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Module 19 Challenge

Start Assignment

• DueSundayby23:59


• Points100


• Submittinga text entry box or a website url

Background

Beks has come a long way since her first day at that boot camp five years ago—and since earlier this week, when she started learning about neural networks! Now, she is finally ready to put her skills to work to help the foundation predict where to make investments.

With your knowledge of machine learning and neural networks, you’ll use the features in the provided dataset to help Beks create a binary classifier that is capable of predicting whether applicants will be successful if funded by Alphabet Soup.

From Alphabet Soup’s business team, Beks 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 the following:

• 
  EINandNAME—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

What You're Creating

This new assignment consists of three technical analysis deliverables and a written report. You will submit the following:

• Deliverable 1: Preprocessing Data for a Neural Network Model


• Deliverable 2: Compile, Train, and Evaluate the Model


• Deliverable 3: Optimize the Model


• Deliverable 4: A Written Report on the Neural Network Model (README.md)

Files

Use the following links to download the dataset and starter code.

Alphabet Soup Charity dataset (charity_data.csv)(Links to an external site.)

Alphabet Soup Charity starter code(Links to an external site.)

Before You Start

Create a new GitHub repository entitled "Neural_Network_Charity_Analysis" and initialize the repository with a README.

Deliverable 1: Preprocessing Data for a Neural Network Model (30 points)

Deliverable 1 Instructions

Using your knowledge of Pandas and the Scikit-Learn’sStandardScaler(), you’ll need to preprocess the dataset in order to compile, train, and evaluate the neural network model later in Deliverable 2.

REWIND

For this deliverable, you’ve already done the following in this module:

• 
  
  Lesson 19.3.3:
  Create a density plot for column values


• 
  
  Lesson 19.3.3:
  Create bins for low counts


• 
  
  Lesson 19.3.3:
  Place rare categorical values in a separate column


• 
  
  Lesson 19.3.3:
  Create an instance ofOneHotEncoderand fit the encoder with values


• 
  
  Lesson 19.3.3:
  Merge DataFrames and drop original columns


• 
  
  Lesson 19.3.4:
  Use theStandardScaler()module to standardize numerical variables


• 
  
  Lesson 19.4.2:
  Generate a categorical variable list


• 
  
  Lesson 19.4.3:
  Split the preprocessed data into features and target arrays, and scale the data

Follow the instructions below and use theAlphabetSoupCharity_starter_code.ipynbfile to complete Deliverable 1.

Open theAlphabetSoupCharity_starter_code.ipynbfile, rename itAlphabetSoupCharity.ipynb, and save it to your Neural_Network_Charity_Analysis GitHub folder.

Using the information we have provided in the starter code, follow the instructions to complete the preprocessing steps.

1. Read in thecharity_data.csvto a Pandas DataFrame, and be sure to identify the following in your dataset:
   
   
   • What variable(s) are considered the target(s) for your model?
   
   
   • What variable(s) are considered the feature(s) for your model?
   
   
   
   


2. Drop theEINandNAMEcolumns.


3. Determine the number of unique values for each column.


4. For those columns that have more than 10 unique values, determine the number of data points for each unique value.


5. Create a density plot to determine the distribution of the column values.


6. Use the density plot to create a cutoff point to bin "rare" categorical variables together in a new column,Other, and then check if the binning was successful.


7. Generate a list of categorical variables.


8. Encode categorical variables using one-hot encoding, and place the variables in a new DataFrame.


9. Merge the one-hot encoding DataFrame with the original DataFrame, and drop the originals.

At this point, your merged DataFrame should look like this:

10. Split the preprocessed data into features and target arrays.


11. Split the preprocessed data into training and testing datasets.


12. Standardize numerical variables using Scikit-Learn’sStandardScalerclass, then scale the data.

Save yourAlphabetSoupCharity.ipynbfile to your Neural_Network_Charity_Analysis folder.

Deliverable 1 Requirements

You will earn a perfect score for Deliverable 1 by completing all requirements below:

• The following preprocessing steps have been performed:
  
  
  • TheEINandNAMEcolumns have been dropped(5 pt)
    
  
  
  • The columns with more than 10 unique values have been grouped together(5 pt)
    
  
  
  • The categorical variables have been encoded using one-hot encoding(5 pt)
    
  
  
  • The preprocessed data is split into features and target arrays(5 pt)
    
  
  
  • The preprocessed data is split into training and testing datasets(5 pt)
    
  
  
  • The numerical values have been standardized using theStandardScaler()module(5 pt)
    
  
  
  
  

Deliverable 2: Compile, Train, and Evaluate the Model (20 points)

Deliverable 2 Instructions

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.

REWIND

For this deliverable, you’ve already done the following in this module:

• 
  
  Lesson 19.4.4:
  Deep learning model design


• 
  
  Lesson 19.4.5:
  Train and evaluate the model


• 
  
  Lesson 19.6.1:
  Create a checkout and callback to save the model’s weights


• 
  
  Lesson 19.6.2:
  Save the results after training

Follow the instructions below and use the information file to complete Deliverable 2.

1. Continue using theAlphabetSoupCharity.ipynbfile where you’ve already performed the preprocessing steps from Deliverable 1.


2. Create a neural network model by assigning the number of input features and nodes for each layer using Tensorflow 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 5 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, and name itAlphabetSoupCharity.h5.

Save yourAlphabetSoupCharity.ipynbfile andAlphabetSoupCharity.h5file to your Neural_Network_Charity_Analysis folder.

Deliverable 2 Requirements

You will earn a perfect score for Deliverable 2 by completing all requirements below:

• The neural network model using Tensorflow Keras contains working code that performs the following steps:
  
  
  • The number of layers, the number of neurons per layer, and activation function are defined(2.5 pt)
    
  
  
  • An output layer with an activation function is created(2.5 pt)
    
  
  
  • There is an output for the structure of the model(5 pt)
    
  
  
  • There is an output of the model’s loss and accuracy(5 pt)
    
  
  
  • The model's weights are saved every 5 epochs(2.5 pt)
    
  
  
  • The results are saved to an HDF5 file(2.5 pt)
    
  
  
  
  

Deliverable 3: Optimize the Model (20 points)

Deliverable 3 Instructions

Using your knowledge of TensorFlow, optimize your model in order to achieve a target predictive accuracy higher than 75%. If you can't achieve an accuracy higher than 75%, you'll need to make at least three attempts to do so.

NOTE

The accuracy for the solution is designed to be lower than 75% after completing the requirements for Deliverables 1 and 2.

REWIND

For this deliverable, you’ve already done the following in this module:

• 
  
  Lesson 19.2.6:
  Optimize a neural network


• 
  
  Lesson 19.4.4:
  Deep learning model design


• 
  
  Lesson 19.4.5:
  Train and evaluate the model


• 
  
  Lesson 19.5.2:
  Logistic vs neural network


• 
  
  Lesson 19.5.3:
  Support vector machine vs deep learning


• 
  
  Lesson 19.5.3:
  Random forest vs deep learning


• 
  
  Lesson 19.6.1:
  Create a checkout and callback to save the model’s weights


• 
  
  Lesson 19.6.2:
  Save the results after training

Optimize your model in order to achieve a target predictive accuracy higher than 75% by using any or all of the following:

• Adjusting the input data to ensure that there are no variables or outliers that 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.
  
  
  
  


• Adding more neurons to a hidden layer.


• Adding more hidden layers.


• Using different activation functions for the hidden layers.


• Adding or reducing the number of epochs to the training regimen.

NOTE

You will not lose points if your model does not achieve target performance, as long as you make three attempts at optimizing the model in yourAlphabetSoupCharity_Optimzation.ipynbfile.

Follow the instructions below and use the information file to complete Deliverable 3.

1. Create a new Jupyter Notebook file and name itAlphabetSoupCharity_Optimzation.ipynb.


2. Import your dependencies, and read in thecharity_data.csvto a Pandas DataFrame.


3. Preprocess the dataset like you did in Deliverable 1, taking into account any modifications to optimize the model.


4. Design a neural network model, taking into account any modifications that will optimize the model to achieve higher than 75% accuracy.


5. Create a callback that saves the model's weights every 5 epochs.


6. Save and export your results to an HDF5 file, and name itAlphabetSoupCharity_Optimization.h5.

Save yourAlphabetSoupCharity_Optimzation.ipynbfile andAlphabetSoupCharity_Optimization.h5file to your Neural_Network_Charity_Analysis folder.

Deliverable 3 Requirements

You will earn a perfect score for Deliverable 3 by completing all requirements below:

• The model is optimized, and the predictive accuracy is increased to over 75%, or there is working code that makes
  three attempts
  to increase model performance using the following steps:
  
  
  • Noisy variables are removed from features(2.5 pt)
    
  
  
  • Additional neurons are added to hidden layers(2.5 pt)
    
  
  
  • Additional hidden layers are added(5 pt)
    
  
  
  • The activation function of hidden layers or output layers is changed for optimization(5 pt)
    
  
  
  • The model's weights are saved every 5 epochs(2.5 pt)
    
  
  
  • The results are saved to an HDF5 file(2.5 pt)
    
  
  
  
  

Deliverable 4: A Written Report on the Neural Network Model (30 points)

Deliverable 4 Instructions

For this part of the Challenge, 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 considered the target(s) for your model?
     
     
     • What variable(s) are considered to be the features for your model?
     
     
     • What variable(s) are neither targets nor features, and should be removed from the input data?
     
     
     
     
   
   
   • 
     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 to try and 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 explain your recommendation.

   
   

Deliverable 4 Requirements

You will earn a perfect score for Deliverable 4 by completing all requirements below:

Structure, Organization, and Formatting (6 points)

The written analysis has the following structure, organization, and formatting:

• There is a title, and there are multiple sections(2 pt)
  


• Each section has a heading and subheading(2 pt)
  


• Links to images are working, and code is formatted and displayed correctly(2 pt).

Analysis (24 points)

The written analysis has the following:

• 
  

  Overview of the loan prediction risk analysis:

  
  
  
  
  • The purpose of this analysis is well defined(4 pt)
    
  
  
  
  


• 
  

  Results:

  
  
  
  
  • There is a bulleted list that answers all six questions(15 pt)
    
  
  
  
  


• 
  

  Summary:

  
  
  
  
  • There is a summary of the results(2 pt)
    
  
  
  • There is a recommendation on using a different model to solve the classification problem, and justification(3 pt)
    
  
  
  
  

Submission

Once you’re ready to submit, make sure to check your work against the rubric to ensure you are meeting the requirements for this Challenge one final time. It’s easy to overlook items when you’re in the zone!

As a reminder, the deliverables for this Challenge are as follows:

• Deliverable 1: Preprocessing Data for a Neural Network Model


• Deliverable 2: Compile, Train, and Evaluate the Model


• Deliverable 3: Optimize the Model


• Deliverable 4: A Written Report on the Neural Network Model (README.md)

Upload the following to your Neural_Network_Charity_Analysis GitHub repository:

• YourAlphabetSoupCharity.ipynbfile for Deliverables 1 and 2.


• YourAlphabetSoupCharity.h5file for Deliverables 1 and 2.


• YourAlphabetSoupCharity_Optimzation.ipynbfile for Deliverable 3.


• YourAlphabetSoupCharity_Optimzation.h5file for Deliverable 3.


• An updated README.md that has your written report.

To submit your challenge assignment for grading in Bootcamp Spot, click Start Assignment, click the Website URL tab, then provide the URL of your Neural_Network_Charity_Analysis GitHub repository, and then click Submit. Comments are disabled for graded submissions in BootCampSpot. If you have questions about your feedback, please notify your instructional staff or the Student Success Manager. If you would like to resubmit your work for an improved grade, you can use theRe-Submit Assignmentbutton to upload new links. You may resubmit up to 3 times for a total of 4 submissions.

IMPORTANT

Once you receive feedback on your Challenge, make any suggested updates or adjustments to your work. Then, add this week’s Challenge to your professional portfolio.

NOTE

You are allowed to miss up to two Challenge assignments and still earn your certificate. If you complete all Challenge assignments, your lowest two grades will be dropped. If you wish to skip this assignment, click Next, and move on to the next Module.

Rubric

Module-19 RubricModule-19 Rubric

Criteria	Ratings	Pts
This criterion is linked to a learning outcomeDeliverable 1: Preprocessing Data for a Neural Network Mode	30to >27.0PtsDemonstrating ProficiencyThe Deliverable Fulfills "Approaching Proficiency" Required Criteria and meets this requirement: ✓The numerical values have been standardized using the StandardScaler. 27to >24.0PtsApproaching ProficiencyThe Deliverable Fulfills "Developing Proficiency" Required Criteria and meets this requirement: ✓The preprocessed data is split into training and testing datasets. AND has this: ✓Code is written to standardize the numerical values using the StandardScaler. 24to >21.0PtsDeveloping ProficiencyThe Deliverable Fulfills "Emerging" Required Criteria and meets this requirement: ✓The preprocessed data is split into features and target arrays. AND has these: ✓Code is written to split the preprocessed data into training and testing datasets. ✓Code is written to standardize the numerical values using the StandardScaler. 21to >0.0PtsEmergingREQUIRED: The Deliverable does the following: ✓The EIN and NAME columns have been dropped. ✓Columns with more than 10 unique values have been grouped together. ✓The categorical variables have been encoded using one-hot encoding. AND has these: ✓Code is written to split the preprocessed data into features and target arrays. ✓Code is written to split the preprocessed data into training and testing datasets. ✓Code is written to standardize the numerical values using the StandardScaler. 0PtsIncomplete	30pts
This criterion is linked to a learning outcomeDeliverable 2: Compile, Train, and Evaluate the Model	20to >19.0PtsDemonstrating ProficiencyThe Deliverable Fulfills "Developing Proficiency" Required Criteria and meets these requirements: ✓The model's weights are saved every 5 epochs. ✓The results are saved to an HDF5 file. 19to >17.0PtsApproaching ProficiencyThe Deliverable Fulfills "Developing Proficiency" Required Criteria. AND does this: ✓The model's weights are saved every 5 epochs. ✓Code is written to save the results to an HDF5 file, but there is an error. OR, does this: ✓Code is written to save the model's weights every 5 epochs but has errors. ✓The results are saved to an HDF5 file. 17to >14.0PtsDeveloping ProficiencyThe Deliverable Fulfills "Emerging" Required Criteria and meets this requirement: ✓There is an output of the model’s loss and accuracy. AND does these: ✓Code is written to save the model's weights every 5 epochs but has errors. ✓Code is written to save the results to an HDF5 file, but there is an error. 14to >0.0PtsEmergingREQUIRED: The Deliverable does the following: ✓The number of layers, number of neurons per layer, and activation function are defined. ✓An output layer with an activation function is created. ✓There is an output of the structure of the model. AND does these: ✓Code is written to create an output of the model’s loss and accuracy. ✓Code is written to save the model's weights every 5 epochs but has errors. ✓Code is written to save the results to an HDF5 file, but there is an error. 0PtsIncomplete	20pts
This criterion is linked to a learning outcomeDeliverable 3: Optimize the Model	20to >17.0PtsDemonstrating ProficiencyStudent produces model that demonstrates predictive accuracy over 75%. OR The student’s solution contains working code that attempts to increase model performance at least THREE times using the following steps: ✓Noisy variables are removed from features. ✓Additional neurons are added to the hidden layers. ✓Additional hidden layers are added. ✓The activation function of hidden layers or output layers are changed for optimization. AND: ✓The model's weights are saved every 5 epochs. ✓The results are saved to an HDF5 file. 17to >14.0PtsApproaching ProficiencyThe student’s solution contains working code that attempts to increase model performance at least TWO times using the following steps: ✓Leaving out noisy variables from features. ✓Additional neurons are added to the hidden layers. ✓Adds additional hidden layers to the model. ✓Changes the activation function of hidden layers or output layers. AND: ✓The model's weights are saved every 5 epochs. ✓The results are saved to an HDF5 file. 14to >12.0PtsDeveloping ProficiencyThe student’s solution contains working code that attempts to increase model performance at least ONE time using the following steps: ✓Leaving out noisy variables from features. ✓Additional neurons are added to the hidden layers. ✓Adds additional hidden layers to the model. ✓Changes the activation function of hidden layers or output layers. AND: ✓The model's weights are saved every 5 epochs. ✓The results are saved to an HDF5 file. 12to >0.0PtsEmergingStudent attempts to produce working code that produces the following steps: ✓Leaving out noisy variables from features. ✓Additional neurons are added to the hidden layers. ✓Adds additional hidden layers to the model. ✓ Changes the activation function of hidden layers or output layer. AND: ✓The model's weights are saved every 5 epochs. ✓The results are saved to an HDF5 file. 0PtsIncomplete	20pts
This criterion is linked to a learning outcomeDeliverable 4: Structure, Organization, and Formatting	6to >5.0PtsDemonstrating ProficiencyThe written analysis has ALL of the following: ✓There is a title, and there are multiple sections. ✓Each section has a heading and subheading. ✓The images are formatted and displayed correctly. 5to >4.0PtsApproaching ProficiencyThe written analysis has ALL of the following: ✓There is a title, and there are multiple sections. ✓Each section has a heading and subheading. ✓The images are formatted and displayed correctly, with one or two minor errors. 4to >3.0PtsDeveloping ProficiencyThe written analysis has ALL of the following: ✓There is a title, and there are multiple sections. AND ONE of the following: ✓Each section may have a heading and subheading. ✓The images are formatted and displayed correctly, with one or two minor errors. 3to >0.0PtsEmergingThe written analysis has ALL of the following: ✓There is a title. ✓There may be a subheading for a section. ✓There are no headings for each section, but there are three sections. 0PtsIncomplete	6pts
This criterion is linked to a learning outcomeDeliverable 4: Analysis	24to >21.0PtsDemonstrating Proficiency✓The purpose is well defined. ✓ALL SIX questions are answered. ✓The results are summarized, and there is a recommendation on using a different model to solve the classification problem, with a justification. 21to >18.0PtsApproaching Proficiency✓The purpose is well defined. ✓FIVE of the SIX questions are answered. ✓The results are summarized, and there is a recommendation on using a different model to solve the classification problem, but there is no justification. 18to >16.0PtsDeveloping Proficiency✓The purpose is well defined. ✓FOUR of the SIX questions are answered. ✓The results are summarized, but there is no recommendation on using a different model to solve the classification problem. 16to >0.0PtsEmerging✓The purpose is well defined. ✓THREE of the SIX questions are answered. ✓The results are summarized, but there is no recommendation on using a different model to solve the classification problem. 0PtsIncomplete	24pts
Total points:100

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