2

 

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.datasets import fetch_california_housing

# Step 1: Load the California Housing dataset

california_housing = fetch_california_housing()

df = pd.DataFrame(california_housing.data, columns=california_housing.feature_names)

# Include target variable in the DataFrame

df['target'] = california_housing.target

# Step 2: Compute the correlation matrix

correlation_matrix = df.corr()

# Step 3: Visualize the correlation matrix using a heatmap

plt.figure(figsize=(12, 8))

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', cbar=True)

plt.title("Correlation Matrix Heatmap")

plt.show()

# Step 4: Create a pair plot to visualize pairwise relationships between features

sns.pairplot(df, height=2.5, plot_kws={'alpha':0.7})

plt.suptitle("Pair Plot of California Housing Dataset", y=1.02)

plt.show()

 

Comments

Post a Comment

Popular posts from this blog

1

  Step 1: Import Required Libraries import pandas as pd from sklearn.datasets import fetch_california_housing california_housing = fetch_california_housing() print(california_housing.DESCR) Step 2: Convert to DataFrame df = pd.DataFrame(california_housing.data, columns=california_housing.feature_names) Step 3: Include target variable in the DataFrame df['target'] = california_housing.target Step 4: View the first few rows of the dataset using the head() method print("First 5 rows of the dataset:") print(df.head()) # Display the first 5 rows Step 5: # Plot histograms for all numerical features def plot_histograms(df): df.hist(bins=30, figsize=(12, 10)) plt.suptitle("Histograms of Numerical Features", fontsize=16) plt.show() Step6:# Plot box plots for all numerical features to detect outliers def plot_boxplots(df): plt.figure(figsize=(12, 10)) for i, feature in enumerate(df.columns): plt.subplot(3, 4, i+1) # Adjust the...
Read more

3

  import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA # Load and prepare the data iris = load_iris() df = pd.DataFrame(data=iris.data, columns=iris.feature_names) df['target'] = iris.target df['target_names'] = pd.Categorical.from_codes(iris.target, iris.target_names) # Standardize the features and perform PCA X = df[iris.feature_names] X_scaled = StandardScaler().fit_transform(X) pca = PCA(n_components=2) X_pca = pca.fit_transform(X_scaled) # Plot PCA results plt.figure(figsize=(10, 8)) for target, target_name in zip(sorted(df['target'].unique()), sorted(df['target_names'].unique())): mask = df['target'] == target plt.scatter(X_pca[mask, 0], X_pca[mask, 1], label=target_name, alpha=0.8) plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)')...
Read more