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import streamlit as st | ||
import pandas as pd | ||
import numpy as np | ||
import pickle | ||
from sklearn.ensemble import RandomForestClassifier | ||
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st.write(""" | ||
# Penguin Prediction App | ||
This app predicts the **Palmer Penguin** species! | ||
Data obtained from the [palmerpenguins library](https://github.com/allisonhorst/palmerpenguins) in R by Allison Horst. | ||
""") | ||
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st.sidebar.header('User Input Features') | ||
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st.sidebar.markdown(""" | ||
[Example CSV input file](https://raw.githubusercontent.com/dataprofessor/data/master/penguins_example.csv) | ||
""") | ||
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# Collects user input features into dataframe | ||
uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) | ||
if uploaded_file is not None: | ||
input_df = pd.read_csv(uploaded_file) | ||
else: | ||
def user_input_features(): | ||
island = st.sidebar.selectbox('Island',('Biscoe','Dream','Torgersen')) | ||
sex = st.sidebar.selectbox('Sex',('male','female')) | ||
bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9) | ||
bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2) | ||
flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0) | ||
body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0) | ||
data = {'island': island, | ||
'bill_length_mm': bill_length_mm, | ||
'bill_depth_mm': bill_depth_mm, | ||
'flipper_length_mm': flipper_length_mm, | ||
'body_mass_g': body_mass_g, | ||
'sex': sex} | ||
features = pd.DataFrame(data, index=[0]) | ||
return features | ||
input_df = user_input_features() | ||
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# Combines user input features with entire penguins dataset | ||
# This will be useful for the encoding phase | ||
penguins_raw = pd.read_csv('penguins_cleaned.csv') | ||
penguins = penguins_raw.drop(columns=['species']) | ||
df = pd.concat([input_df,penguins],axis=0) | ||
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# Encoding of ordinal features | ||
# https://www.kaggle.com/pratik1120/penguin-dataset-eda-classification-and-clustering | ||
encode = ['sex','island'] | ||
for col in encode: | ||
dummy = pd.get_dummies(df[col], prefix=col) | ||
df = pd.concat([df,dummy], axis=1) | ||
del df[col] | ||
df = df[:1] # Selects only the first row (the user input data) | ||
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# Displays the user input features | ||
st.subheader('User Input features') | ||
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if uploaded_file is not None: | ||
st.write(df) | ||
else: | ||
st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).') | ||
st.write(df) | ||
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# Reads in saved classification model | ||
load_clf = pickle.load(open('penguins_clf.pkl', 'rb')) | ||
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# Apply model to make predictions | ||
prediction = load_clf.predict(df) | ||
prediction_proba = load_clf.predict_proba(df) | ||
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st.subheader('Prediction') | ||
penguins_species = np.array(['Adelie','Chinstrap','Gentoo']) | ||
st.write(penguins_species[prediction]) | ||
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st.subheader('Prediction Probability') | ||
st.write(prediction_proba) |