WeatherData_DecisionTree.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/anirudh-bukka/DataAnalysis-SupervisedML/blob/main/WeatherData_DecisionTree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "5a72ec73",
      "metadata": {
        "id": "5a72ec73"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOU_MdpiPn7g",
        "outputId": "f3e9c508-58c4-495f-bcf6-e0ba0dc5ffd9"
      },
      "id": "YOU_MdpiPn7g",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "dbbf8790",
      "metadata": {
        "id": "dbbf8790"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Ojas ML/Material/WeatherData.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "6b5f091c",
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_%  Wind Speed_km/h  \\\n",
              "0        1/1/2012 0:00    -1.8              -3.9         86                4   \n",
              "1        1/1/2012 1:00    -1.8              -3.7         87                4   \n",
              "2        1/1/2012 2:00    -1.8              -3.4         89                7   \n",
              "3        1/1/2012 3:00    -1.5              -3.2         88                6   \n",
              "4        1/1/2012 4:00    -1.5              -3.3         88                7   \n",
              "...                ...     ...               ...        ...              ...   \n",
              "8779  12/31/2012 19:00     0.1              -2.7         81               30   \n",
              "8780  12/31/2012 20:00     0.2              -2.4         83               24   \n",
              "8781  12/31/2012 21:00    -0.5              -1.5         93               28   \n",
              "8782  12/31/2012 22:00    -0.2              -1.8         89               28   \n",
              "8783  12/31/2012 23:00     0.0              -2.1         86               30   \n",
              "\n",
              "      Visibility_km  Press_kPa               Weather  \n",
              "0               8.0     101.24                   Fog  \n",
              "1               8.0     101.24                   Fog  \n",
              "2               4.0     101.26  Freezing Drizzle,Fog  \n",
              "3               4.0     101.27  Freezing Drizzle,Fog  \n",
              "4               4.8     101.23                   Fog  \n",
              "...             ...        ...                   ...  \n",
              "8779            9.7     100.13                  Snow  \n",
              "8780            9.7     100.03                  Snow  \n",
              "8781            4.8      99.95                  Snow  \n",
              "8782            9.7      99.91                  Snow  \n",
              "8783           11.3      99.89                  Snow  \n",
              "\n",
              "[8784 rows x 8 columns]"
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              "      <th></th>\n",
              "      <th>Date/Time</th>\n",
              "      <th>Temp_C</th>\n",
              "      <th>Dew Point Temp_C</th>\n",
              "      <th>Rel Hum_%</th>\n",
              "      <th>Wind Speed_km/h</th>\n",
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              "      <th>0</th>\n",
              "      <td>1/1/2012 0:00</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-3.9</td>\n",
              "      <td>86</td>\n",
              "      <td>4</td>\n",
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              "      <td>101.24</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/1/2012 1:00</td>\n",
              "      <td>-1.8</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/1/2012 2:00</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>89</td>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>101.26</td>\n",
              "      <td>Freezing Drizzle,Fog</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/1/2012 3:00</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-3.2</td>\n",
              "      <td>88</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>101.27</td>\n",
              "      <td>Freezing Drizzle,Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/1/2012 4:00</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-3.3</td>\n",
              "      <td>88</td>\n",
              "      <td>7</td>\n",
              "      <td>4.8</td>\n",
              "      <td>101.23</td>\n",
              "      <td>Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>8779</th>\n",
              "      <td>12/31/2012 19:00</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-2.7</td>\n",
              "      <td>81</td>\n",
              "      <td>30</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.13</td>\n",
              "      <td>Snow</td>\n",
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              "    <tr>\n",
              "      <th>8780</th>\n",
              "      <td>12/31/2012 20:00</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>83</td>\n",
              "      <td>24</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.03</td>\n",
              "      <td>Snow</td>\n",
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              "    <tr>\n",
              "      <th>8781</th>\n",
              "      <td>12/31/2012 21:00</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>93</td>\n",
              "      <td>28</td>\n",
              "      <td>4.8</td>\n",
              "      <td>99.95</td>\n",
              "      <td>Snow</td>\n",
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              "    <tr>\n",
              "      <th>8782</th>\n",
              "      <td>12/31/2012 22:00</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>89</td>\n",
              "      <td>28</td>\n",
              "      <td>9.7</td>\n",
              "      <td>99.91</td>\n",
              "      <td>Snow</td>\n",
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              "    <tr>\n",
              "      <th>8783</th>\n",
              "      <td>12/31/2012 23:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.1</td>\n",
              "      <td>86</td>\n",
              "      <td>30</td>\n",
              "      <td>11.3</td>\n",
              "      <td>99.89</td>\n",
              "      <td>Snow</td>\n",
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              "<p>8784 rows × 8 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e73f7e93",
      "metadata": {
        "id": "e73f7e93"
      },
      "source": [
        "### Convert `Date/Time`'s datatype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d902f6d5",
      "metadata": {
        "id": "d902f6d5"
      },
      "outputs": [],
      "source": [
        "df['Date/Time'] = df['Date/Time'].str.replace('/', '')\n",
        "df['Date/Time'] = df['Date/Time'].str.replace(':', '')\n",
        "df['Date/Time'] = df['Date/Time'].str.replace(' ', '')\n",
        "df['Date/Time'] = df['Date/Time'].astype(float)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e3a2d62",
      "metadata": {
        "id": "8e3a2d62"
      },
      "source": [
        "### Split data into target variables and indepedent variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4535900c",
      "metadata": {
        "id": "4535900c"
      },
      "outputs": [],
      "source": [
        "X = df.iloc[:, :-1].values # Independent variables\n",
        "y = df.iloc[:, -1].values # Target variable"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30f27193",
      "metadata": {
        "id": "30f27193"
      },
      "source": [
        "## Splitting into train and test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d5adc37",
      "metadata": {
        "id": "2d5adc37"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d6b01e77",
      "metadata": {
        "id": "d6b01e77",
        "outputId": "87a28122-2d31-4a18-c82a-43098d4b162c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 1.24201211e+10  1.01000000e+01  7.70000000e+00 ...  1.70000000e+01\n",
            "   2.41000000e+01  1.01060000e+02]\n",
            " [ 1.92012500e+08 -8.40000000e+00 -1.27000000e+01 ...  1.10000000e+01\n",
            "   2.50000000e+01  1.01930000e+02]\n",
            " [ 1.16201230e+09 -5.50000000e+00 -8.90000000e+00 ...  4.00000000e+00\n",
            "   2.50000000e+01  1.01760000e+02]\n",
            " ...\n",
            " [ 2.72012170e+09 -5.90000000e+00 -1.76000000e+01 ...  1.90000000e+01\n",
            "   4.83000000e+01  1.01940000e+02]\n",
            " [ 8.42012800e+08  2.31000000e+01  1.56000000e+01 ...  1.30000000e+01\n",
            "   4.83000000e+01  1.01750000e+02]\n",
            " [ 1.10201219e+10 -5.70000000e+00 -1.38000000e+01 ...  1.50000000e+01\n",
            "   2.50000000e+01  1.01150000e+02]]\n"
          ]
        }
      ],
      "source": [
        "print(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f48d6c5f",
      "metadata": {
        "id": "f48d6c5f",
        "outputId": "301fbb2f-e184-4191-f3e4-c37113eddd11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 9.18201215e+10  1.81000000e+01  1.70000000e+01 ...  3.20000000e+01\n",
            "   2.41000000e+01  9.92800000e+01]\n",
            " [ 2.11201221e+10 -1.39000000e+01 -1.86000000e+01 ...  7.00000000e+00\n",
            "   2.50000000e+01  1.00710000e+02]\n",
            " [ 1.22620121e+11 -1.13000000e+01 -1.45000000e+01 ...  2.00000000e+01\n",
            "   4.83000000e+01  1.02500000e+02]\n",
            " ...\n",
            " [ 7.15201220e+09  2.45000000e+01  1.89000000e+01 ...  9.00000000e+00\n",
            "   2.50000000e+01  1.01280000e+02]\n",
            " [ 5.30201210e+10  2.15000000e+01  1.02000000e+01 ...  1.50000000e+01\n",
            "   4.83000000e+01  1.00100000e+02]\n",
            " [ 5.29201250e+09  1.10000000e+01  8.90000000e+00 ...  1.50000000e+01\n",
            "   1.29000000e+01  1.00140000e+02]]\n"
          ]
        }
      ],
      "source": [
        "print(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70840dc1",
      "metadata": {
        "id": "70840dc1",
        "outputId": "4f3dedab-88c0-435c-a2ff-7c3841f36146"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Cloudy' 'Cloudy' 'Clear' ... 'Clear' 'Mainly Clear' 'Mostly Cloudy']\n"
          ]
        }
      ],
      "source": [
        "print(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5892058b",
      "metadata": {
        "scrolled": true,
        "id": "5892058b",
        "outputId": "95dbca89-c410-4df8-abe2-ade1cad6c68a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Rain' 'Clear' 'Mainly Clear' ... 'Mainly Clear' 'Mainly Clear'\n",
            " 'Thunderstorms,Rain Showers']\n"
          ]
        }
      ],
      "source": [
        "print(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a65dcc0",
      "metadata": {
        "id": "2a65dcc0",
        "outputId": "42b5f4d0-2fca-4ad1-d2a9-06f1c827184c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Date/Time'].dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67bdf606",
      "metadata": {
        "id": "67bdf606"
      },
      "source": [
        "## Scaling Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6bdcc594",
      "metadata": {
        "scrolled": false,
        "id": "6bdcc594"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "# fit and transform the train data\n",
        "X_train = sc.fit_transform(X_train)\n",
        "\n",
        "# transform the test data\n",
        "X_test = sc.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b92de45",
      "metadata": {
        "id": "9b92de45"
      },
      "source": [
        "## Decision Tree"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5acc61d2",
      "metadata": {
        "id": "5acc61d2"
      },
      "source": [
        "Method 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "425c5ce3",
      "metadata": {
        "id": "425c5ce3",
        "outputId": "bd71895e-f982-4038-c48b-933a47f4f32c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "18d56356",
      "metadata": {
        "id": "18d56356",
        "outputId": "86da090b-ce67-4574-95a7-ea7f0f37e85c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classifier.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a3493602",
      "metadata": {
        "id": "a3493602",
        "outputId": "3d2bb3cc-5372-4c67-bc5c-eeb08a994df8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Mostly Cloudy' 'Clear' 'Mainly Clear' ... 'Clear' 'Clear' 'Rain']\n"
          ]
        }
      ],
      "source": [
        "y_predict = classifier.predict(X_test)\n",
        "print(y_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f6ff633f",
      "metadata": {
        "id": "f6ff633f",
        "outputId": "410ffeac-5bc6-42ff-d405-5952a9a252f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.47306525037936265"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, y_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3db877b4",
      "metadata": {
        "scrolled": false,
        "id": "3db877b4",
        "outputId": "6983efee-941b-49c8-c4e5-bcd92f16a45c"
      },
      "outputs": [
        {
          "data": {
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              " Text(0.23275104908753783, 0.75, 'X[0] <= -0.513\\nentropy = 2.163\\nsamples = 59\\nvalue = [0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 11, 0, 0, 0, 24, 0, 0, 0, 0, 0\\n0, 1, 0, 0, 0, 9, 0, 3, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0]'),\n",
              " Text(0.22982336293549332, 0.7115384615384616, 'X[1] <= -0.741\\nentropy = 2.01\\nsamples = 38\\nvalue = [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 9, 0, 0, 0, 16, 0, 0, 0, 0, 0\\n0, 1, 0, 0, 0, 9, 0, 1, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0]'),\n",
              " Text(0.2282619303210696, 0.6730769230769231, 'X[3] <= 0.205\\nentropy = 1.64\\nsamples = 15\\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0]'),\n",
              " Text(0.2274812140138577, 0.6346153846153846, 'X[6] <= -0.97\\nentropy = 0.811\\nsamples = 8\\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 6, 0, 0, 0, 2, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0]'),\n",
              " Text(0.2270908558602518, 0.5961538461538461, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0]'),\n",
              " Text(0.22787157216746365, 0.5961538461538461, 'entropy = 0.0\\nsamples = 6\\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\\n0, 0, 0, 0, 0]'),\n",
              " ...]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(classifier)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2851dbbc",
      "metadata": {
        "id": "2851dbbc"
      },
      "source": [
        "Method 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06c44c05",
      "metadata": {
        "id": "06c44c05"
      },
      "outputs": [],
      "source": [
        "from sklearn import tree\n",
        "model = tree.DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "095d6124",
      "metadata": {
        "id": "095d6124",
        "outputId": "b47ec7c4-b968-4c47-e8ee-5109c56d31de"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ff2c8972",
      "metadata": {
        "id": "ff2c8972",
        "outputId": "a59ae572-7081-40af-e90f-c2109c6785cc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88ac0b07",
      "metadata": {
        "id": "88ac0b07",
        "outputId": "ca65d92a-bc20-49ef-e838-a8b12687f155"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.4613050075872534"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_predict = model.predict(X_test)\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy_score(y_test,y_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ff1230e",
      "metadata": {
        "id": "2ff1230e"
      },
      "outputs": [],
      "source": [
        "# tree.plot_tree(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "080f3b1b",
      "metadata": {
        "id": "080f3b1b"
      },
      "outputs": [],
      "source": [
        "clf = DecisionTreeClassifier(max_depth = 2, random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "021119e9",
      "metadata": {
        "id": "021119e9",
        "outputId": "f5c2ce61-8fc9-412a-ee13-c1049f5d54ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2, random_state=0)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99a8daef",
      "metadata": {
        "id": "99a8daef",
        "outputId": "89d9cb6a-f578-4ff2-8dc1-cf64595a5b86"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Mostly Cloudy', 'Mostly Cloudy', 'Mainly Clear', ...,\n",
              "       'Mainly Clear', 'Mostly Cloudy', 'Rain,Fog'], dtype=object)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bdcda1f",
      "metadata": {
        "id": "7bdcda1f",
        "outputId": "eb4c7d05-615c-4c73-ecc0-66445029e9e7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "tree.plot_tree(clf);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67b742f3",
      "metadata": {
        "id": "67b742f3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}