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https://github.com/elfgk/titanic-data-analysis
Titanic Data Analysis
https://github.com/elfgk/titanic-data-analysis
jupyter-notebook titanic-data-analytics titanic-dataset titanic-kaggle
Last synced: 24 days ago
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Titanic Data Analysis
- Host: GitHub
- URL: https://github.com/elfgk/titanic-data-analysis
- Owner: elfgk
- License: apache-2.0
- Created: 2024-12-23T00:26:04.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-23T00:30:10.000Z (about 1 month ago)
- Last Synced: 2024-12-23T01:24:10.531Z (about 1 month ago)
- Topics: jupyter-notebook, titanic-data-analytics, titanic-dataset, titanic-kaggle
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/elfgkk/titanic
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Titanic Data Analysis
This project focuses on analyzing the Titanic dataset, which includes information about passengers aboard the RMS Titanic. The goal is to explore the data and build a machine learning model to predict passenger survival based on features such as age, class, gender, and ticket information.
Dataset: https://www.kaggle.com/competitions/titanic
## Project Overview
The project involves the following steps:
1. **Data Exploration:**
- The Titanic dataset is explored to understand the features and the relationships between them. Basic data cleaning and preprocessing are done at this stage.
2. **Data Preprocessing:**
- The dataset is cleaned by handling missing values, encoding categorical variables, and scaling features to prepare it for machine learning.
3. **Model Building:**
- A machine learning model (e.g., Logistic Regression, Decision Trees, Random Forest) is built to predict the survival of passengers.
4. **Model Evaluation:**
- The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1 score. Cross-validation and hyperparameter tuning are also performed to optimize the model's performance.
5. **Visualization:**
- Various visualizations are created using libraries like `matplotlib` and `seaborn` to better understand the dataset and the relationships between features.
## Dataset
The dataset used in this project is the Titanic dataset from Kaggle, which contains the following columns:
- `PassengerId`: Unique ID for each passenger.
- `Pclass`: Passenger class (1st, 2nd, or 3rd).
- `Name`: Name of the passenger.
- `Sex`: Gender of the passenger.
- `Age`: Age of the passenger.
- `SibSp`: Number of siblings or spouses aboard the Titanic.
- `Parch`: Number of parents or children aboard the Titanic.
- `Ticket`: Ticket number.
- `Fare`: Fare paid by the passenger.
- `Cabin`: Cabin number.
- `Embarked`: Port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton).
- `Survived`: Survival status (0 = No, 1 = Yes).
## Libraries Used
- `pandas`: For data manipulation and analysis.
- `numpy`: For numerical operations.
- `matplotlib` and `seaborn`: For data visualization.
- `scikit-learn`: For building and evaluating machine learning models.
- `xgboost` (optional): For boosting models and improving prediction accuracy.
## Getting Started
To get started with this project, follow these steps:
1. Clone or download the repository:
```bash
git clone https://github.com/elfgk/Titanic-Data-Analysis.git
```
2. Install the required Python libraries.
3. Open the titanic_data_analysis.ipynb Jupyter notebook and follow the steps for data exploration, preprocessing, model building, and evaluation.
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Contact Meπ§βπ»:
[](https://www.linkedin.com/in/elfgk/)
[](https://stackoverflow.com/users/27559679/elfgk)
[](https://huggingface.co/elfgk)
[](https://www.kaggle.com/elfgkk)