Publications by Souleymane Doumbia, Group Member: Fomba Kassoh
Preprocessing
Exercise 3.1: Exploring the Glass Dataset (a) Using visualizations to explore the predictor variables # Load the Glass data data(Glass) Glass <- Glass # Basic structure of the dataset str(Glass) ## 'data.frame': 214 obs. of 10 variables: ## $ RI : num 1.52 1.52 1.52 1.52 1.52 ... ## $ Na : num 13.6 13.9 13.5 13.2 13.3 ... ## $ Mg : nu...
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Data Preprocessing/Overfitting
Exercise 3.1: Exploring the Glass Dataset (a) Using visualizations to explore the predictor variables # Load the Glass data data(Glass) Glass <- Glass # Basic structure of the dataset str(Glass) ## 'data.frame': 214 obs. of 10 variables: ## $ RI : num 1.52 1.52 1.52 1.52 1.52 ... ## $ Na : num 13.6 13.9 13.5 13.2 13.3 ... ## $ Mg : nu...
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Data Mining: multiple linear regression model
{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) Load Required Libraries options(repos = c(CRAN = "https://cloud.r-project.org")) # Function to install a package if not already installed install_if_needed <- function(pkg) { if (!requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) } } # List of packages to check and ...
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Time Series Forecasting Exercises
Exercise 1.: Forecasting Australian Population, Bricks, NSW Lambs, Household Wealth, and Australian Takeaway Food Turnover Australian Population Forecast (global_economy) # Loading global_economy dataset global_economy <- global_economy aus_pop <- global_economy %>% filter(Country == "Australia") %>% select(Year, Population) # Plotting Australian...
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SouleymaneD Time series decomposition Week 3
Libraries library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ─────────────────────────────────────────�...
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SouleymaneD Week 2 Time Series
Libraries library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ─────────────────────────────────────────�...
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Final Project 605
Libraries library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.3 ✔ tidyr ...
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Functions of Multiple Variables
\(\underline{PROBLEM 1}\) Given points: (5.6, 8.8), (6.3, 12.4), (7, 14.8), (7.7, 18.2), (8.4, 20.8) We use the method of least squares to find the best-fitting line for the given data points. Linear Regression Equation The linear regression equation is given by: \[ y = mx + b \] where: - \(y\) is the dependent variable, - \(x\) is the independen...
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Domain and Range of Multi-variable Functions
Domain and Range of Multi-variable Functions Function 1: \(f(x, y) = x^2 + y^2 + 2\) Domain The function \(f(x, y) = x^2 + y^2 + 2\) involves square terms of \(x\) and \(y\), which are defined for all real numbers. Therefore, the domain of \(f\) is all ordered pairs of real numbers: \[ \text{Domain} = \mathbb{R}^2 \] Range Since \(x^2\) and \(y^...
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Taylor Series Expansions of Popular Functions
Taylor Series Expansions of Popular Functions Function \(f(x) = \frac{1}{1-x}\) The Taylor series expansion of \(f(x) = \frac{1}{1-x}\) around \(x = 0\) is: fx <- function(x) 1/(1 - x) taylor_series_1x <- taylor(fx, x0 = 0, n = 7) print(taylor_series_1x) ## [1] 1.007014 1.001710 1.000293 1.000029 1.000003 1.000000 1.000000 1.000000 Function \(f(x...
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