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import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from sklearn.datasets import fetch_california_housing # Load dataset and convert to DataFrame data = fetch_california_housing() df = pd.DataFrame(data.data, columns=data.feature_names) df['MedHouseVal'] = data.target # Add target column # 1. Correlation Matrix correlation_matrix = df.corr() print("\nCorrelation Matrix:") print(correlation_matrix) # 2. Heatmap of Correlation Matrix plt.figure(figsize=(10, 8)) sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5) plt.title('Correlation Matrix of California Housing Features') plt.tight_layout() plt.show() # 3. Pair Plot for Selected Features selected_features = ['MedInc', 'HouseAge', 'AveRooms', 'AveOccup', 'MedHouseVal'] sns.pairplot(df[selected_features], diag_kind='kde', plot_kws={'alpha': 0.5}) plt.suptitle('Pair Plot of Selected California Housing Features', y=1.02) plt.tight_layout() plt.show()

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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 ou...
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