Deep Learning Simplified is an Open-source repository, containing beginner to advance level deep learning projects for the contributors, who are willing to start their journey in Deep Learning. Devfolio URL, https://devfolio.co/projects/deep-learning-simplified-f013
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
Deep Learning Simplified Repository (Proposing new issue)
π΄ Project Title : Fraudulent Transactions Detection
π΄ Aim : Develop a model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan.
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
Deep Learning Simplified Repository (Proposing new issue)
π΄ Project Title : HackerEarth Keep Babies Safe Project
π΄ Aim : our task, as a Machine Learning expert, is to build a Deep Learning model that will tag each image with the extracted product types and brand names of these products. In case there is no brand name mentioned on a product, the model should tag the image as Unnamed.
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
Deep Learning Simplified Repository (Proposing new issue)
π΄ Project Title : HackerEarth Holiday Session Challenge
π΄ Aim : Your task is to create a solution using deep learning to discern whether a post is holiday-related in an effort to better monetize the platform.
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
Consider starring this repository if you found this project helpful. And also share the repository with other contributors so that they can also get to know about it!
Deep Learning Simplified Repository (Proposing new issue)
π΄ Project Title : Phishing Website Detection
π΄ Aim : The candidates have to read the data and create a model baased on the data analysis to identify if the website is legitimate or a phishing . The Result will be detrmined by the two values [-1,1], where 1 represent Legitimate website and -1 represnets phishing.
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
Approach for this Project : Will be first using Random Forest classification algorithm. If got enough time, I will learn about SVM and will try to apply it. Libraries that can be used->scikit Learn, matplotlib, tensorflow (for SVM)
What is your participant role? (Mention the Open Source program): Social Summer of Code (SSOC)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π
π΄ Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
π΄π‘ Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
β To be Mentioned while taking the issue :
Full name :
GitHub Profile Link :
Email ID :
Participant ID (if applicable):
Approach for this Project :
What is your participant role? (Mention the Open Source program)
Happy Contributing π
All the best. Enjoy your open source journey ahead. π