Matplotlib: Data Visualization

Numbers in a table are hard to understand. But when you turn them into a chart or graph, patterns become clear instantly. In AI & ML, visualization helps you:

1. Understand your data before training a model
2. Find problems like outliers or missing patterns
3. Show results to others in a clear way

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1. Installing Matplotlib

pip install matplotlib

Already installed in Google Colab.

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2. Your First Plot — Line Chart

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May"]
sales = [100, 150, 130, 180, 200]

plt.plot(months, sales)
plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales (in units)")
plt.show()

plt.show() displays the chart. In Colab, charts show automatically.

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3. Bar Chart

Best for comparing different categories:

import matplotlib.pyplot as plt

languages = ["Python", "JavaScript", "Java", "C++"]
popularity = [85, 78, 65, 55]

plt.bar(languages, popularity, color="skyblue")
plt.title("Programming Language Popularity")
plt.xlabel("Language")
plt.ylabel("Popularity Score")
plt.show()

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4. Scatter Plot

Best for seeing the relationship between two numbers:

import matplotlib.pyplot as plt

study_hours = [2, 3, 5, 7, 8, 4, 6, 1]
exam_scores = [55, 60, 75, 85, 90, 65, 80, 40]

plt.scatter(study_hours, exam_scores, color="red")
plt.title("Study Hours vs Exam Score")
plt.xlabel("Hours Studied")
plt.ylabel("Exam Score")
plt.show()

In ML: Scatter plots help you see if two features are related. If the dots form a line going up, it means more study hours = higher scores. This is correlation — the basis of Linear Regression.

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5. Histogram

Best for seeing how data is spread out (distribution):

import matplotlib.pyplot as plt
import numpy as np

# Generate 1000 random exam scores around 70 with some spread
scores = np.random.normal(70, 10, 1000)

plt.hist(scores, bins=20, color="green", edgecolor="black")
plt.title("Exam Score Distribution")
plt.xlabel("Score")
plt.ylabel("Number of Students")
plt.show()

In ML: Histograms show you if your data follows a normal distribution (bell curve). Many ML algorithms work better when data is normally distributed.

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6. Multiple Plots in One Figure

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May"]
product_a = [100, 150, 130, 180, 200]
product_b = [80, 120, 140, 160, 170]

plt.plot(months, product_a, label="Product A", marker="o")
plt.plot(months, product_b, label="Product B", marker="s")
plt.title("Sales Comparison")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.legend()
plt.show()

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7. Saving Charts as Images

plt.plot([1, 2, 3], [10, 20, 30])
plt.title("My Chart")
plt.savefig("my_chart.png", dpi=150)
print("Chart saved!")

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Summary

Chart Type	Best For	Function
Line Chart	Trends over time	plt.plot()
Bar Chart	Comparing categories	plt.bar()
Scatter Plot	Relationship between two numbers	plt.scatter()
Histogram	Data distribution/spread	plt.hist()

• Always add plt.title(), plt.xlabel(), and plt.ylabel() to make charts readable.
• Use plt.legend() when showing multiple lines.
• Visualization is the first step in any ML project — always look at your data before training models!