
+Methodology:
+
+The script implements the following steps:
+
+Data Loading and Preprocessing:
+
+Loads the "insurance.csv" dataset using Pandas.
+
+Separates the features (independent variables) from the target variable (medical insurance charges).
+
+Preprocesses the data by:
+
+One-hot encoding categorical features (sex, smoker, region).
+
+Scaling numerical features (age, bmi, children) using StandardScaler.
+
+Model Building and Evaluation:
+
+Defines multiple regression models:
+
+Linear Regression
+
+Decision Tree Regression
+
+Random Forest Regression
+
+Gradient Boosting Regression
+
+Splits the data into training and testing sets.
+
+Creates pipelines for each model, combining preprocessing and model training.
+
+Trains each model on the training data and predicts on the testing data.
+
+Evaluates the models using the following metrics:
+
+Mean Squared Error (MSE)
+
+Mean Absolute Error (MAE)
+
+R-squared score
+
+Ensemble Learning (Optional):
+
+Implements a VotingRegressor ensemble model to combine predictions from the individual models.
+
+Evaluates the ensemble model using the same metrics.
+
+Results:
+
+The script provides the following results:
+
+Model	MSE,MAE,R-squared
+Linear Regression	33596915.85	4181.19	0.7836
+Decision Tree	47547224.40	3352.59	0.6937
+Random Forest	21898087.07	2569.67	0.8589
+Gradient Boosting	18790018.23	2410.57	0.8790
+Ensemble Model	21434214.97	2703.98	0.8619
+
+Conclusion:
+
+The Gradient Boosting Regressor model achieved the highest R-squared score (0.8790), indicating the best predictive performance among the individual models. The Ensemble Model also showed strong performance, with an R-squared score of 0.8619. These results suggest that the models were able to effectively learn the relationships between the input features and insurance costs, providing reasonable predictions on unseen data.
+
+
+Libraries:
+
+Pandas
+
+NumPy
+
+Matplotlib
+
+Seaborn
+
+Scikit-learn
+
+To run the script:
+
+Install the required libraries using pip install pandas numpy matplotlib seaborn scikit-learn
+
+Download the "insurance.csv" dataset from Kaggle.
+
+Run the script: python issue.py
+
+This README.md file provides a comprehensive overview of the script's functionality, results, and potential improvements. Feel free to modify and experiment with the code to explore different aspects of medical insurance cost prediction.
\ No newline at end of file
diff --git a/Medical Insurance Cost Prediction/model/model.py b/Medical Insurance Cost Prediction/model/model.py
new file mode 100644
index 000000000..7fe73ad19
--- /dev/null
+++ b/Medical Insurance Cost Prediction/model/model.py	
@@ -0,0 +1,93 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, accuracy_score
+
+# Load the dataset
+data = pd.read_csv("dataset/insurance.csv")
+
+# Separate features and target
+X = data.drop('charges', axis=1)
+y = data['charges']
+
+# Preprocessing: Encode categorical features and scale numerical features
+numeric_features = ['age', 'bmi', 'children']
+categorical_features = ['sex', 'smoker', 'region']
+
+numeric_transformer = StandardScaler()
+categorical_transformer = OneHotEncoder()
+
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', numeric_transformer, numeric_features),
+        ('cat', categorical_transformer, categorical_features)
+    ])
+
+# Define multiple models
+models = {
+    'Linear Regression': LinearRegression(),
+    'Decision Tree': DecisionTreeRegressor(),
+    'Random Forest': RandomForestRegressor(),
+    'Gradient Boosting': GradientBoostingRegressor()
+}
+
+# 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)
+
+# Results dictionary to store metrics for each model
+results = {}
+
+# Train and evaluate each model
+for name, model in models.items():
+    # Create pipeline
+    pipeline = Pipeline(steps=[
+        ('preprocessor', preprocessor),
+        ('regressor', model)
+    ])
+    # Train the model
+    pipeline.fit(X_train, y_train)
+    # Predict on the test set
+    y_pred = pipeline.predict(X_test)
+    # Calculate metrics
+    mse = mean_squared_error(y_test, y_pred)
+    mae = mean_absolute_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+    accuracy = accuracy_score(y_test, y_pred)
+    # Store the results
+    results[name] = {"mse": mse, "mae": mae, "r2_score": r2,"accuracy":accuracy}
+
+# Print the results
+for name, metrics in results.items():
+    print(f"{name}:\n\tMean Squared Error: {metrics['mse']}\n\tMean Absolute Error: {metrics['mae']}\n\tR-squared: {metrics['r2_score']}\n\taccuracy_score: {metrics['accuracy']}")
+
+# Ensemble Learning (Optional): Combining predictions
+from sklearn.ensemble import VotingRegressor
+
+ensemble = VotingRegressor(estimators=[
+    ('lr', LinearRegression()),
+    ('dt', DecisionTreeRegressor()),
+    ('rf', RandomForestRegressor()),
+    ('gb', GradientBoostingRegressor())
+])
+
+ensemble_pipeline = Pipeline(steps=[
+    ('preprocessor', preprocessor),
+    ('ensemble', ensemble)
+])
+
+ensemble_pipeline.fit(X_train, y_train)
+ensemble_pred = ensemble_pipeline.predict(X_test)
+
+# Evaluate the ensemble model
+ensemble_mae = mean_absolute_error(y_test, ensemble_pred)
+ensemble_mse = mean_squared_error(y_test, ensemble_pred)
+ensemble_r2 = r2_score(y_test, ensemble_pred)
+ensemble_accuracy = accuracy_score(y_test, ensemble_pred)
+
+print(f"Ensemble Model:\n\tMean Absolute Error: {ensemble_mae}\n\tMean Squared Error: {ensemble_mse}\n\tR-squared: {ensemble_r2}\n")
diff --git a/Snake Game Using Computer vision/README.md b/Snake Game Using Computer vision/README.md
new file mode 100644
index 000000000..a8614d97d
--- /dev/null
+++ b/Snake Game Using Computer vision/README.md	
@@ -0,0 +1,79 @@
+Snake Game with Hand Tracking
+
+This Python project uses OpenCV and MediaPipe's hand tracking module to create a fun and interactive Snake game controlled by your hand!
+
+How it Works:
+
+Hand Tracking: The code utilizes the cvzone.HandTrackingModule to detect and track the index finger tip of your hand.
+
+Snake Movement: The index finger tip's position is used to control the snake's direction. The snake moves towards the fingertip.
+
+Food: A randomly placed food item appears on the screen.
+
+Eating: When the snake's head touches the food, the food is replaced, the snake grows, and the score increases.
+
+Game Over: The game ends if the snake collides with itself or the edges of the screen.
+
+Features:
+
+Hand Control: Control the snake's direction using your index finger.
+
+Snake Growth: The snake grows longer as it eats food.
+
+Score: Track your progress with a score counter.
+
+Game Over: A game over screen appears when the game ends.
+
+Reset Option: Press 'r' to restart the game.
+
+Dependencies:
+
+OpenCV (pip install opencv-python)
+
+cvzone (pip install cvzone)
+
+mediapipe (pip install mediapipe)
+
+How to Run:
+
+Install the necessary dependencies.
+
+Run the model.py script: python model.py
+
+Point your hand at the webcam.
+
+Move your index finger to guide the snake.
+
+If Game over press 'r' to reset and start playing game
+
+press 'Enter' for exit 
+
+Code Explanation:
+
+SnakeGameClass: This class manages all aspects of the game, including:
+
+Snake position and movement.
+
+Food placement and consumption.
+
+Scorekeeping.
+
+Game over logic.
+
+Game reset logic
+
+HandDetector: This object detects and tracks your hand using MediaPipe.
+
+Game Loop: The main loop continuously captures frames from the webcam, tracks your hand, updates the game state, and displays the game on the screen.
+
+Possible Enhancements:
+
+Difficulty Levels: Implement different speeds or food placement strategies for varying difficulty levels.
+
+Multiple Players: Allow for two players to control snakes simultaneously.
+
+Obstacles: Add obstacles to the game to make it more challenging.
+
+Sound Effects: Incorporate sound effects for actions like eating food or game over.
+
+Enjoy playing this fun and engaging Snake game!
\ No newline at end of file
diff --git a/Snake Game Using Computer vision/model.py b/Snake Game Using Computer vision/model.py
new file mode 100644
index 000000000..7a5f49093
--- /dev/null
+++ b/Snake Game Using Computer vision/model.py	
@@ -0,0 +1,156 @@
+import math
+import random
+import time
+import cvzone
+import cv2
+import numpy as np
+from cvzone.HandTrackingModule import HandDetector
+
+cap = cv2.VideoCapture(0)
+cap.set(3, 1280)
+cap.set(4, 720)
+
+detector = HandDetector(detectionCon=0.8, maxHands=1)
+
+# Snake speed control
+snake_speed = 0.1  
+
+# Target frame rate
+FPS = 30
+frame_time = 1.0 / FPS  # Time per frame
+
+class SnakeGameClass:
+    def __init__(self, pathFood):
+        self.points = []
+        self.lengths = []
+        self.currentLength = 0
+        self.allowedLength = 150
+        self.previousHead = 0, 0
+    
+        self.imgFood = cv2.imread(pathFood, cv2.IMREAD_UNCHANGED)
+        self.hFood, self.wFood, _ = self.imgFood.shape
+        self.foodPoint = 0, 0
+        self.randomFoodLocation()
+
+        self.score = 0
+        self.gameOver = False
+        self.last_update_time = time.time()
+
+    def randomFoodLocation(self):
+        self.foodPoint = random.randint(100, 1000), random.randint(100, 600)
+
+    def update(self, imgMain, currentHead):
+        currentTime = time.time()
+
+        # Speed control
+        if currentTime - self.last_update_time > snake_speed:
+            self.last_update_time = currentTime
+
+        if self.gameOver:
+            cvzone.putTextRect(imgMain, "Game Over", [300, 400],
+                            scale=7, thickness=5, offset=20)
+            cvzone.putTextRect(imgMain, f'Your Score: {self.score}', [300, 550],
+                            scale=7, thickness=5, offset=20)
+            return imgMain  # Exit update when game is over
+        
+        px, py = self.previousHead
+        cx, cy = currentHead
+
+        self.points.append([cx, cy])
+        distance = math.hypot(cx - px, cy - py)
+        self.lengths.append(distance)
+        self.currentLength += distance
+        self.previousHead = cx, cy
+
+        # Length Reduction
+        while self.currentLength > self.allowedLength and self.lengths:
+            self.currentLength -= self.lengths.pop(0)
+            self.points.pop(0)
+
+        # Check if snake ate the Food
+        rx, ry = self.foodPoint
+        if rx - self.wFood // 2 < cx < rx + self.wFood // 2 and \
+                ry - self.hFood // 2 < cy < ry + self.hFood // 2:
+            self.randomFoodLocation()
+            self.allowedLength += 50
+            self.score += 1
+            print(self.score)
+
+        # Draw Snake
+        if self.points:
+            for i in range(1, len(self.points)):
+                cv2.line(imgMain, self.points[i - 1], self.points[i], (0, 0, 255), 20)
+            cv2.circle(imgMain, self.points[-1], 20, (0, 255, 0), cv2.FILLED)
+
+        # Draw Food
+        imgMain = cvzone.overlayPNG(imgMain, self.imgFood,
+                                    (rx - self.wFood // 2, ry - self.hFood // 2))
+
+        cvzone.putTextRect(imgMain, f'Score: {self.score}', [50, 80],
+                        scale=3, thickness=3, offset=10)
+
+        # Check for Collision
+        if len(self.points) > 2:
+            pts = np.array(self.points[:-2], np.int32)
+            pts = pts.reshape((-1, 1, 2))
+            cv2.polylines(imgMain, [pts], False, (0, 255, 0), 3)
+            minDist = cv2.pointPolygonTest(pts, (cx, cy), True)
+
+            if -1 <= minDist <= 1:
+                print("Hit")
+                self.gameOver = True
+                self.points = []  # all points of the snake
+                self.lengths = []  # distance between each point
+                self.currentLength = 0  # total length of the snake
+                self.allowedLength = 150  # total allowed Length
+                self.previousHead = 0, 0  # previous head point
+                self.randomFoodLocation()
+
+        return imgMain
+
+    def reset_game(self):
+        self.points = []
+        self.lengths = []
+        self.currentLength = 0
+        self.allowedLength = 150
+        self.previousHead = 0, 0
+        self.randomFoodLocation()
+        self.score = 0
+        self.gameOver = False
+
+game = SnakeGameClass("resource/donut.png")
+
+while True:
+    start_time = time.time()
+
+    success, img = cap.read()
+    img = cv2.flip(img, 1)
+    hands, img = detector.findHands(img, flipType=False)
+
+    if hands:
+        lmList = hands[0]['lmList']
+        pointIndex = lmList[8][0:2]
+        
+        if not game.gameOver:
+            img = game.update(img, pointIndex)
+        else:
+            # Game Over display
+            cvzone.putTextRect(img, "Game Over", [300, 400],
+                               scale=7, thickness=5, offset=20)
+            cvzone.putTextRect(img, f'Your Score: {game.score}', [300, 550],
+                               scale=7, thickness=5, offset=20)
+    
+    cv2.imshow("Image", img)
+    elapsed_time = time.time() - start_time
+    if elapsed_time < frame_time:
+        time.sleep(frame_time - elapsed_time)
+
+    key = cv2.waitKey(1)
+
+    if key == ord('r'):  #'r' key to reset
+        game.reset_game()
+    elif key == 13:  # 'Enter' key to exit
+        break
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/Snake Game Using Computer vision/resource/donut.png b/Snake Game Using Computer vision/resource/donut.png
new file mode 100644
index 000000000..f0110d54d
Binary files /dev/null and b/Snake Game Using Computer vision/resource/donut.png differ