Machine Learning Model Comparison (Titanic Dataset)

Project Overview

This project compares multiple Machine Learning models on the Titanic dataset to predict passenger survival.
The goal is to evaluate different algorithms and identify the best-performing model.

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Dataset

• Dataset used: Titanic Dataset (from seaborn)
• Target variable: survived
• Features include:
  • pclass, sex, age, sibsp, parch, fare, embarked

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Technologies Used

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

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Machine Learning Models Used

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Gaussian Naive Bayes
• Decision Tree Classifier
• Support Vector Machine (SVM)

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Model Performance

Model	Accuracy
SVM	0.83
Logistic Regression	0.80
KNN	0.79
Decision Tree	~0.78
Naive Bayes	0.77

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Best Model

Support Vector Machine (SVM) achieved the highest accuracy.

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Key Steps Performed

1. Data Loading (Seaborn Titanic Dataset)
2. Data Cleaning & Preprocessing
3. Feature Encoding
4. Feature Scaling
5. Train-Test Split
6. Model Training
7. Model Evaluation (Accuracy, Confusion Matrix, Classification Report)
8. Model Comparison

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Evaluation Metrics

• Accuracy Score
• Confusion Matrix
• Precision, Recall, F1-score
• Cross-Validation

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Project Structure

• Notebook