knn-classification-animal-classification.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.facebook.com/codemakerz\"><img src=\"https://scontent.ffjr1-4.fna.fbcdn.net/v/t1.0-9/36189148_736466693143793_2172101683281133568_n.png?_nc_cat=107&_nc_eui2=AeHzxv3SUcQBOfijLP-cEnHkX4z9XQXdeau__2MlErWZ1x07aZ1zx1PzJUDDxL6cpr7oPqYiifggXDptgtP8W5iCoDRjcdILDBYZ5Ig40dqi8Q&_nc_oc=AQmMCNXdzelFB2rdtpk8wN8nC410Wm2yKupYfYS1FxHNejTF0Jhr1G3WIZORKRF3TvFpohMB8Puw29Txxan8CW05&_nc_ht=scontent.ffjr1-4.fna&oh=7b13627e991a4d1b508923041bd7bc22&oe=5D8A7B03\" />\n",
    "</a>\n",
    "\n",
    "Follow Us:\n",
    "Facebook: https://www.facebook.com/codemakerz\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['class.csv', 'zoo.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df_class = pd.read_csv(\"../input/class.csv\")\n",
    "df_animals = pd.read_csv(\"../input/zoo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Class_Number</th>\n",
       "      <th>Number_Of_Animal_Species_In_Class</th>\n",
       "      <th>Class_Type</th>\n",
       "      <th>Animal_Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>aardvark, antelope, bear, boar, buffalo, calf,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Bird</td>\n",
       "      <td>chicken, crow, dove, duck, flamingo, gull, haw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Reptile</td>\n",
       "      <td>pitviper, seasnake, slowworm, tortoise, tuatara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>Fish</td>\n",
       "      <td>bass, carp, catfish, chub, dogfish, haddock, h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Amphibian</td>\n",
       "      <td>frog, frog, newt, toad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Bug</td>\n",
       "      <td>flea, gnat, honeybee, housefly, ladybird, moth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Invertebrate</td>\n",
       "      <td>clam, crab, crayfish, lobster, octopus, scorpi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class_Number  Number_Of_Animal_Species_In_Class    Class_Type  \\\n",
       "0             1                                 41        Mammal   \n",
       "1             2                                 20          Bird   \n",
       "2             3                                  5       Reptile   \n",
       "3             4                                 13          Fish   \n",
       "4             5                                  4     Amphibian   \n",
       "5             6                                  8           Bug   \n",
       "6             7                                 10  Invertebrate   \n",
       "\n",
       "                                        Animal_Names  \n",
       "0  aardvark, antelope, bear, boar, buffalo, calf,...  \n",
       "1  chicken, crow, dove, duck, flamingo, gull, haw...  \n",
       "2    pitviper, seasnake, slowworm, tortoise, tuatara  \n",
       "3  bass, carp, catfish, chub, dogfish, haddock, h...  \n",
       "4                             frog, frog, newt, toad  \n",
       "5  flea, gnat, honeybee, housefly, ladybird, moth...  \n",
       "6  clam, crab, crayfish, lobster, octopus, scorpi...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have 7 different class in class.csv. These classes are the species class of the animals.\n",
    "df_class.Class_Number.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>class_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",
       "   class_type  \n",
       "0           1  \n",
       "1           1  \n",
       "2           4  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Every animal has a class from class.csv\n",
    "df_animals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_animals.loc[:, [\"milk\", \"backbone\", \"toothed\", \"venomous\", \"domestic\", \"aquatic\"]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Class wise animal counts.\n",
    "# We can see, mostly animals belong to the class 1 which is Mammal.\n",
    "plt.figure(figsize=(10,8));\n",
    "df_animals.class_type.value_counts().plot(kind=\"bar\");\n",
    "plt.xlabel('Class Type');\n",
    "plt.ylabel(\"Count\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot how many animals are domestic or not\n",
    "plt.figure(figsize=(10,8));\n",
    "df_animals.domestic.value_counts().plot(kind=\"bar\");\n",
    "plt.xlabel('Is Domestic');\n",
    "plt.ylabel(\"Count\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we can see mostly animals are not domestic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>domestic</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "domestic     0  1\n",
       "class_type       \n",
       "1           33  8\n",
       "2           17  3\n",
       "3            5  0\n",
       "4           12  1\n",
       "5            4  0\n",
       "6            7  1\n",
       "7           10  0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_animals.class_type, df_animals.domestic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see species wise domestic and non-domestic animals\n",
    "pd.crosstab(df_animals.class_type, df_animals.domestic).plot(kind=\"bar\", figsize=(10, 8), title=\"Class wise Domestic & Non-Domestic Count\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see mammals class has most number of domestic animals, which is a kind of true if you will see around\n",
    "# you. Mostly domestic animals are mammals like dogs, cats, cows, pigs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    60\n",
       "1    41\n",
       "Name: milk, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see how many animals provides us milk\n",
    "df_animals.milk.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So there are 41 animals in the list which provides us milk. Lets see to which category they belongs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>milk</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "milk         0   1\n",
       "class_type        \n",
       "1            0  41\n",
       "2           20   0\n",
       "3            5   0\n",
       "4           13   0\n",
       "5            4   0\n",
       "6            8   0\n",
       "7           10   0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_animals.class_type, df_animals.milk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we can observer here only mammals provides milk, which is really a scientific true. \n",
    "# that mean our data exploration is going good till now.. CONGRATS !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_animals.class_type, df_animals.milk).plot(kind=\"bar\", title=\"Class wise Milk providing animals\", figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see mammal bar is orange (milk = 1), this shows all the mammals in our list provides milk.\n",
    "# And no othere class animals gives us milk, Actually our plot really makes sense.\n",
    "# lets find is it correct.\n",
    "df_animals[(df_animals.milk==1)].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_animals[df_animals.class_type == 1].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So yes we can see milk animals and mammals have equal numbers and all animals who provide milk belong to \n",
    "# mammal category. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65\n",
       "1    36\n",
       "Name: aquatic, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see how many animals live under water. i.e aquatic\n",
    "# lets find out all the aquatic animals.\n",
    "df_animals.aquatic.value_counts() # only 36 aquatic animals are there.\n",
    "# lets see there class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    13\n",
       "7     6\n",
       "2     6\n",
       "1     6\n",
       "5     4\n",
       "3     1\n",
       "Name: class_type, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_animals[df_animals.aquatic==1].class_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see mostly aquatic animals are fish means class 4, but wait we have few animals with \n",
    "# class 7, 2, 1, 5, 3\n",
    "# and still they have fins. \n",
    "# Lets find out is it a wrong data or what?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot category wise animals having fins\n",
    "pd.crosstab(df_animals.class_type, df_animals.aquatic).plot(kind=\"bar\", figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AMAZING NATURE...!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    93\n",
       "1     8\n",
       "Name: venomous, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What about venomous?\n",
    "df_animals.venomous.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>venomous</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "venomous     0  1\n",
       "class_type       \n",
       "1           41  0\n",
       "2           20  0\n",
       "3            3  2\n",
       "4           12  1\n",
       "5            3  1\n",
       "6            6  2\n",
       "7            8  2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In our dataset we have 8 venomous. Lets see their class\n",
    "pd.crosstab(df_animals.class_type, df_animals.venomous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb9e96b898>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As per our data we have 2 repltiles which are venomous and , 1 fish and other class types\n",
    "pd.crosstab(df_animals.class_type, df_animals.venomous).plot(kind=\"bar\", figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From above plot, we know that no mammals and birds are venomous. So thank god you can hug them.\n",
    "# but better watch your back before mammal kick your... !@#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So enough EDA i guess, You can also practice few like:\n",
    "# find out the venomous fish.\n",
    "# find out venomous reptiles.\n",
    "# find out venomous animal which gives egg or milk or any kind of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'''\n",
    "For this classfication we will use KNN classification. I will not explain the complete concept of KNN here.\n",
    "But a brief explanation is enough to give you and idea. \n",
    "Lets take a real life example. Suppose there are two basket B1 & B2. B1 has 80% RED BALLS, B2 has 90% BLACK BALLS.\n",
    "Now when your friend tells you to get one ball with your eyes covered from B1 & ask you which color it can be?\n",
    "SO in this case, your mind thins in a programmed way. It thinks B1 has mostly RED BALLS, so probably the\n",
    "ball will be red. \n",
    "So what your mind analyzed? \n",
    "Basically your mind analyzed the probability of ball being red and black. But our ball was form B1 so the\n",
    "probability of being RED was more. Which you calculated according to the number of other balls already presented\n",
    "in the basket 1. You can other balls were neighbour of the ball which you drew. So most of the neighbours of the\n",
    "ball which you drew were RED, so you said it must be red. \n",
    "\n",
    "KNN works in the same way, when you try to predict the category of a point(which we call classification), it first finds out its neighbour. By \n",
    "default it consider 5 nearest points(neighbour) and find out the category of all the points. then it find out\n",
    "which category has most number of points and assign it to the new point. By default KNN algo takes 5 neighbour.\n",
    "But you can change according to your algorithmm and case. That is why KNN decision boundary mostly not \n",
    "linear as Logistic regression. KNN is a type of Non-Linear classification.\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Feature & Target Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 18)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_animals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = df_animals.iloc[:, 1: 17].values\n",
    "X=df_animals.loc[:, [\"milk\", \"backbone\", \"toothed\", \"venomous\", \"domestic\", \"aquatic\"]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_animals.iloc[:, 17].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 6)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Data Into Train & Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=5, metric=\"minkowski\", p=2) \n",
    "# n_neighbors: number of neighbors. Default is 5\n",
    "# metric=\"minkowski\", p=2: will calculate distance as eucledian distance formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 1, 1, 1, 2, 4, 1, 1, 7, 1, 2, 6, 4, 6, 1, 6, 2, 4, 2, 3,\n",
       "       1, 2, 1, 1])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred # here you can see our model predict the class of the animal for the test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN Regression: 0.9230769230769231\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of KNN Regression:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we can see we have an accuracy of 92% which is very good\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets quickly compare with Logistic regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_log = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_log.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_log = clf_log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression Classifier: 0.9230769230769231\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Logistic Regression Classifier:\",accuracy_score(y_test, y_pred_log))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see our both the classfier showing same accuracy but it may not be the case always. Try with someother data and try to change columns or change the number of neighbor in KNN Classifier & see how it affect the accuracy of model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you !! Thats for now. We will be keep updating this notebook. Upvote and share if you liked it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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