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Student Grade Prediction #426
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Hello there! 👋🏻 Welcome to the ML-ProjectKart! 🚀⚡️ |
Can you elaborate your project more? You issue template lack details. Tell me of your approach, how would you proceed, algorithms you would follow and task you are going to take. @sujay2306 |
I will use linear regression for this project |
You should use multiple algorithms and compare the accuracies. Your issue template lacks details about the project. What's the task you are taking up? 'Model Creation', 'Front End' or 'Back End'. How would you proceed? @sujay2306 |
Model creation |
Kindly provide the dataset you will be working on, so that issue can be assigned. @sujay2306 |
Can I work on this issue? |
Electricity consumption is assigned to you. @harshita9621 |
Are you willing to work on this, if so provide the dataset you will be using. @sujay2306 |
can I work on this issue, I can do this task using ensemble learning, we will use different models like linear regression, random forest, xgboast and svm for better prediction on the dataset. |
This is already raised by someone else. If you want to work on this. raise a new issue with this project title. @devkumar24 |
@sujay2306 are you willing to work on this issue or not |
no update. @sujay2306 |
@prathimacode-hub , can you please assign me this mam. I am part of hacktoberfest2021. |
@prathimacode-hub can you please assign me this mam. I am part of hacktoberfest2021. |
Issue assigned to @harshita9621 |
By when are you expected to do? Fest is about to end? @harshita9621 |
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Define You:
Is your feature request related to a problem? Please describe.
The problem is precidicting the student gradw based on how many classes he or she has attended.
Describe the solution you'd like...
I would like to use linear regression algorithm for this project
I will take a student data set and based on n.o of classes he has attended and marks secures we can train the model and we can predict the marks according to n.o of classes attended.
Describe alternatives you've considered?
Approach to be followed (optional):
Additional context
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