build linear - Posts - OneCompiler

Htmlfile

<!DOCTYPEhtml> <html> <head> <titl nCodeigniter.TheJavascri e>NumberCheck</titl e> <scriptsrc=”<?phpechobase_url </head> <body> <h1>NumberCheck</h1> (‘j s/numberCheck.js’ <p>Enteranumbertocheck:</p> <inputtype=”number”i d=”num”/> <buttononcli ck=”checkNumber(document.getEl </body> nstop_words] ptcodeshouldcheckwhetheranumber );?>”></scri pt> ementById(‘ num’).val ue)”>Check</button> </html>

Createisfi
lechecknumber.js
FunctioncheckNumber(num){
If(num>0){
Alert(“Thenumberi
spositive.
}elseif(num<0){
Alert(“Thenumberi
”);
snegative.”);
}else{
Alert(“Thenumberi
szero.”);
}
}

Importpandasaspd
Fromsklearn.model_sel
onmodelforUserData.
ectionimporttrai
Fromsklearn.l
inear_modelimportLi
n_test_spl
it
nearRegressi
on
Fromsklearn.metri
csimportmean_squared_error,r2_score
Importmatplotl
ib.pypl
otasplt
#1.Collectdata
Data=pd.read_csv(‘
user_data.
#2.Preprocessdata
Data.dropna(i
nplace=True)
X=data[‘age’
].val
csv’)
ues.reshape(
1,1)
Y=data[‘income’
].val
ues.reshape(
1,1)
#3.Splitdata
X_trai
n,x_test,y_trai
n,y_test=trai
n_test_spl
#4.Trainthemodel
Regressor=LinearRegressi
on()
Regressor.fi
t(x_trai
n,y_trai
#5.Predictvalues
Y_pred=regressor.predi
#6.Evaluatemodel
n)
ct(x_test)
Mse=mean_squared_error(y_test,y_pred)
R2=r2_score(y_test,y_pred)
Print(“Meansquarederror:“,mse)
Print(“R
squared:“,r2)
#7.Visualizeresul
ts
Plt.scatt
er(x_test,y_t
est,color=’
Plt.pl
ot(x_test,y_pred,col
gray’)
or=’red’
,linewi
Plt.show(