KNN Animal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<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",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
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       "      <td>0</td>\n",
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       "    <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",
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       "    <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",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>wolf</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
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       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 18 columns</p>\n",
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      ],
      "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  \n",
       "0          1     1  \n",
       "1          1     1  \n",
       "2          0     4  \n",
       "3          1     1  \n",
       "4          1     1  \n",
       "..       ...   ...  \n",
       "96         1     1  \n",
       "97         0     6  \n",
       "98         1     1  \n",
       "99         0     7  \n",
       "100        0     2  \n",
       "\n",
       "[101 rows x 18 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading data \n",
    "animal_data = pd.read_csv(\"~/Downloads/Data Science/data set/Zoo.csv\")\n",
    "animal_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature and target arrays \n",
    "\n",
    "y = animal_data.type\n",
    "animal_data.drop(['type','animal name'], axis=1, inplace=True)\n",
    "X = animal_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     hair  feathers  eggs  milk  airborne  aquatic  predator  toothed  \\\n",
       "0       1         0     0     1         0        0         1        1   \n",
       "1       1         0     0     1         0        0         0        1   \n",
       "2       0         0     1     0         0        1         1        1   \n",
       "3       1         0     0     1         0        0         1        1   \n",
       "4       1         0     0     1         0        0         1        1   \n",
       "..    ...       ...   ...   ...       ...      ...       ...      ...   \n",
       "96      1         0     0     1         0        0         0        1   \n",
       "97      1         0     1     0         1        0         0        0   \n",
       "98      1         0     0     1         0        0         1        1   \n",
       "99      0         0     1     0         0        0         0        0   \n",
       "100     0         1     1     0         1        0         0        0   \n",
       "\n",
       "     backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \n",
       "0           1         1         0     0     4     0         0        1  \n",
       "1           1         1         0     0     4     1         0        1  \n",
       "2           1         0         0     1     0     1         0        0  \n",
       "3           1         1         0     0     4     0         0        1  \n",
       "4           1         1         0     0     4     1         0        1  \n",
       "..        ...       ...       ...   ...   ...   ...       ...      ...  \n",
       "96          1         1         0     0     2     1         0        1  \n",
       "97          0         1         1     0     6     0         0        0  \n",
       "98          1         1         0     0     4     1         0        1  \n",
       "99          0         1         0     0     0     0         0        0  \n",
       "100         1         1         0     0     2     1         0        0  \n",
       "\n",
       "[101 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      4\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "96     1\n",
       "97     6\n",
       "98     1\n",
       "99     7\n",
       "100    2\n",
       "Name: type, Length: 101, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split into training and test set \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42) \n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=7) \n",
    "\n",
    "knn.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8571428571428571\n"
     ]
    }
   ],
   "source": [
    "# Calculate the accuracy of the model \n",
    "print(knn.score(X_test, y_test))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 4 1 1 6 1 1 1 1 4 6 6 2 4 1 1 2 4 1 4]\n"
     ]
    }
   ],
   "source": [
    "# Predict on dataset which model has not seen before \n",
    "print(knn.predict(X_test))"
   ]
  }
 ],
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