Face Detection With Deep LearningLast Updated on May 3, 2021
This project is not only capable of identifying face but also recognizing other
facial features such as eyes,nose and mouth technically called landmark
detection.This state-of-the-art face detection is achieved using a Multi-task
cascade CNN via the MTCNN library described by Kaipeng Zhang in the 2016
paper titled “Joint Face detection and Alignment Using Multitask Cascaded
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Determination Of A Person’S HealthLast Updated on May 3, 2021
Determination of person’s health
The project was built with the intend of helping the society. It has been calculated that approx. 1.9 billion people die due to health-related problems every year. This rate is very high, and the disease is easily preventable
The project has been made with the help of Data Analysis and Machine Learning using Python with a GUI output page. In this project, the machine will analyse the already present data first and then conclude upon a person’s health on his/her given factors.
In this project, gender and either height or weight will be given to the machine. If the height is given then the weight will be predicted and vice-versa. Through these predictions the machine will tell us about the health of a person.
The main goal is to help the society for its betterment as far as health is concerned.
The data set used is from UCI repository. It includes four attributes-
The machine will be trained in these aspects to determine a person’s health or weight and the category it will lie in.
The categories are-
1. 0 – Underweight
2. 1 – Normal weight
3. 2 – Healthy
4. 3 – Over weight
5. 4 – Obesity
The methods followed in chronological form are-
1. Loading dataset (using pandas library)
2. Dataset cleaning (using pandas and numpy libraries)
3. Dataset pre-processing
4. Data visualization (using seaborn, matplotlib and matplotlib.pyplot libraries)
4.1 Univariate analysis
4.2 Bivariate analysis
5. Correlation matrix
The machine learning algorithms applied were-
1. Linear Regression
2. Logistic Regression
3. KNN Classifier
4. Decision Tree Classifier
5. Random Forest Classifier
Random Forest Classifier gave highest accuracy of about 95% while logistic regression gave the leas with about 76%.
The user in the GUI page will be asked:
1. Full name
3. Whether they know their height or weight
4. Their height or weight
Hotel Management System Using PythonLast Updated on May 3, 2021
This Project is done using python 3.x which depicts a front end interface of the hotel management system which is done using GUI interface and has the menu, where the user has a list of choices to select the food he wants and this interface has the food rating section where the user has to give the rating in which the food he took, and this interface is done using basic components of GUI. The GUI I used here is Tkinter, and by using List boxes, buttons, the text box is deployed in this interface, which is user-friendly. This interface is done because the situation of covid is increasing tremendously, to reduce the people frequently going outside for food, this interface has been developed. In this interface, we can also set background color and
font color. Here we can also set the background dimension and in this application, we can also change font sizes and also with rows and columns. This interface asks the user to enter his name, mobile number, email id and also asks whether a user prefers a choice of veg or nonveg and also gives a chance to give the food specification whether he needs the food spicy, salty, and some other and the user can choose whether he needs to pay cash, or online payment either which he can also give food rating and he can also select coupons and apply in this interface.
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.
Churn PredictionLast Updated on May 3, 2021
Predicting Customer Churn at a Fictitious Wireless Telecom Company
Churn Management has gotten great attention among the telecommunication Industry because it is proved that instead of going for advertisements to find new customers it’s better to find a technique, solution, and all the available resources in our service to figure out a pattern to make customers stay in the company. Every telecommunication company has huge competition and due to easy access of the plans and services provided by all the companies, a customer can switch the company anytime. For churn Prediction, it is most required to identify the customer who has the highest probability of leaving the service of the company and it will be effective if it’s done at the right time. Through this company can make a decision on what service to provide to make the customer not leave the service.
Here, we can reformulate the given problem as a Classification problem. My goal is to build a Classification model that can predict if Customers will stay with the company or not from the given features. To achieve this, first, I did data analysis and data cleaning, data preparation for training, and then model building. After this, based on the performance I find the best parameters of our model through GridSearchCV which best suits for the given data and gave the expected result.
Fantasy Cricket GameLast Updated on May 3, 2021
It is an online game where you create a virtual team of real cricket players and score points depending on how your chosen players perform in real life matches. To win a tournament, you must try and get the maximum points and the No. 1 rank amongst other participants. Here's how a Fantasy Cricket game may look like.
1 Opening screen of the application. You can see the players of each category by selecting the category. To begin with, the selection is disabled until a new team is created from the Manage Teams menu. A pop up asking the name of the team appears.
2 The toolbar menu options which allow you to create a new team, open an existing team, save your team and finally evaluate the score of a saved team.
3 After clicking create Team, the left box is populated with player names. As you select a different category, the corresponding list of players is displayed.
4 On double-clicking each player name, the right box gets populated. Points available and used are displayed accordingly.
5 Message if the game logic is not followed
6 Pop-up on clicking Evaluate Score. You can select your team here and the match for which the players' performance is compared.
7 The final score for your fantasy team based on the match selected.