Code for Feature Selection using Scikit-Learn in Python Tutorial

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)