A data science blog documenting learning, projects, concepts, and how-tos of this incredible field.
Sentiments derive stock markets. Which markets will go UP or which security will go DOWN is highly correlated to investors’ overall sentiments.
Investors sentiments are fueled by:
Welcome to the final part of the project series “Apartment Pricing: Advanced Regression Techniques.”
I highly recommend visiting previous posts in this series:
You can find all the working shown in this series at my GitHub repository here.
So, this is all we’ve been building up to, Predicting prices.
To reach this stage, we covered the following stages of a data science project lifecycle:
In the final part of the series, we will see how we can:
Welcome to part two of the project series “Apartment Pricing: Advanced Regression Techniques.”
I highly recommend checking part one of the series here, if you have not already. All the working shown below can be found at my GitHub repository here. You can also view the interactive notebook here.
In part one of this project series, we discussed our business understanding related to the project and then moved forward to the data extraction phase. After extracting approximately 2000 properties, we started preparing the data for our data visualization and exploratory analysis phase, and ultimately for our machine learning model development.
Now that our data is tinkered as per our need let’s begin.
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