Udacity capstone project: Kaggle competition on house prices prediction using advanced regression techniques

Repository for source code of kaggle competition: House Prices: Advanced Regression Techniques

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.

This project consists in competing in the following Kaggle competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Kaggle House Prices: Advanced Regression Techniques.Public Leaderboard Score 0.12076.

Deep Learning using Tensorflow for the "House Prices: Advanced Regression Techniques" Kaggle competition.

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

House Prices: Advanced Regression Techniques - Kaggle competition

Kaggle challenge of House Prices: Advanced Regression Techniques is solved using ANN models with only low-level APIs of TensorFlow. The predicted test-result scored 0.1190 in Kaggle leaderboard.

Kaggle Competition - House Prices: Advanced Regression Techniques

This is a streamlit app created by using the Ames Housing dataset on Kaggle (https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview).

Kaggle competition for House price prediction using advanced regression techniques.

A beginner's walkthrough and tutorial for the House Prices: Advanced Regression Techniques Kaggle machine learning problem set.

Repository for source code of Kaggle competition: House Prices: Advanced Regression Techniques

Topic from: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

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This repo is a part of K136. Kodluyoruz & Istanbul Metropolitan Municipality Data Science Bootcamp. The project aims to produce a machine learning model for home price estimation. The model was built on the Kaggle House Prices - Advanced Regression Techniques competition dataset.

kaggle入门竞赛——房屋售价预测竞赛（House Prices: Advanced Regression Techniques）

This repository contains a machine learning model built to predict house prices using the "House Prices - Advanced Regression Techniques" dataset from Kaggle. The model is built using Python and scikit-learn and implements various regression techniques.

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RandomForest model for House Prices: Advanced Regression Techniques kaggle competition

House Price — Advanced Regression Techniques (Kaggle) This repository presents a regression solution for the Kaggle competition "House Prices: Advanced Regression Techniques", where the goal is to predict final house sale prices based on various features.

An optimized Neural Network for the Kaggle Competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data

A Data Mining and Machine Learning project on Advancing House Price Predictions, a "House Prices - Advanced Regression Techniques" Competition on Kaggle.

INTRODUCTION TO PYTHON'S SWEETVIZ LIBRARY Sweetviz is an open source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with a single line of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Make sure you visit sweetviz.PyPI to explore more, and also consult and go through documentation of this library. DATASET USED FOR EDA : I have used train and test datasets from the "House Prices: Advanced Regression Techniques" competition hosted by KAGGLE, for performing the EDA. You can find the datasets at the following link redirected to KAGGLE : https://www.kaggle.com/c/house-prices-advanced-regression-techniques

A data analysis and prediction project that uses the "Kaggle House Prices - Advanced Regression Techniques" dataset to run basic analysis about the provided data and build a Regression Model on Jupyter Notebook.

Kaggle project: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

Predicted sales prices and practiced feature engineering, RFs, and gradient boosting.

Full data science project workflow that placed me in the top 5% of all competitors (0.11931).