machine-learning/classification/decision-tree-classification-implementation-using-scikit-learn.ipynb

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    "colab": {
      "name": "Decision Tree Classification Implementation Using Scikit-Learn",
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      "source": [
        "# **Decision Tree Classification Implementation Using Scikit-Learn**\n",
        "\n",
        "*By Carlos Santiago Bañón*\n",
        "\n",
        "* **Year:** 2020\n",
        "* **Technologies:** Python, Scikit-Learn, Pandas, NumPy\n",
        "* **Areas**: Machine Learning, Classification\n",
        "* **Keywords:** `classification`, `decision-tree`, `decision-tree-classification`, `machine-learning`\n",
        "* **Description:** This notebook presents an implementation of decision tree classification using the Scikit-Learn library. The data used is a preprocessed version of the Kaggle Titanic dataset hosted in the GitHub repository for this notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UX94UXm8z7Nv"
      },
      "source": [
        "## 1. Import Statements\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnrBRGX3z9mQ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wyC0ZRn0LHm"
      },
      "source": [
        "## 2. Load the Data\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWxu2qDP2fsE"
      },
      "source": [
        "First, we import the preprocessed Kaggle Titanic dataset hosted in the GitHub repository for this notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mM6bKcvH0Nva"
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      "source": [
        "# Import the data into Pandas DataFrames.\n",
        "train_df = pd.read_csv('https://bit.ly/39AQRJj')\n",
        "test_df = pd.read_csv('https://bit.ly/3aoJzHG')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
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        "outputId": "4edcc4f2-8851-4b4d-b40c-cae5408ec445"
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      "source": [
        "# Show the training set.\n",
        "train_df"
      ],
      "execution_count": 3,
      "outputs": [
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              "<p>891 rows × 6 columns</p>\n",
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              "     Age  Survived  Pclass  SibSp  Fare  Gender\n",
              "0      2         0       3      1     0       0\n",
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              "4      3         0       3      0     1       0\n",
              "..   ...       ...     ...    ...   ...     ...\n",
              "886    2         0       2      0     1       0\n",
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        "# Show the test set.\n",
        "test_df"
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      "execution_count": 4,
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              "     Age  Pclass  SibSp  Fare  Gender\n",
              "0      3       3      0     0       0\n",
              "1      4       3      1     0       1\n",
              "2      5       2      0     1       0\n",
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              "..   ...     ...    ...   ...     ...\n",
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          "metadata": {
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          "execution_count": 4
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ekK9B6Yd0khJ"
      },
      "source": [
        "# Set up the learning matrices.\n",
        "X_train = train_df.drop('Survived', axis=1, inplace=False).to_numpy()\n",
        "y_train = train_df[['Survived']].to_numpy()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7P0ZAo60rXW"
      },
      "source": [
        "## 3. Decision Tree Classification\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18K0JkTx0upe"
      },
      "source": [
        "### 3.1. Define and Fit the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xAX9ucx0_Cr"
      },
      "source": [
        "# Define the decision tree classifier.\n",
        "clf = tree.DecisionTreeClassifier(max_depth = 10, criterion='gini')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0DSl9dU1CCl"
      },
      "source": [
        "# Fit the decision tree classifier.\n",
        "clf = clf.fit(X_train, y_train)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sCrnIG30y_e"
      },
      "source": [
        "### 3.2. Plot the Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "nfHzkU521FbA",
        "outputId": "373272f7-8945-4d01-86a2-efe163e26793"
      },
      "source": [
        "# Plot the decision tree.\n",
        "tree.plot_tree(clf);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJi74y7y04i_"
      },
      "source": [
        "### 3.3. Get the Cross-Validation Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4BLRMhU1IES",
        "outputId": "9a153185-64cd-4b7d-f105-c6dd3499b4c8"
      },
      "source": [
        "# Get the accuracy from five-fold cross validation.\n",
        "scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
        "scores.mean()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8081036971941498"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    }
  ]
}