Publications by Abdelmalek Hajjam/ Monu Chacko
DATA 621 Final Project
Real Estate “The best investment on earth is earth.” -Louis Glickman Abstract The real estate market in an important aspect in people life. From the dream of owning a home to using it as a security during tough times, real estate has been a part of our lives from generations. Price of real estate is important when buying home. For example, ...
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Final Project - Computational Mathematics
YouTube: https://www.youtube.com/watch?v=g6ddpZHTp24 library(tidyverse) library(knitr) library(kableExtra) library(matrixcalc) Problem 1 Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,...
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Blog 2 - Data Transformation
Data Transformation Data Transformation is the application of a mathematical expression to each point in the data. In contrast, in a Data Engineering context Transformation can also mean transforming data from one format to another in the Extract Transform Load (ETL) process. Importance of tranforming data Interpretability Variables could have...
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Blob 4 - Common Statistical Method
Common statistical method used in Business Mean, Median and Mode Mean is calculated by taking the sum of the values and dividing with the number of values in a data series. The function mean() is used to calculate this in R. Median is the middle most value in a data series is called the median. The median() function is used in R to calculate th...
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Blog 5 - Residual
Residual Residuals are associated to a line of best fit or trend line. So what is a line of best fit or trend line? It is a straight line that best represents the data on a scatter plot. This line may pass through some of the points, none of the points, or all of the points. Residual is a measure of how well a line fits an individual data point. ...
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Blog 3 - Least Squares Models
Generalized, linear, and generalized least squares models (LM, GLM, GLS) Linear regression (LM) Linear regression is used to predict the value of an outcome variable Y based on one or more input predictor variables X. The aim is to establish a linear relationship (a mathematical formula) between the predictor variable(s) and the response variabl...
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Blog 1 - Big Data
Big Data - Real life issues faced by companies Introduction There has been a great transformation in the type and scale of data we used to solve business and personal problems. Data used to be in small structured unit that needed small computation units to solve problems. These computation resources were often hosted on-premise. Problem - The B...
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Data 605 - Assignment 15
1. Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. ( 5.6, 8.8 ), ( 6.3, 12.4 ), ( 7, 14.8 ), ( 7.7, 18.2 ), ( 8.4, 20.8 ) df <- data.frame(X = c(5.6, 6.3, 7, 7.7, 8.4), Y = c(8.8, 12.4, 14.8, 18.2, 20.8)) md1 <- lm(Y ~ X, data = df) s1 <- summary(md1) s1 ## ## Cal...
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ASSIGNMENT 14 - TAYLOR SERIES
library(pracma) Taylor Series expansions of popular functions. Question 1: \[f\left( x \right) = \frac { 1 }{ (1-x) }\] \[f\left( x \right) \quad =\quad \sum _{ n=0 }^{ \infty }{ \frac { { f }^{ (n) }(a) }{ n! } { (x-a) }^{ n } }\] \[f(a)\quad +{ \quad f }^{ (1) }(a)(x-a)\quad +\quad \frac { { f }^{ (2) } }{ 2! } (a)(x-a)\quad +\quad ...\] \(f(...
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Data 605 Discussion 15
Exercises 12.1.7: Give the domain and range of the multi-variable function. \[f(x,~y)= x^2+y^2 + 2\] Domain is all real values of x and y Range is all values greater and 2 fun1 <- function(x,y) { x^2 +y^2 + 2 } x <- runif(100000,-1000,1000) y <- runif(100000,-1000,1000) result <- fun1(x,y) hist(result, breaks = 100) ...
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