Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Diabetes | Machine Learning Project 3

Introduction

In this tutorial, we will explore how to use the Support Vector Machine (SVM) algorithm to classify diabetes diseases. This step-by-step guide is designed for beginners and will cover the fundamental concepts of SVM, its application in healthcare, and how to implement it using a provided dataset.

Step 1: Understand the Basics of SVM

• SVM is a supervised machine learning algorithm used for classification and regression tasks.
• It works by finding the hyperplane that best separates different classes in the dataset.
• Key terms to know:
  • Hyperplane: A decision boundary that separates different classes.
  • Support Vectors: Data points that are closest to the hyperplane and influence its position.

Step 2: Gather Required Tools and Data

• Install Python and relevant libraries if you don't have them yet. Use:

  pip install numpy pandas scikit-learn matplotlib
  

• Download the diabetes dataset from the provided link: Diabetes Dataset.

Step 3: Load the Dataset

• Use the following code to load the dataset into a Pandas DataFrame:

  import pandas as pd
  
  # Load dataset
  data = pd.read_csv('path_to_your_file.csv')  # Replace with your file path
  print(data.head())
  

Step 4: Preprocess the Data

• Clean the dataset by handling missing values and converting categorical data if necessary:

  • Remove or fill missing values.
  • Normalize or standardize the numerical features.

• Example code:

  # Handling missing values
  data.fillna(data.mean(), inplace=True)
  
  # Normalizing the data
  from sklearn.preprocessing import StandardScaler
  scaler = StandardScaler()
  data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])
  

Step 5: Split the Data

• Divide the dataset into training and testing sets to evaluate the model performance:

  from sklearn.model_selection import train_test_split
  
  X = data.drop('label_column', axis=1)  # Replace 'label_column' with your actual label column name
  y = data['label_column']
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  

Step 6: Train the SVM Model

• Import the SVM classifier and fit it with the training data:

  from sklearn.svm import SVC
  
  svm_model = SVC(kernel='linear')  # You can choose different kernels like 'rbf', 'poly', etc.
  svm_model.fit(X_train, y_train)
  

Step 7: Make Predictions

• Use the trained model to make predictions on the test set:

  predictions = svm_model.predict(X_test)
  

Step 8: Evaluate the Model

• Assess the model's performance using accuracy score and confusion matrix:

  from sklearn.metrics import accuracy_score, confusion_matrix
  
  accuracy = accuracy_score(y_test, predictions)
  conf_matrix = confusion_matrix(y_test, predictions)
  
  print(f'Accuracy: {accuracy}')
  print(f'Confusion Matrix:\n{conf_matrix}')
  

Step 9: Visualize Results

• Use Matplotlib to visualize the decision boundaries and results:

  import matplotlib.pyplot as plt
  
  # Example visualization code here
  plt.scatter(X_test['feature1'], X_test['feature2'], c=predictions)
  plt.title('SVM Predictions')
  plt.xlabel('Feature 1')
  plt.ylabel('Feature 2')
  plt.show()
  

Conclusion

In this tutorial, we covered the essential steps to implement SVM for classifying diabetes diseases. You learned about SVM's basic concepts, data preprocessing, model training, prediction, evaluation, and visualization. Next steps could involve experimenting with different SVM kernels, tuning hyperparameters, or applying the model to other datasets. Happy coding!