teerapat-ch / ISLR

Labs from An Introduction to Statistical Learning

README

Project Summary

Do the labs from 'An Introduction to Statistical Learning' book, in Python. (The labs are done in R in the book.) The book is available for free download on the author's website along with slides, video tutorials, and some datasets.

Data

The labs require the datasets listed below. These datasets are available on the CRAN GitHub repo.

• Auto: Gas mileage, horsepower, and other information for cars.
• Boston: Housing values and other information about Boston suburbs.
• Caravan: Information about individuals offered caravan insurance.
• Carseats: Information about car seat sales in 400 stores.
• College: Demographic characteristics, tuition, and more for USA colleges.
• Default: Customer default records for a credit card company.
• Hitters: Records and salaries for baseball players.
• Khan: Gene expression measurements for four cancer types.
• NCI60: Gene expression measurements for 64 cancer cell lines.
• OJ: Sales information for Citrus Hill and Minute Maid orange juice.
• Portfolio: Past values of financial assets, for use in portfolio allocation.
• Smarket: Daily percentage returns for S&P 500 over a 5-year period.
• USArrests: Crime statistics per 100,000 residents in 50 states of USA.
• Wage: Income survey data for males in central Atlantic region of USA.
• Weekly: 1,089 weekly stock market returns for 21 years.

Requirements

$ pip install -r requirements.txt

Work Summary

3 Regression:

• Simple Linear Regression:
  • Coefficient of Determination
  • Residual Plot
• Multiple Linear Regression:
  • Non-linearity
  • Heteroscedasticity
  • Leverage Statistic
  • Studentized Residuals
  • Correlation Heatmap
  • Variance Inflation Factor (VIF)

4 Classification:

• Logistic Regression:
  • Confusion Matrix
  • Sensitivity
  • Precision
  • F1 score
• K-Nearest Neighbors (KNN)
  * Linear Discriminant Analysis
  * Quadratic Discriminant Analysis

5 Resampling Methods:
* Validation Set
* Leave-One-Out Cross-Validation
* k-fold Cross-Validation
* Bootstrap

6 Linear Model Selection and Regularization:
7 Moving Beyond Linearity:

8 Tree-Based Methods:

• Decision trees
  * Bagging
  * Random Forests
  * Boosting

9 Support Vector Machines:

10 Unsupervised Learning:

• Principal Component Analysis (PCA)
• K-Means Clustering
• Hierarchical Clustering
  * Gaussian Mixture Models (GMM) Clustering
  * Spectral Clustering
  * Mean-Shift Clustering
  * DBSCAN