Topic: boston-housing-price-prediction Goto Github
Some thing interesting about boston-housing-price-prediction
Some thing interesting about boston-housing-price-prediction
boston-housing-price-prediction,Implementation of 11 variants of Gradient Descent algorithm from scratch, applied to the Boston Housing Dataset.
User: aahouzi
boston-housing-price-prediction,This is a practice notebook which include dummy and real life exapmle of linear regression implementation.
User: abhilas0
boston-housing-price-prediction,Dataset Boston Housing Price prediction
User: ahmadali-jamali
boston-housing-price-prediction,Repository for Assignment 1 for CS 725
User: akshaykhadse
boston-housing-price-prediction,Notebook for Data Science - Machine Learning
User: alik604
boston-housing-price-prediction,Regression by diviging data into bins and fitting different degree of polynomials on each bin.
User: aps19
boston-housing-price-prediction,
User: armin-abdollahi
boston-housing-price-prediction,
User: arslanali4343
boston-housing-price-prediction,Machine Learning - End to End Data Science Projects
User: ashokkumar-k
Home Page: https://ashok-machine-learning-project.herokuapp.com/
boston-housing-price-prediction,In this repository, a regression analysis is conducted using different machine learning and deep learning models. The study is led in order to choose the most suitable model by looking at different characteristics (models tuning, features scaling, etc).
User: davidcico
boston-housing-price-prediction,In this project, XGBoost is applied to forecast real estate prices using the Boston Housing Dataset. The primary aim is to create an effective predictive model, assess its accuracy through metrics like Mean Absolute Error (MAE), and refine its performance by tuning hyperparameters with HYPEROPT.
User: dhwass
Home Page: https://www.kaggle.com/code/wasswassss/xgboost-on-home-data/notebook
boston-housing-price-prediction,Boston Housing Dataset Example
User: en10
boston-housing-price-prediction,Evaluating the performance and predictive power of a model. Cross questioned several concepts of ML for better understanding.
User: geekquad
boston-housing-price-prediction,House price prediction in the boston area.
User: govardhan26
boston-housing-price-prediction,Deep Learning Projects
User: ibadi123
boston-housing-price-prediction,Predicting Boston House Prices
User: jangirsumit
boston-housing-price-prediction,Leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home.
User: jrbeverly
boston-housing-price-prediction,A machine learning web app for Boston house price prediction.
User: kalsam123
boston-housing-price-prediction,An Implementation of the Gradient Descent Algorithm on the 🏡Boston Housing DataSet🏡.
User: keeratsachdeva
boston-housing-price-prediction,The objective is to build a regression model to predict the price of houses.
User: kunal1198
boston-housing-price-prediction,A repository with my course practice in MGMT590 Machine Learning at Krannert School of Management, Purdue University.
User: lilianchi
boston-housing-price-prediction,Boston Housing Prediction - 2nd project for Udacity's Machine Learning Nanodegree
User: lmego
boston-housing-price-prediction,This repo contains machine learning projects about some popular datasets. In each project, exploratory data analysis is made before building the model.
User: marcogulli01
boston-housing-price-prediction,Implement a perceptron from scratch
User: matin-ghorbani
boston-housing-price-prediction,These are all the assignments from Udacity Nanodegree Machine Learning course
User: mayur29
boston-housing-price-prediction,Regression-PrediksiHargaRumahBoston-kaggle-ensamblemodel-supervisedlearning
User: michstg
boston-housing-price-prediction,This repository includes all the project works that i have carried out through the Machine Learning Foundation nanodegree.
User: mohancr97
boston-housing-price-prediction,Created the model of SGD for Linear Regression on Boston House Price dataset
User: niketan108
boston-housing-price-prediction,This repo contains projects developed in Machine Learning Foundation Nanodegree.
User: optimistanoop
Home Page: https://in.udacity.com/course/machine-learning-engineer-nanodegree--nd009-in-basic/
boston-housing-price-prediction,2018 [Julia v1.0] machine learning (linear regression & kernel-ridge regression) examples on the Boston housing dataset
User: qin-yu
boston-housing-price-prediction,The main motive of this project is Price Prediction on the Boston Housing dataset. and here mainly focused on the Implementation using Linear Regression Model.
User: radadiyamohit81
boston-housing-price-prediction,This repository contains files for Udacity's Machine Learning Nanodegree Project: Boston House Price Prediction
User: rahulravindran0108
boston-housing-price-prediction,The Linear regression model is implemented on Boston house prices dataset.
User: raptor2804
boston-housing-price-prediction,Keras 101: A simple Neural Network for House Pricing regression
User: rodrigobressan
boston-housing-price-prediction,Gradient Descent for N features using two datasets: Boston House data, Power Plant Data
User: sabeelahmad
boston-housing-price-prediction,Boston Data Prices prediction using Scikit Learn
User: sandilya1599
boston-housing-price-prediction,
User: sazzzo99
boston-housing-price-prediction,All the essential resources and template code needed to understand major data science and machine learning libraries like Numpy, Pandas, Matplotlib and Scikit Learn with few small projects to demonstrate their practical application.
User: shushrutsharma
boston-housing-price-prediction,Linear Regression , Cross Validation, k-mean clustering , Watershed , Gradients and Edge Detection , threshold , Correlation , Neural Network, Conventional Neural Network , Pneumonia Classification, Social Distancing, Rainfall Prediction, Boston Housing Price Prediction.
User: sujitmandal
boston-housing-price-prediction,Machine Learning Nano-degree Project : To assist a real estate agent and his/her client with finding the best selling price for their home
User: sushantdhumak
boston-housing-price-prediction,This project is about predicting house price of Boston city using supervised machine learning algorithms. In this we used three models Multiple Linear Regression, Decision Tree and Random Forest and finally choose the best one. Furthermore, we briefly introduced Regression, the data set, analyzed and visualized the dataset.
User: thenomaniqbal
Home Page: https://housepriceprediction404.herokuapp.com/
boston-housing-price-prediction,The repository contains various Machine Learning based solutions for data analysis, regression and clustering problems
User: utkarshmishra04
boston-housing-price-prediction,
User: utkarshraj11
boston-housing-price-prediction,A project built as part of the udacity machine learning ND
User: valerio
boston-housing-price-prediction,Linear Regression model trained on Boston Housing Dataset
User: vetlapavankalyan
boston-housing-price-prediction,Implementing linear regression on Boston Housing dataset using scikit-learn
User: vishaldpatel92
boston-housing-price-prediction,Data: Boston Housing Dataset (HousingData.csv) Programming language(s): R Tool(s): RStudio Business problem: To understand the drivers behind the value of houses in Boston and provide data-driven recommendation to the client on how they can increase the value of housing.The Boston housing dataset consisted of 506 observations and 14 variables. Project challenge(s): MEDV (Median value of homes in Boston) was identified as the dependent variable. While the rest, were the independent variables. The goal was to find out which among the independent variables were statistically significant in driving the house prices (MEDV). The dataset consisted of missing values and outliers. Some of the variables had a skewed distribution. There was multicollinearity among few independent variables. Our Approach: Prior to model building, we tidied up our dataset by eliminating the rows that contained missing values. Replacing the missing values with median and mean of those variables were also done. Considering the three approaches, median imputation(replacing missing values with mean) was found to be the best approach. As the dependent variable "MEDV" (median value of houses) was continuous(numerical) in nature, we implemented the Multiple linear regression to build our model. Additional models were built from Decision trees and Random forest. On further investigation, we discovered that the dependent variable had a skewed distribution. By log transformation of this variable, we were able to get a normal distribution. Post transformation, we found out that the model built from Multiple linear regression with log transformed MEDV was the best in terms of MSE (Mean squared error) value and Adjusted R^2. All the assumptions of linear regression were met.
User: vishalv91
boston-housing-price-prediction,Project #4 from the Cloud DevOps Engineer Nanodegree Program - Udacity
User: y-martinez
boston-housing-price-prediction,This project to predict Boston housing price using linear regression
User: zarahshibli
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