Buffer dtype mismatch, expected 'SIZE_t' but got 'long long'

Question

I developed 3 ML models in spyder, they are Linear Regression, Polynomial Regression, and Random Forest Regression. In sypder all of them worked well. However when I deployed on Django for creating a web app, Random Forest was raising " ValueError: Buffer type mismatch, expected 'SIZE_t' but got 'long long' ". (I tried removing randomforest and the other two models worked well).

Check this out first:-

Model Developed in Sypder

"""****************** Import Lib ******************"""
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

"""****************** Loading dataset ******************"""
boston = load_boston()
dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
dataset['target'] = boston.target

"""****************** Data Preprocessing ******************"""
""" Data Analysis """
# Check Null
dataset.isnull().sum()
# Calculate X and y 
X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,-1].values.reshape(-1,1)
# train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=25)
""" Visualizing Data """
corr = dataset.corr()
sns.heatmap(corr, annot=True, cmap='Blues')
sns.pairplot(dataset)

"""****************** Regression Models ******************"""
""" Linear Regression """
from sklearn.linear_model import LinearRegression
regressor_linear = LinearRegression()
regressor_linear.fit(X_train, y_train)
cv_linear = cross_val_score(estimator = regressor_linear, X=X_train, y=y_train, cv=10)

""" Polynomial Regression """
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 2)
X_poly = poly_reg.fit_transform(X_train)
regressor_poly2 = LinearRegression()
regressor_poly2.fit(X_poly, y_train)
cv_poly2 = cross_val_score(estimator=regressor_poly2, X=X_poly, y=y_train, cv=10)

""" Random Forest Regression """
from sklearn.ensemble import RandomForestRegressor
regressor_rf = RandomForestRegressor(n_estimators=500, random_state=0, n_jobs=-1)
regressor_rf.fit(X_train, y_train.ravel())
cv_rf = cross_val_score(estimator=regressor_rf, X=X_train, y=y_train.ravel(), cv=10)

"""****************** Measuring the Error ******************"""
models=[
        ('Linear Regression', cv_linear.mean()),
        ('Polynomial Regression (2)', cv_poly2.mean()),
        ('Random Forest Regression', cv_rf.mean())
        ]
cv_scores = pd.DataFrame(data=models, columns=['Model','CV Score'])

"""****************** Dump ******************"""
from sklearn.externals import joblib
joblib.dump(regressor_linear,'regressor_linear_jb')
joblib.dump(regressor_poly2,'regressor_poly2_jb')
joblib.dump(regressor_rf,'regressor_rf_jb')

Django Implementation Code

from django.shortcuts import render
from django.http import HttpResponse
import json
from django.http import JsonResponse
import pandas as pd
import numpy as np
from sklearn.externals import joblib
from sklearn.preprocessing import PolynomialFeatures
# Create your views here.

# ML Code

regressor_linear = joblib.load('./models/regressor_linear_jb')
regressor_poly2 = joblib.load('./models/regressor_poly2_jb')
regressor_rf = joblib.load('./models/regressor_rf_jb')

# ML Code End

def predict(request):
    temp_data = [
                 0.16902,
                 0,
                 25.65,
                 0,
                 0.581,
                 5.986,
                 88.4,
                 1.9929,
                 2,
                 188,
                 19.1,
                 385.02,
                 14.81,
                 ]
    temp_df = pd.DataFrame(temp_data).transpose()
    predict = {}

    # Linear Regression
    predict['Linear Regressor'] = round(regressor_linear.predict(temp_df)[0, 0], 2)

    # Polynomial Regression.
    regressor_poly = PolynomialFeatures(degree=2)
    temp_df_poly = regressor_poly.fit_transform(temp_df)
    predict['Polynomial Regressor'] = round(regressor_poly2.predict(temp_df_poly)[0, 0], 2)
    
    # Random Forest Regression
    predict['Random Forest Regressor'] = round(regressor_rf.predict(temp_df)[0],2)
    
    return JsonResponse(predict)

Answer 1

Switch environment for Django to anaconda and this will be resolved

Jupyter notebook was using anaconda environment while Django was using different environment which was installed on system (Major problem --> one was 32 bit while other was 64 bit)