food_demand_forecast.ipynb

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   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# !pip install seaborn\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pbNTxRwSy5I9"
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   "outputs": [],
   "source": [
    "test_file=pd.read_csv('datasets/test_file.csv')\n",
    "train_file=pd.read_csv('datasets/train_file.csv')\n",
    "meal_file=pd.read_csv('datasets/meal_info.csv')\n",
    "fullfilment_file=pd.read_csv('datasets/fulfilment_center_info.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1861,
     "status": "ok",
     "timestamp": 1592037395513,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
      "0  1028232   146         55     1885          158.11      159.11   \n",
      "1  1127204   146         55     1993          160.11      159.11   \n",
      "2  1212707   146         55     2539          157.14      159.14   \n",
      "3  1082698   146         55     2631          162.02      162.02   \n",
      "4  1400926   146         55     1248          163.93      163.93   \n",
      "\n",
      "   emailer_for_promotion  homepage_featured  \n",
      "0                      0                  0  \n",
      "1                      0                  0  \n",
      "2                      0                  0  \n",
      "3                      0                  0  \n",
      "4                      0                  0  \n"
     ]
    }
   ],
   "source": [
    "df=test_file.copy()\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SejBO10Sy5VR"
   },
   "outputs": [],
   "source": [
    "df.rename(columns={\"id,week,center_id,meal_id,checkout_price,base_price,emailer_for_promotion,homepage_featured\":\"new\"},inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cuI3g_Ady5X9"
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   "outputs": [],
   "source": [
    "# df['id']=df.new.str.split(',').str[0]\n",
    "# df['week']=df.new.str.split(',').str[1]\n",
    "# df['center_id']=df.new.str.split(',').str[2]\n",
    "# df['meal_id']=df.new.str.split(',').str[3]\n",
    "# df['checkout_price']=df.new.str.split(',').str[4]\n",
    "# df['base_price']=df.new.str.split(',').str[5]\n",
    "# df['emailer_for_promotion']=df.new.str.split(',').str[6]\n",
    "# df['homepage_featured']=df.new.str.split(',').str[7]\n",
    "# df=df.iloc[:,1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
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    "colab_type": "code",
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     "status": "ok",
     "timestamp": 1592037430960,
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
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       "      <th>0</th>\n",
       "      <td>1028232</td>\n",
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       "      <td>157.14</td>\n",
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       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1028232   146         55     1885          158.11      159.11   \n",
       "1  1127204   146         55     1993          160.11      159.11   \n",
       "2  1212707   146         55     2539          157.14      159.14   \n",
       "3  1082698   146         55     2631          162.02      162.02   \n",
       "4  1400926   146         55     1248          163.93      163.93   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  \n",
       "0                      0                  0  \n",
       "1                      0                  0  \n",
       "2                      0                  0  \n",
       "3                      0                  0  \n",
       "4                      0                  0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['id']=df['id'].astype('int')\n",
    "df['week']=df['week'].astype('int')\n",
    "df['center_id']=df['center_id'].astype('int')\n",
    "df['meal_id']=df['meal_id'].astype('int')\n",
    "df['checkout_price']=df['checkout_price'].astype('float')\n",
    "df['base_price']=df['base_price'].astype('float')\n",
    "\n",
    "df['emailer_for_promotion']=df['emailer_for_promotion'].astype('int')\n",
    "df['homepage_featured']=df['homepage_featured'].astype('int')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CMbujIgszqiw"
   },
   "outputs": [],
   "source": [
    "train_df = pd.merge(train_file,fullfilment_file, on='center_id')\n",
    "test_df= pd.merge(df,fullfilment_file, on='center_id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2jqM_WELzqlh"
   },
   "outputs": [],
   "source": [
    "train_df = pd.merge(train_df,meal_file, on='meal_id')\n",
    "test_df = pd.merge(test_df,meal_file, on='meal_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "      <td>1885</td>\n",
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       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
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       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1028232   146         55     1885          158.11      159.11   \n",
       "1  1262649   147         55     1885          159.11      159.11   \n",
       "2  1453211   149         55     1885          157.14      158.14   \n",
       "3  1262599   150         55     1885          159.14      157.14   \n",
       "4  1495848   151         55     1885          160.11      159.11   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  city_code  region_code  \\\n",
       "0                      0                  0        647           56   \n",
       "1                      0                  0        647           56   \n",
       "2                      0                  0        647           56   \n",
       "3                      0                  0        647           56   \n",
       "4                      0                  0        647           56   \n",
       "\n",
       "  center_type  op_area   category cuisine  \n",
       "0      TYPE_C      2.0  Beverages    Thai  \n",
       "1      TYPE_C      2.0  Beverages    Thai  \n",
       "2      TYPE_C      2.0  Beverages    Thai  \n",
       "3      TYPE_C      2.0  Beverages    Thai  \n",
       "4      TYPE_C      2.0  Beverages    Thai  "
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KWx2iaeO81ok"
   },
   "outputs": [],
   "source": [
    "encoder=LabelEncoder()\n",
    "encoder2=LabelEncoder()\n",
    "encoder3=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1343872</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1379560     1         55     1885          136.83      152.29   \n",
       "1  1018704     2         55     1885          135.83      152.29   \n",
       "2  1196273     3         55     1885          132.92      133.92   \n",
       "3  1116527     4         55     1885          135.86      134.86   \n",
       "4  1343872     5         55     1885          146.50      147.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders  city_code  \\\n",
       "0                      0                  0         177        647   \n",
       "1                      0                  0         323        647   \n",
       "2                      0                  0          96        647   \n",
       "3                      0                  0         163        647   \n",
       "4                      0                  0         215        647   \n",
       "\n",
       "   region_code center_type  op_area   category cuisine  \n",
       "0           56      TYPE_C      2.0  Beverages    Thai  \n",
       "1           56      TYPE_C      2.0  Beverages    Thai  \n",
       "2           56      TYPE_C      2.0  Beverages    Thai  \n",
       "3           56      TYPE_C      2.0  Beverages    Thai  \n",
       "4           56      TYPE_C      2.0  Beverages    Thai  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data=train_df.copy()\n",
    "main_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1565,
     "status": "ok",
     "timestamp": 1592040018504,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "slgBbrbP87hR",
    "outputId": "b1bb8302-98ee-4ac3-86f4-b49dab695b52"
   },
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1379560</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1018704</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1196273</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>132.92</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1116527</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.86</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1343872</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1379560     1         55     1885          136.83      152.29   \n",
       "1  1018704     2         55     1885          135.83      152.29   \n",
       "2  1196273     3         55     1885          132.92      133.92   \n",
       "3  1116527     4         55     1885          135.86      134.86   \n",
       "4  1343872     5         55     1885          146.50      147.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders  city_code  \\\n",
       "0                      0                  0         177        647   \n",
       "1                      0                  0         323        647   \n",
       "2                      0                  0          96        647   \n",
       "3                      0                  0         163        647   \n",
       "4                      0                  0         215        647   \n",
       "\n",
       "   region_code  center_type  op_area  category  cuisine  \n",
       "0           56            2      2.0         0        3  \n",
       "1           56            2      2.0         0        3  \n",
       "2           56            2      2.0         0        3  \n",
       "3           56            2      2.0         0        3  \n",
       "4           56            2      2.0         0        3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data['category']=encoder.fit_transform(main_data['category'])\n",
    "main_data['center_type']=encoder2.fit_transform(main_data['center_type'])\n",
    "main_data['cuisine']=encoder3.fit_transform(main_data['cuisine'])\n",
    "main_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2564,
     "status": "ok",
     "timestamp": 1592040138127,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "IG923i2t87k0",
    "outputId": "bbe944a0-f0ff-401e-c4e6-54fec993007c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['num_orders', 'homepage_featured', 'emailer_for_promotion', 'op_area',\n",
       "       'cuisine', 'city_code', 'region_code', 'category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data1= main_data.drop(['id'], axis=1)\n",
    "correlation = main_data1.corr(method='pearson')\n",
    "columns = correlation.nlargest(8, 'num_orders').index\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2427,
     "status": "ok",
     "timestamp": 1592043360246,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "LCJP9W6JKJfW",
    "outputId": "b65e25c8-d9be-49f1-aff5-5ad9086c9866"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(correlation,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jxNvYFhz87sM"
   },
   "outputs": [],
   "source": [
    "features = columns.drop(['num_orders'])\n",
    "main_data2 = main_data[features]\n",
    "X = main_data2.values\n",
    "y = main_data['num_orders'].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2163,
     "status": "ok",
     "timestamp": 1592042345780,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "A4HR4GNZ_2ab",
    "outputId": "ac7667d4-482b-454c-ddba-d026c4969c4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342411, 7)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1372,
     "status": "ok",
     "timestamp": 1592043168986,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "jTyYCfxwKl6f",
    "outputId": "af30f940-9e13-487d-eb8c-09ed543d1408"
   },
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1028232</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>158.11</td>\n",
       "      <td>159.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1262649</td>\n",
       "      <td>147</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>159.11</td>\n",
       "      <td>159.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1453211</td>\n",
       "      <td>149</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>157.14</td>\n",
       "      <td>158.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>TYPE_C</td>\n",
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       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1028232   146         55     1885          158.11      159.11   \n",
       "1  1262649   147         55     1885          159.11      159.11   \n",
       "2  1453211   149         55     1885          157.14      158.14   \n",
       "3  1262599   150         55     1885          159.14      157.14   \n",
       "4  1495848   151         55     1885          160.11      159.11   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  city_code  region_code  \\\n",
       "0                      0                  0        647           56   \n",
       "1                      0                  0        647           56   \n",
       "2                      0                  0        647           56   \n",
       "3                      0                  0        647           56   \n",
       "4                      0                  0        647           56   \n",
       "\n",
       "  center_type  op_area   category cuisine  \n",
       "0      TYPE_C      2.0  Beverages    Thai  \n",
       "1      TYPE_C      2.0  Beverages    Thai  \n",
       "2      TYPE_C      2.0  Beverages    Thai  \n",
       "3      TYPE_C      2.0  Beverages    Thai  \n",
       "4      TYPE_C      2.0  Beverages    Thai  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data=test_df.copy()\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
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    },
    "id": "0s-TrXT3KmKn",
    "outputId": "ff7026af-562a-43ef-c9e9-29de49ff4e79"
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   "outputs": [
    {
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       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1028232   146         55     1885          158.11      159.11   \n",
       "1  1262649   147         55     1885          159.11      159.11   \n",
       "2  1453211   149         55     1885          157.14      158.14   \n",
       "3  1262599   150         55     1885          159.14      157.14   \n",
       "4  1495848   151         55     1885          160.11      159.11   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  city_code  region_code  \\\n",
       "0                      0                  0        647           56   \n",
       "1                      0                  0        647           56   \n",
       "2                      0                  0        647           56   \n",
       "3                      0                  0        647           56   \n",
       "4                      0                  0        647           56   \n",
       "\n",
       "   center_type  op_area  category  cuisine  \n",
       "0            2      2.0         0        3  \n",
       "1            2      2.0         0        3  \n",
       "2            2      2.0         0        3  \n",
       "3            2      2.0         0        3  \n",
       "4            2      2.0         0        3  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['category']=encoder.fit_transform(test_data['category'])\n",
    "test_data['center_type']=encoder2.fit_transform(test_data['center_type'])\n",
    "test_data['cuisine']=encoder3.fit_transform(test_data['cuisine'])\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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    "colab_type": "code",
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    },
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      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1028232   146         55     1885          158.11      159.11   \n",
       "1  1262649   147         55     1885          159.11      159.11   \n",
       "2  1453211   149         55     1885          157.14      158.14   \n",
       "3  1262599   150         55     1885          159.14      157.14   \n",
       "4  1495848   151         55     1885          160.11      159.11   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  city_code  region_code  \\\n",
       "0                      0                  0        647           56   \n",
       "1                      0                  0        647           56   \n",
       "2                      0                  0        647           56   \n",
       "3                      0                  0        647           56   \n",
       "4                      0                  0        647           56   \n",
       "\n",
       "   center_type  op_area  category  cuisine  \n",
       "0            2      2.0         0        3  \n",
       "1            2      2.0         0        3  \n",
       "2            2      2.0         0        3  \n",
       "3            2      2.0         0        3  \n",
       "4            2      2.0         0        3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data1=test_data.copy()\n",
    "test_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3243,
     "status": "ok",
     "timestamp": 1592045187779,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "3XFyb0GMLjOA",
    "outputId": "ae0f7254-ccbe-4f32-9c46-6ad302f6d251"
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   "outputs": [
    {
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       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   emailer_for_promotion  homepage_featured  city_code  region_code  op_area  \\\n",
       "0                      0                  0        647           56      2.0   \n",
       "1                      0                  0        647           56      2.0   \n",
       "2                      0                  0        647           56      2.0   \n",
       "3                      0                  0        647           56      2.0   \n",
       "4                      0                  0        647           56      2.0   \n",
       "\n",
       "   category  cuisine  \n",
       "0         0        3  \n",
       "1         0        3  \n",
       "2         0        3  \n",
       "3         0        3  \n",
       "4         0        3  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_final=test_data1.drop(columns=['id','center_id','meal_id','week','checkout_price','base_price','center_type'],axis=1)\n",
    "test_data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tRJykGaIBZ8L"
   },
   "outputs": [],
   "source": [
    "#Building Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZDZh0VJ-_2eY"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DTR = DecisionTreeRegressor()\n",
    "DTR.fit(x_train, y_train)\n",
    "y_pred = DTR.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1393,
     "status": "ok",
     "timestamp": 1592042454940,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "S9HBSRS8BbbV",
    "outputId": "dd09d660-f6cd-4b38-88ed-c29835017d99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6295586792091123"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "from sklearn import metrics\n",
    "RMSEL=mean_squared_log_error(y_test, y_pred)\n",
    "RMSEL=np.sqrt(RMSEL)\n",
    "RMSEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 13891,
     "status": "ok",
     "timestamp": 1592042566136,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "g4WoLk1PBYge",
    "outputId": "86387659-1250-4069-c400-524a7e646dd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6852918836223324\n"
     ]
    }
   ],
   "source": [
    "# !pip install xgboost\n",
    "from xgboost import XGBRegressor\n",
    "XG = XGBRegressor()\n",
    "XG.fit(x_train, y_train)\n",
    "y_pred = XG.predict(x_test)\n",
    "y_pred[y_pred<0] = 0\n",
    "RMSEL=mean_squared_log_error(y_test, y_pred)\n",
    "RMSEL=np.sqrt(RMSEL)\n",
    "print(RMSEL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JAuUDA6YCFYo"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 20292,
     "status": "ok",
     "timestamp": 1592042650745,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "g7_5zONYB66T",
    "outputId": "16fab81a-e026-4200-8afc-7c9f769ad40a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9431287796406321"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "GB = GradientBoostingRegressor()\n",
    "GB.fit(x_train, y_train)\n",
    "y_pred = GB.predict(x_test)\n",
    "y_pred[y_pred<0] = 0\n",
    "RMSEL=mean_squared_log_error(y_test, y_pred)\n",
    "RMSEL=np.sqrt(RMSEL)\n",
    "RMSEL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 34391,
     "status": "ok",
     "timestamp": 1592042720678,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "18zOGLoXDUma",
    "outputId": "277d50c9-1bb9-4db5-f5e7-1db8c8d9ef94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6294590656522306"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RF = RandomForestRegressor()\n",
    "RF.fit(x_train, y_train)\n",
    "y_pred = RF.predict(x_test)\n",
    "y_pred[y_pred<0] = 0\n",
    "RMSEL=mean_squared_log_error(y_test, y_pred)\n",
    "RMSEL=np.sqrt(RMSEL)\n",
    "RMSEL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DBgJVLq10Um_"
   },
   "outputs": [],
   "source": [
    "y=train_file['num_orders']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W3KG8gcq0UyG"
   },
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor,plot_importance\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_log_error,mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1544,
     "status": "ok",
     "timestamp": 1592041674979,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "ruM10oES0U0r",
    "outputId": "eecc6c58-9369-4888-fe61-50d2ca278488"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(importance_type='gain', min_child_samples=[40, 45, 50],\n",
       "              n_estimators=[230, 260], num_leaves=[41, 51],\n",
       "              random_state=[2019])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !pip install lightgbm\n",
    "from lightgbm import LGBMRegressor\n",
    "lgb_model=LGBMRegressor(importance_type='gain')\n",
    "lgbm_params = {\n",
    "    \"n_estimators\":[230,260],\n",
    "    \"num_leaves\":[41,51],\n",
    "    'min_child_samples':[40,45,50],\n",
    "    'random_state':[2019]\n",
    "  \n",
    "}\n",
    "lgb_model.set_params(**lgbm_params) #base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fhg5NK1h0U3F"
   },
   "outputs": [],
   "source": [
    "lgb_grid=GridSearchCV(lgb_model,lgbm_params,cv=5,scoring='neg_mean_squared_error',n_jobs=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hbS_5mZqIjrv"
   },
   "outputs": [],
   "source": [
    "model=lgb_grid.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1770,
     "status": "ok",
     "timestamp": 1592037967223,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "M5-O0v-jIj0W",
    "outputId": "8f0db54f-eb51-4cbf-a0aa-0d728b5b54b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(importance_type='gain', min_child_samples=50, n_estimators=260,\n",
       "              num_leaves=51, random_state=2019)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_estimate=model.best_estimator_\n",
    "lgb_estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2330,
     "status": "ok",
     "timestamp": 1592041982587,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "TgTk8t51Ij5O",
    "outputId": "b0631577-39b7-44db-b2be-00fa3879eca1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([396.24319312, 116.54608316,  60.42639822, ..., 463.23949173,\n",
       "        52.8740641 , 144.91169307])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1863,
     "status": "ok",
     "timestamp": 1592037978685,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "vahEq_BcIj_O",
    "outputId": "cdfaa0c3-ab24-4f64-fa27-63ce705856d5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1520,
     "status": "ok",
     "timestamp": 1592042152247,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "MDvELDAOIkC-",
    "outputId": "8c706306-832d-4562-d90e-394cd181236a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6487183707268681"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[y_pred<0] = 0\n",
    "RMSEL=mean_squared_log_error(y_test, y_pred)\n",
    "RMSEL=np.sqrt(RMSEL)\n",
    "RMSEL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p1olFlK0QxNX"
   },
   "outputs": [],
   "source": [
    "#by comparision the error value of differents models for training data, the lowest error value got from Random Forest Regressio Model\n",
    "#prediction on test data by Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1634,
     "status": "ok",
     "timestamp": 1592045275153,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "sKb9qvUhSsRI",
    "outputId": "037170ff-1a1e-4f42-9580-4c4a52fd4dbe"
   },
   "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>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>op_area</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emailer_for_promotion  homepage_featured  city_code  region_code  op_area  \\\n",
       "0                      0                  0        647           56      2.0   \n",
       "1                      0                  0        647           56      2.0   \n",
       "2                      0                  0        647           56      2.0   \n",
       "3                      0                  0        647           56      2.0   \n",
       "4                      0                  0        647           56      2.0   \n",
       "\n",
       "   category  cuisine  \n",
       "0         0        3  \n",
       "1         0        3  \n",
       "2         0        3  \n",
       "3         0        3  \n",
       "4         0        3  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2060,
     "status": "ok",
     "timestamp": 1592045292147,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "KfggcfX1SxKP",
    "outputId": "8c2e611a-2058-4fc1-b94b-2a8040e64aa6"
   },
   "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>homepage_featured</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>op_area</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   homepage_featured  emailer_for_promotion  op_area  cuisine  city_code  \\\n",
       "0                  0                      0      2.0        3        647   \n",
       "1                  0                      0      2.0        3        647   \n",
       "2                  0                      0      2.0        3        647   \n",
       "3                  0                      0      2.0        3        647   \n",
       "4                  0                      0      2.0        3        647   \n",
       "\n",
       "   region_code  category  \n",
       "0           56         0  \n",
       "1           56         0  \n",
       "2           56         0  \n",
       "3           56         0  \n",
       "4           56         0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-8EBGUxsQxQ7"
   },
   "outputs": [],
   "source": [
    "pred_test_data= RF.predict(test_data_final)\n",
    "pred_test_data[pred_test_data<0] = 0\n",
    "submit = pd.DataFrame({\n",
    "    'id' :test_data['id'],\n",
    "    'num_orders' : pred_test_data\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2142,
     "status": "ok",
     "timestamp": 1592045460900,
     "user": {
      "displayName": "amit",
      "photoUrl": "",
      "userId": "12797285159599571225"
     },
     "user_tz": -345
    },
    "id": "VkF0P3sNQxU9",
    "outputId": "acd0dc31-94e9-40e8-8f09-319324d015d3"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1028232</td>\n",
       "      <td>729.665147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1262649</td>\n",
       "      <td>729.665147</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1453211</td>\n",
       "      <td>729.665147</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1262599</td>\n",
       "      <td>729.665147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1495848</td>\n",
       "      <td>729.665147</td>\n",
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      ],
      "text/plain": [
       "        id  num_orders\n",
       "0  1028232  729.665147\n",
       "1  1262649  729.665147\n",
       "2  1453211  729.665147\n",
       "3  1262599  729.665147\n",
       "4  1495848  729.665147"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9p_Lz1ExQxYH"
   },
   "outputs": [],
   "source": [
    "submit.to_csv(\"output/submission_RFR.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P9KLryVRQxbj"
   },
   "outputs": [],
   "source": [
    "pred_test_data= DTR.predict(test_data_final)\n",
    "pred_test_data[pred_test_data<0] = 0\n",
    "submitDTR = pd.DataFrame({\n",
    "    'id' :test_data['id'],\n",
    "    'num_orders' : pred_test_data\n",
    "})\n",
    "submitDTR.head()\n",
    "submitDTR.to_csv(\"output/submission_DTR.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zLM6Bf8rQxfX"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6'] ['emailer_for_promotion', 'homepage_featured', 'city_code', 'region_code', 'op_area', 'category', 'cuisine']\nexpected f0, f3, f6, f2, f4, f5, f1 in input data\ntraining data did not have the following fields: category, cuisine, emailer_for_promotion, city_code, homepage_featured, region_code, op_area",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-a53de2b13e40>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred_test_data\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mXG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_data_final\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpred_test_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpred_test_data\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m submitXG = pd.DataFrame({\n\u001b[0;32m      4\u001b[0m     \u001b[1;34m'id'\u001b[0m \u001b[1;33m:\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;34m'num_orders'\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mpred_test_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, data, output_margin, ntree_limit, validate_features, base_margin)\u001b[0m\n\u001b[0;32m    602\u001b[0m                                           \u001b[0moutput_margin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput_margin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m                                           \u001b[0mntree_limit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mntree_limit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 604\u001b[1;33m                                           validate_features=validate_features)\n\u001b[0m\u001b[0;32m    605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mntree_limit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, data, output_margin, ntree_limit, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features, training)\u001b[0m\n\u001b[0;32m   1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1363\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvalidate_features\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1364\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_features\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1366\u001b[0m         \u001b[0mlength\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc_bst_ulong\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36m_validate_features\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m   1934\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1935\u001b[0m                 raise ValueError(msg.format(self.feature_names,\n\u001b[1;32m-> 1936\u001b[1;33m                                             data.feature_names))\n\u001b[0m\u001b[0;32m   1937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1938\u001b[0m     def get_split_value_histogram(self, feature, fmap='', bins=None,\n",
      "\u001b[1;31mValueError\u001b[0m: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6'] ['emailer_for_promotion', 'homepage_featured', 'city_code', 'region_code', 'op_area', 'category', 'cuisine']\nexpected f0, f3, f6, f2, f4, f5, f1 in input data\ntraining data did not have the following fields: category, cuisine, emailer_for_promotion, city_code, homepage_featured, region_code, op_area"
     ]
    }
   ],
   "source": [
    "pred_test_data= XG.predict(test_data_final)\n",
    "pred_test_data[pred_test_data<0] = 0\n",
    "submitXG = pd.DataFrame({\n",
    "    'id' :test_data['id'],\n",
    "    'num_orders' : pred_test_data\n",
    "})\n",
    "submitXG.head()\n",
    "submitXG.to_csv(\"output/submission_XG.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fclhinAEQxjv"
   },
   "outputs": [],
   "source": [
    "pred_test_data= GB.predict(test_data_final)\n",
    "pred_test_data[pred_test_data<0] = 0\n",
    "submitGB = pd.DataFrame({\n",
    "    'id' :test_data['id'],\n",
    "    'num_orders' : pred_test_data\n",
    "})\n",
    "submitGB.head()\n",
    "submitGB.to_csv(\"output/submission_GB.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DkXYGcKTQxmi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tOzl1YGDQxqO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kmGz_jg0Jee1"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "collapsed_sections": [],
   "mount_file_id": "14FU2vwsy9P61_WJckklSPseXzjZJ0TWh",
   "name": "Food Demand forecast.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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