Linear Model

import sklearn
from sklearn.utils import shuffle
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
from sklearn import linear_model, preprocessing
import matplotlib.pyplot as plt
from matplotlib import style
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

def GetAccuracity(Accuracity):
    Accuracity = str(round(Accuracity*100,2)) + str("%")
    print("- Accuracity:",Accuracity)

def Get_2D_Plot(XLabel, YLabel, Prediction, Data, Style):
    style.use(Style)
    plt.scatter(Data[XLabel], Data[Prediction])
    plt.xlabel(XLabel)
    plt.ylabel(YLabel)
    plt.show()

def Get_3D_Plot(XLabel, YLabel, ZLabel, Data, Style):
    style.use(Style)
    Graph = plt.figure()
    ax = Graph.add_subplot(111, projection='3d')

    x = Data[XLabel]
    y = Data[YLabel]
    z = Data[ZLabel]
    ax.scatter(x, y, z, c='r', marker='o')

    ax.set_xlabel("")
    ax.set_ylabel("")
    ax.set_zlabel("")

    plt.show()

Data = pd.read_csv("Students.csv", sep=",")
Data = Data[["GRE Score", "TOEFL Score", "University Rating", "SOP", "LOR", "CGPA", "Research", "Chance of Admit"]]
MyPrediction = "Chance of Admit"

Features = np.array(Data.drop([MyPrediction], 1))
CA = np.array(Data[MyPrediction])

# Linear Model.
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(Features, CA, test_size = 0.1)
LinearModel = linear_model.LinearRegression()
LinearModel.fit(x_train, y_train)
Accuracity = LinearModel.score(x_test, y_test)

XLabel = "GRE Score"
YLabel = "TOEFL Score"
ZLabel = "Chance of Admit"
Get_3D_Plot(XLabel, YLabel, ZLabel, Data, "ggplot")
Get_2D_Plot(XLabel, YLabel, MyPrediction, Data, "ggplot")
GetAccuracity(Accuracity)