ML Apps using In this article, we are going to see… | by Aniket Wattamwar | Jan, 2021


First, we look at the plot and then later on the ML part. Let’s create 2 functions scatter_handler and bar_handler.

def scatter_handler():
df = get_data()
return px.scatter(df, x="ApplicantIncome", y="LoanAmount", color="Education")
def bar_handler():
df = get_data()
return, x="Gender", y="Loan_Amount_Term", color="Education", barmode="group")

These two functions return the scatter and bar plot respectively from library

Now, we need to add these as tabs on a frame. To do this, we create a frame first. You can see the frame on the dashboard image above.

frame = ds.frame(“Loan_Prediction”)

Once this is done, we add the functions as apps on the frame.

frame.add(, params={“Scatter Plot”:})frame.add(, params={“Bar Plot”:})

We pass function created to the function as a parameter and mention it as a tab.

Then we push the frame to the dstack application.

url = frame.push()

When you run your application you can view your dstack app now.

Let’s look at the ML part with dstack

train_data = get_data()
y = train_data.iloc[:,-1]
train_data = train_data.drop(['Loan_ID','Loan_Status'],axis=1)
test_data = get_testdata()
ids = test_data.iloc[:,0]
test_data = test_data.drop(['Loan_ID'],axis=1)
def encoding(data):
data = pd.get_dummies(data, columns=["Gender","Married","Education","Self_Employed","Property_Area"],drop_first=True)
return data
train = encoding(train_data)
test = encoding(test_data)

We get the training and testing data and separate the output from the training data. Then we do encoding to convert our categorical into numerical values. This is a simple pre-processing of the data, make sure you do all the necessary steps before using any ML model on the data.

from sklearn.ensemble import RandomForestClassifier
random = RandomForestClassifier(),y)

We are training a simple random forest algorithm from the sklearn.

ds.push("Random_Forest", random, "Random Forest Loan Prediction")

Once the model is trained, you will have to push to the dstack application. It will look like this.

Image by Author

This is where all your models will be stored and you can use it. To use the model you will have to pull it.

model = ds.pull('/dstack/Random_Forest')

Once you have pulled it now you can use it to predict on your test data. We are going to create dropdown called as Combobox in dstack and it will contain one value called as ‘Predict’.

values = ctrl.ComboBox(data=['Predict'],label="Predictions",require_apply=True)

Create a function to predict the model on the test data.

def get_predicted(values: ctrl.ComboBox):
y_pred = model.predict(test)
y_pred = pd.DataFrame(y_pred)
y_pred = y_pred.rename(columns={0:'Prediction'})
y_pred['ID'] = ids
return y_pred

We have to now create an app and add the function to the frame as a tab.

p_app =, values = values)
frame.add(p_app, params={"Predicted":})
url = frame.push()

That’s it. When you run the application now it will show you the Predicted tab. When you click on Apply it will show you the predicted values with the loan status.

Image by Author

You can find the code on my Github:

Hope this helps.


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