Diabetes Prediction Models 🩺

📋 Project Overview

Implement machine learning models to predict diabetes using the Diabetes dataset. Evaluate each model's performance using metrics like F1 score, Precision, Recall, and Accuracy.

🛠️ Project Tasks

Task 1: Dataset Description and Preparation

• Dataset: Utilize the Diabetes dataset containing medical information with the Outcome label (0 or 1).
• Splitting: Divide the dataset into training and testing sets (train_test_split function with test_size=0.2).

Task 2: Model Implementation and Evaluation

• SVM (Support Vector Machine):

  • Use SVC from sklearn.svm with kernel='linear' for improved accuracy.
  • Evaluate using F1 score, Precision, Recall, and Accuracy.

• KNN (K-Nearest Neighbors):

  • Use KNeighborsClassifier from sklearn.neighbors.
  • Experiment with different values of n_neighbors for optimal results.

• Logistic Regression:

  • Employ LogisticRegression from sklearn.linear_model.
  • Adjust parameters and evaluate model performance.

• Decision Tree:

  • Implement DecisionTreeClassifier from sklearn.tree.
  • Tune hyperparameters for better training efficiency.

🔑 Key Skills

• Python Programming
• Machine Learning (SVM, KNN, Logistic Regression, Decision Tree)
• Evaluation Metrics (F1 score, Precision, Recall, Accuracy)

🛠️ Tools

📖 Libraries