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– a pet adoption service, • a student tutoring service, • a summer vacation reso

– a pet adoption service,
• a student tutoring service,
• a summer vacation resort,
• a winter vacation resort,
• COVID19 informational website,
• a family owned restaurant or other business,

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Web programming

# Unit 21 Homework: Charity Funding Predictor ## Background The nonprofit founda

# Unit 21 Homework: Charity Funding Predictor
## Background
The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures. With your knowledge of machine learning and neural networks, you’ll use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
From Alphabet Soup’s business team, you have received a CSV containing more than 34,000 organizations that have received funding from Alphabet Soup over the years. Within this dataset are a number of columns that capture metadata about each organization, such as:
* **EIN** and **NAME**—Identification columns
* **APPLICATION_TYPE**—Alphabet Soup application type
* **AFFILIATION**—Affiliated sector of industry
* **CLASSIFICATION**—Government organization classification
* **USE_CASE**—Use case for funding
* **ORGANIZATION**—Organization type
* **STATUS**—Active status
* **INCOME_AMT**—Income classification
* **SPECIAL_CONSIDERATIONS**—Special consideration for application
* **ASK_AMT**—Funding amount requested
* **IS_SUCCESSFUL**—Was the money used effectively
## Instructions
### Step 1: Preprocess the Data
Using your knowledge of Pandas and scikit-learn’s `StandardScaler()`, you’ll need to preprocess the dataset. This step prepares you for Step 2, where you’ll compile, train, and evaluate the neural network model.
Using the information we have provided in the starter code, follow the instructions to complete the preprocessing steps.
1. Read in the charity_data.csv to a Pandas DataFrame, and be sure to identify the following in your dataset:
* What variable(s) are the target(s) for your model?
* What variable(s) are the feature(s) for your model?
2. Drop the `EIN` and `NAME` columns.
3. Determine the number of unique values for each column.
4. For columns that have more than 10 unique values, determine the number of data points for each unique value.
5. Use the number of data points for each unique value to pick a cutoff point to bin “rare” categorical variables together in a new value, `Other`, and then check if the binning was successful.
6. Use `pd.get_dummies()` to encode categorical variables.
### Step 2: Compile, Train, and Evaluate the Model
Using your knowledge of TensorFlow, you’ll design a neural network, or deep learning model, to create a binary classification model that can predict if an Alphabet Soup–funded organization will be successful based on the features in the dataset. You’ll need to think about how many inputs there are before determining the number of neurons and layers in your model. Once you’ve completed that step, you’ll compile, train, and evaluate your binary classification model to calculate the model’s loss and accuracy.
1. Continue using the Jupyter Notebook in which you performed the preprocessing steps from Step 1.
2. Create a neural network model by assigning the number of input features and nodes for each layer using TensorFlow and Keras.
3. Create the first hidden layer and choose an appropriate activation function.
4. If necessary, add a second hidden layer with an appropriate activation function.
5. Create an output layer with an appropriate activation function.
6. Check the structure of the model.
7. Compile and train the model.
8. Create a callback that saves the model’s weights every five epochs.
9. Evaluate the model using the test data to determine the loss and accuracy.
10. Save and export your results to an HDF5 file. Name the file `AlphabetSoupCharity.h5`.
### Step 3: Optimize the Model
Using your knowledge of TensorFlow, optimize your model to achieve a target predictive accuracy higher than 75%.
Using any or all of the following methods to optimize your model:
* Adjust the input data to ensure that no variables or outliers are causing confusion in the model, such as:
* Dropping more or fewer columns.
* Creating more bins for rare occurrences in columns.
* Increasing or decreasing the number of values for each bin.
* Add more neurons to a hidden layer.
* Add more hidden layers.
* Use different activation functions for the hidden layers.
* Add or reduce the number of epochs to the training regimen.
**Note**: If you make at least three attempts at optimizing your model, you will not lose points if your model does not achieve target performance.
1. Create a new Jupyter Notebook file and name it `AlphabetSoupCharity_Optimzation.ipynb`.
2. Import your dependencies and read in the `charity_data.csv` to a Pandas DataFrame.
3. Preprocess the dataset like you did in Step 1, Be sure to adjust for any modifications that came out of optimizing the model.
4. Design a neural network model, and be sure to adjust for modifications that will optimize the model to achieve higher than 75% accuracy.
5. Save and export your results to an HDF5 file. Name the file `AlphabetSoupCharity_Optimization.h5`.
### Step 4: Write a Report on the Neural Network Model
For this part of the assignment, you’ll write a report on the performance of the deep learning model you created for AlphabetSoup.
The report should contain the following:
1. **Overview** of the analysis: Explain the purpose of this analysis.
2. **Results**: Using bulleted lists and images to support your answers, address the following questions.
* Data Preprocessing
* What variable(s) are the target(s) for your model?
* What variable(s) are the features for your model?
* What variable(s) should be removed from the input data because they are neither targets nor features?
* Compiling, Training, and Evaluating the Model
* How many neurons, layers, and activation functions did you select for your neural network model, and why?
* Were you able to achieve the target model performance?
* What steps did you take in your attempts to increase model performance?
3. **Summary**: Summarize the overall results of the deep learning model. Include a recommendation for how a different model could solve this classification problem, and then explain your recommendation.
– – –
## Rubric

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I will provide the working code in a zipped file. I need this app deployed to he

I will provide the working code in a zipped file. I need this app deployed to heroku so my professor can test it. I need it connected to a proper heroku-postgres database and I need the heroku database seeded as well. If everything is done correctly, I will be able to test in a software called Insomnia on any computer. Upon completion, I will test it myself (using Insomnia) before sending it to my instructor.

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Web programming

Home page page, About Page, Contact Page All three pages have nav bar, sticky ba

Home page page, About Page, Contact Page
All three pages have nav bar, sticky banner, footer
Home Page: Any random Pokemon images. The featured products can just be 4 product cards (forget about side arrows). Text can be random because I can change that to the stuff I need to later on. Just don’t have time to finish before class. I can insert logo on the left side of the navbar with a diff image later just some random logo is fine
About us: again random image is fine.
Contact page is an example.
Comments from Customer
copy of the code is needed

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Web programming

I have done the results for both assignments. Please check my publish. I would lik

I have done the results for both assignments. Please check my publish. I would like you to check my informations on the introduction and background for both the assignments. If you could rephrase them and check with the reference please do so. Please adjust or add if needed.
I would like you to add comments on the waveforms/screenshots/ results for both assignments. no comments of the code needed as I have already done this. According to the marking scheme I only need “Detailed comments and explanations for the simulation waveforms” so 4-6 lines for each waveform screenshot giving an explanation. Adjust the conclusion depending on the results.
I have attached a feedback on what is expected to write on the results/waveforms/screenshots. Use these as a guideline to know what to write. The guidelines will help you know what to write but it needs to be worded properly and in your own words.
You won’t need the instructions, but I have added just incase in order of you to understand the assignment.
I am sure that my results and code for assignment 2 is correct. But if there is any mistake is assignment 1 with the code or result please let me know I am 90% sure.

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This is a mini project assignment for my Master’s Degree python class (Object Or

This is a mini project assignment for my Master’s Degree python class (Object Oriented Programming 2). Me passing this course relies on this assignment.
Attached is the pdf of the text book AND the snapshot of the questions that have been assigned to me. The two questions seen in the snapshot can be found in the textbook pdf I have attached (towards the end of Chapter 17 in the pdf (i.e.) the end-of-chapter exercise questions).
There are previous chapters in the pdf book that might help provide context to these two chapter 17 questions.
Example: You may want to look into chapter 13 in the pdf, as it relates to solving the two questions from chapter 17. This is just a suggestion and may not necessarily be needed.

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I have a streaming media assignment I need help with. All the files are enclosed

I have a streaming media assignment I need help with. All the files are enclosed and directions in the files as well as well as the basic directions below from the class.
Main directions from class
1. Code the HTML
1. Add the content
2. (Links to an external site.)
3. into your HTML editor
4. Wrap each, individual piece of the content in an HTML5
tag
5. Wrap each group of content in an HTML5 sectioning
content tag
6. Validate your code and fix all errors and warnings
7. Validate your outline and fix all missing headings and
information architecture errors
2. Code the CSS based on the mockups
1. Style your webpage according to the mockups
3. Upload via SFTP
1. Upload your HTML, CSS, and multimedia to your web

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Assignment: OpenSSL With Apache OpenSSL is an open source library that implement

Assignment: OpenSSL With Apache
OpenSSL is an open source library that implements the SSL (Secure Socket Layer) and TLS (Transport Layer Security) protocols, and it is by far the most widely deployed, freely available implementation of these protocols.
Reference: Viega, J., Messier, M., & Chandra, P. (2002) Network security with OpenSSL. O’Reilly Media.
For this 2-week Assignment, you will install OpenSSL in a Linux server and integrate OpenSSL with Apache server.
Week 5 Tasks
Accessing and Reviewing the IBM® Cloud Virtual Server Management Functionality
A significant proportion of the practical work will entail use of IBM® Virtual Server. To begin, carry out the following familiarization tasks:
Create or activate an IBM Cloud account: https://cloud.ibm.com/login?state=%2Fcatalog
Log on to your IBM Cloud account.
Explore the top-level Cloud Management functions, services, options, and key tools offered by IBM Cloud.
Review relevant information on the IBM Cloud Virtual Services online.
In IBM Cloud console, navigate to Menu icon > VPC Infrastructure > Compute > Virtual server instances.
Click on New instance and enter the following information:
Name of your virtual server instance (CSEC 6735/ITEC 8735-your name)
Specify the IBM Cloud VPC where you want to create your instance
Select the location where you want your virtual server instance to be created
Profile: 1 vCPU and 1 GB memory (no cost)
Generate a SSH key by running the ssh-keygen command and following the prompts. For example, you can generate an SSH key on your Linux or Mac system by running the command ssh-keygen -t rsa -C “user_ID”. That command generates two files. The generated public key is in the .pub file. For Windows systems, you can use PuTTYgen to generate an SSH key
Choose an image from the list of images
Assign a network interface card IP address of your choice
Click on Create virtual server instance.
Connect to your newly created instance: $ ibmcloud is instance-network-interfaces –YOURNAME
Microsoft Windows Instructions:
If your local machine is running Microsoft Windows, please follow the instructions at: https://cloud.ibm.com/docs/vpc-on-classic-vsi?topic=vpc-on-classic-vsi-connecting-to-your-windows-instance#connecting-to-your-windows-instance
MacOS or Linux Instructions:
If your local machine is running MacOS or Linux, you should change the access-permission-property of the private key file to 400 using the command:
chmod 400 ITEC8735-Student-Thomas.key
Then use the following command to connect to the server:
ssh -i “ITEC8735-Student-Thomas.key” ubuntu@ec2-52-32-178-8.us-west-2.compute.amazonaws.com
The following is a screenshot for changing the access permission for the private key and for a successful connection to the server.
After you set up a cloud Linux server, you may use Linux commands to update your system first. The following are example instructions that one might use for Ubuntu Linux at IBM Cloud.
yum -y update
yum -y install make wget openssl-devel ncurses-devel newt-devel libxml2-devel kernel-devel gcc gcc-c++ sqlite-devel
This command should also install the current openSSL to your server. OpenSSL documentation can be found at https://www.openssl.org/docs/.
Get familiar with openSSL command line applications. For example, run the following commands:
Encode/decode with base64
Encrypt a file and decrypt a file using DES or AES with passwords
Generate RSA/DSA private key/public key pairs
Encrypt/decrypt using RSA
Digitally sign a file using DSA
Generate a self-signed certificate
By Day 7
Submit a report describing in detail the steps you have taken, including screenshots for all major steps.
Note: Your document should be 3–5 pages long (not including the Title page or Reference list) but the quality of the work is most important, not the number of pages. Cite and reference all sources using APA format and style guidelines. Submit in a single document.

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Screenshots of functionalities 1-3 are attached in the instructions. Another att

Screenshots of functionalities 1-3 are attached in the instructions. Another attachment is attached with the full code of week 4. Please let me know if there are any questions

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Web programming

Dashboard Group Assignment Outline Marks: 30 Length: 2 report pages, each page m

Dashboard Group Assignment
Outline
Marks: 30
Length: 2 report pages, each page must incorporate at least 6 variables / measures
Group Assignment: You can work as a team on this project. No group can be larger than 4 people.
Submission Details:
You will be presenting your dashboard in a 5 minute overview during class on Friday, July 29th.
Additionally, please email your PBIX file to:
Name your file as: Name(s).PBIX.
For ex. “Bill Stevenson.PBIX” or “Susan Petra and Bill Stevenson.PBIX”
Research Request / Statement of Work
You have been hired to create a summary dashboard showing your client/boss a high-level overview of your dataset. They want to explore an overview of public sentiment of key issues relevant to the election.
Using data visualization best practices, you will need to address the following:
1. What research question(s) were explored in this dataset? And what are the relevant findings based on the data?
2. What is the demographic makeup of the respondents in your dataset? More specifically, your client would like to understand how representative your data is compared to Stats Can Census data for the region.
To complete this assignment you will need to use one of the datasets available on blackboard:
1. Ontario – April 2018
2. Fed Survey – April 2019
Both datasets were collected a few months prior to a major election. Both are available on blackboard.
Please review the marking rubric for specific details on how your dashboard will be evaluated.