PROPERTY PRICE PREDICTION AND RECOMMENDATIONS IN NORTHERN CYPRUS / (Kayıt no. 293077)

MARC ayrıntıları
000 -BAŞLIK
Sabit Uzunluktaki Kontrol Alanı 02431nam a22002657a 4500
003 - KONTROL NUMARASI KİMLİĞİ
Kontrol Alanı KOHA
005 - EN SON İŞLEM TARİHİ ve ZAMANI
Kontrol Alanı 20250110143344.0
008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ
Sabit Alan 240927d2024 cy de||| |||| 00| 0 eng d
040 ## - KATALOGLAMA KAYNAĞI
Özgün Kataloglama Kurumu CY-NiCIU
Kataloglama Dili eng
Çeviri Kurumu CY-NiCIU
Açıklama Kuralları rda
041 ## - DİL KODU
Metin ya da ses kaydının dil kodu eng
090 ## - Yerel Tasnif No
tasnif no YL 3560
Cutter no F33 2024
100 1# - KİŞİ ADI
Yazar Adı (Kişi adı) Al-Fadhli, Sama Mansoor Nasser
245 10 - ESER ADI BİLDİRİMİ
Başlık PROPERTY PRICE PREDICTION AND RECOMMENDATIONS IN NORTHERN CYPRUS /
Sorumluluk Bildirimi SAMA MANSOOR NASSER AL-FADHLI ; SUPERVISOR, ASST. PROF. DR. YASEMİN BAY
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice 2024
300 ## - FİZİKSEL TANIMLAMA
Sayfa, Cilt vb. 49 sheets ;
Boyutları 30 cm
Birlikteki Materyal +1 CD ROM
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
Content type code txt
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
Media type code n
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
Carrier type code nc
502 ## - TEZ NOTU
Tez Notu Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information System
520 ## - ÖZET NOTU
Özet notu This research aims to improve the market stability of Northern Cyprus and consumer trust through property recommendations based on the achieved high-accuracy price predictions. Using a dataset collected between 2023 and 2024, captured from 101Evler.com using Python, a leading property search portal based in Northern Cyprus. The dataset includes data on 430 residential properties on the island, the study explores the unique dynamics of Northern Cyprus characteristics as an unrecognized state offering lower property prices and investment opportunities than other countries around the world. Machine Learning algorithms were used such as Random Forest, Gradient Boosting, and XGBoost, compared their results and their performance was evaluated by using R-squared (R²) and Mean Absolute Error (MAE) metrics. The results proved that Random Forest Regression outperformed the other models, achieving the highest R² scores and the lowest MAE values, the properties recommended were based on the results of the high predicted values rather than the actual prices which illustrate the potential of being overpriced in other platforms in Northern Cyprus. Our research applied the property price prediction analysis methodology and utilized the Dataiku platform and Python for model development and recommendation. Our findings highlight the potential of recommending worth buying properties, enabling more informed decisions through accurate price predictions.
650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ
Konusal terim veya coğrafi ad Management Information System
Alt başlık biçimi Dissertations, Academic
700 1# - EK GİRİŞ - KİŞİ ADI
Yazar Adı (Kişi adı) Bay, Yasemin
İlişkili Terim supervisor
942 ## - EK GİRİŞ ÖGELERİ (KOHA)
Sınıflama Kaynağı Dewey Onlu Sınıflama Sistemi
Materyal Türü Thesis
Mevcut
Geri Çekilme Durumu Kayıp Durumu Sınıflandırma Kaynağı Kredi için değil Koleksiyon Kodu Kalıcı Konum Mevcut Konum Raf Yeri Kayıt Tarih Source of acquisition Toplam Ödünçverme Yer Numarası Demirbaş Numarası Son Görülme Tarihi Kopya Bilgisi Fatura Tarihi Materyal Türü Genel / Bağış Notu
    Dewey Onlu Sınıflama Sistemi   Tez Koleksiyonu CIU LIBRARY CIU LIBRARY Depo 19.11.2024 Bağış   YL 3560 F33 2024 T4007 19.11.2024 C.1 19.11.2024 Thesis Management Information Systems
    Dewey Onlu Sınıflama Sistemi   Tez Koleksiyonu CIU LIBRARY CIU LIBRARY Görsel İşitsel 19.11.2024 Bağış   YL 3560 F33 2024 CDT4007 19.11.2024 C.1 19.11.2024 Suppl. CD Management Information Systems
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