Publications by Mohammed Rahman
Assignment 2: Experimentation & Model Training
Introduction In Machine Learning, Experimentation refers to the systematic process of designing, executing, and analyzing different configurations to identify the optimal settings that performs best on a given task. Experimentation is learning by doing. It involves systematically changing parameters, evaluating results with metrics, and compari...
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Assignment 1: Exploratory Data Analysis
Introduction The data set is derived from a Portuguese bank’s direct marketing initiatives. Phone calls served as the foundation for these marketing strategies. To determine whether a consumer had subscribed to the product (a bank term deposit), multiple interactions with the client were necessary. The categorization goal is to forecast wheth...
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Assignment 1: Exploratory Data Analysis
Background Introduction One of the most important challenges in banking institutions’ success has always been marketing to potential customers. Not surprisingly, banks typically use digital media, social media, customer service, and strategic alliances to connect with their clientele. However, how can banks more precisely sell to a particula...
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Predicting the PH Of Beverages
Introduction: As a data scientist tasked with developing a predictive model for pH regulation in manufacturing processes, the overarching objective is to leverage data-driven insights to enhance operational efficiency, ensure regulatory compliance, and optimize product quality. By harnessing advanced analytics and machine learning techniques, w...
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Walmart Data Analysis
Introduction For the final project I’m using the Walmart sales data from Kaggle that is scraped from the web to perform data and statistical analysis. Data Source: Walmart Dataset The data contains sales of different Walmart stores from 2010-02-05 to 2012-11-01. It has columns with store number, week of sales, sales for the given store, holi...
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Project 4 - Document Classification
Project 4 - Document Classification Overview It can be useful to be able to classify new “test” documents using already classified “training” documents. A common example is using a corpus of labeled spam and ham (non-spam) e-mails to predict whether or not a new document is spam. For this project, you can start with a spam/ham dataset, ...
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Data-624 Homework 9
Exercise 8.1: Recreate the simulated data from Exercise 7.2: library(mlbench) set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <-as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" (a): Fit a random forest model to all of the predictors, then estimate the variab...
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Week 11: Recommender Systems
Introduction: Pandora is a leading music and podcast discovery platform, providing a highly-personalized listening experience to approximately 70 million users each month with its proprietary Music Genome Project® and Podcast Genome Project® technology - whether at home or on the go - through its mobile app, the web, and integration with more...
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Data-624 Homework 8
Question 7.2: Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: \(y = 10 sin(\pi x_1x_2) + 20(x_3 - 0.5)^2 + 10x_4 + 5x_5 + N(0, \sigma^2)\) where the x values are random variables uniformly distributed between [0, 1] (there are also 5 othe...
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Data-624 Homework 7
Exercise 6.2: Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: Start R and use these commands to load the data: library(AppliedPredictiveModeling) data(permeab...
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