ML-Crate

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ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Intention of Chatbots :red_circle: Aim : Tweak and expand the data with your observations to build and understand the working of a chatbot in organizations. A chatbot requires you to understand Natural language processing concepts. :red_circle: Dataset : https://github.com/katanaml/katana-assistant/blob/master/mlbackend/intents.json :red_circle: 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.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

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📍 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.
• A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

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:red_circle::yellow_circle: 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.
• This issue is only for 'SWOC' contributors of 'ML-Crate' project.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID :
• Approach for this Project :
• Are you a participant of SWOC 2.0?
  • [ ] YES
  • [ ] No

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Hi @abhisheks008 can i work on this issue ?

Full name : Harshini Marappan

GitHub Profile Link : https://github.com/HarshiniMarappan

Participant ID : 389

Approach for this Project : For initial text processing i would use nltk library . I would use Naive Bayes , Decision Tree and Support Vector Machines algorithms to compare the accuracies of the chatbot and find the best fit.

Are you a participant of SWOC 2.0? YES

Issue assigned to you @HarshiniMarappan

@abhisheks008 can you unassign this issue for me, apologies 🙏🙏

Yeah sure no worries @HarshiniMarappan

@abhisheks008 I don't understand what I have to do in this???.....can u explain?

You need to create a chatbot using the expressions given in the .json file and also provide an user interface for the chatbot so that others can interact with it easily @yagyesh-bobde

I am not familiar with this .....Do you have like any resources where I can learn to do this?

Hi @abhisheks008 interested to work on this issue.

Full name : Aman Sayyad

GitHub Profile Link : https://github.com/amansayyad2003

Participant ID : NA

Approach for this Project : Steps I will follow

1. Understand different intents. If needed add more (while implementation)
2. Data processing (gather data, data cleaning, spliting data into training and testing, label encoding)
3. Feature extraction (TF-IDF vectorization, word Embeddings)
4. Models (Support Vector Machines, Naive Bayes, Random Forest, if possible neural network )
5. Comparision of different model
6. Creating the UI for user interaction

Are you a participant of SWOC 2.0? No (SSOC)

Hi @abhisheks008 interested to work on this issue.

Full name : Aman Sayyad

GitHub Profile Link : https://github.com/amansayyad2003

Participant ID : NA

Approach for this Project : Steps I will follow

1. Understand different intents. If needed add more (while implementation)
2. Data processing (gather data, data cleaning, spliting data into training and testing, label encoding)
3. Feature extraction (TF-IDF vectorization, word Embeddings)
4. Models (Support Vector Machines, Naive Bayes, Random Forest, if possible neural network )
5. Comparision of different model
6. Creating the UI for user interaction

Are you a participant of SWOC 2.0? No (SSOC)

Implement 5-6 models for this dataset.

Assigned @amansayyad2003

Thank you @abhisheks008 for assigning this task. I will give you regular updates on this.