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import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import fetch_california_housing # Load California housing dataset housing_data = fetch_california_housing() df = pd.DataFrame(housing_data.data, columns=housing_data.feature_names) # 1. Display First Five Rows print("First five rows of the dataset:") print(df.head()) # 2. Dataset Summary print("\nDataset Summary:") print(df.describe()) # 3. Histograms for All Features df.hist(figsize=(12, 8), bins=30, edgecolor='black') plt.suptitle("Histograms for All Numerical Features", fontsize=16) plt.show() # 4. Boxplots for All Features plt.figure(figsize=(12, 6)) df.boxplot(rot=45) plt.title("Box Plots for All Numerical Features", fontsize=16) plt.show() # 5. Outlier Detection using IQR Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - 1.5 * IQR upper_bound = Q3 + 1.5 * IQR outliers = ((dfupper_bound)).sum() print("\nNumber of Outliers in Each Feature:") print(outliers) # 6. Individual Box Plots with Outliers Highlighted plt.figure(figsize=(12, 8)) for i, col in enumerate(df.columns, 1): plt.subplot(3, 3, i) sns.boxplot(x=df[col], color="skyblue") plt.title(col) plt.tight_layout() plt.show()

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