Winston_Challenge.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Feature_3</th>\n",
       "      <th>Feature_4</th>\n",
       "      <th>Feature_5</th>\n",
       "      <th>Feature_6</th>\n",
       "      <th>Feature_7</th>\n",
       "      <th>Feature_8</th>\n",
       "      <th>Feature_9</th>\n",
       "      <th>...</th>\n",
       "      <th>Ret_175</th>\n",
       "      <th>Ret_176</th>\n",
       "      <th>Ret_177</th>\n",
       "      <th>Ret_178</th>\n",
       "      <th>Ret_179</th>\n",
       "      <th>Ret_180</th>\n",
       "      <th>Ret_PlusOne</th>\n",
       "      <th>Ret_PlusTwo</th>\n",
       "      <th>Weight_Intraday</th>\n",
       "      <th>Weight_Daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75751</td>\n",
       "      <td>0.2254</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002688</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>-0.000838</td>\n",
       "      <td>-6.953224e-04</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>-0.001974</td>\n",
       "      <td>-0.019512</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>1251507.965681</td>\n",
       "      <td>1564384.957101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.388896</td>\n",
       "      <td>17369</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000129</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>3.315418e-07</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>-0.002939</td>\n",
       "      <td>-0.010253</td>\n",
       "      <td>1733950.349065</td>\n",
       "      <td>2167437.936331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.696727</td>\n",
       "      <td>0.739591</td>\n",
       "      <td>-0.167928</td>\n",
       "      <td>9</td>\n",
       "      <td>0.471947</td>\n",
       "      <td>8277</td>\n",
       "      <td>0.3650</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000524</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>5.322557e-04</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>-0.024791</td>\n",
       "      <td>0.015711</td>\n",
       "      <td>1529197.383151</td>\n",
       "      <td>1911496.728938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.694350</td>\n",
       "      <td>1.568248</td>\n",
       "      <td>0.479073</td>\n",
       "      <td>5</td>\n",
       "      <td>0.120653</td>\n",
       "      <td>22508</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>-0.000090</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>-1.281102e-04</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>-0.005680</td>\n",
       "      <td>-0.002190</td>\n",
       "      <td>1711569.423695</td>\n",
       "      <td>2139461.779618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.736489</td>\n",
       "      <td>2.765531</td>\n",
       "      <td>1.245280</td>\n",
       "      <td>7</td>\n",
       "      <td>4.866985</td>\n",
       "      <td>22423</td>\n",
       "      <td>0.2138</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.002449</td>\n",
       "      <td>8.619882e-06</td>\n",
       "      <td>0.001209</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.036104</td>\n",
       "      <td>-0.026552</td>\n",
       "      <td>1267270.264754</td>\n",
       "      <td>1584087.830943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 211 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Feature_1  Feature_2  Feature_3  Feature_4  Feature_5  Feature_6  \\\n",
       "0   1        NaN        NaN        NaN        NaN          8        NaN   \n",
       "1   2        NaN        NaN        NaN        NaN          3   0.388896   \n",
       "2   3        NaN  -0.696727   0.739591  -0.167928          9   0.471947   \n",
       "3   4        NaN  -0.694350   1.568248   0.479073          5   0.120653   \n",
       "4   5          6  -1.736489   2.765531   1.245280          7   4.866985   \n",
       "\n",
       "   Feature_7  Feature_8  Feature_9       ...         Ret_175   Ret_176  \\\n",
       "0      75751     0.2254         11       ...       -0.002688  0.002246   \n",
       "1      17369     0.0166         13       ...       -0.000129  0.000123   \n",
       "2       8277     0.3650          9       ...       -0.000524 -0.000394   \n",
       "3      22508     0.2654         13       ...        0.000346 -0.000090   \n",
       "4      22423     0.2138         13       ...       -0.001235  0.000027   \n",
       "\n",
       "    Ret_177       Ret_178   Ret_179   Ret_180  Ret_PlusOne  Ret_PlusTwo  \\\n",
       "0 -0.000838 -6.953224e-04  0.000003 -0.001974    -0.019512     0.028846   \n",
       "1  0.000248  3.315418e-07  0.000003  0.000027    -0.002939    -0.010253   \n",
       "2  0.000116  5.322557e-04  0.000274  0.000784    -0.024791     0.015711   \n",
       "3  0.000288 -1.281102e-04  0.000074  0.000341    -0.005680    -0.002190   \n",
       "4  0.002449  8.619882e-06  0.001209 -0.000004     0.036104    -0.026552   \n",
       "\n",
       "   Weight_Intraday    Weight_Daily  \n",
       "0   1251507.965681  1564384.957101  \n",
       "1   1733950.349065  2167437.936331  \n",
       "2   1529197.383151  1911496.728938  \n",
       "3   1711569.423695  2139461.779618  \n",
       "4   1267270.264754  1584087.830943  \n",
       "\n",
       "[5 rows x 211 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"/Users/jalemrajrohit/Desktop/Data Science/Kaggle/Winston_Challenge/train.csv\")\n",
    "\n",
    "train.head()\n",
    "\n",
    "#len(train) # <-- 40000\n",
    "#train.shape  # <-- (40000, 211)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Feature_3</th>\n",
       "      <th>Feature_4</th>\n",
       "      <th>Feature_5</th>\n",
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       "      <th>Feature_7</th>\n",
       "      <th>Feature_8</th>\n",
       "      <th>Feature_9</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.000382</td>\n",
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       "      <td>0.001579</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.002382</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.124463</td>\n",
       "      <td>0.750519</td>\n",
       "      <td>0.801993</td>\n",
       "      <td>8</td>\n",
       "      <td>0.463234</td>\n",
       "      <td>44942</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>-0.000278</td>\n",
       "      <td>-0.000255</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>-0.000778</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>-0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.404529</td>\n",
       "      <td>1.371021</td>\n",
       "      <td>1.135463</td>\n",
       "      <td>1</td>\n",
       "      <td>3.470399</td>\n",
       "      <td>35071</td>\n",
       "      <td>0.2208</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002180</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>-0.000053</td>\n",
       "      <td>-0.002894</td>\n",
       "      <td>-0.003772</td>\n",
       "      <td>0.001737</td>\n",
       "      <td>0.003756</td>\n",
       "      <td>-0.000863</td>\n",
       "      <td>-0.000873</td>\n",
       "      <td>-0.000302</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>-0.107651</td>\n",
       "      <td>-0.272008</td>\n",
       "      <td>8</td>\n",
       "      <td>0.198879</td>\n",
       "      <td>1188</td>\n",
       "      <td>0.2254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000204</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.000204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000007</td>\n",
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       "</table>\n",
       "<p>5 rows × 147 columns</p>\n",
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       "   Id  Feature_1  Feature_2  Feature_3  Feature_4  Feature_5  Feature_6  \\\n",
       "0   1        NaN  -0.479347   0.716679   0.765301          6        NaN   \n",
       "1   2        NaN  -1.556631  -0.544329  -0.041571          2  -0.401548   \n",
       "2   3        NaN  -1.124463   0.750519   0.801993          8   0.463234   \n",
       "3   4        NaN   2.404529   1.371021   1.135463          1   3.470399   \n",
       "4   5          1   0.180297  -0.107651  -0.272008          8   0.198879   \n",
       "\n",
       "   Feature_7  Feature_8  Feature_9    ...      Ret_111   Ret_112   Ret_113  \\\n",
       "0      36450     0.2064         14    ...    -0.000135 -0.000002 -0.000144   \n",
       "1      77624     0.3418         10    ...    -0.000384 -0.000382  0.000387   \n",
       "2      44942     0.0166         11    ...    -0.000013  0.000801 -0.000278   \n",
       "3      35071     0.2208         12    ...    -0.002180  0.000206 -0.000053   \n",
       "4       1188     0.2254        NaN    ...    -0.000204  0.000176 -0.000204   \n",
       "\n",
       "    Ret_114   Ret_115   Ret_116   Ret_117   Ret_118   Ret_119   Ret_120  \n",
       "0  0.000931  0.000280  0.000796 -0.000272 -0.000683  0.002010 -0.001187  \n",
       "1  0.000392  0.001579 -0.000019  0.000803  0.000001 -0.000002 -0.002382  \n",
       "2 -0.000255  0.000534  0.000272 -0.000778  0.001060  0.000010 -0.000805  \n",
       "3 -0.002894 -0.003772  0.001737  0.003756 -0.000863 -0.000873 -0.000302  \n",
       "4       NaN -0.000004  0.000026  0.000201  0.000004       NaN  0.000007  \n",
       "\n",
       "[5 rows x 147 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"/Users/jalemrajrohit/Desktop/Data Science/Kaggle/Winston_Challenge/test.csv\")\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_1</td>\n",
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1_10</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1_11</td>\n",
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       "      <th>11</th>\n",
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       "      <th>12</th>\n",
       "      <td>1_13</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1_14</td>\n",
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       "      <th>14</th>\n",
       "      <td>1_15</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1_16</td>\n",
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       "      <th>16</th>\n",
       "      <td>1_17</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1_18</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1_19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1_20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1_21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1_22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1_23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1_24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1_25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1_26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1_27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1_28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1_29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1_30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  Predicted\n",
       "0    1_1          0\n",
       "1    1_2          0\n",
       "2    1_3          0\n",
       "3    1_4          0\n",
       "4    1_5          0\n",
       "5    1_6          0\n",
       "6    1_7          0\n",
       "7    1_8          0\n",
       "8    1_9          0\n",
       "9   1_10          0\n",
       "10  1_11          0\n",
       "11  1_12          0\n",
       "12  1_13          0\n",
       "13  1_14          0\n",
       "14  1_15          0\n",
       "15  1_16          0\n",
       "16  1_17          0\n",
       "17  1_18          0\n",
       "18  1_19          0\n",
       "19  1_20          0\n",
       "20  1_21          0\n",
       "21  1_22          0\n",
       "22  1_23          0\n",
       "23  1_24          0\n",
       "24  1_25          0\n",
       "25  1_26          0\n",
       "26  1_27          0\n",
       "27  1_28          0\n",
       "28  1_29          0\n",
       "29  1_30          0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = pd.read_csv(\"/Users/jalemrajrohit/Desktop/Data Science/Kaggle/Winston_Challenge/sample_submission.csv\")\n",
    "\n",
    "sample.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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