Update app.py

app.py

@@ -9,9 +9,32 @@ import pandas as pd
 
 def load_data():
     # Load data from CSV files
+    df = pd.read_csv('slump_test.data.csv')
+    df.drop(columns=['SLUMP(cm)'], inplace=True)
+    df.dropna(inplace=True)
+    # df = pd.get_dummies(df, drop_first=True)
+    # numeric_columns = df.select_dtypes(include=['int64', 'float64']).columns
+    # # Creating a MinMaxScaler object
+    # scaler = MinMaxScaler()
+    # # Normalizing numeric features
+    # df[numeric_columns] = scaler.fit_transform(df[numeric_columns])
+    # scaler = StandardScaler()
+    # df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
+    
+    # # Perform PCA
+    # pca = PCA()
+    # df_pca = pca.fit_transform(df_scaled)
+    
+    # Calculate explained variance
+    # explained_variance = pca.explained_variance_ratio_
+    
+    # Create a DataFrame with the PCA results
+    pca_columns = [f'PC{i+1}' for i in range(df_scaled.shape[1])]
+    df_pca = pd.DataFrame(df_pca, columns=pca_columns)
+
     X_train = pd.read_csv('slump_test.data.csv').values[:,:-1]
-    X_train = X_train.drop('SLUM (cm)',axis=1)
     y_train = pd.read_csv('slump_test.data.csv').values[:, -1] 
+    
 
     
     return X_train, y_train