Machine Leaning Project 1: Housing Price Prediction

Introduction

 
This chapter demonstrates a house price prediction with machine learning using Jupyter notebook.
 

Housing Price Prediction

 
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.
 
Github Link: Click and download.
 
Step 1
 
First, download and install Anaconda. The link is given below:
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
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:
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
Click the house price prediction, and open the new tab. 
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
Step 3
 
Once the Anaconda software is installed, to complete, go to Windows >> Anaconda >> click to open. 
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
Open The Anaconda software and Click the Jupyter notebook. To launch this, the best browser is Chrome.
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
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.
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
Then, click the Desktop open a new window. 
 
House Price Prediction In Machine Learning Using Jupyter Note Book 
 
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.
 
House Price Prediction In Machine Learning Using Jupyter Note Book 
 
The code is saved in Module.pickle.

House Price Prediction In Machine Learning Using Jupyter Note Book
 
Next, click File >> NewNotebook >> Python 3. 
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
The New Python file creates in the import pickle and the packages previously save the python3 file calling module.pickle.
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 
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:
 
House Price Prediction In Machine Learning Using Jupyter Note Book
 

Conclusion

 
So in this chapter, you learned how to build a housing price preditor.
Author
Ajithkumar J
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