Property_Lassso_Regression.ipynb

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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Menghubungkan Colab dengan Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Memuat file CSV\n",
        "import pandas as pd\n",
        "raw_data = pd.read_csv('/content/drive/MyDrive/AIEngineerGAIA.csv')\n",
        "\n",
        "# Menampilkan data\n",
        "raw_data.head()"
      ],
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              "   id  squareMeters  numberOfRooms  hasYard  hasPool  floors  cityCode  \\\n",
              "0   0         34291             24        1        0      47     35693   \n",
              "1   1         95145             60        0        1      60     34773   \n",
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              "3   3         97184             99        0        0      59     15113   \n",
              "4   4         61752            100        0        0      57     64245   \n",
              "\n",
              "   cityPartRange  numPrevOwners  made  isNewBuilt  hasStormProtector  \\\n",
              "0              2              1  2000           0                  1   \n",
              "1              1              4  2000           0                  1   \n",
              "2              4              8  2020           1                  1   \n",
              "3              1              1  2000           0                  1   \n",
              "4              8              4  2018           1                  0   \n",
              "\n",
              "   basement  attic  garage  hasStorageRoom  hasGuestRoom      price  \n",
              "0         8   5196     369               0             3  3436795.2  \n",
              "1       729   4496     277               0             6  9519958.0  \n",
              "2      7473   8953     245               1             9  9276448.1  \n",
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              "(22730, 18)"
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        "df = raw_data.drop(['id', 'price'], axis=1)"
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              "   squareMeters  numberOfRooms  hasYard  hasPool  floors  cityCode  \\\n",
              "0         34291             24        1        0      47     35693   \n",
              "1         95145             60        0        1      60     34773   \n",
              "2         92661             45        1        1      62     45457   \n",
              "3         97184             99        0        0      59     15113   \n",
              "4         61752            100        0        0      57     64245   \n",
              "\n",
              "   cityPartRange  numPrevOwners  made  isNewBuilt  hasStormProtector  \\\n",
              "0              2              1  2000           0                  1   \n",
              "1              1              4  2000           0                  1   \n",
              "2              4              8  2020           1                  1   \n",
              "3              1              1  2000           0                  1   \n",
              "4              8              4  2018           1                  0   \n",
              "\n",
              "   basement  attic  garage  hasStorageRoom  hasGuestRoom  \n",
              "0         8   5196     369               0             3  \n",
              "1       729   4496     277               0             6  \n",
              "2      7473   8953     245               1             9  \n",
              "3      6424   8522     256               1             9  \n",
              "4      7151   2786     863               0             7  "
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b878f082-277d-4a18-93ab-af8eac275381 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b878f082-277d-4a18-93ab-af8eac275381');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Eksplorasi Dan Analisis Data**\n"
      ],
      "metadata": {
        "id": "2Sss77pnltzX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# Mengecek missing value\n",
        "raw_data.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04lgCAWZl8Dq",
        "outputId": "6a8c242a-2e41-4d38-e03c-b1cfac4f84a9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "squareMeters         0\n",
              "numberOfRooms        0\n",
              "hasYard              0\n",
              "hasPool              0\n",
              "floors               0\n",
              "cityCode             0\n",
              "cityPartRange        0\n",
              "numPrevOwners        0\n",
              "made                 0\n",
              "isNewBuilt           0\n",
              "hasStormProtector    0\n",
              "basement             0\n",
              "attic                0\n",
              "garage               0\n",
              "hasStorageRoom       0\n",
              "hasGuestRoom         0\n",
              "price                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengecek duplikasi\n",
        "raw_data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5dbB-qqmlTZ",
        "outputId": "a600ad75-06f5-453f-bed0-c33afc6b1013"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Mengecek outliers menggunakan boxplot\n",
        "fig, ax = plt.subplots(figsize=(25,6))\n",
        "raw_data.boxplot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "1xU_mRZZnHXR",
        "outputId": "85e291d3-6c22-432f-823a-1d5284e615e6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(25,6))\n",
        "df.boxplot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "zHx4RP2Ur6o_",
        "outputId": "60b6480b-2d65-4642-928c-8283a2fd4565"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'squareMeters'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "_8Lk3iQptAPg",
        "outputId": "b0ac3d7b-8baa-4d23-d4fe-633c06565437"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom squareMeters ada pada baris ke:\n",
            "Int64Index([15334], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAGwCAYAAADMjZ3mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAaE0lEQVR4nO3de5CVBf348c/ZXdldEUQgDQQW71rCBqKohHnBC5OUmkqpiWGjFSRJZsM0X/FSoo6QlxzNLKgZGRSNUqdNCRUl7xR5NzUUU9LyxkV31d3n90fj/tw+kAsJ56iv18yZ4ZzzPM/58Oiw7/M8z9lTKoqiCACA96gq9wAAQOURCABAIhAAgEQgAACJQAAAEoEAACQCAQBIatZ3xba2tnjhhReiW7duUSqVPsiZAIANpCiKWLlyZfTt2zeqqtZ+nGC9A+GFF16I/v37r+/qAEAZPffcc9GvX7+1Pr/egdCtW7f2F+jevfv6bgYA2IhWrFgR/fv3b/85vjbrHQjvnlbo3r27QACAD5n3uzzARYoAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgqSn3AGtSFEU0NzdHURTR0tISERG1tbVRKpWirq4uSqVSmScEgI+2igyE5ubmGD169Bqfa2pqivr6+o08EQB8vDjFAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAAJKacg/wXkVRRHNzczQ3N3d62YiIurq6KJVKG3o8APjYqKhAaG5ujtGjR6/zsk1NTVFfX78hRwOAjxWnGACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgBUmLvuuivGjh0bd911V9lmEAgAUEGam5tjxowZ8eKLL8aMGTOiubm5LHMIBACoIFdffXW8/PLLERHx8ssvx+zZs8syR0UFQlEU77tMc3NzvPnmmx2KqjPrAUCl+/vf/x6zZ89u/7lWFEXMnj07/v73v2/0WWo6u2BLS0u0tLS031+xYsUHPsx7t782hx9++BrX23TTTT/weQBgYymKIi6++OK1Pn7BBRdEqVTaaPN0+gjCtGnTYvPNN2+/9e/ff0POBQAfK8uWLYv7778/WltbOzze2toa999/fyxbtmyjztPpQJgyZUq8/vrr7bfnnnvuAx+mtrb2fZeZN29eNDU1xbx589ZpPQCoZAMGDIjdd989qqurOzxeXV0de+yxRwwYMGCjztPpQKitrY3u3bt3uH3QOnPopK6uLurr66Ourm6d1gOASlYqlWLSpElrfXxj/6yrqIsUAeDjrF+/fnHMMce0x0CpVIpjjjkmtt56640+i0AAgApy7LHHRq9evSIionfv3nHMMceUZQ6BAAAVpK6uLiZPnhxbbbVVnHrqqR1OqW9Mnf6YIwCwcey9996x9957l3UGRxAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQ15R7gverq6qKpqSmam5vj8MMP79Sy7/4ZAPjgVFQglEqlqK+v/8CXBQDWjVMMAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIKkp9wBrUldXF01NTVEURbS0tERERG1tbZRKpairqyvzdADw0VeRgVAqlaK+vj4iIjbddNMyTwMAHz9OMQAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAASc36rlgURURErFix4gMbBgDYsN79uf3uz/G1We9AWLlyZURE9O/ff303AQCUycqVK2PzzTdf6/Ol4v0SYi3a2trihRdeiG7dukWpVFrvAf/TihUron///vHcc89F9+7dP7DtfhTZV51jP3WO/dQ59lPn2Veds7H3U1EUsXLlyujbt29UVa39SoP1PoJQVVUV/fr1W9/V31f37t39D9VJ9lXn2E+dYz91jv3UefZV52zM/fTfjhy8y0WKAEAiEACApOICoba2NqZOnRq1tbXlHqXi2VedYz91jv3UOfZT59lXnVOp+2m9L1IEAD66Ku4IAgBQfgIBAEgEAgCQCAQAIKm4QLjsssti4MCBUVdXF8OHD4/77ruv3CNVnDvuuCPGjBkTffv2jVKpFL/5zW/KPVJFmjZtWuy+++7RrVu32HLLLeOwww6LJ554otxjVZzLL788Bg8e3P5LWvbaa69oamoq91gV77zzzotSqRTf+c53yj1KRTnzzDOjVCp1uO28887lHqtiPf/883HcccdFr169or6+PgYNGhQPPPBAuceKiAoLhGuuuSYmT54cU6dOjT/96U/R2NgYBx98cLz00kvlHq2irF69OhobG+Oyyy4r9ygVbeHChTFhwoS45557Yv78+fH222/HQQcdFKtXry73aBWlX79+cd5558XixYvjgQceiP333z+++MUvxiOPPFLu0SrW/fffHz/96U9j8ODB5R6lIn3605+O5cuXt98WLVpU7pEq0quvvhojRoyITTbZJJqamuLRRx+N6dOnxxZbbFHu0f6tqCB77LFHMWHChPb7ra2tRd++fYtp06aVcarKFhHFvHnzyj3Gh8JLL71URESxcOHCco9S8bbYYoviqquuKvcYFWnlypXFDjvsUMyfP7/43Oc+V0yaNKncI1WUqVOnFo2NjeUe40Ph+9//fvHZz3623GOsVcUcQXjrrbdi8eLFMWrUqPbHqqqqYtSoUXH33XeXcTI+Kl5//fWIiOjZs2eZJ6lcra2tMWfOnFi9enXstdde5R6nIk2YMCE+//nPd/i3io6efPLJ6Nu3b2y77bZx7LHHxrJly8o9UkW64YYbYtiwYXHUUUfFlltuGUOGDImf/exn5R6rXcUEwr/+9a9obW2NrbbaqsPjW221VfzjH/8o01R8VLS1tcV3vvOdGDFiROy6667lHqfiPPTQQ7HZZptFbW1tfOMb34h58+bFpz71qXKPVXHmzJkTf/rTn2LatGnlHqViDR8+PGbNmhW///3v4/LLL4+lS5fGyJEjY+XKleUereL87W9/i8svvzx22GGHuPnmm+Ob3/xmnHLKKfHLX/6y3KNFxP/wbY7wYTJhwoR4+OGHnQtdi5122imWLFkSr7/+elx33XUxbty4WLhwoUh4j+eeey4mTZoU8+fPj7q6unKPU7FGjx7d/ufBgwfH8OHDo6GhIa699to48cQTyzhZ5Wlra4thw4bFueeeGxERQ4YMiYcffjiuuOKKGDduXJmnq6AjCL17947q6up48cUXOzz+4osvxic/+ckyTcVHwcSJE+Omm26K2267bYN+RfmHWZcuXWL77beP3XbbLaZNmxaNjY1x8cUXl3usirJ48eJ46aWXYujQoVFTUxM1NTWxcOHCuOSSS6KmpiZaW1vLPWJF6tGjR+y4447x1FNPlXuUitOnT58U4bvsskvFnJKpmEDo0qVL7LbbbrFgwYL2x9ra2mLBggXOhbJeiqKIiRMnxrx58+LWW2+NbbbZptwjfWi0tbVFS0tLuceoKAcccEA89NBDsWTJkvbbsGHD4thjj40lS5ZEdXV1uUesSKtWrYqnn346+vTpU+5RKs6IESPSR6//+te/RkNDQ5km6qiiTjFMnjw5xo0bF8OGDYs99tgjLrrooli9enV87WtfK/doFWXVqlUdanzp0qWxZMmS6NmzZwwYMKCMk1WWCRMmxOzZs+O3v/1tdOvWrf1als033zzq6+vLPF3lmDJlSowePToGDBgQK1eujNmzZ8ftt98eN998c7lHqyjdunVL16907do1evXq5bqW9zjttNNizJgx0dDQEC+88EJMnTo1qqur4ytf+Uq5R6s4p556auy9995x7rnnxtFHHx333XdfXHnllXHllVeWe7R/K/fHKP7TpZdeWgwYMKDo0qVLscceexT33HNPuUeqOLfddlsREek2bty4co9WUda0jyKimDlzZrlHqyjjx48vGhoaii5duhSf+MQnigMOOKC45ZZbyj3Wh4KPOWZjx44t+vTpU3Tp0qXYeuuti7FjxxZPPfVUuceqWDfeeGOx6667FrW1tcXOO+9cXHnlleUeqZ2vewYAkoq5BgEAqBwCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQACACnLHHXfEmDFjom/fvlEqleI3v/nNOm+jKIq48MILY8cdd4za2trYeuut40c/+tE6baOiftUyAHzcrV69OhobG2P8+PFxxBFHrNc2Jk2aFLfccktceOGFMWjQoHjllVfilVdeWadtOIIArNHtt98epVIptthii2hubu7w3P333x+lUilKpdI6bXPgwIFx0UUXfYBTwkfP6NGj44c//GEcfvjha3y+paUlTjvttNh6662ja9euMXz48Lj99tvbn3/sscfi8ssvj9/+9rfxhS98IbbZZpvYbbfd4sADD1ynOQQCfMy0trZGW1tbp5fv1q1bzJs3r8NjP//5z8v6xWBvvfVW2V4bym3ixIlx9913x5w5c+LBBx+Mo446Kg455JB48sknIyLixhtvjG233TZuuumm2GabbWLgwIHx9a9/3REEqDTXXXddDBo0KOrr66NXr14xatSoWL16dbS2tsbkyZOjR48e0atXrzj99NNj3Lhxcdhhh7Wvu6Z33J/5zGfizDPPbL8/Y8aMGDRoUHTt2jX69+8f3/rWt2LVqlXtz8+aNSt69OgRN9xwQ3zqU5+K2traWLZs2fu+C3nXuHHj4he/+EX7/TfffDPmzJkT48aNS8suWrQoRo4cGfX19dG/f/845ZRTYvXq1RERse+++8azzz4bp556ajr68N/We3c/nHPOOXH88cdH9+7d46STToq33norJk6cGH369Im6urpoaGiIadOmdfY/C3woLVu2LGbOnBlz586NkSNHxnbbbRennXZafPazn42ZM2dGRMTf/va3ePbZZ2Pu3Lnxq1/9KmbNmhWLFy+OI488cp1eSyDABrR8+fL4yle+EuPHj4/HHnssbr/99jjiiCOiKIqYPn16zJo1K37xi1/EokWL4pVXXknv1DujqqoqLrnkknjkkUfil7/8Zdx6661x+umnd1jmjTfeiPPPPz+uuuqqeOSRR2LLLbd833ch7/rqV78ad955ZyxbtiwiIq6//voYOHBgDB06tMNyTz/9dBxyyCHxpS99KR588MG45pprYtGiRTFx4sSIiPj1r38d/fr1i7PPPjuWL18ey5cv79R677rwwgujsbEx/vznP8f//d//xSWXXBI33HBDXHvttfHEE0/E1VdfHQMHDlzn/QcfJg899FC0trbGjjvuGJtttln7beHChfH0009HRERbW1u0tLTEr371qxg5cmTsu+++8fOf/zxuu+22eOKJJzr/YuX9Mkn4aFu8eHEREcUzzzyTnuvTp09xwQUXtN9/++23i379+hVf/OIX2x9raGgofvzjH3dYr7GxsZg6depaX3Pu3LlFr1692u/PnDmziIhiyZIl7Y89++yzRXV1dfH88893WPeAAw4opkyZUhTF//9a8VdffbU47LDDirPOOqsoiqLYb7/9iosvvriYN29e8d5/Qk488cTipJNO6rC9O++8s6iqqirefPPNtf59OrveYYcd1mGZb3/728X+++9ftLW1rXVfwIddRBTz5s1rvz9nzpyiurq6ePzxx4snn3yyw2358uVFURTFGWecUdTU1HTYzhtvvFFExDp9lbtPMcAG1NjYGAcccEAMGjQoDj744DjooIPiyCOPjKqqqli+fHkMHz68fdmampoYNmxYFOv4Dex/+MMfYtq0afH444/HihUr4p133onm5uZ44403YtNNN42IiC5dusTgwYPb13nvu5D3amlpiV69eqXXGD9+fEyaNCmOO+64uPvuu2Pu3Llx5513dljmL3/5Szz44INx9dVXtz9WFEW0tbXF0qVLY5dddlnj/J1db9iwYR3WO+GEE+LAAw+MnXbaKQ455JA49NBD46CDDurMLoMPrSFDhkRra2u89NJLMXLkyDUuM2LEiHjnnXfi6aefju222y4iIv76179GRERDQ0OnX0sgwAZUXV0d8+fPj7vuuituueWWuPTSS+MHP/hBzJ8/v1PrV1VVpWB4++232//8zDPPxKGHHhrf/OY340c/+lH07NkzFi1aFCeeeGK89dZb7YFQX1/f4Zz/qlWrorq6OhYvXhzV1dUdtr/ZZpulOUaPHh0nnXRSnHjiiTFmzJg1RsSqVavi5JNPjlNOOSU9998uaOzsel27du3w3NChQ2Pp0qXR1NQUf/jDH+Loo4+OUaNGxXXXXbfW14IPg1WrVsVTTz3Vfn/p0qWxZMmS6NmzZ+y4445x7LHHxvHHHx/Tp0+PIUOGxD//+c9YsGBBDB48OD7/+c/HqFGjYujQoTF+/Pi46KKLoq2tLSZMmBAHHnhgelPw3wgE2MBKpVKMGDEiRowYEWeccUY0NDTEggULok+fPnHvvffGPvvsExER77zzTixevLjDuf1PfOIT7efqIyJWrFgRS5cubb+/ePHiaGtri+nTp0dV1b8vKbr22mvfd6bOvAt5r5qamjj++OPjggsuiKampjUuM3To0Hj00Udj++23X+t2unTpEq2treu83tp07949xo4dG2PHjo0jjzwyDjnkkHjllVeiZ8+e67wtqBQPPPBA7Lfffu33J0+eHBH/vmB41qxZMXPmzPjhD38Y3/3ud+P555+P3r17x5577hmHHnpoRPz7jcWNN94Y3/72t2OfffaJrl27xujRo2P69OnrNIdAgA3o3nvvjQULFsRBBx0UW265Zdx7773xz3/+M3bZZZeYNGlSnHfeebHDDjvEzjvvHDNmzIjXXnutw/r7779/zJo1K8aMGRM9evSIM844o8M7/u233z7efvvtuPTSS2PMmDHxxz/+Ma644or3nasz70L+0znnnBPf+9731nj0ICLi+9//fuy5554xceLE+PrXvx5du3aNRx99NObPnx8/+clPIuLfn0a444474stf/nLU1tZG7969O7XemsyYMSP69OkTQ4YMiaqqqpg7d2588pOfjB49erzv3x8q2b777vtfTzVusskmcdZZZ8VZZ5211mX69u0b119//f80h0CADah79+5xxx13xEUXXRQrVqyIhoaGmD59eowePToOPPDAWL58eYwbNy6qqqpi/Pjxcfjhh8frr7/evv6UKVNi6dKlceihh8bmm28e55xzTocjCI2NjTFjxow4//zzY8qUKbHPPvvEtGnT4vjjj3/f2d7vXch/6tKlS/Tu3Xut2xs8eHAsXLgwfvCDH8TIkSOjKIrYbrvtYuzYse3LnH322XHyySfHdtttFy0tLVEURafWW5Nu3brFBRdcEE8++WRUV1fH7rvvHr/73e/aj6QA/5tSsa5XRAEbzAknnBCvvfbaev3udYAPktQGABKBAAAkTjEAAIkjCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACD5fw6IXN24Z31GAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nilai_kolom = df.loc[15334, 'squareMeters']\n",
        "print(nilai_kolom)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "id": "vTQn6RKTzzLV",
        "outputId": "f34892e9-b953-4acf-daf2-78d0e9bc3677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a0c170c4871f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnilai_kolom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m15334\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'squareMeters'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnilai_kolom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil pengamatan data outliers pada kolom square Meters, didapatkan bahwa nilai outlier terlalu jauh dari nilai yang ada. Penulis memutuskan untuk menghapus nilai tersebut karena diasumsikan bahwa data tersebut merupakan kesalahan input yang mana penulis tidak menemukan metode transformasi maupun pendekatan statistik untuk menangani data ini"
      ],
      "metadata": {
        "id": "YXASluRDO7gl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'floors'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom floors\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "mf_zs0QJ1sLj",
        "outputId": "14cd0f58-4358-44f0-b337-b0bbde7d80ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom floors floors ada pada baris ke:\n",
            "Int64Index([5659], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nilai_kolom = df.loc[5659, 'floors']\n",
        "print(nilai_kolom)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mx71fM7l13W6",
        "outputId": "7e506e7f-518f-449c-cebb-d64c49e4cbe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil pengamatan data floors, penulis juga menghapus kolom yang berisi data outliers karena penulis mengasumsikan bahwa data ini merupakan kesalahan input. dimana data puluhan terdapat satu data yang ribuan. Untuk kasus ini penulis belum mendapatkan proses transformasi maupun pendekatan statistik untuk menangani data ini"
      ],
      "metadata": {
        "id": "Z3YwhX5jPm8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'cityCode'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom floors\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "wkS3lcTT2ZKG",
        "outputId": "4549316e-df92-4c8c-ee72-56c70d134f58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom floors cityCode ada pada baris ke:\n",
            "Int64Index([299, 1326, 2425, 10987, 20735, 22631], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.io.parsers.c_parser_wrapper import is_index_col\n",
        "# memilih kolom yang diinginkan\n",
        "columns = 'cityCode'\n",
        "\n",
        "# memilih index yang diinginkan\n",
        "index_col = [299, 1326, 2425, 10987, 20735, 22631]\n",
        "\n",
        "# mencetak nilai kolom pada index yang diinginkan\n",
        "print(df.loc[index_col, columns])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqCMN9ATLx-x",
        "outputId": "6135903c-06e1-4d21-a5f1-f9e782a1cf5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "299      201035\n",
            "1326     491100\n",
            "2425     465360\n",
            "10987    200812\n",
            "20735    200801\n",
            "22631    200801\n",
            "Name: cityCode, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'made'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom made\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "xOjoN70JNAYH",
        "outputId": "a386ca2a-d564-47ab-8a24-c71da901dbd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom made made ada pada baris ke:\n",
            "Int64Index([2113, 3608, 19124, 19748, 21400], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.io.parsers.c_parser_wrapper import is_index_col\n",
        "# memilih kolom yang diinginkan\n",
        "columns = 'made'\n",
        "\n",
        "# memilih index yang diinginkan\n",
        "index_col = [2113, 3608, 19124, 19748, 21400]\n",
        "\n",
        "# mencetak nilai kolom pada index yang diinginkan\n",
        "print(df.loc[index_col, columns])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ka2KSk1NVRE",
        "outputId": "7b6e290b-6a51-49f4-97ce-350c93754d3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2113     10000\n",
            "3608     10000\n",
            "19124    10000\n",
            "19748    10000\n",
            "21400    10000\n",
            "Name: made, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'basement'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom made\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "cdTZFCKhNtPo",
        "outputId": "5f592a5a-e2ae-44a2-d3c6-6607d1cd10eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom made basement ada pada baris ke:\n",
            "Int64Index([2107, 3995, 15068, 18926], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.io.parsers.c_parser_wrapper import is_index_col\n",
        "# memilih kolom yang diinginkan\n",
        "columns = 'basement'\n",
        "\n",
        "# memilih index yang diinginkan\n",
        "index_col = [2107, 3995, 15068, 18926]\n",
        "\n",
        "# mencetak nilai kolom pada index yang diinginkan\n",
        "print(df.loc[index_col, columns])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfI4k4L9N6DN",
        "outputId": "dce2baf5-c20f-400b-b816-097128113db2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2107     84333\n",
            "3995     81851\n",
            "15068    91992\n",
            "18926    91978\n",
            "Name: basement, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'attic'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom made\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "bgwoTAcjVrbE",
        "outputId": "d10162d0-cee9-4d79-c4ce-9f812fb9d81c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom made attic ada pada baris ke:\n",
            "Int64Index([3828, 4909, 12858, 13633, 13642, 17168, 19994], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.io.parsers.c_parser_wrapper import is_index_col\n",
        "# memilih kolom yang diinginkan\n",
        "columns = 'attic'\n",
        "\n",
        "# memilih index yang diinginkan\n",
        "index_col = [3828, 4909, 12858, 13633, 13642, 17168, 19994]\n",
        "\n",
        "# mencetak nilai kolom pada index yang diinginkan\n",
        "print(df.loc[index_col, columns])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CijtZOWcVz4o",
        "outputId": "62786f3a-fb0e-4f0a-bbd9-3c58f42eb3b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3828     71001\n",
            "4909     71001\n",
            "12858    71024\n",
            "13633    96381\n",
            "13642    30000\n",
            "17168    71965\n",
            "19994    30000\n",
            "Name: attic, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Pilih kolom luas bangunan\n",
        "kolom = 'garage'\n",
        "\n",
        "# Buat boxplot\n",
        "sns.boxplot(x=df[kolom])\n",
        "\n",
        "# Hitung batas bawah dan atas data outliers\n",
        "q1 = df[kolom].quantile(0.25)\n",
        "q3 = df[kolom].quantile(0.75)\n",
        "iqr = q3 - q1\n",
        "batas_bawah = q1 - 1.5 * iqr\n",
        "batas_atas = q3 + 1.5 * iqr\n",
        "\n",
        "# Cari indeks baris yang mengandung data outliers\n",
        "outlier_idx = df[(df[kolom] < batas_bawah) | (df[kolom] > batas_atas)].index\n",
        "\n",
        "# Tampilkan indeks baris yang mengandung data outliers\n",
        "print(\"Data outliers pada kolom made\", kolom, \"ada pada baris ke:\")\n",
        "print(outlier_idx)\n",
        "\n",
        "# Tampilkan grafik\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "YWpn8YEpV4-x",
        "outputId": "65413726-7b9e-452c-cf32-7b53d290b09e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data outliers pada kolom made garage ada pada baris ke:\n",
            "Int64Index([14878, 17629], dtype='int64')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.io.parsers.c_parser_wrapper import is_index_col\n",
        "# memilih kolom yang diinginkan\n",
        "columns = 'garage'\n",
        "\n",
        "# memilih index yang diinginkan\n",
        "index_col = [14878, 17629]\n",
        "\n",
        "# mencetak nilai kolom pada index yang diinginkan\n",
        "print(df.loc[index_col, columns])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVZsp8R6WAt1",
        "outputId": "c948d9a2-9f92-4c14-dce2-f05aca35eef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14878    2048\n",
            "17629    9017\n",
            "Name: garage, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Cleaning**"
      ],
      "metadata": {
        "id": "-dBvRgMpWyVf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean = raw_data.drop(['id'], axis=1)"
      ],
      "metadata": {
        "id": "UPhXLARWWgo7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# memilih index yang akan dihapus\n",
        "index_delete = [15334,5659,299, 1326, 2425, 10987, 20735, 22631,2113, 3608,\n",
        "                19124, 19748, 21400,2107, 3995, 15068, 18926,3828, 4909, 12858,\n",
        "                13633, 13642, 17168, 19994,14878, 17629]\n",
        "\n",
        "# menghapus baris pada index yang diinginkan\n",
        "df_clean = df_clean.drop(index_delete)"
      ],
      "metadata": {
        "id": "V2P42WsdXLkc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "JsaODYO-Ytaj",
        "outputId": "97630e15-6c6b-418f-e10e-12da91fd0504"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   squareMeters  numberOfRooms  hasYard  hasPool  floors  cityCode  \\\n",
              "0         34291             24        1        0      47     35693   \n",
              "1         95145             60        0        1      60     34773   \n",
              "2         92661             45        1        1      62     45457   \n",
              "3         97184             99        0        0      59     15113   \n",
              "4         61752            100        0        0      57     64245   \n",
              "\n",
              "   cityPartRange  numPrevOwners  made  isNewBuilt  hasStormProtector  \\\n",
              "0              2              1  2000           0                  1   \n",
              "1              1              4  2000           0                  1   \n",
              "2              4              8  2020           1                  1   \n",
              "3              1              1  2000           0                  1   \n",
              "4              8              4  2018           1                  0   \n",
              "\n",
              "   basement  attic  garage  hasStorageRoom  hasGuestRoom      price  \n",
              "0         8   5196     369               0             3  3436795.2  \n",
              "1       729   4496     277               0             6  9519958.0  \n",
              "2      7473   8953     245               1             9  9276448.1  \n",
              "3      6424   8522     256               1             9  9725732.2  \n",
              "4      7151   2786     863               0             7  6181908.8  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-240c9a16-29ff-4572-b3cf-27b169fb5fed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>squareMeters</th>\n",
              "      <th>numberOfRooms</th>\n",
              "      <th>hasYard</th>\n",
              "      <th>hasPool</th>\n",
              "      <th>floors</th>\n",
              "      <th>cityCode</th>\n",
              "      <th>cityPartRange</th>\n",
              "      <th>numPrevOwners</th>\n",
              "      <th>made</th>\n",
              "      <th>isNewBuilt</th>\n",
              "      <th>hasStormProtector</th>\n",
              "      <th>basement</th>\n",
              "      <th>attic</th>\n",
              "      <th>garage</th>\n",
              "      <th>hasStorageRoom</th>\n",
              "      <th>hasGuestRoom</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34291</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>35693</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>5196</td>\n",
              "      <td>369</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3436795.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>95145</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>34773</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>729</td>\n",
              "      <td>4496</td>\n",
              "      <td>277</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>9519958.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>92661</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>45457</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7473</td>\n",
              "      <td>8953</td>\n",
              "      <td>245</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>9276448.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>97184</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "      <td>15113</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6424</td>\n",
              "      <td>8522</td>\n",
              "      <td>256</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>9725732.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61752</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>64245</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7151</td>\n",
              "      <td>2786</td>\n",
              "      <td>863</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>6181908.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-240c9a16-29ff-4572-b3cf-27b169fb5fed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-240c9a16-29ff-4572-b3cf-27b169fb5fed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-240c9a16-29ff-4572-b3cf-27b169fb5fed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMen8IGHYxLD",
        "outputId": "9c9f8af3-ad53-4b98-a5bd-4074470919ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22704, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(25,6))\n",
        "df_clean.boxplot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "wUnYwxNSY1_e",
        "outputId": "076d505c-3240-499d-8e5e-5cdbe0d0835d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x600 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pembuatan Model Prediksi**"
      ],
      "metadata": {
        "id": "DDhRgo_VZTRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Load data\n",
        "df = df_clean\n",
        "\n",
        "# Split data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit Lasso regression model\n",
        "alphas = np.logspace(-4, 0, 100)\n",
        "mse = []\n",
        "for alpha in alphas:\n",
        "    lasso = Lasso(alpha=alpha)\n",
        "    lasso.fit(X_train, y_train)\n",
        "    mse.append(mean_squared_error(y_test, lasso.predict(X_test)))\n",
        "\n",
        "best_alpha = alphas[np.argmin(mse)]\n",
        "lasso = Lasso(alpha=best_alpha)\n",
        "lasso.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate model\n",
        "y_pred = lasso.predict(X_test)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "print(f\"RMSE: {rmse:.2f}\")\n",
        "\n",
        "# Create table of predicted vs actual values\n",
        "results_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "results_df.index.name = 'Observation'\n",
        "print(results_df.head(10))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPZG3xSBHzm_",
        "outputId": "fd2d5699-441b-45e1-e34e-c076ae3b675e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 95751.11\n",
            "                Actual     Predicted\n",
            "Observation                         \n",
            "15454        7382091.9  7.366020e+06\n",
            "8188         6243323.3  6.228006e+06\n",
            "21646        1563887.5  1.576674e+06\n",
            "7745         7091010.3  7.073206e+06\n",
            "18855        5183003.6  5.176186e+06\n",
            "18695        3898194.1  3.887345e+06\n",
            "13022        4954300.1  4.941842e+06\n",
            "6966         7351183.7  7.335611e+06\n",
            "14179        5659030.2  5.663814e+06\n",
            "21435        2786587.7  2.794054e+06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pandas as pd\n",
        "# import numpy as np\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# from sklearn.linear_model import Lasso\n",
        "# from sklearn.metrics import mean_squared_error\n",
        "# from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# # Load data\n",
        "# df = df_clean\n",
        "\n",
        "# # Split data into training and testing sets\n",
        "# X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], test_size=0.2, random_state=42)\n",
        "\n",
        "# # Standardize data\n",
        "# scaler = StandardScaler()\n",
        "# X_train = scaler.fit_transform(X_train)\n",
        "# X_test = scaler.transform(X_test)\n",
        "\n",
        "# # Fit Lasso regression model\n",
        "# alphas = np.logspace(-4, 0, 100)\n",
        "# mse = []\n",
        "# for alpha in alphas:\n",
        "#     lasso = Lasso(alpha=alpha)\n",
        "#     lasso.fit(X_train, y_train)\n",
        "#     mse.append(mean_squared_error(y_test, lasso.predict(X_test)))\n",
        "\n",
        "# best_alpha = alphas[np.argmin(mse)]\n",
        "# lasso = Lasso(alpha=best_alpha)\n",
        "# lasso.fit(X_train, y_train)\n",
        "\n",
        "# # Evaluate model\n",
        "# y_pred = lasso.predict(X_test)\n",
        "# rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "# rmse /= 10000\n",
        "# print(f\"RMSE: {rmse:.2f}\")\n",
        "\n",
        "# # Create table of predicted vs actual values\n",
        "# results_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "# results_df = results_df.apply(lambda x: x/10000) # convert to thousands\n",
        "# results_df.index.name = 'Observation'\n",
        "# print(results_df.head(10))\n"
      ],
      "metadata": {
        "id": "9-019GaKEjau",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f39fe3b9-b3b8-4831-f568-0915ae75d453"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 9.58\n",
            "                Actual   Predicted\n",
            "Observation                       \n",
            "15454        738.20919  736.601744\n",
            "8188         624.33233  622.800467\n",
            "21646        156.38875  157.667113\n",
            "7745         709.10103  707.320631\n",
            "18855        518.30036  517.618942\n",
            "18695        389.81941  388.734079\n",
            "13022        495.43001  494.184143\n",
            "6966         735.11837  733.561385\n",
            "14179        565.90302  566.381282\n",
            "21435        278.65877  279.405455\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.plot(y_test, y_test, color='red')\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('Actual vs Predicted')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "E7ccB_JdFDCh",
        "outputId": "8258bdb1-f0b0-4e6a-e7ba-9c8329a546bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(f\"R-squared: {r2:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVOTTijgFLRv",
        "outputId": "a5976377-c345-4f52-99aa-367daeaf4e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-squared: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "# Save model to file\n",
        "joblib.dump(lasso, 'lasso_model.pkl')\n",
        "\n",
        "# Load model from file\n",
        "loaded_model = joblib.load('lasso_model.pkl')\n"
      ],
      "metadata": {
        "id": "l7PSc4OrItvK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}