Review of various Machine Learning Models

Knn, Regression (LASSO, Ridge), Logistic, PCA, LDA, QDA, Trees, RF, Boosting

M2 - knn and imputing missing values - k-nearest neighbors methods is used to predict heart disease in a data set. Various methods are used to impute missing values.
M3 - Logistic Regression - Logistic Regression used to predict heart disease. ROC curves and related parameters are evaluated.
M4 - Disciminant Analysis (Linear and Quadratic) - A supervised learning method used to determine the separability of classes. Data set is the temporal distribution of households of different income levels. This method is compared with knn and logistic regression.
M5 - Trees, Bagging, Random Forest, Boosting
- Decision Trees - Predict voting outcome based on various demographics characteristics. Trees are generated and various parameters are studied.
- Bagging - Bootstrap aggregation is demonstrated and the process is visualized with a simple data set.
- Random Forest - Multiple trees fit on bootstrapped samples on another data set to predict heart disease. Feature importance gives list of important predictors.
- Boosting - Regression and classification on residuals. First principles method is used to demonstrate how the algorithm works and then skit-learn is used on another
M6 - Regression (LASSO and Ridge) and cross validation - Simple function used to see how prediction various with different penalty value (lambda of LASSO). Cross validation is used to pick the best lambda.

Name		Name	Last commit message	Last commit date
Latest commit History 28 Commits
Cheat Sheets		Cheat Sheets
data		data
images		images
M2 - knn and imputing missing values.ipynb		M2 - knn and imputing missing values.ipynb
M3 - Logistic Regression.ipynb		M3 - Logistic Regression.ipynb
M4 - LDA and QDA.ipynb		M4 - LDA and QDA.ipynb
M5 - Trees - RF - Boosting - 2020.ipynb		M5 - Trees - RF - Boosting - 2020.ipynb
M5 - Trees et al- Old.ipynb		M5 - Trees et al- Old.ipynb
M6 - Regression-LASSO-Ridge-Cross Validation.ipynb		M6 - Regression-LASSO-Ridge-Cross Validation.ipynb
README.md		README.md
T2 - Numpy.ipynb		T2 - Numpy.ipynb
T3 - Pandas.ipynb		T3 - Pandas.ipynb
T4 - Matplotlib.ipynb		T4 - Matplotlib.ipynb
T5 - Machine Learning - Intro.ipynb		T5 - Machine Learning - Intro.ipynb
T6 - String Operations.ipynb		T6 - String Operations.ipynb
T7 - Web Scraping - Bourbon.ipynb		T7 - Web Scraping - Bourbon.ipynb
T7 - Web Scraping - Nobel Prize.ipynb		T7 - Web Scraping - Nobel Prize.ipynb
helper.py		helper.py

SBalas/Machine-Learning-Models