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Plotly + pandas + sklearn: shoot the first shot of kaggle

2022-02-01 11:50:01 PI dada

official account : Youer cottage
author :Peter
edit :Peter

Hello everyone , I am a Peter~

Many readers have asked me : Is there any better data analysis 、 Cases of data mining ? The answer is, of course , All in Kaggle Come on .

It's just that you have to spend time studying , Even playing games .Peter I have no competition experience , But I often go shopping Kaggle, Learn the problem-solving ideas and methods of the big guys in the game .

A good way to record the big guys , It's to improve yourself ,Peter Decided to open a column :Kaggle Case sharing .

The case analysis will be updated from time to time later , Ideas come from the big guys on the Internet , In especial Top1 The share of ,Peter Mainly responsible for : Organize your thoughts 、 Learning technology .

Today I decided to share an article about clustering The case of , It uses : Supermarket user segmentation data set , Please move to the official website address : The supermarket

For the convenience of everyone to practice , Official account back office reply The supermarket , You can get this data set ~

Here is the ranking Top1 Of Notebook Source code , Welcome to learn ~

Import library

#  Data processing 
import numpy as np
import pandas as pd
# KMeans clustering 
from sklearn.cluster import KMeans

#  Drawing library 
import matplotlib.pyplot as plt
import seaborn as sns
import plotly as py
import as px
import plotly.graph_objects as go
py.offline.init_notebook_mode(connected = True)
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data EDA

Import data

First, let's import the dataset :

We found in the data 5 Attribute fields , It's the customer ID、 Gender 、 Age 、 Average income 、 Consumption level

Data exploration

1、 Data shape shape


#  result 
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The total is 200 That's ok ,5 Columns of data

2、 Missing values


#  result 
CustomerID                0
Gender                    0
Age                       0
Annual Income (k$)        0
Spending Score (1-100)    0
dtype: int64
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You can see : All fields are complete , No missing value

3、 data type


#  result 
CustomerID                 int64
Gender                    object
Age                        int64
Annual Income (k$)         int64
Spending Score (1-100)     int64
dtype: object
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In the field type , Except for gender Gender Is string , Others are int64 Numerical type of

4、 Describe statistics

Description statistics are mainly used to view Numerical type The values of the relevant statistical parameters of the data , such as : Number 、 The median 、 variance 、 The most value 、 Quartile, etc

For the convenience of subsequent data processing and display , Deal with two points :

# 1、 Set the drawing style"fivethirtyeight")

# 2、 Take out the key analysis 3 A field 
cols = df.columns[2:].tolist()
#  result 
['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
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3 Attribute histogram

see 'Age'、 'Annual Income (k$)'、 'Spending Score (1-100)' Histogram , Observe the overall distribution :

#  mapping 
plt.figure(1,figsize=(15,6))  #  Canvas size 
n = 0

for col in cols:
    n += 1 #  Subgraph location 
    plt.subplot(1,3,n)  #  Subgraphs 
    plt.subplots_adjust(hspace=0.5,wspace=0.5)  #  Adjust width and height 
    sns.distplot(df[col],bins=20)  #  Draw histogram 
    plt.title(f'Distplot of {col}')  #  title  #  The graphics 
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The gender factor

Gender statistics

See how many men and women in this data set . In the follow-up, we will consider whether gender has an impact on the overall analysis .

Data distribution under different genders

             hue="Gender",  #  Grouping field 
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Through the bivariate distribution diagram above , We observed that : Gender factors affect other 3 Fields have little effect

The relationship between age and average income under different gender

plt.figure(1,figsize=(15,6))  #  Drawing size 

for gender in ["Male", "Female"]:
    plt.scatter(x="Age", y="Annual Income (k$)", #  Specify two fields for analysis 
                data=df[df["Gender"] == gender],  #  Data to be analyzed , Some gender Next 
                s=200,alpha=0.5,label=gender  #  The size of the scatter 、 transparency 、 Label classification 
#  Horizontal and vertical axis 、 Title Setting  
plt.ylabel("Annual Income (k$)")
plt.title("Age vs Annual Income w.r.t Gender")
#  The graphics
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The relationship between average income and consumption score under different gender


for gender in ["Male", "Female"]:  #  Please refer to the above for explanation 
    plt.scatter(x = 'Annual Income (k$)',y = 'Spending Score (1-100)',
                data=df[df["Gender"] == gender],
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)') 
plt.title("Annual Income vs Spending Score w.r.t Gender")
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Data distribution under different genders

Observe the data distribution through violin diagram and cluster scatter diagram :

#  Clustering scatter graph :Swarmplots
#  Violin chart :violinplot

n = 0

for col in cols:
    n += 1  #  Subgraph order 
    plt.subplot(1,3,n)  #  The first n Subtext 
    plt.subplots_adjust(hspace=0.5,wspace=0.5)  #  Adjust width and height 
    #  Draw a col The following two graphics , adopt Gender Display in groups 
    sns.violinplot(x=col,y="Gender",data=df,palette = "vlag") 
    sns.swarmplot(x=col, y="Gender",data=df)
    #  Axis and title settings 
    plt.ylabel("Gender" if n == 1 else '')
    plt.title("Violinplots & Swarmplots" if n == 2 else '')
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give the result as follows :

  • See different Gender The distribution of different fields
  • See if there are outliers 、 Outliers, etc

Attribute correlation analysis

It is mainly to observe the regression between two attributes :

cols = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']  #  this 3 Correlation analysis of attributes 
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n = 0

for x in cols:
    for y in cols:
        n += 1  #  Every cycle n increase , The subgraph moves once 
        plt.subplot(3,3,n)  # 3*3 Matrix , The first n A graph 
        plt.subplots_adjust(hspace=0.5, wspace=0.5)  #  Width between subgraphs 、 High parameter 
        sns.regplot(x=x,y=y,data=df,color="#AE213D")  #  Drawing data and color 
        plt.ylabel(y.split()[0] + " " + y.split()[1] if len(y.split()) > 1 else y)
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The specific figure is :

The figure above shows two points :

  • The main diagonal is the relationship between itself and itself , In proportion
  • Other graphs are between attributes , Scatter distribution with data , At the same time, there are relevant trend charts of simulation

Clustering between two attributes

The principle and process of clustering algorithm are not explained in detail here , The default is based on

K It's worth choosing

We plot the data by ELBOW Figure to determine k value . Information broadcast :

1、 Parameter interpretation from the official website…

2、 Chinese explanation and reference…

df1 = df[['Age' , 'Spending Score (1-100)']].iloc[:,:].values  #  Data to be fitted 
inertia = []   #  An empty list , Used to store the sum of distances to the center of mass 

for k in range(1,11):  # k The default value is 1-10 Between , The empirical value is 5 perhaps 10
    algorithm = (KMeans(n_clusters=k,  # k value 
                       init="k-means++",  #  Initial algorithm selection 
                       n_init=10,  #  Number of random runs 
                       max_iter=300,  #  At most iterations 
                       tol=0.0001,  #  Tolerance of minimum error 
                       random_state=111,  #  Random seeds 
                       algorithm="full"))  #  Algorithm to choose  auto、full、elkan  #  Fitting data 
    inertia.append(algorithm.inertia_)  #  Sum of center of mass 
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Draw out K The relationship between the change of value and the sum of centroid distance :

plt.plot(np.arange(1,11), inertia, 'o')  #  The data is drawn twice , Different marks 
plt.plot(np.arange(1,11), inertia, '-', alpha=0.5)

plt.xlabel("Choose of K")
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In the end, we found out :k=4 It's more appropriate . So we use k=4 To carry out the real fitting process of data

Clustering modeling

algorithm = (KMeans(n_clusters=4,  # k=4
                       algorithm="elkan"))  #  Analog data 
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The data are fit After the operation , We got the label label and 4 A center of mass :

labels1 = algorithm.labels_  #  The result of the classification (4 class )
centroids1 = algorithm.cluster_centers_  #  The position of the final center of mass 

print("labels1:", labels1)
print("centroids1:", centroids1)
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In order to show the classification effect of the original data , The case of the official website is the following operation , Personally, I think it's a little cumbersome :

Data consolidation :

Show the classification effect :


Z = Z.reshape(xx.shape)

           cmap =, 
           aspect = 'auto', 

            y='Spending Score (1-100)', 
            data = df , 
            c = labels1 , 
            s = 200)

plt.scatter(x = centroids1[:,0], 
            y =  centroids1[:,1], 
            s = 300 , 
            c = 'red', 
            alpha = 0.5)

plt.ylabel("Spending Score(1-100)")
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If I were , How do you do it? ? Use, of course Pandas+Plolty To solve the problem perfectly :

Look at the results of classification Visualization :

px.scatter(df3,x="Age",y="Spending Score(1-100)",color="Labels",color_continuous_scale="rainbow")
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The above process is based on Age and Spending Score(1-100) To cluster . Based on the same method on the official website :Annual Income (k$) and Spending Score (1-100) Clustering of fields .

The effect is as follows , Divided into 5 Classes :

3 Clustering of attributes

according to Age 、 Annual Income 、 Spending Score To cluster , Final drawing 3 Dimension graphics .

K It's worth choosing

The methods are the same , Just chose 3 A field ( There's something on it 2 individual )

X3 = df[['Age' , 'Annual Income (k$)' ,'Spending Score (1-100)']].iloc[: , :].values  #  selection 3 Data of two fields 
inertia = []
for n in range(1 , 11):
    algorithm = (KMeans(n_clusters = n,
                        n_init = 10 ,
                        random_state= 111  , 
                        algorithm='elkan') )   #  Fitting data 
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Draw an elbow to determine k:

plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(1 , 11) , inertia , 'o')
plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)
plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')
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We finally chose k=6 To cluster

Modeling and fitting

algorithm = (KMeans(n_clusters=6,  #  affirmatory k value 

labels2 = algorithm.labels_
centroids2 = algorithm.cluster_centers_

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Get the label and center of mass :

labels2 = algorithm.labels_
centroids2 = algorithm.cluster_centers_
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3 For dimensional clustering, we finally choose plotly To show :

df["labels2"] = labels2

trace = go.Scatter3d(
    y= df['Spending Score (1-100)'],
    z= df['Annual Income (k$)'],
    marker = dict(

data = [trace]
layout = go.Layout(
    title="six Clusters",
        yaxis = dict(title  = 'Spending Score'),
        zaxis = dict(title  = 'Annual Income')

fig = go.Figure(data=data,layout=layout)
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The following is the final clustering effect :

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author[PI dada],Please bring the original link to reprint, thank you.

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