KNN_Assignment_zoo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('Zoo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>animal name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
       "0    aardvark     1         0     0     1         0        0         1   \n",
       "1    antelope     1         0     0     1         0        0         0   \n",
       "2        bass     0         0     1     0         0        1         1   \n",
       "3        bear     1         0     0     1         0        0         1   \n",
       "4        boar     1         0     0     1         0        0         1   \n",
       "\n",
       "   toothed  backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \\\n",
       "0        1         1         1         0     0     4     0         0        1   \n",
       "1        1         1         1         0     0     4     1         0        1   \n",
       "2        1         1         0         0     1     0     1         0        0   \n",
       "3        1         1         1         0     0     4     0         0        1   \n",
       "4        1         1         1         0     0     4     1         0        1   \n",
       "\n",
       "   type  \n",
       "0     1  \n",
       "1     1  \n",
       "2     4  \n",
       "3     1  \n",
       "4     1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101 entries, 0 to 100\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   animal name  101 non-null    object\n",
      " 1   hair         101 non-null    int64 \n",
      " 2   feathers     101 non-null    int64 \n",
      " 3   eggs         101 non-null    int64 \n",
      " 4   milk         101 non-null    int64 \n",
      " 5   airborne     101 non-null    int64 \n",
      " 6   aquatic      101 non-null    int64 \n",
      " 7   predator     101 non-null    int64 \n",
      " 8   toothed      101 non-null    int64 \n",
      " 9   backbone     101 non-null    int64 \n",
      " 10  breathes     101 non-null    int64 \n",
      " 11  venomous     101 non-null    int64 \n",
      " 12  fins         101 non-null    int64 \n",
      " 13  legs         101 non-null    int64 \n",
      " 14  tail         101 non-null    int64 \n",
      " 15  domestic     101 non-null    int64 \n",
      " 16  catsize      101 non-null    int64 \n",
      " 17  type         101 non-null    int64 \n",
      "dtypes: int64(17), object(1)\n",
      "memory usage: 14.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,1:17]\n",
    "y = data.iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_folds = 10\n",
    "kfold = KFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=17)\n",
    "results = cross_val_score(model, x, y, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.76\n"
     ]
    }
   ],
   "source": [
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search for Algorithm Tuning\n",
    "import numpy \n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors = numpy.array(range(1,40))\n",
    "param_grid = dict(n_neighbors=n_neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier()\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid)\n",
    "grid.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n",
      "{'n_neighbors': 1}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the CV results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "C:\\Users\\sande\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "# choose k between 1 to 41\n",
    "k_range = range(1, 41)\n",
    "k_scores = []\n",
    "# use iteration to caclulator different k in models, then return the average accuracy based on the cross validation\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, x, y, cv=5)\n",
    "    k_scores.append(scores.mean())\n",
    "# plot to see clearly\n",
    "plt.plot(k_range, k_scores)\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Cross-Validated Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 4, 1, 1, 1, 1, 4, 4, 1, 1, 2, 4, 7, 7, 7, 2, 1, 4, 1, 2, 2,\n",
       "       1, 2, 6, 5, 5, 1, 1, 1, 6, 1, 1, 2, 4, 1, 1, 2, 4, 6, 6, 2, 6, 2,\n",
       "       1, 1, 7, 1, 1, 1, 1, 6, 5, 7, 1, 1, 2, 2, 2, 2, 4, 4, 3, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 2, 7, 4, 1, 1, 3, 7, 2, 2, 3, 7, 4, 2, 1, 7, 4, 2,\n",
       "       6, 5, 3, 3, 4, 1, 1, 2, 1, 6, 1, 7, 2], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 1)\n",
    "knn.fit(x,y)\n",
    "y_pred = knn.predict(x)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 1, 2, 1, 1, 7, 1, 2, 7, 4, 4, 2, 4, 1, 1, 1, 5, 3, 1, 7, 6,\n",
       "       1, 1, 4, 4, 2, 1, 1, 1, 6, 2, 2, 1, 1, 1, 7, 1, 4, 1, 3, 2, 2, 1,\n",
       "       3, 2, 1, 1, 2, 2, 1, 7, 2, 1, 1, 5, 2, 4, 2, 2, 7, 1, 4, 4, 2, 4,\n",
       "       2, 7, 4, 6], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train ,y_test = train_test_split( x, y , test_size = 0.3, random_state = 42)\n",
    "knn = KNeighborsClassifier(n_neighbors = 1)\n",
    "knn.fit(x_train, y_train)\n",
    "y1_pred = knn.predict(x_train)\n",
    "y1_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>animal name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>wallaby</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>wasp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>wolf</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>worm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>wren</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    animal name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
       "0      aardvark     1         0     0     1         0        0         1   \n",
       "1      antelope     1         0     0     1         0        0         0   \n",
       "2          bass     0         0     1     0         0        1         1   \n",
       "3          bear     1         0     0     1         0        0         1   \n",
       "4          boar     1         0     0     1         0        0         1   \n",
       "..          ...   ...       ...   ...   ...       ...      ...       ...   \n",
       "96      wallaby     1         0     0     1         0        0         0   \n",
       "97         wasp     1         0     1     0         1        0         0   \n",
       "98         wolf     1         0     0     1         0        0         1   \n",
       "99         worm     0         0     1     0         0        0         0   \n",
       "100        wren     0         1     1     0         1        0         0   \n",
       "\n",
       "     toothed  backbone  breathes  venomous  fins  legs  tail  domestic  \\\n",
       "0          1         1         1         0     0     4     0         0   \n",
       "1          1         1         1         0     0     4     1         0   \n",
       "2          1         1         0         0     1     0     1         0   \n",
       "3          1         1         1         0     0     4     0         0   \n",
       "4          1         1         1         0     0     4     1         0   \n",
       "..       ...       ...       ...       ...   ...   ...   ...       ...   \n",
       "96         1         1         1         0     0     2     1         0   \n",
       "97         0         0         1         1     0     6     0         0   \n",
       "98         1         1         1         0     0     4     1         0   \n",
       "99         0         0         1         0     0     0     0         0   \n",
       "100        0         1         1         0     0     2     1         0   \n",
       "\n",
       "     catsize  type  category  \n",
       "0          1     1         1  \n",
       "1          1     1         1  \n",
       "2          0     4         4  \n",
       "3          1     1         1  \n",
       "4          1     1         1  \n",
       "..       ...   ...       ...  \n",
       "96         1     1         1  \n",
       "97         0     6         6  \n",
       "98         1     1         1  \n",
       "99         0     7         7  \n",
       "100        0     2         2  \n",
       "\n",
       "[101 rows x 19 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['category'] = y_pred                                            \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.95122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04878</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.97561</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>3.365854</td>\n",
       "      <td>0.853659</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             hair  feathers     eggs  milk  airborne   aquatic  predator  \\\n",
       "category                                                                   \n",
       "1         0.95122       0.0  0.02439   1.0   0.04878  0.146341  0.536585   \n",
       "2         0.00000       1.0  1.00000   0.0   0.80000  0.300000  0.450000   \n",
       "3         0.00000       0.0  0.80000   0.0   0.00000  0.200000  0.800000   \n",
       "4         0.00000       0.0  1.00000   0.0   0.00000  1.000000  0.692308   \n",
       "5         0.00000       0.0  1.00000   0.0   0.00000  1.000000  0.750000   \n",
       "6         0.50000       0.0  1.00000   0.0   0.75000  0.000000  0.125000   \n",
       "7         0.00000       0.0  0.90000   0.0   0.00000  0.600000  0.800000   \n",
       "\n",
       "          toothed  backbone  breathes  venomous      fins      legs      tail  \\\n",
       "category                                                                        \n",
       "1         0.97561       1.0       1.0  0.000000  0.097561  3.365854  0.853659   \n",
       "2         0.00000       1.0       1.0  0.000000  0.000000  2.000000  1.000000   \n",
       "3         0.80000       1.0       0.8  0.400000  0.000000  1.600000  1.000000   \n",
       "4         1.00000       1.0       0.0  0.076923  1.000000  0.000000  1.000000   \n",
       "5         1.00000       1.0       1.0  0.250000  0.000000  4.000000  0.250000   \n",
       "6         0.00000       0.0       1.0  0.250000  0.000000  6.000000  0.000000   \n",
       "7         0.00000       0.0       0.3  0.200000  0.000000  3.700000  0.100000   \n",
       "\n",
       "          domestic   catsize  type  \n",
       "category                            \n",
       "1         0.195122  0.780488   1.0  \n",
       "2         0.150000  0.300000   2.0  \n",
       "3         0.000000  0.200000   3.0  \n",
       "4         0.076923  0.307692   4.0  \n",
       "5         0.000000  0.000000   5.0  \n",
       "6         0.125000  0.000000   6.0  \n",
       "7         0.000000  0.100000   7.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = data.groupby(data.category).mean()\n",
    "result1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}