ValueError: dtype=‘numeric’ is not compatible with arrays of bytes/strings. Convert your data to numeric values explicitly instead.
i get the above error when i want to execute the result of my prediction model, it is a multiple disease prediction model in one app, and when i execute the other two models they respond perfectly, dont what could be the matter
the arrays in question are all numeric
Hi @Osita,
Thanks for posting!
Can you share a code snippet so we can try to reproduce the issue?
Caroline 
ValueError: dtype=‘numeric’ is not compatible with arrays of bytes/strings.Convert your data to numeric values explicitly instead.
Traceback:
File "C:\Users\CCRN\anaconda3\envs\MachineLearning\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 556, in _run_script
exec(code, module.__dict__)File "C:\Users\CCRN\Multiple Disease Predtiction Web App.py", line 318, in <module>
cardio_prediction = Cardio_Disease_model.predict([[age,gender,height,weight,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active]])File "C:\Users\CCRN\anaconda3\envs\MachineLearning\lib\site-packages\sklearn\linear_model\_base.py", line 447, in predict
scores = self.decision_function(X)File "C:\Users\CCRN\anaconda3\envs\MachineLearning\lib\site-packages\sklearn\linear_model\_base.py", line 429, in decision_function
X = self._validate_data(X, accept_sparse="csr", reset=False)File "C:\Users\CCRN\anaconda3\envs\MachineLearning\lib\site-packages\sklearn\base.py", line 577, in _validate_data
X = check_array(X, input_name="X", **check_params)File "C:\Users\CCRN\anaconda3\envs\MachineLearning\lib\site-packages\sklearn\utils\validation.py", line 887, in check_array
raise ValueError(
==.> above is the whole error message
==> below are the codes
import numpy as np
import pickle
import streamlit as st
from streamlit_option_menu import option_menu
Loading the saved model
Diabetes_model = pickle.load(open(‘Diabetes_prediction_model.sav’,‘rb’))
Breast_Cancer_model = pickle.load(open(‘Breast_Cancer_Prediction_model.sav’,‘rb’))
Heart_Disease_model = pickle.load(open(‘Heart_Disease_Prediction_model.sav’,‘rb’))
Parkinsons_model = pickle.load(open(‘Parkinsons_Disease_Prediction_model.sav’,‘rb’))
Cardio_Disease_model = pickle.load(open(‘Cardio_Disease_Prediction_model.sav’,‘rb’))
sidebar for navigation
with st.sidebar:
selected = option_menu(‘Multiple Disease Detection System’,
[‘Diabetes Detection’,
‘Breast Cancer Detection’,
‘Heart Disease Detection’,
“Parkinson’s Disease Detection”,
‘Cardiovascular Disease Detection’],
icons = ['activity','bookmark-star','heart','person-check','postage-heart'],
default_index = 0)
Diabetes Prediction Page
if (selected == ‘Diabetes Detection’):
#title page
st.title('Diabetes Detection Model')
#columns for input fields
col1,col2,col3 = st.columns(3)
with col1:
Pregnancies = st.text_input('Number of Pregnancies')
with col2:
Glucose = st.text_input('Glucose Level')
with col3:
BloodPressure = st.text_input('BloodPressure Level')
with col1:
SkinThickness = st.text_input('Skin Thickness Value')
with col2:
Insulin = st.text_input('Insulin Level')
with col3:
BMI = st.text_input('BMI Values')
with col1:
DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function Value')
with col2:
Age = st.text_input('Age Of Patient')
#code for prediction
diab_diagnosis =''
#creating a button for Prediction
if st.button('Diabetes Test Result'):
diab_prediction = Diabetes_model.predict([[Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,
BMI,DiabetesPedigreeFunction,Age]])
if diab_prediction[0]==0:
diab_diagnosis = 'Patient is not Diabetic'
else:
diab_diagnosis = 'Patient is Diabetic'
st.success(diab_diagnosis)
Breast Cancer Prediction Page
if (selected == ‘Breast Cancer Detection’):
#title page
st.title('Breast Cancer Detection Model')
#columns for input fields
col1,col2,col3 = st.columns(3)
with col1:
mean_radius = st.text_input('Mean Radius')
with col2:
mean_texture = st.text_input('Mean Texture')
with col3:
mean_perimeter = st.text_input('Mean Perimeter')
with col1:
mean_area = st.text_input('Mean Area')
with col2:
mean_smoothness = st.text_input('Mean Smoothness')
with col3:
mean_compactness = st.text_input('Mean Compactness')
with col1:
mean_concavity = st.text_input('Mean Concavity')
with col2:
mean_concave_points = st.text_input('Mean Concave Points')
with col3:
mean_symmetry = st.text_input('Mean Symmetry')
with col1:
mean_fractal_dimension = st.text_input('Mean Fractal Dimension')
with col2:
radius_error = st.text_input('Radius Error')
with col3:
texture_error = st.text_input('Texture Error')
with col1:
perimeter_error = st.text_input('Perimeter Error')
with col2:
area_error = st.text_input('Area Error')
with col3:
smoothness_error = st.text_input('Smoothness Error')
with col1:
compactness_error = st.text_input('Compactness Error')
with col2:
concavity_error = st.text_input('Concavity Error')
with col3:
concave_points_error = st.text_input('Concave Points Error')
with col1:
symmetry_error = st.text_input('Symmetry Error')
with col2:
fractal_dimension_error = st.text_input('Fractal Dimension Error')
with col3:
worst_radius = st.text_input('Worst Radius')
with col1:
worst_texture = st.text_input('Worst Texture')
with col2:
worst_perimeter = st.text_input('Worst Perimeter')
with col3:
worst_area = st.text_input('Worst Area')
with col1:
worst_smoothness = st.text_input('Worst Smoothness')
with col2:
worst_compactness = st.text_input('Worst Compactness')
with col3:
worst_concavity = st.text_input('Worst Concavity')
with col1:
worst_concave_points = st.text_input('Worst Concave Points')
with col2:
worst_symmetry = st.text_input('Worst Symmetry')
with col3:
worst_fractal_dimension = st.text_input('Worst Fractal Dimension')
#code for prediction
breast_cancer_diagnosis =''
#creating a button for Prediction
if st.button('Breast Cancer Test Result'):
breast_cancer_prediction = Breast_Cancer_model.predict([[mean_radius,mean_texture,mean_perimeter,mean_area,mean_smoothness,
mean_compactness,mean_concavity,mean_concave_points,mean_symmetry,
mean_fractal_dimension,radius_error,texture_error,perimeter_error,area_error,
smoothness_error,compactness_error,concavity_error,concave_points_error,
symmetry_error,fractal_dimension_error,worst_radius,worst_texture,
worst_perimeter,worst_area,worst_smoothness,worst_compactness,
worst_concavity,worst_concave_points,worst_symmetry,
worst_fractal_dimension]])
if breast_cancer_prediction[0]==0:
breast_cancer_diagnosis = 'Breast Cancer is Malignant'
else:
breast_cancer_diagnosis = 'Breast Cancer is Benign'
st.success(breast_cancer_diagnosis)
Heart Disease Prediction Page
if (selected == ‘Heart Disease Detection’):
#title page
st.title('Heart Disease Detection Model')
#columns for input fields
col1,col2,col3 = st.columns(3)
with col1:
age = st.text_input('Age')
with col2:
sex = st.text_input('Sex')
with col3:
cp = st.text_input('Chest Pain Type (4 values)')
with col1:
trestbps = st.text_input('Resting blood pressure')
with col2:
chol = st.text_input('Serum Cholestoral in mg/dl')
with col3:
fbs = st.text_input('Fasting Blood Sugar > 120 mg/dl')
with col1:
restecg = st.text_input('Resting Electrocardiographic Results (values 0,1,2)')
with col2:
thalach = st.text_input('Maximum Heart Rate Achieved')
with col3:
exang = st.text_input('Exercise Induced Angina')
with col1:
oldpeak = st.text_input('Oldpeak = ST Depression induced By Exercise Relative To Rest')
with col2:
slope = st.text_input('The slope of the peak exercise ST segment')
with col3:
ca = st.text_input('Number of major vessels (0-3) colored by flourosopy')
with col1:
thal = st.text_input('Thal: 0 = Normal; 1 = Fixed defect; 2 = Reversable defect')
#code for prediction
heart_diagnosis =''
#creating a button for Prediction
if st.button('Heart Test Result'):
heart_disease_prediction = Heart_Disease_model.predict([[age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal]])
if heart_disease_prediction[0]==0:
heart_diagnosis = 'Patient does not have a Heart Disease'
else:
heart_diagnosis = 'Patient has a Heart Disease'
st.success(heart_diagnosis)
Parkinson Prediction Page
if (selected == “Parkinson’s Disease Detection”):
#title page
st.title("Parkinson's Disease Detection Model")
#columns for input fields
col1,col2,col3 = st.columns(3)
with col1:
spread1 = st.text_input('spread1')
with col2:
PPE = st.text_input('PPE')
with col3:
spread2 = st.text_input('spread2')
with col1:
MDVPFo = st.text_input('MDVP:Fo(Hz)')
with col2:
MDVPFlo = st.text_input('MDVP:Flo(Hz)')
with col3:
MDVPShimmer = st.text_input('MDVP:Shimmer')
with col1:
MDVPAPQ = st.text_input('MDVP:APQ')
with col2:
HNR = st.text_input('HNR')
with col3:
ShimmerAPQ5 = st.text_input('Shimmer:APQ5')
with col1:
MDVPShimmer_dB = st.text_input('MDVP:Shimmer(dB)')
with col2:
ShimmerAPQ3 = st.text_input('Shimmer:APQ3')
with col3:
ShimmerDDA = st.text_input('Shimmer:DDA')
with col1:
D2 = st.text_input('D2')
with col2:
MDVPJitter_abs = st.text_input('MDVP:Jitter(Abs)')
with col3:
RPDE = st.text_input('RPDE')
with col1:
MDVPPPQ = st.text_input('MDVP:PPQ')
with col2:
MDVPJitter_percent = st.text_input('MDVP:Jitter(%)')
with col3:
MDVPRAP = st.text_input('MDVP:RAP')
with col1:
JitterDDP = st.text_input('Jitter:DDP')
with col2:
DFA = st.text_input('DFA')
with col3:
NHR = st.text_input('NHR')
with col1:
MDVPFhi = st.text_input('MDVP:Fhi(Hz)')
#code for prediction
parkinsons_diagnosis =''
#creating a button for Prediction
if st.button('Parkinsons Disease Test Result'):
parkinsons_disease_prediction = Parkinsons_model.predict([[spread1,PPE,spread2,MDVPFo,MDVPFlo,MDVPShimmer,MDVPAPQ,
HNR,ShimmerAPQ5,MDVPShimmer_dB,ShimmerAPQ3,ShimmerDDA,D2,
MDVPJitter_abs,RPDE,MDVPPPQ,MDVPJitter_percent,MDVPRAP,JitterDDP,
DFA,NHR,MDVPFhi]])
if parkinsons_disease_prediction[0]==0:
parkinsons_diagnosis = 'Patient does not have Parkinsons Disease'
else:
parkinsons_diagnosis = 'Patient has Parkinsons Disease'
st.success(parkinsons_diagnosis)
Cardio Prediction Page
if (selected == ‘Cardiovascular Disease Detection’):
#title page
st.title('Cardiovascular Disease Detection Model')
#columns for input fields
col1,col2,col3 = st.columns(3)
with col1:
age = st.text_input('Age')
with col2:
gender = st.text_input('Gender 1: Female, 2: Male')
with col3:
height = st.text_input('Height (cm)')
with col1:
weight = st.text_input('Weight (kg)')
with col2:
ap_hi = st.text_input('Systolic blood pressure')
with col3:
ap_lo = st.text_input('Diastolic blood pressure')
with col1:
cholesterol = st.text_input('Cholesterol 1: normal, 2: above normal, 3: well above normal')
with col2:
gluc = st.text_input('Glucose 1: normal, 2: above normal, 3: well above normal')
with col3:
smoke = st.text_input('Smoking 1: Yes, 0: No')
with col1:
alco = st.text_input('Alcohol intake 1: Yes, 0: No')
with col2:
active = st.text_input('Physical activity 1: Yes, 0: No')
#code for prediction
cardio_disease_diagnosis =''
#creating a button for Prediction
if st.button('Cardio Disease Test Result'):
cardio_prediction = Cardio_Disease_model.predict([[age,gender,height,weight,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active]])
if cardio_prediction[0]==0:
cardio_disease_diagnosis = 'Patient does not have Cardiovascular Disease'
else:
cardio_disease_diagnosis = 'Patient has Cardiovascular Disease'
st.success(cardio_disease_diagnosis)
“Please note the code for diabetes and heart disease prediction are running smoothly, the cardio and the parkinson disease throws up this error message”
i will appreciate your help
many thanks
Thanks for the response, have sent the whole codes and the error message
looking forward to your response
msny thanks
Hi @Osita
Could you perhaps share the code along with the various pickled files (*.sav) as a GitHub repo, which will allow reproduction of the error.
Looking at the error message, it seems that there’s a data type mismatch. Try using type() to evaluate the data type of various variables in the code. For instance, some of the functions may be expected a number but a string is provided which may be the case for your error ValueError: dtype=‘numeric’ is not compatible with arrays of bytes/strings. Convert your data to numeric values explicitly instead.
If that’s the case then you could try converting the string in question to a numerical value (e.g. float(), int(), etc.)
Hope this helps.
Hi @Osita ,
I looked at the code and it looks like you have a bunch of text inputs in all 4 of the different pages and you basically take all of those inputs (strings) and toss them into an [] and then call predict. I am also thinking that maybe you need to convert from what dataprofessor said but I’m not sure what you’re predict method is doing exactly and what the models are doing. Have you tried also doing this in a python console and it’s possible that Streamlit is the not the issue?
It’s cool to see what you’re doing with streamlit!
Thanks,
William
Hi @dataprofessor
Thanks for your response, but i assure you all the variables there are numeric, again the code runs perfectly and do the needed prediction on jupyter notebook, why are they throwing up errors on streamlit
Hi @Osita,
Thanks for confirming, if you share the entire project as a GitHub repo, we can take a look and tinker around with the code to troubleshoot various possibilities.
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