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House Pricing Prediction using machine learning.

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machine-learning model natural-language-processing prediction-model python tensorflow

ames_house_pricing_prediction's Introduction

Ames_House_Pricing_Prediction

Real estate di Indonesia adalah industri properti yang mencakup berbagai jenis properti seperti rumah, apartemen, tanah, komersial, perumahan, dan properti industri. Perkembangan real estate di Indonesia di pengaruhi oleh berbagai faktor diantaranya: -Pertumbuhan Ekonomi -Urbanisasi -Kebijakan Pemerintah -Pasar Properti Komersial -Pembangunan Infrastruktur dll Perkembangan real estate di Indonesia di pengaruhi oleh berbagai faktor diantaranya: -Pertumbuhan Ekonomi -Urbanisasi -Kebijakan Pemerintah -Pasar Properti Komersial -Pembangunan Infrastruktur dll

Seberapa penting sebuah sistem yang mampus menentukan harga rumah?

Pentingnya sistem yang mampu menentukan harga rumah tergantung pada berbagai faktor seperti:

  • Pemilihan Properti untuk pembeli
  • Penentuan Harga Jual untuk penjual
  • Pengembangan Properti untuk developer
  • Investasi Properti untuk investor

kemampuan untuk menentukan harga rumah dengan akurat dapat memiliki dampak besar pada keputusan dan transaksi properti. Artificial Intelligence diharapkan dapat mendukung sistem tersebut untuk menghasilkan rekomendasi yang baik dan akurat

Dataset Info

RangeIndex: 1460 entries, 0 to 1459 Data columns (total 81 columns):

  • Id
  • MSSubClass
  • MSZoning
  • LotFrontage
  • LotArea
  • ..........
  • MoSold
  • YrSold
  • SaleType
  • SaleCondition
  • SalePrice dtypes: float64(3), int64(35), object(43)

Data Modeling

Linear Regression

evaluation:

  • Mean Absolute Error: 4.689454779681475e+16
  • Mean Squared Error: 1.5268715044279774e+35
  • R-squared scores: 0,70

Random Forest Regressor

evaluation:

  • Mean Absolute Error: 17357.92
  • Mean Squared Error: 943047692.15
  • R-squared scores: 0.86

XGBoost Regressor

evaluation:

  • Mean Absolute Error: 17588.08
  • Mean Squared Error: 875453541.77
  • R-squared scores: 0.87

Features Imortance

OverallQual is the most feature importance image

image

OverallQual vs SalePrice

image

Modeling With Top 10 Features

Linear Regression

evaluation:

  • Mean Absolute Error: 23482.96
  • Mean Squared Error: 2025148129.29
  • R-squared scores: 0.7

Random Forest Regressor

evaluation:

  • Mean Absolute Error: 18754.26
  • Mean Squared Error: 989850716.99
  • R-squared scores: 0.85

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