└── AMAZON SALES REPORT.py


/AMAZON SALES REPORT.py:
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 1 | #!/usr/bin/env python
 2 | # coding: utf-8
 3 | 
 4 | # In[16]:
 5 | 
 6 | 
 7 | import pandas as pd
 8 | import matplotlib.pyplot as plt
 9 | 
10 | # Load the dataset
11 | ecommerce_data = pd.read_csv('Amazon Sale Report.csv')
12 | 
13 | # Display the first few rows of the dataset
14 | print(ecommerce_data.head())
15 | 
16 | 
17 | # In[11]:
18 | 
19 | 
20 | print(ecommerce_data.info())  # Summary information about the dataset
21 | print(ecommerce_data.describe()) 
22 | 
23 | 
24 | # In[13]:
25 | 
26 | 
27 | #Check for missing values
28 | print(ecommerce_data.isnull().sum())
29 | 
30 | 
31 | # In[14]:
32 | 
33 | 
34 | # Example 1: Sales analysis
35 | sales_by_category = ecommerce_data.groupby('Category')['Amount'].sum()
36 | print("\nSales by Category:")
37 | print(sales_by_category)
38 | 
39 | 
40 | # In[15]:
41 | 
42 | 
43 | # Visualize sales by category
44 | sales_by_category.plot(kind='bar', xlabel='Category', ylabel='Total Sales', title='Sales by Category')
45 | plt.show()
46 | 
47 | 
48 | # In[8]:
49 | 
50 | 
51 | # Example 2: Market basket analysis (dummy example, replace with actual analysis)
52 | # For demonstration purposes, let's create a histogram showing the distribution of purchase amounts
53 | plt.hist(ecommerce_data['Amount'], bins=20, color='skyblue', edgecolor='black')
54 | plt.xlabel('Purchase Amount')
55 | plt.ylabel('Frequency')
56 | plt.title('Distribution of Purchase Amounts')
57 | plt.show()
58 | 
59 | 
60 | # In[9]:
61 | 
62 | 
63 | # Example 4: Sales forecasting (dummy example, replace with actual analysis)
64 | # For demonstration purposes, let's create a line plot showing sales trend over time
65 | ecommerce_data['Date'] = pd.to_datetime(ecommerce_data['Date'])
66 | sales_over_time = ecommerce_data.groupby('Date')['Amount'].sum()
67 | print("\nSales Trend over Time:")
68 | print(sales_over_time)
69 | 
70 | # Visualize sales trend over time
71 | sales_over_time.plot(kind='line', xlabel='Date', ylabel='Total Sales', title='Sales Trend over Time')
72 | plt.show()
73 | 
74 | 


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