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| import os | |
| import pandas as pd | |
| import pandas_profiling | |
| import streamlit as st | |
| #ML stuff | |
| from pycaret.classification import compare_models, pull, save_model, setup | |
| #from pycaret.regression import compare_models, pull, save_model, setup | |
| from streamlit_pandas_profiling import st_profile_report | |
| with st.sidebar: | |
| st.image("https://cdn.pixabay.com/photo/2018/09/18/11/19/artificial-intelligence-3685928_1280.png") | |
| st.title("EasyAutoML") | |
| choice = st.radio("Navigation",["Data loading","Exploratory","Modeling","Download"]) | |
| st.info("This application to explore your data & build an automated ML pipeline.") | |
| if os.path.exists("source_data.csv"): | |
| df = pd.read_csv("source_data.csv", index_col=None) | |
| if choice == "Data loading": | |
| st.title("Upload your data for modeling") | |
| file = st.file_uploader("Upload your dataset here") | |
| if file: | |
| df = pd.read_csv(file, index_col=None) | |
| df.to_csv("source_data.csv", index=None) | |
| st.dataframe(df) | |
| elif choice == "Exploratory": | |
| st.title('Automated EDA') | |
| profile_report = df.profile_report() | |
| st_profile_report(profile_report) | |
| elif choice == "Modeling": | |
| st.title('Time for ML') | |
| target = st.selectbox('Choose the target column', df.columns) | |
| if st.button("Train model"): | |
| setup(df, target=target, silent=True) | |
| setup_df = pull() | |
| st.info("This is the ML experiment settings") | |
| st.dataframe(setup_df) | |
| best_model = compare_models() | |
| compare_df = pull() | |
| st.info("This is the ML model") | |
| st.dataframe(compare_df) | |
| best_model | |
| save_model(best_model, 'best_model') | |
| elif choice == "Download": | |
| with open("best_model.pkl",'rb') as f: | |
| st.download_button("Download the model file",f,"best_model.pkl") | |
| else: | |
| pass | |
| st.write("Made with <3 by Amdjed") | |