Heart Disease Data Preprocessing
This project focuses on preprocessing the Heart Disease UCI dataset for machine learning model training. The notebook demonstrates comprehensive data preprocessing techniques including exploratory data analysis (EDA), data transformation, missing value imputation, feature encoding, and pipeline creation. The project prepares the dataset for predictive modeling by handling different variable types (numeric, categorical, ordinal, binary) and creating a reusable preprocessing pipeline using scikit-learn's Pipeline and ColumnTransformer.
Overview
This project focuses on preprocessing the Heart Disease UCI dataset for machine learning model training. The notebook demonstrates comprehensive data preprocessing techniques including exploratory data analysis (EDA), data transformation, missing value imputation, feature encoding, and pipeline creation. The project prepares the dataset for predictive modeling by handling different variable types (numeric, categorical, ordinal, binary) and creating a reusable preprocessing pipeline using scikit-learn's Pipeline and ColumnTransformer.
Key Features
Comprehensive exploratory data analysis (EDA) with visualizations
Type-specific preprocessing pipelines (numeric, categorical, ordinal, binary)
Missing value imputation with appropriate strategies
Feature encoding (One-Hot Encoding, Ordinal Encoding)
Data normalization using MinMaxScaler
Train/test split with stratification for balanced classes
Unified preprocessing with ColumnTransformer
Export preprocessed datasets for model training
Correlation analysis and bivariate visualizations
Technical Highlights
Created type-specific preprocessing pipelines for different variable types
Implemented unified preprocessing workflow using ColumnTransformer
Performed comprehensive EDA with correlation analysis and visualizations
Applied appropriate imputation strategies for missing values
Used stratified train/test split to maintain class distribution
Prevented data leakage by fitting on training data only
Challenges and Solutions
Type-Specific Processing
Created separate pipelines for numeric, categorical, ordinal, and binary variables
Missing Value Handling
Applied appropriate imputation strategies (mean for numeric, most_frequent for categorical)
Feature Encoding
Used OneHotEncoder for categorical and OrdinalEncoder for ordinal variables
Data Leakage Prevention
Ensured test set doesn't influence preprocessing by fitting only on training data
Technologies
Data Processing
Machine Learning
Preprocessing
Visualization
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Pipeline-Based Preprocessing Architecture
- Category
- Data Science