V3GeneticScoreAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPIFB4 rs2070325</th>\n",
       "      <th>ST6GAL1_rs11710456</th>\n",
       "      <th>SLC2A2_rs5400</th>\n",
       "      <th>CD36_rs1761667</th>\n",
       "      <th>CD36_rs1049673</th>\n",
       "      <th>ZC3H10_rs61732294</th>\n",
       "      <th>HDAC1_rs1741981</th>\n",
       "      <th>HDAC1_rs1741981_CCeCTvsTT</th>\n",
       "      <th>HDAC1_rs1741981_CCvsCTeTT</th>\n",
       "      <th>HDAC3_rs2547547</th>\n",
       "      <th>...</th>\n",
       "      <th>rs_9420907</th>\n",
       "      <th>rs_9420907_1vs2e3</th>\n",
       "      <th>rs_9420907_1e2vs3</th>\n",
       "      <th>rs_8105767</th>\n",
       "      <th>rs_8105767_1vs2e3</th>\n",
       "      <th>rs_8105767_1e2vs3</th>\n",
       "      <th>rs_755017</th>\n",
       "      <th>rs_755017_1vs2e3</th>\n",
       "      <th>rs_755017_1e2vs3</th>\n",
       "      <th>SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2027.000000</td>\n",
       "      <td>2090.000000</td>\n",
       "      <td>2272.000000</td>\n",
       "      <td>2215.000000</td>\n",
       "      <td>2213.000000</td>\n",
       "      <td>2287.000000</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1860.000000</td>\n",
       "      <td>1860.000000</td>\n",
       "      <td>1860.000000</td>\n",
       "      <td>1842.000000</td>\n",
       "      <td>1842.000000</td>\n",
       "      <td>1842.000000</td>\n",
       "      <td>1762.000000</td>\n",
       "      <td>1762.000000</td>\n",
       "      <td>1762.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.670449</td>\n",
       "      <td>1.559330</td>\n",
       "      <td>1.346831</td>\n",
       "      <td>2.004515</td>\n",
       "      <td>2.061003</td>\n",
       "      <td>1.547442</td>\n",
       "      <td>1.248568</td>\n",
       "      <td>1.019833</td>\n",
       "      <td>1.228735</td>\n",
       "      <td>1.330101</td>\n",
       "      <td>...</td>\n",
       "      <td>1.343011</td>\n",
       "      <td>1.310215</td>\n",
       "      <td>1.032796</td>\n",
       "      <td>1.534745</td>\n",
       "      <td>1.456026</td>\n",
       "      <td>1.078719</td>\n",
       "      <td>1.131669</td>\n",
       "      <td>1.128263</td>\n",
       "      <td>1.003405</td>\n",
       "      <td>2.851420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.704610</td>\n",
       "      <td>0.633039</td>\n",
       "      <td>0.598949</td>\n",
       "      <td>0.705494</td>\n",
       "      <td>0.698993</td>\n",
       "      <td>0.503097</td>\n",
       "      <td>0.475969</td>\n",
       "      <td>0.139455</td>\n",
       "      <td>0.420111</td>\n",
       "      <td>0.568017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.539539</td>\n",
       "      <td>0.462706</td>\n",
       "      <td>0.178149</td>\n",
       "      <td>0.637535</td>\n",
       "      <td>0.498198</td>\n",
       "      <td>0.269372</td>\n",
       "      <td>0.348154</td>\n",
       "      <td>0.334478</td>\n",
       "      <td>0.058271</td>\n",
       "      <td>3.643885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.518080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.598360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.816545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.489526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BPIFB4 rs2070325  ST6GAL1_rs11710456  SLC2A2_rs5400  CD36_rs1761667  \\\n",
       "count       2027.000000         2090.000000    2272.000000     2215.000000   \n",
       "mean           1.670449            1.559330       1.346831        2.004515   \n",
       "std            0.704610            0.633039       0.598949        0.705494   \n",
       "min            1.000000            1.000000       1.000000        1.000000   \n",
       "25%            1.000000            1.000000       1.000000        2.000000   \n",
       "50%            2.000000            1.000000       1.000000        2.000000   \n",
       "75%            2.000000            2.000000       2.000000        3.000000   \n",
       "max            3.000000            3.000000       3.000000        3.000000   \n",
       "\n",
       "       CD36_rs1049673  ZC3H10_rs61732294  HDAC1_rs1741981  \\\n",
       "count     2213.000000        2287.000000      2269.000000   \n",
       "mean         2.061003           1.547442         1.248568   \n",
       "std          0.698993           0.503097         0.475969   \n",
       "min          1.000000           0.000000         1.000000   \n",
       "25%          2.000000           1.000000         1.000000   \n",
       "50%          2.000000           2.000000         1.000000   \n",
       "75%          3.000000           2.000000         1.000000   \n",
       "max          3.000000           3.000000         3.000000   \n",
       "\n",
       "       HDAC1_rs1741981_CCeCTvsTT  HDAC1_rs1741981_CCvsCTeTT  HDAC3_rs2547547  \\\n",
       "count                2269.000000                2269.000000      2269.000000   \n",
       "mean                    1.019833                   1.228735         1.330101   \n",
       "std                     0.139455                   0.420111         0.568017   \n",
       "min                     1.000000                   1.000000         1.000000   \n",
       "25%                     1.000000                   1.000000         1.000000   \n",
       "50%                     1.000000                   1.000000         1.000000   \n",
       "75%                     1.000000                   1.000000         2.000000   \n",
       "max                     2.000000                   2.000000         3.000000   \n",
       "\n",
       "       ...   rs_9420907  rs_9420907_1vs2e3  rs_9420907_1e2vs3   rs_8105767  \\\n",
       "count  ...  1860.000000        1860.000000        1860.000000  1842.000000   \n",
       "mean   ...     1.343011           1.310215           1.032796     1.534745   \n",
       "std    ...     0.539539           0.462706           0.178149     0.637535   \n",
       "min    ...     1.000000           1.000000           1.000000     1.000000   \n",
       "25%    ...     1.000000           1.000000           1.000000     1.000000   \n",
       "50%    ...     1.000000           1.000000           1.000000     1.000000   \n",
       "75%    ...     2.000000           2.000000           1.000000     2.000000   \n",
       "max    ...     3.000000           2.000000           2.000000     3.000000   \n",
       "\n",
       "       rs_8105767_1vs2e3  rs_8105767_1e2vs3    rs_755017  rs_755017_1vs2e3  \\\n",
       "count        1842.000000        1842.000000  1762.000000       1762.000000   \n",
       "mean            1.456026           1.078719     1.131669          1.128263   \n",
       "std             0.498198           0.269372     0.348154          0.334478   \n",
       "min             1.000000           1.000000     1.000000          1.000000   \n",
       "25%             1.000000           1.000000     1.000000          1.000000   \n",
       "50%             1.000000           1.000000     1.000000          1.000000   \n",
       "75%             2.000000           1.000000     1.000000          1.000000   \n",
       "max             2.000000           2.000000     3.000000          2.000000   \n",
       "\n",
       "       rs_755017_1e2vs3        SCORE  \n",
       "count       1762.000000  1445.000000  \n",
       "mean           1.003405     2.851420  \n",
       "std            0.058271     3.643885  \n",
       "min            1.000000     0.000000  \n",
       "25%            1.000000     0.518080  \n",
       "50%            1.000000     1.598360  \n",
       "75%            1.000000     3.816545  \n",
       "max            2.000000    30.489526  \n",
       "\n",
       "[8 rows x 105 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_DIR = \"./rawdata/geneticdata/DBPLIC1_wSCORECLass.txt\"\n",
    "genetic_data = pd.read_csv(DATA_DIR,delimiter='\\t')\n",
    "updated_genetic_data = genetic_data.drop(labels=['cod_pz','Unnamed: 0','Score','ScoreClass'],axis=1)\n",
    "updated_genetic_data.apply(pd.to_numeric)\n",
    "updated_genetic_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_data = updated_genetic_data.dropna(subset=['SCORE']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X=cleaned_data.iloc[:,:104]  # Features\n",
    "y=cleaned_data['SCORE']  # Labels\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1011x104 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 79018 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train += 1\n",
    "X_test +=1\n",
    "X_trainnew = X_train.fillna(0)\n",
    "X_testnew = X_test.fillna(0)\n",
    "X_trained = scipy.sparse.csr_matrix(X_trainnew.values)\n",
    "X_tested = scipy.sparse.csr_matrix(X_testnew.values)\n",
    "\n",
    "X_trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "clf=RandomForestRegressor(n_estimators=100)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf.fit(X_trained,y_train.values)\n",
    "\n",
    "y_pred=clf.predict(X_tested)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error:  2.695056425774667\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Mean Squared Error: \",metrics.mean_absolute_error(y_test, y_pred))\n",
    "#print(\"Accuracy:\",metrics.mse(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-620dfa8f5453>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
     ]
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-e5b86ff07486>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y_test' is not defined"
     ]
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TelomereLength                0.081336\n",
       "ApoE_Alleli                   0.037544\n",
       "BPIFB4 rs2070325              0.027547\n",
       "TLR4_SNP_399                  0.024816\n",
       "P2RX7_rs1718119_SNP1          0.023753\n",
       "LOX1_SNP_1                    0.022911\n",
       "VISFATIN_SNP_1001             0.022486\n",
       "ST6GAL1_rs11710456            0.022457\n",
       "BDNF_SNP_V66M                 0.020824\n",
       "Fractalkine_R_SNP_V249I       0.019628\n",
       "RAGE_SNP_374                  0.019330\n",
       "CD36_rs1761667                0.018492\n",
       "PCSK9_SNP_I474V               0.017780\n",
       "rs2967374                     0.017499\n",
       "CFDP1_SNP                     0.016458\n",
       "SLC2A2_rs5400                 0.016266\n",
       "Fractalkine_R_SNP_T280M       0.015386\n",
       "PCSK9_rs7552841               0.014739\n",
       "CD36_rs1049673                0.014670\n",
       "PTX3_SNP_rs3816527            0.014004\n",
       "PTX3_Aplotipo                 0.013758\n",
       "TLR4_SNP_299                  0.012854\n",
       "rs_2736100                    0.012845\n",
       "Adiponectin_SNP_276           0.012530\n",
       "rs17845226                    0.011873\n",
       "rs_1799945                    0.011506\n",
       "rs1035539                     0.011446\n",
       "rs_10936599                   0.011158\n",
       "rs2967374_1e2vs3              0.011118\n",
       "rs1801075                     0.011068\n",
       "                                ...   \n",
       "rs_10936599_1e2vs3            0.004647\n",
       "rs11125529_1vs2e3             0.004642\n",
       "rs_1800562                    0.004614\n",
       "rs_755017_1vs2e3              0.004514\n",
       "rs_9420907_1vs2e3             0.004506\n",
       "GCK_rs4607517_GGvsGAeAA       0.004303\n",
       "rs_1800562_1_2e3              0.004270\n",
       "GCK_rs3757840_GTeTTvsGG       0.004086\n",
       "rs_755017                     0.004076\n",
       "HDAC3_rs2547547_AAVSAGeGG     0.003797\n",
       "rs_8105767_1e2vs3             0.003736\n",
       "PTX3_SNP_rs1840680            0.003584\n",
       "PCSK9_SNP_R46L                0.003385\n",
       "SLC16A7_rs995343_TTeCTvsCC    0.003172\n",
       "HDAC3_rs2530223_TTeTCvsCC     0.002990\n",
       "GCK_rs3757840_GTeGGvsTT       0.002925\n",
       "eNOS_SNP_Esone7_1e2vs3        0.002560\n",
       "eNOS_SNP_Introne19_1e2vs3     0.002468\n",
       "GCKR_rs_1260326_TTeTCvsCC     0.002372\n",
       "HDAC3_rs2547547_AAeAGvsGG     0.002306\n",
       "eNOS_SNP_Introne18_1e2vs3     0.002275\n",
       "rs_7675998_1e2_vs3            0.002271\n",
       "TLR5_SNP_1174                 0.002220\n",
       "HDAC1_rs1741981_CCeCTvsTT     0.001579\n",
       "GCK_rs4607517_GGeGAvsAA       0.001545\n",
       "rs11125529_1e2vs3             0.001458\n",
       "rs_755017_1e2vs3              0.001349\n",
       "ALOX_5_SNP                    0.000349\n",
       "SLC5A12_rs138192976           0.000027\n",
       "Cod_SHIP2_SNP                 0.000000\n",
       "Length: 104, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.Series(clf.feature_importances_,index=cleaned_data.columns[:104]).sort_values(ascending=False)\n",
    "feature_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# Creating a bar plot\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.barplot(x=feature_imp[:20], y=feature_imp.index[:20])\n",
    "# Add labels to your graph\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features\")\n",
    "plt.legend()\n",
    "plt.savefig('test5.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}