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Training and Testing a Set of Machine Learning/Deep Learning Models to Predict Airbnb Prices for NYC
Math Techniques viz. ARIMA, Frequency Decomposition, Fourier Filtering, Linear Regression & Bi-directional LSTMs on Feature Engineered Stock Market Data.
Classifying whether the credit card transaction is fraudulent or not using Logistic Regression
Classifying whether the credit card transaction is fraudulent or not using Support Vector Machines
Sudoku solver using computer vision and deep learning to extract and interpret Sudoku puzzles from images. Includes a fully functional Sudoku game built using Pygame.
Machine Learning project to recognise faces from an Image just like facebook or video stream
An application of Multilayer Perceptron, Random Forest Regression and Recurrent Neural Networks (LSTM)
The code for the paper: https://arxiv.org/abs/1806.06317
In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.
Using the principal component analysis to find the two main features to represent the 13 various wine characteristics and develop a logistic regression model to predict the customer types using the two features as the inputs.
Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Regularisation Optimization
Face recognition on The Database of Faces at a glance. Data pre-processing. Dimension reduction using PCA. Support Vector Machines (For classification)
Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models
This project combines logistic regression, gradient boosting, and LSTMs to predict next-month returns.
Neural sentiment classification of text using the Stanford Sentiment Treebank (SST-2) movie reviews dataset, logistic regression, naive bayes, continuous bag of words, and multiple CNN variants.
Improve S&P 500 stock price prediction (random forest and gradient boosting trees) with time series similarity measurements: DTW, SAX, co-integration, Euclidean and Pearson.
This project uses Support Vector Machine and BERT word embeddings to make sentiment prediction on messages from stockttwits, one of the biggest financial forum in the world.
Built Logistic regression, SVM, Naive Bayes, RandomForest, KNN for text classification on scrapped news data. Built Text rank, LDA and K-means clustering for text summarization.
Exploratory Data Analysis of Time Series Data and Forecasting using Naïve Approach, Moving Average Method, Simple Exponential Smoothening, Holt’s Linear Trend Model, Holt’s Winter Model, ARIMA and SARIMAX models.
Time series forecasting for individual household power prediction: ARIMA, xgboost, RNN
Time-series demand forecasting is constructed by using LSTM, GRU, LSTM with seq2seq architecture, and prophet models.
time series forecasting using keras, inlcuding LSTM,RNN,MLP,GRU,SVR and multi-lag training and forecasting method, ICONIP2017 paper.
Time Series Analysis & Forecasting of Rossmann Sales with Python. EDA, TSA and seasonal decomposition, Forecasting with Prophet and XGboost modeling for regression.
time series forecasting using pytorch,including ANN,RNN,LSTM,GRU and TSR-RNN,experimental code
Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.
This is a project of twitter sentiment analysis using machine learning(Support Vector Machines,Naive Bayes), deep learning(LSTM), Transformer(BERT,ROBERTA).
Sentiment Analysis Twitter
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 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. 📊📈🎉
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 ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.