pyspark-ny-taxi-fare-pred.ipynb

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    "# Imports and Loads"
   ]
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     "text": [
      "Starting Spark application\n"
     ]
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       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>1</td><td>application_1603703473778_0002</td><td>pyspark</td><td>idle</td><td></td><td></td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
     ]
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   "source": [
    "import pyspark"
   ]
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "import pyspark.sql.functions as F\n",
    "from pyspark.sql import Row\n",
    "\n",
    "from pyspark_dist_explore import Histogram, hist, distplot, pandas_histogram"
   ]
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   ],
   "source": [
    "train_data_path = 's3://ny-taxi-emr/train.csv'\n",
    "test_data_path = 's3://ny-taxi-emr/test.csv'"
   ]
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   "execution_count": 4,
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   "source": [
    "train_data = spark.read.csv(train_data_path, header = True, inferSchema \\\n",
    "                           = True, mode=\"DROPMALFORMED\")\n",
    "test_data = spark.read.csv(test_data_path, header = True, inferSchema \\\n",
    "                          = True, mode=\"DROPMALFORMED\")"
   ]
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   "source": [
    "The data consists of eight features:\n",
    "    1. key\n",
    "    2. fare_amount\n",
    "    3. pickup_datetime\n",
    "    4. pickup_longitude\n",
    "    5. pickup_latitude\n",
    "    6. dropoff_longitude\n",
    "    7. dropoff_latitude\n",
    "    8. passenger_count\n",
    "    \n",
    "**key** is for identification.\n",
    "\n",
    "**fare_amount** is the target value we will be predicting for the test set.\n",
    "\n",
    "Rest of the properties are the input features based on which we need to predict the fare amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Cleaning"
   ]
  },
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     "text": [
      "Shape of train data (55423856, 8)\n",
      "Shape of test data (9914, 7)"
     ]
    }
   ],
   "source": [
    "print('Shape of train data', (train_data.count(), len(train_data.columns)))\n",
    "print('Shape of test data', (test_data.count(), len(test_data.columns)))"
   ]
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     "text": [
      "+-------------------+-----------+--------------------+----------------+---------------+-----------------+----------------+---------------+\n",
      "|                key|fare_amount|     pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|\n",
      "+-------------------+-----------+--------------------+----------------+---------------+-----------------+----------------+---------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:...|      -73.844311|      40.721319|        -73.84161|       40.712278|              1|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:...|      -74.016048|      40.711303|       -73.979268|       40.782004|              1|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:...|      -73.982738|       40.76127|       -73.991242|       40.750562|              2|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:...|       -73.98713|      40.733143|       -73.991567|       40.758092|              1|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:...|      -73.968095|      40.768008|       -73.956655|       40.783762|              1|\n",
      "+-------------------+-----------+--------------------+----------------+---------------+-----------------+----------------+---------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data.show(5)"
   ]
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     "text": [
      "['key', 'fare_amount', 'pickup_datetime', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count']"
     ]
    }
   ],
   "source": [
    "train_data.columns"
   ]
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   "execution_count": 8,
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     "text": [
      "root\n",
      " |-- key: timestamp (nullable = true)\n",
      " |-- fare_amount: double (nullable = true)\n",
      " |-- pickup_datetime: string (nullable = true)\n",
      " |-- pickup_longitude: double (nullable = true)\n",
      " |-- pickup_latitude: double (nullable = true)\n",
      " |-- dropoff_longitude: double (nullable = true)\n",
      " |-- dropoff_latitude: double (nullable = true)\n",
      " |-- passenger_count: integer (nullable = true)"
     ]
    }
   ],
   "source": [
    "train_data.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create new columns based on datetime**"
   ]
  },
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   "execution_count": 5,
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      "root\n",
      " |-- key: timestamp (nullable = true)\n",
      " |-- fare_amount: double (nullable = true)\n",
      " |-- pickup_datetime: timestamp (nullable = true)\n",
      " |-- pickup_longitude: double (nullable = true)\n",
      " |-- pickup_latitude: double (nullable = true)\n",
      " |-- dropoff_longitude: double (nullable = true)\n",
      " |-- dropoff_latitude: double (nullable = true)\n",
      " |-- passenger_count: integer (nullable = true)"
     ]
    }
   ],
   "source": [
    "train_data = train_data.withColumn(\"pickup_datetime\", F.to_timestamp(F.col(\"pickup_datetime\"), \"yyyy-MM-dd HH:mm:ss\"))\n",
    "train_data.printSchema()"
   ]
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     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data.show(5)"
   ]
  },
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   "source": [
    "def create_date_columns():\n",
    "    \"\"\"\n",
    "        Create new rows from pickup_datetime, \n",
    "        namely, date, day, hour, day_of_week, month and year\n",
    "    \"\"\"\n",
    "    # Get datetime.date objects and create a new column pickup_date\n",
    "    new_train_data = train_data.withColumn(\"pickup_date\", F.to_date(F.col(\"pickup_datetime\")))\n",
    "    new_train_data = new_train_data.withColumn(\"pickup_day\", F.dayofmonth(F.col(\"pickup_datetime\")))\n",
    "    new_train_data = new_train_data.withColumn(\"pickup_hour\", F.hour(F.col(\"pickup_datetime\")))\n",
    "    new_train_data = new_train_data.withColumn(\"pickup_day_of_week\", F.dayofweek(F.col(\"pickup_datetime\")))\n",
    "    new_train_data = new_train_data.withColumn(\"pickup_month\", F.month(F.col(\"pickup_datetime\")))\n",
    "    new_train_data = new_train_data.withColumn(\"pickup_year\", F.year(F.col(\"pickup_datetime\")))\n",
    "    \n",
    "    return new_train_data"
   ]
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   "source": [
    "new_train_data = create_date_columns()"
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     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "new_train_data.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check for null entries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e89cda5d6adb42678c3f62b929a64990",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----------+---------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "|key|fare_amount|pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|\n",
      "+---+-----------+---------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "|  0|          0|              0|               0|              0|              376|             376|              0|          0|         0|          0|                 0|           0|          0|\n",
      "+---+-----------+---------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+"
     ]
    }
   ],
   "source": [
    "new_train_data.select([F.count(F.when(F.isnull(col), col)).alias(col) for col in new_train_data.columns]).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drop rows with null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2989a90d8d4346bcb56a1baff66256e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length before removal 55423856\n",
      "Length after removal 55423480"
     ]
    }
   ],
   "source": [
    "print('Length before removal', new_train_data.count())\n",
    "new_train_data = new_train_data.filter(new_train_data.dropoff_longitude.isNotNull())\n",
    "new_train_data = new_train_data.filter(new_train_data.dropoff_latitude.isNotNull())\n",
    "print('Length after removal', new_train_data.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare Amount Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fff5ae470f614e829caef02032d1dace",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Fare:Row(max(fare_amount)=93963.36)\n",
      " Minimum Fare: Row(min(fare_amount)=-300.0)"
     ]
    }
   ],
   "source": [
    "print(\"Maximum Fare:{}\\n Minimum Fare: {}\".format(new_train_data.agg({\"fare_amount\": \"max\"}).collect()[0],\\\n",
    "      new_train_data.agg({\"fare_amount\": \"min\"}).collect()[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are negative fares as well as extremely high fares. While negative fares don't make any sense, based on data of NY Taxi base fares, we know that the base fare is 2.5. So, we remove all records who have fares less than 2.5. Also, we keep the upper limit as 100, based on intuition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc3d4cf49535408ab3b6041fb9b2d9de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data_fare_filtered = new_train_data.filter(F.col(\"fare_amount\").between(2.5, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab18b899fcda470f83e0ef14d2d610ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|               Mon|           6|       2009|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|               Tue|           1|       2010|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|               Thu|           8|       2011|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|               Sat|           4|       2012|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|               Tue|           3|       2010|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data_fare_filtered.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c041a61b91a4a768e0d7594f5ade070",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "ranges = [2.5,5,10,15,20,30,40,50,60,70,80,90,100]\n",
    "\n",
    "plt.xticks(ticks = ranges[1:], labels= ['2.5-5', '5-10', '10-15', '15-20', '20-30', '30-40', \\\n",
    "            '40-50', '50-60', '60-70','70-80', '80-90', '90-100'], rotation = 'vertical')\n",
    "plt.title('Binned Fare (less than $100)')\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of records')\n",
    "\n",
    "hist(axes, train_data_fare_filtered.select(F.col((\"fare_amount\"))), bins = ranges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above plot that trips with fares in the range 2.5(base rate) to 15 is maximum and then there are some trips at the rate of 40-60 which can be some other kind of fixed rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passenger Count Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f28135fa26de4d3f98765a807250ded6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Passengers:Row(max(passenger_count)=208)\n",
      " Minimum Passengers: Row(min(passenger_count)=0)"
     ]
    }
   ],
   "source": [
    "print(\"Maximum Passengers:{}\\n Minimum Passengers: {}\".format(train_data_fare_filtered.agg({\"passenger_count\": \"max\"}).collect()[0],\\\n",
    "      train_data_fare_filtered.agg({\"passenger_count\": \"min\"}).collect()[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can see that the passenger count has high numbers as well as zero value which does not make sense. Again, we filter only those rows where count is [1,6], that is 7 seater cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7662f396d25642cea6e85485882b8c5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data_passenger_filtered = train_data_fare_filtered.filter(F.col(\"passenger_count\").between(1,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc7a57f802df471e8fc9b5b2d8818fc5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "ranges = [1,2,3,4,5,6]\n",
    "\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.title('Passenger Count')\n",
    "plt.xlabel('Passenger Count')\n",
    "plt.ylabel('Number of records')\n",
    "\n",
    "hist(axes, train_data_passenger_filtered.select(F.col((\"passenger_count\"))), bins = ranges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0437d490e417487596e4c1f68e31fd18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of Pickup Latitude:  Row(max(pickup_latitude)=3408.789565) Row(min(pickup_latitude)=-3492.263768)\n",
      "Range of Pickup Longitude:  Row(max(pickup_longitude)=3457.625683) Row(min(pickup_longitude)=-3442.059565)\n",
      "Range of Dropoff Latitude:  Row(max(dropoff_latitude)=3537.132528) Row(min(dropoff_latitude)=-3547.886698)\n",
      "Range of Dropoff Longitude:  Row(max(dropoff_longitude)=3457.62235) Row(min(dropoff_longitude)=-3442.024565)"
     ]
    }
   ],
   "source": [
    "print(\"Range of Pickup Latitude: \", train_data_passenger_filtered.agg({\"pickup_latitude\": \"max\"}).collect()[0],\\\n",
    "      train_data_passenger_filtered.agg({\"pickup_latitude\": \"min\"}).collect()[0])\n",
    "print(\"Range of Pickup Longitude: \", train_data_passenger_filtered.agg({\"pickup_longitude\": \"max\"}).collect()[0],\\\n",
    "      train_data_passenger_filtered.agg({\"pickup_longitude\": \"min\"}).collect()[0])\n",
    "print(\"Range of Dropoff Latitude: \", train_data_passenger_filtered.agg({\"dropoff_latitude\": \"max\"}).collect()[0],\\\n",
    "      train_data_passenger_filtered.agg({\"dropoff_latitude\": \"min\"}).collect()[0])\n",
    "print(\"Range of Dropoff Longitude: \", train_data_passenger_filtered.agg({\"dropoff_longitude\": \"max\"}).collect()[0],\\\n",
    "      train_data_passenger_filtered.agg({\"dropoff_longitude\": \"min\"}).collect()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we see that latitude and longitude has extremities. Now, the central co-ordinate of NY are (40.771133, -73.974187). Using intuition, we keep the records whose co-ordinates are +/- 2 degrees from the centre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2fad530a22a4e5e959b264513c125fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55202765"
     ]
    }
   ],
   "source": [
    "train_data_passenger_filtered.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff823393b9224fd69fbfaa780096cc7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data_filtered = train_data_passenger_filtered.filter((F.col(\"pickup_latitude\").between(38.5, 42.5)))\n",
    "train_data_filtered = train_data_filtered.filter((F.col(\"dropoff_latitude\").between(38.5, 42.5)))\n",
    "train_data_filtered = train_data_filtered.filter((F.col(\"pickup_longitude\").between(-76, -72)))\n",
    "train_data_filtered = train_data_filtered.filter((F.col(\"dropoff_longitude\").between(-76, -72)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21c44e7c04384719a6862821598b9bec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54057758"
     ]
    }
   ],
   "source": [
    "train_data_filtered.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Airport Fare Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we plot the pickups and drops for a small part of the dataset in Pandas, we see that there are many pickups and drops to and from airports. So, we try to confirm that using the co-ordinates of the airports and comparing the average fare for all trips against those for the airports. We do this so we can engineer features that differentiate trips to and from airports from other trips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](dropoffs.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](pickups.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coordinates of the three major airports in New York are as follows:\n",
    "\n",
    "    1. Coordinates of Newark Airport = 40.6895° N, 74.1745° W\n",
    "\n",
    "    2. Coordinates of JFK Airport = 40.6413° N, 73.7781° W\n",
    "\n",
    "    3. Coordinates of La Guardia Airport = 40.7769° N, 73.8740° W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "835616f7767848e3bb6a9ac3514dff90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trips with pickups at JFK: 827600\n",
      "Number of trips with drops at JFK: 421014"
     ]
    }
   ],
   "source": [
    "JFK = {\n",
    "    'min_lng': -73.8352, \n",
    "    'min_lat': 40.6195, \n",
    "    'max_lng': -73.7401,  \n",
    "    'max_lat': 40.6659\n",
    "}\n",
    "\n",
    "JFK_center=[40.6437, -73.7900]\n",
    "\n",
    "# Get all pickups from JFK\n",
    "train_data_JFK_pickup = train_data_filtered.filter(F.col(\"pickup_latitude\").between(JFK['min_lat'], JFK['max_lat']))\n",
    "train_data_JFK_pickup = train_data_JFK_pickup.filter(F.col(\"pickup_longitude\").between(JFK['min_lng'], JFK['max_lng']))\n",
    "\n",
    "print('Number of trips with pickups at JFK:', train_data_JFK_pickup.count())\n",
    "\n",
    "# Get all dropoffs at JFK\n",
    "train_data_JFK_drops = train_data_filtered.filter(F.col(\"dropoff_latitude\").between(JFK['min_lat'], JFK['max_lat']))\n",
    "train_data_JFK_drops = train_data_JFK_drops.filter(F.col(\"dropoff_longitude\").between(JFK['min_lng'], JFK['max_lng']))\n",
    "\n",
    "print('Number of trips with drops at JFK:', train_data_JFK_drops.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d28438293b884c3c85946d5506f7a3f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.title('Pickup Fares')\n",
    "plt.xlabel('Fare')\n",
    "\n",
    "distplot(axes, train_data_JFK_pickup.select(F.col((\"fare_amount\"))), alpha = 0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10a11811491544b4a68c3ff0da9f78d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.title('Dropoff Fares')\n",
    "plt.xlabel('Fare')\n",
    "\n",
    "distplot(axes, train_data_JFK_drops.select(F.col((\"fare_amount\"))), alpha = 0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the pickup and dropoff fares for JFK are more in the 40-60 range which can suggest fixed #. To confirm, let's compare this with average fare for all trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d75714a0d2a74aeba4f7fade837c6f47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.title('All Fares')\n",
    "plt.xlabel('Fare')\n",
    "\n",
    "distplot(axes, train_data_filtered.select(F.col((\"fare_amount\"))), alpha = 0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, the average fare is much higher when trips are related to airports. So, differentiating these records by feature engineering will help us build a better model as it will be able to differentiate and recognize the difference in pattern between airport trips and non-airport trips. So, we create features that specify whether a record (trip) is to any of the 3 airports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Airport Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d46853500fe54cf8a9b0da6b5612ef79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nyc_airports = {\n",
    "    'JFK': {\n",
    "        'min_lng': -73.8352, \n",
    "        'min_lat': 40.6195,\n",
    "        'max_lng': -73.7401,  \n",
    "        'max_lat': 40.6659\n",
    "    },     \n",
    "    'EWR': {\n",
    "        'min_lng': -74.1925,\n",
    "        'min_lat': 40.6700, \n",
    "        'max_lng': -74.1531, \n",
    "        'max_lat': 40.7081\n",
    "    },\n",
    "    'LaGuardia': {\n",
    "        'min_lng': -73.8895, \n",
    "        'min_lat': 40.7664, \n",
    "        'max_lng': -73.8550, \n",
    "        'max_lat': 40.7931\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8f88046297946d78e415ba76bc116be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x7fc549d24b00>"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import BooleanType\n",
    "\n",
    "def isTripRelatedToAirport(latitude,longitude,airport_name='JFK'):   \n",
    "    if ((latitude >= nyc_airports[airport_name]['min_lat']) & \n",
    "        (latitude <= nyc_airports[airport_name]['max_lat']) & \n",
    "        (longitude >= nyc_airports[airport_name]['min_lng']) & \n",
    "       (longitude <= nyc_airports[airport_name]['max_lng'])):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "featureEngineeringUDF = udf(lambda x, y, z: isTripRelatedToAirport(x,y,z),BooleanType())\n",
    "spark.udf.register(\"featureEngineeringUDF\", featureEngineeringUDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create 6 new columns related to trips to the three airports JFK, EWR, and La Guardia respectively: one for pickup and one for dropoff from each airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e269cb461e34d358d6ad2a69d0bb69d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|        false|         false|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|        false|         false|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|        false|         false|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|        false|         false|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|        false|         false|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import lit\n",
    "\n",
    "train_data_filtered = train_data_filtered.withColumn(\"is_pickup_JFK\", featureEngineeringUDF(F.col(\"pickup_latitude\"), \\\n",
    "                                                     F.col(\"pickup_longitude\"), lit(\"JFK\")))\n",
    "train_data_filtered = train_data_filtered.withColumn(\"is_dropoff_JFK\", featureEngineeringUDF(F.col(\"dropoff_latitude\"), \\\n",
    "                                                     F.col(\"dropoff_longitude\"), lit(\"JFK\")))\n",
    "\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data_filtered = train_data_filtered.withColumn(\"is_pickup_EWR\", featureEngineeringUDF(F.col(\"pickup_latitude\"), \\\n",
    "                                                     F.col(\"pickup_longitude\"), lit(\"EWR\")))\n",
    "train_data_filtered = train_data_filtered.withColumn(\"is_dropoff_EWR\", featureEngineeringUDF(F.col(\"dropoff_latitude\"), \\\n",
    "                                                     F.col(\"dropoff_longitude\"), lit(\"EWR\")))\n",
    "\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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       "VBox()"
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     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|is_pickup_LaGuardia|is_dropoff_LaGuardia|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|              false|               false|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|              false|               false|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|              false|               false|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|              false|               false|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|              false|               false|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data_filtered = train_data_filtered.withColumn(\"is_pickup_LaGuardia\", featureEngineeringUDF(F.col(\"pickup_latitude\"), \\\n",
    "                                                     F.col(\"pickup_longitude\"), lit(\"LaGuardia\")))\n",
    "train_data_filtered = train_data_filtered.withColumn(\"is_dropoff_LaGuardia\", featureEngineeringUDF(F.col(\"dropoff_latitude\"), \\\n",
    "                                                     F.col(\"dropoff_longitude\"), lit(\"LaGuardia\")))\n",
    "\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Haversine Formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can calculate the trip distance based on the trip fare using the Haversine distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "VBox()"
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     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x7fc549cfc9e0>"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import DoubleType, ArrayType, DecimalType\n",
    "import math\n",
    "\n",
    "def trip_distance(lat1, lat2, lon1, lon2):\n",
    "    \"\"\"\n",
    "    Calculate trip distance based on Haversine formula\n",
    "    Args:\n",
    "        lat1: Latitude of first point\n",
    "        lat2: Latitude of second point\n",
    "        lon1: Longitude of first point\n",
    "        lon2: :ongitude of second point\n",
    "    Returns:\n",
    "        Distance between the two points in miles\n",
    "    \"\"\"\n",
    "    # pi / 180\n",
    "    p = 0.017453292519943295\n",
    "    a = 0.5 - math.cos((lat2 - lat1) * p)/2 + math.cos(lat1 * p) * math.cos(lat2 * p) * (1 - math.cos((lon2 - lon1) * p)) / 2\n",
    "    return 0.6213712 * 12742 * math.asin(math.sqrt(a))\n",
    "\n",
    "haversineUDF = udf(lambda a, b, c, d: trip_distance(a,b,c,d), DoubleType())\n",
    "spark.udf.register(\"haversineUDF\", haversineUDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "VBox()"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|is_pickup_LaGuardia|is_dropoff_LaGuardia|     trip_distance|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|              false|               false|0.6404870231407276|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|              false|               false| 5.250669652366417|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|              false|               false|0.8634109565058572|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|              false|               false|1.7393859082192853|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|              false|               false|1.2422184522938338|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data_filtered = train_data_filtered.withColumn(\"trip_distance\", haversineUDF(F.col(\"pickup_latitude\"), \\\n",
    "                                                     F.col(\"dropoff_latitude\"), F.col(\"pickup_longitude\"), \\\n",
    "                                                     F.col(\"dropoff_longitude\")))\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
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   "outputs": [
    {
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       "VBox()"
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   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "ranges = [0,1,5,10,20,30,40,50,60,70,80,90,100]\n",
    "\n",
    "plt.xticks(ranges, ranges, rotation = 'vertical')\n",
    "plt.title('Distribution of Trip Distance')\n",
    "plt.xlabel('Distance (in miles)')\n",
    "\n",
    "distplot(axes, train_data_filtered.select(F.col((\"trip_distance\"))), alpha = 0.6, bins=ranges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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     "data": {
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xdu1a7N69GwsXLiz3vjhrCgoKwnvvvYeUlBS0aNECq1atwoEDB7B06dIKLaJ94cIFeY3JnJwcHD16VH4SyPPPP28xweRW2dnZaNSoEYYNG4YOHTrAw8MDv/76K/bs2YP58+fL/bp06YJVq1Zh6tSp6Nq1Kzw8PDBw4EAMGDAA69atw5AhQxAdHY0zZ85gyZIlaNOmjXz/YWV98MEHuOeee9C5c2dMnDgRTZo0QUpKCjZs2CA/anD27NnYvn07wsPD8dhjj6FNmzbIyMjAvn378Ouvv5b6CxWR07PdBGQix2ReBsb8UqvVQqfTiT59+ohFixZZLDdidusyMPHx8WLQoEEiKChIqNVqERQUJGJiYsTff/9t8bn169eLNm3aCJVKZbHsSs+ePUXbtm1Lra+sZWBWrFghpk2bJvz9/YWrq6uIjo4usfSGEELMnz9fNGzYUGg0GnH33XeLvXv3lthnebXdugyMEEJkZ2eL5557TgQFBQkXFxfRvHlzMXfuXIulPoQoWrKktCVKylqe5lZpaWkiNjZW1K9fX6jVatGuXbtSl6qp6WVg1qxZU6J/WcvAtG3bVuzdu1dEREQIrVYrQkJCxOLFiytUQ0hIiPz3TpIk4eXlJdq2bSsee+wxkZiYWOpnitecn58vXnzxRdGhQwfh6ekp3N3dRYcOHcTHH39s8ZmcnBzx8MMPCx8fHwFA/n6aTCbx7rvvipCQEKHRaESnTp3Ezz//XOJ7bl4GZu7cueXWY3b48GExZMgQ4ePjI7RarWjZsqV4/fXXLfqkpaWJyZMni+DgYOHi4iJ0Op3o3bu3WLp0aYXOHZGz4bOAiYjqiPvuuw9Xrlwp9543IqKawHsAiYiIiJwMAyARERGRk2EAJCIiInIyvAeQiIiIyMlwBJCIiIjIyTAAEhERETkZBkAiIiIiJ8MngVSDyWTCxYsX4enpWePPZSUiIqLaIYRAdnY2goKCLJ4N7kwYAKvh4sWLCA4OtnUZREREVAXnz59Ho0aNbF2GTTAAVoP52aHnz5+Hl5eXjashIiKiitDr9QgODq4zzwC3BQbAajBf9vXy8mIAJCIisjPOfPuWc174JiIiInJiDIBEREREToYBkIiIiMjJMAASERERORkGQCIiIiInwwBIRERE5GQYAImIiIicDAMgERERkZNhACQiIiJyMgyARERERE6GAZCIiIjIyTAAEhERETkZla0LoLKl6fPw29+X4alRoX+7QFuXQ0RERA6CAbAOe+vnozh2KRtaFwV2nryCuIfa2bokIiIicgC8BFyHKSQJAGASwsaVEBERkSNhAKzDFIqiAGg02bgQIiIicigMgHWYstgIoOAoIBEREdUQBsA6THlzBFCIohcRERFRTVAAgBACM2bMQGBgIFxdXREZGYmTJ09adMzIyMCoUaPg5eUFHx8fjB8/Hjk5ORZ9kpOT0aNHD2i1WgQHB2POnDklDrhmzRq0atUKWq0W7dq1w8aNGy3arVXLunXrEBYWBh8fH7i7u6Njx474+uuvK3n6atfN/AcAMDIBEhERUQ1RAMCcOXPwwQcfYMmSJUhMTIS7uzuioqKQl5cndxw1ahSOHDmCrVu34ueff8bOnTsxceJEuV2v16Nv374ICQlBUlIS5s6di1mzZmHp0qVynz/++AMxMTEYP3489u/fj8GDB2Pw4ME4fPiw3Mdatfj5+WH69OlISEhAcnIyYmNjERsbi19++aWGT3HVSZIkh0CTiQGQiIiIaojJZBI6nU7MnTtXmGVmZgqNRiNWrFghhBDi6NGjAoDYs2eP3GfTpk1CkiRx4cIFIYQQH3/8sfD19RX5+flyn5dfflm0bNlSfj98+HARHR0tigsPDxePP/64EEIIa9ZSmk6dOonXXnut3D7FZWVlCQAiKyurwp+pjFe+SxZ93/9N3D9vh3h6xb5aOQYREZGzqe2f3/ZAcebMGaSmpiIyMlIOhd7e3ggPD0dCQgIAICEhAT4+PggLC5P7REZGQqFQIDExUe5z7733Qq1Wy32ioqJw4sQJXLt2Te5T/DjmPubjWLOW4oQQiI+Px4kTJ3DvvfeWGZbz8/Oh1+stXrXtv4kgtX4oIiIichKK1NRUAEBAQIBFQ0BAAMxtqamp8Pf3t2hXqVTw8/Oz6FPaPsxt5fUp3m6tWgAgKysLHh4eUKvViI6Oxocffog+ffrceo5kcXFx8Pb2ll/BwcFl9q0p5qVgeAmYiIiIaopTzwL29PTEgQMHsGfPHrzzzjuYOnUqduzYUWb/adOmISsrS36dP3++1ms0jwByEggRERHVFJVOpwMApKWlITDwv+fNpqWloWPHjgAAnU6H9PR0iw8WFhYiIyMD5s/rdDqkpaVZ9DG/v12f4u3WqgUAFAoFmjVrBgDo2LEjjh07hri4ONx3330lzxQAjUYDjUZTalttUdyM6EaOABIREVENUTRp0gQ6nQ7x8fHyRr1ej8TERERERAAAIiIikJmZiaSkJLnPtm3bYDKZEB4eLvfZuXMnCgoK5D5bt25Fy5Yt4evrK/cpfhxzH/NxrFlLaUwmE/Lz8yty3qxGvgeQAZCIiIhqihBCzJ49W/j4+Ij169eL5ORkMWjQINGkSRORm5srzxbp16+f6NSpk0hMTBS7du0SzZs3FzExMXJ7ZmamCAgIEKNHjxaHDx8WK1euFG5ubuLTTz+V++zevVuoVCoxb948cezYMTFz5kzh4uIiDh06JPexVi3vvvuu2LJlizh9+rQ4evSomDdvnlCpVOKzzz6r8Awaa8wC/n9L/hD3z9shxn3xV60cg4iIyNlwFnDRI8aEyWQSr7/+uggICBAajUb07t1bnDhxwqLj1atXRUxMjPDw8BBeXl4iNjZWZGdnW/Q5ePCguOeee4RGoxENGzYUs2fPLnHA1atXixYtWgi1Wi3atm0rNmzYYNFurVqmT58umjVrJrRarfD19RURERFi5cqVlTp51giAIz9NEPfP2yFGf55YK8cgIiJyNgyAQkhCcHZBVen1enh7eyMrKwteXl41vv9p6w7hXMYNpGblIdBbi28mhNf4MYiIiJxNbf/8tgdOPQvYHihvPgmEk0CIiIiopjAA1nHyOoAcqCUiIqIawgBYx3EdQCIiIqppDIB1HJ8EQkRERDWNAbCOU8gjgDYuhIiIiBwGA2Adp+QIIBEREdUwBsA6TsFZwERERFTDGADrOCVnARMREVENYwCs4+RnATMAEhERUQ1hAKzjzLOAjSaAD20hIiKimsAAWMeZZwEDgMFosmElRERE5CgYAOs4hQSYI6ChkAGQiIiIqo8BsI6TJAmKm9+lfAZAIiIiqgEMgHbAfBmYI4BERERUExgA7YB5KZgC3gNIRERENYAB0A6YRwB5CZiIiIhqAgOgHTCPAPISMBEREdUEBkA7wBFAIiIiqkkMgHZAefO7xBFAIiIiqgkMgHZAngXMSSBERERUAxgA7YCC9wASERFRDWIAtANKrgNIRERENYgB0A7Is4CNRhtXQkRERI6AAdAOyLOACzgCSERERNXHAGgHzM8C5iQQIiIiqgkMgHaA9wASERFRTWIAtAPmWcBcCJqIiIhqAgOgHeAIIBEREdUkBkA7IK8DyHsAiYiIqAYwANoBjgASERFRTWIAtAMKPguYiIiIahADoB0wjwAWmgSMJmHjaoiIiMjeMQDaAfM9gABHAYmIiKj6GADtgEKScHMQkAGQiIiIqo0B0E6YLwPn83nAREREVE0KABBCYMaMGQgMDISrqysiIyNx8uRJi44ZGRkYNWoUvLy84OPjg/HjxyMnJ8eiT3JyMnr06AGtVovg4GDMmTOnxAHXrFmDVq1aQavVol27dti4caNFuzVrMVu5ciUkScLgwYNvf8ZsRF4KhiOAREREVE0KAJgzZw4++OADLFmyBImJiXB3d0dUVBTy8vLkjqNGjcKRI0ewdetW/Pzzz9i5cycmTpwot+v1evTt2xchISFISkrC3LlzMWvWLCxdulTu88cffyAmJgbjx4/H/v37MXjwYAwePBiHDx+W+1irFrOUlBS88MIL6NGjRw2d0tqh5CVgIiIiqikmk0nodDoxd+5cYZaZmSk0Go1YsWKFEEKIo0ePCgBiz549cp9NmzYJSZLEhQsXhBBCfPzxx8LX11fk5+fLfV5++WXRsmVL+f3w4cNFdHS0KC48PFw8/vjjQgghrFmLEEIUFhaK7t27i88//1yMHTtWDBo0SFRGVlaWACCysrIq9bmKeuW7ZPk18MPfxSvfJYtjl2rnWERERM6itn9+2wPFmTNnkJqaisjISDkUent7Izw8HAkJCQCAhIQE+Pj4ICwsTO4TGRkJhUKBxMREuc+9994LtVot94mKisKJEydw7do1uU/x45j7mI9jzVoA4M0334S/vz/Gjx9fobCcn58PvV5v8bIWBReDJiIiohqiSE1NBQAEBARYNAQEBMDclpqaCn9/f4t2lUoFPz8/iz6l7cPcVl6f4u3WqmXXrl343//+h88++6y081KquLg4eHt7y6/g4OAKf7a6lLwHkIiIiGqIU84Czs7OxujRo/HZZ5+hfv36Ff7ctGnTkJWVJb/Onz9fi1VaMo8A5jMAEhERUTWpdDodACAtLQ2BgYFyQ1paGjp27AgA0Ol0SE9Pt/hgYWEhMjIyYP68TqdDWlqaRR/z+9v1Kd5ujVpOnz6NlJQUDBw4UG43mYqClUqlwokTJ3DHHXeUOFkajQYajabEdmtQ8nFwREREVEMUTZo0gU6nQ3x8vLxRr9cjMTERERERAICIiAhkZmYiKSlJ7rNt2zaYTCaEh4fLfXbu3ImCggK5z9atW9GyZUv4+vrKfYofx9zHfBxr1dKqVSscOnQIBw4ckF8PPvggevXqhQMHDlj10m5FmdcBNBgZAImIiKiahBBi9uzZwsfHR6xfv14kJyeLQYMGiSZNmojc3Fx5tki/fv1Ep06dRGJioti1a5do3ry5iImJkdszMzNFQECAGD16tDh8+LBYuXKlcHNzE59++qncZ/fu3UKlUol58+aJY8eOiZkzZwoXFxdx6NAhuY+1arlVXZ8F/Mjnf4pXvksW6w9cqJVjEREROQvOAhZCBQAvvfQSrl+/jokTJyIzMxP33HMPNm/eDK1WKwfFb7/9FlOmTEHv3r2hUCgwdOhQfPDBB3K7t7c3tmzZgsmTJ6NLly6oX78+ZsyYYbE+X/fu3bF8+XK89tprePXVV9G8eXP88MMPuPPOO+U+1qrF3nAWMBEREdUUSQghbF2EvdLr9fD29kZWVha8vLxqfP/T1h2S/5ymz0OAlxbtGnrj4fDGNX4sIiIiZ1HbP7/tgVPOArZH/80C5rOAiYiIqHoYAO2EeR1ALgNDRERE1cUAaCe4EDQRERHVFAZAO2FeBiavgJeAiYiIqHoYAO2E4uZ3ipeAiYiIqLoYAO2E6mYC5CQQIiIiqi4GQDtx8xZAGE1AIZ8GQkRERNXAAGgnzJNAAF4GJiIiouphALQTkiRBreRSMERERFR9DIB2ROOiBMD7AImIiKh6GADtiEZ1cyJIAUcAiYiIqOoYAO2IHAB5CZiIiIiqgQHQjmhUvARMRERE1ccAaEc0LhwBJCIioupjALQjvAeQiIiIagIDoB3hJWAiIiKqCQyAdsQ8ApjHEUAiIiKqBgZAO2K+B9Bg5AggERERVR0DoB2RLwFzBJCIiIiqgQHQjvx3CZgjgERERFR1DIB2xDwCaDByBJCIiIiqjgHQjsjrAPISMBEREVUDA6AdkS8BcxkYIiIiqgYGQDsiXwLmk0CIiIioGhgA7QjXASQiIqKawABoR8z3ABaaBIwmYeNqiIiIyF4xANoR8yVggI+DIyIioqpjALQjSoUEF6UEgDOBiYiIqOoYAO2M+T7AfE4EISIioipiALQz8uPgeAmYiIiIqogB0M5wBJCIiIiqiwHQzvBpIERERFRdDIB2hpeAiYiIqLoYAO0MLwETERFRdTEA2hn5EjBHAImIiKiKFAAghMCMGTMQGBgIV1dXREZG4uTJkxYdMzIyMGrUKHh5ecHHxwfjx49HTk6ORZ/k5GT06NEDWq0WwcHBmDNnTokDrlmzBq1atYJWq0W7du2wceNGi3Zr1pKZmYnJkycjMDAQGo0GLVq0KFFPXSNfAuY9gERERFRFCgCYM2cOPvjgAyxZsgSJiYlwd3dHVFQU8vLy5I6jRo3CkSNHsHXrVvz888/YuXMnJk6cKLfr9Xr07dsXISEhSEpKwty5czFr1iwsXbpU7vPHH38gJiYG48ePx/79+zF48GAMHjwYhw8flvtYqxaDwYA+ffogJSUFa9euxYkTJ/DZZ5+hYcOGNXyKaxYvARMREVG1mUwmodPpxNy5c4VZZmam0Gg0YsWKFUIIIY4ePSoAiD179sh9Nm3aJCRJEhcuXBBCCPHxxx8LX19fkZ+fL/d5+eWXRcuWLeX3w4cPF9HR0aK48PBw8fjjjwshhLBmLZ988olo2rSpMBgMoqqysrIEAJGVlVXlfZTnle+SLV5CCPH735fFK98li5V/na2VYxIRETm62v75bQ8UZ86cQWpqKiIjI+VQ6O3tjfDwcCQkJAAAEhIS4OPjg7CwMLlPZGQkFAoFEhMT5T733nsv1Gq13CcqKgonTpzAtWvX5D7Fj2PuYz6ONWv58ccfERERgcmTJyMgIAB33nkn3n33XRiNZd9bl5+fD71eb/Gytv/uAeQIIBEREVWNIjU1FQAQEBBg0RAQEABzW2pqKvz9/S3aVSoV/Pz8LPqUtg9zW3l9irdbq5Z//vkHa9euhdFoxMaNG/H6669j/vz5ePvtt0s7TwCAuLg4eHt7y6/g4OAy+9YW+RIw7wEkIiKiKnLaWcAmkwn+/v5YunQpunTpghEjRmD69OlYsmRJmZ+ZNm0asrKy5Nf58+etWHERrgNIRERE1aXQ6XQAgLS0NIuGtLQ0mNt0Oh3S09Mt2gsLC5GRkWHRp7R9mNvK61O83Vq1BAYGokWLFlAqlXKf1q1bIzU1FQaDAaXRaDTw8vKyeFkbJ4EQERFRdSmaNGkCnU6H+Ph4eaNer0diYiIiIiIAABEREcjMzERSUpLcZ9u2bTCZTAgPD5f77Ny5EwUFBXKfrVu3omXLlvD19ZX7FD+OuY/5ONas5e6778apU6dgMv0XpP7++28EBgZa3DtY1/AeQCIiIqo2IYSYPXu28PHxEevXrxfJycli0KBBokmTJiI3N1eeLdKvXz/RqVMnkZiYKHbt2iWaN28uYmJi5PbMzEwREBAgRo8eLQ4fPixWrlwp3NzcxKeffir32b17t1CpVGLevHni2LFjYubMmcLFxUUcOnRI7mOtWs6dOyc8PT3FlClTxIkTJ8TPP/8s/P39xdtvv13hGTS2mAV8NSdfvPJdsnj9h0O3+TQRERGVhrOAhYAQRcuvvP766yIgIEBoNBrRu3dvceLECYuOV69eFTExMcLDw0N4eXmJ2NhYkZ2dbdHn4MGD4p577hEajUY0bNhQzJ49u8QBV69eLVq0aCHUarVo27at2LBhg0W7NWv5448/RHh4uNBoNKJp06binXfeEYWFhRU+ebYIgNfzC+T3RqOpVo5LRETkyBgAhZCEEMK2Y5D2S6/Xw9vbG1lZWbVyP+C0dYcs3sc91A6FRhNeX38EADBjQBu4qpWlfZSIiIjKUNs/v+2B084CtlcqpQIqhQSAM4GJiIioahgA7RBnAhMREVF1MADaIXkmMBeDJiIioipgALRDXAyaiIiIqoMB0A7xEjARERFVBwOgHfovAHIEkIiIiCqPAdAOaVxuXgLmPYBERERUBQyAdoiXgImIiKg6GADtECeBEBERUXUwANoh7c1lYPJ4CZiIiIiqgAHQDmlv3gOYV8ARQCIiIqo8BkA7ZB4BzGUAJCIioipgALRD/40A8hIwERERVR4DoB3iJWAiIiKqDgZAOyQHQM4CJiIioipgALRDrlwImoiIiKqBAdAOmSeB5BeaYDQJG1dDRERE9oYB0A5pby4EDXAxaCIiIqo8BkA7pFBI8uPgcg0MgERERFQ5DIB2SmN+GgifB0xERESVxABop8yXgbkUDBEREVUWA6CdMi8Fw0vAREREVFkMgHbKVZ4JzABIRERElcMAaKf4ODgiIiKqKgZAO8XHwREREVFVMQDaKfNi0BwBJCIiospiALRTGvMkEI4AEhERUSUxANopV14CJiIioipiALRTvAeQiIiIqooB0E6ZRwDz+SQQIiIiqiQGQDv13yQQjgASERFR5TAA2ik+CYSIiIiqigHQTsnPAi40Qghh42qIiIjInjAA2inNzUvARhNQYGQAJCIioopjALRTGpUCklT05zw+D5iIiIgqwaEC4EcffYTQ0FBotVqEh4fjr7/+KrPvZ599hh49esDX1xe+vr6IjIwst39dI0nSf5eBeR8gERERVYLDBMBVq1Zh6tSpmDlzJvbt24cOHTogKioK6enppfbfsWMHYmJisH37diQkJCA4OBh9+/bFhQsXrFx51fFxcERERFQVDhMAFyxYgMceewyxsbFo06YNlixZAjc3N3zxxRel9v/222/x5JNPomPHjmjVqhU+//xzmEwmxMfHW7nyqpOfBsJLwERERFQJDhEADQYDkpKSEBkZKW9TKBSIjIxEQkJChfZx48YNFBQUwM/Pr8w++fn50Ov1Fi9b4tNAiIiIqCocIgBeuXIFRqMRAQEBFtsDAgKQmppaoX28/PLLCAoKsgiRt4qLi4O3t7f8Cg4Orlbd1cVLwERERFQVDhEAq2v27NlYuXIlvv/+e2i12jL7TZs2DVlZWfLr/PnzVqyyJI15MWiOABIREVElqGxdQE2oX78+lEol0tLSLLanpaVBp9OV+9l58+Zh9uzZ+PXXX9G+ffty+2o0Gmg0mmrXW1N4CZiIiIiqwiFGANVqNbp06WIxgcM8oSMiIqLMz82ZMwdvvfUWNm/ejLCwMGuUWqNcGQCJiIioChxiBBAApk6dirFjxyIsLAzdunXDwoULcf36dcTGxgIAxowZg4YNGyIuLg4A8N5772HGjBlYvnw5QkND5XsFPTw84OHhYbOvozLM9wDm8x5AIiIiqgSHCYAjRozA5cuXMWPGDKSmpqJjx47YvHmzPDHk3LlzUCj+G/D85JNPYDAYMGzYMIv9zJw5E7NmzbJm6VXGZWCIiIioKhwmAALAlClTMGXKlFLbduzYYfE+JSWl9guqZeZ7AHP5JBAiIiKqBIe4B9BZcRkYIiIiqgoGQDumUXEZGCIiIqo8BkA7xmVgiIiIqCoYAO2Yq7ooABqMJgghbFwNERER2QsGQDumVRV9+4TgfYBERERUcQyAdkylVMBFKQHgZWAiIiKqOAZAO6flWoBERERUSQyAds58GZiXgImIiKiiGADtnIYzgYmIiKiSGADtnPw0EAZAIiIiqiAGQDvnyhFAIiIiqiQGQDvnpubzgImIiKhyGADtHC8BExERUWUxANo58wjgDY4AEhERUQUxANo58+PgeA8gERERVRQDoJ0zTwLhCCARERFVFAOgnXPlJBAiIiKqJAZAO+fKSSBERERUSQyAdq74MjBCCBtXQ0RERPaAAdDOmZeBKTQJFBgZAImIiOj2GADtnEalgEIq+jMvAxMREVFFMADaOUmS+DQQIiIiqhQGQAfAiSBERERUGQyADkArPw2k0MaVEBERkT1gAHQAbi58GggRERFVHAOgA/hvMWiTjSshIiIie8AA6ABc1SoAvARMREREFcMA6AA4CYSIiIgqgwHQAcgBkMvAEBERUQUwADoA+R5AjgASERFRBTAAOgDzCOANjgASERFRBTAAOgA+CYSIiIgqgwHQAbipOQJIREREFccA6ADcNEXLwOQWGGEyCRtXQ0RERHUdA6ADMD8JBABucCIIERER3YYcAD/66COEhoZCq9UiPDwcf/31l0XHvLw8TJ48GfXq1YOHhweGDh2KtLQ0iz7nzp1DdHQ03Nzc4O/vjxdffBGFhZaLE+/YsQOdO3eGRqNBs2bNsGzZshJFWaOWdevWoU+fPmjQoAG8vLwQERGBX375pXJnr45QKCRoXYq+lVwMmoiIiG5HAQCrVq3C1KlTMXPmTOzbtw8dOnRAVFQU0tPT5Y7PPfccfvrpJ6xZswa//fYbLl68iIceekhuNxqNiI6OhsFgwB9//IGvvvoKy5Ytw4wZM+Q+Z86cQXR0NHr16oUDBw7g2WefxYQJEyyCl7Vq2blzJ/r06YONGzciKSkJvXr1wsCBA7F///4aPsXWwYkgREREVGFCCNGtWzcxefJkYWY0GkVQUJCIi4sTQgiRmZkpXFxcxJo1a+Q+x44dEwBEQkKCEEKIjRs3CoVCIVJTU+U+n3zyifDy8hL5+flCCCFeeukl0bZtW1HciBEjRFRUlPzeWrWUpk2bNuKNN94os/1WWVlZAoDIysqq8Gcq45Xvki1e5Vm87aR45btkceRC7dRCRETkKGr757c9UBgMBiQlJSEyMlIOhQqFApGRkUhISAAAJCUloaCgwKJPq1at0LhxY7lPQkIC2rVrh4CAALlPVFQU9Ho9jhw5Ivcpvg9zH/M+rFnLrUwmE7Kzs+Hn51dmWM7Pz4der7d41RXyCGABLwETERFR+RRXrlyB0Wi0CEsAEBAQgNTUVABAamoq1Go1fHx8yu1T2j7MbeX10ev1yM3NhTVrudW8efOQk5OD4cOHl9oOAHFxcfD29pZfwcHBZfa1Nnd10Uzg6/m8BExERETl4yxgAMuXL8cbb7yB1atXw9/fv8x+06ZNQ1ZWlvw6f/68FassnyvXAiQiIqIKUtSvXx9KpbLELNq0tDTodDoAgE6ng8FgQGZmZrl9StuHua28Pl5eXnB1dYU1azFbuXIlJkyYgNWrV5e4PH0rjUYDLy8vi1ddwUvAREREVFEKtVqNLl26ID4+Xt5oMpkQHx+PiIgIAECXLl3g4uJi0efEiRM4d+6c3CciIgKHDh2ymK27detWeHl5oU2bNnKf4vsw9zHvw5q1AMCKFSsQGxuLFStWIDo6uirnr85wu3kJmCOAREREdDsqAJg6dSrGjh2LsLAwdOvWDQsXLsT169cRGxsLAPD29sb48eMxdepU+Pn5wcvLC0899RQiIiJw1113AQD69u2LNm3aYPTo0ZgzZw5SU1Px2muvYfLkydBoNACAJ554AosXL8ZLL72ERx99FNu2bcPq1auxYcMGuSBr1bJ8+XKMHTsWixYtQnh4uHxvoKurK7y9va1x7muU/Dg43gNIREREt2OeDvzhhx+Kxo0bC7VaLbp16yb+/PNPi+nCubm54sknnxS+vr7Czc1NDBkyRFy6dMmiT0pKiujfv79wdXUV9evXF88//7woKCiw6LN9+3bRsWNHoVarRdOmTcWXX35ZYmqyNWrp2bOnAFDiNXbs2ApPoa5Ly8CcTNOLV75LFgu3/l0rtRARETkKLgMjhCSE4MNjq0iv18Pb2xtZWVm1cj/gtHWHLN7HPdSuzL4XMnOxeNspeLmqMK1/6xqvhYiIyFHU9s9ve8BZwA7C/DzgG/lGMNMTERFReRgAHYSbpigAFpoEDEaTjashIiKiuowB0EGolQqoFBIAPg+YiIiIyqeydQFUcbfeE3ir/eeuocAoMGP9Ebhryv7WlncvIRERETk+jgA6EBdl0bez0MR7AImIiKhsDIAORKUsugRcyHsAiYiIqBwMgA5EpSj6dhYYOQJIREREZWMAdCDyCKCJI4BERERUNgZAB+LCEUAiIiKqAAZAB8IRQCIiIqoIBkAHIs8C5gggERERlYMB0IGYF4Iu4CxgIiIiKgcDoAP5bxkYjgASERFR2RgAHUjxhaCFYAgkIiKi0jEAOhCVQoJ08898GggRERGVhQHQgUiSBCXvAyQiIqLbYAB0MC68D5CIiIhugwHQwaiU5sWgOQJIREREpWMAdDD/LQbNEUAiIiIqHQOggzE/Dq6QI4BERERUBgZAB2MeASzgCCARERGVgQHQwaj4ODgiIiK6DQZAB+PCZWCIiIjoNhgAHYwLZwETERHRbTAAOpj/AiAvARMREVHpGAAdjHkhaKNJwMSJIERERFQKBkAHo1RIuHkbIApMvAxMREREJTEAOhhJkoo9DYQjgERERFQSA6ADMl8G5kQQIiIiKg0DoAPiTGAiIiIqDwOgAzKPAHIxaCIiIioNA6AD4gggERERlYcB0AExABIREVF5GAAdkEp+HBwvARMREVFJDIAOiCOAREREVB45AH700UcIDQ2FVqtFeHg4/vrrL4uOeXl5mDx5MurVqwcPDw8MHToUaWlpFn3OnTuH6OhouLm5wd/fHy+++CIKCwst+uzYsQOdO3eGRqNBs2bNsGzZshJFWaOWdevWoU+fPmjQoAG8vLwQERGBX375pXJnr45yUTEAEhERUdkUALBq1SpMnToVM2fOxL59+9ChQwdERUUhPT1d7vjcc8/hp59+wpo1a/Dbb7/h4sWLeOihh+R2o9GI6OhoGAwG/PHHH/jqq6+wbNkyzJgxQ+5z5swZREdHo1evXjhw4ACeffZZTJgwwSJ4WauWnTt3ok+fPti4cSOSkpLQq1cvDBw4EPv376/hU2x9Lgrz4+DAx8ERERFRCZIQQoSHh6Nr165YvHgxAMBkMiE4OBhPPfUUXnnlFWRlZaFBgwZYvnw5hg0bBgA4fvw4WrdujYSEBNx1113YtGkTBgwYgIsXLyIgIAAAsGTJErz88su4fPky1Go1Xn75ZWzYsAGHDx+WCxg5ciQyMzOxefNmAIC1ailN27ZtMWLECIugWB69Xg9vb29kZWXBy8ursuf+tqatO1SlzwkhkHT2GkwC6NDIGxoXpUV73EPtaqI8IiIiu1TbP7/tgcJgMCApKQmRkZH/bVQoEBkZiYSEBABAUlISCgoKLPq0atUKjRs3lvskJCSgXbt2cuACgKioKOj1ehw5ckTuU3wf5j7mfVizlluZTCZkZ2fDz8+vzJOVn58PvV5v8aqLJEmS7wM08DIwERER3UJx5coVGI1Gi7AEAAEBAUhNTQUApKamQq1Ww8fHp9w+pe3D3FZeH71ej9zcXFizllvNmzcPOTk5GD58eKntABAXFwdvb2/5FRwcXGZfW1PfvA/QUMgASERERJY4CxjA8uXL8cYbb2D16tXw9/cvs9+0adOQlZUlv86fP2/FKivnv5nAvAeQiIiILCnq168PpVJZYhZtWloadDodAECn08FgMCAzM7PcPqXtw9xWXh8vLy+4urrCmrWYrVy5EhMmTMDq1atLXJ6+lUajgZeXl8WrrlJzJjARERGVQaFWq9GlSxfEx8fLG00mE+Lj4xEREQEA6NKlC1xcXCz6nDhxAufOnZP7RERE4NChQxazdbdu3QovLy+0adNG7lN8H+Y+5n1YsxYAWLFiBWJjY7FixQpER0dX5fzVWebnAfMeQCIiIrqVCgCmTp2KsWPHIiwsDN26dcPChQtx/fp1xMbGAgC8vb0xfvx4TJ06FX5+fvDy8sJTTz2FiIgI3HXXXQCAvn37ok2bNhg9ejTmzJmD1NRUvPbaa5g8eTI0Gg0A4IknnsDixYvx0ksv4dFHH8W2bduwevVqbNiwQS7IWrUsX74cY8eOxaJFixAeHi7fG+jq6gpvb29rnPtapVbyHkAiIiIqnQoARowYgcuXL2PGjBlITU1Fx44dsXnzZouJFO+//z4UCgWGDh2K/Px8REVF4eOPP5bblUolfv75Z0yaNAkRERFwd3fH2LFj8eabb8p9mjRpgg0bNuC5557DokWL0KhRI3z++eeIioqS+1irlqVLl6KwsBCTJ0/G5MmT5e1jx44tdXFqe8OngRAREVFZJCEEZwlUUV1dBxAA8gqMSP43C0oF0LmxLyRJktu4DiARETkzrgPIWcAOyzwCaDQBfBgIERERFccA6KCUCgnKm4+E432AREREVBwDoANT35wJzPsAiYiIqDgGQAfmouLj4IiIiKgkBkAHxqVgiIiIqDQMgA5MzRFAIiIiKgUDoAOTAyBHAImIiKgYBkAHpuElYCIiIioFA6ADU6uUABgAiYiIyBIDoAMzXwIuNAkYuRo0ERER3cQA6MCUCgmqm4tB5xcabVwNERER1RUMgA6OE0GIiIjoVgyADk7DAEhERES3YAB0cOYRwHwGQCIiIrqJAdDBcTFoIiIiuhUDoIMzPw6OI4BERERkxgDo4DQuXAuQiIiILDEAOjjzJJCCQhNMgmsBEhEREQOgw1MpJCgVgAAvAxMREVERBkAHJ0kSNDcfCZdfwMWgiYiIiAHQKWhdir7NeQUcASQiIiIGQKcgjwDycXBEREQEBkCnoLk5ApjPEUAiIiICA6BT0N4cAczjCCARERGBAdApaF3Ml4BNEFwKhoiIyOkxADoBF6UEhQQIwQWhiYiIiAHQKRQtBXNzJjADIBERkdNjAHQS5svAeVwLkIiIyOkxADoJcwDMNTAAEhEROTsGQCfhqr4ZADkCSERE5PQYAJ2Ea7ERQM4EJiIicm4MgE7C1UUJCUChSSAnv9DW5RAREZENMQA6CYVCkp8IkqbPs3E1REREZEsMgE7EfBk4TZ9v40qIiIjIlhgAnYh5Ikh6NkcAiYiInJldBcCPPvoIoaGh0Gq1CA8Px19//VVu/zVr1qBVq1bQarVo164dNm7caNE+btw4SJJk8erXr19tfgk2ZR4BTM3iCCAREZEzs5sAuGrVKkydOhUzZ87Evn370KFDB0RFRSE9Pb3U/n/88QdiYmIwfvx47N+/H4MHD8bgwYNx+PBhi379+vXDpUuX5NeKFSus8eXYhJtGBQC4lJULo4kzgYmIiJyV3QTABQsW4LHHHkNsbCzatGmDJUuWwM3NDV988UWp/RctWoR+/frhxRdfROvWrfHWW2+hc+fOWLx4sUU/jUYDnU4nv3x9fa3x5diEVqWASiGhwChwKSvX1uUQERGRjdhFADQYDEhKSkJkZKS8TaFQIDIyEgkJCaV+JiEhwaI/AERFRZXov2PHDvj7+6Nly5aYNGkSrl69WmYd+fn50Ov1Fi97IkkS3G+OAp7PYAAkIiJyVnYRAK9cuQKj0YiAgACL7QEBAUhNTS31M6mpqbft369fP/zf//0f4uPj8d577+G3335D//79YTSW/rSMuLg4eHt7y6/g4OBqfmXW56Epug/w/LUbNq6EiIiIbEVl6wJsaeTIkfKf27Vrh/bt2+OOO+7Ajh070Lt37xL9p02bhqlTp8rv9Xq93YVA8wjgvxkMgERERM7KLkYA69evD6VSibS0NIvtaWlp0Ol0pX5Gp9NVqj8ANG3aFPXr18epU6dKbddoNPDy8rJ42RtzALycY0Cugc8FJiIickZ2EQDVajW6dOmC+Ph4eZvJZEJ8fDwiIiJK/UxERIRFfwDYunVrmf0B4N9//8XVq1cRGBhYM4XXQS5KBep7qAEAKVev27gaIiIisgW7CIAAMHXqVHz22Wf46quvcOzYMUyaNAnXr19HbGwsAGDMmDGYNm2a3P+ZZ57B5s2bMX/+fBw/fhyzZs3C3r17MWXKFABATk4OXnzxRfz5559ISUlBfHw8Bg0ahGbNmiEqKsomX6O1NG3gDgA4fTnHxpUQERGRLdjNPYAjRozA5cuXMWPGDKSmpqJjx47YvHmzPNHj3LlzUCj+y7Pdu3fH8uXL8dprr+HVV19F8+bN8cMPP+DOO+8EACiVSiQnJ+Orr75CZmYmgoKC0LdvX7z11lvQaDQ2+RqtpVkDT/x15hpOpTMAEhEROSNJCMEVgatIr9fD29sbWVlZtXI/4LR1h2p8nwDwWnRrvLPxGIQAXn2gFTy1LrVyHCIiorqotn9+2wO7uQRMNcddo0KQtxYAOApIRETkhBgAnVQzfw8AwOnLnAhCRETkbBgAnZQ5AJ5KzwHvAiAiInIuDIBOKqSeO1QKCVm5BbiSY7B1OURERGRFDIBOykWpQEg9NwC8D5CIiMjZMAA6sTvMl4G5HiAREZFTYQB0Ys0a3JwIkp4Dk4n3ARIRETkLBkAn1tDHFW5qJfILTTibccPW5RAREZGVMAA6MYVCQkudJwDg2CW9jashIiIia2EAdHJtAotWQD92Sc/lYIiIiJwEA6CTa+bvAZVCwpUcAy7n5Nu6HCIiIrICBkAnp3VRomkDdwDAsUvZNq6GiIiIrIEBkNBK999lYCIiInJ8DIAk3wd4LuMGcvILbVwNERER1TYGQIK3mwuCvLUQAjiRylFAIiIiR8cASACA1vJsYN4HSERE5OgYAAkA0DqoKAD+nZaN/EKjjashIiKi2sQASACAIG8t6nuoUWAUOHqRl4GJiIgcGQMgAQAkSUKHRj4AgAPnM21aCxEREdUuBkCSdWzsAwA4lZ7D2cBEREQOjAGQZPU9NGjk6wqTAJL/zbR1OURERFRLGADJQsdgHwC8DExEROTIGADJQvtG3pAk4HxGLq7y2cBEREQOiQGQLHhqXdCsgQcA4CAvAxMRETkkBkAqwTwZJOnsNQghbFsMERER1TgGQCqhbZAXtC4KZFwvwKn0HFuXQ0RERDWMAZBK0KiU6NzYFwCQeCbDxtUQERFRTWMApFKFN/EDABy7pEfWjQIbV0NEREQ1SWXrAsj6pq07VKF+xy7pkZ1XiMnL9yHYz61Cn4l7qF11SiMiIiIr4AgglUnnrQUApGfnw2jiZBAiIiJHwQBIZfJxdYHWRQGjSeByNtcEJCIichQMgFQmSZLkUcBLWbkwcRSQiIjIITAAUrnqe2igUSlQYBRI5yggERGRQ2AApHIpJAmBPv+NAvJeQCIiIvvHAEi3Vd9DA61L0SjgpaxcW5dzW0IIFBpNyC808kkmREREpXD6ZWA++ugjzJ07F6mpqejQoQM+/PBDdOvWzdZl1SkKSUKwrxtOpucgNSsPDTw00LgobVJLXoERl7LykKbPQ+aNAuhzC5CVWwB9XgFyDUYUGE0oMAkUz30qhQQXpQIuKgleWhd4ubrA29UFXloV/NzVqO+hQT0PNTQq23xNRERE1ubUAXDVqlWYOnUqlixZgvDwcCxcuBBRUVE4ceIE/P39bV1eneLjVhSY9HmFSLl6Ay0CPCBJUq0e02QSuJiVi38uX8fZq9dxKSsP16qwKHWhSaDQZERuAaDPLQSulT6K6eWqQn13Dep7qlHPXYP6HhrU91DD282F4ZCIiByKJJz4Gll4eDi6du2KxYsXAwBMJhOCg4Px1FNP4ZVXXrnt5/V6Pby9vZGVlQUvL68ar6+iCzZbS67BiCMXs2ASQJP67mjgqanxYxgKTbh2wwB9bgH0eYWl3nOoVing5qKExkUBtVIBtUoBF6UCKqUEpSRBkiQoFIAECSYhbr6KAqXBaIKh0IQCown5hSbkF5gQFuqLGwZjuXW5uijh4+YCH7ebo4euLvBxdYGnVgV3zc2XWgWlonZDMRERVV9t//y2B047AmgwGJCUlIRp06bJ2xQKBSIjI5GQkFDqZ/Lz85Gf/99MWL1eX+t11iWuaiUa+rji/LVcnMu4Dg+NCq7q6o+MGQpNyLhuwLUbBuTkFaJ45FMqJHhpVfDUusBNrYSbWgmVsuK3riphGcjcS+nz+oA2uGEoxNUcAy7n5ONqjgFXcvJxNScfV3IMyC80IbfAiNysosvP5XF1UcJDo4T7zXOjUSmgUSmhdSn6r+bmf12UEpQKCSqFAkqF+c9F/5UkyJewBSDfxyhE0csoBIymomBrNFn+2Rx2jSYBk0lY9DWZij5763ajCaVsK7qMbv5eFP89sfivjALFaiulvSylDR5Lt2kv6lOy4XYD0WWNVEtye1nHKn9ft/saiJyJSqlATLfGti6DKsFpA+CVK1dgNBoREBBgsT0gIADHjx8v9TNxcXF44403rFFe0fHq4GPVTCaB/+06g3+uXEcDTw2evO8OaKtwP6DJJHA8NRt/nbmKv9NzoPPWymsONvZzQ5sgL9zRwB1B3q5QWGFUzU2tgpufqtRH3uUVGJGVW4DMGwU3/2tAZm7R/Yc5+YW4YTAiJ78QQqAoKBYYcTnHUOs1ExHVFRoV55TaG6cNgFUxbdo0TJ06VX6v1+sRHBxsw4qsT6GQEBPeGB9uO4nL2flY9kcKYu8OrfA9ctl5BfjznwzsPZtRdD/eTaH13NCuoTfaBnnD282ltsqvEq2LEloXJQK8tGX2EULghsGI6/mFyMkvxPV8I/ILjcgrMMn/zSswFl12LjSi0ChQaBIwmkw3/1v0Kix2yds8wmQe9ZIkQCEBSkmCQiH999/if5aKvkcK6ZbtCpTcJv034qi8ZX8KqWj7f6NkUokRs+J1FVf0ubJDe/FRw4r6b0S09A+Vtq/SRi9Lay99P1U5zu1HI4kclYJ/+e2O0wbA+vXrQ6lUIi0tzWJ7WloadDpdqZ/RaDTQaGr+vjd746FRYVz3UHy28wzOXr2Bz38/g0fCQ8oNbhnXDfj95GXsTbkmhxwPjRJdQnwRFuqH+h72fV4lSZLvBeT0ISIiquucdsxWrVajS5cuiI+Pl7eZTCbEx8cjIiLChpXZh0BvV8TeHQo3tRL/XsvFB9tOIuH0VRQYTRb9LmTmYvWe85i/5QT+/CcDhSaBxn5uiOkWjJf7tUK/OwPtPvwRERHZG6cdAQSAqVOnYuzYsQgLC0O3bt2wcOFCXL9+HbGxsbYuzS4E+7lhcq9m+PbPs7iYlYcfD17EL0dSEeznBrVSwuXsfIt74VoEeKBniwZoUt+91peQISIiorI5dQAcMWIELl++jBkzZiA1NRUdO3bE5s2bS0wMobL5uavxZK9mSDxzFb/9fRn63EKcSs+R21UKCW2DvNCjRQM09HG1YaVERERk5tTrAFYX1xGyJITAv9dycTknH/kFJvi5qxHs5wo3tVP/nkFERHUMf347+Qgg1SxJkhDs51bqUipERERUdzjtJBAiIiIiZ8UASERERORkGACJiIiInAwDIBEREZGTYQAkIiIicjIMgEREREROhgGQiIiIyMkwABIRERE5GQZAIiIiIifDAEhERETkZBgAiYiIiJwMAyARERGRk2EAJCIiInIyKlsXYM+EEAAAvV5v40qIiIioosw/t80/x50RA2A1ZGdnAwCCg4NtXAkRERFVVnZ2Nry9vW1dhk1IwpnjbzWZTCZcvHgRnp6ekCSpRvet1+sRHByM8+fPw8vLq8T7mtpvTarNfdcW1mwdrNk6WLN1sGbrqM2ahRDIzs5GUFAQFArnvBuOI4DVoFAo0KhRo1o9hpeXl8Vf/Fvf19R+a1Jt7ru2sGbrYM3WwZqtgzVbR23V7Kwjf2bOGXuJiIiInBgDIBEREZGTYQCsozQaDWbOnAmNRlPq+5rab02qzX3XFtZsHazZOlizdbBm67DHmu0JJ4EQERERORmOABIRERE5GQZAIiIiIifDAEhERETkZBgAiYiIiJwMAyARERGRk+GTQOqAK1eu4IsvvkBCQgJSU1MBADqdDt27d8e4cePQoEEDG1foWP76668S5zoiIgLdunWzcWWOheeZiKju4jIwNrZnzx5ERUXBzc0NkZGRCAgIAACkpaUhPj4eN27cwC+//IKwsDCLz50/fx4zZ87EF198UeFjXb9+HatXr8apU6cQGBiImJgY1KtXr0a/nrosPT0dQ4cOxe7du9G4cWOLc33u3Dncfffd+O677+Dv72/jSkuypzDF82w9BoMBP/zwQ6m/PA4aNAhqtdrGFZaO57n22WPNZqmpqUhMTLSoOzw8HDqdzsaVORYGQBu766670KFDByxZsgSSJFm0CSHwxBNPIDk5GQkJCRZtBw8eROfOnWE0Gsvcd5s2bbBr1y74+fnh/PnzuPfee3Ht2jW0aNECp0+fhkqlwp9//okmTZpUqXZ7+wdm2LBhuHjxIr788ku0bNnSou3EiRN49NFHERQUhDVr1tiowpLsMUzxPFvHqVOnEBUVhYsXLyI8PNyi5sTERDRq1AibNm1Cs2bNbFzpf3iercMeawaKBikef/xxrFy5EpIkwc/PDwCQkZEBIQRiYmLw6aefws3NzcaVOghBNqXVasWxY8dKbF+/fr1Yv369+Oijj4SLi4v83vx6//33hUKhKHffkiSJtLQ0IYQQo0aNEt27dxeZmZlCCCGys7NFZGSkiImJqVLdJ0+eFE2bNhVarVb07NlTDB8+XAwfPlz07NlTaLVa0axZM3Hy5Mkq7bu2eHh4iH379pXZvnfvXuHh4WHFim5v6NChIiIiQhw/frxE2/Hjx0X37t3FsGHDbFBZ2XierSMyMlIMGjRIZGVllWjLysoSgwYNEn379rVBZWXjebYOe6xZCCHGjx8vmjdvLjZv3iwKCwvl7YWFheKXX34RLVq0EBMmTLBhhY6FAdDGQkNDxVdffVViuyRJQqFQCEmSBAAhSVKJV2UCYNOmTcWWLVss2nfv3i2Cg4OrVLc9/gNTr149sWPHjjLbt2/fLurVq2fFim7PHsMUz7N1uLq6ikOHDpXZnpycLFxdXa1Y0e3xPFuHPdYshBA+Pj5i9+7dZbbv2rVL+Pj4WLEix8ZZwDb2wgsvYOLEiXjmmWfw448/IjExEYmJifD19UX//v2h1Wrx0UcfwWQyWbz27dtXof2bLyvn5eUhMDDQoq1hw4a4fPlylerevXs33n77bXh5eZVo8/LywltvvYXff/+9SvuuLSNGjMDYsWPx/fffQ6/Xy9v1ej2+//57xMbGIiYmxoYVlqTRaCxqvVV2dnade04mz7N1+Pj4ICUlpcz2lJQU+Pj4WK2eiuB5tg57rBkATCZTubcOqdVqmEwmK1bk4GydQEmIlStXivDwcKFSqSxG+Bo2bChWrVpV6mcOHDggJEkqd7+SJIl27dqJTp06CQ8PD7F27VqL9t9++000bNiwSjUHBgaKn376qcz2H3/8UQQGBlZp37UlLy9PPPHEE0KtVguFQiG0Wq3QarVCoVAItVotJk2aJPLy8mxdpoUnn3xShISEiHXr1lmMtmZlZYl169aJ0NBQMWXKFBtWWFJZ51mSJJ7nGvT6668LX19fsWDBAnHw4EGRmpoqUlNTxcGDB8WCBQuEn5+fmDlzpq3LtMDzbB32WLMQQjz88MOiU6dOpY4S79u3T3Tp0kWMGjXKBpU5JgbAOsRgMIiLFy+Kixcvim3btolNmzaV2TcnJ6fcy2xCCDFr1iyL1+bNmy3aX3jhBTFy5Mgq1Wqv/8AIUfTDZtu2bWL58uVi+fLlYtu2baVeyq4L7DG0mmVlZYn4+Hj5PMfHx/M817DZs2eLwMBA+ZYQ820jgYGB4r333rN1eSXwPFuPPdackZEh+vXrJyRJEn5+fqJVq1aiVatWws/PTygUCtG/f39x7do1W5fpMDgLmKrsvffew6JFi5CamipfahZCQKfT4dlnn8VLL71k4wodh16vR1JSksVs6y5dupR6Cb6uUqvVOHjwIFq3bm3rUspkr+f5zJkzFjVXdWa/tej1euzduxdpaWkAeJ5rkz3WfOzYMfz5558llglq1aqVjStzLAyAVG329A9Mbm4ukpKS4OfnhzZt2li05eXlYfXq1RgzZoyNqiud+R9D8z+Ax48fx6JFi5Cfn49HHnkE999/v61LtDB16tRSty9atAiPPPKIvPbkggULrFlWpRRfMzMoKAgjR450qjUza8tTTz2F4cOHo0ePHrYuxeFdunQJn3zyCXbt2oVLly5BoVCgadOmGDx4MMaNGwelUmnrEsnGGACpVlRloera9vfff6Nv3744d+4cJEnCPffcgxUrViAoKAhA0RpZQUFB5a6taG2bN2/GoEGD4OHhgRs3buD777/HmDFj0KFDB5hMJvz222/YsmVLnQqBCoUCHTp0KHGT+W+//YawsDC4u7tDkiRs27bNNgWWorbXzKwN+/btg6+vr1zT119/jSVLluDcuXMICQnBlClTMHLkSBtXaUmhUECSJNxxxx0YP348xo4daxeL+y5evBh//fUXHnjgAYwcORJff/014uLiYDKZ8NBDD+HNN9+ESlV3Hqy1d+9eREZGolmzZnB1dUVCQgIefvhhGAwG/PLLL2jTpg02b94MT09PW5dagr2tL2vXbHj5mRzYgQMHbrtMjbUNHjxYREdHi8uXL4uTJ0+K6Oho0aRJE3H27FkhhBCpqal1ruaIiAgxffp0IYQQK1asEL6+vuLVV1+V21955RXRp08fW5VXqri4ONGkSRMRHx9vsV2lUokjR47YqKry1eaambWlffv2YuvWrUIIIT777DPh6uoqnn76afHJJ5+IZ599Vnh4eIj//e9/Nq7SkiRJ4tdffxXPPPOMqF+/vnBxcREPPvig+Omnn4TRaLR1eaV66623hKenpxg6dKjQ6XRi9uzZol69euLtt98W7777rmjQoIGYMWOGrcu0cPfdd4tZs2bJ77/++msRHh4uhCi6z65jx47i6aeftlV5ZbLH9WXtGQMgVcmtC1NXZaFqa/P39xfJycnye5PJJJ544gnRuHFjcfr06ToZAL28vOR/8IxGo1CpVBYz5A4dOiQCAgJsVV6Z/vrrL9GiRQvx/PPPC4PBIISwnwBY02tm1hZXV1eRkpIihBCiU6dOYunSpRbt3377rWjTpo0tSitT8fNsMBjEqlWrRFRUlFAqlSIoKEi8+uqrde4H/B133CG+++47IUTRL7ZKpVJ88803cvu6detEs2bNbFVeqVxdXcXp06fl90ajUbi4uIjU1FQhhBBbtmwRQUFBtiqvTPa4vqw9YwCkKim+UHVZr7oWpjw9PcXRo0dLbJ88ebJo1KiR2LlzZ52r2cvLS5w6dUp+7+HhYfEPe0pKitBqtbYo7bays7PFmDFjRPv27cWhQ4eEi4tLnQ6A6enpQgghgoKCSiyiWxfPc7169cTevXuFEEW/3Bw4cMCi/dSpU3Vusd/iAbC4s2fPipkzZ4qQkJA69/+gq6urfJVACCFcXFzE4cOH5fcpKSnCzc3NFqWVKSQkROzatUt+f/HiRSFJkrhx44YQQogzZ87Uub/PQtjvAtb2igtBU5UEBgZi3bp1JRaoruxC1dbUqlUr7N27t8T2xYsXY9CgQXjwwQdtUFX5QkNDcfLkSfl9QkICGjduLL8/d+5ciQW+6woPDw989dVXmDZtGiIjI+vUvZWl6d27Nzp37gy9Xo8TJ05YtJ09e7bOTQLp378/PvnkEwBAz549sXbtWov21atX17lnvZalcePGmDVrFs6cOYPNmzfbuhwLOp0OR48eBQCcPHkSRqNRfg8AR44cqVPPLgaAwYMH44knnsDmzZuxfft2jBo1Cj179oSrqyuAomdyN2zY0MZVlmSvC1jbq7pz1yrZlS5duiApKQmDBg0qtV2SJIg6Nr9oyJAhWLFiBUaPHl2ibfHixTCZTFiyZIkNKivbpEmTLILTnXfeadG+adOmOjUBpDQjR47EPffcg6SkJISEhNi6nFLNnDnT4r2Hh4fF+59++qnOzVx97733cPfdd6Nnz54ICwvD/PnzsWPHDrRu3RonTpzAn3/+ie+//97WZVoICQkpd/apJEno06ePFSu6vVGjRmHMmDEYNGgQ4uPj8dJLL+GFF17A1atXIUkS3nnnHQwbNszWZVp4++23cenSJQwcOBBGoxERERH45ptv5HZJkhAXF2fDCks3YcIEjBkzBq+//jp69+6NgIAAAEUT9OLj4/H222/jqaeesnGVjoOzgKlKfv/9d1y/fh39+vUrtf369evYu3cvevbsaeXKiJxHZmYmZs+ejZ9++gn//PMPTCYTAgMDcffdd+O5555DWFiYrUu0eyaTCbNnz0ZCQgK6d++OV155BatWrcJLL72EGzduYODAgVi8eDHc3d1tXWoJeXl5KCwsLPELTV3G9WWthwGQiIiI6hR7Wl/WXjEAEhERUZ1XF9eXtWcMgERERFTnHTx4EJ07d67zE8rsBSeBEBERkc39+OOP5bb/888/VqrEOXAEkIiIiGzO/KjA8mKJJEkcAawhXAeQiIiIbM4e15e1ZwyAREREZHPm9WXLUhfXl7VnvAeQiIiIbO7FF1/E9evXy2xv1qwZtm/fbsWKHBvvASQiIiJyMrwETERERORkGACJiIiInAwDIBEREZGTYQAkIiIicjIMgERUIyRJwg8//GDrMmqFwWBAs2bN8McffwAAUlJSIEkSDhw4YPVaxo0bh8GDB8vv77vvPjz77LM1su+jR4+iUaNG5c7EJCLHwABIRGUaN24cJEmCJElwcXFBQEAA+vTpgy+++AImk8mi76VLl9C/f/8K7dfewuKSJUvQpEkTdO/eHQAQHByMS5cu4c4777R6LYsWLcKyZctqZd9t2rTBXXfdhQULFtTK/omo7mAAJKJy9evXD5cuXUJKSgo2bdqEXr164ZlnnsGAAQNQWFgo99PpdNBoNDastHYIIbB48WKMHz9e3qZUKqHT6aBSWX8pVW9vb/j4+NTa/mNjY/HJJ59YfG+JyPEwABJRuTQaDXQ6HRo2bIjOnTvj1Vdfxfr167Fp0yaLkajio3oGgwFTpkxBYGAgtFotQkJCEBcXBwAIDQ0FAAwZMgSSJMnvT58+jUGDBiEgIAAeHh7o2rUrfv31V4taQkND8e677+LRRx+Fp6cnGjdujKVLl1r0+ffffxETEwM/Pz+4u7sjLCwMiYmJcvv69evRuXNnaLVaNG3aFG+88Ua5YScpKQmnT59GdHS0vO3WS8A7duyAJEmIj49HWFgY3Nzc0L17d5w4caLM/Zr3sXr1avTo0QOurq7o2rUr/v77b+zZswdhYWHw8PBA//79cfnyZflzt14CvlV+fj5eeOEFNGzYEO7u7ggPD8eOHTvk9rNnz2LgwIHw9fWFu7s72rZti40bN8rtffr0QUZGBn777bcyj0FE9o8BkIgq7f7770eHDh2wbt26Uts/+OAD/Pjjj1i9ejVOnDiBb7/9Vg56e/bsAQB8+eWXuHTpkvw+JycHDzzwAOLj47F//37069cPAwcOxLlz5yz2PX/+fISFhWH//v148sknMWnSJDlo5eTkoGfPnrhw4QJ+/PFHHDx4EC+99JJ8ufr333/HmDFj8Mwzz+Do0aP49NNPsWzZMrzzzjtlfq2///47WrRoAU9Pz9uel+nTp2P+/PnYu3cvVCoVHn300dt+ZubMmXjttdewb98+qFQqPPzww3jppZewaNEi/P777zh16hRmzJhx2/2YTZkyBQkJCVi5ciWSk5Px//7f/0O/fv1w8uRJAMDkyZORn5+PnTt34tChQ3jvvffg4eEhf16tVqNjx474/fffK3xMIrJDgoioDGPHjhWDBg0qtW3EiBGidevW8nsA4vvvvxdCCPHUU0+J+++/X5hMplI/W7xvedq2bSs+/PBD+X1ISIh45JFH5Pcmk0n4+/uLTz75RAghxKeffio8PT3F1atXS91f7969xbvvvmux7euvvxaBgYFl1vDMM8+I+++/32LbmTNnBACxf/9+IYQQ27dvFwDEr7/+KvfZsGGDACByc3NL3a95H59//rm8bcWKFQKAiI+Pl7fFxcWJli1byu9v/Z707NlTPPPMM0IIIc6ePSuUSqW4cOFCia972rRpQggh2rVrJ2bNmlXm1yuEEEOGDBHjxo0rtw8R2Tc+C5iIqkQIAUmSSm0bN24c+vTpg5YtW6Jfv34YMGAA+vbtW+7+cnJyMGvWLGzYsAGXLl1CYWEhcnNzS4wAtm/fXv6zJEnQ6XRIT08HABw4cACdOnWCn59fqcc4ePAgdu/ebTHiZzQakZeXhxs3bsDNza3EZ3Jzc6HVasutvbTaAgMDAQDp6elo3LhxhT4TEBAAAGjXrp3FNvPXdzuHDh2C0WhEixYtLLbn5+ejXr16AICnn34akyZNwpYtWxAZGYmhQ4da1AAArq6uuHHjRoWOSUT2iQGQiKrk2LFjaNKkSaltnTt3xpkzZ7Bp0yb8+uuvGD58OCIjI7F27doy9/fCCy9g69atmDdvHpo1awZXV1cMGzYMBoPBop+Li4vFe0mS5Eu8rq6u5dack5ODN954Aw899FCJtrJCXv369XHo0KFy91tabeZwfOts6Yp85tZtt9uHWU5ODpRKJZKSkqBUKi3azJd5J0yYgKioKGzYsAFbtmxBXFwc5s+fj6eeekrum5GRgTvuuKNCxyQi+8R7AImo0rZt24ZDhw5h6NChZfbx8vLCiBEj8Nlnn2HVqlX47rvvkJGRAaAo4BiNRov+u3fvxrhx4zBkyBC0a9cOOp0OKSkplaqrffv2OHDggHycW3Xu3BknTpxAs2bNSrwUitL/OezUqROOHz8OIUSlarGFTp06wWg0Ij09vcTXp9Pp5H7BwcF44oknsG7dOjz//PP47LPPLPZz+PBhdOrUydrlE5EVcQSQiMqVn5+P1NRUGI1GpKWlYfPmzYiLi8OAAQMwZsyYUj+zYMECBAYGolOnTlAoFFizZg10Op28fEloaCji4+Nx9913Q6PRwNfXF82bN8e6deswcOBASJKE119/vcIjX2YxMTF49913MXjwYMTFxSEwMBD79+9HUFAQIiIiMGPGDAwYMACNGzfGsGHDoFAocPDgQRw+fBhvv/12qfvs1asXcnJycOTIEZus+1cZLVq0wKhRozBmzBjMnz8fnTp1wuXLlxEfH4/27dsjOjoazz77LPr3748WLVrg2rVr2L59O1q3bi3vIyUlBRcuXEBkZKQNvxIiqm0cASSicm3evBmBgYEIDQ1Fv379sH37dnzwwQdYv359icuMZp6enpgzZw7CwsLQtWtXpKSkYOPGjfIo2/z587F161YEBwfLI00LFiyAr68vunfvjoEDByIqKgqdO3euVK1qtRpbtmyBv78/HnjgAbRr1w6zZ8+W64yKisLPP/+MLVu2oGvXrrjrrrvw/vvvIyQkpMx91qtXD0OGDMG3335bqVps5csvv8SYMWPw/PPPo2XLlhg8eDD27Nkj34doNBoxefJktG7dGv369UOLFi3w8ccfy59fsWIF+vbtW+45ISL79/8BYw/wngw4qqsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, trips within 0 to 1 miles have the highest distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, on plotting the trip fare vs trip distance for some part of the data, we can see that fare seems to be approximately \n",
    "fixed for trip distances > 50 miles. Assuming airports pickup or dropoff to have fixed prices, we can remove the \n",
    "trips related to airport pickup and dropoff and plot the distribution of Fare Amount vs Trip distribution again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](farevsdist.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On plotting the same graph without airport trip data, we see that the cluster for distances > 50 miles still exists. Upon deeper research (visualizing the trips through a map) we can see that these longer trips are more distributed across Lower Manhattan. So, we decide to look through the training data for each of the boroughs that NY is made up of: Bronx, Brooklyn, Queens, Manhattan, Staten Island. For this, we create two new variables the represent the borough from which the pickup or dropoff happened"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](farevsdistmod.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Borough-wise Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3145d1c58f984ddba18b310f677cdda7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nyc_boroughs = {\n",
    "    'manhattan': {\n",
    "        'min_lng': -74.0479,\n",
    "        'min_lat': 40.6829,\n",
    "        'max_lng': -73.9067,\n",
    "        'max_lat': 40.8820\n",
    "    },\n",
    "    'queens': {\n",
    "        'min_lng': -73.9630,\n",
    "        'min_lat': 40.5431,\n",
    "        'max_lng': -73.7004,\n",
    "        'max_lat': 40.8007\n",
    "    },\n",
    "    'brooklyn': {\n",
    "        'min_lng': -74.0421,\n",
    "        'min_lat': 40.5707,\n",
    "        'max_lng': -73.8334,\n",
    "        'max_lat': 40.7395\n",
    "    },\n",
    "    'bronx': {\n",
    "        'min_lng': -73.9339,\n",
    "        'min_lat': 40.7855,\n",
    "        'max_lng': -73.7654,\n",
    "        'max_lat': 40.9176\n",
    "    },\n",
    "    'staten_island': {\n",
    "        'min_lng': -74.2558,\n",
    "        'min_lat': 40.4960,\n",
    "        'max_lng': -74.0522,\n",
    "        'max_lat': 40.6490\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29a3f3c3a7ec462ba7955a8a01e15208",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x7fc549cb4c20>"
     ]
    }
   ],
   "source": [
    "def getBorough(lat,lng):\n",
    "    \"\"\"\n",
    "    Get the borough based on latitude and longitude\n",
    "    Args:\n",
    "        lat: Latitude of the place\n",
    "        lng: Longitude of the place\n",
    "    Returns:\n",
    "        A string representing the name of the borough\n",
    "    \"\"\"\n",
    "    locations = nyc_boroughs.keys()\n",
    "    for location in locations:\n",
    "        if (lat >= nyc_boroughs[location]['min_lat'] and \n",
    "            lat <= nyc_boroughs[location]['max_lat'] and \n",
    "            lng >= nyc_boroughs[location]['min_lng'] and \n",
    "            lng <= nyc_boroughs[location]['max_lng']):\n",
    "            return location\n",
    "    return 'others'\n",
    "\n",
    "boroughUDF = udf(lambda a, b: getBorough(a,b))\n",
    "spark.udf.register(\"boroughUDF\", boroughUDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "056fbd1e064d486db2e0bdbc05e66065",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+--------------+---------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|is_pickup_LaGuardia|is_dropoff_LaGuardia|     trip_distance|pickup_borough|dropoff_borough|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+--------------+---------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|              false|               false|0.6404870231407276|        queens|         queens|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|              false|               false| 5.250669652366417|     manhattan|      manhattan|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|              false|               false|0.8634109565058572|     manhattan|      manhattan|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|              false|               false|1.7393859082192853|     manhattan|      manhattan|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|              false|               false|1.2422184522938338|     manhattan|      manhattan|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+--------------+---------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data_filtered = train_data_filtered.withColumn(\"pickup_borough\", boroughUDF(F.col(\"pickup_latitude\"), \\\n",
    "                                                     F.col(\"pickup_longitude\")))\n",
    "train_data_filtered = train_data_filtered.withColumn(\"dropoff_borough\", boroughUDF(F.col(\"dropoff_latitude\"), \\\n",
    "                                                     F.col(\"dropoff_longitude\")))\n",
    "\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integer encode the categorical borough variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10936a2c09c8416fb7e44424a2f172b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x7f2d4817ef80>"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import IntegerType\n",
    "def getBoroughMapping(borough):\n",
    "    if borough == \"manhattan\":\n",
    "        return 1\n",
    "    elif borough == \"queens\":\n",
    "        return 2\n",
    "    elif borough == \"brooklyn\":\n",
    "        return 3\n",
    "    elif borough == \"bronx\":\n",
    "        return 4\n",
    "    elif borough == \"staten_island\":\n",
    "        return 5\n",
    "    else:\n",
    "        return 6\n",
    "\n",
    "bmapUDF = udf(lambda s: getBoroughMapping(s), IntegerType())\n",
    "spark.udf.register(\"bmapUDF\", bmapUDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e9000dbb72c49ee8d562dcccb21201e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.title('Distribution of Pickups vs Boroughs')\n",
    "plt.xlabel('Boroughs')\n",
    "\n",
    "pickup_boroughs_numeric = train_data_filtered.withColumn(\"pickup_borough\", bmapUDF(F.col(\"pickup_borough\")))\n",
    "\n",
    "hist(axes, pickup_boroughs_numeric.select(F.col(\"pickup_borough\")))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4f8702723834c65adb886d9b2b53a5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.title('Distribution of Drops vs Boroughs')\n",
    "plt.xlabel('Boroughs')\n",
    "\n",
    "dropoff_boroughs_numeric = train_data_filtered.withColumn(\"dropoff_borough\", bmapUDF(F.col(\"dropoff_borough\")))\n",
    "\n",
    "hist(axes, dropoff_boroughs_numeric.select(F.col(\"dropoff_borough\")))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5+vqqc+fOFbKdEidOnNDgwYP122+/6e9///tFQ3FwcLDatWunV155xem47dixQ+vWrVP37t1L9XnhhRec5hcsWCBJjmNWnvPoQvr376/U1FQtWbJER44ccfrzr/THk+Znc3Nzc7zgvDzjl+W9996TJEVGRkqS2rdvr0aNGmnx4sVOY3744YfatWuXevTocdExy3uO+fv7q0GDBqWuPi5cuNBpvlatWoqJidHq1aud7tfbu3fvZV+tO3PmjHJzc53aGjVqpJCQkMs+loAr8RoYGOvDDz/U7t27dfr0aeXk5Gjjxo36+OOP1bRpU7377rsXfMntk08+qc2bN6tHjx5q2rSpDh06pIULF6px48bq1KmTpD/CWN26dbV48WL5+fnJx8dHHTp0KHVvWXnVr19fnTp10rBhw5STk6OkpCQ1b97c6VU1I0aM0MqVK9WtWzfdd999+uGHH/Taa6853dx+qbX16tVLd955p/7+97/rxx9/VGRkpNatW6d33nlHEyZMKDX25Ro1apT++c9/aujQoUpPT1dYWJhWrlypLVu2KCkp6YL3ZF7ML7/84nivY35+vnbu3KkVK1YoOztbDz/8sP7yl7+Ua5xnn31WcXFxio6O1vDhwx2vgbHb7aXeQSdJ+/fv1913361u3bpp69ateu211/TAAw84wlN5zqMLue+++/TII4/okUceUf369RUTE+O0fMSIEfrtt9/UpUsXNW7cWD/99JMWLFigdu3aXfSKpyR9//33juN2/Phxpaam6pVXXlHz5s0dP79Xu3ZtPf300xo2bJg6d+6sAQMGOF4DExYWpokTJ150O5dyjo0YMUKzZ8/WiBEj1L59e23evFnff/99qTFnzJihdevWqWPHjho9erQjYLZt2/ayfgLx2LFjaty4se69915FRkbK19dX69ev1xdffKHnnnvukscDXM6FTyADLlHyGpiSycPDwwoKCrLuuusua968eU6vGylx7mtgNmzYYPXu3dsKCQmxPDw8rJCQEGvAgAHW999/79TvnXfesVq3bu149UTJ6ys6d+5stWnTpsz6zvcamP/85z/WlClTrEaNGlne3t5Wjx49rJ9++qlU/+eee8665pprLE9PT6tjx47Wl19+WWrMC9V27mtgLMuyjh07Zk2cONEKCQmxateubbVo0cJ69tlnreLiYqf1JJX5mozzvZ7mXDk5OdawYcOsBg0aWB4eHtYNN9xQ5is/LvU1MCWftc1ms/z9/a02bdpYI0eOtNLS0kqtX/KqkWeffbbM8davX2917NjR8vb2tvz9/a1evXpZO3fudFqn5HzZuXOnde+991p+fn5WvXr1rHHjxjm9KqW859GFdOzY0ZJkjRgxotSylStXWn/+85+tRo0aWR4eHlaTJk2sv/zlL9bBgwcvOq7Oef1LrVq1rMaNG1ujRo2ycnJySq3/xhtvWDfeeKPl6elp1a9f3xo4cKD1888/O60THx9v+fj4lLm98p5jx48ft4YPH27Z7XbLz8/Puu+++6xDhw6Veg2MZf1xfG+88UbLw8PDatasmfXSSy9ZDz/8sOXl5VVqXy923hYVFVl/+9vfrMjISMvPz8/y8fGxIiMjrYULF17sUALVks2yqumd2QBQQ82YMUNPPPGEDh8+rAYNGri6HJylT58++u6770rdnwmYhnsAAQBXpRMnTjjN79mzRx988EGZP7UImIZ7AAEAV6Vrr71WQ4cO1bXXXquffvpJixYtkoeHhyZPnuzq0gCXIwACAK5K3bp103/+8x9lZ2fL09NT0dHReuqpp0q9oBswEfcAAgAAGIZ7AAEAAAxDAAQAADAMARAAAMAwPARyBYqLi5WVlSU/P78K/91XAABQOSzL0rFjxxQSEiI3NzOvhREAr0BWVpZCQ0NdXQYAALgMBw4cUOPGjV1dhksQAK9AyW+THjhwQP7+/i6uBgAAlEdeXp5CQ0Ov6DfGazoC4BUo+bOvv78/ARAAgBrG5Nu3zPzDNwAAgMEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIZxd3UBOL+wR993dQmX7MfZPVxdAgAAuAiuAAIAABjGJQFwxowZstlsTlPLli0dywsLCzV27FgFBATI19dX/fr1U05OzgXHtCxL06ZNU3BwsLy9vRUTE6M9e/Y4rVOyrdTUVKf2oqIiBQQEyGazKSUlpcL2EwAAoDpy2RXANm3a6ODBg47p008/dSybOHGi3nvvPa1YsUKbNm1SVlaW7rnnnguO98wzz2j+/PlavHix0tLS5OPjo9jYWBUWFjqtFxoaqqVLlzq1rVq1Sr6+vhW3cwAAANWYywKgu7u7goKCHFODBg0kSbm5uXr55Zc1Z84cdenSRVFRUVq6dKk+++yzUlfuSliWpaSkJD3++OPq3bu3IiIitGzZMmVlZWn16tVO68bHx2v58uU6ceKEo23JkiWKj4+vtH0FAACoTlwWAPfs2aOQkBBde+21GjhwoDIzMyVJ6enpOnXqlGJiYhzrtmzZUk2aNNHWrVvLHGv//v3Kzs526mO329WhQ4dSfaKiohQWFqa33npLkpSZmanNmzdr8ODBF625qKhIeXl5ThMAAEBN45IA2KFDByUnJ2vt2rVatGiR9u/fr9tuu03Hjh1Tdna2PDw8VLduXac+gYGBys7OLnO8kvbAwMBy9UlISNCSJUskScnJyerevbsaNmx40boTExNlt9sdU2hoaHl2FwAAoFpxSQCMi4vTf/3XfykiIkKxsbH64IMPdPToUb355ptVsv1BgwZp69at2rdvn5KTk5WQkFCuflOmTFFubq5jOnDgQCVXCgAAUPGqxWtg6tatq+uuu0579+5VUFCQTp48qaNHjzqtk5OTo6CgoDL7l7Sf+6Tw+foEBASoZ8+eGj58uAoLCxUXF1euOj09PeXv7+80AQAA1DTVIgDm5+frhx9+UHBwsKKiolS7dm1t2LDBsTwjI0OZmZmKjo4us394eLiCgoKc+uTl5SktLe28fRISEpSSkqIhQ4aoVq1aFbtDAAAA1ZhLfgnkkUceUa9evdS0aVNlZWVp+vTpqlWrlgYMGCC73a7hw4dr0qRJql+/vvz9/TV+/HhFR0fr1ltvdYzRsmVLJSYmqm/fvrLZbJowYYJmzpypFi1aKDw8XFOnTlVISIj69OlTZg3dunXT4cOHuYoHAACM45IA+PPPP2vAgAH69ddf1bBhQ3Xq1EmpqamOBzHmzp0rNzc39evXT0VFRYqNjdXChQudxsjIyFBubq5jfvLkySooKNCoUaN09OhRderUSWvXrpWXl1eZNdhsNserZwAAAExisyzLcnURNVVeXp7sdrtyc3Mr5UoivwUMAEDFq+zv75qgWtwDCAAAgKpDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMIzLA+Ds2bNls9k0YcIER1thYaHGjh2rgIAA+fr6ql+/fsrJybngOJZladq0aQoODpa3t7diYmK0Z88ep3VsNptsNptSU1Od2ouKihQQECCbzaaUlJSK2jUAAIBqyaUB8IsvvtA///lPRUREOLVPnDhR7733nlasWKFNmzYpKytL99xzzwXHeuaZZzR//nwtXrxYaWlp8vHxUWxsrAoLC53WCw0N1dKlS53aVq1aJV9f34rZKQAAgGrOZQEwPz9fAwcO1Isvvqh69eo52nNzc/Xyyy9rzpw56tKli6KiorR06VJ99tlnpa7clbAsS0lJSXr88cfVu3dvRUREaNmyZcrKytLq1aud1o2Pj9fy5ct14sQJR9uSJUsUHx9fKfsJAABQ3bgsAI4dO1Y9evRQTEyMU3t6erpOnTrl1N6yZUs1adJEW7duLXOs/fv3Kzs726mP3W5Xhw4dSvWJiopSWFiY3nrrLUlSZmamNm/erMGDB1+05qKiIuXl5TlNAAAANY1LAuDy5cu1bds2JSYmllqWnZ0tDw8P1a1b16k9MDBQ2dnZZY5X0h4YGFiuPgkJCVqyZIkkKTk5Wd27d1fDhg0vWndiYqLsdrtjCg0NvWgfAACA6qbKA+CBAwf00EMP6d///re8vLyqevOSpEGDBmnr1q3at2+fkpOTlZCQUK5+U6ZMUW5urmM6cOBAJVcKAABQ8ao8AKanp+vQoUO66aab5O7uLnd3d23atEnz58+Xu7u7AgMDdfLkSR09etSpX05OjoKCgsocs6T93CeFz9cnICBAPXv21PDhw1VYWKi4uLhy1e7p6Sl/f3+nCQAAoKap8gDYtWtXffvtt9q+fbtjat++vQYOHOj459q1a2vDhg2OPhkZGcrMzFR0dHSZY4aHhysoKMipT15entLS0s7bJyEhQSkpKRoyZIhq1apVsTsJAABQjblX9Qb9/PzUtm1bpzYfHx8FBAQ42ocPH65Jkyapfv368vf31/jx4xUdHa1bb73V0adly5ZKTExU3759He8RnDlzplq0aKHw8HBNnTpVISEh6tOnT5l1dOvWTYcPH+YqHgAAME6VB8DymDt3rtzc3NSvXz8VFRUpNjZWCxcudFonIyNDubm5jvnJkyeroKBAo0aN0tGjR9WpUyetXbv2vPcZ2mw2NWjQoFL3AwAAoDqyWZZlubqImiovL092u125ubmVciUx7NH3K3zMyvbj7B6uLgEAgAuq7O/vmsDlPwUHAACAqkUABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwjEsC4KJFixQRESF/f3/5+/srOjpaH374oWN5YWGhxo4dq4CAAPn6+qpfv37Kycm54JiWZWnatGkKDg6Wt7e3YmJitGfPHqd1bDabbDabUlNTndqLiooUEBAgm82mlJSUCttPAACA6sglAbBx48aaPXu20tPT9eWXX6pLly7q3bu3vvvuO0nSxIkT9d5772nFihXatGmTsrKydM8991xwzGeeeUbz58/X4sWLlZaWJh8fH8XGxqqwsNBpvdDQUC1dutSpbdWqVfL19a3YnQQAAKimXBIAe/Xqpe7du6tFixa67rrrNGvWLPn6+io1NVW5ubl6+eWXNWfOHHXp0kVRUVFaunSpPvvss1JX7kpYlqWkpCQ9/vjj6t27tyIiIrRs2TJlZWVp9erVTuvGx8dr+fLlOnHihKNtyZIlio+Pr8xdBgAAqDZcfg/gmTNntHz5chUUFCg6Olrp6ek6deqUYmJiHOu0bNlSTZo00datW8scY//+/crOznbqY7fb1aFDh1J9oqKiFBYWprfeekuSlJmZqc2bN2vw4MGVsHcAAADVj8sC4LfffitfX195enrqwQcf1KpVq9S6dWtlZ2fLw8NDdevWdVo/MDBQ2dnZZY5V0h4YGFiuPgkJCVqyZIkkKTk5Wd27d1fDhg0vWnNRUZHy8vKcJgAAgJrGZQHw+uuv1/bt25WWlqbRo0crPj5eO3furJJtDxo0SFu3btW+ffuUnJyshISEcvVLTEyU3W53TKGhoZVcKQAAQMVzWQD08PBQ8+bNFRUVpcTEREVGRmrevHkKCgrSyZMndfToUaf1c3JyFBQUVOZYJe3nPil8vj4BAQHq2bOnhg8frsLCQsXFxZWr5ilTpig3N9cxHThwoFz9AAAAqhOX3wNYori4WEVFRYqKilLt2rW1YcMGx7KMjAxlZmYqOjq6zL7h4eEKCgpy6pOXl6e0tLTz9klISFBKSoqGDBmiWrVqlatGT09Px6trSiYAAICaxt0VG50yZYri4uLUpEkTHTt2TK+//rpSUlL00UcfyW63a/jw4Zo0aZLq168vf39/jR8/XtHR0br11lsdY7Rs2VKJiYnq27evbDabJkyYoJkzZ6pFixYKDw/X1KlTFRISoj59+pRZQ7du3XT48GFCHAAAMI5LAuChQ4c0ZMgQHTx4UHa7XREREfroo4901113SZLmzp0rNzc39evXT0VFRYqNjdXChQudxsjIyFBubq5jfvLkySooKNCoUaN09OhRderUSWvXrpWXl1eZNdhsNjVo0KDydhIAAKCaslmWZbm6iJoqLy9Pdrtdubm5lXIlMezR9yt8zMr24+weri4BAIALquzv75qg2twDCAAAgKpBAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMIxLAmBiYqJuvvlm+fn5qVGjRurTp48yMjKc1iksLNTYsWMVEBAgX19f9evXTzk5ORcc17IsTZs2TcHBwfL29lZMTIz27NnjtI7NZpPNZlNqaqpTe1FRkQICAmSz2ZSSklIh+wkAAFAduSQAbtq0SWPHjlVqaqo+/vhjnTp1Sn/+859VUFDgWGfixIl67733tGLFCm3atElZWVm65557LjjuM888o/nz52vx4sVKS0uTj4+PYmNjVVhY6LReaGioli5d6tS2atUq+fr6VtxOAgAAVFMuCYBr167V0KFD1aZNG0VGRio5OVmZmZlKT0+XJOXm5urll1/WnDlz1KVLF0VFRWnp0qX67LPPSl25K2FZlpKSkvT444+rd+/eioiI0LJly5SVlaXVq1c7rRsfH6/ly5frxIkTjrYlS5YoPj6+0vYZAACguqgW9wDm5uZKkurXry9JSk9P16lTpxQTE+NYp2XLlmrSpIm2bt1a5hj79+9Xdna2Ux+73a4OHTqU6hMVFaWwsDC99dZbkqTMzExt3rxZgwcPrtD9AgAAqI5cHgCLi4s1YcIEdezYUW3btpUkZWdny8PDQ3Xr1nVaNzAwUNnZ2WWOU9IeGBhYrj4JCQlasmSJJCk5OVndu3dXw4YNL1hrUVGR8vLynCYAAICaxuUBcOzYsdqxY4eWL19epdsdNGiQtm7dqn379ik5OVkJCQkX7ZOYmCi73e6YQkNDq6BSAACAiuXSADhu3DitWbNGn3zyiRo3buxoDwoK0smTJ3X06FGn9XNychQUFFTmWCXt5z4pfL4+AQEB6tmzp4YPH67CwkLFxcVdtN4pU6YoNzfXMR04cOCifQAAAKoblwRAy7I0btw4rVq1Shs3blR4eLjT8qioKNWuXVsbNmxwtGVkZCgzM1PR0dFljhkeHq6goCCnPnl5eUpLSztvn4SEBKWkpGjIkCGqVavWRev29PSUv7+/0wQAAFDTuLtio2PHjtXrr7+ud955R35+fo579Ox2u7y9vWW32zV8+HBNmjRJ9evXl7+/v8aPH6/o6GjdeuutjnFatmypxMRE9e3bVzabTRMmTNDMmTPVokULhYeHa+rUqQoJCVGfPn3KrKNbt246fPgwQQ4AABjFJQFw0aJFkqQ77rjDqX3p0qUaOnSoJGnu3Llyc3NTv379VFRUpNjYWC1cuNBp/YyMDMcTxJI0efJkFRQUaNSoUTp69Kg6deqktWvXysvLq8w6bDabGjRoUHE7BgAAUAPYLMuyXF1ETZWXlye73a7c3NxKuYoY9uj7FT5mZftxdg9XlwAAwAVV9vd3TeDyp4ABAABQtQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYxiUBcPPmzerVq5dCQkJks9m0evVqp+WWZWnatGkKDg6Wt7e3YmJitGfPnouO+8ILLygsLExeXl7q0KGDPv/8c6flYWFhstlsWr58eam+bdq0kc1mU3Jy8pXsGgAAQLXnkgBYUFCgyMhIvfDCC2Uuf+aZZzR//nwtXrxYaWlp8vHxUWxsrAoLC8875htvvKFJkyZp+vTp2rZtmyIjIxUbG6tDhw45rRcaGqqlS5c6taWmpio7O1s+Pj5XvnMAAADVnEsCYFxcnGbOnKm+ffuWWmZZlpKSkvT444+rd+/eioiI0LJly5SVlVXqSuHZ5syZo5EjR2rYsGFq3bq1Fi9erDp16mjJkiVO6w0cOFCbNm3SgQMHHG1LlizRwIED5e7uXmH7CAAAUF1Vu3sA9+/fr+zsbMXExDja7Ha7OnTooK1bt5bZ5+TJk0pPT3fq4+bmppiYmFJ9AgMDFRsbq1deeUWSdPz4cb3xxhtKSEi4aG1FRUXKy8tzmgAAAGqaahcAs7OzJf0R1M4WGBjoWHauI0eO6MyZM+Xuk5CQoOTkZFmWpZUrV6pZs2Zq167dRWtLTEyU3W53TKGhoeXcKwAAgOqj2gXAqtCjRw/l5+dr8+bNWrJkSbmu/knSlClTlJub65jO/jMyAABATVHtbnoLCgqSJOXk5Cg4ONjRnpOTc96rdA0aNFCtWrWUk5Pj1J6Tk+MY72zu7u4aPHiwpk+frrS0NK1atapctXl6esrT07OcewIAAFA9VbsrgOHh4QoKCtKGDRscbXl5eUpLS1N0dHSZfTw8PBQVFeXUp7i4WBs2bDhvn4SEBG3atEm9e/dWvXr1KnYnAAAAqjGXXAHMz8/X3r17HfP79+/X9u3bVb9+fTVp0kQTJkzQzJkz1aJFC4WHh2vq1KkKCQlRnz59HH26du2qvn37aty4cZKkSZMmKT4+Xu3bt9ctt9yipKQkFRQUaNiwYWXW0KpVKx05ckR16tSp1H0FAACoblwSAL/88kvdeeedjvlJkyZJkuLj45WcnKzJkyeroKBAo0aN0tGjR9WpUyetXbtWXl5ejj4//PCDjhw54pjv37+/Dh8+rGnTpik7O1vt2rXT2rVrSz0YcraAgIBK2DsAAIDqzWZZluXqImqqvLw82e125ebmyt/fv8LHD3v0/Qofs7L9OLuHq0sAAOCCKvv7uyaodvcAAgAAoHIRAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAAADDEAABAAAMQwAEAAAwDAEQAADAMARAAAAAwxAAAQAADEMABAAAMIy7qwsAXC3s0fddXcIl+3F2D1eXAACowbgCCAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYYz/KbgXXnhBzz77rLKzsxUZGakFCxbolltucXVZwFWHn9wDgOrD6CuAb7zxhiZNmqTp06dr27ZtioyMVGxsrA4dOuTq0gAAACqN0QFwzpw5GjlypIYNG6bWrVtr8eLFqlOnjpYsWeLq0gAAACqNsX8CPnnypNLT0zVlyhRHm5ubm2JiYrR169Yy+xQVFamoqMgxn5ubK0nKy8urlBqLi45XyriVqbKORWXiOFcNjnPVaDv9I1eXcMl2PBHr6hKMwLnx/0r+3bYsq1LGrwmMDYBHjhzRmTNnFBgY6NQeGBio3bt3l9knMTFRTzzxRKn20NDQSqmxJrInuboCM3CcqwbHuWpwnHE+lX1uHDt2THa7vXI3Uk0ZGwAvx5QpUzRp0iTHfHFxsX777TcFBATIZrNV6Lby8vIUGhqqAwcOyN/fv0LHxv/jOFcNjnPV4DhXDY5z1ajM42xZlo4dO6aQkJAKHbcmMTYANmjQQLVq1VJOTo5Te05OjoKCgsrs4+npKU9PT6e2unXrVlaJkiR/f3/+A1MFOM5Vg+NcNTjOVYPjXDUq6zibeuWvhLEPgXh4eCgqKkobNmxwtBUXF2vDhg2Kjo52YWUAAACVy9grgJI0adIkxcfHq3379rrllluUlJSkgoICDRs2zNWlAQAAVBqjA2D//v11+PBhTZs2TdnZ2WrXrp3Wrl1b6sEQV/D09NT06dNL/ckZFYvjXDU4zlWD41w1OM5Vg+NcuWyWyc9AAwAAGMjYewABAABMRQAEAAAwDAEQAADAMARAAAAAwxAAAeAqwnN9AMqDAAgAVxFPT0/t2rXL1WUAqOaMfg9gTXHgwAFNnz5dS5YscXUpNd6JEyeUnp6u+vXrq3Xr1k7LCgsL9eabb2rIkCEuqu7qsWvXLqWmpio6OlotW7bU7t27NW/ePBUVFWnQoEHq0qWLq0us8c7+XfKznTlzRrNnz1ZAQIAkac6cOVVZ1lWvoKBAb775pvbu3avg4GANGDDAcaxx+bZt26Z69eopPDxckvTqq69q8eLFyszMVNOmTTVu3Djdf//9Lq7y6sJ7AGuAr7/+WjfddJPOnDnj6lJqtO+//15//vOflZmZKZvNpk6dOmn58uUKDg6W9MfvQIeEhHCcr9DatWvVu3dv+fr66vjx41q1apWGDBmiyMhIFRcXa9OmTVq3bh0h8Aq5ubkpMjKy1O+Rb9q0Se3bt5ePj49sNps2btzomgKvEq1bt9ann36q+vXr68CBA7r99tv1+++/67rrrtMPP/wgd3d3paamOoILLk9kZKSee+45xcTE6KWXXtJf//pXjRw5Uq1atVJGRoZeeuklzZs3TwkJCa4u9apBAKwG3n333Qsu37dvnx5++GGCyRXq27evTp06peTkZB09elQTJkzQzp07lZKSoiZNmhAAK8if/vQndenSRTNnztTy5cs1ZswYjR49WrNmzZIkTZkyRenp6Vq3bp2LK63ZZs+erX/961966aWXnMJ07dq19fXXX5e6wo3L4+bmpuzsbDVq1EiDBg3S/v379cEHH8hutys/P199+/ZVw4YN9frrr7u61BqtTp062rVrl5o2baqbbrpJo0eP1siRIx3LX3/9dc2aNUvfffedC6u8ylhwOZvNZrm5uVk2m+28k5ubm6vLrPEaNWpkffPNN4754uJi68EHH7SaNGli/fDDD1Z2djbHuQL4+/tbe/bssSzLss6cOWO5u7tb27Ztcyz/9ttvrcDAQFeVd1X5/PPPreuuu856+OGHrZMnT1qWZVnu7u7Wd9995+LKrh42m83KycmxLMuyrr32WmvdunVOy7ds2WKFhoa6orSrSkBAgPXll19alvXHf6u3b9/utHzv3r2Wt7e3K0q7avEQSDUQHByst99+W8XFxWVO27Ztc3WJV4UTJ07I3f3/b3u12WxatGiRevXqpc6dO+v77793YXVXF5vNJumPqydeXl6y2+2OZX5+fsrNzXVVaVeVm2++Wenp6Tp8+LDat2+vHTt2OI49Kk7JMS0sLHTcMlLimmuu0eHDh11R1lUlLi5OixYtkiR17txZK1eudFr+5ptvqnnz5q4o7arFQyDVQFRUlNLT09W7d+8yl9tsNl7tUAFatmypL7/8Uq1atXJqf/755yVJd999tyvKuuqEhYVpz549atasmSRp69atatKkiWN5ZmZmqS9RXD5fX1+98sorWr58uWJiYriFoRJ07dpV7u7uysvLU0ZGhtq2betY9tNPP/EQSAV4+umn1bFjR3Xu3Fnt27fXc889p5SUFMc9gKmpqVq1apWry7yqEACrgb/97W8qKCg47/LmzZvrk08+qcKKrk59+/bVf/7zHw0ePLjUsueff17FxcVavHixCyq7uowePdophJz9ZSlJH374IQ+AVIL7779fnTp1Unp6upo2berqcq4a06dPd5r39fV1mn/vvfd02223VWVJV6WQkBB99dVXmj17tt577z1ZlqXPP/9cBw4cUMeOHbVlyxa1b9/e1WVeVXgIBAAAwDDcAwgAAGAYAiAAAIBhCIAAAACGIQACQCWz2WxavXq1q8sAAAcCIIBqbejQobLZbI4pICBA3bp10zfffOPq0gCgxiIAAqj2unXrpoMHD+rgwYPasGGD3N3d1bNnz8se7+TJkxVYHQDUPARAANWep6engoKCFBQUpHbt2unRRx/VgQMHHL/A8O2336pLly7y9vZWQECARo0apfz8fEf/oUOHqk+fPpo1a5ZCQkJ0/fXXl6vfHXfcoQkTJjjV0qdPHw0dOtQxf/DgQfXo0UPe3t4KDw/X66+/rrCwMCUlJTn1O3LkiPr27as6deqoRYsWTr8B/vvvv2vgwIFq2LChvL291aJFCy1durSCjh4AlEYABFCj5Ofn67XXXlPz5s0VEBCggoICxcbGql69evriiy+0YsUKrV+/XuPGjXPqt2HDBmVkZOjjjz/WmjVryt3vYoYMGaKsrCylpKTorbfe0r/+9S8dOnSo1HpPPPGE7rvvPn3zzTfq3r27Bg4cqN9++02SNHXqVO3cuVMffvihdu3apUWLFqlBgwaXf5AA4CL4JRAA1d6aNWscv8BQUFCg4OBgrVmzRm5ubnr99ddVWFioZcuWycfHR9Ifv+zSq1cvPf300woMDJQk+fj46KWXXpKHh4ck6cUXXyxXvwvZvXu31q9fry+++MLxKwUvvfSSWrRoUWrdoUOHasCAAZKkp556SvPnz9fnn3+ubt26KTMzUzfeeKNjjLCwsCs4WgBwcVwBBFDt3Xnnndq+fbu2b9+uzz//XLGxsYqLi9NPP/2kXbt2KTIy0hHiJKljx44qLi5WRkaGo+2GG25whD9J5e53IRkZGXJ3d9dNN93kaGvevLnq1atXat2IiAjHP/v4+Mjf399xpXD06NFavny52rVrp8mTJ+uzzz4r1/YB4HIRAAFUez4+PmrevLmaN2+um2++WS+99JIKCgr04osvXtIYl8rNzU3n/lrmqVOnLnkcSapdu7bTvM1mU3FxsSQ5wuzEiROVlZWlrl276pFHHrms7QBAeRAAAdQ4NptNbm5uOnHihFq1aqWvv/5aBQUFjuVbtmyRm5ub42GPspSnX8OGDXXw4EHH8jNnzmjHjh2O+euvv16nT5/WV1995Wjbu3evfv/990vep4YNGyo+Pl6vvfaakpKS9K9//euSxwCA8iIAAqj2ioqKlJ2drezsbO3atUvjx49Xfn6+evXqpYEDB8rLy0vx8fHasWOHPvnkE40fP16DBw++4H185enXpUsXvf/++3r//fe1e/dujR49WkePHnWM0bJlS8XExGjUqFH6/PPP9dVXX2nUqFHy9vaWzWYr9/5NmzZN77zzjvbu3avvvvtOa9asUatWrS77eAHAxRAAAVR7a9euVXBwsIKDg9WhQwfHU7t33HGH6tSpo48++ki//fabbr75Zt17773q2rWrnn/++QuOWZ5+CQkJio+P15AhQ9S5c2dde+21uvPOO53GWbZsmQIDA3X77berb9++GjlypPz8/OTl5VXu/fPw8NCUKVMUERGh22+/XbVq1dLy5csv7SABwCWwWefe4AIAuGw///yzQkNDtX79enXt2tXV5QBAmQiAAHAFNm7cqPz8fN1www06ePCgJk+erF9++UXff/99qQc/AKC64D2AAHAFTp06pccee0z79u2Tn5+f/vSnP+nf//434Q9AtcYVQAAAAMPwEAgAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACG+T9zExWTkJ8+7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As hypothesised earlier, most of the pickups and dropoffs relate to Manhattan. Next, we try to visualize the average pickup and dropoff fares for these boroughs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fd7cbaf6dc14b2089445c75e30c1af6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "plt.title(\"Distribution of Fare Amount Across Manhattan Borough\")\n",
    "\n",
    "pickup_borough_fares_manhattan = train_data_filtered.filter(F.col(\"pickup_borough\") == \"manhattan\").select(F.col(\"fare_amount\")).alias(\"pickup_rates\")\n",
    "dropoff_borough_fares_manhattan = train_data_filtered.filter(F.col(\"dropoff_borough\") == \"manhattan\").select(F.col(\"fare_amount\")).alias(\"dropoff_rates\")\n",
    "    \n",
    "distplot(axes, [pickup_borough_fares_manhattan.select(F.col((\"fare_amount\"))), dropoff_borough_fares_manhattan.select(F.col((\"fare_amount\")))],\\\n",
    "        alpha = 0.6, color = ['red', 'tan'])\n",
    "    \n",
    "axes.set_title(\"Fare Amount Distribution for Manhattan\")\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0c6984a965e493ea79f152acf1e478b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "plt.title(\"Distribution of Fare Amount Across Bronx Borough\")\n",
    "\n",
    "pickup_borough_fares_bronx = train_data_filtered.filter(F.col(\"pickup_borough\") == \"bronx\").select(F.col(\"fare_amount\"))\n",
    "dropoff_borough_fares_bronx = train_data_filtered.filter(F.col(\"dropoff_borough\") == \"bronx\").select(F.col(\"fare_amount\"))\n",
    "    \n",
    "distplot(axes, [pickup_borough_fares_bronx.select(F.col((\"fare_amount\"))), dropoff_borough_fares_bronx.select(F.col((\"fare_amount\")))],\\\n",
    "        alpha = 0.6, color = ['red', 'tan'])\n",
    "    \n",
    "axes.set_title(\"Fare Amount Distribution for Bronx\")\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31a6a9098ecb42bc9cf17f71bbc0bb81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "plt.title(\"Distribution of Fare Amount Across Queens\")\n",
    "\n",
    "pickup_borough_fares_queens = train_data_filtered.filter(F.col(\"pickup_borough\") == \"queens\").select(F.col(\"fare_amount\"))\n",
    "dropoff_borough_fares_queens = train_data_filtered.filter(F.col(\"dropoff_borough\") == \"queens\").select(F.col(\"fare_amount\"))\n",
    "    \n",
    "distplot(axes, [pickup_borough_fares_queens.select(F.col((\"fare_amount\"))), dropoff_borough_fares_queens.select(F.col((\"fare_amount\")))],\\\n",
    "        alpha = 0.6, color = ['red', 'tan'])\n",
    "    \n",
    "axes.set_title(\"Fare Amount Distribution for Queens\")\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c27cd4fdc1c43678ac0f93ccee7d597",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "plt.title(\"Distribution of Fare Amount Across Brooklyn\")\n",
    "\n",
    "pickup_borough_fares_brooklyn = train_data_filtered.filter(F.col(\"pickup_borough\") == \"brooklyn\").select(F.col(\"fare_amount\"))\n",
    "dropoff_borough_fares_brooklyn = train_data_filtered.filter(F.col(\"dropoff_borough\") == \"brooklyn\").select(F.col(\"fare_amount\"))\n",
    "    \n",
    "distplot(axes, [pickup_borough_fares_brooklyn.select(F.col((\"fare_amount\"))), dropoff_borough_fares_brooklyn.select(F.col((\"fare_amount\")))],\\\n",
    "        alpha = 0.6, color = ['red', 'tan'])\n",
    "    \n",
    "axes.set_title(\"Fare Amount Distribution for Brooklyn\")\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc25075ebd714b66b8aff7c3fb48dc2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "plt.title(\"Distribution of Fare Amount Across Staten Island\")\n",
    "\n",
    "pickup_borough_fares_staten = train_data_filtered.filter(F.col(\"pickup_borough\") == \"staten_island\").select(F.col(\"fare_amount\"))\n",
    "dropoff_borough_fares_staten = train_data_filtered.filter(F.col(\"dropoff_borough\") == \"staten_island\").select(F.col(\"fare_amount\"))\n",
    "    \n",
    "distplot(axes, [pickup_borough_fares_staten.select(F.col((\"fare_amount\"))), dropoff_borough_fares_staten.select(F.col((\"fare_amount\")))],\\\n",
    "        alpha = 0.6, color = ['red', 'tan'])\n",
    "    \n",
    "axes.set_title(\"Fare Amount Distribution for Staten Island\")\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We conclude the following from the above graphs:\n",
    "\n",
    "1. Pickup and dropoff fare amount are almost similarly distributed for Manhattan.\n",
    "2. Dropoff fare is higher for Bronx, Brooklyn and Staten Island meaning people come from far out into the Bronx, Brooklyn and Staten Island\n",
    "3. For Queens, both averages are higher meaning people usually take longer trips in general"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we split the Manhattan borough also into two: upper and lower Manhattan and for that we introduce two new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Manhattan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "498d627722674cbd90d6f097259d31f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function <lambda> at 0x7fc549cdc200>"
     ]
    }
   ],
   "source": [
    "lower_manhattan_boundary={\n",
    "    'min_lng': -74.0194,\n",
    "    'min_lat': 40.6997,           \n",
    "    'max_lng': -73.9716,        \n",
    "    'max_lat': 40.7427\n",
    "}\n",
    "\n",
    "def isLowerManhattan(lat,lng):\n",
    "    \"\"\"\n",
    "    Check if lat and lng corresponds to lower manhattan\n",
    "    Args:\n",
    "        lat: Latitude\n",
    "        lng: Longitude\n",
    "    Returns:\n",
    "        0 or 1 indicating whether the pick up or drop off location is in lower manhattan\n",
    "    \"\"\"\n",
    "    if (lat >= lower_manhattan_boundary['min_lat'] and \n",
    "        lat <= lower_manhattan_boundary['max_lat'] and \n",
    "        lng >= lower_manhattan_boundary['min_lng'] and \n",
    "        lng <= lower_manhattan_boundary['max_lng']):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "manhattanUDF = udf(lambda x,y : isLowerManhattan(x, y),BooleanType())\n",
    "spark.udf.register(\"manhattanUDF\", manhattanUDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3379b4e73c54add99a9992a9055be59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+--------------+---------------+----------------------------+--------------------------+\n",
      "|                key|fare_amount|    pickup_datetime|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_date|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|is_pickup_LaGuardia|is_dropoff_LaGuardia|     trip_distance|pickup_borough|dropoff_borough|is_pickup_in_lower_manhattan|is_drop_in_lower_manhattan|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+--------------+---------------+----------------------------+--------------------------+\n",
      "|2009-06-15 17:26:21|        4.5|2009-06-15 17:26:21|      -73.844311|      40.721319|        -73.84161|       40.712278|              1| 2009-06-15|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|              false|               false|0.6404870231407276|        queens|         queens|                       false|                     false|\n",
      "|2010-01-05 16:52:16|       16.9|2010-01-05 16:52:16|      -74.016048|      40.711303|       -73.979268|       40.782004|              1| 2010-01-05|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|              false|               false| 5.250669652366417|     manhattan|      manhattan|                        true|                     false|\n",
      "|2011-08-18 00:35:00|        5.7|2011-08-18 00:35:00|      -73.982738|       40.76127|       -73.991242|       40.750562|              2| 2011-08-18|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|              false|               false|0.8634109565058572|     manhattan|      manhattan|                       false|                     false|\n",
      "|2012-04-21 04:30:42|        7.7|2012-04-21 04:30:42|       -73.98713|      40.733143|       -73.991567|       40.758092|              1| 2012-04-21|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|              false|               false|1.7393859082192853|     manhattan|      manhattan|                        true|                     false|\n",
      "|2010-03-09 07:51:00|        5.3|2010-03-09 07:51:00|      -73.968095|      40.768008|       -73.956655|       40.783762|              1| 2010-03-09|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|              false|               false|1.2422184522938338|     manhattan|      manhattan|                       false|                     false|\n",
      "+-------------------+-----------+-------------------+----------------+---------------+-----------------+----------------+---------------+-----------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+--------------+---------------+----------------------------+--------------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "train_data_filtered = train_data_filtered.withColumn(\"is_pickup_in_lower_manhattan\", manhattanUDF(F.col(\"pickup_latitude\"),\\\n",
    "                                                                                                  F.col(\"pickup_longitude\")))\n",
    "train_data_filtered = train_data_filtered.withColumn(\"is_drop_in_lower_manhattan\", manhattanUDF(F.col(\"dropoff_latitude\"),\\\n",
    "                                                                                                  F.col(\"dropoff_longitude\")))\n",
    "\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c090d876b8c242d2ba57d4e46763ca09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.gca()\n",
    "plt.title(\"Distribution of Fare Amount - Lower Manhattan vs Other Regions in Manhattan\")\n",
    "\n",
    "lower_manhattan_pickup = train_data_filtered.filter(F.col(\"is_pickup_in_lower_manhattan\") == True).select(F.col(\"fare_amount\"))\n",
    "other_manhattan_pickup = train_data_filtered.filter(F.col(\"is_pickup_in_lower_manhattan\") == False).select(F.col(\"fare_amount\"))\n",
    "    \n",
    "distplot(axes, [lower_manhattan_pickup.select(F.col((\"fare_amount\"))), other_manhattan_pickup.select(F.col((\"fare_amount\")))],\\\n",
    "        alpha = 0.6, color = ['red', 'tan'])\n",
    "    \n",
    "axes.set_title(\"Fare Amount Distribution for Manhattan - Lower vs Other Regions\")\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that for Lower Manhattan and Rest of Manhattan, the fare amounts are similar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-based Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0980c6dae7942508c6fe276a6d4555b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pickup_year    count\n",
      "6         2009  8432908\n",
      "0         2010  8168683\n",
      "1         2011  8504583\n",
      "5         2012  8629726\n",
      "2         2013  8476306\n",
      "4         2014  8068283\n",
      "3         2015  3777269"
     ]
    }
   ],
   "source": [
    "trips_by_year = train_data_filtered.groupBy(\"pickup_year\").count().toPandas()\n",
    "trips_by_year = trips_by_year.sort_values(by = \"pickup_year\")\n",
    "trips_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b59f4d9bf1af49a8b9422098f4db78a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Trips per year')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_year\",\n",
    "            y = \"count\",\n",
    "            data = trips_by_year).set_title(\"Trips per year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad635868893d456ca4ef6a28d922d177",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    pickup_month    count\n",
      "1              1  4817304\n",
      "8              2  4545047\n",
      "3              3  5069527\n",
      "4              4  4984445\n",
      "7              5  5082105\n",
      "2              6  4862991\n",
      "9              7  4098227\n",
      "5              8  3856419\n",
      "10             9  4118322\n",
      "11            10  4368670\n",
      "6             11  4107319\n",
      "0             12  4147382"
     ]
    }
   ],
   "source": [
    "trips_by_month = train_data_filtered.groupBy(\"pickup_month\").count().toPandas()\n",
    "trips_by_month = trips_by_month.sort_values(by = \"pickup_month\")\n",
    "trips_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6627a9951f1484ab0fd10c788d3c810",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Trips per month')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_month\",\n",
    "            y = \"count\",\n",
    "            data = trips_by_month).set_title(\"Trips per month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "472df2e1671d40199477e59ef15d985c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pickup_day_of_week    count\n",
      "0                   1  7090814\n",
      "5                   2  6929640\n",
      "2                   3  7573406\n",
      "3                   4  7842666\n",
      "4                   5  8099058\n",
      "1                   6  8317144\n",
      "6                   7  8205030"
     ]
    }
   ],
   "source": [
    "trips_by_day = train_data_filtered.groupBy(\"pickup_day_of_week\").count().toPandas()\n",
    "trips_by_day = trips_by_day.sort_values(by = \"pickup_day_of_week\")\n",
    "trips_by_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20d8a9d1315f454e80e289919e53a661",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Trips per day of week (in numbers starting Sunday)')\n",
      "<string>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`)."
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_day_of_week\",\n",
    "            y = \"count\",\n",
    "            data = trips_by_day).set_title(\"Trips per day of week (in numbers starting Sunday)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf274fefb4cb4f949a2057b7f5acb521",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    pickup_hour    count\n",
      "20            0  2139866\n",
      "1             1  1584703\n",
      "15            2  1181754\n",
      "3             3   865742\n",
      "4             4   627217\n",
      "13            5   528350\n",
      "2             6  1108962\n",
      "19            7  1946680\n",
      "5             8  2455370\n",
      "22            9  2526974\n",
      "23           10  2439216\n",
      "6            11  2516889\n",
      "0            12  2658788\n",
      "17           13  2642588\n",
      "10           14  2722529\n",
      "14           15  2599788\n",
      "11           16  2231623\n",
      "18           17  2679117\n",
      "16           18  3255772\n",
      "7            19  3380180\n",
      "12           20  3184018\n",
      "9            21  3095506\n",
      "21           22  3010717\n",
      "8            23  2675409"
     ]
    }
   ],
   "source": [
    "trips_by_hour = train_data_filtered.groupBy(\"pickup_hour\").count().toPandas()\n",
    "trips_by_hour = trips_by_hour.sort_values(by = \"pickup_hour\")\n",
    "trips_by_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d384d10bdac74cd8ba0143540d35dcb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Trips per hour')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_hour\",\n",
    "            y = \"count\",\n",
    "            data = trips_by_hour).set_title(\"Trips per hour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d02ed924b5ef4ce081c1fa6ac7359393",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pickup_year  avg(pickup_year)  avg(fare_amount)\n",
      "6         2009            2009.0         10.036191\n",
      "0         2010            2010.0         10.137589\n",
      "1         2011            2011.0         10.425817\n",
      "5         2012            2012.0         11.124391\n",
      "2         2013            2013.0         12.506418\n",
      "4         2014            2014.0         12.824710\n",
      "3         2015            2015.0         12.887004"
     ]
    }
   ],
   "source": [
    "avg_fares_year = train_data_filtered.select(\"pickup_year\", \"fare_amount\").groupBy(\"pickup_year\").mean().toPandas()\n",
    "avg_fares_year.sort_values(by = \"pickup_year\", inplace = True)\n",
    "avg_fares_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af7607ce2278466cb8ac206cec04d64c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Average Fares Each Year')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_year\",\n",
    "            y = \"avg(fare_amount)\",\n",
    "            data = avg_fares_year).set_title(\"Average Fares Each Year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20d3d12514d347e7a5d4a0d27d94c721",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    pickup_month  avg(pickup_month)  avg(fare_amount)\n",
      "1              1                1.0         10.690427\n",
      "8              2                2.0         10.850605\n",
      "3              3                3.0         11.135737\n",
      "4              4                4.0         11.258153\n",
      "7              5                5.0         11.539938\n",
      "2              6                6.0         11.506472\n",
      "9              7                7.0         11.067508\n",
      "5              8                8.0         11.175669\n",
      "10             9                9.0         11.675097\n",
      "11            10               10.0         11.589168\n",
      "6             11               11.0         11.506481\n",
      "0             12               12.0         11.539273"
     ]
    }
   ],
   "source": [
    "avg_fares_month = train_data_filtered.select(\"pickup_month\", \"fare_amount\").groupBy(\"pickup_month\").mean().toPandas()\n",
    "avg_fares_month.sort_values(by = \"pickup_month\", inplace = True)\n",
    "avg_fares_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b63c4e14c274dc3a22af1bee242f0b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Average Fares Each Month')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_month\",\n",
    "            y = \"avg(fare_amount)\",\n",
    "            data = avg_fares_month).set_title(\"Average Fares Each Month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "489a656fbe8344ac8eb954f5f1b9e882",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pickup_day_of_week  avg(pickup_day_of_week)  avg(fare_amount)\n",
      "0                   1                      1.0         11.583543\n",
      "5                   2                      2.0         11.320434\n",
      "2                   3                      3.0         11.138204\n",
      "3                   4                      4.0         11.257655\n",
      "4                   5                      5.0         11.441898\n",
      "1                   6                      6.0         11.368184\n",
      "6                   7                      7.0         10.947569"
     ]
    }
   ],
   "source": [
    "avg_fares_day = train_data_filtered.select(\"pickup_day_of_week\", \"fare_amount\").groupBy(\"pickup_day_of_week\").mean().toPandas()\n",
    "avg_fares_day.sort_values(by = \"pickup_day_of_week\", inplace = True)\n",
    "avg_fares_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11013e7b2f94405f83aa4eb2049bd5b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Average Fares for Each Day (starting Sunday in numbers)')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_day_of_week\",\n",
    "            y = \"avg(fare_amount)\",\n",
    "            data = avg_fares_day).set_title(\"Average Fares for Each Day (starting Sunday in numbers)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aeca7487c8b140bca16a840053bfbf01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    pickup_hour  avg(pickup_hour)  avg(fare_amount)\n",
      "20            0               0.0         11.622322\n",
      "1             1               1.0         11.349003\n",
      "15            2               2.0         11.331435\n",
      "3             3               3.0         11.786527\n",
      "4             4               4.0         13.521306\n",
      "13            5               5.0         15.098579\n",
      "2             6               6.0         12.128896\n",
      "19            7               7.0         10.955931\n",
      "5             8               8.0         10.877159\n",
      "22            9               9.0         10.831554\n",
      "23           10              10.0         10.885888\n",
      "6            11              11.0         11.078682\n",
      "0            12              12.0         11.092259\n",
      "17           13              13.0         11.481311\n",
      "10           14              14.0         11.827846\n",
      "14           15              15.0         11.917005\n",
      "11           16              16.0         11.822765\n",
      "18           17              17.0         11.445367\n",
      "16           18              18.0         10.877042\n",
      "7            19              19.0         10.523951\n",
      "12           20              20.0         10.704785\n",
      "9            21              21.0         10.984665\n",
      "21           22              22.0         11.289432\n",
      "8            23              23.0         11.566282"
     ]
    }
   ],
   "source": [
    "avg_fares_hour = train_data_filtered.select(\"pickup_hour\", \"fare_amount\").groupBy(\"pickup_hour\").mean().toPandas()\n",
    "avg_fares_hour.sort_values(by = \"pickup_hour\", inplace = True)\n",
    "avg_fares_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ead8629d9b64664b17dc05fd7075ec9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0.5, 1.0, 'Average Fares for Each Hour')"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.barplot(x = \"pickup_hour\",\n",
    "            y = \"avg(fare_amount)\",\n",
    "            data = avg_fares_hour).set_title(\"Average Fares for Each Hour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a ML Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41401d2743d54a74a2b925a91e3fd725",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- key: timestamp (nullable = true)\n",
      " |-- fare_amount: double (nullable = true)\n",
      " |-- pickup_datetime: timestamp (nullable = true)\n",
      " |-- pickup_longitude: double (nullable = true)\n",
      " |-- pickup_latitude: double (nullable = true)\n",
      " |-- dropoff_longitude: double (nullable = true)\n",
      " |-- dropoff_latitude: double (nullable = true)\n",
      " |-- passenger_count: integer (nullable = true)\n",
      " |-- pickup_date: date (nullable = true)\n",
      " |-- pickup_day: integer (nullable = true)\n",
      " |-- pickup_hour: integer (nullable = true)\n",
      " |-- pickup_day_of_week: integer (nullable = true)\n",
      " |-- pickup_month: integer (nullable = true)\n",
      " |-- pickup_year: integer (nullable = true)\n",
      " |-- is_pickup_JFK: boolean (nullable = true)\n",
      " |-- is_dropoff_JFK: boolean (nullable = true)\n",
      " |-- is_pickup_EWR: boolean (nullable = true)\n",
      " |-- is_dropoff_EWR: boolean (nullable = true)\n",
      " |-- is_pickup_LaGuardia: boolean (nullable = true)\n",
      " |-- is_dropoff_LaGuardia: boolean (nullable = true)\n",
      " |-- trip_distance: double (nullable = true)\n",
      " |-- pickup_borough: string (nullable = true)\n",
      " |-- dropoff_borough: string (nullable = true)\n",
      " |-- is_pickup_in_lower_manhattan: boolean (nullable = true)\n",
      " |-- is_drop_in_lower_manhattan: boolean (nullable = true)"
     ]
    }
   ],
   "source": [
    "train_data_filtered.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we drop the 'pickup_datetime' and 'pickup_date' columns because of their type. Next, we encode the 'pickup_borough' and 'dropoff_borough' columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7017c5a357564332a09a9e4101ec4cf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.ml.feature import StringIndexer, OneHotEncoderEstimator, VectorAssembler\n",
    "from pyspark.ml import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_filtered = train_data_filtered.drop(\"pickup_datetime\", \"pickup_date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding data and removing un-encoded columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cff5db027b742bb979b5a254f64bbf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "+-------------------+-----------+----------------+---------------+-----------------+----------------+---------------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+----------------------------+--------------------------+------------------+-------------------+\n",
      "|                key|fare_amount|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|is_pickup_LaGuardia|is_dropoff_LaGuardia|     trip_distance|is_pickup_in_lower_manhattan|is_drop_in_lower_manhattan|pickup_borough_OHE|dropoff_borough_OHE|\n",
      "+-------------------+-----------+----------------+---------------+-----------------+----------------+---------------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+----------------------------+--------------------------+------------------+-------------------+\n",
      "|2009-06-15 17:26:21|        4.5|      -73.844311|      40.721319|        -73.84161|       40.712278|              1|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|              false|               false|0.6404870231407276|                       false|                     false|     (5,[1],[1.0])|      (5,[1],[1.0])|\n",
      "|2010-01-05 16:52:16|       16.9|      -74.016048|      40.711303|       -73.979268|       40.782004|              1|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|              false|               false| 5.250669652366417|                        true|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|\n",
      "|2011-08-18 00:35:00|        5.7|      -73.982738|       40.76127|       -73.991242|       40.750562|              2|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|              false|               false|0.8634109565058572|                       false|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|\n",
      "|2012-04-21 04:30:42|        7.7|       -73.98713|      40.733143|       -73.991567|       40.758092|              1|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|              false|               false|1.7393859082192853|                        true|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|\n",
      "|2010-03-09 07:51:00|        5.3|      -73.968095|      40.768008|       -73.956655|       40.783762|              1|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|              false|               false|1.2422184522938338|                       false|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|\n",
      "+-------------------+-----------+----------------+---------------+-----------------+----------------+---------------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+----------------------------+--------------------------+------------------+-------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "# create object of StringIndexer class and specify input and output column\n",
    "SI_pickup = StringIndexer(inputCol='pickup_borough',outputCol='pickup_borough_encoded')\n",
    "SI_dropoff = StringIndexer(inputCol='dropoff_borough',outputCol='dropoff_borough_encoded')\n",
    "\n",
    "# transform the data\n",
    "train_data_filtered = SI_pickup.fit(train_data_filtered).transform(train_data_filtered)\n",
    "train_data_filtered = SI_dropoff.fit(train_data_filtered).transform(train_data_filtered)\n",
    "\n",
    "# create object and specify input and output column\n",
    "OHE = OneHotEncoderEstimator(inputCols=['pickup_borough_encoded', 'dropoff_borough_encoded'],\\\n",
    "                             outputCols=['pickup_borough_OHE', 'dropoff_borough_OHE'])\n",
    "\n",
    "# transform the data\n",
    "train_data_filtered = OHE.fit(train_data_filtered).transform(train_data_filtered)\n",
    "\n",
    "train_data_filtered = train_data_filtered.drop(\"pickup_borough\", \"dropoff_borough\", \"pickup_borough_encoded\", \n",
    "                                               \"dropoff_borough_encoded\")\n",
    "\n",
    "print(len(train_data_filtered.columns))\n",
    "train_data_filtered.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "2b733e45edfe4046bf5097fdbee2a799",
       "version_major": 2,
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      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "train_data_filtered = train_data_filtered.drop(\"key\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VectorAssembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0414b450f6444ca8f62a5744bc9d075",
       "version_major": 2,
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureColumns = ['pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count', \\\n",
    "                  'pickup_day', 'pickup_hour', 'pickup_day_of_week', 'pickup_month', 'pickup_year', 'is_pickup_JFK', \\\n",
    "                  'is_dropoff_JFK', 'is_pickup_EWR', 'is_dropoff_EWR', 'is_pickup_LaGuardia', 'is_dropoff_LaGuardia', \\\n",
    "                  'trip_distance', 'is_pickup_in_lower_manhattan', 'is_drop_in_lower_manhattan', 'pickup_borough_OHE', \\\n",
    "                  'dropoff_borough_OHE']\n",
    "\n",
    "assembler = VectorAssembler(inputCols= featureColumns, outputCol=\"features\")\n",
    "\n",
    "full_set = assembler.transform(train_data_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e37360ec7cfa497d86a89289355fc7e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+----------------+---------------+-----------------+----------------+---------------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+----------------------------+--------------------------+------------------+-------------------+--------------------+\n",
      "|fare_amount|pickup_longitude|pickup_latitude|dropoff_longitude|dropoff_latitude|passenger_count|pickup_day|pickup_hour|pickup_day_of_week|pickup_month|pickup_year|is_pickup_JFK|is_dropoff_JFK|is_pickup_EWR|is_dropoff_EWR|is_pickup_LaGuardia|is_dropoff_LaGuardia|     trip_distance|is_pickup_in_lower_manhattan|is_drop_in_lower_manhattan|pickup_borough_OHE|dropoff_borough_OHE|            features|\n",
      "+-----------+----------------+---------------+-----------------+----------------+---------------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+----------------------------+--------------------------+------------------+-------------------+--------------------+\n",
      "|        4.5|      -73.844311|      40.721319|        -73.84161|       40.712278|              1|        15|         17|                 2|           6|       2009|        false|         false|        false|         false|              false|               false|0.6404870231407276|                       false|                     false|     (5,[1],[1.0])|      (5,[1],[1.0])|(29,[0,1,2,3,4,5,...|\n",
      "|       16.9|      -74.016048|      40.711303|       -73.979268|       40.782004|              1|         5|         16|                 3|           1|       2010|        false|         false|        false|         false|              false|               false| 5.250669652366417|                        true|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|(29,[0,1,2,3,4,5,...|\n",
      "|        5.7|      -73.982738|       40.76127|       -73.991242|       40.750562|              2|        18|          0|                 5|           8|       2011|        false|         false|        false|         false|              false|               false|0.8634109565058572|                       false|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|(29,[0,1,2,3,4,5,...|\n",
      "|        7.7|       -73.98713|      40.733143|       -73.991567|       40.758092|              1|        21|          4|                 7|           4|       2012|        false|         false|        false|         false|              false|               false|1.7393859082192853|                        true|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|(29,[0,1,2,3,4,5,...|\n",
      "|        5.3|      -73.968095|      40.768008|       -73.956655|       40.783762|              1|         9|          7|                 3|           3|       2010|        false|         false|        false|         false|              false|               false|1.2422184522938338|                       false|                     false|     (5,[0],[1.0])|      (5,[0],[1.0])|(29,[0,1,2,3,4,5,...|\n",
      "+-----------+----------------+---------------+-----------------+----------------+---------------+----------+-----------+------------------+------------+-----------+-------------+--------------+-------------+--------------+-------------------+--------------------+------------------+----------------------------+--------------------------+------------------+-------------------+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "full_set.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a0e0e1ff945446c9ce3e96e959ceab0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set, test_set = full_set.randomSplit([0.9, 0.1], seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4903d69b2b8c408e832f9e0fb3d2fd16",
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       "version_minor": 0
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      "text/plain": [
       "VBox()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5406428 23\n",
      "48651330 23"
     ]
    }
   ],
   "source": [
    "print(test_set.count(), len(test_set.columns))\n",
    "print(train_set.count(), len(train_set.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e851fcee0c7e42a4a8d4a985cb184a5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.ml.regression import RandomForestRegressor\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "\n",
    "rf = RandomForestRegressor(labelCol=\"fare_amount\", featuresCol= \"features\")\n",
    "model = rf.fit(train_set)\n",
    "\n",
    "predictions = model.transform(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "1f2ef672276541878d061a08814222e3",
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      "text/plain": [
       "VBox()"
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     },
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    },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-----------+\n",
      "|        prediction|fare_amount|\n",
      "+------------------+-----------+\n",
      "| 18.42720878127786|        2.5|\n",
      "|11.877214852627052|        2.5|\n",
      "| 16.08345399361932|        2.5|\n",
      "| 14.91263213808153|        2.5|\n",
      "| 8.857089332423525|        2.5|\n",
      "+------------------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "predictions.select(\"prediction\", \"fare_amount\").show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb2185f745044879a1eb1e8d0875a99d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Error on test set:  4.4201998172430255"
     ]
    }
   ],
   "source": [
    "evaluator = RegressionEvaluator(predictionCol=\"prediction\", labelCol=\"fare_amount\",metricName=\"rmse\")\n",
    "print(\"RMSE Error on test set: \", evaluator.evaluate(predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fef7921f89a741229470ec6c2244be07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.ml.regression import DecisionTreeRegressor\n",
    "from pyspark.ml.evaluation import RegressionEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41502c7b1ac54de1bcfcf691561c3144",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor(labelCol=\"fare_amount\", featuresCol= \"features\")\n",
    "model = dt.fit(train_set)\n",
    "\n",
    "predictions = model.transform(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fa47eebc3a4451e94dc5ba92818508b",
       "version_major": 2,
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      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-----------+\n",
      "|        prediction|fare_amount|\n",
      "+------------------+-----------+\n",
      "|26.292193752552055|        2.5|\n",
      "|26.292193752552055|        2.5|\n",
      "|26.292193752552055|        2.5|\n",
      "|26.292193752552055|        2.5|\n",
      "|26.292193752552055|        2.5|\n",
      "+------------------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "predictions.select(\"prediction\", \"fare_amount\").show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3760dea99dea4614a97be03b4304b0cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Error on test set:  4.281480982734822"
     ]
    }
   ],
   "source": [
    "evaluator = RegressionEvaluator(predictionCol=\"prediction\", labelCol=\"fare_amount\",metricName=\"rmse\")\n",
    "print(\"RMSE Error on test set: \", evaluator.evaluate(predictions))"
   ]
  }
 ],
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