AI/ML Practical Task 3 Progress

AI/ML Practical Task 3 Progress

Today, I started working on Task 3 of my AI/ML practical assignment using Google Colab. The goal of this task is to improve a credit risk classification model using feature selection and evaluation techniques.

Dataset Overview

The dataset used is the Credit Risk Dataset, which contains various financial and behavioral features used to predict credit score classification.

Initial Setup

I began by loading the dataset into Google Colab and preparing the data for analysis. The features and target variables were defined as follows:

  • Features (X): Input variables derived from the dataset
  • Target (y): Credit_Score_Coded (classification label)

The dataset was then split into training and testing sets using a 60/40 ratio.

Train-Test Split

X_train, X_test, y_train, y_test = train_test_split(
    features, target, test_size=0.4, random_state=42
)

Next Steps

The next phase of the task involves applying Mutual Information for feature importance analysis, followed by training Decision Tree models using the top 5, top 10, and top 15 features.

Each model will be evaluated using:

  • Classification Report
  • Confusion Matrix
  • ROC-AUC Curve

Conclusion

This task helps in understanding how feature selection impacts model performance and improves interpretability in machine learning workflows.

Deadline: April 13, 7:59 AM (WAT)

More updates coming soon as I progress with model training and evaluation.

AI/ML Practical (3MTT) – Organized by 3MTT, Facilitated by Ibadan Digital Academy

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