Publications by Olivia Sim
Footballers' salary - Neural Network
Introduction Import library and connect to Python to use keras library library(keras) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(reticulate) library(t...
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Dimensionality reduction on pizza data
knitr::opts_chunk$set(echo = TRUE) Introduction Import dataset and activate required libraries library(ggcorrplot) ## Loading required package: ggplot2 library(FactoMineR) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'pack...
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Using categorical predictors in neural network - Footballers' salary prediction
Introduction Import library and connect to Python to use keras library ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ## ## Attaching package: 'neuralnet' ## The following obje...
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Time series forecasting - Weekly sales for 45 Walmart Stores
Introduction The dataset is the weekly sales of Walmart across its 45 stores in US with 143 observations per store totaling 143x45 =6,435 data points. As the sales are recorded at regularly spaced intervals, this is a time-series data. We will attempt to forecast Walmart’s weekly sales half a year into the future for each of its 45 stores. librar...
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Bitcoin Random Forest and Text Mining
Random Forest Part 1: Introduction This is a continuation from previous attempt to classify Bitcoin using logistic regression and KNN. The purpose is still the same i.e. identifying the factors that best predict the likelihood of Bitcoin price closing at a price higher vs the previous week, and this time, applying Random Forest classifier. Few im...
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Application of Logistic Regression and KNN on Bitcoin Price Behavior
Data sourcing from Yahoo Finance API We will explore which factors best predict the likelihood of Bitcoin price closing at a price higher vs the previous week. Firstly, we will load all the libraries we need for this exercise and start to collect our price data from Yahoo Finance API. The choice of explanatory variables are based on numerous litera...
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YouTube trending analysis US 2022
YouTube trending analysis US 2022 Column Chart A Column Chart B Chart C...
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Analysis of NYC Flights 2013 Dataset
Introduction The dataset we are analyzing today is the NYC Flights dataset, which is part of the Tidyverse package. Firstly, we load the dataset, inspect its overall structure and look through the first six rows of the data. library(nycflights13) require(nycflights13) str(flights) ## tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame) ## $ year ...
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