This code is the result of my learning of various ML models and their implementations.
Linear Regression: By using the numpy, matplotlib, and sklearn Python libraries, this program implements Linear Regression on a plot of points to find the best-fit line (prediction of future test lines), prints out the test scores, and opens the graph with points and line.
K-Nearest Neighbor: By using the numpy and sklearn Python libraries, this program uses breast cancer data to train and test the KNeighbor model. It outputs the test's score.
Support Vector Machines: By using the sklearn Python library, this program compares the score of SVMs and the KNearest Neighbor model on breast cancer data.
Decision Trees: By using the sklearn Python library, this program compares the score of Decision Trees, Random Tree Classifiers, SVMs, and the KNearest Neighbor model on breast cancer data.
K-Means Clustering: By using the sklearn Python library, this program uses a digit database to train the model on data.