nursery
Demo.html
<html> <head> <title>Form for number input</title> </head> <body> <script type="text/javascript"> function getName() { var name = document.getElementById("name").value; if (name == "") { document.getElementById("response").innerHTML = "Stranger, please tell me your name!"; } else { var xhttp = new XMLHttpRequest(); xhttp.onreadystatechange = function() { if (this.readyState == 4 && this.status == 200) { var response = this.responseText; document.getElementById("response").innerHTML = response; } }; xhttp.open("GET", "demo.php?name=" + name, true); xhttp.send(); } } </script> <form> Name: <input type="text" id="name" onkeyup="getName()"> </form> <div id="response"></div> </body> </html>Demo.php
<?php if (isset($_GET['name'])) { $name = $_GET['name']; if ($name == 'Rohit' || $name == 'Virat' || $name == 'Dhoni' || $name == 'Ashwin' || $name == 'Harbhajan') { echo "Hello, master!"; } else { echo "I don't know you!"; } } ?>Importpandasaspd
Importnumpyasnp
Fromsklearn.model_selectionimporttrain_test_split
Fromsklearn.linear_modelimportLinearRegression
#Loadthedataset
url=https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data
names=[‘parents’,‘has_nurs’,‘form’,‘children’,‘housing’,‘finance’,‘social’,‘health’,‘class’]
dataset=pd.read_csv(url,names=names)
#Identifyindependentandtargetvariables
X=dataset.drop(‘class’,axis=1)
Y=dataset[‘class’]
#Convertcategoricalvariablesintonumericalvariablesusingone-hotencoding
X=pd.get_dummies(X)
#Splitintotrai
ningandtestingsets
X_trai
n,X_test,y_trai
n,y_test=trai
#Buildalinearregressi
Model=LinearRegression()
Model.fi
t(X_trai
n,y_trai
#Printthecoeffi
n_test_spl
onmodel
n)
cientsofthemodel
Print(“I
ntercept:“,model
.intercept
Print(“Coeffi
cients:“,model
#Predictthetargetvari
)
.coef)
ableforthetesti
Y_pred=model.predict(X_test)
it(X,y,test
_size=0.2,random_state=42)
ngset
#EvaluatethemodelusingMeanSquaredError(MSE)
Mse=np.mean((y_test–y_pred)**2)
Print(“MSE:“,mse)