AlicanAKCA

@author: Alican AKCA
"""
import pandas as pd #Kütüphaneleri ekledik!
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix

veriler = pd.read_csv('Datasets.csv') # We have loaded our dataset. Note that it is located in the same directory.
x = veriler.iloc[:,3:7].values # We will view our columns.

AlicanAKCA

#####################################################################################
#                            Dec 16 2020                                            #
#                            github.com/Alicanakca                                  #
#####################################################################################
import requests
import json
from datetime import datetime
from matplotlib import pyplot as plt

x = [] #For dates : year-month-day

AlicanAKCA

# Dataset : https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/diamonds.csv
# Alican AKCA - 20.01.2021

#### Required Libraries

# For Visualization and Calculation
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.pylab as pylab
import seaborn as sns

AlicanAKCA

from matplotlib import pyplot as plt
from random import randint
num1 =0
num2=0
summary = 0
mean = 0
numberOfBeats = 0
plt.title('')
plt.ylabel('Mean')
plt.xlabel('Number Of Beats')

AlicanAKCA

Machine Learning Week 1 Quiz 2 (Linear Regression with One Variable) Stanford Coursera

Github repo for the Course: Stanford Machine Learning (Coursera)

Question 1

Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year.

Specifically, let x be equal to the number of "A" grades (including A-. A and A+ grades) that a student receives in their first year of college (freshmen year). We would like to predict the value of y, which we define as the number of "A" grades they get in their second year (sophomore year).

AlicanAKCA

import random
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix