Wholesale data set - classification and clustering

This data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories. The wholesale distributor operating in different regions of Portugal has information on annual spending of several items in their stores across different regions and channels. The dataset consist of 440 large retailers annual spending on 6 different varieties of product in 3 different regions (lisbon , oporto, other) and across different sales channel ( Hotel, channel)

Algorithms to be used, XGBoost classifier, logistic regression, k means clustering etc.

To predict which region and which channel will spend more and which region and channel to spend less.

Data set Link : UCI MLR - https://archive.ics.uci.edu/ml/datasets/wholesale+customers

Kaggle : - https://www.kaggle.com/binovi/wholesale-customers-data-set

Detailed overview of dataset

Data

import pandas as pd
 #Load Data
 
 file_loc = "data\\Wholesale customers data.csv"
 wholesale_cust_data = pd.read_csv(file_loc)
 wholesale_cust_data.head()

Total number of Rows and Columns

row_col = wholesale_cust_data.shape
 print("Tota number of rows in the dataset : {}".format(row_col[0]))
 print("Total number of columns in the dataset : {}".format(row_col[1]))
 

Details about dataset

# check information
 wholesale_cust_data.info()

Check the number of Missing values in the dataset

# missing values
 wholesale_cust_data.isna().sum()

Statistical information

# statistical information
 wholesale_cust_data.describe()

Data Visualization

Correlation

import seaborn as sns
 import matplotlib.pyplot as plt
 # correlation
 corr = wholesale_cust_data.corr()
 corr.style.background_gradient(cmap='cubehelix')

Count the value in Channel and Regions column

# Replace data values
 wholesale_cust_data["Channel"] = wholesale_cust_data["Channel"].replace(1,"Hotel")
 wholesale_cust_data["Channel"] = wholesale_cust_data["Channel"].replace(2,"Retail")
 
 # Replace values
 wholesale_cust_data["Region"] = wholesale_cust_data["Region"].replace(1,"Lisbon")
 wholesale_cust_data["Region"] = wholesale_cust_data["Region"].replace(2,"Oporto")
 wholesale_cust_data["Region"] = wholesale_cust_data["Region"].replace(3,"Other")
 
 import matplotlib.pyplot as plt
 import seaborn as sns
 colmns = ['Channel','Region']
 for col in colmns:
  sns.set_style("whitegrid")
  plt.figure(figsize = (8,5))
  sns.countplot(x=wholesale_cust_data[col], data=wholesale_cust_data)
  plt.show()

Channel count by Regions

sns.set_style('whitegrid')
 sns.countplot(x="Channel",hue='Region',data=wholesale_cust_data)

Box Plots of Each columns

cols = wholesale_cust_data.select_dtypes(exclude ='object').columns
 
 for i in cols:
  sns.set_theme(style="whitegrid")
  plt.figure(figsize=(10,3))
  ax = sns.boxplot(x=wholesale_cust_data[i])

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