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demonstrates usage of evaluation metrics for machine learning models
uses oversampling on customer usage statistics to balance the data fed to the ml model
uses fbprophet with tutorial-like informative comments to demonstrate how fbprophet works
use fb prophet to forecast sales as well as search trends. Also analyzed search trends to see if there was any effect on sales and stock price.
time series analysis of Apple from October 2015 to October 2020 - analyzes whether any predictable relationship exists between google trends data, aapl stock price, and aapl volatility/return metrics.
Class files
Usage of deep learning and optimization to identify fraudulent transactions
Uses a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
uses logistic regression and evaluative statistics on the ml model
Losing employees can carry a great financial cost for companies. This app was constructed using a human resources dataset from IBM that was shared on Kaggle. The dataset contains employee profiles and attrition info such as age, department, role, work/life balance, and so forth. This data is used to generate a deep learning model that predicts whether an employee is likely to depart from the company within the next two years given their profile.
use logistic regression to make predictions
Usage of neural network machine learning techniques to predict credit card default risk
Taking a look at data from the SBA (small business administration) to measure effectiveness of random oversampling. This dataset is imbalanced. Using the dataset and oversampling the model predicts which SBA loans are likely to default.
Use time series forecasting to analyze google trends data to validate market opportunities that would help the Aymara indigenous people in Bolivia export alpaca wool scarves to different countries/regions
Trading bot using twitter data provided by augmento.ai
Config files for my GitHub profile.
Explanation of neural networks using dummy data and tensorflow.keras modules specifically Sequential and Dense
Testing Github and Git interactivity
This app looks at a dataset of more than 34k organizations that have received funding from a venture capital firm over the years. The csv contains a variety of info about each business including whether or not it ultimately succeeded. This app uses a deep neural network machine learning approach to create a binary classification model that will predict whether an applicant will become a successful business.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
š Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ššš
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ā¤ļø Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.