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import streamlit as st |
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from sklearn import neighbors, datasets |
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with st.form(key='my_form'): |
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sLen = st.slider('Sepal lenght(cm) ', 0.0, 10.0) |
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sWid = st.slider('Sepal width(cm) ', 0.0, 10.0) |
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pLen = st.slider('Petal lenght(cm) ', 0.0, 10.0) |
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pWid = st.slider('Petal width(cm) ', 0.0, 10.0) |
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st.form_submit_button('Predict') |
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iris = datasets.load_iris() |
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X,y = iris.data, iris.target |
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knn = neighbors.KNeighborsClassifier(n_neighbors=3) |
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knn.fit(X,y) |
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predict = knn.predict([[sLen,sWid,pLen,pWid]]) |
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st.text(iris.target_names[predict]) |
