2. Z-SCORE

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Using Z score method,we can find out how many standard deviations value away from the mean.

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Figure in the left shows area under normal curve and how much area that standard deviation covers.* 68% of the data points lie between + or - 1 standard deviation.* 95% of the data points lie between + or - 2 standard deviation* 99.7% of the data points lie between + or - 3 standard deviation

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Z-score formula

$Zscore=X−Mean/StandardDeviation$

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If the z score of a data point is more than 3 (because it covers 99.7% of the area), it indicates that the data value is quite different from the other values. It is taken as outlier.

import pandas as pd
import numpy as np
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
out=[]
def Zscore_outlier(df):
    m = np.mean(df)
    sd = np.std(df)
    for i in df: 
        z = (i-m)/sd
        if np.abs(z) > 3: 
            out.append(i)
    print("Outliers:",out)
Zscore_outlier(train['LotArea'])

Outliers: [50271, 159000, 215245, 164660, 53107, 70761, 53227, 46589, 115149, 53504, 45600, 63887, 57200