Customer Analysis

!pip install dataidea --upgrade --quiet

import opendatasets as od

# download the dataset
dataset_url = 'https://raw.githubusercontent.com/Kaushik-Varma/Marketing_Data_Analysis/master/Marketing_Analysis.csv'
od.download(dataset_url)

#import the useful libraries.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# Read the data set of "Marketing Analysis" in data.
data= pd.read_csv("Marketing_Analysis.csv", low_memory=False)

# Printing the data
data.head()

#import the useful libraries.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# Read the file in data without first two rows as it is of no use.
data = pd.read_csv("Marketing_Analysis.csv",skiprows = 2)

#print the head of the data frame.
data.head()

# Drop the customer id as it is of no use.
data.drop('customerid', axis = 1, inplace = True)

#Extract job  & Education in newly from "jobedu" column.
data['job']= data["jobedu"].apply(lambda x: x.split(",")[0])
data['education']= data["jobedu"].apply(lambda x: x.split(",")[1])

# Drop the "jobedu" column from the dataframe.
data.drop('jobedu', axis = 1, inplace = True)

# Printing the Dataset
data.sample(n=5)

# Checking the missing values
data.isnull().sum()

# Dropping the records with age missing in data dataframe.
data = data[~data.age.isnull()].copy()

# Checking the missing values in the dataset.
data.isnull().sum()

# Find the mode of month in data
month_mode = data.month.mode()[0]

# Fill the missing values with mode value of month in data.
data.month.fillna(month_mode, inplace = True)

# Let's see the null values in the month column.
data.month.isnull().sum()

#drop the records with response missing in data.
data = data[~data.response.isnull()].copy()
# Calculate the missing values in each column of data frame
data.isnull().sum()

# Let's calculate the percentage of each job status category.
data.job.value_counts(normalize=True)

#plot the bar graph of percentage job categories
data.job.value_counts(normalize=True).plot.barh()
plt.show()

#calculate the percentage of each education category.
data.education.value_counts(normalize=True)

#plot the pie chart of education categories
data.education.value_counts(normalize=True).plot.pie()
plt.show()

data.salary.describe()

#plot the scatter plot of balance and salary variable in data
plt.scatter(data.salary,data.balance)
plt.show()

#plot the scatter plot of balance and age variable in data
data.plot.scatter(x="age",y="balance")
plt.show()

#plot the pair plot of salary, balance and age in data dataframe.
sns.pairplot(data = data, vars=['salary','balance','age'])
plt.show()

# Creating a matrix using age, salary, balance as rows and columns
data[['age','salary','balance']].corr()

#plot the correlation matrix of salary, balance and age in data dataframe.
sns.heatmap(data[['age','salary','balance']].corr(), annot=True, cmap = 'Greens')
plt.show()

#create response_rate of numerical data type where response "yes"= 1, "no"= 0
data['response_rate'] = np.where(data.response=='yes',1,0)
data.response_rate.value_counts()

#plot the bar graph of marital status with average value of response_rate
data.groupby('marital')['response_rate'].mean().plot.bar()
plt.show()

result = pd.pivot_table(data=data, index='education', columns='marital',values='response_rate')
print(result)

#create heat map of to show correlations betwenn education vs marital vs response_rate
sns.heatmap(result, annot=True, cmap = 'RdYlGn', center=0.117)
plt.show()

#plot the bar graph of age groups with average salary for that group
bins = [18, 30, 40, 50, 60, 70, 120]
labels = ['18-29', '30-39', '40-49', '50-59', '60-69', '70+']
data['agerange'] = pd.cut(data.age, bins, labels = labels,include_lowest = True)

#plot the bar graph of average salary per age group
data.groupby('agerange')['salary'].mean().plot.bar()
plt.title('Avg Salary per Age',fontsize = 12)
plt.show()