Merge pull request #6 from Team-Fourth-Dimension/master

EDA

Author: EshikaShah

EDA.py

@@ -0,0 +1,47 @@
+import Notebook
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+cleaned_portfolio, cleaned_profile, offers, transactions = Notebook.cleaning_data()
+
+''' Exploratory Data Analysis '''
+
+# To find out the maximum no of customer's belonging to which age group
+def by_age_count(cleaned_profile):
+    sns.countplot(x="age_by_decade",data=cleaned_profile)
+  
+# Gender Distribution of our customer
+def by_gender_count(cleaned_profile):
+    '''cleaned_profile['F'].value_counts() 
+    0    8696
+    1    6129
+    
+    cleaned_profile['M'].value_counts() 
+    1    8484
+    0    6341
+    
+    cleaned_profile['O'].value_counts() 
+    0    14613
+    1      212
+    '''
+    x = ["F", "M", "O"]
+    y = [6129,8484,212]
+    plt.bar(x, y)
+
+# An Overview of what income range facilitates more membership
+def by_income_range(cleaned_profile):
+    sns.countplot(x="income_range",data=cleaned_profile)
+    
+def by_member_year(cleaned_profile):
+    sns.countplot(x="became_member_on",data=cleaned_profile)
+
+# Comparing the Gender-wise distribution of our customer's income
+def income_by_gender(cleaned_profile):
+    x = cleaned_profile[cleaned_profile['F']==1]
+    y = cleaned_profile[cleaned_profile['M']==1]
+    z = cleaned_profile[cleaned_profile['O']==1]
+    sns.kdeplot(x['income'],label='Female')
+    sns.kdeplot(y['income'],label='Male')
+    sns.kdeplot(z['income'],label='Other')