diff --git a/.gitignore b/.gitignore
index 32378b8..2d4aa43 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,7 +8,7 @@ __pycache__/
 *.py[cod]
 .history
 *$py.class
-eculidean.*
+*.xls
 
 # C extensions
 *.so
diff --git a/co.py b/co.py
new file mode 100644
index 0000000..a0758c5
--- /dev/null
+++ b/co.py
@@ -0,0 +1,89 @@
+import pandas as pd
+import xlsxwriter
+from random import uniform
+import math
+import numpy as np
+
+
+def calucateMean(df) -> tuple:
+    mean = []
+    maximum = []
+    minimum = []
+    for col in range(df.shape[1]):  # 0 ta 10
+        mean.append(0)
+        maximum.append(0)
+        minimum.append(math.inf)
+        for row in df:
+            if (row[col] > maximum[col]):
+                maximum[col] = row[col]
+            if (row[col] < minimum[col]):
+                minimum[col] = row[col]
+            mean[col] += row[col]
+        mean[col] = ((int)((mean[col] / len(df))*10000))/10000
+    v = []
+    for i in range(df.shape[1]):
+        v.append(uniform(minimum[i], maximum[i]))
+    vm = []
+    for j in range(df.shape[1]):
+        vm.append(mean[j] - v[j])
+    return (mean, vm)
+
+
+def matrixToxls(matrix, filename):
+    workbook = xlsxwriter.Workbook(filename)
+    worksheet = workbook.add_worksheet()
+    row = 0
+    for col, data in enumerate(matrix):
+        worksheet.write_column(row, col, data)
+    workbook.close()
+
+
+def variance(col) -> list:
+    return ((int)(sum([cell ** 2 for cell in matrixSubs[col]])*10000))/10000
+
+
+def covariance(col1, col2):
+    return ((int)(sum([p*q for p, q in zip(matrixSubs[col1], matrixSubs[col2])])*10000))/10000
+
+
+def matrixCov():
+    matrix = []
+    for col1 in range(len(matrixSubs)):
+        tmp = []
+        for col2 in range(len(matrixSubs)):
+            if (col1 == col2):
+                tmp.append(variance(col1))
+            else:
+                tmp.append(covariance(col1, col2))
+        matrix.append(tmp)
+    return matrix
+
+
+def corrMatrix(matrix):
+    matrixCorr = []
+    for col1 in range(len(matrix)):
+        tmp = []
+        for col2 in range(len(matrix)):
+            if (col1 == col2):
+                tmp.append(1)
+            else:
+                tmp.append((matrix[col1][col2]) /
+                           ((matrix[col1][col1]*matrix[col2][col2])**0.5))
+        matrixCorr.append(tmp)
+    return matrixCorr
+
+
+if __name__ == "__main__":
+    df = pd.read_excel('dataset2.xls', sheet_name="forestfires").to_numpy()
+    (mean, vm) = calucateMean(df)
+    # print(mean)
+    matrixSubs = []
+    for col in range(df.shape[1]):
+        tmp = []
+        for row in df:
+            tmp.append(row[col] - mean[col])
+        matrixSubs.append(tmp)
+    matrix = matrixCov()
+    #
+    CorrelationMatrix = corrMatrix(matrix)
+    matrixToxls(CorrelationMatrix, "CorrelationMatrix.xls")
diff --git a/entropy.py b/entropy.py
new file mode 100644
index 0000000..ade16c2
--- /dev/null
+++ b/entropy.py
@@ -0,0 +1,53 @@
+import pandas as pd
+import xlsxwriter
+from random import uniform
+import math
+import numpy as np
+
+
+def calucateMean(df) -> tuple:
+    mean = []
+    maximum = []
+    minimum = []
+    for col in range(df.shape[1]):  # 0 ta 10
+        mean.append(0)
+        maximum.append(0)
+        minimum.append(math.inf)
+        for row in df:
+            if (row[col] > maximum[col]):
+                maximum[col] = row[col]
+            if (row[col] < minimum[col]):
+                minimum[col] = row[col]
+            mean[col] += row[col]
+        mean[col] = ((int)((mean[col] / len(df))*10000))/10000
+    v = []
+    for i in range(df.shape[1]):
+        v.append(uniform(minimum[i], maximum[i]))
+    vm = []
+    for j in range(df.shape[1]):
+        vm.append(mean[j] - v[j])
+    return (mean, vm)
+
+
+def matrixToxls(matrix, filename):
+    workbook = xlsxwriter.Workbook(filename)
+    worksheet = workbook.add_worksheet()
+    row = 0
+    for col, data in enumerate(matrix):
+        worksheet.write_column(row, col, data)
+    workbook.close()
+
+
+def entropy(col):
+    total = sum([row[col] for row in df])
+    print(total)
+    return [-(math.fabs(row[col]) / total*100) * math.log((math.fabs(row[col]) / total*100), 2) for row in df]
+
+
+if __name__ == "__main__":
+    df = pd.read_excel('dataset2.xls', sheet_name="forestfires").to_numpy()
+    (mean, vm) = calucateMean(df)
+    entropyMatrix = []
+    for col in range(df.shape[1]):
+        entropyMatrix.append(entropy(col))
+    matrixToxls(entropyMatrix, "entropiesMatrix.xls")