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https://github.com/fralfaro/titanic-challenge

Step-by-Step tutorial to solve the Titanic Challenge with streamlit, jupyter notebooks and more!

https://github.com/fralfaro/titanic-challenge

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Step-by-Step tutorial to solve the Titanic Challenge with streamlit, jupyter notebooks and more!

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Created about 2 years ago · Last pushed about 2 years ago
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README.md

Titanic Challenge

example workflow documentation documentation Streamlit App

Welcome to the Titanic Survival Prediction Challenge! This project is aimed at predicting the survival of passengers on the Titanic using machine learning models. Below you'll find a detailed overview of the project, including data preprocessing, feature engineering, model training, evaluation, and results.

📊 Exploratory Data Analysis (EDA)

The initial data analysis involved examining the Titanic dataset to uncover patterns and insights. We focused on: - Handling missing values - Understanding the distribution of features - Visualizing relationships between features and survival

🔧 Feature Engineering

Key steps in feature engineering included: - Removing non-contributory columns like Name and Ticket - Filling missing values in Age with the mean age - Replacing missing values in Cabin with the most frequent value and simplifying the data - Converting categorical variables (Pclass, SibSp, Parch) to string types

🧠 Machine Learning Models

We trained several machine learning models to predict survival: - Random Forest 🌲 - Logistic Regression 📉 - K-Nearest Neighbors (KNN) 📍 - LightGBM 💡 - AdaBoost 🚀 - Decision Tree 🌳 - Gaussian Naive Bayes 🧪

Model Evaluation

We evaluated models using metrics such as: - Accuracy - Precision - Recall - F1-Score - AUC (Area Under the ROC Curve)

The Random Forest model emerged as the best performer with the highest AUC.

🛠 Tools and Libraries

  • pandas
  • numpy
  • scikit-learn
  • seaborn
  • matplotlib
  • lightgbm
  • loguru

Owner

  • Name: Francisco
  • Login: fralfaro
  • Kind: user

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Dependencies

.github/workflows/documentation.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • peaceiris/actions-gh-pages v3 composite
pyproject.toml pypi
  • mkdocs-dracula-theme *
  • mkdocs-jupyter *
  • mkdocs-material *
  • neoteroi-mkdocs *
  • python ^3.10
requirements.txt pypi
  • altair *
  • great_tables *
  • itables *
  • pandas *
  • plotly *
  • scikit-learn *