Dive into the depths of Automated Machine Learning (AutoML) with this comprehensive analysis, focusing on a targeted dataset to unveil predictive insights and model interpretations. This project showcases the power of AutoML through detailed data preprocessing, feature engineering, and an exploration of model complexities.
This project begins with an abstract detailing the dataset under investigation. It provides an overview of the dataset's origin, its significance, and the analytical objectives aimed at uncovering through AutoML methodologies.
Initial explorations to understand the dataset's structure, identify key variables, and perform exploratory data analysis (EDA) to glean initial insights.
A thorough phase of data cleaning and preprocessing ensures the dataset is primed for AutoML processing, focusing on handling missing values, outlier identification, and normalization.
- Correlation Analysis: Investigates the relationships between features to understand their impact on the target variable.
- Checking for Multicollinearity: Ensures the independence of predictors before feeding them into the AutoML models.
Discusses the application of regularization techniques to prevent overfitting and enhance model generalizability.
Guides through the installation and setup of the H2O AutoML environment, preparing the stage for advanced model training and evaluation.
Transforms the dataset into H2OFrame to leverage H2O's efficient data handling and modeling capabilities.
This section dives deep into the analytical techniques and model evaluations:
- Model Correlation Heatmap: Visualizes the correlation between different models' predictions.
- Partial Dependence Plots: Illustrates the effect of specific features on the model's prediction.
- Shap Analysis: Provides interpretability by explaining the contribution of each feature to the model's output.
- Learning Curve Plot: Evaluates the model's performance over various training sizes to assess learning efficacy.
- Regularization: Explores the impact of regularization in model performance tuning.
- Hyperparameter Tuning Analysis: Details the process and outcomes of hyperparameter optimization to achieve optimal model performance.
We welcome contributions, improvements, and feedback from the community. Feel free to fork the repository, make your changes, and submit a pull request.
This project is open-sourced under the MIT License. See LICENSE for more information.