Created
June 15, 2020 03:23
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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.write(prediction) | |
st.subheader('Prediction Probability') | |
st.write(prediction_proba) |
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Thank You sir