Regression

Analysis Of Regression Models For House Sales Price Prediction

Problem Statement

Wrong estimate or prediction of real estate/ housing properties leads to wrong budgeting and delay of payment for both customers and brokers. Hence the problem is to have an accurate method or model to have justified and correct pricing of real estate.

Motivation

In real estate, there is usually a discrepancy between real pricing and expected pricing. As a result, it's preferable to utilise precise and best machine learning models based on historical records to forecast sales and purchase pricing. But various models can be used for predicting the price hence a comparative examination of the various models is required. Thus we planned to compile a short simulation on Comparative Analysis Of Various Models For House Sales Price Prediction.

Literature

House Price Index (HPI)-

The House Price Index (HPI) is a broad measure of the movement of single-family property prices in the United States. In addition to serving as a trend indicator, it also serves as an analytical tool for estimating changes in mortgage default, prepayment, and housing affordability rates [1].

Housing Price Prediction via Improved Machine Learning Techniques-

Before building models, the data should be processed accordingly so that the models could learn the patterns more efficiently. Specifically, numerical values were standardised, while categorical values were one-hot-encoded[2].

Housing Price Prediction Based on Multiple Linear Regression-

According to economics principles, the market price of properties is attained when the demand and supply curves intersect with each other, which is subject to various factors, both subjectively and objectively[3].

Models used/proposed(Flow Diagram)

Linear Regression:

Linear Regression is a supervised machine learning algorithm. It carries out a regression task. Based on independent variables, regression models a goal prediction value. It is mostly used in forecasting and determining the link between variables. Different regression models differ in terms of the type of relationship they evaluate between dependent and independent variables and the number of independent variables they employ. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output). Hence, the name is Linear Regression.

Multivariate Regression

Multivariate Regression comes under the class of Supervised Learning Algorithms i.e when we are provided with a training dataset. In the case of multivariate linear regression, the output value is dependent on multiple input values. The relationship between input values, the format of different input values and the range of input values plays an important role in linear model creation and prediction.

For multiple input values, the hypothesis function will look like,

y = Θ1 + Θ2x2 + Θ3 x3 + …………… Θn* xn where x2, x3...xn are multiple feature values

References

1. Investopedia.
2. Housing Price Prediction via Improved Machine Learning Techniques - ScienceDirect
3. Housing Price Prediction Based on Multiple Linear Regression
4. Kumari, Khushbu & Yadav, Suniti. (2018). Linear regression analysis study. Journal of the Practice of Cardiovascular Sciences. 4. 33. 10.4103/jpcs.jpcs_8_18.
5. Manorathna, Rukshan. (2020). Linear Regression with Gradient Descent.
6. Bargiela, Andrzej & Nakashima, Tomoharu & Pedrycz, W.. (2005). Iterative gradient descent approach to multiple regression with fuzzy data. 304- 309. 10.1109/NAFIPS.2005.1548552.

Source Code : ML