Preliminary EDA

Imports

import pandas as pd 
import seaborn as sns
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt

sns.set()

Datasets

df = pd.read_csv("segmentation data.csv", index_col="ID"); df

	Sex	Marital status	Age	Education	Income	Occupation	Settlement size
ID
100000001	0	0	67	2	124670	1	2
100000002	1	1	22	1	150773	1	2
100000003	0	0	49	1	89210	0	0
100000004	0	0	45	1	171565	1	1
100000005	0	0	53	1	149031	1	1
...	...	...	...	...	...	...	...
100001996	1	0	47	1	123525	0	0
100001997	1	1	27	1	117744	1	0
100001998	0	0	31	0	86400	0	0
100001999	1	1	24	1	97968	0	0
100002000	0	0	25	0	68416	0	0

2000 rows × 7 columns

df.describe()

	Sex	Marital status	Age	Education	Income	Occupation	Settlement size
count	2000.000000	2000.000000	2000.000000	2000.00000	2000.000000	2000.000000	2000.000000
mean	0.457000	0.496500	35.909000	1.03800	120954.419000	0.810500	0.739000
std	0.498272	0.500113	11.719402	0.59978	38108.824679	0.638587	0.812533
min	0.000000	0.000000	18.000000	0.00000	35832.000000	0.000000	0.000000
25%	0.000000	0.000000	27.000000	1.00000	97663.250000	0.000000	0.000000
50%	0.000000	0.000000	33.000000	1.00000	115548.500000	1.000000	1.000000
75%	1.000000	1.000000	42.000000	1.00000	138072.250000	1.000000	1.000000
max	1.000000	1.000000	76.000000	3.00000	309364.000000	2.000000	2.000000

Correlation

df.corr()

	Sex	Marital status	Age	Education	Income	Occupation	Settlement size
Sex	1.000000	0.566511	-0.182885	0.244838	-0.195146	-0.202491	-0.300803
Marital status	0.566511	1.000000	-0.213178	0.374017	-0.073528	-0.029490	-0.097041
Age	-0.182885	-0.213178	1.000000	0.654605	0.340610	0.108388	0.119751
Education	0.244838	0.374017	0.654605	1.000000	0.233459	0.064524	0.034732
Income	-0.195146	-0.073528	0.340610	0.233459	1.000000	0.680357	0.490881
Occupation	-0.202491	-0.029490	0.108388	0.064524	0.680357	1.000000	0.571795
Settlement size	-0.300803	-0.097041	0.119751	0.034732	0.490881	0.571795	1.000000

sns.heatmap(df.corr(), cmap='RdYlGn', vmin=-1, vmax=1, annot=True)

<AxesSubplot:>

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0xe578cab9190>

fig, ax = plt.subplots(figsize=(12,12))
sns.boxenplot(data=(df-df.mean())/df.std(),ax=ax)

<AxesSubplot:>