Nuha_Alanezi_Assignment5_Decision_Trees_for_Swine_Flu_Detection.ipynb

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        "#**Decision Trees for Swine Flu Detection**\n",
        "---\n",
        "\n",
        "<br>**Dataset Explanation:** We will work on a Swine Flu dataset that tells us if a person has the flu or not based on the symptoms he/she is experiencing. The columns in this dataset are:<br>\n",
        "* Chills: A feature telling if the person has chills or not.\n",
        "* Runny nose: A feature telling if the person has a runny nose or not.\n",
        "* Headache: A feature telling the level of headache that a person is experiencing (It could be Miild, Moderate, or No headache).\n",
        "* Fever: A feature telling if the person has fever or not.\n",
        "* Flu: The target value that tells us if the person has the flu or not."
      ],
      "metadata": {
        "id": "5r_EIDfNe-og"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Nuha Alanezi\n"
       
      ],
      "metadata": {
        "id": "WaW6vqg-Q3k1"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bw-ThFM9eDIS"
      },
      "source": [
        "#Importing Librarires"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rapla3NhdNO9"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWCQa0V4eHia"
      },
      "source": [
        "#Loading the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this notebook we will work with the flu.csv file we've seen earlier"
      ],
      "metadata": {
        "id": "-IAM5Pt3ezvt"
      }
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "First we need to take a look at the dataset before starting to work on it."
      ]
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      "source": [
        "df = pd.read_csv('flu.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
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              "  Chills runny nose head ache fever Flu\n",
              "0      Y          N      Mild     Y   N\n",
              "1      Y          Y        No     N   Y\n",
              "2      Y          N    Strong     Y   Y\n",
              "3      N          Y      Mild     Y   Y\n",
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      "source": [
        "As we can see, we have 4 features that will predict if the person has the Swine Flu or not."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y69c9g1wfz7I"
      },
      "source": [
        "Let's check the shape of our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vt-IgNONfzcR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48eebc34-601a-470c-8530-48641d5b2021"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 6
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      "source": [
        "So we have 8 examples in this dataset (A small one! Will be good enough for our demonstration purposes).\n",
        "\n",
        "Let's next explore some information about it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRB5c4jfgdsf",
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        "outputId": "5aa0e9f1-2ea3-441a-ad28-5964e2033482"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8 entries, 0 to 7\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Chills      8 non-null      object\n",
            " 1   runny nose  8 non-null      object\n",
            " 2   head ache   8 non-null      object\n",
            " 3   fever       8 non-null      object\n",
            " 4   Flu         8 non-null      object\n",
            "dtypes: object(5)\n",
            "memory usage: 448.0+ bytes\n"
          ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQagSolzMHfK"
      },
      "source": [
        "We do not have any missing values. However, all our features and target variables are of data type object which we can't feed to the model, so we need to clean those."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_NAc27IfwVJ"
      },
      "source": [
        "#Dataset Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_y0yDQSf9kQ"
      },
      "source": [
        "## Converting Categorical Features to Numerical"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyO94JZRgEDy"
      },
      "source": [
        "First thing to consider is changing the categorical features to Numerical in order to feed them to the algorithm so we are able to run the classification.\n",
        "\n",
        "This would be useful when we use the decision trees later as well."
      ]
    },
    {
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        "encoder = preprocessing.LabelEncoder()\n",
        "df['Chills'] = encoder.fit_transform(df['Chills'])\n",
        "df['runny nose'] = encoder.fit_transform(df['runny nose'])\n",
        "df['head ache'] = encoder.fit_transform(df['head ache'])\n",
        "df['fever'] = encoder.fit_transform(df['fever'])\n",
        "df['Flu'] = encoder.fit_transform(df['Flu'])\n",
        "\n",
        "df.head(8)"
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      "execution_count": null,
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              "   Chills  runny nose  head ache  fever  Flu\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DyR3R0b9PxD3"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "#Decision Trees"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPFklmQeP4AW"
      },
      "source": [
        "Now, you have to solve the same problem but using decision trees that we learned about."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 1:** Import DecisionTreeClassifier, prepare X and Y, and fit your classifier"
      ],
      "metadata": {
        "id": "P1hv5LWLishi"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYCJwq4R9IFG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "4d0ca19e-62e4-421a-b333-776906e8b0cb"
      },
      "source": [
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "X = df.drop('Flu', axis=1)\n",
        "Y = df['Flu']\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X,Y)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xl1l_sH9TmFy"
      },
      "source": [
        "##Printing Text Structure of the tree"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 2:** Check how you can print the text structure of the built tree using sklearn lirary"
      ],
      "metadata": {
        "id": "OpF35uZqi2hc"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzRf8M8XTo3W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a367c28-78b4-4eee-ac3b-539b1b9f6207"
      },
      "source": [
        "from sklearn.tree import export_text\n",
        "feature_names = list(X.columns)\n",
        "tree_rules = export_text(clf, feature_names=feature_names)\n",
        "print(tree_rules)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|--- fever <= 0.50\n",
            "|   |--- Chills <= 0.50\n",
            "|   |   |--- class: 0\n",
            "|   |--- Chills >  0.50\n",
            "|   |   |--- class: 1\n",
            "|--- fever >  0.50\n",
            "|   |--- runny nose <= 0.50\n",
            "|   |   |--- head ache <= 1.00\n",
            "|   |   |   |--- class: 0\n",
            "|   |   |--- head ache >  1.00\n",
            "|   |   |   |--- class: 1\n",
            "|   |--- runny nose >  0.50\n",
            "|   |   |--- class: 1\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "tree_rules variable contains the rules of the Decision Tree in a text format, showing the conditions at each node and the predicted class at the leaves"
      ],
      "metadata": {
        "id": "WXrocPcwOlIL"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2ylgKJBRUC_"
      },
      "source": [
        "##Plotting the tree to see its structure"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 3:** Plot the tree structure to see nodes and gini index at the nodes level"
      ],
      "metadata": {
        "id": "4ONDUyd4i-fa"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3htzrgkQ5ZF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "8c49a866-442e-4ceb-d1a9-b6cbb78a40dc"
      },
      "source": [
        "from sklearn.tree import plot_tree\n",
        "plt.figure(figsize=(12, 8))\n",
        "plot_tree(clf,  feature_names=list(X.columns), filled=True, rounded=True,class_names=['No Flu', 'Flu'])\n",
        "plt.title(\"Decision tree if has Flu or Not\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "When plotting the tree structure to see nodes and Gini index at the nodes level, It is creating a visual representation of the decision tree model it was built. Each node in the tree represents a decision point based on a specific feature, and the Gini index is a measure of impurity or disorder at that node. The Gini index helps the decision tree algorithm determine the optimal split for making predictions."
      ],
      "metadata": {
        "id": "BxoH8_f0Pe8B"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dXzXR04U6vo"
      },
      "source": [
        "##Predicting using a Decision Tree"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 4:** Confirm the prediction result you got using Naive Bayes previously"
      ],
      "metadata": {
        "id": "TyYDTFpHjIcd"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuv3z00uRuwn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff87f145-520b-4496-c716-a3a6f05fbf46"
      },
      "source": [
        "print(clf.predict([[1,1,1,1]]))\n",
        "#We can predict the probability of the class here.\n",
        "print(clf.predict_proba([[1,1,1,1]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "[[0. 1.]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class 1 has a higher probability 100%, which means the person HAS the flu."
      ],
      "metadata": {
        "id": "sUcIcZEtMCJ4"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNVfax5SghoK"
      },
      "source": [
        "Once again, we get the same result from our DT as our NB algorithm."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(clf.predict([[0,0,0,0]]))\n",
        "#We can predict the probability of the class here.\n",
        "print(clf.predict_proba([[0,0,0,0]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BAWDABrNu3_",
        "outputId": "ece14cac-5d6d-4bf2-e71f-f350ef486805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "[[1. 0.]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class 0 has a higher probability 100%, which means the person HASn't the flu."
      ],
      "metadata": {
        "id": "hlvt_UIVN1Jx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AND Predicting using Naive Bayes previously"
      ],
      "metadata": {
        "id": "0OCmljQ1DdWw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import CategoricalNB\n",
        "\n",
        "X = df.drop('Flu', axis=1)\n",
        "Y = df['Flu']\n",
        "\n",
        "clf = CategoricalNB()\n",
        "clf.fit(X, Y)\n",
        "\n",
        "result = clf.predict([[1,1,1,1]])\n",
        "print(result)\n",
        "\n",
        "#We can predict the probability of the class here.\n",
        "print(clf.predict_proba([[1,1,1,1]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzuW_MYyDceO",
        "outputId": "0de7570c-3e2c-4e66-ee5b-4babc2bc8a4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "[[0.14938211 0.85061789]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but CategoricalNB was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but CategoricalNB was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class 1 has a higher probability 85%, which means the person HAS the flu.\n"
      ],
      "metadata": {
        "id": "jec2Mx95ELTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = clf.predict([[0,0,0,0]])\n",
        "print(result)\n",
        "\n",
        "#We can predict the probability of the class here.\n",
        "print(clf.predict_proba([[0,0,0,0]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CchyWMduMyqb",
        "outputId": "899caaf4-d238-49ec-af2e-bc7de7145e40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "[[0.76705181 0.23294819]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but CategoricalNB was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but CategoricalNB was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class 0 has a higher probability 77%, which means the person HASn't the flu."
      ],
      "metadata": {
        "id": "mzi379wtM9BW"
      }
    }
  ]
}