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Stars1
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LanguageJupyter Notebook
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Created about 4 years ago
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Updated over 2 years ago
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Repository Details
Performing the model like MLP for Hand-written digit recognition with no hidden layer with 10 output neurons. MLP for Hand-written digit recognition with two hidden layers.
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Investigation of open data from internet-based expressions and opinions could yield fascinating outcomes and bits of knowledge into the universe of popular feelings about any item, administration or identity. The blast of Web 2.0 has prompted expanded action in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. Subsequently there has been a sudden increase of enthusiasm for individuals to mine these tremendous assets of information for suppositions. Sentiment analysis or Opinion Mining is mining of sentiment polarities from online social media. In this project we will talk about a procedure which permits use and understanding of twitter information for sentiment analysis. We perform several steps of text pre-processing, and then experiment with multiple classification mechanisms. Using a dataset of 50000 tweets and TFIDF features, we comparison the accuracy obtained using various classifiers for this task. We find that linear SVMs provide us the best accuracy results among the various classifiers tried. Sentiment analysis classifier could be useful for many applications like market analysis of different features of a new product or public opinion for a new movie or speech by a political candidate.Jupyter Notebook
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learning variance and covarianceR
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Python-Mini-Project---Word-Ladders-Game
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Customers of a big international bank decided to leave the bank. The bank is investigating a very high rate of customer leaving the bank. The dataset contains 10000 records, and we use it to investigate and predict which of the customers are more likely to leave the bank soon. The approach here is supervised classification; the classification model to be built on historical data and then used to predict the classes for the current customers to identify the churn. The dataset contains 13 features, and also the label column (Exited or not). The best accuracy was obtained with the NaΓ―ve Bayes model (83.29%). Such churn prediction models could be very useful for applications such as churn prediction in Telecom sector to identify the customers who are switching from current network, and also for Churn prediction in subscription services.Jupyter Notebook
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