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Online Marketplace for Medical Supplies and TeleHealth
A stock attractiveness model based on the GS Marquee dataset
We have decided to take action and publicly shame all those bad actors by detecting toxic communication to support inclusiveness.
Lightbulb Companion is an extension to NCR's Silver API. Since existing solutions only analysed historical data, we set out to create a dashboard that provides management with predictive insights.
Mdoc uses AI in a privacy preserving manner to predict how likely Bob is likely to sign this document
"Have you heard of the app Tinder?" *YES* "We have created Tinder for news!" Get your personalised and curated news articles with few swipes of your fingers.
We’ve built a platform to democratize access to global medical data, all while keeping patient medical information private, using a concept in artificial intelligence called federated learning.
We are trying to build a classifier mode to predict down hole failures based on sensor information
Solving the ConocoPhilips with ANN
Classification Model for ConocoPhillips Competition
Equipment failure detection to drive down cost
Predict equipment failure from data using classical machine learning approach
After several testing of algorithms we found the best one! We are able to predict correctly the target values with the given data.
Using machine learning to address barriers to adoption for solar power
A binary classifier that accounts for class imbalance to predict equipment failures given data from 107 sensors.
A SMS ChatBox/Alexa Integrated Tool with various helpful functionality
Exploratory data analysis, feature engineering, statistical inference, and market analysis for Mexican restaurant data set.
Assisted Restricted Access Intelligence - An AI based age restricted access to multilingual chatroom with personal chatbot assistance called Alfred.
A machine learning model to predict toothpaste prices that helps brands break into emerging markets
The idea of our project is to use stock price datasets from Yahoo Finance and design a program to find correlations between factors that affect stock prices and predict future stock prices using ML.
Binary classification of equipment failure as above- or below-ground based on the sensor data
Unbiased, unsupervised classification and ranking for tacos by treating all restaurants equally, giving small businesses better exposure.
What happens when a bunch of data science master's students are asked to work with a challenging data set? Data Pre-Processing/ EDA/ Model Evaluation/ Biases/ outliers/ Accuracy/ Visualization
We predict equipment failures using a simple model accurately and maintain interpretability of our results
