Unnecessary lines for Admission Prediction using ML

[Ahmet]

Describe the bug
There is a two line of code in the script that affects to run correctly.
And they are useless. pandas can read csv file without them. so we can clean those.

To Reproduce
Steps to reproduce the behavior:
just run all the script.

Clean version;

Admission_Prediction_using_Machine_Learning By Zahra Shahid

"""# Import libraries"""

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

"""# Upload and Read file"""

#from google.colab import files
#files.upload()

df = pd.read_csv("Admission_Predict_Ver1.1.csv")

df.head(8)

"""# Cleaning the data"""

df.columns

df.drop('Serial No.',axis=1,inplace=True)

df.head()

"""#Exploratory Data Aanalysis"""

df.describe()

df.corr()

sns.heatmap(df.corr(), annot=True)

sns.distplot(df.CGPA)

sns.pairplot(df,x_vars=['SOP','GRE Score','TOEFL Score','CGPA'],y_vars=['Chance of Admit '],height=5, aspect=0.8, kind='reg')

"""# Creating Model"""

df.columns

x=df[['GRE Score', 'TOEFL Score', 'CGPA']]

y=df[['Chance of Admit ']]

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import random

x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.20,random_state=0)

x_train.shape

y_train.shape

linreg = LinearRegression()
linreg.fit(x_train,y_train)

"""# Testing and Evaluating the Model"""

y_pred=linreg.predict(x_test)

y_pred[:7]

y_test.head(7)

from sklearn import metrics
print(metrics.mean_absolute_error(y_test,y_pred)) #96% prediction