Data Analysis & Visualization - Predict the future price of houses

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

In this Data science project I tried to create Jupyter notebooks for EDA and feature engineering of Advanced House Price Prediction Dataset from Kaggle Competition.

The repository contains a Pune house price prediction system build using R programming Language. The System efficiently calculates and analyze house prices in multiple areas across Pune using machine learning models and Data science and analytical tools

PROJECT HOUSING: PRICE PREDICTION Problem Statement: Houses are one of the necessary need of each and every person around the globe and therefore housing and real estate market is one of the markets which is one of the major contributors in the world’s economy. It is a very large market and there are various companies working in the domain. Data science comes as a very important tool to solve problems in the domain to help the companies increase their overall revenue, profits, improving their marketing strategies and focusing on changing trends in house sales and purchases. Predictive modelling, Market mix modelling, recommendation systems are some of the machine learning techniques used for achieving the business goals for housing companies. Our problem is related to one such housing company. A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. The company is looking at prospective properties to buy houses to enter the market. You are required to build a model using Machine Learning in order to predict the actual value of the prospective properties and decide whether to invest in them or not. For this company wants to know: • Which variables are important to predict the price of variable? • How do these variables describe the price of the house? Business Goal: You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for the management to understand the pricing dynamics of a new market. Technical Requirements: • Data contains 1460 entries each having 81 variables. • Data contains Null values. You need to treat them using the domain knowledge and your own understanding. • Extensive EDA has to be performed to gain relationships of important variable and price. • Data contains numerical as well as categorical variable. You need to handle them accordingly. • You have to build Machine Learning models, apply regularization and determine the optimal values of Hyper Parameters. • You need to find important features which affect the price positively or negatively. • Two datasets are being provided to you (test.csv, train.csv). You will train on train.csv dataset and predict on test.csv file. The “Data file.csv” and “Data description.txt” are enclosed with this file.

The Boston House Price Prediction project utilizes data science methodologies and machine learning algorithms to provide accurate predictions for housing prices in the Boston area.

data science project house-price-prediction

Metropolitan House Price Prediction Web App: Streamlit-based ML model for real-time house price estimations in cities. Input location, size, bedrooms for instant predictions. Check out the GitHub repo for a seamless blend of data science and user-friendly design.

House Price Prediction is Data Science Project using Machine Learning..

This repo is for house price prediction system in data science and machine learning technology

This project demonstrates the power of Data Science and AI in solving real-world House Price Prediction problems.

The objective of this project is to apply my knowledge of data science and machine learning to create a house price prediction model in a well documented fashion

This project involves predicting house prices using the LightGBM algorithm, showcased through a Kaggle competition notebook. The Jupyter Notebook demonstrates thorough data preprocessing, feature engineering, and model evaluation to achieve accurate price predictions. This portfolio piece highlights my proficiency in data science and machine learni

⚽ House ⚾ Price 🥎 Prediction 🏀 Machine 🏐 Learning ⛸ is 🚁 a data 🛸 science 🚟 project 🛬 that 🛩 uses 🚢 machine ⛴ learning 🛥 algorithms 🚠 to 🚟 predict ✈ housing 🚃 prices 🚂 based 🚅 on 🕍 various 🏦 property 🏯 features 🏡 such 🏭 as 🏜 location 🛖 size 🚈 number 🚒 of 🚖 rooms ☂ and 🎃 other 🎳 real 🦧 estate 🐯 attributes 🦄

Predicting house prices is a classic problem in machine learning and data science. It involves analyzing various factors such as location, size, number of bedrooms, and other features to estimate the likely selling price of a property.

Machine learning - Regression - Enhanced house price predictions - Kaggle competition

Data Science Mini Project on Boston House Price Prediction

Developed a House Price Prediction system using Machine Learning , Data science and Flask

Advance House Prediction- Kaggle Competition: Use Machine/Deep Learning to predict House Prices using Data Science Pipelines

The Boston House Price Prediction project utilizes data science methodologies and machine learning algorithms to provide accurate predictions for housing prices in the Boston area. Topics data-science machine-learning scikit-learn supervised-learning boston regression-models house-price-prediction regression-algorithms

Prediction of Boston houses price by Multilinear regression using different data science and machine learning aspects.

Machine Learning, Predictive Modeling, Regression, Boston Housing Dataset, House Price Prediction, Data Science, Python, Jupyter Notebook, GitHub.

This Boston House Price Prediction project is a comprehensive machine learning analysis that demonstrates the complete data science workflow from data exploration to model deployment.

MLOps_California_House_Price_Prediction is an end-to-end machine learning project designed to predict house prices in California using modern MLOps practices. This project combines data science, model development, and deployment pipelines to create a scalable and reproducible housing price prediction system.

house price prediction for Data Science and Machine Learning.1.preprocessing 2.data virualization 3. create models for data 4.ouputs using pandas,numpy,sklearn,matplotlib, and seaborn.

Projects completed during the Pinnacle Full-Stack Interns Data Science Internship, covering tasks such as heart disease prediction, fraud detection, customer segmentation, and house price prediction using machine learning techniques.

This project implements a Machine Learning based House Price Prediction system using Python and data science libraries. It includes data preprocessing, exploratory data analysis, feature selection, and training of regression models to predict house prices. Different models are compared using performance metrics and visualization graphs.

Welcome to our House Price Prediction project focused on Washington, USA. This data science project leverages advanced machine learning techniques to predict house prices in the Washington area. Our project emphasizes the use of Folium, an interactive mapping library, to visualize housing trends and predictions geospatially.

The objective of this project is to apply my knowledge of data science and machine learning to create a house price prediction model in a well documented fashion

This data science project series walks through step by step process of how to build a real estate price prediction website. We will first build a model using sklearn and linear regression using house prices dataset.