Use the DecisionTreeRegressor for predicting the house price based on Boston housing dataset.
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Updated
Sep 29, 2019 - Jupyter Notebook
Use the DecisionTreeRegressor for predicting the house price based on Boston housing dataset.
Regression by diviging data into bins and fitting different degree of polynomials on each bin.
A project on Data manipulation and visualisation in jupyter notebook. This task focused is on The Boston House Dataset. The goal is to make predictions of a house to determine the factors on which the price depends.
A simple Linear Regression model using the Boston Housing Prices Dataset
This is the final project of CEBD-1160 course, based on Boston housing dataset.
A collection of small data science projects to predict house pricing for two different datasets
In this project, you will apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home.
This repository is dedicated for learning linear regression on Boston housing data set using R
Implementation of various algorithms on scikit-learn's Toy Datasets.
Predicting Boston housing prices using linear regression.
Boston_House_Price_Prediction
Implementation of streaming regression algorithms - ASR and IMSR
In this project, for supervised learning, I used regression and decision tree techniques to build predictive models and tested model accuracy by evaluating MSE and misclassification cost. For unsupervised learning, I performed cluster analysis on Iris dataset to identify subgroups and I used association rules to analyze transaction details in th…
This project is based on Boston House Price Prediction using Linear Regression and Random Forest Regressor.
Udacity project on using linear regression to predict housing prices in Boston.
A repository with my course practice in MGMT590 Machine Learning at Krannert School of Management, Purdue University.
A Deep Learning Project on "Regression" using the Boston Housing Prices dataset. We used algorithms such as "k-fold", which will help us in getting more combinations if training and testing sets, which will give a robust performance of our model. Many other features are included to improve our models perrformance.
Understanding regression.
Udacity MLND P1: Predicting Boston Housing prices
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