In [5]: 1 # Name: Sakshi Anil Ugle

2 # Roll_no:2441016
3 # Batch:c

In [6]: 1 import pandas as pd

2 import numpy as np
3 import seaborn as sns
4 import matplotlib.pyplot as plt

In [7]: 1 from sklearn.cluster import KMeans, k_means

2 from sklearn.decomposition import PCA

In [8]: 1 df = pd.read_csv("sales_data_sample.csv", encoding='ISO-8859-1')

In [9]: 1 df.head()

Out[9]: ORDERNUMBER QUANTITYORDERED PRICEEACH ORDERLINENUMBER SALES ORDER

2/24
0 10107 30 95.70 2 2871.00

1 10121 34 81.35 5 2765.90 5/7/200

2 10134 41 94.74 2 3884.34 7/1/200

8/25
3 10145 45 83.26 6 3746.70

10/10
4 10159 49 100.00 14 5205.27

5 rows × 25 columns
 

In [10]: 1 df.shape

Out[10]: (2823, 25)

In [11]: 1 df.describe()

Out[11]: ORDERNUMBER QUANTITYORDERED PRICEEACH ORDERLINENUMBER SALE

count 2823.000000 2823.000000 2823.000000 2823.000000 2823.00000

mean 10258.725115 35.092809 83.658544 6.466171 3553.88907

std 92.085478 9.741443 20.174277 4.225841 1841.86510

min 10100.000000 6.000000 26.880000 1.000000 482.13000

25% 10180.000000 27.000000 68.860000 3.000000 2203.43000

50% 10262.000000 35.000000 95.700000 6.000000 3184.80000

75% 10333.500000 43.000000 100.000000 9.000000 4508.00000

max 10425.000000 97.000000 100.000000 18.000000 14082.80000

 

In [12]: 1 df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2823 entries, 0 to 2822
Data columns (total 25 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ORDERNUMBER 2823 non-null int64
1 QUANTITYORDERED 2823 non-null int64
2 PRICEEACH 2823 non-null float64
3 ORDERLINENUMBER 2823 non-null int64
4 SALES 2823 non-null float64
5 ORDERDATE 2823 non-null object
6 STATUS 2823 non-null object
7 QTR_ID 2823 non-null int64
8 MONTH_ID 2823 non-null int64
9 YEAR_ID 2823 non-null int64
10 PRODUCTLINE 2823 non-null object
11 MSRP 2823 non-null int64
12 PRODUCTCODE 2823 non-null object
13 CUSTOMERNAME 2823 non-null object
14 PHONE 2823 non-null object
15 ADDRESSLINE1 2823 non-null object
16 ADDRESSLINE2 302 non-null object
17 CITY 2823 non-null object
18 STATE 1337 non-null object
19 POSTALCODE 2747 non-null object
20 COUNTRY 2823 non-null object
21 TERRITORY 1749 non-null object
22 CONTACTLASTNAME 2823 non-null object
23 CONTACTFIRSTNAME 2823 non-null object
24 DEALSIZE 2823 non-null object
dtypes: float64(2), int64(7), object(16)
memory usage: 551.5+ KB
In [13]: 1 df.isnull().sum()

Out[13]: ORDERNUMBER 0
QUANTITYORDERED 0
PRICEEACH 0
ORDERLINENUMBER 0
SALES 0
ORDERDATE 0
STATUS 0
QTR_ID 0
MONTH_ID 0
YEAR_ID 0
PRODUCTLINE 0
MSRP 0
PRODUCTCODE 0
CUSTOMERNAME 0
PHONE 0
ADDRESSLINE1 0
ADDRESSLINE2 2521
CITY 0
STATE 1486
POSTALCODE 76
COUNTRY 0
TERRITORY 1074
CONTACTLASTNAME 0
CONTACTFIRSTNAME 0
DEALSIZE 0
dtype: int64

In [14]: 1 df.dtypes

Out[14]: ORDERNUMBER int64

QUANTITYORDERED int64
PRICEEACH float64
ORDERLINENUMBER int64
SALES float64
ORDERDATE object
STATUS object
QTR_ID int64
MONTH_ID int64
YEAR_ID int64
PRODUCTLINE object
MSRP int64
PRODUCTCODE object
CUSTOMERNAME object
PHONE object
ADDRESSLINE1 object
ADDRESSLINE2 object
CITY object
STATE object
POSTALCODE object
COUNTRY object
TERRITORY object
CONTACTLASTNAME object
CONTACTFIRSTNAME object
DEALSIZE object
dtype: object
In [15]: 1 df_drop = ['ADDRESSLINE1', 'ADDRESSLINE2', 'STATUS',
2 'POSTALCODE', 'CITY', 'TERRITORY', 'PHONE',
3 'STATE', 'CONTACTFIRSTNAME', 'CONTACTLASTNAME',
4 'CUSTOMERNAME', 'ORDERNUMBER']
5 df = df.drop(df_drop, axis=1)

In [16]: 1 df.isnull().sum()

Out[16]: QUANTITYORDERED 0
PRICEEACH 0
ORDERLINENUMBER 0
SALES 0
ORDERDATE 0
QTR_ID 0
MONTH_ID 0
YEAR_ID 0
PRODUCTLINE 0
MSRP 0
PRODUCTCODE 0
COUNTRY 0
DEALSIZE 0
dtype: int64

In [17]: 1 df.dtypes

Out[17]: QUANTITYORDERED int64

PRICEEACH float64
ORDERLINENUMBER int64
SALES float64
ORDERDATE object
QTR_ID int64
MONTH_ID int64
YEAR_ID int64
PRODUCTLINE object
MSRP int64
PRODUCTCODE object
COUNTRY object
DEALSIZE object
dtype: object

In [18]: 1 df['COUNTRY'].unique()

Out[18]: array(['USA', 'France', 'Norway', 'Australia', 'Finland', 'Austria', 'UK',

'Spain', 'Sweden', 'Singapore', 'Canada', 'Japan', 'Italy',
'Denmark', 'Belgium', 'Philippines', 'Germany', 'Switzerland',
'Ireland'], dtype=object)

In [19]: 1 df['PRODUCTLINE'].unique()

Out[19]: array(['Motorcycles', 'Classic Cars', 'Trucks and Buses', 'Vintage Cars',

'Planes', 'Ships', 'Trains'], dtype=object)

In [20]: 1 df['DEALSIZE'].unique()

Out[20]: array(['Small', 'Medium', 'Large'], dtype=object)

In [21]: 1 productline = pd.get_dummies(df['PRODUCTLINE'])
2 Dealsize = pd.get_dummies(df['DEALSIZE'])

In [22]: 1 df = pd.concat([df,productline,Dealsize], axis = 1)

In [23]: 1 df_drop = ['COUNTRY','PRODUCTLINE','DEALSIZE']

2 df = df.drop(df_drop, axis=1)

In [24]: 1 df['PRODUCTCODE'] = pd.Categorical(df['PRODUCTCODE']).codes

In [25]: 1 df.drop('ORDERDATE', axis=1, inplace=True)

In [26]: 1 df.dtypes

Out[26]: QUANTITYORDERED int64

PRICEEACH float64
ORDERLINENUMBER int64
SALES float64
QTR_ID int64
MONTH_ID int64
YEAR_ID int64
MSRP int64
PRODUCTCODE int8
Classic Cars uint8
Motorcycles uint8
Planes uint8
Ships uint8
Trains uint8
Trucks and Buses uint8
Vintage Cars uint8
Large uint8
Medium uint8
Small uint8
dtype: object
In [27]: 1 distortions = []
2 K = range(1,10)
3 for k in K:
4 kmeanModel = KMeans(n_clusters=k)
5 kmeanModel.fit(df)
6 distortions.append(kmeanModel.inertia_)

C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(
In [28]: 1 plt.figure(figsize=(16,8))
2 plt.plot(K, distortions, 'bx-')
3 plt.xlabel('k')
4 plt.ylabel('Distortion')
5 plt.title('The Elbow Method showing the optimal k')
6 plt.show()

In [29]: 1 X_train = df.values

In [30]: 1 X_train.shape

Out[30]: (2823, 19)

In [31]: 1 model = KMeans(n_clusters=3,random_state=2)

2 model = model.fit(X_train)
3 predictions = model.predict(X_train)

C:\Users\bagal\AppData\Roaming\Python\Python311\site-packages\sklearn\clus
ter\_kmeans.py:870: FutureWarning: The default value of `n_init` will chan
ge from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppr
ess the warning
warnings.warn(

In [32]: 1 unique,counts = np.unique(predictions,return_counts=True)

In [33]: 1 counts = counts.reshape(1,3)

In [34]: 1 counts_df = pd.DataFrame(counts,columns=['Cluster1','Cluster2','Cluster

In [35]: 1 counts_df.head()

Out[35]: Cluster1 Cluster2 Cluster3

0 1083 1367 373

In [36]: 1 pca = PCA(n_components=2)

In [37]: 1 reduced_X = pd.DataFrame(pca.fit_transform(X_train),

2 columns=['PCA1','PCA2'])

In [38]: 1 reduced_X.head()

Out[38]: PCA1 PCA2

0 -682.488323 -42.819535

1 -787.665502 -41.694991

2 330.732170 -26.481208

3 193.040232 -26.285766

4 1651.532874 -6.891196

In [39]: 1 plt.figure(figsize=(14,10))
2 plt.scatter(reduced_X['PCA1'],reduced_X['PCA2'])

Out[39]: <matplotlib.collections.PathCollection at 0x1d945ebf790>

In [40]: 1 model.cluster_centers_

Out[40]: array([[ 3.72031394e+01, 9.52120960e+01, 6.44967682e+00,

4.13868425e+03, 2.72022161e+00, 7.09879963e+00,
2.00379409e+03, 1.13248384e+02, 5.04469067e+01,
3.74884580e-01, 1.15420129e-01, 9.41828255e-02,
8.21791320e-02, 1.84672207e-02, 1.16343490e-01,
1.98522622e-01, 2.08166817e-17, 1.00000000e+00,
-6.66133815e-16],
[ 3.08302853e+01, 7.00755230e+01, 6.67300658e+00,
2.12409474e+03, 2.71762985e+00, 7.09509876e+00,
2.00381127e+03, 7.84784199e+01, 6.24871982e+01,
2.64813460e-01, 1.21433797e-01, 1.29480614e-01,
1.00219459e-01, 3.87710315e-02, 9.21726408e-02,
2.53108998e-01, 2.08166817e-17, 6.21799561e-02,
9.37820044e-01],
[ 4.45871314e+01, 9.98931099e+01, 5.75603217e+00,
7.09596863e+03, 2.71045576e+00, 7.06434316e+00,
2.00389008e+03, 1.45823056e+02, 3.14959786e+01,
5.33512064e-01, 1.07238606e-01, 7.23860590e-02,
2.14477212e-02, 1.07238606e-02, 1.31367292e-01,
1.23324397e-01, 4.20911528e-01, 5.79088472e-01,
5.55111512e-17]])

In [41]: 1 reduced_centers = pca.transform(model.cluster_centers_)

In [42]: 1 reduced_centers

Out[42]: array([[ 5.84994044e+02, -4.36786931e+00],

[-1.43005891e+03, 2.60041009e+00],
[ 3.54247180e+03, 3.15185487e+00]])
In [43]: 1 plt.figure(figsize=(14,10))
2 plt.scatter(reduced_X['PCA1'],reduced_X['PCA2'])
3 plt.scatter(reduced_centers[:,0],reduced_centers[:,1],
4 color='black',marker='x',s=300)

Out[43]: <matplotlib.collections.PathCollection at 0x1d945ee9950>

In [44]: 1 reduced_X['Clusters'] = predictions

In [45]: 1 reduced_X.head()

Out[45]: PCA1 PCA2 Clusters

0 -682.488323 -42.819535 1

1 -787.665502 -41.694991 1

2 330.732170 -26.481208 0

3 193.040232 -26.285766 0

4 1651.532874 -6.891196 0
In [46]: 1 plt.figure(figsize=(14,10))
2 plt.scatter(reduced_X[reduced_X['Clusters'] == 0].loc[:,'PCA1'],
3 reduced_X[reduced_X['Clusters'] == 0].loc[:,'PCA2'],
4 color='slateblue')
5 plt.scatter(reduced_X[reduced_X['Clusters'] == 1].loc[:,'PCA1'],
6 reduced_X[reduced_X['Clusters'] == 1].loc[:,'PCA2'],
7 color='springgreen')
8 plt.scatter(reduced_X[reduced_X['Clusters'] == 2].loc[:,'PCA1'],
9 reduced_X[reduced_X['Clusters'] == 2].loc[:,'PCA2'],
10 color='indigo')
11 ​
12 ​
13 plt.scatter(reduced_centers[:,0],reduced_centers[:,1],
14 color='black',marker='x',s=300)

Out[46]: <matplotlib.collections.PathCollection at 0x1d9485be150>

In [ ]: 1 ​