https://github.com/seyedhosseinzadeh/ws_tm

Weather web scraping and Time series model to predict temperature, humidity and barometer
https://github.com/seyedhosseinzadeh/ws_tm

data-analysis deep-learning lstm-model machine-learning prediction prediction-model weather web-scraping

Last synced: 29 days ago
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Weather web scraping and Time series model to predict temperature, humidity and barometer

• Host: GitHub
• URL: https://github.com/seyedhosseinzadeh/ws_tm
• Owner: Seyedhosseinzadeh
• Created: 2022-01-23T19:23:17.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-01-29T21:22:19.000Z (over 4 years ago)
• Last Synced: 2025-02-28T09:20:07.408Z (over 1 year ago)
• Topics: data-analysis, deep-learning, lstm-model, machine-learning, prediction, prediction-model, weather, web-scraping
• Language: Jupyter Notebook
• Homepage:
• Size: 2.29 MB
• Stars: 2
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Weather web Scraping - Time series Model

Scraping www.timeanddate.com web with bs4 tool in python for London weather of 7 years.

"YearLondon_Weather" is cleand data which I used it for models has 10222 records.

I got two different types of data - Actually they have different date and hours split (raw data):

- cleaned the data with separate months and days and some extra strings like wind units along with their numbers (dataset folder)



Analyse features (process_data) :

- Temp, Barometer and Humidity changes in 7 years: 



Test some ML models, RNN and LSTM models on dataset that you can see some results here.

- Result of RNN:



- Result of LSTM: