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.