Last Updated on September 7, 2023 by GeeksGod
Course : Forecasting Real Estate Market with Linear Regression & LSTM
Welcome to Forecasting Real Estate Market with Linear Regression & LSTM Course
Welcome to the 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 visualization on real estate market data. This course will mainly concentrate on forecasting the future housing market using two different forecasting models: linear regression and LSTM (Long Short Term Memory).
About the Course
In this course, we will be using Python programming language alongside several libraries like Pandas for performing data modeling, Numpy for complex calculations, Matplotlib for data visualization, and Scikit-learn for implementing the linear regression model and various evaluation metrics.
The data for this course will be downloaded from Kaggle, a popular platform for data scientists and machine learning practitioners. Throughout the course, you will learn the basic fundamentals of real estate market forecasting, the characteristics of the real estate market, forecasting models that will be used, and major problems in the real estate market nowadays such as limited housing supply and population growth.
Linear Regression Fundamentals
Before diving into the code, it is important to understand the basic mathematics behind linear regression. In this course, you will be guided step by step on how to analyze case studies and perform basic linear regression calculations. This session is designed to prepare your knowledge and understanding about linear regression before implementing this concept in your code.
The Impact of Factors on Real Estate Market
This course will also cover several factors that can potentially impact the real estate market. These factors include population growth, government policies, and infrastructure development. Understanding these factors is crucial for accurate forecasting and making informed investment decisions.
Setting Up the Environment
Once you have gained all the necessary knowledge about the real estate market, we will proceed to set up your coding environment. You will be guided step by step on how to set up Google Colab IDE, which is a cloud-based platform for coding. Additionally, you will also learn how to find and download datasets from Kaggle.
The main part of this course is the project section. The project will be divided into two main parts: forecasting the real estate market trend using linear regression and forecasting the real estate market trend using a long short term memory (LSTM) model. By completing this project, you will gain hands-on experience and practical knowledge that you can apply to real-world scenarios.
Evaluating Forecasting Models
At the end of the course, you will learn how to evaluate the accuracy and performance of your forecasting models using R-squared analysis and directional symmetry analysis. These evaluation methods will help you assess the effectiveness of your models and make any necessary adjustments.
The Importance of Forecasting the Real Estate Market
Before we delve into the course material, it is important to understand why it is crucial to learn how to forecast the real estate market. By understanding market trends and predicting future outcomes, investors and buyers can make informed decisions and maximize their returns.
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. Additionally, rental income from properties can provide a steady cash flow, making it an attractive investment avenue.
With the advancement of big data technology, integrating data-driven forecasting models into real estate market analysis has become increasingly important. By analyzing historical data and identifying patterns, investors can make informed investment decisions based on data rather than speculation.
These forecasting skill sets are not only valuable in the real estate market but can also be applied to other industries and markets. The ability to analyze data and predict future trends is a highly sought-after skill in today’s data-driven world.
In this course, you can expect to learn the following:
- Basic fundamentals of real estate market forecasting
- Analysis of market characteristics and major problems faced by the real estate market
- Linear regression calculations and understanding of regression coefficients, intercepts, dependent variables, and independent variables
- Factors that can potentially impact the real estate market, such as population growth, job market, and infrastructure development
- How to find and download datasets from Kaggle
- Uploading data and setting up the coding environment using Google Colab Studio
- Data cleaning techniques, including removing rows with missing values and duplicates
- Detecting and handling potential outliers in the dataset
- Analysis of property price trends, including calculating annual mean and median
- Correlation analysis between property price and property type
- Analysis of real estate market trends and identifying investment opportunities using sales ratio calculation
- Forecasting real estate market trends using a linear regression model
- Forecasting real estate market trends using LSTM (Long Short Term Memory) model
- Evaluating the accuracy and performance of forecasting models using R-squared analysis and directional symmetry analysis
By the end of this course, you will have gained valuable skills and knowledge that can be applied to real estate market analysis and forecasting. Whether you are a beginner or already have some experience in data analysis, this course will provide you with actionable insights and practical techniques to enhance your forecasting abilities.
Enroll now and start your journey into the exciting world of real estate market forecasting!