Category: R

The assignment is for a group of 3 students, so the solution must cover the work of all three members.

Each students contribution should be included clearly in the final solution.

The work must be written in the students own words and should not be copied from websites or other sources.

The final report should have low similarity by using proper paraphrasing, original explanation, and correct references.

The solution should avoid direct copy-paste and must follow academic integrity rules.

The final work should not look AI-generated and should be written in a natural student style.

The answer should be original, clear, and simple, without depending on AI-generated text.

In the assignment, you are required to use any 3 algorithms only.

The task should apply the concept of Machine Learning, not only normal programming.

Machine Learning is a part of Artificial Intelligence.

In Machine Learning, we give the system:

Data

Previous results

Then the system analyzes them and discovers patterns.

The aim of the algorithms is to perform:

Prediction

Grouping

Analysis

Programming will be used, especially R, to implement the algorithms.

The difference should be explained between:

Programming: rules and conditions are written manually.

Machine Learning: the system depends on data and results to discover patterns.

The final answer should be prepared as a complete group solution for the three students, not as an individual solution for one student only.

I just need the coding using R stedio not the full reportI have attached the dataset and rubric

I will share the questions being answered by the code and sample code of what is expected

I just need the coding using R stedio not the full report.

Business Problem Identification

• Clearly state the business problem in a single, bold sentence. This should be concise and specific, highlighting the core issue you aim to address.
• Elaborate on the problem: Provide context and explain how you identified this problem. Discuss its significance and potential impact on the business. Why is it important to solve this problem? What are the potential benefits of finding a solution?

2. Data Preprocessing

• Discuss potential data issues: Explain how you handled potential data issues such as errors, outliers, and missing values. Justify your approach. For example, if you removed outliers, explain why and mention the proportion of data removed.

3. Analysis and Visualization

• Visualize data using ggplot2: Create four insightful visualizations using the ggplot2 package in R. Focus on quality over quantity, leveraging advanced features like facet_wrap() to create compelling and informative visuals.
• Develop a predictive model: Identify the most relevant independent variables and develop a predictive model. Explore different iterations by including and excluding variables to improve accuracy.
• Explain your model: Discuss the business implications of your predictive model. Analyze variable importance and interpret the model’s results in the context of the business problem.

4. Interpretation and Communication

• Provide clear and concise interpretations: Explain the key findings from your analysis and visualizations.
• Offer data-driven recommendations: Based on your analysis, provide specific and actionable recommendations to address the business problem. Clearly connect your recommendations to your findings and explain how they can help solve the problem.

Guidelines:

Some guidelines for completing the assessment are provided below.

Word Limit: 2000 words (+/- 10%) Note that anything beyond word limit will not be marked.

Font Size: 11pt or 12pt

Font Style: Calibri or Times New Roman

Document Margins: 2.5cm for top, bottom, left and right

Spacing: Single or 1.5 spacing

Some notes and guidelines:

• The report covers a wider audience, including management and business users as well as data analysts.

• Use headings for each section and subsection (i.e. 1.0 Introduction, 1.1. Business Background, 1.2. Objectives, 2.0 Data Pre-processing, 3.0 Methodology, etc.).
• Label the figures and tables in the report properly.
• Use Appendix as you see fit.
• Use References as you see fit. See next section about referencing guidelines.

Veriset of films and TV shows broadcast on the digital streaming platform Netflix,- until mid-2021- It contains a list of all movies and TV shows available on Netflix, with details such as cast, directors, ratings, release year, duration, etc. Prepare an R script that performs the following visualizations using this dataset.

• Number of movies and TV shows streaming on Netflix as a column chart
• Change in the number of movies on Netflix by year As a dot plot
• Distribution of film and TV program durations by year dot graph
• Histogram of the duration of movies from Netflix releases
• Change in average film durations by years after 2000 as XY scatter plot

Task 1: Apply the tree-based models and ensemble learning techniques we have reviewed in class, including CART, Bagging, Random Forest, and AdaBoost, to analyze a Human Resources dataset and predict employee attrition.

Please download the following three files to get started (Task 1)

The report template where you will paste your screenshots and written answers: Assignment_4_template.docx

The starter R script to help you organize your code: Assignment4_Template.RDownload Assignment4_Template.R

Submission Requirements:

You are required to submit two files for this assignment: Assignment Report, R script

Task 2: Only R Script required.

Please let me know if any additional materials are needed. Thanks.

Hi, Im working on my SOC 402 SEM project where I had to replicate a published model using LISREL. Ive completed my report and compared my results with the article, including the coefficients and model fit.

Would you be able to review my report and fix them

I’m sharing the assignment itself as a Word document. I’ve also shared an Excel file, which you can convert to a CSV file and use.

ECON253: Data Analysis IReport Assignment : Part 1

You need to complete the data analysis for the designated city.

will shrae allocated data set

Question 1

Assume you are going to run a Two-Way independent ANOVA on the memory.csv data. Answer the question is the data balanced?

Question 2
Without doing any analyses, what kinds of results do you expect to see for the main effects? i.e., what are your predictions?

Question 3
Assume the researchers anticipated a moderate effect size ( = 0.13) for a main effect of strategy, delay, and their interaction. Does the study have a reasonable sample size to achieve a power level of at least 0.8 to assess those effects? What is the minimum total sample size (N) they would need for each, (round to an integer)?

Question 4
Plot the classical means and their 95% confidence intervals for this dataset. Based on the plot, does the pattern suggest an interaction between strategy and delay? Give the plot interesting colours.

Question 5
Calculate the total sum of squares and its associated degrees of freedom.

Question 6
Calculate the models sum of squares and the models degrees of freedom.

Question 7
Calculate the sum of squares for a main effect of Strategy and its degrees of freedom.

Question 8
Calculate the sum of squares for a main effect of Delay and its degrees of freedom.

Question 9
Calculate the sum of squares for a possible interaction and its degrees of freedom.

Question 10
Calculate the sum of squares for the residuals/error and its degrees of freedom.

Question 11
Calculate the mean squares for the effect of Strategy, Delay, the interaction between Strategy and Delay, and the mean square error/residuals.

Question 12
Calculate the F-ratios for each main effect and the interaction. Also report the associated degrees of freedom and p-value.

Question 13
Use one of Rs cheat codes (i.e., functions) to quickly check and see if your results in questions 5 – 12 are correct. Make sure your output displays the degrees of freedom, sum of squares, F-Ratios, and p-values.

Question 14
In plain English, what do the results of the ANOVA you ran tell you?

Question 15
Pretend the memory.csv data is unbalanced and the researchers dont want to violate the principle of marginality. Re-run question 13 in a way that does not violate this principle.

Question 16
Pretend the memory.csv data is unbalanced. Re-run question 13s analyses using Type-III sum of squares.

Question 17
The significant interaction suggests that it would be useful to examine some simple effects. Conduct a simple effects analyses that allows you to evaluate the effect delay has on each type of strategy. i.e., compare delay (5m vs 2d) across each type of strategy. Display the summary output and list the comparisons you made that are classified as simple effects and state which ones are significant.
e.g., Generated 5m vs Generated 2d: significant.

Question 18
How does delay impact the three different strategies, if at all, and what strategy seems best overall.

Question 19
Write R code to verify that the contrasts used for the simple effects analysis are centered.

Question 20
Write R code to verify that the contrasts used for the simple effects analysis are orthogonal.

Question 21
Instead of doing a simple effects analysis, a bunch of t-tests could have been conducted across the 6 combinations of levels. If you took that approach, how many pairwise comparisons would you be making? Find an automated way of determining this in R.
Hint: combinations, not permutations, are what you want

Question 22
Given the previous question, if you are making 15 pairwise comparisons, what would your familywise error be if you are using = 0.05? In plain English what does this mean?

Question 23
In the lectures I have said repeatedly that an ANOVA is just multiple linear regression. Consequently, it has all the same assumptions. For the model Recall ~ Strategy + Delay + Strategy:Delay does it look like the residuals are homoscedastic? Use of lm() is permitted.
Make the plot an interesting set of colours.

Question 24
Are the residuals normally distributed?
When you create the Q-Q plot, be sure to include a line to help visualize how well the points conform to a straight path. By default, the line ignores the bottom and top 25% of the data when fitting. It does this to give strong protection against outliers. Pretend you believe that is too conservative for the line. Figure out a way to change behaviour that so it only ignores the bottom and top 10%.

Everything i think you need is already documented in the PDF and the dataset.

You will need to read the PDF very well to understand. Please ask me any question if you need further clarification.

DataSet: