We are going to perform data analysis and linear regression on the House Prices Kaggle Dataset downloaded via kaggle API call inside the notebook. The IDE we're using is the Google Colab notebooks IDE and we're going to access our Google Drive to store and retrieve data used in the notebooks.
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The Google Colab is an IDE as simple as Jupyter is to use. The nice thing about it is that it runs on browser, provides more processing power and memory than conventional laptops and it is also possible to use GPU and TPU to run your notebooks. All is very simple and easy to use, and the best of all, it's FREE. You can use Google Colab just as an IDE and connect to a local kernel of jupyter whether you want to as well.
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Kaggle is a Data Science platform that stores a lot of datasets. We'll learn to download a dataset from Kaggle using the it's API inside our notebook.
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Afterwards, we'll work on this data and use our Google Drive to store our cleaned data. We'll use Google Drive API for that.
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After we downloaded, explored, cleaned and stored the data, we'll create a linear model just for fun. 🤓
The details are in the notebooks themselves. You can follow the notebooks on the order below:
Keep on Learning! 🦾