From b6855e92f027d200ff0aaa748edcdfcb667149b2 Mon Sep 17 00:00:00 2001 From: Madhumitha Date: Mon, 8 Apr 2024 01:14:43 +0530 Subject: [PATCH] Added an example to 14_imbalanced/handling_imbalanced_data_exercise.md --- .../handling_imbalanced_data_exercise.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/14_imbalanced/handling_imbalanced_data_exercise.md b/14_imbalanced/handling_imbalanced_data_exercise.md index 8aa2cea..c86c30b 100644 --- a/14_imbalanced/handling_imbalanced_data_exercise.md +++ b/14_imbalanced/handling_imbalanced_data_exercise.md @@ -14,6 +14,17 @@ 1. Improve f1 score in minority class using various techniques such as undersampling, oversampling, ensemble etc [Solution](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/14_imbalanced/Handling%20Imbalanced%20Data%20In%20Customer%20Churn%20Using%20ANN/Bank%20Turnover%20Customer%20Churn%20Using%20ANN.ipynb) - - Thanks https://github.com/src-sohail for providing this solution. + Thanks https://github.com/src-sohail for providing this solution. + 3. Exercise: Predicting Customer Satisfaction + Use the Customer Satisfaction dataset from Kaggle. - https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction + + 1. Build a classification model to predict customer satisfaction. + 2. Initially, use a logistic regression model from scikit-learn. + 3. Print the classification report and analyze precision, recall, and f1-score. + 4. Try to improve the f1-score for the minority class using techniques like undersampling, oversampling, or ensemble methods. + + 5. [Solution] : https://www.kaggle.com/code/teejmahal20/classification-predicting-customer-satisfaction + + Thanks https://kaggle/teejmahal20 for providing this solution. +