Model Training.

Data scientists have access to many libraries and packages that help with model development. Some of the most common for Python are XGBoost, Keras, and scikit-learn. These packages are already available in the Katonic SDK(auto ML).

Once we completed doing the Text preprocessing like removing unnecessary punctuations, symbols and converted those text into numerical features. We need to use them to build Machine Learning models. For that we are going to Katonic's Auto ML tool which will keep track of all your experiments that you're doing inside a Notebook and store them in a Experiments Registry from there we can compare different models and find the best model.

Before doing any model training in the Notebook you need to set an experiment using katonic auto ml package.

python
from katonic.ml.client import set_exp

exp_name = set_exp("bad_loan_prediction")

The models that you're going to train will get stored under the Experiment name that you given.

We are using so many Machine Learning models in order to do the training.

Before starting the model training we need to initialize a Auto ML Classifier.

python
classifier = Classifier(X_train, X_test, Y_train, Y_test, experiment_name = "bad_loan_prediction)

Now we have configured the Classifier with the training and validation data along with the Experiemnt name.

Now, we can train any model using a single line of code.

Logistic Regression.

python
classifier.LogisticRegression()

That's it just by writing one line of code, we can train the model.

Gradient Boosting Classifier.

python
classifier.GradientBoostingClassifier()

Random Forest Classifier.

python
classifier.RandomForestClassifier()

XG Boost Classifier.

python
classifier.XGBClassifier()

Decision Tree Classifier.

python
classifier.DecisionTreeClassifier()

K Neighbors Classifier.

python
classifier.KNeighborsClassifier()

All the performance metrices and the model information will get catalouged in the Experiments Section.