Notebooks_invest
Analyze historical market data using Jupyter Notebooks
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Stock Prediction Deep Neural Learning | 385 | 4 months ago | cc0-1.0 | Jupyter Notebook | ||||||
| Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting | ||||||||||
| Speculator | 90 | 1 | 6 years ago | 11 | December 19, 2017 | mit | Python | |||
| API for predicting the next Bitcoin and Ethereum with machine learning and technical analysis | ||||||||||
| Commercial Centers Using Poi | 7 | a year ago | Jupyter Notebook | |||||||
| Identify commercial centers using Points of Interest (POI) data by clustering these points into commercial centers/markets | ||||||||||
| Notebooks_invest | 6 | 10 months ago | Jupyter Notebook | |||||||
| Analyze historical market data using Jupyter Notebooks | ||||||||||
| Market Basket Analysis On Food Items | 5 | 4 years ago | gpl-3.0 | Jupyter Notebook | ||||||
| Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store. | ||||||||||
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