13102022 - YZ.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "f63076bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n1- Centroid based clustering (kümeleme) : K-means\\n2- Hierarchical clustering (Agglomerative/Divisive)\\n3- Density models (e.g. DBSCAN, OPTICS)\\nNerelerde kullanılır? Sahte Haber Tespiti, Spam e-posta filtreleme, Satış-Pazarlama, Network trafiğini \\nsınıflandırma, Suç içeren aktivite tespiti, Deprem çalışmaları\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Danışmansız Öğrenme (Unsupervised Learning)\n",
    "\"\"\"\n",
    "1- Centroid based clustering (kümeleme) : K-means\n",
    "2- Hierarchical clustering : Agglomerative/Divisive\n",
    "3- Density models : DBSCAN, OPTICS\n",
    "Nerelerde kullanılır? Sahte Haber Tespiti, Spam e-posta filtreleme, Satış-Pazarlama, Network trafiğini \n",
    "sınıflandırma, Suç içeren aktivite tespiti, Deprem çalışmaları\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "513c6a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n1- Rastgele K adet merkez ata\\n2- Bütün noktaların kendi içindeki merkezlere uzaklığını ölç ve noktaları en kısa mesafeye göre kümeye gönder.\\n3- Within-cluster sum of squared distances (WCSS/Inertia) 'ın en düşük değere sahip olacağı modeli seç.\\n4- Bütün noktalar kümelere atandıktan sonra iterasyon yoluyla tekrardan küme merkezlerini hesapla \\n(centroidleri hesapla)\\n5- Küme merkezlerinin değişimi durana dek 2, 3 ve 4. adımları tekrarla ve noktalar sabit kalana dek devam et.\\n\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#K-means\n",
    "\"\"\"\n",
    "1- Rastgele K adet merkez ata\n",
    "2- Bütün noktaların kendi içindeki merkezlere uzaklığını ölç ve noktaları en kısa mesafeye göre kümeye gönder.\n",
    "3- Within-cluster sum of squared distances (WCSS/Inertia) 'ın en düşük değere sahip olacağı modeli seç.\n",
    "4- Bütün noktalar kümelere atandıktan sonra iterasyon yoluyla tekrardan küme merkezlerini hesapla \n",
    "(centroidleri hesapla)\n",
    "5- Küme merkezlerinin değişimi durana dek 2, 3 ve 4. adımları tekrarla ve noktalar sabit kalana dek devam et.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93cf5a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "361dacad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('data/Mall_Customers.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad8ce49d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Annual Income (k$)  Spending Score (1-100)\n",
       "0                  15                      39\n",
       "1                  15                      81\n",
       "2                  16                       6\n",
       "3                  16                      77\n",
       "4                  17                      40"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.iloc[:, 3:]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04857f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters = i, init='k-means++', random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Dirsek metodu')\n",
    "plt.xlabel('Küme sayısı')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e6bce87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
       "       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 0,\n",
       "       2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 0, 4, 1, 4, 1, 4,\n",
       "       0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n",
       "       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n",
       "       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n",
       "       1, 4])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a50abac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bu nokta 2 kümesine aittir\n"
     ]
    }
   ],
   "source": [
    "test = kmeans.predict([[15, #Annual Income (k$)\n",
    "                        39, #Spending Score (1-100)\n",
    "                       ]])\n",
    "print(\"Bu nokta\", test[0], \"kümesine aittir\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf1bc2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>39</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Annual Income (k$)  Spending Score (1-100)\n",
       "43                  39                      61\n",
       "46                  40                      55\n",
       "47                  40                      47\n",
       "48                  40                      42\n",
       "49                  40                      42"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[y_kmeans == 0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7194063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55.2962963 , 88.2       , 26.30434783, 25.72727273, 86.53846154])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51aed324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([49.51851852, 17.11428571, 20.91304348, 79.36363636, 82.12820513])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4e78c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y_kmeans == 0]['Annual Income (k$)'], [X[y_kmeans == 0]['Spending Score (1-100)']], label = 'Cluster 1')\n",
    "plt.scatter(X[y_kmeans == 1]['Annual Income (k$)'], [X[y_kmeans == 1]['Spending Score (1-100)']], label = 'Cluster 2')\n",
    "plt.scatter(X[y_kmeans == 2]['Annual Income (k$)'], [X[y_kmeans == 2]['Spending Score (1-100)']], label = 'Cluster 3')\n",
    "plt.scatter(X[y_kmeans == 3]['Annual Income (k$)'], [X[y_kmeans == 3]['Spending Score (1-100)']], label = 'Cluster 4')\n",
    "plt.scatter(X[y_kmeans == 4]['Annual Income (k$)'], [X[y_kmeans == 4]['Spending Score (1-100)']], label = 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, label = 'Centroids')\n",
    "plt.title('Müşteri kümeleri')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90367c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "300d427b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Agglomerative Clustering -> bottom-up approach (aşağıdan yukarıya yaklaşımı)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "04654722",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83691cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_data = pd.read_csv('data/Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01484c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eeaa9c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79f5e355",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = customer_data.iloc[:, 3:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c044e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "plt.figure(figsize = (10, 7))\n",
    "plt.title(\"Müşteri Dendrogramları\")\n",
    "dend = shc.dendrogram(shc.linkage(data, method = 'ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfb3c4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
       "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1,\n",
       "       4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 0, 2, 0, 2,\n",
       "       1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "cluster = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\n",
    "\n",
    "cluster.fit_predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "424c6d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c4d80772b0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5,5))\n",
    "plt.scatter(data[:, 0], data[:, 1], c = cluster.labels_, cmap = 'rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9dc3540b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nAgent (Ajan) : \\n1 - İnsan \\n2 - Robotik \\n3 - Yazılım\\n\\nEnvironment (Ortam) :\\n1 - Ayrık/Sürekli -> Satranç ayrık, otonom sürüş sürekli bir ortamdır \\n2 - Gözlemlenebilir / Parçalı Gözlemlenebilir \\n3 - Statik/Dinamik -> Agent hareket ederken değişmezse ortam statik, değişirse dinamiktir \\n4 - Erişilebilir / Erişilemez -> Sensörlerin tüm durumlara erişebiliyor/erişemiyor olması\\n5 - Deterministik / Non. Det. \\n6 - Episodic / Non. Epi. \\n'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Agent (Ajan) : \n",
    "1 - İnsan \n",
    "2 - Robotik \n",
    "3 - Yazılım\n",
    "\n",
    "Environment (Ortam) :\n",
    "1 - Ayrık/Sürekli -> Satranç ayrık, otonom sürüş sürekli bir ortamdır \n",
    "2 - Gözlemlenebilir / Parçalı Gözlemlenebilir \n",
    "3 - Statik/Dinamik -> Agent hareket ederken değişmezse ortam statik, değişirse dinamiktir \n",
    "4 - Erişilebilir / Erişilemez -> Sensörlerin tüm durumlara erişebiliyor/erişemiyor olması\n",
    "5 - Deterministik / Non. Det. \n",
    "6 - Episodic / Non. Epi. \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e916f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('CartPole-v0')\n",
    "for _ in range(20):\n",
    "    observation = env.reset()\n",
    "    for i in range(100):\n",
    "        env.render()\n",
    "        print(observation)\n",
    "        action = env.action_space.sample()\n",
    "        observation, reward, done, info = env.step(action)\n",
    "        if done:\n",
    "            print(\"Episode finished after {} timesteps\".format(i+1))\n",
    "            break\n",
    "#Reinforcement Learning"
   ]
  }
 ],
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