leap year program using functions in python

def is_leap(year):

    leap = False

    

    # Write your logic here

    if year>= 1900 and year <=10**5:

        if (year % 4) == 0:

            if (year % 100) == 0:

                if (year % 400) == 0:

                     return True

                else:

                    return leap

            else:

                return True

        else:

            return leap

    

year = int(raw_input())

print(is_leap(year))

print a character and a string and sentence

#include <stdio.h>
#include <stdlib.h>


int main()
{
char a,a1[50],a2[100];
scanf("%c%*c",&a);
scanf("%s%*c",&a1);
scanf("%[^\n]",&a2);
printf("%c\n%s\n%s",a,a1,a2);
return 0;
}

output:
h
hello
how are you

h
hello
how are you

polynomial regression in machine learning using python

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(2)
page_speeds = np.random.normal(3.0,1.0,100)
purchase_amount = np.random.normal(50.0,30.0,100)/page_speeds

plt.scatter(page_speeds,purchase_amount)

Out[3]:

<matplotlib.collections.PathCollection at 0x27ca0f7e788>

In [4]:

train_x = page_speeds[:80]
test_x = page_speeds[80:]

train_y = purchase_amount[:80]
test_y = purchase_amount[80:]

In [5]:

plt.scatter(train_x,train_y)

Out[5]:

<matplotlib.collections.PathCollection at 0x27ca0fee248>

In [7]:

plt.scatter(test_x,test_y)

Out[7]:

<matplotlib.collections.PathCollection at 0x27ca10fcb48>

In [10]:

x =np.array(train_x)
y =np.array(train_y)

p4 = np.poly1d(np.polyfit(x,y,8))

In [12]:

import matplotlib.pyplot as plt

xp =np.linspace(0,7,100)
axes = plt.axes()
axes.set_xlim([0,7])
axes.set_ylim([0,200])
plt.scatter(x,y)
plt.plot(xp,p4(xp),c='r')
plt.show()

In [13]:

testx =np.array(test_x)
testy =np.array(test_y)


axes = plt.axes()
axes.set_xlim([0,7])
axes.set_ylim([0,200])
plt.scatter(testx,testy)
plt.plot(xp,p4(xp),c='r')
plt.show()

In [14]:

from sklearn.metrics import r2_score
r2 =r2_score(testy,p4(testx))
print(r2)

0.30018168611266705

In [ ]: