Code for Feature Selection using Scikit-Learn in Python Tutorial

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feature_selection.py

# To add a new cell, type '# %%' # To add a new markdown cell, type '# %% [markdown]' # %% import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.feature_selection import SelectKBest,chi2,RFE from sklearn.ensemble import RandomForestClassifier df = pd.read_csv("data/Heart_Disease_Prediction.csv") print(df.shape) df.head(5) # %% label = df["Heart Disease"] df.drop("Heart Disease", axis=1, inplace=True) # %% print(label.value_counts()) label.value_counts().plot(kind="bar") # %% categorical_features = ["Sex", "Chest pain type", "FBS over 120", "EKG results", "Exercise angina", "Slope of ST", "Number of vessels fluro", "Thallium"] df[categorical_features] = df[categorical_features].astype("category") # %% continuous_features = set(df.columns) - set(categorical_features) scaler = MinMaxScaler() df_norm = df.copy() df_norm[list(continuous_features)] = scaler.fit_transform(df[list(continuous_features)]) # %% X_new = SelectKBest(k=5, score_func=chi2).fit_transform(df_norm, label) X_new # %% rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=5) X_new = rfe.fit_transform(df_norm, label) X_new # %% clf = RandomForestClassifier() clf.fit(df_norm, label) # create a figure to plot a bar, where x axis is features, and Y indicating the importance of each feature plt.figure(figsize=(12,12)) plt.bar(df_norm.columns, clf.feature_importances_) plt.xticks(rotation=45)