friday7/Datawarehouse-and-Business-Intelligence

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Created almost 6 years ago
Updated almost 6 years ago

friday7/Datawarehouse-and-Business-Intelligence

friday7

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Data Warehouse and Business Intelligence- Designed a Data warehouse for TED talks by automating the entire ETL process using data from three diverse sources and formats which included semi-structured data of locations from TED website and unstructured data of likes, dislikes, views and comments from YouTube by scraping it. Finally, business intelligence was drawn for TED talks. Technologies used: R, Python, SQL, SSMS, SSIS, SSAS and Tableau.

Continous-Sign-Language-Translator-

Sign language interpreters are currently required for interpreting the speech impaired people. This skill-based job of interpreters is cumbersome and hence the number of interpreters per capita across majority countries are very low or decreasing. We aim to harness technology in developing a powerful continuous sign language gestures recognition system. This computer vision-based approach will be used to recognise Argentinian sign language gestures from a video. Translating these sign language gestures is considered a monumental task in this field. The project proposes to investigate whether sign language gestures can be recognised by using a trained modified Inception V3 working as a feature selector and classifier, with a LTSM Recurrent Neural Network. Two separate approaches have been applied to recognise the Argentinian gestures. The Global Max Pooling approach outperforms the SoftMax approach, with a model accuracy of 86.10% on validation set and 75.2% on test set. Using the Inception V3 model as a feature extractor for LTSM RNN worked more efficiently and produced better results than using the Inception V3 model as a classifier. These results show the effectiveness of the research conducted. This research will help in classifying and recognising continuous sign language gestures based on machine vision. This in turn will assist people that are affected by speech and hearing impairment in understanding, translating and recognising sign gestures.

Jupyter Notebook

exoplanet-detection

Exoplanets are defined as planets orbiting a star outside our solar system. There have been considerable improvements over the past decades in the detection of exoplanets. NASA launched the Kepler satellite in 2009 with the mission to hunt exoplanets. The data gathered by the satellite have been made publicly available by NASA in an attempt to increase development in the field of astroinformatics, a crossdisciplinary field consisting of astronomy, data science and informatics. This has led to increased contribution in detecting exoplanets among non-exoplanets. In this paper, we explore various techniques to detect exoplanets using the intensity of light captured by Kepler space telescope. The dimming in flux (light intensity) of the stars indicates an orbiting body around that star. We perform preliminary analysis and prepare the data to be fed in to our data mining models. We have used multiple models to efficiently classify the exoplanets. The first model, H2O’s Deep Learning, is based on a multi-layer feedforward artificial neural network. We have then performed the Gradient Boosting technique using XGBoost algorithm, an ensemble technique that works on the concept of Decision Tree and Bootstrap Aggregation. Finally, we have used Convolutional Neural Network, a Black Box method, to classify the exoplanets. This paper will provide the results, model performance and efficiency of the various algorithms used to detect such exoplanets.

for-Prediction-and-Prevention-of-Employee-attrition

Employee attrition has been a nightmare for many organizations irrespective of their size or stature. Losing talent costs a company direly in terms of time and money which makes research in this field a necessity. This research sheds light on this topic and narrows down the reasons for attrition to allow prediction based on probabilistic methods using the Naïve Bayes probabilistic approach for consequential event occurrence in the HR (human resources) management domain.