https://github.com/shervinnd/titanic-survival-predictor

🚢 Dive into the Titanic dataset with this ML project! Using TensorFlow, predict passenger survival via neural networks. Features data preprocessing, model training, and visualization with pandas, scikit-learn, and matplotlib. Perfect for beginners in deep learning. 📊💻
https://github.com/shervinnd/titanic-survival-predictor

classification data-preprocessing data-science data-visualization deep-learning jupyter-notebook machine-learning neural-network pandas predictive-modeling python scikit-learn tensorflow titanic-dataset

Last synced: 27 days ago
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🚢 Dive into the Titanic dataset with this ML project! Using TensorFlow, predict passenger survival via neural networks. Features data preprocessing, model training, and visualization with pandas, scikit-learn, and matplotlib. Perfect for beginners in deep learning. 📊💻

• Host: GitHub
• URL: https://github.com/shervinnd/titanic-survival-predictor
• Owner: shervinnd
• License: mit
• Created: 2025-09-09T08:45:46.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-09-09T09:19:03.000Z (about 1 month ago)
• Last Synced: 2025-09-09T11:13:15.030Z (about 1 month ago)
• Topics: classification, data-preprocessing, data-science, data-visualization, deep-learning, jupyter-notebook, machine-learning, neural-network, pandas, predictive-modeling, python, scikit-learn, tensorflow, titanic-dataset
• Language: Jupyter Notebook
• Homepage:
• Size: 176 KB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Titanic Survival Predictor 🚢💥

![Titanic

Banner](https://img.shields.io/badge/Project-Titanic%20Survival%20Predictor-blue?style=for-the-badge&logo=ship)

![Python](https://img.shields.io/badge/Python-3.12-brightgreen?style=flat&logo=python)

![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange?style=flat&logo=tensorflow)

![License](https://img.shields.io/badge/License-MIT-green?style=flat)

Welcome to the **Titanic Survival Predictor** project! 🌊 This Jupyter

Notebook-based machine learning model dives into the historic Titanic

dataset to predict passenger survival using neural networks. Whether

you're a data science newbie or a seasoned sailor in ML waters, this

project is your ticket to exploring predictive modeling with a dash of

history! 📜

## Overview 📊

The Titanic disaster of 1912 is one of history's most infamous events.

In this project, we use the famous Titanic dataset to build a deep

learning model that predicts whether a passenger survived based on

features like age, sex, class, and more.

-   **Goal**: Predict survival (0 = Did not survive 😢, 1 = Survived 🎉)

    using a neural network built with TensorFlow/Keras.

-   **Dataset**: Sourced from Kaggle's Titanic challenge (via GitHub raw

    URL).

-   **Tech Stack**: Python, Pandas, NumPy, Matplotlib, Scikit-learn,

    TensorFlow.

-   **Why Fun?**: It's a classic ML problem with real-world data

    insights -- who survived and why? 🚀

## Features ✨

-   **Data Loading & Exploration**: Load the Titanic dataset and peek

    into its structure 🔍.

-   **Preprocessing**: Handle missing values, encode categorical

    variables (e.g., Sex, Embarked), and scale features for optimal

    model performance 📈.

-   **Neural Network Model**: Build and train a TensorFlow/Keras model

    to predict survival 🧠.

-   **Visualization**: Plot key insights using Matplotlib to understand

    data distributions 📊.

-   **Scalable Workflow**: Easily adaptable for further experimentation

    or hyperparameter tuning ⚙️.

## Getting Started 🚀

### Prerequisites

To run this project, you'll need:

-   Python 3.12+ 🐍

-   Jupyter Notebook 📓

-   Libraries: Install via `pip install -r requirements.txt` or

    manually:

    -   `pandas`

    -   `numpy`

    -   `matplotlib`

    -   `scikit-learn`

    -   `tensorflow`

### Installation

1.  Clone this repository:

    ``` bash

    git clone https://github.com/shervinnd/titanic-survival-predictor.git

    ```

2.  Navigate to the project directory:

    ``` bash

    cd titanic-survival-predictor

    ```

3.  Install dependencies:

    ``` bash

    pip install -r requirements.txt

    ```

4.  Launch Jupyter Notebook:

    ``` bash

    jupyter notebook Titanic.ipynb

    ```

### Dataset

The dataset is sourced directly from:

    https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv

It includes features like:

-   `PassengerId`, `Survived`, `Pclass`, `Sex`, `Age`, `SibSp`, `Parch`,

    `Fare`, `Embarked`

-   Preprocessing steps handle missing values (`Age`, `Embarked`) and

    drop irrelevant columns (`Name`, `Ticket`, `Cabin`).

## Usage 🛠️

1.  Open `Titanic.ipynb` in Jupyter Notebook.

2.  Run the cells sequentially to:

    -   Load and preprocess the data 🧹.

    -   Build and train the neural network 🧠.

    -   Evaluate model performance 📊.

3.  Experiment with hyperparameters or feature engineering to improve

    accuracy 🚧.

## Project Structure 📂

-   `Titanic.ipynb`: Main Jupyter Notebook with data processing, model

    training, and evaluation.

-   `README.md`: This file, your guide to the project! 📖

-   `requirements.txt`: List of required Python libraries.

## Future Improvements 🌟

-   Add feature engineering (e.g., family size, title extraction) 🛠️.

-   Experiment with other models (e.g., Random Forest, XGBoost) 🌳.

-   Implement cross-validation for robust evaluation ✅.

-   Visualize predictions with confusion matrices or ROC curves 📈.

## License 📜

This project is licensed under the MIT License. See the LICENSE file for

details.

## Acknowledgments 🙌

-   Kaggle for providing the Titanic dataset.

-   TensorFlow and Scikit-learn communities for awesome tools.

-   The data science community for endless inspiration! 🌍

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