diff --git a/Validation/dataset_info.py b/Validation/dataset_info.py
new file mode 100644
index 0000000..f851c25
--- /dev/null
+++ b/Validation/dataset_info.py
@@ -0,0 +1,101 @@
+import os
+import pandas as pd
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+
+# Custom function to convert int64 to int
+def convert_int64_to_int(obj):
+    if isinstance(obj, np.int64):
+        return int(obj)
+    raise TypeError
+
+# Function to perform statistical analysis and create plots on columns
+def analyze_and_visualize_columns(df):
+    column_stats = []
+    for column in df.columns:
+        null_count = df[column].isnull().sum()
+        blank_count = df[column].map(lambda x: str(x).strip() == '').sum()
+        data_type = df[column].dtype
+        mean_value = None  
+
+        # Calculate the mean for numeric columns
+        if data_type in ['int64', 'float64']:
+            mean_value = df[column].mean()
+
+            plt.figure(figsize=(8, 6))
+            df[column].plot(kind='hist', bins=20, title=f'Histogram for {column}')
+            plt.xlabel(column)
+            plt.ylabel('Frequency')
+            plt.savefig(f'{column}_histogram.png')
+            plt.close()
+
+        column_stats.append({
+            'Column Name': column,
+            'Number of Null Values': null_count,
+            'Number of Blank Values': blank_count,
+            'Data Type': str(data_type),
+            'Mean': mean_value 
+        })
+    
+    return column_stats
+
+# Function to generate dataset report in JSON format
+def generate_json_report(dataset_path, column_stats):
+    report_data = {
+        'Dataset Path': dataset_path,
+        'Column Stats': column_stats
+    }
+
+    with open('report.json', 'w') as json_file:
+        json.dump(report_data, json_file, indent=4, default=convert_int64_to_int)  # Use custom conversion
+
+# Function to generate dataset report in CSV format
+def generate_csv_report(column_stats):
+    df = pd.DataFrame(column_stats)
+    df.to_csv('report.csv', index=False)
+
+# Function to generate dataset report in TXT format
+def generate_txt_report(column_stats):
+    with open('report.txt', 'w') as txt_file:
+        txt_file.write("************ VALIDATION REPORT OF UPLOADED DATASET ************\n\n")
+        # Write column-wise analysis and statistics to the TXT report
+        for stats in column_stats:
+            txt_file.write(f"Column Name: {stats['Column Name']}\n")
+            txt_file.write(f"Number of Null Values: {stats['Number of Null Values']}\n")
+            txt_file.write(f"Number of Blank Values: {stats['Number of Blank Values']}\n")
+            txt_file.write(f"Data Type: {stats['Data Type']}\n")
+            
+            # Add the mean to the report
+            if 'Mean' in stats:
+                txt_file.write(f"Mean: {stats['Mean']}\n")
+
+            txt_file.write("---\n\n")
+
+# Main function
+def main():
+    dataset_path = input("Enter the directory path of the dataset: ")
+    if not os.path.exists(dataset_path):
+        print("The specified directory does not exist.")
+        return
+
+    # Read the dataset into a DataFrame (modify the read_csv options as needed)
+    df = pd.read_csv(os.path.join(dataset_path, 'your_dataset.csv'))
+
+    # Perform statistical analysis and create plots on columns
+    column_stats = analyze_and_visualize_columns(df)
+
+    # Generate reports in different formats
+    generate_json_report(dataset_path, column_stats)
+    generate_csv_report(column_stats)
+    generate_txt_report(column_stats)
+
+    print("Report generated successfully.")
+    
+    
+    
+    # Calculate basic statistics for numeric columns
+    # numeric_stats = df.describe()    // on analyzing these statistical values you can judge whether this dataset would be useful for you or not in a particular project!!
+
+if __name__ == "__main__":
+    main()