Term Deposit Predcition

[Sgvkamalakar]

Pull Request for ML-Crate 💡

Issue Title: Term Deposit Prediction #680

• Info about the related issue (Aim of the project) : Implemented various machine learning models to predict client response in a bank marketing campaign, evaluating performance through metrics like accuracy, precision, and recall.
• Name: S G V Kamalakar
• Email ID for further communication: sgvkamalakar@gmail.com
• GitHub ID: Sgvkamalakar
• Idenitfy yourself: SSOC

Closes: #680

Describe the add-ons or changes you've made 📃

Give a clear description of what have you added or modifications made

Term Deposit Prediction
|- Dataset
   |- dataset.csv
   |- README.md
|- Images
   |- img1.png
   |- img2.png
   :
   :
   |- README.md
|- Models
   |- Notebook.ipynb
   |- README.md
|- requirements.txt

Structured the project into specific folders: Dataset for storing the main dataset and its README, Images for storing visualizations, Models for storing notebooks and model-related files, and included a requirements.txt file for dependencies.

Organized README.md files within each folder to provide context and instructions specific to their contents, ensuring clarity and ease of navigation.

Type of change ☑️

What sort of change have you made:

• New feature (non-breaking change which adds functionality)

How Has This Been Tested? ⚙️

Describe how it has been tested
Describe how have you verified the changes made

• The dataset has an imbalance in the target variable 'y', with the 'no' class having approximately 27,000 instances and the 'yes' class having only 3,859 instances.
• This imbalance indicates that the dataset is heavily skewed towards the 'no' class, which can lead to biased model performance if not addressed properly.

• Adjusting Class Weights: In models that support it, I adjusted the class weights to give more importance to the minority class during training.

• Evaluation Metrics: Focused on metrics that are sensitive to class imbalance, such as precision, recall, and the F1 score, rather than just accuracy.

• Verified dataset modifications, model retraining, and output consistency to confirm the accuracy and reliability of updated results.

• Evaluated the project's performance metrics (e.g., accuracy, precision, recall) against benchmarks and thresholds.

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.

[github-actions]

Our team will soon review your PR. Thanks @Sgvkamalakar :)

[abhisheks008]

Hi @Sgvkamalakar looks good to me. Approved ✅