MeanNearestNeighbors (MNN) - algorithm for balancing dataset - In progress #1

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One of the challenges in classification problems are unbalanced datasets. I was Data Science Intern when the company that I worked for, assigned me such an interesting challenge where the dataset was unbalanced.  However, I realized this type of problem like unbalanced dataset is а common thing in real life. I tried most of the algorithms (undersampling, oversampling) like SMOTE, NearMiss, CondensedNearestNeighbors, RandomUnderSampler, RandomOverSampler,  KMeansSMOTЕ and rest of them. Anyway, they didn't help me in that case, on the contrary, they worsened my model.  I was like: "but, but, you should have been helpful in creating the predictive model" So, I'm trying to create another algorithm based on undersampling concept when it comes to balancing datasets. I called it Mean Nearest Neighbors (MNN). What's the initial idea: It's simple. Actually, the algorithm is just a modification of the other undersampling algorithms. In the data where target labe...

Competitive Programming #5: [Smallest Positive missing number]

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You are given an unsorted array with both positive and negative elements. You have to find the smallest positive number missing from the array in O(n) time using constant extra space.
Input:
First line consists of T test cases. First line of every test case consists of N, denoting the number of elements in array. Second line of every test case consists of elements in array.
Output:
Single line output, print the smallest positive number missing.
Constraints:
1<=T<=100
1<=N<=100
-100<=a[i]<=100
Example:
Input:
2
5
1 2 3 4 5
5
0 -10 1 3 -20
Output:
6
2

Solution:
Everything is said in the text above.

 

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