Data.md

@@ -168,16 +168,7 @@ Step 5: Handle Missing or Infinite Values
 After generating the synthetic data, any missing or invalid values (such as NaN or inf) are replaced with the respective feature's mean from the original dataset to ensure data integrity.
 
 
-### Replace NaN or infinite values in each column of synthetic data with mean values of respective columns
-synthetic_data = synthetic_data.replace([np.inf, -np.inf], np.nan)
-synthetic_data = synthetic_data.fillna({
-    'Open': data['Open'].mean(),
-    'High': data['High'].mean(),
-    'Low': data['Low'].mean(),
-    'Close': data['Close'].mean(),
-    'Adj Close': data['Adj Close'].mean(),
-    'Volume': abs(data['Volume'].mean())
-})
+### Replace NaN or infinite values in each column of synthetic data with mean ![image](uploads/6be78c8af008ebc35b7a51cb788b8f80/image.png){width=809 height=194}
 Purpose: This step ensures that the synthetic data is clean and free of missing or extreme values that could interfere with model training.
 Step 6: Create DataFrame for Synthetic Data
 The synthetic data for all features (Open, High, Low, Close, Adj Close, and Volume) is compiled into a DataFrame, which is then concatenated with the original data.