makine-renmesiyle-d-nya-mutluluk-raporu-analizi.ipynb

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"
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   "source": [
    "# **1. GİRİŞ**\n",
    "Dünya Mutluluk Raporu küresel mutluluğun durumunu gösteren bir ankettir. Yaklaşık olarak 155 ülkenin mutluluk seviyelerine göre sıralandığı bu rapor hükümetlerin nasıl politika izleyeceklerine karar vermesi; kuruluşların ve sivil toplumun bazı kararlar alabilmesi için bu raporlar paylaşılmaya ve takip edilmeye devam etmektedir. Alanında önde gelen uzmanlar ekonomi, psikoloji, anket analizi, ulusal istatistikler, sağlık, kamu politikası gibi konular üzerinde çalışarak refah ölçümlerinin ulusların ilerlemesini nasıl etkili bir şekilde kullanabileceklerini açıklar. Küresel Terörizm Veri Tabanı (GTD), 1970'ten 2017'ye kadar dünyanın dört bir yanındaki terörist saldırılara ilişkin bilgileri içeren açık kaynaklı bir veri tabanıdır. GTD, bu süre zarfında meydana gelen ve şimdi 180.000’den daha da fazla olan ulusal ve uluslararası terör olaylarına ilişkin sistematik verileri içermektedir.\n",
    "\n",
    "2015, 2016 ve 2017 Dünya Mutluluk Raporları’nda Dünya Mutluluk Sıralaması’nı hangi faktörün ne kadar etkilediğini ve Küresel Terörizm Raporu’nu kullanarak bu yıllarda ki terör olaylarını veri setimize ekleyerek terör olaylarının etkisinin de sıralamaya etkisini izledik. 2018, 2019 ve 2020 Mutluluk Raporları’nda Dünya Mutluluk Sıralaması’nı hangi faktörün ne kadar etkilediğini ve bu analizimiz sonucunda bu 5 senelik rapor ile Makine Öğrenmesi yardımıyla mantıklı rastgele değerler ile mutluluk puanı tahmini yapıldı.\n",
    "\n",
    "Herkes hayatta mutlu olmayı arzular ve ilginç bir şekilde mutlu olmanın gereklilikleri kişiden kişiye değişir. Hepimiz mutluluk kavramına farklı anlamlar yatırıyoruz. İnsanlar hayatımızın farklı noktalarında mutluluğa farklı değerler verir. Bununla birlikte, hayatta mutlu olmanın ana bileşenleri olarak kabul edilen bazı hayati faktörler vardır. Fiziksel ve psikolojik sağlamlık mutlu olmak için çok önemlidir ve insanlar hastalandıklarında bunu gerçekten anlayabilirler. Bence mutlu olmanın en önemli bir diğer faktörü de hayattaki ihtiyaçları karşılama becerisidir (Ekonomik özgürlük). Her zaman boş mideyle mutlu olmanın mümkün olmadığı söylenir. Bireysel doğum ve hak özgürlüğü de hayatta mutlu olmak için büyük bir etki olarak kabul edilir.\n",
    "\n",
    "Ana analiz korelasyonları (regresyon; çıktı = mutluluk, değişkenler = ekonomi, sağlık, özgürlük vb.) içerecek ve bir Makine Öğrenimi algoritması oluşturacak, ülkeleri mutluluk derecelerine ve puanlarına göre sıralandıracaktır. Örneğin mutluluk puanının 7,00'den fazla olduğu bir ülke gelişmiş bir ülke, mutluluk puanının 5,00 ile 7,00 arasında olduğu bir ülke gelişmekte olan bir ülke ve mutluluk puanı 5,00'ün altında olan bir ülke ise gelişmemiş ülkedir.\n",
    "\n",
    "Bu analiz, insanların mutluluğu ve ülkeleri arasındaki bağlantıyı değerlendirecek. Bence yaşadığımız yer mutluluk oranımızın büyük bir faktörü. Birleşmiş Milletler tarafından hazırlanan Dünya Mutluluk Raporu, yaklaşık 155 ülkeyi vatandaşlarının kendilerini ne kadar mutlu gördüklerine göre sıralamaktadır. Ekonomik refah, ortalama yaşam süresi, sosyal destek, yaşam seçimleri yapma özgürlüğü ve hükümet yolsuzluğu seviyeleri gibi faktörlere dayanır. Mutluluk puanları ve sıralamaları Gallup Dünya Anketinden alınan verileri kullanır. Puanlar, ankette sorulan temel yaşam değerlendirme sorusuna verilen yanıtlara dayanmaktadır.\n",
    "\n",
    "Louise Millard 2011'de Makine Öğrenimi Yöntemleri ile Veri Madenciliği ve Küresel Mutluluk analizi yapıyor. Bu rapor, mutluluk değerleri olan 123 ülkeyi kapsıyor. Bu raporda ekonomi, sağlık, iklim verileri ele alınmaktadır. [1]\n",
    "\n",
    "Natasha Jaques ve arkadaşları, 2015'te Makine Öğrenimi Yöntemleriyle Öğrencilerin Mutluluğunu Fizyoloji, Telefon, Hareketlilik ve Davranışsal Verilerden Tahmin Etme analizini yapıyor. Bu rapor, elektro termal aktivite (fizyolojik stresin bir ölçüsü) ve 3 eksenli ivmeölçer (adımlar ve fiziksel aktivite ölçüsü); akademik faaliyet, uyku, uyuşturucu ve alkol kullanımı ve egzersizle ilgili sorular içeren anket verileri; Telefon araması, SMS ve kullanım modelleriyle birlikte telefon verileri; gün boyunca kaydedilen koordinatlarla konum verileri. Bu makalede, mutlu ve mutsuz üniversite öğrencilerini ayırt etmek için bir makine öğrenimi algoritmasını analiz ediyorlar, hangi önlemlerin mutluluk hakkında en fazla bilgiyi sağladığını değerlendiriyorlar ve mutluluk, sağlık, enerji, uyanıklık ve stres dahil olmak üzere refahın farklı bileşenleri arasındaki ilişkiyi değerlendiriyorlar. [2]\n",
    "\n",
    "# **2. VERİ HAKKINDA**\n",
    "Dünya Mutluluk Raporu olan ilk kullanılan veri, küresel mutluluk durumuna ilişkin dönüm noktası niteliğindeki bir araştırmadır. 155 ülkeyi mutluluk seviyelerine göre sıralayan rapor, hükümetler, kuruluşlar ve sivil toplum politika belirleme kararlarını bilgilendirmek için mutluluk göstergelerini giderek daha fazla kullandıkça küresel kabul görmeye devam ediyor. Ekonomi, psikoloji, anket analizi, ulusal istatistikler, sağlık, kamu politikası vb. alanlarda önde gelen uzmanlar, ulusların ilerlemesini değerlendirmek için refah ölçümlerinin nasıl etkili bir şekilde kullanılabileceğini açıklamaktadır.\n",
    "\n",
    "Küresel Terörizm Veri Tabanı (GTD) olan ikinci kullanılan veri, 1970'den 2017'ye kadar dünyanın dört bir yanındaki terör saldırıları hakkındaki bilgileri içeren açık kaynaklı bir veri tabanıdır. GTD, bu sırada meydana gelen yerel ve uluslararası terör olaylarına ilişkin sistematik verileri ve 180.000'den fazla terör olayı içermektedir.\n",
    "\n",
    "# **3. PROJE TANIMI**\n",
    "Çalışma, Mutluluk Puanının etkilerini incelemektedir. İnsanların mutluluk düzeyi bazı durumlardan etkilenebilir. CRISP-DM yöntemiyle Makine Öğrenimi, en azından en fazla etkiyi ölçer. Değerler mutluluk için en önemli şeyleri söyler. Projenin amacı, hükümetlere, kuruluşlara ve sivil topluma püf noktaları sağlar. Kurmak için Çoklu Doğrusal Regresyon modeli seçilir.\n",
    "\n",
    "Öte yandan bu veri setleri ve tüm çıktılar küresel terör saldırıları ile tekrar analiz edilecektir. Planım, Dünya Mutluluk Raporlarında olmayan yeni bir bağımsız değişken yaratmak. Bu amacın temel amacı, coğrafyanın terör saldırılarının tetikleyicisi olup olmadığını ve bu saldırıların vatandaşların mutluluğu üzerinde bir rolü olup olmadığını görmektir.\n",
    "\n",
    "Bu projenin temel amacı, sosyoloji ve siyasete makine öğrenimi ve istatistik ile yaklaşmaktır. Çok ilginç olacak çünkü bu, veri biliminin hayatımızdaki yararını anlamak için güzel bir örnek. Veriler sayısal veya sözel değerler içerse de, daha iyi bir dünya inşa etmeye yardımcı olacak analizler ve çıkarımlar yapabiliriz.\n",
    "     \n",
    "# **4. METODOLOJİ**\n",
    "Veri analizi için Python programlama dili ve Çoklu Doğrusal Regresyon Makine Öğrenme Metodu kullanıyoruz. Küresel Terörizm Raporu'ndan ülke, silah saldırısı vb. Olarak uygun verileri alıyoruz ve bu sütunlar Dünya Mutluluk Raporumuza ekleniyor. Bu veri çerçevesi, test (1/3) ve tren (2/3) olarak rastgele bölünür. Ve bu veri çerçevesi üzerinde çoklu doğrusal regresyon yöntemi çalışır. Ve hangi özelliğin mutluluk puanını diğerlerinden daha fazla etkilediğini buluyoruz. Yöntem için herhangi bir sorun varsa, diğer yöntemlere geçeceğiz ve çıktı değerlerini karşılaştıracağız.\n",
    "\n",
    "# **4.1. CRISP-DM Metodu**\n",
    "Veri madenciliği projelerinin daha etkili, daha hızlı, daha güvenli, daha az maliyetli hale gelmesi için veri madenciliği için standart süreçleri tanımlayan bir model olan CRISP-DM (Cross Industry Standard Process for Data Mining) yöntemi. Bu yöntem, problemi ve ne yapmamız gerektiğini anlamakla başlar ve Makine Öğrenimi için veri hazırlama, modelleme ve değerlendirme ile devam eder.\n",
    "\n",
    "![image.png](attachment:12535ccd-3cba-4364-b401-8a2dab6e53f3.png)\n",
    "\n",
    " **Görsel-1:** CRISP-DM Metodu Yapısı\n",
    "\n",
    "# **4.2. Veriyi Anlamak**\n",
    "Dünya Mutluluk Raporu mutluluk puanları, ekonomik durumu (Kişi Başına GSYİH), sağlık durumu (Yaşam Beklentisi), özgürlük durumu, güven durumu (Devlet Yolsuzlukları), cömertlik durumu olan ülkeleri içerir. Mutluluk puanlarının bu değerlerle ilişkisini bulmayı anlıyoruz.\n",
    "\n",
    "Küresel Terörizm Veritabanı raporu, terör olaylarının görüldüğü ülkeleri, saldırı türlerini, silah türlerini vb. İçeren yılları içerir. Verimize yeni özellikler almanın ve eklemenin yeterli olduğunu anlıyoruz. Verimize bu veriden yıllar ve ülkeleri almak yeterlidir.\n",
    "\n",
    "# **4.3. Veri Ön İşleme**\n",
    "Veri ön işleme kısmı, verilerin işlenmesi için önemlidir. Bu bölüm kütüphaneyi ve veri içe aktarmayı, veri temizlemeyi, kullanılacak uygun veri düzenlemeyi, Makine Öğrenimi bölümüne geçmek için veri çerçevesine veri eklemeyi içerir. Makine Öğrenimi bölümü için ön işlemlere ihtiyacımız var.\n",
    "\n",
    "# **4.3.1. Kütüphane ve Veri İçe Aktarma**\n",
    "Süreçlere göre hangi kütüphanelerin kullanılacağına karar veriyoruz. Python'da veri ve veri çerçevesi işlemleri için Pandas kütüphanesi, hesaplamalar için NumPy kütüphanesi, çizim için Matplotlib ve Seaborn kütüphanesi, \"import\" komutu ile Makine Öğrenimi işlemi için Sci-Kit Learn kütüphanesi kullanılır. Veri kullanımı csv (virgülle ayrılmış değerler), excel, html vb. Olabilir ve \"read_csv / excel (\" dosya_adı \")\" komutu alınır. Veri dosyası çalışma dizini olmalıdır. Çalışma dizininde değilse, dosya_adı için dosya dizinini yazabilirsiniz. Ve bu herhangi bir değişken olarak tanımlanır.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75e9eb8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:32.595036Z",
     "iopub.status.busy": "2021-11-29T19:17:32.593988Z",
     "iopub.status.idle": "2021-11-29T19:17:44.840095Z",
     "shell.execute_reply": "2021-11-29T19:17:44.840968Z",
     "shell.execute_reply.started": "2021-11-29T19:15:53.454937Z"
    },
    "papermill": {
     "duration": 12.326342,
     "end_time": "2021-11-29T19:17:44.841324",
     "exception": false,
     "start_time": "2021-11-29T19:17:32.514982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bubbly\r\n",
      "  Downloading bubbly-1.0.2.tar.gz (5.1 kB)\r\n",
      "Requirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from bubbly) (5.1.0)\r\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from bubbly) (1.2.4)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->bubbly) (2.8.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->bubbly) (2021.1)\r\n",
      "Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.7/site-packages (from pandas->bubbly) (1.19.5)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->bubbly) (1.15.0)\r\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from plotly->bubbly) (7.0.0)\r\n",
      "Building wheels for collected packages: bubbly\r\n",
      "  Building wheel for bubbly (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for bubbly: filename=bubbly-1.0.2-py3-none-any.whl size=5428 sha256=c1bc74ec1aa5c24c4086cb42b40cfb9588eb185274684807a191bbecc5f4059b\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/85/c7/3d/38784ece9ac882d3afdb852ea8cf867df942fe6c71da5c4360\r\n",
      "Successfully built bubbly\r\n",
      "Installing collected packages: bubbly\r\n",
      "Successfully installed bubbly-1.0.2\r\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install bubbly\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd   \n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import matplotlib.gridspec as grid_spec\n",
    "import seaborn as sns\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as po\n",
    "from bubbly.bubbly import bubbleplot\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected = True)\n",
    "\n",
    "\n",
    "data1 = pd.read_csv('../input/world-happiness-report-with-terrorism/WorldHappinessReportwithTerrorism-2015.csv')\n",
    "data2 = pd.read_csv('../input/world-happiness-report-with-terrorism/WorldHappinessReportwithTerrorism-2016.csv')\n",
    "data3 = pd.read_csv('../input/world-happiness-report-with-terrorism/WorldHappinessReportwithTerrorism-2017.csv')\n",
    "data4 = pd.read_csv('../input/world-happiness/2018.csv')\n",
    "data5 = pd.read_csv('../input/world-happiness/2019.csv')\n",
    "data6 = pd.read_csv('../input/world-happiness-report/2020.csv')\n",
    "data7 = pd.read_csv('../input/global-terrorism-report-for-world-happiness-report/GlobalTerrorismReport-2015.csv')\n",
    "data8 = pd.read_csv('../input/global-terrorism-report-for-world-happiness-report/GlobalTerrorismReport-2016.csv')\n",
    "data9 = pd.read_csv('../input/global-terrorism-report-for-world-happiness-report/GlobalTerrorismReport-2017.csv')"
   ]
  },
  {
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"
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   "source": [
    "# **4.3.2. Veri Temizleme**\n",
    "Bazı Makine Öğrenimi algoritmaları, eksik değerlere sahip verileri çalıştıramaz. İşlemler için düzeltilmesi gerekir. Eksik değerler NaN (sayı değil), \"?\", Boşluk vb. Görülebilir. Eksik sayısal değerler sabit istatistiksel yöntemler (ortalama vb.), belirli sayılar koyarak veya satırları silerek görülebilir. 2018 yılında sadece bir tane ve sütunların ortalamasına değiştirildi. Onun dışında çalışmamız için veriler eksik değere sahip olmadığı için başka bir işlem uygulanmamıştır. Büyük-küçük harf veya kelimeler veya farklı diller arasındaki boşluk gibi sütun adı tutarsızlığı olduğundan, keşfetmeden önce verileri teşhis etmemiz gerekir. Büyük-küçük harf veya boşluk sorunu düzeltildi. Verilerimizin sütun adlarında büyük-küçük harf veya boşluk varsa, sütun adlarını değiştirmeliyiz. IDE'de kodlayarak veya Excel'de yazarak sütun adlarını değiştirebilirsiniz.\n",
    "\n",
    "Terörizm raporu, raporumuz için pek çok gereksiz bilgiye sahiptir. Dolayısıyla bu raporu raporumuza göre gereksiz sütunları silecek şekilde düzenliyoruz. Ve 2015, 2016 ve 2017'de sadece terörist saldırılarını filtreledik.\n",
    "\n",
    "# **4.3.3. Kullanıma Uygun Olarak Veriyi Düzenleme**\n",
    "Veriler kategorik (nominal veya sıralı) veya sayısal (oran veya aralık) olabilir. Yalnızca sayısal verilere sahipsek, uygun veriler için düzenlememiz gerekmez. Yapmadıysak, verileri düzenlemeliyiz. Örneğin raporumuzda 2 kategorik verimiz var. Birincisi endeks numarası, ikincisi ülkeler. Bunlar mutluluk puanını etkiliyorsa, bu verileri sayı olarak kategorik olarak değiştirirken düzenlememiz gerekir. Ancak endeks numarası ve ülkeler mutluluk puanını etkilemez. Dolayısıyla, Makine Öğrenimi süreci için bunları göz ardı ediyoruz.\n",
    "\n",
    "Terörün dünya mutluluk raporuna etkisini görmek istiyoruz. O halde Terörizm Raporundan uygun verileri almamız gerekiyor. Uygun veriler için sadece aynı yıl içinde kaç terör saldırısı olan ülkeleri alıyoruz. Dünya Mutluluk Raporu'nda olmayan diğer özellikleri ve ülkeleri görmezden geliyoruz. Mutluluk raporu için sadece etkilenmeye ihtiyacımız var. Terörizm olay sayılarını yalnızca yıllarla ve ülkelerle yıl olarak ayrı ayrı düzenlenmiş Küresel Terörizm Raporundan alıyoruz.\n",
    "      \n",
    "# **4.3.4. Veri Setine Veri Eklemek**\n",
    "Şimdi 2 veri çerçevemiz var: Dünya Mutluluğu veri çerçevesi ve sadece ülkeler ve sayıları içeren Terörizm veri çerçevesi. Dünya Mutluluk Veri Çerçevesine terörizm özelliği eklemeliyiz. Bu değerleri aynı yıl ve aynı ülkelerde ekliyoruz. Dünya Mutluluk Raporumuza terörizm olay sayılarını ekliyoruz.\n",
    "\n",
    "# **4.4. Keşif Analizi**\n",
    "Verilerimizi anlamak ve üzerinde düşünmek istiyoruz. İlk olarak, istatistiksel bilgiler, veri çerçevelerinin kaç satır ve sütun olduğu vb. verilerden özet ve bilgi alıyoruz. Ve verileri teşhis etmek için keşif analizine ihtiyaç vardır.\n",
    "\n",
    "# **4.5. Çoklu Doğrusal Regresyon Yöntemine Karar Verme**\n",
    "Çoklu Doğrusal Regresyon, problemdeki birden çok bağımsız değerden etkilenen çıktıyı kullanmaktır. Mutluluk puanları birden çok bağımsız değerden etkilenir. Ben de bu problem için bu yöntemi denemeye karar verdim.\n",
    "\n",
    "![image.png](attachment:301487f4-74ca-4a27-b656-4ed4bc7c8f24.png)\n",
    "\n",
    "**Formül-1:** Çoklu Doğrusal Regresyon Yönteminin Formülü\n",
    "\n",
    "Formülde, β0 yanlılık olarak bilinen sabit sayıdır, β1,2,3,4… katsayı olarak bilinen değişkenlerin çarpanı ve ϵ hata oranıdır. Bu formülden, doğrudan problem için inşa etmeyi anlıyoruz. Ve bu, ϵ (hata) olabileceği ve hesaplama eklenmesi gerektiği anlamına gelir. Çoklu Doğrusal Regresyon, uzayda 3 boyuttan fazla olabilir. Β0 için her veride yalnızca 1 olan bir sütun ekliyoruz.\n",
    "\n",
    "Y ve düz y (γi) değerleri arasındaki fark, artık olarak bilinir. Düz, minimum kare hatası (MSE) üzerine inşa edilmelidir. \"N\" örnek sayıdır.\n",
    "\n",
    "![image.png](attachment:814872b5-63e9-4428-aa64-bc0f0802b43d.png)\n",
    "\n",
    "**Formül-2:** Minimum Kare Hata Formülü\n",
    "\n",
    "# **4.6. Test ve Eğitim Kümesi Olarak Veriyi Bölme**\n",
    "Başarıyı ölçmek için test ve train kümesi olarak veri bölme yapılır. Makine Öğrenimi Algoritmasında amaç değerlendirme geliştirmektir. Genel olarak 1/3 testi ve 2/3 train için rastgele kullanılır, yüzdelik bölünme olarak bilinir. Makine öğrenimi algoritması train verileri üzerinde çalışır ve test verileri üzerinde tahmin yapar, tahmin ve test verilerinin mutluluk puanı karşılaştırılır. Ve başarı görülür ve Makine Öğrenimi Yönteminin iyi olup olmadığına karar verebiliriz. Başarıya göre kullanılan Makine Öğrenimi Yöntemi değiştirebiliriz.\n",
    "\n",
    "# **4.7. Özellik Ölçekleme**\n",
    "Özellik Ölçekleme süreci Makine Öğrenimi için önemlidir. Farklı sütunların farklı veri hareketleri ve istatistiksel özellikleri (ortalama, minimum / maksimum değerler, standart sapma vb.) vardır. Yani sütunların etkileri farklıdır ve bu bir problemdir. Özellik ölçeklendirmeyi kullanarak bu sorunu çözmeliyiz. Özellik Ölçeklendirme iki klasik yöntemle yapılır: Standardizasyon ve Normalleştirme. Normalleştirme yöntemini kullanıyoruz. Sayısal veriler 0 ile 1 arasında ölçeklenir.\n",
    "\n",
    "![image.png](attachment:399256b1-e4e5-4729-97eb-58b1020af811.png)\n",
    "\n",
    "**Formül-3:** Standardizasyon ve Normalleştirme Formülleri\n",
    "\n",
    "# **4.8. Sahte Değişken Tuzağına Dikkat Edin**\n",
    "\n",
    "Kukla değişken, veri çerçevesinin değişkenlerinin aynı değerlere sahip olduğu durumdur. Bazı Makine Öğrenimi Algoritmaları kukla değişkenden etkilenebilir. Veri çerçevesinde herhangi bir durum varsa, birini iptal etmek zorundayız. Örneğin, dize değerini sayısal değere dönüştürmek için erkek ve kadın değerleri 0 ve 1 olarak kodlanabilir. Ve bu iki kodlanmış sütunu kullanırsak sorun yaşayabiliriz. Çıktı, erkek veya kadın olabilen durumdan iki kez etkilenir. Sadece bir sütun yeterlidir ve ikincisi iptal edilir. Veri çerçevemiz için gerekli değildir.\n",
    "\n",
    "# **4.9. Fine Tuning**\n",
    "Verilerin ve seçilen Makine Öğrenimi yönteminin başarılarına bakıyoruz. Herhangi bir sorun yaşarsak yöntemi değiştiririz. Veri başarısı için P-Değerlerine bakarız ve yöntemin başarısı için R^2 ve Düzeltilmiş R^2'ye bakarız.\n",
    "\n",
    "# **4.9.1. P Değerlerine Bakmak**\n",
    "Bir p değeri, sonucun tesadüfen oluşma olasılığını temsil eden bir sonucun önemini belirtir. Düşük bir değer, sonucun rastgele oluşma ihtimalinin düşük olduğu ve dolayısıyla istatistiksel olarak anlamlı olduğu anlamına gelir. Yaygın olarak %5 ve %1 eşik değerleri kullanılır, bunun altında bir sonuç anlamlı olarak belirtilebilir. İstatistiksel testler, veri kümesinin boyutu gibi test parametrelerine göre olabilir ve bir p değeri, verilerin bu tür yönlerini hesaba katan karşılaştırılabilir bir değer sağlar. [1]\n",
    "     \n",
    "# **4.9.2. Yöntem Başarısının R^2 ve Düzeltilmiş R^2 ile Karşılaştırılması**\n",
    "R^2  aynı zamanda belirleme katsayısı olarak da bilinir ve R değerinin bir uzantısıdır. Regresyon modeli ile açıklanabilen etiketteki çeşitlilik oranını temsil eder. Bununla birlikte, R modelde kullanılan değişkenlerin kullanımı farklı olduğunda karşılaştırılamaz. Düzeltilmiş R^2, regresyonda kullanılan değişkenlerin sayısını dikkate alır. [1]\n",
    "\n",
    "![image.png](attachment:81504f8c-7377-4a53-8bd0-9c47ea5d57d7.png)\n",
    "\n",
    "**Formül-4:** R^2 ve Düzeltilmiş R^2’nin Formülleri\n",
    "\n",
    "R^2 ve Düzeltilmiş R^2 değerleri 1'e ne kadar yaklaşıyorsa, seçilen yöntem için çok iyi bir durumdur. Çoklu Doğrusal Regresyon Yöntemi analizimiz için iyidir. Mutluluk puanlarının etkilerini analiz ediyoruz. Veri setinde bulunan tüm değerler önemlidir çünkü tüm değerler mutluluk puanlarını etkiler. Bu nedenle, hiçbir özelliği iptal etmiyoruz veya yok saymıyoruz.\n",
    "\n",
    "# **5. VERİ ANALİZİ**\n",
    "\n",
    "# **World Happiness Rank 2015**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0201dacf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:45.141520Z",
     "iopub.status.busy": "2021-11-29T19:17:45.140855Z",
     "iopub.status.idle": "2021-11-29T19:17:45.206184Z",
     "shell.execute_reply": "2021-11-29T19:17:45.206726Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.257288Z"
    },
    "papermill": {
     "duration": 0.143141,
     "end_time": "2021-11-29T19:17:45.206911",
     "exception": false,
     "start_time": "2021-11-29T19:17:45.063770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'region', 'happinessrank', 'happinessscore', 'standarderror',\n",
      "       'economysituation', 'family', 'healthlifeexpectancy', 'freedom',\n",
      "       'governmentcorruption', 'generosity', 'dystopiaresidual',\n",
      "       'terrorismevent'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   country               158 non-null    object \n",
      " 1   region                158 non-null    object \n",
      " 2   happinessrank         158 non-null    int64  \n",
      " 3   happinessscore        158 non-null    float64\n",
      " 4   standarderror         158 non-null    float64\n",
      " 5   economysituation      158 non-null    float64\n",
      " 6   family                158 non-null    float64\n",
      " 7   healthlifeexpectancy  158 non-null    float64\n",
      " 8   freedom               158 non-null    float64\n",
      " 9   governmentcorruption  158 non-null    float64\n",
      " 10  generosity            158 non-null    float64\n",
      " 11  dystopiaresidual      158 non-null    float64\n",
      " 12  terrorismevent        158 non-null    int64  \n",
      "dtypes: float64(9), int64(2), object(2)\n",
      "memory usage: 16.2+ KB\n",
      "None\n",
      "       happinessrank  happinessscore  standarderror  economysituation  \\\n",
      "count     158.000000      158.000000     158.000000        158.000000   \n",
      "mean       79.493671        5.375734       0.047885          0.846137   \n",
      "std        45.754363        1.145010       0.017146          0.403121   \n",
      "min         1.000000        2.839000       0.018480          0.000000   \n",
      "25%        40.250000        4.526000       0.037268          0.545808   \n",
      "50%        79.500000        5.232500       0.043940          0.910245   \n",
      "75%       118.750000        6.243750       0.052300          1.158448   \n",
      "max       158.000000        7.587000       0.136930          1.690420   \n",
      "\n",
      "           family  healthlifeexpectancy     freedom  governmentcorruption  \\\n",
      "count  158.000000            158.000000  158.000000            158.000000   \n",
      "mean     0.991046              0.630259    0.428615              0.143422   \n",
      "std      0.272369              0.247078    0.150693              0.120034   \n",
      "min      0.000000              0.000000    0.000000              0.000000   \n",
      "25%      0.856823              0.439185    0.328330              0.061675   \n",
      "50%      1.029510              0.696705    0.435515              0.107220   \n",
      "75%      1.214405              0.811013    0.549092              0.180255   \n",
      "max      1.402230              1.025250    0.669730              0.551910   \n",
      "\n",
      "       generosity  dystopiaresidual  terrorismevent  \n",
      "count  158.000000        158.000000      158.000000  \n",
      "mean     0.237296          2.098977       90.145570  \n",
      "std      0.126685          0.553550      316.008825  \n",
      "min      0.000000          0.328580        0.000000  \n",
      "25%      0.150553          1.759410        0.000000  \n",
      "50%      0.216130          2.095415        1.500000  \n",
      "75%      0.309882          2.462415       18.750000  \n",
      "max      0.795880          3.602140     2750.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data1.columns)\n",
    "\n",
    "print(data1.info())\n",
    "\n",
    "print(data1.describe())\n",
    "\n",
    "df1 = data1[\"country\"]\n",
    "dff1 = df1.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01af115e",
   "metadata": {
    "papermill": {
     "duration": 0.074062,
     "end_time": "2021-11-29T19:17:45.357734",
     "exception": false,
     "start_time": "2021-11-29T19:17:45.283672",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2015 verilerimizin 158 ülke, boş olmayan değerlere sahip 12 özellik içerdiğini ve diğer tüm istatistiksel bilgilere kod çıktısından ulaşıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd494a32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:45.514046Z",
     "iopub.status.busy": "2021-11-29T19:17:45.513228Z",
     "iopub.status.idle": "2021-11-29T19:17:45.516036Z",
     "shell.execute_reply": "2021-11-29T19:17:45.516654Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.312452Z"
    },
    "papermill": {
     "duration": 0.084979,
     "end_time": "2021-11-29T19:17:45.516832",
     "exception": false,
     "start_time": "2021-11-29T19:17:45.431853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x1 = data1.iloc[:,5:].values\n",
    "y1 = data1.iloc[:,3:4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55e354c",
   "metadata": {
    "papermill": {
     "duration": 0.075565,
     "end_time": "2021-11-29T19:17:45.666592",
     "exception": false,
     "start_time": "2021-11-29T19:17:45.591027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak x mutluluk puanını etkileyen girdiler ve y sonuç olan mutluluk puanı sütunu olarak x ve y değişkenine atanırlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18278ab9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:45.820322Z",
     "iopub.status.busy": "2021-11-29T19:17:45.819640Z",
     "iopub.status.idle": "2021-11-29T19:17:46.010102Z",
     "shell.execute_reply": "2021-11-29T19:17:46.009527Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.320823Z"
    },
    "papermill": {
     "duration": 0.269605,
     "end_time": "2021-11-29T19:17:46.010259",
     "exception": false,
     "start_time": "2021-11-29T19:17:45.740654",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x1, y1, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48810d5c",
   "metadata": {
    "papermill": {
     "duration": 0.073883,
     "end_time": "2021-11-29T19:17:46.158683",
     "exception": false,
     "start_time": "2021-11-29T19:17:46.084800",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak test ve eğitim kümeleri oluşturulur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0aad8cd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:46.315827Z",
     "iopub.status.busy": "2021-11-29T19:17:46.314733Z",
     "iopub.status.idle": "2021-11-29T19:17:46.320703Z",
     "shell.execute_reply": "2021-11-29T19:17:46.321228Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.388653Z"
    },
    "papermill": {
     "duration": 0.08849,
     "end_time": "2021-11-29T19:17:46.321401",
     "exception": false,
     "start_time": "2021-11-29T19:17:46.232911",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train1 = sc.fit_transform(x_train1)\n",
    "X_test1 = sc.fit_transform(x_test1)\n",
    "Y_train1 = sc.fit_transform(y_train1)\n",
    "Y_test1 = sc.fit_transform(y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd93e64",
   "metadata": {
    "papermill": {
     "duration": 0.07388,
     "end_time": "2021-11-29T19:17:46.469587",
     "exception": false,
     "start_time": "2021-11-29T19:17:46.395707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak normalizasyon işlemi yapılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84ffefab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:46.622948Z",
     "iopub.status.busy": "2021-11-29T19:17:46.622247Z",
     "iopub.status.idle": "2021-11-29T19:17:46.875773Z",
     "shell.execute_reply": "2021-11-29T19:17:46.875198Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.399034Z"
    },
    "papermill": {
     "duration": 0.33222,
     "end_time": "2021-11-29T19:17:46.875935",
     "exception": false,
     "start_time": "2021-11-29T19:17:46.543715",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0:  [0.00019598]\n",
      "other b:  [[1.00018309e+00 9.99923896e-01 9.99793362e-01 9.99531428e-01\n",
      "  9.99958783e-01 9.99985848e-01 1.00003007e+00 7.95088220e-08]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train1, y_train1)\n",
    "print(\"b0: \", lr.intercept_)\n",
    "print(\"other b: \", lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6360e127",
   "metadata": {
    "papermill": {
     "duration": 0.075422,
     "end_time": "2021-11-29T19:17:47.026867",
     "exception": false,
     "start_time": "2021-11-29T19:17:46.951445",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak makine öğrenmesi modeli oluşturulur ve modele göre ağırlık çarpan değerleri hesaplanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66687c3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:47.185361Z",
     "iopub.status.busy": "2021-11-29T19:17:47.184688Z",
     "iopub.status.idle": "2021-11-29T19:17:47.188079Z",
     "shell.execute_reply": "2021-11-29T19:17:47.188594Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.453636Z"
    },
    "papermill": {
     "duration": 0.087123,
     "end_time": "2021-11-29T19:17:47.188792",
     "exception": false,
     "start_time": "2021-11-29T19:17:47.101669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is  [[5.38965305]]\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = lr.predict(x_test1)\n",
    "prediction1 = lr.predict(np.array([[1.16492,0.87717,0.64718,0.23889,0.12348,0.04707,2.29074,542]]))\n",
    "print(\"Prediction is \", prediction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff041053",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:47.346928Z",
     "iopub.status.busy": "2021-11-29T19:17:47.346263Z",
     "iopub.status.idle": "2021-11-29T19:17:47.349879Z",
     "shell.execute_reply": "2021-11-29T19:17:47.349027Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.462721Z"
    },
    "papermill": {
     "duration": 0.085712,
     "end_time": "2021-11-29T19:17:47.350026",
     "exception": false,
     "start_time": "2021-11-29T19:17:47.264314",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is  [[5.50012402]]\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = lr.predict(x_test1)\n",
    "prediction1 = lr.predict(np.array([[1.198274,1.337753,0.637606,0.300741,0.099672,0.046693,1.879278,181]]))\n",
    "print(\"Prediction is \", prediction1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ef0239",
   "metadata": {
    "papermill": {
     "duration": 0.075013,
     "end_time": "2021-11-29T19:17:47.500268",
     "exception": false,
     "start_time": "2021-11-29T19:17:47.425255",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktıları olarak Türkiye için 2015 raporunda gerçek değerin 5.332 mutluluk puanı olacağını rapordan görüyoruz, modelimiz 2016 raporundan 5.38965305 ve 2017 raporundan 5.50012402 olarak tahmin ediyor. 2017 değerinin ve diğerlerinin farklılıklarının nedeninin özelliklerin ve terörizmin etkisinin değiştiğini tahmin ediyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "addab9e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:47.659327Z",
     "iopub.status.busy": "2021-11-29T19:17:47.658565Z",
     "iopub.status.idle": "2021-11-29T19:17:49.006033Z",
     "shell.execute_reply": "2021-11-29T19:17:49.006620Z",
     "shell.execute_reply.started": "2021-11-29T19:16:01.483623Z"
    },
    "papermill": {
     "duration": 1.431708,
     "end_time": "2021-11-29T19:17:49.006824",
     "exception": false,
     "start_time": "2021-11-29T19:17:47.575116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low_c = '#dd4124'\n",
    "high_c = '#009473'\n",
    "background_color = '#fbfbfb'\n",
    "fig = plt.figure(figsize=(12, 10), dpi=150,facecolor=background_color)\n",
    "gs = fig.add_gridspec(3, 3)\n",
    "gs.update(wspace=0.2, hspace=0.5)\n",
    "\n",
    "newdata1 = data1.iloc[:,4:]\n",
    "categorical = [var for var in newdata1.columns if newdata1[var].dtype=='O']\n",
    "continuous = [var for var in newdata1.columns if newdata1[var].dtype!='O']\n",
    "\n",
    "happiness_mean = data1['happinessscore'].mean()\n",
    "\n",
    "data1['lower_happy'] = data1['happinessscore'].apply(lambda x: 0 if x < happiness_mean else 1)\n",
    "\n",
    "plot = 0\n",
    "for row in range(0, 3):\n",
    "    for col in range(0, 3):\n",
    "        locals()[\"ax\"+str(plot)] = fig.add_subplot(gs[row, col])\n",
    "        locals()[\"ax\"+str(plot)].set_facecolor(background_color)\n",
    "        locals()[\"ax\"+str(plot)].tick_params(axis='y', left=False)\n",
    "        locals()[\"ax\"+str(plot)].get_yaxis().set_visible(False)\n",
    "        locals()[\"ax\"+str(plot)].set_axisbelow(True)\n",
    "        for s in [\"top\",\"right\",\"left\"]:\n",
    "            locals()[\"ax\"+str(plot)].spines[s].set_visible(False)\n",
    "        plot += 1\n",
    "\n",
    "plot = 0\n",
    "\n",
    "Yes = data1[data1['lower_happy'] == 1]\n",
    "No = data1[data1['lower_happy'] == 0]\n",
    "\n",
    "for variable in continuous:\n",
    "        sns.kdeplot(Yes[variable],ax=locals()[\"ax\"+str(plot)], color=high_c,ec='black', shade=True, linewidth=1.5, alpha=0.9, zorder=3, legend=False)\n",
    "        sns.kdeplot(No[variable],ax=locals()[\"ax\"+str(plot)], color=low_c, shade=True, ec='black',linewidth=1.5, alpha=0.9, zorder=3, legend=False)\n",
    "        locals()[\"ax\"+str(plot)].grid(which='major', axis='x', zorder=0, color='gray', linestyle=':', dashes=(1,5))\n",
    "        locals()[\"ax\"+str(plot)].set_xlabel(variable, fontfamily='monospace')\n",
    "        plot += 1\n",
    "        \n",
    "Xstart, Xend = ax0.get_xlim()\n",
    "Ystart, Yend = ax0.get_ylim()\n",
    "\n",
    "ax0.text(Xstart, Yend+(Yend*0.5), 'Mutlu ve Mutsuz Ülkeler Arasındaki Farklar', fontsize=17, fontweight='bold', fontfamily='sansserif',color='#323232')\n",
    "ax0.text(Xstart, Yend+(Yend*0.25), 'GSYİH ve Sosyal Desteğin açık olması belki daha ilginç olsa da, büyük farklılıklar var,\\nmutsuz ülkeler daha cömert görünüyor.', fontsize=12, fontweight='light', fontfamily='monospace',color='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "731f1751",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:49.181846Z",
     "iopub.status.busy": "2021-11-29T19:17:49.180939Z",
     "iopub.status.idle": "2021-11-29T19:17:49.331021Z",
     "shell.execute_reply": "2021-11-29T19:17:49.332561Z",
     "shell.execute_reply.started": "2021-11-29T19:16:02.874378Z"
    },
    "papermill": {
     "duration": 0.242886,
     "end_time": "2021-11-29T19:17:49.332925",
     "exception": false,
     "start_time": "2021-11-29T19:17:49.090039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 3.212e+08\n",
      "Date:                Mon, 29 Nov 2021   Prob (F-statistic):               0.00\n",
      "Time:                        19:17:49   Log-Likelihood:                 1071.3\n",
      "No. Observations:                 158   AIC:                            -2125.\n",
      "Df Residuals:                     149   BIC:                            -2097.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       7.059e-05      0.000      0.546      0.586      -0.000       0.000\n",
      "x1             1.0001      0.000   8690.407      0.000       1.000       1.000\n",
      "x2             1.0000      0.000   8440.900      0.000       1.000       1.000\n",
      "x3             0.9999      0.000   6135.033      0.000       1.000       1.000\n",
      "x4             0.9997      0.000   4798.002      0.000       0.999       1.000\n",
      "x5             0.9999      0.000   4453.117      0.000       0.999       1.000\n",
      "x6             1.0001      0.000   4837.157      0.000       1.000       1.000\n",
      "x7             1.0000   4.19e-05   2.39e+04      0.000       1.000       1.000\n",
      "x8          -1.55e-08   7.95e-08     -0.195      0.846   -1.73e-07    1.42e-07\n",
      "==============================================================================\n",
      "Omnibus:                       35.265   Durbin-Watson:                   2.047\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                7.700\n",
      "Skew:                          -0.072   Prob(JB):                       0.0213\n",
      "Kurtosis:                       1.928   Cond. No.                     3.75e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.75e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.regression.linear_model as sm\n",
    "X1 = np.append(arr = np.ones((158,1)).astype(int), values=x1, axis=1)\n",
    "r_ols1 = sm.OLS(endog = y1, exog = X1)\n",
    "r1 = r_ols1.fit()\n",
    "print(r1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f15b5c",
   "metadata": {
    "papermill": {
     "duration": 0.085744,
     "end_time": "2021-11-29T19:17:49.542599",
     "exception": false,
     "start_time": "2021-11-29T19:17:49.456855",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak R-squared ve Adj. R-squared değerleri(1.000 ve 1.000) Makine Öğrenimi Yönteminin Dünya Mutluluk Sıralaması 2015 Verilerindeki Başarısı'nı göstermektedir. Bu değerlerin 1 veya 1'e yakın olması modelimizin çok başarılı olduğunu anlıyoruz. Bu nedenle, seçili Makine Öğrenimi yöntemimizi değiştirmiyoruz.\n",
    "\n",
    "P>|t| değeri Değişkenlerin Dünya Mutluluk Sıralaması 2015 Verileri Üzerindeki Başarısı'nı göstermektedir. 1 olarak eklediğimiz sabit değer ve eklenen terör olayı değerlerimizin p değerlerinin %5 veya %1 eşik değerlerinden fazla olduğunu anlıyoruz. Bu, terörizm olaylarının değerlerinin modelimiz için uygun ve etkilenebilir olmadığı anlamına gelir."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6913bad3",
   "metadata": {
    "papermill": {
     "duration": 0.084211,
     "end_time": "2021-11-29T19:17:49.710347",
     "exception": false,
     "start_time": "2021-11-29T19:17:49.626136",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **World Happiness Rank 2016**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b0ff57c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:49.900801Z",
     "iopub.status.busy": "2021-11-29T19:17:49.900081Z",
     "iopub.status.idle": "2021-11-29T19:17:49.933930Z",
     "shell.execute_reply": "2021-11-29T19:17:49.934428Z",
     "shell.execute_reply.started": "2021-11-29T19:16:02.947454Z"
    },
    "papermill": {
     "duration": 0.140649,
     "end_time": "2021-11-29T19:17:49.934637",
     "exception": false,
     "start_time": "2021-11-29T19:17:49.793988",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'region', 'happinessrank', 'happinessscore',\n",
      "       'lowerconfidenceinterval', 'upperconfidenceinterval',\n",
      "       'economysituation', 'family', 'healthlifeexpectancy', 'freedom',\n",
      "       'governmentcorruption', 'generosity', 'dystopiaresidual',\n",
      "       'terrorismevent'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157 entries, 0 to 156\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   country                  157 non-null    object \n",
      " 1   region                   157 non-null    object \n",
      " 2   happinessrank            157 non-null    int64  \n",
      " 3   happinessscore           157 non-null    float64\n",
      " 4   lowerconfidenceinterval  157 non-null    float64\n",
      " 5   upperconfidenceinterval  157 non-null    float64\n",
      " 6   economysituation         157 non-null    float64\n",
      " 7   family                   157 non-null    float64\n",
      " 8   healthlifeexpectancy     157 non-null    float64\n",
      " 9   freedom                  157 non-null    float64\n",
      " 10  governmentcorruption     157 non-null    float64\n",
      " 11  generosity               157 non-null    float64\n",
      " 12  dystopiaresidual         157 non-null    float64\n",
      " 13  terrorismevent           157 non-null    int64  \n",
      "dtypes: float64(10), int64(2), object(2)\n",
      "memory usage: 17.3+ KB\n",
      "None\n",
      "       happinessrank  happinessscore  lowerconfidenceinterval  \\\n",
      "count     157.000000      157.000000               157.000000   \n",
      "mean       78.980892        5.382185                 5.282395   \n",
      "std        45.466030        1.141674                 1.148043   \n",
      "min         1.000000        2.905000                 2.732000   \n",
      "25%        40.000000        4.404000                 4.327000   \n",
      "50%        79.000000        5.314000                 5.237000   \n",
      "75%       118.000000        6.269000                 6.154000   \n",
      "max       157.000000        7.526000                 7.460000   \n",
      "\n",
      "       upperconfidenceinterval  economysituation      family  \\\n",
      "count               157.000000        157.000000  157.000000   \n",
      "mean                  5.481975          0.953880    0.793621   \n",
      "std                   1.136493          0.412595    0.266706   \n",
      "min                   3.078000          0.000000    0.000000   \n",
      "25%                   4.465000          0.670240    0.641840   \n",
      "50%                   5.419000          1.027800    0.841420   \n",
      "75%                   6.434000          1.279640    1.021520   \n",
      "max                   7.669000          1.824270    1.183260   \n",
      "\n",
      "       healthlifeexpectancy     freedom  governmentcorruption  generosity  \\\n",
      "count            157.000000  157.000000            157.000000  157.000000   \n",
      "mean               0.557619    0.370994              0.137624    0.242635   \n",
      "std                0.229349    0.145507              0.111038    0.133756   \n",
      "min                0.000000    0.000000              0.000000    0.000000   \n",
      "25%                0.382910    0.257480              0.061260    0.154570   \n",
      "50%                0.596590    0.397470              0.105470    0.222450   \n",
      "75%                0.729930    0.484530              0.175540    0.311850   \n",
      "max                0.952770    0.608480              0.505210    0.819710   \n",
      "\n",
      "       dystopiaresidual  terrorismevent  \n",
      "count        157.000000      157.000000  \n",
      "mean           2.325807       84.783439  \n",
      "std            0.542220      330.136516  \n",
      "min            0.817890        0.000000  \n",
      "25%            2.031710        0.000000  \n",
      "50%            2.290740        2.000000  \n",
      "75%            2.664650       16.000000  \n",
      "max            3.837720     3360.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data2.columns)\n",
    "\n",
    "print(data2.info())\n",
    "\n",
    "print(data2.describe())\n",
    "\n",
    "df2 = data2[\"country\"]\n",
    "dff2 = df2.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee103e1",
   "metadata": {
    "papermill": {
     "duration": 0.08778,
     "end_time": "2021-11-29T19:17:50.108640",
     "exception": false,
     "start_time": "2021-11-29T19:17:50.020860",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2016 verilerimizin 157 ülke, boş olmayan değerlere sahip 13 özellik içerdiğini ve diğer tüm istatistiksel bilgilere kod çıktısından ulaşıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "433445da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:50.282446Z",
     "iopub.status.busy": "2021-11-29T19:17:50.281722Z",
     "iopub.status.idle": "2021-11-29T19:17:50.286606Z",
     "shell.execute_reply": "2021-11-29T19:17:50.287142Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.029574Z"
    },
    "papermill": {
     "duration": 0.092496,
     "end_time": "2021-11-29T19:17:50.287342",
     "exception": false,
     "start_time": "2021-11-29T19:17:50.194846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x2 = data2.iloc[:,6:].values\n",
    "y2 = data2.iloc[:,3:4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4551e058",
   "metadata": {
    "papermill": {
     "duration": 0.083747,
     "end_time": "2021-11-29T19:17:50.457699",
     "exception": false,
     "start_time": "2021-11-29T19:17:50.373952",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak x mutluluk puanını etkileyen girdiler ve y sonuç olan mutluluk puanı sütunu olarak x ve y değişkenine atanırlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6c0b7298",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:50.630666Z",
     "iopub.status.busy": "2021-11-29T19:17:50.629978Z",
     "iopub.status.idle": "2021-11-29T19:17:50.633150Z",
     "shell.execute_reply": "2021-11-29T19:17:50.633673Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.039755Z"
    },
    "papermill": {
     "duration": 0.09248,
     "end_time": "2021-11-29T19:17:50.633858",
     "exception": false,
     "start_time": "2021-11-29T19:17:50.541378",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train2, x_test2, y_train2, y_test2 = train_test_split(x2, y2, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f1be7f",
   "metadata": {
    "papermill": {
     "duration": 0.08344,
     "end_time": "2021-11-29T19:17:50.801557",
     "exception": false,
     "start_time": "2021-11-29T19:17:50.718117",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak test ve eğitim kümeleri oluşturulur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41541276",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:50.974685Z",
     "iopub.status.busy": "2021-11-29T19:17:50.973957Z",
     "iopub.status.idle": "2021-11-29T19:17:50.980424Z",
     "shell.execute_reply": "2021-11-29T19:17:50.980913Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.050018Z"
    },
    "papermill": {
     "duration": 0.094881,
     "end_time": "2021-11-29T19:17:50.981099",
     "exception": false,
     "start_time": "2021-11-29T19:17:50.886218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train2 = sc.fit_transform(x_train2)\n",
    "X_test2 = sc.fit_transform(x_test2)\n",
    "Y_train2 = sc.fit_transform(y_train2)\n",
    "Y_test2 = sc.fit_transform(y_test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16573d1f",
   "metadata": {
    "papermill": {
     "duration": 0.084223,
     "end_time": "2021-11-29T19:17:51.151105",
     "exception": false,
     "start_time": "2021-11-29T19:17:51.066882",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak normalizasyon işlemi yapılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7127142c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:51.323884Z",
     "iopub.status.busy": "2021-11-29T19:17:51.323160Z",
     "iopub.status.idle": "2021-11-29T19:17:51.330585Z",
     "shell.execute_reply": "2021-11-29T19:17:51.331221Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.063316Z"
    },
    "papermill": {
     "duration": 0.095506,
     "end_time": "2021-11-29T19:17:51.331422",
     "exception": false,
     "start_time": "2021-11-29T19:17:51.235916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0:  [-6.53325261e-05]\n",
      "other b:  [[ 1.00000516e+00  9.99993864e-01  9.99829543e-01  1.00007547e+00\n",
      "   9.99873091e-01  1.00035778e+00  1.00004516e+00 -4.71162706e-07]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train2, y_train2)\n",
    "print(\"b0: \", lr.intercept_)\n",
    "print(\"other b: \", lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfed1a58",
   "metadata": {
    "papermill": {
     "duration": 0.089519,
     "end_time": "2021-11-29T19:17:51.509848",
     "exception": false,
     "start_time": "2021-11-29T19:17:51.420329",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak makine öğrenmesi modeli oluşturulur ve modele göre ağırlık çarpan değerleri hesaplanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3c04850",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:51.682247Z",
     "iopub.status.busy": "2021-11-29T19:17:51.681532Z",
     "iopub.status.idle": "2021-11-29T19:17:51.690057Z",
     "shell.execute_reply": "2021-11-29T19:17:51.689010Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.075440Z"
    },
    "papermill": {
     "duration": 0.095966,
     "end_time": "2021-11-29T19:17:51.690336",
     "exception": false,
     "start_time": "2021-11-29T19:17:51.594370",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is  [[5.33218602]]\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = lr.predict(x_test2)\n",
    "prediction2 = lr.predict(np.array([[1.06098,0.94632,0.73172,0.22815,0.15746,0.12253,2.08528,422]]))\n",
    "print(\"Prediction is \", prediction2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f8a4ecf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:51.866298Z",
     "iopub.status.busy": "2021-11-29T19:17:51.865606Z",
     "iopub.status.idle": "2021-11-29T19:17:51.873824Z",
     "shell.execute_reply": "2021-11-29T19:17:51.874900Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.087219Z"
    },
    "papermill": {
     "duration": 0.098028,
     "end_time": "2021-11-29T19:17:51.875226",
     "exception": false,
     "start_time": "2021-11-29T19:17:51.777198",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is  [[5.49986729]]\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = lr.predict(x_test2)\n",
    "prediction2 = lr.predict(np.array([[1.198274,1.337753,0.637606,0.300741,0.099672,0.046693,1.879278,181]]))\n",
    "print(\"Prediction is \", prediction2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08bafd6",
   "metadata": {
    "papermill": {
     "duration": 0.085963,
     "end_time": "2021-11-29T19:17:52.048731",
     "exception": false,
     "start_time": "2021-11-29T19:17:51.962768",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Türkiye için 2016 raporunda gerçek değerin 5.389 mutluluk puanı olacağını kod çıktısından görüyoruz, modelimiz 2015 raporundan 5.33218602 ve 2017 raporundan 5.49986729 olarak tahmin ediyor. 2017 değerinin ve diğerlerinin farklılıklarının nedeninin özelliklerin ve terörizmin etkisinin değiştiğini tahmin ediyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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   ],
   "source": [
    "figure = bubbleplot(dataset = data2, x_column = 'happinessscore', y_column = 'healthlifeexpectancy', \n",
    "    bubble_column = 'country', size_column = 'economysituation', color_column = 'region', \n",
    "    x_title = \"Happiness Score\", y_title = \"Health Life Expectancy\", title = 'Happiness vs Health Life Expectancy vs Economy',\n",
    "    x_logscale = False, scale_bubble = 1, height = 650)\n",
    "\n",
    "po.iplot(figure)"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 2.791e+08\n",
      "Date:                Mon, 29 Nov 2021   Prob (F-statistic):               0.00\n",
      "Time:                        19:17:53   Log-Likelihood:                 1054.5\n",
      "No. Observations:                 157   AIC:                            -2091.\n",
      "Df Residuals:                     148   BIC:                            -2063.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0001      0.000      1.083      0.281      -0.000       0.000\n",
      "x1             1.0000      0.000   8035.348      0.000       1.000       1.000\n",
      "x2             1.0000      0.000   7526.126      0.000       1.000       1.000\n",
      "x3             0.9998      0.000   5028.477      0.000       0.999       1.000\n",
      "x4             1.0002      0.000   4545.688      0.000       1.000       1.001\n",
      "x5             0.9997      0.000   3825.124      0.000       0.999       1.000\n",
      "x6             1.0002      0.000   4808.159      0.000       1.000       1.001\n",
      "x7             0.9999   4.59e-05   2.18e+04      0.000       1.000       1.000\n",
      "x8         -3.834e-08   7.69e-08     -0.499      0.619    -1.9e-07    1.14e-07\n",
      "==============================================================================\n",
      "Omnibus:                       61.489   Durbin-Watson:                   2.041\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                9.415\n",
      "Skew:                          -0.083   Prob(JB):                      0.00903\n",
      "Kurtosis:                       1.812   Cond. No.                     4.10e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.1e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.regression.linear_model as sm\n",
    "X2 = np.append(arr = np.ones((157,1)).astype(int), values=x2, axis=1)\n",
    "r_ols2 = sm.OLS(endog = y2, exog = X2)\n",
    "r2 = r_ols2.fit()\n",
    "print(r2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ffc6842",
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   "source": [
    "Kod çıktısı olarak R-squared ve Adj. R-squared değerleri(1.000 ve 1.000) Makine Öğrenimi Yönteminin Dünya Mutluluk Sıralaması 2016 Verilerindeki Başarısı'nı göstermektedir. Bu değerlerin 1 veya 1'e yakın olması modelimizin çok başarılı olduğunu anlıyoruz. Bu nedenle, seçili Makine Öğrenimi yöntemimizi değiştirmiyoruz.\n",
    "\n",
    "P>|t| değeri Değişkenlerin Dünya Mutluluk Sıralaması 2016 Verileri Üzerindeki Başarısı'nı göstermektedir. 1 olarak eklediğimiz sabit ve eklenen terör olayı değerlerimizin p değerlerinin %5 veya %1 eşik değerlerinden fazla olduğunu anlıyoruz. Bu, terörizm olaylarının değerlerinin modelimiz için uygun ve etkilenebilir olmadığı anlamına gelir. Ancak bu p değerlerinin azaldığını görüyoruz, bu modelimize olan etkilerinin artması anlamına geliyor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb0eb6a",
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     "start_time": "2021-11-29T19:17:53.786910",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **World Happiness Rank 2017**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91212340",
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    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'happinessrank', 'happinessscore', 'whiskerhigh',\n",
      "       'whiskerlow', 'economysituation', 'family', 'healthlifeexpectancy',\n",
      "       'freedom', 'governmentcorruption.', 'generosity', 'dystopiaresidual',\n",
      "       'terrorismevent'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155 entries, 0 to 154\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   country                155 non-null    object \n",
      " 1   happinessrank          155 non-null    int64  \n",
      " 2   happinessscore         155 non-null    float64\n",
      " 3   whiskerhigh            155 non-null    float64\n",
      " 4   whiskerlow             155 non-null    float64\n",
      " 5   economysituation       155 non-null    float64\n",
      " 6   family                 155 non-null    float64\n",
      " 7   healthlifeexpectancy   155 non-null    float64\n",
      " 8   freedom                155 non-null    float64\n",
      " 9   governmentcorruption.  155 non-null    float64\n",
      " 10  generosity             155 non-null    float64\n",
      " 11  dystopiaresidual       155 non-null    float64\n",
      " 12  terrorismevent         155 non-null    int64  \n",
      "dtypes: float64(10), int64(2), object(1)\n",
      "memory usage: 15.9+ KB\n",
      "None\n",
      "       happinessrank  happinessscore  whiskerhigh  whiskerlow  \\\n",
      "count     155.000000      155.000000   155.000000  155.000000   \n",
      "mean       78.000000        5.354019     5.452326    5.255713   \n",
      "std        44.888751        1.131230     1.118542    1.145030   \n",
      "min         1.000000        2.693000     2.864884    2.521116   \n",
      "25%        39.500000        4.505500     4.608172    4.374955   \n",
      "50%        78.000000        5.279000     5.370032    5.193152   \n",
      "75%       116.500000        6.101500     6.194600    6.006527   \n",
      "max       155.000000        7.537000     7.622030    7.479556   \n",
      "\n",
      "       economysituation      family  healthlifeexpectancy     freedom  \\\n",
      "count        155.000000  155.000000            155.000000  155.000000   \n",
      "mean           0.984718    1.188898              0.551341    0.408786   \n",
      "std            0.420793    0.287263              0.237073    0.149997   \n",
      "min            0.000000    0.000000              0.000000    0.000000   \n",
      "25%            0.663371    1.042635              0.369866    0.303677   \n",
      "50%            1.064578    1.253918              0.606042    0.437454   \n",
      "75%            1.318027    1.414316              0.723008    0.516561   \n",
      "max            1.870766    1.610574              0.949492    0.658249   \n",
      "\n",
      "       governmentcorruption.  generosity  dystopiaresidual  terrorismevent  \n",
      "count             155.000000  155.000000        155.000000      155.000000  \n",
      "mean                0.123120    0.246883          1.850238       69.761290  \n",
      "std                 0.101661    0.134780          0.500028      259.039988  \n",
      "min                 0.000000    0.000000          0.377914        0.000000  \n",
      "25%                 0.057271    0.154106          1.591292        0.000000  \n",
      "50%                 0.089848    0.231538          1.832910        2.000000  \n",
      "75%                 0.153297    0.323762          2.144653       22.000000  \n",
      "max                 0.464308    0.838075          3.117485     2466.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data3.columns)\n",
    "\n",
    "print(data3.info())\n",
    "\n",
    "print(data3.describe())\n",
    "\n",
    "df3 = data3[\"country\"]\n",
    "dff3 = df3.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8feb0e4",
   "metadata": {
    "papermill": {
     "duration": 0.095177,
     "end_time": "2021-11-29T19:17:54.314994",
     "exception": false,
     "start_time": "2021-11-29T19:17:54.219817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2017 verilerimizin 155 ülke, boş olmayan değerlere sahip 12 özellik içerdiğini ve diğer tüm istatistiksel bilgilere kod çıktısından ulaşıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2b27260c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:54.514009Z",
     "iopub.status.busy": "2021-11-29T19:17:54.513224Z",
     "iopub.status.idle": "2021-11-29T19:17:54.515735Z",
     "shell.execute_reply": "2021-11-29T19:17:54.516252Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.603569Z"
    },
    "papermill": {
     "duration": 0.105582,
     "end_time": "2021-11-29T19:17:54.516425",
     "exception": false,
     "start_time": "2021-11-29T19:17:54.410843",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x3 = data3.iloc[:,5:].values\n",
    "y3 = data3.iloc[:,2:3].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b78c824",
   "metadata": {
    "papermill": {
     "duration": 0.095401,
     "end_time": "2021-11-29T19:17:54.708131",
     "exception": false,
     "start_time": "2021-11-29T19:17:54.612730",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak x mutluluk puanını etkileyen girdiler ve y sonuç olan mutluluk puanı sütunu olarak x ve y değişkenine atanırlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "37b52645",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:54.902326Z",
     "iopub.status.busy": "2021-11-29T19:17:54.901592Z",
     "iopub.status.idle": "2021-11-29T19:17:54.907275Z",
     "shell.execute_reply": "2021-11-29T19:17:54.906758Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.612302Z"
    },
    "papermill": {
     "duration": 0.103668,
     "end_time": "2021-11-29T19:17:54.907428",
     "exception": false,
     "start_time": "2021-11-29T19:17:54.803760",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train3, x_test3, y_train3, y_test3 = train_test_split(x3, y3, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00386b24",
   "metadata": {
    "papermill": {
     "duration": 0.096789,
     "end_time": "2021-11-29T19:17:55.100109",
     "exception": false,
     "start_time": "2021-11-29T19:17:55.003320",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak test ve eğitim kümeleri oluşturulur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ba49ff2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:55.299019Z",
     "iopub.status.busy": "2021-11-29T19:17:55.298240Z",
     "iopub.status.idle": "2021-11-29T19:17:55.305021Z",
     "shell.execute_reply": "2021-11-29T19:17:55.305597Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.630551Z"
    },
    "papermill": {
     "duration": 0.10956,
     "end_time": "2021-11-29T19:17:55.305785",
     "exception": false,
     "start_time": "2021-11-29T19:17:55.196225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train3 = sc.fit_transform(x_train3)\n",
    "X_test3 = sc.fit_transform(x_test3)\n",
    "Y_train3 = sc.fit_transform(y_train3)\n",
    "Y_test3 = sc.fit_transform(y_test3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5954ea57",
   "metadata": {
    "papermill": {
     "duration": 0.111936,
     "end_time": "2021-11-29T19:17:55.515455",
     "exception": false,
     "start_time": "2021-11-29T19:17:55.403519",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak normalizasyon işlemi yapılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e8eb3d74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:55.722747Z",
     "iopub.status.busy": "2021-11-29T19:17:55.722037Z",
     "iopub.status.idle": "2021-11-29T19:17:55.731100Z",
     "shell.execute_reply": "2021-11-29T19:17:55.731626Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.643099Z"
    },
    "papermill": {
     "duration": 0.10859,
     "end_time": "2021-11-29T19:17:55.731820",
     "exception": false,
     "start_time": "2021-11-29T19:17:55.623230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0:  [0.00036227]\n",
      "other b:  [[1.00005690e+00 9.99784445e-01 1.00014678e+00 1.00008395e+00\n",
      "  9.99666469e-01 1.00010484e+00 9.99879151e-01 9.60444835e-09]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train3, y_train3)\n",
    "print(\"b0: \", lr.intercept_)\n",
    "print(\"other b: \", lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa3fe1b",
   "metadata": {
    "papermill": {
     "duration": 0.095914,
     "end_time": "2021-11-29T19:17:55.975379",
     "exception": false,
     "start_time": "2021-11-29T19:17:55.879465",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak makine öğrenmesi modeli oluşturulur ve modele göre ağırlık çarpan değerleri hesaplanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d092383c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:56.176888Z",
     "iopub.status.busy": "2021-11-29T19:17:56.175986Z",
     "iopub.status.idle": "2021-11-29T19:17:56.180380Z",
     "shell.execute_reply": "2021-11-29T19:17:56.179739Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.655037Z"
    },
    "papermill": {
     "duration": 0.106828,
     "end_time": "2021-11-29T19:17:56.180545",
     "exception": false,
     "start_time": "2021-11-29T19:17:56.073717",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is  [[5.33249758]]\n"
     ]
    }
   ],
   "source": [
    "y_pred3 = lr.predict(x_test3)\n",
    "prediction3 = lr.predict(np.array([[1.06098,0.94632,0.73172,0.22815,0.15746,0.12253,2.08528,422]]))\n",
    "print(\"Prediction is \", prediction3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2c1f844",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:56.380812Z",
     "iopub.status.busy": "2021-11-29T19:17:56.380003Z",
     "iopub.status.idle": "2021-11-29T19:17:56.383846Z",
     "shell.execute_reply": "2021-11-29T19:17:56.383197Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.671855Z"
    },
    "papermill": {
     "duration": 0.107627,
     "end_time": "2021-11-29T19:17:56.383990",
     "exception": false,
     "start_time": "2021-11-29T19:17:56.276363",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is  [[5.38949664]]\n"
     ]
    }
   ],
   "source": [
    "y_pred3 = lr.predict(x_test3)\n",
    "prediction3 = lr.predict(np.array([[1.16492,0.87717,0.64718,0.23889,0.12348,0.04707,2.29074,542]]))\n",
    "print(\"Prediction is \", prediction3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5bd4e5",
   "metadata": {
    "papermill": {
     "duration": 0.096245,
     "end_time": "2021-11-29T19:17:56.577053",
     "exception": false,
     "start_time": "2021-11-29T19:17:56.480808",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    " Kod çıktısı olarak Türkiye için 2017 raporunda gerçek değerin 5,5 mutluluk puanı olacağını rapordan görüyoruz, modelimiz 2015 raporundan 5.33249758 ve 2016 raporundan 5.38949664 olarak tahmin ediyor. Bu modeli kullanmak isterseniz, denkliği nedeniyle 2015 ve 2016 raporlarını kullanabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d03c8067",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:56.773734Z",
     "iopub.status.busy": "2021-11-29T19:17:56.772708Z",
     "iopub.status.idle": "2021-11-29T19:17:56.887954Z",
     "shell.execute_reply": "2021-11-29T19:17:56.888493Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.688648Z"
    },
    "papermill": {
     "duration": 0.215279,
     "end_time": "2021-11-29T19:17:56.888694",
     "exception": false,
     "start_time": "2021-11-29T19:17:56.673415",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "colorscale": [
          [
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           0.4,
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          [
           0.6,
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          ],
          [
           0.8,
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          ],
          [
           1.0,
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         ],
         "locationmode": "country names",
         "locations": [
          "Norway",
          "Denmark",
          "Iceland",
          "Switzerland",
          "Finland",
          "Netherlands",
          "Canada",
          "New Zealand",
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          "Australia",
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          "Kenya",
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          "India",
          "Mauritania",
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          "Georgia",
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          "Guinea",
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          "Rwanda",
          "Syria",
          "Tanzania",
          "Burundi",
          "Central African Republic"
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         "text": [
          "Norway",
          "Denmark",
          "Iceland",
          "Switzerland",
          "Finland",
          "Netherlands",
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          "New Zealand",
          "Sweden",
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          "Costa Rica",
          "Austria",
          "United States",
          "Ireland",
          "Germany",
          "Belgium",
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          "United Kingdom",
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          "Malta",
          "Uruguay",
          "Guatemala",
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          "Ecuador",
          "El Salvador",
          "Poland",
          "Uzbekistan",
          "Italy",
          "Russia",
          "Belize",
          "Japan",
          "Lithuania",
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          "Latvia",
          "South Korea",
          "Moldova",
          "Romania",
          "Bolivia",
          "Turkmenistan",
          "Kazakhstan",
          "North Cyprus",
          "Slovenia",
          "Peru",
          "Mauritius",
          "Cyprus",
          "Estonia",
          "Belarus",
          "Libya",
          "Turkey",
          "Paraguay",
          "Hong Kong S.A.R., China",
          "Philippines",
          "Serbia",
          "Jordan",
          "Hungary",
          "Jamaica",
          "Croatia",
          "Kosovo",
          "China",
          "Pakistan",
          "Indonesia",
          "Venezuela",
          "Montenegro",
          "Morocco",
          "Azerbaijan",
          "Dominican Republic",
          "Greece",
          "Lebanon",
          "Portugal",
          "Bosnia and Herzegovina",
          "Honduras",
          "Macedonia",
          "Somalia",
          "Vietnam",
          "Nigeria",
          "Tajikistan",
          "Bhutan",
          "Kyrgyzstan",
          "Nepal",
          "Mongolia",
          "South Africa",
          "Tunisia",
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          "Egypt",
          "Bulgaria",
          "Sierra Leone",
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          "Albania",
          "Bangladesh",
          "Namibia",
          "Kenya",
          "Mozambique",
          "Myanmar",
          "Senegal",
          "Zambia",
          "Iraq",
          "Gabon",
          "Ethiopia",
          "Sri Lanka",
          "Armenia",
          "India",
          "Mauritania",
          "Congo (Brazzaville)",
          "Georgia",
          "Congo (Kinshasa)",
          "Mali",
          "Ivory Coast",
          "Cambodia",
          "Sudan",
          "Ghana",
          "Ukraine",
          "Uganda",
          "Burkina Faso",
          "Niger",
          "Malawi",
          "Chad",
          "Zimbabwe",
          "Lesotho",
          "Angola",
          "Afghanistan",
          "Botswana",
          "Benin",
          "Madagascar",
          "Haiti",
          "Yemen",
          "South Sudan",
          "Liberia",
          "Guinea",
          "Togo",
          "Rwanda",
          "Syria",
          "Tanzania",
          "Burundi",
          "Central African Republic"
         ],
         "type": "choropleth",
         "z": [
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mercator\",\"method\":\"relayout\"},{\"args\":[\"geo.projection.type\",\"albers usa\"],\"label\":\"albers usa\",\"method\":\"relayout\"},{\"args\":[\"geo.projection.type\",\"winkel tripel\"],\"label\":\"winkel tripel\",\"method\":\"relayout\"}],\"x\":0.1,\"y\":0.8,\"yanchor\":\"top\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('8b89367d-a5c0-407d-bd48-22581d6bb9dd');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1 = [go.Choropleth(\n",
    "               colorscale = 'Electric',\n",
    "               locationmode = 'country names',\n",
    "               locations = data3['country'],\n",
    "               text = data3['country'], \n",
    "               z = data3['happinessrank'],\n",
    "               )]\n",
    "\n",
    "layout = dict(title = 'Happiness Rank World',\n",
    "                  geo = dict(\n",
    "                      showframe = True,\n",
    "                      showocean = True,\n",
    "                      showlakes = True,\n",
    "                      showcoastlines = True,\n",
    "                      projection = dict(\n",
    "                          type = 'hammer'\n",
    "        )))\n",
    "\n",
    "\n",
    "projections = [ \"equirectangular\", \"mercator\", \"orthographic\", \"natural earth\",\"kavrayskiy7\", \n",
    "               \"miller\", \"robinson\", \"eckert4\", \"azimuthal equal area\",\"azimuthal equidistant\", \n",
    "               \"conic equal area\", \"conic conformal\", \"conic equidistant\", \"gnomonic\", \"stereographic\", \n",
    "               \"mollweide\", \"hammer\", \"transverse mercator\", \"albers usa\", \"winkel tripel\" ]\n",
    "\n",
    "buttons = [dict(args = ['geo.projection.type', y],\n",
    "           label = y, method = 'relayout') for y in projections]\n",
    "\n",
    "annot = list([ dict( x=0.1, y=0.8, text='Projection', yanchor='bottom', \n",
    "                    xref='paper', xanchor='right', showarrow=False )])\n",
    "\n",
    "\n",
    "# Update Layout Object\n",
    "layout[ 'updatemenus' ] = list([ dict( x=0.1, y=0.8, buttons=buttons, yanchor='top' )])\n",
    "layout[ 'annotations' ] = annot\n",
    "\n",
    "\n",
    "fig = go.Figure(data = trace1, layout = layout)\n",
    "po.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3fb0254",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:57.106809Z",
     "iopub.status.busy": "2021-11-29T19:17:57.105752Z",
     "iopub.status.idle": "2021-11-29T19:17:57.121950Z",
     "shell.execute_reply": "2021-11-29T19:17:57.122646Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.772237Z"
    },
    "papermill": {
     "duration": 0.129369,
     "end_time": "2021-11-29T19:17:57.122899",
     "exception": false,
     "start_time": "2021-11-29T19:17:56.993530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 2.991e+08\n",
      "Date:                Mon, 29 Nov 2021   Prob (F-statistic):               0.00\n",
      "Time:                        19:17:57   Log-Likelihood:                 1048.9\n",
      "No. Observations:                 155   AIC:                            -2080.\n",
      "Df Residuals:                     146   BIC:                            -2052.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0002      0.000      1.133      0.259      -0.000       0.000\n",
      "x1             1.0001      0.000   8408.636      0.000       1.000       1.000\n",
      "x2             0.9999      0.000   8460.692      0.000       1.000       1.000\n",
      "x3             0.9999      0.000   5357.305      0.000       1.000       1.000\n",
      "x4             1.0000      0.000   5047.282      0.000       1.000       1.000\n",
      "x5             0.9998      0.000   3593.245      0.000       0.999       1.000\n",
      "x6             1.0002      0.000   5212.624      0.000       1.000       1.001\n",
      "x7             1.0000   4.73e-05   2.12e+04      0.000       1.000       1.000\n",
      "x8          7.172e-09   9.29e-08      0.077      0.939   -1.76e-07    1.91e-07\n",
      "==============================================================================\n",
      "Omnibus:                       49.910   Durbin-Watson:                   2.033\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                8.692\n",
      "Skew:                          -0.075   Prob(JB):                       0.0130\n",
      "Kurtosis:                       1.850   Cond. No.                     3.48e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.48e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.regression.linear_model as sm\n",
    "X3 = np.append(arr = np.ones((155,1)).astype(int), values=x3, axis=1)\n",
    "r_ols3 = sm.OLS(endog = y3, exog = X3)\n",
    "r3 = r_ols3.fit()\n",
    "print(r3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a38b68e",
   "metadata": {
    "papermill": {
     "duration": 0.105109,
     "end_time": "2021-11-29T19:17:57.336286",
     "exception": false,
     "start_time": "2021-11-29T19:17:57.231177",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak R-squared ve Adj. R-squared değerleri(1.000 ve 1.000) Makine Öğrenimi Yönteminin Dünya Mutluluk Sıralaması 2017 Verilerindeki Başarısı'nı göstermektedir. Bu değerlerin 1 veya 1'e yakın olması modelimizin çok başarılı olduğunu anlıyoruz. Bu nedenle, seçili Makine Öğrenimi yöntemimizi değiştirmiyoruz.\n",
    "\n",
    "P>|t| değeri Değişkenlerin Dünya Mutluluk Sıralaması 2017 Verileri Üzerindeki Başarısı'nı göstermektedir. 1 olarak eklediğimiz sabit değer ve eklenen terör olayı değerlerimizin p değerlerinin %5 veya %1 eşik değerlerinden fazla olduğunu anlıyoruz. Bu, terörizm olaylarının değerlerinin modelimiz için uygun ve etkilenebilir olmadığı anlamına gelir."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a01365",
   "metadata": {
    "papermill": {
     "duration": 0.104574,
     "end_time": "2021-11-29T19:17:57.545375",
     "exception": false,
     "start_time": "2021-11-29T19:17:57.440801",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **World Happiness Rank 2018**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1d3e7d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:57.762316Z",
     "iopub.status.busy": "2021-11-29T19:17:57.761681Z",
     "iopub.status.idle": "2021-11-29T19:17:57.842383Z",
     "shell.execute_reply": "2021-11-29T19:17:57.841836Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.803289Z"
    },
    "papermill": {
     "duration": 0.191463,
     "end_time": "2021-11-29T19:17:57.842557",
     "exception": false,
     "start_time": "2021-11-29T19:17:57.651094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='constant',fill_value= 0)\n",
    "impdata = data4.iloc[:,2:9].values\n",
    "imputer = imputer.fit(impdata[:,2:9])\n",
    "impdata[:,2:9] = imputer.transform(impdata[:,2:9])\n",
    "data4.iloc[:,2:9] = impdata[:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db5085b",
   "metadata": {
    "papermill": {
     "duration": 0.106496,
     "end_time": "2021-11-29T19:17:58.054746",
     "exception": false,
     "start_time": "2021-11-29T19:17:57.948250",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak eksik verilerin 0 olarak doldurulması sağlanmıştır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16a5f761",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:58.269213Z",
     "iopub.status.busy": "2021-11-29T19:17:58.268584Z",
     "iopub.status.idle": "2021-11-29T19:17:58.313827Z",
     "shell.execute_reply": "2021-11-29T19:17:58.313166Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.849419Z"
    },
    "papermill": {
     "duration": 0.153773,
     "end_time": "2021-11-29T19:17:58.313988",
     "exception": false,
     "start_time": "2021-11-29T19:17:58.160215",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Overall rank', 'Country or region', 'Score', 'GDP per capita',\n",
      "       'Social support', 'Healthy life expectancy',\n",
      "       'Freedom to make life choices', 'Generosity',\n",
      "       'Perceptions of corruption'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Overall rank                  156 non-null    int64  \n",
      " 1   Country or region             156 non-null    object \n",
      " 2   Score                         156 non-null    float64\n",
      " 3   GDP per capita                156 non-null    float64\n",
      " 4   Social support                156 non-null    float64\n",
      " 5   Healthy life expectancy       156 non-null    float64\n",
      " 6   Freedom to make life choices  156 non-null    float64\n",
      " 7   Generosity                    156 non-null    float64\n",
      " 8   Perceptions of corruption     156 non-null    float64\n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 11.1+ KB\n",
      "None\n",
      "       Overall rank       Score  GDP per capita  Social support  \\\n",
      "count    156.000000  156.000000      156.000000      156.000000   \n",
      "mean      78.500000    5.375917        0.891449        1.213237   \n",
      "std       45.177428    1.119506        0.391921        0.302372   \n",
      "min        1.000000    2.905000        0.000000        0.000000   \n",
      "25%       39.750000    4.453750        0.616250        1.066750   \n",
      "50%       78.500000    5.378000        0.949500        1.255000   \n",
      "75%      117.250000    6.168500        1.197750        1.463000   \n",
      "max      156.000000    7.632000        2.096000        1.644000   \n",
      "\n",
      "       Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
      "count               156.000000                    156.000000  156.000000   \n",
      "mean                  0.597346                      0.454506    0.181006   \n",
      "std                   0.247579                      0.162424    0.098471   \n",
      "min                   0.000000                      0.000000    0.000000   \n",
      "25%                   0.422250                      0.356000    0.109500   \n",
      "50%                   0.644000                      0.487000    0.174000   \n",
      "75%                   0.777250                      0.578500    0.239000   \n",
      "max                   1.030000                      0.724000    0.598000   \n",
      "\n",
      "       Perceptions of corruption  \n",
      "count                 156.000000  \n",
      "mean                    0.111282  \n",
      "std                     0.096597  \n",
      "min                     0.000000  \n",
      "25%                     0.050750  \n",
      "50%                     0.082000  \n",
      "75%                     0.136500  \n",
      "max                     0.457000  \n"
     ]
    }
   ],
   "source": [
    "print(data4.columns)\n",
    "\n",
    "print(data4.info())\n",
    "\n",
    "print(data4.describe())\n",
    "\n",
    "df4 = data4[\"Country or region\"]\n",
    "dff4 = data4.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121cbf8b",
   "metadata": {
    "papermill": {
     "duration": 0.105649,
     "end_time": "2021-11-29T19:17:58.527469",
     "exception": false,
     "start_time": "2021-11-29T19:17:58.421820",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2018 verilerimizin 156 ülke, boş olmayan değerlere sahip 7 özellik içerdiğini ve diğer tüm istatistiksel bilgilere kod çıktısından ulaşıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2f129aeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:58.745463Z",
     "iopub.status.busy": "2021-11-29T19:17:58.742557Z",
     "iopub.status.idle": "2021-11-29T19:17:58.747374Z",
     "shell.execute_reply": "2021-11-29T19:17:58.747984Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.916098Z"
    },
    "papermill": {
     "duration": 0.114566,
     "end_time": "2021-11-29T19:17:58.748165",
     "exception": false,
     "start_time": "2021-11-29T19:17:58.633599",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x4 = data4.iloc[:,3:].values\n",
    "y4 = data4.iloc[:,2:3].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e8d7c1",
   "metadata": {
    "papermill": {
     "duration": 0.105557,
     "end_time": "2021-11-29T19:17:58.959617",
     "exception": false,
     "start_time": "2021-11-29T19:17:58.854060",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak x mutluluk puanını etkileyen girdiler ve y sonuç olan mutluluk puanı sütunu olarak x ve y değişkenine atanırlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "41f47224",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:59.175344Z",
     "iopub.status.busy": "2021-11-29T19:17:59.174635Z",
     "iopub.status.idle": "2021-11-29T19:17:59.180279Z",
     "shell.execute_reply": "2021-11-29T19:17:59.179716Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.923775Z"
    },
    "papermill": {
     "duration": 0.114189,
     "end_time": "2021-11-29T19:17:59.180436",
     "exception": false,
     "start_time": "2021-11-29T19:17:59.066247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train4, x_test4, y_train4, y_test4 = train_test_split(x4, y4, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cb4bb6",
   "metadata": {
    "papermill": {
     "duration": 0.105871,
     "end_time": "2021-11-29T19:17:59.392344",
     "exception": false,
     "start_time": "2021-11-29T19:17:59.286473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak test ve eğitim kümeleri oluşturulur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ac60add",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:17:59.606942Z",
     "iopub.status.busy": "2021-11-29T19:17:59.606182Z",
     "iopub.status.idle": "2021-11-29T19:17:59.613036Z",
     "shell.execute_reply": "2021-11-29T19:17:59.613589Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.937556Z"
    },
    "papermill": {
     "duration": 0.11581,
     "end_time": "2021-11-29T19:17:59.613764",
     "exception": false,
     "start_time": "2021-11-29T19:17:59.497954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train4 = sc.fit_transform(x_train4)\n",
    "X_test4 = sc.fit_transform(x_test4)\n",
    "Y_train4 = sc.fit_transform(y_train4)\n",
    "Y_test4 = sc.fit_transform(y_test4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f692f3f6",
   "metadata": {
    "papermill": {
     "duration": 0.105276,
     "end_time": "2021-11-29T19:17:59.825434",
     "exception": false,
     "start_time": "2021-11-29T19:17:59.720158",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak normalizasyon işlemi yapılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "335b1bca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:00.040130Z",
     "iopub.status.busy": "2021-11-29T19:18:00.039448Z",
     "iopub.status.idle": "2021-11-29T19:18:00.047392Z",
     "shell.execute_reply": "2021-11-29T19:18:00.047931Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.954162Z"
    },
    "papermill": {
     "duration": 0.117374,
     "end_time": "2021-11-29T19:18:00.048124",
     "exception": false,
     "start_time": "2021-11-29T19:17:59.930750",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0:  [1.89454482]\n",
      "other b:  [[1.17446738 0.96919352 0.74789035 1.67866448 0.13118774 0.37835954]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train4, y_train4)\n",
    "print(\"b0: \", lr.intercept_)\n",
    "print(\"other b: \", lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bd1dbc",
   "metadata": {
    "papermill": {
     "duration": 0.105742,
     "end_time": "2021-11-29T19:18:00.262844",
     "exception": false,
     "start_time": "2021-11-29T19:18:00.157102",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak makine öğrenmesi modeli oluşturulur ve modele göre ağırlık çarpan değerleri hesaplanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cc608a65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:00.478000Z",
     "iopub.status.busy": "2021-11-29T19:18:00.476996Z",
     "iopub.status.idle": "2021-11-29T19:18:00.497220Z",
     "shell.execute_reply": "2021-11-29T19:18:00.496420Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.970186Z"
    },
    "papermill": {
     "duration": 0.12909,
     "end_time": "2021-11-29T19:18:00.497432",
     "exception": false,
     "start_time": "2021-11-29T19:18:00.368342",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.789\n",
      "Model:                            OLS   Adj. R-squared:                  0.780\n",
      "Method:                 Least Squares   F-statistic:                     92.66\n",
      "Date:                Mon, 29 Nov 2021   Prob (F-statistic):           9.36e-48\n",
      "Time:                        19:18:00   Log-Likelihood:                -117.24\n",
      "No. Observations:                 156   AIC:                             248.5\n",
      "Df Residuals:                     149   BIC:                             269.8\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.8856      0.195      9.647      0.000       1.499       2.272\n",
      "x1             1.1114      0.209      5.307      0.000       0.698       1.525\n",
      "x2             0.9991      0.202      4.958      0.000       0.601       1.397\n",
      "x3             0.8085      0.331      2.440      0.016       0.154       1.463\n",
      "x4             1.3831      0.320      4.317      0.000       0.750       2.016\n",
      "x5             0.6056      0.472      1.283      0.202      -0.327       1.539\n",
      "x6             0.5951      0.525      1.133      0.259      -0.443       1.633\n",
      "==============================================================================\n",
      "Omnibus:                        6.928   Durbin-Watson:                   1.642\n",
      "Prob(Omnibus):                  0.031   Jarque-Bera (JB):                6.563\n",
      "Skew:                          -0.454   Prob(JB):                       0.0376\n",
      "Kurtosis:                       3.431   Cond. No.                         27.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.regression.linear_model as sm\n",
    "X4 = np.append(arr = np.ones((156,1)).astype(int), values=x4, axis=1)\n",
    "r_ols4 = sm.OLS(endog = y4, exog = X4)\n",
    "r4 = r_ols4.fit()\n",
    "print(r4.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4603b38c",
   "metadata": {
    "papermill": {
     "duration": 0.107505,
     "end_time": "2021-11-29T19:18:00.712673",
     "exception": false,
     "start_time": "2021-11-29T19:18:00.605168",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **World Happiness Rank 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9f4e9e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:00.930148Z",
     "iopub.status.busy": "2021-11-29T19:18:00.929443Z",
     "iopub.status.idle": "2021-11-29T19:18:00.937713Z",
     "shell.execute_reply": "2021-11-29T19:18:00.938247Z",
     "shell.execute_reply.started": "2021-11-29T19:16:03.994837Z"
    },
    "papermill": {
     "duration": 0.118808,
     "end_time": "2021-11-29T19:18:00.938429",
     "exception": false,
     "start_time": "2021-11-29T19:18:00.819621",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='constant',fill_value= 0)\n",
    "impdata = data5.iloc[:,3:9].values\n",
    "imputer = imputer.fit(impdata[:,3:9])\n",
    "impdata[:,3:9] = imputer.transform(impdata[:,3:9])\n",
    "data5.iloc[:,3:9] = impdata[:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ea38f0",
   "metadata": {
    "papermill": {
     "duration": 0.107197,
     "end_time": "2021-11-29T19:18:01.153206",
     "exception": false,
     "start_time": "2021-11-29T19:18:01.046009",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak eksik verilerin 0 olarak doldurulması sağlanmıştır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "67d28659",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:01.369468Z",
     "iopub.status.busy": "2021-11-29T19:18:01.368813Z",
     "iopub.status.idle": "2021-11-29T19:18:01.413480Z",
     "shell.execute_reply": "2021-11-29T19:18:01.412823Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.006551Z"
    },
    "papermill": {
     "duration": 0.154079,
     "end_time": "2021-11-29T19:18:01.413655",
     "exception": false,
     "start_time": "2021-11-29T19:18:01.259576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Overall rank', 'Country or region', 'Score', 'GDP per capita',\n",
      "       'Social support', 'Healthy life expectancy',\n",
      "       'Freedom to make life choices', 'Generosity',\n",
      "       'Perceptions of corruption'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Overall rank                  156 non-null    int64  \n",
      " 1   Country or region             156 non-null    object \n",
      " 2   Score                         156 non-null    float64\n",
      " 3   GDP per capita                156 non-null    float64\n",
      " 4   Social support                156 non-null    float64\n",
      " 5   Healthy life expectancy       156 non-null    float64\n",
      " 6   Freedom to make life choices  156 non-null    float64\n",
      " 7   Generosity                    156 non-null    float64\n",
      " 8   Perceptions of corruption     156 non-null    float64\n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 11.1+ KB\n",
      "None\n",
      "       Overall rank       Score  GDP per capita  Social support  \\\n",
      "count    156.000000  156.000000      156.000000      156.000000   \n",
      "mean      78.500000    5.407096        0.905147        1.208814   \n",
      "std       45.177428    1.113120        0.398389        0.299191   \n",
      "min        1.000000    2.853000        0.000000        0.000000   \n",
      "25%       39.750000    4.544500        0.602750        1.055750   \n",
      "50%       78.500000    5.379500        0.960000        1.271500   \n",
      "75%      117.250000    6.184500        1.232500        1.452500   \n",
      "max      156.000000    7.769000        1.684000        1.624000   \n",
      "\n",
      "       Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
      "count               156.000000                    156.000000  156.000000   \n",
      "mean                  0.725244                      0.392571    0.184846   \n",
      "std                   0.242124                      0.143289    0.095254   \n",
      "min                   0.000000                      0.000000    0.000000   \n",
      "25%                   0.547750                      0.308000    0.108750   \n",
      "50%                   0.789000                      0.417000    0.177500   \n",
      "75%                   0.881750                      0.507250    0.248250   \n",
      "max                   1.141000                      0.631000    0.566000   \n",
      "\n",
      "       Perceptions of corruption  \n",
      "count                 156.000000  \n",
      "mean                    0.110603  \n",
      "std                     0.094538  \n",
      "min                     0.000000  \n",
      "25%                     0.047000  \n",
      "50%                     0.085500  \n",
      "75%                     0.141250  \n",
      "max                     0.453000  \n"
     ]
    }
   ],
   "source": [
    "print(data5.columns)\n",
    "\n",
    "print(data5.info())\n",
    "\n",
    "print(data5.describe())\n",
    "\n",
    "df5 = data5[\"Country or region\"]\n",
    "dff5 = data5.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a452a046",
   "metadata": {
    "papermill": {
     "duration": 0.107011,
     "end_time": "2021-11-29T19:18:01.632597",
     "exception": false,
     "start_time": "2021-11-29T19:18:01.525586",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2019 verilerimizin 156 ülke, boş olmayan değerlere sahip 7 özellik içerdiğini ve diğer tüm istatistiksel bilgilere kod çıktısından ulaşıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e7ff62ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:01.850616Z",
     "iopub.status.busy": "2021-11-29T19:18:01.849910Z",
     "iopub.status.idle": "2021-11-29T19:18:01.854925Z",
     "shell.execute_reply": "2021-11-29T19:18:01.855426Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.059134Z"
    },
    "papermill": {
     "duration": 0.115517,
     "end_time": "2021-11-29T19:18:01.855660",
     "exception": false,
     "start_time": "2021-11-29T19:18:01.740143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x5 = data5.iloc[:,3:].values\n",
    "y5 = data5.iloc[:,2:3].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a2c526",
   "metadata": {
    "papermill": {
     "duration": 0.107279,
     "end_time": "2021-11-29T19:18:02.071496",
     "exception": false,
     "start_time": "2021-11-29T19:18:01.964217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak x mutluluk puanını etkileyen girdiler ve y sonuç olan mutluluk puanı sütunu olarak x ve y değişkenine atanırlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "36182b76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:02.297529Z",
     "iopub.status.busy": "2021-11-29T19:18:02.296811Z",
     "iopub.status.idle": "2021-11-29T19:18:02.298796Z",
     "shell.execute_reply": "2021-11-29T19:18:02.299381Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.067577Z"
    },
    "papermill": {
     "duration": 0.116205,
     "end_time": "2021-11-29T19:18:02.299605",
     "exception": false,
     "start_time": "2021-11-29T19:18:02.183400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train5, x_test5, y_train5, y_test5 = train_test_split(x5, y5, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7691f158",
   "metadata": {
    "papermill": {
     "duration": 0.106279,
     "end_time": "2021-11-29T19:18:02.513275",
     "exception": false,
     "start_time": "2021-11-29T19:18:02.406996",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak test ve eğitim kümeleri oluşturulur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "74384ba6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:02.729833Z",
     "iopub.status.busy": "2021-11-29T19:18:02.729104Z",
     "iopub.status.idle": "2021-11-29T19:18:02.736214Z",
     "shell.execute_reply": "2021-11-29T19:18:02.736735Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.081358Z"
    },
    "papermill": {
     "duration": 0.117069,
     "end_time": "2021-11-29T19:18:02.736927",
     "exception": false,
     "start_time": "2021-11-29T19:18:02.619858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train5 = sc.fit_transform(x_train5)\n",
    "X_test5 = sc.fit_transform(x_test5)\n",
    "Y_train5 = sc.fit_transform(y_train5)\n",
    "Y_test5 = sc.fit_transform(y_test5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09eed29",
   "metadata": {
    "papermill": {
     "duration": 0.106587,
     "end_time": "2021-11-29T19:18:02.950277",
     "exception": false,
     "start_time": "2021-11-29T19:18:02.843690",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak normalizasyon işlemi yapılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2d827188",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:03.172735Z",
     "iopub.status.busy": "2021-11-29T19:18:03.171980Z",
     "iopub.status.idle": "2021-11-29T19:18:03.181374Z",
     "shell.execute_reply": "2021-11-29T19:18:03.181915Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.094338Z"
    },
    "papermill": {
     "duration": 0.122393,
     "end_time": "2021-11-29T19:18:03.182113",
     "exception": false,
     "start_time": "2021-11-29T19:18:03.059720",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0:  [1.69595399]\n",
      "other b:  [[0.74133276 1.46301485 0.86884491 1.22151726 0.21556991 1.25487745]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train5, y_train5)\n",
    "print(\"b0: \", lr.intercept_)\n",
    "print(\"other b: \", lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85a8493",
   "metadata": {
    "papermill": {
     "duration": 0.109462,
     "end_time": "2021-11-29T19:18:03.400587",
     "exception": false,
     "start_time": "2021-11-29T19:18:03.291125",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak makine öğrenmesi modeli oluşturulur ve modele göre ağırlık çarpan değerleri hesaplanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "81e98fff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:03.619362Z",
     "iopub.status.busy": "2021-11-29T19:18:03.618679Z",
     "iopub.status.idle": "2021-11-29T19:18:03.636155Z",
     "shell.execute_reply": "2021-11-29T19:18:03.637004Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.106410Z"
    },
    "papermill": {
     "duration": 0.128963,
     "end_time": "2021-11-29T19:18:03.637192",
     "exception": false,
     "start_time": "2021-11-29T19:18:03.508229",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.779\n",
      "Model:                            OLS   Adj. R-squared:                  0.770\n",
      "Method:                 Least Squares   F-statistic:                     87.62\n",
      "Date:                Mon, 29 Nov 2021   Prob (F-statistic):           2.40e-46\n",
      "Time:                        19:18:03   Log-Likelihood:                -119.76\n",
      "No. Observations:                 156   AIC:                             253.5\n",
      "Df Residuals:                     149   BIC:                             274.9\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.7952      0.211      8.505      0.000       1.378       2.212\n",
      "x1             0.7754      0.218      3.553      0.001       0.344       1.207\n",
      "x2             1.1242      0.237      4.745      0.000       0.656       1.592\n",
      "x3             1.0781      0.335      3.223      0.002       0.417       1.739\n",
      "x4             1.4548      0.375      3.876      0.000       0.713       2.197\n",
      "x5             0.4898      0.498      0.984      0.327      -0.494       1.473\n",
      "x6             0.9723      0.542      1.793      0.075      -0.099       2.044\n",
      "==============================================================================\n",
      "Omnibus:                        8.188   Durbin-Watson:                   1.648\n",
      "Prob(Omnibus):                  0.017   Jarque-Bera (JB):                7.971\n",
      "Skew:                          -0.498   Prob(JB):                       0.0186\n",
      "Kurtosis:                       3.483   Cond. No.                         28.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.regression.linear_model as sm\n",
    "X5 = np.append(arr = np.ones((156,1)).astype(int), values=x5, axis=1)\n",
    "r_ols5 = sm.OLS(endog = y5, exog = X5)\n",
    "r5 = r_ols5.fit()\n",
    "print(r5.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2321f8fb",
   "metadata": {
    "papermill": {
     "duration": 0.107333,
     "end_time": "2021-11-29T19:18:03.852065",
     "exception": false,
     "start_time": "2021-11-29T19:18:03.744732",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **World Happiness Rank 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "868b4312",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:04.071627Z",
     "iopub.status.busy": "2021-11-29T19:18:04.070946Z",
     "iopub.status.idle": "2021-11-29T19:18:04.080975Z",
     "shell.execute_reply": "2021-11-29T19:18:04.081629Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.129341Z"
    },
    "papermill": {
     "duration": 0.122779,
     "end_time": "2021-11-29T19:18:04.081831",
     "exception": false,
     "start_time": "2021-11-29T19:18:03.959052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='constant',fill_value= 0)\n",
    "impdata = data6.iloc[:,4:20].values\n",
    "imputer = imputer.fit(impdata[:,4:20])\n",
    "impdata[:,4:20] = imputer.transform(impdata[:,4:20])\n",
    "data6.iloc[:,4:20] = impdata[:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220391ce",
   "metadata": {
    "papermill": {
     "duration": 0.106895,
     "end_time": "2021-11-29T19:18:04.297277",
     "exception": false,
     "start_time": "2021-11-29T19:18:04.190382",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak eksik verilerin 0 olarak doldurulması sağlanmıştır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eb5d8069",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:04.515052Z",
     "iopub.status.busy": "2021-11-29T19:18:04.514331Z",
     "iopub.status.idle": "2021-11-29T19:18:04.584645Z",
     "shell.execute_reply": "2021-11-29T19:18:04.585367Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.145412Z"
    },
    "papermill": {
     "duration": 0.181014,
     "end_time": "2021-11-29T19:18:04.585644",
     "exception": false,
     "start_time": "2021-11-29T19:18:04.404630",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Country name', 'Regional indicator', 'Ladder score',\n",
      "       'Standard error of ladder score', 'upperwhisker', 'lowerwhisker',\n",
      "       'Logged GDP per capita', 'Social support', 'Healthy life expectancy',\n",
      "       'Freedom to make life choices', 'Generosity',\n",
      "       'Perceptions of corruption', 'Ladder score in Dystopia',\n",
      "       'Explained by: Log GDP per capita', 'Explained by: Social support',\n",
      "       'Explained by: Healthy life expectancy',\n",
      "       'Explained by: Freedom to make life choices',\n",
      "       'Explained by: Generosity', 'Explained by: Perceptions of corruption',\n",
      "       'Dystopia + residual'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 153 entries, 0 to 152\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country name                                153 non-null    object \n",
      " 1   Regional indicator                          153 non-null    object \n",
      " 2   Ladder score                                153 non-null    float64\n",
      " 3   Standard error of ladder score              153 non-null    float64\n",
      " 4   upperwhisker                                153 non-null    float64\n",
      " 5   lowerwhisker                                153 non-null    float64\n",
      " 6   Logged GDP per capita                       153 non-null    float64\n",
      " 7   Social support                              153 non-null    float64\n",
      " 8   Healthy life expectancy                     153 non-null    float64\n",
      " 9   Freedom to make life choices                153 non-null    float64\n",
      " 10  Generosity                                  153 non-null    float64\n",
      " 11  Perceptions of corruption                   153 non-null    float64\n",
      " 12  Ladder score in Dystopia                    153 non-null    float64\n",
      " 13  Explained by: Log GDP per capita            153 non-null    float64\n",
      " 14  Explained by: Social support                153 non-null    float64\n",
      " 15  Explained by: Healthy life expectancy       153 non-null    float64\n",
      " 16  Explained by: Freedom to make life choices  153 non-null    float64\n",
      " 17  Explained by: Generosity                    153 non-null    float64\n",
      " 18  Explained by: Perceptions of corruption     153 non-null    float64\n",
      " 19  Dystopia + residual                         153 non-null    float64\n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 24.0+ KB\n",
      "None\n",
      "       Ladder score  Standard error of ladder score  upperwhisker  \\\n",
      "count     153.00000                      153.000000    153.000000   \n",
      "mean        5.47324                        0.053538      5.578175   \n",
      "std         1.11227                        0.018183      1.096823   \n",
      "min         2.56690                        0.025902      2.628270   \n",
      "25%         4.72410                        0.040698      4.826248   \n",
      "50%         5.51500                        0.050606      5.607728   \n",
      "75%         6.22850                        0.060677      6.363886   \n",
      "max         7.80870                        0.120590      7.869766   \n",
      "\n",
      "       lowerwhisker  Logged GDP per capita  Social support  \\\n",
      "count    153.000000             153.000000      153.000000   \n",
      "mean       5.368304               9.295706        0.808721   \n",
      "std        1.128631               1.201588        0.121453   \n",
      "min        2.505530               6.492642        0.319460   \n",
      "25%        4.603149               8.350645        0.737217   \n",
      "50%        5.430644               9.456313        0.829204   \n",
      "75%        6.138881              10.265124        0.906747   \n",
      "max        7.747634              11.450681        0.974670   \n",
      "\n",
      "       Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
      "count               153.000000                    153.000000  153.000000   \n",
      "mean                 64.445529                      0.783360   -0.014568   \n",
      "std                   7.057848                      0.117786    0.151809   \n",
      "min                  45.200001                      0.396573   -0.300907   \n",
      "25%                  58.961712                      0.714839   -0.127015   \n",
      "50%                  66.305145                      0.799805   -0.033665   \n",
      "75%                  69.289192                      0.877709    0.085429   \n",
      "max                  76.804581                      0.974998    0.560664   \n",
      "\n",
      "       Perceptions of corruption  Ladder score in Dystopia  \\\n",
      "count                 153.000000              1.530000e+02   \n",
      "mean                    0.733120              1.972317e+00   \n",
      "std                     0.175172              1.559417e-15   \n",
      "min                     0.109784              1.972317e+00   \n",
      "25%                     0.683019              1.972317e+00   \n",
      "50%                     0.783122              1.972317e+00   \n",
      "75%                     0.849151              1.972317e+00   \n",
      "max                     0.935585              1.972317e+00   \n",
      "\n",
      "       Explained by: Log GDP per capita  Explained by: Social support  \\\n",
      "count                        153.000000                    153.000000   \n",
      "mean                           0.868771                      1.155607   \n",
      "std                            0.372416                      0.286866   \n",
      "min                            0.000000                      0.000000   \n",
      "25%                            0.575862                      0.986718   \n",
      "50%                            0.918549                      1.203987   \n",
      "75%                            1.169229                      1.387139   \n",
      "max                            1.536676                      1.547567   \n",
      "\n",
      "       Explained by: Healthy life expectancy  \\\n",
      "count                             153.000000   \n",
      "mean                                0.692869   \n",
      "std                                 0.254094   \n",
      "min                                 0.000000   \n",
      "25%                                 0.495443   \n",
      "50%                                 0.759818   \n",
      "75%                                 0.867249   \n",
      "max                                 1.137814   \n",
      "\n",
      "       Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
      "count                                  153.000000                153.000000   \n",
      "mean                                     0.463583                  0.189375   \n",
      "std                                      0.141172                  0.100401   \n",
      "min                                      0.000000                  0.000000   \n",
      "25%                                      0.381457                  0.115006   \n",
      "50%                                      0.483293                  0.176745   \n",
      "75%                                      0.576665                  0.255510   \n",
      "max                                      0.693270                  0.569814   \n",
      "\n",
      "       Explained by: Perceptions of corruption  Dystopia + residual  \n",
      "count                               153.000000           153.000000  \n",
      "mean                                  0.130718             1.972317  \n",
      "std                                   0.113097             0.563638  \n",
      "min                                   0.000000             0.257241  \n",
      "25%                                   0.055805             1.629928  \n",
      "50%                                   0.098435             2.046272  \n",
      "75%                                   0.163064             2.350267  \n",
      "max                                   0.533162             3.440810  \n"
     ]
    }
   ],
   "source": [
    "print(data6.columns)\n",
    "\n",
    "print(data6.info())\n",
    "\n",
    "print(data6.describe())\n",
    "\n",
    "df6 = data6[\"Country name\"]\n",
    "dff6 = data6.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4adb616",
   "metadata": {
    "papermill": {
     "duration": 0.107997,
     "end_time": "2021-11-29T19:18:04.802392",
     "exception": false,
     "start_time": "2021-11-29T19:18:04.694395",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2020 verilerimizin 153 ülke, boş olmayan değerlere sahip 16 özellik içerdiğini ve diğer tüm istatistiksel bilgilere kod çıktısından ulaşıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "477349f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:05.025314Z",
     "iopub.status.busy": "2021-11-29T19:18:05.024612Z",
     "iopub.status.idle": "2021-11-29T19:18:05.027305Z",
     "shell.execute_reply": "2021-11-29T19:18:05.026807Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.228091Z"
    },
    "papermill": {
     "duration": 0.116853,
     "end_time": "2021-11-29T19:18:05.027461",
     "exception": false,
     "start_time": "2021-11-29T19:18:04.910608",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x6 = data6.iloc[:,4:].values\n",
    "y6 = data6.iloc[:,2:3].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb834496",
   "metadata": {
    "papermill": {
     "duration": 0.109057,
     "end_time": "2021-11-29T19:18:05.246319",
     "exception": false,
     "start_time": "2021-11-29T19:18:05.137262",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak x mutluluk puanını etkileyen girdiler ve y sonuç olan mutluluk puanı sütunu olarak x ve y değişkenine atanırlar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e0242a7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:05.468410Z",
     "iopub.status.busy": "2021-11-29T19:18:05.467370Z",
     "iopub.status.idle": "2021-11-29T19:18:05.472900Z",
     "shell.execute_reply": "2021-11-29T19:18:05.473457Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.235663Z"
    },
    "papermill": {
     "duration": 0.118495,
     "end_time": "2021-11-29T19:18:05.473656",
     "exception": false,
     "start_time": "2021-11-29T19:18:05.355161",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train6, x_test6, y_train6, y_test6 = train_test_split(x6, y6, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33549873",
   "metadata": {
    "papermill": {
     "duration": 0.107068,
     "end_time": "2021-11-29T19:18:05.688168",
     "exception": false,
     "start_time": "2021-11-29T19:18:05.581100",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak test ve eğitim kümeleri oluşturulur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7fd9d73a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:05.909653Z",
     "iopub.status.busy": "2021-11-29T19:18:05.908953Z",
     "iopub.status.idle": "2021-11-29T19:18:05.915042Z",
     "shell.execute_reply": "2021-11-29T19:18:05.914367Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.248858Z"
    },
    "papermill": {
     "duration": 0.119463,
     "end_time": "2021-11-29T19:18:05.915186",
     "exception": false,
     "start_time": "2021-11-29T19:18:05.795723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train6 = sc.fit_transform(x_train6)\n",
    "X_test6 = sc.fit_transform(x_test6)\n",
    "Y_train6 = sc.fit_transform(y_train6)\n",
    "Y_test6 = sc.fit_transform(y_test6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e152d62c",
   "metadata": {
    "papermill": {
     "duration": 0.108401,
     "end_time": "2021-11-29T19:18:06.132414",
     "exception": false,
     "start_time": "2021-11-29T19:18:06.024013",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak normalizasyon işlemi yapılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ead67ba5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:06.359801Z",
     "iopub.status.busy": "2021-11-29T19:18:06.358712Z",
     "iopub.status.idle": "2021-11-29T19:18:06.365947Z",
     "shell.execute_reply": "2021-11-29T19:18:06.365139Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.262311Z"
    },
    "papermill": {
     "duration": 0.126115,
     "end_time": "2021-11-29T19:18:06.366147",
     "exception": false,
     "start_time": "2021-11-29T19:18:06.240032",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0:  [1.73497471]\n",
      "other b:  [[ 4.99999938e-01  4.99999940e-01 -5.69744363e-04 -6.82275733e-03\n",
      "  -8.57745785e-05 -3.10780224e-03 -5.00118885e-03  2.45136455e-03\n",
      "  -8.76017936e-01  1.83838424e-03  2.88874285e-03  2.38264435e-03\n",
      "   2.59309351e-03  7.56203260e-03  3.79697548e-03  1.21159092e-07]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train6, y_train6)\n",
    "print(\"b0: \", lr.intercept_)\n",
    "print(\"other b: \", lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8664fb22",
   "metadata": {
    "papermill": {
     "duration": 0.129576,
     "end_time": "2021-11-29T19:18:06.610870",
     "exception": false,
     "start_time": "2021-11-29T19:18:06.481294",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Kod çıktısı olarak makine öğrenmesi modeli oluşturulur ve modele göre ağırlık çarpan değerleri hesaplanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5de85604",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:06.834435Z",
     "iopub.status.busy": "2021-11-29T19:18:06.833770Z",
     "iopub.status.idle": "2021-11-29T19:18:06.853795Z",
     "shell.execute_reply": "2021-11-29T19:18:06.854695Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.279317Z"
    },
    "papermill": {
     "duration": 0.135119,
     "end_time": "2021-11-29T19:18:06.854977",
     "exception": false,
     "start_time": "2021-11-29T19:18:06.719858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 5.223e+15\n",
      "Date:                Mon, 29 Nov 2021   Prob (F-statistic):               0.00\n",
      "Time:                        19:18:06   Log-Likelihood:                 2366.6\n",
      "No. Observations:                 153   AIC:                            -4701.\n",
      "Df Residuals:                     137   BIC:                            -4653.\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0015      0.148      0.010      0.992      -0.291       0.294\n",
      "x1             0.5000   7.18e-05   6967.954      0.000       0.500       0.500\n",
      "x2             0.5000   7.18e-05   6966.782      0.000       0.500       0.500\n",
      "x3            -0.0007      0.056     -0.012      0.990      -0.111       0.110\n",
      "x4            -0.0034      0.313     -0.011      0.991      -0.623       0.616\n",
      "x5         -9.059e-05      0.008     -0.011      0.991      -0.016       0.016\n",
      "x6          9.399e-05      0.376      0.000      1.000      -0.744       0.744\n",
      "x7            -0.0036      0.313     -0.012      0.991      -0.623       0.616\n",
      "x8             0.0014      0.652      0.002      0.998      -1.289       1.292\n",
      "x9             0.0029      0.292      0.010      0.992      -0.575       0.580\n",
      "x10            0.0022      0.181      0.012      0.990      -0.355       0.359\n",
      "x11            0.0014      0.133      0.011      0.991      -0.261       0.264\n",
      "x12            0.0025      0.228      0.011      0.991      -0.448       0.453\n",
      "x13        -7.837e-05      0.314     -0.000      1.000      -0.621       0.621\n",
      "x14            0.0055      0.474      0.012      0.991      -0.932       0.943\n",
      "x15            0.0021      1.011      0.002      0.998      -1.996       2.001\n",
      "x16         4.455e-08      0.000      0.000      1.000      -0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                       15.611   Durbin-Watson:                   0.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                6.859\n",
      "Skew:                          -0.286   Prob(JB):                       0.0324\n",
      "Kurtosis:                       2.134   Cond. No.                     1.31e+18\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 3.88e-31. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.regression.linear_model as sm\n",
    "X6 = np.append(arr = np.ones((153,1)).astype(int), values=x6, axis=1)\n",
    "r_ols6 = sm.OLS(endog = y6, exog = X6)\n",
    "r6 = r_ols6.fit()\n",
    "print(r6.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326d2767",
   "metadata": {
    "papermill": {
     "duration": 0.109503,
     "end_time": "2021-11-29T19:18:07.164196",
     "exception": false,
     "start_time": "2021-11-29T19:18:07.054693",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Not: 2021 yılı açıklandığında eklenecektir.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a71748",
   "metadata": {
    "papermill": {
     "duration": 0.109812,
     "end_time": "2021-11-29T19:18:07.383441",
     "exception": false,
     "start_time": "2021-11-29T19:18:07.273629",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Global Terrorism Report 2015**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a045b8f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:07.608236Z",
     "iopub.status.busy": "2021-11-29T19:18:07.607493Z",
     "iopub.status.idle": "2021-11-29T19:18:07.634407Z",
     "shell.execute_reply": "2021-11-29T19:18:07.635242Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.306458Z"
    },
    "papermill": {
     "duration": 0.143245,
     "end_time": "2021-11-29T19:18:07.635660",
     "exception": false,
     "start_time": "2021-11-29T19:18:07.492415",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['year', 'country'], dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14963 entries, 0 to 14962\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   year     14963 non-null  int64 \n",
      " 1   country  14963 non-null  object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 233.9+ KB\n",
      "None\n",
      "          year\n",
      "count  14963.0\n",
      "mean    2015.0\n",
      "std        0.0\n",
      "min     2015.0\n",
      "25%     2015.0\n",
      "50%     2015.0\n",
      "75%     2015.0\n",
      "max     2015.0\n",
      "Iraq           2750\n",
      "Afghanistan    1928\n",
      "Pakistan       1243\n",
      "India           884\n",
      "Philippines     721\n",
      "               ... \n",
      "Hungary           1\n",
      "Spain             1\n",
      "Uzbekistan        1\n",
      "South Korea       1\n",
      "Lesotho           1\n",
      "Name: country, Length: 99, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data7.columns)\n",
    "\n",
    "print(data7.info())\n",
    "\n",
    "print(data7.describe())\n",
    "\n",
    "df7 = data7[\"country\"]\n",
    "dff7 = df7.value_counts()\n",
    "print(dff7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce651c0",
   "metadata": {
    "papermill": {
     "duration": 0.108497,
     "end_time": "2021-11-29T19:18:07.854232",
     "exception": false,
     "start_time": "2021-11-29T19:18:07.745735",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2015 Terör Raporu verilerimizin yıl ve 14963 sıfır olmayan değeri olan ülkeler olduğunu anlıyoruz.\n",
    "\n",
    "Örnek olarak ülkeler terörizm sayılarına göre sıralanıyor. 2015 rapor modelimize tüm değerler yeni bir özellik olarak eklenmiştir."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6b3377",
   "metadata": {
    "papermill": {
     "duration": 0.11094,
     "end_time": "2021-11-29T19:18:08.075588",
     "exception": false,
     "start_time": "2021-11-29T19:18:07.964648",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Global Terrorism Report 2016**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9c5a7d96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-29T19:18:08.304301Z",
     "iopub.status.busy": "2021-11-29T19:18:08.303565Z",
     "iopub.status.idle": "2021-11-29T19:18:08.330796Z",
     "shell.execute_reply": "2021-11-29T19:18:08.331453Z",
     "shell.execute_reply.started": "2021-11-29T19:16:04.361937Z"
    },
    "papermill": {
     "duration": 0.14365,
     "end_time": "2021-11-29T19:18:08.331657",
     "exception": false,
     "start_time": "2021-11-29T19:18:08.188007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['year', 'country'], dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13587 entries, 0 to 13586\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   year     13587 non-null  int64 \n",
      " 1   country  13587 non-null  object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 212.4+ KB\n",
      "None\n",
      "          year\n",
      "count  13587.0\n",
      "mean    2016.0\n",
      "std        0.0\n",
      "min     2016.0\n",
      "25%     2016.0\n",
      "50%     2016.0\n",
      "75%     2016.0\n",
      "max     2016.0\n",
      "Iraq               3360\n",
      "Afghanistan        1617\n",
      "India              1025\n",
      "Pakistan            864\n",
      "Philippines         632\n",
      "                   ... \n",
      "Denmark               1\n",
      "Slovak Republic       1\n",
      "New Zealand           1\n",
      "Panama                1\n",
      "Haiti                 1\n",
      "Name: country, Length: 108, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data8.columns)\n",
    "\n",
    "print(data8.info())\n",
    "\n",
    "print(data8.describe())\n",
    "\n",
    "df8 = data8[\"country\"]\n",
    "dff8 = df8.value_counts()\n",
    "print(dff8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c3ce85",
   "metadata": {
    "papermill": {
     "duration": 0.108608,
     "end_time": "2021-11-29T19:18:08.553610",
     "exception": false,
     "start_time": "2021-11-29T19:18:08.445002",
     "status": "completed"
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   },
   "source": [
    "2016 Terör Raporu verilerimizin yıl ve 13587 sıfır olmayan değeri olan ülkeler olduğunu anlıyoruz.\n",
    "\n",
    "Örnek olarak ülkeler terör sayılarına göre sıralanıyor. 2016 rapor modelimize tüm değerler yeni bir özellik olarak eklenmiştir."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c17adc8",
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    "tags": []
   },
   "source": [
    "# **Global Terrorism Report 2017**"
   ]
  },
  {
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   "id": "89a92ed2",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['year', 'country'], dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10900 entries, 0 to 10899\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   year     10900 non-null  int64 \n",
      " 1   country  10900 non-null  object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 170.4+ KB\n",
      "None\n",
      "          year\n",
      "count  10900.0\n",
      "mean    2017.0\n",
      "std        0.0\n",
      "min     2017.0\n",
      "25%     2017.0\n",
      "50%     2017.0\n",
      "75%     2017.0\n",
      "max     2017.0\n",
      "Iraq           2466\n",
      "Afghanistan    1414\n",
      "India           966\n",
      "Pakistan        719\n",
      "Philippines     692\n",
      "               ... \n",
      "Norway            1\n",
      "Serbia            1\n",
      "Honduras          1\n",
      "Djibouti          1\n",
      "Malawi            1\n",
      "Name: country, Length: 102, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data9.columns)\n",
    "\n",
    "print(data9.info())\n",
    "\n",
    "print(data9.describe())\n",
    "\n",
    "df9 = data9[\"country\"]\n",
    "dff9 = df9.value_counts()\n",
    "print(dff9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecdff0c",
   "metadata": {
    "papermill": {
     "duration": 0.110943,
     "end_time": "2021-11-29T19:18:09.245593",
     "exception": false,
     "start_time": "2021-11-29T19:18:09.134650",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Terör Raporu 2017 verilerimizin yıl ve 10900 sıfır olmayan değeri olan ülkeler olduğunu anlıyoruz.\n",
    "\n",
    "Örnek olarak ülkeler terör sayılarına göre sıralanıyor. Tüm değerler 2017 rapor modelimize yeni bir özellik olarak eklenmiştir."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b15bc8",
   "metadata": {
    "papermill": {
     "duration": 0.109375,
     "end_time": "2021-11-29T19:18:09.464767",
     "exception": false,
     "start_time": "2021-11-29T19:18:09.355392",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **6. SONUÇ**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392a9fd4",
   "metadata": {
    "papermill": {
     "duration": 0.109378,
     "end_time": "2021-11-29T19:18:09.684080",
     "exception": false,
     "start_time": "2021-11-29T19:18:09.574702",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2015, 2016 ve 2017 Dünya Mutluluk Raporlarında, Küresel Terörizm Raporu'nu kullanarak Dünya Mutluluk Raporu'na hangi faktörün ne kadar etki ettiğini ve terör olaylarının bu sıralamaya etkisini takip ettik. Analizin sonunda, Makine Öğrenimini kullanarak mantıksal rastgele değerlerle mutluluk puanını tahmin ediyoruz ve bunların gerçekten işe yaradığını gördük. Modelimiz için Çok Doğrusal Regresyon Makine Öğrenme Metodunun iyi bir seçenek olduğunu gördük. Terör olaylarının değerlerinin mutluluk puanlarından etkilenmediğini, ancak etkisinin 2016’da arttığını gördük. 2018, 2019 ve 2020 verileri için Terörizm verileri olmadığından yalnızca kendi verileriyle analizi yapıldı."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79eaaee0",
   "metadata": {
    "papermill": {
     "duration": 0.109788,
     "end_time": "2021-11-29T19:18:09.903545",
     "exception": false,
     "start_time": "2021-11-29T19:18:09.793757",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **7. KAYNAKÇA**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154fafb6",
   "metadata": {
    "papermill": {
     "duration": 0.11009,
     "end_time": "2021-11-29T19:18:10.123759",
     "exception": false,
     "start_time": "2021-11-29T19:18:10.013669",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**[1]** Louise Millard (2011) Data Mining and Analysis of Global Happiness: A Machine Learning Approach\n",
    "\n",
    "**[2]** Natasha Jaques, Sara Taylor, Asaph Azaria, Asma Ghandeharioun, Akane Sano, Rosalind Picard (2015) Predicting Students’ Happiness from Physiology, Phone, Mobility, and Behavioral Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b5451f",
   "metadata": {
    "papermill": {
     "duration": 0.109944,
     "end_time": "2021-11-29T19:18:10.345045",
     "exception": false,
     "start_time": "2021-11-29T19:18:10.235101",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Not: Güncellemeler devam edecektir.**"
   ]
  }
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