Predicting house sales in King County, based on a number of features using regression models

MLflow - House Sales Prediction

Primary real estate house price prediction model using Numpy & Seaborn. Takes in house features, uses machine learning to predict price. Trained on past sales data. Can be used to make informed decisions about house prices.

PROJECT HOUSING: PRICE PREDICTION Problem Statement: Houses are one of the necessary need of each and every person around the globe and therefore housing and real estate market is one of the markets which is one of the major contributors in the world’s economy. It is a very large market and there are various companies working in the domain. Data science comes as a very important tool to solve problems in the domain to help the companies increase their overall revenue, profits, improving their marketing strategies and focusing on changing trends in house sales and purchases. Predictive modelling, Market mix modelling, recommendation systems are some of the machine learning techniques used for achieving the business goals for housing companies. Our problem is related to one such housing company. A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. The company is looking at prospective properties to buy houses to enter the market. You are required to build a model using Machine Learning in order to predict the actual value of the prospective properties and decide whether to invest in them or not. For this company wants to know: • Which variables are important to predict the price of variable? • How do these variables describe the price of the house? Business Goal: You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for the management to understand the pricing dynamics of a new market. Technical Requirements: • Data contains 1460 entries each having 81 variables. • Data contains Null values. You need to treat them using the domain knowledge and your own understanding. • Extensive EDA has to be performed to gain relationships of important variable and price. • Data contains numerical as well as categorical variable. You need to handle them accordingly. • You have to build Machine Learning models, apply regularization and determine the optimal values of Hyper Parameters. • You need to find important features which affect the price positively or negatively. • Two datasets are being provided to you (test.csv, train.csv). You will train on train.csv dataset and predict on test.csv file. The “Data file.csv” and “Data description.txt” are enclosed with this file.

I use various regression methods and try to predict the house prices by using them. As you can guess, there are various methods to suceed this and each method has pros and cons. I think regression is one of the most important methods because it gives us more insight about the data. When we ask why, it is easier to interpret the relation between the response and explanatory variables. I start with a very simple model and continue with more complex ones after visualizing some features and a data mining process. I try to find the best regression for this dataset. On the other hand, if you are looking for more theory and do not want to use built in functions, I recommend you to check my other kernel k-NN, Logistic Regression, k-Fold CV from Scratch.

Machine Learning project for predicting house prices based on key features. Includes data preprocessing, model training, and evaluation.

House Prediction on Kaggle Dataset - using several machine learning algorithms I was able to train the model and predict the 'House Sales Price' for the test dataset.

House price prediction is the process of using data and statistical models to estimate the future value of a residential property. This prediction is based on a number of factors such as location, size, condition, and recent sales data of similar properties in the same area.

An example on regression problem: House Sales in King County

House sales : prices prediction website

Analyze and predict housing prices in King County using linear regression and exploratory data analysis.

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IBM Data Science Certificate - Capstone Project (Data Analysis with Python)

House sales predictions for King County

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Data Analysis Project: Used house attributes or features to analyze and predict housing prices, determining the market value of a house based on a given set of features.

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Real estate transactions are quite opaque sometimes and it may be difficult for a newbie to know the fair price of any given home. Thus, multiple real estate websites have the functionality to predict the prices of houses given different features regarding it. Such forecasting models will help buyers to identify a fair price for the home and also give insights to sellers as to how to build homes that fetch them more money. Chennai house sale price data is shared here and the participants are expected to build a sale price prediction model that will aid the customers to find a fair price for their homes and also help the sellers understand what factors are fetching more money for the houses?

Given data about houses in London, let's try to predict how many houses will be sold in a given month and area.

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Prediction of sale price of houses and properties in Chennai

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Machine Learning :Linear Regression prediction of house sales in Seattle ares

This is my personal Data analysis project. Performed predictive analytics using regression models.