AI Development Lesson 4: Data Visualization

In AI/ML, visualization reveals patterns, outliers, and model performance. You spend more time understanding data than building models.

Matplotlib Basics

import matplotlib.pyplot as plt
import numpy as np

# Line chart
x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x), label="sin")
plt.plot(x, np.cos(x), label="cos")
plt.title("Sine & Cosine")
plt.xlabel("x")
plt.legend()
plt.show()

# Subplots
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
axes[0].hist(data, bins=30)
axes[1].scatter(x, y, alpha=0.5)
axes[2].bar(categories, values)
plt.tight_layout()
plt.savefig("analysis.png", dpi=150)

Seaborn for Statistical Plots

import seaborn as sns
import pandas as pd

df = sns.load_dataset("tips")

# Box plot
sns.boxplot(x="day", y="total_bill", data=df)

# Correlation heatmap
corr = df[["total_bill","tip","size"]].corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")

# Pair plot (explore all relationships at once)
sns.pairplot(df, hue="sex")

# Distribution
sns.histplot(df["total_bill"], kde=True)