editing readme.md

README.md

@@ -2,7 +2,7 @@
 
 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. 
 
-### STEP 1: Fill missing values
+## STEP 1: Fill missing values
 
   All the columns in our data contain missing values a range from 25 to 70. By using `from sklearn.impute import KNNImputer` 
   
@@ -16,7 +16,7 @@ We are dealing with an exteremly imbalance dataset related to electrocardiogram
   return data_frame_imputed
   ```
 
-### STEP 2: Scaling
+## STEP 2: Scaling
 
   We used `from sklearn.preprocessing import RobustScaler` to handle scaling.
 
@@ -28,7 +28,7 @@ We are dealing with an exteremly imbalance dataset related to electrocardiogram
   data_frame_scaled["label"] = labels.values
   ```
 
-### STEP 3: k-fold cross validation + stratify classes + balancing training data
+## STEP 3: k-fold cross validation + stratify classes + balancing training data
 
   First of all we split the dataset into 2 parts train (85%) and test (15%). For making sure that majority class and imbalanced class
   distributed fairly we passed `stratify=y`
@@ -79,16 +79,18 @@ We are dealing with an exteremly imbalance dataset related to electrocardiogram
   model.fit(X_train, y_train)
   ```
 
-### STEP 4: Train different models to find the best possible approach 
+## STEP 4: Train different models to find the best possible approach 
 
-What we are looking for:
+#### What we are looking for:
-Dangerous: Sick → predicted healthy : high recall score or low FN
+
-Costly: Healthy → predicted sick : high precision score or low FP
+#### Dangerous: Sick → predicted healthy : high recall score or low FN
+
+#### Costly: Healthy → predicted sick : high precision score or low FP
 
 
 
+## next steps: 
 ```
-next steps: 
 ✅ 1. Stratified K-fold only apply on train.
 🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10
 🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10