Network_intrusion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Dataset Description:**\n",
    "#### The dataset used in this project comprises features extracted from network traffic flows, with each flow representing a series of packets transferred between a source and destination. These flows can be categorized into normal network behavior or malicious activity (intrusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **IMPORTING ALL THE NECESSARY LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Data Frame\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "# For  Visualtion\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# For Modelling\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "# Evaluation.\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn import metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_2021=pd.read_csv(r'A:\\DAIICT_sem1\\classification_learning\\Network_Intrusion_Detection\\2021.02.15.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **IMPORTING DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flow_2021=pd.read_csv(r'2021.02.15.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_ipt</th>\n",
       "      <th>bytes_in</th>\n",
       "      <th>bytes_out</th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>dest_port</th>\n",
       "      <th>entropy</th>\n",
       "      <th>num_pkts_out</th>\n",
       "      <th>num_pkts_in</th>\n",
       "      <th>proto</th>\n",
       "      <th>src_ip</th>\n",
       "      <th>src_port</th>\n",
       "      <th>time_end</th>\n",
       "      <th>time_start</th>\n",
       "      <th>total_entropy</th>\n",
       "      <th>label</th>\n",
       "      <th>duration</th>\n",
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       "  </thead>\n",
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       "      <td>15163.3050</td>\n",
       "      <td>benign</td>\n",
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       "      <td>benign</td>\n",
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       "      <td>malicious</td>\n",
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       "      <td>1613368358749721</td>\n",
       "      <td>1613368358637848</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>malicious</td>\n",
       "      <td>0.111873</td>\n",
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       "      <td>3510.7970</td>\n",
       "      <td>benign</td>\n",
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       "      <td>2106.1604</td>\n",
       "      <td>outlier</td>\n",
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       "      <td>benign</td>\n",
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       "      <td>malicious</td>\n",
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      ],
      "text/plain": [
       "           avg_ipt  bytes_in  bytes_out  dest_ip  dest_port   entropy  \\\n",
       "494809    2.666667       329       2383      786     9200.0  5.591189   \n",
       "308185    0.000000         0          0      786    47350.0  0.000000   \n",
       "862007   40.250000       270        191      786      445.0  4.578202   \n",
       "573513    0.000000         0          0      786    50613.0  0.000000   \n",
       "512794    0.000000         0        699      786     9200.0  5.022600   \n",
       "565103    0.000000         0          0      786    47358.0  0.000000   \n",
       "182766  138.500000       270        191      786      445.0  4.568678   \n",
       "564705    0.000000         0       7644      786     9200.0  3.087754   \n",
       "642711    0.000000         0       1448      786     9200.0  4.972262   \n",
       "451709    0.000000         0          0      786      445.0  0.000000   \n",
       "\n",
       "        num_pkts_out  num_pkts_in  proto  src_ip  src_port          time_end  \\\n",
       "494809             2            2      6     786   47358.0  1613367029729746   \n",
       "308185             1            0      6     786    9200.0  1613360978072282   \n",
       "862007             6            7      6     786    5558.0  1613425928659117   \n",
       "573513             1            1      6     786     445.0  1613368358749721   \n",
       "512794             1            1      6     786   47356.0  1613364018341952   \n",
       "565103             1            0      6     786    9200.0  1613368536207164   \n",
       "182766             6            6      6     786   14470.0  1613376108762604   \n",
       "564705             6            1      6     786   47356.0  1613368555329574   \n",
       "642711             1            2      6     786   47356.0  1613368298200404   \n",
       "451709             1            1      6     786   50764.0  1613363841495161   \n",
       "\n",
       "              time_start  total_entropy      label  duration  \n",
       "494809  1613367029729718     15163.3050     benign  0.000028  \n",
       "308185  1613360978072282         0.0000     benign  0.000000  \n",
       "862007  1613425928292428      2110.5510  malicious  0.366689  \n",
       "573513  1613368358637848         0.0000  malicious  0.111873  \n",
       "512794  1613364018341424      3510.7970     benign  0.000528  \n",
       "565103  1613368536207164         0.0000     benign  0.000000  \n",
       "182766  1613376107714267      2106.1604    outlier  1.048337  \n",
       "564705  1613368555328901     23602.7950     benign  0.000673  \n",
       "642711   161336829820004      7199.8360     benign  0.000364  \n",
       "451709  1613363841495161         0.0000  malicious  0.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_2021.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Understanding The Columns**\n",
    "\n",
    "| src_ip | The source IP address associated with the flow. This feature is anonymised to the corresponding Autonomous System |\n",
    "| --- | --- |\n",
    "| src_port | The source port number associated with the flow. |\n",
    "| dest_ip | The destination IP address associated with the flow. The feature is also anonymised in the same manner as before. |\n",
    "| dest_port | The destination port number associated with the flow |\n",
    "| protocol | The protocol number associated with the flow. For example TCP is 6 |\n",
    "| bytes_in | The number of bytes transmitted from source to destination |\n",
    "| bytes_out | The number of bytes transmitted from destination to source. |\n",
    "| num_pkts_in | The packet count from source to destination |\n",
    "| num_pkts_out | The packet count from destination to source |\n",
    "| entropy | The entropy in bits per byte of the data fields within the flow. This number ranges from 0 to 8. |\n",
    "| total_entropy | The total entropy in bytes over all of the bytes in the data fields of the flow |\n",
    "| mean_ipt | The mean of the inter-packet arrival times of the flow |\n",
    "| time_start | The start time of the flow in seconds since the epoch. |\n",
    "| time_end | The end time of the flow in seconds since the epoch |\n",
    "| duration | The flow duration time, with microsecond precision |\n",
    "| label | The label of the flow, as decided by Tangerine. Either benign, outlier, or malicious |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Observing the DataFrame's Columns and Rows** #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of Rows in the DataFrame is : 878526\n",
      "Total Number of columns in the DataFrame is :  16\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of Rows in the DataFrame is :\",len(flow_2021))\n",
    "print(\"Total Number of columns in the DataFrame is : \",len(list(flow_2021.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 878526 entries, 0 to 878525\n",
      "Data columns (total 16 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   avg_ipt        878526 non-null  float64\n",
      " 1   bytes_in       878526 non-null  int64  \n",
      " 2   bytes_out      878526 non-null  int64  \n",
      " 3   dest_ip        878526 non-null  int64  \n",
      " 4   dest_port      875764 non-null  float64\n",
      " 5   entropy        878526 non-null  float64\n",
      " 6   num_pkts_out   878526 non-null  int64  \n",
      " 7   num_pkts_in    878526 non-null  int64  \n",
      " 8   proto          878526 non-null  int64  \n",
      " 9   src_ip         878526 non-null  int64  \n",
      " 10  src_port       875764 non-null  float64\n",
      " 11  time_end       878526 non-null  int64  \n",
      " 12  time_start     878526 non-null  int64  \n",
      " 13  total_entropy  878526 non-null  float64\n",
      " 14  label          878526 non-null  object \n",
      " 15  duration       878526 non-null  float64\n",
      "dtypes: float64(6), int64(9), object(1)\n",
      "memory usage: 107.2+ MB\n"
     ]
    }
   ],
   "source": [
    "flow_2021.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **VISUALISATIONS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91951\\AppData\\Local\\Temp\\ipykernel_16720\\1873958844.py:1: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(y='label', data=flow_2021, saturation=0.7, palette=['#7fccb6','#74a7b2','#8faff2'])\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='label', data=flow_2021, saturation=0.7, palette=['#7fccb6','#74a7b2','#8faff2'])\n",
    "plt.title('Data Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **CORRELATION HEATMAP**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation heatmap. \n",
    "plt.figure(figsize=(12, 8)) # Plot size. \n",
    "sns.heatmap(X.corr(), annot=True, cmap='YlGnBu', vmin=-1, vmax=1, center=0)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Removing the Null Rows and unnecessary Columns Based on Visualization and Domain Knowledge** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_2021.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the unnecessary columns. \n",
    "flow_2021.drop([\"src_ip\",\"dest_ip\",\"time_end\",\"time_start\"],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "benign       387301\n",
       "malicious    271570\n",
       "outlier      216893\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_2021['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Separating the Data into X and Y axis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating the data.\n",
    "X = flow_2021[['avg_ipt','bytes_in','bytes_out','dest_port','entropy','num_pkts_out',\n",
    "        'num_pkts_in','proto','src_port','total_entropy','duration']]\n",
    "y = flow_2021['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Label Encoding the Y-Axis*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting a dictionary to encode the label.\n",
    "mapping = {'benign': 1, 'malicious': 0, 'outlier': 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_true = [mapping[x] for x in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 387301, 0: 271570, 2: 216893})\n"
     ]
    }
   ],
   "source": [
    "counted_values = Counter(y_true)\n",
    "print(counted_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Importing all Required Model** #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV,RandomizedSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Splitting The Data set into Train and Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_true, test_size=0.25, random_state=42,stratify=y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Training the model using DecisionTree and Finetuning the model using GridSearchCV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\91951\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [2, 5, 6, 7],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 5],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 4, 6, 9]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [2, 5, 6, 7],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 5],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 4, 6, 9]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=7, min_samples_leaf=2, min_samples_split=4)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=7, min_samples_leaf=2, min_samples_split=4)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [2, 5, 6, 7],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 4, 5],\n",
       "                                        'min_samples_split': [2, 4, 6, 9]})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from random import randint\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "parameters = {'max_depth': [2,5,6,7], 'min_samples_split': [2,4,6,9], 'min_samples_leaf': [1, 2, 3, 4, 5], 'criterion': ['gini', 'entropy']}\n",
    "\n",
    "random_search = RandomizedSearchCV(dt, param_distributions=parameters, n_iter=20, cv=5, n_jobs=-1)\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Predicting The flow Type* based on decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "y_dt = random_search.predict(X_train)\n",
    "y_pred_dt = random_search.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Plotting the Evaluation Metrice for Decision Tree*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.82      0.76     67893\n",
      "           1       0.99      1.00      1.00     96825\n",
      "           2       0.72      0.57      0.63     54223\n",
      "\n",
      "    accuracy                           0.84    218941\n",
      "   macro avg       0.81      0.79      0.80    218941\n",
      "weighted avg       0.84      0.84      0.83    218941\n",
      "\n",
      "Accuracy on Training set:  0.8356924163739698\n",
      "Accuracy on Testing set:  0.835640652047812\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# generate classification report\n",
    "target_names = ['0', '1','2']\n",
    "print(classification_report(y_test, y_pred_dt, target_names=target_names))\n",
    "# Accuracy\n",
    "print(\"Accuracy on Training set: \",metrics.accuracy_score(y_train, y_dt))\n",
    "print(\"Accuracy on Testing set: \",metrics.accuracy_score(y_test, y_pred_dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55485,   438, 11970],\n",
       "       [   22, 96735,    68],\n",
       "       [23425,    62, 30736]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Training the model using XgBoost and Finetuning the model using GridSearchCV*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_bin=None,\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_bin=None,\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_bin=None,\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 0.2],\n",
       "                         'max_depth': [3, 5, 7]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBoost with GridSearchCV\n",
    "xgb_params = {'learning_rate': [0.01, 0.1, 0.2], 'max_depth': [3, 5, 7]}\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_grid = GridSearchCV(xgb_model, xgb_params, cv=3, scoring='accuracy')\n",
    "xgb_grid.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_best_model = xgb_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Plotting the Evaluation Metrice for XGBoost*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.92      0.87     67893\n",
      "           1       1.00      1.00      1.00     96825\n",
      "           2       0.88      0.77      0.82     54223\n",
      "\n",
      "    accuracy                           0.92    218941\n",
      "   macro avg       0.90      0.89      0.90    218941\n",
      "weighted avg       0.92      0.92      0.92    218941\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# XGBoost Evaluation\n",
    "xgb_pred = xgb_grid.predict(X_test)\n",
    "report = classification_report(y_test, xgb_pred)\n",
    "print(f'XGBoost Report:\\n {report}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Plotting the Confusion Matrix for XGboost*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[62244,    12,  5637],\n",
       "       [   14, 96811,     0],\n",
       "       [12567,     0, 41656]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, xgb_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Understanding the Importacne of Each Feature*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=list(X_train.columns)\n",
    "l2 = list(xgb_best_model.feature_importances_)\n",
    "feat_imp=pd.DataFrame(l1,l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.018093</th>\n",
       "      <td>avg_ipt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.044497</th>\n",
       "      <td>bytes_in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.031394</th>\n",
       "      <td>bytes_out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.525385</th>\n",
       "      <td>dest_port</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.046276</th>\n",
       "      <td>entropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.013332</th>\n",
       "      <td>num_pkts_out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010343</th>\n",
       "      <td>num_pkts_in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.109598</th>\n",
       "      <td>proto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.117142</th>\n",
       "      <td>src_port</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.054305</th>\n",
       "      <td>total_entropy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.029636</th>\n",
       "      <td>duration</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "0.018093        avg_ipt\n",
       "0.044497       bytes_in\n",
       "0.031394      bytes_out\n",
       "0.525385      dest_port\n",
       "0.046276        entropy\n",
       "0.013332   num_pkts_out\n",
       "0.010343    num_pkts_in\n",
       "0.109598          proto\n",
       "0.117142       src_port\n",
       "0.054305  total_entropy\n",
       "0.029636       duration"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Training the model using RandomForest and Finetuning the model using GridSearchCV*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=20, n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=20, n_estimators=200)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5, min_samples_leaf=20, n_estimators=200)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=200,max_depth=5,min_samples_leaf=20)\n",
    "\n",
    "rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Plotting the Evaluation Metrice for XGBoost*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.70      0.71     67893\n",
      "           1       0.99      1.00      1.00     96825\n",
      "           2       0.64      0.65      0.64     54223\n",
      "\n",
      "    accuracy                           0.82    218941\n",
      "   macro avg       0.78      0.78      0.78    218941\n",
      "weighted avg       0.82      0.82      0.82    218941\n",
      "\n",
      "Accuracy on Training set:  0.8184290136003155\n",
      "Accuracy on Testing set:  0.8190973823998247\n"
     ]
    }
   ],
   "source": [
    "y_rf = rf.predict(X_train)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# generate classification report\n",
    "target_names = ['0', '1','2']\n",
    "print(classification_report(y_test, y_pred_rf, target_names=target_names))\n",
    "# Accuracy\n",
    "print(\"Accuracy on Training set: \",metrics.accuracy_score(y_train, y_rf))\n",
    "print(\"Accuracy on Testing set: \",metrics.accuracy_score(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[47407,   498, 19988],\n",
       "       [   39, 96780,     6],\n",
       "       [19010,    66, 35147]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_rf)"
   ]
  }
 ],
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