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BuildWithAI Challenge 1

by BuildwithAIHack21

The Pandemic has impacted education - classes have moved online, students have been isolated on screens and coping with this change. Despite the challenges, the digital school has the potential to transform education. How can we empower students and teachers in this new digital school paradigm. In this challenge, we are inviting AI-powered solutions for the digital school of tomorrow.

DATASET: Feel free to use any dataset of your choice.

There is no restriction and you can use any data set. Please see the section - DATASETS below for sample datasets to help as a reference. Here are sample areas you could choose to tackle in this challenge. Feel free to come up with your own ideas as well. 1. Higher Education and Career Recommendation 2. Mental Health Monitor and Virtual Companion 3. Adaptive Learning Curriculum 4. Class availability scheduling for social distancing 5. Compliance of COVID guidelines - masking, distancing, temperature Please see below for guidelines and reusable libraries to jumpstart your solution. This kit provides reference to open-source libraries which can be reused as core building blocks for creating a predictive solution. You may use any other open-source libraries also as relevant to your solution. Reusability is a key design principle and will be scored positively in your submission. These reference reusable libraries are spread over functions in Data Analysis and Mining, Data Visualization, Machine Learning, and other key areas to build AI solution. Below are the guidelines for creating your submission kit for this challenge. 1. See Product Tour > Creating a kit from the kandi header. This will guide you on creating your kit. 2. Your submission kit should contain the kit heading/ name, description of the solution, and other relevant information. 3. Create groups with logical names and add the libraries to the respective sections. 4. You solution can be built with any libraries other than the libraries provided here for reference. 5. The project source library for the solution built in the hackathon should be hosted in GitHub and listed in your kit under 'Kit Solution Source' section. 6. Any deployment instructions should be added under 'Kit Deployment Instructions' section of the kit. 7. Add any additional information, links under the kit description or group descriptions.

DATASETS

https://data.ed.gov/ https://data.world/datasets/education https://data.gov.in/sector/higher-education https://github.com/mdsaifk/Student-Dropout-Prediction/tree/main/Data https://github.com/hilmarh/student-dropout-prediction/tree/master/datasets https://github.com/iampratheesh/Student-Dropout-Prediction/blob/master/student%20info.csv https://www.kaggle.com/spscientist/students-performance-in-exams https://www.kaggle.com/aljarah/xAPI-Edu-Data https://www.kaggle.com/janiobachmann/math-students?select=student-mat.csv https://www.kaggle.com/kwadwoofosu/predict-test-scores-of-students https://www.kaggle.com/namanmanchanda/entrepreneurial-competency-in-university-students https://www.kaggle.com/uciml/student-alcohol-consumption?select=student-por.csv https://www.kaggle.com/passnyc/data-science-for-good https://www.kaggle.com/landlord/education-and-covid19

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.

Data Analysis and Mining

Data Mining and Analysis plays vital role in Predictive Analytics. It lets you inspect, cleanse, explore, manipulate, transform your data to identify hidden patterns and relationships in data. You can make use of these popular libraries to model the solution.

Data Visualization

Data Visualization helps you depict insight found in data. Avail the libraries added here to represent identified patterns and relationships from data graphically for better understanding and presentation.

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.

Image Analysis

Image Analysis plays vital role in Visual Analytics. It lets us inspect, cleanse, explore, augment and transform images to prepare data for training and prediction.

Machine learning algorithms and techniques

To build a model for Predictive Analytics, you can apply traditional machine learning algorithms and techniques using the most popular scikit-learn. Or you can build your own neural network to implement deep learning techniques by using the library of your choice from this section.

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.