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README.md
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README.md
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# Electrocardiogram
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# Electrocardiogram
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We are dealing with an exteremly imbalance dataset related to electrocardiogram signals that contain binary class labels as good and bad signals.
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We are dealing with an exteremly imbalance dataset related to electrocardiogram signals that contain binary classes and labeled as good(0) and bad(1) signals.
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### STEP 1: Fill missing values
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### STEP 1: Fill missing values
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model.fit(X_train, y_train)
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model.fit(X_train, y_train)
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```
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```
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```
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✅ 1. Stratified K-fold only apply on train. Of each fold
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🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10
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🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10
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🗹 4. implement proposed methods of this article : https://1drv.ms/b/c/ab2a38fe5c318317/IQBEDsSFcYj6R6AMtOnh0X6DAZUlFqAYq19WT8nTeXomFwg
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🗹 5. compare proposed model with SMOTE vs oversampling balancing method
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```
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