Python for Linear Regression in Machine Learning

Explore python basics in a crash course that covers data types, variables, and core structures, including lists, sets, tuples, dictionaries, booleans, and comparison and logical operators.

Explore Python string fundamentals, including single and double quotes, escaping quotes, raw strings, and string formatting with curly braces to construct and print text for machine learning workflows.

Learn boolean data types and comparison operators in Python, including true, false, and greater than. Identify how double equals work for comparison versus assignment, and how None and strings compare.

Explore how logical operators and, or, work with comparison operators to evaluate boolean expressions, and how or yields true when any condition is true, while and requires all conditions.

Learn how Python function definitions use def and lambda, contrast return and print, and see lambda with single and multiple parameters for powers and products.

Learn to work with NumPy arrays in Python: install or import NumPy, create 1D and 2D arrays from lists, inspect shapes, and convert data types between int, float, and string.

Master shape, reshape, ravel, and flatten in numpy. Transform a 2d array from 3x2 to 2x3 and to a single row of 6 elements, or into a vector.

Learn to save and load 2d numpy arrays as text and csv with delimiters, use np.save and np.load, handle loading with loadtxt, and save multiple arrays while noting UTF-8 considerations.

Learn pandas DataFrame and Series by building a dictionary with fruit and vegetables, converting it to a DataFrame, and noting that a Series has column while a DataFrame has columns.

Learn to view and update data frame columns using df.columns, and rename them with df.rename via a dictionary, transforming names like 'name' and 'team' in pandas.

Apply imputation to handle missing data in pandas by filling nulls with zeros or the mean. Learn to fill revenue and meta score columns and verify no null values remain.

Learn to label axes and add a title to a matplotlib plot, using xlabel, ylabel, and title to clearly describe the x and y axes.

Create scatter plots, bar plots, and hist plots from x and y data with matplotlib. Adjust line width, bar width, and histogram bins, and preview the box plot.

Learn to customize matplotlib plots by setting x and y limits and tick labels, create subplots, and apply axis labeling for clear linear regression visuals.

Learn to create and customize pie plots with Matplotlib in pyplot, including color mappings, labels, sizes, percentages, shadows, start angles, explode effects, and inline plotting.

Learn to label a pie chart in matplotlib by placing ingredient labels and auto percentages inside slices, then add a legend with a bounding box anchored to the center left.

Apply linear regression in Python to predict continuous targets from input features in a supervised learning setup, such as house prices using the Boston housing dataset.

Learn the types of linear regression, from simple linear regression with a single input to multiple linear regression with several inputs, and how coefficients and gradient descent shape predictions.

Apply a train-test split to create training and test sets, then train a linear regression model and generate predictions for evaluation.

Assess a house price prediction model using the R2 score, mean absolute error, mean squared error, and RMSE on test set, then plot true versus predicted prices to inspect accuracy.

Plot a learning curve to show how a linear regression model improves with more training samples, using cross validation and tracking training and test scores.

Plot residuals with the Yellowbrick residual plot to interpret model errors and compare training versus testing R2 scores.

Explore the prediction error plot to compare actual targets with predicted values, interpret the identity and best-fit lines, and diagnose regression models using r-squared and skewness trends.

Apply data pre-processing to ensure linear regression assumptions—linear relation, normally distributed inputs, no collinearity, homoscedastic errors—and use R2 to assess simple versus multiple regression and handle outliers.

Create a reference data set to validate four linear model assumptions by generating normally distributed numbers and a linear y = beta0 + beta1 x, and compare with Boston data.

Assess linear model assumptions for the Boston dataset by visualizing the target against features with a Seaborn dot plot. Reveal nonlinearity and imperfect linear fit, indicating a transformation is needed.

The lecture tests square root and exponential transformations on the average number of rooms per house to achieve a normal distribution for regression, noting that log and inverse transformations fail.

Check multicollinearity by computing a correlation matrix and visualizing a heatmap, then drop highly correlated variables like radius and tax to improve model performance.

Apply standardization and normalization to a Boston dataset using StandardScaler, compare mean squared error with and without pre-processing, and discuss upcoming topics like outlier detection and feature selection.

Explain how a shap heatmap reveals model interpretability for a linear regression on the Boston housing dataset. Visualize feature importance through SAP values, showing positive and negative impacts on predictions.

Explain feature selection with SHAP by comparing models trained on most versus least important features, and visualize residuals to compare R2 scores, highlighting group effects over individual features.

Detect outliers in linear regression with box plots and IQR, applying 1.5 times the IQR for normal data and 3 times the IQR for skewed data, noting when outliers matter.

Visualize and diagnose outliers using histograms, qq plots, and box plots on the Boston housing data, then remove outliers and explore transformations to normalize skewed data.

Detect outliers for normal and skewed distributions using the three-sigma rule and the IQR rule, then implement a Python function to compute bounds.

Detect outliers in skewed variables using an iqr-based method that computes q3 minus q1 and applies a distance factor to define lower and upper bounds.

Identify the most correlated features with house price using a correlation matrix. Visualize relationships with heatmaps and bar plots, then apply absolute values to pick top features.

Evaluate model performance using selected features highly correlated with the target, applying outlier removal and transformations, and compare boolean-configured setups achieving R-squared around 0.52–0.58 on the Boston dataset.

Explore recursive feature elimination, starting with all features and removing the least performing one to optimize a linear regression model using scikit-learn's RFE.

Explore incremental feature selection using incremental RFE to optimize linear regression models, showing how adding features and handling outliers improves R-squared and overall accuracy.