🌳 Decision Tree Classifier – Heart Disease Prediction

📌 Project Overview

This project implements a Decision Tree Classifier to predict the presence of heart disease based on patient medical data. It covers the complete machine learning workflow including EDA, preprocessing, model building, evaluation, and hyperparameter tuning.

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🎯 Objective

• Apply Decision Tree Classification on a real-world dataset
• Perform data preprocessing and feature engineering
• Evaluate model performance using multiple metrics
• Optimize model using hyperparameter tuning
• Interpret results using visualization and feature importance

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📂 Dataset

• File: heart_disease.xlsx
• Contains features such as:
  • Age, Sex
  • Chest pain type (cp)
  • Resting ECG (restecg)
  • Cholesterol, Blood Pressure
  • Maximum heart rate
  • Target variable (num) → Heart disease presence

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🔍 Exploratory Data Analysis (EDA)

• Checked dataset structure using .info() and .describe()
• Identified missing values and duplicates
• Detected outliers using:
  • Boxplots
  • IQR (Interquartile Range) method
• Visualizations performed:
  • Histograms for feature distribution
  • Pairplot for relationships
  • Correlation heatmap
  • Target class distribution

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⚙️ Data Preprocessing & Feature Engineering

Outlier Handling

• Removed outliers using IQR method
• Applied capping (winsorization) to limit extreme values

Encoding Techniques

• Label Encoding for categorical variables
• One-Hot Encoding using pd.get_dummies()

Feature Scaling

• Applied Standard Scaling using StandardScaler

Target Transformation

• Converted target variable into binary:
  • 0 → No Disease
  • 1 → Disease

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🤖 Model Building – Decision Tree

• Split dataset into:
  • 80% Training
  • 20% Testing
• Trained using DecisionTreeClassifier

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📊 Model Evaluation

Performance evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix
• ROC-AUC Score

Visualizations:

• ROC Curve
• Decision Tree Structure

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🔧 Hyperparameter Tuning

Used GridSearchCV to optimize:

• criterion (gini / entropy)
• max_depth
• min_samples_split
• min_samples_leaf

✔ Improved model performance after tuning

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🌳 Model Interpretation

• Visualized decision tree using plot_tree()
• Extracted feature importance to identify key predictors

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💡 Key Learnings

• Understanding how Decision Trees split data using Gini and Entropy
• Importance of hyperparameter tuning to avoid overfitting
• Handling outliers improves model stability
• Difference between Label Encoding and One-Hot Encoding
• Model interpretability using tree visualization and feature importance

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📊 Visualizations

🔹 Feature Distribution

🔹 Outlier Detection

🔹 Correlation Heatmap

🔹 Decision Tree Structure

🔹 ROC Curve

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🧠 Interview Questions

1. What are common hyperparameters of Decision Trees?

• max_depth → Controls tree depth (overfitting vs underfitting)
• min_samples_split → Minimum samples to split a node
• min_samples_leaf → Minimum samples in a leaf node
• criterion → Split quality (gini / entropy)

👉 Proper tuning balances bias and variance

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2. Label Encoding vs One-Hot Encoding

Feature	Label Encoding	One-Hot Encoding
Output	Integer values	Binary columns
Use Case	Ordinal data	Nominal data
Risk	Introduces false order	High dimensionality

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🛠️ Tech Stack

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

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🚀 How to Run

bash git clone https://github.com/MeghanaCVarghese/Decision-Tree-Classifier

pip install -r requirements.txt

Open Jupyter Notebook

jupyter notebook

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👩‍💻 Author

Meghana C Varghese