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1. Predict-students-performance-by-building-a-neural-network-with-Tensorflow Predict-students-performance-by-building-a-neural-network-with-Tensorflow Public

   This neural network predicts grades of students on a basis of their activities in school, description of personal life, etc. DNNRegressor of Tensorflow is used

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2. Youtube-API-Extraction-and-sentiment-analysis-of-comments-about-Asus-Zenbook-Pro-Regex-NLTK Youtube-API-Extraction-and-sentiment-analysis-of-comments-about-Asus-Zenbook-Pro-Regex-NLTK Public

   The topic of analysis is "Asus Zenbook Pro", a laptop from Asus. The idea is to find out what people think about the product by analysing comments, extracted from videos on this topic.

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3. RNN-LSTM-model-for-sentiment-analysis-of-Amazon-reviews-Keras RNN-LSTM-model-for-sentiment-analysis-of-Amazon-reviews-Keras Public

   RNN LSTM model built with Keras, trained on Amazon reviews to distinguish positive and negative reviews. 560 000 reviews available, however, model was trained on 30 000 (15 000 positive/15 000 nega…

   Jupyter Notebook 1

4. Spark-Scala-predict-price-of-a-diamond-with-decision-tree-and-random-forest Spark-Scala-predict-price-of-a-diamond-with-decision-tree-and-random-forest Public

   Usage of Spark machine learning (Linear Regression, Decision tree, Random forest) to create a model that predicts a price of diamonds on a basis of different features of them. GridSearch is applied…

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5. Analyze-film-industry-with-MongoDB-and-Python Analyze-film-industry-with-MongoDB-and-Python Public

   Small code that explains how to merge 3 datasets into one with MongoDb Compass and save it as a separate table in a database. Datasets are taken from Unesco, World Bank and the idea is to analyze t…

   Python 1

6. Usage-of-RNN-LSTM-to-predict-sales-for-the-next-month-of-different-items-across-different-shops Usage-of-RNN-LSTM-to-predict-sales-for-the-next-month-of-different-items-across-different-shops Public

   Usage of RNN LSTM to predict sales for the next month of different items across different shops on the basis of historical data of sales for last 33 months

   Jupyter Notebook 1