gl-ankit-kumar / PRODIGY_DS_02

EXPLORATORY DATA ANALYSIS OVER TITANIC DATASET

PRODIGY_DS_02

EXPLORATORY DATA ANALYSIS OVER TITANIC DATASET

Prodigy Infotech Data Science Internship Task 02

Titanic Dataset Analysis with Python, Matplotlib, Pandas, NumPy and Scikit-learn 🚢📊

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.

Tools Utilized

• Python
• Jupyter Notebook
• Matplotlib Library
• Pandas
• Numpy
• Scikit-learn (for model training and evaluation)

Overview of the Task

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.

Task Requirements

1. Import Libraries

1.1 Import the necessary libraries: Matplotlib, Pandas, Numpy, and Scikit-learn.

2. Load the Dataset

2.1 Utilize the Titanic dataset for analysis.

3. Data Exploration

3.1 Uncover patterns and trends in the Titanic dataset. 3.2 Create visualizations to represent key insights.

4. Feature Engineering

4.1 Engineer impactful features for model training.

5. Model Training

5.1 Train machine learning models using Scikit-learn. 5.2 Utilize relevant algorithms for prediction.

6. Evaluation Mastery

6.1 Rigorously assess model performance for accuracy. 6.2 Generate evaluation metrics such as precision, recall, and F1-score.

7. Conclusion

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.

🛠️ Setup and Usage

1. Clone the repository:

   git clone https://github.com/gl-ankit-kumar/PRODIGY_DS_02.git

2. Explore with Jupyter Notebook:

   • Open EDA on Titanic Dataset.ipynb in Jupyter Notebook for an interactive experience.

Inferences

• 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!

🤝 Contributing

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.

📬 Contact

Questions or suggestions? Reach out to me:

• Ankit Kumar
• Email: [e-mail]

Explore the world's data, analyze global trends, and uncover the stories with the Titanic dataset! 🌍📊