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Education: Starter Kit - Student Counsellor

by kandikits Updated: Jan 24, 2022

Student Counsellor can help the student across many areas such as: 1. Clarifications on subjects of your choice. 2. Help students to prepare for admissions applications and tests. 3. Provide one-on-one career guidance and skills assessment to assist with career development 4. Offer referrals to outside resources, such as for mental health, substance abuse, or vocational-related activities. In this challenge, we are inviting to build a virtual counsellor application which will help with addressing student queries round the clock. You can also choose any topics of your choice. Please see below a sample solution kit to jumpstart your solution on creating a Virtual Agent application. To use this kit to build your own solution, scroll down to refer sections Kit Deployment Instructions and Instruction to Run. Complexity : Simple

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

jupyterby jupyter

Python star image 12379 Version:Current

License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat

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jupyterby jupyter

Python star image 12379 Version:Current License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat
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vscodeby microsoft

TypeScript star image 130477 Version:1.66.2

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

TypeScript star image 130477 Version:1.66.2 License: Permissive (MIT)

Visual Studio Code
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Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation. For building Virtual Agent, we use pandas to load, view and analyse data from csv file; and we use numpy to find out the indices of maximum values of an array.

pandasby pandas-dev

Python star image 33259 Version:v1.4.1

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 33259 Version:v1.4.1 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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numpyby numpy

Python star image 20101 Version:v1.22.3

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 20101 Version:v1.22.3 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms. We use py-lingualytics to manage pre-processing of data like removing numerical values, stopwords, punctuations and so on.

py-lingualyticsby lingualytics

Python star image 32 Version:Current

License: Permissive (MIT)

A text analytics library with support for codemixed data

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py-lingualyticsby lingualytics

Python star image 32 Version:Current License: Permissive (MIT)

A text analytics library with support for codemixed data
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spaCyby explosion

Python star image 23063 Version:v3.1.6

License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python

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spaCyby explosion

Python star image 23063 Version:v3.1.6 License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python
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Machine Learning

Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics. For generating sentence embedding, we use sentence-transformers with pretrained models. We reference bert-cosine-sim to build similarity engine for comparing two sets of input text.

sentence-transformersby UKPLab

Python star image 5944 Version:v2.0.0

License: Permissive (Apache-2.0)

Multilingual Sentence & Image Embeddings with BERT

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sentence-transformersby UKPLab

Python star image 5944 Version:v2.0.0 License: Permissive (Apache-2.0)

Multilingual Sentence & Image Embeddings with BERT
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bert-cosine-simby beekbin

Python star image 61 Version:Current

License: No License (null)

Fine-tune BERT to generate sentence embedding for cosine similarity

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bert-cosine-simby beekbin

Python star image 61 Version:Current License: No License

Fine-tune BERT to generate sentence embedding for cosine similarity
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faissby facebookresearch

C++ star image 14276 Version:v1.7.1

License: Permissive (MIT)

A library for efficient similarity search and clustering of dense vectors.

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faissby facebookresearch

C++ star image 14276 Version:v1.7.1 License: Permissive (MIT)

A library for efficient similarity search and clustering of dense vectors.
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Kit Solution Source

faq-virtual-agentby kandikits

Jupyter Notebook star image 0 Version:Current

License: Permissive (Apache-2.0)

Build your FAQ virtual agent in 5 minutes

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faq-virtual-agentby kandikits

Jupyter Notebook star image 0 Version:Current License: Permissive (Apache-2.0)

Build your FAQ virtual agent in 5 minutes
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Deployment Information

Virtual Counsellor created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.

For Windows OS, Download, extract and double-click kit_installer file to install the kit. Note: Do ensure to extract the zip file before running it. The installation may take from 2 to 10 minutes based on bandwidth. 1. When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar to see how the chatbot works. It is loaded with minimal questions. 2. To run the app manually, press N when you're prompted and locate the zip file faq-virtual-agent.zip 3. Extract the zip file and navigate to the directory faq-virtual-agent-main 4. Open command prompt in the extracted directory faq-virtual-agent-main and run the command jupyter notebook For other Operating System, 1. Click here to install python 2. Click here to download the repository 3. Extract the zip file and navigate to the directory faq-virtual-agent-main 4. Open terminal in the extracted directory faq-virtual-agent-main 5. Install dependencies by executing the command pip install -r requirements.txt 6. Run the command jupyter notebook

Instruction to Run

Follow below instructions to run the solution. 1. Locate and open the Student Counsellor App.ipynb notebook from the Jupyter Notebook browser window. 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar For customizing to your Questions and Answers, 1. Open the input file faqs.csv in the faq-virtual-agent-main directory from the kit_installer.bat location 2. Update questions for training under the column Q and update corresponding answer in the column A 3. Execute cells in the notebook by selecting Cell --> Run All from Menu bar 4. Type in the query when you're prompted and hit Enter key Input file: faqs.csv - contains questions and answers to train You can additionally build interfaces to the chatbot and other enhancements for additional score. For any support, you can direct message us at #help-with-kandi-kits

Troubleshooting

1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway 2. During kit installer, if you encounter Windows security alert, click Allow

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.

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