Delete iris-ml-app.py

iris-ml-app.py

@@ -1,49 +0,0 @@
-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)