Machine Learning Regression Masterclass in Python

Apply simple linear regression to predict ice cream stand revenue from outside temperature using a dataset of 500 samples, following six steps: import libraries, load data, visualize, split, train, test.

Split the dataset into training and testing sets using train_test_split, with X as temperature and Y as revenue. Shuffle data for generalization and use an 80/20 (training/testing) split.

Explore polynomial regression as an extension of simple linear regression, modeling y as an nth degree polynomial in x to capture curvature, with salary versus experience as an example.

Visualize the relationship using seaborn joint plot, lm plot, and pair plot; identify that a simple linear model fits poorly, then apply polynomial regression with the full training data.

Train a linear regression model with sklearn, inspect slope and intercept, visualize data with the fit line, and introduce polynomial regression to improve accuracy.

Explore polynomial regression in python by applying polynomial features to transform data, fitting a linear model with different degrees, and visualizing improved accuracy over linear regression.

Train and evaluate a multilinear regression model with scikit-learn, fit intercept options, predict on test data, and visualize predictions versus ground truth while assessing rmse and r-squared.

Predict graduate school admission probability using a real dataset with a multi regression model, using GRE, TOEFL, university rating, SOP strength, GPA, and research experience.

Explore how a confusion matrix evaluates a classification model, detailing true positives, true negatives, false positives, and false negatives, and measuring precision, recall, and accuracy.

Clean the data by dropping emails, names, and country, define x from time on site and salary and y from clicked, and apply standard scaling before training.

Take a single neuron model with inputs x1, x2, x3, weights, and bias; apply an activation function to produce a binary classification, then assemble mini neural networks in TensorFlow playground.

Train the model on minmax-scaled data with a train-test split, using a seven-input dense network in Keras and a linear output, and assess performance with Adam and mean squared error.