Oceane22/Mapping

1import streamlit as st2import pandas as pd3from sklearn import datasets4from sklearn.ensemble import RandomForestClassifier5 6st.title('Iris Flower Prediction App')7 8iris = datasets.load_iris()9X = iris.data10y = iris.target11 12clf = RandomForestClassifier()13clf.fit(X,y)14 15st.sidebar.header('User Input Parameters')16 17def user_input_features():18    sepal_length = st.sidebar.slider('Sepal length',4.3,8.0,5.0)19    sepal_width = st.sidebar.slider('Sepal length',2.0,4.4,3.4)20    pepal_length = st.sidebar.slider('Sepal length',1.0,6.9,1.3)21    petal_width= st.sidebar.slider('Sepal length',0.1,2.5,0.2)22    data ={'sepal_length':sepal_length,23           'sepal_width':sepal_width,24           'petal_length':pepal_length,25           'petal_width':petal_width}26    features = pd.DataFrame(data,index=[0])27    return features28 29 30df = user_input_features()31st.subheader('User Input Parameters')32st.write(df)33 34prediction = clf.predict(df)35prediction_proba = clf.predict_proba(df)36 37st.subheader('Class names and corresponding numbers')38st.write(iris.target_names)39 40st.subheader('Prediction')41st.write(iris.target_names[prediction])42 43st.subheader('Prediction Probability')44st.write(prediction_proba )