penguin_ml_with_image.py

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

# package
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, ax=ax, palette="viridis")

plt.title('Which features are the most important for species prediction?')

plt.xlabel('Important')
plt.ylabel('Feature')
plt.tight_layout()
fig.savefig('feature_important.png')

fig, ax = plt.subplots()
ax = sns.displot(x=penguin_df['bill_length_mm'],hue=penguin_df['species'])

plt.axvline(bill_length) # reference lines 
plt.title("Bill length by species")
st.pyplot(ax)
fig, ax = plt.subplots()
ax = sns.displot(x=penguin_df["bill_depth_mm"],hue=penguin_df['species'])

plt.axvline(bill_depth)
plt.title('Bill depth by species')

st.pyplot(ax)
fig, ax = plt.subplots()
ax = sns.displot(x=penguin_df['flipper_length_mm'], 
                 hue= penguin_df['species'])

plt.axvline(flipper_length)

plt.title('Flipper length by species')

st.pyplot(ax)