Predicting Diabetes using KNeighborsClassifier.

1. 🎯 Aim:

Train a model with the diabetes data to predict a patient has diabetes or not.

2. 📚 Concepts Covered:

• Data Manupulation
• Feature Engineering
• Scaling a dataset
• Hyperparameter tuning with GridSearchCV
• confusion_matix
• F1_score, precision
• Data Visualization

3. 🔎 Approach:

• Importing the data
• Understanding the features
• Cleaning the data
• Scale and Impute the data
• Instantiate a KNeighborsClassifier() model from sklearn.neighbors
• To have the right number of n_neighbors performed GridSearchCV
• After getting the grid.best_params_ visualized confusion_matrix
• Calculated Precision, Recall, F1_score
• Lastly visualized a accuracy vs n_neighbors plot

👉 In the notebook I've provided detailed codes and concepts. If you like it please give a star ⭐️

❗If you are trying it in google colab you have to upload the dataset [dibetes.csv]

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