GitHub Link : github.com/MuhammadAsifff/AIM...
0:00 : Demo
0:25 : Life Cycle of Project
0:56 : Importing Important Libraries
1:13 : Data Analysis
10:45 : Correlation Matrix
12:29 : Model Training
16:34 : Hyperparameter Tuning
18:50 : Hyperparameter Tuning using Randomized Search CV
21:44 : Hyperparameter Tuning using Grid Search CV
23:05 : Area Under The Curve
23:45 : Confusion Matrix
24:15 : Plotting The Classification Report
26:21 : Cross Validation Layers
28:00 : Features Improvement
29:19 : Conclusion
Welcome to our RU-vid channel! In this exciting video, we're diving deep into the world of machine learning and predictive analytics to bring you an end-to-end project on heart disease prediction using Python and the powerful scikit-learn library.
Heart disease is a significant health concern worldwide, and early detection is crucial for effective treatment and prevention. We'll guide you through the entire process of building a machine learning model that can predict the likelihood of heart disease in patients based on various health-related features.
Here's what you can expect in this comprehensive video:
1. **Introduction to Heart Disease Prediction**: We'll start by discussing why predicting heart disease is important and how machine learning can play a vital role in healthcare.
2. **Data Collection and Exploration**: We'll walk you through the process of gathering a dataset containing various patient attributes and explore the data to gain insights into its structure and characteristics.
3. **Data Preprocessing**: Data often requires cleaning and transformation. You'll learn how to handle missing values, encode categorical variables, and scale numeric features to prepare the data for modeling.
4. **Model Building**: The heart of this project is creating a machine learning model. We'll use scikit-learn to build, train, and evaluate several classification algorithms, such as Logistic Regression, Random Forest, and Support Vector Machines, to find the best-performing model.
5. **Model Evaluation**: We'll discuss essential evaluation metrics like accuracy, precision, recall, and F1-score to assess the model's performance. You'll also learn about techniques like cross-validation to ensure the model's robustness.
6. **Hyperparameter Tuning**: Fine-tuning the model is crucial for improving its accuracy. We'll show you how to optimize hyperparameters to achieve the best results.
7. **Making Predictions**: Once we have a well-trained model, we'll demonstrate how to use it to make predictions on new, unseen data.
8. **Conclusion and Future Work**: We'll summarize our findings, discuss the project's limitations, and suggest ways to enhance the model and its real-world applications.
By the end of this video, you'll not only have a working heart disease prediction model but also a solid understanding of the entire machine learning project workflow, from data preprocessing to model evaluation.
Whether you're a beginner looking to learn the basics of machine learning or an experienced data scientist seeking a practical project, this video has something for everyone. Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest tutorials and projects. Let's get started on this exciting journey into machine learning and healthcare!
1 авг 2024
