bike-sharing-rental-ridership/bike-sharing-rental-ridership.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "editable": true
   },
   "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "editable": true
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the 3 rows of dteday column\n",
    "rides[\"dteday\"][:3]\n",
    "#Get the first value of the previous data\n",
    "rides[\"dteday\"][:3][0]\n",
    "# get first 20 rows and 20 columns from the 60th\n",
    "rides.iloc[:20, 60:80]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa428f7e0b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa428fc5978>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true
   },
   "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>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Scaling target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std\n",
    "    \n",
    "# (data[each] - mean)/std is to calculate z-score which is how many std is the value far from the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true
   },
   "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>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.947345</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.930162</td>\n",
       "      <td>-0.956312</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.561326</td>\n",
       "      <td>-0.804632</td>\n",
       "      <td>-0.823998</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.622172</td>\n",
       "      <td>-0.837666</td>\n",
       "      <td>-0.868103</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.636351</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.949983</td>\n",
       "      <td>-0.972851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.636351</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.723582</td>\n",
       "      <td>-1.009445</td>\n",
       "      <td>-1.039008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday      temp       hum  windspeed    casual  registered       cnt  \\\n",
       "0   0        0 -1.334609  0.947345  -1.553844 -0.662736   -0.930162 -0.956312   \n",
       "1   0        0 -1.438475  0.895513  -1.553844 -0.561326   -0.804632 -0.823998   \n",
       "2   0        0 -1.438475  0.895513  -1.553844 -0.622172   -0.837666 -0.868103   \n",
       "3   0        0 -1.334609  0.636351  -1.553844 -0.662736   -0.949983 -0.972851   \n",
       "4   0        0 -1.334609  0.636351  -1.553844 -0.723582   -1.009445 -1.039008   \n",
       "\n",
       "   season_1  season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  \\\n",
       "0         1         0    ...          0      0      0          0          0   \n",
       "1         1         0    ...          0      0      0          0          0   \n",
       "2         1         0    ...          0      0      0          0          0   \n",
       "3         1         0    ...          0      0      0          0          0   \n",
       "4         1         0    ...          0      0      0          0          0   \n",
       "\n",
       "   weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          1  \n",
       "1          0          0          0          0          1  \n",
       "2          0          0          0          0          1  \n",
       "3          0          0          0          0          1  \n",
       "4          0          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            cnt    casual  registered\n",
      "0     -0.956312 -0.662736   -0.930162\n",
      "1     -0.823998 -0.561326   -0.804632\n",
      "2     -0.868103 -0.622172   -0.837666\n",
      "3     -0.972851 -0.662736   -0.949983\n",
      "4     -1.039008 -0.723582   -1.009445\n",
      "5     -1.039008 -0.723582   -1.009445\n",
      "6     -1.033495 -0.683018   -1.016052\n",
      "7     -1.027981 -0.703300   -1.002838\n",
      "8     -1.000416 -0.703300   -0.969804\n",
      "9     -0.967338 -0.561326   -0.976411\n",
      "10    -0.846051 -0.480199   -0.857487\n",
      "11    -0.735789 -0.196252   -0.817845\n",
      "12    -0.581424 -0.135406   -0.652673\n",
      "13    -0.526293  0.229668   -0.705528\n",
      "14    -0.460137 -0.013715   -0.546963\n",
      "15    -0.438084  0.087695   -0.553570\n",
      "16    -0.531806  0.107976   -0.672494\n",
      "17    -0.675146 -0.419353   -0.672494\n",
      "18    -0.851564 -0.541045   -0.844273\n",
      "19    -0.840538 -0.601890   -0.811239\n",
      "20    -0.846051 -0.500481   -0.850880\n",
      "21    -0.857077 -0.662736   -0.811239\n",
      "22    -0.890155 -0.500481   -0.903735\n",
      "23    -0.829511 -0.419353   -0.857487\n",
      "24    -0.950799 -0.642454   -0.930162\n",
      "25    -0.950799 -0.703300   -0.910342\n",
      "26    -0.994903 -0.703300   -0.963197\n",
      "27    -1.011442 -0.683018   -0.989624\n",
      "28    -1.027981 -0.683018   -1.009445\n",
      "29    -1.033495 -0.723582   -1.002838\n",
      "...         ...       ...         ...\n",
      "15405 -1.005929 -0.683018   -0.983018\n",
      "15406 -1.027981 -0.723582   -0.996231\n",
      "15407 -1.005929 -0.703300   -0.976411\n",
      "15408 -0.939773 -0.723582   -0.890521\n",
      "15409 -0.256154 -0.683018   -0.084481\n",
      "15410  0.951206 -0.622172    1.342606\n",
      "15411  2.886288 -0.419353    3.595553\n",
      "15412  1.061467  0.026849    1.263323\n",
      "15413 -0.140379 -0.054279   -0.150550\n",
      "15414  0.019499  0.026849    0.014622\n",
      "15415  0.388874  0.148540    0.417642\n",
      "15416  0.267587  0.371641    0.199615\n",
      "15417  0.300665  0.148540    0.311932\n",
      "15418  0.355796  0.473051    0.272290\n",
      "15419  1.293015  0.696152    1.322785\n",
      "15420  3.399003  0.756997    3.826794\n",
      "15421  3.277715 -0.196252    3.991966\n",
      "15422  1.789190 -0.094843    2.175073\n",
      "15423  0.940180 -0.115125    1.164220\n",
      "15424  0.195917 -0.399071    0.364787\n",
      "15425 -0.151406 -0.581608    0.008015\n",
      "15426 -0.581424 -0.561326   -0.513929\n",
      "15427 -0.862590 -0.723582   -0.798025\n",
      "15428 -1.000416 -0.683018   -0.976411\n",
      "15429 -1.000416 -0.683018   -0.976411\n",
      "15430 -1.011442 -0.723582   -0.976411\n",
      "15431 -1.011442 -0.723582   -0.976411\n",
      "15432 -0.823998 -0.683018   -0.764990\n",
      "15433 -0.079736 -0.662736    0.120332\n",
      "15434  1.750599 -0.541045    2.274176\n",
      "\n",
      "[15435 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "print(train_targets)\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure. You'll implement both the forward pass and backwards pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "#############\n",
    "# In the my_answers.py file, fill out the TODO sections as specified\n",
    "#############\n",
    "\n",
    "from my_answers import NeuralNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.013s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 0.19962801]])))\n",
    "\n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of iterations\n",
    "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, this process can have sharply diminishing returns and can waste computational resources if you use too many iterations.  You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. The ideal number of iterations would be a level that stops shortly after the validation loss is no longer decreasing.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. Normally a good choice to start at is 0.1; however, if you effectively divide the learning rate by n_records, try starting out with a learning rate of 1. In either case, if the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "In a model where all the weights are optimized, the more hidden nodes you have, the more accurate the predictions of the model will be.  (A fully optimized model could have weights of zero, after all.) However, the more hidden nodes you have, the harder it will be to optimize the weights of the model, and the more likely it will be that suboptimal weights will lead to overfitting. With overfitting, the model will memorize the training data instead of learning the true pattern, and won't generalize well to unseen data.  \n",
    "\n",
    "Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.  You'll generally find that the best number of hidden nodes to use ends up being between the number of input and output nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n",
      "Progress: 0.2% ... Training loss: 2.369 ... Validation loss: 2.041"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:28: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.0% ... Training loss: 0.061 ... Validation loss: 0.155"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "####################\n",
    "### Set the hyperparameters in you myanswers.py file ###\n",
    "####################\n",
    "\n",
    "from my_answers import iterations, learning_rate, hidden_nodes, output_nodes\n",
    "\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "print(N_i)\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "losses = {'train':[], 'validation':[]}\n",
    "\n",
    "# Tinkering:\n",
    "# batch = np.random.choice(train_features.index, size=128)\n",
    "# print(train_features.ix[[1,2,3]])\n",
    "# print(train_features.ix[[1,2,3]].values)\n",
    "# print(train_targets.ix[batch]['cnt'])\n",
    "# print(train_targets)\n",
    "\n",
    "if True:\n",
    "    for ii in range(iterations):\n",
    "        # Go through a random batch of 128 records from the training data set\n",
    "        # train_features.index return a Range object of the index column\n",
    "        # np.random.choice select a random item of the passed array (train_features.index) and how many items (size = 128)\n",
    "        batch = np.random.choice(train_features.index, size=128)\n",
    "        X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "\n",
    "        network.train(X, y)\n",
    "\n",
    "        # Printing out the training progress\n",
    "        train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "        val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "        sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                         + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                         + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "        sys.stdout.flush()\n",
    "\n",
    "        losses['train'].append(train_loss)\n",
    "        losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa424fcd240>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 373
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:10: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa424cba4e0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 502
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Submitting:\n",
    "Open up the 'jwt' file in the first-neural-network directory (which also contains this notebook) for submission instructions"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}