# -*- coding: utf-8 -*-
"""model.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1hA9Xz_VjzMVj66qS_j3A5dKcGkAfScKM
"""

pip install pycaret

from scipy import stats

import numpy as np

import pandas as pd

import pickle

import matplotlib.pyplot as plt
# create fictitious data set 
simple_life_dataset = pd.DataFrame({'Age':[0, 60], 'Life Expectancy':[90, 30]})
simple_life_dataset.head()

import numpy as np
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(simple_life_dataset['Age'],simple_life_dataset['Life Expectancy'])
print('intercept: ', intercept)
print('slope: ', slope)

np.ceil(slope * 20 + intercept)

fig, axes = plt.subplots(figsize=(5,5))
x = [0,20,60]
y = [90, 70, 30]
axes.plot(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life_expectancy')
axes.set_ylim([0,100])
plt.grid()
plt.show()

# load WHO longevity data
# http://apps.who.int/gho/data/node.main.688
who_list = pd.read_csv('/content/drive/MyDrive/WHOSIS_000001,WHOSIS_000015.csv')
# save a local copy of the data set for our Flask prototype later on
who_list.to_csv('WHOSIS_000001,WHOSIS_000015.csv')

# Keep only useful features fix case display of country text
who_list = who_list[['GHO (DISPLAY)', 'YEAR (CODE)' , 'COUNTRY (DISPLAY)', 'SEX (DISPLAY)', 'Numeric']]
who_list['COUNTRY (DISPLAY)'] = [ctry.title() for ctry in who_list['COUNTRY (DISPLAY)'].values]
# print a few rows
who_list[who_list['COUNTRY (DISPLAY)']=='France'].head(10)

country = 'United States Of America'
sex = 'Male'
# pull latest entries for birth and 60 years for a country and gender
sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
# sort by year in descending order to work with the latest read
sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']
print('sub_set_birth:')
print(sub_set_birth.head(5))
print('sub_set_60:')
print(sub_set_60.head(5))

# create data set with both points as shown in first example
lf_at_birth = sub_set_birth['Numeric'].values[0]
lf_at_60 = sub_set_60['Numeric'].values[0]
# let's organize our data and plot
age = [0,60]
life_expectancy = [lf_at_birth, lf_at_60]
fig, axes = plt.subplots(figsize=(5,5))
x = age
y = life_expectancy
axes.plot(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life expectancy')
axes.set_ylim([0,100])
plt.grid()
plt.show()

# model 
slope, intercept, r_value, p_value, std_err = stats.linregress(age, life_expectancy)
print('intercept: ', intercept)
print('slope: ', slope)

# predict life expectancy for an 49-year-old male in the USA:
np.ceil(slope * 49 + intercept)

def get_life_expectancy(age, country, sex):
    # pull latest entries for birth and 60 years
    sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
    sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
    sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
    sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
    sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']

    # not all combinations exsits so check that we have data for both
    if len(sub_set_birth['Numeric']) > 0 and len(sub_set_60['Numeric']) > 0:
        # create data set with both points as shown in first example
        lf_at_birth = sub_set_birth['Numeric'].values[0]
        lf_at_60 = sub_set_60['Numeric'].values[0]

        # model
        slope, intercept, r_value, p_value, std_err = stats.linregress([0,60],[lf_at_birth, lf_at_60])

        # predict for the age variable
        return(np.ceil(slope * age + intercept))
    else:
        return None

list(set(who_list['COUNTRY (DISPLAY)']))[0:10]

# test the function out using a 22-year-old Japanese female:
get_life_expectancy(22, 'Japan', 'Female')

get_life_expectancy(22,'Pakistan','Female')

get_life_expectancy(21,'India','Male')

missing_values_count = who_list.isnull().sum()

print(missing_values_count)

get_life_expectancy(80,'Pakistan','Female')

pickle.dump(get_life_expectancy,open('model.pkl','wb'))

model=pickle.load(open('model.pkl','rb'))
{"language": ["en"], "tags": ["code"]}
code en region:us
TAGS
#code #en #region-us 

# -*- coding: utf-8 -*-
"""URL

Automatically generated by Colaboratory.

Original file is located at
    URL
"""

pip install pycaret

from scipy import stats

import numpy as np

import pandas as pd

import pickle

import URL as plt
# create fictitious data set 
simple_life_dataset = pd.DataFrame({'Age':[0, 60], 'Life Expectancy':[90, 30]})
simple_life_dataset.head()

import numpy as np
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(simple_life_dataset['Age'],simple_life_dataset['Life Expectancy'])
print('intercept: ', intercept)
print('slope: ', slope)

URL(slope * 20 + intercept)

fig, axes = plt.subplots(figsize=(5,5))
x = [0,20,60]
y = [90, 70, 30]
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life_expectancy')
axes.set_ylim([0,100])
URL()
URL()

# load WHO longevity data
# URL
who_list = pd.read_csv('/content/drive/MyDrive/WHOSIS_000001,WHOSIS_000015.csv')
# save a local copy of the data set for our Flask prototype later on
who_list.to_csv('WHOSIS_000001,WHOSIS_000015.csv')

# Keep only useful features fix case display of country text
who_list = who_list[['GHO (DISPLAY)', 'YEAR (CODE)' , 'COUNTRY (DISPLAY)', 'SEX (DISPLAY)', 'Numeric']]
who_list['COUNTRY (DISPLAY)'] = [URL() for ctry in who_list['COUNTRY (DISPLAY)'].values]
# print a few rows
who_list[who_list['COUNTRY (DISPLAY)']=='France'].head(10)

country = 'United States Of America'
sex = 'Male'
# pull latest entries for birth and 60 years for a country and gender
sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
# sort by year in descending order to work with the latest read
sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']
print('sub_set_birth:')
print(sub_set_birth.head(5))
print('sub_set_60:')
print(sub_set_60.head(5))

# create data set with both points as shown in first example
lf_at_birth = sub_set_birth['Numeric'].values[0]
lf_at_60 = sub_set_60['Numeric'].values[0]
# let's organize our data and plot
age = [0,60]
life_expectancy = [lf_at_birth, lf_at_60]
fig, axes = plt.subplots(figsize=(5,5))
x = age
y = life_expectancy
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life expectancy')
axes.set_ylim([0,100])
URL()
URL()

# model 
slope, intercept, r_value, p_value, std_err = stats.linregress(age, life_expectancy)
print('intercept: ', intercept)
print('slope: ', slope)

# predict life expectancy for an 49-year-old male in the USA:
URL(slope * 49 + intercept)

def get_life_expectancy(age, country, sex):
    # pull latest entries for birth and 60 years
    sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
    sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
    sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
    sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
    sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']

    # not all combinations exsits so check that we have data for both
    if len(sub_set_birth['Numeric']) > 0 and len(sub_set_60['Numeric']) > 0:
        # create data set with both points as shown in first example
        lf_at_birth = sub_set_birth['Numeric'].values[0]
        lf_at_60 = sub_set_60['Numeric'].values[0]

        # model
        slope, intercept, r_value, p_value, std_err = stats.linregress([0,60],[lf_at_birth, lf_at_60])

        # predict for the age variable
        return(URL(slope * age + intercept))
    else:
        return None

list(set(who_list['COUNTRY (DISPLAY)']))[0:10]

# test the function out using a 22-year-old Japanese female:
get_life_expectancy(22, 'Japan', 'Female')

get_life_expectancy(22,'Pakistan','Female')

get_life_expectancy(21,'India','Male')

missing_values_count = who_list.isnull().sum()

print(missing_values_count)

get_life_expectancy(80,'Pakistan','Female')

URL(get_life_expectancy,open('URL','wb'))

model=URL(open('URL','rb'))
# -*- coding: utf-8 -*-
"""URL

Automatically generated by Colaboratory.

Original file is located at
    URL
"""

pip install pycaret

from scipy import stats

import numpy as np

import pandas as pd

import pickle

import URL as plt # create fictitious data set 
simple_life_dataset = pd.DataFrame({'Age':[0, 60], 'Life Expectancy':[90, 30]})
simple_life_dataset.head()

import numpy as np
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(simple_life_dataset['Age'],simple_life_dataset['Life Expectancy'])
print('intercept: ', intercept)
print('slope: ', slope)

URL(slope * 20 + intercept)

fig, axes = plt.subplots(figsize=(5,5))
x = [0,20,60]
y = [90, 70, 30]
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life_expectancy')
axes.set_ylim([0,100])
URL()
URL() # load WHO longevity data # URL
who_list = pd.read_csv('/content/drive/MyDrive/WHOSIS_000001,WHOSIS_000015.csv') # save a local copy of the data set for our Flask prototype later on
who_list.to_csv('WHOSIS_000001,WHOSIS_000015.csv') # Keep only useful features fix case display of country text
who_list = who_list[['GHO (DISPLAY)', 'YEAR (CODE)' , 'COUNTRY (DISPLAY)', 'SEX (DISPLAY)', 'Numeric']]
who_list['COUNTRY (DISPLAY)'] = [URL() for ctry in who_list['COUNTRY (DISPLAY)'].values] # print a few rows
who_list[who_list['COUNTRY (DISPLAY)']=='France'].head(10)

country = 'United States Of America'
sex = 'Male' # pull latest entries for birth and 60 years for a country and gender
sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex] # sort by year in descending order to work with the latest read
sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']
print('sub_set_birth:')
print(sub_set_birth.head(5))
print('sub_set_60:')
print(sub_set_60.head(5)) # create data set with both points as shown in first example
lf_at_birth = sub_set_birth['Numeric'].values[0]
lf_at_60 = sub_set_60['Numeric'].values[0] # let's organize our data and plot
age = [0,60]
life_expectancy = [lf_at_birth, lf_at_60]
fig, axes = plt.subplots(figsize=(5,5))
x = age
y = life_expectancy
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life expectancy')
axes.set_ylim([0,100])
URL()
URL() # model 
slope, intercept, r_value, p_value, std_err = stats.linregress(age, life_expectancy)
print('intercept: ', intercept)
print('slope: ', slope) # predict life expectancy for an 49-year-old male in the USA:
URL(slope * 49 + intercept)

def get_life_expectancy(age, country, sex):
    # pull latest entries for birth and 60 years
    sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
    sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
    sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
    sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
    sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']

    # not all combinations exsits so check that we have data for both
    if len(sub_set_birth['Numeric']) > 0 and len(sub_set_60['Numeric']) > 0:
        # create data set with both points as shown in first example
        lf_at_birth = sub_set_birth['Numeric'].values[0]
        lf_at_60 = sub_set_60['Numeric'].values[0]

        # model
        slope, intercept, r_value, p_value, std_err = stats.linregress([0,60],[lf_at_birth, lf_at_60])

        # predict for the age variable
        return(URL(slope * age + intercept))
    else:
        return None

list(set(who_list['COUNTRY (DISPLAY)']))[0:10] # test the function out using a 22-year-old Japanese female:
get_life_expectancy(22, 'Japan', 'Female')

get_life_expectancy(22,'Pakistan','Female')

get_life_expectancy(21,'India','Male')

missing_values_count = who_list.isnull().sum()

print(missing_values_count)

get_life_expectancy(80,'Pakistan','Female')

URL(get_life_expectancy,open('URL','wb'))

model=URL(open('URL','rb'))
TAGS
#code #en #region-us 
 # -*- coding: utf-8 -*-
"""URL

Automatically generated by Colaboratory.

Original file is located at
    URL
"""

pip install pycaret

from scipy import stats

import numpy as np

import pandas as pd

import pickle

import URL as plt # create fictitious data set 
simple_life_dataset = pd.DataFrame({'Age':[0, 60], 'Life Expectancy':[90, 30]})
simple_life_dataset.head()

import numpy as np
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(simple_life_dataset['Age'],simple_life_dataset['Life Expectancy'])
print('intercept: ', intercept)
print('slope: ', slope)

URL(slope * 20 + intercept)

fig, axes = plt.subplots(figsize=(5,5))
x = [0,20,60]
y = [90, 70, 30]
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life_expectancy')
axes.set_ylim([0,100])
URL()
URL() # load WHO longevity data # URL
who_list = pd.read_csv('/content/drive/MyDrive/WHOSIS_000001,WHOSIS_000015.csv') # save a local copy of the data set for our Flask prototype later on
who_list.to_csv('WHOSIS_000001,WHOSIS_000015.csv') # Keep only useful features fix case display of country text
who_list = who_list[['GHO (DISPLAY)', 'YEAR (CODE)' , 'COUNTRY (DISPLAY)', 'SEX (DISPLAY)', 'Numeric']]
who_list['COUNTRY (DISPLAY)'] = [URL() for ctry in who_list['COUNTRY (DISPLAY)'].values] # print a few rows
who_list[who_list['COUNTRY (DISPLAY)']=='France'].head(10)

country = 'United States Of America'
sex = 'Male' # pull latest entries for birth and 60 years for a country and gender
sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex] # sort by year in descending order to work with the latest read
sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']
print('sub_set_birth:')
print(sub_set_birth.head(5))
print('sub_set_60:')
print(sub_set_60.head(5)) # create data set with both points as shown in first example
lf_at_birth = sub_set_birth['Numeric'].values[0]
lf_at_60 = sub_set_60['Numeric'].values[0] # let's organize our data and plot
age = [0,60]
life_expectancy = [lf_at_birth, lf_at_60]
fig, axes = plt.subplots(figsize=(5,5))
x = age
y = life_expectancy
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life expectancy')
axes.set_ylim([0,100])
URL()
URL() # model 
slope, intercept, r_value, p_value, std_err = stats.linregress(age, life_expectancy)
print('intercept: ', intercept)
print('slope: ', slope) # predict life expectancy for an 49-year-old male in the USA:
URL(slope * 49 + intercept)

def get_life_expectancy(age, country, sex):
    # pull latest entries for birth and 60 years
    sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
    sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
    sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
    sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
    sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']

    # not all combinations exsits so check that we have data for both
    if len(sub_set_birth['Numeric']) > 0 and len(sub_set_60['Numeric']) > 0:
        # create data set with both points as shown in first example
        lf_at_birth = sub_set_birth['Numeric'].values[0]
        lf_at_60 = sub_set_60['Numeric'].values[0]

        # model
        slope, intercept, r_value, p_value, std_err = stats.linregress([0,60],[lf_at_birth, lf_at_60])

        # predict for the age variable
        return(URL(slope * age + intercept))
    else:
        return None

list(set(who_list['COUNTRY (DISPLAY)']))[0:10] # test the function out using a 22-year-old Japanese female:
get_life_expectancy(22, 'Japan', 'Female')

get_life_expectancy(22,'Pakistan','Female')

get_life_expectancy(21,'India','Male')

missing_values_count = who_list.isnull().sum()

print(missing_values_count)

get_life_expectancy(80,'Pakistan','Female')

URL(get_life_expectancy,open('URL','wb'))

model=URL(open('URL','rb'))
TAGS
#code #en #region-us 
# -*- coding: utf-8 -*-
"""URL

Automatically generated by Colaboratory.

Original file is located at
    URL
"""

pip install pycaret

from scipy import stats

import numpy as np

import pandas as pd

import pickle

import URL as plt# create fictitious data set 
simple_life_dataset = pd.DataFrame({'Age':[0, 60], 'Life Expectancy':[90, 30]})
simple_life_dataset.head()

import numpy as np
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(simple_life_dataset['Age'],simple_life_dataset['Life Expectancy'])
print('intercept: ', intercept)
print('slope: ', slope)

URL(slope * 20 + intercept)

fig, axes = plt.subplots(figsize=(5,5))
x = [0,20,60]
y = [90, 70, 30]
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life_expectancy')
axes.set_ylim([0,100])
URL()
URL()# load WHO longevity data# URL
who_list = pd.read_csv('/content/drive/MyDrive/WHOSIS_000001,WHOSIS_000015.csv')# save a local copy of the data set for our Flask prototype later on
who_list.to_csv('WHOSIS_000001,WHOSIS_000015.csv')# Keep only useful features fix case display of country text
who_list = who_list[['GHO (DISPLAY)', 'YEAR (CODE)' , 'COUNTRY (DISPLAY)', 'SEX (DISPLAY)', 'Numeric']]
who_list['COUNTRY (DISPLAY)'] = [URL() for ctry in who_list['COUNTRY (DISPLAY)'].values]# print a few rows
who_list[who_list['COUNTRY (DISPLAY)']=='France'].head(10)

country = 'United States Of America'
sex = 'Male'# pull latest entries for birth and 60 years for a country and gender
sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]# sort by year in descending order to work with the latest read
sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']
print('sub_set_birth:')
print(sub_set_birth.head(5))
print('sub_set_60:')
print(sub_set_60.head(5))# create data set with both points as shown in first example
lf_at_birth = sub_set_birth['Numeric'].values[0]
lf_at_60 = sub_set_60['Numeric'].values[0]# let's organize our data and plot
age = [0,60]
life_expectancy = [lf_at_birth, lf_at_60]
fig, axes = plt.subplots(figsize=(5,5))
x = age
y = life_expectancy
URL(x,y, color='blue', linestyle='--', marker='o')
fig.suptitle('Life Expectancy')
axes.set_xlabel('age')
axes.set_xlim([-5,100])
axes.set_ylabel('life expectancy')
axes.set_ylim([0,100])
URL()
URL()# model 
slope, intercept, r_value, p_value, std_err = stats.linregress(age, life_expectancy)
print('intercept: ', intercept)
print('slope: ', slope)# predict life expectancy for an 49-year-old male in the USA:
URL(slope * 49 + intercept)

def get_life_expectancy(age, country, sex):
    # pull latest entries for birth and 60 years
    sub_set = who_list[who_list['COUNTRY (DISPLAY)'].str.startswith(country, na=False)]
    sub_set = sub_set[sub_set['SEX (DISPLAY)'] == sex]
    sub_set = sub_set.sort_values('YEAR (CODE)', ascending=False)
    sub_set_birth = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at birth (years)']
    sub_set_60 = sub_set[sub_set['GHO (DISPLAY)'] == 'Life expectancy at age 60 (years)']

    # not all combinations exsits so check that we have data for both
    if len(sub_set_birth['Numeric']) > 0 and len(sub_set_60['Numeric']) > 0:
        # create data set with both points as shown in first example
        lf_at_birth = sub_set_birth['Numeric'].values[0]
        lf_at_60 = sub_set_60['Numeric'].values[0]

        # model
        slope, intercept, r_value, p_value, std_err = stats.linregress([0,60],[lf_at_birth, lf_at_60])

        # predict for the age variable
        return(URL(slope * age + intercept))
    else:
        return None

list(set(who_list['COUNTRY (DISPLAY)']))[0:10]# test the function out using a 22-year-old Japanese female:
get_life_expectancy(22, 'Japan', 'Female')

get_life_expectancy(22,'Pakistan','Female')

get_life_expectancy(21,'India','Male')

missing_values_count = who_list.isnull().sum()

print(missing_values_count)

get_life_expectancy(80,'Pakistan','Female')

URL(get_life_expectancy,open('URL','wb'))

model=URL(open('URL','rb'))