Build a Heart Disease Baseline
Machine Learning Baseline: Heart Disease Prediction
Problem Description
You are provided with a tabular dataset designed for predicting the presence of heart disease in patients. This dataset includes a binary target column, has_heart_disease, indicating whether a patient has the condition, alongside various features such as age, height, weight, blood pressure, cholesterol levels, smoking status, and other relevant clinical measurements.
Task Requirements
Using Python, pandas, and seaborn, describe and walk through the process you would follow to:
- Data Loading and Inspection: Load the dataset and perform an initial inspection to understand its structure and contents.
- Data Cleaning: Address missing values, identify and remove duplicate records, and correct any obviously invalid data entries.
- Exploratory Data Analysis (EDA): Conduct exploratory data analysis, including visualizing relationships between individual features and the target variable (
has_heart_disease). - Feature Engineering: Create new, useful features from existing ones where appropriate (e.g., calculating Body Mass Index (BMI) from height and weight).
- Model Training: Train a reasonable baseline model suitable for binary classification.
- Model Evaluation: Evaluate the trained model's performance and explain which metrics you would choose to report, justifying your selection.
- Summary and Pre-Deployment Checks: Summarize the key patterns and insights discovered from the data and model. Outline critical checks and considerations before deploying this model into a practical setting.
Assumptions
You may assume that standard Python machine learning libraries are readily available for use.
AI interview prep
Core dilemma
Building a robust, interpretable, and ethically sound baseline machine learning model for a critical medical application, balancing predictive performance with data quality challenges, potential biases, and the real-world costs of misclassification.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Build a Heart Disease Baseline
Machine Learning Baseline: Heart Disease Prediction
Problem Description
You are provided with a tabular dataset designed for predicting the presence of heart disease in patients. This dataset includes a binary target column, has_heart_disease, indicating whether a patient has the condition, alongside various features such as age, height, weight, blood pressure, cholesterol levels, smoking status, and other relevant clinical measurements.
Task Requirements
Using Python, pandas, and seaborn, describe and walk through the process you would follow to:
- Data Loading and Inspection: Load the dataset and perform an initial inspection to understand its structure and contents.
- Data Cleaning: Address missing values, identify and remove duplicate records, and correct any obviously invalid data entries.
- Exploratory Data Analysis (EDA): Conduct exploratory data analysis, including visualizing relationships between individual features and the target variable (
has_heart_disease). - Feature Engineering: Create new, useful features from existing ones where appropriate (e.g., calculating Body Mass Index (BMI) from height and weight).
- Model Training: Train a reasonable baseline model suitable for binary classification.
- Model Evaluation: Evaluate the trained model's performance and explain which metrics you would choose to report, justifying your selection.
- Summary and Pre-Deployment Checks: Summarize the key patterns and insights discovered from the data and model. Outline critical checks and considerations before deploying this model into a practical setting.
Assumptions
You may assume that standard Python machine learning libraries are readily available for use.