Assignment 2(a) .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment-02(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 01 K-Nearest Neighbour Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(iris.data)\n",
    "y=pd.DataFrame(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "146  6.3  2.5  5.0  1.9\n",
       "147  6.5  3.0  5.2  2.0\n",
       "148  6.2  3.4  5.4  2.3\n",
       "149  5.9  3.0  5.1  1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
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       "..  ..\n",
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       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "scaler.fit(x_train)\n",
    "x_train=scaler.transform(x_train)\n",
    "x_test=scaler.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "Classifier = KNeighborsClassifier(n_neighbors=10)\n",
    "Classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=Classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  0  0]\n",
      " [ 0 15  0]\n",
      " [ 0  0 10]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00         5\n",
      "          1       1.00      1.00      1.00        15\n",
      "          2       1.00      1.00      1.00        10\n",
      "\n",
      "avg / total       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "error=[]\n",
    "for i in range(1, 40):\n",
    "    knn=KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x_train,y_train)\n",
    "    pred_i=knn.predict(x_test)\n",
    "    pred_i=pred_i.reshape(30,1)\n",
    "    error.append(np.mean(pred_i != y_test))                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25b664c6be0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=[]\n",
    "for i in range(1,40):\n",
    "    a.append(i)\n",
    "plt.plot(a,error)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}