DNA Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46b2f704",
   "metadata": {},
   "source": [
    "<h1>DNA Classification</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8155f0bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request  # URL Library\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "  \n",
    "urllib.request.urlretrieve('https://www.genome.gov/sites/default/files/tg/en/illustration/promoter.jpg',\"gfg.png\")\n",
    "img = Image.open(\"gfg.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "575c6006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b255e62a90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,1,figsize=(20,20))\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08af9658",
   "metadata": {},
   "source": [
    "A promoter is a sequence of DNA needed to turn a gene on or off. The process of transcription is initiated at the promoter. Usually found the beginning of a gene, the promoter has a binding site for the enzyme used to make a messenger RNA (mRNA) molecule.<br>\n",
    "Source: Google"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59dfd0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv(r\"https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/promoters.data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d533c72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns=[\"class\",\"id\",\"Sequence\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ef7f46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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</th>\n",
       "      <th>id</th>\n",
       "      <th>Sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>+</td>\n",
       "      <td>AMPC</td>\n",
       "      <td>\\t\\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+</td>\n",
       "      <td>AROH</td>\n",
       "      <td>\\t\\tgtactagagaactagtgcattagcttatttttttgttatcat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+</td>\n",
       "      <td>DEOP2</td>\n",
       "      <td>\\taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+</td>\n",
       "      <td>LEU1_TRNA</td>\n",
       "      <td>\\ttcgataattaactattgacgaaaagctgaaaaccactagaatgc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+</td>\n",
       "      <td>MALEFG</td>\n",
       "      <td>\\taggggcaaggaggatggaaagaggttgccgtataaagaaactag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>-</td>\n",
       "      <td>799</td>\n",
       "      <td>\\t\\tcctcaatggcctctaaacgggtcttgaggggttttttgctga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>-</td>\n",
       "      <td>987</td>\n",
       "      <td>\\t\\tgtattctcaacaagattaaccgacagattcaatctcgtggat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>-</td>\n",
       "      <td>1226</td>\n",
       "      <td>\\t\\tcgcgactacgatgagatgcctgagtgcttccgttactggatt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>-</td>\n",
       "      <td>794</td>\n",
       "      <td>\\t\\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>-</td>\n",
       "      <td>1442</td>\n",
       "      <td>\\t\\ttaacattaataaataaggaggctctaatggcactcattagcc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class         id                                           Sequence\n",
       "0       +       AMPC  \\t\\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat...\n",
       "1       +       AROH  \\t\\tgtactagagaactagtgcattagcttatttttttgttatcat...\n",
       "2       +      DEOP2  \\taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa...\n",
       "3       +  LEU1_TRNA  \\ttcgataattaactattgacgaaaagctgaaaaccactagaatgc...\n",
       "4       +     MALEFG  \\taggggcaaggaggatggaaagaggttgccgtataaagaaactag...\n",
       "..    ...        ...                                                ...\n",
       "100     -        799  \\t\\tcctcaatggcctctaaacgggtcttgaggggttttttgctga...\n",
       "101     -        987  \\t\\tgtattctcaacaagattaaccgacagattcaatctcgtggat...\n",
       "102     -       1226  \\t\\tcgcgactacgatgagatgcctgagtgcttccgttactggatt...\n",
       "103     -        794  \\t\\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt...\n",
       "104     -       1442  \\t\\ttaacattaataaataaggaggctctaatggcactcattagcc...\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393132fd",
   "metadata": {},
   "source": [
    "According to this dataset:\n",
    "\n",
    "<li>\"+\" means the promoters</li>\n",
    "<li>\"-\" means is anything that is not promoters.</li>\n",
    "DNA molecules as a linear, non-overlapping sequence of the nitrogenous bases Adenine (A), Guanine (G), Cytosine (C) and Thymine (T). These are the \"alphabet\" of letters that are used to write the \"code words\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f0f421b",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68460d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=d1.pop(\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ad4b2b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMPC</td>\n",
       "      <td>\\t\\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AROH</td>\n",
       "      <td>\\t\\tgtactagagaactagtgcattagcttatttttttgttatcat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEOP2</td>\n",
       "      <td>\\taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LEU1_TRNA</td>\n",
       "      <td>\\ttcgataattaactattgacgaaaagctgaaaaccactagaatgc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MALEFG</td>\n",
       "      <td>\\taggggcaaggaggatggaaagaggttgccgtataaagaaactag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>799</td>\n",
       "      <td>\\t\\tcctcaatggcctctaaacgggtcttgaggggttttttgctga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>987</td>\n",
       "      <td>\\t\\tgtattctcaacaagattaaccgacagattcaatctcgtggat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1226</td>\n",
       "      <td>\\t\\tcgcgactacgatgagatgcctgagtgcttccgttactggatt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>794</td>\n",
       "      <td>\\t\\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1442</td>\n",
       "      <td>\\t\\ttaacattaataaataaggaggctctaatggcactcattagcc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                           Sequence\n",
       "0         AMPC  \\t\\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat...\n",
       "1         AROH  \\t\\tgtactagagaactagtgcattagcttatttttttgttatcat...\n",
       "2        DEOP2  \\taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa...\n",
       "3    LEU1_TRNA  \\ttcgataattaactattgacgaaaagctgaaaaccactagaatgc...\n",
       "4       MALEFG  \\taggggcaaggaggatggaaagaggttgccgtataaagaaactag...\n",
       "..         ...                                                ...\n",
       "100        799  \\t\\tcctcaatggcctctaaacgggtcttgaggggttttttgctga...\n",
       "101        987  \\t\\tgtattctcaacaagattaaccgacagattcaatctcgtggat...\n",
       "102       1226  \\t\\tcgcgactacgatgagatgcctgagtgcttccgttactggatt...\n",
       "103        794  \\t\\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt...\n",
       "104       1442  \\t\\ttaacattaataaataaggaggctctaatggcactcattagcc...\n",
       "\n",
       "[105 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a12840ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "x={}\n",
    "start=1\n",
    "for i in d1[\"Sequence\"]:\n",
    "    w1=i.replace(\"\\t\",\"\")\n",
    "    w2=list(w1)\n",
    "    x[start]=w2\n",
    "    start+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fdf64b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27196bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b13ced6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "loc=pd.DataFrame(x)\n",
    "DNA_codes=loc.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7697bf3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 57)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DNA_codes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "887bc7d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 57)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DNA_codes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2937021c",
   "metadata": {},
   "source": [
    "DNA molecules as a linear, non-overlapping sequence of the nitrogenous bases Adenine (A), Guanine (G), Cytosine (C) and Thymine (T). These are the \"alphabet\" of letters that are used to write the \"code words\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3997ab11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    +\n",
       "1    +\n",
       "2    +\n",
       "3    +\n",
       "4    +\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6b7b4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6bfb7343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 57)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DNA_codes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c966e9a",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "DNA_codes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9acc408f",
   "metadata": {},
   "outputs": [
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       "      <th>103</th>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0_a  0_c  0_g  0_t  1_a  1_c  1_g  1_t  2_a  2_c  ...  54_g  54_t  55_a  \\\n",
       "1      0    0    0    1    0    0    1    0    0    1  ...     0     0     1   \n",
       "2      0    0    1    0    0    0    0    1    1    0  ...     1     0     0   \n",
       "3      1    0    0    0    1    0    0    0    0    0  ...     0     0     0   \n",
       "4      0    0    0    1    0    1    0    0    0    0  ...     0     1     1   \n",
       "5      1    0    0    0    0    0    1    0    0    0  ...     1     0     0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   \n",
       "101    0    1    0    0    0    1    0    0    0    0  ...     0     1     1   \n",
       "102    0    0    1    0    0    0    0    1    1    0  ...     0     1     0   \n",
       "103    0    1    0    0    0    0    1    0    0    1  ...     0     1     0   \n",
       "104    0    1    0    0    0    0    0    1    0    1  ...     0     0     1   \n",
       "105    0    0    0    1    1    0    0    0    1    0  ...     0     0     0   \n",
       "\n",
       "     55_c  55_g  55_t  56_a  56_c  56_g  56_t  \n",
       "1       0     0     0     1     0     0     0  \n",
       "2       1     0     0     0     0     1     0  \n",
       "3       0     0     1     0     1     0     0  \n",
       "4       0     0     0     0     0     1     0  \n",
       "5       0     1     0     0     0     0     1  \n",
       "..    ...   ...   ...   ...   ...   ...   ...  \n",
       "101     0     0     0     0     0     0     1  \n",
       "102     0     0     1     0     0     1     0  \n",
       "103     1     0     0     0     1     0     0  \n",
       "104     0     0     0     0     1     0     0  \n",
       "105     1     0     0     0     0     0     1  \n",
       "\n",
       "[105 rows x 228 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(DNA_codes) # sparse matrix of DNA_codes dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b7e25cd",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9   ...  47  48  49  50  51  52  53  \\\n",
       "1   3   2   1   3   0   3   1   1   3   2  ...   2   1   0   3   1   2   1   \n",
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       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=x.keys()\n",
    "new_data={}\n",
    "for i in k:\n",
    "    \"Enconding string indicators to numeric\"\n",
    "    \"a:0\"\n",
    "    \"c:1\"\n",
    "    \"g:3\"\n",
    "    \"t:4\"\n",
    "    new_data[i]=[]\n",
    "    for j in x[i]:\n",
    "        if j==\"a\":\n",
    "            new_data[i].append(0)\n",
    "        elif j==\"c\":\n",
    "            new_data[i].append(1)\n",
    "        elif j==\"g\":\n",
    "            new_data[i].append(2)\n",
    "        elif j==\"t\":\n",
    "            new_data[i].append(3)\n",
    "#print(new_data)\n",
    "data_new=pd.DataFrame(new_data).T\n",
    "data_new.head()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5696bc7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      +\n",
       "1      +\n",
       "2      +\n",
       "3      +\n",
       "4      +\n",
       "      ..\n",
       "100    -\n",
       "101    -\n",
       "102    -\n",
       "103    -\n",
       "104    -\n",
       "Name: class, Length: 105, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d9ad83fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c455c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "y_encode=le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8ac1bedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "252466b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_encode.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65dee440",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new[\"target\"]=y_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ba366486",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1  2  3  4  5  6  7  8  9  ...  48  49  50  51  52  53  54  55  56  \\\n",
       "1    3  2  1  3  0  3  1  1  3  2  ...   1   0   3   1   2   1   1   0   0   \n",
       "2    2  3  0  1  3  0  2  0  2  0  ...   1   0   1   1   1   2   2   1   2   \n",
       "3    0  0  3  3  2  3  2  0  3  2  ...   0   0   1   0   0   0   1   3   1   \n",
       "4    3  1  2  0  3  0  0  3  3  0  ...   1   1   2   3   2   2   3   0   2   \n",
       "5    0  2  2  2  2  1  0  0  2  2  ...   1   2   3   3   3   0   2   2   3   \n",
       "..  .. .. .. .. .. .. .. .. .. ..  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "101  1  1  3  1  0  0  3  2  2  1  ...   2   0   0   1   3   0   3   0   3   \n",
       "102  2  3  0  3  3  1  3  1  0  0  ...   3   1   0   0   1   0   3   3   2   \n",
       "103  1  2  1  2  0  1  3  0  1  2  ...   0   0   2   2   1   3   3   1   1   \n",
       "104  1  3  1  2  3  1  1  3  1  0  ...   0   2   2   0   2   2   0   0   1   \n",
       "105  3  0  0  1  0  3  3  0  0  3  ...   3   1   0   0   2   0   0   1   3   \n",
       "\n",
       "     target  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "..      ...  \n",
       "101       1  \n",
       "102       1  \n",
       "103       1  \n",
       "104       1  \n",
       "105       1  \n",
       "\n",
       "[105 rows x 58 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e78234",
   "metadata": {},
   "source": [
    "<h1> Train _test data split </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "129549ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b9a4b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(data_new.iloc[:,:-1],data_new.iloc[:,-1],train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aa8d357d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 57)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "409e6c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Information\n",
    "link=\"data:image/png;base64,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\"\n",
    "urllib.request.urlretrieve(link,\"gfg.png\")\n",
    "img = Image.open(\"gfg.png\")\n",
    "plt.subplots(1,1,figsize=(10,10))\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "raw",
   "id": "553172e0",
   "metadata": {},
   "source": [
    "F-score is a way of combining the precision and recall of the model, and it is defined as the harmonic mean of the model's precision and recall.\n",
    "higher (F-1) score, higher accuracy of model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88e3b85",
   "metadata": {},
   "source": [
    "<h1>Models | Supervised Learning </h1>\n",
    "<h4>Supervised Algo-1: LogisticRegression</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5bf884ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "result={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "57da0e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "be3650c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression(solver=\"liblinear\") # for small datasets\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c3bd9f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c6235bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ad7ed4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8095238095238095"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc=accuracy_score(pred,ytest)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "87a1c756",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"LogisticRegression\"]=acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5456ad41",
   "metadata": {},
   "source": [
    "<h2>Supervised Algo 2: KNeighbourClassifier</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0fbed4f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ef95be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=2,weights=\"distance\") # points which are more closer will get more importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b770f0e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7e3635d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"KNeighborsClassifier\"]=acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c69f3215",
   "metadata": {},
   "source": [
    "<h2>Supervised Algo 3: Decision Tree</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "19f19adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from  sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bec63501",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6ed82d50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d111a9ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.67      0.70        12\n",
      "           1       0.60      0.67      0.63         9\n",
      "\n",
      "    accuracy                           0.67        21\n",
      "   macro avg       0.66      0.67      0.66        21\n",
      "weighted avg       0.67      0.67      0.67        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "813e6c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"DecisionTreeClassifier\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3278f247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54fb4c88",
   "metadata": {},
   "source": [
    "<h2>Supervised Algo 3: GaussianProcess classifier</h2>"
   ]
  },
  {
   "cell_type": "raw",
   "id": "842fe508",
   "metadata": {},
   "source": [
    "GaussinProcessClassifier: prediction is Probabilistic(Gaussian) and classify using Laplace approximation. <br>\n",
    "Gauss : normal distribution, sigma=1 <br>\n",
    "#Gaussian Processes: Kernal(mathmatical function): map non linear data into higher dimention space to  become seprable.<br>\n",
    "#Kernel Example: RBF(Radius basis kernel function) , seprate non linear data data into hyperplane based on radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "af8f64bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kernel for Gaussian Classifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF # radius based function kernel\n",
    "model=GaussianProcessClassifier(kernel=RBF())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d19a1953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "27e9ba9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.78      0.70         9\n",
      "           1       0.80      0.67      0.73        12\n",
      "\n",
      "    accuracy                           0.71        21\n",
      "   macro avg       0.72      0.72      0.71        21\n",
      "weighted avg       0.73      0.71      0.72        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f782a1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"GaussianProcessClassifier\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "220d416b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666,\n",
       " 'GaussianProcessClassifier': 0.7142857142857143}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d4dee5",
   "metadata": {},
   "source": [
    "<h2>Supervised Algo 3: RandomForestClassifier</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a7e7a512",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# ensemble: combine prediction from multiple models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9a6e5056",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier(n_estimators=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ff8a23cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8095238095238095\n"
     ]
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6830df6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82        11\n",
      "           1       0.80      0.80      0.80        10\n",
      "\n",
      "    accuracy                           0.81        21\n",
      "   macro avg       0.81      0.81      0.81        21\n",
      "weighted avg       0.81      0.81      0.81        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a84ec260",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"RandomForestClassifier\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a3075f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666,\n",
       " 'GaussianProcessClassifier': 0.7142857142857143,\n",
       " 'RandomForestClassifier': 0.8095238095238095}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d413ab07",
   "metadata": {},
   "source": [
    "<h2>Second Gaussian Algo 3: GaussianNB</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40811602",
   "metadata": {},
   "source": [
    "\n",
    "<b>Literature:</b><a> https://www.analyticsvidhya.com/blog/2021/03/introduction-to-naive-bayes-algorithm/</a>"
   ]
  },
  {
   "cell_type": "raw",
   "id": "473f0a9d",
   "metadata": {},
   "source": [
    "# GaussianNB: classification based on probabibity(gaussian) using basian knowledge / gaussin:: Normal_Distribution.\n",
    "# Naive: unknown,unbiased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3ad88698",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB # try BernouliNaiveBayes algorithm(Bernouli distribution this time rather than  Gaussian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f71e4f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7619047619047619\n"
     ]
    }
   ],
   "source": [
    "model=GaussianNB()\n",
    "model.fit(xtrain,ytrain)  #Fit Gaussian Naive Bayes\n",
    "pred=model.predict(xtest) \n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b4322d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"GaussianNaiveByes\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0979f15d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.80      0.76        10\n",
      "           1       0.80      0.73      0.76        11\n",
      "\n",
      "    accuracy                           0.76        21\n",
      "   macro avg       0.76      0.76      0.76        21\n",
      "weighted avg       0.77      0.76      0.76        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9015808",
   "metadata": {},
   "source": [
    "<h3>BOOsting Algorithms 1: Adaaptive boosting </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff1b589",
   "metadata": {},
   "source": [
    "AdaBoost classifier builds a strong classifier by combining multiple poorly performing classifiers \n",
    "so that you will get high accuracy strong classifier.<b> (Week learner convert into strong learner)</b> <br>\n",
    "<b>Bagging </b>merging the same type of predictions. <br>\n",
    "<b>Boosting</b>  merging different types of predictions(improve weak learners).<br> Bagging decreases variance, not bias, and solves over-fitting issues in a model. Boosting decreases bias, not variance.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "acf92ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2e95adcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=AdaBoostClassifier(n_estimators=10) # default estimator is \"DecisionTreeClassifier\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e493b2e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8095238095238095\n"
     ]
    }
   ],
   "source": [
    "# base estimator as DecisionTreeClassifier\n",
    "model=AdaBoostClassifier(n_estimators=10)\n",
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4bd40ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"AdaboostClassifier\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "74ff100b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b25e359280>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "accf=[]\n",
    "for i in range(500):\n",
    "    model=AdaBoostClassifier(n_estimators=i+1)\n",
    "    model.fit(xtrain,ytrain)\n",
    "    pred=model.predict(xtest)\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    acc=accuracy_score(pred,ytest)\n",
    "    accf.append(acc)\n",
    "plt.plot(accf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f7dd57f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iterations vs Accuracy for AdaBoostClassifier Algorithm')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "accf=[]\n",
    "for i in range(100):\n",
    "    model=AdaBoostClassifier(n_estimators=i+1)\n",
    "    model.fit(xtrain,ytrain)\n",
    "    pred=model.predict(xtest)\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    acc=accuracy_score(pred,ytest)\n",
    "    accf.append(acc)\n",
    "plt.plot(accf)\n",
    "plt.xlabel(\"Number of estimators\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Iterations vs Accuracy for AdaBoostClassifier Algorithm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "41f1d0b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.80      0.76        10\n",
      "           1       0.80      0.73      0.76        11\n",
      "\n",
      "    accuracy                           0.76        21\n",
      "   macro avg       0.76      0.76      0.76        21\n",
      "weighted avg       0.77      0.76      0.76        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded2e1bb",
   "metadata": {},
   "source": [
    "<h3>Deep Learning Algo 1: Multi layer Perceptrons(used for feature detection in nn)</h3> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f390cf39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b84d2c25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7619047619047619\n"
     ]
    }
   ],
   "source": [
    "model= MLPClassifier(max_iter=1000)\n",
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "92defb3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.75      0.78        12\n",
      "           1       0.70      0.78      0.74         9\n",
      "\n",
      "    accuracy                           0.76        21\n",
      "   macro avg       0.76      0.76      0.76        21\n",
      "weighted avg       0.77      0.76      0.76        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "549ae713",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"MLPClassifier\"]=acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb96a42",
   "metadata": {},
   "source": [
    "<h1>Models | Unsupervised Learning </h1>\n",
    "<h4> Unsupervised Algo 1: KMean clustering</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9d60f536",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3eaf6bf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "model=KMeans(n_clusters=2) # 0/1 classes\n",
    "# if datasize is too large use \"sklearn.cluster.MiniBatchKMeans\".\n",
    "model.fit(xtrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)\n",
    "result[\"Kmeans\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "616ddd06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotting(interation):\n",
    "    global xtrain,ytrain,xtest,ytest\n",
    "    model=KMeans(n_clusters=2) # 0/1 classes\n",
    "    i=0\n",
    "    data=[]\n",
    "    for i in range(interation):\n",
    "        model.fit(xtrain)\n",
    "        pred=model.predict(xtest)\n",
    "        acc=accuracy_score(pred,ytest)\n",
    "        data.append(acc)\n",
    "    return data\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "09ea6974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data=plotting(100)\n",
    "plt.plot(range(1,101,1),data,\"--\")\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "06c4c1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7619047619047619"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4108ee0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"Kmeans\"]=max(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "616e90b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666,\n",
       " 'GaussianProcessClassifier': 0.7142857142857143,\n",
       " 'RandomForestClassifier': 0.8095238095238095,\n",
       " 'GaussianNaiveByes': 0.7619047619047619,\n",
       " 'AdaboostClassifier': 0.8095238095238095,\n",
       " 'MLPClassifier': 0.7619047619047619,\n",
       " 'Kmeans': 0.7619047619047619}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ca8376",
   "metadata": {},
   "source": [
    "<h2>Unsupervised Algo 2: DBSCAN </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "74c74d77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN # density based clustering.\n",
    "model=DBSCAN() # 0/1 classes\n",
    "model.fit(xtrain)\n",
    "pred=model.fit_predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)\n",
    "result[\"DBSCAN\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cfca7a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1273d570",
   "metadata": {},
   "source": [
    "<h3>supervised Algo 3:Multiclass<br> 1-OnevsAll <br>2-OneVsOne<h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1c43d5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#oneVsALL: this algo used if classiication is more than binary. i.e. red vs[green +blue]\n",
    "from sklearn.multiclass import OneVsOneClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3755e7ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8095238095238095\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "estimator=LinearSVC\n",
    "model=OneVsOneClassifier(estimator(random_state=90)) # LinearSVC will used as estimator foe oneVsone classififcation\n",
    "model.fit(xtrain,ytrain) # it will return fitted underlying estimator\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3089f823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.77      0.83        13\n",
      "           1       0.70      0.88      0.78         8\n",
      "\n",
      "    accuracy                           0.81        21\n",
      "   macro avg       0.80      0.82      0.81        21\n",
      "weighted avg       0.83      0.81      0.81        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2c4d1dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"OneVsOne\"]=acc"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2bd01cb9",
   "metadata": {},
   "source": [
    "# One vs all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "25acc510",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d0aba9fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8095238095238095\n"
     ]
    }
   ],
   "source": [
    "estimator=LinearSVC\n",
    "model=OneVsRestClassifier(estimator(random_state=90)) # LinearSVC will used as estimator foe oneVsone classififcation\n",
    "model.fit(xtrain,ytrain) # it will return fitted underlying estimator\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "39ba9986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.77      0.83        13\n",
      "           1       0.70      0.88      0.78         8\n",
      "\n",
      "    accuracy                           0.81        21\n",
      "   macro avg       0.80      0.82      0.81        21\n",
      "weighted avg       0.83      0.81      0.81        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "97304ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"OneVsRest\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "160954e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['LogisticRegression', 'KNeighborsClassifier', 'DecisionTreeClassifier', 'GaussianProcessClassifier', 'RandomForestClassifier', 'GaussianNaiveByes', 'AdaboostClassifier', 'MLPClassifier', 'Kmeans', 'DBSCAN', 'OneVsOne', 'OneVsRest'])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2f3bbc1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666,\n",
       " 'GaussianProcessClassifier': 0.7142857142857143,\n",
       " 'RandomForestClassifier': 0.8095238095238095,\n",
       " 'GaussianNaiveByes': 0.7619047619047619,\n",
       " 'AdaboostClassifier': 0.8095238095238095,\n",
       " 'MLPClassifier': 0.7619047619047619,\n",
       " 'Kmeans': 0.7619047619047619,\n",
       " 'DBSCAN': 0.0,\n",
       " 'OneVsOne': 0.8095238095238095,\n",
       " 'OneVsRest': 0.8095238095238095}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0815c07",
   "metadata": {},
   "source": [
    "<h2> Decomposition Algorithm: Principal Component Analysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ddb6026e",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  3  4  5  6  7  8  9  ...  48  49  50  51  52  53  54  55  56  \\\n",
       "1  3  2  1  3  0  3  1  1  3  2  ...   1   0   3   1   2   1   1   0   0   \n",
       "2  2  3  0  1  3  0  2  0  2  0  ...   1   0   1   1   1   2   2   1   2   \n",
       "3  0  0  3  3  2  3  2  0  3  2  ...   0   0   1   0   0   0   1   3   1   \n",
       "4  3  1  2  0  3  0  0  3  3  0  ...   1   1   2   3   2   2   3   0   2   \n",
       "5  0  2  2  2  2  1  0  0  2  2  ...   1   2   3   3   3   0   2   2   3   \n",
       "\n",
       "   target  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "5       0  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# useful for Dimentionality Reduction.\n",
    "data_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d8db52bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ca3a8de0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=10)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo=PCA(n_components=10)\n",
    "algo.fit(data_new.iloc[:,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "36c5e679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05952619, 0.04836548, 0.04749917, 0.04121658, 0.03995174,\n",
       "       0.03915788, 0.0373445 , 0.03595196, 0.03349461, 0.03254408])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var=algo.explained_variance_ratio_\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3e846b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No. of Featues and its explaineing varinace (ratio)')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(var,\"--g\");\n",
    "plt.xlabel(\"features\")\n",
    "plt.ylabel(\"explained_variance_ratio\");\n",
    "plt.grid(True)\n",
    "plt.title(\"No. of Featues and its explaineing varinace (ratio)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "18553389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,2,figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(var,\"r\",drawstyle=\"steps-post\");\n",
    "plt.xlabel(\"features\")\n",
    "plt.grid(True)\n",
    "plt.ylabel(\"explained_variance_ratio\");\n",
    "plt.subplot(1,2,2)\n",
    "sns.ecdfplot(var)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e0b651e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.58223789, 19.45405659, 19.27904162, 17.95884484, 17.68113997,\n",
       "       17.50459329, 17.09447389, 16.7727291 , 16.18936849, 15.95799818])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value=algo.singular_values_\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0a0fc483",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data_pca=pd.DataFrame(algo.transform(data_new.iloc[:,:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "afa102fe",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.230236</td>\n",
       "      <td>0.289239</td>\n",
       "      <td>-2.461704</td>\n",
       "      <td>-0.543399</td>\n",
       "      <td>-0.153456</td>\n",
       "      <td>-1.724301</td>\n",
       "      <td>-1.379031</td>\n",
       "      <td>-1.024634</td>\n",
       "      <td>-1.644045</td>\n",
       "      <td>-1.218050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.125637</td>\n",
       "      <td>2.811716</td>\n",
       "      <td>-0.506727</td>\n",
       "      <td>1.262677</td>\n",
       "      <td>2.044616</td>\n",
       "      <td>-0.812740</td>\n",
       "      <td>-0.612925</td>\n",
       "      <td>-1.206329</td>\n",
       "      <td>2.646910</td>\n",
       "      <td>-0.615524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.703202</td>\n",
       "      <td>4.765086</td>\n",
       "      <td>0.653617</td>\n",
       "      <td>-0.654654</td>\n",
       "      <td>2.598087</td>\n",
       "      <td>1.284911</td>\n",
       "      <td>0.254510</td>\n",
       "      <td>-1.031569</td>\n",
       "      <td>1.080535</td>\n",
       "      <td>-0.980305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.568098</td>\n",
       "      <td>1.125480</td>\n",
       "      <td>-1.624598</td>\n",
       "      <td>-1.609144</td>\n",
       "      <td>-1.813896</td>\n",
       "      <td>-3.089332</td>\n",
       "      <td>-1.229804</td>\n",
       "      <td>2.129800</td>\n",
       "      <td>-1.645069</td>\n",
       "      <td>-0.231393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.741449</td>\n",
       "      <td>1.262325</td>\n",
       "      <td>0.587383</td>\n",
       "      <td>-1.176392</td>\n",
       "      <td>1.001032</td>\n",
       "      <td>0.077763</td>\n",
       "      <td>-2.707422</td>\n",
       "      <td>3.136705</td>\n",
       "      <td>-1.611335</td>\n",
       "      <td>0.406388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.230236  0.289239 -2.461704 -0.543399 -0.153456 -1.724301 -1.379031   \n",
       "1  1.125637  2.811716 -0.506727  1.262677  2.044616 -0.812740 -0.612925   \n",
       "2  0.703202  4.765086  0.653617 -0.654654  2.598087  1.284911  0.254510   \n",
       "3  2.568098  1.125480 -1.624598 -1.609144 -1.813896 -3.089332 -1.229804   \n",
       "4  0.741449  1.262325  0.587383 -1.176392  1.001032  0.077763 -2.707422   \n",
       "\n",
       "          7         8         9  \n",
       "0 -1.024634 -1.644045 -1.218050  \n",
       "1 -1.206329  2.646910 -0.615524  \n",
       "2 -1.031569  1.080535 -0.980305  \n",
       "3  2.129800 -1.645069 -0.231393  \n",
       "4  3.136705 -1.611335  0.406388  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cdfae29b",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.458842e-18</td>\n",
       "      <td>5.921189e-17</td>\n",
       "      <td>2.347329e-16</td>\n",
       "      <td>2.537653e-17</td>\n",
       "      <td>-3.013462e-17</td>\n",
       "      <td>-6.767074e-17</td>\n",
       "      <td>2.194012e-17</td>\n",
       "      <td>-1.480297e-16</td>\n",
       "      <td>-9.727668e-17</td>\n",
       "      <td>3.806479e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.116313e+00</td>\n",
       "      <td>1.907627e+00</td>\n",
       "      <td>1.890466e+00</td>\n",
       "      <td>1.761010e+00</td>\n",
       "      <td>1.733778e+00</td>\n",
       "      <td>1.716467e+00</td>\n",
       "      <td>1.676251e+00</td>\n",
       "      <td>1.644701e+00</td>\n",
       "      <td>1.587498e+00</td>\n",
       "      <td>1.564810e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.171711e+00</td>\n",
       "      <td>-4.149034e+00</td>\n",
       "      <td>-3.325157e+00</td>\n",
       "      <td>-3.813485e+00</td>\n",
       "      <td>-4.125933e+00</td>\n",
       "      <td>-4.668352e+00</td>\n",
       "      <td>-3.573782e+00</td>\n",
       "      <td>-3.822494e+00</td>\n",
       "      <td>-3.487912e+00</td>\n",
       "      <td>-3.170728e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.619756e+00</td>\n",
       "      <td>-1.287512e+00</td>\n",
       "      <td>-1.533344e+00</td>\n",
       "      <td>-1.403180e+00</td>\n",
       "      <td>-1.142137e+00</td>\n",
       "      <td>-1.030900e+00</td>\n",
       "      <td>-1.253251e+00</td>\n",
       "      <td>-1.031569e+00</td>\n",
       "      <td>-1.016303e+00</td>\n",
       "      <td>-1.095037e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.578639e-01</td>\n",
       "      <td>3.950345e-02</td>\n",
       "      <td>-8.054369e-02</td>\n",
       "      <td>4.645147e-02</td>\n",
       "      <td>-1.743386e-01</td>\n",
       "      <td>7.776263e-02</td>\n",
       "      <td>-1.715169e-01</td>\n",
       "      <td>-5.766741e-02</td>\n",
       "      <td>-5.033714e-02</td>\n",
       "      <td>1.502355e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.125637e+00</td>\n",
       "      <td>1.262404e+00</td>\n",
       "      <td>1.368722e+00</td>\n",
       "      <td>1.147255e+00</td>\n",
       "      <td>1.016725e+00</td>\n",
       "      <td>1.091344e+00</td>\n",
       "      <td>1.265956e+00</td>\n",
       "      <td>1.066888e+00</td>\n",
       "      <td>8.119276e-01</td>\n",
       "      <td>9.454803e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.431015e+00</td>\n",
       "      <td>4.765086e+00</td>\n",
       "      <td>3.914578e+00</td>\n",
       "      <td>4.703092e+00</td>\n",
       "      <td>4.249479e+00</td>\n",
       "      <td>4.756542e+00</td>\n",
       "      <td>3.662598e+00</td>\n",
       "      <td>4.268755e+00</td>\n",
       "      <td>5.058996e+00</td>\n",
       "      <td>4.304728e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0             1             2             3             4  \\\n",
       "count  1.050000e+02  1.050000e+02  1.050000e+02  1.050000e+02  1.050000e+02   \n",
       "mean   8.458842e-18  5.921189e-17  2.347329e-16  2.537653e-17 -3.013462e-17   \n",
       "std    2.116313e+00  1.907627e+00  1.890466e+00  1.761010e+00  1.733778e+00   \n",
       "min   -4.171711e+00 -4.149034e+00 -3.325157e+00 -3.813485e+00 -4.125933e+00   \n",
       "25%   -1.619756e+00 -1.287512e+00 -1.533344e+00 -1.403180e+00 -1.142137e+00   \n",
       "50%    1.578639e-01  3.950345e-02 -8.054369e-02  4.645147e-02 -1.743386e-01   \n",
       "75%    1.125637e+00  1.262404e+00  1.368722e+00  1.147255e+00  1.016725e+00   \n",
       "max    5.431015e+00  4.765086e+00  3.914578e+00  4.703092e+00  4.249479e+00   \n",
       "\n",
       "                  5             6             7             8             9  \n",
       "count  1.050000e+02  1.050000e+02  1.050000e+02  1.050000e+02  1.050000e+02  \n",
       "mean  -6.767074e-17  2.194012e-17 -1.480297e-16 -9.727668e-17  3.806479e-17  \n",
       "std    1.716467e+00  1.676251e+00  1.644701e+00  1.587498e+00  1.564810e+00  \n",
       "min   -4.668352e+00 -3.573782e+00 -3.822494e+00 -3.487912e+00 -3.170728e+00  \n",
       "25%   -1.030900e+00 -1.253251e+00 -1.031569e+00 -1.016303e+00 -1.095037e+00  \n",
       "50%    7.776263e-02 -1.715169e-01 -5.766741e-02 -5.033714e-02  1.502355e-01  \n",
       "75%    1.091344e+00  1.265956e+00  1.066888e+00  8.119276e-01  9.454803e-01  \n",
       "max    4.756542e+00  3.662598e+00  4.268755e+00  5.058996e+00  4.304728e+00  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_pca.describe() # new data set using PCA  decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c84281ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666,\n",
       " 'GaussianProcessClassifier': 0.7142857142857143,\n",
       " 'RandomForestClassifier': 0.8095238095238095,\n",
       " 'GaussianNaiveByes': 0.7619047619047619,\n",
       " 'AdaboostClassifier': 0.8095238095238095,\n",
       " 'MLPClassifier': 0.7619047619047619,\n",
       " 'Kmeans': 0.7619047619047619,\n",
       " 'DBSCAN': 0.0,\n",
       " 'OneVsOne': 0.8095238095238095,\n",
       " 'OneVsRest': 0.8095238095238095}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result # adaboost classifier is best among all algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "fb541840",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train Adaptive boosting using pca decomposed data\n",
    "xtrain_pca,xtest_pca,ytrain_pca,ytest_pca=train_test_split(new_data_pca,data_new.iloc[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d9072c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7407407407407407\n"
     ]
    }
   ],
   "source": [
    "model=AdaBoostClassifier(n_estimators=10)\n",
    "model.fit(xtrain_pca,ytrain_pca)\n",
    "pred=model.predict(xtest_pca)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest_pca)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "754a7cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"PCA_adaboost\"]=acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0035084",
   "metadata": {},
   "source": [
    "<h1>Hyperparameters Tuning</h1>"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a15bc7f6",
   "metadata": {},
   "source": [
    "task 1: identifying \"number of suitable estimators\" for dabosst classifier with  10 Princiapl decomposed data from original data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "9dd002da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no of estimators between 1 to 150\n",
    "track1=[]\n",
    "for i in range(150):\n",
    "    model=AdaBoostClassifier(n_estimators=i+1)\n",
    "    model.fit(xtrain_pca,ytrain_pca)\n",
    "    pred=model.predict(xtest_pca)\n",
    "    acc=accuracy_score(pred,ytest_pca)\n",
    "    track1.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d2379f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Examining # of estimators with 10 P.C. data')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(track1)\n",
    "plt.xlabel(\"# interations\")\n",
    "plt.ylabel(\"accuracy\");\n",
    "plt.grid(True)\n",
    "plt.axvline(x=10,color=\"red\")\n",
    "plt.title(\"Examining # of estimators with 10 P.C. data\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8e6b573b",
   "metadata": {},
   "source": [
    "tAsk 2: Findidng best number of threshold features by decomposing data through PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "138ad55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "track=[]\n",
    "for i in range(1,50):\n",
    "    algo=PCA(n_components=i)\n",
    "    algo.fit(data_new.iloc[:,:-1])\n",
    "    new_data_pca=pd.DataFrame(algo.transform(data_new.iloc[:,:-1]))\n",
    "    xtrain_pca,xtest_pca,ytrain_pca,ytest_pca=train_test_split(new_data_pca,data_new.iloc[:,-1])\n",
    "    model=AdaBoostClassifier(n_estimators=50)\n",
    "    model.fit(xtrain_pca,ytrain_pca)\n",
    "    pred=model.predict(xtest_pca)\n",
    "    acc=accuracy_score(pred,ytest_pca)\n",
    "    track.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b4d1752d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Examining # of Decomposed features with 10 estimators of Adaboost Algo')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(track,\"g\")\n",
    "plt.xlabel(\"# PCA Components\")\n",
    "plt.ylabel(\"accuracy\");\n",
    "plt.grid(True)\n",
    "plt.title(\"Examining # of Decomposed features with 10 estimators of Adaboost Algo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed504391",
   "metadata": {},
   "source": [
    "<h1> Support vector Classifier </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4ead173b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for higher dimentional dataset\n",
    "# algorithm Target: Find a \"maximum marginal hyperplane\" and classify dataset across ,ultidemntional dataset.\n",
    "# support vectors: cordinates which are closer to hyperplane\n",
    "# It convert input data into higher dimentional space using Kernels(Functions like RBF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2b404b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "384a0f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1d140c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)\n",
    "pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(pred,ytest)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3e1e0e36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.78      0.70         9\n",
      "           1       0.80      0.67      0.73        12\n",
      "\n",
      "    accuracy                           0.71        21\n",
      "   macro avg       0.72      0.72      0.71        21\n",
      "weighted avg       0.73      0.71      0.72        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=classification_report(pred,ytest)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d09dbab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"SVC\"]=acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fe08f183",
   "metadata": {},
   "outputs": [],
   "source": [
    "def try_model(kernal=None):\n",
    "    model=SVC(kernel=kernal)\n",
    "    model.fit(xtrain,ytrain)\n",
    "    pred=model.predict(xtest)\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    acc=accuracy_score(pred,ytest)\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "fa361434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'svc_rbf': 0.7142857142857143, 'svc_sigmoid': 0.7619047619047619, 'svc_linear': 0.8095238095238095, 'svc_poly': 0.8095238095238095}\n"
     ]
    }
   ],
   "source": [
    "kernal=[\"rbf\",\"sigmoid\",\"linear\",\"poly\"]\n",
    "\"\"\"\n",
    "Kernal: map non_linear data into higher dimentinal space, think as a mathmatical function.\n",
    "RBF: radius based function, computation using euclidean distance\n",
    "Sigmoid: same as 2 layer of NN. [tanhh(gama.(x.T).y+r]\n",
    "Polynomial : Polynomila degree kernal\n",
    "Linear: Linear degree function (A.X=Y)\n",
    "Gausian Kernal: e-(((x-x')^2)/2.sigma^2)\n",
    "\"\"\"\n",
    "track3={}\n",
    "for i in kernal:\n",
    "    out=try_model(i)\n",
    "    track3[\"svc_\"+str(i)]=out\n",
    "print(track3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "6ef888c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8095238095238095,\n",
       " 'KNeighborsClassifier': 0.7142857142857143,\n",
       " 'DecisionTreeClassifier': 0.6666666666666666,\n",
       " 'GaussianProcessClassifier': 0.7142857142857143,\n",
       " 'RandomForestClassifier': 0.8095238095238095,\n",
       " 'GaussianNaiveByes': 0.7619047619047619,\n",
       " 'AdaboostClassifier': 0.8095238095238095,\n",
       " 'MLPClassifier': 0.7619047619047619,\n",
       " 'Kmeans': 0.7619047619047619,\n",
       " 'DBSCAN': 0.0,\n",
       " 'OneVsOne': 0.8095238095238095,\n",
       " 'OneVsRest': 0.8095238095238095,\n",
       " 'PCA_adaboost': 0.7407407407407407,\n",
       " 'SVC': 0.7142857142857143}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result  # conclusion of modal performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8160aab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=[]\n",
    "label=[]\n",
    "for i in result.keys():\n",
    "    label.append(i)\n",
    "    accuracy.append(result[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "848416ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,1,figsize=(20,5))\n",
    "plt.plot(accuracy,\"--r\")\n",
    "plt.xticks(range(len(label)),label,rotation=90,color=\"blue\",fontsize=25);\n",
    "plt.ylabel(\"Accuracy\");\n",
    "plt.grid(True)\n",
    "plt.title(\"Accuracy vs Machine learning Models performance for DNA Classifocation Task\",fontsize=30,color=\"green\");"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}