Merge pull request #326 from Noth2006/main

New Problem : Min-Max-Normalization

Author: moe18

Problems/112_Min_Max_Normalization/learn.md

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+# Min-Max Normalization
+
+Min-Max Normalization is a technique used to scale numerical data between *0 and 1*. It ensures that values are transformed while maintaining their original distribution. A machine learning model usually assigns a higher weight to a feature with larger values and a lower weight to a feature with smaller values.  
+The goal of normalization is to make every datapoint have the same scale so each feature is equally important to the model.
+
+
+The formula used for Min-Max Normalization is:
+
+$$
+X' = \frac{X - X_{\min}}{X_{\max} - X_{\min}}
+$$
+
+- Note 1 : In case the input array has identical elements just return an array of the same size filled with zeros.
+- Note 2 : Round the resultant values upto the 4th decimal place.
+
+## Where:
+- \( X \) is the original value.
+- $$(X_{\min})$$ is the minimum value in the dataset.
+- $$( X_{\text{max}} )$$ is the maximum value in the dataset.
+- \( X' \) is the normalized value.

Problems/112_Min_Max_Normalization/solution.py

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+def min_max(x:list[int])->list[float]:
+
+    #trying to modify the input list in-place instead creating a new list
+
+    largest=max(x) #finding the largest value in the input array
+    smallest=min(x) #finding the minimum value in the input array 
+
+    if largest==smallest: # if input has identical elements
+        return[0.0]*len(x)
+    for i in range(len(x)):
+        #using the formula to normalize the input
+        x[i]=round((x[i]-smallest)/(largest-smallest),4)
+
+    return(x)
+
+def test_min_max():
+    #first_test_case:
+
+    assert min_max([1,2,3,4,5])==[0.0, 0.25, 0.5, 0.75, 1.0],"Test Case 1 failed"
+
+    assert min_max([30,45,56,70,88])==[0.0, 0.2586, 0.4483, 0.6897, 1.0],"Test Case 2 failed"
+
+    assert min_max([5,5,5,5])==[0.0,0.0,0.0,0.0], "Test Case 3 failed"
+
+
+if __name__=="__main__":
+    test_min_max()
+    print("All Min Max Normalization test cases passed.")
+