feature_selection_rfecv.py

import pandas as pd

my_df = pd.read_csv("feature_selection_sample_data.csv")



# Recursive Feature Elimination with Cross Validation

from sklearn.feature_selection import RFECV 
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

X = my_df.drop(["output"], axis = 1)
y = my_df["output"]

regressor = LinearRegression()
feature_selector = RFECV(regressor)

fit = feature_selector.fit(X,y)

optimal_feature_count = feature_selector.n_features_
print(f"Optimal numer of features: {optimal_feature_count}")


X_new = X.loc[:, feature_selector.get_support()]

plt.plot(range(1, len(fit.grid_scores_) + 1), fit.grid_scores_, marker = "o")
plt.ylabel("Model Score")
plt.xlabel("Number of Features")
plt.title(f"Feature Selection using RFE \n Optimal number of features is {optimal_feature_count} (at score of {round(max(fit.grid_scores_), 4)})")
plt.tight_layout() 
plt.show()