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Project overview
In this project, you’ll assume the role of a data scientist working for a healthcare solutions company. The company has collected anonymized patient data and wants you to build a model that predicts the likelihood of a patient developing heart disease in the future based on their medical information.
You’ll apply data analysis and machine learning skills to clean the dataset, conduct exploratory analysis to identify relevant features, and train a K Nearest Neighbors classifier. Through hyperparameter tuning, you’ll optimize the model’s performance. This project allows you to showcase your ability to build predictive models with real-world healthcare applications.
Objective: Develop a K Nearest Neighbors classifier to predict a patient’s risk of heart disease based on their medical data, demonstrating proficiency in data preparation, feature selection, model training and evaluation.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Basic Python programming for data analysis tasks
- Using pandas to load, explore, and manipulate data
- Understanding fundamental supervised machine learning concepts
- Establishing a supervised machine learning workflow
Projects steps
Step 1: Introduction
Step 2: EDA: Descriptive Statistics
Step 3: EDA: Visualizations
Step 4: Data Cleaning
Step 5: Feature Selection
Step 6: Building a Classifier I
Step 7: Building a Classifier II
Step 8: Hyperparameter Tuning
Step 9: Model Evaluation on Test Set
Step 10: Next Steps
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