Weather_Prediction_With_Gradient_Boost__Ichwan_Maulana_.ipynb

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      "name": "Weather Prediction With Gradient Boost <Ichwan Maulana>.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
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    },
    {
      "cell_type": "code",
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      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import scipy\n",
        "import re\n",
        "from scipy import stats\n",
        "from scipy.stats import ttest_ind\n",
        "from scipy.stats import pearsonr\n",
        "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Datasets"
      ],
      "metadata": {
        "id": "Iu19tX340lsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Kumpulan Data Set Bootcamp DS 11A/seattle-weather.csv\")\n",
        "data.head(10)"
      ],
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        "colab": {
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        "id": "ZEhSe4NHwiEn",
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              "         date  precipitation  temp_max  temp_min  wind  weather\n",
              "0  2012-01-01            0.0      12.8       5.0   4.7  drizzle\n",
              "1  2012-01-02           10.9      10.6       2.8   4.5     rain\n",
              "2  2012-01-03            0.8      11.7       7.2   2.3     rain\n",
              "3  2012-01-04           20.3      12.2       5.6   4.7     rain\n",
              "4  2012-01-05            1.3       8.9       2.8   6.1     rain\n",
              "5  2012-01-06            2.5       4.4       2.2   2.2     rain\n",
              "6  2012-01-07            0.0       7.2       2.8   2.3     rain\n",
              "7  2012-01-08            0.0      10.0       2.8   2.0      sun\n",
              "8  2012-01-09            4.3       9.4       5.0   3.4     rain\n",
              "9  2012-01-10            1.0       6.1       0.6   3.4     rain"
            ],
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              "      <td>drizzle</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-02</td>\n",
              "      <td>10.9</td>\n",
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              "      <td>rain</td>\n",
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              "      <td>rain</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-05</td>\n",
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              "      <td>rain</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2012-01-06</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>2.2</td>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2012-01-07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.3</td>\n",
              "      <td>rain</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2012-01-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>sun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2012-01-09</td>\n",
              "      <td>4.3</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2012-01-10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>3.4</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "# About Datasets\n",
        "\n",
        "Terdapat 6 kolom pada data yaitu : {`date`, `precipitation`, `temp_max`, `temp_min`,\t`wind`,\t`weather`}\n",
        "\n",
        "*   **`date`**: Tanggal \n",
        "*   **`precipitation`**: Produk dari kondensasi uap air di atmosfer\n",
        "*   **`temp_max`**: Suhu tertinggi\n",
        "*   **`temp_min`**: Suhu terendah\n",
        "*   **`wind`**: Kecepatan angin\n",
        "*   **`weather`**: Cuaca yang dihasilkan (drizzle, rain, sun, snow, fog)\n"
      ],
      "metadata": {
        "id": "Z6pTYxy9wMXA"
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    {
      "cell_type": "markdown",
      "source": [
        "# Analysing Dataset"
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              "         date  precipitation  temp_max  temp_min  wind  weather\n",
              "0  2012-01-01            0.0      12.8       5.0   4.7  drizzle\n",
              "1  2012-01-02           10.9      10.6       2.8   4.5     rain\n",
              "2  2012-01-03            0.8      11.7       7.2   2.3     rain\n",
              "3  2012-01-04           20.3      12.2       5.6   4.7     rain\n",
              "4  2012-01-05            1.3       8.9       2.8   6.1     rain"
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>10.9</td>\n",
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              "      <td>8.9</td>\n",
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          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "1.   Data berisi 6 kolom dengan 1461 baris \n",
        "2.   Terdapat dua tipe kolom (Numerical & Categorical) pada data.\n",
        "3.  Terdapat 4 kolom yang numerical, dan isi/variabel dari pada kolom tsb adalah kontinu \n",
        "4.   Terdapat 1 kolom berbentuk tanggal.\n",
        "5.   Terdapat 1 kolom yang isinya berbentuk categorical pada kolom `weather`\n",
        "\n"
      ],
      "metadata": {
        "id": "gSkG_-QC5ZPq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "-BAeH3Sp7849"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## General Information & Missing Values"
      ],
      "metadata": {
        "id": "wH718m4d13_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxSI-GBh1XKI",
        "outputId": "12ab572d-9abe-4796-d98f-8b296f316c1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1461 entries, 0 to 1460\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   date           1461 non-null   object \n",
            " 1   precipitation  1461 non-null   float64\n",
            " 2   temp_max       1461 non-null   float64\n",
            " 3   temp_min       1461 non-null   float64\n",
            " 4   wind           1461 non-null   float64\n",
            " 5   weather        1461 non-null   object \n",
            "dtypes: float64(4), object(2)\n",
            "memory usage: 68.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "1. Data berisi 6 kolom dengan 1461 baris \n",
        "2. Tidak terlihatnya missing value pada data (akan dicek lebih lanjut)"
      ],
      "metadata": {
        "id": "C2Iv9cWC2DL7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# number of missing values per column\n",
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLDvr5zA3iGk",
        "outputId": "114ab60c-e568-4099-c5df-a1315afc055d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date             0\n",
              "precipitation    0\n",
              "temp_max         0\n",
              "temp_min         0\n",
              "wind             0\n",
              "weather          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Duplicated data check"
      ],
      "metadata": {
        "id": "QzzT_lC-9UNO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# number of duplicated data\n",
        "data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGkqZlJB3tVH",
        "outputId": "c9cee5eb-2334-4192-d090-196f974cd738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "1. Telah dibuktikan bahwa tidak terdapat missing value \n",
        "2. Tidak terdapat duplicate pada data diatas"
      ],
      "metadata": {
        "id": "ItXFCzko3xND"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistical summary"
      ],
      "metadata": {
        "id": "z3-5tQId5Fmv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# group column names based on type\n",
        "# it will make our life easier onwards\n",
        "categoricals = ['weather']\n",
        "\n",
        "numericals = ['precipitation', 'temp_max', 'temp_min', 'wind']"
      ],
      "metadata": {
        "id": "pcG6JGMn7hBa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# numerical statistical sumary\n",
        "data[numericals].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "0VuFR3v599CG",
        "outputId": "8f355fdb-7676-4d13-e635-7315e3e5d036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       precipitation     temp_max     temp_min         wind\n",
              "count    1461.000000  1461.000000  1461.000000  1461.000000\n",
              "mean        3.029432    16.439083     8.234771     3.241136\n",
              "std         6.680194     7.349758     5.023004     1.437825\n",
              "min         0.000000    -1.600000    -7.100000     0.400000\n",
              "25%         0.000000    10.600000     4.400000     2.200000\n",
              "50%         0.000000    15.600000     8.300000     3.000000\n",
              "75%         2.800000    22.200000    12.200000     4.000000\n",
              "max        55.900000    35.600000    18.300000     9.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d10f8b18-64d4-4da1-98c0-7acc639d4e61\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_max</th>\n",
              "      <th>temp_min</th>\n",
              "      <th>wind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1461.000000</td>\n",
              "      <td>1461.000000</td>\n",
              "      <td>1461.000000</td>\n",
              "      <td>1461.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.029432</td>\n",
              "      <td>16.439083</td>\n",
              "      <td>8.234771</td>\n",
              "      <td>3.241136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.680194</td>\n",
              "      <td>7.349758</td>\n",
              "      <td>5.023004</td>\n",
              "      <td>1.437825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.600000</td>\n",
              "      <td>-7.100000</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.600000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>2.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>8.300000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.800000</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>55.900000</td>\n",
              "      <td>35.600000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>9.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "      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-d10f8b18-64d4-4da1-98c0-7acc639d4e61 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-d10f8b18-64d4-4da1-98c0-7acc639d4e61');\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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "1.   Dibagian min dan max masih make sense karena tidak ada nilai yang aneh dikeduanya (misal tertukar)\n",
        "2.   Mean ~ 50% (Median) `temp_max, temp_min, wind nilai` tidak berbeda jauh\n",
        "\n",
        "3.   Mean > 50% (Median) pada kolom `precipitation`, menunjukkan keduanya memiliki distribusi yang sedikit condong positif\n"
      ],
      "metadata": {
        "id": "NxcRMJKC_0Yd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# describe method on categorical data\n",
        "data[categoricals].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "0QWuGcuFWnci",
        "outputId": "4574beeb-6b0e-4745-af8b-087ab011a4e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       weather\n",
              "count     1461\n",
              "unique       5\n",
              "top       rain\n",
              "freq       641"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>weather</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>1461</td>\n",
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              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>641</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6553d28-ff1c-4f88-ac32-b073997fb181')\"\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",
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              "    <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",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\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-c6553d28-ff1c-4f88-ac32-b073997fb181 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-c6553d28-ff1c-4f88-ac32-b073997fb181');\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": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "1.   Terdapat 5 uniqe value pada kolom weather yang berisikan 5 cuaca\n",
        "2.   Cuaca yang paling sering terjadi adalah hujan (rain)\n",
        "3.   Tercatat 641 kali terjadi cuaca hujan dari 1461 data\n"
      ],
      "metadata": {
        "id": "3t1csHiTW3V6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "zW3a1tjkus71"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplot to detect outliers"
      ],
      "metadata": {
        "id": "nFnwSmEZu1Ia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# adjust the figure size for better readability\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "# plotting\n",
        "features = numericals\n",
        "for i in range(0, len(features)):\n",
        "    plt.subplot(1, len(features), i+1)\n",
        "    sns.boxplot(y=data[features[i]], color='#F24A72')\n",
        "    plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "Qnt17WP6vNh1",
        "outputId": "4476f9c7-100d-4ce6-af2c-8a064d2d02bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "1.   Terdapat outliers pada kolom `precipitation` yang nilainya lebih dari 10\n",
        "2.   Terdapat outliers pada kolom `wind` yang nilainya lebih dari 7\n"
      ],
      "metadata": {
        "id": "FaOD2yBJ3LZV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histrogram to detect skewness"
      ],
      "metadata": {
        "id": "ncmXQYtf20vJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num=['precipitation', 'temp_max', 'temp_min', 'wind']\n",
        "for col in num:\n",
        "  plt.figure(figsize=(8,4))\n",
        "  plt.hist(data [col], color = \"lightblue\", ec=\"red\")\n",
        "  plt.title(col)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b1KLnrgq1ZNb",
        "outputId": "60fec7c2-71a7-4598-d84b-d38bb6be9355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dilihat dari data histogram diatas, terdapat skewness pada beberapa data\n",
        "\n",
        "precipitation\ttemp_max\ttemp_min\twind\n",
        "\n",
        "1.   Kolom `precipitation`, `temp_max`,\t`wind` diidentifikasi terdapat poistive skew\n",
        "2.   Kolom `temp_min` diidentifikasi terdapat negative skew"
      ],
      "metadata": {
        "id": "UyzEBdzO3koC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplot to detect skewness\n",
        "\n",
        "Mendeteksi skewness antara numerical kolom dengan categorical kolom"
      ],
      "metadata": {
        "id": "N_OhToU1kDcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot antara precipitation dengan weather\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(\"precipitation\",\"weather\",data=data,palette=\"bone\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "109C40U8k3Q1",
        "outputId": "6d43b4e4-5b29-4d25-fc41-b9e226091874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a90cf4690>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dari Boxplot antara `weather` dan `precipitation` di atas, nilai rain memiliki banyak outlier positive dan juga rain dan snow keduanya memiliki skewness positive."
      ],
      "metadata": {
        "id": "JEJjDyCilzMY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot antara temp_max dengan weather\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(\"temp_max\",\"weather\",data=data,palette=\"hot\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "J8Oktptom4Kb",
        "outputId": "23a4f20b-796e-4bb2-8ee7-49f9066895de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a90266150>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dari Boxplot antara `weather` dan `temp_max` di atas, nilai rain memiliki outlier positive dan juga beberapa cuaca memiliki skewness positive & negative."
      ],
      "metadata": {
        "id": "sg0EUmSenOMc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot antara temp_min dengan weather\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(\"temp_min\",\"weather\",data=data,palette=\"cool\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "LeDFSMDhn0_X",
        "outputId": "d206f923-ddc6-4ae3-bc97-f4c6542fd3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a9025c210>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dari Boxplot antara `weather` dan `temp_min` di atas, data cenderung memiliki nagative skew dan beberapa ada yang mempunyai oulier portive dan negative"
      ],
      "metadata": {
        "id": "uOFvRSUyofrs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot antara wind dengan weather\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(\"wind\",\"weather\",data=data,palette=\"winter\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "rwpwHc_Kp6yH",
        "outputId": "1f6ab14e-c94a-4b6d-bd61-4a7fae6c0c7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a90b96b50>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dari Boxplot antara `weather` dan `wind` di atas, data cenderung memiliki positive skew dan banyak yang mempunyai oulier positive "
      ],
      "metadata": {
        "id": "Yt49O8zEqgVo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Heatmap to detect Correlation"
      ],
      "metadata": {
        "id": "vPspMeeZbZYy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = data.corr()\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(correlation, annot=True, fmt='.2f', cmap=\"PuBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "7NlByYo0aF65",
        "outputId": "0d4cb900-d1b6-441b-c477-d03b25e82467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a90bc7fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Terlihat korelasi positif antara `temp_max` dan `temp_min`\n"
      ],
      "metadata": {
        "id": "jskox6Pgbg9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "NmlfWdaDcV-_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Drop Unnecessary Variables"
      ],
      "metadata": {
        "id": "92aX8F4pfvDw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=data.drop([\"date\"],axis=1)"
      ],
      "metadata": {
        "id": "Lr6xr-WZfFa4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dalam kumpulan data ini Tanggal (date) adalah variabel yang tidak diperlukan karena tidak mempengaruhi pengolahan data sehingga kolom date akan dibuang.\n"
      ],
      "metadata": {
        "id": "lWv1ZC4sf5NP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Remove Outliers & Infinite Values"
      ],
      "metadata": {
        "id": "Cc3WpFPbgHWe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Q1=df.quantile(0.25)\n",
        "Q3=df.quantile(0.75)\n",
        "IQR=Q3-Q1\n",
        "df=df[~((df<(Q1-1.5*IQR))|(df>(Q3+1.5*IQR))).any(axis=1)]"
      ],
      "metadata": {
        "id": "ox2-pTEKrI02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Karena data terdapat ouliers, maka dari itu data outlier tersebut akan dihapus supaya data terstruktur dan lebih merata"
      ],
      "metadata": {
        "id": "YDux3t5WrOdH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handling Skewed Data"
      ],
      "metadata": {
        "id": "0Usrnk0SrpMH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.precipitation=np.sqrt(df.precipitation)\n",
        "df.wind=np.sqrt(df.wind)"
      ],
      "metadata": {
        "id": "U1bY1_5Hs8iT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Karena terdapat outlier yang cenderung paling banyak diantara kolom lainnya, kita akan rubah nilainya dengan sqrt (nilai kuadrat) "
      ],
      "metadata": {
        "id": "zu7iI-8ktG4W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p5j5tWOyuUt0",
        "outputId": "92da2616-37b1-41db-9817-7a9da7d2a474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   precipitation  temp_max  temp_min      wind  weather\n",
              "0       0.000000      12.8       5.0  2.167948  drizzle\n",
              "2       0.894427      11.7       7.2  1.516575     rain\n",
              "4       1.140175       8.9       2.8  2.469818     rain\n",
              "5       1.581139       4.4       2.2  1.483240     rain\n",
              "6       0.000000       7.2       2.8  1.516575     rain"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38f93075-93aa-4387-abdf-2f6e49a49890\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_max</th>\n",
              "      <th>temp_min</th>\n",
              "      <th>wind</th>\n",
              "      <th>weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.167948</td>\n",
              "      <td>drizzle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.894427</td>\n",
              "      <td>11.7</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1.516575</td>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.140175</td>\n",
              "      <td>8.9</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.469818</td>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.581139</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.483240</td>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1.516575</td>\n",
              "      <td>rain</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38f93075-93aa-4387-abdf-2f6e49a49890')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-38f93075-93aa-4387-abdf-2f6e49a49890');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling the weather variables using label Encoder"
      ],
      "metadata": {
        "id": "F1VWqaAFvCk_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lc=LabelEncoder()\n",
        "df[\"weather\"]=lc.fit_transform(df[\"weather\"])"
      ],
      "metadata": {
        "id": "Tb7kAok_v9YJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Data  scaling  memiliki  peran  penting  dalam  praproses  data  yang  berdampak  pada  kinerja  dari  algoritme  machine  learning. Agar lebih memaksimalkan penganalisaan data, maka weather akan dirubah menjadi encode"
      ],
      "metadata": {
        "id": "UWsDROB5wLMl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NhzfrStmwA1z",
        "outputId": "6cfc7eb1-9b9d-4fd9-fe24-1f4a747f1483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   precipitation  temp_max  temp_min      wind  weather\n",
              "0       0.000000      12.8       5.0  2.167948        0\n",
              "2       0.894427      11.7       7.2  1.516575        2\n",
              "4       1.140175       8.9       2.8  2.469818        2\n",
              "5       1.581139       4.4       2.2  1.483240        2\n",
              "6       0.000000       7.2       2.8  1.516575        2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa254a8a-e97a-4723-b3f1-1cd285aff84e\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_max</th>\n",
              "      <th>temp_min</th>\n",
              "      <th>wind</th>\n",
              "      <th>weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.894427</td>\n",
              "      <td>11.7</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1.516575</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.140175</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.581139</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.2</td>\n",
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              "      <td>2</td>\n",
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              "      <td>7.2</td>\n",
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              "      <td>2</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa254a8a-e97a-4723-b3f1-1cd285aff84e')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-fa254a8a-e97a-4723-b3f1-1cd285aff84e 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-fa254a8a-e97a-4723-b3f1-1cd285aff84e');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting The Dataset Into Depandant and Independant Variables"
      ],
      "metadata": {
        "id": "sco_zZHCxAZa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=((df.loc[:,df.columns!=\"weather\"]).astype(int)).values[:,0:]\n",
        "y=df[\"weather\"].values"
      ],
      "metadata": {
        "id": "lWqsWhMoxXe1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.weather.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-xkPPFfxao9",
        "outputId": "5ab14062-dc6d-4399-903c-1b97a5ca09e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 4, 3, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=2)"
      ],
      "metadata": {
        "id": "sYt42_HExdOw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Algorithms & Model Training\n",
        "\n",
        "Algoritma & Machine Learning yang akan dipakai:\n",
        "\n",
        "1.  K-Nearest Neighbour(KNN)\n",
        "\n",
        "2.   Support Vector Machine(SVM)\n",
        "\n",
        "3.   Gradient Boost\n",
        "\n",
        "4.   Extreme Gradient Boosting(XGBC)\n",
        "\n",
        "5.   Naive Bayes Classifier(GNB)"
      ],
      "metadata": {
        "id": "WVH4_eDGxlL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# K-Nearest Neighbour(KNN)\n",
        "knn=KNeighborsClassifier()\n",
        "knn.fit(x_train,y_train)\n",
        "print(\"KNN Accuracy:{:.2f}%\".format(knn.score(x_test,y_test)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gaf815CDxtNk",
        "outputId": "96f3fab2-e794-49a7-d4a6-1d0114c46eee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Accuracy:75.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dengan K-Nearest Neighbour(KNN) didapat persentasi akurasi 75%"
      ],
      "metadata": {
        "id": "c7tg-j3bykfM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Support Vector Machine(SVM)\n",
        "svm=SVC()\n",
        "svm.fit(x_train,y_train)\n",
        "print(\"SVM Accuracy:{:.2f}%\".format(svm.score(x_test,y_test)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhIlMC9fy5yc",
        "outputId": "581279bd-26a4-4372-fe8e-8c80594292cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Accuracy:77.42%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dengan Support Vector Machine(SVM) didapat persentasi akurasi 77.42%. Terdapat Kenaikan persentase dari model sebelumnya"
      ],
      "metadata": {
        "id": "j5YY-NKiy_50"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gradient Boost\n",
        "gbc=GradientBoostingClassifier()\n",
        "gbc.fit(x_train,y_train)\n",
        "print(\"Gradient Boosting Accuracy:{:.2f}%\".format(gbc.score(x_test,y_test)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8BNhDVgzMpt",
        "outputId": "c91602e2-3683-4093-e168-8e47a33c1326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Accuracy:85.48%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dengan Gradient Boost didapat persentasi akurasi 85.48%. Terdapat Kenaikan persentase dari model sebelumnya"
      ],
      "metadata": {
        "id": "gRBYd-5nzgkm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extreme Gradient Boosting(XGBC)\n",
        "xgb=XGBClassifier()\n",
        "xgb.fit(x_train,y_train)\n",
        "print(\"XGB Accuracy:{:.2f}%\".format(xgb.score(x_test,y_test)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_s9wAP3zrFi",
        "outputId": "2a3c00c1-6062-41fd-a0ea-8b85d5658ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGB Accuracy:83.06%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dengan Extreme Gradient Boosting(XGBC) didapat persentasi akurasi 83.06% tidak jauh berbeda dengan Gradient Boost . Terdapat Kenaikan persentase dari model sebelumnya. "
      ],
      "metadata": {
        "id": "8r_mr1A0zp91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Naive Bayes Classifier(GNB)\n",
        "gnb=GaussianNB()\n",
        "gnb.fit(x_train,y_train)\n",
        "print(\"GNB Accuracy:{:.2f}%\".format(gnb.score(x_test,y_test)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQGpTe7242em",
        "outputId": "53ee36d2-1d91-4fb8-9c69-df973612251a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GNB Accuracy:81.45%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Dengan Naive Bayes Classifier(GNB) didapat persentasi akurasi 81.45%. Terdapat Kenaikan persentase dari model sebelumnya"
      ],
      "metadata": {
        "id": "1CuMAuw35w9V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking With Function"
      ],
      "metadata": {
        "id": "NDvFhApc0BnE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "mpdb9PeH0S7t",
        "outputId": "14b1f0a8-6831-4a23-e3fa-a9ceef3f8aac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    precipitation  temp_max  temp_min      wind  weather\n",
              "0        0.000000      12.8       5.0  2.167948        0\n",
              "2        0.894427      11.7       7.2  1.516575        2\n",
              "4        1.140175       8.9       2.8  2.469818        2\n",
              "5        1.581139       4.4       2.2  1.483240        2\n",
              "6        0.000000       7.2       2.8  1.516575        2\n",
              "7        0.000000      10.0       2.8  1.414214        4\n",
              "8        2.073644       9.4       5.0  1.843909        2\n",
              "9        1.000000       6.1       0.6  1.843909        2\n",
              "10       0.000000       6.1      -1.1  2.258318        4\n",
              "11       0.000000       6.1      -1.7  1.378405        4\n",
              "12       0.000000       5.0      -2.8  1.140175        4\n",
              "13       2.024846       4.4       0.6  2.302173        3\n",
              "14       2.302173       1.1      -3.3  1.788854        3\n",
              "15       1.581139       1.7      -2.8  2.236068        3\n",
              "21       2.469818       6.7       2.2  2.190890        2\n",
              "22       0.000000       8.3       1.1  1.897367        2\n",
              "25       2.190890       8.9       1.1  2.190890        2\n",
              "26       0.000000       6.7      -2.2  1.183216        0\n",
              "27       0.000000       6.7       0.6  1.483240        2\n",
              "29       1.897367       8.3       6.1  2.258318        2"
            ],
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              "      <th></th>\n",
              "      <th>precipitation</th>\n",
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              "      <th>temp_min</th>\n",
              "      <th>wind</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>0.894427</td>\n",
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              "      <td>-2.8</td>\n",
              "      <td>1.140175</td>\n",
              "      <td>4</td>\n",
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              "      <th>13</th>\n",
              "      <td>2.024846</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.302173</td>\n",
              "      <td>3</td>\n",
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              "      <th>14</th>\n",
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              "      <th>15</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
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              "      <td>2.190890</td>\n",
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              "      <th>22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.3</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.897367</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2.190890</td>\n",
              "      <td>8.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.190890</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.7</td>\n",
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              "      <td>1.183216</td>\n",
              "      <td>0</td>\n",
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              "      <td>1.483240</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.897367</td>\n",
              "      <td>8.3</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.258318</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Akan dibuat uji coba untuk test model manakah yang lebih akurat dalam menebak\n",
        "\n",
        "Akan di ambil sample pada baris ke 15 :\n",
        "*   `precipitation`\t= 1.581139\t\n",
        "*   `temp_max` = 1.7\n",
        "*   `temp_min` = -2.8\n",
        "*   `wind`     = 2.236068\n",
        "*   `weather` = 3 (Snow)\n",
        "\n",
        "Akan dilihat manakah model yang bisa menebak cuaca pada baris ke 15 adalah Snow"
      ],
      "metadata": {
        "id": "vq4CvdBR0eBk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking K-Nearest Neighbour(KNN)"
      ],
      "metadata": {
        "id": "D1qER2tL11r8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input=[[1.581139,1.7,-2.8,5.0]]\n",
        "ot=knn.predict(input)\n",
        "print(\"The weather is:\")\n",
        "if(ot==0):\n",
        "    print(\"Drizzle\")\n",
        "elif(ot==1):\n",
        "    print(\"Fog\")\n",
        "elif(ot==2):\n",
        "    print(\"Rain\")\n",
        "elif(ot==3):\n",
        "    print(\"Snow\")\n",
        "else:\n",
        "    print(\"Sun\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjaW5slG13QF",
        "outputId": "8247ec6a-5c0c-4d33-85e9-dd6c49c1d27f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The weather is:\n",
            "Sun\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "K-Nearest Neighbour(KNN) dengan keakuratan 75% masih **Salah** menebak cuaca yang seharusnya Snow. Maka dari itu model ini kurang baik untuk dipilih"
      ],
      "metadata": {
        "id": "SUovDteL2hbd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking Support Vector Machine(SVM)"
      ],
      "metadata": {
        "id": "N5O2nZmL2PKk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input=[[1.581139,1.7,-2.8,5.0]]\n",
        "ot=svm.predict(input)\n",
        "print(\"The weather is:\")\n",
        "if(ot==0):\n",
        "    print(\"Drizzle\")\n",
        "elif(ot==1):\n",
        "    print(\"Fog\")\n",
        "elif(ot==2):\n",
        "    print(\"Rain\")\n",
        "elif(ot==3):\n",
        "    print(\"Snow\")\n",
        "else:\n",
        "    print(\"Sun\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81T_7IDl15L8",
        "outputId": "58d18c14-69a0-474a-b70c-0b948305be7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The weather is:\n",
            "Sun\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Support Vector Machine(SVM) dengan keakuratan 77.42% masih **Salah** menebak cuaca yang seharusnya Snow. Maka dari itu model ini kurang baik untuk dipilih"
      ],
      "metadata": {
        "id": "PMPX53_X3KZH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## Checking Gradient Boost"
      ],
      "metadata": {
        "id": "LGjc90XX2TQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input=[[1.581139,1.7,-2.8,5.0]]\n",
        "ot=gbc.predict(input)\n",
        "print(\"The weather is:\")\n",
        "if(ot==0):\n",
        "    print(\"Drizzle\")\n",
        "elif(ot==1):\n",
        "    print(\"Fog\")\n",
        "elif(ot==2):\n",
        "    print(\"Rain\")\n",
        "elif(ot==3):\n",
        "    print(\"Snow\")\n",
        "else:\n",
        "    print(\"Sun\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlsecJ7v19-F",
        "outputId": "86e89868-43df-44ef-9d92-8f61eb6a4304"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The weather is:\n",
            "Snow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Gradient Boost dengan keakuratan 85.48% sudah **Benar** menebak cuaca yang seharusnya Snow. Maka dari itu model ini baik untuk dipilih"
      ],
      "metadata": {
        "id": "d-FiEmMU3UjN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking Extreme Gradient Boosting(XGBC)"
      ],
      "metadata": {
        "id": "wPmChKtV2XAx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input=[[1.581139,1.7,-2.8,5.0]]\n",
        "ot=xgb.predict(input)\n",
        "print(\"The weather is:\")\n",
        "if(ot==0):\n",
        "    print(\"Drizzle\")\n",
        "elif(ot==1):\n",
        "    print(\"Fog\")\n",
        "elif(ot==2):\n",
        "    print(\"Rain\")\n",
        "elif(ot==3):\n",
        "    print(\"Snow\")\n",
        "else:\n",
        "    print(\"Sun\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyqIvoMp2Aqw",
        "outputId": "d882773c-66c3-4351-e631-bf7e6d11113a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The weather is:\n",
            "Snow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Checking Extreme Gradient Boosting(XGBC) dengan keakuratan 83.06% sudah **Benar** menebak cuaca yang seharusnya Snow. Maka dari itu model ini baik untuk dipilih"
      ],
      "metadata": {
        "id": "N-hE35aQ3zlT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking Naive Bayes Classifier(GNB)"
      ],
      "metadata": {
        "id": "3uzUPqKQ55mP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input=[[1.581139,1.7,-2.8,5.0]]\n",
        "ot=gnb.predict(input)\n",
        "print(\"The weather is:\")\n",
        "if(ot==0):\n",
        "    print(\"Drizzle\")\n",
        "elif(ot==1):\n",
        "    print(\"Fog\")\n",
        "elif(ot==2):\n",
        "    print(\"Rain\")\n",
        "elif(ot==3):\n",
        "    print(\"Snow\")\n",
        "else:\n",
        "    print(\"Sun\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uT_6l8455iFg",
        "outputId": "ee2293c7-b2c6-4238-f5b3-61b6e6782596"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The weather is:\n",
            "Snow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observasi:**\n",
        "\n",
        "Checking Naive Bayes Classifier(GNB) dengan keakuratan 81.45% sudah **Benar** menebak cuaca yang seharusnya Snow. Maka dari itu model ini baik untuk dipilih"
      ],
      "metadata": {
        "id": "8-9zcIQ15-7c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommended Models"
      ],
      "metadata": {
        "id": "H691Y3aa6lql"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kesimpulan dari test dengan beberapa model, model yang baik atau bisa menebak cuaca dengan benar adalah model yang akurasinya diatas 80% seperti Gradient Boost, Extreme Gradient Boosting(XGBC), Naive Bayes Classifier(GNB).\n",
        "\n",
        "Dari 3 model tersebut terdapat model yang persentasenya paling besar yaitu Gradient Boost dengan persentase akurasi 85.48%. **Maka dipilihnya Gradient Boost Sebagai model yang paling baik dari 5 model yang dipilih**"
      ],
      "metadata": {
        "id": "jqqa6zgD6n7O"
      }
    }
  ]
}