penguins_ml.py

import pickle

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

penguin_df = pd.read_csv("penguins.csv")
penguin_df.dropna(inplace=True)
output = penguin_df["species"]
features = penguin_df[
    [
        "island",
        "bill_length_mm",
        "bill_depth_mm",
        "flipper_length_mm",
        "body_mass_g",
        "sex",
    ]
]
features = pd.get_dummies(features)
output, uniques = pd.factorize(output)

x_train, x_test, y_train, y_test = train_test_split(features, output, test_size=0.8)
rfc = RandomForestClassifier(random_state=15)
rfc.fit(x_train.values, y_train)
y_pred = rfc.predict(x_test.values)
score = accuracy_score(y_pred, y_test)
print("Our accuracy score for this model is {}".format(score))


rf_pickle = open("random_forest_penguin.pickle", "wb")
pickle.dump(rfc, rf_pickle)
rf_pickle.close()
output_pickle = open("output_penguin.pickle", "wb")
pickle.dump(uniques, output_pickle)
output_pickle.close()


fig, ax = plt.subplots()
ax = sns.barplot(x=rfc.feature_importances_, y=features.columns)
plt.title("Which features are the most important for species prediction?")
plt.xlabel("Importance")
plt.ylabel("Feature")
plt.tight_layout()
fig.savefig("feature_importance.png")