add imbalance learning tutorial

Author: x4nth055

Diff for: machine-learning/imbalance-learning/README.md

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+# [Imbalance Learning With Imblearn and Smote Variants Libraries in Python](https://www.thepythoncode.com/article/handling-imbalance-data-imblearn-smote-variants-python)
+To run this:
+- `pip3 install -r requirements.txt`
+- Download and extract `creditcard.csv` from this [Kaggle link](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+- Then, you're free to run `imb_learning.py` as you wish.

Diff for: machine-learning/imbalance-learning/imb_learning.py

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+import numpy as np 
+import pandas as pd 
+from imblearn.under_sampling import RandomUnderSampler,EditedNearestNeighbours,InstanceHardnessThreshold,TomekLinks
+from imblearn.over_sampling import RandomOverSampler 
+import smote_variants as sv
+
+
+df=pd.read_csv("creditcard.csv")
+y=df["Class"]
+X=df.drop(["Time","Class"],axis=1)
+print(y.value_counts())
+
+
+under=RandomUnderSampler()
+X_und,y_und=under.fit_resample(X,y)
+print(len(X_und[X_und==1])==len(X_und[X_und==0]))
+
+
+over=RandomOverSampler()
+X_und,y_und=over.fit_resample(X,y)
+print(len(X_und[X_und==1])==len(X_und[X_und==0]))
+
+
+under_samp_models=[EditedNearestNeighbours(),InstanceHardnessThreshold(),TomekLinks()]
+for under_samp_model in under_samp_models:
+    X_und,y_und=under_samp_model.fit_resample(X,y)
+    print(X_und.shape)
+    
+svs=[sv.kmeans_SMOTE(),sv.Safe_Level_SMOTE(),sv.SMOTE_Cosine()]
+for over_sampler in svs: 
+    X_over_samp, y_over_samp= over_sampler.sample(X, y)
+    print(X_over_samp.shape)

Diff for: machine-learning/imbalance-learning/requirements.txt

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+numpy
+pandas
+imblearn
+smote-variants