protein_classification_with_ml_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId    classification experimentalTechnique macromoleculeType  \\\n",
       "0        100D    DNA-RNA HYBRID     X-RAY DIFFRACTION    DNA/RNA Hybrid   \n",
       "1        100D    DNA-RNA HYBRID     X-RAY DIFFRACTION    DNA/RNA Hybrid   \n",
       "2        101D               DNA     X-RAY DIFFRACTION               DNA   \n",
       "3        101D               DNA     X-RAY DIFFRACTION               DNA   \n",
       "4        101M  OXYGEN TRANSPORT     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight  \\\n",
       "0            20        1.90                   6360.30   \n",
       "1            20        1.90                   6360.30   \n",
       "2            24        2.25                   7939.35   \n",
       "3            24        2.25                   7939.35   \n",
       "4           154        2.07                  18112.80   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "2                            NaN                   NaN             2.00   \n",
       "3                            NaN                   NaN             2.00   \n",
       "4                            NaN                   NaN             3.09   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "2              38.45                                                NaN   \n",
       "3              38.45                                                NaN   \n",
       "4              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "\n",
       "   phValue  publicationYear chainId  \\\n",
       "0      7.0           1994.0       A   \n",
       "1      7.0           1994.0       B   \n",
       "2      NaN           1995.0       A   \n",
       "3      NaN           1995.0       B   \n",
       "4      9.0           1999.0       A   \n",
       "\n",
       "                                            sequence  \n",
       "0                                         CCGGCGCCGG  \n",
       "1                                         CCGGCGCCGG  \n",
       "2                                       CGCGAATTCGCG  \n",
       "3                                       CGCGAATTCGCG  \n",
       "4  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('storage/pdb/pdb_data_no_dups.csv')\n",
    "df2 = pd.read_csv('storage/pdb/pdb_data_seq.csv')\n",
    "\n",
    "df = pd.merge(df1,df2[['structureId','chainId','sequence']], on='structureId', how='inner')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total number of protein classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(110228, 16)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2706"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['macromoleculeType'] == \"Protein\"]\n",
    "print(df.shape)\n",
    "len(df[df['macromoleculeType'] == \"Protein\"]['classification'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values per column in percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "structureId  :  0.0\n",
      "classification  :  0.0\n",
      "experimentalTechnique  :  0.0\n",
      "macromoleculeType  :  0.0\n",
      "residueCount  :  0.0\n",
      "resolution  :  2.6\n",
      "structureMolecularWeight  :  0.0\n",
      "crystallizationMethod  :  10.9\n",
      "crystallizationTempK  :  10.8\n",
      "densityMatthews  :  3.1\n",
      "densityPercentSol  :  3.1\n",
      "pdbxDetails  :  5.1\n",
      "phValue  :  6.1\n",
      "publicationYear  :  2.7\n",
      "chainId  :  0.0\n",
      "sequence  :  0.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for i, v in df.isnull().sum().items():\n",
    "    print(i, \" : \", round((v/471149)*100, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df.isnull(), yticklabels=False,cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102L</td>\n",
       "      <td>hydrolase/o-glycosyl</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId        classification experimentalTechnique macromoleculeType  \\\n",
       "0        101M      oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "1        102L  hydrolase/o-glycosyl     X-RAY DIFFRACTION           Protein   \n",
       "2        102M      oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight crystallizationMethod  \\\n",
       "0           154        2.07                  18112.80                   NaN   \n",
       "1           165        1.74                  18926.61                   NaN   \n",
       "2           154        1.84                  18010.64                   NaN   \n",
       "\n",
       "   crystallizationTempK  densityMatthews  densityPercentSol  \\\n",
       "0                   NaN             3.09              60.20   \n",
       "1                   NaN             2.75              55.28   \n",
       "2                   NaN             3.09              60.20   \n",
       "\n",
       "                                         pdbxDetails  phValue  \\\n",
       "0  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...      9.0   \n",
       "1                                                NaN      NaN   \n",
       "2  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...      9.0   \n",
       "\n",
       "   publicationYear chainId                                           sequence  \n",
       "0           1999.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...  \n",
       "1           1993.0       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...  \n",
       "2           1999.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['classification'] = df['classification'].astype('str')\n",
    "df = df[df['macromoleculeType'] == \"Protein\"]\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "df['classification'] = df['classification'].str.lower()\n",
    "df['classification'] = df['classification'].str.replace('(', '/')\n",
    "df['classification'] = df['classification'].str.replace(',', '/')\n",
    "df['classification'] = df['classification'].str.replace(', ', '/')\n",
    "df['classification'] = df['classification'].str.replace('/ ', '/')\n",
    "df['classification'] = df['classification'].str.replace(')', '')\n",
    "# pattern = '|'.join([', ', ',', '('])\n",
    "# df['classification'] = df['classification'].str.replace(pattern, '/')\n",
    "\n",
    "# Maintaing class ordering. There are the same classes with different order \n",
    "#ex. viral protein/immune system and immune system/viral protein\n",
    "df['classification'] = df['classification'].apply(lambda x:\"/\".join(sorted(x.split('/'))))\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting outliar 23 values for <b>Molecular weight</b> which was for Virus classification. It was a very small number so was deleted.\n",
    "\n",
    "<b> Note: </b> Molecular weight is calculated using sequence using the weight of each amino acid. Thus, if sequence is long, then it can have large weight\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c36faa4e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"structureMolecularWeight\", y=\"densityMatthews\", data=df)\n",
    "\n",
    "# Later check: You can compare weight with sequence length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c34305a90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#density_weight = df[['classification','densityMatthews','structureMolecularWeight']].dropna().copy()\n",
    "weight_max = df['structureMolecularWeight'] == df['structureMolecularWeight'].max()\n",
    "df = df[~weight_max].copy()\n",
    "sns.scatterplot(x=\"structureMolecularWeight\", y=\"densityMatthews\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dealing with outliar of phValue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c342a23c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['phValue'] = df['phValue'].replace(100, np.nan)\n",
    "sns.boxplot(df['phValue'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28751"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phValue.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['structureId', 'classification', 'experimentalTechnique',\n",
       "       'macromoleculeType', 'residueCount', 'resolution',\n",
       "       'structureMolecularWeight', 'crystallizationMethod',\n",
       "       'crystallizationTempK', 'densityMatthews', 'densityPercentSol',\n",
       "       'pdbxDetails', 'phValue', 'publicationYear', 'chainId', 'sequence'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['crystallizationMethod','crystallizationTempK', 'publicationYear', 'pdbxDetails'], axis=1, inplace=True) \n",
    "df.dropna(subset=['densityMatthews','densityPercentSol','resolution', 'sequence'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter dataset's records for classification types which are > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>phValue</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102L</td>\n",
       "      <td>hydrolase/o-glycosyl</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId        classification experimentalTechnique macromoleculeType  \\\n",
       "0        101M      oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "1        102L  hydrolase/o-glycosyl     X-RAY DIFFRACTION           Protein   \n",
       "2        102M      oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight  densityMatthews  \\\n",
       "0           154        2.07                  18112.80             3.09   \n",
       "1           165        1.74                  18926.61             2.75   \n",
       "2           154        1.84                  18010.64             3.09   \n",
       "\n",
       "   densityPercentSol  phValue chainId  \\\n",
       "0              60.20      9.0       A   \n",
       "1              55.28      NaN       A   \n",
       "2              60.20      9.0       A   \n",
       "\n",
       "                                            sequence  \n",
       "0  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...  \n",
       "1  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...  \n",
       "2  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df['classification'].value_counts()\n",
    "\n",
    "types = np.asarray(counts[(counts > 100)].index)\n",
    "\n",
    "data = df[df.classification.isin(types)].copy()\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10667"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.phValue.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Outliar of PhValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hydrolase': 0,\n",
       " 'transferase': 1,\n",
       " 'oxidoreductase': 2,\n",
       " 'lyase': 3,\n",
       " 'immune system': 4,\n",
       " 'structural genomics/unknown function': 5,\n",
       " 'hydrolase/hydrolase inhibitor': 6,\n",
       " 'transcription': 7,\n",
       " 'isomerase': 8,\n",
       " 'signaling protein': 9,\n",
       " 'ligase': 10,\n",
       " 'transport protein': 11,\n",
       " 'electron transport': 12,\n",
       " 'viral protein': 13,\n",
       " 'sugar binding protein': 14,\n",
       " 'membrane protein': 15,\n",
       " 'chaperone': 16,\n",
       " 'toxin': 17,\n",
       " 'dna binding protein': 18,\n",
       " 'structural protein': 19,\n",
       " 'metal binding protein': 20,\n",
       " 'unknown function': 21,\n",
       " 'oxygen storage/transport': 22,\n",
       " 'protein binding': 23,\n",
       " 'photosynthesis': 24,\n",
       " 'cell adhesion': 25,\n",
       " 'protein transport': 26,\n",
       " 'growth factor/hormone': 27,\n",
       " 'cell cycle': 28,\n",
       " 'oxygen transport': 29,\n",
       " 'metal transport': 30,\n",
       " 'lectin': 31,\n",
       " 'blood clotting': 32,\n",
       " 'hormone': 33,\n",
       " 'immunoglobulin': 34,\n",
       " 'rna binding protein': 35,\n",
       " 'biosynthetic protein': 36,\n",
       " 'luminescent protein': 37,\n",
       " 'gene regulation': 38,\n",
       " 'translation': 39,\n",
       " 'apoptosis': 40,\n",
       " 'virus': 41,\n",
       " 'cytokine': 42,\n",
       " 'plant protein': 43,\n",
       " 'contractile protein': 44,\n",
       " 'lipid binding protein': 45,\n",
       " 'hydrolase /o-glycosyl': 46,\n",
       " 'peptide binding protein': 47,\n",
       " 'immune system/viral protein': 48,\n",
       " 'replication': 49,\n",
       " 'receptor': 50,\n",
       " 'hydrolase/immune system': 51,\n",
       " 'biotin-binding protein': 52,\n",
       " 'signaling protein/transferase': 53,\n",
       " 'electron transport/oxidoreductase': 54,\n",
       " 'de novo protein': 55,\n",
       " 'hydrolase/hydrolase activator': 56,\n",
       " 'phosphotransferase': 57,\n",
       " 'hydrolase inhibitor': 58,\n",
       " 'flavoprotein': 59,\n",
       " 'transcription regulation': 60,\n",
       " 'endocytosis/exocytosis': 61,\n",
       " 'hydrolase/protein binding': 62,\n",
       " 'transferase/transferase inhibitor': 63,\n",
       " 'antibiotic': 64,\n",
       " 'immunosuppressant/isomerase': 65,\n",
       " 'glycoprotein': 66,\n",
       " 'growth factor receptor/hormone': 67,\n",
       " 'transport': 68,\n",
       " 'hydrolase/inhibitor': 69,\n",
       " 'transcription regulator': 70,\n",
       " 'endonuclease': 71,\n",
       " 'fluorescent protein': 72,\n",
       " 'serine protease': 73,\n",
       " 'enterotoxin': 74}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_dict = dict()\n",
    "count = 0\n",
    "\n",
    "classes = data['classification'].value_counts().items()\n",
    "\n",
    "for cat, num in classes:\n",
    "    \n",
    "    # Remove all other classes that have number of values are less than 100\n",
    "    if num < 100:\n",
    "        temp = data['classification'] == cat\n",
    "        data = data[~temp].copy()\n",
    "        continue\n",
    "        \n",
    "    # Remove all a class if all phValues are null\n",
    "    if data[(data['classification'] == cat) & (data['phValue'].notnull())].phValue.count() == 0:\n",
    "        temp = data['classification'] == cat\n",
    "        data = data[~temp].copy()\n",
    "        continue\n",
    "    \n",
    "    ph_mean = round(data[data['classification'] == cat]['phValue'].dropna().mean(),2)\n",
    "    data.loc[(data['classification'] == cat) & (data['phValue'].isna()),'phValue'] = ph_mean\n",
    "    \n",
    "    \n",
    "    \n",
    "    if num >= 100:\n",
    "        class_dict[cat] = count\n",
    "        count += 1\n",
    "\n",
    "class_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>phValue</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18093.78</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>153</td>\n",
       "      <td>1.71</td>\n",
       "      <td>18030.63</td>\n",
       "      <td>1.87</td>\n",
       "      <td>34.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>A</td>\n",
       "      <td>VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>153</td>\n",
       "      <td>2.02</td>\n",
       "      <td>18030.63</td>\n",
       "      <td>1.83</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId    classification experimentalTechnique macromoleculeType  \\\n",
       "0        101M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "2        102M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "4        103M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "7        104M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "8        105M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight  densityMatthews  \\\n",
       "0           154        2.07                  18112.80             3.09   \n",
       "2           154        1.84                  18010.64             3.09   \n",
       "4           154        2.07                  18093.78             3.09   \n",
       "7           153        1.71                  18030.63             1.87   \n",
       "8           153        2.02                  18030.63             1.83   \n",
       "\n",
       "   densityPercentSol  phValue chainId  \\\n",
       "0               60.2      9.0       A   \n",
       "2               60.2      9.0       A   \n",
       "4               60.3      9.0       A   \n",
       "7               34.3      7.0       A   \n",
       "8               33.0      9.0       A   \n",
       "\n",
       "                                            sequence  class  \n",
       "0  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...     29  \n",
       "2  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...     29  \n",
       "4  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...     29  \n",
       "7  VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...     29  \n",
       "8  VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...     29  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'] = data['classification'].map(class_dict)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structureId                 0\n",
       "classification              0\n",
       "experimentalTechnique       0\n",
       "macromoleculeType           0\n",
       "residueCount                0\n",
       "resolution                  0\n",
       "structureMolecularWeight    0\n",
       "densityMatthews             0\n",
       "densityPercentSol           0\n",
       "phValue                     0\n",
       "chainId                     0\n",
       "sequence                    0\n",
       "class                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 80659 entries, 0 to 110227\n",
      "Data columns (total 13 columns):\n",
      "structureId                 80659 non-null object\n",
      "classification              80659 non-null object\n",
      "experimentalTechnique       80659 non-null object\n",
      "macromoleculeType           80659 non-null object\n",
      "residueCount                80659 non-null int64\n",
      "resolution                  80659 non-null float64\n",
      "structureMolecularWeight    80659 non-null float64\n",
      "densityMatthews             80659 non-null float64\n",
      "densityPercentSol           80659 non-null float64\n",
      "phValue                     80659 non-null float64\n",
      "chainId                     80659 non-null object\n",
      "sequence                    80659 non-null object\n",
      "class                       80659 non-null int64\n",
      "dtypes: float64(5), int64(2), object(6)\n",
      "memory usage: 8.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>phValue</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>80659.000000</td>\n",
       "      <td>80659.000000</td>\n",
       "      <td>8.065900e+04</td>\n",
       "      <td>80659.000000</td>\n",
       "      <td>80659.000000</td>\n",
       "      <td>80659.000000</td>\n",
       "      <td>80659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1205.753518</td>\n",
       "      <td>2.228303</td>\n",
       "      <td>1.387004e+05</td>\n",
       "      <td>2.716949</td>\n",
       "      <td>52.116063</td>\n",
       "      <td>6.750320</td>\n",
       "      <td>10.557917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1429.433141</td>\n",
       "      <td>0.528228</td>\n",
       "      <td>1.822397e+05</td>\n",
       "      <td>0.691406</td>\n",
       "      <td>9.913671</td>\n",
       "      <td>1.177819</td>\n",
       "      <td>15.250602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>8.368100e+02</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>370.000000</td>\n",
       "      <td>1.890000</td>\n",
       "      <td>4.230552e+04</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>45.450000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>716.000000</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>8.088973e+04</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>51.480000</td>\n",
       "      <td>6.770000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1416.000000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>1.601955e+05</td>\n",
       "      <td>2.960000</td>\n",
       "      <td>58.160000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11814.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.652705e+06</td>\n",
       "      <td>12.010000</td>\n",
       "      <td>89.680000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       residueCount    resolution  structureMolecularWeight  densityMatthews  \\\n",
       "count  80659.000000  80659.000000              8.065900e+04     80659.000000   \n",
       "mean    1205.753518      2.228303              1.387004e+05         2.716949   \n",
       "std     1429.433141      0.528228              1.822397e+05         0.691406   \n",
       "min        6.000000      0.540000              8.368100e+02         0.970000   \n",
       "25%      370.000000      1.890000              4.230552e+04         2.280000   \n",
       "50%      716.000000      2.190000              8.088973e+04         2.550000   \n",
       "75%     1416.000000      2.570000              1.601955e+05         2.960000   \n",
       "max    11814.000000      9.000000              4.652705e+06        12.010000   \n",
       "\n",
       "       densityPercentSol       phValue         class  \n",
       "count       80659.000000  80659.000000  80659.000000  \n",
       "mean           52.116063      6.750320     10.557917  \n",
       "std             9.913671      1.177819     15.250602  \n",
       "min             0.320000      0.000000      0.000000  \n",
       "25%            45.450000      6.000000      1.000000  \n",
       "50%            51.480000      6.770000      3.000000  \n",
       "75%            58.160000      7.500000     14.000000  \n",
       "max            89.680000     11.800000     74.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb17d6e6a90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 12\n",
    "sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_phlevel(x):\n",
    "    if x > 7:\n",
    "        return 'Basic'\n",
    "    elif x < 7:\n",
    "        return 'Acidic'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "\n",
    "data['ph_level'] = data['phValue'].apply(set_phlevel)\n",
    "ph_level = data.groupby('ph_level')['ph_level'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# labels = ph_level.index.tolist()\n",
    "# sizes = np.round((ph_level.values) * 100 / sum(ph_level.values), 2).tolist()\n",
    "# fig1, ax1 = plt.subplots()\n",
    "# ax1.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "# ax1.axis('equal')  \n",
    "# plt.tight_layout()\n",
    "# plt.show()\n",
    "\n",
    "sizes = np.round((ph_level.values) / sum(ph_level.values), 2).tolist()\n",
    "plt.bar(x=labels, height=sizes)\n",
    "plt.xlabel('ph level')\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>phValue</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>class</th>\n",
       "      <th>ph_level</th>\n",
       "      <th>seq_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>29</td>\n",
       "      <td>Basic</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>29</td>\n",
       "      <td>Basic</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18093.78</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>29</td>\n",
       "      <td>Basic</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>153</td>\n",
       "      <td>1.71</td>\n",
       "      <td>18030.63</td>\n",
       "      <td>1.87</td>\n",
       "      <td>34.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>A</td>\n",
       "      <td>VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...</td>\n",
       "      <td>29</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105M</td>\n",
       "      <td>oxygen transport</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>153</td>\n",
       "      <td>2.02</td>\n",
       "      <td>18030.63</td>\n",
       "      <td>1.83</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...</td>\n",
       "      <td>29</td>\n",
       "      <td>Basic</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId    classification experimentalTechnique macromoleculeType  \\\n",
       "0        101M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "2        102M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "4        103M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "7        104M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "8        105M  oxygen transport     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight  densityMatthews  \\\n",
       "0           154        2.07                  18112.80             3.09   \n",
       "2           154        1.84                  18010.64             3.09   \n",
       "4           154        2.07                  18093.78             3.09   \n",
       "7           153        1.71                  18030.63             1.87   \n",
       "8           153        2.02                  18030.63             1.83   \n",
       "\n",
       "   densityPercentSol  phValue chainId  \\\n",
       "0               60.2      9.0       A   \n",
       "2               60.2      9.0       A   \n",
       "4               60.3      9.0       A   \n",
       "7               34.3      7.0       A   \n",
       "8               33.0      9.0       A   \n",
       "\n",
       "                                            sequence  class ph_level  seq_len  \n",
       "0  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...     29    Basic      154  \n",
       "2  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...     29    Basic      154  \n",
       "4  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...     29    Basic      154  \n",
       "7  VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...     29  Neutral      153  \n",
       "8  VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...     29    Basic      153  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['seq_len'] = data['sequence'].apply(lambda x: len(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb178228dd8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.scatterplot(x='densityMatthews', y='densityPercentSol', data=data, alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a signle train-test split model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80659, 6)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[['residueCount','resolution','structureMolecularWeight','densityMatthews','densityPercentSol','phValue']]\n",
    "y = data['class']\n",
    "\n",
    "scaler = StandardScaler().fit(X)\n",
    "X_norm =  scaler.transform(X)\n",
    "X_scaled = pd.DataFrame(X_norm, columns=X.columns)\n",
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>phValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.152404</td>\n",
       "      <td>0.324894</td>\n",
       "      <td>6.382702</td>\n",
       "      <td>0.204500</td>\n",
       "      <td>0.488665</td>\n",
       "      <td>-1.061108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.729687</td>\n",
       "      <td>-1.006610</td>\n",
       "      <td>-0.661070</td>\n",
       "      <td>0.117968</td>\n",
       "      <td>-0.385143</td>\n",
       "      <td>-0.033988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.166680</td>\n",
       "      <td>-0.340858</td>\n",
       "      <td>-0.160096</td>\n",
       "      <td>-0.559865</td>\n",
       "      <td>-0.496885</td>\n",
       "      <td>0.891268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.409765</td>\n",
       "      <td>-0.816395</td>\n",
       "      <td>-0.381731</td>\n",
       "      <td>-0.083939</td>\n",
       "      <td>0.170551</td>\n",
       "      <td>0.016943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.674504</td>\n",
       "      <td>-0.569116</td>\n",
       "      <td>-0.617517</td>\n",
       "      <td>-0.603131</td>\n",
       "      <td>-0.566347</td>\n",
       "      <td>-1.103551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   residueCount  resolution  structureMolecularWeight  densityMatthews  \\\n",
       "0      7.152404    0.324894                  6.382702         0.204500   \n",
       "1     -0.729687   -1.006610                 -0.661070         0.117968   \n",
       "2     -0.166680   -0.340858                 -0.160096        -0.559865   \n",
       "3     -0.409765   -0.816395                 -0.381731        -0.083939   \n",
       "4     -0.674504   -0.569116                 -0.617517        -0.603131   \n",
       "\n",
       "   densityPercentSol   phValue  \n",
       "0           0.488665 -1.061108  \n",
       "1          -0.385143 -0.033988  \n",
       "2          -0.496885  0.891268  \n",
       "3           0.170551  0.016943  \n",
       "4          -0.566347 -1.103551  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_norm =  scaler.transform(X_train)\n",
    "X_train_scaled = pd.DataFrame(X_train_norm, columns=X.columns)\n",
    "X_train_scaled.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>phValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.603927</td>\n",
       "      <td>1.104776</td>\n",
       "      <td>3.119560</td>\n",
       "      <td>1.675542</td>\n",
       "      <td>1.627232</td>\n",
       "      <td>-0.042477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.997053</td>\n",
       "      <td>1.846614</td>\n",
       "      <td>5.177880</td>\n",
       "      <td>0.521784</td>\n",
       "      <td>0.797719</td>\n",
       "      <td>0.636610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.050726</td>\n",
       "      <td>9.074780</td>\n",
       "      <td>-0.043027</td>\n",
       "      <td>1.415946</td>\n",
       "      <td>1.598038</td>\n",
       "      <td>0.551725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.182048</td>\n",
       "      <td>0.229787</td>\n",
       "      <td>-0.197343</td>\n",
       "      <td>-0.675241</td>\n",
       "      <td>-0.723391</td>\n",
       "      <td>-0.297135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.239858</td>\n",
       "      <td>0.515109</td>\n",
       "      <td>0.207901</td>\n",
       "      <td>-0.026252</td>\n",
       "      <td>0.128270</td>\n",
       "      <td>0.636610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   residueCount  resolution  structureMolecularWeight  densityMatthews  \\\n",
       "0      3.603927    1.104776                  3.119560         1.675542   \n",
       "1      5.997053    1.846614                  5.177880         0.521784   \n",
       "2     -0.050726    9.074780                 -0.043027         1.415946   \n",
       "3     -0.182048    0.229787                 -0.197343        -0.675241   \n",
       "4      0.239858    0.515109                  0.207901        -0.026252   \n",
       "\n",
       "   densityPercentSol   phValue  \n",
       "0           1.627232 -0.042477  \n",
       "1           0.797719  0.636610  \n",
       "2           1.598038  0.551725  \n",
       "3          -0.723391 -0.297135  \n",
       "4           0.128270  0.636610  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_norm = scaler.transform(X_test)\n",
    "X_test_scaled = pd.DataFrame(X_test_norm, columns=X.columns)\n",
    "X_test_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.91      0.82      2828\n",
      "           1       0.81      0.91      0.85      2332\n",
      "           2       0.89      0.90      0.90      2232\n",
      "           3       0.95      0.88      0.92       855\n",
      "           4       0.90      0.91      0.90       659\n",
      "           5       0.81      0.72      0.77       585\n",
      "           6       0.90      0.93      0.91       429\n",
      "           7       0.90      0.83      0.86       423\n",
      "           8       0.94      0.81      0.87       394\n",
      "           9       0.85      0.74      0.79       307\n",
      "          10       0.91      0.71      0.80       264\n",
      "          11       0.89      0.78      0.83       284\n",
      "          12       0.76      0.81      0.79       241\n",
      "          13       0.95      0.77      0.85       256\n",
      "          14       0.93      0.79      0.85       221\n",
      "          15       0.90      0.78      0.83       205\n",
      "          16       0.97      0.82      0.89       199\n",
      "          17       0.97      0.80      0.87       152\n",
      "          18       0.93      0.77      0.84       161\n",
      "          19       0.86      0.76      0.81       159\n",
      "          20       0.93      0.77      0.84       144\n",
      "          21       0.99      0.66      0.79       155\n",
      "          22       0.90      0.90      0.90       133\n",
      "          23       0.88      0.75      0.81       111\n",
      "          24       1.00      0.95      0.97       117\n",
      "          25       0.87      0.58      0.70       113\n",
      "          26       0.94      0.72      0.82       108\n",
      "          27       0.93      0.84      0.88        95\n",
      "          28       0.99      0.79      0.88        87\n",
      "          29       0.86      0.89      0.87        81\n",
      "          30       0.92      0.74      0.82        74\n",
      "          31       0.87      0.89      0.88        76\n",
      "          32       1.00      0.97      0.98        67\n",
      "          33       0.85      0.93      0.89        60\n",
      "          34       0.98      0.95      0.96        57\n",
      "          35       0.95      0.86      0.91        73\n",
      "          36       0.92      0.75      0.83        65\n",
      "          37       0.87      0.81      0.84        58\n",
      "          38       0.94      0.54      0.69        57\n",
      "          39       0.97      0.66      0.79        53\n",
      "          40       0.92      0.80      0.86        56\n",
      "          41       0.98      1.00      0.99        50\n",
      "          42       0.85      0.79      0.82        43\n",
      "          43       0.93      0.69      0.80        59\n",
      "          44       0.92      0.73      0.81        48\n",
      "          45       0.74      0.58      0.65        43\n",
      "          46       0.83      0.73      0.78        41\n",
      "          47       0.90      0.95      0.93        40\n",
      "          48       0.82      0.93      0.87        30\n",
      "          49       1.00      0.86      0.92        35\n",
      "          50       0.96      0.71      0.81        31\n",
      "          51       1.00      0.81      0.89        36\n",
      "          52       1.00      0.97      0.99        37\n",
      "          53       0.94      0.75      0.84        44\n",
      "          54       1.00      0.92      0.96        37\n",
      "          55       0.86      0.86      0.86        35\n",
      "          56       1.00      1.00      1.00        43\n",
      "          57       0.95      0.74      0.83        27\n",
      "          58       0.95      0.64      0.77        28\n",
      "          59       1.00      0.91      0.95        34\n",
      "          60       1.00      0.77      0.87        30\n",
      "          61       0.96      0.71      0.81        31\n",
      "          62       0.91      1.00      0.95        20\n",
      "          63       0.70      0.70      0.70        20\n",
      "          64       0.73      0.89      0.80        27\n",
      "          65       0.92      1.00      0.96        22\n",
      "          66       1.00      0.89      0.94        28\n",
      "          67       1.00      0.81      0.90        27\n",
      "          68       0.93      0.57      0.70        23\n",
      "          69       1.00      0.46      0.63        26\n",
      "          70       0.92      0.57      0.71        21\n",
      "          71       1.00      0.65      0.79        23\n",
      "          72       1.00      0.81      0.89        26\n",
      "          73       1.00      0.68      0.81        22\n",
      "          74       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           0.85     16132\n",
      "   macro avg       0.92      0.80      0.85     16132\n",
      "weighted avg       0.86      0.85      0.85     16132\n",
      "\n",
      "Accuracy : 0.854574758244483 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Random Forest with singal train-test split\n",
    "m = RandomForestClassifier(n_estimators=40, min_samples_leaf=3, n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "\n",
    "predictions = m.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.8506051730283447\n",
      "{'max_features': 4, 'n_estimators': 40}\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=4, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=3, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=40, n_jobs=-1,\n",
      "                       oob_score=False, random_state=None, verbose=0,\n",
      "                       warm_start=False)\n",
      "Test Accuracy : 0.8646788990825688 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest with cross validation and grid search\n",
    "param_grid = [{'n_estimators':[30, 40], \n",
    "               'max_features':[4,6]}]\n",
    "\n",
    "m = RandomForestClassifier(n_estimators=40, min_samples_leaf=3, n_jobs=-1)\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "grid_search = GridSearchCV(m, param_grid, cv=kfold)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train Accuracy :\",grid_search.best_score_)\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "predictions = grid_search.predict(X_test)\n",
    "print(\"Test Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.88      0.88      2828\n",
      "           1       0.91      0.89      0.90      2332\n",
      "           2       0.94      0.90      0.92      2232\n",
      "           3       0.92      0.91      0.91       855\n",
      "           4       0.93      0.93      0.93       659\n",
      "           5       0.80      0.81      0.81       585\n",
      "           6       0.89      0.93      0.91       429\n",
      "           7       0.85      0.90      0.87       423\n",
      "           8       0.91      0.88      0.89       394\n",
      "           9       0.79      0.83      0.81       307\n",
      "          10       0.79      0.81      0.80       264\n",
      "          11       0.82      0.82      0.82       284\n",
      "          12       0.82      0.80      0.81       241\n",
      "          13       0.87      0.81      0.84       256\n",
      "          14       0.94      0.85      0.89       221\n",
      "          15       0.88      0.84      0.86       205\n",
      "          16       0.86      0.87      0.87       199\n",
      "          17       0.81      0.83      0.82       152\n",
      "          18       0.85      0.85      0.85       161\n",
      "          19       0.83      0.84      0.83       159\n",
      "          20       0.82      0.83      0.82       144\n",
      "          21       0.84      0.86      0.85       155\n",
      "          22       0.89      0.93      0.91       133\n",
      "          23       0.71      0.82      0.76       111\n",
      "          24       0.97      0.97      0.97       117\n",
      "          25       0.71      0.81      0.76       113\n",
      "          26       0.79      0.84      0.82       108\n",
      "          27       0.78      0.93      0.85        95\n",
      "          28       0.88      0.91      0.89        87\n",
      "          29       0.91      0.85      0.88        81\n",
      "          30       0.81      0.85      0.83        74\n",
      "          31       0.86      0.92      0.89        76\n",
      "          32       0.92      0.97      0.94        67\n",
      "          33       0.87      0.97      0.91        60\n",
      "          34       0.80      1.00      0.89        57\n",
      "          35       0.83      0.89      0.86        73\n",
      "          36       0.95      0.86      0.90        65\n",
      "          37       0.82      0.79      0.81        58\n",
      "          38       0.73      0.72      0.73        57\n",
      "          39       0.76      0.83      0.79        53\n",
      "          40       0.78      0.91      0.84        56\n",
      "          41       1.00      1.00      1.00        50\n",
      "          42       0.81      0.81      0.81        43\n",
      "          43       0.81      0.80      0.80        59\n",
      "          44       0.98      0.96      0.97        48\n",
      "          45       0.55      0.72      0.63        43\n",
      "          46       0.71      0.78      0.74        41\n",
      "          47       0.87      0.97      0.92        40\n",
      "          48       0.83      1.00      0.91        30\n",
      "          49       0.85      0.94      0.89        35\n",
      "          50       0.93      0.84      0.88        31\n",
      "          51       0.83      0.94      0.88        36\n",
      "          52       1.00      1.00      1.00        37\n",
      "          53       0.72      0.75      0.73        44\n",
      "          54       0.97      0.95      0.96        37\n",
      "          55       0.82      0.89      0.85        35\n",
      "          56       1.00      1.00      1.00        43\n",
      "          57       0.84      0.78      0.81        27\n",
      "          58       0.64      0.82      0.72        28\n",
      "          59       1.00      0.91      0.95        34\n",
      "          60       0.77      0.67      0.71        30\n",
      "          61       0.85      0.90      0.88        31\n",
      "          62       1.00      1.00      1.00        20\n",
      "          63       0.86      0.90      0.88        20\n",
      "          64       0.89      0.89      0.89        27\n",
      "          65       0.92      1.00      0.96        22\n",
      "          66       0.83      0.89      0.86        28\n",
      "          67       0.96      0.85      0.90        27\n",
      "          68       0.86      0.83      0.84        23\n",
      "          69       0.92      0.88      0.90        26\n",
      "          70       0.60      0.57      0.59        21\n",
      "          71       0.85      0.74      0.79        23\n",
      "          72       1.00      0.88      0.94        26\n",
      "          73       0.59      0.77      0.67        22\n",
      "          74       0.95      1.00      0.97        19\n",
      "\n",
      "    accuracy                           0.88     16132\n",
      "   macro avg       0.85      0.87      0.86     16132\n",
      "weighted avg       0.88      0.88      0.88     16132\n",
      "\n",
      "Accuracy : 0.8786883213488718 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Decision tree with singal train-test split\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train, y_train)\n",
    "\n",
    "predictions = dtree.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.33275992995180315\n",
      "{'max_depth': 10, 'min_samples_leaf': 1}\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
      "                       max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort=False,\n",
      "                       random_state=None, splitter='best')\n",
      "Test Accuracy : 0.33951152987850236 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree with cross validation and grid search\n",
    "param_grid = [{'max_depth':[5,10],\n",
    "              'min_samples_leaf':[1, 5, 10]}]\n",
    "\n",
    "dtree = DecisionTreeClassifier()\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "grid_search = GridSearchCV(dtree, param_grid, cv=kfold)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(\"Train Accuracy :\",grid_search.best_score_)\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "predictions = grid_search.predict(X_test)\n",
    "print(\"Test Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.20      0.93      0.32      2828\n",
      "           1       0.85      0.12      0.21      2332\n",
      "           2       0.44      0.31      0.36      2232\n",
      "           3       0.61      0.06      0.10       855\n",
      "           4       0.93      0.06      0.11       659\n",
      "           5       0.89      0.01      0.03       585\n",
      "           6       0.80      0.02      0.04       429\n",
      "           7       0.54      0.02      0.03       423\n",
      "           8       0.00      0.00      0.00       394\n",
      "           9       1.00      0.02      0.04       307\n",
      "          10       0.86      0.09      0.17       264\n",
      "          11       0.69      0.12      0.21       284\n",
      "          12       1.00      0.02      0.05       241\n",
      "          13       0.88      0.03      0.05       256\n",
      "          14       0.00      0.00      0.00       221\n",
      "          15       0.76      0.11      0.19       205\n",
      "          16       1.00      0.14      0.25       199\n",
      "          17       0.36      0.03      0.05       152\n",
      "          18       0.91      0.19      0.31       161\n",
      "          19       1.00      0.07      0.13       159\n",
      "          20       0.00      0.00      0.00       144\n",
      "          21       0.00      0.00      0.00       155\n",
      "          22       0.41      0.05      0.09       133\n",
      "          23       0.00      0.00      0.00       111\n",
      "          24       0.89      0.14      0.24       117\n",
      "          25       0.00      0.00      0.00       113\n",
      "          26       1.00      0.02      0.04       108\n",
      "          27       1.00      0.04      0.08        95\n",
      "          28       1.00      0.02      0.04        87\n",
      "          29       1.00      0.19      0.31        81\n",
      "          30       1.00      0.30      0.46        74\n",
      "          31       0.54      0.09      0.16        76\n",
      "          32       0.50      0.01      0.03        67\n",
      "          33       0.57      0.22      0.31        60\n",
      "          34       1.00      0.19      0.32        57\n",
      "          35       1.00      0.11      0.20        73\n",
      "          36       1.00      0.09      0.17        65\n",
      "          37       1.00      0.02      0.03        58\n",
      "          38       0.00      0.00      0.00        57\n",
      "          39       1.00      0.08      0.14        53\n",
      "          40       1.00      0.05      0.10        56\n",
      "          41       0.96      0.44      0.60        50\n",
      "          42       1.00      0.05      0.09        43\n",
      "          43       0.67      0.07      0.12        59\n",
      "          44       1.00      0.04      0.08        48\n",
      "          45       1.00      0.12      0.21        43\n",
      "          46       0.91      0.51      0.66        41\n",
      "          47       0.70      0.17      0.28        40\n",
      "          48       1.00      0.03      0.06        30\n",
      "          49       0.75      0.17      0.28        35\n",
      "          50       1.00      0.58      0.73        31\n",
      "          51       1.00      0.17      0.29        36\n",
      "          52       1.00      0.11      0.20        37\n",
      "          53       1.00      0.02      0.04        44\n",
      "          54       1.00      0.22      0.36        37\n",
      "          55       0.75      0.17      0.28        35\n",
      "          56       1.00      0.98      0.99        43\n",
      "          57       1.00      0.44      0.62        27\n",
      "          58       0.80      0.14      0.24        28\n",
      "          59       1.00      0.47      0.64        34\n",
      "          60       0.00      0.00      0.00        30\n",
      "          61       1.00      0.19      0.32        31\n",
      "          62       1.00      0.35      0.52        20\n",
      "          63       0.00      0.00      0.00        20\n",
      "          64       1.00      0.48      0.65        27\n",
      "          65       1.00      0.05      0.09        22\n",
      "          66       1.00      0.18      0.30        28\n",
      "          67       1.00      0.04      0.07        27\n",
      "          68       1.00      0.04      0.08        23\n",
      "          69       1.00      0.08      0.14        26\n",
      "          70       0.00      0.00      0.00        21\n",
      "          71       0.90      0.39      0.55        23\n",
      "          72       1.00      0.08      0.14        26\n",
      "          73       0.50      0.09      0.15        22\n",
      "          74       0.67      0.11      0.18        19\n",
      "\n",
      "    accuracy                           0.26     16132\n",
      "   macro avg       0.75      0.15      0.20     16132\n",
      "weighted avg       0.60      0.26      0.20     16132\n",
      "\n",
      "Accuracy : 0.2626456731961319 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "xgboost = GradientBoostingClassifier(n_estimators=40, learning_rate=0.01, max_depth=3)\n",
    "xgboost.fit(X_train, y_train)\n",
    "predictions = xgboost.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-acec87787480>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomizedSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxgboost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    686\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1467\u001b[0m         evaluate_candidates(ParameterSampler(\n\u001b[1;32m   1468\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1469\u001b[0;31m             random_state=self.random_state))\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    665\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 667\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    922\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    514\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m   1544\u001b[0m         n_stages = self._fit_stages(\n\u001b[1;32m   1545\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rng\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1546\u001b[0;31m             sample_weight_val, begin_at_stage, monitor, X_idx_sorted)\n\u001b[0m\u001b[1;32m   1547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1548\u001b[0m         \u001b[0;31m# change shape of arrays after fit (early-stopping or additional ests)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[0;34m(self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage, monitor, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1608\u001b[0m             raw_predictions = self._fit_stage(\n\u001b[1;32m   1609\u001b[0m                 \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1610\u001b[0;31m                 random_state, X_idx_sorted, X_csc, X_csr)\n\u001b[0m\u001b[1;32m   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m             \u001b[0;31m# track deviance (= loss)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[0;34m(self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr)\u001b[0m\n\u001b[1;32m   1242\u001b[0m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_csr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX_csr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m             tree.fit(X, residual, sample_weight=sample_weight,\n\u001b[0;32m-> 1244\u001b[0;31m                      check_input=False, X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1246\u001b[0m             \u001b[0;31m# update tree leaves\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1155\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1156\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1157\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1158\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    378\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Gradient Boosting\n",
    "parameters = {\n",
    "    \"learning_rate\": [0.075, 0.1, 0.15,],\n",
    "    \"max_depth\":[5,8],\n",
    "    \"n_estimators\":[30, 40],\n",
    "    }\n",
    "# \"subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
    "# \"validation_fraction\":[0.1, 0.20, 0.50]\n",
    "\n",
    "xgboost = GradientBoostingClassifier(n_estimators=40, learning_rate=0.01, max_depth=1)\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "grid_search = RandomizedSearchCV(xgboost, parameters, cv=kfold)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train Accuracy :\",grid_search.best_score_)\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "predictions = grid_search.predict(X_test)\n",
    "print(\"Test Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.19      0.01      0.01      2828\n",
      "           1       0.18      0.31      0.22      2332\n",
      "           2       0.31      0.25      0.28      2232\n",
      "           3       0.19      0.04      0.07       855\n",
      "           4       0.00      0.00      0.00       659\n",
      "           5       0.04      0.32      0.08       585\n",
      "           6       0.00      0.00      0.00       429\n",
      "           7       0.00      0.00      0.00       423\n",
      "           8       0.00      0.00      0.00       394\n",
      "           9       0.03      0.17      0.05       307\n",
      "          10       0.00      0.00      0.00       264\n",
      "          11       0.00      0.00      0.00       284\n",
      "          12       0.00      0.00      0.00       241\n",
      "          13       0.00      0.00      0.00       256\n",
      "          14       0.00      0.00      0.00       221\n",
      "          15       0.00      0.00      0.00       205\n",
      "          16       0.00      0.00      0.00       199\n",
      "          17       0.00      0.00      0.00       152\n",
      "          18       0.00      0.00      0.00       161\n",
      "          19       0.00      0.00      0.00       159\n",
      "          20       0.00      0.00      0.00       144\n",
      "          21       0.00      0.00      0.00       155\n",
      "          22       0.00      0.00      0.00       133\n",
      "          23       0.00      0.00      0.00       111\n",
      "          24       0.00      0.00      0.00       117\n",
      "          25       0.00      0.00      0.00       113\n",
      "          26       0.00      0.00      0.00       108\n",
      "          27       0.00      0.00      0.00        95\n",
      "          28       0.00      0.00      0.00        87\n",
      "          29       0.00      0.00      0.00        81\n",
      "          30       0.00      0.00      0.00        74\n",
      "          31       0.00      0.00      0.00        76\n",
      "          32       0.00      0.00      0.00        67\n",
      "          33       0.00      0.00      0.00        60\n",
      "          34       0.00      0.00      0.00        57\n",
      "          35       0.00      0.00      0.00        73\n",
      "          36       0.00      0.00      0.00        65\n",
      "          37       0.00      0.00      0.00        58\n",
      "          38       0.00      0.00      0.00        57\n",
      "          39       0.00      0.00      0.00        53\n",
      "          40       0.00      0.00      0.00        56\n",
      "          41       0.65      0.30      0.41        50\n",
      "          42       0.00      0.00      0.00        43\n",
      "          43       0.00      0.00      0.00        59\n",
      "          44       0.00      0.00      0.00        48\n",
      "          45       0.00      0.00      0.00        43\n",
      "          46       0.00      0.00      0.00        41\n",
      "          47       0.00      0.00      0.00        40\n",
      "          48       0.00      0.00      0.00        30\n",
      "          49       0.00      0.00      0.00        35\n",
      "          50       0.00      0.00      0.00        31\n",
      "          51       0.00      0.00      0.00        36\n",
      "          52       0.01      0.16      0.01        37\n",
      "          53       0.00      0.00      0.00        44\n",
      "          54       0.00      0.00      0.00        37\n",
      "          55       0.01      0.29      0.02        35\n",
      "          56       0.08      0.56      0.14        43\n",
      "          57       0.00      0.00      0.00        27\n",
      "          58       0.00      0.00      0.00        28\n",
      "          59       0.00      0.00      0.00        34\n",
      "          60       0.00      0.00      0.00        30\n",
      "          61       0.00      0.00      0.00        31\n",
      "          62       0.00      0.00      0.00        20\n",
      "          63       0.00      0.00      0.00        20\n",
      "          64       0.03      0.85      0.05        27\n",
      "          65       0.00      0.00      0.00        22\n",
      "          66       0.00      0.00      0.00        28\n",
      "          67       0.00      0.00      0.00        27\n",
      "          68       0.00      0.00      0.00        23\n",
      "          69       0.00      0.00      0.00        26\n",
      "          70       0.00      0.00      0.00        21\n",
      "          71       0.00      0.00      0.00        23\n",
      "          72       0.00      0.00      0.00        26\n",
      "          73       0.00      0.00      0.00        22\n",
      "          74       0.00      0.00      0.00        19\n",
      "\n",
      "    accuracy                           0.10     16132\n",
      "   macro avg       0.02      0.04      0.02     16132\n",
      "weighted avg       0.12      0.10      0.08     16132\n",
      "\n",
      "Accuracy : 0.10259112323332507 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "m = GaussianNB()\n",
    "m.fit(X_train, y_train)\n",
    "predictions = m.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10196808 0.09669921 0.10289788 0.09840384 0.09700914 0.10227801\n",
      " 0.09979854 0.09686919 0.09795412 0.09779913]\n",
      "0.09916771458922445\n"
     ]
    }
   ],
   "source": [
    "nb = GaussianNB()\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "scores = cross_val_score(nb, X_train, y_train, cv=kfold)\n",
    "\n",
    "print(scores)\n",
    "print(scores.mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.15      0.07      0.09      2828\n",
      "           1       0.21      0.09      0.13      2332\n",
      "           2       0.24      0.13      0.17      2232\n",
      "           3       0.02      0.01      0.02       855\n",
      "           4       0.09      0.14      0.11       659\n",
      "           5       0.05      0.10      0.07       585\n",
      "           6       0.02      0.03      0.02       429\n",
      "           7       0.00      0.00      0.00       423\n",
      "           8       0.00      0.00      0.00       394\n",
      "           9       0.02      0.13      0.04       307\n",
      "          10       0.00      0.00      0.00       264\n",
      "          11       0.00      0.00      0.00       284\n",
      "          12       0.12      0.00      0.01       241\n",
      "          13       0.00      0.00      0.00       256\n",
      "          14       0.00      0.00      0.00       221\n",
      "          15       0.00      0.00      0.00       205\n",
      "          16       0.02      0.05      0.03       199\n",
      "          17       0.00      0.00      0.00       152\n",
      "          18       0.00      0.00      0.00       161\n",
      "          19       0.00      0.00      0.00       159\n",
      "          20       0.00      0.00      0.00       144\n",
      "          21       0.00      0.00      0.00       155\n",
      "          22       0.00      0.00      0.00       133\n",
      "          23       0.00      0.00      0.00       111\n",
      "          24       0.31      0.17      0.22       117\n",
      "          25       0.00      0.00      0.00       113\n",
      "          26       0.00      0.00      0.00       108\n",
      "          27       0.01      0.12      0.02        95\n",
      "          28       0.00      0.00      0.00        87\n",
      "          29       0.00      0.00      0.00        81\n",
      "          30       0.00      0.00      0.00        74\n",
      "          31       0.00      0.00      0.00        76\n",
      "          32       0.00      0.00      0.00        67\n",
      "          33       0.03      0.37      0.06        60\n",
      "          34       0.00      0.00      0.00        57\n",
      "          35       0.00      0.00      0.00        73\n",
      "          36       0.00      0.00      0.00        65\n",
      "          37       0.00      0.00      0.00        58\n",
      "          38       0.00      0.00      0.00        57\n",
      "          39       0.00      0.00      0.00        53\n",
      "          40       0.00      0.00      0.00        56\n",
      "          41       0.67      0.36      0.47        50\n",
      "          42       0.00      0.00      0.00        43\n",
      "          43       0.00      0.00      0.00        59\n",
      "          44       0.00      0.00      0.00        48\n",
      "          45       0.03      0.02      0.03        43\n",
      "          46       0.00      0.00      0.00        41\n",
      "          47       0.00      0.00      0.00        40\n",
      "          48       0.00      0.00      0.00        30\n",
      "          49       0.00      0.00      0.00        35\n",
      "          50       0.00      0.00      0.00        31\n",
      "          51       0.00      0.00      0.00        36\n",
      "          52       0.04      0.08      0.05        37\n",
      "          53       0.00      0.00      0.00        44\n",
      "          54       0.00      0.00      0.00        37\n",
      "          55       0.01      0.31      0.02        35\n",
      "          56       0.05      0.98      0.09        43\n",
      "          57       0.00      0.00      0.00        27\n",
      "          58       0.00      0.00      0.00        28\n",
      "          59       0.00      0.00      0.00        34\n",
      "          60       0.00      0.00      0.00        30\n",
      "          61       0.00      0.00      0.00        31\n",
      "          62       0.00      0.00      0.00        20\n",
      "          63       0.00      0.00      0.00        20\n",
      "          64       0.03      0.78      0.05        27\n",
      "          65       0.00      0.00      0.00        22\n",
      "          66       0.00      0.00      0.00        28\n",
      "          67       0.00      0.00      0.00        27\n",
      "          68       0.00      0.00      0.00        23\n",
      "          69       0.00      0.00      0.00        26\n",
      "          70       0.00      0.00      0.00        21\n",
      "          71       0.00      0.00      0.00        23\n",
      "          72       0.00      0.00      0.00        26\n",
      "          73       0.00      0.00      0.00        22\n",
      "          74       0.00      0.00      0.00        19\n",
      "\n",
      "    accuracy                           0.07     16132\n",
      "   macro avg       0.03      0.05      0.02     16132\n",
      "weighted avg       0.11      0.07      0.07     16132\n",
      "\n",
      "Accuracy : 0.065707909744607 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "m = MultinomialNB()\n",
    "m.fit(X_train, y_train)\n",
    "predictions = m.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.05919727 0.06090191 0.06136681 0.06260654 0.06059197 0.06105687\n",
      " 0.06229661 0.06633602 0.05951643 0.05719157]\n",
      "0.06110619934940894\n"
     ]
    }
   ],
   "source": [
    "m = MultinomialNB()\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "scores = cross_val_score(m, X_train, y_train, cv=kfold)\n",
    "print(scores)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.18      0.76      0.29      2828\n",
      "           1       0.16      0.18      0.17      2332\n",
      "           2       0.25      0.17      0.20      2232\n",
      "           3       0.00      0.00      0.00       855\n",
      "           4       0.00      0.00      0.00       659\n",
      "           5       0.00      0.00      0.00       585\n",
      "           6       0.00      0.00      0.00       429\n",
      "           7       0.00      0.00      0.00       423\n",
      "           8       0.00      0.00      0.00       394\n",
      "           9       0.00      0.00      0.00       307\n",
      "          10       0.00      0.00      0.00       264\n",
      "          11       0.00      0.00      0.00       284\n",
      "          12       0.00      0.00      0.00       241\n",
      "          13       0.00      0.00      0.00       256\n",
      "          14       0.00      0.00      0.00       221\n",
      "          15       0.00      0.00      0.00       205\n",
      "          16       0.00      0.00      0.00       199\n",
      "          17       0.00      0.00      0.00       152\n",
      "          18       0.00      0.00      0.00       161\n",
      "          19       0.00      0.00      0.00       159\n",
      "          20       0.00      0.00      0.00       144\n",
      "          21       0.00      0.00      0.00       155\n",
      "          22       0.00      0.00      0.00       133\n",
      "          23       0.00      0.00      0.00       111\n",
      "          24       0.00      0.00      0.00       117\n",
      "          25       0.00      0.00      0.00       113\n",
      "          26       0.00      0.00      0.00       108\n",
      "          27       0.00      0.00      0.00        95\n",
      "          28       0.00      0.00      0.00        87\n",
      "          29       0.00      0.00      0.00        81\n",
      "          30       0.00      0.00      0.00        74\n",
      "          31       0.00      0.00      0.00        76\n",
      "          32       0.00      0.00      0.00        67\n",
      "          33       0.00      0.00      0.00        60\n",
      "          34       0.00      0.00      0.00        57\n",
      "          35       0.00      0.00      0.00        73\n",
      "          36       0.00      0.00      0.00        65\n",
      "          37       0.00      0.00      0.00        58\n",
      "          38       0.00      0.00      0.00        57\n",
      "          39       0.00      0.00      0.00        53\n",
      "          40       0.00      0.00      0.00        56\n",
      "          41       0.00      0.00      0.00        50\n",
      "          42       0.00      0.00      0.00        43\n",
      "          43       0.00      0.00      0.00        59\n",
      "          44       0.00      0.00      0.00        48\n",
      "          45       0.00      0.00      0.00        43\n",
      "          46       0.00      0.00      0.00        41\n",
      "          47       0.00      0.00      0.00        40\n",
      "          48       0.00      0.00      0.00        30\n",
      "          49       0.00      0.00      0.00        35\n",
      "          50       0.00      0.00      0.00        31\n",
      "          51       0.00      0.00      0.00        36\n",
      "          52       0.00      0.00      0.00        37\n",
      "          53       0.00      0.00      0.00        44\n",
      "          54       0.00      0.00      0.00        37\n",
      "          55       0.00      0.00      0.00        35\n",
      "          56       0.00      0.00      0.00        43\n",
      "          57       0.00      0.00      0.00        27\n",
      "          58       0.00      0.00      0.00        28\n",
      "          59       0.00      0.00      0.00        34\n",
      "          60       0.00      0.00      0.00        30\n",
      "          61       0.00      0.00      0.00        31\n",
      "          62       0.00      0.00      0.00        20\n",
      "          63       0.00      0.00      0.00        20\n",
      "          64       0.12      0.11      0.11        27\n",
      "          65       0.00      0.00      0.00        22\n",
      "          66       0.00      0.00      0.00        28\n",
      "          67       0.00      0.00      0.00        27\n",
      "          68       0.00      0.00      0.00        23\n",
      "          69       0.00      0.00      0.00        26\n",
      "          70       0.00      0.00      0.00        21\n",
      "          71       0.00      0.00      0.00        23\n",
      "          72       0.00      0.00      0.00        26\n",
      "          73       0.00      0.00      0.00        22\n",
      "          74       0.00      0.00      0.00        19\n",
      "\n",
      "    accuracy                           0.18     16132\n",
      "   macro avg       0.01      0.02      0.01     16132\n",
      "weighted avg       0.09      0.18      0.10     16132\n",
      "\n",
      "Accuracy : 0.18292834118522192 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "svm = LinearSVC()\n",
    "\n",
    "svm.fit(X_train_scaled, y_train)\n",
    "\n",
    "predictions = svm.predict(X_test_scaled)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# Support Vector Machine\n",
    "param_grid = [{'C': [1, 10, 100]}, {'dual' : [True, False]}]\n",
    "\n",
    "svm = LinearSVC()\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "grid_search = GridSearchCV(svm, param_grid, cv=kfold)\n",
    "grid_search.fit(X_train_scaled, y_train)\n",
    "\n",
    "print(\"Train Accuracy :\",grid_search.best_score_)\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "predictions = grid_search.predict(X_test_scaled)\n",
    "print(\"Test Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.69      0.64      2828\n",
      "           1       0.67      0.67      0.67      2332\n",
      "           2       0.75      0.72      0.74      2232\n",
      "           3       0.77      0.72      0.75       855\n",
      "           4       0.72      0.78      0.75       659\n",
      "           5       0.59      0.56      0.58       585\n",
      "           6       0.65      0.76      0.70       429\n",
      "           7       0.61      0.69      0.65       423\n",
      "           8       0.61      0.58      0.60       394\n",
      "           9       0.61      0.56      0.58       307\n",
      "          10       0.64      0.55      0.59       264\n",
      "          11       0.62      0.57      0.60       284\n",
      "          12       0.56      0.51      0.54       241\n",
      "          13       0.75      0.66      0.70       256\n",
      "          14       0.70      0.66      0.68       221\n",
      "          15       0.77      0.67      0.72       205\n",
      "          16       0.83      0.68      0.75       199\n",
      "          17       0.65      0.62      0.64       152\n",
      "          18       0.68      0.60      0.64       161\n",
      "          19       0.67      0.59      0.63       159\n",
      "          20       0.70      0.63      0.66       144\n",
      "          21       0.75      0.59      0.66       155\n",
      "          22       0.66      0.70      0.68       133\n",
      "          23       0.59      0.55      0.57       111\n",
      "          24       0.92      0.91      0.92       117\n",
      "          25       0.69      0.50      0.58       113\n",
      "          26       0.71      0.56      0.63       108\n",
      "          27       0.61      0.63      0.62        95\n",
      "          28       0.69      0.67      0.68        87\n",
      "          29       0.61      0.62      0.61        81\n",
      "          30       0.70      0.65      0.67        74\n",
      "          31       0.75      0.72      0.74        76\n",
      "          32       0.82      0.91      0.87        67\n",
      "          33       0.78      0.78      0.78        60\n",
      "          34       0.62      0.61      0.62        57\n",
      "          35       0.65      0.71      0.68        73\n",
      "          36       0.66      0.60      0.63        65\n",
      "          37       0.60      0.62      0.61        58\n",
      "          38       0.43      0.35      0.38        57\n",
      "          39       0.88      0.43      0.58        53\n",
      "          40       0.77      0.71      0.74        56\n",
      "          41       0.94      1.00      0.97        50\n",
      "          42       0.58      0.58      0.58        43\n",
      "          43       0.56      0.54      0.55        59\n",
      "          44       0.80      0.73      0.76        48\n",
      "          45       0.62      0.42      0.50        43\n",
      "          46       0.68      0.61      0.64        41\n",
      "          47       0.67      0.85      0.75        40\n",
      "          48       0.73      0.80      0.76        30\n",
      "          49       0.82      0.80      0.81        35\n",
      "          50       0.67      0.65      0.66        31\n",
      "          51       0.47      0.69      0.56        36\n",
      "          52       0.74      0.86      0.80        37\n",
      "          53       0.71      0.57      0.63        44\n",
      "          54       0.97      0.86      0.91        37\n",
      "          55       0.75      0.60      0.67        35\n",
      "          56       0.91      1.00      0.96        43\n",
      "          57       0.68      0.48      0.57        27\n",
      "          58       0.76      0.46      0.58        28\n",
      "          59       0.90      0.79      0.84        34\n",
      "          60       0.68      0.50      0.58        30\n",
      "          61       0.66      0.61      0.63        31\n",
      "          62       0.67      0.90      0.77        20\n",
      "          63       0.59      0.50      0.54        20\n",
      "          64       0.71      0.81      0.76        27\n",
      "          65       0.75      0.82      0.78        22\n",
      "          66       0.92      0.79      0.85        28\n",
      "          67       0.63      0.44      0.52        27\n",
      "          68       0.67      0.43      0.53        23\n",
      "          69       0.63      0.46      0.53        26\n",
      "          70       0.50      0.43      0.46        21\n",
      "          71       0.73      0.48      0.58        23\n",
      "          72       0.94      0.62      0.74        26\n",
      "          73       0.54      0.32      0.40        22\n",
      "          74       0.65      0.89      0.76        19\n",
      "\n",
      "    accuracy                           0.67     16132\n",
      "   macro avg       0.70      0.65      0.67     16132\n",
      "weighted avg       0.68      0.67      0.67     16132\n",
      "\n",
      "Accuracy : 0.6728242003471361 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#KNN\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "predictions = knn.predict(X_test_scaled)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.8219969935065942\n",
      "{'n_neighbors': 15, 'weights': 'distance'}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=15, p=2,\n",
      "                     weights='distance')\n",
      "Test Accuracy : 0.8408132903545747 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# K-nearest naighbour with grid search and cross validation\n",
    "param_grid = [{'n_neighbors':[3, 5, 10, 15], \n",
    "               'weights':['uniform','distance']}]\n",
    "#'metric':['euclidean','manhattan']\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "\n",
    "grid_search = GridSearchCV(knn, param_grid, cv=kfold)\n",
    "grid_search.fit(X_train_scaled, y_train)\n",
    "\n",
    "print(\"Train Accuracy :\",grid_search.best_score_)\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "predictions = grid_search.predict(X_test_scaled)\n",
    "print(\"Test Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression with OneVsRest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.17      0.91      0.29      2828\n",
      "           1       0.00      0.00      0.00      2332\n",
      "           2       0.20      0.11      0.14      2232\n",
      "           3       0.00      0.00      0.00       855\n",
      "           4       0.00      0.00      0.00       659\n",
      "           5       0.00      0.00      0.00       585\n",
      "           6       0.00      0.00      0.00       429\n",
      "           7       0.00      0.00      0.00       423\n",
      "           8       0.00      0.00      0.00       394\n",
      "           9       0.00      0.00      0.00       307\n",
      "          10       0.00      0.00      0.00       264\n",
      "          11       0.00      0.00      0.00       284\n",
      "          12       0.00      0.00      0.00       241\n",
      "          13       0.00      0.00      0.00       256\n",
      "          14       0.00      0.00      0.00       221\n",
      "          15       0.00      0.00      0.00       205\n",
      "          16       0.52      0.06      0.11       199\n",
      "          17       0.00      0.00      0.00       152\n",
      "          18       0.00      0.00      0.00       161\n",
      "          19       0.00      0.00      0.00       159\n",
      "          20       0.00      0.00      0.00       144\n",
      "          21       0.00      0.00      0.00       155\n",
      "          22       0.00      0.00      0.00       133\n",
      "          23       0.00      0.00      0.00       111\n",
      "          24       0.00      0.00      0.00       117\n",
      "          25       0.00      0.00      0.00       113\n",
      "          26       0.00      0.00      0.00       108\n",
      "          27       0.00      0.00      0.00        95\n",
      "          28       0.00      0.00      0.00        87\n",
      "          29       0.00      0.00      0.00        81\n",
      "          30       0.00      0.00      0.00        74\n",
      "          31       0.00      0.00      0.00        76\n",
      "          32       0.00      0.00      0.00        67\n",
      "          33       0.00      0.00      0.00        60\n",
      "          34       0.00      0.00      0.00        57\n",
      "          35       0.00      0.00      0.00        73\n",
      "          36       0.00      0.00      0.00        65\n",
      "          37       0.00      0.00      0.00        58\n",
      "          38       0.00      0.00      0.00        57\n",
      "          39       0.00      0.00      0.00        53\n",
      "          40       0.00      0.00      0.00        56\n",
      "          41       0.69      0.18      0.29        50\n",
      "          42       0.00      0.00      0.00        43\n",
      "          43       0.00      0.00      0.00        48\n",
      "          44       0.00      0.00      0.00        59\n",
      "          45       0.00      0.00      0.00        43\n",
      "          46       0.00      0.00      0.00        41\n",
      "          47       0.00      0.00      0.00        40\n",
      "          48       0.00      0.00      0.00        30\n",
      "          49       0.00      0.00      0.00        35\n",
      "          50       0.00      0.00      0.00        31\n",
      "          51       0.00      0.00      0.00        36\n",
      "          52       0.00      0.00      0.00        37\n",
      "          53       0.00      0.00      0.00        44\n",
      "          54       0.00      0.00      0.00        37\n",
      "          55       0.00      0.00      0.00        35\n",
      "          56       0.00      0.00      0.00        43\n",
      "          57       0.00      0.00      0.00        27\n",
      "          58       0.00      0.00      0.00        28\n",
      "          59       0.00      0.00      0.00        34\n",
      "          60       0.00      0.00      0.00        30\n",
      "          61       0.00      0.00      0.00        31\n",
      "          62       0.00      0.00      0.00        20\n",
      "          63       0.00      0.00      0.00        20\n",
      "          64       1.00      0.11      0.20        27\n",
      "          65       0.00      0.00      0.00        22\n",
      "          66       0.00      0.00      0.00        28\n",
      "          67       0.00      0.00      0.00        27\n",
      "          68       0.00      0.00      0.00        23\n",
      "          69       0.00      0.00      0.00        26\n",
      "          70       0.00      0.00      0.00        21\n",
      "          71       0.00      0.00      0.00        23\n",
      "          72       0.00      0.00      0.00        26\n",
      "          73       0.00      0.00      0.00        22\n",
      "          74       0.00      0.00      0.00        19\n",
      "\n",
      "    accuracy                           0.18     16132\n",
      "   macro avg       0.03      0.02      0.01     16132\n",
      "weighted avg       0.07      0.18      0.07     16132\n",
      "\n",
      "Accuracy : 0.1764815273989586 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Logistic with single train test split\n",
    "logreg = LogisticRegression(random_state=42, solver='lbfgs', multi_class='multinomial')\n",
    "mulreg = OneVsRestClassifier(logreg)\n",
    "mulreg.fit(X_train, y_train)\n",
    "predictions = mulreg.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(\"Accuracy :\",accuracy_score(y_test, predictions),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.18162095 0.17650705 0.18084612 0.17666202 0.18456532 0.17480242\n",
      " 0.18084612 0.17622443 0.18133912 0.17622443]\n",
      "0.17896379649733027\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(solver='lbfgs', multi_class='multinomial')\n",
    "mulreg = OneVsRestClassifier(logreg)\n",
    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "scores = cross_val_score(mulreg, X_train, y_train, cv=kfold)\n",
    "print(scores)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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