diff --git a/machine_learning/Multi-Armed Bandits .py b/machine_learning/Multi-Armed Bandits .py
new file mode 100644
index 000000000000..416c7d711384
--- /dev/null
+++ b/machine_learning/Multi-Armed Bandits .py	
@@ -0,0 +1,241 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from abc import ABC, abstractmethod
+
+
+class BanditAlgorithm(ABC):
+    """Base class for bandit algorithms"""
+
+    def __init__(self, n_arms):
+        self.n_arms = n_arms
+        self.reset()
+
+    def reset(self):
+        self.counts = np.zeros(self.n_arms)
+        self.rewards = np.zeros(self.n_arms)
+        self.t = 0
+
+    @abstractmethod
+    def select_arm(self):
+        pass
+
+    def update(self, arm, reward):
+        self.t += 1
+        self.counts[arm] += 1
+        self.rewards[arm] += reward
+
+
+class EpsilonGreedy(BanditAlgorithm):
+    """Epsilon-Greedy Algorithm"""
+
+    def __init__(self, n_arms, epsilon=0.1):
+        super().__init__(n_arms)
+        self.epsilon = epsilon
+
+    def select_arm(self):
+        if np.random.random() < self.epsilon:
+            # Explore: random arm
+            return np.random.randint(self.n_arms)
+        else:
+            # Exploit: best arm so far
+            avg_rewards = np.divide(
+                self.rewards,
+                self.counts,
+                out=np.zeros_like(self.rewards),
+                where=self.counts != 0,
+            )
+            return np.argmax(avg_rewards)
+
+
+class UCB(BanditAlgorithm):
+    """Upper Confidence Bound Algorithm"""
+
+    def __init__(self, n_arms, c=2.0):
+        super().__init__(n_arms)
+        self.c = c
+
+    def select_arm(self):
+        # If any arm hasn't been tried, try it
+        if 0 in self.counts:
+            return np.where(self.counts == 0)[0][0]
+
+        # Calculate UCB values
+        avg_rewards = self.rewards / self.counts
+        confidence = self.c * np.sqrt(np.log(self.t) / self.counts)
+        ucb_values = avg_rewards + confidence
+
+        return np.argmax(ucb_values)
+
+
+class ThompsonSampling(BanditAlgorithm):
+    """Thompson Sampling (Beta-Bernoulli)"""
+
+    def __init__(self, n_arms):
+        super().__init__(n_arms)
+        self.alpha = np.ones(n_arms)  # Prior successes
+        self.beta = np.ones(n_arms)  # Prior failures
+
+    def select_arm(self):
+        # Sample from Beta distribution for each arm
+        samples = np.random.beta(self.alpha, self.beta)
+        return np.argmax(samples)
+
+    def update(self, arm, reward):
+        super().update(arm, reward)
+        # Update Beta parameters
+        if reward > 0:
+            self.alpha[arm] += 1
+        else:
+            self.beta[arm] += 1
+
+
+class GradientBandit(BanditAlgorithm):
+    """Gradient Bandit Algorithm"""
+
+    def __init__(self, n_arms, alpha=0.1):
+        super().__init__(n_arms)
+        self.alpha = alpha
+        self.preferences = np.zeros(n_arms)
+        self.avg_reward = 0
+
+    def select_arm(self):
+        # Softmax to get probabilities
+        exp_prefs = np.exp(self.preferences - np.max(self.preferences))
+        probs = exp_prefs / np.sum(exp_prefs)
+        return np.random.choice(self.n_arms, p=probs)
+
+    def update(self, arm, reward):
+        super().update(arm, reward)
+
+        # Update average reward
+        self.avg_reward += (reward - self.avg_reward) / self.t
+
+        # Get action probabilities
+        exp_prefs = np.exp(self.preferences - np.max(self.preferences))
+        probs = exp_prefs / np.sum(exp_prefs)
+
+        # Update preferences
+        for a in range(self.n_arms):
+            if a == arm:
+                self.preferences[a] += (
+                    self.alpha * (reward - self.avg_reward) * (1 - probs[a])
+                )
+            else:
+                self.preferences[a] -= (
+                    self.alpha * (reward - self.avg_reward) * probs[a]
+                )
+
+
+# Testbed for comparing algorithms
+class BanditTestbed:
+    """Environment for testing bandit algorithms"""
+
+    def __init__(self, n_arms=10, true_rewards=None):
+        self.n_arms = n_arms
+        if true_rewards is None:
+            self.true_rewards = np.random.normal(0, 1, n_arms)
+        else:
+            self.true_rewards = true_rewards
+        self.optimal_arm = np.argmax(self.true_rewards)
+
+    def get_reward(self, arm):
+        """Get noisy reward for pulling an arm"""
+        return np.random.normal(self.true_rewards[arm], 1)
+
+    def run_experiment(self, algorithm, n_steps=1000):
+        """Run bandit algorithm for n_steps"""
+        algorithm.reset()
+        rewards = []
+        optimal_actions = []
+
+        for _ in range(n_steps):
+            arm = algorithm.select_arm()
+            reward = self.get_reward(arm)
+            algorithm.update(arm, reward)
+
+            rewards.append(reward)
+            optimal_actions.append(1 if arm == self.optimal_arm else 0)
+
+        return np.array(rewards), np.array(optimal_actions)
+
+
+# Example usage and comparison
+def compare_algorithms():
+    """Compare different bandit algorithms"""
+
+    # Create testbed
+    testbed = BanditTestbed(n_arms=10)
+
+    # Initialize algorithms
+    algorithms = {
+        "ε-greedy (0.1)": EpsilonGreedy(10, epsilon=0.1),
+        "ε-greedy (0.01)": EpsilonGreedy(10, epsilon=0.01),
+        "UCB (c=2)": UCB(10, c=2),
+        "Thompson Sampling": ThompsonSampling(10),
+        "Gradient Bandit": GradientBandit(10, alpha=0.1),
+    }
+
+    n_steps = 2000
+    n_runs = 100
+
+    results = {}
+
+    for name, algorithm in algorithms.items():
+        print(f"Running {name}...")
+        avg_rewards = np.zeros(n_steps)
+        optimal_actions = np.zeros(n_steps)
+
+        for run in range(n_runs):
+            rewards, optimal = testbed.run_experiment(algorithm, n_steps)
+            avg_rewards += rewards
+            optimal_actions += optimal
+
+        avg_rewards /= n_runs
+        optimal_actions /= n_runs
+
+        results[name] = {"rewards": avg_rewards, "optimal_actions": optimal_actions}
+
+    # Plot results
+    plt.figure(figsize=(15, 5))
+
+    # Average reward over time
+    plt.subplot(1, 2, 1)
+    for name, result in results.items():
+        plt.plot(np.cumsum(result["rewards"]) / np.arange(1, n_steps + 1), label=name)
+    plt.xlabel("Steps")
+    plt.ylabel("Average Reward")
+    plt.title("Average Reward vs Steps")
+    plt.legend()
+    plt.grid(True)
+
+    # Percentage of optimal actions
+    plt.subplot(1, 2, 2)
+    for name, result in results.items():
+        plt.plot(
+            np.cumsum(result["optimal_actions"]) / np.arange(1, n_steps + 1) * 100,
+            label=name,
+        )
+    plt.xlabel("Steps")
+    plt.ylabel("% Optimal Action")
+    plt.title("Optimal Action Selection vs Steps")
+    plt.legend()
+    plt.grid(True)
+
+    plt.tight_layout()
+    plt.show()
+
+    return results
+
+
+# Run the comparison
+if __name__ == "__main__":
+    results = compare_algorithms()
+
+    # Print final performance
+    print("\nFinal Performance (last 100 steps):")
+    for name, result in results.items():
+        avg_reward = np.mean(result["rewards"][-100:])
+        optimal_pct = np.mean(result["optimal_actions"][-100:]) * 100
+        print(
+            f"{name:20s}: Avg Reward = {avg_reward:.3f}, Optimal = {optimal_pct:.1f}%"
+        )
diff --git a/machine_learning/kernel_svm.ipynb b/machine_learning/kernel_svm.ipynb
new file mode 100644
index 000000000000..5f91ec14c08f
--- /dev/null
+++ b/machine_learning/kernel_svm.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copy of kernel_svm.ipynb","provenance":[{"file_id":"1U2p46TcDjQyYx80tQdZkiANbGTjEv8Po","timestamp":1660747043874}],"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"0MRC0e0KhQ0S"},"source":["# Kernel SVM"]},{"cell_type":"markdown","metadata":{"id":"LWd1UlMnhT2s"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"YvGPUQaHhXfL","executionInfo":{"status":"ok","timestamp":1660747109613,"user_tz":-330,"elapsed":562,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"K1VMqkGvhc3-"},"source":["## Importing the dataset"]},{"cell_type":"code","metadata":{"id":"M52QDmyzhh9s","executionInfo":{"status":"error","timestamp":1660747110167,"user_tz":-330,"elapsed":15,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}},"outputId":"8888c572-e012-4e97-8043-757e6a2c6b04","colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["dataset = pd.read_csv('Social_Network_Ads.csv')\n","X = dataset.iloc[:, :-1].values\n","y = dataset.iloc[:, -1].values"],"execution_count":3,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-3-f66964059c2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Social_Network_Ads.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m 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ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding_errors\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"strict\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m         )\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    705\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m                 \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m                 \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    708\u001b[0m             )\n\u001b[1;32m    709\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Social_Network_Ads.csv'"]}]},{"cell_type":"markdown","metadata":{"id":"YvxIPVyMhmKp"},"source":["## Splitting the dataset into the Training set and Test set"]},{"cell_type":"code","metadata":{"id":"AVzJWAXIhxoC","executionInfo":{"status":"aborted","timestamp":1660747110169,"user_tz":-330,"elapsed":12,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"P3nS3-6r1i2B","executionInfo":{"status":"aborted","timestamp":1660747110170,"user_tz":-330,"elapsed":13,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(X_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8dpDLojm1mVG","executionInfo":{"status":"aborted","timestamp":1660747110171,"user_tz":-330,"elapsed":14,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qbb7i0DH1qui","executionInfo":{"status":"aborted","timestamp":1660747110171,"user_tz":-330,"elapsed":13,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kj1hnFAR1s5w","executionInfo":{"status":"aborted","timestamp":1660747110172,"user_tz":-330,"elapsed":14,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kW3c7UYih0hT"},"source":["## Feature Scaling"]},{"cell_type":"code","metadata":{"id":"9fQlDPKCh8sc","executionInfo":{"status":"aborted","timestamp":1660747110173,"user_tz":-330,"elapsed":15,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["from sklearn.preprocessing import StandardScaler\n","sc = StandardScaler()\n","X_train = sc.fit_transform(X_train)\n","X_test = sc.transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"syrnD1Op2BSR","executionInfo":{"status":"aborted","timestamp":1660747110173,"user_tz":-330,"elapsed":14,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(X_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JUd6iBRp2C3L","executionInfo":{"status":"aborted","timestamp":1660747110174,"user_tz":-330,"elapsed":15,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bb6jCOCQiAmP"},"source":["## Training the Kernel SVM model on the Training set"]},{"cell_type":"code","metadata":{"id":"e0pFVAmciHQs","executionInfo":{"status":"aborted","timestamp":1660747110175,"user_tz":-330,"elapsed":16,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["from sklearn.svm import SVC\n","classifier = SVC(kernel = 'rbf', random_state = 0)\n","classifier.fit(X_train, y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yyxW5b395mR2"},"source":["## Predicting a new result"]},{"cell_type":"code","metadata":{"id":"f8YOXsQy58rP","executionInfo":{"status":"aborted","timestamp":1660747110176,"user_tz":-330,"elapsed":17,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["print(classifier.predict(sc.transform([[30,87000]])))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vKYVQH-l5NpE"},"source":["## Predicting the Test set results"]},{"cell_type":"code","metadata":{"id":"p6VMTb2O4hwM","executionInfo":{"status":"aborted","timestamp":1660747110177,"user_tz":-330,"elapsed":18,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["y_pred = classifier.predict(X_test)\n","print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"h4Hwj34ziWQW"},"source":["## Making the Confusion Matrix"]},{"cell_type":"code","metadata":{"id":"D6bpZwUiiXic","executionInfo":{"status":"aborted","timestamp":1660747110179,"user_tz":-330,"elapsed":20,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["from sklearn.metrics import confusion_matrix, accuracy_score\n","cm = confusion_matrix(y_test, y_pred)\n","print(cm)\n","accuracy_score(y_test, y_pred)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6OMC_P0diaoD"},"source":["## Visualising the Training set results"]},{"cell_type":"code","metadata":{"id":"_NOjKvZRid5l","executionInfo":{"status":"aborted","timestamp":1660747110181,"user_tz":-330,"elapsed":21,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = sc.inverse_transform(X_train), y_train\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n","plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n","plt.title('Kernel SVM (Training set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SZ-j28aPihZx"},"source":["## Visualising the Test set results"]},{"cell_type":"code","metadata":{"id":"qeTjz2vDilAC","executionInfo":{"status":"aborted","timestamp":1660747110182,"user_tz":-330,"elapsed":22,"user":{"displayName":"Alien Editz","userId":"03181622927197882991"}}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = sc.inverse_transform(X_test), y_test\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n","plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n","plt.title('Kernel SVM (Test set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[]}]}
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