Metis Data Science Bootcamp : Project Directory

Project - 1- Multiple linear regression problem using House Price Data. “LetsUpgrade-FS Data Science-[Suwarna Baraskar]”.

Within this undertaking, a range of regression methodologies shall be explored in order to address a compelling conundrum.

Linear Regression, Logistic Regression, ML Pipeline

This repository uses a simple linear regression to predict house prices in US $ based upon areas in sq ft.

Predicting Property Prices in Pakistan

This project aims to analyze the Bengaluru House Data using Python. The dataset is loaded from a CSV file named "Bengaluru_House_Data.csv" and is analyzed to gain insights into the housing market in Bengaluru.

My project (XGBoost.ipynb) using the gradient boosting algorithm for evaluating real estate objects in St. Peresburg on the example of the XGBoost library based on collected dataset (deals_dataset.csv). This dataset is colleted from https://portal.rosreestr.ru/wps/portal/p/is/cc_informSections/ais_mrn during 2016-2017 years for training a model …

Train a neural network to predict the prices of houses in Ames, Iowa.

Austrian housing market information deployed as Dash web app.

A Flask app that scrapes, aggregates, and plots rentals in Canada.

Statistical Methods project

Analyse and Visualize Home Buying and Home Rental for Different Areas in 🇸🇬

Coursework in pattern recognition class

Predicting the house price using Linear Regression

House price prediction and exploratory data analysis and trained and validated models using SVM, RANDOMFORESTREGRESSION & LINEAR REGRESSION

This project is a Python script that uses the scikit-learn, matplotlib, and seaborn libraries to predict house prices based on various features. The linear regression and multi regression machine learning algorithms are used for this purpose.

House prices dataset exploration and prediction. Workflow includes useful examples of Tensorflow pipelines including k-Nearest Neighbors imputer, Decision Tree Regression and XGBoost Regression