🔍 📈 Exploratory data analysis and Sklearn algorithm test harness for QA Datascience Summative assignment.

An analysis of factors that influence housing prices in King County, WA

Dallas house price Analysis

house price analysis

This repository contains a machine learning algorithm that trains a Random Forest model to predict house prices based on specified features of the homes, using the California Housing Dataset. The dataset used to train and evaluate the Random Forest model to predict median housing prices.

This repository contains a machine learning algorithm that trains a model to predict house prices based on specified features of the homes, using the California Housing Dataset.

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

Segment the Chicago's housing market and determine the main factor's influencing the housing price.

This project employs linear regression to predict property prices based on key features. Through thorough data cleaning, preprocessing, and feature engineering, the model is fine-tuned for accuracy. With insights from exploratory data analysis, the model offers reliable estimates, aiding stakeholders in informed decision-making.

An analysis of house prices in Beijing

This tool utilizes Python, Flask, and Linear Regression to predict house prices based on housing data from Kaggle. Whether you're a real estate enthusiast or just curious about predicting house prices, it provides an intuitive interface to explore and predict potential prices.