I need help identifying the source of this error code. I initially wrote some codes to design linear regression algorithms to determine the probability of a customer purchasing a bike, After the initial preprocessing and training/testing (Which worked), I am trying to combine the codes to create a module for reusability. I encountered the Mismatch error code. What do you think I'm not doing right and how do I avoid the scenario. import numpy as np import pandas as pd import pickle from sklearn.base import BaseEstimator, TransformerMixin from sklearn.preprocessing import StandardScaler class CustomScaler(BaseEstimator,TransformerMixin): def __init__(self,columns): self.scaler = StandardScaler() self.columns = columns self.mean_ = None self.var_ = None def fit(self, X, y=None): self.scaler.fit(X[self.columns], y) self.mean_ = np.mean(X[self.columns]) self.var_ = np.var(X[self.columns]) return self def transform(self, X, y=None, copy=None): init_col_order = X.columns X_scaled = pd.DataFrame(self.scaler.transform(X[self.columns]), columns=self.columns) X_not_scaled = X.loc[:,~X.columns.isin(self.columns)] return pd.concat([X_not_scaled, X_scaled], axis=1)[init_col_order] class Purchased_Bike_model(): def __init__(self, model_file, scaler_file): with open('model','rb') as model_file, open('scaler', 'rb') as scaler_file: self.reg = pickle.load(model_file) self.scaler = pickle.load(scaler_file) self.data = None def load_and_clean_data(self, data_file): # import the data df = pd.read_csv(data_file,delimiter=',') # store the data in a new variable for later use self.df_with_predictions = df.copy() # drop the 'ID' column df = df.drop(['ID'], axis = 1) # create a separate dataframe, containing dummy values for ALL occupations Occupation_column = pd.get_dummies(df['Occupation'], drop_first = True) # to avoid multicollinearity, drop the 'Occupation' column from df df = df.drop(['Occupation'], axis = 1) # concatenate df and the separate occupation dummies df = pd.concat([df, Occupation_column], axis = 1) # note: there is a more universal version of this code, however the following will best suit our current purposes column_names = ['Marital Status', 'Gender', 'Income', 'Children', 'Education','Home Owner', 'Cars', 'Commute Distance', 'Region', 'Age', 'Purchased Bike', 'Clerical', 'Management', 'Manual', 'Professional', 'Skilled Manual'] df.columns = column_names # re-order the columns in df column_names_reordered = ['Marital Status', 'Gender', 'Income', 'Children', 'Education', 'Clerical', 'Management', 'Manual', 'Professional', 'Skilled Manual', 'Home Owner', 'Cars', 'Commute Distance', 'Region', 'Age', 'Purchased Bike'] df = df[column_names_reordered] # encode all categorical variables df['Marital Status'] = df['Marital Status'].map({'M':1, 'S':0}) df['Gender'] = df['Gender'].map({'M':1, 'F':0}) df['Education'] = df['Education'].map({'Partial High School':0,'High School':1,'Partial College':2, 'Bachelors':3, 'Graduate Degree':4}) df['Home Owner'] = df['Home Owner'].map({'Yes':1, 'No':0}) df['Commute Distance'] = df['Commute Distance'].map({'0-1 Miles':0, '1-2 Miles':1, '2-5 Miles':2, '5-10 Miles':3, '10+ Miles':4}) df['Region'] = df['Region'].map({'Europe':0, 'Pacific':1, 'North America':2}) df['Purchased Bike'] = df['Purchased Bike'].map({'Yes':1, 'No':0}) df = df.drop(['Purchased Bike'],axis=1) # we have included this line of code if you want to call the 'preprocessed data' self.preprocessed_data = df.copy() # we need this line so we can use it in the next functions self.data = self.scaler.transform(df) # a function which outputs the probability of a data point to be 1 def predicted_probability(self): if (self.data is not None): pred = self.reg.predict_proba(self.data)[:,1] return pred # a function which outputs 0 or 1 based on our model def predicted_output_category(self): if (self.data is not None): pred_outputs = self.reg.predict(self.data) return pred_outputs # predict the outputs and the probabilities and # add columns with these values at the end of the new data def predicted_outputs(self): if (self.data is not None): self.preprocessed_data['Probability'] = self.reg.predict_proba(self.data)[:,1] self.preprocessed_data ['Prediction'] = self.reg.predict(self.data) return self.preprocessed_data

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