3. ROBUST Z-SCORE

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It is also called as Median absolute deviation method. It is similar to the Z-score method with some changes in parameters. Since mean and standard deviations are heavily influenced by outliers, alter to these parameters we use median and absolute deviation from median.

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Suppose x follows a standard normal distribution. The MAD will converge to the median of the half-normal distribution, which is the 75% percentile of a normal distribution, and N(0.75)≃0.6745.

import pandas as pd
import numpy as np
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
out=[]
def ZRscore_outlier(df):
    med = np.median(df)
    ma = stats.median_absolute_deviation(df)
    for i in df: 
        z = (0.6745*(i-med))/ (np.median(ma))
        if np.abs(z) > 3: 
            out.append(i)
    print("Outliers:",out)
ZRscore_outlier(train['LotArea'])