Discover niharikabalachandra/Gesture-Recognition-Using-RNN Open Source

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Python
License
MIT License
Created over 7 years ago
Updated over 7 years ago

niharikabalachandra

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This project investigates gesture recognition techniques using Leap Motion Controller to collect 3 dimensional hand features such as coordinates, position of the hand, etc. We then use this raw data to train a Long Short Term Memory Recurrent Neural Network that is able to recognize basic hand gestures

Stock-Market-Prediction-Using-Natural-Language-Processing

We used Machine learning techniques to evaluate past data pertaining to the stock market and world affairs of the corresponding time period, in order to make predictions in stock trends. We built a model that will be able to buy and sell stock based on profitable prediction, without any human interactions. The model uses Natural Language Processing (NLP) to make smart “decisions” based on current affairs, article, etc. With NLP and the basic rule of probability, our goal is to increases the accuracy of the stock predictions.

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Market-Risk-Management-with-Time-Series-Prediction-of-Stock-Market-Trends-

Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time series analysis and prediction of short-term tends in stock prices.

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PropensityToConvert

Propensity models make true predictions about a customer’s future behavior. With propensity models you can truly anticipate a customer's future behavior. Here we focus on building a combination of a Propensity to convert and a Propensity to buy models that can influence the kind of marketing campaigns we adopt and who we decide to target (predicted converters vs non-converters) leading to spend optimizations that eventually increase the ROI on digital marketing campaigns.

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Language-Detection-MultinomialLogisticRegression

Language Detection using the European Parliament Proceedings Parallel Corpus. European Parliament Proceedings Parallel Corpus is a text dataset used for evaluating language detection engines. The 1.5GB corpus includes 21 languages spoken in EU. This project aims to build a machine learning model trained on this dataset to predict new unseen data.

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