Upload _211.py

_211.py

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+# -*- coding: utf-8 -*-
+""".211
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1uZZV_SkJj2tua-CdVEbGu85Tl8vrTbWD
+"""
+
+import numpy as np
+import pandas as pd
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+  for filename in filenames:
+    print(os.path.join(dirname, filename))
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+data = pd.read_csv('/content/synthetic_ecommerce_data.csv')
+
+print("Dataset Preview:")
+print(data.head())
+
+print("\nDescriptive Statistics:")
+print(data.describe(include='all'))
+
+print("\nMissing Values:")
+print(data.isnull().sum())
+
+data['Transaction_Date'] = pd.to_datetime(data['Transaction_Date'])
+daily_revenue = data.groupby('Transaction_Date')['Revenue'].sum()
+
+plt.figure(figsize=(10, 5))
+plt.plot(daily_revenue, label='Daily Revenue')
+plt.title('Revenue Over Time')
+plt.xlabel('Date')
+plt.ylabel('Revenue')
+plt.legend()
+plt.show()
+
+top_products = data.groupby('Product_ID')['Revenue'].sum().sort_values(ascending=False).head(10)
+
+plt.figure(figsize=(10, 5))
+top_products.plot(kind='bar')
+plt.title('Top 10 Products by Revenue')
+plt.xlabel('Product Id')
+plt.show()
+
+category_revenue = data.groupby('Category')['Revenue'].sum()
+
+plt.figure(figsize=(10, 5))
+sns.scatterplot(x=data=['Ad_Spend'], y=data['Revenue'])
+plt.title('Ad Spend vs Revenue')
+plt.xlabel('Ad Spend')
+plt.ylabel('Revenue')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.histplot(data['Ad_CTR'], bins=20, kde=True)
+plt.title('Distribution of Ad Click-Through Rate (CTR)')
+plt.xlabel('CTR')
+plt.ylabel('Frequency')
+plt.show()
+
+region_revenue = data.groupby('Region')['Revenue'].sum()
+
+plt.figure(figsize=(10, 5))
+region_revenue.plot(kind='bar')
+plt.title('Revenue by Region')
+plt.xlabel('Region')
+plt.ylabel('Revenue')
+plt.show()
+
+data['Month'] = data['Transaction_Date'].dt.month
+monthly_revenue = data.groupby('Month')['Revenue'].sum()
+
+plt.figure(figsize=(10, 5))
+monthly_revenue.plot(kind='bar')
+plt.title('Monthly Reveneu Trend')
+plt.xlabel('Month')
+plt.ylabel('Revenue')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.scatterplot(x=data['Discount_Applied'], y=data['Revenue'])
+plt.title('Discount Applied vs Revenue')
+plt.xlabel('Discount (%)')
+plt.ylabel('Revenue')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.scatterplot(x=data['Clicks'], y=data['Revenue'])
+plt.title('Clicks vs Revenue')
+plt.ylabel('Revenue')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.histplot(data['Conversion_Rate'], bins=20, kde=True)
+plt.title('Distribution of Conversion Rate')
+plt.xlabel('Conversion Rate')
+plt.ylabel('Frequency')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.scatterplot(x=data['Conversion_Rate'], y=data['Revenue'])
+plt.title('Conversion Rate vs Revenue')
+plt.xlabel('Conversion Rate')
+plt.ylabel('Revenue')
+plt.show()
+
+region_revenue = data.groupby('Region')['Revenue'].sum()
+total_revenue = region_revenue.sum()
+region_contribution = (region_revenue / total_revenue) * 100
+
+plt.figure(figsize=(10, 5))
+region_contribution.plot(kind='bar')
+plt.title('Revenue Contribution by Reigion (%)')
+plt.xlabel('Region')
+plt.ylabel('Revenue Contribution (%)')
+plt.show()
+
+data['Ad_Efficiency'] = data['Revenue'] / data['Ad_Spend']
+plt.figure(figsize=(10, 5))
+sns.boxplot(data=data, x='Category', y='Ad_Efficiency')
+plt.title('Ad Spend Efficiency by Category')
+plt.xlabel('Category')
+plt.ylabel('Revenue per Unit of Ad Spend')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.histplot(data['Units_Sold'], bins=20, kde=True)
+plt.title('Distribution of Units Sold')
+plt.xlabel('Units Sold')
+plt.ylabel('Frequency')
+plt.show()
+
+plt.figure(figsize=(10, 5))
+sns.scatterplot(x=data['Units_Sold'], y=data['Revenue'])
+plt.title('Units Sold vs Revenue')
+plt.xlabel('Units Sold')
+plt.ylabel('Revenue')
+plt.show()
+
+units_by_category = data.groupby('Category')['Units_Sold'].sum()
+
+plt.figure(figsize=(10, 5))
+units_by_category.plot(kind='bar')
+plt.title('Units Sold by Category')
+plt.xlabel('Category')
+plt.ylabel('Units Sold')
+plt.show()
+
+data['Revenue_per_Impression'] = data['Revenue'] / data ['Impressions'].astype(float)
+plt.figure(figsize=(10, 5))
+sns.boxplot(data=data, x='Category', y='Revenue_per_Impression')
+plt.title('Revenue per Impression by Category')
+plt.xlabel('Category')
+plt.ylabel('Revenue per Impression')
+plt.show()