How do I get this program to prepare data for linear regression?

import numpy as np
import pandas as pd

# Do not change these options; This allows the CodeGrade auto grading to function correctly
pd.set_option('display.max_columns', None)
import warnings
warnings.filterwarnings('ignore') 

fileName = "JohnnyPiesData.csv"
pie_df = pd.read_csv('JohnnyPiesData.csv')
pie_df

pie_df =pie_df.drop(['Example'],axis=1);
features = pie_df.drop(['Example'], axis =  1); 
response = pd.get_dummies(pie_df['Example'])
pie_df

from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
reg_model = LinearRegression()
reg_model.fit(features, response)

reg_model.coef_
reg_model.intercept_

preds = reg_model.predict (features)
preds [preds <= 0.5] = 0
preds[preds > 0.5] = 1

resp_comp = response.copy() 
reg_outputs = [float(reg_model.predict(np.reshape(row, (1, -1)))) for row in features.itertuples(index=False)]
predicted_resp = np.array([1 if reg_output > 0.5 else 0 for reg_output in reg_outputs])
resp_comp = resp_comp.assign(Regression_Predictions = reg_outputs)
resp_comp = resp_comp.assign(Predicted_Responses = predicted_resp)
resp_comp

acc_score = accuracy_score(response, preds)

pie_df =pie_df.drop(['Example'],axis=1);
features = pie_df.drop(['Example'], axis =  1); 
response = pd.get_dummies(pie_df['Example'])
pie_df