quora_ml_other classes.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Nt8FJ</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12087620705283</td>\n",
       "      <td>2</td>\n",
       "      <td>4.797982</td>\n",
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       "      <td>4.653960</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VCaTF</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>282114466020</td>\n",
       "      <td>2</td>\n",
       "      <td>3.151926</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.737670</td>\n",
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       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>2.890372</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gParY</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>173284955</td>\n",
       "      <td>2</td>\n",
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       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DtWDw</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4708728355523</td>\n",
       "      <td>2</td>\n",
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       "      <td>3.091042</td>\n",
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       "      <td>19</td>\n",
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       "      <td>20</td>\n",
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       "      <td>21</td>\n",
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       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>4hUov</td>\n",
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       "      <td>1</td>\n",
       "      <td>1459162488</td>\n",
       "      <td>2</td>\n",
       "      <td>2.002920</td>\n",
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       "      <td>0.0</td>\n",
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       "<p>5 rows × 48 columns</p>\n",
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      ],
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       "      0   1   2               3   4         5   6   7   8         9  ...   38  \\\n",
       "0  Nt8FJ   1   1  12087620705283   2  4.797982   3   1   4  4.990433 ...   19   \n",
       "1  VCaTF   1   1    282114466020   2  3.151926   3   1   4  3.737670 ...   19   \n",
       "2  gParY   1   1       173284955   2  1.785813   3   1   4  1.791759 ...   19   \n",
       "3  DtWDw   1   1   4708728355523   2  2.394989   3   1   4  3.091042 ...   19   \n",
       "4  4hUov   1   1      1459162488   2  2.002920   3   1   4  4.836282 ...   19   \n",
       "\n",
       "   39  40  41  42        43  44   45  46   47  \n",
       "0   0  20  14  21  4.653960  22  0.0  23  0.0  \n",
       "1   0  20   2  21  2.890372  22  0.0  23  0.0  \n",
       "2   0  20   6  21  3.637586  22  0.0  23  0.0  \n",
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       "4   0  20   7  21  2.397895  22  0.0  23  0.0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/home/piyush/Documents/quora ml/input/input00.csv\",header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>6</td>\n",
       "      <td>3.637586</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2.394989</td>\n",
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       "      <td>3.091042</td>\n",
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       "      <td>10</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   1         5   7         9         11    13   15   17  19  21        23  25  \\\n",
       "0   1  4.797982   1  4.990433  6.492240  80.0  0.0  0.0   0   0  3.850148   0   \n",
       "1   1  3.151926   1  3.737670  3.891820  83.0  0.0  0.0   1   0  2.564949   0   \n",
       "2   1  1.785813   1  1.791759  1.945910   0.0  0.0  0.0   0   0  4.262680   2   \n",
       "3   1  2.394989   1  3.091042  2.890372   0.0  0.0  0.0   0   0  4.804021   8   \n",
       "4   1  2.002920   1  4.836282  4.158883   0.0  0.0  0.0   0   0  4.521789  10   \n",
       "\n",
       "   27  29  31  33  35  37  41        43  \n",
       "0   1   0   0   1   1   0  14  4.653960  \n",
       "1   1   0   0   1   0   0   2  2.890372  \n",
       "2   1   0   0   1   0   0   6  3.637586  \n",
       "3   1   0   0   1   1   0   5  2.772589  \n",
       "4   1   0   0   1   0   0   7  2.397895  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    if i>0 and i%2==0:\n",
    "        df.drop(i,inplace=True,axis=1)\n",
    "df.drop([0,3,45,47,39],inplace=True,axis=1)\n",
    "df.head()\n",
    "##drop 39 because we find feature importance 0 in dec tree\n",
    "##0,3 are drpped because all values are different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2531\n",
      "2\n",
      "415\n",
      "524\n",
      "172\n",
      "161\n",
      "227\n",
      "7\n",
      "2\n",
      "336\n",
      "45\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "12\n",
      "79\n",
      "232\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(df[i].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>5</th>\n",
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       "      <th>0</th>\n",
       "      <td>4.797982</td>\n",
       "      <td>1</td>\n",
       "      <td>4.990433</td>\n",
       "      <td>6.492240</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.151926</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.785813</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3.637586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.394989</td>\n",
       "      <td>1</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>2.890372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.002920</td>\n",
       "      <td>1</td>\n",
       "      <td>4.836282</td>\n",
       "      <td>4.158883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.521789</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         5   7         9         11    13   15   17  19  21        23  25  27  \\\n",
       "0  4.797982   1  4.990433  6.492240  80.0  0.0  0.0   0   0  3.850148   0   1   \n",
       "1  3.151926   1  3.737670  3.891820  83.0  0.0  0.0   1   0  2.564949   0   1   \n",
       "2  1.785813   1  1.791759  1.945910   0.0  0.0  0.0   0   0  4.262680   2   1   \n",
       "3  2.394989   1  3.091042  2.890372   0.0  0.0  0.0   0   0  4.804021   8   1   \n",
       "4  2.002920   1  4.836282  4.158883   0.0  0.0  0.0   0   0  4.521789  10   1   \n",
       "\n",
       "   29  31  33  35  37  41        43  \n",
       "0   0   0   1   1   0  14  4.653960  \n",
       "1   0   0   1   0   0   2  2.890372  \n",
       "2   0   0   1   0   0   6  3.637586  \n",
       "3   0   0   1   1   0   5  2.772589  \n",
       "4   0   0   1   0   0   7  2.397895  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,1:]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[1]\n",
    "y.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9852777777777778"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "rf.fit(X_train,y_train)\n",
    "rf.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8111111111111111"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8241666666666667"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)\n",
    "lr.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8211111111111111"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sv =SVC(kernel=\"linear\",probability=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8213888888888888"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv.fit(X_train,y_train)\n",
    "sv.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8133333333333334"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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