Create DataProcessor.py

.ai/models/DataProcessor.py

@@ -0,0 +1,33 @@
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+
+class DataProcessor:
+    def __init__(self, data_path):
+        self.data_path = data_path
+        self.data = pd.read_csv(data_path)
+
+    def preprocess_data(self):
+        self.data.dropna(inplace=True)
+        self.data = self.data.apply(lambda x: x.astype(str).str.lower())
+        self.data = self.data.apply(lambda x: x.str.replace(r'[^\w\s]', ''))
+        scaler = StandardScaler()
+        self.data[['feature1', 'feature2', 'feature3']] = scaler.fit_transform(self.data[['feature1', 'feature2', 'feature3']])
+        pca = PCA(n_components=0.95)
+        self.data[['feature1', 'feature2', 'feature3']] = pca.fit_transform(self.data[['feature1', 'feature2', 'feature3']])
+        return self.data
+
+    def feature_engineering(self):
+        self.data['new_feature1'] = self.data['feature1'] * self.data['feature2']
+        self.data['new_feature2'] = self.data['feature2'] / self.data['feature3']
+        return self.data
+
+    def data_visualization(self):
+        import matplotlib.pyplot as plt
+        plt.scatter(self.data['feature1'], self.data['feature2'])
+        plt.xlabel('Feature 1')
+        plt.ylabel('Feature 2')
+        plt.title('Feature 1 vs Feature 2')
+        plt.show()
+        return self.data