curiousily

{
  "adSupported": null,
  "androidVersion": "Varies",
  "androidVersionText": "Varies with device",
  "appId": "com.anydo",
  "containsAds": null,
  "contentRating": "Everyone",
  "contentRatingDescription": null,
  "currency": "USD",
  "description": "<b>\ud83c\udfc6 Editor's Choice by Google</b>\r\n\r\nAny.do is a To Do List, Calendar, Planner, Tasks & Reminders App That Helps Over 25M People Stay Organized and Get More Done.\r\n\r\n<b>\ud83e\udd47 \"It\u2019s A MUST HAVE PLANNER & TO DO LIST APP\" (NYTimes, USA TODAY, WSJ & Lifehacker).</b>\r\n\r\nAny.do is a free to-do list, planner & calendar app for managing and organizing your daily tasks, to-do lists, notes, reminders, checklists, calendar events, grocery lists and more.\r\n\r\n\ud83d\udcc5 Organize Your Tasks & To-Do List in Seconds\r\n\r\n\u2022 ADVANCED CALENDAR & DAILY PLANNER - Keep your to-do list and calendar events always at hand with our calendar widget. Any.do to-do list & planner support daily calendar view, 3-day Calendar view, Weekly cal

curiousily

print_json(app_infos[0])

curiousily

def print_json(json_object):
  json_str = json.dumps(
    json_object,
    indent=2,
    sort_keys=True,
    default=str
  )
  print(highlight(json_str, JsonLexer(), TerminalFormatter()))

curiousily

app_infos = []

for ap in tqdm(app_packages):
  info = app(ap, lang='en', country='us')
  del info['comments']
  app_infos.append(info)

curiousily

app_packages = [
  'com.anydo',
  'com.todoist',
  'com.ticktick.task',
  'com.habitrpg.android.habitica',
  'cc.forestapp',
  'com.oristats.habitbull',
  'com.levor.liferpgtasks',
  'com.habitnow',
  'com.microsoft.todos',

curiousily

import json
import pandas as pd
from tqdm import tqdm

import seaborn as sns
import matplotlib.pyplot as plt

from pygments import highlight
from pygments.lexers import JsonLexer
from pygments.formatters import TerminalFormatter

curiousily

y_pred = model.predict(X_test)

curiousily

model.evaluate(X_test, y_test)

curiousily

history = model.fit(
    X_train, y_train,
    epochs=20,
    batch_size=32,
    validation_split=0.1,
    shuffle=False
)

curiousily

model = keras.Sequential()
model.add(
    keras.layers.Bidirectional(
      keras.layers.LSTM(
          units=128,
          input_shape=[X_train.shape[1], X_train.shape[2]]
      )
    )
)
model.add(keras.layers.Dropout(rate=0.5))