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![]() Model_predictor = load_model_n_predict("models/xgboost_model4.pickle")įinal_result = get_key(prediction,prediction_label) Prediction = model_predictor.predict(sample_data) Model_predictor = load_model_n_predict("models/lgbm_model4.pickle") # final_result = get_key(prediction,prediction_label) # prediction = loaded_model.predict(sample_data) # loaded_model = joblib.load(open("models/catboost3_model.pickle","rb")) Policy_end_date_quarter= st.number_input("Policy End Data by quarter",1,5)įirst_transaction_date_day= st.number_input("First Transaction by Day",1,30)įirst_transaction_date_month= st.number_input("First Transaction by month",1,12) Policy_end_date_month= st.number_input("Policy End Data by month",1,12) Policy_end_date_day= st.number_input("Policy End Data by Day",1,30) Policy_start_date_quarter= st.number_input("Policy Start Data by quarter",1,5) Policy_start_date_month= st.number_input("Policy Start Data by month",1,12) Policy_start_date_day= st.number_input("Policy Start Data by Day",1,30) St.subheader("Automated EDA with pandas_profiling")ĭata_file=st.file_uploader("upload your dataset") This Python write-up presented a complete guide on how to fix the “could not convert string to float” error.From streamlit_pandas_profiling import st_profile_reportįrom pandas_profiling import ProfileReport The try-except block is also beneficial to rectify or handle this error in Python script. The “float()” function is used along with the combination of both these functions to convert the string value into float. findall()” and “ str.repalce()” functions are used to remove symbols and characters from the given string. To fix the “ ValueError: could not convert string to float”, the “ str.repalce()”, “ re.findall()” and “ try-except” is used in Python. The above output verified that the string value had been converted into a float value.
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