Iris Dataset Visualization

 import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

# Load the iris Dataset

df = pd.read_csv("iris.csv")

# Display basic info

df.head()

# Create a figure with subplots (2 rows, 2 columns)

fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Histogram for Sepal Length

sns.histplot(df["sepal_length"], bins=20, kde=True, color="blue", ax=axes[0, 0])

axes[0, 0].set_title("Distribution of Sepal Length")

axes[0, 0].set_xlabel("Sepal Length (cm)")

axes[0, 0].set_ylabel("Count")

# Histogram for Sepal Width

sns.histplot(df["sepal_width"], bins=20, kde=True, color="green", ax=axes[0, 1])

axes[0, 1].set_title("Distribution of Sepal Width")

axes[0, 1].set_xlabel("Sepal Width (cm)")

axes[0, 1].set_ylabel("Count")

# Histogram for Petal Length

sns.histplot(df["petal_length"], bins=20, kde=True, color="black", ax=axes[1, 0])

axes[1, 0].set_title("Distribution of Petal Length")

axes[1, 0].set_xlabel("Petal Length (cm)")

axes[1, 0].set_ylabel("Count")

# Histogram for Petal Width

sns.histplot(df["petal_width"], bins=20, kde=True, color="red", ax=axes[1, 1])

axes[1, 1].set_title("Distribution of Petal Width")

axes[1, 1].set_xlabel("Petal Width (cm)")

axes[1, 1].set_ylabel("Count")

# Adjust layout

plt.tight_layout()

plt.show()

# 2️⃣ Scatter Plot: Sepal Length vs Sepal Width

# ------------------------------ #

plt.figure(figsize=(6, 4))

sns.scatterplot(x="sepal_length", y="sepal_width", hue="species", data=df, palette="deep")

plt.title("Sepal Length vs Sepal Width")

plt.xlabel("Sepal Length (cm)")

plt.ylabel("Sepal Width (cm)")

plt.show()

# 8️⃣ Correlation Heatmap

# ------------------------------ #

plt.figure(figsize=(6, 4))

# Select only numerical columns for correlation calculation

numerical_df = df.select_dtypes(include=['number'])

sns.heatmap(numerical_df.corr(), annot=True, cmap="coolwarm", linewidths=0.5)

plt.title("Feature Correlation Heatmap")

plt.show()