Define the input-output behavior of the system and the scope of the project. What is your evaluation metric for success? Collect some preliminary data, and give concrete examples of inputs and outputs. Implement a baseline and an oracle and discuss the gap. What are the challenges? Which topics (e.g., search, MDPs Markov Decision Process, etc.) might be able to address those challenges (at a high level, since we haven't covered any techniques in detail at this point)? Search the Internet for similar projects and mention the related work. You should basically have all the infrastructure (e.g., building a simulator, cleaning data) completed doing something interesting by now.

Python Project

8 puzzle game using A* search algorithm (GUI)

Note: Attach Sample of Another Project Proposal.

Reference link  related work of another project proposal:

[1] Rawat, A. (2020, November 28). Email Slicer with Python. Retrieved December 11, 2020, from, https://ayushirawat.com/email-slicer-with-python

 

Transcribed Image Text:

Code:- user_email=input ("Enter email address:").strip() User_name = user_email[:user_email.index ("@")] domain_email= user_email[user_email.index("@")+1:] user_output= "user_email is {} and user_name is {} and user_domain is {}". format(user_email,User_name, domain_email) print(user_output) Explanation: Program description: 1. Take inputs from the user as user email. 2. strip() is used to eliminate additional attributes from input. 3. Illustrating sliced user email on the output screen. Input and Output : C: \Users\techno\Desktop>python t.py Enter enail address: sur123@123456gnail.com User email is sur1230123456gnail.com and user usernane is sur123 and user domain is 123456gnail.com Project scope: Helping to find domain and user name from the user input and modify information and send back to the host. Evaluation metrics for the project: 1. Sharing Email: It is the percentage of data to a recipient who circulates it on their friends and social media platform. 2. Forwarding email: It keeps all the information related to brands you have. Examples of inputs and outputs: ENUsers\techno\Desktopapython t.py Enter emuil address:pankaji1218grail.com User enail is pankaj1121genail.con and user usernane is pankaj1121 and user domain is gnail.con C:\Users\techno\Desktop>python t.py Enter enail adress: lanturnegnail.com User enail is lanturnegnail.com and user usernane is lanturn and user domain is gnail.com BaseLine: 1. Schedule baseline: the time for finishing the project. 2. Cost baseline: the cost of the project. 3. Scope baseline: the future scope of the project. 'Fit gap analysis' is a process to compare the project's needs with 'Oracle Communications Data Model', 1. Add non-technical and technical person to the project as per need. 2. Review project code against 'Oracle Communications Data Model'. 3. Find the difference between a requirement and a data model schema. 4. Built a warehouse of the data model schema. Challenges: 1. To distinguish a fraudulent email. 2. To stop transmitting faulty information. Markov decision process is based on the mathematical framework which helps in decision making. We can use this framework to identify fake email and to prevent wrong information to others. Proposal example: To create a program that demonstrates Ludwig's Python API. This proposal includes a python program for • Titanic Survivors • MNIST Data set • Text Classification Programs would include the following method and functions: • Training of data set • Prediction • Visualization • Analyzing and accessing results files (like training.json) The program can be in the python program and Jupiter notebook formats. A sub-directory is called a model created in the root directory. The structure directory as follows: model/ Model/ trainingmodel.py trainingmoel.ipynb definitionmodel.yaml mnist_dataset/ Code:- user_email=input ("Enter email address:").strip() User_name = user_email[:user_email.index ("@")] domain_email= user_email[user_email.index("@")+1:] user_output= "user_email is {} and user_name is {} and user_domain is {}". format(user_email,User_name, domain_email) print(user_output) Explanation: Program description: 1. Take inputs from the user as user email. 2. strip() is used to eliminate additional attributes from input. 3. Illustrating sliced user email on the output screen. Input and Output : C: \Users\techno\Desktop>python t.py Enter enail address: sur123@123456gnail.com User email is sur1230123456gnail.com and user usernane is sur123 and user domain is 123456gnail.com Project scope: Helping to find domain and user name from the user input and modify information and send back to the host. Evaluation metrics for the project: 1. Sharing Email: It is the percentage of data to a recipient who circulates it on their friends and social media platform. 2. Forwarding email: It keeps all the information related to brands you have. Examples of inputs and outputs: ENUsers\techno\Desktopapython t.py Enter emuil address:pankaji1218grail.com User enail is pankaj1121genail.con and user usernane is pankaj1121 and user domain is gnail.con C:\Users\techno\Desktop>python t.py Enter enail adress: lanturnegnail.com User enail is lanturnegnail.com and user usernane is lanturn and user domain is gnail.com BaseLine: 1. Schedule baseline: the time for finishing the project. 2. Cost baseline: the cost of the project. 3. Scope baseline: the future scope of the project. 'Fit gap analysis' is a process to compare the project's needs with 'Oracle Communications Data Model', 1. Add non-technical and technical person to the project as per need. 2. Review project code against 'Oracle Communications Data Model'. 3. Find the difference between a requirement and a data model schema. 4. Built a warehouse of the data model schema. Challenges: 1. To distinguish a fraudulent email. 2. To stop transmitting faulty information. Markov decision process is based on the mathematical framework which helps in decision making. We can use this framework to identify fake email and to prevent wrong information to others. Proposal example: To create a program that demonstrates Ludwig's Python API. This proposal includes a python program for • Titanic Survivors • MNIST Data set • Text Classification Programs would include the following method and functions: • Training of data set • Prediction • Visualization • Analyzing and accessing results files (like training.json) The program can be in the python program and Jupiter notebook formats. A sub-directory is called a model created in the root directory. The structure directory as follows: model/ Model/ trainingmodel.py trainingmoel.ipynb definitionmodel.yaml mnist_dataset/

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