Dataset Link

📊 Suggested Visuals for EDA in Power BI

1. Overall Success Rate

• Visual Type: Card + Donut Chart
• What it shows: Total campaigns, % Success vs. Failure
• Columns: Campaign_Success

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2. Success Rate by Channel

• Visual Type: Stacked Bar or Column Chart
• X-axis: Channel
• Y-axis: Count of Campaign_ID
• Legend: Campaign_Success

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3. Budget vs. Success

• Visual Type: Box Plot or Scatter Plot
• X-axis: Campaign_Success
• Y-axis: Budget
• Tooltip: Channel, Region

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4. Previous Engagement Distribution

• Visual Type: Histogram
• Axis: Previous_Engagement
• Color Split: Campaign_Success

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5. CTR vs. Conversion Rate

• Visual Type: Scatter Plot
• X-axis: CTR
• Y-axis: Conversion_Rate
• Color: Campaign_Success
• Size: Budget or Audience_Size

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6. Success by Region and Product Category

• Visual Type: Matrix or Heatmap
• Rows: Region
• Columns: Product_Category
• Values: Count of Campaign_ID (filter by Campaign_Success = 1)

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7. Time of Year Analysis

• Visual Type: Line Chart or Bar Chart
• Axis: Time_of_Year
• Value: Success Rate (filter by Campaign_Success)

Interactive Power BI Dashboard with Python Seaborn – Visuals & Filters – Part 2

Python Scrips used in the Python Viduals are here:

# The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script: 

# dataset = pandas.DataFrame(Channel, Campaign_Success)
# dataset = dataset.drop_duplicates()

# Paste or type your script code here:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
df = dataset
# Plot 1: Success count by Channel
plt.figure(figsize=(8, 5))
sns.countplot(data=df, x='Channel', hue="Campaign_Success", stat="percent", palette='Set2')
plt.title("Campaign Success by Channel")
plt.show()

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# The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script: 

# dataset = pandas.DataFrame(CTR, Conversion_Rate, Campaign_Success)
# dataset = dataset.drop_duplicates()

# Paste or type your script code here:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
df = dataset
plt.figure(figsize=(8, 5))
sns.scatterplot(data=df, x='CTR', y='Conversion_Rate', hue='Campaign_Success')
plt.title("CTR vs. Conversion Rate")
plt.show()

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import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
df = dataset
sns.jointplot(data=df, x="Previous_Engagement", y="Conversion_Rate", hue='Campaign_Success')
plt.show()