code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import sklearn \n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import keras\n",
    "import xgboost\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>traffic_volume</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
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       "          id  traffic_volume holiday        temp   rain_1h    snow_1h  \\\n",
       "0          0     5977.718652     NaN  334.271282  7.077448   2.175000   \n",
       "1          1     4868.417932     NaN  335.523582  5.055320   7.612501   \n",
       "2          2     5139.005377     NaN  335.778680  8.088512   3.262500   \n",
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       "4          4     5301.789059     NaN  337.587557  5.055320  13.050001   \n",
       "...      ...             ...     ...         ...       ...        ...   \n",
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       "28921  28921     6594.356175     NaN  308.297656  3.033192   4.350000   \n",
       "28922  28922     6594.356175     NaN  308.297656  3.033192   8.700001   \n",
       "\n",
       "       clouds_all weather_main weather_description         date_time  \n",
       "0              40       Clouds    scattered clouds  02-10-2012 09:00  \n",
       "1              75       Clouds       broken clouds  02-10-2012 10:00  \n",
       "2              90       Clouds     overcast clouds  02-10-2012 11:00  \n",
       "3              90       Clouds     overcast clouds  02-10-2012 12:00  \n",
       "4              75       Clouds       broken clouds  02-10-2012 13:00  \n",
       "...           ...          ...                 ...               ...  \n",
       "28918          90         Haze                haze  08-12-2016 04:00  \n",
       "28919          75         Snow          light snow  08-12-2016 05:00  \n",
       "28920           1         Snow          light snow  08-12-2016 06:00  \n",
       "28921           1         Mist                mist  08-12-2016 07:00  \n",
       "28922           1         Snow          light snow  08-12-2016 07:00  \n",
       "\n",
       "[28923 rows x 10 columns]"
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     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:\\\\Users\\\\Hemanth M\\\\C\\\\Python\\\\Project\\\\train.csv\")\n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "          id holiday        temp   rain_1h    snow_1h  clouds_all  \\\n",
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       "3      28926     NaN  311.161712  5.055320   8.700001          90   \n",
       "4      28927     NaN  311.509574  1.011064   5.437500          90   \n",
       "...      ...     ...         ...       ...        ...         ...   \n",
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       "19277  48200     NaN  327.870638  9.099576   1.087500          90   \n",
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       "19280  48203     NaN  327.128535  4.044256   2.175000          90   \n",
       "\n",
       "       weather_main     weather_description         date_time  \n",
       "0              Snow              light snow  08-12-2016 08:00  \n",
       "1              Snow              light snow  08-12-2016 09:00  \n",
       "2              Snow              light snow  08-12-2016 10:00  \n",
       "3              Snow              light snow  08-12-2016 11:00  \n",
       "4              Snow              light snow  08-12-2016 12:00  \n",
       "...             ...                     ...               ...  \n",
       "19276        Clouds           broken clouds  30-09-2018 19:00  \n",
       "19277        Clouds         overcast clouds  30-09-2018 20:00  \n",
       "19278  Thunderstorm  proximity thunderstorm  30-09-2018 21:00  \n",
       "19279        Clouds         overcast clouds  30-09-2018 22:00  \n",
       "19280        Clouds         overcast clouds  30-09-2018 23:00  \n",
       "\n",
       "[19281 rows x 9 columns]"
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     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdata = pd.read_csv(\"C:\\\\Users\\\\Hemanth M\\\\C\\\\Python\\\\Project\\\\test.csv\")\n",
    "vdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check holidays and add a new column to check if it was a holiday or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>02-10-2012 11:00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <td>nan</td>\n",
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       "      <td>8.700001</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>nan</td>\n",
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       "      <td>5.055320</td>\n",
       "      <td>13.050001</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>02-10-2012 13:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  traffic_volume holiday        temp   rain_1h    snow_1h  clouds_all  \\\n",
       "0   0     5977.718652     nan  334.271282  7.077448   2.175000          40   \n",
       "1   1     4868.417932     nan  335.523582  5.055320   7.612501          75   \n",
       "2   2     5139.005377     nan  335.778680  8.088512   3.262500          90   \n",
       "3   3     5418.217122     nan  336.416425  6.066384   8.700001          90   \n",
       "4   4     5301.789059     nan  337.587557  5.055320  13.050001          75   \n",
       "\n",
       "  weather_main weather_description         date_time  isHoliday  \n",
       "0       Clouds    scattered clouds  02-10-2012 09:00          0  \n",
       "1       Clouds       broken clouds  02-10-2012 10:00          0  \n",
       "2       Clouds     overcast clouds  02-10-2012 11:00          0  \n",
       "3       Clouds     overcast clouds  02-10-2012 12:00          0  \n",
       "4       Clouds       broken clouds  02-10-2012 13:00          0  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"holiday\"].unique() # checking all the unique days\n",
    "\n",
    "train = data\n",
    "train.holiday = train.holiday.fillna('nan')\n",
    "isHoliday = []\n",
    "train['holiday'] = train['holiday'].astype('string')\n",
    "\n",
    "for col in train.holiday:\n",
    "    if col == 'nan':\n",
    "        isHoliday.append(0)\n",
    "    else:\n",
    "        isHoliday.append(1)\n",
    "\n",
    "train['isHoliday'] = isHoliday\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      id holiday        temp   rain_1h   snow_1h  clouds_all weather_main  \\\n",
       "0  28923     nan  308.529564  2.022128  9.787501          40         Snow   \n",
       "1  28924     nan  308.900616  2.022128  9.787501          40         Snow   \n",
       "2  28925     nan  310.199297  3.033192  9.787501          75         Snow   \n",
       "3  28926     nan  311.161712  5.055320  8.700001          90         Snow   \n",
       "4  28927     nan  311.509574  1.011064  5.437500          90         Snow   \n",
       "\n",
       "  weather_description         date_time  isHoliday  \n",
       "0          light snow  08-12-2016 08:00          0  \n",
       "1          light snow  08-12-2016 09:00          0  \n",
       "2          light snow  08-12-2016 10:00          0  \n",
       "3          light snow  08-12-2016 11:00          0  \n",
       "4          light snow  08-12-2016 12:00          0  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid = vdata.copy()\n",
    "valid.holiday = valid.holiday.fillna('nan')\n",
    "isHoliday = []\n",
    "valid['holiday'] = valid['holiday'].astype('string')\n",
    "\n",
    "for col in valid.holiday:\n",
    "    if col == 'nan':\n",
    "        isHoliday.append(0)\n",
    "    else:\n",
    "        isHoliday.append(1)\n",
    "\n",
    "valid['isHoliday'] = isHoliday\n",
    "valid.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ordinal encoding the weather_main column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   id  traffic_volume holiday        temp   rain_1h    snow_1h  clouds_all  \\\n",
       "0   0     5977.718652     nan  334.271282  7.077448   2.175000          40   \n",
       "1   1     4868.417932     nan  335.523582  5.055320   7.612501          75   \n",
       "2   2     5139.005377     nan  335.778680  8.088512   3.262500          90   \n",
       "3   3     5418.217122     nan  336.416425  6.066384   8.700001          90   \n",
       "4   4     5301.789059     nan  337.587557  5.055320  13.050001          75   \n",
       "\n",
       "   weather_main weather_description         date_time  isHoliday  \n",
       "0           1.0    scattered clouds  02-10-2012 09:00          0  \n",
       "1           1.0       broken clouds  02-10-2012 10:00          0  \n",
       "2           1.0     overcast clouds  02-10-2012 11:00          0  \n",
       "3           1.0     overcast clouds  02-10-2012 12:00          0  \n",
       "4           1.0       broken clouds  02-10-2012 13:00          0  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe = OrdinalEncoder()\n",
    "train['weather_main'] = oe.fit_transform(train[['weather_main']])\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "      id holiday        temp   rain_1h   snow_1h  clouds_all  weather_main  \\\n",
       "0  28923     nan  308.529564  2.022128  9.787501          40           8.0   \n",
       "1  28924     nan  308.900616  2.022128  9.787501          40           8.0   \n",
       "2  28925     nan  310.199297  3.033192  9.787501          75           8.0   \n",
       "3  28926     nan  311.161712  5.055320  8.700001          90           8.0   \n",
       "4  28927     nan  311.509574  1.011064  5.437500          90           8.0   \n",
       "\n",
       "  weather_description         date_time  isHoliday  \n",
       "0          light snow  08-12-2016 08:00          0  \n",
       "1          light snow  08-12-2016 09:00          0  \n",
       "2          light snow  08-12-2016 10:00          0  \n",
       "3          light snow  08-12-2016 11:00          0  \n",
       "4          light snow  08-12-2016 12:00          0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid['weather_main'] = oe.transform(valid[['weather_main']])\n",
    "valid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting time from time field and putting it in a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "      <td>335.523582</td>\n",
       "      <td>5.055320</td>\n",
       "      <td>7.612501</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>02-10-2012 10:00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5139.005377</td>\n",
       "      <td>nan</td>\n",
       "      <td>335.778680</td>\n",
       "      <td>8.088512</td>\n",
       "      <td>3.262500</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>02-10-2012 11:00</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5418.217122</td>\n",
       "      <td>nan</td>\n",
       "      <td>336.416425</td>\n",
       "      <td>6.066384</td>\n",
       "      <td>8.700001</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>02-10-2012 12:00</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5301.789059</td>\n",
       "      <td>nan</td>\n",
       "      <td>337.587557</td>\n",
       "      <td>5.055320</td>\n",
       "      <td>13.050001</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>02-10-2012 13:00</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  traffic_volume holiday        temp   rain_1h    snow_1h  clouds_all  \\\n",
       "0   0     5977.718652     nan  334.271282  7.077448   2.175000          40   \n",
       "1   1     4868.417932     nan  335.523582  5.055320   7.612501          75   \n",
       "2   2     5139.005377     nan  335.778680  8.088512   3.262500          90   \n",
       "3   3     5418.217122     nan  336.416425  6.066384   8.700001          90   \n",
       "4   4     5301.789059     nan  337.587557  5.055320  13.050001          75   \n",
       "\n",
       "   weather_main weather_description         date_time  isHoliday  time  \n",
       "0           1.0    scattered clouds  02-10-2012 09:00          0     9  \n",
       "1           1.0       broken clouds  02-10-2012 10:00          0    10  \n",
       "2           1.0     overcast clouds  02-10-2012 11:00          0    11  \n",
       "3           1.0     overcast clouds  02-10-2012 12:00          0    12  \n",
       "4           1.0       broken clouds  02-10-2012 13:00          0    13  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['date_time'] = train['date_time'].astype('string')\n",
    "time = []\n",
    "\n",
    "for field in train['date_time']:\n",
    "    time.append(int(field[-5:-3]))\n",
    "train['time'] = time\n",
    "\n",
    "valid['date_time'] = valid['date_time'].astype('string')\n",
    "time = []\n",
    "\n",
    "for field in valid['date_time']:\n",
    "    time.append(int(field[-5:-3]))\n",
    "valid['time'] = time\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "217/217 [==============================] - 5s 15ms/step - loss: 3479.0076 - val_loss: 3434.9543\n",
      "Epoch 2/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 3357.5325 - val_loss: 3229.1655\n",
      "Epoch 3/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 3110.2195 - val_loss: 2865.5115\n",
      "Epoch 4/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 2748.4084 - val_loss: 2498.5605\n",
      "Epoch 5/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 2286.8889 - val_loss: 1733.8541\n",
      "Epoch 6/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 1757.2350 - val_loss: 1312.2812\n",
      "Epoch 7/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 1265.2094 - val_loss: 969.9324\n",
      "Epoch 8/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 963.5389 - val_loss: 876.5973\n",
      "Epoch 9/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 861.6867 - val_loss: 858.3013\n",
      "Epoch 10/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 813.6732 - val_loss: 944.8875\n",
      "Epoch 11/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 789.3198 - val_loss: 855.2117\n",
      "Epoch 12/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 775.8173 - val_loss: 789.2966\n",
      "Epoch 13/20\n",
      "217/217 [==============================] - 3s 13ms/step - loss: 765.6409 - val_loss: 787.2383\n",
      "Epoch 14/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 753.7933 - val_loss: 850.7504\n",
      "Epoch 15/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 760.6127 - val_loss: 810.7764\n",
      "Epoch 16/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 756.6030 - val_loss: 766.0911\n",
      "Epoch 17/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 759.5251 - val_loss: 748.6863\n",
      "Epoch 18/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 749.7496 - val_loss: 850.8814\n",
      "Epoch 19/20\n",
      "217/217 [==============================] - 3s 14ms/step - loss: 743.6035 - val_loss: 835.6427\n",
      "Epoch 20/20\n",
      "217/217 [==============================] - 3s 15ms/step - loss: 740.3412 - val_loss: 764.8994\n"
     ]
    }
   ],
   "source": [
    "features = ['isHoliday', 'rain_1h', 'snow_1h', 'weather_main', 'time', 'clouds_all']\n",
    "X = train[features]\n",
    "y = train.traffic_volume\n",
    "\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(512, activation='relu', input_shape=[6]),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dense(512, activation='relu'),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dense(512, activation='relu'),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dense(1),\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='mae',\n",
    ")\n",
    "\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    min_delta= 0.005,\n",
    "    patience= 10, \n",
    "    restore_best_weights=True,\n",
    ")\n",
    "\n",
    "history = model.fit(\n",
    "    train_X, train_y,\n",
    "    validation_data=(val_X, val_y),\n",
    "    batch_size=100,\n",
    "    epochs = 20,\n",
    "    callbacks = [early_stopping],\n",
    "    verbose = 1\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "features = ['isHoliday', 'rain_1h', 'snow_1h', 'weather_main', 'time', 'clouds_all']\n",
    "X_testing = valid[features]\n",
    "\n",
    "pred = model.predict(X_testing)\n",
    "pred.shape\n",
    "pred = pred.reshape((19281))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making it into a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'id': valid.id,\n",
    "                       'traffic_volume': pred})\n",
    "\n",
    "output.to_csv('submission.csv', index = False)"
   ]
  }
 ],
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