Forecasting Real Estate Market with Linear Regression & LSTM

https://www.udemy.com/course/forecasting-real-estate-market-with-linear-regression-lstm

What you'll learn
Learn basic fundamentals of real estate market forecasting, such as getting to know market characteristics and major problems faced by real estate market
Learn how to perform linear regression calculations and gain an understanding of regression coefficients, intercepts, dependent and independent variables
Learn how to forecast real estate market trend using linear regression model
Learn how to forecast real estate market trend using LSTM (Long Short Term Memory) model
Learn how to evaluate the accuracy and performance of the forecasting models using R-squared analysis and directional symmetry analysis
Learn several factors that can potentially impact real estate market, such as population growth, job market, and infrastructure development
Learn how to analsze property price trend by calculating its annual mean and median
Learn how to find correlation between property price and property type
Learn how to analyse real estate market trend and find investment opportunities using sales ratio calculation
Learn how to clean datasets by removing rows with missing values and duplicates
Learn how to detect potential outliers in the dataset


Requirements
No previous experience in real estate market forecasting is required
No previous experience in linear regression is required
Willingness to learn and conduct a lot of experiments with big dataset
Description
Welcome to Forecasting Real Estate Market with Linear Regression & LSTM course. This is a comprehensive project based course where you will learn step by step on how to perform complex analysis and visualisation on real estate market data. This course will be mainly concentrating on forecasting the future housing market using two different forecasting models, those are linear regression and LSTM which stands for long short term memory. Regarding programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, Matplotlib for visualising the data, and Scikit-learn for implementing the linear regression model and various evaluation metrics. Whereas, for the data, we are going to download the real estate market dataset from Kaggle. In the introduction session, you will learn basic fundamentals of real estate market forecasting, such as getting to know the characteristics of the real estate market, forecasting models that will be used, and major problems in the real estate market nowadays like limited housing supply and population growth. Then, continue by learning the basic mathematics behind linear regression where you will be guided step by step on how to analyze case study and perform basic linear regression calculation. This session was designed to prepare your knowledge and understanding about linear regression before implementing this concept to your code. Afterward, you will learn several different factors that can potentially impact the real estate market, such as population growth, government policies, and infrastructure development. Once you’ve learnt all necessary knowledge about the real estate market, we will start the forecasting project. Firstly, you will be guided step by step on how to set up Google Colab IDE, then, you will also learn how to find and download datasets from Kaggle. Once everything is all set, you will enter the main section of the course which is the project section. The project will consist of two main parts, the first one is forecasting the real estate market trend using linear regression while the second one is forecasting the real estate market trend using a long short term memory model. Lastly, at the end of the course, you will learn how to evaluate the accuracy and performance of your forecasting models using R-squared and directional symmetry methods.
First of all, before getting into the course, we need to ask ourselves these questions: why should we learn to forecast the real estate market? What’s the benefit? Well, I have a ton of answers to those questions. Firstly, the real estate market has always been considered a strong investment option due to its potential for long-term appreciation and income generation. Property values tend to appreciate over time, offering the opportunity for capital gains, while rental income from properties can provide a steady cash flow. Meanwhile, as big data technology has advanced very rapidly in the past few years, integrating this technology to forecast future market trends and prices by identifying patterns in the historical data can be very beneficial as it allows investors to make a data driven investment decision. In addition, these skill sets that you learn are extremely valuable as they can be applied to different markets other than real estate.
Below are things that you can expect to learn from this course:
Learn basic fundamentals of real estate market forecasting, such as getting to know market characteristics and major problems faced by real estate market
Learn how to perform linear regression calculations and gain an understanding of regression coefficients, intercepts, dependent variables, and independent variables
Learn several factors that can potentially impact real estate market, such as population growth, job market, and infrastructure development
Learn how to find and download datasets from Kaggle
Learn how to upload data to Google Colab Studio
Learn how to clean datasets by removing rows with missing values and duplicates
Learn how to detect potential outliers in the dataset
Learn how to analyze property price trend by calculating its annual mean and median
Learn how to find correlation between property price and property type
Learn how to analyze real estate market trend and find investment opportunities using sales ratio calculation
Learn how to forecast real estate market trend using linear regression model
Learn how to forecast real estate market trend using LSTM (Long Short Term Memory) model
Learn how to evaluate the accuracy and performance of the forecasting models using R-squared analysis and directional symmetry analysis
Who this course is for:
People who are interested in forecasting real estate market using linear regression and LSTM model
People who are interested in analysing and visualising complex dataset using Pandas, Matplotlib, Numpy, Seaborn, and Scikit Learn
People who are interested in real estate investment


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