Python-ML

SUPERVISED LEARNING:

1. HelloWorldML.ipynb = Basic ML model which will guess the genre of songs for people based on their ages using Decision Tree algorithm. Ex: Most of the people like Hip-hop during the age 20-25 and classical music above 30. (Just an assumption)
2. Support Vector Machine.ipynb = ML model which will guess whether if it's a muffin or cupcake using SVM algorithm.
3. Linear Regression.ipynb = ML model which will find out the total profits of a company using Linear Regression algorithm.
4. Decision Tree.ipynb = Little detailed ML model which will predict whether a customer will repay loan or not using Decision Tree algorithm.
5. Logistic Regression.ipynd = ML model which will predict and give us a good accuracy score whether a patient will get a heart disease or not using Logistic Regression algorithm.
6. Naive Bayes.ipynd = Classifying texts into different groups based on which category they belong to using Naive Bayes algorithm.
7. Naive Bayes Part 2.ipynb = Email classification which tells whether an email is a spam or not using Naive Bayes algorithm.
8. Random Forest.ipynb = Predict what species of iris a set of flowers belong to using Random Forest algorithm.

UNSUPERVISED LEARNING:

1. K-Means Clustering.ipynb = Clustering cars based on brands using parameters such as horsepower, make year, cubic inches, etc using K-Means Clustering algorithm.