OneCompiler

heights

93

contact.dat file:
1,John,1234567890,0987654321,123 Main St
2,Alex,1234567891,0987654322,456 Park Ave
3,Jack,1234567892,0987654323,789 Pine St

<html> <head> <script> function readData() { var xhttp = new XMLHttpRequest(); xhttp.onreadystatechange = function() { if (this.readyState == 4 && this.status == 200) { document.getElementById("printTable").innerHTML = this.responseText; } }; xhttp.open("GET", "contact.dat", true); xhttp.send(); } </script> </head> <body> <button type="button" onclick="readData()">Print</button><br /><br /> <table id="printTable"></table> </body> </html>

Importnumpyasnp
Importpandasaspd
Fromsklearn.l
inear_modelimportLi
Fromsklearn.model_sel
nearRegressi
on
ectionimporttrai
n_test_spl
it
#Createarandomdatasetwith10samples
Heights=np.random.normal(170,10,10)
Weights=np.random.normal(70,5,10)
#Combinethetwoarraysintoasingledataset
Dataset=pd.DataFrame({‘
Height’
:heights,‘
Weight’:wei
ghts})
ng
ntthem.Builda
#Splitthedataseti
ntotraini
ngandtestingsets
X_trai
n,X_test,y_trai
n,y_test=trai
random_state=42)
n_test_spl
it(dataset[
‘Height’
],dataset[
‘Weight’
#CreateaLinearRegressionmodelandfitittothetrai
ningdata
Lr_model=LinearRegressi
on()
Lr_model.fi
t(X_trai
n.values.
#Printthemodelcoeffici
Print(‘
ModelCoefficients:
reshape(-1,1),y_trai
ents
’,l
r_model.coef_)
#Predicttheweightsforthetestdataandpri
Y_pred=lr_model.predi
ct(X_test.
Print(‘
Predicti
ons:’
@Slip-13
,y_pred)
n)
ntthepredicti
ons
values.reshape(
1,1))
Print(‘
Predicti
ons:’