Adding ML Model for Absenteeism Prediction with Hyperparameter Tuning

[murtaza-sadri-19]

Pull Request for PyVerse 💡

Issue Title : Adding ML model for HyperParameter Tuning including Resources

• Info about the related issue (Aim of the project) : To demonstrate the application of hyperparameter tuning to an ML model for predicting absenteeism time using the UCI machine learning dataset.
• Name: Murtaza Sadriwala
• GitHub ID: murtaza-sadri-19
• Email ID: [email protected]
• Idenitfy yourself: GSSOC'24 Extd

Closes: #141
I have added a new Jupyter Notebook to the project that demonstrates the process of building and training an ML model for predicting absenteeism time using the UCI dataset. The notebook includes the following steps:

Data loading and preprocessing: Loading the dataset, handling missing values, and performing feature engineering.
Model selection: Choosing a suitable ML algorithm (e.g., Random Forest, XGBoost) for the task.
Hyperparameter tuning: Using techniques like grid search or random search to find optimal hyperparameter values for the selected model.
Model training and evaluation: Training the model with the tuned hyperparameters and evaluating its performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
The notebook also includes visualizations to illustrate the impact of hyperparameter tuning on the model's performance.

Type of change ☑️

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

I have thoroughly tested the notebook by running it multiple times and verifying that the results are consistent. I have also compared the performance of the model with and without hyperparameter tuning to demonstrate its effectiveness.

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

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