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# Pyre type checker | ||
.pyre/ | ||
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# PyCharm | ||
.idea/ |
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# ml-storybooks-recommender | ||
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Machine learning model which predicts the rating of unread storybooks based on the student's previously read storybooks. | ||
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One model will be trained per language. | ||
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## 1. Prepare the Data | ||
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To prepare the data, follow these steps: | ||
* Open `prepare_data.py` and select environment and language. | ||
* Go to the website corresponding to the chosen environment and language, e.g. http://eng.test.elimu.ai. | ||
* Download `storybooks.csv` from http://eng.test.elimu.ai/content/storybook/list. | ||
* Download `storybook-learning-events.csv` from http://eng.test.elimu.ai/analytics/storybook-learning-event/list. | ||
* Add the two datasets to `RAW_DATA_DIR`. | ||
* Execute the script: `python prepare_data.py` | ||
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## 2. Train the Model | ||
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TODO | ||
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## 3. Make Predictions on New Samples | ||
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TODO |
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id,time,android_id,package_name,storybook_id,learning_event_type | ||
45,1603495877976,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
44,1603495780823,7394f9d23bfd74af,ai.elimu.vitabu.test,24,STORYBOOK_OPENED | ||
43,1603495757361,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
42,1603494702681,7394f9d23bfd74af,ai.elimu.vitabu.test,23,STORYBOOK_OPENED | ||
41,1603494681753,7394f9d23bfd74af,ai.elimu.vitabu.test,24,STORYBOOK_OPENED | ||
40,1603494581648,7394f9d23bfd74af,ai.elimu.vitabu.test,24,STORYBOOK_OPENED | ||
39,1603494430877,7394f9d23bfd74af,ai.elimu.vitabu.test,27,STORYBOOK_OPENED | ||
38,1603494363802,7394f9d23bfd74af,ai.elimu.vitabu.test,27,STORYBOOK_OPENED | ||
37,1603494110370,7394f9d23bfd74af,ai.elimu.vitabu.test,26,STORYBOOK_OPENED | ||
36,1603493867772,7394f9d23bfd74af,ai.elimu.vitabu.test,25,STORYBOOK_OPENED | ||
35,1603493388681,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
34,1603493323420,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
33,1603493261035,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
32,1603493235566,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
31,1603493014112,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
30,1603492973830,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
29,1603492930084,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
28,1603492919979,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
27,1603492918728,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
26,1603492908493,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
25,1603492907090,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
24,1603492903577,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
23,1603492807897,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
22,1603492757816,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED | ||
21,1603492731620,7394f9d23bfd74af,ai.elimu.vitabu.test,22,STORYBOOK_OPENED |
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