Linear Regression on Lagos Rent

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As part of our practice labs, this task is to scrape contents from nigeriapropertycentre. Afterwards, build a regression model to predict the housing price (rent) in Lagos.

Method

Rent data was scraped from nigeriapropertycentre using Python and BeautifulSoup and were preprocessed in Excel to inspect the data for irregularities. EDA was then carried out on the data using python in jupyter-lab environment. Encoding of the categorical variables and model building were done in Python using MultiLabelBinarizer and LinearRegression algorithm; respectively.

Discussions

After splitting, the linear regression model gave a score of 0.71 and an intercept of 5.6758. This leaves us with a linear model (where 5.6758 is the intercept; X, the independent variable and; m, the model coefficients.)

y = 5.6758 + mX

[View project on GitHub]

Also see: Auto MPG Regression This went a step further to use RidgeRegression (normalization algorithm) to perform regression task.

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