Replace dsm020-cw2.ipynb

    "\n",

    "Following data preprocessing, this study addressed the issue of class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) (Imbalanced-learn, n.d.). The dataset is divided into training and test sets, with SMOTE applied only to the training set to prevent data leakage. This results in two versions of the training data: one with SMOTE and one without.\n",

    "\n",

    "Logistic Regression is trained on both versions of the training set using the built-in classification algorithm from scikit-learn (Scikit-learn, n.d.), with the number of epochs set to the default value of 100 and L2 penalty. For evaluation, only out-of-sample (OOS) results are reported."

    "Logistic Regression is trained on both versions of the training set using the built-in classification algorithm from Scikit-learn (Scikit-learn, n.d.), with the number of epochs set to the default value of 100 and L2 penalty. For evaluation, only out-of-sample (OOS) results are reported."

   ]

  },

  {