This is a Python script that trains a decision tree classifier on a heart disease dataset to predict whether a patient has heart disease

I recently watched a video on building a gender classifier using Scikit, and I was inspired to create something similar but a little more complex. After some research, I found a dataset on Kaggle called The Cleveland Clinic Heart Disease Dataset (https://www.kaggle.com/datasets/aavigan/cleveland-clinic-heart-disease-dataset?resource=download). My goal was to use this dataset and a decision tree classifier to create a model that could predict whether or not a patient had heart disease based on the features in the dataset.

To accomplish this, I needed to learn how to clean the data, remove rows with missing values, normalize the data, split it into features and labels, and train and test the model. I also wanted to test the model's accuracy by generating some values for new patients using ChatGPT, a language model.

However, to my surprise, the model was not able to detect heart disease in a few of the high-risk patients that ChatGPT generated for me. When I tested the model's accuracy, I found that it was only 55% accurate, which makes sense given that it was wrong nearly half the time.

Moving forward, I plan to make some improvements to the model. For example, I could use a dataset with more features to improve the model's accuracy. I could also explore other machine learning algorithms to see if they might be better suited to this task. Overall, I learned a lot from this project, and I'm excited to continue exploring the fascinating world of machine learning and data science.