diff --git a/Correlation/calculate the correlation.py b/Correlation/calculate the correlation.py
deleted file mode 100644
index 6dce5689f90fd9ba6cc6506ed25553caaf1808fe..0000000000000000000000000000000000000000
--- a/Correlation/calculate the correlation.py	
+++ /dev/null
@@ -1,53 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Dec 17 17:47:52 2023
-
-@author: qsy
-"""
-import pandas as pd
-import matplotlib.pyplot as plt
-
-filepath = "D:\Deep Learning in Crowd Farming\emerged.xlsx"
-df = pd.read_excel(filepath)
-#%%
-
-df = df.set_index(df.columns[0])
-#%%
-#Data cleaning
-
-for column in df.columns:
-    # convert the data type of the column to numeric
-    df[column] = pd.to_numeric(df[column], errors='coerce')
-
-    # for numeric columns, fill non-numeric or null values as the column average
-    if pd.api.types.is_numeric_dtype(df[column]):
-        df[column] = df[column].fillna(df[column].mean())
-    else:
-        # For non-numeric columns, fill the null value with the column average
-        df[column] = df[column].fillna(df[column].mean())
-
-
-#%%
-# Computed correlation matrix
-correlation_matrix = df.corr()
-
-# Find column pairs with correlations greater than or equal to 0.8
-high_correlation_pairs = []
-
-for i in range(len(correlation_matrix.columns)):
-    for j in range(i + 1, len(correlation_matrix.columns)):
-        if abs(correlation_matrix.iloc[i, j]) >= 0.8:
-            column_pair = (correlation_matrix.columns[i], correlation_matrix.columns[j])
-            high_correlation_pairs.append(column_pair)
-
-
-print("Column pairs' correlation greater than or equal to 0.8:")
-for pair in high_correlation_pairs:
-    print(pair)
-
-# Draw a heat map of the correlation matrix
-f1 = plt.figure()
-plt.pcolormesh(correlation_matrix)
-cbar = plt.colorbar()
-
-plt.show()
\ No newline at end of file