EXPLORATORY DATA ANALYSIS OVER TITANIC DATASET
Embark on a comprehensive data exploration journey with this repository, offering a deep dive into the Titanic dataset using Python in a Jupyter Notebook. Discover hidden patterns, engineer impactful features, train machine learning models, and rigorously evaluate their performance.
- Python
- Jupyter Notebook
- Matplotlib Library
- Pandas
- Numpy
- Scikit-learn (for model training and evaluation)
The objective of this task is to perform a holistic analysis of the Titanic dataset, including data exploration, feature engineering, machine learning model training, and evaluation.
1.1 Import the necessary libraries: Matplotlib, Pandas, Numpy, and Scikit-learn.
2.1 Utilize the Titanic dataset for analysis.
3.1 Uncover patterns and trends in the Titanic dataset. 3.2 Create visualizations to represent key insights.
4.1 Engineer impactful features for model training.
5.1 Train machine learning models using Scikit-learn. 5.2 Utilize relevant algorithms for prediction.
6.1 Rigorously assess model performance for accuracy. 6.2 Generate evaluation metrics such as precision, recall, and F1-score.
This data science project provides a holistic analysis of the Titanic dataset, spanning data exploration, feature engineering, machine learning model training, and evaluation. Explore visualizations, discover trends, and evaluate model accuracy for predictive insights.
-
Clone the repository:
git clone https://github.com/gl-ankit-kumar/PRODIGY_DS_02.git
-
Explore with Jupyter Notebook:
- Open
EDA on Titanic Dataset.ipynb
in Jupyter Notebook for an interactive experience.
- Open
-
Data Exploration
- Uncovered patterns and trends in the Titanic dataset.
- Visualizations provide insights into various aspects of passenger demographics and survival factors.
-
Feature Engineering
- Engineered impactful features to enhance model predictive power.
-
Model Training
- Successfully trained machine learning models for prediction using Scikit-learn.
-
Evaluation Mastery
- Rigorously assessed model performance for accuracy, precision, recall, and F1-score.
Feel free to delve into the provided analyses, explore visualizations, and leverage the trained models for predictive tasks!
Contributions are encouraged! Whether you want to enhance the analysis or add new features:
- Open issues to discuss potential changes.
- Submit pull requests to collaborate on improvements.
Questions or suggestions? Reach out to me:
- Ankit Kumar
- Email: ankitkumarbgp.official@gmail.com
Explore the world's data, analyze global trends, and uncover the stories with the Titanic dataset! 🌍📊