k nearest neighbours.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#instance based learning algorihtm\n",
    "#no explicit training required\n",
    "#compares distance between instance being queried with instance from the training set\n",
    "#classified points based on k number of nearest points\n",
    "#not good with high dimensional data\n",
    "#normalization is required for the data for accuracy,data with larger range will dominate so needs normalization\n",
    "\n",
    "#not chosen for very large data set\n",
    "###############################################################\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn.datasets as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 14 columns):\n",
      "alcohol                         178 non-null float64\n",
      "malic_acid                      178 non-null float64\n",
      "ash                             178 non-null float64\n",
      "alcalinity_of_ash               178 non-null float64\n",
      "magnesium                       178 non-null float64\n",
      "total_phenols                   178 non-null float64\n",
      "flavanoids                      178 non-null float64\n",
      "nonflavanoid_phenols            178 non-null float64\n",
      "proanthocyanins                 178 non-null float64\n",
      "color_intensity                 178 non-null float64\n",
      "hue                             178 non-null float64\n",
      "od280/od315_of_diluted_wines    178 non-null float64\n",
      "proline                         178 non-null float64\n",
      "wine_type                       178 non-null int64\n",
      "dtypes: float64(13), int64(1)\n",
      "memory usage: 19.5 KB\n"
     ]
    }
   ],
   "source": [
    "wine=data.load_wine()\n",
    "wine.keys()\n",
    "df=pd.DataFrame(wine.data,columns=wine.feature_names)\n",
    "wine.target_names\n",
    "df['wine_type']=wine.target\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7865168539325843"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#x=df.drop('proline',axis=1)\n",
    "#x=df\n",
    "x=df.drop('wine_type',axis=1)\n",
    "y=df['wine_type']\n",
    "clf.fit(x,y)\n",
    "pred=clf.predict(x)\n",
    "accuracy_score(y,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############preProcessing of the data\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9775280898876404"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['wine_type']\n",
    "clf.fit(x,y)\n",
    "pred=clf.predict(x)\n",
    "accuracy_score(y,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################\n",
    "#plotting of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################Example 2 for iris dataset calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import neighbors, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f113cc5b490>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)\n",
    "# i am taking the complete df for analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking 14 neighbours for calculation\n",
    "number_of_neighbours=14\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "X=iris.data[:, :2]\n",
    "y=iris.target\n",
    "h=.02\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "plt.figure()\n",
    "plt.scatter(X[:, 0], X[:, 1])\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"Data points\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = neighbors.KNeighborsClassifier(number_of_neighbours, weights='distance')\n",
    "clf.fit(X, y)\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create color maps\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA','#00AAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00','#00AAFF'])\n",
    "  \n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"3-Class classification (k = %i)\" % (number_of_neighbours))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter sepal length (cm): 12.5\n",
      "Enter sepal width (cm): 14.6\n",
      "Prediction:  Iris Virginica\n"
     ]
    }
   ],
   "source": [
    "sl = raw_input('Enter sepal length (cm): ')\n",
    "sw = raw_input('Enter sepal width (cm): ')\n",
    "dataClass = clf.predict([[sl,sw]])\n",
    "print('Prediction: '),\n",
    " \n",
    "if dataClass == 0:\n",
    "    print('Iris Setosa')\n",
    "elif dataClass == 1:\n",
    "    print('Iris Versicolour')\n",
    "else:\n",
    "    print('Iris Virginica')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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