Final_Classification model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2397b22e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dd583bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "org_data=pd.read_csv(\"data_640_validated.csv\")\n",
    "dq_data=pd.read_csv(\"DT_data_640.csv\")\n",
    "cat_data=pd.read_csv(\"Cat_Data_640.csv\")\n",
    "check=pd.read_excel(\"/Users/sathyasris/Documents on the Mac/Semester 1/COMP5122 - Data Science/Coursework 1/Pred_check.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7785da32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for j in range(2,15,2):\n",
    "#     b=\"C\"+str(j)\n",
    "#     for i in range(0,len(cat_data[b])):\n",
    "#         if cat_data[b][i]==1:\n",
    "#             cat_data[b][i]=5\n",
    "#         elif cat_data[b][i]==2:\n",
    "#             cat_data[b][i]=4\n",
    "#         elif cat_data[b][i]==3:\n",
    "#             cat_data[b][i]=3\n",
    "#         elif cat_data[b][i]==4:\n",
    "#             cat_data[b][i]=2\n",
    "#         elif cat_data[b][i]==5:\n",
    "#             cat_data[b][i]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4b7a0092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    203\n",
       "4    181\n",
       "2    155\n",
       "1     59\n",
       "5     42\n",
       "Name: C2, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq_data['C2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a5a40f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cat_data[['A1_1','A1_2','A2','A3','A4','A5','A6','A7','A8']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "647e2130",
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       "     A1_1  A1_2  A2  A3  A4  A5  A6  A7  A8\n",
       "0      12     0   1   3   1   0   0   3   1\n",
       "1       0     3   1   3   2   1   6   3   1\n",
       "2       0     3   1   3   2   1   6   3   1\n",
       "3      12     0   0   1   1   1   0   3   1\n",
       "4       0     3   1   3   1   2   2   3   1\n",
       "..    ...   ...  ..  ..  ..  ..  ..  ..  ..\n",
       "635     4     1   0   0   2  34   6   1   2\n",
       "636     0     3   0   4   1  35   6   4   5\n",
       "637     0     3   0   4   1  36   6   1   7\n",
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       "\n",
       "[640 rows x 9 columns]"
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    "X"
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  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b2aa8b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pos=cat_data[['C1','C3','C5','C7','C9','C11','C13','C15']]\n",
    "y_neg=cat_data[['C2','C4','C6','C8','C10','C12','C14']]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "33ed5b28",
   "metadata": {},
   "outputs": [
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       "     C1  C3  C5  C7  C9  C11  C13  C15\n",
       "0     4   5   5   4   3    5    3    4\n",
       "1     4   5   5   5   5    5    5    5\n",
       "2     5   5   5   5   5    5    5    5\n",
       "3     4   5   4   5   4    5    5    5\n",
       "4     3   3   5   3   5    3    4    4\n",
       "..   ..  ..  ..  ..  ..  ...  ...  ...\n",
       "635   3   3   4   4   4    3    4    4\n",
       "636   4   4   5   4   4    4    4    4\n",
       "637   4   4   4   4   5    4    4    5\n",
       "638   4   4   4   2   4    4    4    4\n",
       "639   4   3   2   5   4    3    4    5\n",
       "\n",
       "[640 rows x 8 columns]"
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     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y_pos"
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  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "35d3fb4a",
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       "      <th>638</th>\n",
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      "text/plain": [
       "     C2  C4  C6  C8  C10  C12  C14\n",
       "0     3   1   2   3    4    5    4\n",
       "1     2   5   3   5    5    5    3\n",
       "2     2   4   3   5    5    5    3\n",
       "3     4   3   1   4    3    5    2\n",
       "4     4   1   1   4    3    4    2\n",
       "..   ..  ..  ..  ..  ...  ...  ...\n",
       "635   2   2   2   2    4    4    3\n",
       "636   3   3   3   4    5    5    3\n",
       "637   3   3   2   3    5    4    4\n",
       "638   4   2   4   3    4    4    3\n",
       "639   4   5   2   5    4    4    5\n",
       "\n",
       "[640 rows x 7 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "509e5e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>635</th>\n",
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       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pos\n",
       "0      4\n",
       "1      5\n",
       "2      5\n",
       "3      5\n",
       "4      4\n",
       "..   ...\n",
       "635    4\n",
       "636    4\n",
       "637    4\n",
       "638    4\n",
       "639    4\n",
       "\n",
       "[640 rows x 1 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pos1=pd.DataFrame(data = { 'Pos' : round(y_pos.sum(axis=1)/8,0)})\n",
    "y_pos1['Pos']=y_pos1['Pos'].astype('int64')\n",
    "y_pos1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "73c58eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>3</td>\n",
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       "      <th>639</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Neg\n",
       "0      3\n",
       "1      4\n",
       "2      4\n",
       "3      3\n",
       "4      3\n",
       "..   ...\n",
       "635    3\n",
       "636    4\n",
       "637    3\n",
       "638    3\n",
       "639    4\n",
       "\n",
       "[640 rows x 1 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_neg1=pd.DataFrame(data = { 'Neg' : round(y_neg.sum(axis=1)/7,0)})\n",
    "y_neg1['Neg']=y_neg1['Neg'].astype('int64')\n",
    "# y_neg1.rename(columns={\"0\":\"P\"})\n",
    "y_neg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a2a8c0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=cat_data[['C1','C3','C5','C7','C9','C11','C13','C15','C2','C4','C6','C8','C10','C12','C14']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8c710a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>all</th>\n",
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       "      <th>...</th>\n",
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       "      <td>3</td>\n",
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       "      <th>639</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     all\n",
       "0      4\n",
       "1      4\n",
       "2      4\n",
       "3      4\n",
       "4      3\n",
       "..   ...\n",
       "635    3\n",
       "636    4\n",
       "637    4\n",
       "638    4\n",
       "639    4\n",
       "\n",
       "[640 rows x 1 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_all=pd.DataFrame(data = { 'all' : round(y.sum(axis=1)/15,0)})\n",
    "y_all['all']=y_all['all'].astype('int64')\n",
    "y_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7e88d94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "all\n",
       "4      324\n",
       "3      287\n",
       "5       28\n",
       "2        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_all.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "14543c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pos\n",
       "4      430\n",
       "5      134\n",
       "3       67\n",
       "2        7\n",
       "1        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pos1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cb45ed40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neg\n",
       "3      267\n",
       "4      190\n",
       "2      159\n",
       "5       15\n",
       "1        9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_neg1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b2889ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
    "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7c8ef0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.concat([y_all,y_pos1,y_neg1],axis=1)\n",
    "#X=pd.concat([y_all,X],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "18638f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "result['Mid']=(result['Pos']+result['Neg'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6e8556cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.5, 4.5, 4. , 3. , 2.5, 5. ])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Mid'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "660962d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d072ceca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     all  Pos  Neg  Mid\n",
       "0      4    4    3  3.5\n",
       "1      4    5    4  4.5\n",
       "2      4    5    4  4.5\n",
       "3      4    5    3  4.0\n",
       "4      3    4    3  3.5\n",
       "..   ...  ...  ...  ...\n",
       "635    3    4    3  3.5\n",
       "636    4    4    4  4.0\n",
       "637    4    4    3  3.5\n",
       "638    4    4    3  3.5\n",
       "639    4    4    4  4.0\n",
       "\n",
       "[640 rows x 4 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b8ddc541",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/43/ys05gj490c52sd_1b4cdfwz40000gn/T/ipykernel_2772/3722948799.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  result['View'][i]=check['Prediction '][j]\n"
     ]
    }
   ],
   "source": [
    "result['View']=' '\n",
    "for i in range(0,len(result['Pos'])):\n",
    "    for j in range(0,len(check['Pos_Value '])):\n",
    "        if check['Pos_Value '][j]==result['Pos'][i] and check['Neg_Value'][j]==result['Neg'][i]:\n",
    "            result['View'][i]=check['Prediction '][j]\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bf730a78",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     all  Pos  Neg  Mid View\n",
       "0      4    4    3  3.5    B\n",
       "1      4    5    4  4.5    A\n",
       "2      4    5    4  4.5    A\n",
       "3      4    5    3  4.0    A\n",
       "4      3    4    3  3.5    B\n",
       "..   ...  ...  ...  ...  ...\n",
       "635    3    4    3  3.5    B\n",
       "636    4    4    4  4.0    A\n",
       "637    4    4    3  3.5    B\n",
       "638    4    4    3  3.5    B\n",
       "639    4    4    4  4.0    A\n",
       "\n",
       "[640 rows x 5 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "79623d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "result['View']=result['View'].astype('category').cat.codes\n",
    "result['Mid']=result['Mid'].astype('category').cat.codes\n",
    "result['Pos']=result['Pos'].astype('category').cat.codes\n",
    "result['Neg']=result['Neg'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bcb76650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "635    1\n",
       "636    0\n",
       "637    1\n",
       "638    1\n",
       "639    0\n",
       "Name: View, Length: 640, dtype: int8"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['View']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0b1b470d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2], dtype=int8)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['View'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ebe1aaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 2, 0, 1], dtype=int8)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Pos'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7c6480d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, 0, 4], dtype=int8)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Neg'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "676bf309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 3, 1, 0, 5], dtype=int8)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Mid'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7819576f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.svm import SVC, SVR\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.naive_bayes import GaussianNB , MultinomialNB , CategoricalNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score , mean_squared_error , r2_score, f1_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X1 = X \n",
    "y=result[[\"all\",'View']]\n",
    "# X1['Age'] = sc.fit_transform(X1[['Age']])\n",
    "X_train, X_test , y_train, y_test = train_test_split(X1,y,test_size=0.15,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "090e83e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    MultiOutputClassifier(RandomForestClassifier(n_estimators=25, random_state=42)),\n",
    "     MultiOutputClassifier(SVC(kernel='linear', C=1, random_state=42)),\n",
    "    MultiOutputClassifier(KNeighborsClassifier(n_neighbors=3)),\n",
    "     MultiOutputClassifier(DecisionTreeClassifier(random_state=42)),\n",
    "#     MultiOutputRegressor(RandomForestRegressor(n_estimators=100, random_state=42)),\n",
    "#     MultiOutputRegressor(SVR(kernel='linear', C=1)),\n",
    "#     MultiOutputRegressor(KNeighborsRegressor(n_neighbors=3)),\n",
    "#     MultiOutputRegressor(DecisionTreeRegressor(random_state=42)),\n",
    "#     MultiOutputRegressor(LogisticRegression(random_state=42))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "addd8de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classifier: MultiOutputClassifier\n",
      "Accuracy for each output: [0.6770833333333334, 0.6041666666666666]\n",
      "Average Accuracy: 0.640625\n",
      "\n",
      "Classifier: MultiOutputClassifier\n",
      "Accuracy for each output: [0.6979166666666666, 0.59375]\n",
      "Average Accuracy: 0.6458333333333333\n",
      "\n",
      "Classifier: MultiOutputClassifier\n",
      "Accuracy for each output: [0.625, 0.6666666666666666]\n",
      "Average Accuracy: 0.6458333333333333\n",
      "\n",
      "Classifier: MultiOutputClassifier\n",
      "Accuracy for each output: [0.59375, 0.6354166666666666]\n",
      "Average Accuracy: 0.6145833333333333\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "for model in models:\n",
    "    # Fit the model on the training data\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Predict the labels for the test set\n",
    "    if 'Classifier' in model.__class__.__name__:\n",
    "        y_pred = model.predict(X_test)\n",
    "        # For classifiers, calculate accuracy for each output separately\n",
    "        accuracies = [accuracy_score(y_test.iloc[:, i], y_pred[:, i]) for i in range(y_test.shape[1])]\n",
    "        average_accuracy = np.mean(accuracies)\n",
    "        print(f\"\\nClassifier: {model.__class__.__name__}\")\n",
    "        print(f\"Accuracy for each output: {accuracies}\")\n",
    "        print(f\"Average Accuracy: {average_accuracy}\")\n",
    "    else:\n",
    "        y_pred = model.predict(X_test)\n",
    "        # For regressors, calculate Mean Squared Error (MSE) for each output separately\n",
    "        mse = [mean_squared_error(y_test.iloc[:, i], y_pred[:, i]) for i in range(y_test.shape[1])]\n",
    "        average_mse = np.mean(mse)\n",
    "        r2= [r2_score(y_test.iloc[:, i], y_pred[:, i]) for i in range(y_test.shape[1])]\n",
    "        r2_score_avg = np.mean(r2)\n",
    "        print(f\"\\nRegressor: {model.__class__.__name__}\")\n",
    "        print(f\"MSE for each output: {mse}\")\n",
    "        print(f\"Average MSE: {average_mse}\")\n",
    "        print(f\"R2_score: {r2_score_avg}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8007b50d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "939a85c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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