Linear Regression Prediction Model for predicting Graduate Admissions in Python with Scikit-Learn

The program code is used for prediction of  Graduate Admissions which uses the Linear Regression Model and cross-validation to split the dataset into training and testing data, including R squared to show how the data fit the model. The program code also allows the user to input it performs or scores to check whether he or she has the chance of admission. The dataset was downloaded from www.kaggle.com, below is the dataset download link;
https://www.kaggle.com/mohansacharya/graduate-admissions


#importing the essential libraries
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

#importing dataset
df=pd.read_csv('Admission_Predict_Ver1.1.csv',index_col=0)
#independent or explanatory variables
feature_cols=['GRE Score','TOEFL Score','LOR','CGPA','Research']
X=np.array(df[feature_cols],dtype=np.float64)
#dependent or response variable
y=np.array(df['Chance of Admit'],dtype=np.float64)
#cross validation; splitting dataset into training and testing data
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)
#Linear Regression model
lr=LinearRegression(n_jobs=-1)
#fitting the model
lr.fit(X_train,y_train,sample_weight=None)
y_pred=lr.predict(X_test) #predicting using the test dataset
accuracy=lr.score(X_test,y_test) #R squared shows how the data fit the model
#pred_x=np.array([337,118,4,4.5,4.5,9.65,1]) //array numbers for prediction on test dataset
#pred_x=pred_x.reshape(1,-1)
#pred_y=lr.predict(pred_x)
#print(pred_y)
print("R^2 or Accuracy : " , round(accuracy,2))
#print(X_test[:5])
#print(y_pred[:5])

#inputting of independent variables to show whether you can be admitted or not
v1 = input('Please enter your GRE score : ')
v2 = input('Please enter your TOEFL score : ')
v3 = input('Please enter your LOR : ')
v4 = input('Please enter your CGPA : ')
v5 = input('Please enter "1" if you did a research or "0" if you did not do a research : ')
pred_n=np.array([v1,v2,v3,v4,v5],dtype=np.float64)
pred_n=pred_n.reshape(1,-1)
pred_ad=lr.predict(pred_n)
print('============================================================================== \n ===Records entered===')
print(' GRE Score : ', v1, '\n TOEFL Score : ', v2, '\n LOR : ', v3, '\n CGPA : ', v4)
if(v5=="1"):
    print (' Research : Yes')
elif(v5=="0"):
    print(' Research : No')
elif(v5>"1"):
    print(' Research : Invalid input')
else:
    print(' Research : Invalid input')

pre_ad_round=np.round(np.array(pred_ad*100),2)#round-off the result
predict_admissions = str(pre_ad_round)[1:-1]#remove the square bracket
print(' Your chance of admission is', predict_admissions,'%')
if(pre_ad_round > 55.5):
    print (" Congratulations!!! You have the chance of admission")
else :
    print (" Whooops!!! You did not reach the pass mark, sorry more room for improvement!")
print(" ==========\n Thank you\n ==========")