🌊 Titanic Dataset - Machine Learning Pipeline 🛠️

✨ Project Overview

This project demonstrates how to build a Machine Learning Pipeline for the Titanic Dataset using Decision Tree Classifier. The pipeline includes:

• Data Preprocessing (handling missing values, encoding, and scaling)
• Feature Selection using SelectKBest
• Model Training with Decision Tree Classifier
• Pipeline Serialization using Pickle

📊 Data Preprocessing

• Handling Missing Values: SimpleImputer()
• Encoding Categorical Features: OneHotEncoder()
• Feature Scaling: MinMaxScaler()
• Feature Selection: SelectKBest(score_func=chi2, k=8)

🧠 Model Training

• Algorithm: DecisionTreeClassifier()
• Pipeline: Created with ColumnTransformer and Pipeline
• Serialization: Saved using Pickle