R-based statistical analysis of Boston Housing Data. Explored feature scales, computed descriptive stats, visualized data, and identified outliers (e.g., higher crime rates in specific areas). Examined variable relationships, calculated correlation coefficients, and presented findings via cross-classifications.

In this project, I created a linear regression analysis of the Boston Housing Dataset with Python

Boston Housing Dataset Example

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.

Gradient Descent Algorithm Projects

A machine learning web app for Boston house price prediction.

GRADIENT DESCENT PROJECT ON BOSTON DATASET | GRADIENT DESCENT PROJECT ON COMBINED CYCLE DATASET | GRADIENT DESCIENT BASICS

This project is a Web Application that can be used to predict the Price of house in city of Boston. Boston-Housing-Dataset is used during our Data Analysis process, `Multivariate Regression` is performed and a Regressor model is created. An API is created to run the Dockered Model over the `Heroku Cloud Platform` using `Github Actions`.

Worked in R to analyze a Boston Housing dataset. I explored the dataset, used the ggplot2 package to produce visualizations, and created a multiple linear regression model to predict median house values.

Boston house price prediction using Linear Regression Machine Learning model

Perform exploratory data analysis techniques, such as predictive models and advanced visualization, on the Boston Housing Dataset.

Boston House 🏡 price prediction ML project ✔️

Quick analysis on multivariate regression using PCA and R2 score to select variables.

Objective of the repository is to learn and build machine learning models using Python for data Housing Median Price ( BostonHousing).

Machine learning model implemented to accurately predict the housing prices in Boston suburbs.

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 code file predicts the House prices (MEDV) based on given features.

ML Project Using Linear Regression - Predict Prices of Houses in Boston

A repository with my course practice in MGMT590 Machine Learning at Krannert School of Management, Purdue University.

This project is based on Boston House Price Prediction using Linear Regression and Random Forest Regressor.