House Price Prediction using Machine Learning and Python

Machine learning is a branch of Artificial Intelligence which is used to analyse the data more smartly. It automates the process using certain algorithms to minimize human intervention in the process.

In this machine learning project, we are going to predict the house price using python. This project will help the sellers and buyers to have an overview of the situation so that they can act accordingly.

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Here we are going to use linear regression to predict the house price. In a nutshell linear regression is a machine learning tool that helps you to make predictions by using the existing data (basically the relationship between the target data and set of other data). In our case, the house price basically depends on the parameters such as the number of bedrooms, location, size of living area, nearby places, etc.

Project Implementation:

1. Import the dependencies and libraries

            import pandas

            import numpy

            sklearn - it is the machine learning library for python

            linear_model from sklearn

            train_test_split from sklearn.model_selection

(it is a function that splits our data into training and testing sets)

2. Now load the dataset for the particular location which you want to analyze. Here we are going to use Boston housing dataset from sklearn.datasets. Now create a variable called boston and assign it to load_boston() function. Now print it using print(Boston)

3. Next step is to transform the dataset into the data frame. Create variable df_x and df_y.

4. Now get some statistics from the data set, count, mean, etc.

5. Initialize the linear regression model - reg = linear_model.LinearRegrssion()

6. Split the data into 67% as training and 33% as testing data.

7. Now train the model with our training data

8. Print the coefficients/weights for each feature/column of our model - print(reg.coef)

9. Now print the predictions on our test data.

10. Print the actual values - print(y_test)

11. Check the model performance using MSE (Mean Squared Error).

12. Now check the model performance using MSE and sklearn.metrics. Visualize the differences between the actual price and predicted price

13. Similarly train the model using various models - Random Forest Regressor, XGBoost Regressor, SVM Regressor,

14. Finally, evaluate and compare all the models to get proper output. (As far as with my experience XGBoost Regression works best for this dataset)

Similarly, you can predict the house price of various locations by importing the data of the particular place. (You can get the data from the real-estate websites like 99acres, airbnb, homes.com, trulia, realtor, etc.

pandas- It is an open-source library written for python to perform data analysis and manipulation.

Matplotlib - It is a plotting library for python program and its mathematics extension NumPy.

NumPy - It is a package for python for scientific computing to perform different operations.

sklearn/scikit-learn  - It is a free machine learning library developed for python programming language under BSD license which is majorly used for data analysis and data mining. It also supports various machine learning algorithms such as SVM, random forests, k-neighbours, etc.        

Software requirements and libraries used:

Jupyter notebook - online editor

Libraries - pandas, numpy, matplotlib

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• Machine Learning
• Python

House Price machine learning project using python