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Machine Learning
Risk Management
Expected Credit Loss Model (ECLM) for Agriculture Sector of Pakistan
Expected Credit Loss Model (ECLM) Demonstration through TensorFlow
KSE-100 Index Estimate through Machine Learning
Economy
Estimating GDP Growth of Pakistan through Machine Learning
Population Estimate through Machine Learning
Diamonds are highly precious and water is cheap. Why? diamonds have no real purpose but water is essential for life. Still diamonds are so expensive and water is so cheap. This is because water is available in abundance and with little effort, more water can be arrranged but diamonds are scars and more supply needs more effort. So it is the affect of demand and supply that determines the price of everything.
If we ignore all other factors, including demand, and determine the price on the basis of supply, price will increase as quantitly supplied is reduced and price will decrease with an increase in supply. Supply and price have inverse relationship. Anything vaialbe in abundance and easily has very little monetary value while on the other hand anything scarce has huge value.
Same rule applies to the currency. If supply of PKR is increased, its value will decrease and if supply is decreased, its value will increase. Provided that other factors remain constant. Here we have taken actual data of Pakistan's PKR value in term of USD in comparison with Broad Money.
Year
PKR_Per_USD
Broad_Money
1960
4.76190
7252000000.00000
1961
4.76190
6964300000.00000
1962
4.76190
7596400000.00000
1963
4.76190
8875500000.00000
1964
4.76190
10779400000.00000
1965
4.76190
12058000000.00000
1966
4.76190
14605000000.00000
1967
4.76190
15081400000.00000
1968
4.76190
16959700000.00000
1969
4.76190
18580100000.00000
1970
4.76190
20752100000.00000
1971
4.76190
23609900000.00000
1972
8.68138
27734200000.00000
1973
9.99424
31562200000.00000
1974
9.90000
31182300000.00000
1975
9.90000
37797600000.00000
1976
9.90000
49967300000.00000
1977
9.90000
58935200000.00000
1978
9.90000
70629400000.00000
1979
9.90000
84117500000.00000
1980
9.90000
97321600000.00000
1981
9.90000
108537600000.00000
1982
11.84746
132232500000.00000
1983
13.11697
159881600000.00000
1984
14.04633
167312300000.00000
1985
15.92839
191985100000.00000
1986
16.64750
222842300000.00000
1987
17.39880
259396300000.00000
1988
18.00329
279377500000.00000
1989
20.54149
300054000000.00000
1990
21.70737
334991300000.00000
1991
23.80076
398453100000.00000
1992
25.08279
515202100000.00000
1993
28.10718
608626000000.00000
1994
30.56659
714348000000.00000
1995
31.64268
812998000000.00000
1996
36.07868
976155000000.00000
1997
41.11152
1170525000000.00000
1998
45.04666
1262521000000.00000
1999
49.50069
1316989000000.00000
2000
53.64818
1476676000000.00000
2001
61.92716
1650126000000.00000
2002
59.72378
1927995000000.00000
2003
57.75199
2266163400000.00000
2004
58.25786
2731053000000.00000
2005
59.51447
3182515000000.00000
2006
60.27133
4631578000000.00000
2007
60.73851
5439249000000.00000
2008
70.40803
5794143796000.00000
2009
81.71289
6814495817363.99000
2010
85.19381
7807082726900.00000
2011
86.34338
8790979839286.22000
2012
93.39519
10320172533548.00000
2013
101.62889
11694370341859.60000
2014
101.10008
13044335106623.40000
2015
102.76927
14633262962941.40000
2016
104.76911
16625735215555.40000
2017
105.45516
18276890431006.40000
2018
121.82406
20074311419085.40000
2019
150.03625
22417591739487.40000