Data Visualization Project

🎯 Project: Complete Data Visualization Analysis

This project will help you apply everything you've learned about data visualization. You'll analyze a dataset, create multiple visualizations, and extract insights using Matplotlib and Seaborn.

Visualization is crucial for understanding data before building ML models. This project will give you hands-on experience with real visualization workflows.

Exploring Data with Visualizations

Visualizations help you understand your data. Here's a complete workflow:

exploratory_analysis.py
# Complete Data Visualization Workflow
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Sample dataset
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 120, 140, 130, 150, 180],
    'region': ['North', 'South', 'North', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

print("Dataset Overview:")
print(df)
print(f"\nDataset shape: {df.shape}")
print(f"Columns: {list(df.columns)}")

print("\nVisualization Plan:")
print("  1. Time series: Sales trend over months")
print("  2. Comparison: Sales by region")
print("  3. Distribution: Sales distribution")
print("  4. Relationship: Sales vs other features")

Creating Multiple Visualizations

A complete analysis requires multiple visualization types:

multiple_viz.py
# Creating Multiple Visualizations
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Sample sales data
df = pd.DataFrame({
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'sales': [100, 120, 140, 130, 150],
    'region': ['North', 'South', 'North', 'South', 'North']
})

print("Visualization 1: Time Series (Line Plot)")
print("  plt.plot(df['month'], df['sales'])")
print("  Shows trend over time")

print("\nVisualization 2: Comparison (Bar Chart)")
print("  sns.barplot(data=df, x='region', y='sales')")
print("  Compares sales by region")

print("\nVisualization 3: Distribution (Histogram)")
print("  sns.histplot(data=df, x='sales')")
print("  Shows sales distribution")

print("\nVisualization 4: Statistical Summary (Box Plot)")
print("  sns.boxplot(data=df, x='region', y='sales')")
print("  Shows quartiles and outliers")

Combining Visualizations

Create comprehensive dashboards with multiple plots:

dashboard.py
# Creating a Visualization Dashboard
import matplotlib.pyplot as plt
import seaborn as sns

# Create figure with multiple subplots
fig = plt.figure(figsize=(14, 10))

print("Dashboard Layout:")
print("  Top Row: Overview plots (trend, summary)")
print("  Bottom Row: Detailed analysis (distribution, comparison)")

# Layout structure
print("\nSubplot Structure:")
print("  [0,0] - Time series line plot")
print("  [0,1] - Summary statistics bar chart")
print("  [1,0] - Distribution histogram")
print("  [1,1] - Comparison box plot")

print("\nBenefits of dashboard:")
print("  - See multiple perspectives at once")
print("  - Identify patterns and outliers")
print("  - Communicate insights effectively")

Exercise: Complete Visualization Project

Complete the exercise on the right side:

Task 1: Create a DataFrame with sales data (month, sales, region)
Task 2: Create a line plot showing sales trend over months
Task 3: Create a bar chart comparing sales by region
Task 4: Calculate and print summary statistics (mean, max, min sales)
Task 5: Create a dashboard with 2x2 subplots showing all visualizations

Write your code to complete this comprehensive visualization project!

💡 Project Tips

Break the project into smaller tasks. Complete and test each part before moving to the next. Don't try to do everything at once—iterative development leads to better results!

🎉

Lesson Complete!

Great work! Continue to the next lesson.