student score
<!DOCTYPEhtml>
<html>
<head>
<titl
</head>
<body>
e>NumberOperations</ti
tle>
<h1>NumberOperations</h1>
<?php
//definevari
ablesandsettoemptyvalues
$num=$op=“”;
If($_SERVER[“REQUEST_METHOD”]==“POST”){
$num=test_input($_POST[“num”]);
$op=test_input($_POST[“op”]
//performoperati
);
onbasedonuser’schoice
Switch($op){
Case“fib”:
$result=fi
bonacci($num);
Echo“<p>TheFibonacciseriesof$numnumbersis:$result</p>”;
Break;
Case“sum”:
$result=sumOfDigits($num);
Echo“<p>Thesumofdigitsin$numis:$result</p>”;
Break;
Default:
Echo“<p>Invali
doperationselected</p>”;
}
}
Functiontest_i
nput($data){
$data=trim($data);
$data=stripsl
ashes($data);
$data=htmlspecialchars($data);
Return$data;
}
Functionfi
bonacci($num){
$first=0;
$second=1;
$result=“”;
For($i=0;$i<$num;$i++){
$result.=$fi
rst.““;
$third=$first+$second;
$first=$second;
$second=$third;
}
Return$result;
}
FunctionsumOfDigits($num){
$sum=0;
While($num>0){
$digit=$num%10;
$sum+=$digit;
$num=(int)($num/10);
}
Return$sum;
}
?>
<formmethod=”post”action=”<?phpecho
htmlspecial
chars($_SERVER[“PHP_SELF”]);
?>”>
<labelfor=”num”>Enteranumber:
</label
>
<inputtype=”number”name=”num”id=”num”requi
<br><br>
<labelfor=”op”>Sel
ectanoperation:
<selectname=”op”id=”op”requi
<optionvalue=””>--Select
<optionvalue=”fi
</label
>
red>-</opti
on>
b”>FibonacciSeri
red>
es</opti
on>
<optionvalue=”sum”>SumofDigits</opti
on>
</select>
<br><br>
<inputtype=”submi
</form>
</body>
</html>
Importpandasaspd
Fromsklearn.l
inear_modelimportLogi
Fromsklearn.model_sel
sticRegressi
on
ectionimporttrai
n_test_spl
it
Fromsklearn.metri
csimportaccuracy_score
#Loadthedataset
Data=pd.read_csv(‘
student_scores.
#Splitthedataintoi
csv’)
nputandoutputvariabl
es
X=data.iloc[:
,:-1]
.values
Y=data.iloc[:
,-1].
values
#Splitthedataintotrai
X_trai
n,X_test,y_trai
#Createthelogisti
ningandtestingsets
n,y_test=trai
n_test_spl
it(X,y,test
_size=0.2,random_state=0)
cregressionmodelandfitittothetrai
ningdata
Classifi
er=LogisticRegressi
on()
Classifi
er.fi
t(X_trai
n,y_trai
n)
#Makepredictionsonthetestingset
Y_pred=classifi
er.predi
ct(X_test)
#Evaluatethemodel’
saccuracy
Accuracy=accuracy_score(y_test,y_pred)
Print(“Accuracy:
”,accuracy