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S Y ED  M OH AM MAD  M OIE Z  U R R A H M AN
ASP IR IN G D ATA  A N ALY ST	
M OIE Z R16 1	w ww.lin ke d in .c o m /in /s y e d -m -m oie z-u r-
r a h m an -9 a1a 17 2 4 8	
http s:/ /g it h ub .c o m /M oie zR	O KH LA , N EW  D ELH I	
P R O JEC TS
Auth ore d  R ese arc h  P ap er T it le d  &quot; C hro n ic  K id n ey D is ea se	 	
P re d ic tio n  u sin g M ach in e L e arn in g: F eatu re  S ele ctio n &quot;
Pap er c u rre n tly  u n d er p ro ce ss	
S ele cte d  t h e b est 1 0  f e atu re s f ro m  t h e a va ila b le  2 6  f e atu re  s p ace  u sin g	 	
S ele ctK Best m eth o d .
R ed uce d  t h e r is k  o f o ve rfit tin g  o n d ata  m od el  b y lo w erin g  t h e d im en sio n a n d	 	
th e f e atu re  s p ace  o f t h e d ata se t.
C om pare d  t w o d if fe re n t m od els ,  t r a in in g  b oth  o n t h e b est 1 0  f e atu re s	 	
se le cte d :  G au ssia n  N aiv e  B aye s a n d  K
A ch ie ve d  t h e h ig hest a ccu ra cy u sin g  t h e K	 	
T e ch niq ue (X G Boost),  9 8% .	
S peech  E m otio n  D ete ctio n  w it h  F u zzy  c la ssi
Pro je ct L in k	
Tra in ed  a  n eura l  n etw ork  m od el  t o  d ete ct t h e e m otio n in  a u d io  f ile s o f	 	
p eo p le &#039;s  s p eech .
N eura l  N etw ork  w as t r a in ed  o n t h e L o ng  S ho rt T e rm  M em ory  m od el  t o	 	
c a p tu re  e ffe ctiv e  d ep en d en cie s in  p ast d ata .
T ES S d ata se t w as u se d  f o r t h e d eve lo p m en t o f t h is  p ro je ct.	
U ni	 	
an d F acia l A naly sis  ( c u rre n tly  o n -g oin g)
In te g ra tin g  t e xt,  s p eech  a n d  f a cia l  a n aly sis  o n a  s in g ula r b acke n d  p la tfo rm .	
Rob erta  m od el  f ro m  t h e H ug gin g  F ace  lib ra ry  b y M eta  f o r s e n tim en t a n aly sis	 	
o f d ata  in  t e xtu al  f o rm at o r &quot; c h ats &quot;
S elf  d eve lo p ed  m od el  ( m en tio ned  a b ove ) f o r t h e s e n tim en t a n aly sis  o f d ata  in	 	
a u d io  f o rm at o r &quot; s p eech &quot;
O pen C V lib ra ry  u se d  f o r t h e s e n tim en t a n aly sis  o f f a cia l  e xp re ssio ns.	
E D U CATIO N
SH ARD A U NIV ER SIT Y  |  G REATE R  N OID A, U TTA R	 	
PR A D ES H
Bac h elo r o f T e ch nolo g y i n  C om pute r S cie n ce	
10/2021 	&nbsp;	
GP A	
8.0 1	10 .0	
R ele va n t C ours e w ork :  D ata b ase  M an ag em en t S yste m s,  N eura l  N etw ork s,	 	
O pera tin g  S yste m s,  C om pute r N etw ork s,  In tro d uctio n t o  E ntre p re n eurs h ip ,	 	
D eep  le arn in g	
IN TE R N ATIO N AL I N DIA N  S C H OOL A l- J ubail   |  K S A
Hig h S ch ool	
04/200 7 	&nbsp;	
Cla ss X IIt h :  8 3 %
C la ss X th :  8 2.6 %	
S U M MARY
Deta il  a n d  g oal  o rie n te d  d ata  d riv e n  in d iv id ual  w it h  a	 	
p assio n in  e xp lo rin g  p atte rn s a n d  d ep en d en cie s	 	
am ong  v a rio us la rg e d ata se ts .  O rg an iz in g ,	 	
pre p ro ce ssin g  a n d  c le an in g  d ata se ts  o f v a ry in g  s iz e	 	
to  e ffic ie n tly  u se  t h em  a n aly sis  a n d  p re d ic tio ns	 	
le ve ra g in g  v a rio us t e ch nic a l  t o ols .
A C H IE V EM EN TS	
Hac kath on  W in ner i n  t h e D ata  S cie n ce	 	
D om ain
W on a  h acka th o n c o nd ucte d  in  a  S hard a	 	
U niv e rs it y  b ootc a m p in  t h e D ata  S cie n ce	 	
d om ain .
W in ner o f S park ath on &#039;2 3
W on a n  in te r- d ep artm en t a p tit u d e a n d  s o ft	 	
s k ills  b ase d  c o m petit io n.
T e ch noK ra ts  C od in g  C om petit io n  F in alis t
R each ed  t h e f in al  r o un d  o f p ro b le m  s o lv in g	 	
b ase d  c o d in g  c o m petit io n o rg an iz e d  in  S hard a	 	
U niv e rs it y .
B est D ele g ate  a w ard  a t I IS M UN &#039;2 0
Jo rd an  b ase d  v ir tu al  M od el  U nit e d  N atio ns.	
S KIL LS
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Sta tis tic al A naly sis	D ata  V is u aliz a tio n	
Jav a	E ffe ctiv e  C om munic atio n	
Te am  C olla b ora tio n
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