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import streamlit as st |
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import pandas as pd |
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from sklearn import datasets |
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from sklearn.ensemble import RandomForestClassifier |
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st.write(""" |
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# Simple Iris Flower Prediction App |
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This app predicts the **Iris flower** type! |
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""") |
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st.sidebar.header('User Input Parameters') |
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def user_input_features(): |
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sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4) |
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sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4) |
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petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3) |
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petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2) |
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data = {'sepal_length': sepal_length, |
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'sepal_width': sepal_width, |
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'petal_length': petal_length, |
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'petal_width': petal_width} |
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features = pd.DataFrame(data, index=[0]) |
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return features |
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df = user_input_features() |
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st.subheader('User Input parameters') |
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st.write(df) |
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iris = datasets.load_iris() |
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X = iris.data |
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Y = iris.target |
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clf = RandomForestClassifier() |
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clf.fit(X, Y) |
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prediction = clf.predict(df) |
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prediction_proba = clf.predict_proba(df) |
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st.subheader('Class labels and their corresponding index number') |
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st.write(iris.target_names) |
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st.subheader('Prediction') |
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st.write(iris.target_names[prediction]) |
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st.subheader('Prediction Probability') |
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st.write(prediction_proba) |
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