Recently Published
Actively open-minded thinking - original items - MW-C
EGA plot of AOT - original items only - MW condition, contiguous order
Análisis de Regresión Múltiple con Resultados de las Pruebas Saber 11
Adjunto el taller final de Estadística el cual se trabajó con un modelo de regresión lineal múltiple con base a datos obtenidos por medio de la data base de datos.gov.co entorno a las PRUEBAS SABER 11 del año 2020-1
Kelp Reforestation
CO2 removal
Locus of control - WNMC -all items
EGA plot - LC - all items - WNM condition - contiguous order
Locus of control - WNMR -all items
EGA plot of LC data - all items - WNM condition - random order
Data 612 Project 1
Briefly describe the recommender system that you’re going to build out from a business
perspective, e.g. “This system recommends data science books to readers.”
• Find a dataset, or build out your own toy dataset. As a minimum requirement for complexity,
please include numeric ratings for at least five users, across at least five items, with some missing
data.
• Load your data into (for example) an R or pandas dataframe, a Python dictionary or list of lists, (or
another data structure of your choosing). From there, create a user-item matrix.
• If you choose to work with a large dataset, you’re encouraged to also create a small, relatively
dense “user-item” matrix as a subset so that you can hand-verify your calculations.
• Break your ratings into separate training and test datasets.
• Using your training data, calculate the raw average (mean) rating for every user-item combination.
• Calculate the RMSE for raw average for both your training data and your test data.
• Using your training data, calculate the bias for each user and each item.
• From the raw average, and the appropriate user and item biases, calculate the baseline predictors
for every user-item combination.
• Calculate the RMSE for the baseline predictors for both your training data and your test data.
• Summarize your results.
Locus of control - WNMC - original items
EGA plot - LC data - original items only - WNM condition - contiguous order
Locus of control - WNMR - original items
EGA plot - original items only - WNM condition - random order
RNA TRABAJO 2: APLICACIÓN DE REDES NEURONALES A DATOS TABULARES
Autores: - Marcos David Carrillo Builes - Tomás Escobar Rivera Monsalve - Jose Fernando López Ramírez - Esteban Vásquez Pérez
Locus of control - MNWC - all items
EGA plot - LC data - all items - MNW condition - contiguous order