FeatureImp.py

# import libraries
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor

# load data file
train = pd.read_csv('50-Startups.csv')

# perform one-hot encoding for categorical variable
trainDummies = pd.get_dummies(train['State'], prefix = 'state')

# combine original and one-hot encoded data frames together
train = pd.concat([train, trainDummies], axis=1)

# extract dependent (predictor) feature
y = train.Profit

# extract independent features from combined data frame by removing unwanted features
X = train.drop(["Profit", "State", "state_California"], axis=1)

# create regressor object
rf = RandomForestRegressor(n_estimators=10000, random_state=0, n_jobs=-1)

# fit dependent and independent features in regressor object (creates Machine Learning model)
rf.fit(X, y)

# extract feature importance, combine it with dataset's feature, and print the output in descending order
featureImp = pd.DataFrame({'Feature':X.columns, 'Importance':rf.feature_importances_}).sort_values('Importance', ascending=False)
featureImp