Hospital Mangement SystemLast Updated on May 3, 2021
• Build a Hospital Management System from scratch .In which the list of doctors should be visible to the users that is approved by the Admin .There are multiple dynamic card which shows the precautions of several disease.The application data is stored in MYSQL
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Comcast Telecom Consumer ComplaintsLast Updated on May 3, 2021
Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them a $2.3 million, after receiving over 1000 consumer complaints.
The existing database will serve as a repository of public customer complaints filed against Comcast.
It will help to pin down what is wrong with Comcast's customer service.
- Ticket #: Ticket number assigned to each complaint
- Customer Complaint: Description of complaint
- Date: Date of complaint
- Time: Time of complaint
- Received Via: Mode of communication of the complaint
- City: Customer city
- State: Customer state
- Zipcode: Customer zip
- Status: Status of complaint
- Filing on behalf of someone
To perform these tasks, you can use any of the different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup.
- Import data into Python environment.
- Provide the trend chart for the number of complaints at monthly and daily granularity levels.
- Provide a table with the frequency of complaint types.
- Which complaint types are maximum i.e., around internet, network issues, or across any other domains.
- Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed.
- Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on:
- Which state has the maximum complaints
- Which state has the highest percentage of unresolved complaints
- Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.
The analysis results to be provided with insights wherever applicable.
Cluster AiLast Updated on May 3, 2021
Explore a galaxy of research papers in 3D space using a state-of-the-art machine learning model.
Search engines like Google Scholar make it easy to find research papers on a specific topic. However, it can be hard to branch out from a general position to find topics for your research that need to be specified. Wouldn’t it be great to have a tool that not only recommends you research papers, but does it in a way that makes it easy to explore other related topics and solutions to your topic?
What it does
Users will input either a text query or research paper into Cluster AI. Cluster AI uses BERT (Bidirectional Encoder Representations from Transformers), a Natural Language Processing model, in order to connect users to similar papers. Cluster AI uses the CORE Research API to fetch research articles that may be relevant, then visualizes the similarity of these papers in a 3d space. Each node represents a research paper, and the distances between the nodes show the similarity between those papers. Using this, users can visualize clusters of research papers with close connections in order to quickly find resources that pertain to their topic.
Test Cluster AI here
Note: Running on CPU based server, deploying your own Django server using instructions in the Source Code is highly recommended. Demo may have delays depending on the query and number of users at any given point. 10-100 papers, but up to 20 papers requested in the query will be optimal.
Check out the Source Code!
How we built it
We used a multitude of technologies, languages, and frameworks in order to build ClusterAI.
- BERT (Bidirectional Encoder Representations from Transformers) and MDS (Multidimensional Scaling) with PyTorch for the Machine Learning
- Python and Django for the backend
Challenges we ran into
The CORE Research API did not always provide all the necessary information that was requested. It sometimes returned papers not in English or without abstracts. We were able to solve this problem by validating the results ourselves. Getting the HTML/CSS to do exactly what we wanted gave us trouble.
Accomplishments that we're proud of
We worked with a state-of-the-art natural language processing model which successfully condensed each paper into a 3D point.
The visualization of the graph turned out great and let us see the results of the machine learning techniques we used and the similarities between large amounts of research papers.
What we learned
What's next for Cluster AI
We can add filtering to the nodes so that only nodes of a given specification are shown. We can expand Cluster AI to visualize other corpora of text, such as books, movie scripts, or news articles. Some papers are in different languages; we would like to use an API to convert the different languages into a person’s native language, so anyone will be able to read the papers.
Hyderabad House Price PredictorLast Updated on May 3, 2021
Hyderabad House Price Predictor
ML model which predicts the price of a house based on features like total Sq. ft area,total number of bedrooms,balconies etc.
The front-end of this model is made by boot-strap and Flask,where as the backend is a Machine learning model which is trained on the housing-price dataset and the algorithm used is Random-Forest
the model is hosted at------> https://homepricepredictor.herokuapp.com/
General Overview of the Project
Starting of with the home page which is designed using bootstrap classes,here we in this template the general overview of the project is mentioned,along with that the parameters which are required for predicting the price of the house are also mentioned here,here's a glimpse of it