{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b9f3e77-3cea-442e-a8b0-6a6e8efd0010",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7cbb637-9c96-43e5-99c5-fb0345a34caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('titanic.csv') \n",
    "\n",
    "# Load the IRIS dataset\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0c4476c-099d-411e-9bbf-a195d76d6d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a decision tree classifier\n",
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "\n",
    "# Display the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=iris.target_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b167caf3-1a8d-461e-ae0c-58dd54845fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a KNN classifier\n",
    "clf = KNeighborsClassifier(n_neighbors=5)  # you can change k value (default=5)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Display the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=iris.target_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for KNN Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00a4cdd4-c81e-4b61-8e58-450c15dc91b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "anaconda-panel-2023.05-py310",
   "language": "python",
   "name": "conda-env-anaconda-panel-2023.05-py310-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}