The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you've come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for your clients homes.
This is a modified Boston housing dataset consists of 489 data points, with each data point having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository.
Dataset name
housing.csv
Features
RM: average number of rooms per dwellingLSTAT: percentage of the population considered the lower statusPTRATIO: pupil-teacher ratio by the town
Target Variable
MEDV: Median value of owner-occupied homes in $1000's (Price)
We want to predict the price of the houses given the other variables, so your task is to create a model that can do this task, this is a multiple linear regression model.
The repository of this course can be found at this Link, in which you can find in it some code example, lessons and so one to help you get started with your assignment. If you need extra help you can get it by making an issue in this repository, tag me (@Younes-Charfaoui) and then describe what do you need, We will review the solution and reply to all kind of help.