Challenge

Project 2

KKothapally Ravinder6 Oct 2022

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.cluster import KMeans

import plotly.express as exp

In [3]:

df = pd.read_csv('Country-data.csv')

In [4]:

df

Out[4]:

	country	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
0	Afghanistan	90.2	10.0	7.58	44.9	1610	9.44	56.2	5.82	553
1	Albania	16.6	28.0	6.55	48.6	9930	4.49	76.3	1.65	4090
2	Algeria	27.3	38.4	4.17	31.4	12900	16.10	76.5	2.89	4460
3	Angola	119.0	62.3	2.85	42.9	5900	22.40	60.1	6.16	3530
4	Antigua and Barbuda	10.3	45.5	6.03	58.9	19100	1.44	76.8	2.13	12200
...	...	...	...	...	...	...	...	...	...	...
162	Vanuatu	29.2	46.6	5.25	52.7	2950	2.62	63.0	3.50	2970
163	Venezuela	17.1	28.5	4.91	17.6	16500	45.90	75.4	2.47	13500
164	Vietnam	23.3	72.0	6.84	80.2	4490	12.10	73.1	1.95	1310
165	Yemen	56.3	30.0	5.18	34.4	4480	23.60	67.5	4.67	1310
166	Zambia	83.1	37.0	5.89	30.9	3280	14.00	52.0	5.40	1460

167 rows × 10 columns

In [5]:

df.corr()

Out[5]:

	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
child_mort	1.000000	-0.318093	-0.200402	-0.127211	-0.524315	0.288276	-0.886676	0.848478	-0.483032
exports	-0.318093	1.000000	-0.114408	0.737381	0.516784	-0.107294	0.316313	-0.320011	0.418725
health	-0.200402	-0.114408	1.000000	0.095717	0.129579	-0.255376	0.210692	-0.196674	0.345966
imports	-0.127211	0.737381	0.095717	1.000000	0.122406	-0.246994	0.054391	-0.159048	0.115498
income	-0.524315	0.516784	0.129579	0.122406	1.000000	-0.147756	0.611962	-0.501840	0.895571
inflation	0.288276	-0.107294	-0.255376	-0.246994	-0.147756	1.000000	-0.239705	0.316921	-0.221631
life_expec	-0.886676	0.316313	0.210692	0.054391	0.611962	-0.239705	1.000000	-0.760875	0.600089
total_fer	0.848478	-0.320011	-0.196674	-0.159048	-0.501840	0.316921	-0.760875	1.000000	-0.454910
gdpp	-0.483032	0.418725	0.345966	0.115498	0.895571	-0.221631	0.600089	-0.454910	1.000000

In [7]:

fig = plt.figure(figsize=(12,10))

ax = sns.heatmap(df.corr(),annot = True,cmap='viridis')

plt.show()

In [11]:

from sklearn.preprocessing import MinMaxScaler

scalar = MinMaxScaler()

In [12]:

scaled_data = scalar.fit_transform(df.drop('country',axis = 1))

In [14]:

scaled_df = pd.DataFrame(data = scaled_data,columns=df.columns[1:])

scaled_df['country'] = df['country']

scaled_df

Out[14]:

	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp	country
0	0.426485	0.049482	0.358608	0.257765	0.008047	0.126144	0.475345	0.736593	0.003073	Afghanistan
1	0.068160	0.139531	0.294593	0.279037	0.074933	0.080399	0.871795	0.078864	0.036833	Albania
2	0.120253	0.191559	0.146675	0.180149	0.098809	0.187691	0.875740	0.274448	0.040365	Algeria
3	0.566699	0.311125	0.064636	0.246266	0.042535	0.245911	0.552268	0.790221	0.031488	Angola
4	0.037488	0.227079	0.262275	0.338255	0.148652	0.052213	0.881657	0.154574	0.114242	Antigua and Barbuda
...	...	...	...	...	...	...	...	...	...	...
162	0.129503	0.232582	0.213797	0.302609	0.018820	0.063118	0.609467	0.370662	0.026143	Vanuatu
163	0.070594	0.142032	0.192666	0.100809	0.127750	0.463081	0.854043	0.208202	0.126650	Venezuela
164	0.100779	0.359651	0.312617	0.460715	0.031200	0.150725	0.808679	0.126183	0.010299	Vietnam
165	0.261441	0.149536	0.209447	0.197397	0.031120	0.257000	0.698225	0.555205	0.010299	Yemen
166	0.391918	0.184556	0.253574	0.177275	0.021473	0.168284	0.392505	0.670347	0.011731	Zambia

167 rows × 10 columns

In [17]:

exp.histogram(data_frame = df,x = 'gdpp',nbins = 167, opacity = 0.75,barmode ='overlay')

020k40k60k80k100k051015202530

gdppcount

In [18]:

exp.scatter(data_frame=df,x = 'child_mort',y = 'health',color = 'country')

05010015020024681012141618

countryAfghanistanAlbaniaAlgeriaAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCentral African RepublicChadChileChinaColombiaComorosCongo, Dem. Rep.Congo, Rep.Costa RicaCote d'IvoireCroatiaCyprusCzech RepublicDenmarkDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaFijiFinlandFranceGabonGambiaGeorgiaGermanyGhanaGreeceGrenadaGuatemalaGuineaGuinea-BissauGuyanaHaitiHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiKuwaitKyrgyz RepublicLaoLatviaLebanonLesothoLiberiaLibyaLithuaniaLuxembourgMacedonia, FYRMadagascarMalawiMalaysiaMaldivesMaliMaltaMauritaniaMauritiusMicronesia, Fed. Sts.MoldovaMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNepalNetherlandsNew ZealandNigerNigeriaNorwayOmanPakistanPanamaParaguayPeruPhilippinesPolandPortugalQatarRomaniaRussiaRwandaSamoaSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovak RepublicSloveniaSolomon IslandsSouth AfricaSouth KoreaSpainSri LankaSt. Vincent and the GrenadinesSudanSurinameSwedenSwitzerlandTajikistanTanzaniaThailandTimor-LesteTogoTongaTunisiaTurkeyTurkmenistanUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVenezuelaVietnamYemenZambiachild_morthealth

In [20]:

df = scaled_df.drop('country',axis = 1)

In [21]:

ssd = []

K = range(1,10)

for k in K:

    km = KMeans(n_clusters = k)

    km = km.fit(df)

    ssd.append(km.inertia_)

C:\Users\DELL\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:881: UserWarning:

KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.

In [22]:

plt.figure(figsize = (10,6))

plt.plot(K, ssd, 'bx-')

plt.xlabel('k')

plt.ylabel('ssd')

plt.title('Elbow Method For Optimal k')

plt.show()

In [23]:

kmean = KMeans(n_clusters = 3)

kmean.fit(df)

Out[23]:

KMeans(n_clusters=3)

In [24]:

pred = kmean.labels_

print(pred)

[1 2 2 1 2 2 2 0 0 2 2 2 2 2 2 0 2 1 2 2 2 2 2 0 2 1 1 2 1 0 2 1 1 2 2 2 1
 1 1 2 1 2 0 0 0 2 2 2 2 1 1 2 2 0 0 1 1 2 0 1 0 2 2 1 1 2 1 2 0 2 2 2 1 0
 0 0 2 0 2 2 1 1 0 2 1 2 2 1 1 2 2 0 2 1 1 2 2 1 0 1 2 2 2 2 2 2 1 2 1 2 0
 0 1 1 0 2 1 2 2 2 2 2 0 0 2 2 1 2 2 1 2 2 1 0 0 0 1 2 0 0 2 2 1 2 0 0 2 1
 2 1 1 2 2 2 2 1 2 0 0 0 2 2 2 2 2 1 1]

In [25]:

exp.scatter(data_frame = df,x = 'gdpp', y = 'income',color=kmean.labels_)

00.20.40.60.8100.20.40.60.81

00.511.52colorgdppincome

In [27]:

from sklearn.decomposition import PCA

pca = PCA(n_components = 2)

pca_model = pca.fit_transform(df)

data_transform = pd.DataFrame(data = pca_model,columns = ['PCA1','PCA2'])

data_transform['Cluster'] = pred

In [28]:

data_transform.head()

Out[28]:

	PCA1	PCA2	Cluster
0	-0.599078	0.095490	1
1	0.158474	-0.212092	2
2	0.003686	-0.135867	2
3	-0.650235	0.275975	1
4	0.200711	-0.064662	2

In [32]:

plt.figure(figsize = (8,8))

g = sns.scatterplot(data = data_transform, x = 'PCA1', y = 'PCA2', palette = sns.color_palette()[:3],hue = 'Cluster')

title = plt.title('Countries Clusters with PCA')

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