Introduction
This chapter demonstrates a house price prediction with machine learning using Jupyter notebook.
House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. The Dataset is downloaded from Kaggle and the dataset is in CSV format. This project uses a supervised learning technique.
Step 1
First, download and install Anaconda. The link is given below:
Step 2
Go to Chrome Browser or any browser search to Kaggle and search the House price prediction or the link is given click below:
Click the house price prediction, and open the new tab.
Step 3
Once the Anaconda software is installed, to complete, go to Windows >> Anaconda >> click to open.
Open The Anaconda software and Click the Jupyter notebook. To launch this, the best browser is Chrome.
Step 4
The first step is to create a new folder and dataset, copy this folder and launch the Jupyter notebook file. The example is one I saved on the desktop in the House folder.
Then, click the Desktop open a new window.
Open the House folder in a new window. The shown CSV file and Click the New >> Python 3.
The code is given below.
The code explains the neatly in the source code. The first column imports the packages.
The code is saved in Module.pickle.
Next, click File >> NewNotebook >> Python 3.
The New Python file creates in the import pickle and the packages previously save the python3 file calling module.pickle.
Once run, put the input for "Bedroom, Bathroom, Sqft_living, Sqft_lot, waterfront, floors, Sqft_above, Sqft_basement, year_built" in to predict the house prices.
Example output:
Conclusion
So in this chapter, you learned how to build a housing price preditor.