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Homework – Chapter 3 & 4 – Questions & Answers
Homework – Chapter 3 & 4 – Questions & Answers 1. Given the following observations from a sample, calculate the mean, the median, and the mode. (Round "mean" to 2 decimal places.) 29 29 30 28 31 2. Consider the following observations from a population: 93 240 78 143 143 76 234 158 78 a. Calculate the mean and median. (Round "mean" to 2 decimal places.) b. Select the mode. (You may select more than one answer. Single click the box with the question mark to produce a check mark for a...
- Exam (elaborations)
- • 10 pages •
Homework – Chapter 3 & 4 – Questions & Answers 1. Given the following observations from a sample, calculate the mean, the median, and the mode. (Round "mean" to 2 decimal places.) 29 29 30 28 31 2. Consider the following observations from a population: 93 240 78 143 143 76 234 158 78 a. Calculate the mean and median. (Round "mean" to 2 decimal places.) b. Select the mode. (You may select more than one answer. Single click the box with the question mark to produce a check mark for a...
PyTorch and KNN
PyTorch for Burn and Not Burn Do these problems qualify or suitable for deep learning? why or why not? and How does Pytorch compares with KNN for Burn dataset? Include differences you can think of!
- Exam (elaborations)
- • 2 pages •
PyTorch for Burn and Not Burn Do these problems qualify or suitable for deep learning? why or why not? and How does Pytorch compares with KNN for Burn dataset? Include differences you can think of!
KNN Classifier
Problem: A healthcare facility (aka hospital) brought in a new research physician who specializes in treating burn victims. This physician has been tasked with running the hospital’s telemedicine division and is interested in using machine learning to triage patients who schedule appointments with the telemedicine service. As part of the triage process, patients are instructed to send in photographs of their skin, so that the most in-need patients can be treated first.
- Exam (elaborations)
- • 2 pages •
Problem: A healthcare facility (aka hospital) brought in a new research physician who specializes in treating burn victims. This physician has been tasked with running the hospital’s telemedicine division and is interested in using machine learning to triage patients who schedule appointments with the telemedicine service. As part of the triage process, patients are instructed to send in photographs of their skin, so that the most in-need patients can be treated first.
Linear Regression
Linear Regression Examine the suitability of polynomial regression in predicting progression of diabetes using features: BMI, DiabetesPedigreeFunction, and Age. The outcome is a binary result (1: diabetes, 0: no diabetes) 2. Clean the data: Replace the missing values with average values, and remove unecessary columns 3. Linear Regression 4. Heatmap Analysis 5. Logistic Regression
- Exam (elaborations)
- • 2 pages •
Linear Regression Examine the suitability of polynomial regression in predicting progression of diabetes using features: BMI, DiabetesPedigreeFunction, and Age. The outcome is a binary result (1: diabetes, 0: no diabetes) 2. Clean the data: Replace the missing values with average values, and remove unecessary columns 3. Linear Regression 4. Heatmap Analysis 5. Logistic Regression
Predicting Sentiments using Logistic Regression
The goal of this project is to predict sentiments in twitter data using logistic regression, closely following the approach outlined in Chapter 5. The input data can be found at In order to use logistic regression, we need to come up with a set of features. One approach suggested in the book is to use positive and negative lexicons. You can find such lexicons at The file socialsent_hist_ has historical adjectives for each decade from 1850 to 2000 in the form of tab separated values. For exampl...
- Exam (elaborations)
- • 2 pages •
The goal of this project is to predict sentiments in twitter data using logistic regression, closely following the approach outlined in Chapter 5. The input data can be found at In order to use logistic regression, we need to come up with a set of features. One approach suggested in the book is to use positive and negative lexicons. You can find such lexicons at The file socialsent_hist_ has historical adjectives for each decade from 1850 to 2000 in the form of tab separated values. For exampl...
Hands-On Exercise 6-1: Outlier Detection with Titanic dataset
Hands-On Exercise 6-1: Outlier Detection with Titanic dataset In this Hands-on exercise, you will learn. • How to use quantiles to detect the outliers in data (the Titanic Training dataset) Related DM Book Chapters/Sections: • Section 2.2.2 Measuring the Dispersion of Data: Range, Quartiles, Variance, Standard Deviation, and Interquartile Range Related Hands-on Exercises: • Exercise 1-2 Apache Spark and Basic Statistics Finish the assignments shown below. Submit a word document (...
- Exam (elaborations)
- • 7 pages •
Hands-On Exercise 6-1: Outlier Detection with Titanic dataset In this Hands-on exercise, you will learn. • How to use quantiles to detect the outliers in data (the Titanic Training dataset) Related DM Book Chapters/Sections: • Section 2.2.2 Measuring the Dispersion of Data: Range, Quartiles, Variance, Standard Deviation, and Interquartile Range Related Hands-on Exercises: • Exercise 1-2 Apache Spark and Basic Statistics Finish the assignments shown below. Submit a word document (...
Hands-On Experiment 5-2: Clustering with Spark - Part II
Hands-On Experiment 5-2: Clustering with Spark - Part II
- Exam (elaborations)
- • 5 pages •
Hands-On Experiment 5-2: Clustering with Spark - Part II
Hands-On Experiment 5-1: Clustering with Spark
Hands-On Experiment 5-1: Clustering with Spark In this Hands-on exercise, you will learn. • How to use the k-means clustering algorithm in Apache Spark • How to handle data and features for clustering • Training and prediction for clustering • Evaluation for clustering Related DM Book Chapters/Sections: • Section 10.1 Cluster Analysis • Section 10.2 Partitioning Methods • Section 10.2.1 k-Means: A Centroid-Based Technique Submit a word document (or PDF) with answers/expl...
- Exam (elaborations)
- • 4 pages •
Hands-On Experiment 5-1: Clustering with Spark In this Hands-on exercise, you will learn. • How to use the k-means clustering algorithm in Apache Spark • How to handle data and features for clustering • Training and prediction for clustering • Evaluation for clustering Related DM Book Chapters/Sections: • Section 10.1 Cluster Analysis • Section 10.2 Partitioning Methods • Section 10.2.1 k-Means: A Centroid-Based Technique Submit a word document (or PDF) with answers/expl...
Hands-On Experiment 4-2: Classification with Titanic dataset
Hands-On Experiment 4-2: Classification with Titanic dataset 2.2.1 (20pts) Assignment 1: Index the Gender values We have learned how to index values using StringIndexer in previous hands-on exercises • Write codes for indexing the gender values 1. Import a Class 2. Define an indexer – Input column: Gender – Output column: IndexedGender 3. Train and transform • Take a screenshot of running your codes and outputs using the show (5) function 3 Building a Model 3.1 Training and T...
- Exam (elaborations)
- • 4 pages •
Hands-On Experiment 4-2: Classification with Titanic dataset 2.2.1 (20pts) Assignment 1: Index the Gender values We have learned how to index values using StringIndexer in previous hands-on exercises • Write codes for indexing the gender values 1. Import a Class 2. Define an indexer – Input column: Gender – Output column: IndexedGender 3. Train and transform • Take a screenshot of running your codes and outputs using the show (5) function 3 Building a Model 3.1 Training and T...
Hands-On Experiment 4-1: Classification with Spark
Hands-On Experiment 4-1: Classification with Spark In this Hands-on exercise, you will learn • Decision Tree classifier in Apache Spark • How to handle data, features, and training & testing data • Training & Testing • Evaluation Related DM Book Chapters/Sections: • Section 8.1 Basic Concepts • Section 8.2 Decision Tree DataFrame-based Spark ML is new, much easier, and better. However, some features are missing. The evaluator for DataFrame provides limited metrics only. Th...
- Exam (elaborations)
- • 7 pages •
Hands-On Experiment 4-1: Classification with Spark In this Hands-on exercise, you will learn • Decision Tree classifier in Apache Spark • How to handle data, features, and training & testing data • Training & Testing • Evaluation Related DM Book Chapters/Sections: • Section 8.1 Basic Concepts • Section 8.2 Decision Tree DataFrame-based Spark ML is new, much easier, and better. However, some features are missing. The evaluator for DataFrame provides limited metrics only. Th...
Santander_Bank_Case_Study_ML_Week6_NEC
Drawing_Maps_VisualAnalytics_Week13_NEC_Solved
MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved
Santander_Bank_Case_Study_ML_Week6_NEC
Fundamentals_of_ensemble_modeling_Week5_NEC