Machine Learning – Scatter Plot

Welcome to The Coding College! A Scatter Plot is one of the simplest yet most powerful tools for visualizing relationships between variables in a dataset. In Machine Learning, scatter plots help identify patterns, correlations, and outliers, offering insights that guide data preprocessing and model development.

This guide will explain what scatter plots are, their importance in ML, and how to create them using Python.

What Is a Scatter Plot?

A Scatter Plot is a two-dimensional graph used to display the relationship between two variables. Each point on the graph represents a data pair, with one variable plotted along the x-axis and the other along the y-axis.

Features of a Scatter Plot:

  • X-axis: Represents the independent variable.
  • Y-axis: Represents the dependent variable.
  • Points: Indicate individual data values.

Why Are Scatter Plots Important in Machine Learning?

  1. Visualizing Relationships: Helps determine if variables are positively correlated, negatively correlated, or uncorrelated.
  2. Outlier Detection: Outliers are easy to spot as points far from the general trend.
  3. Feature Selection: Understanding variable relationships guides the selection of meaningful features.
  4. Clustering Insights: Scatter plots reveal clusters and patterns in the data.

Creating a Scatter Plot in Python

Basic Scatter Plot

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 7, 10]

# Create scatter plot
plt.scatter(x, y, color='blue', label='Data points')
plt.title("Scatter Plot Example")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()
plt.show()

Output:

A simple scatter plot showing the relationship between x and y.

Scatter Plot with Color and Size

Adding color and size variations to a scatter plot can enhance visualization, especially for multivariate data.

import numpy as np

# Sample data
x = np.random.rand(50)
y = np.random.rand(50)
sizes = np.random.rand(50) * 100
colors = np.random.rand(50)

# Create scatter plot
plt.scatter(x, y, s=sizes, c=colors, alpha=0.5, cmap='viridis')
plt.title("Scatter Plot with Colors and Sizes")
plt.colorbar(label='Color Scale')
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Use Cases of Scatter Plots in Machine Learning

1. Correlation Analysis

Scatter plots help identify correlations between features.

Example: Visualizing the relationship between study hours and test scores.

hours = [1, 2, 3, 4, 5]
scores = [50, 60, 65, 70, 85]

plt.scatter(hours, scores, color='green')
plt.title("Study Hours vs. Test Scores")
plt.xlabel("Hours Studied")
plt.ylabel("Test Scores")
plt.show()

2. Clustering

Scatter plots are useful for visualizing clusters in unsupervised learning.

Example: K-Means Clustering Visualization

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

# Generate sample data
data, labels = make_blobs(n_samples=300, centers=3, random_state=42)

# Apply K-Means clustering
kmeans = KMeans(n_clusters=3)
kmeans.fit(data)
predicted_labels = kmeans.predict(data)

# Plot clusters
plt.scatter(data[:, 0], data[:, 1], c=predicted_labels, cmap='rainbow', alpha=0.6)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], color='black', marker='X', label='Centroids')
plt.title("K-Means Clustering")
plt.legend()
plt.show()

3. Outlier Detection

Scatter plots make it easy to detect outliers, which are data points that deviate significantly from the rest.

x = [1, 2, 3, 4, 5, 100]
y = [1, 4, 9, 16, 25, 36]

plt.scatter(x, y, color='red')
plt.title("Scatter Plot with Outlier")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Exercises

Exercise 1: Basic Scatter Plot

Create a scatter plot using x = [10, 20, 30, 40, 50] and y = [15, 25, 35, 45, 55].

Exercise 2: Multivariate Scatter Plot

Enhance the scatter plot with size and color variations for a dataset of 100 points.

Exercise 3: Outlier Identification

Using x = [1, 2, 3, 4, 5, 20] and y = [2, 4, 6, 8, 10, 50], identify and highlight the outlier.

Why Choose The Coding College?

At The Coding College, we make Machine Learning concepts like Scatter Plot approachable and practical. With hands-on examples and exercises, we ensure you gain the skills needed to excel in your ML journey.

Conclusion

Scatter plots are an essential tool in Machine Learning for visualizing data relationships, detecting patterns, and identifying anomalies. By mastering scatter plots, you can better understand your data and make informed decisions in model building.

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