ServerClassification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e81fc326",
   "metadata": {},
   "source": [
    "## Server Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c708ec13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jindi\\Downloads\\Day3\\Network AI\n"
     ]
    }
   ],
   "source": [
    "# Changing the current working directory to specified path\n",
    "import os\n",
    "os.chdir(r\"C:\\Users\\jindi\\Downloads\\Day3\\Network AI\")\n",
    "\n",
    "# Verifing the path using getcwd()\n",
    "cwd = os.getcwd()\n",
    "print(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db2ce9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f818a0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset\n",
    "dataset = pd.read_csv('Dataset\\datacentertraffic.csv')\n",
    "\n",
    "# Splitting data into X(features) and Y(label class)\n",
    "featureset = dataset.iloc[:, :-1].values\n",
    "selectedfeature = dataset.iloc[:, 4:5].values\n",
    "serverclass = dataset.iloc[:,6].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "399892ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into Train/Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(selectedfeature, serverclass, test_size = 0.25, random_state = 0, stratify=serverclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b7cc9eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Training\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8dcd02b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Testing (Predict labels of the unseen Test set data, by using trained model)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ff79609",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the Confusion Matrix (to evaluate the accuracy of classification model)\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classifier.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da3944a0-8b85-4ba2-bcfa-1c05ae8d99d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}