blackfriday_case4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
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       "      <td>3</td>\n",
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       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <td>A</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <td>A</td>\n",
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       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN      8370  \n",
       "1                 6.0                14.0     15200  \n",
       "2                 NaN                 NaN      1422  \n",
       "3                14.0                 NaN      1057  \n",
       "4                 NaN                 NaN      7969  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000004  P00128942      M  46-50           7             B   \n",
       "1  1000009  P00113442      M  26-35          17             C   \n",
       "2  1000010  P00288442      F  36-45           1             B   \n",
       "3  1000010  P00145342      F  36-45           1             B   \n",
       "4  1000011  P00053842      F  26-35           1             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               1                   1   \n",
       "1                          0               0                   3   \n",
       "2                         4+               1                   5   \n",
       "3                         4+               1                   4   \n",
       "4                          1               0                   4   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  \n",
       "0                11.0                 NaN  \n",
       "1                 5.0                 NaN  \n",
       "2                14.0                 NaN  \n",
       "3                 9.0                 NaN  \n",
       "4                 5.0                12.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('Sample_Submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.head()\n",
    "test_to_check=submission['Purchase']\n",
    "test_to_check.to_csv(r'C:\\Users\\sk\\AnacondaProjects\\blackfriday\\test_to_check.csv')\n",
    "res=np.array(submission['Purchase'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(550068, 12)\n",
      "(233599, 11)\n",
      "(233599, 3)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)\n",
    "print(submission.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(783667, 12)\n",
      "    Age City_Category Gender  Marital_Status  Occupation  Product_Category_1  \\\n",
      "0  0-17             A      F               0          10                   3   \n",
      "1  0-17             A      F               0          10                   1   \n",
      "2  0-17             A      F               0          10                  12   \n",
      "3  0-17             A      F               0          10                  12   \n",
      "4   55+             C      M               0          16                   8   \n",
      "\n",
      "   Product_Category_2  Product_Category_3 Product_ID  Purchase  \\\n",
      "0                 NaN                 NaN  P00069042    8370.0   \n",
      "1                 6.0                14.0  P00248942   15200.0   \n",
      "2                 NaN                 NaN  P00087842    1422.0   \n",
      "3                14.0                 NaN  P00085442    1057.0   \n",
      "4                 NaN                 NaN  P00285442    7969.0   \n",
      "\n",
      "  Stay_In_Current_City_Years  User_ID  \n",
      "0                          2  1000001  \n",
      "1                          2  1000001  \n",
      "2                          2  1000001  \n",
      "3                          2  1000001  \n",
      "4                         4+  1000002  \n"
     ]
    }
   ],
   "source": [
    "frames=[train,test]\n",
    "input=pd.concat(frames)\n",
    "input.to_csv(r'C:\\Users\\sk\\AnacondaProjects\\blackfriday\\input_total.csv')\n",
    "print (input.shape)\n",
    "print(input.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Product_Category_3</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>User_ID</th>\n",
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       "      <td>1422.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
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       "      <th>4</th>\n",
       "      <td>55+</td>\n",
       "      <td>C</td>\n",
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       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>4+</td>\n",
       "      <td>1000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age City_Category Gender  Marital_Status  Occupation  Product_Category_1  \\\n",
       "0  0-17             A      F               0          10                   3   \n",
       "1  0-17             A      F               0          10                   1   \n",
       "2  0-17             A      F               0          10                  12   \n",
       "3  0-17             A      F               0          10                  12   \n",
       "4   55+             C      M               0          16                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3 Product_ID  Purchase  \\\n",
       "0                 NaN                 NaN  P00069042    8370.0   \n",
       "1                 6.0                14.0  P00248942   15200.0   \n",
       "2                 NaN                 NaN  P00087842    1422.0   \n",
       "3                14.0                 NaN  P00085442    1057.0   \n",
       "4                 NaN                 NaN  P00285442    7969.0   \n",
       "\n",
       "  Stay_In_Current_City_Years  User_ID  \n",
       "0                          2  1000001  \n",
       "1                          2  1000001  \n",
       "2                          2  1000001  \n",
       "3                          2  1000001  \n",
       "4                         4+  1000002  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                            object\n",
       "City_Category                  object\n",
       "Gender                         object\n",
       "Marital_Status                  int64\n",
       "Occupation                      int64\n",
       "Product_Category_1              int64\n",
       "Product_Category_2            float64\n",
       "Product_Category_3            float64\n",
       "Product_ID                     object\n",
       "Purchase                      float64\n",
       "Stay_In_Current_City_Years     object\n",
       "User_ID                         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "input.fillna(999,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Product_ID</th>\n",
       "      <th>Purchase</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55+</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>4+</td>\n",
       "      <td>1000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age City_Category Gender  Marital_Status  Occupation  Product_Category_1  \\\n",
       "0  0-17             A      F               0          10                   3   \n",
       "1  0-17             A      F               0          10                   1   \n",
       "2  0-17             A      F               0          10                  12   \n",
       "3  0-17             A      F               0          10                  12   \n",
       "4   55+             C      M               0          16                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3 Product_ID  Purchase  \\\n",
       "0               999.0               999.0  P00069042    8370.0   \n",
       "1                 6.0                14.0  P00248942   15200.0   \n",
       "2               999.0               999.0  P00087842    1422.0   \n",
       "3                14.0               999.0  P00085442    1057.0   \n",
       "4               999.0               999.0  P00285442    7969.0   \n",
       "\n",
       "  Stay_In_Current_City_Years  User_ID  \n",
       "0                          2  1000001  \n",
       "1                          2  1000001  \n",
       "2                          2  1000001  \n",
       "3                          2  1000001  \n",
       "4                         4+  1000002  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=input.Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=np.array(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "input.drop(['Purchase'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Age', 'City_Category', 'Gender', 'Marital_Status', 'Occupation',\n",
      "       'Product_Category_1', 'Product_Category_2', 'Product_Category_3',\n",
      "       'Product_ID', 'Stay_In_Current_City_Years', 'User_ID'],\n",
      "      dtype='object') Age                            object\n",
      "City_Category                  object\n",
      "Gender                         object\n",
      "Marital_Status                  int64\n",
      "Occupation                      int64\n",
      "Product_Category_1              int64\n",
      "Product_Category_2            float64\n",
      "Product_Category_3            float64\n",
      "Product_ID                     object\n",
      "Stay_In_Current_City_Years     object\n",
      "User_ID                         int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(input.columns,input.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           object\n",
       "City_Category                 object\n",
       "Gender                        object\n",
       "Marital_Status                object\n",
       "Occupation                    object\n",
       "Product_Category_1            object\n",
       "Product_Category_2            object\n",
       "Product_Category_3            object\n",
       "Product_ID                    object\n",
       "Stay_In_Current_City_Years    object\n",
       "User_ID                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input=input.applymap(str)\n",
    "input.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_pd=input.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn import linear_model\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "input=np.array(input)\n",
    "\n",
    "for i in range(input.shape[1]):\n",
    "    lbl=preprocessing.LabelEncoder()\n",
    "    lbl.fit(list(input[:,i]))\n",
    "    input[:,i]=lbl.transform(input[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 2 1 0 8 18 17 15 2769 4 1]\n"
     ]
    }
   ],
   "source": [
    "print(input[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "input=input.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "#xgboost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={}\n",
    "params['min_child_weight']=10\n",
    "params['subsample']=0.7\n",
    "params['colsample_bytree']=0.7\n",
    "params['scale_pos_weight']=0.8\n",
    "params['silent']=1\n",
    "params['max_depth']=6\n",
    "params['nthread']=6\n",
    "params['objective']='reg:linear'\n",
    "params['eta']=0.1\n",
    "params['base_score']=1800\n",
    "params['eval_metric']='rmse'\n",
    "params['seed']=0\n",
    "plst=list(params.items())\n",
    "num_rounds=300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+pCVlUVWVhaDBg2idevW1Kyp4RaRPIW90fyTu2/fVVTFzCpRwBTXB6AHcIm7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa44afe550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'f0': 126083841.39129546,\n",
       " 'f1': 238378383.7134553,\n",
       " 'f10': 193910198.75650272,\n",
       " 'f2': 92954207.15447155,\n",
       " 'f3': 97840719.41645244,\n",
       " 'f4': 109823364.47848412,\n",
       " 'f5': 16769464181.98379,\n",
       " 'f6': 1144273232.8295887,\n",
       " 'f7': 865824058.9723403,\n",
       " 'f8': 684836281.6213766,\n",
       " 'f9': 102460940.94430234}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "xgtrain=xgb.DMatrix(input[:train.shape[0],:],label=target[:train.shape[0]])\n",
    "watchlist=[(xgtrain,'train')]\n",
    "model_1_xgboost=xgb.train(plst,xgtrain,num_rounds)\n",
    "plot_importance(model_1_xgboost)\n",
    "plt.show()\n",
    "'''print(model_1_xgboost.feature_importances_)\n",
    "plt.bar(range(len(model_1_xgboost.feature_importances_)),model_1_xgboost.feature_importances_)\n",
    "plt.show()'''\n",
    "model_1_xgboost.get_score(importance_type='gain')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_predict=model_1_xgboost.predict(xgb.DMatrix(input[train.shape[0]:,:]))\n",
    "model_1_predict[model_1_predict<0]=25\n",
    "submission.Purchase=model_1_predict\n",
    "#submission.to_csv('../submission/submit_13.csv',index=False)\n",
    "test_to_check=submission['Purchase']\n",
    "test_to_check.to_csv(r'C:\\Users\\sk\\AnacondaProjects\\blackfriday\\test_to_check.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        User_ID Product_ID      Purchase\n",
      "0       1000004  P00128942  15300.768555\n",
      "1       1000009  P00113442  11046.294922\n",
      "2       1000010  P00288442   6833.807129\n",
      "3       1000010  P00145342   2857.034180\n",
      "4       1000011  P00053842   2299.368652\n",
      "5       1000013  P00350442  10489.735352\n",
      "6       1000013  P00155442  12089.356445\n",
      "7       1000013   P0094542   9814.919922\n",
      "8       1000015  P00161842  12556.844727\n",
      "9       1000022  P00067942   5222.706055\n",
      "10      1000026  P00046742  14146.349609\n",
      "11      1000026  P00040042   4620.926758\n",
      "12      1000026  P00196542   5893.847656\n",
      "13      1000026  P00004542   5579.830078\n",
      "14      1000028  P00159542  16736.949219\n",
      "15      1000029  P00111542  13166.423828\n",
      "16      1000033  P00121042  15811.344727\n",
      "17      1000033  P00344442   6019.159180\n",
      "18      1000034  P00265242   7724.531250\n",
      "19      1000035   P0096642  10922.911133\n",
      "20      1000036  P00303042   6883.804688\n",
      "21      1000036  P00059642  15128.433594\n",
      "22      1000042  P00030842  14829.981445\n",
      "23      1000045  P00346442  11236.896484\n",
      "24      1000045  P00357242   5779.479492\n",
      "25      1000045  P00284742   6068.187012\n",
      "26      1000048  P00110842  15942.547852\n",
      "27      1000048  P00251642   9359.730469\n",
      "28      1000053  P00136742  14504.949219\n",
      "29      1000053   P0097342  13618.897461\n",
      "...         ...        ...           ...\n",
      "233569  1005996  P00184242  16486.650391\n",
      "233570  1005996  P00045342  11845.574219\n",
      "233571  1006001  P00124642  15090.599609\n",
      "233572  1006002  P00121142   8559.880859\n",
      "233573  1006002  P00233542  14031.758789\n",
      "233574  1006002  P00226342   5133.806641\n",
      "233575  1006002  P00284842  15653.131836\n",
      "233576  1006003  P00313342   6760.653809\n",
      "233577  1006010  P00223542  14445.941406\n",
      "233578  1006013  P00020342  10577.417969\n",
      "233579  1006016  P00014842   9931.306641\n",
      "233580  1006016  P00109542   7312.892578\n",
      "233581  1006018  P00071442  18633.371094\n",
      "233582  1006021  P00112542  15682.200195\n",
      "233583  1006021  P00003442   2357.438232\n",
      "233584  1006023  P00262242  12507.671875\n",
      "233585  1006025  P00161142   3581.994141\n",
      "233586  1006026  P00110742  16268.005859\n",
      "233587  1006028  P00162442  12310.365234\n",
      "233588  1006030  P00129842  15145.045898\n",
      "233589  1006030  P00241442  10617.435547\n",
      "233590  1006032  P00252442   7301.714355\n",
      "233591  1006033  P00127642  14735.732422\n",
      "233592  1006035  P00135742  13808.451172\n",
      "233593  1006036  P00255442   6797.984375\n",
      "233594  1006036  P00118942   7626.685059\n",
      "233595  1006036  P00254642   5821.574219\n",
      "233596  1006036  P00031842  10220.858398\n",
      "233597  1006037  P00124742  17770.888672\n",
      "233598  1006039  P00316642   2683.623047\n",
      "\n",
      "[233599 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(r'C:\\Users\\sk\\AnacondaProjects\\blackfriday\\submit_xgboost.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:8326.14+2.94996\ttest-rmse:8326.19+12.3312\n",
      "[1]\ttrain-rmse:7658.58+58.8422\ttest-rmse:7658.65+65.6778\n",
      "[2]\ttrain-rmse:7068.71+97.7133\ttest-rmse:7068.81+95.8446\n",
      "[3]\ttrain-rmse:6533.33+57.0043\ttest-rmse:6533.22+53.5358\n",
      "[4]\ttrain-rmse:6044.63+94.8667\ttest-rmse:6044.49+91.2869\n",
      "[5]\ttrain-rmse:5612.83+82.9465\ttest-rmse:5612.89+78.2282\n",
      "[6]\ttrain-rmse:5264.58+40.8786\ttest-rmse:5264.96+37.0614\n",
      "[7]\ttrain-rmse:4946.07+68.307\ttest-rmse:4946.5+68.4038\n",
      "[8]\ttrain-rmse:4635.86+57.7096\ttest-rmse:4636.49+58.0789\n",
      "[9]\ttrain-rmse:4402.8+47.4362\ttest-rmse:4403.62+49.6353\n",
      "[10]\ttrain-rmse:4181.28+58.8049\ttest-rmse:4182.38+63.6757\n",
      "[11]\ttrain-rmse:4030.2+72.374\ttest-rmse:4031.4+75.6029\n",
      "[12]\ttrain-rmse:3877.62+72.8604\ttest-rmse:3879.07+76.321\n",
      "[13]\ttrain-rmse:3718.02+60.9663\ttest-rmse:3719.67+63.9805\n",
      "[14]\ttrain-rmse:3581.36+52.409\ttest-rmse:3583.23+54.9733\n",
      "[15]\ttrain-rmse:3471.24+39.3609\ttest-rmse:3473.23+41.6566\n",
      "[16]\ttrain-rmse:3388.44+26.5429\ttest-rmse:3390.4+29.8892\n",
      "[17]\ttrain-rmse:3317.65+38.239\ttest-rmse:3319.79+41.9152\n",
      "[18]\ttrain-rmse:3252.39+27.5969\ttest-rmse:3254.8+30.0134\n",
      "[19]\ttrain-rmse:3194.03+23.799\ttest-rmse:3196.53+23.6344\n",
      "[20]\ttrain-rmse:3144.4+27.2086\ttest-rmse:3147.28+28.1877\n",
      "[21]\ttrain-rmse:3107.55+30.1954\ttest-rmse:3110.72+32.1205\n",
      "[22]\ttrain-rmse:3066.92+25.6749\ttest-rmse:3070.19+27.4261\n",
      "[23]\ttrain-rmse:3038.62+28.628\ttest-rmse:3042.11+30.318\n",
      "[24]\ttrain-rmse:3015.13+21.7128\ttest-rmse:3018.61+23.6893\n",
      "[25]\ttrain-rmse:2996.24+23.3792\ttest-rmse:2999.87+25.9213\n",
      "[26]\ttrain-rmse:2979.63+25.1629\ttest-rmse:2983.32+26.699\n",
      "[27]\ttrain-rmse:2965+27.0161\ttest-rmse:2968.91+28.8836\n",
      "[28]\ttrain-rmse:2948.46+21.8558\ttest-rmse:2952.48+23.7405\n",
      "[29]\ttrain-rmse:2937.48+22.5004\ttest-rmse:2941.71+25.1251\n",
      "[30]\ttrain-rmse:2925.15+18.2788\ttest-rmse:2929.38+20.6895\n",
      "[31]\ttrain-rmse:2914.2+14.864\ttest-rmse:2918.62+17.6084\n",
      "[32]\ttrain-rmse:2905.16+12.0713\ttest-rmse:2909.7+14.0088\n",
      "[33]\ttrain-rmse:2898.74+12.962\ttest-rmse:2903.38+15.0666\n",
      "[34]\ttrain-rmse:2891.52+11.8685\ttest-rmse:2896.31+13.1912\n",
      "[35]\ttrain-rmse:2885.42+12.4192\ttest-rmse:2890.32+13.9588\n",
      "[36]\ttrain-rmse:2878.31+11.2822\ttest-rmse:2883.29+12.4866\n",
      "[37]\ttrain-rmse:2872.63+9.94459\ttest-rmse:2877.65+10.5004\n",
      "[38]\ttrain-rmse:2865.87+9.15924\ttest-rmse:2870.88+10.7766\n",
      "[39]\ttrain-rmse:2860.79+8.72048\ttest-rmse:2865.96+10.7477\n",
      "[40]\ttrain-rmse:2857.04+7.5319\ttest-rmse:2862.36+9.52433\n",
      "[41]\ttrain-rmse:2853.45+7.47412\ttest-rmse:2858.9+9.93337\n",
      "[42]\ttrain-rmse:2849.48+6.89842\ttest-rmse:2855.13+9.8129\n",
      "[43]\ttrain-rmse:2845.29+5.82985\ttest-rmse:2851.08+8.78434\n",
      "[44]\ttrain-rmse:2841.95+5.66986\ttest-rmse:2847.89+8.31548\n",
      "[45]\ttrain-rmse:2838.45+5.17979\ttest-rmse:2844.42+9.28446\n",
      "[46]\ttrain-rmse:2834.71+4.28052\ttest-rmse:2840.68+9.66907\n",
      "[47]\ttrain-rmse:2831.06+4.44687\ttest-rmse:2837.12+9.78329\n",
      "[48]\ttrain-rmse:2828.14+3.99854\ttest-rmse:2834.33+10.0635\n",
      "[49]\ttrain-rmse:2825.42+4.16476\ttest-rmse:2831.66+10.7497\n",
      "test-rmse-mean     3683.411766\n",
      "test-rmse-std        32.376238\n",
      "train-rmse-mean    3680.159913\n",
      "train-rmse-std       30.239733\n",
      "dtype: float64 test-rmse-mean     1358.989243\n",
      "test-rmse-std        24.507392\n",
      "train-rmse-mean    1360.654710\n",
      "train-rmse-std       25.645301\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "early_stopping=10\n",
    "cv=xgb.cv (params,xgtrain,50,nfold=5,early_stopping_rounds=early_stopping, verbose_eval=1)\n",
    "print(cv.mean(),cv.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#second model\n",
    "transformer = TfidfTransformer()\n",
    "tfidf = transformer.fit_transform(input.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<783667x11 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 7318716 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_tfidf=tfidf.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {}\n",
    "params[\"min_child_weight\"] = 10\n",
    "params[\"subsample\"] = 0.7\n",
    "params[\"colsample_bytree\"] = 0.7\n",
    "params[\"scale_pos_weight\"] = 0.8\n",
    "params[\"silent\"] = 1\n",
    "params[\"max_depth\"] = 12\n",
    "params[\"nthread\"] = 6\n",
    "#params[\"gamma\"] = 1\n",
    "params[\"objective\"] = \"reg:linear\"\n",
    "params[\"eta\"] = 0.1\n",
    "params[\"base_score\"] = 1800\n",
    "params[\"eval_metric\"] = \"rmse\"\n",
    "params[\"seed\"] = 0\n",
    "\n",
    "plst = list(params.items())\n",
    "num_rounds = 1800"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgtrain = xgb.DMatrix(input_tfidf[:train.shape[0],:], label=target[:train.shape[0]])\n",
    "watchlist = [(xgtrain, 'train')]\n",
    "model_1_xgboost = xgb.train(plst, xgtrain, num_rounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:8318.54+3.09273\ttest-rmse:8323.19+12.3731\n",
      "[1]\ttrain-rmse:7689.1+47.102\ttest-rmse:7697.18+55.4565\n",
      "[2]\ttrain-rmse:7125.53+68.7822\ttest-rmse:7137.25+69.2255\n",
      "[3]\ttrain-rmse:6627.9+40.0221\ttest-rmse:6643.88+40.8092\n",
      "[4]\ttrain-rmse:6175.9+65.1232\ttest-rmse:6195.91+63.7328\n",
      "[5]\ttrain-rmse:5776.98+51.2547\ttest-rmse:5801.85+48.7318\n",
      "[6]\ttrain-rmse:5447.59+24.8923\ttest-rmse:5478.13+24.7198\n",
      "[7]\ttrain-rmse:5148.19+44.0996\ttest-rmse:5184.01+47.4186\n",
      "[8]\ttrain-rmse:4866.61+37.4918\ttest-rmse:4907.48+41.2358\n",
      "[9]\ttrain-rmse:4651.28+34.7541\ttest-rmse:4698.26+41.0349\n",
      "[10]\ttrain-rmse:4450.4+49.9343\ttest-rmse:4502.92+58.1262\n",
      "[11]\ttrain-rmse:4301.06+56.1844\ttest-rmse:4360.29+62.8836\n",
      "[12]\ttrain-rmse:4159.44+58.9134\ttest-rmse:4224.78+65.0268\n",
      "[13]\ttrain-rmse:4018.34+51.1001\ttest-rmse:4089.49+57.13\n",
      "[14]\ttrain-rmse:3895.85+41.6571\ttest-rmse:3972.26+48.3499\n",
      "[15]\ttrain-rmse:3792.96+33.6571\ttest-rmse:3874.42+40.1782\n",
      "[16]\ttrain-rmse:3716.04+23.4102\ttest-rmse:3803.48+32.7461\n",
      "[17]\ttrain-rmse:3646.4+34.9641\ttest-rmse:3739.14+43.7222\n",
      "[18]\ttrain-rmse:3583.83+26.7708\ttest-rmse:3681.92+34.6275\n",
      "[19]\ttrain-rmse:3527.51+22.5633\ttest-rmse:3630.7+27.3406\n",
      "[20]\ttrain-rmse:3479.85+26.396\ttest-rmse:3587.76+31.8821\n",
      "[21]\ttrain-rmse:3440.71+30.1039\ttest-rmse:3553.74+35.8289\n",
      "[22]\ttrain-rmse:3400.79+24.7175\ttest-rmse:3517.99+29.7281\n",
      "[23]\ttrain-rmse:3369.04+30.1254\ttest-rmse:3490.84+34.9184\n",
      "[24]\ttrain-rmse:3343.12+20.7291\ttest-rmse:3469.05+25.5909\n",
      "[25]\ttrain-rmse:3320.15+22.1416\ttest-rmse:3450.47+28.0353\n",
      "[26]\ttrain-rmse:3298.18+26.137\ttest-rmse:3432.89+29.3439\n",
      "[27]\ttrain-rmse:3278.87+26.9753\ttest-rmse:3418.19+32.37\n",
      "[28]\ttrain-rmse:3258.6+19.9434\ttest-rmse:3401.53+26.9768\n",
      "[29]\ttrain-rmse:3241.94+23.6584\ttest-rmse:3388.62+30.6831\n",
      "[30]\ttrain-rmse:3226.83+19.729\ttest-rmse:3377.37+26.3304\n",
      "[31]\ttrain-rmse:3209.03+11.7\ttest-rmse:3363.25+20.2643\n",
      "[32]\ttrain-rmse:3191.33+10.5292\ttest-rmse:3349.69+16.5469\n",
      "[33]\ttrain-rmse:3178.96+12.0602\ttest-rmse:3341.15+21.4729\n",
      "[34]\ttrain-rmse:3169.38+9.43853\ttest-rmse:3335.11+19.2338\n",
      "[35]\ttrain-rmse:3159.59+12.3419\ttest-rmse:3328.41+20.7462\n",
      "[36]\ttrain-rmse:3144.52+13.8278\ttest-rmse:3316.7+22.9584\n",
      "[37]\ttrain-rmse:3135.61+11.9506\ttest-rmse:3310.91+20.9901\n",
      "[38]\ttrain-rmse:3122.44+8.5509\ttest-rmse:3301.16+17.5991\n",
      "[39]\ttrain-rmse:3112.33+7.18661\ttest-rmse:3294.2+16.1556\n",
      "[40]\ttrain-rmse:3104.14+5.75544\ttest-rmse:3288.88+14.4575\n",
      "[41]\ttrain-rmse:3096.74+6.17315\ttest-rmse:3284.81+15.7919\n",
      "[42]\ttrain-rmse:3091.37+7.03155\ttest-rmse:3282.01+16.4228\n",
      "[43]\ttrain-rmse:3082.73+7.0272\ttest-rmse:3277.13+16.936\n",
      "[44]\ttrain-rmse:3071.77+10.1638\ttest-rmse:3269.43+19.6104\n",
      "[45]\ttrain-rmse:3063.08+11.9435\ttest-rmse:3263.48+20.5079\n",
      "[46]\ttrain-rmse:3054.79+11.3728\ttest-rmse:3258.07+20.1362\n",
      "[47]\ttrain-rmse:3049.15+10.9454\ttest-rmse:3254.99+19.8825\n",
      "[48]\ttrain-rmse:3043.75+10.3665\ttest-rmse:3252.16+19.2967\n",
      "[49]\ttrain-rmse:3038.25+7.31256\ttest-rmse:3248.98+16.5108\n",
      "test-rmse-mean     4053.110358\n",
      "test-rmse-std        32.041540\n",
      "train-rmse-mean    3933.930259\n",
      "train-rmse-std       25.442070\n",
      "dtype: float64 test-rmse-mean     1241.192400\n",
      "test-rmse-std        15.390820\n",
      "train-rmse-mean    1295.122801\n",
      "train-rmse-std       17.235993\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "model_1_predict = model_1_xgboost.predict(xgb.DMatrix(input_tfidf[train.shape[0]:,:]))\n",
    "model_1_predict[model_1_predict<0] = 25\n",
    "submission.Purchase = model_1_predict\n",
    "submission.to_csv(r'C:\\Users\\sk\\AnacondaProjects\\blackfriday\\submit_xgboost_tfidf.csv',index=False)\n",
    "\n",
    "early_stopping=10\n",
    "cv=xgb.cv(params,xgtrain,50,nfold=5,early_stopping_rounds=early_stopping, verbose_eval=1)\n",
    "print(cv.mean(),cv.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#third model\n",
    "input_w_tfidf=np.concatenate((input,input_tfidf),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:8256.93+2.54563\ttest-rmse:8257.18+12.6469\n",
      "[1]\ttrain-rmse:7567.19+37.7388\ttest-rmse:7567.56+37.3852\n",
      "[2]\ttrain-rmse:6978.68+78.2502\ttest-rmse:6979.53+79.2647\n",
      "[3]\ttrain-rmse:6428.05+92.9816\ttest-rmse:6429.48+95.0165\n",
      "[4]\ttrain-rmse:5932.77+73.4161\ttest-rmse:5934.73+73.9507\n",
      "[5]\ttrain-rmse:5495.05+63.4562\ttest-rmse:5497.49+62.3264\n",
      "[6]\ttrain-rmse:5130.84+60.4316\ttest-rmse:5133.67+55.1301\n",
      "[7]\ttrain-rmse:4798.5+35.7103\ttest-rmse:4802.25+30.1517\n",
      "[8]\ttrain-rmse:4517.22+35.7841\ttest-rmse:4521.76+36.8793\n",
      "[9]\ttrain-rmse:4264.1+21.7762\ttest-rmse:4269.43+21.43\n",
      "[10]\ttrain-rmse:4045.9+21.0686\ttest-rmse:4051.84+21.7542\n",
      "[11]\ttrain-rmse:3845.34+18.9074\ttest-rmse:3852.09+19.3222\n",
      "[12]\ttrain-rmse:3681.18+24.9011\ttest-rmse:3688.73+20.8567\n",
      "[13]\ttrain-rmse:3540.75+31.3852\ttest-rmse:3549.22+25.5737\n",
      "[14]\ttrain-rmse:3424.42+27.1194\ttest-rmse:3433.88+21.9143\n",
      "[15]\ttrain-rmse:3324.69+23.5498\ttest-rmse:3335.3+19.2145\n",
      "[16]\ttrain-rmse:3240.65+28.6902\ttest-rmse:3252.45+26.2518\n",
      "[17]\ttrain-rmse:3163.91+23.8616\ttest-rmse:3176.74+21.2533\n",
      "[18]\ttrain-rmse:3105.16+28.9742\ttest-rmse:3118.65+25.4111\n",
      "[19]\ttrain-rmse:3051.73+22.0576\ttest-rmse:3066.02+18.3681\n",
      "[20]\ttrain-rmse:3007.29+15.6686\ttest-rmse:3022.45+12.3736\n",
      "[21]\ttrain-rmse:2971.3+18.9595\ttest-rmse:2987.36+14.1207\n",
      "[22]\ttrain-rmse:2942.99+16.8411\ttest-rmse:2960.04+13.4103\n",
      "[23]\ttrain-rmse:2913.68+12.9256\ttest-rmse:2931.53+9.64316\n",
      "[24]\ttrain-rmse:2888.7+10.1171\ttest-rmse:2907.55+6.85458\n",
      "[25]\ttrain-rmse:2866.8+9.83449\ttest-rmse:2886.44+6.00279\n",
      "[26]\ttrain-rmse:2849.16+8.38024\ttest-rmse:2869.7+5.46996\n",
      "[27]\ttrain-rmse:2833.19+7.94638\ttest-rmse:2854.51+4.94431\n",
      "[28]\ttrain-rmse:2820.71+8.08881\ttest-rmse:2842.83+4.29523\n",
      "[29]\ttrain-rmse:2810.16+8.07941\ttest-rmse:2833.17+2.60234\n",
      "[30]\ttrain-rmse:2800.63+8.10712\ttest-rmse:2824.38+2.69697\n",
      "[31]\ttrain-rmse:2790.45+7.75822\ttest-rmse:2815.03+2.48388\n",
      "[32]\ttrain-rmse:2782.3+7.68633\ttest-rmse:2807.66+2.03167\n",
      "[33]\ttrain-rmse:2774.2+7.47882\ttest-rmse:2800.44+2.90684\n",
      "[34]\ttrain-rmse:2767.8+7.49409\ttest-rmse:2794.96+3.42931\n",
      "[35]\ttrain-rmse:2760.74+7.93401\ttest-rmse:2788.64+4.15542\n",
      "[36]\ttrain-rmse:2755.53+7.4285\ttest-rmse:2784.01+4.31222\n",
      "[37]\ttrain-rmse:2749.73+6.68784\ttest-rmse:2779+4.13523\n",
      "[38]\ttrain-rmse:2745.27+6.90466\ttest-rmse:2775.33+3.67143\n",
      "[39]\ttrain-rmse:2740.57+6.23484\ttest-rmse:2771.3+3.91662\n",
      "[40]\ttrain-rmse:2736.76+6.45093\ttest-rmse:2768.28+4.01849\n",
      "[41]\ttrain-rmse:2732.96+6.15627\ttest-rmse:2765.44+4.59394\n",
      "[42]\ttrain-rmse:2728.93+5.65162\ttest-rmse:2762.3+5.2007\n",
      "[43]\ttrain-rmse:2725.05+5.23347\ttest-rmse:2759.24+6.51035\n",
      "[44]\ttrain-rmse:2721.72+5.37483\ttest-rmse:2756.61+6.63765\n",
      "[45]\ttrain-rmse:2718.63+5.47661\ttest-rmse:2754.32+7.4519\n",
      "[46]\ttrain-rmse:2715.4+5.98164\ttest-rmse:2751.74+7.2086\n",
      "[47]\ttrain-rmse:2711.87+5.45478\ttest-rmse:2748.77+7.63298\n",
      "[48]\ttrain-rmse:2708.83+5.50094\ttest-rmse:2746.38+7.20726\n",
      "[49]\ttrain-rmse:2705.3+5.81869\ttest-rmse:2743.63+7.19322\n",
      "test-rmse-mean     3570.220081\n",
      "test-rmse-std        18.024261\n",
      "train-rmse-mean    3551.393657\n",
      "train-rmse-std       20.005225\n",
      "dtype: float64 test-rmse-mean     1359.658339\n",
      "test-rmse-std        21.180805\n",
      "train-rmse-mean    1368.857679\n",
      "train-rmse-std       20.748835\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "params = {}\n",
    "params[\"min_child_weight\"] = 10\n",
    "params[\"subsample\"] = 0.7\n",
    "params[\"colsample_bytree\"] = 0.7\n",
    "params[\"scale_pos_weight\"] = 0.8\n",
    "params[\"silent\"] = 1\n",
    "params[\"max_depth\"] = 8\n",
    "params[\"nthread\"] = 6\n",
    "#params[\"gamma\"] = 1\n",
    "params[\"objective\"] = \"reg:linear\"\n",
    "params[\"eta\"] = 0.1\n",
    "params[\"base_score\"] = 1800\n",
    "params[\"eval_metric\"] = \"rmse\"\n",
    "params[\"seed\"] = 0\n",
    "\n",
    "plst = list(params.items())\n",
    "num_rounds = 1750\n",
    "\n",
    "xgtrain=xgb.DMatrix(input_w_tfidf[:train.shape[0],:],label=target[:train.shape[0]])\n",
    "watchlist=[(xgtrain,'train')]\n",
    "model_1_xgboost = xgb.train(plst, xgtrain, num_rounds)\n",
    "\n",
    "model_1_predict = model_1_xgboost.predict(xgb.DMatrix(input_w_tfidf[train.shape[0]:,:]))\n",
    "model_1_predict[model_1_predict<0] = 25\n",
    "submission.Purchase = model_1_predict\n",
    "submission.to_csv(r\"C:\\Users\\sk\\AnacondaProjects\\blackfriday\\third_model.csv\", index=False)\n",
    "\n",
    "early_stopping=10\n",
    "cv=xgb.cv(params,xgtrain,50,nfold=5,early_stopping_rounds=early_stopping, verbose_eval=1)\n",
    "print(cv.mean(),cv.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(550068, 12) (324731, 12)\n"
     ]
    }
   ],
   "source": [
    "print (train.shape, train[train.Marital_Status==0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   45.8s\n",
      "[Parallel(n_jobs=6)]: Done 188 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=6)]: Done 438 tasks      | elapsed:  7.8min\n",
      "[Parallel(n_jobs=6)]: Done 788 tasks      | elapsed: 15.2min\n",
      "[Parallel(n_jobs=6)]: Done 1238 tasks      | elapsed: 26.1min\n",
      "[Parallel(n_jobs=6)]: Done 1450 out of 1450 | elapsed: 29.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "          min_samples_leaf=10, min_samples_split=10,\n",
       "          min_weight_fraction_leaf=0.0, n_estimators=1450, n_jobs=6,\n",
       "          oob_score=True, random_state=123, verbose=1, warm_start=False)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fourth model\n",
    "model_4_et = ExtraTreesRegressor(n_estimators=1450, max_depth=8,min_samples_split=10, min_samples_leaf=10, oob_score=True, n_jobs=6, random_state=123, verbose=1, bootstrap=True)\n",
    "model_4_et.fit(input_w_tfidf[:train.shape[0],:], target[:train.shape[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=6)]: Done 188 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=6)]: Done 438 tasks      | elapsed:    2.7s\n",
      "[Parallel(n_jobs=6)]: Done 788 tasks      | elapsed:    5.1s\n",
      "[Parallel(n_jobs=6)]: Done 1238 tasks      | elapsed:    8.1s\n",
      "[Parallel(n_jobs=6)]: Done 1450 out of 1450 | elapsed:    9.4s finished\n"
     ]
    }
   ],
   "source": [
    "model_4_predict = model_4_et.predict(input_w_tfidf[train.shape[0]:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.Purchase = model_4_predict\n",
    "submission.to_csv(r\"C:\\Users\\sk\\AnacondaProjects\\blackfriday\\fourth_model.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4124.88950007\n",
      "4057.15709681\n",
      "4267.88371959\n",
      "3727.30867414\n",
      "1023.26036168\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "temp=pd.read_csv('Sample_Submission.csv')\n",
    "temp_sample=temp['Purchase']\n",
    "temp=pd.read_csv('submit_xgboost.csv')\n",
    "temp_first=temp['Purchase']\n",
    "print (np.sqrt(mean_squared_error(temp_sample, temp_first)))\n",
    "temp=pd.read_csv('submit_xgboost_tfidf.csv')\n",
    "temp_second=temp['Purchase']\n",
    "print (np.sqrt(mean_squared_error(temp_sample, temp_second)))\n",
    "temp=pd.read_csv('third_model.csv')\n",
    "temp_third=temp['Purchase']\n",
    "print (np.sqrt(mean_squared_error(temp_sample, temp_third)))\n",
    "temp=pd.read_csv('fourth_model.csv')\n",
    "temp_fourth=temp['Purchase']\n",
    "print (np.sqrt(mean_squared_error(temp_sample, temp_fourth)))\n",
    "print (np.sqrt(mean_squared_error(temp_fourth, temp_first)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3LeURh5079cTes6ealoM/NpKylQYNUhdgUeKQlGHms5firIdGSkynEYfaYDQk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\n+vYJ4Ebg9uD5LcDFwePvAR8poU/XA38ZPB4KjC1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa037e7e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hhBOAe+8FXnwx2HwiTamIQ19f8EqsgLc4WCfCijjkAC/LwT5oWcUBiBaUzmPM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa033929e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/+zveke2z0oaVsiSkobYiXDNzjEuXqoDOnJn+NX7n4J/LzIWVSmbCBJ0y+Ec/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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa0294e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dXfr/muVQISZNUpdOs7hD2mJ6gwerQKQVh/oEOE/VxKGojX6SZqJlLWVtFm8A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\nv+a/Hzr+80G/FgBnt6tPdc/3UgtId/pcDQVuDK6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa0330a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IZtPtk8lk9te1UspxUkqrlHKilPIJKeX1UsoFUsqFUsovSSlrk/b/uZRyupRy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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa030290b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "temp=pd.read_csv('Sample_Submission.csv')\n",
    "temp_first=pd.read_csv('submit_xgboost.csv')\n",
    "x1=temp_first['User_ID'].astype(int)\n",
    "plt.plot(x1[:100],temp_first['Purchase'][:100],color='red')\n",
    "x=temp['User_ID'].astype(int)\n",
    "plt.plot(x[:100],temp['Purchase'][:100],color='blue')\n",
    "plt.show()\n",
    "\n",
    "temp_second=pd.read_csv('submit_xgboost_tfidf.csv')\n",
    "x2=temp_second['User_ID'].astype(int)\n",
    "plt.plot(x2[:100],temp_second['Purchase'][:100],color='red')\n",
    "plt.plot(x[:100],temp['Purchase'][:100],color='blue')\n",
    "plt.show()\n",
    "\n",
    "temp_third=pd.read_csv('third_model.csv')\n",
    "x3=temp_third['User_ID'].astype(int)\n",
    "plt.plot(x3[:100],temp_third['Purchase'][:100],color='red')\n",
    "plt.plot(x[:100],temp['Purchase'][:100],color='blue')\n",
    "plt.show()\n",
    "\n",
    "temp_fourth=pd.read_csv('fourth_model.csv')\n",
    "x4=temp_fourth['User_ID'].astype(int)\n",
    "plt.plot(x4[:100],temp_fourth['Purchase'][:100],color='red')\n",
    "plt.plot(x[:100],temp['Purchase'][:100],color='blue')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#plotting all together\n",
    "\n",
    "plt.plot(x1[:100],temp_first['Purchase'][:100],color='red',label='Model1')\n",
    "plt.plot(x2[:100],temp_second['Purchase'][:100],color='green',label='Model2')\n",
    "plt.plot(x3[:100],temp_third['Purchase'][:100],color='cyan',label='Model3')\n",
    "plt.plot(x4[:100],temp_fourth['Purchase'][:100],color='black',label='Model4')\n",
    "plt.plot(x[:100],temp['Purchase'][:100],color='blue',label='Original')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Users')\n",
    "plt.ylabel('Purchase')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}