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