Graph
Extra and last post! Graph netowrk: a brief analysis. Privacy policy for Uber and Lyft Conclusions 1. Graph netowrk: a brief analysis. library(kni...
Extra and last post! Graph netowrk: a brief analysis. Privacy policy for Uber and Lyft Conclusions 1. Graph netowrk: a brief analysis. library(kni...
XGboost To conclude with the estimation part of the project we will use the Xgboost methodology to compare results with the previous estimations. Once again...
Gradient Boosting Machine (GBM) Whereas random forests build an ensemble of deep independent trees, GBMs build an ensemble of shallow and weak successive tr...
Random Forest in R Due to computational restrictions, this time we select randomly 260.000 observations from the original data, although this sub-sample re...
Tree classifiers in R library(data.table) library(tree) library(readr) NY <- read_delim("C:/Users/D/Desktop/NY.csv", ";", escape_double = FALSE, trim_ws...
Performing Principal Components Regression (PCR) and Partial Least Squares Regression (PLS) in R For UBER’s dataset library(tidyverse) library(caret) libra...
Part 1. Cleaning the database R code block: library(readr) NY <- read_delim("C:/Users/DDD/Desktop/NY.csv", ";", escape_double = FALSE, ...
Public spending efficiency in the Regional Government of Madrid after Covid-19
Fluctuations in coin tossing The ideal coin-tossing game will be described in the terminology of random walks which is better suited for generalizations. Fo...
Combinatorial analysis Probability Theory: An Introduction “What are the chances…” is an expression you probably use very often. Determining the chances of...
test for jupyter def trapezoidal(f, a, b, n): h = float(b - a) / n s = 0.0 s += f(a)/2.0 for i in range(1, n): s += f(a + i*h) s...
Pandas and classification exercise import numpy as np import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection impor...
Sentiment Analysis of Monetary Policy Communication