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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+
+st.title('Principal Component Analysis Demo App 🤖')
+st.subheader('Credit: @BenjaminJack')
+st.subheader('Raw Data')
+
+iris = px.data.iris().drop('species_id',axis=1)
+st.write(iris)
+
+xvar = st.selectbox('Select x-axis',iris.columns[:-1])
+yvar = st.selectbox('Select y-axis',iris.columns[:-1])
+
+st.write(px.scatter(iris, x=xvar, y=yvar, color='species'))
+iris_scaled = StandardScaler().fit_transform(iris.drop('species',axis=1))
+iris_pca = PCA()
+
+iris_transformed = iris_pca.fit_transform(iris_scaled)
+col_names = [f'component{i+1}' for i in range(iris_transformed.shape[1])]
+st.write(col_names)
+
+iris_transformed_df = pd.DataFrame(iris_transformed, columns=col_names)
+iris_transformed_df = pd.concat([iris_transformed_df, iris['species']],axis=1)
+
+st.subheader('Transformed Data')
+
+st.write(iris_transformed_df)
+
+st.subheader('Explore principal components')
+xvar = st.selectbox('Select x-axis:', iris_transformed_df.columns[:-1])
+yvar = st.selectbox('Select y-axis:', iris_transformed_df.columns[:-1])
+
+st.write(px.scatter(iris_transformed_df,x=xvar, y=yvar, color = 'species'))
+
+st.subheader('Explore Loadings')
+
+loadings = iris_pca.components_.T * np.sqrt(iris_pca.explained_variance_)
+
+loadings_df = pd.DataFrame(loadings,columns=col_names)
+loadings_df = pd.concat([loadings_df,
+                          pd.Series(iris.columns[0:4],name='var')],
+                          axis=1)
+
+component =st.selectbox('Select Component:',loadings_df.columns[0:4])
+
+bar_chart = px.bar(loadings_df[['var',component]].sort_values(component),
+                   y='var',
+                   x=component,
+                   orientation='h',
+                   range_x=[-1,1]
+                   )
+st.write(bar_chart)
+st.write(loadings_df)
+
+#Credits : benjaminjack

requirements.txt

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+streamlit
+pandas
+numpy
+plotly