My first ML project! Predicting house prices using Supervised learning ( Linear Regression model).
The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository and you can find the main dataset on the Kaggle
Features RM: average number of rooms per dwelling (Total number of rooms in home) LSTAT: percentage of population considered lower status (Neighborhood poverty level ) PTRATIO: pupil-teacher ratio by town (Student-teacher ratio of nearby schools) Target Variable: MEDV: median value of owner-occupied homes (house price)