Dataset-2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# *Required* python Library"
      ],
      "metadata": {
        "id": "YRkHp2YPOfyF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "w0Q1O0ktKGNy"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "from google.colab import files\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics,svm\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,precision_score\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import numpy as np "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBBRa_ZjKKno",
        "outputId": "5bf7aefa-d7c5-4fd3-c043-82430aec0201"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.6.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Please attach Kaggel API Json file for downloading the dataset"
      ],
      "metadata": {
        "id": "63V0C6InO0G_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "uploaded = files.upload()\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "  \n",
        "# Then move kaggle.json into the folder where the API expects to find it.\n",
        "!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "BuweU7lSKNBn",
        "outputId": "ebeafe8c-21e2-491f-8311-8e610a353327"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6795f8b2-929a-4d00-baf3-41a611b456de\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6795f8b2-929a-4d00-baf3-41a611b456de\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "User uploaded file \"kaggle.json\" with length 67 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Downloading the Dataset from Kaggel"
      ],
      "metadata": {
        "id": "E_0nOQhPPGs_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d preeti5607/ddos-attack-prevention\n",
        "!unzip ddos-attack-prevention"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xk6sefJUKNvy",
        "outputId": "11a9140c-0a62-467c-84f1-ee58b78d550d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ddos-attack-prevention.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  ddos-attack-prevention.zip\n",
            "replace dataset_attack.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: dataset_attack.csv      \n",
            "replace dataset_normal.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: dataset_normal.csv      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load the file and create a Pandas DataFrame"
      ],
      "metadata": {
        "id": "2avk7RKbPTen"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.concat(map(pd.read_csv, ['dataset_attack.csv', 'dataset_normal.csv']))\n",
        "raw_df.dataframeName = 'dataset.csv'\n",
        "nRow, nCol = raw_df.shape\n",
        "print(f'There are {nRow} rows and {nCol} columns')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2nTWZRDK-J0",
        "outputId": "04c4f285-c564-489c-f6fb-e9bbffc585e2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 5658998 rows and 29 columns\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mykok52ENf_-",
        "outputId": "bce1e5a6-921e-48be-8618-be25097c37d8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5658998 entries, 1 to 1\n",
            "Data columns (total 29 columns):\n",
            " #   Column            Dtype  \n",
            "---  ------            -----  \n",
            " 0   frame.encap_type  int64  \n",
            " 1   frame.len         object \n",
            " 2   frame.protocols   int64  \n",
            " 3   ip.hdr_len        int64  \n",
            " 4   ip.len            int64  \n",
            " 5   ip.flags.rb       int64  \n",
            " 6   ip.flags.df       int64  \n",
            " 7   p.flags.mf        int64  \n",
            " 8   ip.frag_offset    int64  \n",
            " 9   ip.ttl            int64  \n",
            " 10  ip.proto          object \n",
            " 11  ip.src            object \n",
            " 12  ip.dst            int64  \n",
            " 13  tcp.srcport       int64  \n",
            " 14  tcp.dstport       int64  \n",
            " 15  tcp.len           int64  \n",
            " 16  tcp.ack           int64  \n",
            " 17  tcp.flags.res     int64  \n",
            " 18  tcp.flags.ns      int64  \n",
            " 19  tcp.flags.cwr     int64  \n",
            " 20  tcp.flags.ecn     int64  \n",
            " 21  tcp.flags.urg     int64  \n",
            " 22  tcp.flags.ack     int64  \n",
            " 23  tcp.flags.push    int64  \n",
            " 24  tcp.flags.reset   int64  \n",
            " 25  tcp.flags.syn     int64  \n",
            " 26  tcp.flags.fin     int64  \n",
            " 27  tcp.window_size   float64\n",
            " 28  tcp.time_delta    object \n",
            "dtypes: float64(1), int64(24), object(4)\n",
            "memory usage: 1.3+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "List of the Dataset classifiers of ML model Building"
      ],
      "metadata": {
        "id": "O9_X8SDfPb2V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df['tcp.time_delta'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2W7GEsCNzbs",
        "outputId": "26c3b597-752c-4bb1-adc0-5b9d51508362"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['attack', 'normal'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = raw_df.assign(\n",
        "      result = lambda dataframe: dataframe['tcp.time_delta'].map(lambda val: 'ATTACK' if val == 'attack' else 'NORMAL') \n",
        "  )"
      ],
      "metadata": {
        "id": "FFHvUl--N2B7"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Balance Distribuation check of the dataset classifiers"
      ],
      "metadata": {
        "id": "ihdfM_DbPhQq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.histogram(raw_df,x='result',color='result').show()"
      ],
      "metadata": {
        "id": "XW83eX_1OE9J"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preprocessing of the Dataset for ML Model "
      ],
      "metadata": {
        "id": "HkOPlKKyQBYX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "def handle_non_numerical_data(df):\n",
        "    columns = df.columns.values\n",
        "    for column in columns:\n",
        "        text_digit_vals = {}\n",
        "        def convert_to_int(val):\n",
        "            return text_digit_vals[val]\n",
        "\n",
        "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
        "            column_contents = df[column].values.tolist()\n",
        "            unique_elements = set(column_contents)\n",
        "            x = 0\n",
        "            for unique in unique_elements:\n",
        "                if unique not in text_digit_vals:\n",
        "                    text_digit_vals[unique] = x\n",
        "                    x+=1\n",
        "\n",
        "            df[column] = list(map(convert_to_int, df[column]))\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "uiIdMNvGQOKi"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_df=handle_non_numerical_data(raw_df)\n",
        "clean_df.info()\n",
        "clean_df.nunique(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cst-7sMOJHh",
        "outputId": "89e9ed45-05ef-4344-a331-db23e36e337a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5658998 entries, 1 to 1\n",
            "Data columns (total 30 columns):\n",
            " #   Column            Dtype  \n",
            "---  ------            -----  \n",
            " 0   frame.encap_type  int64  \n",
            " 1   frame.len         int64  \n",
            " 2   frame.protocols   int64  \n",
            " 3   ip.hdr_len        int64  \n",
            " 4   ip.len            int64  \n",
            " 5   ip.flags.rb       int64  \n",
            " 6   ip.flags.df       int64  \n",
            " 7   p.flags.mf        int64  \n",
            " 8   ip.frag_offset    int64  \n",
            " 9   ip.ttl            int64  \n",
            " 10  ip.proto          int64  \n",
            " 11  ip.src            int64  \n",
            " 12  ip.dst            int64  \n",
            " 13  tcp.srcport       int64  \n",
            " 14  tcp.dstport       int64  \n",
            " 15  tcp.len           int64  \n",
            " 16  tcp.ack           int64  \n",
            " 17  tcp.flags.res     int64  \n",
            " 18  tcp.flags.ns      int64  \n",
            " 19  tcp.flags.cwr     int64  \n",
            " 20  tcp.flags.ecn     int64  \n",
            " 21  tcp.flags.urg     int64  \n",
            " 22  tcp.flags.ack     int64  \n",
            " 23  tcp.flags.push    int64  \n",
            " 24  tcp.flags.reset   int64  \n",
            " 25  tcp.flags.syn     int64  \n",
            " 26  tcp.flags.fin     int64  \n",
            " 27  tcp.window_size   float64\n",
            " 28  tcp.time_delta    int64  \n",
            " 29  result            int64  \n",
            "dtypes: float64(1), int64(29)\n",
            "memory usage: 1.3 GB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "frame.encap_type      1455\n",
              "frame.len              450\n",
              "frame.protocols          3\n",
              "ip.hdr_len            1463\n",
              "ip.len                   1\n",
              "ip.flags.rb              2\n",
              "ip.flags.df              2\n",
              "p.flags.mf               5\n",
              "ip.frag_offset         113\n",
              "ip.ttl                   5\n",
              "ip.proto              6479\n",
              "ip.src               10500\n",
              "ip.dst               10897\n",
              "tcp.srcport          11179\n",
              "tcp.dstport           1461\n",
              "tcp.len             851063\n",
              "tcp.ack                  2\n",
              "tcp.flags.res            2\n",
              "tcp.flags.ns             2\n",
              "tcp.flags.cwr            2\n",
              "tcp.flags.ecn            1\n",
              "tcp.flags.urg            2\n",
              "tcp.flags.ack            2\n",
              "tcp.flags.push           2\n",
              "tcp.flags.reset          2\n",
              "tcp.flags.syn            2\n",
              "tcp.flags.fin        18953\n",
              "tcp.window_size     450604\n",
              "tcp.time_delta           2\n",
              "result                   2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_column = ['frame.encap_type','frame.len','frame.protocols','ip.hdr_len','ip.len','ip.flags.rb','ip.flags.df','p.flags.mf','ip.frag_offset','ip.ttl','ip.proto','ip.src','ip.dst','tcp.srcport','tcp.dstport','tcp.len','tcp.ack','tcp.flags.res','tcp.flags.ns','tcp.flags.cwr','tcp.flags.ecn','tcp.flags.urg','tcp.flags.ack','tcp.flags.push','tcp.flags.reset','tcp.flags.syn','tcp.flags.fin','tcp.window_size']\n",
        "X = clean_df[feature_column] \n",
        "Y = clean_df['result']\n",
        "print(X.shape)\n",
        "print(Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQ7QL5uUOQ6V",
        "outputId": "8d1a4538-3ca7-41a6-b1e2-8d839f9a7531"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5658998, 28)\n",
            "(5658998,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spliting the data for the Traning in to 70-30 ratio for Traning and Testing"
      ],
      "metadata": {
        "id": "95gEyYiMQjTW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=3) \n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMpQSvewOYRF",
        "outputId": "9e0ef926-061a-455d-92f7-970e85dab1c1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4244248, 28)\n",
            "(4244248,)\n",
            "(1414750, 28)\n",
            "(1414750,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **List of models**"
      ],
      "metadata": {
        "id": "9sPGD5f6RJxF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Decision Tree**:\n",
        "Decision Trees are a type of Supervised Machine Learning (you explain what the input is and what the corresponding output is in the training data) in which the data is continuously split based on a specific parameter.\n",
        "\n",
        "**2. KNN : k-nearest neighbors**:\n",
        "KNN is an abbreviation for \"K-Nearest Neighbour.\" It is a machine learning algorithm that is supervised. The algorithm can solve classification and regression problem statements. The symbol 'K' represents the number of nearest neighbors to a new unknown variable that must be predicted or classified.\n",
        "\n",
        "**3. Random Forest**:\n",
        "Random forests, also known as random decision forests, are an ensemble learning method for classification, regression, and other tasks that works by constructing a large number of decision trees during training. For classification tasks, the random forest output is the class chosen by the majority of trees.\n",
        "\n",
        "**4. SVM : Support vector machine**:\n",
        "Support-vector machines are supervised learning models that analyze data for classification and regression analysis using learning algorithms.\n",
        "\n",
        "**5. Neural Network**:\n",
        "Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning that form the foundation of deep learning algorithms. Their name and structure are inspired by the human brain, and they mimic the way biological neurons communicate with one another.\n",
        "\n",
        "**6. GBT : Gradient boosting tree (*Advance ML Model*)**\n",
        "Gradient boosting is a machine learning technique that is commonly used in regression and classification tasks. It returns a prediction model in the form of an ensemble of weak prediction models, usually decision trees.\n",
        "\n"
      ],
      "metadata": {
        "id": "ue7Twzc4QzuJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Temp List to store all the requied Model Data"
      ],
      "metadata": {
        "id": "gMMS5VZnQ8Hk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "master_models=[] #[model_name,ml_model]\n",
        "master_preduction=[] #[model_name,model_fit]\n",
        "master_model_result=[] #[model_name,Accuracy, Precision, Detection  rate, False positive rate]"
      ],
      "metadata": {
        "id": "M8xy9w9sR4HV"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k=3 #Hyper paramaters \n",
        "master_models.insert(-1,[\"Decision Tree\",DecisionTreeClassifier()])\n",
        "master_models.insert(-1,[\"KNeighbors\",KNeighborsClassifier(n_neighbors=k)])\n",
        "master_models.insert(-1,[\"RandomForest\",RandomForestClassifier()])\n",
        "master_models.insert(-1,[\"Support vector machine\",svm.SVC(kernel='linear')])\n",
        "master_models.insert(-1,[\"Neural Network\",MLPClassifier(hidden_layer_sizes=(21,21,21), activation='relu', solver='adam', max_iter=500)])\n",
        "master_models.insert(-1,[\"Gradient boosting\",GradientBoostingClassifier(random_state=0)])"
      ],
      "metadata": {
        "id": "AzEeMhpoR8rR"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODELS Building"
      ],
      "metadata": {
        "id": "Hv9MHTQHSAm4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for name,model in master_models:\n",
        "    print(\"Building model : \",name)\n",
        "    model.fit(X_train,y_train)\n",
        "    master_preduction.insert(-1,[name,model.predict(X_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJCTgaIDR9zi",
        "outputId": "5d3c5094-ad9c-42f0-8773-a0dc9357ef44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Building model :  KNeighbors\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML Model Result "
      ],
      "metadata": {
        "id": "NiaqJg5ISR5s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def detection_rate_score(cm):\n",
        "    # tp/(tp+fn)\n",
        "    return cm[0][0]/(cm[0][0]+cm[1][0])\n",
        "def false_positive_score(cm):\n",
        "    # fp/(fp+tn)\n",
        "    return cm[0][1]/(cm[0][1]+cm[1][1])"
      ],
      "metadata": {
        "id": "diwoxjOgSVSc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name,predict in master_preduction:\n",
        "    tmp_cm = confusion_matrix(y_test,predict)\n",
        "    #Detection  rate\n",
        "    #False positive rate\n",
        "    master_model_result.insert(-1,[name,accuracy_score(y_test,predict),precision_score(y_test,predict),detection_rate_score(tmp_cm),false_positive_score(tmp_cm)])\n",
        "    tmp_cm=0"
      ],
      "metadata": {
        "id": "XAe18Sx7SYSy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Comparison DataFrame"
      ],
      "metadata": {
        "id": "VJ3yiquSScr3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result_df = pd.DataFrame(master_model_result,columns=[\"model_name\",\"Accuracy\", \"Precision\", \"Detection  rate\", \"False positive rate\"])\n",
        "display(result_df)"
      ],
      "metadata": {
        "id": "e0MvkNnTSdFX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Feature Importance"
      ],
      "metadata": {
        "id": "edFqEexLSgjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "importance = model.feature_importances_\n",
        "# summarize feature importance\n",
        "for i,v in enumerate(importance):\n",
        "\tprint('Feature: %0d, Score: %.5f' % (i,v))"
      ],
      "metadata": {
        "id": "JrZf1O6nSekk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}