📊 Day 12: Scatter Plot in Python

 

📊 Day 12: Scatter Plot in Python

🔹 What is a Scatter Plot?

A Scatter Plot displays data points on a 2D plane using dots to represent the relationship between two numerical variables.

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🔹 When Should You Use It?

Use a scatter plot when:

• Exploring relationships or correlations

• Identifying patterns or trends

• Detecting outliers

• Comparing two continuous variables

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🔹 Example Scenario

Suppose you are analyzing:

• Study hours vs exam scores

• Advertising spend vs sales

• Temperature vs electricity usage

A scatter plot helps you see:

• Positive or negative correlation

• Clusters of data points

• Unusual or extreme values

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🔹 Key Idea Behind It

👉 Each dot represents one observation
👉 X-axis and Y-axis show two variables
👉 Pattern of dots reveals relationships

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🔹 Python Code (Scatter Plot)

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(50) 
y = np.random.rand(50)

plt.scatter(x, y) 

plt.xlabel("X Values")
 plt.ylabel("Y Values")

plt.title("Scatter Plot Example")

plt.show()

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🔹 Output Explanation

• Each point corresponds to a data pair (x, y)

• Random spread → weak or no correlation

• Tight upward pattern → positive correlation

• Isolated points → potential outliers

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🔹 Scatter Plot vs Line Chart

Feature	Scatter Plot	Line Chart
Data order	Not required	Required
Relationship	Shows correlation	Shows trend
Points	Individual	Connected
Use case	Exploration	Time series

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🔹 Key Takeaways

• Scatter plots reveal relationships quickly

• Ideal for correlation analysis

• Excellent for outlier detection

• Foundation of many ML visualizations