🤖 Diabetes Prediction using Machine Learning 📊

• Pandas, NumPy, Matplotlib, and Seaborn are used for data manipulation and visualization 📈

• Data Cleaning 🧹

  • Missing Values: No missing values 😊
  • Duplicated Values: None found 👍

• Exploratory Data Analysis 🔎

  • Outcome Distribution: Balanced ⚖️
  • Outlier Detection: A few in some features 👀

• Data Preprocessing 🛠️

  • Standard Scaling: Applied for better model performance 🎚️
  • Label Encoding: Outcome variable (0/1) 🎯

• Model Training and Evaluation 🚂

  • K-Nearest Neighbors Classifier 🤝
  • Grid Search for Optimal Hyperparameter (k) 🔧
  • Max Train Score: 94.23% at k = 14 🥇
  • Max Test Score: 88.89% at k = 13 🏆

• Performance Evaluation 📊

  • Confusion Matrix: Shows model's prediction accuracy ⬜⬛
  • Classification Report: Detailed metrics (Precision, Recall, F1-score) 👌

• Conclusion 🏁

  • KNN Classifier with k = 13 provides the best balance between train and test scores 🏆
  • Model can effectively predict diabetes based on patient's features 😄