The "Santa" project is a comprehensive exploration of data analysis and predictive modeling. It encompasses Exploratory Data Analysis (EDA), Logistic Regression, and Decision Tree modeling. The project aims to extract meaningful insights from data and apply predictive algorithms to model and understand complex relationships.
To set up the project, install the necessary Python libraries:
pip install numpy pandas matplotlib seaborn scikit-learn
Run the Santa.ipynb notebook to engage with the data analysis and modeling. The notebook is organized into sections, guiding through EDA, model implementation, and evaluation.
- Visualization and Statistical Analysis: Uses
matplotlibandseabornfor visualizing data and understanding underlying patterns. - Correlation Studies: Analyzes correlations between different variables, particularly focusing on their relationship with the target variable.
- Manual and Scikit-Learn Implementations: Includes both manual implementation and utilization of scikit-learn for logistic regression.
- Comparative Evaluation: Compares the performance of manual and scikit-learn implementations in different scenarios.
- Implementation using Scikit-Learn: Explores decision tree modeling for predictive analysis using scikit-learn's API.
- Model Evaluation: Focuses on training and evaluating the decision tree model, assessing its predictive power and accuracy.
- F1 Score Analysis: Compares different models based on F1 score to evaluate their performance and reliability.
- Python Libraries: Utilizes libraries like
numpy,pandas,matplotlib,seaborn, andscikit-learn. - Structured Approach: The notebook is structured into various sections, each focusing on different aspects of data analysis and modeling.
This project is licensed under the MIT License. See the LICENSE file for more details.