circuit

Real Estate House Price Prediction Using Data Science


Data Science and data analytics can be used in many ways in the Real estate market. The main purpose of using data science in real estate is to collect datasets from multiple sources and extract useful information from them, Humans are not able to analyze such Big data, unlike algorithms.

Technologies used: Python, NumPy, sk-learn, Flask, matplotlib, etc.

Benefits of Data Science in Real Estate

  1. Reduces Risks: with the help of predictive analytics company can use it to estimate the overall condition like its ages, deconstruction history, owner information. the company can provide their customer with up-to-date information so it increases their satisfaction from working with them.
  2. It helps calculate the price: precise cost calculation in real estate is time-consuming, how the Machine learning algorithm can use for the estimate the price of properties with the help of historical data.
  3. Data-driven decision: Machine learning open many opportunities for the business. just feed the algorithm with data and it will process it to help you make the right decision.
  4. Marketing strategy: with the help of customer information company can plan their future marketing strategy according to customer needs.

American online real estate database company Zillow has used data science in the real estate market. Zillow determines an estimate, also known as a “Zestimate” for a house, based on a range of publicly available information, including sales of comparable houses in a neighborhood.

For this project I used the “Bengaluru House price data” dataset from Kaggle:

Bengaluru house price data

Let's start with the data, this dataset contains some basic information about houses like area_type, availability, balcony, location, size, price, society, bath, total_sqft i.e., a total of 9 columns & 13320 rows.

First of all, to start, we will import some libraries:

Below is the image before outline removal. We can see that some 2 BHK houses, having the same square feet and the same area, are still more costly than 3 BHK ones.

To fix this we remove inappropriate data:

After outlier removal:

We can see in the general case there are not 12,13,16 bathrooms in a normal house

And there you have it. Thanks for reading!

Code link : https://github.com/varun8487/housepriceprediction




Continue Learning