How to Build a Decision Tree Classifier-Based Machine Learning Model for Diabetes Prediction| Python

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In this video, we will learn how to create a Decision Tree Classifier based machine learning model using Python. We will use this model to predict the likelihood of an individual developing diabetes based on various health-related features like age, BMI, blood pressure, and glucose levels.

We will start by exploring the dataset and understanding the features we have. We will then split the dataset into training and testing sets.

Next, we will create a Decision Tree Classifier object and train it on the training data. We will evaluate the model's performance on the testing data using metrics like accuracy, precision, and recall.

Finally, we will save the trained model as a pickle file, which can be used later to make predictions on new data.

This video will be a step-by-step guide for beginners who want to learn how to create a machine learning model for diabetes prediction using Decision Tree Classifier in Python.

[dinethliyanage]

Very detailed and helpful guide, thank you!

[amor]

this helps me a lot thank you so much. you explained so clearly ❤