Book Recommendation SystemLast Updated on May 3, 2021
It is based on unsupervised Machine Learning and uses GUI. It recommends the user which book he/she should read next based on a book already read.
Tools Used: Python3, Tkinter , ML
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Jeevika Special Purpose Vehicle For Agricultural Transformation (Jspvat)Last Updated on May 3, 2021
JSPVAT, a program initiated in 2020 to support the Bihar state government’s rural livelihoods project, JEEViKA, aims to diversify and enhance household incomes and improve nutrition and sanitation for tens of thousands of farming households. JSPVAT has already facilitated an important systems-level partnership between JEEViKA and the World Bank to strengthen the agricultural and livestock market ecosystems in Bihar.
Reaching 50,000 farmers, JSPVAT’s work includes catalyzing key institutional changes to support market linkages and testing innovative private-sector models and approaches for inclusive agricultural transformation. The latter includes digital solutions related to procurement, quality testing, and access to finance and technologies, with farmer producer companies serving as a point for engagement with smallholder farmers. JSPVAT has also designed and supported the implementation of interventions to strengthen market ecosystems for fruits, vegetables, high-value crops, and livestock.
In the very first year, smallholder farmers supported by JEEViKA and JSPVAT engaged in expanded trade with institutional buyers of maize (more than 3,610 metric tons) and fruits and vegetables (210 metric tons) and procured a greater amount of agricultural inputs (more than 220 metric tons). JSPVAT introduced derivative trading on the NCDEX platform, enabling farmers to realize prices 6 to 8 percent higher than before. JSPVAT also facilitated access to public funding for participating farmer collectives—among the first such instances in the country.
In its first year, JSPVAT reached 7.5 million rural women from poor and marginalized-caste households through self-help groups formed under JEEViKA. This has expanded their access to financial services, value chains in the agriculture and nonfarm sectors, and nutrition and sanitation services.
Bank_Loan_Default_CaseLast Updated on May 3, 2021
The Objective of this problem is to predict whether a person is ‘Defaulted’ or ‘Not Defaulted’ on the basis of the given 8 predictor variables.
The data consists of 8 Independent Variables and 1 dependent variable. The Independent Variables are I. Age: It is a continuous variable. This feature depicts the age of the person. II. Ed: It is a categorical variable. This feature has the education category of the person converted to numerical form. III. Employ: It is a categorical variable. This feature contains information about the geographic location of the person. This column has also been converted to numeric values. IV. Income: It is a continuous variable. This feature contains the gross income of each person. V. DebtInc: It is a continuous variable. This feature tells us an individual’s debt to his or her gross income. VI. Creddebt: It is a continuous variable. This feature tells us about the debt-to-credit ratio. It is a measurement of how much a person owes their creditors as a percentage of its available credit. VII. Othdebt: It is a continuous variable. It tells about any other debt a person owes. VIII. Default: It is a categorical variable. It tells whether a person is a Default (1) or Not-Default (0).
After performing extensive exploratory data analysis the data is given to multiple models like Logistic Regression, Decision Tree classifier, Random Forest classifier, KNN, Gradient Boosting classifier with and without hyperparameter tuning, the final results are obtained and compared on metrics like precision score, recall score, AUC-ROC score.
ResistarLast Updated on May 3, 2021
Augmented Reality Circuit Visualizer and Solver.
TartanHacks Grand Prize
Facebook Company Prize
Attempted Prize Categories
Duolingo’s Social Impact Prize (Educational)
GoDaddy’s Social Impact Prize (“Best app that improves STEM education”)
Long hours spent on ECE problem sets and frustration visualizing convoluted circuits caused these four CMU undergrads to create a circuit visualization system that would also help them solve circuits. A member of the team is currently in the intro ECE course: "Well it's not bad, I guess." - Team Member
What it does
ResistAR is an Augmented Reality Circuit Visualizer and Solver. A user can place down circuit elements in parallel and series configurations and ResistAR will solve the current through and voltage across each element of the circuit. It gives the user an easy way to see (sharp) the circuit.
How we built it
We first began with 3D printed chassis for the VuMark targets. These targets are identified and parsed by the program and cross checked against our cloud database on Vuforia. We then created 3D, textured, models in Blender that will hover over the VuMark targets. We then wrote the code in Unity that will calculate voltage and current values using concepts from vector calculus and matrix algebra.
Challenges we ran into
The math was very difficult and attempting to rush a 3D printed design was also difficult but there was a rush because 3D printing would be a very time consuming process. Thus we also had to create a lot of our latter designs around the already 3D printed parts. VuMarks were also difficult to create. VuMarks must be very easily distinguishable from each other and non-symmetric along any axis, and therefore took a while to get finely tuned and calibrated. Finally the math was a very difficult thing to visualize. We had to go from 3D space to 2D space and there were some difficulties with projections. The coders did end up writing relatively bug-free code, but not before a long, arduous thinking process.
Accomplishments that we're proud of
The two logic/algorithm gods that we had on our team solved an extremely complex math problem very quickly. Also our 3D printed parts are actually fire though. Just saying.
What we learned
Two 5 hour energies in 72 hours is actually not as bad an idea as some might think. Math is hard.
What's next for ResistAR
Norton and Thevenin Equivalents. Yikes.
Social Distance Monitoring System(Python, Deep Learning And Opencv)(Research Paper)Last Updated on May 3, 2021
Social distancing is one of the community mitigation measures that may be recommended during Covid-19 pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as ATM,Airport Or market place .
Covid-19 pandemics have demonstrated that we cannot expect to contain geographically the next influenza pandemic in the location it emerges, nor can we expect to prevent international spread of infection for more than a short period. Vaccines are not expected to be available during the early stage of the next pandemic (1), a Therefore, we came up with this system to limit the spread of COVID via ensuring social distancing among people. It will use cctv camera feed to identify social distancing violations
We are first going to apply object detection using a YOLOv3 model trained on a coco dataset that has 80 classes. YOLO uses darknet frameworks to process incoming feed frame by frame. It returns the detections with their IDs, centroids, corner coordinates and the confidences in the form of multidimensional ndarrays. We receive that information and remove the IDs that are not a “person”. We will draw bounding boxes to highlight the detections in frames. Then we use centroids to calculate the euclidean distance between people in pixels. Then we will check if the distance between two centroids is less than the configured value then the system will throw an alert with a beeping sound and will turn the bounding boxes of violators to red.
Research paper link: https://ieeexplore.ieee.org/document/9410745
Real Estate Price PredictionLast Updated on May 3, 2021
People looking to buy a new home tend to be more conservative with their budgets and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase. The goal of this project is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. By analyzing previous market trends and price ranges, and also upcoming developments future prices will be predicted. The functioning of this project involves a website which accepts customer’s specifications and then combines the application of multiple linear regression algorithm of data mining. This application will help customers to invest in an estate without approaching an agent. It also decreases the risk involved in the transaction.
Housing prices are an important reflection of the economy, and housing price ranges are of great interest for both buyers and sellers. In this project. house prices will be predicted given explanatory variables that cover many aspects of residential houses. Thus, there is a need to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. This project uses random forest algorithm to predict prices by analyzing current house prices, thereby forecasting the future prices according to the user’s requirements. The goal of this project is to create a regression model that are able to accurately estimate the price of the house given the features.