Titanic Survival Prediction

 Titanic Survival Prediction – IT and Computer Engineering Guide

1. Project Overview

Objective: Predict whether a passenger survived the Titanic disaster based on their attributes.
Scope: Use the Titanic dataset to build and evaluate classification models for survival prediction.

2. Prerequisites

Knowledge: Understanding of Python programming, classification algorithms, and basic data preprocessing.
Tools: Python, Jupyter Notebook, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
Dataset: Titanic dataset from Kaggle or other public sources.

3. Project Workflow

- Data Collection: Load the Titanic dataset from Kaggle or another source.

- Data Preprocessing: Handle missing values, encode categorical variables, and normalize numerical features.

- Exploratory Data Analysis (EDA): Analyze the dataset using visualizations to understand feature correlations with survival.

- Feature Engineering: Create new features (e.g., family size) or modify existing ones for better predictions.

- Data Splitting: Split the dataset into training and testing sets.

- Model Development: Train classification models such as Logistic Regression, Decision Trees, or Random Forest.

- Model Evaluation: Evaluate models using accuracy, precision, recall, F1-score, and confusion matrix.

- Optimization: Fine-tune model parameters using Grid Search or Random Search.

- Deployment: Deploy the trained model using a web framework like Flask or Django.

4. Technical Implementation

Step 1: Import Libraries


import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns

Step 2: Load the Dataset


data = pd.read_csv('titanic.csv')
print(data.head())

Step 3: Data Preprocessing


# Handle missing values
data['Age'].fillna(data['Age'].median(), inplace=True)
data['Embarked'].fillna(data['Embarked'].mode()[0], inplace=True)
data['Cabin'].fillna('Unknown', inplace=True)

# Encode categorical variables
data = pd.get_dummies(data, columns=['Sex', 'Embarked'], drop_first=True)

Step 4: Feature Engineering


data['FamilySize'] = data['SibSp'] + data['Parch']
data['IsAlone'] = (data['FamilySize'] == 0).astype(int)

Step 5: Split the Dataset


X = data[['Pclass', 'Age', 'Fare', 'FamilySize', 'IsAlone', 'Sex_male', 'Embarked_Q', 'Embarked_S']]
y = data['Survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 6: Train and Evaluate the Model


model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))
print(confusion_matrix(y_test, predictions))

5. Results and Visualization

Visualize the confusion matrix.
Analyze feature importance and plot survival probabilities.

6. Challenges and Mitigation

Data imbalance: Use techniques like SMOTE or class weighting.
Feature relevance: Perform feature selection and engineering.

7. Future Enhancements

Incorporate advanced models like Gradient Boosting or XGBoost.
Build a user-friendly interface for input and predictions.

8. Conclusion

The Titanic Survival Prediction project highlights the application of machine learning for binary classification problems.
It demonstrates the complete workflow from data preprocessing to model evaluation and deployment.