Can you please do read instruction document that I have attached below. Please prepare the document accordingly by the topic name "Facial emotion recognition using neural networks and deep learning"....

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Can you please do read instruction document that I have attached below. Please prepare the document accordingly by the topic name "Facial emotion recognition using neural networks and deep learning".
I think we need to prepare documents for the assignment. Please find the additional details in the instruction document.
Requirements:1. One document has to be about 1300 words with 6 to 7 screenshots on the topic findings with 1400 words.2. The second file is the presentation file on the analytics and findings.


Student Guidelines Assessment 3 Group Assignment Due: 31 May 2020 - 11:59 pm (week 12) Total Weightage: 25% Group assignment (3 members in one group) Submission only by one of the group members Task For this assignment, you are required to work in group and two files need to be uploaded in the moodle (provided links) by one of the group members. The first file is a report containing 1200-1500 words with 6-10 screenshots from your findings. The second file is a presentation file of your analytics and findings. Note that both files need to be uploaded by only one of the group members. 1. Data There is a data available in the moodle. You need to add a section in your report and talk about the data and challenges there. What kind of issues available there? What are the features? Add some information on the data in this section of your report. 2. Data Analytics & Visulaisation Find out what kind of data it is and what you can do with this data if this data passes to you by your company. For doing this you need to look at the data and do data processing, such as cleaning and feature engineering, if it is required. Then chose proper methods to analysis the data. Suppose this is a company data that you are working for them. What are the issues available there and what you can recommend for your manager in company to enhance their objectives and to the benefits of the company? 3. Report (Weightage: 15%) Your report should have 1200-1500 words, excluding references, addressing the business questions, challenges, analytics, recommendation and visualisation related to the data. It should cover what are the issues in the data, you are going to solve and how, plots and recommendations. The report should have some plots (6-10 screenshots) from your findings. Note that plots need to be labelled and explained inside the report. All coding, including data uploading, cleaning, analytics and visualisation should be coded in Python. The python code should be included at the end of your report in a section called Appendix. Note: Structure and font of your report should follow the word file template provided in the moodle. Your report should be a single word or pdf document containing your report and need to be submitted through moodle. One submission per group and make sure all group members participate and add their names in the report. Your report should have a contribution table at the end of the report. 4. Presentation (Weightage: 10%) The presentation should be a maximum of 10 minutes for the whole team. Each member must participate in video presentation file and talk at least 2 minutes in the video related to the methodology used, findings, contribution or recommendation. Note: Presentation file should be submitted by the same person who submitted the report. Your presentation file should have a standard video format and it should not exceed 200 MB. Slides and your faces should be clear in the video file. The same person, who submitted the report, needs to submit the presentation file (not link to your video) in the provided video submission link. Only video file will be marked; the link for your video is not accepted and will not be considered for marking. Submission Guidelines All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days). The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference web-sites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re- submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission. Note: You must find group by yourself and provide group member names by week 6. It is your responsibility to find group members. If you could not find any students to create a group, you have to work by yourself and provide a quality report and presentation as others. Student Guidelines Assessment 3 Group Assignment Due: 31 May 2020 - 11:59 pm (week 12) Total Weightage: 25% Group assignment (3 members in one group) Submission only by one of the group members Task For this assignment, you are required to work in group and two files need to be uploaded in the moodle (provided links) by one of the group members. The first file is a report containing 1200-1500 words with 6-10 screenshots from your findings. The second file is a presentation file of your analytics and findings. Note that both files need to be uploaded by only one of the group members. 1. Data There is a data available in the moodle. You need to add a section in your report and talk about the data and challenges there. What kind of issues available there? What are the features? Add some information on the data in this section of your report. 2. Data Analytics & Visulaisation Find out what kind of data it is and what you can do with this data if this data passes to you by your company. For doing this you need to look at the data and do data processing, such as cleaning and feature engineering, if it is required. Then chose proper methods to analysis the data. Suppose this is a company data that you are working for them. What are the issues available there and what you can recommend for your manager in company to enhance their objectives and to the benefits of the company? 3. Report (Weightage: 15%) Your report should have 1200-1500 words, excluding references, addressing the business questions, challenges, analytics, recommendation and visualisation related to the data. It should cover what are the issues in the data, you are going to solve and how, plots and recommendations. The report should have some plots (6-10 screenshots) from your findings. Note that plots need to be labelled and explained inside the report. All coding, including data uploading, cleaning, analytics and visualisation should be coded in Python. The python code should be included at the end of your report in a section called Appendix. Note: Structure and font of your report should follow the word file template provided in the moodle. Your report should be a single word or pdf document containing your report and need to be submitted through moodle. One submission per group and make sure all group members participate and add their names in the report. Your report should have a contribution table at the end of the report. 4. Presentation (Weightage: 10%) The presentation should be a maximum of 10 minutes for the whole team. Each member must participate in video presentation file and talk at least 2 minutes in the video related to the methodology used, findings, contribution or recommendation. Note: Presentation file should be submitted by the same person who submitted the report. Your presentation file should have a standard video format and it should not exceed 200 MB. Slides and your faces should be clear in the video file. The same person, who submitted the report, needs to submit the presentation file (not link to your video) in the provided video submission link. Only video file will be marked; the link for your video is not accepted and will not be considered for marking. Submission Guidelines All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days). The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference web-sites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re- submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission. Note: You must find group by yourself and provide group member names by week 6. It is your responsibility to find group members. If you could not find any students to create a group, you have to work by yourself and provide a quality report and presentation as others.
Answered Same DayMay 23, 2021

Answer To: Can you please do read instruction document that I have attached below. Please prepare the document...

Sandeep Kumar answered on May 30 2021
152 Votes
The Abalone Data Set was acquired from the open-source UCI Machine Learning repository https://archive.ics.uci.edu/ml/datasets/Abalone, which was provided by the Department of Primary Industry and Fisheries, Tasmania. It contains 4177 recordings having 9 features.
    Data Set Features
    Number of recordings
    Number of Attributes
    Attribute Characteristics
    Multivariate
    4177
    9
    Categorical, Integer, Real
In detail the data can be summarized as this:
    Att
ribute
    Data Type
    Units
    Description
    Sex
    nominal
    N/A
    M, F, and I (infant)
    Length
    continuous
    mm
    Longest shell measurement
    Diameter
    continuous
    mm
    perpendicular to length
    Height
    continuous
    mm
    with meat in shell
    Whole weight
    continuous
    grams
    whole abalone
    Shucked weight
    continuous
    grams
    weight of meat
    Viscera weight
    continuous
    grams
    gut weight (after bleeding)
    Shell weight
    continuous
    grams
    after being dried
    Rings
    integer
    N/A
    +1.5 gives the age in years
After inspecting the dataset we can make test various hypothesizes based on the visualizations and arrive at different conclusions like:
Hypothesis 1: "the mean of length is the same for Male and Female" is null hypothesis
Hypothesis 2: "the mean of length is not the same for Male and Female" is alternative hypothesis and original claim
Using boxplot, Anova test, T test with, important metrics: α = 0.05 and p-value = 8.987874966189928e-07, we see that because we success to reject the null hypothesis, we conclude that mean of length is not the same between two sexes of abalone, which means the growth patteren is different between male and female.
Hypothesis: "the median of Rings of female and male is the same" is null hypothesis
Using Mann Whitney U-test, median_test and histogram plot with important metrics α = 0.05, p-value for Mann Whitney U-test=6.689638084926974e-05 and p-value for median-test=0.0032854772243561072, it is Because we succeed to reject the null hypothesis, we conclude that also the sample medians look the same, medians of Rings of male and female are not the same actually, which means the age distribution between male and female are different.
Hypothesis: When the rings of abalone is less than the median rings of infant, the abalone's length, height and weight are increasing when rings increase. When the rings of abalone is larger than the median rings of infant, the abalone's length, height and weight are less likely to increase with rings' increase.
Necessary Numbers: Pearsonr, p-value
For Length (less than the median rings of infant): pearsonr=0.74 ; p=1.1e-243
For Length (larger than the median rings of infant): pearsonr=0.14; p=1.2e-13
For Height (less than the median rings of infant): pearsonr=0.54 ; p=3.2e-107
For Height (larger than the median rings of infant): pearsonr=0.27 ; p=5.6e-46
For Whole weight (less than the median rings of infant): pearsonr=0.62 ; p=4.1e-148
For Whole weight (larger than the median rings of infant): pearsonr=0.2 ; p=3e-26
Because the pearsonr of 'larger than the median rings of infant' are all larger than the one of 'less than the median rings of infant', and the low p-values show the reliability of these pearsonr results, we could not reject the H0. This conclusion could suggest that abalones grows (in length, height and weight) until a certain age (the median rings of infant), and after that, it's growth speed slow down dramatically.
Analysis: Which elements in the dataset are likely to have linear relationship with Rings?
Conclusion from Analysis: Because Length has the largest p-value, it is the only element are likely to have linear relationship with Rings.
H0: "Length and Rings have linear relationship" is null hypothesis
Using Linear Regression with learning rate as 0.05 and p-value as 0 we see that, because the p-value is 0, it leads to the conclusion conclude that Length and Rings don't have linear relationship.
H0: "Length and Rings have linear relationship for Infant" is null hypothesis
Using Linear Regression with learning rate as 0.05 and p-value as 0, we can conclude that for infant, Length and Rings also there is no linear relationship.
H0: "Height for infant is Gaussian distribution" is null hypothesis
Using QQ plot and normaltest with learning rate as 0.05 and p-value = 0.5639596947723188, we conclude that as the p-value is larger than α, so we can we clear that Height for infant is Gaussian distribution.
References:
https://archive.ics.uci.edu/ml/datasets/Abalone
https://rpubs.com/AlistairGJ/Abalone
https://datahub.io/machine-learning/abalone
Appendix:
#read in and parse column headers
import pandas as...
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