DrugDiscovery.ipynb

{
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>cls</th>\n",
       "      <th>ABC</th>\n",
       "      <th>ABCGG</th>\n",
       "      <th>nAcid</th>\n",
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       "      <td>10.368070</td>\n",
       "      <td>80.145118</td>\n",
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       "      <td>CCCCN=C1N(C(=CS1)C2=CC3=C(C=C2)OCC(=O)N3)N=C4C...</td>\n",
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       "<p>5 rows × 1615 columns</p>\n",
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      ],
      "text/plain": [
       "                                                name  cls  ABC  ABCGG  nAcid  \\\n",
       "0                       CC1=CC(=C(N1C)C)C2=CSC(=N2)N    1  NaN    NaN      0   \n",
       "1  CCCOC1=CC=C(C=C1)C(=O)N/N=C/C2=CC=C(O2)C3=CC=C...    1  NaN    NaN      0   \n",
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       "\n",
       "   nBase    SpAbs_A   SpMax_A  SpDiam_A     SpAD_A  ...      SRW10     TSRW10  \\\n",
       "0      0  17.701129  2.439857  4.640601  17.701129  ...   9.512739  61.847126   \n",
       "1      0  46.736633  2.407624  4.793640  46.736633  ...  10.344288  85.856674   \n",
       "2      0  39.586705  2.537049  4.973884  39.586705  ...  10.368070  80.145118   \n",
       "3      0  26.395318  2.442924  4.716386  26.395318  ...   9.777074  69.229338   \n",
       "4      0  35.597476  2.464844  4.867887  35.597476  ...  10.147649  77.687460   \n",
       "\n",
       "           MW       AMW  WPath  WPol  Zagreb1  Zagreb2   mZagreb1  mZagreb2  \n",
       "0  207.083018  7.669741    288    19       74       88   5.666667  3.027778  \n",
       "1  505.141990  8.561729   5352    52      184      211  10.701389  8.013889  \n",
       "2  402.157957  7.733807   2195    50      158      189   9.111111  6.777778  \n",
       "3  285.068431  9.195756    853    27      104      122   5.666667  4.444444  \n",
       "4  384.161997  7.532588   1886    39      142      167   7.138889  6.027778  \n",
       "\n",
       "[5 rows x 1615 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "file_path = 'test2k.csv'\n",
    "\n",
    "df = pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22b4a507-7d0f-402e-8814-650c5f86b648",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc3fd7dd-5745-4747-bde8-12925ebafb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2401, 1615)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d23a414-4a04-4795-bb48-4ef56c72a19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2401 entries, 0 to 2400\n",
      "Columns: 1615 entries, name to mZagreb2\n",
      "dtypes: float64(1273), int64(341), object(1)\n",
      "memory usage: 29.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "474f6083-ca97-42b7-813f-a7e4114b7f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>211</td>\n",
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       "      <td>39.586705</td>\n",
       "      <td>2.537049</td>\n",
       "      <td>4.973884</td>\n",
       "      <td>39.586705</td>\n",
       "      <td>...</td>\n",
       "      <td>8.683893</td>\n",
       "      <td>7.065613</td>\n",
       "      <td>10.368070</td>\n",
       "      <td>80.145118</td>\n",
       "      <td>402.157957</td>\n",
       "      <td>7.733807</td>\n",
       "      <td>2195</td>\n",
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       "      <td>158</td>\n",
       "      <td>189</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC1=C(C(=O)N(N1)C2=CC=CC=C2)N=NC3=NC=CS3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.395318</td>\n",
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       "      <td>4.716386</td>\n",
       "      <td>26.395318</td>\n",
       "      <td>...</td>\n",
       "      <td>8.160804</td>\n",
       "      <td>7.390799</td>\n",
       "      <td>9.777074</td>\n",
       "      <td>69.229338</td>\n",
       "      <td>285.068431</td>\n",
       "      <td>9.195756</td>\n",
       "      <td>853</td>\n",
       "      <td>27</td>\n",
       "      <td>104</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCCCN=C1N(C(=CS1)C2=CC3=C(C=C2)OCC(=O)N3)N=C4C...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>2.464844</td>\n",
       "      <td>4.867887</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>...</td>\n",
       "      <td>8.501267</td>\n",
       "      <td>7.273093</td>\n",
       "      <td>10.147649</td>\n",
       "      <td>77.687460</td>\n",
       "      <td>384.161997</td>\n",
       "      <td>7.532588</td>\n",
       "      <td>1886</td>\n",
       "      <td>39</td>\n",
       "      <td>142</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 1613 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  cls  ABC  ABCGG  nAcid  \\\n",
       "0                       CC1=CC(=C(N1C)C)C2=CSC(=N2)N    1  NaN    NaN      0   \n",
       "1  CCCOC1=CC=C(C=C1)C(=O)N/N=C/C2=CC=C(O2)C3=CC=C...    1  NaN    NaN      0   \n",
       "2  COC1=CC(=C(C=C1)C(C[N+](=O)[O-])C2=C(C=C3N2C=C...    1  NaN    NaN      0   \n",
       "3           CC1=C(C(=O)N(N1)C2=CC=CC=C2)N=NC3=NC=CS3    1  NaN    NaN      0   \n",
       "4  CCCCN=C1N(C(=CS1)C2=CC3=C(C=C2)OCC(=O)N3)N=C4C...    1  NaN    NaN      0   \n",
       "\n",
       "   nBase    SpAbs_A   SpMax_A  SpDiam_A     SpAD_A  ...     SRW08     SRW09  \\\n",
       "0      0  17.701129  2.439857  4.640601  17.701129  ...  7.881182  7.486053   \n",
       "1      0  46.736633  2.407624  4.793640  46.736633  ...  8.732788  6.580639   \n",
       "2      0  39.586705  2.537049  4.973884  39.586705  ...  8.683893  7.065613   \n",
       "3      0  26.395318  2.442924  4.716386  26.395318  ...  8.160804  7.390799   \n",
       "4      0  35.597476  2.464844  4.867887  35.597476  ...  8.501267  7.273093   \n",
       "\n",
       "       SRW10     TSRW10          MW       AMW  WPath  WPol  Zagreb1  Zagreb2  \n",
       "0   9.512739  61.847126  207.083018  7.669741    288    19       74       88  \n",
       "1  10.344288  85.856674  505.141990  8.561729   5352    52      184      211  \n",
       "2  10.368070  80.145118  402.157957  7.733807   2195    50      158      189  \n",
       "3   9.777074  69.229338  285.068431  9.195756    853    27      104      122  \n",
       "4  10.147649  77.687460  384.161997  7.532588   1886    39      142      167  \n",
       "\n",
       "[5 rows x 1613 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "features = df.iloc[:, : -2]\n",
    "predictions= df.iloc[:, -2]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a38e4678-be17-4812-8717-4558de38ac3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     5.666667\n",
      "1    10.701389\n",
      "2     9.111111\n",
      "3     5.666667\n",
      "4     7.138889\n",
      "Name: mZagreb1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(predictions.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebcb2249-a9e9-4c12-8e49-ad85dfb824a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\extmath.py:1050: RuntimeWarning: invalid value encountered in divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\extmath.py:1055: RuntimeWarning: invalid value encountered in divide\n",
      "  T = new_sum / new_sample_count\n",
      "C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\extmath.py:1075: RuntimeWarning: invalid value encountered in divide\n",
      "  new_unnormalized_variance -= correction**2 / new_sample_count\n"
     ]
    },
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      ],
      "text/plain": [
       "       name       cls  ABC  ABCGG     nAcid     nBase   SpAbs_A   SpMax_A  \\\n",
       "0 -0.260880  1.000417  NaN    NaN -0.266852 -0.428501 -2.052503 -0.102792   \n",
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       "\n",
       "   SpDiam_A    SpAD_A  ...     SRW10    TSRW10        MW       AMW     WPath  \\\n",
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       "4  0.240448  0.511657  ...  0.237012  0.797653  0.386277 -0.691240 -0.138579   \n",
       "\n",
       "       WPol   Zagreb1   Zagreb2  mZagreb1  mZagreb2  \n",
       "0 -1.699277 -1.808588 -1.664314 -1.201977 -2.104432  \n",
       "1  1.189534  1.777010  1.516096  1.259681  2.170765  \n",
       "2  1.014455  0.929505  0.947243  0.482137  1.110897  \n",
       "3 -0.998959 -0.830698 -0.785176 -1.201977 -0.889752  \n",
       "4  0.051518  0.407963  0.378389 -0.482154  0.467831  \n",
       "\n",
       "[5 rows x 1615 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == 'object':\n",
    "        df[column] = label_encoder.fit_transform(df[column])\n",
    "\n",
    "scaler = StandardScaler()\n",
    "features_scaled = scaler.fit_transform(df)\n",
    "\n",
    "features_scaled = pd.DataFrame(features_scaled, columns=df.columns)\n",
    "\n",
    "features_scaled.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "310bf44e-f3e8-4f7f-90e2-ad5b1d4e5a24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'ABC':\n",
      "[nan]\n",
      "\n",
      "Unique values in column 'ABCGG':\n",
      "[nan]\n",
      "\n",
      "Unique values in column 'nAcid':\n",
      "[0 1 2 4]\n"
     ]
    }
   ],
   "source": [
    "unique_values_abc = df['ABC'].unique()\n",
    "unique_values_abcgg = df['ABCGG'].unique()\n",
    "unique_values_nAcid = df['nAcid'].unique()\n",
    "\n",
    "\n",
    "print(\"Unique values in column 'ABC':\")\n",
    "print(unique_values_abc)\n",
    "\n",
    "print(\"\\nUnique values in column 'ABCGG':\")\n",
    "print(unique_values_abcgg)\n",
    "\n",
    "print(\"\\nUnique values in column 'nAcid':\")\n",
    "print(unique_values_nAcid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71e3a2db-dddb-45cc-8c44-567a7d3047d7",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "       name       cls     nAcid     nBase   SpAbs_A   SpMax_A  SpDiam_A  \\\n",
       "0 -0.260880  1.000417 -0.266852 -0.428501 -2.052503 -0.102792 -1.163514   \n",
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       "4  0.551509  1.000417 -0.266852 -0.428501  0.511657  0.177328  0.240448   \n",
       "\n",
       "     SpAD_A   SpMAD_A   LogEE_A  ...     SRW10    TSRW10        MW       AMW  \\\n",
       "0 -2.052503 -0.585163 -2.484848  ... -1.557890 -0.772131 -1.953020 -0.573303   \n",
       "1  2.107659  0.252818  1.758861  ...  0.792914  1.607226  1.984479  0.193708   \n",
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       "3 -0.806813  0.785344 -0.857198  ... -0.810610 -0.040550 -0.922796  0.738902   \n",
       "4  0.511657  0.752172  0.529149  ...  0.237012  0.797653  0.386277 -0.691240   \n",
       "\n",
       "      WPath      WPol   Zagreb1   Zagreb2  mZagreb1  mZagreb2  \n",
       "0 -0.138580 -1.699277 -1.808588 -1.664314 -1.201977 -2.104432  \n",
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       "\n",
       "[5 rows x 1613 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "features_scaled = features_scaled.drop(columns=['ABC', 'ABCGG'])\n",
    "\n",
    "features_scaled.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e8a4eff-4584-49f2-a86c-3b482b24130f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0     5.666667\n",
       "1    10.701389\n",
       "2     9.111111\n",
       "3     5.666667\n",
       "4     7.138889\n",
       "Name: mZagreb1, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1938aa5-5722-4626-90cd-e780eff5fe76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa81989a-bd51-44fb-85c6-e29396fff3ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27bd344a-4d68-41d4-bde9-cb3fe0e717d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa82333f-47e3-431f-bc0d-78ef4b78a768",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "86780f63-a3f3-4905-b0b3-168c154332ad",
   "metadata": {},
   "source": [
    "***Variance threshold based method***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78a82b00-679c-4e8f-beb9-a355edfabb67",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>nAcid</th>\n",
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       "      <th>SpMax_A</th>\n",
       "      <th>SpDiam_A</th>\n",
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       "      <td>...</td>\n",
       "      <td>9.512739</td>\n",
       "      <td>61.847126</td>\n",
       "      <td>207.083018</td>\n",
       "      <td>7.669741</td>\n",
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       "      <td>85.856674</td>\n",
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       "      <td>39.586705</td>\n",
       "      <td>1.319557</td>\n",
       "      <td>4.334574</td>\n",
       "      <td>4.469321</td>\n",
       "      <td>...</td>\n",
       "      <td>10.368070</td>\n",
       "      <td>80.145118</td>\n",
       "      <td>402.157957</td>\n",
       "      <td>7.733807</td>\n",
       "      <td>2195</td>\n",
       "      <td>50</td>\n",
       "      <td>158</td>\n",
       "      <td>189</td>\n",
       "      <td>9.111111</td>\n",
       "      <td>6.777778</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2.442924</td>\n",
       "      <td>4.716386</td>\n",
       "      <td>26.395318</td>\n",
       "      <td>1.319766</td>\n",
       "      <td>3.934751</td>\n",
       "      <td>3.715307</td>\n",
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       "      <td>9.777074</td>\n",
       "      <td>69.229338</td>\n",
       "      <td>285.068431</td>\n",
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       "      <td>853</td>\n",
       "      <td>27</td>\n",
       "      <td>104</td>\n",
       "      <td>122</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.444444</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2.464844</td>\n",
       "      <td>4.867887</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>1.318425</td>\n",
       "      <td>4.235035</td>\n",
       "      <td>4.417538</td>\n",
       "      <td>...</td>\n",
       "      <td>10.147649</td>\n",
       "      <td>77.687460</td>\n",
       "      <td>384.161997</td>\n",
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       "      <td>1886</td>\n",
       "      <td>39</td>\n",
       "      <td>142</td>\n",
       "      <td>167</td>\n",
       "      <td>7.138889</td>\n",
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       "<p>5 rows × 1173 columns</p>\n",
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      ],
      "text/plain": [
       "   cls  nAcid  nBase    SpAbs_A   SpMax_A  SpDiam_A     SpAD_A   SpMAD_A  \\\n",
       "0    1      0      0  17.701129  2.439857  4.640601  17.701129  1.264366   \n",
       "1    1      0      0  46.736633  2.407624  4.793640  46.736633  1.298240   \n",
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       "4    1      0      0  35.597476  2.464844  4.867887  35.597476  1.318425   \n",
       "\n",
       "    LogEE_A     VE1_A  ...      SRW10     TSRW10          MW       AMW  WPath  \\\n",
       "0  3.582199  3.443387  ...   9.512739  61.847126  207.083018  7.669741    288   \n",
       "1  4.501393  4.490651  ...  10.344288  85.856674  505.141990  8.561729   5352   \n",
       "2  4.334574  4.469321  ...  10.368070  80.145118  402.157957  7.733807   2195   \n",
       "3  3.934751  3.715307  ...   9.777074  69.229338  285.068431  9.195756    853   \n",
       "4  4.235035  4.417538  ...  10.147649  77.687460  384.161997  7.532588   1886   \n",
       "\n",
       "   WPol  Zagreb1  Zagreb2   mZagreb1  mZagreb2  \n",
       "0    19       74       88   5.666667  3.027778  \n",
       "1    52      184      211  10.701389  8.013889  \n",
       "2    50      158      189   9.111111  6.777778  \n",
       "3    27      104      122   5.666667  4.444444  \n",
       "4    39      142      167   7.138889  6.027778  \n",
       "\n",
       "[5 rows x 1173 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "df = pd.read_csv('test2k.csv')\n",
    "df = df.apply(pd.to_numeric, errors='coerce').fillna(0)\n",
    "threshold = 0.01\n",
    "selector = VarianceThreshold(threshold)\n",
    "features_selected = selector.fit_transform(df)\n",
    "selected_indices = selector.get_support(indices=True)\n",
    "selected_df = df.iloc[:, selected_indices]\n",
    "selected_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b949c181-25d5-47a8-9216-9aad976f6f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2401, 1173)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa00232f-111d-4f57-844b-3999fd878bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2401 entries, 0 to 2400\n",
      "Columns: 1173 entries, cls to mZagreb2\n",
      "dtypes: float64(1006), int64(167)\n",
      "memory usage: 21.5 MB\n"
     ]
    }
   ],
   "source": [
    "selected_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "087f7f30-da67-4ea3-a39a-3e6ffe272ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cls</th>\n",
       "      <th>nAcid</th>\n",
       "      <th>nBase</th>\n",
       "      <th>SpAbs_A</th>\n",
       "      <th>SpMax_A</th>\n",
       "      <th>SpDiam_A</th>\n",
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       "      <th>SpMAD_A</th>\n",
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       "      <th>TSRW10</th>\n",
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       "      <th>Zagreb1</th>\n",
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       "      <th>mZagreb1</th>\n",
       "      <th>mZagreb2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
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       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
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       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2.401000e+03</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.499792</td>\n",
       "      <td>0.111620</td>\n",
       "      <td>0.302791</td>\n",
       "      <td>30.345719</td>\n",
       "      <td>2.320506</td>\n",
       "      <td>4.575546</td>\n",
       "      <td>30.345719</td>\n",
       "      <td>1.220427</td>\n",
       "      <td>3.904189</td>\n",
       "      <td>3.888108</td>\n",
       "      <td>...</td>\n",
       "      <td>10.063810</td>\n",
       "      <td>69.638520</td>\n",
       "      <td>354.921808</td>\n",
       "      <td>8.336458</td>\n",
       "      <td>2.273653e+08</td>\n",
       "      <td>38.411495</td>\n",
       "      <td>129.484382</td>\n",
       "      <td>152.366097</td>\n",
       "      <td>7.749393</td>\n",
       "      <td>5.482152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500104</td>\n",
       "      <td>0.418373</td>\n",
       "      <td>0.706774</td>\n",
       "      <td>9.858906</td>\n",
       "      <td>0.553081</td>\n",
       "      <td>1.088502</td>\n",
       "      <td>9.858906</td>\n",
       "      <td>0.289958</td>\n",
       "      <td>0.942887</td>\n",
       "      <td>0.982258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.353803</td>\n",
       "      <td>10.092874</td>\n",
       "      <td>75.713298</td>\n",
       "      <td>1.163181</td>\n",
       "      <td>1.641023e+09</td>\n",
       "      <td>11.425765</td>\n",
       "      <td>30.684671</td>\n",
       "      <td>38.682313</td>\n",
       "      <td>2.627438</td>\n",
       "      <td>1.166531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.448700</td>\n",
       "      <td>39.179718</td>\n",
       "      <td>147.054495</td>\n",
       "      <td>5.373257</td>\n",
       "      <td>1.120000e+02</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.357355</td>\n",
       "      <td>2.378866</td>\n",
       "      <td>4.701235</td>\n",
       "      <td>26.357355</td>\n",
       "      <td>1.259340</td>\n",
       "      <td>3.947471</td>\n",
       "      <td>3.802040</td>\n",
       "      <td>...</td>\n",
       "      <td>9.845276</td>\n",
       "      <td>62.253768</td>\n",
       "      <td>304.108402</td>\n",
       "      <td>7.574640</td>\n",
       "      <td>1.049000e+03</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>4.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.355701</td>\n",
       "      <td>2.443865</td>\n",
       "      <td>4.816265</td>\n",
       "      <td>31.355701</td>\n",
       "      <td>1.289165</td>\n",
       "      <td>4.117967</td>\n",
       "      <td>4.060620</td>\n",
       "      <td>...</td>\n",
       "      <td>10.097779</td>\n",
       "      <td>70.872742</td>\n",
       "      <td>352.078073</td>\n",
       "      <td>8.190260</td>\n",
       "      <td>1.601000e+03</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>7.916667</td>\n",
       "      <td>5.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.344374</td>\n",
       "      <td>2.501420</td>\n",
       "      <td>4.933070</td>\n",
       "      <td>36.344374</td>\n",
       "      <td>1.315166</td>\n",
       "      <td>4.271702</td>\n",
       "      <td>4.325792</td>\n",
       "      <td>...</td>\n",
       "      <td>10.300282</td>\n",
       "      <td>77.113054</td>\n",
       "      <td>404.080681</td>\n",
       "      <td>8.877421</td>\n",
       "      <td>2.362000e+03</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>6.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>119.145518</td>\n",
       "      <td>2.882367</td>\n",
       "      <td>5.450456</td>\n",
       "      <td>119.145518</td>\n",
       "      <td>1.395657</td>\n",
       "      <td>5.409243</td>\n",
       "      <td>5.492726</td>\n",
       "      <td>...</td>\n",
       "      <td>11.652000</td>\n",
       "      <td>148.727472</td>\n",
       "      <td>1300.958738</td>\n",
       "      <td>18.768044</td>\n",
       "      <td>3.600001e+10</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>28.861111</td>\n",
       "      <td>21.972222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1173 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               cls        nAcid        nBase      SpAbs_A      SpMax_A  \\\n",
       "count  2401.000000  2401.000000  2401.000000  2401.000000  2401.000000   \n",
       "mean      0.499792     0.111620     0.302791    30.345719     2.320506   \n",
       "std       0.500104     0.418373     0.706774     9.858906     0.553081   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000    26.357355     2.378866   \n",
       "50%       0.000000     0.000000     0.000000    31.355701     2.443865   \n",
       "75%       1.000000     0.000000     0.000000    36.344374     2.501420   \n",
       "max       1.000000     4.000000     4.000000   119.145518     2.882367   \n",
       "\n",
       "          SpDiam_A       SpAD_A      SpMAD_A      LogEE_A        VE1_A  ...  \\\n",
       "count  2401.000000  2401.000000  2401.000000  2401.000000  2401.000000  ...   \n",
       "mean      4.575546    30.345719     1.220427     3.904189     3.888108  ...   \n",
       "std       1.088502     9.858906     0.289958     0.942887     0.982258  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       4.701235    26.357355     1.259340     3.947471     3.802040  ...   \n",
       "50%       4.816265    31.355701     1.289165     4.117967     4.060620  ...   \n",
       "75%       4.933070    36.344374     1.315166     4.271702     4.325792  ...   \n",
       "max       5.450456   119.145518     1.395657     5.409243     5.492726  ...   \n",
       "\n",
       "             SRW10       TSRW10           MW          AMW         WPath  \\\n",
       "count  2401.000000  2401.000000  2401.000000  2401.000000  2.401000e+03   \n",
       "mean     10.063810    69.638520   354.921808     8.336458  2.273653e+08   \n",
       "std       0.353803    10.092874    75.713298     1.163181  1.641023e+09   \n",
       "min       8.448700    39.179718   147.054495     5.373257  1.120000e+02   \n",
       "25%       9.845276    62.253768   304.108402     7.574640  1.049000e+03   \n",
       "50%      10.097779    70.872742   352.078073     8.190260  1.601000e+03   \n",
       "75%      10.300282    77.113054   404.080681     8.877421  2.362000e+03   \n",
       "max      11.652000   148.727472  1300.958738    18.768044  3.600001e+10   \n",
       "\n",
       "              WPol      Zagreb1      Zagreb2     mZagreb1     mZagreb2  \n",
       "count  2401.000000  2401.000000  2401.000000  2401.000000  2401.000000  \n",
       "mean     38.411495   129.484382   152.366097     7.749393     5.482152  \n",
       "std      11.425765    30.684671    38.682313     2.627438     1.166531  \n",
       "min       7.000000    42.000000    40.000000     0.000000     2.208333  \n",
       "25%      30.000000   108.000000   125.000000     6.500000     4.722222  \n",
       "50%      38.000000   128.000000   151.000000     7.916667     5.472222  \n",
       "75%      45.000000   150.000000   177.000000     9.333333     6.250000  \n",
       "max     150.000000   434.000000   491.000000    28.861111    21.972222  \n",
       "\n",
       "[8 rows x 1173 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f05cfaa-267b-4e78-a086-9e0c9642845b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with NaN values:\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "nan_counts = selected_df.isnull().sum()\n",
    "\n",
    "features_with_nan = nan_counts[nan_counts > 0].index.tolist()\n",
    "\n",
    "print(\"Features with NaN values:\")\n",
    "print(features_with_nan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f58a204-7440-438c-94fd-60ba493a9aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = selected_df.iloc[:, -1]\n",
    "y = selected_df.iloc[:, -2]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel('mZagreb1')\n",
    "plt.ylabel('mZagreb2')\n",
    "plt.title('Selected Features Scatter Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ef49ef-524f-45d5-8e29-6fa74b068a73",
   "metadata": {},
   "source": [
    "*used scatter plot to find the most useful features*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a850179-7617-4a97-baf2-c18800de07b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation_matrix = df.corr()\n",
    "\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# plt.figure(figsize=(12, 8))\n",
    "# sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
    "# plt.title('Correlation Matrix')\n",
    "# plt.show()\n",
    "# # import pandas as pd\n",
    "\n",
    "# df = pd.read_csv('test2k.csv')\n",
    "# correlation_matrix = df.corr()  # Assuming df is your DataFrame\n",
    "# threshold = 0.5\n",
    "# highly_correlated = (correlation_matrix.abs() > threshold).sum() > 1\n",
    "# selected_features = df.columns[highly_correlated].tolist()\n",
    "# selected_df = df[selected_features]\n",
    "# selected_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94af5ad6-742a-4872-9b72-ba3829c8311d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed30c400-97f6-468f-8056-8e08c69f0f55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65ff8942-6027-4b61-a850-c92d4f945aa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c62ab22f-1542-47bd-92f2-8d4604fd1396",
   "metadata": {},
   "source": [
    "***Correlation based method***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "85dacbda-9e48-45d5-986c-6a27700c7ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "\n",
    "# df = pd.read_csv('test2k.csv')\n",
    "# correlation_matrix = features_scaled.corr()\n",
    "# threshold = 0.5\n",
    "# highly_correlated = (correlation_matrix.abs() > threshold).sum() > 1\n",
    "# selected_features = df.columns[highly_correlated]\n",
    "# selected_df = df[selected_features]\n",
    "# selected_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f835670-a8dc-4311-9c0f-74d10c121729",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selected_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ada0c29-5e88-40b2-a7f5-374f51556b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selected_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "facac757-701a-478c-bdc6-962590e16ab6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fa79ff9-1980-4151-b9a6-b72c2b390ed3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfdf304f-d200-4452-86f0-eb843de358bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ecd47c1-b83f-4150-88f1-0e1ebdbf0e11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af8c5e75-3e40-4e69-881f-67091f2561ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887ff1ae-3563-4cb3-bb03-705b0e6b231e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a6a286c-3045-490d-8a10-659dd059ed0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "889bfbe5-c407-4bee-9e70-ec367c98fd58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1567a84f-ee67-48e0-881a-7af39806feb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d2469431-e5d8-40db-949b-e8d1e63a7750",
   "metadata": {},
   "source": [
    "***SVC Method***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b803c3b-0016-4442-bced-1cbaaff1b06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# X_train,X_test,y_train,y_test=train_test_split(features_scaled,predictions,test_size=0.2,random_state=0,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3edfe06-2a7b-48fd-be64-38e1ca411281",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cls</th>\n",
       "      <th>nAcid</th>\n",
       "      <th>nBase</th>\n",
       "      <th>SpAbs_A</th>\n",
       "      <th>SpMax_A</th>\n",
       "      <th>SpDiam_A</th>\n",
       "      <th>SpAD_A</th>\n",
       "      <th>SpMAD_A</th>\n",
       "      <th>LogEE_A</th>\n",
       "      <th>VE1_A</th>\n",
       "      <th>...</th>\n",
       "      <th>SRW10</th>\n",
       "      <th>TSRW10</th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>WPath</th>\n",
       "      <th>WPol</th>\n",
       "      <th>Zagreb1</th>\n",
       "      <th>Zagreb2</th>\n",
       "      <th>mZagreb1</th>\n",
       "      <th>mZagreb2</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>17.701129</td>\n",
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       "      <td>4.640601</td>\n",
       "      <td>17.701129</td>\n",
       "      <td>1.264366</td>\n",
       "      <td>3.582199</td>\n",
       "      <td>3.443387</td>\n",
       "      <td>...</td>\n",
       "      <td>9.512739</td>\n",
       "      <td>61.847126</td>\n",
       "      <td>207.083018</td>\n",
       "      <td>7.669741</td>\n",
       "      <td>288</td>\n",
       "      <td>19</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>3.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46.736633</td>\n",
       "      <td>2.407624</td>\n",
       "      <td>4.793640</td>\n",
       "      <td>46.736633</td>\n",
       "      <td>1.298240</td>\n",
       "      <td>4.501393</td>\n",
       "      <td>4.490651</td>\n",
       "      <td>...</td>\n",
       "      <td>10.344288</td>\n",
       "      <td>85.856674</td>\n",
       "      <td>505.141990</td>\n",
       "      <td>8.561729</td>\n",
       "      <td>5352</td>\n",
       "      <td>52</td>\n",
       "      <td>184</td>\n",
       "      <td>211</td>\n",
       "      <td>10.701389</td>\n",
       "      <td>8.013889</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.586705</td>\n",
       "      <td>2.537049</td>\n",
       "      <td>4.973884</td>\n",
       "      <td>39.586705</td>\n",
       "      <td>1.319557</td>\n",
       "      <td>4.334574</td>\n",
       "      <td>4.469321</td>\n",
       "      <td>...</td>\n",
       "      <td>10.368070</td>\n",
       "      <td>80.145118</td>\n",
       "      <td>402.157957</td>\n",
       "      <td>7.733807</td>\n",
       "      <td>2195</td>\n",
       "      <td>50</td>\n",
       "      <td>158</td>\n",
       "      <td>189</td>\n",
       "      <td>9.111111</td>\n",
       "      <td>6.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.395318</td>\n",
       "      <td>2.442924</td>\n",
       "      <td>4.716386</td>\n",
       "      <td>26.395318</td>\n",
       "      <td>1.319766</td>\n",
       "      <td>3.934751</td>\n",
       "      <td>3.715307</td>\n",
       "      <td>...</td>\n",
       "      <td>9.777074</td>\n",
       "      <td>69.229338</td>\n",
       "      <td>285.068431</td>\n",
       "      <td>9.195756</td>\n",
       "      <td>853</td>\n",
       "      <td>27</td>\n",
       "      <td>104</td>\n",
       "      <td>122</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>4.444444</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>2.464844</td>\n",
       "      <td>4.867887</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>1.318425</td>\n",
       "      <td>4.235035</td>\n",
       "      <td>4.417538</td>\n",
       "      <td>...</td>\n",
       "      <td>10.147649</td>\n",
       "      <td>77.687460</td>\n",
       "      <td>384.161997</td>\n",
       "      <td>7.532588</td>\n",
       "      <td>1886</td>\n",
       "      <td>39</td>\n",
       "      <td>142</td>\n",
       "      <td>167</td>\n",
       "      <td>7.138889</td>\n",
       "      <td>6.027778</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>2396</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32.192686</td>\n",
       "      <td>2.407128</td>\n",
       "      <td>4.750619</td>\n",
       "      <td>32.192686</td>\n",
       "      <td>1.287707</td>\n",
       "      <td>4.139277</td>\n",
       "      <td>3.939072</td>\n",
       "      <td>...</td>\n",
       "      <td>9.907231</td>\n",
       "      <td>74.844887</td>\n",
       "      <td>360.100459</td>\n",
       "      <td>8.782938</td>\n",
       "      <td>1669</td>\n",
       "      <td>33</td>\n",
       "      <td>126</td>\n",
       "      <td>145</td>\n",
       "      <td>7.527778</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.378395</td>\n",
       "      <td>2.374633</td>\n",
       "      <td>4.749267</td>\n",
       "      <td>34.378395</td>\n",
       "      <td>1.322246</td>\n",
       "      <td>4.198466</td>\n",
       "      <td>4.550302</td>\n",
       "      <td>...</td>\n",
       "      <td>10.140100</td>\n",
       "      <td>60.998556</td>\n",
       "      <td>353.089937</td>\n",
       "      <td>8.611950</td>\n",
       "      <td>1955</td>\n",
       "      <td>39</td>\n",
       "      <td>138</td>\n",
       "      <td>160</td>\n",
       "      <td>6.888889</td>\n",
       "      <td>5.638889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.203154</td>\n",
       "      <td>2.476216</td>\n",
       "      <td>4.952431</td>\n",
       "      <td>34.203154</td>\n",
       "      <td>1.266783</td>\n",
       "      <td>4.216998</td>\n",
       "      <td>4.307242</td>\n",
       "      <td>...</td>\n",
       "      <td>10.275706</td>\n",
       "      <td>62.286547</td>\n",
       "      <td>366.121572</td>\n",
       "      <td>8.136035</td>\n",
       "      <td>1780</td>\n",
       "      <td>46</td>\n",
       "      <td>140</td>\n",
       "      <td>166</td>\n",
       "      <td>9.861111</td>\n",
       "      <td>6.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.956769</td>\n",
       "      <td>2.484376</td>\n",
       "      <td>4.890443</td>\n",
       "      <td>30.956769</td>\n",
       "      <td>1.238271</td>\n",
       "      <td>4.121173</td>\n",
       "      <td>3.848883</td>\n",
       "      <td>...</td>\n",
       "      <td>10.070822</td>\n",
       "      <td>73.491791</td>\n",
       "      <td>374.087118</td>\n",
       "      <td>8.699700</td>\n",
       "      <td>1541</td>\n",
       "      <td>40</td>\n",
       "      <td>124</td>\n",
       "      <td>146</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>5.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.384354</td>\n",
       "      <td>2.403781</td>\n",
       "      <td>4.714653</td>\n",
       "      <td>30.384354</td>\n",
       "      <td>1.266015</td>\n",
       "      <td>4.105688</td>\n",
       "      <td>3.429538</td>\n",
       "      <td>...</td>\n",
       "      <td>9.924417</td>\n",
       "      <td>74.033326</td>\n",
       "      <td>349.146013</td>\n",
       "      <td>7.428639</td>\n",
       "      <td>1597</td>\n",
       "      <td>32</td>\n",
       "      <td>124</td>\n",
       "      <td>143</td>\n",
       "      <td>7.888889</td>\n",
       "      <td>5.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 1173 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cls  nAcid  nBase    SpAbs_A   SpMax_A  SpDiam_A     SpAD_A   SpMAD_A  \\\n",
       "0       1      0      0  17.701129  2.439857  4.640601  17.701129  1.264366   \n",
       "1       1      0      0  46.736633  2.407624  4.793640  46.736633  1.298240   \n",
       "2       1      0      0  39.586705  2.537049  4.973884  39.586705  1.319557   \n",
       "3       1      0      0  26.395318  2.442924  4.716386  26.395318  1.319766   \n",
       "4       1      0      0  35.597476  2.464844  4.867887  35.597476  1.318425   \n",
       "...   ...    ...    ...        ...       ...       ...        ...       ...   \n",
       "2396    0      2      0  32.192686  2.407128  4.750619  32.192686  1.287707   \n",
       "2397    0      0      0  34.378395  2.374633  4.749267  34.378395  1.322246   \n",
       "2398    0      1      0  34.203154  2.476216  4.952431  34.203154  1.266783   \n",
       "2399    0      0      0  30.956769  2.484376  4.890443  30.956769  1.238271   \n",
       "2400    0      0      0  30.384354  2.403781  4.714653  30.384354  1.266015   \n",
       "\n",
       "       LogEE_A     VE1_A  ...      SRW10     TSRW10          MW       AMW  \\\n",
       "0     3.582199  3.443387  ...   9.512739  61.847126  207.083018  7.669741   \n",
       "1     4.501393  4.490651  ...  10.344288  85.856674  505.141990  8.561729   \n",
       "2     4.334574  4.469321  ...  10.368070  80.145118  402.157957  7.733807   \n",
       "3     3.934751  3.715307  ...   9.777074  69.229338  285.068431  9.195756   \n",
       "4     4.235035  4.417538  ...  10.147649  77.687460  384.161997  7.532588   \n",
       "...        ...       ...  ...        ...        ...         ...       ...   \n",
       "2396  4.139277  3.939072  ...   9.907231  74.844887  360.100459  8.782938   \n",
       "2397  4.198466  4.550302  ...  10.140100  60.998556  353.089937  8.611950   \n",
       "2398  4.216998  4.307242  ...  10.275706  62.286547  366.121572  8.136035   \n",
       "2399  4.121173  3.848883  ...  10.070822  73.491791  374.087118  8.699700   \n",
       "2400  4.105688  3.429538  ...   9.924417  74.033326  349.146013  7.428639   \n",
       "\n",
       "      WPath  WPol  Zagreb1  Zagreb2   mZagreb1  mZagreb2  \n",
       "0       288    19       74       88   5.666667  3.027778  \n",
       "1      5352    52      184      211  10.701389  8.013889  \n",
       "2      2195    50      158      189   9.111111  6.777778  \n",
       "3       853    27      104      122   5.666667  4.444444  \n",
       "4      1886    39      142      167   7.138889  6.027778  \n",
       "...     ...   ...      ...      ...        ...       ...  \n",
       "2396   1669    33      126      145   7.527778  5.666667  \n",
       "2397   1955    39      138      160   6.888889  5.638889  \n",
       "2398   1780    46      140      166   9.861111  6.027778  \n",
       "2399   1541    40      124      146  10.250000  5.833333  \n",
       "2400   1597    32      124      143   7.888889  5.250000  \n",
       "\n",
       "[2401 rows x 1173 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train_data= pd.concat([X_train,y_train], axis='columns')\n",
    "selected_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "942b1c97-dc9e-4bb0-97b7-b295468ac316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with NaN values:\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "nan_counts = selected_df.isnull().sum()\n",
    "\n",
    "features_with_nan = nan_counts[nan_counts > 0].index.tolist()\n",
    "\n",
    "print(\"Features with NaN values:\")\n",
    "print(features_with_nan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54c7f762-5241-46bc-a6a2-293f6d926a80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\svm\\_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATS0dv</th>\n",
       "      <th>ATS1dv</th>\n",
       "      <th>ATS2dv</th>\n",
       "      <th>ATS3dv</th>\n",
       "      <th>ATS4dv</th>\n",
       "      <th>ATS5dv</th>\n",
       "      <th>ATS6dv</th>\n",
       "      <th>ATS7dv</th>\n",
       "      <th>ATS8dv</th>\n",
       "      <th>ATS3d</th>\n",
       "      <th>...</th>\n",
       "      <th>MPC7</th>\n",
       "      <th>MPC10</th>\n",
       "      <th>TMPC10</th>\n",
       "      <th>TopoPSA(NO)</th>\n",
       "      <th>TopoPSA</th>\n",
       "      <th>Vabc</th>\n",
       "      <th>TMWC10</th>\n",
       "      <th>TSRW10</th>\n",
       "      <th>MW</th>\n",
       "      <th>WPath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>160.444444</td>\n",
       "      <td>177.666667</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>159.666667</td>\n",
       "      <td>133.666667</td>\n",
       "      <td>83.666667</td>\n",
       "      <td>39.333333</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>43.84</td>\n",
       "      <td>72.08</td>\n",
       "      <td>175.956864</td>\n",
       "      <td>99.591492</td>\n",
       "      <td>61.847126</td>\n",
       "      <td>207.083018</td>\n",
       "      <td>288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>550.444444</td>\n",
       "      <td>491.333333</td>\n",
       "      <td>775.666667</td>\n",
       "      <td>700.666667</td>\n",
       "      <td>697.666667</td>\n",
       "      <td>606.666667</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>562.666667</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>397.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>135.78</td>\n",
       "      <td>144.16</td>\n",
       "      <td>431.100250</td>\n",
       "      <td>152.577884</td>\n",
       "      <td>85.856674</td>\n",
       "      <td>505.141990</td>\n",
       "      <td>5352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>442.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>613.000000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>819.000000</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>434.0</td>\n",
       "      <td>...</td>\n",
       "      <td>171.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>66.01</td>\n",
       "      <td>66.01</td>\n",
       "      <td>358.787649</td>\n",
       "      <td>141.057525</td>\n",
       "      <td>80.145118</td>\n",
       "      <td>402.157957</td>\n",
       "      <td>2195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>296.444444</td>\n",
       "      <td>309.666667</td>\n",
       "      <td>424.666667</td>\n",
       "      <td>415.333333</td>\n",
       "      <td>341.666667</td>\n",
       "      <td>297.333333</td>\n",
       "      <td>262.666667</td>\n",
       "      <td>190.666667</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>75.40</td>\n",
       "      <td>103.64</td>\n",
       "      <td>224.826273</td>\n",
       "      <td>115.081609</td>\n",
       "      <td>69.229338</td>\n",
       "      <td>285.068431</td>\n",
       "      <td>853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>344.444444</td>\n",
       "      <td>358.666667</td>\n",
       "      <td>500.333333</td>\n",
       "      <td>523.333333</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>425.666667</td>\n",
       "      <td>410.666667</td>\n",
       "      <td>354.666667</td>\n",
       "      <td>293.333333</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>124.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>67.98</td>\n",
       "      <td>96.22</td>\n",
       "      <td>344.871636</td>\n",
       "      <td>133.209092</td>\n",
       "      <td>77.687460</td>\n",
       "      <td>384.161997</td>\n",
       "      <td>1886.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ATS0dv      ATS1dv      ATS2dv      ATS3dv      ATS4dv      ATS5dv  \\\n",
       "0  160.444444  177.666667  230.000000  159.666667  133.666667   83.666667   \n",
       "1  550.444444  491.333333  775.666667  700.666667  697.666667  606.666667   \n",
       "2  442.000000  433.000000  603.000000  613.000000  707.000000  819.000000   \n",
       "3  296.444444  309.666667  424.666667  415.333333  341.666667  297.333333   \n",
       "4  344.444444  358.666667  500.333333  523.333333  435.000000  425.666667   \n",
       "\n",
       "       ATS6dv      ATS7dv      ATS8dv  ATS3d  ...   MPC7  MPC10  TMPC10  \\\n",
       "0   39.333333    6.000000    0.000000  156.0  ...   31.0    3.0   261.0   \n",
       "1  584.000000  562.666667  504.000000  397.0  ...  110.0  132.0   949.0   \n",
       "2  905.000000  690.000000  511.000000  434.0  ...  171.0  196.0  1290.0   \n",
       "3  262.666667  190.666667   97.000000  197.0  ...   59.0   54.0   509.0   \n",
       "4  410.666667  354.666667  293.333333  361.0  ...  124.0  167.0  1028.0   \n",
       "\n",
       "   TopoPSA(NO)  TopoPSA        Vabc      TMWC10     TSRW10          MW   WPath  \n",
       "0        43.84    72.08  175.956864   99.591492  61.847126  207.083018   288.0  \n",
       "1       135.78   144.16  431.100250  152.577884  85.856674  505.141990  5352.0  \n",
       "2        66.01    66.01  358.787649  141.057525  80.145118  402.157957  2195.0  \n",
       "3        75.40   103.64  224.826273  115.081609  69.229338  285.068431   853.0  \n",
       "4        67.98    96.22  344.871636  133.209092  77.687460  384.161997  1886.0  \n",
       "\n",
       "[5 rows x 250 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "df_selected = selected_df.apply(pd.to_numeric, errors='coerce').fillna(0)\n",
    "\n",
    "X = df_selected.drop('mZagreb2', axis=1)\n",
    "y = df_selected['mZagreb2']\n",
    "\n",
    "lsvr = LinearSVR(C=0.01, epsilon=0.1).fit(X, y)\n",
    "\n",
    "model = SelectFromModel(lsvr, prefit=True)\n",
    "X_new = model.transform(X)\n",
    "\n",
    "feature = pd.DataFrame(X_new, columns=X.columns[model.get_support(indices=True)])\n",
    "feature.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def42795-2bdd-4564-9477-6b1bd2ffc5f8",
   "metadata": {},
   "source": [
    "*Completed using SVC method - for novelty, Here MayankPuvvala*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbc88291-e4a1-47b7-9405-3dbdb3964f3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eba1c4d5-867a-45b4-9d82-c2beb1cfb14b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80804e92-a7b7-420d-bdb3-acc54f56e1c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ea043d19-1f36-4f8d-a0c0-6aa0fd18de83",
   "metadata": {},
   "source": [
    "***Correlation based method***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af4100bb-4350-4bae-98d5-e6d9a5e5b58f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cls</th>\n",
       "      <th>nAcid</th>\n",
       "      <th>nBase</th>\n",
       "      <th>SpAbs_A</th>\n",
       "      <th>SpMax_A</th>\n",
       "      <th>SpDiam_A</th>\n",
       "      <th>SpAD_A</th>\n",
       "      <th>SpMAD_A</th>\n",
       "      <th>LogEE_A</th>\n",
       "      <th>VE1_A</th>\n",
       "      <th>...</th>\n",
       "      <th>SRW10</th>\n",
       "      <th>TSRW10</th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>WPath</th>\n",
       "      <th>WPol</th>\n",
       "      <th>Zagreb1</th>\n",
       "      <th>Zagreb2</th>\n",
       "      <th>mZagreb1</th>\n",
       "      <th>mZagreb2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cls</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027769</td>\n",
       "      <td>0.049100</td>\n",
       "      <td>-0.139667</td>\n",
       "      <td>-0.130963</td>\n",
       "      <td>-0.126240</td>\n",
       "      <td>-0.139667</td>\n",
       "      <td>-0.128202</td>\n",
       "      <td>-0.125934</td>\n",
       "      <td>-0.106679</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.132138</td>\n",
       "      <td>-0.168492</td>\n",
       "      <td>-0.023301</td>\n",
       "      <td>0.169950</td>\n",
       "      <td>0.067608</td>\n",
       "      <td>-0.087488</td>\n",
       "      <td>-0.104896</td>\n",
       "      <td>-0.117327</td>\n",
       "      <td>-0.026797</td>\n",
       "      <td>-0.061719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nAcid</th>\n",
       "      <td>-0.027769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001617</td>\n",
       "      <td>-0.170458</td>\n",
       "      <td>-0.236264</td>\n",
       "      <td>-0.236562</td>\n",
       "      <td>-0.170458</td>\n",
       "      <td>-0.238102</td>\n",
       "      <td>-0.232522</td>\n",
       "      <td>-0.221394</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021538</td>\n",
       "      <td>0.044190</td>\n",
       "      <td>0.070523</td>\n",
       "      <td>0.050870</td>\n",
       "      <td>0.428506</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>0.025512</td>\n",
       "      <td>-0.008080</td>\n",
       "      <td>0.056767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nBase</th>\n",
       "      <td>0.049100</td>\n",
       "      <td>-0.001617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.153450</td>\n",
       "      <td>-0.281423</td>\n",
       "      <td>-0.279553</td>\n",
       "      <td>-0.153450</td>\n",
       "      <td>-0.286249</td>\n",
       "      <td>-0.262466</td>\n",
       "      <td>-0.253069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018788</td>\n",
       "      <td>-0.049739</td>\n",
       "      <td>0.077472</td>\n",
       "      <td>-0.148307</td>\n",
       "      <td>0.161197</td>\n",
       "      <td>0.016348</td>\n",
       "      <td>0.035944</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>-0.135600</td>\n",
       "      <td>0.048570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpAbs_A</th>\n",
       "      <td>-0.139667</td>\n",
       "      <td>-0.170458</td>\n",
       "      <td>-0.153450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.763133</td>\n",
       "      <td>0.765618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.746570</td>\n",
       "      <td>0.856234</td>\n",
       "      <td>0.845010</td>\n",
       "      <td>...</td>\n",
       "      <td>0.611388</td>\n",
       "      <td>0.564146</td>\n",
       "      <td>0.565228</td>\n",
       "      <td>-0.079097</td>\n",
       "      <td>-0.426634</td>\n",
       "      <td>0.659439</td>\n",
       "      <td>0.698103</td>\n",
       "      <td>0.691766</td>\n",
       "      <td>0.777275</td>\n",
       "      <td>0.669675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpMax_A</th>\n",
       "      <td>-0.130963</td>\n",
       "      <td>-0.236264</td>\n",
       "      <td>-0.281423</td>\n",
       "      <td>0.763133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998057</td>\n",
       "      <td>0.763133</td>\n",
       "      <td>0.984683</td>\n",
       "      <td>0.979867</td>\n",
       "      <td>0.932670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236055</td>\n",
       "      <td>0.188182</td>\n",
       "      <td>-0.018096</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>-0.581542</td>\n",
       "      <td>0.173507</td>\n",
       "      <td>0.157136</td>\n",
       "      <td>0.178669</td>\n",
       "      <td>0.593285</td>\n",
       "      <td>0.088969</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>WPol</th>\n",
       "      <td>-0.087488</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>0.016348</td>\n",
       "      <td>0.659439</td>\n",
       "      <td>0.173507</td>\n",
       "      <td>0.175053</td>\n",
       "      <td>0.659439</td>\n",
       "      <td>0.101999</td>\n",
       "      <td>0.269280</td>\n",
       "      <td>0.303088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.931906</td>\n",
       "      <td>0.686434</td>\n",
       "      <td>0.855989</td>\n",
       "      <td>-0.124517</td>\n",
       "      <td>0.077345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.959920</td>\n",
       "      <td>0.973885</td>\n",
       "      <td>0.623240</td>\n",
       "      <td>0.890307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zagreb1</th>\n",
       "      <td>-0.104896</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>0.035944</td>\n",
       "      <td>0.698103</td>\n",
       "      <td>0.157136</td>\n",
       "      <td>0.156290</td>\n",
       "      <td>0.698103</td>\n",
       "      <td>0.107990</td>\n",
       "      <td>0.280661</td>\n",
       "      <td>0.309806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.890907</td>\n",
       "      <td>0.776545</td>\n",
       "      <td>0.919542</td>\n",
       "      <td>-0.104028</td>\n",
       "      <td>0.105390</td>\n",
       "      <td>0.959920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993597</td>\n",
       "      <td>0.614074</td>\n",
       "      <td>0.947828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zagreb2</th>\n",
       "      <td>-0.117327</td>\n",
       "      <td>0.025512</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.691766</td>\n",
       "      <td>0.178669</td>\n",
       "      <td>0.175441</td>\n",
       "      <td>0.691766</td>\n",
       "      <td>0.122411</td>\n",
       "      <td>0.286907</td>\n",
       "      <td>0.316469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925337</td>\n",
       "      <td>0.784362</td>\n",
       "      <td>0.887872</td>\n",
       "      <td>-0.097578</td>\n",
       "      <td>0.087921</td>\n",
       "      <td>0.973885</td>\n",
       "      <td>0.993597</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.594363</td>\n",
       "      <td>0.914858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mZagreb1</th>\n",
       "      <td>-0.026797</td>\n",
       "      <td>-0.008080</td>\n",
       "      <td>-0.135600</td>\n",
       "      <td>0.777275</td>\n",
       "      <td>0.593285</td>\n",
       "      <td>0.600626</td>\n",
       "      <td>0.777275</td>\n",
       "      <td>0.519610</td>\n",
       "      <td>0.673182</td>\n",
       "      <td>0.661819</td>\n",
       "      <td>...</td>\n",
       "      <td>0.533488</td>\n",
       "      <td>0.420124</td>\n",
       "      <td>0.575586</td>\n",
       "      <td>-0.052547</td>\n",
       "      <td>-0.124079</td>\n",
       "      <td>0.623240</td>\n",
       "      <td>0.614074</td>\n",
       "      <td>0.594363</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.638581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mZagreb2</th>\n",
       "      <td>-0.061719</td>\n",
       "      <td>0.056767</td>\n",
       "      <td>0.048570</td>\n",
       "      <td>0.669675</td>\n",
       "      <td>0.088969</td>\n",
       "      <td>0.093596</td>\n",
       "      <td>0.669675</td>\n",
       "      <td>0.058260</td>\n",
       "      <td>0.238020</td>\n",
       "      <td>0.258431</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750268</td>\n",
       "      <td>0.704021</td>\n",
       "      <td>0.942138</td>\n",
       "      <td>-0.143264</td>\n",
       "      <td>0.144061</td>\n",
       "      <td>0.890307</td>\n",
       "      <td>0.947828</td>\n",
       "      <td>0.914858</td>\n",
       "      <td>0.638581</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1173 rows × 1173 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               cls     nAcid     nBase   SpAbs_A   SpMax_A  SpDiam_A  \\\n",
       "cls       1.000000 -0.027769  0.049100 -0.139667 -0.130963 -0.126240   \n",
       "nAcid    -0.027769  1.000000 -0.001617 -0.170458 -0.236264 -0.236562   \n",
       "nBase     0.049100 -0.001617  1.000000 -0.153450 -0.281423 -0.279553   \n",
       "SpAbs_A  -0.139667 -0.170458 -0.153450  1.000000  0.763133  0.765618   \n",
       "SpMax_A  -0.130963 -0.236264 -0.281423  0.763133  1.000000  0.998057   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "WPol     -0.087488  0.008518  0.016348  0.659439  0.173507  0.175053   \n",
       "Zagreb1  -0.104896  0.036812  0.035944  0.698103  0.157136  0.156290   \n",
       "Zagreb2  -0.117327  0.025512  0.022279  0.691766  0.178669  0.175441   \n",
       "mZagreb1 -0.026797 -0.008080 -0.135600  0.777275  0.593285  0.600626   \n",
       "mZagreb2 -0.061719  0.056767  0.048570  0.669675  0.088969  0.093596   \n",
       "\n",
       "            SpAD_A   SpMAD_A   LogEE_A     VE1_A  ...     SRW10    TSRW10  \\\n",
       "cls      -0.139667 -0.128202 -0.125934 -0.106679  ... -0.132138 -0.168492   \n",
       "nAcid    -0.170458 -0.238102 -0.232522 -0.221394  ... -0.021538  0.044190   \n",
       "nBase    -0.153450 -0.286249 -0.262466 -0.253069  ... -0.018788 -0.049739   \n",
       "SpAbs_A   1.000000  0.746570  0.856234  0.845010  ...  0.611388  0.564146   \n",
       "SpMax_A   0.763133  0.984683  0.979867  0.932670  ...  0.236055  0.188182   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "WPol      0.659439  0.101999  0.269280  0.303088  ...  0.931906  0.686434   \n",
       "Zagreb1   0.698103  0.107990  0.280661  0.309806  ...  0.890907  0.776545   \n",
       "Zagreb2   0.691766  0.122411  0.286907  0.316469  ...  0.925337  0.784362   \n",
       "mZagreb1  0.777275  0.519610  0.673182  0.661819  ...  0.533488  0.420124   \n",
       "mZagreb2  0.669675  0.058260  0.238020  0.258431  ...  0.750268  0.704021   \n",
       "\n",
       "                MW       AMW     WPath      WPol   Zagreb1   Zagreb2  \\\n",
       "cls      -0.023301  0.169950  0.067608 -0.087488 -0.104896 -0.117327   \n",
       "nAcid     0.070523  0.050870  0.428506  0.008518  0.036812  0.025512   \n",
       "nBase     0.077472 -0.148307  0.161197  0.016348  0.035944  0.022279   \n",
       "SpAbs_A   0.565228 -0.079097 -0.426634  0.659439  0.698103  0.691766   \n",
       "SpMax_A  -0.018096 -0.002115 -0.581542  0.173507  0.157136  0.178669   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "WPol      0.855989 -0.124517  0.077345  1.000000  0.959920  0.973885   \n",
       "Zagreb1   0.919542 -0.104028  0.105390  0.959920  1.000000  0.993597   \n",
       "Zagreb2   0.887872 -0.097578  0.087921  0.973885  0.993597  1.000000   \n",
       "mZagreb1  0.575586 -0.052547 -0.124079  0.623240  0.614074  0.594363   \n",
       "mZagreb2  0.942138 -0.143264  0.144061  0.890307  0.947828  0.914858   \n",
       "\n",
       "          mZagreb1  mZagreb2  \n",
       "cls      -0.026797 -0.061719  \n",
       "nAcid    -0.008080  0.056767  \n",
       "nBase    -0.135600  0.048570  \n",
       "SpAbs_A   0.777275  0.669675  \n",
       "SpMax_A   0.593285  0.088969  \n",
       "...            ...       ...  \n",
       "WPol      0.623240  0.890307  \n",
       "Zagreb1   0.614074  0.947828  \n",
       "Zagreb2   0.594363  0.914858  \n",
       "mZagreb1  1.000000  0.638581  \n",
       "mZagreb2  0.638581  1.000000  \n",
       "\n",
       "[1173 rows x 1173 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('test2k.csv')\n",
    "\n",
    "corr_mat = selected_df.corr(method='pearson',numeric_only=True)\n",
    "\n",
    "print(\"Correlation Matrix:\")\n",
    "corr_mat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53fac743-3747-4ee9-9b5d-5ce4abb70e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 3000x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "plt.matshow(corr_mat)\n",
    "plt.xticks(range(len(corr_mat.columns)),corr_mat.columns,rotation=90)\n",
    "plt.yticks(range(len(corr_mat.columns)),corr_mat.columns)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab8022f-880a-4d31-be3c-4cc12ccb3d64",
   "metadata": {},
   "source": [
    "**Thanks for these infinite features, now i have to code instead of just dropping them off**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "af845beb-50b9-4743-b26c-e0a89f698c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame after dropping highly correlated and low correlated features:\n"
     ]
    },
    {
     "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>cls</th>\n",
       "      <th>nAcid</th>\n",
       "      <th>nBase</th>\n",
       "      <th>SpAbs_A</th>\n",
       "      <th>SpMax_A</th>\n",
       "      <th>VR1_A</th>\n",
       "      <th>nAromAtom</th>\n",
       "      <th>nAtom</th>\n",
       "      <th>nSpiro</th>\n",
       "      <th>nBridgehead</th>\n",
       "      <th>...</th>\n",
       "      <th>TopoPSA(NO)</th>\n",
       "      <th>TopoPSA</th>\n",
       "      <th>GGI5</th>\n",
       "      <th>GGI6</th>\n",
       "      <th>GGI7</th>\n",
       "      <th>GGI8</th>\n",
       "      <th>GGI9</th>\n",
       "      <th>SRW05</th>\n",
       "      <th>TSRW10</th>\n",
       "      <th>WPath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.701129</td>\n",
       "      <td>2.439857</td>\n",
       "      <td>61.215418</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>43.84</td>\n",
       "      <td>72.08</td>\n",
       "      <td>0.357778</td>\n",
       "      <td>0.248299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>61.847126</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46.736633</td>\n",
       "      <td>2.407624</td>\n",
       "      <td>2230.324539</td>\n",
       "      <td>23</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>135.78</td>\n",
       "      <td>144.16</td>\n",
       "      <td>1.233056</td>\n",
       "      <td>0.653832</td>\n",
       "      <td>0.413194</td>\n",
       "      <td>0.389810</td>\n",
       "      <td>0.256258</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>85.856674</td>\n",
       "      <td>5352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.586705</td>\n",
       "      <td>2.537049</td>\n",
       "      <td>308.603378</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.01</td>\n",
       "      <td>66.01</td>\n",
       "      <td>1.653056</td>\n",
       "      <td>1.077098</td>\n",
       "      <td>0.619827</td>\n",
       "      <td>0.401612</td>\n",
       "      <td>0.189691</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>80.145118</td>\n",
       "      <td>2195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.395318</td>\n",
       "      <td>2.442924</td>\n",
       "      <td>185.555867</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.40</td>\n",
       "      <td>103.64</td>\n",
       "      <td>0.621667</td>\n",
       "      <td>0.301270</td>\n",
       "      <td>0.215313</td>\n",
       "      <td>0.050887</td>\n",
       "      <td>0.013758</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>69.229338</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>2.464844</td>\n",
       "      <td>319.313188</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.98</td>\n",
       "      <td>96.22</td>\n",
       "      <td>0.812778</td>\n",
       "      <td>0.461270</td>\n",
       "      <td>0.353387</td>\n",
       "      <td>0.255244</td>\n",
       "      <td>0.171566</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>77.687460</td>\n",
       "      <td>1886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 474 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cls  nAcid  nBase    SpAbs_A   SpMax_A        VR1_A  nAromAtom  nAtom  \\\n",
       "0    1      0      0  17.701129  2.439857    61.215418         10     27   \n",
       "1    1      0      0  46.736633  2.407624  2230.324539         23     59   \n",
       "2    1      0      0  39.586705  2.537049   308.603378         21     52   \n",
       "3    1      0      0  26.395318  2.442924   185.555867         16     31   \n",
       "4    1      0      0  35.597476  2.464844   319.313188         11     51   \n",
       "\n",
       "   nSpiro  nBridgehead  ...  TopoPSA(NO)  TopoPSA      GGI5      GGI6  \\\n",
       "0       0            0  ...        43.84    72.08  0.357778  0.248299   \n",
       "1       0            0  ...       135.78   144.16  1.233056  0.653832   \n",
       "2       0            0  ...        66.01    66.01  1.653056  1.077098   \n",
       "3       0            0  ...        75.40   103.64  0.621667  0.301270   \n",
       "4       0            0  ...        67.98    96.22  0.812778  0.461270   \n",
       "\n",
       "       GGI7      GGI8      GGI9     SRW05     TSRW10  WPath  \n",
       "0  0.000000  0.000000  0.000000  3.044522  61.847126    288  \n",
       "1  0.413194  0.389810  0.256258  2.397895  85.856674   5352  \n",
       "2  0.619827  0.401612  0.189691  2.397895  80.145118   2195  \n",
       "3  0.215313  0.050887  0.013758  3.044522  69.229338    853  \n",
       "4  0.353387  0.255244  0.171566  3.044522  77.687460   1886  \n",
       "\n",
       "[5 rows x 474 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat = selected_df.corr()\n",
    "\n",
    "high_corr_threshold = 0.9\n",
    "\n",
    "corr_cols = set()\n",
    "for i in range(len(corr_mat.columns)):\n",
    "    for j in range(i):\n",
    "        if abs(corr_mat.iloc[i, j]) > high_corr_threshold:\n",
    "            colname = corr_mat.columns[i]\n",
    "            corr_cols.add(colname)\n",
    "\n",
    "df_dropped_high_corr = selected_df.drop(columns=corr_cols)\n",
    "\n",
    "low_corr_threshold = 0.1\n",
    "\n",
    "low_corr_cols = corr_mat.columns[(corr_mat.abs() < low_corr_threshold).all()]\n",
    "df_dropped_low_corr = df_dropped_high_corr.drop(columns=low_corr_cols)\n",
    "\n",
    "print(\"DataFrame after dropping highly correlated and low correlated features:\")\n",
    "df_dropped_low_corr.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "076b34d7-22fd-4b91-b631-98908914a1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "plt.matshow(df_dropped_low_corr)\n",
    "plt.xticks(range(len(df_dropped_low_corr.columns)),df_dropped_low_corr.columns,rotation=90)\n",
    "plt.yticks(range(len(df_dropped_low_corr.columns)),df_dropped_low_corr.columns)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d966a6eb-eea3-4476-ab96-5b0f62a27bb8",
   "metadata": {},
   "source": [
    "*Yeah even i don't know how to plot a graph with 400 features. sorry sir*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b168935a-096d-47e6-a2b2-05b442e08580",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame after dropping highly correlated and low correlated features:\n"
     ]
    },
    {
     "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>cls</th>\n",
       "      <th>nAcid</th>\n",
       "      <th>nBase</th>\n",
       "      <th>SpAbs_A</th>\n",
       "      <th>SpMax_A</th>\n",
       "      <th>VR1_A</th>\n",
       "      <th>nAromAtom</th>\n",
       "      <th>nAtom</th>\n",
       "      <th>nSpiro</th>\n",
       "      <th>nBridgehead</th>\n",
       "      <th>...</th>\n",
       "      <th>TopoPSA(NO)</th>\n",
       "      <th>TopoPSA</th>\n",
       "      <th>GGI5</th>\n",
       "      <th>GGI6</th>\n",
       "      <th>GGI7</th>\n",
       "      <th>GGI8</th>\n",
       "      <th>GGI9</th>\n",
       "      <th>SRW05</th>\n",
       "      <th>TSRW10</th>\n",
       "      <th>WPath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.701129</td>\n",
       "      <td>2.439857</td>\n",
       "      <td>61.215418</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>43.84</td>\n",
       "      <td>72.08</td>\n",
       "      <td>0.357778</td>\n",
       "      <td>0.248299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>61.847126</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46.736633</td>\n",
       "      <td>2.407624</td>\n",
       "      <td>2230.324539</td>\n",
       "      <td>23</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>135.78</td>\n",
       "      <td>144.16</td>\n",
       "      <td>1.233056</td>\n",
       "      <td>0.653832</td>\n",
       "      <td>0.413194</td>\n",
       "      <td>0.389810</td>\n",
       "      <td>0.256258</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>85.856674</td>\n",
       "      <td>5352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.586705</td>\n",
       "      <td>2.537049</td>\n",
       "      <td>308.603378</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.01</td>\n",
       "      <td>66.01</td>\n",
       "      <td>1.653056</td>\n",
       "      <td>1.077098</td>\n",
       "      <td>0.619827</td>\n",
       "      <td>0.401612</td>\n",
       "      <td>0.189691</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>80.145118</td>\n",
       "      <td>2195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.395318</td>\n",
       "      <td>2.442924</td>\n",
       "      <td>185.555867</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.40</td>\n",
       "      <td>103.64</td>\n",
       "      <td>0.621667</td>\n",
       "      <td>0.301270</td>\n",
       "      <td>0.215313</td>\n",
       "      <td>0.050887</td>\n",
       "      <td>0.013758</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>69.229338</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.597476</td>\n",
       "      <td>2.464844</td>\n",
       "      <td>319.313188</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.98</td>\n",
       "      <td>96.22</td>\n",
       "      <td>0.812778</td>\n",
       "      <td>0.461270</td>\n",
       "      <td>0.353387</td>\n",
       "      <td>0.255244</td>\n",
       "      <td>0.171566</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>77.687460</td>\n",
       "      <td>1886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 474 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cls  nAcid  nBase    SpAbs_A   SpMax_A        VR1_A  nAromAtom  nAtom  \\\n",
       "0    1      0      0  17.701129  2.439857    61.215418         10     27   \n",
       "1    1      0      0  46.736633  2.407624  2230.324539         23     59   \n",
       "2    1      0      0  39.586705  2.537049   308.603378         21     52   \n",
       "3    1      0      0  26.395318  2.442924   185.555867         16     31   \n",
       "4    1      0      0  35.597476  2.464844   319.313188         11     51   \n",
       "\n",
       "   nSpiro  nBridgehead  ...  TopoPSA(NO)  TopoPSA      GGI5      GGI6  \\\n",
       "0       0            0  ...        43.84    72.08  0.357778  0.248299   \n",
       "1       0            0  ...       135.78   144.16  1.233056  0.653832   \n",
       "2       0            0  ...        66.01    66.01  1.653056  1.077098   \n",
       "3       0            0  ...        75.40   103.64  0.621667  0.301270   \n",
       "4       0            0  ...        67.98    96.22  0.812778  0.461270   \n",
       "\n",
       "       GGI7      GGI8      GGI9     SRW05     TSRW10  WPath  \n",
       "0  0.000000  0.000000  0.000000  3.044522  61.847126    288  \n",
       "1  0.413194  0.389810  0.256258  2.397895  85.856674   5352  \n",
       "2  0.619827  0.401612  0.189691  2.397895  80.145118   2195  \n",
       "3  0.215313  0.050887  0.013758  3.044522  69.229338    853  \n",
       "4  0.353387  0.255244  0.171566  3.044522  77.687460   1886  \n",
       "\n",
       "[5 rows x 474 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "target_feature = 'mZagreb1'\n",
    "\n",
    "features_for_corr = selected_df.drop(columns=[target_feature])\n",
    "\n",
    "corr_mat = features_for_corr.corr()\n",
    "\n",
    "high_corr_threshold = 0.9\n",
    "\n",
    "corr_cols = set()\n",
    "for i in range(len(corr_mat.columns)):\n",
    "    for j in range(i):\n",
    "        if abs(corr_mat.iloc[i, j]) > high_corr_threshold:\n",
    "            colname = corr_mat.columns[i]\n",
    "            corr_cols.add(colname)\n",
    "\n",
    "df_dropped_high_corr = features_for_corr.drop(columns=corr_cols)\n",
    "\n",
    "low_corr_threshold = 0.1\n",
    "\n",
    "low_corr_cols = corr_mat.columns[(corr_mat.abs() < low_corr_threshold).all()]\n",
    "df_dropped_low_corr = df_dropped_high_corr.drop(columns=low_corr_cols)\n",
    "\n",
    "print(\"DataFrame after dropping highly correlated and low correlated features:\")\n",
    "df_dropped_low_corr.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2db10d-62c2-46f6-aa4e-740a9368ae6d",
   "metadata": {},
   "source": [
    "**brought down number of features to 474** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24f6a3c5-712a-4433-bc53-7809f63d177f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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