Lab4-2(KFold_confusionmatrix).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da1f6ab9-2732-4b5e-9719-b88e7e1c0e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4665846b-1a89-4bcd-8e10-8dacdcf9b8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X,y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "046a95eb-dbb4-4971-9ea6-e6b39894cd4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits = 5, shuffle=True, random_state =42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "00d9f9a2-70f8-4b1a-9ccc-206a71d46276",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2814a635-e5fe-42ca-958b-dbe9c1f4c2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "85bb6604-4d2e-4e59-809a-3c47ccfb0d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in kf.split(X):\n",
    "    x_train, x_test = X[train_index],X[test_index]\n",
    "    y_train, y_test = y[train_index],y[test_index]\n",
    "    knn.fit(x_train,y_train)\n",
    "    y_pred = knn.predict(x_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    scores.append(accuracy)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e6c3076-1048-4246-b47c-5b0a8b00895f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_accuracy= np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1e4fdd61-db23-4903-a9bb-097d5fa722c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k-fold result: [1.0, 0.9666666666666667, 0.9666666666666667, 0.9333333333333333, 0.9666666666666667]\n",
      "mean accuracy: 0.9666666666666668\n"
     ]
    }
   ],
   "source": [
    "print(\"k-fold result:\", scores)\n",
    "print(\"mean accuracy:\", mean_accuracy )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1189041b-be77-4cbd-af93-e4b9afb04a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \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.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f218f14f-7af1-475b-9e12-52e8e5b99546",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "x_train, x_test, y_train,y_test= train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "beb705eb-7bb0-4b8d-aa67-5fd1bee0bed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2f515902-aa00-40e3-9fd4-c79d3c958e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "523d82c5-b16b-4f38-939b-0d3dcb1a4673",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "40e57612-d4e1-4356-ba5e-0c1cdc1f9bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"Setosa\", \"Versicolor\",\"Virginica\"]\n",
    "df_cm= pd.DataFrame(cm, index=labels, columns=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "11ecfb4a-15cc-44b3-881f-e2b851dd9a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap (df_cm, annot=True, cmap=plt.cm.Blues)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1f1b27b-0a0b-4b02-97ca-38060b192f71",
   "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
}