Project 06: Heart Disease Prediction Using Python & Machine Learning

Summary

In this tutorial, the presenter walks through a machine learning project for heart disease prediction using Python. They explain how to load a dataset, import libraries like pandas and scikit-learn, and explore the data. The presenter also demonstrates splitting data into training and testing sets, building a logistic regression model, and evaluating its accuracy. The video encourages viewers to experiment with other algorithms such as decision trees and random forests. Additionally, viewers are assigned a homework task to build a Streamlit application for real-time heart disease prediction.

Takeaways

• 😀 The video discusses building heart disease predictions using Python and machine learning.
• 📁 The dataset used is 'heart.csv', which is available on the presenter's GitHub repository.
• 🔧 Essential Python libraries used include pandas, numpy, matplotlib, and scikit-learn for machine learning tasks.
• 📊 The target column in the dataset helps with classification, as it's a supervised learning classification task.
• 🧑‍🔬 The presenter explains basic dataset analysis, including checking null values, data types, and shape of the data.
• 📈 Key steps include splitting the data into training and test sets (80% training, 20% testing) and evaluating model performance.
• 🔍 Logistic regression is used as the machine learning model, providing an accuracy of 85% on training data and 81% on test data.
• 🧑‍🏫 The presenter gives homework to try decision tree, random forest, and SVC algorithms to improve the model.
• 🤖 There's a demonstration of making predictions using the trained model, specifically predicting heart disease from new input data.
• 🚀 Homework assignment: Build a Streamlit application to take user input and predict heart disease using the trained model.

Q & A

What is the main topic of the video?

The main topic of the video is discussing heart disease predictions using Python and machine learning.

What dataset is mentioned for building the heart disease prediction model?

The dataset mentioned for building the heart disease prediction model is 'hard.csb'.

Where can the 'hard.csb' dataset be obtained from?

The 'hard.csb' dataset can be obtained from the presenter's GitHub repository.

What programming environment is used for coding in the video?

The programming environment used for coding in the video is Jupyter Notebook.

Which libraries are imported for the heart disease prediction project?

The libraries imported for the project include pandas, numpy, matplotlib, and sklearn.

How does the presenter load the 'hard.csb' dataset into the Jupyter Notebook?

The presenter loads the 'hard.csb' dataset into the Jupyter Notebook using the `pd.read_csv` function.

What does the 'Target' column in the dataset represent?

The 'Target' column in the dataset represents the classification for heart disease, where the values help in making predictions.

What type of machine learning problem is the heart disease prediction?

The heart disease prediction is a classification task, specifically a supervised machine learning problem.

How does the presenter check for missing values in the dataset?

The presenter checks for missing values in the dataset using the `data.info()` method.

What is the significance of the 'data.describe()' function in the video?

The 'data.describe()' function is used to get a statistical summary of the numerical columns in the dataset, providing insights like mean, standard deviation, and percentiles.

How does the presenter determine if the dataset is balanced or imbalanced?

The presenter determines if the dataset is balanced or imbalanced by checking the value counts of the 'Target' column using the `value_counts()` method.

What machine learning model is initially used for the heart disease prediction?

Initially, a logistic regression model is used for the heart disease prediction.

How is the dataset split into training and testing sets in the video?

The dataset is split into training and testing sets using the `train_test_split` function from sklearn.model_selection, with 80% for training and 20% for testing.

What is the accuracy of the model on the training data?

The accuracy of the model on the training data is 85%.

What is the accuracy of the model on the testing data?

The accuracy of the model on the testing data is 81%.

What additional homework is suggested by the presenter?

The presenter suggests trying different algorithms like Decision Tree, Random Forest, and SVM, and exploring ensemble methods for the homework.

What additional application is recommended to build as an extension of the project?

The presenter recommends building a streamlit application as an extension of the project to take user inputs and make predictions.