sklearn_DecisionTreeClassifier.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeClassifier\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "music_data = pd.read_csv('music.csv')\n",
        "X = music_data.drop (columns=['genre'])\n",
        "Y = music_data['genre']"
      ],
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              "    age  gender\n",
              "0    20       1\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier()\n",
        "model.fit (X,Y)\n",
        "predictions = model.predict([ [21,1], [22, 0] ])\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCyuR_n15tmb",
        "outputId": "43f1f928-f62c-4b7a-e1a8-4141c9f7e81f"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['HipHop', 'Dance'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Measure Accuracy"
      ],
      "metadata": {
        "id": "JXIzeDya-9zW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "EhtmCudSAaW9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# test_size=0.2\n",
        "#0.2 means 20%\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n",
        "#here, X train test is input, Y train test is output.\n",
        "\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit (X_train,Y_train)\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "score = accuracy_score(Y_test, predictions)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfDVNAai6fx1",
        "outputId": "1eeed383-0f13-414d-f97f-9618db86ccfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If once I do the model training, second time it will remember and trained by itsel. for this, we will need a library 'from sklearn. externals import joblib'"
      ],
      "metadata": {
        "id": "EEE6WwUJIhvT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.tree import DecisionTreeClassifier\n",
        "import joblib\n",
        "\n",
        "# !pip install joblib"
      ],
      "metadata": {
        "id": "scvI60CqAFcR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "music_data = pd.read_csv('music.csv')\n",
        "X = music_data.drop (columns=['genre'])\n",
        "Y = music_data['genre']\n",
        "\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit (X,Y)\n",
        "\n",
        "joblib.dump(model, 'music-recommender.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnkR9ULFI6-X",
        "outputId": "c9437869-da16-4e6c-8ebf-f93de2207685"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['music-recommender.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model is saved"
      ],
      "metadata": {
        "id": "soHY6Ea5No2o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "now, load the model and check"
      ],
      "metadata": {
        "id": "_XZ0sla7OTdw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = joblib.load ('music-recommender.joblib')\n",
        "predictions = model.predict([[21,1]])\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkW0PhOnMxUb",
        "outputId": "c3aa45b3-03af-4ab5-fb8e-3ce7b0d73569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['HipHop'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Revise and review the whole model"
      ],
      "metadata": {
        "id": "2CDh9lDvP8MH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import tree"
      ],
      "metadata": {
        "id": "3IUFHbOoPxcn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "music_data = pd.read_csv('music.csv')\n",
        "X = music_data.drop (columns=['genre'])\n",
        "Y = music_data['genre']\n",
        "\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit (X,Y)\n",
        "\n",
        "tree.export_graphviz(model, out_file='music-recommender.dot', feature_names=['age', 'gender'], class_names=sorted(Y.unique()), label='all', rounded=True, filled=True)"
      ],
      "metadata": {
        "id": "TcnTQXEZOJ7G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pydot\n",
        "\n",
        "(graph,) = pydot.graph_from_dot_file('music-recommender.dot')\n",
        "graph.write_png('decision_tree_graph.png')\n",
        "\n"
      ],
      "metadata": {
        "id": "S0sv_T9DQTRN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}