KNN classifiers.ipynb

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   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy in data from lesson\n",
    "\n",
    "music = pd.DataFrame()\n",
    "\n",
    "music['duration'] = [184, 134, 243, 186, 122, 197, 294, 382, 102, 264, \n",
    "                     205, 110, 307, 110, 397, 153, 190, 192, 210, 403,\n",
    "                     164, 198, 204, 253, 234, 190, 182, 401, 376, 102]\n",
    "\n",
    "music['loudness'] = [18, 34, 43, 36, 22, 9, 29, 22, 10, 24, \n",
    "                     20, 10, 17, 51, 7, 13, 19, 12, 21, 22,\n",
    "                     16, 18, 4, 23, 34, 19, 14, 11, 37, 42]\n",
    "\n",
    "music['jazz'] = [ 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
    "                  0, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
    "                  1, 1, 1, 1, 0, 0, 1, 1, 0, 0]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nearest_neighbor(a, b):\n",
    "    shortest_dist = 1000\n",
    "    index_shortest_dist = []\n",
    "    \n",
    "    for index, row in music.iterrows():\n",
    "        duration = row[\"duration\"]\n",
    "        loudness = row[\"loudness\"]\n",
    "        \n",
    "        dist = np.sqrt((a-duration)**2 + (b-loudness**2))\n",
    "        \n",
    "        if dist < shortest_dist:\n",
    "            shortest_dist = dist\n",
    "            index_shortest_dist = index\n",
    "            \n",
    "        return music.iloc[index_shortest_dist][\"jazz\"]"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
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     "name": "stdout",
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     "text": [
      "From scratch test: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"From scratch test:\", nearest_neighbor(120, 18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN algo result: [1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neighbors = KNeighborsClassifier(n_neighbors=5)\n",
    "X = music[[\"duration\", \"loudness\"]]\n",
    "Y = music.jazz\n",
    "neighbors.fit(X,Y)\n",
    "\n",
    "#Get results from KNN algo\n",
    "print(\"KNN algo result:\", neighbors.predict([[120, 18]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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