Here’s a couple of Google Colab or Jupyter notebooks that I’ve created in order to explore various ML algorithms and techniques. The same can be found on my GitHub repository by clicking on the icon on the sidebar.

Supervised Learning Algorithms

Logistic Regression on HR Analytics dataset : https://github.com/trajnishBrown/machine-learning/blob/main/hr_analytics.ipynb

Logistic Regression on Iris dataset : https://github.com/trajnishBrown/machine-learning/blob/main/iris_analytics.ipynb

Decision Tree on Titanic dataset : https://github.com/trajnishBrown/machine-learning/blob/main/titanic_decision_tree.ipynb

SVM on Digits dataset : https://github.com/trajnishBrown/machine-learning/blob/main/digits_svm.ipynb

Random Forest on Iris dataset : https://github.com/trajnishBrown/machine-learning/blob/main/iris_random_forest.ipynb

Un-Supervised Learning Algorithms

K-Means clustering of Iris dataset : https://github.com/trajnishBrown/machine-learning/blob/main/iris_kmeans.ipynb

Naive Bayes on Wine dataset : https://github.com/trajnishBrown/machine-learning/blob/main/wine_naive_bayes.ipynb

Other Techniques

Cross Validation on Iris dataset : https://github.com/trajnishBrown/machine-learning/blob/main/iris_cross_validation.ipynb

Hyper-parameter tuning on Digits dataset : https://github.com/trajnishBrown/machine-learning/blob/main/digits_hyper_parameter_tuning.ipynb

Full-Stack Projects

Bangalore Home Price Prediction using K-fold cross validation and Grid Search CV (linear regression, lasso, decision tree) :
https://github.com/trajnishBrown/machine-learning/blob/main/blore_real_estate_price_prediction_project.ipynb

Frontend and backend code: https://github.com/trajnishBrown/blore-home-price-prediction