how to do pca to explain varaibility in target feature in python

Here are the steps for performing PCA in Python to explain variability in a target feature:

  1. Load the dataset, e.g., using pandas read_csv() function.
  2. Split the dataset into feature and target variables.
  3. Standardize the feature variables to have zero mean and unit variance.
  4. Create a PCA object from the sklearn.decomposition module.
  5. Fit the PCA object to the standardized feature variables using the fit() method.
  6. Print the explained variance ratio of each principal component using the explained_variance_ratio_ attribute.
  7. Plot the cumulative explained variance ratio using matplotlib.pyplot module.
  8. Choose the number of principal components based on the cumulative explained variance ratio where the curve starts to level off.
  9. Transform the standardized feature variables into principal components using the transform() method.
  10. Add the principal components to the target variable as new features to create a new dataset.

Here is an example code for the above steps:

main.py
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# Load the dataset
df = pd.read_csv('dataset.csv')

# Split the dataset into feature and target variables
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

# Standardize the feature variables
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

# Create a PCA object with 2 components
pca = PCA(n_components=2)

# Fit the PCA object to the standardized feature variables
X_pca = pca.fit_transform(X_std)

# Print the explained variance ratio of each principal component
print(pca.explained_variance_ratio_)

# Plot the cumulative explained variance ratio
plt.plot(range(1, pca.n_components_ + 1), np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Number of components')
plt.ylabel('Cumulative explained variance ratio')
plt.show()

# Transform the standardized feature variables into principal components
X_pca = pca.transform(X_std)

# Add the principal components to the target variable as new features
new_df = pd.DataFrame(X_pca, columns=['PC1', 'PC2'])
new_df['target'] = y

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This will give you a new dataset with the principal components and the target variable. You can now use this dataset for further analysis or modeling.

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