big-data-weather-forecasting

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big-data-weather-forecasting

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Big Data weather forecasting, experimenting with logistic regression, SVM and random forest in a distributed setting by using PySpark

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# Weather Forecasting with PySpark

In this project I am addressing **weather forecasting** with _Machine Learning_
and _Big Data_ tools, in order to show whether is possible to make valuable
predictions of meteorological conditions only based on previously seen meteorological
data. The classification goal is therefore, given a set of weather measurements,
to predict which meteorological condition should occur.

For further details you can refer to the [presentation slides](https://github.com/andrea-gasparini/big-data-weather-forecasting/blob/master/presentation.pdf) or to the [Python Notebook](https://github.com/andrea-gasparini/big-data-weather-forecasting/blob/master/project.ipynb)
(also published on [DataBricks](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/4264875385131584/1252952709241176/4274404022451383/latest.html)).

This project has been developed during the A.Y. 2020-2021 for the
[Big Data Computing course](https://github.com/gtolomei/big-data-computing)
@ Sapienza University of Rome.

## Dataset

The dataset comes from [Kaggle](https://www.kaggle.com/selfishgene/historical-hourly-weather-data)
and contains hourly weather measurements data of **36** cities, collected from
2012 to 2017. This 5 years of data result in approximately **45.000** measurements
(for each city) of temperature, humidity, air pressure and the like.

## Author

[Andrea Gasparini](https://github.com/andrea-gasparini)

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