Machine learning models. Building own models as well as using scikit learn
Data_preprocessing_analysis - General characteristics of the data as a whole: examine the means, standard deviations, and other statistics associated with the numerical attributes and distributions of values associated with categorical attributes.
KNN_without_scikit - Without scikit functions creating own KNN classifer. Parameter input to use various distance methods.
Data_analysis_Predictive_modeling_Census_data - Data Analysis and Predictive Modeling on Census data. Scikit-learn to build classifiers usinng Naive Bayes (Gaussian), decision tree (using "gini" as selection criteria), and linear discriminant analysis (LDA)
Classification_using_scikit-learn - Classification using scikit-learn. Various classifiers provided as part of the scikit-learn (sklearn) machine learning module, as well as with some of its preprocessing and model evaluation capabilities
Multiple_linear_regression - Experiment with multiple linear regression models to make predictions with numerical data. Exploring more systematic methods for feature selection and for optimizing model parameters (model selection).
Automatic_document_clustering - Using Pandas and other modules from scikit-learn for preprocessing or evaluation. Perform clustering on the documents and comparing the clusters to the actual categories.
Item_based_Recommendation - Using both standard item-based collaborative filtering (based on the rating prediction function "standEst") and the SVD-based version of the item-based CF (using "svdEst" as the prediction engine) to generate these recommendations for the users.
PCA_and_Clustering - PCA to reduce dimensionality and noise in the data. Comapring the results of clustering the data with and without PCA.
Execute item based and user based collaborative filtering. Item based was created by finding the similarity in an item by item matrix, which then was able to make predictions and recommend. User based collaborative filtering was executed through Kmeans clustering.
RentTheRunway_InitialAnalysis - Initial analysis
RentTheRunway_item_user_als_recommender - Item based and user based recommendation.
RentTheRunway_MatrixFactorization_2500Users - Matrix Factorization
RentTheRunway_MatrixFactorization_DistinctUsers - Matrix Factorization