cars-cancellation/cars_cancellation.ipynb

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    "#Predicting Cab Booking Cancellations\n",
    "\n",
    "###Notebook by [Abhishek Sharma](http://github.com/numb3r33/)\n",
    "####Data hosted at [Kaggle](https://inclass.kaggle.com/c/predicting-cab-booking-cancellations/data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Table of contents\n",
    "\n",
    "1. [Introduction](#Introduction)\n",
    "\n",
    "2. [Required libraries](#Required-libraries)\n",
    "\n",
    "3. [Step 1: Answering the question](#Step-1:-Answering-the-question)\n",
    "\n",
    "4. [Step 2: Checking the data](#Step-2:-Checking-the-data)\n",
    "\n",
    "5. [Step 3: Exploratory analysis](#Step-3:-Exploratory-analysis)\n",
    "\n",
    "6. [Step 4: Data Preparation](#Step-4:-Data-Preparation)\n",
    "\n",
    "7. [Step 5: Feature Extraction](#Step-5:-Feature-Extraction)\n",
    "\n",
    "8. [Step 6: Fitting Model](#Step-6:-Fitting-Model)\n",
    "    - [Validation](#Validation)\n",
    "    - [Learning Curves](#Learning-Curves)\n",
    "\n",
    "9. [Step 7: Performance on Test Set](#Step-7:-Performance-on-test-set)\n",
    "\n",
    "10. [Step 8: Model Training on full dataset](#Step-8:-Model-Training-on-full-dataset)\n",
    "\n",
    "11. [Step 9: Prepare Submission](#Step-9:-Prepare-Submission)\n",
    "\n",
    "12. [Further improvements](#Further-Improvements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Introduction\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The business problem tackled here is trying to improve customer service for [YourCabs](#http://www.yourcabs.com/), a cab company in Bangalore. The problem of interest is booking cancellations by the company due to unavailability of a car. The challenge is that cancellations can occur very close to the trip start time, thereby causing passengers inconvenience.\n",
    "\n",
    "###Competition Goal\n",
    "\n",
    "The goal of the competition is to create a predictive model for classifying new bookings as to whether they will eventually get cancelled due to car unavailability. This is a classification task that includes misclassification costs. The winning entry will be the one with the lowest **average-cost-per-booking.**\n",
    "\n",
    "Participants need to upload their classifications (0=no cancellation or 1=cancellation), and the system will compute the average-cost-per-booking based on these classifications."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Required libraries\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:\n",
    "\n",
    "* **NumPy**: Provides a fast numerical array structure and helper functions.\n",
    "* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\n",
    "* **scikit-learn**: The essential Machine Learning package in Python.\n",
    "* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\n",
    "* **Seaborn**: Advanced statistical plotting library.\n",
    "\n",
    "To make sure you have all of the packages you need, install them with `conda`:\n",
    "\n",
    "    conda install numpy pandas scikit-learn matplotlib seaborn\n",
    "\n",
    "`conda` may ask you to update some of them if you don't have the most recent version. Allow it to do so."
   ]
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   "cell_type": "markdown",
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   "source": [
    "##Step 1: Answering the question\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task.\n",
    "\n",
    ">Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?\n",
    "\n",
    "The goal of the competition is to create a predictive model for classifying new bookings as to whether they will eventually get cancelled due to car unavailability, so this is a classification task.\n",
    "\n",
    ">Did you define the metric for success before beginning?\n",
    "\n",
    "Let's do that now. Since we're taking into account penalties for misclassification, we can use [Weighted Mean Absolute Error](https://www.kaggle.com/wiki/WeightedMeanAbsoluteError)\n",
    "\n",
    ">Did you understand the context for the question and the scientific or business application?\n",
    "\n",
    "The business problem tackled here is trying to improve customer service for YourCabs.com, a cab company in Bangalore. The problem of interest is booking cancellations by the company due to unavailability of a car. The challenge is that cancellations can occur very close to the trip start time, thereby causing passengers inconvenience.\n",
    "\n",
    ">Did you record the experimental design?\n",
    "\n",
    "The cab bookings data are made available through a collaboration between Prof. Galit Shmueli at the Indian School of Business and YourCabs co-founder Mr. Rajath Kedilaya and IDRC managing partner, Mr. Amit Batra.\n",
    "\n",
    ">Did you consider whether the question could be answered with the available data?\n",
    "\n",
    "The data set we currently have is only for three types of *Iris* flowers. The model built off of this data set will only work for those *Iris* flowers, so we will need more data to create a general flower classifier."
   ]
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   "cell_type": "markdown",
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   "source": [
    "##Step 2: Checking the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The next step is to look at the data we're working with.\n",
    "\n",
    "Generally, we're looking to answer the following questions:\n",
    "\n",
    "* Is there anything wrong with the data?\n",
    "* Are there any quirks with the data?\n",
    "* Do I need to fix or remove any of the data?\n",
    "\n",
    "Let's start by reading the data into a pandas DataFrame.\n",
    "\n",
    "##Data fields\n",
    "\n",
    "* id - booking *ID*\n",
    "* user_id - the *ID* of the customer (based on mobile number)\n",
    "* vehicle_model_id - vehicle model type.\n",
    "* package_id - type of package (1=4hrs & 40kms, 2=8hrs & 80kms, 3=6hrs & 60kms, 4= 10hrs &   100kms, 5=5hrs & 50kms, 6=3hrs &     30kms, 7=12hrs & 120kms)\n",
    "* travel_type_id - type of travel (1=long distance, 2= point to point, 3= hourly rental).\n",
    "* from_area_id - unique identifier of area. Applicable only for point-to-point travel and packages\n",
    "* to_area_id - unique identifier of area. Applicable only for point-to-point travel\n",
    "* from_city_id - unique identifier of city\n",
    "* to_city_id - unique identifier of city (only for intercity)\n",
    "* from_date - time stamp of requested trip start\n",
    "* to_date - time stamp of trip end\n",
    "* online_booking - if booking was done on desktop website\n",
    "* mobile_site_booking - if booking was done on mobile website\n",
    "* booking_created - time stamp of booking\n",
    "* from_lat - latitude of from area\n",
    "* from_long -  longitude of from area\n",
    "* to_lat - latitude of to area\n",
    "* to_long - longitude of to area\n",
    "* Car_Cancellation (available only in training data) - whether the booking was cancelled (1) or not (0) due to unavailability of   a car.\n",
    "* Cost_of_error (available only in training data) - the cost incurred if the booking is misclassified. The cost of misclassifying an uncancelled booking as a cancelled booking (cost=1 unit). The cost associated with misclassifying a cancelled booking as uncancelled, This cost is a function of how close the cancellation occurs relative to the trip start time. The closer the trip, the higher the cost. Cancellations occurring less than 15 minutes prior to the trip start incur a fixed penalty of 100 units."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
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   "cell_type": "code",
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       "      <th>vehicle_model_id</th>\n",
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       "      <th>to_area_id</th>\n",
       "      <th>from_city_id</th>\n",
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       "      <th>to_date</th>\n",
       "      <th>online_booking</th>\n",
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       "      <th>from_long</th>\n",
       "      <th>to_lat</th>\n",
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       "      <th>Cost_of_error</th>\n",
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       "       id  user_id  vehicle_model_id  package_id  travel_type_id  \\\n",
       "0  132512    22177                28         NaN               2   \n",
       "1  132513    21413                12         NaN               2   \n",
       "2  132514    22178                12         NaN               2   \n",
       "3  132515    13034                12         NaN               2   \n",
       "4  132517    22180                12         NaN               2   \n",
       "\n",
       "   from_area_id  to_area_id  from_city_id  to_city_id      from_date  to_date  \\\n",
       "0            83         448           NaN         NaN  1/1/2013 2:00      NaN   \n",
       "1          1010         540           NaN         NaN  1/1/2013 9:00      NaN   \n",
       "2          1301        1034           NaN         NaN  1/1/2013 3:30      NaN   \n",
       "3           768         398           NaN         NaN  1/1/2013 5:45      NaN   \n",
       "4          1365         849           NaN         NaN  1/1/2013 9:00      NaN   \n",
       "\n",
       "   online_booking  mobile_site_booking booking_created   from_lat  from_long  \\\n",
       "0               0                    0   1/1/2013 1:39  12.924150  77.672290   \n",
       "1               0                    0   1/1/2013 2:25  12.966910  77.749350   \n",
       "2               0                    0   1/1/2013 3:08  12.937222  77.626915   \n",
       "3               0                    0   1/1/2013 4:39  12.989990  77.553320   \n",
       "4               0                    0   1/1/2013 7:53  12.845653  77.677925   \n",
       "\n",
       "      to_lat    to_long  Car_Cancellation  Cost_of_error  \n",
       "0  12.927320  77.635750                 0              1  \n",
       "1  12.927680  77.626640                 0              1  \n",
       "2  13.047926  77.597766                 0              1  \n",
       "3  12.971430  77.639140                 0              1  \n",
       "4  12.954340  77.600720                 0              1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cancel_train = pd.read_csv('./data/Kaggle_YourCabs_training.csv')\n",
    "cars_cancel_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As you can see there are various columns with missing values and some columns which need to be converted to date-time.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert from_date column to date-time\n",
    "# Note: when we try to convert NaN to date-time, it gets converted to NaT( Not a timestamp )\n",
    "\n",
    "cars_cancel_train['from_date'] = pd.to_datetime(cars_cancel_train['from_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert to_date column to date-time\n",
    "cars_cancel_train['to_date'] = pd.to_datetime(cars_cancel_train['to_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert booking_created column to date-time\n",
    "cars_cancel_train['booking_created'] = pd.to_datetime(cars_cancel_train['booking_created'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next thing, we could do is to print out the summary statistics about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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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>user_id</th>\n",
       "      <th>vehicle_model_id</th>\n",
       "      <th>package_id</th>\n",
       "      <th>travel_type_id</th>\n",
       "      <th>from_area_id</th>\n",
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       "      <th>from_city_id</th>\n",
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       "      <th>from_long</th>\n",
       "      <th>to_lat</th>\n",
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       "      <th>Car_Cancellation</th>\n",
       "      <th>Cost_of_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43431.000000</td>\n",
       "      <td>43431.000000</td>\n",
       "      <td>43431.00000</td>\n",
       "      <td>7550.000000</td>\n",
       "      <td>43431.000000</td>\n",
       "      <td>43343.000000</td>\n",
       "      <td>34293.000000</td>\n",
       "      <td>16345.000000</td>\n",
       "      <td>1588.000000</td>\n",
       "      <td>43431.000000</td>\n",
       "      <td>43431.000000</td>\n",
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       "      <td>34293.000000</td>\n",
       "      <td>43431.000000</td>\n",
       "      <td>43431.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>159206.473556</td>\n",
       "      <td>30739.198153</td>\n",
       "      <td>25.71723</td>\n",
       "      <td>2.030066</td>\n",
       "      <td>2.137252</td>\n",
       "      <td>714.544494</td>\n",
       "      <td>669.490917</td>\n",
       "      <td>14.915081</td>\n",
       "      <td>68.537783</td>\n",
       "      <td>0.351592</td>\n",
       "      <td>0.043241</td>\n",
       "      <td>12.982461</td>\n",
       "      <td>77.636255</td>\n",
       "      <td>13.026648</td>\n",
       "      <td>77.640595</td>\n",
       "      <td>0.072114</td>\n",
       "      <td>8.000509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15442.386279</td>\n",
       "      <td>10996.476709</td>\n",
       "      <td>26.79825</td>\n",
       "      <td>1.461756</td>\n",
       "      <td>0.437712</td>\n",
       "      <td>419.883553</td>\n",
       "      <td>400.638225</td>\n",
       "      <td>1.165306</td>\n",
       "      <td>49.880732</td>\n",
       "      <td>0.477473</td>\n",
       "      <td>0.203402</td>\n",
       "      <td>0.085933</td>\n",
       "      <td>0.059391</td>\n",
       "      <td>0.113487</td>\n",
       "      <td>0.064045</td>\n",
       "      <td>0.258680</td>\n",
       "      <td>25.350698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>132512.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.776630</td>\n",
       "      <td>77.386930</td>\n",
       "      <td>12.776630</td>\n",
       "      <td>77.386930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>145778.000000</td>\n",
       "      <td>24614.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.926450</td>\n",
       "      <td>77.593661</td>\n",
       "      <td>12.951850</td>\n",
       "      <td>77.582030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>159248.000000</td>\n",
       "      <td>31627.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.968887</td>\n",
       "      <td>77.635750</td>\n",
       "      <td>12.982750</td>\n",
       "      <td>77.645030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>172578.500000</td>\n",
       "      <td>39167.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>1054.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.007750</td>\n",
       "      <td>77.688900</td>\n",
       "      <td>13.199560</td>\n",
       "      <td>77.706880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185941.000000</td>\n",
       "      <td>48730.000000</td>\n",
       "      <td>91.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1403.000000</td>\n",
       "      <td>1403.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.366072</td>\n",
       "      <td>77.786420</td>\n",
       "      <td>13.366072</td>\n",
       "      <td>77.786420</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id       user_id  vehicle_model_id   package_id  \\\n",
       "count   43431.000000  43431.000000       43431.00000  7550.000000   \n",
       "mean   159206.473556  30739.198153          25.71723     2.030066   \n",
       "std     15442.386279  10996.476709          26.79825     1.461756   \n",
       "min    132512.000000     16.000000           1.00000     1.000000   \n",
       "25%    145778.000000  24614.000000          12.00000     1.000000   \n",
       "50%    159248.000000  31627.000000          12.00000     2.000000   \n",
       "75%    172578.500000  39167.000000          24.00000     2.000000   \n",
       "max    185941.000000  48730.000000          91.00000     7.000000   \n",
       "\n",
       "       travel_type_id  from_area_id    to_area_id  from_city_id   to_city_id  \\\n",
       "count    43431.000000  43343.000000  34293.000000  16345.000000  1588.000000   \n",
       "mean         2.137252    714.544494    669.490917     14.915081    68.537783   \n",
       "std          0.437712    419.883553    400.638225      1.165306    49.880732   \n",
       "min          1.000000      2.000000      2.000000      1.000000     4.000000   \n",
       "25%          2.000000    393.000000    393.000000     15.000000    32.000000   \n",
       "50%          2.000000    590.000000    541.000000     15.000000    49.000000   \n",
       "75%          2.000000   1089.000000   1054.000000     15.000000   108.000000   \n",
       "max          3.000000   1403.000000   1403.000000     31.000000   203.000000   \n",
       "\n",
       "       online_booking  mobile_site_booking      from_lat     from_long  \\\n",
       "count    43431.000000         43431.000000  43338.000000  43338.000000   \n",
       "mean         0.351592             0.043241     12.982461     77.636255   \n",
       "std          0.477473             0.203402      0.085933      0.059391   \n",
       "min          0.000000             0.000000     12.776630     77.386930   \n",
       "25%          0.000000             0.000000     12.926450     77.593661   \n",
       "50%          0.000000             0.000000     12.968887     77.635750   \n",
       "75%          1.000000             0.000000     13.007750     77.688900   \n",
       "max          1.000000             1.000000     13.366072     77.786420   \n",
       "\n",
       "             to_lat       to_long  Car_Cancellation  Cost_of_error  \n",
       "count  34293.000000  34293.000000      43431.000000   43431.000000  \n",
       "mean      13.026648     77.640595          0.072114       8.000509  \n",
       "std        0.113487      0.064045          0.258680      25.350698  \n",
       "min       12.776630     77.386930          0.000000       0.150000  \n",
       "25%       12.951850     77.582030          0.000000       1.000000  \n",
       "50%       12.982750     77.645030          0.000000       1.000000  \n",
       "75%       13.199560     77.706880          0.000000       1.000000  \n",
       "max       13.366072     77.786420          1.000000     100.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cancel_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see that many of the columns are missing values like package_id, to_area_id etc.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also set id to be the index of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars_cancel_train.set_index('id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40299\n",
       "1     3132\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets find out the class balance\n",
    "cars_cancel_train.Car_Cancellation.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Step-3:-Exploratory-analysis\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Major class imbalance, very few cancellations as compared to large amount of non-cancellations. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3503\n",
       "2    2651\n",
       "6     502\n",
       "4     412\n",
       "3     375\n",
       "7     101\n",
       "5       6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the distribution of package_id\n",
    "cars_cancel_train.package_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MzBMRsRXYGRH7gBPATRM5AEmSVDPWmflPwOXVzy8BV44zZzuwfczYceCGc16lJElzmF+K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\nYy1JUuGMtSRJhfu/hg4lT/+cFqkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10495870>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cars_cancel_train.package_id.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Most of the packages that people opt for are for a journey of 4hrs and around 40kms, followed by 8hrs and 80kms.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NOtrahDlDQ0ONbXw0EXEj8I3MvKN+vzUzl87oRiV1LdsEqTPauIa/CbgQICJ+\nCvhWC9uU1L1sE6QOaOOU/leBn42ITfX7D7WwTUndyzZB6oAZP6UvSZI6z4F3JEkqgIEvSVIBDHxJ\nkgpg4B9BRMzv4LaPi4h5ndp+XcNrOrn9uoa5EfG6eijWTtbxLyKiuZtnJ77dRW1vU6PrZHtQb7/4\nNqFb2oO6ltbbhCbag4512ouI+4F5vHIUgqF6yM226vgF4I+BA8CnMvMrw/Vl5rkt1fAm4FpgO/Bf\ngVXAILAiM+9qqYaTR7ydA3wR+ABAZm5uo4a6js9n5i9HxBnAl4EXqAZq+VBmfqOlGi4Bfhy4s67h\nJeB44PLMvLeNGuo69gHLM3N1W9vspG5oE7qhPai3Z5tAd7QHdR0dbxOaaA/auC3vSH6T6h/xxVQ/\nrk65Cngz1dmOOyJifmauabmG/1LX8XpgLXAysI9q+NFWftzAfcAe4Nn6fQB/Ur9uraGj+lEBfAa4\nIDOfiIgfAb4CnNVSDb8KnEP1//5dmbm5ruFOoLXABx4F3lwH4e9m5gMtbrsTuqFN6Ib2AGwThnVD\newDd0SZMuz3oWODXY2jfDpyames6VQfwcmZuB4iIi4C/jYjmxqOcmDn1X94DEXFuZv6grmd/izWc\nTvVjvi0z72n7iGYUBzLzCYDM/F5EtLnt/Zm5JyJ2At8dUUMzYz5P3L7M/NV6rPmVEXErVSP8ZGbe\n0nItM65L2oRuaA/ANuFwnWwPoDvahGm3B508wiczP9vJ7deejoibgKszc1dEXAzcAyxusYbNEbEa\n+JXM/CBARPwW8P22CsjM5yLi3cANEfFWmhzweXIWR8Q/AAsi4pepTp/dCLTZ6N4VEXcC3wb+OiLu\nAX4OuL/FGv6/zHwYuDgifpjqqObkcRaZtbqgTeiG9gBsE4Z1Q3sAXdQmTKc96Hjnhy7wS1RDew4B\nZOZWqlM3d7RYw6XAXZl5cMS0fwI+2GINZOb+zPwY8M90KPAz8zRgGXAJ8E2q65bfosXR2DLzOuAm\nqv8HW4E+4ObM/GRbNdTWHFbXi5l5Z2b+Qct1lKQb2gOwTRjefsfbg7qObmgT1hxW06TbA0fakySp\nAB7hS5JUAANfkqQCGPiSJBXAwJckqQAGviRJBfh/32wRuok5yTYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10633110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see whether package type affect car cancellations\n",
    "cars_cancel_train.hist(column='package_id', by='Car_Cancellation');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As you can see most of the times 1st package ( 4hrs & 40kms ) gets cancelled followed by packages ( 3hrs & 30kms ) and ( 8hrs & 80kms ).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2m3he+uF4gEFG0uS0OxOwkupU/XURMXptwArg9vrCv2eBB+vVAbcDG6k+LliZmXsjYhVw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\nI9WLRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077f6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets take a look at travel_type variable\n",
    "cars_cancel_train.hist(column='travel_type_id', by='Car_Cancellation');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "** Not surprisingly, most people rent car from point to point travel and around ( 1 / 10 )th of these bookings get cancelled. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Lets take a look at from_area_id variable and see if it has any relation with car cancellation.\n",
    "g = cars_cancel_train.groupby(['from_area_id', 'Car_Cancellation']).size().unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Car_Cancellation</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>from_area_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "Car_Cancellation   0   1\n",
       "from_area_id            \n",
       "2                 27   4\n",
       "6                  7 NaN\n",
       "15                 6 NaN\n",
       "16                 5 NaN\n",
       "17                 2 NaN"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g['percent_cancelled'] = (g[1] / (g[1] + g[0])) * 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "from_area_id\n",
       "130     80.000000\n",
       "1148    66.666667\n",
       "1174    66.666667\n",
       "630     66.666667\n",
       "176     52.830189\n",
       "1381    50.000000\n",
       "1160    50.000000\n",
       "1100    50.000000\n",
       "1385    50.000000\n",
       "1276    45.454545\n",
       "211     44.444444\n",
       "1372    40.000000\n",
       "356     40.000000\n",
       "987     40.000000\n",
       "626     34.375000\n",
       "1258    33.333333\n",
       "34      33.333333\n",
       "326     33.333333\n",
       "177     33.333333\n",
       "833     33.333333\n",
       "Name: percent_cancelled, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.percent_cancelled.order(ascending=False).iloc[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**So as you can see there are certain areas ( from area ) for which more than 50% of the bookings were cancelled.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to = cars_cancel_train.groupby(['to_area_id', 'Car_Cancellation']).size().unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "Car_Cancellation  0   1\n",
       "to_area_id             \n",
       "2                 4 NaN\n",
       "6                 2 NaN\n",
       "15                3   2\n",
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    "to.head()"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to['percent_cancelled'] = (to[1] / (to[1] + to[0])) * 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "to_area_id\n",
       "1247    75.000000\n",
       "677     66.666667\n",
       "355     66.666667\n",
       "1218    60.000000\n",
       "845     60.000000\n",
       "1310    50.000000\n",
       "1311    50.000000\n",
       "1387    50.000000\n",
       "1197    50.000000\n",
       "1225    50.000000\n",
       "1193    50.000000\n",
       "1235    50.000000\n",
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       "1357    50.000000\n",
       "1336    50.000000\n",
       "118     50.000000\n",
       "122     41.463415\n",
       "626     40.000000\n",
       "520     40.000000\n",
       "525     40.000000\n",
       "Name: percent_cancelled, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to.percent_cancelled.order(ascending=False).iloc[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from_to = cars_cancel_train.groupby(['from_area_id', 'to_area_id', 'Car_Cancellation']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f_t = from_to.unstack(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "Car_Cancellation          0   1\n",
       "from_area_id to_area_id        \n",
       "2            222          1 NaN\n",
       "             393         12 NaN\n",
       "             452          1 NaN\n",
       "             455        NaN   1\n",
       "             572        NaN   1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_t['percent_cancelled'] = (f_t[1] / (f_t[0] + f_t[1])) * 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "from_area_id  to_area_id\n",
       "626           122           90.909091\n",
       "1349          1052          83.333333\n",
       "1330          176           80.000000\n",
       "1052          1349          78.571429\n",
       "625           452           75.000000\n",
       "1296          793           75.000000\n",
       "1365          293           75.000000\n",
       "1285          61            70.588235\n",
       "122           626           70.000000\n",
       "176           136           66.666667\n",
       "1068          352           66.666667\n",
       "264           585           66.666667\n",
       "1384          160           66.666667\n",
       "839           585           66.666667\n",
       "571           396           66.666667\n",
       "83            176           66.666667\n",
       "1095          25            66.666667\n",
       "363           1068          66.666667\n",
       "542           1237          66.666667\n",
       "541           83            66.666667\n",
       "Name: percent_cancelled, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_t.percent_cancelled.order(ascending=False).iloc[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "** So there are some routes for which the cancellation rate is pretty high, e.g. from area-626 to-122 almost 90% of the bookings were cancelled **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_booking  mobile_site_booking\n",
       "0               0                       940\n",
       "                1                       289\n",
       "1               0                      1903\n",
       "Name: Car_Cancellation, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see if online or mobile booking has any effect on cancellation\n",
    "pd.pivot_table(cars_cancel_train[['online_booking', 'mobile_site_booking', 'Car_Cancellation']]\n",
    "              , values='Car_Cancellation', index=['online_booking', 'mobile_site_booking'], aggfunc= np.count_nonzero)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**So most of the cancellations are of orders that were booked online.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "booking_diff = cars_cancel_train[['from_date', 'to_date', 'booking_created', 'Car_Cancellation']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:249: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:415: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "booking_diff.loc[:, 'difference'] = booking_diff.loc[:, ('from_date')] - booking_diff.loc[:, ('booking_created')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>booking_created</th>\n",
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      "text/plain": [
       "                 from_date to_date     booking_created  Car_Cancellation  \\\n",
       "id                                                                         \n",
       "132512 2013-01-01 02:00:00     NaT 2013-01-01 01:39:00                 0   \n",
       "132513 2013-01-01 09:00:00     NaT 2013-01-01 02:25:00                 0   \n",
       "132514 2013-01-01 03:30:00     NaT 2013-01-01 03:08:00                 0   \n",
       "132515 2013-01-01 05:45:00     NaT 2013-01-01 04:39:00                 0   \n",
       "132517 2013-01-01 09:00:00     NaT 2013-01-01 07:53:00                 0   \n",
       "\n",
       "        difference  \n",
       "id                  \n",
       "132512    00:21:00  \n",
       "132513    06:35:00  \n",
       "132514    00:22:00  \n",
       "132515    01:06:00  \n",
       "132517    01:07:00  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booking_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def convert_to_days(x):\n",
    "    seconds = int(x / (1e9))\n",
    "    days = seconds / ( 24. * 60 * 60 )\n",
    "    return days\n",
    "\n",
    "booking_diff.loc[:, 'diff_in_days'] = booking_diff.difference.map(convert_to_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>00:22:00</td>\n",
       "      <td>0.015278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132515</th>\n",
       "      <td>2013-01-01 05:45:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2013-01-01 04:39:00</td>\n",
       "      <td>0</td>\n",
       "      <td>01:06:00</td>\n",
       "      <td>0.045833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132517</th>\n",
       "      <td>2013-01-01 09:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2013-01-01 07:53:00</td>\n",
       "      <td>0</td>\n",
       "      <td>01:07:00</td>\n",
       "      <td>0.046528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 from_date to_date     booking_created  Car_Cancellation  \\\n",
       "id                                                                         \n",
       "132512 2013-01-01 02:00:00     NaT 2013-01-01 01:39:00                 0   \n",
       "132513 2013-01-01 09:00:00     NaT 2013-01-01 02:25:00                 0   \n",
       "132514 2013-01-01 03:30:00     NaT 2013-01-01 03:08:00                 0   \n",
       "132515 2013-01-01 05:45:00     NaT 2013-01-01 04:39:00                 0   \n",
       "132517 2013-01-01 09:00:00     NaT 2013-01-01 07:53:00                 0   \n",
       "\n",
       "        difference  diff_in_days  \n",
       "id                                \n",
       "132512    00:21:00      0.014583  \n",
       "132513    06:35:00      0.274306  \n",
       "132514    00:22:00      0.015278  \n",
       "132515    01:06:00      0.045833  \n",
       "132517    01:07:00      0.046528  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booking_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qyhAgSVJNGQIkSaopQ4AkSTVlCJAkqaYMAZIk1ZQhQJKkmjIESJJUU4YASZJqyhAgSVJN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\nqf8PPjwrXjQR7MYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bcf5b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "booking_diff.hist(column='diff_in_days', by='Car_Cancellation', bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** There seems to be no relation between number of days between date of booking and trip's start date with cancellation.\n",
    "Generally people tend to cancel their booking 5 days prior to their trip's start date which is not unusual **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Step-4:-Data-Preparation\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "** Following features will be considered for analysis **\n",
    "\n",
    "* vehicle_model_id\n",
    "* package_id\n",
    "* travel_type_id\n",
    "* from_area_id\n",
    "* to_area_id\n",
    "* from_city_id\n",
    "* to_city_id\n",
    "* days_before_booking\n",
    "* online_booking\n",
    "* mobile_site_booking\n",
    "* from_month\n",
    "* from_weekday\n",
    "* booking_month\n",
    "* booking_weekday\n",
    "\n",
    "\n",
    "** Many of them have missing values which have to be dealt accordingly **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'user_id', u'vehicle_model_id', u'package_id', u'travel_type_id',\n",
       "       u'from_area_id', u'to_area_id', u'from_city_id', u'to_city_id',\n",
       "       u'from_date', u'to_date', u'online_booking', u'mobile_site_booking',\n",
       "       u'booking_created', u'from_lat', u'from_long', u'to_lat', u'to_long',\n",
       "       u'Car_Cancellation', u'Cost_of_error', u'days_before_booking'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cancel_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 43431 entries, 132512 to 185941\n",
      "Data columns (total 20 columns):\n",
      "user_id                43431 non-null int64\n",
      "vehicle_model_id       43431 non-null int64\n",
      "package_id             7550 non-null float64\n",
      "travel_type_id         43431 non-null int64\n",
      "from_area_id           43343 non-null float64\n",
      "to_area_id             34293 non-null float64\n",
      "from_city_id           16345 non-null float64\n",
      "to_city_id             1588 non-null float64\n",
      "from_date              43431 non-null datetime64[ns]\n",
      "to_date                25541 non-null datetime64[ns]\n",
      "online_booking         43431 non-null int64\n",
      "mobile_site_booking    43431 non-null int64\n",
      "booking_created        43431 non-null datetime64[ns]\n",
      "from_lat               43338 non-null float64\n",
      "from_long              43338 non-null float64\n",
      "to_lat                 34293 non-null float64\n",
      "to_long                34293 non-null float64\n",
      "Car_Cancellation       43431 non-null int64\n",
      "Cost_of_error          43431 non-null float64\n",
      "days_before_booking    43431 non-null float64\n",
      "dtypes: datetime64[ns](3), float64(11), int64(6)\n",
      "memory usage: 7.0 MB\n"
     ]
    }
   ],
   "source": [
    "cars_cancel_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars_cancel_train.loc[:, 'from_month'] = cars_cancel_train.from_date.dt.month\n",
    "cars_cancel_train.loc[:, 'from_weekday'] = cars_cancel_train.from_date.dt.weekday\n",
    "\n",
    "cars_cancel_train.loc[:, 'booking_month'] = cars_cancel_train.booking_created.dt.month\n",
    "cars_cancel_train.loc[:, 'booking_weekday'] = cars_cancel_train.booking_created.dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cars_cancel_train.loc[:, 'days_before_booking'] = booking_diff['diff_in_days']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_cols = ['vehicle_model_id', 'travel_type_id', 'from_area_id',\n",
    "                 'to_area_id','from_month', 'from_weekday',\n",
    "                 'days_before_booking', 'online_booking', 'mobile_site_booking',\n",
    "                 'booking_month', 'booking_weekday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = cars_cancel_train[features_cols]\n",
    "y = cars_cancel_train.Car_Cancellation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Add a new level ( -9999 ) for missing values\n",
    "features.from_area_id.fillna(-9999, inplace=True)\n",
    "features.to_area_id.fillna(-9999, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Step-5:-Feature-Extraction\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "\n",
    "sss = StratifiedShuffleSplit(y, n_iter=2, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_index, test_index = next(iter(sss))\n",
    "\n",
    "X_train = features.iloc[train_index]\n",
    "y_train = y.iloc[train_index]\n",
    "\n",
    "X_test = features.iloc[test_index]\n",
    "y_test = y.iloc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training and test dataset (30401, 11) (13030, 11) \n"
     ]
    }
   ],
   "source": [
    "print 'Shape of training and test dataset %s %s ' %(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Take a sample from the training data to do feature selection\n",
    "sss = StratifiedShuffleSplit(y_train, n_iter=2, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_index, test_index = next(iter(sss))\n",
    "\n",
    "X_train_features = X_train.iloc[test_index]\n",
    "y_train_features = y_train.iloc[test_index]\n",
    "\n",
    "X_train_rest = X_train.iloc[train_index]\n",
    "y_train_rest = y_train.iloc[train_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the training data used for feature selection (6081, 11) and rest of the dataset (24320, 11) \n"
     ]
    }
   ],
   "source": [
    "print 'Shape of the training data used for feature selection %s and rest of the dataset %s ' %(X_train_features.shape, X_train_rest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=sklearn.cross_validation.StratifiedKFold(labels=[0 0 ..., 0 0], n_folds=5, shuffle=False, random_state=None),\n",
       "   estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0),\n",
       "   estimator_params=None, scoring='roc_auc', step=1, verbose=0)"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "rfecv = RFECV(estimator=logreg, step=1, cv=StratifiedKFold(y_train_features, 5), scoring='roc_auc')\n",
    "\n",
    "rfecv.fit(X_train_features, y_train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 11\n"
     ]
    }
   ],
   "source": [
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BLfGFJFGpYp2IiPQlSov+X4Fnzez5cPso4JPxhSRR1ayuZ2RRPhNVyEZERHrQ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9a23b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True], dtype=bool)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfecv.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features_cols_np = np.array(features_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features_to_consider = features_cols_np[rfecv.support_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features are ['vehicle_model_id' 'travel_type_id' 'from_area_id' 'to_area_id'\n",
      " 'from_month' 'from_weekday' 'days_before_booking' 'online_booking'\n",
      " 'mobile_site_booking' 'booking_month' 'booking_weekday'] \n"
     ]
    }
   ],
   "source": [
    "print 'Selected features are %s ' %(features_to_consider)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Step-6:-Fitting-Model\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Xt = X_train_rest[features_cols]\n",
    "yt = y_train_rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "Xtr, Xv, ytr, yv = train_test_split(Xt, yt, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import ExtraTreesClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "logreg = LogisticRegression(C=13., penalty='l1', class_weight='auto')\n",
    "etr = ExtraTreesClassifier(n_estimators=75, class_weight='auto')\n",
    "knn = KNeighborsClassifier(weights='distance', n_neighbors=10)\n",
    "rf = RandomForestClassifier(n_estimators=50)\n",
    "gbc = GradientBoostingClassifier(subsample=0.8, learning_rate=0.05 , n_estimators=160, random_state=5, max_depth=9, max_leaf_nodes=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "sss = StratifiedShuffleSplit(ytr, 5, test_size=.2)\n",
    "\n",
    "cv_scores = cross_val_score(gbc, Xtr, ytr, scoring='roc_auc', cv=sss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation scores are [ 0.86118732  0.87140339  0.87477837  0.87601177  0.87260335] \n"
     ]
    }
   ],
   "source": [
    "print 'cross validation scores are %s ' %(cv_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross validation score 0.871196841017 \n"
     ]
    }
   ],
   "source": [
    "print 'Mean cross validation score %s ' %(cv_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard deviation in cv scores 0.00525780070108 \n"
     ]
    }
   ],
   "source": [
    "print 'Standard deviation in cv scores %s ' %(cv_scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Validation\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.learning_curve import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "C = np.linspace(0.1, 50, 5)\n",
    "val_train, val_test = validation_curve(LogisticRegression(penalty='l1', class_weight='auto'),\n",
    "                                       Xtr, ytr, 'C', C, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jJMhd8Ou/JprJZshkMwPLQViIwaWC/v8G1oXH2XxlNRgoM3jPYus2x5C3dsh7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\nZ06+xrFSfVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1220f830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_with_err(x, data, **kwargs):\n",
    "    mu, std = data.mean(1), data.std(1)\n",
    "    lines = plt.plot(x, mu, '-', **kwargs)\n",
    "    plt.fill_between(x, mu - std, mu + std, edgecolor='none',\n",
    "                     facecolor=lines[0].get_color(), alpha=0.2)\n",
    "\n",
    "plot_with_err(C, val_train, label='training scores')\n",
    "plot_with_err(C, val_test, label='validation scores')\n",
    "plt.xlabel('C'); plt.ylabel('ROC AUC score')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Learning-Curves\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.learning_curve import learning_curve\n",
    "\n",
    "def plot_learning_curve(C=13):\n",
    "    train_sizes = np.linspace(0.05, 1, 10)\n",
    "    N_train, val_train, val_test = learning_curve(LogisticRegression(C=C, penalty='l2', class_weight='auto'),\n",
    "                                                  Xtr, ytr, train_sizes, cv=5,\n",
    "                                                  scoring='roc_auc')\n",
    "    plot_with_err(N_train, val_train, label='training scores')\n",
    "    plot_with_err(N_train, val_test, label='validation scores')\n",
    "    plt.xlabel('Training Set Size'); plt.ylabel('ROC AUC Score')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d4h/6OPA3pdSvlVINlUyEGJ5hGJwxwnDCVKY6zQP9qv+TuSkXBAyUzlls3ZWi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10656d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Step-7:-Performance-on-test-set\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=6, max_features=None, max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "              random_state=None, subsample=0.8, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn.fit(Xtr, ytr)\n",
    "# logreg.fit(Xtr, ytr)\n",
    "gbc.fit(Xtr, ytr)\n",
    "# rf.fit(Xtr, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ytest_pred_knn = knn.predict(Xv)\n",
    "# ytest_pred_logreg = logreg.predict_proba(Xv)[:, 1]\n",
    "ytest_pred_gbc = gbc.predict_proba(Xv)[:, 1]\n",
    "ytest_preds_threshold = [1 if pred > 0.10 else 0 for pred in ytest_pred_gbc]\n",
    "# ytest_pred_rf = rf.predict(Xv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score on the test dataset  0.746771205593\n"
     ]
    }
   ],
   "source": [
    "print 'ROC AUC score on the test dataset ', roc_auc_score(yv, ytest_preds_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Step-8:-Model-Training-on-full-dataset\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_features = features[features_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=6, max_features=None, max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "              random_state=None, subsample=0.8, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logreg.fit(final_features, y)\n",
    "# knn.fit(final_features, y)\n",
    "gbc.fit(final_features, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Step-9:-Prepare-Submission\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>vehicle_model_id</th>\n",
       "      <th>package_id</th>\n",
       "      <th>travel_type_id</th>\n",
       "      <th>from_area_id</th>\n",
       "      <th>to_area_id</th>\n",
       "      <th>from_city_id</th>\n",
       "      <th>to_city_id</th>\n",
       "      <th>from_date</th>\n",
       "      <th>to_date</th>\n",
       "      <th>online_booking</th>\n",
       "      <th>mobile_site_booking</th>\n",
       "      <th>booking_created</th>\n",
       "      <th>from_lat</th>\n",
       "      <th>from_long</th>\n",
       "      <th>to_lat</th>\n",
       "      <th>to_long</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132516</th>\n",
       "      <td>22179</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1096</td>\n",
       "      <td>393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1/2013 9:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2013 7:51</td>\n",
       "      <td>12.965190</td>\n",
       "      <td>77.719320</td>\n",
       "      <td>13.19956</td>\n",
       "      <td>77.70688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132529</th>\n",
       "      <td>22187</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1061</td>\n",
       "      <td>393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/2/2013 1:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2013 12:11</td>\n",
       "      <td>12.924878</td>\n",
       "      <td>77.566255</td>\n",
       "      <td>13.19956</td>\n",
       "      <td>77.70688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132532</th>\n",
       "      <td>23180</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1192</td>\n",
       "      <td>353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/2/2013 4:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2013 12:22</td>\n",
       "      <td>12.976770</td>\n",
       "      <td>77.572700</td>\n",
       "      <td>13.03241</td>\n",
       "      <td>77.59914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132547</th>\n",
       "      <td>30849</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>515</td>\n",
       "      <td>849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1/2013 20:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2013 16:51</td>\n",
       "      <td>12.978960</td>\n",
       "      <td>77.673450</td>\n",
       "      <td>12.95434</td>\n",
       "      <td>77.60072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132548</th>\n",
       "      <td>22198</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/3/2013 0:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2013 17:04</td>\n",
       "      <td>12.924150</td>\n",
       "      <td>77.672290</td>\n",
       "      <td>12.96037</td>\n",
       "      <td>77.58132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  vehicle_model_id  package_id  travel_type_id  from_area_id  \\\n",
       "id                                                                            \n",
       "132516    22179                12         NaN               2          1096   \n",
       "132529    22187                65         NaN               2          1061   \n",
       "132532    23180                12         NaN               2          1192   \n",
       "132547    30849                12         NaN               2           515   \n",
       "132548    22198                12         NaN               2            83   \n",
       "\n",
       "        to_area_id  from_city_id  to_city_id       from_date to_date  \\\n",
       "id                                                                     \n",
       "132516         393           NaN         NaN   1/1/2013 9:30     NaN   \n",
       "132529         393           NaN         NaN   1/2/2013 1:00     NaN   \n",
       "132532         353           NaN         NaN   1/2/2013 4:30     NaN   \n",
       "132547         849           NaN         NaN  1/1/2013 20:15     NaN   \n",
       "132548         463           NaN         NaN   1/3/2013 0:30     NaN   \n",
       "\n",
       "        online_booking  mobile_site_booking booking_created   from_lat  \\\n",
       "id                                                                       \n",
       "132516               0                    0   1/1/2013 7:51  12.965190   \n",
       "132529               0                    0  1/1/2013 12:11  12.924878   \n",
       "132532               0                    0  1/1/2013 12:22  12.976770   \n",
       "132547               0                    0  1/1/2013 16:51  12.978960   \n",
       "132548               0                    0  1/1/2013 17:04  12.924150   \n",
       "\n",
       "        from_long    to_lat   to_long  Unnamed: 18  Unnamed: 19  \n",
       "id                                                               \n",
       "132516  77.719320  13.19956  77.70688          NaN          NaN  \n",
       "132529  77.566255  13.19956  77.70688          NaN          NaN  \n",
       "132532  77.572700  13.03241  77.59914          NaN          NaN  \n",
       "132547  77.673450  12.95434  77.60072          NaN          NaN  \n",
       "132548  77.672290  12.96037  77.58132          NaN          NaN  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cancel_test = pd.read_csv('./data/Kaggle_YourCabs_score.csv', index_col='id')\n",
    "cars_cancel_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars_cancel_test['from_date'] = pd.to_datetime(cars_cancel_test['from_date'])\n",
    "cars_cancel_test['to_date'] = pd.to_datetime(cars_cancel_test['to_date'])\n",
    "cars_cancel_test['booking_created'] = pd.to_datetime(cars_cancel_test['booking_created'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars_cancel_test.loc[:, 'difference'] = cars_cancel_test.loc[:, ('from_date')] - cars_cancel_test.loc[:, ('booking_created')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars_cancel_test.loc[:, 'from_month'] = cars_cancel_test.from_date.dt.month\n",
    "cars_cancel_test.loc[:, 'from_weekday'] = cars_cancel_test.from_date.dt.weekday\n",
    "\n",
    "cars_cancel_test.loc[:, 'booking_month'] = cars_cancel_test.booking_created.dt.month\n",
    "cars_cancel_test.loc[:, 'booking_weekday'] = cars_cancel_test.booking_created.dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars_cancel_test.loc[:, 'days_before_booking'] = cars_cancel_test.difference.map(convert_to_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_test = cars_cancel_test[features_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000 entries, 132516 to 185942\n",
      "Data columns (total 11 columns):\n",
      "vehicle_model_id       10000 non-null int64\n",
      "travel_type_id         10000 non-null int64\n",
      "from_area_id           9980 non-null float64\n",
      "to_area_id             7850 non-null float64\n",
      "from_month             10000 non-null int64\n",
      "from_weekday           10000 non-null int64\n",
      "days_before_booking    10000 non-null float64\n",
      "online_booking         10000 non-null int64\n",
      "mobile_site_booking    10000 non-null int64\n",
      "booking_month          10000 non-null int64\n",
      "booking_weekday        10000 non-null int64\n",
      "dtypes: float64(3), int64(8)\n",
      "memory usage: 937.5 KB\n"
     ]
    }
   ],
   "source": [
    "features_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features_test.from_area_id.fillna(-9999, inplace=True)\n",
    "features_test.to_area_id.fillna(-9999, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# preds_logreg = logreg.predict(features_test)\n",
    "# preds_knn = knn.predict(features_test)\n",
    "preds_gbc = gbc.predict_proba(features_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds_threshold = [1 if pred > 0.10 else 0 for pred in preds_gbc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'id': features_test.index.values, 'Car_Cancellation': preds_threshold})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.to_csv('./submissions/GBC_threshold.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Further-Improvements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** The above approach would get to you to 2nd place in the leaderboard **\n",
    "<p>But there is enough room to improve the model. As you can see I did not perform any kind of grid search for hyper-parameter selection, we could further set up a pipeline in which we could search for optimal parameters for Gradient Boosting Classifier.</p>\n",
    "\n",
    "* Hyper-parameter selection.\n",
    "* Training Models on different sample of data.\n",
    "* Ensemble learning ( averaging predictions of different models )."
   ]
  }
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