commiting this forking stuff

data_analyis.py

@@ -43,4 +43,5 @@ def main():
     analyze_data(data)
 
 if __name__ == "__main__":
+    print("i am in main")
     main()

data_analysis.py

@@ -0,0 +1,54 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+
+def load_data(abFilePath):
+    """Load data from an Excel file."""
+    try:
+        abData = pd.read_excel(abFilePath)
+        return abData
+    except FileNotFoundError:
+        print("File not found. Please provide a valid file path.")
+        return None
+    except Exception as abError:
+        print(f"An error occurred: {abError}")
+        return None
+
+def analyze_data(abData):
+    """Perform basic data analysis."""
+    if abData is not None:
+        # Display summary statistics
+        print("Summary Statistics:")
+        print(abData.describe())
+
+        # Plot histograms for numeric columns
+        print("Histograms:")
+        for abCol in abData.select_dtypes(include=['int64', 'float64']):
+            abData[abCol].plot(kind='hist', bins=10)
+            plt.title(abCol)
+            plt.xlabel(abCol)
+            plt.ylabel('Frequency')
+            plt.show()
+
+        # Encode categorical class labels
+        if 'Class' in abData.columns:
+            abLe = LabelEncoder()
+            abData['Class'] = abLe.fit_transform(abData['Class'])
+
+            # Plot bar plot for the class label (numeric type)
+            abClassLabelCounts = abData['Class'].value_counts()
+            abClassLabelCounts.plot(kind='bar')
+            plt.title('Class Label Distribution')
+            plt.xlabel('Class Label')
+            plt.ylabel('Count')
+            plt.show()
+        else:
+            print("Class column not found.")
+
+def main():
+    abFilePath = "DryBeanDataset/Dry_Bean_Dataset.xlsx"
+    abData = load_data(abFilePath)
+    analyze_data(abData)
+
+if __name__ == "__main__":
+    main()