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所在平台: Udemy |
课程主页: https://www.udemy.com/course/forecasting-real-estate-market-with-linear-regression-lstm/
课程评论：没有评论
课程名称：使用线性回归和LSTM预测房地产市场 课程概述：欢迎参加“使用线性回归和LSTM预测房地产市场”课程。这是一门全面的项目导向课程，您将逐步学习如何对房地产市场数据进行复杂分析和可视化。本课程将主要集中在使用两种不同的预测模型进行未来住房市场预测，即线性回归和长期短期记忆（LSTM）。 在编程语言方面，我们将使用Python及多个库，如Pandas进行数据建模，Numpy进行复杂计算，Matplotlib进行数据可视化，以及Scikit-learn用于实施线性回归模型和各种评估指标。数据部分，我们将从Kaggle下载房地产市场数据集。 在介绍部分，您将学习房地产市场预测的基本基础知识，例如了解房地产市场的特征、将使用的预测模型以及当今房地产市场面临的主要问题，如住房供应不足和人口增长。接下来，您将学习线性回归背后的基本数学知识，并逐步指导您如何分析案例研究并进行基本的线性回归计算。这一部分旨在为您实施线性回归概念做好知识和理解的准备。 随后，您将学习可能影响房地产市场的多个因素，如人口增长、政府政策和基础设施发展。在掌握所有必要的房地产市场知识后，我们将开始预测项目。首先，您将逐步指导如何设置Google Colab IDE，并学习如何从Kaggle查找和下载数据集。所有准备工作完成后，您将进入课程的主要部分，即项目部分。项目分为两个主要部分，第一部分是使用线性回归预测房地产市场趋势，第二部分是使用长期短期记忆模型进行预测。最后，在课程结束时，您将学会如何使用R平方和方向对称等方法评估预测模型的准确性和性能。 在开始课程之前，我们需要问自己几个问题：为什么要学习预测房地产市场？这有什么好处？答案有很多。首先，房地产市场一直被视为一种强大的投资选择，因其长期增值和收入生成的潜力。随着大数据技术在过去几年的迅速发展，将这一技术融合到预测未来市场趋势和价格中，通过识别历史数据中的模式能够为投资者提供数据驱动的投资决策。此外，您学习到的这些技能在其他市场也同样适用。 以下是您可以期待在此课程中学习到的内容： - 学习房地产市场预测的基本基础知识，如市场特征和面临的主要问题 - 学会进行线性回归计算，理解回归系数、截距、因变量和自变量 - 学习多个可能影响房地产市场的因素，如人口增长、就业市场和基础设施发展 - 学会从Kaggle查找和下载数据集 - 学习如何将数据上传到Google Colab工作室 - 学习如何清理数据集，去除缺失值和重复行 - 学习如何检测数据集中的潜在异常值 - 学习如何通过计算年均值和中位数分析房价趋势 - 学习如何找出房价与房产类型之间的相关性 - 学习如何分析房地产市场趋势，利用销售比计算寻找投资机会 - 学会使用线性回归模型预测房地产市场趋势 - 学会使用LSTM模型预测房地产市场趋势 - 学习如何使用R平方分析和方向对称分析评估预测模型的准确性和性能
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 marketLearn how to perform linear regression calculations and gain an understanding of regression coefficients, intercepts, dependent variables, and independent variablesLearn several factors that can potentially impact real estate market, such as population growth, job market, and infrastructure developmentLearn how to find and download datasets from KaggleLearn how to upload data to Google Colab StudioLearn how to clean datasets by removing rows with missing values and duplicatesLearn how to detect potential outliers in the datasetLearn how to analyze property price trend by calculating its annual mean and medianLearn how to find correlation between property price and property typeLearn how to analyze real estate market trend and find investment opportunities using sales ratio calculationLearn how to forecast real estate market trend using linear regression modelLearn how to forecast real estate market trend using LSTM (Long Short Term Memory) modelLearn how to evaluate the accuracy and performance of the forecasting models using R-squared analysis and directional symmetry analysis
