diff --git a/classification_algo_names.py b/classification_algo_names.py
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+++ b/classification_algo_names.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Mar 18 09:47:48 2022
+
+@author: tanu
+"""
+
+#%% Useful links
+# https://stackoverflow.com/questions/41844311/list-of-all-classification-algorithms
+# https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
+# https://github.com/davidsbatista/machine-learning-notebooks/blob/master/hyperparameter-across-models.ipynb
+# https://scikit-learn.org/stable/modules/svm.html#classification
+# https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/ # [params]
+# https://uk.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html [ algo]
+# As a general rule of thumb, it is required to run baseline models on the dataset. I know H2O- AutoML and other AutoML packages do this. But I want to try using Scikit-learn Pipeline,
+    #  https://codereview.stackexchange.com/questions/256934/model-pipeline-to-run-multiple-classifiers-for-ml-classification
+
+names = [
+    "Nearest Neighbors",
+    "Linear SVM",
+    "RBF SVM",
+    "Gaussian Process",
+    "Decision Tree",
+    "Random Forest",
+    "Neural Net",
+    "AdaBoost",
+    "Naive Bayes",
+    "QDA",
+]
+
+classifiers = [
+    KNeighborsClassifier(3),
+    SVC(kernel="linear", C=0.025),
+    SVC(gamma=2, C=1),
+    GaussianProcessClassifier(1.0 * RBF(1.0)),
+    DecisionTreeClassifier(max_depth=5),
+    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
+    MLPClassifier(alpha=1, max_iter=1000),
+    AdaBoostClassifier(),
+    GaussianNB(),
+    QuadraticDiscriminantAnalysis(),
+]