Many of you asked for a practical example after our first post. Here's a complete guide to deploy your first classification model using Jaqpot. We'll use a simple dataset to show you the basics.

What we'll build

We'll create a binary classification model that:

• Uses two features to make predictions
• Runs on logistic regression
• Gets deployed privately on Jaqpot

At the end of the post you'll find the complete code sample. Here's a quick overview of the steps we'll cover:

Breaking it down

1. Prepare your dataset

First, we create a dataset object that Jaqpot understands:

dataset = JaqpotpyDataset(
    df=data,
    x_cols=['feature1', 'feature2'],  # Feature columns
    y_cols=['target'],                # Target column
    task='binary_classification'      # Specify the task type
)

2. Train your model

Next, we wrap our scikit-learn model with Jaqpot's model class:

model = SklearnModel(
    model=LogisticRegression(),
    dataset=dataset
)
model.fit()

3. Deploy to Jaqpot

Finally, we deploy our trained model:

jaqpot = Jaqpot()
jaqpot.login()
model.deploy_on_jaqpot(
    jaqpot=jaqpot,
    name="My first Jaqpot Model",
    description="This is my first attempt to train and upload a Jaqpot model.",
    visibility="PRIVATE",
)

What just happened?

Let's break down what we did:

1. Created a simple dataset with two features
2. Wrapped it in a Jaqpot-friendly format
3. Trained a basic classification model
4. Deployed it privately on Jaqpot

The model is now:

• Accessible through an API
• Protected with authentication
• Ready for predictions
• Private to your account

The complete code

import pandas as pd
from sklearn.linear_model import LogisticRegression
from jaqpotpy.datasets import JaqpotpyDataset
from jaqpotpy.models import SklearnModel
from jaqpotpy import Jaqpot

# Sample data
data = pd.DataFrame({
    'feature1': [1, 2, 3, 4, 5],
    'feature2': [2.1, 3.2, 4.3, 5.4, 6.5],
    'target': [0, 1, 0, 1, 0]
})

# Create dataset for binary classification
dataset = JaqpotpyDataset(
    df=data,
    x_cols=['feature1', 'feature2'],  # Feature columns
    y_cols=['target'],                # Target column
    task='binary_classification'      # Specify the task type
)

# Τrain a model
model = SklearnModel(
    model =LogisticRegression(),
    dataset = dataset
)
model.fit()

# Upload the pretrained model on Jaqpot
jaqpot = Jaqpot()
jaqpot.login()
model.deploy_on_jaqpot(
    jaqpot=jaqpot,
    name="My first Jaqpot Model",
    description="This is my first attempt to train and upload a Jaqpot model.",
    visibility="PRIVATE",
)

Next steps

Try this with your own data:

1. Replace our sample data with your dataset
2. Adjust the feature and target columns
3. Choose a different model type if needed
4. Deploy with your preferred visibility settings

Need help?

If you run into any issues:

• Check our documentation
• Join our Discord community
• Open a GitHub issue in our repository
• Contact our support team

Coming up next

In future posts, we'll cover:

• Working with larger datasets
• Using different model types
• Advanced deployment options
• Model monitoring and updates

Try it out and let us know what you build!