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import numpy
import pandas
import statsmodels.api as sm
import seaborn
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
# Đọc dữ liệu csv
data = pandas.read_csv('2014and2015CSMdataset.csv', low_memory=False)
# Tinh chỉnh câu hỏi nghiên cứu
sub1=data[(data['Sequel']>=3) & (data['Sequel']<=7)]
recode1 = {"2014":0 ,"2015":1}
# Mã hóa biến phân loại theo kiểu nhị phân cho biến Year
# M tim thu xem con bien nao qui ve 1 voi 0 duoc khong thay cho bien year
recode1 = {"2014":0 ,"2015":1}
#ne . No k co y nghia
sub1['Year1']= sub1['Year'].map(recode1)
sub1['Year1'] = pandas.to_numeric(sub1['Year1'], errors='coerce')
data['Likes'] =pandas.to_numeric(data['Likes'], errors='coerce')
data['Genre'] = pandas.to_numeric(data['Genre'], errors='coerce')
data['Views'] =pandas.to_numeric(data['Views'], errors='coerce')
data['Comments'] =pandas.to_numeric(data['Comments'], errors='coerce')
data['Sequel'] =pandas.to_numeric(data['Sequel'], errors='coerce')
sub1['Year1']= sub1['Year'].map(recode1)
# Chuyển sang dữ liệu số
sub1['Year1'] = pandas.to_numeric(sub1['Year1'], errors='coerce')
# Canh chuẩn biến Comments
sub1['Comments_c'] = (sub1['Comments'] - sub1['Comments'].mean())
sub2 = sub1[['Comments_c', 'Year1']].dropna()
log_reg = smf.logit(formula = 'Year1 ~ Comments_c', data = sub2).fit()
############'Year1 ~ Comments_c' bien truoc dau "~" phai la dang 0 va 1 nghia la co hoac khong.
print(log_reg.summary())
### Đánh giá mô hình logistic
X = sub2['Comments_c']
y = sub2['Year1']
# Chia dữ liệu thành 2 tập: train và test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=50, shuffle=True)
log_reg2 = sm.Logit(y_train, X_train).fit()
yhat = log_reg2.predict(X_test)
prediction = list(map(round, yhat))
from sklearn.metrics import (confusion_matrix, accuracy_Genre)
#confusion_matrix
cm = confusion_matrix(y_test, prediction)
print ("Confusion Matrix : \n", cm)
print('Test accuracy = ', accuracy_Genre(y_test, prediction))

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