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Q1:In March of 2023, Goldman Sachs published a report &, indicating that ~25% of the tasks in US and Europe can be automated using Al. However, as you can see in this visualization &, not all industries will be affected equally. According to the report, certain jobs, like office tasks, legal, architecture and social sciences have a potential for 30%+ automation, while positions like construction, installation and building maintenance are going to be largely unaffected. You can also see supporting evidence in the Facebook Research paper &, which highlights Moravec's paradox, the thesis that the hardest problems in Al involve sensorimotor skills, not abstract thought or reasoning, which coincide with Goldman Sachs predictions. While both of these papers are very impressive, they also heavily influenced by the recent advances in Large Language Models (LLMs). For this final project I have prepared a collection of ~200K news articles (about 900 MB) articles on our favorite topics: Data Science, Machine Learning and Artificial Intelligence, and I want you to identify what industries and job lines are going to be most impacted by the Al over the next several years, based on the information you can disseminate from this text corpus. The objective of your final project is to identify what types of tasks and jobs are most likely to see the biggest impact from Al by extracting meaningful insights from unstructured text. Your goal is to provide actionable recommendations on what can be done with Al to automate the jobs and / or improve employee productivity. Please pay attention to the introduction of novel technologies and algorithms, such as Al for image generation and Conversational Al, as they represent the entire paradigm shift in adoption of Al technologies and data science in general. You can access the data by using one of the following methods: · Download data by following this think from your browser: https://storage.googleapis.com/msca-bdp-data- open/news final_project/news final_project.parquet @> · Use Pandas from anywhere (your laptop, Colab or any cloud) df_news_final_project = pd.read_parquet('https://storage.googleapis.com/msca-bdp- data-open/news_final_project/news_final_project.parquet', engine='pyarrow') o NLP GCP 11.1 Final Project Starter.ipynb & Note: this is live data, so the layout and record counts in your dataframe will vary from the counts in the attached notebook To complete your assignment, I suggest considering the following steps: . Clean-up the noise, by eliminating newlines, tabs, remnants of web crawls, and other irrelevant text · Discard irrelevant articles · Detect major topics · Identify top candidates for Al integration - these can be related to any industry and yield positive or negative results (sentiment analysis). . Suggest why certain types of jobs are more likely to be impacted by Al o Plot a timeline to illustrate how the sentiment is changing over time . Identify new technologies and Al solutions that might be affecting the employment landscape · Plot a timeline to illustrate the introduction of some of these technologies · Demonstrate what companies, academic institutions and government entities can do to accelerate the development of these transformative capabilities · Leverage appropriate NLP techniques to identify organizations, people and locations, then apply targeted sentiment o What types of companies (based on the lines of business) are planning to invest in these technologies today or near future (success stories)? · Create appropriate visualization to summarize your recommendations (i.e. word cloud chart or bubble chart) . What types of applications cannot currently be transformed by Al, based on today's state of technology (failures)? · Create appropriate visualization to summarize your recommendations (i.e. word cloud chart or bubble chart) Additional guidance: . Clean-up or sample data if you need to shorten processing times or reduce memory usage . Default sentiment will likely be wrong from any software package and will require some tweaking · Keyword / dictionary approach o Data annotation and development of custom classifier · Building custom model on open-source data (i.e. Yelp) o Fine-tuning Transformer Pipeline · You are encouraged to explore a combination several techniques to identify key topics: · Topic modeling (i.e. LDA using gensim or ktrain) or using BERTopic · Classification (hand-label several topics on a sample and then train classifier) · Clustering (cluster topics around pre-selected keywords or word vectors) · Zero-shot (NLI) modeling . Please ensure your PowerPoint presentation (in PPTX or PDF format) is submitted to the course module as-is (not zipped). Otherwise we are unable to use Canvas SpeedGrader. The presentation should look professional - not a collection of screenshots from your analytical software o Roughly 8-12 pages is reasonable for this kind of project but there are no strict restrictions. . On your slides you will want to provide: · Executive Summary · Methodology and source data overview · Actionable recommendations · Apply text summarization algorithms where possible to synthesize your insights . Please submit your actual program codes (Jupyter notebooks) along with your PowerPoint . The slides should be self-sufficient and after reading them, there should not be any need to read the notebook (we are still asking you to provide the notebooks as a proof or work though). . The slides should clearly answer all the questions and the answers should be supported with the plots/tables/numbers produced in the notebook based on the actual data. . The slides should contain the RIGHT amount of supporting material for each question, putting too much supporting material is as bad as putting too little: too much - you would not be able to keep the audience attention and your presentation would be a mess, too little - your statements would not look convincing. 0 Everything should be clear, logical, well organized, as simple as possible. Use proper English grammar and run spell check. . All the plots should be of production quality and easily readable. Fuzzy plots, untitled plots, unreadable labels, overlapping labels are unacceptable. If you formatting somehow gets corrupted when you put your slides into Canvas (sometimes it happens), it is your responsibility to fix it. For example, try saving it in some other format like PDF, HTML. . Any statements you make should be supported by data. Only recommendations or goals of the project sections can contain elements not directly supported by the data . Please submit your actual program codes (i.e. Python Notebook) along with your PowerPoint - as a separate attachment · Your presentation should be targeted toward business audience and must not contain any code snippets · You are welcome to use any software packages of your choice to complete the assignment Grading Rubric: Rubric Points Executive Summary with meaningful insights 20 Visualize actionable recommendations 10 Article clean-up and filtering 10 Topic detection 20 Sentiment analysis (explicitly customized) 10 Sentiment over time analysis and visualization 10 Entity (organizations and people) identification 10 Targeted (entity) sentiment identification 10 Total Points 100/nInstructions for submitting the solution 1. Submit the source code file for every programming assignment like .c file, .java file. 2. If any dataset is used then you need to share all the files with solution in a single zip file. 3. Share the readme file in which you have to include the system specification, required software and the execution instructions. 4. Share the output of the programs. If it is a single program then you can submit the screenshot, if multiple files are included then please share the screen recording. Below are some steps that need to be covered in screen recording. Show the complete solution according to the instructions. Need to cover all the compilation steps. Show the complete output of program/ project. * Show all pass test cases if given in assignment. 5. Always add proper comments in code. If any specific package is used then please mention it in comment./nInstructions for submitting the solution 1. Submit the source code file for every programming assignment like .c file, .java file. 2. If any dataset is used then you need to share all the files with solution in a single zip file. 3. Share the readme file in which you have to include the system specification, required software and the execution instructions. 4. Share the output of the programs. If it is a single program then you can submit the screenshot, if multiple files are included then please share the screen recording. Below are some steps that need to be covered in screen recording. Show the complete solution according to the instructions. Need to cover all the compilation steps. Show the complete output of program/ project. * Show all pass test cases if given in assignment. 5. Always add proper comments in code. If any specific package is used then please mention it in comment.See Answer
Q2:Student note: Using this link for the data set: https://1.facebook.com/l.php?u=https%3A %2F%2Fwww.kaggle.com%2Fdatasets%2Fazminetoushikwasi%2Faqi- air-quality-index-scheduled-daily-update%3Ffbclid %3DIWAR1Ovg9fzOTZQ1iy39G 1bv6H1Qgg1qZmpTp6- OzTPvRFynGk3e5uvP9ROI&h=AT0qA- Tv00PuEJdPKLT1B41zi0suT2nodD4Mju1mAr3eYp2fGQTBmfwyG7Cg HYRhCIMwpLZtftsbd KTRp2dCHKMA4XbGNon- i0fOtcIWzGmJ5M2e9WcO7HjUoCp1BHs fg I only need the project proposal part done Not the entire project Need this in around 500 words single spaced/n Instructions: ● ● STEVENS INSTITUTE OF TECHNOLOGY 1870 Course Project Instructions and Guidelines The Course Project for MA 541 is a group-based and semester-long project. It is an opportunity for you to integrate and apply what you have learned in the course and to perform an in-depth analysis of your chosen data set. Including the proposal period, you have over ten weeks to complete the project. You will be assigned to a group of three or four students. All team members must make approximately equal contributions to the project to receive the same credit. A Group Performance Assessment form from each team member must be submitted as a reference of grading. To make sure you are on track to submit the project report on time, several assignments related to the course project require your submissions to Canvas. Their due dates are given below. Please note all submissions are due at 11:00 pm. Submission Course Project Proposal Draft of project report (PDF) Final report (PDF) Group assessment form https://datasetsearch.research.google.com https://www.kaggle.com/datasets https://data.gov Your course project report must be a complete exploratory data analysis. The goal is to gain insights into the data, identify patterns, and test hypotheses that can guide further analysis and make conclusions. You will choose your own data set that you want to analyze. The data set must include at least 100 values, however, make sure it is manageable. A good number of values for data sets is 200-500. Some places to find data sets can be found below. https://datahub.io/collections https://www.earthdata.nasa.gov https://apps.who.int/gho/data/node.home https://www.bfi.org.uk/industry-data-insights MA 541-B- Spring 2024 February 25, 2024 April 7, 2024 April 21, 2024 April 21, 2024 https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page https://crime-data-explorer.fr.cloud.gov https://finance.yahoo.com/lookup?s=DATA Due date You can use any programming language (Python, R, Excel, Minitab, etc) to conduct the analysis for this project. The whole coding must be submitted to Canvas as a pdf file that is separate from the project report. Each group will submit one report by uploading it to Canvas. Its grade will be used for the Course Project component in our course. Group Member Registration: Please use the following link to sign up for groups. Each group should have only 3-4 members. MA 541-B Project Groups.xlsx This registration is due Thursday, February 15, 2024, at 11:00 pm. After this time, if you don't have team members to work with, I will randomly assign you to a group. Proposal Requirements: Course project proposal states the problem you will work on and what you plan to do to analyze your dataset. In your project proposal, you will need to include the following. 1) A link to your dataset source. 2) Summary of your data set (What is it about? Has it been cleaned? How many variables does it have? What types are the variables: qualitative, quantitative, text, numeric, tables, pictures, etc?) 3) Describe what you plan to do. (You don't need to be specific in this part. It is just a plan; you can freely make changes as you are working on the project.) 4) Suggestion section: If you need any suggestions from me, please put them in this section. If you don't need them, please enter N/A. (Of course, you can always discuss any questions with me if they arise while you are working on the project.) Project Guidelines: Your project should include the main steps of an exploratory data analysis as follows. 1) Data sourcing: introduce the source of your data set and summarize it. (What is it about? Has it been cleaned? How many variables does it have? What types are the variables: qualitative, quantitative, text, numeric, tables, pictures, etc?) 2) Data cleaning: If your data set does not require cleaning, state so and then skip this step. If it requires cleaning, include what you have done to clean it (identify and correct errors, missing values, duplicate values, outliers, inconsistent data, data formatting issues, etc). 3) Data Visualization and Summary Statistics: This is where you use descriptive statistics to explore and get quick insights of the data. a) Data Visualization: make use of graphs and charts to explore data. Suggestions are pie charts, bar graphs, line graphs, histograms. Make sure you include conclusions (trends, percentages, comparisons, outliers, etc) at the end of this part. b) Summary Statistics: the measures of center (mean, median, mode), measures of dispersion (range, variance, standard deviation) and measures of locations (percentiles, 5-number summary) can help understand the data set and identify patterns or unusual values. You can also draw box plots and use them to compare different groups or characteristics in data. 4) Statistical Inference: This is where you use statistical methods to explore and analyze data. Some things you may include are: check for types of distributions, estimate parameters (using different methods), test hypotheses, perform correlation analysis, (one variable and multivariable) regressions, factor analysis, sample size determination, classification, etc. The goal is to make conclusions and predictions. It includes two main parts. a) Univariate Analysis: identify variables in your data (you should focus on several main variables) and explore them. Use graphs (histograms, QQ plots, normal probability plots) to get some insights about the distribution of the data. Then estimate parameter, test hypotheses, and make conclusions. b) Multivariate Analysis: Determine the type of the variables in your data to use the appropriate methods. Categorical vs Categorical variables: Chi-square test of independence. ● Categorical vs Numerical variables: scatter plot, contingency table, T-test, Chi- square test, ANOVA. Numerical vs Numerical variables: scatter plot, simple linear regression, ANOVA, correlation coefficient, etc. ● Please note you don't need to include all methods/techniques in your report. Explore data and see what characteristics are worth to study further and focus on them. Your project report should be between 12-15 pages (not including the code). Please try to have a transparent analysis rather than a lengthy one. Tips and Suggestions: 1) Choose the right charts for your data. There are several types of charts and graphs that can be used in EDA, and choosing the right one depends on the type of data being analyzed and the question being asked. 1. Histograms — Used to display the distribution of a continuous variable, such as age or income. 2. Scatterplots — Used to visualize the relationship between two continuous variables. 3. Boxplots - Used to display the distribution of a continuous variable by showing the median, quartiles, and outliers. 4. Bar charts — Used to display the distribution of a categorical variable, such as gender or location. 5. Pie charts — Used to display the relative proportions of different categories. 6. Line charts — Used to display trends in a continuous variable over time. 7. Heatmaps — Used to display the relationship between two categorical variables, such as the frequency of purchases by customer segment. 8. Area chart — Used to show how a quantity changes over time, by filling in the area under a line. 9. Bubble chart - Used to visualize three dimensions of data on a two-dimensional plot, by showing the relationship between two continuous variables and the size of the points representing each data point. 10. Stacked bar chart Used to display the distribution of a categorical variable, but with each category divided into sub-categories to show the relative proportions of each sub- category. 11. Waterfall chart Used to show how an initial value is affected by a series of intermediate positive or negative values, and how the final value is reached. 12. Radar chart Used to show the performance of a variable across several categories, by plotting each category as an axis and showing the values for each variable as a point on the corresponding axis. 13. Violin plot - Used to display the distribution of a continuous variable, by showing the density of values at different points along the range of the variable. 14. Sankey diagram Used to show the flow of data or resources between different categories, by using arrows of different widths to show the relative amounts flowing between each category. A complete guide to types of charts can be found here. https://chartio.com/learn/charts/ 2) Reduce the number of variables. To make the project manageable, you should reduce the number of independent variables to three or four at most. (I would suggest two independent variables for the course project.) Make sure you choose the most relevant ones. 3) Write a complete data analysis report. Your project report should include the following: a) Title page (including the project title, your name, class, group, date) b) Table of contents c) Introduction Summary of the study and data, as well as any relevant substantive context, background, or framing issues. ● The "big questions" answered by your data analyses, and summaries of your conclusions about these questions. ● Brief outline of remainder of paper. d) Body The body can be organized in several ways. Here are two that often work well: Traditional: includes several sections such as Data, Methods, Analysis, Results. ● Question-oriented: each section in the body is dedicated to answering a specific question and includes its own Methods, Analysis, Conclusions subsections. e) Conclusions / Predictions / Discussion The conclusion should reprise the questions and conclusions of the introduction, perhaps augmented by some additional observations or details gleaned from the analysis section. New questions, future work, etc., can also be raised here. f) Appendix/Appendices (optional). One or more appendices are the place to out details and ancillary materials. These might include such items as • Technical descriptions of (unusual) statistical procedures • Detailed tables or computer output • Figures that were not central to the arguments presented in the body of the reportSee Answer
Q3:. . • • ASSIGNMENT INSTRUCTIONS Need 12 PPT Slides With Speaker Notes Need video showing the working NOTE 2 Files have been attached. Please review these 2 documents for the best result of this project. Project should be on Power point with max 15mns audio presenting the project. Project should be also on a Word document with more details, attached the visualization Please using only Tableau for the visualizations and excel to export the raw data from the clinical trial website. Its a public website Provide a separate video showing how export the dataset to tableau and getting all the visualizations that you will use along the project. Need to get aces with the tableau account as well The Main topic is " Respiratory Disease". so on the public data web, you will input " Respiratory Disease" on Condition Disease section/n Need 12 PPT Slides With Speaker Notes Here are few criteria need to apply for the PowerPoint, do not focus on the gesture part (Not applicable) *Content Introduced topic, established rapport, and explained the purpose of presentation in creative, clear way, capturing attention. *Content Selection Instruction For Presentations All information was relevant and appropriate to requirements of the assignment. *Understanding of Topic Contains a clear central message and clearly identifiable sections featuring purposeful organizational pattern (e.g., chronological, problem-solution, analysis of parts, etc.) *Transitions Effective, smooth transitions that indicated transitions in presentation topic or focus. *Conclusion Ends with an accurate conclusion tying the content back to the opening Transitioned into close so audience was ready for it. ### Introduction - Brief overview of the project scope - Importance of studying Respiratory Diseases - Objectives of the analysis Slide update: ### Understanding Respiratory Diseases - Overview of common respiratory diseases included in the analysis (e.g., Asthma, COPD, Pneumonia) - Impact on public health ### Data Source - Introduction to Clinical Trials.gov Criteria for selecting the dataset ### Overview of Clinical Trials - Brief explanation of clinical trials and their significance in medical research - Key points about clinical trials relevant to your data (referencing the provided information) ### Data Cleaning and Preparation - Steps taken to clean and prepare the data (expanding on "A) Cleaning the CDC data") - Challenges faced and how they were addressed ### Data Importation into Tableau - Process of importing cleaned data into Tableau (elaborating on "B) Importing into Tableau") - Setting up the environment for analysis ### Creating Visualizations · Detailed explanation of the visualization process (building on "C) How to make visualizations from the data") - Types of visualizations created and their purposes ### Analysis of Clinical Trials Data - Descriptive statistics of the dataset (e.g., number of trials, phase distribution) - Trends observed in the data (e.g., increase in trials over years, prevalent diseases) - Geographic distribution of trials ### Insights from Data Visualizations - Key findings from the visualizations - Patterns or anomalies identified in the clinical trials for respiratory diseases - Comparisons with global or national trends in respiratory disease research ### Implications of Findings - Potential impact of findings on respiratory disease research and treatment - Suggestions for future clinical trials or areas of research ### Conclusion - Summary of key findings and insights - Reflection on the project process and learning outcomes ### Live Demo - Walkthrough of the Tableau dashboard - Live demonstration of how to interact with the visualizations ### Q&A - Opening the floor for questions from the audience/nInstruction For Making Video Need to INCLUDE FOLLOWING IN VIDEO : Well the exporting the data and all the visualizations that you will be using along the project will be covered in this he live demo portion on of the my power point slide you will show the dataset you export from the clinical trial website and when u import it to TABLEAU. u will show how to play with the Data on tableau that giving you the visualizations that u need that you will use for your power point. Explain along the steps and why doing it .... Remember need the Tableau access as well so i can show the presentation here are few links; the clinical trial : https://clinicaltrials.gov/search?cond=Respiratory%20Disease&viewType=Card free tableau link : https://www.tableau.com/products/triaSee Answer
Q4:Assignment Content Imagine that your team serves an organization as a part of the analytics team that is growing rapidly. To help with the efficiency of the expanding team, your group has been tasked to develop a resource that can be referenced in developing and implementing business rules in data models. Create a reference resource that includes the following: ⚫ Defines the criteria for creating a business rule • Explains the steps needed to implement business rules in data models • Explains how to implement constraints on business rules ⚫ Defines the functional classifications of business rules and their uses • • Each person in the group should research and contribute 1 business rule that is not included in the reading. Compile all work as 1 reference resource in PDF format. Ensure that the layout and design are conducive to its intent as a reference. Cite any sources to support your assignment. Submit your assignment.See Answer
Q5: Assessment 2A - Data Analysis & Storytelling Report (40%) Learning Objectives This task aims to develop your skills and knowledge towards the following learning objectives: Analyse various data sources to produce actionable insights (SLO 1) . Apply storytelling skills to visualise and convey data insights in a form appropriate to the audience. (SLO 2) Use principles of cultural diversity and social justice to produce responsible data- driven decisions (SLO 3) Evaluate the role that data plays in enhancing marketing decision-making in an increasingly complex business environment. (SLO 5) Task This task builds on the data collection, evaluation, and reflection from Assessment 1 to create an insightful slide deck about the company you chose to look at in Assessment 1. Using the gathered data, you will conduct basic statistical and visual analysis of the primary and secondary data. You should use data storytelling to provide insights about the data and integrate learning from your reflection report. A visualisation slide using an infographic must be included. The report should use appropriate marketing research methods to help interpret and convey completed data and information. Length The slide deck should contain between 10 to 15 slides. Assessment 2, Task A Criteria: Accuracy of data interpretation and analysis (SLO 1) Appropriate and well-communicated data narrative. (SLO 2) Appropriate selection and use of visualisations and infographics. (SLO 2) Feasibility of insights and recommendations (SLO 1, SLO 3) Consideration of possible ethical and social issues related to data insights (SLO 3, SLO 5)See Answer
Q6: In this assignment you will be using Dynamic Programming to select a set of investments options that maximize your return on investment. You will be given a file of investment options along with the estimated return on investment for each one. You also have a given amount of money to invest. The specific file of investment options you will be given will vary, but you will always have the following 3 pieces of information you can obtain from the file: InvestmentName, Investment Cost, EstimatedReturnOnInvestment For example: Algorithms for Data Science Assignment3 Portfolio Optimization A, 10, 60 B, 20,100 C, 30, 120 Start by putting all your code in a file called portfolio.py. Create a function called loadInvestments that takes in the investmentFilename and returns a list of possible investment options: name, cost, and estimated return. You will be given a file of actual investment options along with specific directions for that file on how to obtain the 3 pieces of information necessary for this assignment. Then make a function called optimizelnvestments that takes the list of possible investments along with the amount of money available to spend. This function should return both the optimal return on investment amount as well as the actual investments selected to achieve this optimal. Implement this function using Dynamic Programming. For Dynamic Programming, one needs a recursive breakdown of the problem. We have a number of possible investments and an amount we are able to spend. At each step we can decide to either include investment x or exclude it. If we include it, then we are left with a smaller problem of finding the optimal with all the possible investments without investment x with a smaller amount we are able to spend (because we purchased investment x). If we exclude it, then we are left with a smaller problem of finding the optimal with all the possible investments without investment x, but with the same amount of money to spend as we had before. A DAC solution could be attempted, but it ends up having too much repeated work. So we will implement our solution with Dynamic Programming. Note that this is a 2D problem (number of investments and amount of money to spend). Thus, we will need a 2D table. The second step is filling in the base-cases. Note that when we don't select any investments, no matter how much money we have to spend (top-row) then we get no return on investment. So this is a natural base-case. The third step is to identify the goal location in the table. This will be the position where we are allowed to include all investments with our full allocation of funds to spend. This will be the lower right corner. The last step in computing any optimal table is to determine the order to fill in the table, keeping in mind that one always needs all the results for the recursive sub-problems available in order to fill in the next location. Once the optimal table is computed, a trace-back table needs to be added to the implementation. This is so we can find the actual investments that produce the optimal. Recall that trace-back tables store the “winning” options at each step and are then used at the end to trace-back through these winning options. Your code should return the optimal return on investment number as well as a list of the investment names used to obtain this optimal number. Note that this is a similar problem to the 0/1 Knapsack problem and you should use that idea to help guide you through your implementation.See Answer
Q7: Algorithms for Data Science Lab 8 Chained Matrix Multiplication DP Below is code to solve the chained matrix multiplication problem using dynamic programming. The dims parameter it takes is a list of the dimensions of the matrices to be multiplied. For example, if Ao is a 10 x 15, A₁ is a 15 x 8, and A2 is an 8 x 3, then dims would be [10, 15, 8, 3] because the inner dimensions must match during multiplication. It returns the optimal cost of the full multiplication. Also included in the code is a function to print out the optimal cost matrix. It is called only once right now, but I would suggest printing at a number of different locations if you want to follow what the code is doing. The code as given produces only the optimal table and can tell you the optimal cost. If you want to know how to actually apply the parentheses, then you need to produce a traceback table and apply that table to the matrices. This is your task in this lab. The first thing you need to do is to add the creation of the traceback table to the chainMatrix function. Second, define a new function called parenStr that will return a string representation of the matrices with parentheses. You should call this function and print out the resulting string as the last thing you do in chainMatrix before it returns. parenStr will obviously need parameters, but I'm leaving it up to you to figure what to pass. Note that this function will most likely be a recursive function. Note that the example hard-coded below is similar to the one from the video/PowerPoint slides. However, there was a slight mistake in the video/PowerPoint slides' tables. The traceback table value for (0,5) is listed as a 0 when it should really be a 2. This, unfortunately, has an impact on the placing of parentheses as well because that value is used. So, although the video correctly describes the procedure, it uses a wrong value and thus comes up with the wrong answer. The correct answer you should come produce with your code is: ((A0) ((A1) (A2))) (((A3) (A4)) (A5)) def printMatrix (m): for row in m: print (row) def chainMatrix (dims) : # Create the empty 2-D table n = len (dims) -1 m = [[None for i in range (n)] for j in range(n)] # Fill in the base case values for i in range(n): m[i] [i] = 0 # Fill in the rest of the table diagonal by diagonal for chainLength in range (2, n+1) : for i in range (n+1-chainLength): j = i + chainLength 1 # Fill in m[i][j] with the best of the recursive options m[i][j] float ("inf") for k in range (i, j): = # Two previous table values plus # what it cost to mult the resulting matrices q = m[i] [k]+m[k+1] [j]+dims [i] *dims [k+1] *dims [j+1] if q m[i] [j] : m[i][j] = q printMatrix (m) return m [0] [n 1] dims = [30, 35,15, 5, 10, 20, 25] print (chainMatrix (dims))See Answer
Q8:Student note : Project is regarding solving Business problems using AI and ML. Tutor need to scrape the instagram influencer dataset using online tools or he can get the datasets from kaggle or huggingface etc.. tutor need to write a python code for the analysis and to build ML models so the deliverable will be WORD DOC + ANALYSIS + PROTOTYPE No need of report I require analysis, ML, scraping. Need a 2 page word document for the analysis done/n Task Your challenge is to identify a data science or Al-related problem or opportunity within an organization and devise a strategic plan to address it using data science and Al tools. Take Up how Organizations can promote their sustainable products using Instagram influencers to better reach the customers to increase customer engagement and profit. Scrape the Instagram Influencer dataset from online and mention how you scrape the data and Use Machine learning algorithms and analysis to solve the business problem Main Objectives Problem Identification: Identify a significant problem or opportunity where data science or Al can be applied within an organization. Explain its relevance and tential impact. Strategy and Implementation Plan: Develop a strategy that uses data science and Al to solve the identified problem or seize the opportunity. Outline a clear implementation plan, considering the organization's context and goals. Proof of Concept: Develop a small, running and well documented prototype for your solution. Deliverables 1. Word Document Report: Create a concise and engaging that outlines: The problem or opportunity identified. Your proposed data science or Al strategy. The implementation plan along the data science pipeline includes data used and how acquired, model development, validation/testing, roll-out, and impact analysis. O O Include visuals, charts, and key findings to support your strategy. Your document should not exceed 10 pages. O 2. Appendix: Provide an appendix with detailed information supporting your presentation. This can include data sources, model specifications, financial and risk analyses, and any additional information that substantiates your strategy. 3. Analyses: If you conducted any form of data analysis, wrangling, etc., then include all materials required to check and replicate your analyses. 4. Proof of Concept / Prototype: Develop a working prototype that demonstrates the practical application of your strategy with real data. Requirements for the prototype include: ● O Functionality: Must replicating key results and demonstrating the utility of your proposed solution. Documentation: Must be thoroughly commented. In case of a Python notebook, you'd need to use markdown to state assumptions, motivate each step, and describe the flow and decisions in your analysis. Citations: If you use code, models, or data from third-party sources, ensure to properly cite these in your notebook. Report: Include a brief overview in your report presentation that showcases the prototype, highlighting its functionality and key results. O O O O Data: provide a link to all data you used (e.g., drop box or google drive link). Evaluation Criteria Innovation and Creativity (20%): Originality and significance of the identified problem or opportunity. Creativity in the application of data science and Al strategies to address the challenge. Strategic Thought (15%): Depth and clarity of the strategic approach and implementation plan. How well the strategy aligns with the organization's goals and the feasibility of the proposed solutions. Technical Execution (25%): Technical accuracy and appropriateness of the data science and Al methodologies employed. This includes the appropriateness of data selection (and strategy for acquisition), model development, and the functionality of the prototype. Make sure to cover the full data science pipeline. Impact Analysis (10%): Comprehensiveness of the impact assessment, including a detailed analysis of potential risks and mitigation strategies. The realism and practicality of the expected outcomes. Report and Storytelling (15%): Effectiveness of the Report in conveying the project's key points, insights, and outcomes. I value clarity, coherence, and visual appeal of a presentation. Prototype (15%): The operational effectiveness of the provided prototype. This includes execution without errors, replication of presented findings, clear documentation and assumptions, motivated steps, and proper citation of sources. You must provide the data (via a link). Data Need data for your idea? There are many datasets available on the internet. Perhaps there is something you are looking for? ● ● ● ● ● ● https://data.gov/. https://www.kaggle.com/datasets. https://huggingface.co/datasets. https://archive.ics.uci.edu/. https://datasetsearch.research.google.com. https://www.microsoft.com/en- us/research/tools/?facet%5Bdate%5D%5Bfixed%5D=any&facet%5Btax%5D %5Bmsr-product-type%5D[]=243083&pg=1&sort_by=most-recent. https://github.com/awesomedata/awesome-public- datasets/blob/master/README.rst.See Answer
Q9:Consider a state lottery and the probability of winning the grand prize. The game is played by selecting a group of 6 numbers from {1, 2, 3, ..., 51), and the state selects a group of 6 numbers from the same set. You win the grand prize if all 6 of your numbers match the state's. The probability of winning is so low that some people are trying different ways of increasing their odds. #1 Amir decides to increase his chance of winning by organizing N of his friends to buy one ticket each for this week's drawing. Each friend buys a ticket independently, and the minimum value of N (Nmin(epsilon)) to ensure that the probability that there is at least one winner among the group is greater than or equal to epsilon needs to be calculated for various values of epsilon. Write a Python function that calculates the value of Nmin(epsilon) for epsilon = 10-5, 10-3, 0.1, and 0.5. #2 If Amir has gathered enough friends to win with probability 0.5 using the above strategy, write a Python program that determines a way that they can increase their win probability above 0.5 without adding more people to the group.See Answer
Q10: GV950 Homework 1 Spring 2024 Guidelines ● This assignment is worth a total of 55 marks. There are a total of 10 marks you can get just from following instructions and having a file that knits correctly. More details on this are given within these guidelines. The remaining 45 marks are based on the questions below, where the marks for each question are stated next to it. • This homework is due on Faser on Wednesday, February 07 at 9:45am. Faser is different from Moodle and you should be able to access it from https://faser .essex.ac.uk/Account/Login?ReturnUrl=%2f. Please do not submit it directly to the instructor or TA, we cannot accept that. • If you are unable to submit on time, please do not email us as there is noth- ing we can do. If you are able to submit within 7 days of the deadline, do so and make sure you submit an extenuating circumstances form within those 7 days as well. See here: https://www.essex.ac.uk/student/exams-and-coursework/ late-submission-of-coursework. • You need to upload two files to Faser: an RMarkdown (.Rmd) file and a .docx (MS- Word) file, which is the output of the Rmd file. Submitting just a Word file or just an Rmd file will lead to a deduction of 30 marks from the total because this is considered an incomplete submission. Similarly, submitting a Word file that is not the output from your .Rmd file will be penalized the same. • We should be able to run the Rmd file by 'knitting' it; of course we will load the datasets from our own computers to do so but, once we do that, the entire file should run without any errors. If it does all run without errors and gives us the exact same output that you have submitted as the Word file, you will receive 6 marks for this. • In the Rmd file, the author field must have your 7 digit student number not your actual name (this is the number in the following format: 231XXXX). Without the student number, we cannot link your marks to your profile. And the assignment must not have your name mentioned anywhere. The title field should say "GV950 Homework 1" and the date can be whatever you want. Doing all of this correctly will get you 2 marks. • Your filename (i.e., the .Rmd file you submit) should be GV950 HW1_231XXXX.Rmd (where 231XXXX is your same 7 digit student number as above). Note that the filename of the Word file will also then automatically be the same. Doing this correctly will get you 2 marks. • Unless the question specifies differently, you should have both the code and its output in the Word document. 1 GV950 Homework 1 Spring 2024 • You must comment your code (at least briefly) to indicate what you are doing, how, why et cetera. The comments do not need to be very detailed but not including brief comments will lead to a mark deduction. • Any regular text that is part of the answer should not be in R comments but, rather, should be written in the Markdown file such that it displays as normal text in the resultant Word file. Not doing so will incur a mark deduction. • All questions must be completed using packages and functions we have learned in class (in lectures and labs). Using other functions will receive a mark of 0 for that question, even if the output is correct. • Please do not include long unnecessary output, especially using head(dataset_name) as that takes up many pages of output and provides no information, and make it harder to find the actual answer. Unnecessary output will have marks deducted. • When a question asks you to explain something, make sure to justify and explain your answer. E.g., if a question asks what direction a variable is skewed, say more than 'left' or 'right.' How can you tell? Is it only slightly skewed or very much skewed? And so on. • Any answer you give must have the relevant code and its output given as well. E.g., if you look at a histogram to determine how a variable is skewed, you must also give the code and output of the histogram before sharing your conclusion. Any interpretation based on code that is not shown or with the output not shown will receive no more than half the mark that question. • Any figure you make must have informative axes and title, and other aesthetic choices must be made appropriately to convey the most informative and useful figure in order to attain full marks. • Please remember that the homework is to be done individually, not in consultation with anyone else. . Please note that not following the instructions given in these guidelines will ad- versely affect your overall marks. ● Reminder that I have academic support hours on Tuesdays, 9:30am-11am in Office 5.016 and Qi is available to answer questions in the second hour of the lab sessions (Thursdays, 2-4pm) in Lab J. Dataset & Codebook For this homework, you will need two files that are available on Moodle under the Home- works section. One is a .csv file called “BES_HW1" (where BES stands for the British Election Study) and the other is a PDF file called "BES_codebook." This codebook lists 2 GV950 Homework 1 Spring 2024 all the variables in the original BES dataset along with a brief description. However, note that BES_HW1 does not have all the variables listed in the codebook as there would have been too many for you to be able to comprehend so I have reduced the columns by about one half. In addition, there are three variables in the dataset that are not in the codebook as I have added them for your ease. These are as follows: Country Name: Name of the country (note that the Country variable in the dataset refers to the same countries but assigns a number to each of these instead of giving the name in words). ● Region_Name: Name of the region (same explanation as the variable above, for the variable called Region). ● WinnerParty_Name19: The name of the party that won in the given constituency in the 2019 General Election (same explanation as the variable above, for the variable called Winner 19). Questions 1. Import the BES_HW1.csv dataset into R. What is the unit of analysis in this dataset and how can you tell? [1 mark] 2. Say that a friend of yours who knows nothing about the UK 2019 elections asks you to explain to them how well the three main parties - Conservatives, Labour, and Liberal Democrats did descriptively in constituencies across England versus Scotland. You choose to do this by using variables that measure the percentage of votes that these three parties each got at the constituency-level. Generate code, output and a description (in words) to explain this to your friend. In doing so, think carefully about various ways we have learned (last term) of describing data and choose what you think will be the most effective and succinct. You must also describe the patterns based on your output; just code and output is not enough. You can choose how to best describe it (be creative!) but, as a rough guideline, a good answer will have one type of figure (rather than several types), perhaps some numbers in addition to it, and approximately 6-8 lines of text to describe the figures and numbers. Remember that you should not do any formal statistical tests for this question. (5 marks) 3. Now, you want to more systematically study whether the Labour party received a significantly different percentage of votes in England versus Wales. To do so, state (in words) a hypothesis and conduct an appropriate test. Make sure to display the output of your test, identify the test statistic and the conclusion of the test. Don't forget to explain how you reached that conclusion. (3 marks) 4. Make a scatterplot that plots the total number of votes cast in each constituency in 2017 and in 2019. Based just on the figure, describe the correlation between these 3 GV950 Homework 1 Spring 2024 two variables. Think about the direction, whether the correlation is high or low, and why it likely is high or low. (Remember to make the figure as informative as possible, using good axes labels, axis markings etc). (4 marks) 5. Create a new variable that measures the change in the number of voters in a con- stituency between 2017 and 2019. Do more constituencies have more voters in 2019 compared to 2017, or fewer voters? How can you tell? (2 marks) 6. Create a variable that measures whether the same gender person ran for the Conser- vative party in 2019 and 2017. Then, using table(), summarize the information from this new variable and describe it in a sentence to state how many constituencies had a same gender person versus different gender person. (3 marks) 7. I want to try to understand the Labour party's vote share in 2019. To help me do so, first choose a continuous main independent variable that you think would help to explain this dependent variable. (Note that the X variable can not be the Labour vote share in 2017). State a hypothesis and a credible mechanism to link X to Y (Note: this is causal hurdle 1 from class and the readings and does not require you to write any code). (3 marks) 8. Do the chosen X and given Y variable fulfill causal hurdle 2? That is, is it possible that Y affects X, and why/how? (Note that this question does not require you to write any code.) (3 marks) 9. Is there covariation between X and Y (Causal hurdle 3)? To analyze this, make an appropriate figure describe what the figure shows; also calculate the correlation. (3 marks) 10. For causal hurdle 4 think of at least three potential confounders that we should be concerned about. For each, state what the confounding variable is, why we should be concerned about it and what variable you will use from the dataset to control for it. (Note: for this question, it does not have to be the case that each confounding variable will entirely cause both X and Y but each confounding variable must have a reason for being correlated with X and Y. Also note that this answer does not require any code.) (6 marks). 11. Now, run a regression of Y on just your main independent variable, X. Display the model results and interpret the coefficient on X. Have you found support for your hypothesis? Why or why not? (3 marks) 12. Run a second regression, this time of Y on all four variables (the original inde- pendent variables plus the three confounders you identified above). Interpret each variable in a sentence each, focusing on both the substantive and statistical signifi- cance for each. Now explain whether/how the coefficient on the main independent variable has changed. (6 marks) 4 GV950 Homework 1 13. Run an ANOVA test to compare both models: which one explains variation in Y better? How can you tell? Make sure to show the output of the test, and explain how you reached your conclusion. (3 marks) 5 Cr Spring 2024See Answer
Q11:1. Download the data file csv_result-churn.csv. Consider the costumer churn example described in the file MegaTelCo.pdf with the following data structure: Data instances: 20000 Features: id, COLLEGE, INCOME, OVERAGE, LEFTOVER, HOUSE, HAND- SET PRICE, OVER 15MINS_CALLS PER MONTH, AVERAGE_CALL DURATION, REPORTED SATISFACTION, REPORTED_USAGE_LEVEL, CONSIDERING CHANGE OF PLAN (total: 12 features) Target: LEAVE With this data configuration do the following: 1.a 3 points Use the software to compute a decision tree model for classification with the following specifications: Training sample using 75 % of the data randomly selected as training/estimation sample Parameters: at least two instances in leaves, do not split subsets smaller than 5, maximum depth 6; Splitting: Stop splitting when majority reaches 95%. Use the remaining 25 % of the sample to compute predictions and appraise those with a Confusion Matrix./n1.b 3 points Your employer would like to target the actively looking into it (leaving) customers, which according to the prediction sample decision tree model, will leave with probability one? Note that since random sample selection occurred the answer will differ depending on the random draws Note also that we are only selecting a small set of customers here for illustrative purpose. In practice such efforts would involve larger selections Hint: Save the output of the decision tree predictions into an Excel file to facilitate the analysis of your results. Then cut/paste your findings into your answers document (PDF file you submit). 1.c 3 points For comparison, using the same specifications compute the Confusion Matrix for the full sample. 1.d 3 points Compute the decision tree for both the aforementioned training and the full samples with the following tuning parameters: at least two instances in leaves, do not split subsets smaller than 5, maximum depth 50; Splitting: Stop splitting when majority reaches 99%. Appraise the results again with a Confusion Matrix. 1.e 3 points Explain why your answers in 1.a and 1.c yielded comparable confusion matrices while in 1.d there is a dramatic improvement in the full sample and deterioration in the prediction sample.See Answer
Q12:Assignment #1: Using the example tables in the "PIVOT TABLES" tab as a template, provide summary data in pivot table form for the additional fields provided (pool, location, FICO bucket, LTV bucket) on a weighted average (WA) basis (weighting should be based on UPB_MONTHEND). FICO should use a non-zero weighted average (nzWA). Data is on the "DATA" tab.See Answer
Q13:HW6: DQL SQL For all of the below assignments you must first run the scripts provided in D2L in order for your commands to work in Oracle SQL Developer. These are also the scripts I will be running when grading. Run the scripts in this order: 1. IS451 HW6_SQL_HOTEL_DDL.sql 2. IS451_HW6_SQL_HOTEL_INSERTS.sql Now you will have the full Hotel database with data in all of the tables. Submit a single sql file with multiple queries in it, one for each assignment below. Each query should end with a semicolon so that the entire script can be run at once with no errors. Use a comment above each query to indicate which number of the assignment each query goes with. 1. Write a query that will return all fields for all records in the Hotel table. 2. Write a query that will return all fields in the Reservation table for reservations with children. 3. Write a query that will return hotel name, city, and country for all Hotels in Italy (country code: ITA). 4. Write a query that will return hotel ID, hotel name, and amenity name for all hotels that have any kind of pool amenity 5. Write a query for the marketing department who wants the email addresses of all guests who have stayed at any hotels in the state of Florida. 6. Write a query for the guest loyalty department who wants to identify guests booking the most profitable reservations. For every reservation with a total price larger than $15,000, they want the guest's first and last name, email address, and the name, city, and country for the hotel where they stayed.See Answer
Q14:Question 6 (2 points) Outcome Prediction 1 .75 .73 .6 .51 .4 .2 .1 OTOP 0 1 0 1 0 OO 0 The table above shows the predicted probabilities and the outcome for seven data points. In order to change the predicted probabilities into predicted outcomes, we are going to use a threshold of 0.55 What is the True Positive Rate (or recall)? What is the Positive Predictive Value (or precision)?See Answer
Q15:Question 5 (3 points) We are performing k-fold cross-validation on a dataset with 100,000 rows. I have set aside 16,000 rows into a holdout set. I am going to perform cross-validation with the remaining rows not in the holdout set. I have decided to set k to 7. How many rows would each of the 7 folds have? (1 Point) In each iteration of cross-validation, how many rows would the training set have and how many rows would the validation set have? (1 point) How does k-fold cross validation help ridge regression prevent overfitting? (1 point) (Answer Box)See Answer
Q16:Question 4 (3 points) We have extracted how often the words "Hot" and "Sweet" appear in a collection of articles. The articles are either about Cooking or Music. The following probabilities describe the data. o P(Cooking = 1) = 0.5 (when Cooking = 0, the article is about Music) o P(Hot 1|Cooking = 1) = 0.25 o P(Sweet = 1|Cooking = 1) = 0.35 o P(Hot = 1|Cooking = 0) = 0.25 o P(Sweet = 1|Cooking = 0) = 0.25 We have a new article from the collection and it contains both of the words ("Sweet" and "Hot"). Use the Naïve Bayes algorithm to classify whether this article is about Cooking or Music. Show all work. (Answer Box)See Answer
Q17:Question 3 (2 points) We are going to use the Naïve Bayes algorithm to predict whether someone will be hospitalized with covid, and we are going to use two predictors: o o whether a person is above 60 years old whether a person has been vaccinated Argue for or against the following statement: "Naïve Bayes' conditional independence assumption is appropriate here" (1 point) Now we are going to use the Naïve Bayes algorithm to predict whether a summer was unusually hot or not. We are going to use two predictors: o Ice cream sales volume o The number of emergency room visits for excessive sunburn Argue for or against the following statement: "Naïve Bayes' conditional independence assumption is appropriate here" (1 point)See Answer
Q18:1. Vary the value of k from 1 through 10. Based on the continuous (kNN regression) prediction error measures, what is the optimal number of nearest neighbors employ? a. 1 b. 2 c. 3 d. 4 e. 5 f. 6 g. 7 h. 8 i. 9 j. 10/n2. Based on the confusion matrix you observe when k = 5, calculate the prediction accuracy of the KNN classifier in cell B30 (Hint: overall accuracy is the proportion of the 20 predictions that were correct, i.e., on the diagonal of the confusion matrix). a) 65% b) 35% c) 40% d) We cannot answer this question without more information./n3.Vary the value of k from 1 through 9. Considering the discrete, KNN classifier's prediction accuracy, what is the optimal number of nearest neighbors employ, i.e., what value of k maximizes the prediction accuracy? a. 1 b. 2 c. 3 d. 4 e. 5 f. 6 60 h. i. 7 8 9/n4. Yes, or no? Would either of these kNN predictive models be useful for generating recommendations related to a new song that has just been released, i.e., which has not yet been rated by any users, or for a new user, i.e., a user who has not yet entered any song ratings? (Hint: you can assume that you would have details on the new user or the new song). a. YES b. NOSee Answer
Q19:NJIT CS 331: Database System Design and Management 14. Find the person id, average budget, and average revenue for all movies starring Al Pacino where revenue is greater than thrice its budget. (1 row) 15. Find the number of movies for each genre id for movies released in 1996. (12 rows) 16. Find the genre id, genre name, and total revenue made by comedy movies whose budget was more than a hundred million. (1 row) 17. Find the number of movies and average revenue for all action movies (genre_name='Action') starring Harrison Ford. (1 row) 18. Find the genre id, genre name, average genre budget, and average genre revenue for all genres whose average revenue is greater than five times the average genre budget. (7 rows) 19. Find all horror movies along with the movie id, title, and genre id. (42 rows) 20. Find the id, title, and budget for all movies, where the budget is greater than the average budget for those movies starring George Clooney whose revenue is over three times its budget. (104 rows)/nQuestion 3 [100 Points (5 points each)]. For answering the following questions, use the file containing the data. You MUST provide the following for each query: RA queries written over the given template AND WRITE RA QUERY on the answer sheet as well; not doing so will result in zero point for this question. (2 points for writing the query on the template + 2 point to write the same query on the answer sheet + 1 point for pasting screenshot of the output = 5 points) Use relational algebra to perform these queries. You see the number of returned rows in front of each query. 1. Find the movie titles of all action movies (genre_id = 28). (158 rows) 2. Find all movie titles, the release date, and the character name of the lead cast member (cast order 0) for movies released in 2005. (17 rows) 3. Find the movies that have 8 or more crew members. (58 rows) 4. Find the movie ids where cast members have a person id of 114, or crew members have a person id of 60. (7 rows) 5. Find all movie ids with cast members with person id 1461 minus those with genre id 28. (5 rows) 6. Find the movie ids of movies with a character named Ricky Fitts and a crew member with person id 494. (1 row) 7. Find the movie title, character names, and person names of cast members with a cast order of 1 for movies with an average vote greater than 8 and a runtime greater than 150 minutes. (9 rows) 8. Find all genre names and the sum of movie budgets for each genre where the sum of budgets is more than a billion. (14 rows) 9. Find the number of movies released in 2001 and 2002 grouped by genre id. (17 rows) 10. Find the character names and movie ids of all Harrison Ford movies. (6 rows) 11. Find the genre name and average revenue for each movie genre. (18 rows) 12. Find the person id, person name, and number of movies for all cast members with more than 3 movies. (28 rows) 13. Find the titles of all movies with a total profit greater than 750 million. (15 rows) Note: a movie is said to be profitable if its revenue is greater than its budget.See Answer
Q20:3. (20 pts) Calcualte the standard deviation for the POPULATION data you created in part 2b. Use EXCEL.See Answer

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