Notebooks
Practice with hands-on Python notebooks to learn AI, ML and data analysis.
Simple Linear Regression
Predict salaries based on years of experience using a simple linear regression model in Python.
Multiple Linear Regression
Predict company profits using multiple factors such as R&D Spend, Administration, and Marketing Spend.
Polynomial Regression
Model nonlinear relationships between employee position level and salary using polynomial regression.
Logistic Regression
Classify whether a person will make a purchase based on Age and Estimated Salary using logistic regression.
K-Nearest Neighbors (KNN)
Classify whether a person will make a purchase based on Age and Estimated Salary using K-Nearest Neighbors.
Support Vector Machine (SVM)
Classify whether a person will make a purchase based on Age and Estimated Salary using a linear Support Vector Machine.
Kernel SVM (Non-linear)
Classify whether a person will make a purchase based on Age and Estimated Salary using a Kernel Support Vector Machine with an RBF kernel.
Naive Bayes Classification
Classify whether a person will make a purchase based on Age and Estimated Salary using the Naive Bayes algorithm.
Decision Tree Classification
Classify whether a person will make a purchase based on Age and Estimated Salary using a Decision Tree classifier.
Random Forest Classification
Classify whether a person will make a purchase based on Age and Estimated Salary using a Random Forest ensemble classifier.
K-Means Clustering
Segment mall customers based on their Annual Income and Spending Score using the K-Means clustering algorithm.
Hierarchical Clustering
Group mall customers based on their Annual Income and Spending Score using Hierarchical Clustering and visualize dendrograms.
Apriori Algorithm
Discover product association rules from a supermarket dataset using the Apriori algorithm, and interpret the results using metrics such as Support, Confidence, and Lift.
Artificial Neural Network (ANN)
Predict customer churn for a bank using an Artificial Neural Network (ANN) trained on customer demographic and financial data.