1st Attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_test = pd.read_csv(\"test.csv\")\n",
    "\n",
    "y = df_train[\"Purchase\"]\n",
    "\n",
    "df_train.drop([\"Purchase\"],axis=1,inplace=True)\n",
    "\n",
    "df = pd.concat([df_train,df_test]).iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>20</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Age  Occupation City_Category Stay_In_Current_City_Years  \\\n",
       "0      F   0-17          10             A                          2   \n",
       "1      F   0-17          10             A                          2   \n",
       "2      F   0-17          10             A                          2   \n",
       "3      F   0-17          10             A                          2   \n",
       "4      M    55+          16             C                         4+   \n",
       "5      M  26-35          15             A                          3   \n",
       "6      M  46-50           7             B                          2   \n",
       "7      M  46-50           7             B                          2   \n",
       "8      M  46-50           7             B                          2   \n",
       "9      M  26-35          20             A                          1   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \n",
       "0               0                   3                 NaN                 NaN  \n",
       "1               0                   1                 6.0                14.0  \n",
       "2               0                  12                 NaN                 NaN  \n",
       "3               0                  12                14.0                 NaN  \n",
       "4               0                   8                 NaN                 NaN  \n",
       "5               0                   1                 2.0                 NaN  \n",
       "6               1                   1                 8.0                17.0  \n",
       "7               1                   1                15.0                 NaN  \n",
       "8               1                   1                16.0                 NaN  \n",
       "9               1                   8                 NaN                 NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            245982\n",
       "Product_Category_3            545809\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender: ['F' 'M']\n",
      "Age: ['0-17' '55+' '26-35' '46-50' '51-55' '36-45' '18-25']\n",
      "Occupation: [10 16 15  7 20  9  1 12 17  0  3  4 11  8 19  2 18  5 14 13  6]\n",
      "City_Category: ['A' 'C' 'B']\n",
      "Stay_In_Current_City_Years: ['2' '4+' '3' '1' '0']\n",
      "Marital_Status: [0 1]\n",
      "Product_Category_1: [ 3  1 12  8  5  4  2  6 14 11 13 15  7 16 18 10 17  9 20 19]\n",
      "Product_Category_2: [ 0.  6. 14.  2.  8. 15. 16. 11.  5.  3.  4. 12.  9. 10. 17. 13.  7. 18.]\n",
      "Product_Category_3: [ 0. 14. 17.  5.  4. 16. 15.  8.  9. 13.  6. 12.  3. 18. 11. 10.]\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i+\":\",df[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "df[df.columns] = df[df.columns].apply(lambda col: le.fit_transform(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "ohe = OneHotEncoder()\n",
    "\n",
    "df = np.array(ohe.fit_transform(df).toarray(),dtype=np.str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.1468</td>\n",
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       "      <td>-0.057528</td>\n",
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       "      <td>5.368100</td>\n",
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       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.745599</td>\n",
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       "      <td>5.977729</td>\n",
       "      <td>-0.470329</td>\n",
       "      <td>-0.815516</td>\n",
       "      <td>-0.499981</td>\n",
       "      <td>-0.301442</td>\n",
       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
       "      <td>-0.379928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>-0.130462</td>\n",
       "      <td>-0.100584</td>\n",
       "      <td>-0.186286</td>\n",
       "      <td>-0.231824</td>\n",
       "      <td>-0.251067</td>\n",
       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.745599</td>\n",
       "      <td>-1.745599</td>\n",
       "      <td>5.977729</td>\n",
       "      <td>-0.470329</td>\n",
       "      <td>-0.815516</td>\n",
       "      <td>-0.499981</td>\n",
       "      <td>-0.301442</td>\n",
       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
       "      <td>-0.379928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>-0.130462</td>\n",
       "      <td>-0.100584</td>\n",
       "      <td>-0.186286</td>\n",
       "      <td>-0.231824</td>\n",
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       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.572869</td>\n",
       "      <td>-0.167288</td>\n",
       "      <td>-0.470329</td>\n",
       "      <td>-0.815516</td>\n",
       "      <td>-0.499981</td>\n",
       "      <td>-0.301442</td>\n",
       "      <td>-0.274156</td>\n",
       "      <td>4.962622</td>\n",
       "      <td>-0.379928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>-0.130462</td>\n",
       "      <td>-0.100584</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
       "      <td>-0.379928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>-0.130462</td>\n",
       "      <td>-0.100584</td>\n",
       "      <td>-0.186286</td>\n",
       "      <td>-0.231824</td>\n",
       "      <td>-0.251067</td>\n",
       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
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       "    <tr>\n",
       "      <th>783663</th>\n",
       "      <td>1.745599</td>\n",
       "      <td>-1.745599</td>\n",
       "      <td>-0.167288</td>\n",
       "      <td>-0.470329</td>\n",
       "      <td>1.226217</td>\n",
       "      <td>-0.499981</td>\n",
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       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
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       "      <td>-0.130462</td>\n",
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       "    </tr>\n",
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       "      <th>783664</th>\n",
       "      <td>1.745599</td>\n",
       "      <td>-1.745599</td>\n",
       "      <td>-0.167288</td>\n",
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       "      <td>1.226217</td>\n",
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       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
       "      <td>-0.379928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>7.665082</td>\n",
       "      <td>-0.100584</td>\n",
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       "      <td>-0.231824</td>\n",
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       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783665</th>\n",
       "      <td>1.745599</td>\n",
       "      <td>-1.745599</td>\n",
       "      <td>-0.167288</td>\n",
       "      <td>-0.470329</td>\n",
       "      <td>-0.815516</td>\n",
       "      <td>-0.499981</td>\n",
       "      <td>3.317389</td>\n",
       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
       "      <td>-0.379928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>-0.130462</td>\n",
       "      <td>-0.100584</td>\n",
       "      <td>-0.186286</td>\n",
       "      <td>-0.231824</td>\n",
       "      <td>-0.251067</td>\n",
       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783666</th>\n",
       "      <td>1.745599</td>\n",
       "      <td>-1.745599</td>\n",
       "      <td>-0.167288</td>\n",
       "      <td>-0.470329</td>\n",
       "      <td>-0.815516</td>\n",
       "      <td>-0.499981</td>\n",
       "      <td>3.317389</td>\n",
       "      <td>-0.274156</td>\n",
       "      <td>-0.201506</td>\n",
       "      <td>2.632079</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1468</td>\n",
       "      <td>-0.056583</td>\n",
       "      <td>-0.057528</td>\n",
       "      <td>-0.130462</td>\n",
       "      <td>-0.100584</td>\n",
       "      <td>-0.186286</td>\n",
       "      <td>-0.231824</td>\n",
       "      <td>-0.251067</td>\n",
       "      <td>-0.177047</td>\n",
       "      <td>-0.092308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6   \\\n",
       "0       1.745599 -1.745599  5.977729 -0.470329 -0.815516 -0.499981 -0.301442   \n",
       "1       1.745599 -1.745599  5.977729 -0.470329 -0.815516 -0.499981 -0.301442   \n",
       "2       1.745599 -1.745599  5.977729 -0.470329 -0.815516 -0.499981 -0.301442   \n",
       "3       1.745599 -1.745599  5.977729 -0.470329 -0.815516 -0.499981 -0.301442   \n",
       "4      -0.572869  0.572869 -0.167288 -0.470329 -0.815516 -0.499981 -0.301442   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "783662  1.745599 -1.745599 -0.167288 -0.470329  1.226217 -0.499981 -0.301442   \n",
       "783663  1.745599 -1.745599 -0.167288 -0.470329  1.226217 -0.499981 -0.301442   \n",
       "783664  1.745599 -1.745599 -0.167288 -0.470329  1.226217 -0.499981 -0.301442   \n",
       "783665  1.745599 -1.745599 -0.167288 -0.470329 -0.815516 -0.499981  3.317389   \n",
       "783666  1.745599 -1.745599 -0.167288 -0.470329 -0.815516 -0.499981  3.317389   \n",
       "\n",
       "              7         8         9   ...      84        85        86  \\\n",
       "0      -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "1      -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "2      -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "3      -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "4      -0.274156  4.962622 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "...          ...       ...       ...  ...     ...       ...       ...   \n",
       "783662 -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "783663 -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "783664 -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "783665 -0.274156 -0.201506 -0.379928  ... -0.1468 -0.056583 -0.057528   \n",
       "783666 -0.274156 -0.201506  2.632079  ... -0.1468 -0.056583 -0.057528   \n",
       "\n",
       "              87        88        89        90        91        92        93  \n",
       "0      -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "1      -0.130462 -0.100584  5.368100 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "2      -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "3      -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "4      -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "...          ...       ...       ...       ...       ...       ...       ...  \n",
       "783662 -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "783663 -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "783664  7.665082 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "783665 -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "783666 -0.130462 -0.100584 -0.186286 -0.231824 -0.251067 -0.177047 -0.092308  \n",
       "\n",
       "[783667 rows x 94 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "st = StandardScaler()\n",
    "df = st.fit_transform(df)\n",
    "\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:550068]\n",
    "X_val = df.iloc[550068:]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.3 , random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3038.189712043257\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model1 = RandomForestRegressor(random_state=0)\n",
    "\n",
    "model1.fit(X_train,y_train)\n",
    "\n",
    "y_pred1 = model1.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "print(sqrt(mean_squared_error(y_test,y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:14:13] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "3045.1113884173565\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "model3 = XGBRegressor()\n",
    "\n",
    "model3.fit(X_train,y_train)\n",
    "\n",
    "y_pred3 = model3.predict(X_test)\n",
    "\n",
    "print(sqrt(mean_squared_error(y_test,y_pred3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3015.439763685617\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model4 = LinearRegression()\n",
    "\n",
    "model4.fit(X_train,y_train)\n",
    "\n",
    "y_pred4 = model4.predict(X_test)\n",
    "\n",
    "print(sqrt(mean_squared_error(y_test,y_pred4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\20114\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.regularizers import l2\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\20114\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Epoch 1/100\n",
      "385047/385047 [==============================] - 79s 206us/step - loss: 11227050.5400\n",
      "Epoch 2/100\n",
      "385047/385047 [==============================] - 70s 181us/step - loss: 8989595.5553\n",
      "Epoch 3/100\n",
      "385047/385047 [==============================] - 63s 163us/step - loss: 8791223.2697\n",
      "Epoch 4/100\n",
      "385047/385047 [==============================] - 60s 156us/step - loss: 8579445.8083\n",
      "Epoch 5/100\n",
      "385047/385047 [==============================] - 60s 156us/step - loss: 8516445.3054\n",
      "Epoch 6/100\n",
      "385047/385047 [==============================] - 60s 157us/step - loss: 8484268.5114\n",
      "Epoch 7/100\n",
      "385047/385047 [==============================] - 60s 156us/step - loss: 8449953.3624\n",
      "Epoch 8/100\n",
      "385047/385047 [==============================] - 60s 157us/step - loss: 8423196.4517\n",
      "Epoch 9/100\n",
      "385047/385047 [==============================] - 60s 156us/step - loss: 8393833.1798\n",
      "Epoch 10/100\n",
      "385047/385047 [==============================] - 60s 155us/step - loss: 8369081.9983\n",
      "Epoch 11/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 8344210.7392\n",
      "Epoch 12/100\n",
      "385047/385047 [==============================] - 63s 165us/step - loss: 8318298.6938\n",
      "Epoch 13/100\n",
      "385047/385047 [==============================] - 62s 161us/step - loss: 8294083.9431\n",
      "Epoch 14/100\n",
      "385047/385047 [==============================] - 69s 178us/step - loss: 8270707.8302\n",
      "Epoch 15/100\n",
      "385047/385047 [==============================] - 61s 159us/step - loss: 8245073.5536\n",
      "Epoch 16/100\n",
      "385047/385047 [==============================] - 70s 182us/step - loss: 8222231.6526\n",
      "Epoch 17/100\n",
      "385047/385047 [==============================] - 65s 170us/step - loss: 8198869.2567\n",
      "Epoch 18/100\n",
      "385047/385047 [==============================] - 70s 182us/step - loss: 8178751.9324\n",
      "Epoch 19/100\n",
      "385047/385047 [==============================] - 67s 173us/step - loss: 8158848.8778\n",
      "Epoch 20/100\n",
      "385047/385047 [==============================] - 67s 173us/step - loss: 8138214.7674\n",
      "Epoch 21/100\n",
      "385047/385047 [==============================] - 66s 171us/step - loss: 8120030.9073\n",
      "Epoch 22/100\n",
      "385047/385047 [==============================] - 67s 175us/step - loss: 8106863.9888\n",
      "Epoch 23/100\n",
      "385047/385047 [==============================] - 64s 167us/step - loss: 8087775.2962\n",
      "Epoch 24/100\n",
      "385047/385047 [==============================] - 65s 170us/step - loss: 8075119.4362\n",
      "Epoch 25/100\n",
      "385047/385047 [==============================] - 78s 203us/step - loss: 8058343.7640\n",
      "Epoch 26/100\n",
      "385047/385047 [==============================] - 77s 201us/step - loss: 8047527.2217\n",
      "Epoch 27/100\n",
      "385047/385047 [==============================] - 75s 196us/step - loss: 8034859.9821\n",
      "Epoch 28/100\n",
      "385047/385047 [==============================] - 74s 191us/step - loss: 8021981.7028\n",
      "Epoch 29/100\n",
      "385047/385047 [==============================] - 68s 175us/step - loss: 8011424.4710\n",
      "Epoch 30/100\n",
      "385047/385047 [==============================] - 66s 173us/step - loss: 8000074.8546\n",
      "Epoch 31/100\n",
      "385047/385047 [==============================] - 60s 157us/step - loss: 7994245.3028\n",
      "Epoch 32/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7980384.1813\n",
      "Epoch 33/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7971438.0926\n",
      "Epoch 34/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7965286.1761\n",
      "Epoch 35/100\n",
      "385047/385047 [==============================] - 61s 157us/step - loss: 7956341.2251\n",
      "Epoch 36/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7946707.8601\n",
      "Epoch 37/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7941153.7834\n",
      "Epoch 38/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7931923.8665\n",
      "Epoch 39/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7926248.6118\n",
      "Epoch 40/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7915547.0953\n",
      "Epoch 41/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7911123.4695\n",
      "Epoch 42/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7904942.0706\n",
      "Epoch 43/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7898276.9196\n",
      "Epoch 44/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7890946.8810\n",
      "Epoch 45/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7886695.3116\n",
      "Epoch 46/100\n",
      "385047/385047 [==============================] - 59s 152us/step - loss: 7876766.7219\n",
      "Epoch 47/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7872469.8622\n",
      "Epoch 48/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7865137.6087\n",
      "Epoch 49/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7860418.7022\n",
      "Epoch 50/100\n",
      "385047/385047 [==============================] - 50s 130us/step - loss: 7856831.6957\n",
      "Epoch 51/100\n",
      "385047/385047 [==============================] - 43s 112us/step - loss: 7853695.68680s - loss: 7854843.\n",
      "Epoch 52/100\n",
      "385047/385047 [==============================] - 47s 121us/step - loss: 7846508.0648\n",
      "Epoch 53/100\n",
      "385047/385047 [==============================] - 49s 126us/step - loss: 7841908.3179\n",
      "Epoch 54/100\n",
      "385047/385047 [==============================] - 53s 137us/step - loss: 7835256.9067\n",
      "Epoch 55/100\n",
      "385047/385047 [==============================] - 50s 129us/step - loss: 7830032.8404\n",
      "Epoch 56/100\n",
      "385047/385047 [==============================] - 48s 123us/step - loss: 7825961.7229\n",
      "Epoch 57/100\n",
      "385047/385047 [==============================] - 47s 122us/step - loss: 7821303.5854\n",
      "Epoch 58/100\n",
      "385047/385047 [==============================] - 46s 120us/step - loss: 7816417.3015\n",
      "Epoch 59/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7812268.3260\n",
      "Epoch 60/100\n",
      "385047/385047 [==============================] - 59s 154us/step - loss: 7808592.5953\n",
      "Epoch 61/100\n",
      "385047/385047 [==============================] - 64s 167us/step - loss: 7803232.9992\n",
      "Epoch 62/100\n",
      "385047/385047 [==============================] - 61s 157us/step - loss: 7798483.5888\n",
      "Epoch 63/100\n",
      "385047/385047 [==============================] - 49s 128us/step - loss: 7797508.9915\n",
      "Epoch 64/100\n",
      "385047/385047 [==============================] - 46s 119us/step - loss: 7794900.66801s - loss: \n",
      "Epoch 65/100\n",
      "385047/385047 [==============================] - 45s 117us/step - loss: 7784358.10010s - loss: 778\n",
      "Epoch 66/100\n",
      "385047/385047 [==============================] - 45s 118us/step - loss: 7785035.5075\n",
      "Epoch 67/100\n",
      "385047/385047 [==============================] - 45s 116us/step - loss: 7775535.23370s - loss: 77\n",
      "Epoch 68/100\n",
      "385047/385047 [==============================] - 47s 121us/step - loss: 7775566.5442\n",
      "Epoch 69/100\n",
      "385047/385047 [==============================] - 51s 132us/step - loss: 7771228.4479\n",
      "Epoch 70/100\n",
      "385047/385047 [==============================] - 60s 157us/step - loss: 7766286.7237\n",
      "Epoch 71/100\n",
      "385047/385047 [==============================] - 52s 136us/step - loss: 7766260.4340\n",
      "Epoch 72/100\n",
      "385047/385047 [==============================] - 58s 152us/step - loss: 7763058.4526\n",
      "Epoch 73/100\n",
      "385047/385047 [==============================] - 54s 141us/step - loss: 7760574.2786\n",
      "Epoch 74/100\n",
      "385047/385047 [==============================] - 54s 140us/step - loss: 7754573.2078\n",
      "Epoch 75/100\n",
      "385047/385047 [==============================] - 56s 144us/step - loss: 7752523.8196\n",
      "Epoch 76/100\n",
      "385047/385047 [==============================] - 59s 153us/step - loss: 7750885.1532\n",
      "Epoch 77/100\n",
      "385047/385047 [==============================] - 51s 132us/step - loss: 7746698.4288\n",
      "Epoch 78/100\n",
      "385047/385047 [==============================] - 53s 137us/step - loss: 7743176.1176\n",
      "Epoch 79/100\n",
      "385047/385047 [==============================] - 54s 141us/step - loss: 7743172.1777\n",
      "Epoch 80/100\n",
      "385047/385047 [==============================] - 46s 119us/step - loss: 7736938.4002\n",
      "Epoch 81/100\n",
      "385047/385047 [==============================] - 51s 133us/step - loss: 7738074.2759\n",
      "Epoch 82/100\n",
      "385047/385047 [==============================] - 53s 138us/step - loss: 7731946.3301\n",
      "Epoch 83/100\n",
      "385047/385047 [==============================] - 47s 123us/step - loss: 7730323.8253\n",
      "Epoch 84/100\n",
      "385047/385047 [==============================] - 46s 119us/step - loss: 7730016.4987\n",
      "Epoch 85/100\n",
      "385047/385047 [==============================] - 49s 128us/step - loss: 7724545.0246\n",
      "Epoch 86/100\n",
      "385047/385047 [==============================] - 48s 124us/step - loss: 7725058.2113\n",
      "Epoch 87/100\n",
      "385047/385047 [==============================] - 49s 126us/step - loss: 7721998.7318\n",
      "Epoch 88/100\n",
      "385047/385047 [==============================] - 49s 127us/step - loss: 7723250.3983\n",
      "Epoch 89/100\n",
      "385047/385047 [==============================] - 52s 134us/step - loss: 7718472.3983\n",
      "Epoch 90/100\n",
      "385047/385047 [==============================] - 51s 133us/step - loss: 7716032.0600\n",
      "Epoch 91/100\n",
      "385047/385047 [==============================] - 50s 129us/step - loss: 7711345.6469\n",
      "Epoch 92/100\n",
      "385047/385047 [==============================] - 48s 124us/step - loss: 7712922.1998\n",
      "Epoch 93/100\n",
      "385047/385047 [==============================] - 49s 127us/step - loss: 7709523.9246\n",
      "Epoch 94/100\n",
      "385047/385047 [==============================] - 47s 123us/step - loss: 7707999.8092\n",
      "Epoch 95/100\n",
      "385047/385047 [==============================] - 50s 131us/step - loss: 7706552.6237\n",
      "Epoch 96/100\n",
      "385047/385047 [==============================] - 47s 123us/step - loss: 7702325.8142\n",
      "Epoch 97/100\n",
      "385047/385047 [==============================] - 49s 127us/step - loss: 7699776.55160s - loss: 76991\n",
      "Epoch 98/100\n",
      "385047/385047 [==============================] - 50s 130us/step - loss: 7700891.3842\n",
      "Epoch 99/100\n",
      "385047/385047 [==============================] - 55s 142us/step - loss: 7695707.4561\n",
      "Epoch 100/100\n",
      "385047/385047 [==============================] - 45s 117us/step - loss: 7696214.8547\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x24c5748fec8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(100,input_dim = 94,kernel_initializer = \"uniform\",\n",
    "                     kernel_regularizer = l2(0.1) , bias_regularizer = l2(0.1),\n",
    "                     bias_initializer=\"zeros\",activation=\"relu\"))\n",
    "model.add(Dense(50,kernel_initializer = \"uniform\",\n",
    "                     kernel_regularizer = l2(0.1) , bias_regularizer = l2(0.1),\n",
    "                     bias_initializer=\"zeros\",activation=\"relu\"))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer=\"adam\",loss = \"mean_squared_error\")\n",
    "\n",
    "model.fit(X_train,y_train,batch_size=10 ,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2894.100727632681\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(np.sqrt(mean_squared_error(y_test,y_pred)))"
   ]
  }
 ],
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