08_spark_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5d6e94f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "22/04/02 15:18:46 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
      "22/04/02 15:18:48 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n",
      "/home/f_dev/.local/lib/python3.9/site-packages/pyspark/sql/context.py:77: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# import classes for python application\n",
    "from pyspark.sql import SparkSession, DataFrame, functions, Row, SQLContext\n",
    "from pyspark import SparkContext, SparkConf\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "os.environ['PYSPARK_PYTHON'] = sys.executable\n",
    "os.environ['PYSPARK_DRIVER_PYTHON'] = sys.executable\n",
    "\n",
    "# create spark session, configuration and context\n",
    "spark = SparkSession.builder.appName(\"DataFrame\").getOrCreate()\n",
    "conf = SparkConf().setMaster(\"local\").setAppName(\"Assignment 8\")\n",
    "sc = SparkContext.getOrCreate(conf=conf)\n",
    "sqlContext = SQLContext(sc)\n",
    "sc.setLogLevel(\"ERROR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9350a0df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#read json file\n",
    "df = spark.read.json(\"simple-ml.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f626a711",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features from the dataframe\n",
    "from pyspark.ml.feature import RFormula\n",
    "supervised = RFormula(formula=\"lab ~ .\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "001d802d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#fit RFormula transformer to the data\n",
    "fittedRF = supervised.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9f18bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+------+------------------+---------------------------------+-----+\n",
      "|color|lab |value1|value2            |features                         |label|\n",
      "+-----+----+------+------------------+---------------------------------+-----+\n",
      "|green|good|1     |14.386294994851129|[0.0,1.0,1.0,14.386294994851129] |1.0  |\n",
      "|blue |bad |8     |14.386294994851129|[0.0,0.0,8.0,14.386294994851129] |0.0  |\n",
      "|blue |bad |12    |14.386294994851129|[0.0,0.0,12.0,14.386294994851129]|0.0  |\n",
      "|green|good|15    |38.97187133755819 |[0.0,1.0,15.0,38.97187133755819] |1.0  |\n",
      "|green|good|12    |14.386294994851129|[0.0,1.0,12.0,14.386294994851129]|1.0  |\n",
      "|green|bad |16    |14.386294994851129|[0.0,1.0,16.0,14.386294994851129]|0.0  |\n",
      "|red  |good|35    |14.386294994851129|[1.0,0.0,35.0,14.386294994851129]|1.0  |\n",
      "|red  |bad |1     |38.97187133755819 |[1.0,0.0,1.0,38.97187133755819]  |0.0  |\n",
      "|red  |bad |2     |14.386294994851129|[1.0,0.0,2.0,14.386294994851129] |0.0  |\n",
      "|red  |bad |16    |14.386294994851129|[1.0,0.0,16.0,14.386294994851129]|0.0  |\n",
      "+-----+----+------+------------------+---------------------------------+-----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#apply transformation and display 10\n",
    "preparedDF = fittedRF.transform(df)\n",
    "preparedDF.show(10, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e58a69fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create 70/30 train/test split \n",
    "train, test = preparedDF.randomSplit([0.7, 0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a781763",
   "metadata": {},
   "outputs": [],
   "source": [
    "#instantiate instance of Logistic Regression with label and features\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "lr = LogisticRegression(labelCol=\"label\",featuresCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d630f633",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#train model by passing the train dataset to the fit() method of the model lr.\n",
    "fittedLR = lr.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29037aeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21.044362887445534,24.154706947490652,0.11660897229865765,0.04511143328251713]\n"
     ]
    }
   ],
   "source": [
    "#review coefficients\n",
    "print(fittedLR.coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "139427fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-25.123156463501612\n"
     ]
    }
   ],
   "source": [
    "#review intercept\n",
    "print(fittedLR.intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e83016dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----------+\n",
      "|label|prediction|\n",
      "+-----+----------+\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       0.0|\n",
      "|  0.0|       1.0|\n",
      "|  0.0|       1.0|\n",
      "|  0.0|       1.0|\n",
      "|  0.0|       1.0|\n",
      "|  0.0|       1.0|\n",
      "|  0.0|       1.0|\n",
      "|  0.0|       1.0|\n",
      "|  1.0|       0.0|\n",
      "|  1.0|       0.0|\n",
      "+-----+----------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#make predictions for training data\n",
    "fittedLR.transform(train).select(\"label\", \"prediction\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "289c802b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set variable for model summary and review contents\n",
    "summary = fittedLR.summary\n",
    "#dir(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60b4521f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run predictions for 2 labels\n",
    "summary.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28df9a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8378378378378378, 0.7878787878787878]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the precision of those labels\n",
    "summary.precisionByLabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef4294b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/f_dev/.local/lib/python3.9/site-packages/pyspark/sql/context.py:125: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+\n",
      "|                FPR|    TPR|\n",
      "+-------------------+-------+\n",
      "|                0.0|    0.0|\n",
      "|                0.0|0.21875|\n",
      "|                0.0|0.40625|\n",
      "|0.18421052631578946|0.40625|\n",
      "|0.18421052631578946|0.65625|\n",
      "|0.18421052631578946| 0.8125|\n",
      "|0.18421052631578946|    1.0|\n",
      "|0.34210526315789475|    1.0|\n",
      "| 0.5526315789473685|    1.0|\n",
      "| 0.7105263157894737|    1.0|\n",
      "| 0.8421052631578947|    1.0|\n",
      "|                1.0|    1.0|\n",
      "|                1.0|    1.0|\n",
      "+-------------------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display summary of roc curve\n",
    "summary.roc.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6004d494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------------+\n",
      "| recall|          precision|\n",
      "+-------+-------------------+\n",
      "|    0.0|                1.0|\n",
      "|0.21875|                1.0|\n",
      "|0.40625|                1.0|\n",
      "|0.40625|               0.65|\n",
      "|0.65625|               0.75|\n",
      "| 0.8125| 0.7878787878787878|\n",
      "|    1.0| 0.8205128205128205|\n",
      "|    1.0| 0.7111111111111111|\n",
      "|    1.0| 0.6037735849056604|\n",
      "|    1.0| 0.5423728813559322|\n",
      "|    1.0|                0.5|\n",
      "|    1.0|0.45714285714285713|\n",
      "+-------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display recall and precision\n",
    "summary.pr.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8dd724bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8142857142857143"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check accuracy of the model\n",
    "summary.accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66d06e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate error rate\n",
    "errors = (1 - summary.accuracy) / df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34c0f385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.169%'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#express errors as a percent\n",
    "\"{:.3%}\".format(errors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc86c7ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----------+\n",
      "|label|prediction|\n",
      "+-----+----------+\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |0.0       |\n",
      "|0.0  |1.0       |\n",
      "+-----+----------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#make predictions for test data\n",
    "lr_predictions = fittedLR.transform(test)\n",
    "lr_predictions.select(\"label\", \"prediction\").show(10, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11cb7481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC 0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "# Use Binary Classification Evaluator for ROC curve for test data\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
    "lr_evaluator = BinaryClassificationEvaluator()\n",
    "print(\"ROC\", lr_evaluator.evaluate(lr_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f6828d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.825\n"
     ]
    }
   ],
   "source": [
    "#check accuracy of the model using test predictions\n",
    "accuracy = lr_predictions.filter(lr_predictions.label == lr_predictions.prediction).count() / float(lr_predictions.count())\n",
    "print(\"Accuracy : \",accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "82186331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0015909090909090914"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the error rate\n",
    "test_errors = (1 - accuracy) / df.count()\n",
    "test_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "10b79935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.159%'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert error rate to percentage\n",
    "\"{:.3%}\".format(test_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "788f8fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import structtype for schema\n",
    "from pyspark.sql.types import StructType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "272859f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create schema for spark df\n",
    "schema = StructType([\\\n",
    "    StructField(\"mpg\", IntegerType(), True),\\\n",
    "    StructField(\"cylinders\", IntegerType(), True),\\\n",
    "    StructField(\"displacement\", IntegerType(), True),\\\n",
    "    StructField(\"horsepower\", IntegerType(), True),\\\n",
    "    StructField(\"weight\", IntegerType(), True),\\\n",
    "    StructField(\"acceleration\", FloatType(), True),\\\n",
    "    StructField(\"model year\", IntegerType(), True),\\\n",
    "    StructField(\"origin\", IntegerType(), True),\\\n",
    "    StructField(\"carName\", StringType(), True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39aa4340",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv and load data\n",
    "car_df = spark.read.format('csv').option(\"header\", True).schema(schema).load(\"auto_mpg_original.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "48e51055",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+--------+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin| carName|\n",
      "+---+---------+------------+----------+------+------------+----------+------+--------+\n",
      "| 15|        8|         350|       165|  3693|        11.5|        70|     1|   buick|\n",
      "| 18|        8|         318|       150|  3436|        11.0|        70|     1|plymouth|\n",
      "| 16|        8|         304|       150|  3433|        12.0|        70|     1|     amc|\n",
      "| 17|        8|         302|       140|  3449|        10.5|        70|     1|    ford|\n",
      "| 15|        8|         429|       198|  4341|        10.0|        70|     1|    ford|\n",
      "+---+---------+------------+----------+------+------------+----------+------+--------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display 5 rows of df\n",
    "car_df.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "78751634",
   "metadata": {},
   "outputs": [],
   "source": [
    "#select features for df\n",
    "car_supervised = RFormula(formula=\"carName ~ .\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f3b7a9ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#call fit to return train version of transformer\n",
    "car_fitted_rf = car_supervised.fit(car_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "845e16bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+---------+-------------------------------------------+-----+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin|carName  |features                                   |label|\n",
      "+---+---------+------------+----------+------+------------+----------+------+---------+-------------------------------------------+-----+\n",
      "|15 |8        |350         |165       |3693  |11.5        |70        |1     |buick    |[15.0,8.0,350.0,165.0,3693.0,11.5,70.0,1.0]|7.0  |\n",
      "|18 |8        |318         |150       |3436  |11.0        |70        |1     |plymouth |[18.0,8.0,318.0,150.0,3436.0,11.0,70.0,1.0]|2.0  |\n",
      "|16 |8        |304         |150       |3433  |12.0        |70        |1     |amc      |[16.0,8.0,304.0,150.0,3433.0,12.0,70.0,1.0]|3.0  |\n",
      "|17 |8        |302         |140       |3449  |10.5        |70        |1     |ford     |[17.0,8.0,302.0,140.0,3449.0,10.5,70.0,1.0]|0.0  |\n",
      "|15 |8        |429         |198       |4341  |10.0        |70        |1     |ford     |[15.0,8.0,429.0,198.0,4341.0,10.0,70.0,1.0]|0.0  |\n",
      "|14 |8        |454         |220       |4354  |9.0         |70        |1     |chevrolet|[14.0,8.0,454.0,220.0,4354.0,9.0,70.0,1.0] |1.0  |\n",
      "|14 |8        |440         |215       |4312  |8.5         |70        |1     |plymouth |[14.0,8.0,440.0,215.0,4312.0,8.5,70.0,1.0] |2.0  |\n",
      "|14 |8        |455         |225       |4425  |10.0        |70        |1     |pontiac  |[14.0,8.0,455.0,225.0,4425.0,10.0,70.0,1.0]|8.0  |\n",
      "|15 |8        |390         |190       |3850  |8.5         |70        |1     |amc      |[15.0,8.0,390.0,190.0,3850.0,8.5,70.0,1.0] |3.0  |\n",
      "|26 |4        |133         |115       |3090  |17.5        |70        |2     |citroen  |[26.0,4.0,133.0,115.0,3090.0,17.5,70.0,2.0]|31.0 |\n",
      "+---+---------+------------+----------+------+------------+----------+------+---------+-------------------------------------------+-----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#apply transformation and display 10\n",
    "car_prepared_df = car_fitted_rf.transform(car_df)\n",
    "car_prepared_df.show(10, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f846f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data 80/20 for train/test split\n",
    "car_train, car_test = car_prepared_df.randomSplit([0.8, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6189fd6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+-------+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin|carName|\n",
      "+---+---------+------------+----------+------+------------+----------+------+-------+\n",
      "|140|        0|           1|         1|     0|           0|         0|     0|      0|\n",
      "+---+---------+------------+----------+------+------------+----------+------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#check for null values\n",
    "from pyspark.sql.functions import col,isnan, when, count\n",
    "car_df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in car_df.columns]\n",
    "   ).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ca22c10f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+---------+------------+----------+------+------------+----------+------+----------+\n",
      "| mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin|   carName|\n",
      "+----+---------+------------+----------+------+------------+----------+------+----------+\n",
      "|  25|        4|        null|        80|  2126|        17.0|        72|     1|     dodge|\n",
      "|null|        8|         305|       140|  4215|        13.0|        76|     1| chevrolet|\n",
      "|null|        8|         304|       120|  3962|        13.9|        76|     1|       amc|\n",
      "|null|        8|         351|       152|  4215|        12.8|        76|     1|      ford|\n",
      "|null|        6|         232|        90|  3085|        17.6|        76|     1|       amc|\n",
      "|null|        4|          98|        60|  2164|        22.1|        76|     1| chevrolet|\n",
      "|null|        6|         250|       110|  3645|        16.2|        76|     1|   pontiac|\n",
      "|null|        6|         258|        95|  3193|        17.8|        76|     1|       amc|\n",
      "|null|        4|          97|        71|  1825|        12.2|        76|     2|volkswagen|\n",
      "|null|        4|         140|        72|  2565|        13.6|        76|     1|      ford|\n",
      "+----+---------+------------+----------+------+------------+----------+------+----------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display columns with null values\n",
    "car_df.filter(\"mpg is NULL OR displacement is NULL OR horsepower is NULL\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd4149d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create estimator to remove null values\n",
    "from pyspark.ml.feature import Imputer\n",
    "\n",
    "imputer = Imputer(\n",
    "    inputCols=['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin'], \n",
    "    outputCols=[c for c in car_df.columns if c not in 'carName']\n",
    ").setStrategy('mean')\n",
    "\n",
    "car_df = imputer.fit(car_df).transform(car_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ee1ed91b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+---------+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin|  carName|\n",
      "+---+---------+------------+----------+------+------------+----------+------+---------+\n",
      "| 15|        8|         350|       165|  3693|        11.5|        70|     1|    buick|\n",
      "| 18|        8|         318|       150|  3436|        11.0|        70|     1| plymouth|\n",
      "| 16|        8|         304|       150|  3433|        12.0|        70|     1|      amc|\n",
      "| 17|        8|         302|       140|  3449|        10.5|        70|     1|     ford|\n",
      "| 15|        8|         429|       198|  4341|        10.0|        70|     1|     ford|\n",
      "| 14|        8|         454|       220|  4354|         9.0|        70|     1|chevrolet|\n",
      "| 14|        8|         440|       215|  4312|         8.5|        70|     1| plymouth|\n",
      "| 14|        8|         455|       225|  4425|        10.0|        70|     1|  pontiac|\n",
      "| 15|        8|         390|       190|  3850|         8.5|        70|     1|      amc|\n",
      "| 26|        4|         133|       115|  3090|        17.5|        70|     2|  citroen|\n",
      "+---+---------+------------+----------+------+------------+----------+------+---------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display data\n",
    "car_df.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0d296da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+-------+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin|carName|\n",
      "+---+---------+------------+----------+------+------------+----------+------+-------+\n",
      "|  0|        0|           0|         0|     0|           0|         0|     0|      0|\n",
      "+---+---------+------------+----------+------+------------+----------+------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#check for null values\n",
    "car_df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in car_df.columns]\n",
    "   ).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7f768a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+--------+--------+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin| carName|features|\n",
      "+---+---------+------------+----------+------+------------+----------+------+--------+--------+\n",
      "| 15|        8|         350|       165|  3693|        11.5|        70|     1|   buick|  [15.0]|\n",
      "| 18|        8|         318|       150|  3436|        11.0|        70|     1|plymouth|  [18.0]|\n",
      "| 16|        8|         304|       150|  3433|        12.0|        70|     1|     amc|  [16.0]|\n",
      "| 17|        8|         302|       140|  3449|        10.5|        70|     1|    ford|  [17.0]|\n",
      "| 15|        8|         429|       198|  4341|        10.0|        70|     1|    ford|  [15.0]|\n",
      "+---+---------+------------+----------+------+------------+----------+------+--------+--------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#create features vector\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "vectorAssembler = VectorAssembler(inputCols = ['mpg'], outputCol = 'features')\n",
    "mpg_df = vectorAssembler.transform(car_df)\n",
    "mpg_df.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c0eac8b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+----------+---+\n",
      "|features|horsepower|mpg|\n",
      "+--------+----------+---+\n",
      "|[15.0]  |165       |15 |\n",
      "|[18.0]  |150       |18 |\n",
      "|[16.0]  |150       |16 |\n",
      "+--------+----------+---+\n",
      "only showing top 3 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#select columns and display \n",
    "mpg_dff = mpg_df.select(['features', 'horsepower', 'mpg'])\n",
    "mpg_dff.show(3, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9ae16c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into 80/20 train/test split\n",
    "splits = mpg_dff.randomSplit([0.8, 0.2])\n",
    "train_df = splits[0]\n",
    "test_df = splits[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2ea5cea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-4.203877714542793]\n",
      "Intercept: 195.69077051488338\n"
     ]
    }
   ],
   "source": [
    "#instantiate an instance of Linear Regression and train model\n",
    "from pyspark.ml.regression import LinearRegression\n",
    "\n",
    "lr = LinearRegression(featuresCol ='features', labelCol='horsepower', maxIter=20, regParam=0.3, elasticNetParam=0.8)\n",
    "lr_model = lr.fit(train_df)\n",
    "\n",
    "#check coefficients and intercept\n",
    "\n",
    "print(\"Coefficients: \" + str(lr_model.coefficients))\n",
    "print(\"Intercept: \" + str(lr_model.intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "32956631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 29.942243\n",
      "r2: 0.423148\n"
     ]
    }
   ],
   "source": [
    "#check the rmse and r2 which both indicate that the model may need more work\n",
    "trainingSummary = lr_model.summary\n",
    "print(\"RMSE: %f\" % trainingSummary.rootMeanSquaredError)\n",
    "print(\"r2: %f\" % trainingSummary.r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2a5d49cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+----------+--------+\n",
      "|prediction        |horsepower|features|\n",
      "+------------------+----------+--------+\n",
      "|145.24423794036986|167       |[12.0]  |\n",
      "|145.24423794036986|198       |[12.0]  |\n",
      "|141.04036022582707|140       |[13.0]  |\n",
      "|141.04036022582707|145       |[13.0]  |\n",
      "|136.83648251128426|148       |[14.0]  |\n",
      "|136.83648251128426|215       |[14.0]  |\n",
      "|132.63260479674148|145       |[15.0]  |\n",
      "|132.63260479674148|150       |[15.0]  |\n",
      "|132.63260479674148|170       |[15.0]  |\n",
      "|132.63260479674148|190       |[15.0]  |\n",
      "+------------------+----------+--------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#make predictions using test data\n",
    "lr_predictions = lr_model.transform(test_df)\n",
    "lr_predictions.select(\"prediction\",\"horsepower\",\"features\").show(10, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5428504b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared (R2) on test data = 0.315397\n"
     ]
    }
   ],
   "source": [
    "# Use Regression Evalutor to check r2 for test data, which is even lower than the 40% for our training data\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "lr_evaluator = RegressionEvaluator(predictionCol=\"prediction\", \\\n",
    "                 labelCol=\"horsepower\",metricName=\"r2\")\n",
    "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d1f1c684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE) on test data = 28.5161\n"
     ]
    }
   ],
   "source": [
    "#check the RMSE \n",
    "test_result = lr_model.evaluate(test_df)\n",
    "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % test_result.rootMeanSquaredError)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5c0df1c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numIterations: 2\n",
      "objectiveHistory: [0.49999999999999956, 0.4383841686529122, 0.292658520890466]\n",
      "+-------------------+\n",
      "|          residuals|\n",
      "+-------------------+\n",
      "|  35.14412891600176|\n",
      "| 46.348006630544546|\n",
      "| 61.348006630544546|\n",
      "| 0.5518843450873305|\n",
      "|  30.55188434508733|\n",
      "|  58.55188434508733|\n",
      "|  60.55188434508733|\n",
      "| 14.755762059630143|\n",
      "|  34.75576205963014|\n",
      "|  34.75576205963014|\n",
      "|  79.75576205963014|\n",
      "|-12.040360225827072|\n",
      "|-11.040360225827072|\n",
      "|-11.040360225827072|\n",
      "| 3.9596397741729277|\n",
      "|  8.959639774172928|\n",
      "|  8.959639774172928|\n",
      "|  8.959639774172928|\n",
      "|  8.959639774172928|\n",
      "| 13.959639774172928|\n",
      "+-------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display additional info about our model including totalIterations, objective history and the residuals\n",
    "print(\"numIterations: %d\" % trainingSummary.totalIterations)\n",
    "print(\"objectiveHistory: %s\" % str(trainingSummary.objectiveHistory))\n",
    "trainingSummary.residuals.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5513d8d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+----------+--------+\n",
      "|prediction        |horsepower|features|\n",
      "+------------------+----------+--------+\n",
      "|145.24423794036986|167       |[12.0]  |\n",
      "|145.24423794036986|198       |[12.0]  |\n",
      "|141.04036022582707|140       |[13.0]  |\n",
      "|141.04036022582707|145       |[13.0]  |\n",
      "|136.83648251128426|148       |[14.0]  |\n",
      "|136.83648251128426|215       |[14.0]  |\n",
      "|132.63260479674148|145       |[15.0]  |\n",
      "|132.63260479674148|150       |[15.0]  |\n",
      "|132.63260479674148|170       |[15.0]  |\n",
      "|132.63260479674148|190       |[15.0]  |\n",
      "|128.4287270821987 |100       |[16.0]  |\n",
      "|124.22484936765589|100       |[17.0]  |\n",
      "|120.02097165311311|88        |[18.0]  |\n",
      "|115.81709393857031|97        |[19.0]  |\n",
      "|115.81709393857031|100       |[19.0]  |\n",
      "|111.61321622402752|90        |[20.0]  |\n",
      "|111.61321622402752|95        |[20.0]  |\n",
      "|111.61321622402752|102       |[20.0]  |\n",
      "|107.40933850948473|48        |[21.0]  |\n",
      "|107.40933850948473|60        |[21.0]  |\n",
      "+------------------+----------+--------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display summary for predictions\n",
    "predictions = lr_model.transform(test_df)\n",
    "predictions.select(\"prediction\",\"horsepower\",\"features\").show(20,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ee256f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
      "0  18.0          8         307.0       130.0    3504          12.0   \n",
      "1  15.0          8         350.0       165.0    3693          11.5   \n",
      "2  18.0          8         318.0       150.0    3436          11.0   \n",
      "3  16.0          8         304.0       150.0    3433          12.0   \n",
      "4  17.0          8         302.0       140.0    3449          10.5   \n",
      "\n",
      "   model year  origin    carName  \n",
      "0          70       1  chevrolet  \n",
      "1          70       1      buick  \n",
      "2          70       1   plymouth  \n",
      "3          70       1        amc  \n",
      "4          70       1       ford  \n"
     ]
    }
   ],
   "source": [
    "#create pandas dataframe for data\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "from pandas import read_csv\n",
    "\n",
    "col_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin', 'carName']\n",
    "\n",
    "data = read_csv('auto_mpg_original.csv',names=col_names, header=None)\n",
    "print(data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f3c7fef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a correlation matrix for the data. There appears to be a number of features that are highly correlated with weight, displacement, cylinders and mpg.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.heatmap(data.corr().abs(),  annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "72b89aac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f93dc1c3220>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DlBSKJJ3t3rjkRNwOlXA0ycOr3+Fnf9xEIDzU6GY4E5xCSoTBAyYl0wrgHT1DJWTFIFQP+RGlFsF4UZaAu2LFChYuXMjcuXPZvn175vjChQu55JJLWLp0KUuXLuWvf/1r5rE9e/awbNkyFi9ezLJly3jvvffKsdSCrbuSJHHO+6Zwx83nMH92HQBvbuvknofX5Q2CaROc7v4Yum4NdxzORWxBS9OQAZN6qlZspl57pIGh0GtNZoT1pGC8KEtJYdGiRVx//fVce+21Qx770Y9+xJw5c4Ycv/POO1m+fDlLly5l9erV3HHHHfzqV78a87X6PQ5sikQommS4vga/18HnPn4K695pY+XarYRS2e5v1m5j2UVzOPPE5pznxxIanX0a1R4HLoeNVX/ZzqZdPYD1w49psKClCVcqc4bc5gc5VbLY1xYkoRkc6Ahz2ddWc2yzhwf+v0UA/OcTG3n5rUMYxsDYnq9duyATXNP1ynSwzp7skF3LnD+7jk27uid0bVOUWgTjRVkC7oIFC0b0/O7ubjZv3swvf/lLAJYsWcK9995LT08PtbW1Y7HEDLIs4a1y4HSoBMJJYsM0NkiShNdto8ppA5IkkgahaJJHnnqXgx0hrhh0257eUPvB02/wXmsw57FNu3q4/Sd/xdDz+zSYmJlgm83ethCf/48XOG5qNS+9cTBz3DDMzL8/dPqMgmN7sh871Blk8+5u/F4Hfo99wtY2hfWkYLwY902zW2+9FdM0OeOMM/jqV7+Kz+ejtbWVpqYmFMW67VMUhcbGRlpbW/MG3EAgQCAQyDnW1tZ2ROuyqQp11QqRWJJgOIGWJ91du34fdpuMx+UiHNPoDcYxTfjfdXvp7Ity9cVz8boHPBdMYN277dR4HdT7FXoCcYzUddMZbz5MkyHBNs3ethB720J5H3vpjYN0B+J5h1mmM93sxyIxDSTrzxqvI+e5EyngCutJwXgxrgH3iSeeYMqUKSQSCe677z7uuecevvvd7474Oo899hgPPPDAGKwQ3E4bDrtKKJIgHEuSLcHt6o9S5VCQJAmPy4bTbgXRWELn9a0dbN/XyzUXz+P0eY2ZcwzDpLs/hsdlo7HGRW8wTjxx5CN9hqOQNMyEnMeSmoEiWX8Ofu5Eo5QNJgJBsYxrwJ0yZQoAdrud5cuXc8stt2SOt7e3o+s6iqKg6zodHR2Z5w/mhhtu4Morr8w51tbWlrdmPBoUWUrVX1UC4UTGYKa+2kV/KJbZgFEVGV+VjWqPg/5wnGAkyc/+uIkFLY1cfdFcPFnZbiiaJJHUqfE5ica1vEqHUnC4emX2YzZVJqEZ2FU573MFAsGRMW6ysEgkQjBo1TJN0+SZZ56hpaUFgLq6OlpaWlizZg0Aa9asoaWlZdj6rc/nY/r06Tlfzc3NeZ97JNhtCvV+l9XCK0tcfPYx6LpJPKljmtafhgGfWHQCd950DifNspQMG7d0cPfD63hzWwdzZlRnrpfQDDp7I6iKzAfmN2NT8iskZAmObfbkfezYZg9+T36rSL/HXlAaNvgxt1MFE9xOVcjIBIIxoCwTH77zne+wdu1aurq6qKmpwe/389BDD/GFL3wBXdcxDIPZs2fzrW99i8ZG6/Z7165d3HbbbQQCAXw+HytWrGDWrFlFv+ZoJz4Ui26YhMIJNmxt49m/76W7P0pdtYuLzz6G98222oFN0+Tvb7fyuxe3ZzrJFrQ0sWNfN/3hgc24Gq+NFZ8/n8ee2cJb2zsIRwceU2Q4cVY999/yQa799tMEIgOPuewyxx9Ty762AMFwIkdV4bQrfOP6M1nQ0pRRIqTrldlKBLdDxcQkFtdzHsv33ImqWhAIykVRAVfXdW688UZ+8YtfYLcXNt6uFMY64KaJJ3WCoQRxbfg6bE8gxuPPbmHznoGNMSnVTaan/BQ++sHjOG6qj+fX7wVA0wxiWSN2duzvzXShKRIkdRPTBF+VjQa/i75QnGA4idtpY0azd9jAOJIxPmLkj0BQWooqKSiKwoEDBzCM/DvlkxmHTaHO77Sm+w7zadb6nHzhn07lkx9pyRwzTdCMlMZWghc27OekWfV8+Kxj8bjt2GwK9X53JrgN7kJLE4poSJJEjddJY62LGc1e7r/lg8MGxJF0WYmOLIGgtBS9afav//qv3HXXXXzhC1+gubk5xz9Alid3h7AkSVS57Djt1qZaNK4N8f6SJIkPnjKVXz+7BQlLImaaoOkmSso3AeB9s+t53+x6JImMzy5ANK6RtZeVUUsYpsl7rYGUCY79sIqCkRjaCPMbgaC0FB1wv/WtbwGwevXqzDHTNJEkiS1btpR+ZUchiiJT43PijmuWG5g+9I7A5VCJJTQUScqM6NENSwkRiibxuKxx7Nk+u9UeB26nagXdPPtqsiShaSadfTGmN+XfXEszki4r0ZElEJSWogPuCy+8MJbrmFA4HCoNdoVQJDGkRXjRmTN45pU9mJioslVWAGsT7p6H17F88TxOndOQeX48qdPdH+ETC4/nN8/vIKHpOX4LsiwhSSlbXRMkCo9xH0mXlejIEghKy4hVCoZh0NXVlVETVCrl2jQ7HEnNIBixygxpHvnTJjZs6ciUBWZO8dLaHck0QHjddhRForHGnVE9SMArbx/kuXV76eyLZWRchpFuVDDRdOuCVS5bjo/uYL8ETIN39/QO8V3IR/rc/W1BkrqBqkgc0+wTagWBYBQUHXADgQB33303zz33HKqq8tZbb/HCCy/w9ttv85WvfGWs1zliKiXgponGkwTDSd7a3sHKtdtQFAl7qtFA100++sHj+MvG/ezvsNp0ZVnC57ahKjJXXzw3IzWzZqA5+M4j6+nqi+C0q/QEYvQErGGXkpRyPDNh2UVzOGFGTY7SoL03QiiiIcugyhK6CaZhUu93gZTfc3c4tcLCM2cIyZhAMAKK3u2688478Xg8vPjii9hsVp3xtNNO49lnnx2zxU0kXA4b9X4Xb2zvxFtlw2GzWoIdNgVFkVj3ThtOh4rXbUOSrBbgvpBVknj21fcy10n77F527nGoskwsodEbGJgsrEgSqixjmCa/eX479z/6Gr3BGLpu1dsjKY2vaYIkychIGCZ09g3vuZtPrZDUdX73wo68494FAkF+ig64r776aqYxIa1QqK2tpbu7e8wWN9GQZYndB/vxexzUV7uwpWQHdlWmuz9KdyCG32NnSp0bh91qF44ldHYf7OftnV2Z65gmTGv0suziuRzT7EOS0rpeCUWR0XUD07SCdvqrsy9KOKuenL6v0VJSPysA55d+5fOPDUc1DMMUkjGBYAQUHXC9Xi+9vb05xw4dOkRDQ8MwZwjyUe930dkXJZbUqfE6qfY40HSTumoX9dUugpEk3f1xkkkdNWWGbprwk9//g0fXvEs4NjCk8rip1XxqyYkc0+zF5VBRUkLgtHG5LEnWiB9JQkKiLxQn7a+eVvWlA6+cJfMbLP1qqnVn/CPSpOexZSMkYwJBYYoOuJ/4xCf44he/yLp16zAMgzfffJNvfOMbXH311WO5vglH2r8gEI7TG4wRT2jU+pxc+oGZzDnGTyCcIKnrKDIYmEhAXbUTgHXvtHHPw+tysl3dgFPnNFBdZcflVDBNIxNE/V47fq8D07Q8dZOagTulq5UkMM0B2Vp1lh/DYOlXPj8GRZaoSknYhjtPIBDkUnTA/cxnPsMll1zCPffcg6Zp3H777SxatIgbbrhhLNc34Rg89sbhsPGJRXM4//TpdPdHafA7sSkyhgk2RabaY6PW5+Sai+fisCn0hxKpbHczkVS2e8n7j2NBSxNuhw2/z4ndZrmW1fqcVDlVGmpcKLKMJEkcN9XPBadPw+WwoRngsCs4bDKRWJL3WgPsbw8SiiRzpF/5RvV8fNEJ2BRZzEsTCEZAWcxrxoNKUykUwy3//gJNNS6SSYNAJIFhmJimSSSuc98tH6SrL8qvn9nCtn1Waafa4+C6j8xjfkrBAJaz2IH2EL96ZjOaYRTll/CjJ98kEtPQdANVkXE7Vb647LTDKg4Gm+IIlYJAUJgRtfaeddZZnH322cybN28s1zRpqfE6ae+JUON10FDtIhRN0BOMU1ftAqz675euOY2X3zjAqpd20h+K8+Pf/YP3z5/CJxadgNtp4+2dXbywcb+ldDChJxBnetPwZjarXtpJlUvNlC3AajMuZsqDMPEWCEZG0QH3ggsuYOPGjTz22GOEQiFOP/10zjrrLBYsWMDJJ588lmucNKQ7u3qDcVwOHZsqU+tzcvHZx2aeI0sSF5wxg5Nm1/OrpzezY38fr25qZct7PZx3ylRe3dSa0fi6HQp+j4OPX3g8p8/LHxiFX4JAUD5GVVI4ePAgv/3tb3n88ceJRCIV6aVwNJYUYOht+scvPJ6TjqvL681gmCb/98YB/uelnSSS1mNOu0J9tTMz7t0EZjT5+MYnF+QdAX/7g68M8UuIJaw67f23fHDs3qigIIO7A0W5ZmJQdIa7a9cuNmzYwIYNG3j99depr69n2bJlnHXWWWO5vknHcLfp9baUN0PWXDVZkrjwjBm8b1Ydv3pmCzv29xFL6LR2R6j1WSOBME227+uhqy9KtdcxRE8r/BIqj+zOvsGTlkXQPbopOuBeeumlHHPMMfzzP/8z9957L263kP+MlpVrt7L65d1E4xouh5rjezAcsiyx40Afa9fvo6MnjE1VWHTmDN43u56GGjdfWX46d/7sVTp7o+iG5RxW5VRxO1XsNoUVv95IOJZk5hQfi88+ltPmDnhhOBwKhzrDAExr8HDT5SeKH+xxJLuzD5iw05MnI0UH3BUrVvD666/zyCOP8PDDD3PmmWdmvoYb7igYysq1W/nN2m2Zjq9wNMlv1m4DKBh0s7Oe6irL3Oapv+4GLA9dWZJY9uE5PPHsFkIxjaRmEI5pROIaLruCrlvDIfcc7OPRp4L8/gU7+ztChKNJvFU2jmnyEE/qxLJMdgTjg6irT1yK1uEuXbqUe+65h2eeeYbHH3+c2tpa7r77bhYuXDiW65tw/OEvOzFMkBj4MkzreCGys5540iAW16lyqby+rSNjyPi+2fVc+5EWZk7x4XaqqeYGiMR1QhGrFJHUDN5rC3KoK4zHbUNRZfpDCSIxTbTnVgj5OvtEU8nEoOgMd/Pmzbz22mu89tprvP766zgcDi644AJRwx0haQvGHNtaM+v4MAzOegzTJJ4wONAepM7nzGyqpSdGAHT0Rrjn4XVoukk4phFL6MiSpdXtDcaJxDTqqx2EIhp9oThVLpvIpCoAUVefuBQdcD//+c9z5plnsnDhQm677TaOOeaYsVzXhCWddeY7Xojhpi/U+FwDhufRJKHIwPTexho3x02tprU7TDiSRDdMdKxYb1OtiRPtPRFqfU7sNhuyJBFJaCKTGmcWtDTBVSeLppIJSNEB98UXXxzLdUwaGvwuOnqjQ4Jug99V8LzDZT2SJOF1D8xVS89IW3zOsfxm7TYc1TLBSJJE0sAENMPE61YJRQw6+qL4PQ6cDoV4QheZVAUgmkomJkUHXIA//OEPrF69mvb2dpqamli6dCkf+9jHxmptE5JbPnYK3318I5G4lrJEBLdD5ZaPnVLwvGKzHpsqU1ftJBJLEgjHed/ses6ZH+CFDftJJA1kGQzDsm7sDyWwqzK6YZLQDDTd5NpL5uUoGMqpBxXaU8FEp+jGhwcffJA//vGPfPrTn2bq1KkcOnSIRx99lMsvv5xbbrllrNc5Yiq58aFcHgS6bvDau208+vRmdMPITJgIRZIkNYNEaqCa123jpsvfx7yZtUDKNMfr4J2dXTyUZ9JDPk+GI2W4qRJj8VoCwXhRdIb7u9/9jl//+tdMmzYtc+zcc8/luuuuq8iAW8mU63ZRUWT+d/1e6vwOMCWC4QQOmwRu8FU5OOWEBla/vItgJMkPfvMm5582zSonOFS6+6M8v2EfHrdKeodvLPWgQnsqmAwULQuLRqPU1tbmHPP7/cRisZIvSlA6DnSEiEQ1JKw6scuhYldlegIxPnzWMXzzU2cxa1o1AC+/eZB7H1nP1vd6ME3Yvq8Xj8tujf1JXW+sVAz5pkoIxYRgolF0hnveeedx66238rWvfY2pU6dy8OBBfvCDH3DuueeO5foER0ha3SBJEvGkTpXLhqJIeKusENpcV8Wt157Bnzfs408v76a7P5bJdqurHLR2h6n3W5MpQtEkoWiCxlp3yeutw6kwhGJCMJEoOsO94447qKqqYunSpZx66qksXboUl8vFt7/97bFcn+AIyZ7WoOkGHb0RIlGNTyw6ITPCR5YlLj77WL716bM4bqoPsLLd9p4I0bhGV1+MvmAMu03GoSrMn11nuZoNM0By45Z2bn/wFW6673luf/CVogZL5psqIbSngonGiN3CDMOgt7eXmpoaZLnoeF12KnnTrFQUyjKzH3M5FCQkonEtZ5NON0xCkQQbtrTx7N/30tUfpc7npKHGxWvvtqOl3MmqPQ4U2dL1XnbeLP6+qZUD7QFs6kAJIO0wlpavjWbz63CbiULFIDjaGVHAfe+993j22Wfp6OigsbGRj3zkI8ycOXMMlzd6JnrALbSrDxQd9F7f2s6Ta7eBZBKNaUQTOrpucvE5x/Lqplb2HAoAUF/t5JMfPZG5x9Zw7yPrmVLrIpbQiaU65EzTJBS1AvpY2D0KFYNgIlB0ivrUU09x5ZVXsm3bNlwuF9u3b+fKK6/kqaeeGsv1CYYhe1d/8JjyQo8N5g9/2UkwmkCRZRpq3NRWO1EUiY1bOrj1ujO48oLjURWZrv4Y31/5Br9Zuw2XXeVQVxhVsWanSdJAvXWsNr9G8p4Egkql6E2zH/zgB/zsZz/jzDPPzBzbuHEjX//617nsssvGZHGC4SnkKGVC0W5T6evEEjqJpEGVS2VaQxWt3VEUWWbxOccy//g6Hnt6C3tbA7z0xgF8VXYkoNUIU+tz4nKomQ61VS/tHJPNL+GgJZgIFJ3hhsNhTj311Jxjp5xyCpGI+A8/HhRylBqJ21T2cw3TJBhJ0heKM2taNTbF+u8xtd7D1z95BldeMBtVkQiEE/SHE8QTBu09ETQdrrukhdPmNo7Z5pdw0BJMBIoOuJ/61Kf43ve+RzweByAWi/H973+fT33qU2O2OMHwFApsIwl6+Z4biep8+MxjqPe78LmtkoGV7c7k9k+dxbFTLCVDXyiOLMt85P3HcswUH919UebPquNfPpY7Ur0UddZi3tNo1BECQTkpetPsQx/6EF1dXUiShM/nIxAIYJomDQ0NOc976aWXxmKdI2aib5pB4V39kbQPH+65SU2nP5TIZJi6YfD8+n2s+dtuNN3673PhGdO54kPH47QrVDlteKvseWeojeX7FZtqgkqn6ID72muvFXXBSvHHnQwBt9yEY0mC4TjpWZaHOkM89vRm9rYFAauT7fpLWzhhRo3lx+Cx887ubh5ds5mDnSEApjZUceOlJ5U0CG7c0s53n3idWFzDblPwex1UOVViCQ1VUfBW2XOkZMCo5GVCliY4UkY1tfdoQATcsUHXDYKRJJFYEpOh2a4EXLhgBld8aDa7DvTxzCvv0dEbJhzTMtMtvG4bX7r69JIEq3Rm29kXRZHBRMI0oaHGhWmadPRGmVrvzmS9oUgSSZKocqkjyoRFBi0oBUXXcBOJBN///vdZtGgRZ5xxBgB/+9vfePzxx8dscYLKQ1Fk/F4HdT4nNkVGkWUuef9M/u3Gszim2YsJvLhxP/c+sp5Vf9nFvo4gDrtKU40bu01BlqwGjFLJudJyMbtNxjQlZElCkqAvGKc3EEeWoLs/xt62IN39MULRZGac0EjkZUKWJigFRQfc+++/n+3bt/Pd734XKTWe4IQTTmDlypVjtjjB2FNoo6nQYw6HSkONtakmS9a0329cv4Cl589GkSU6e6Ps7wgST+j0BKKEYklqfU6cDoVE0uDd3d0l2dhK6379HgcmJoZhImGSSOokNB3DMNE0E1mS0DQTTTdJarlqh2LkZcJcR1AKitbh/vnPf2bt2rW43e5MS29TUxPt7WIn+Ggl+zY52xOBQd1qgx9L30JLkoS3yoHToaZkYvCRD8zk5BPqeezpzexL1XY1HULRJNFokmqvE7tNIRrX8l5zpKRNb6pcNsBSTiSSJk6HiqbpaIaZ2byTJEAfOuKoGHmZMNcRlIKiM1ybzYau52YGPT09+P3+Uq9JUCZK1a1mUxXqq134vZbnwrQGD9/45ALOPqk58xxdN0noJp19UTRNZ9bUanxVjiO+Lc+Wi7mdKnXVThr8Lm699gxcDhuYlr4YTAzTqjFLEiPWCQtzHUEpKDrDveSSS/jGN77Bv/3bvwHQ0dHB/fffz6WXXjpmixPkUqxZzeDHVq7dyuqXdxONa7gcKme2NNIdiPPu7m5UWcLEyvpsqoyEycHOMKZpTfetrrJTl5q3lr6F3rilnceezlUefPDkqbyzu5twNInHZaM/HCcQTuJyKETjub+oJVkiEkvidKgkkjqb91jlhULvZ/7sOjbt6h7y/ha0NLHjzBk572/p+bNY0NLEjGYvhzpDRGIaSc3Apsr4qux43DZ8VY4RTdwQgx0FpaBolUIikeA//uM/+P3vf080GsXlcvGJT3yCW2+9FbvdPtbrHDETTaUwWrOaHft7efL57SCBIkFSNzFN8FXZiMY1kpr17VdlSE3cGUKNxwq6sYSGosgEQgkC4QSSZM2C0HQTSYIanxObKtMfSlBdZUOSTNp746m6KmT/R3M5VBJJjWqvA7dDRVVkYnE97/vpCyXoC8bxe+34PY6i33uhx0SgFIwHo5KF9fT0UFNTk9k8q0QmWsC9/cFXhnXhAoZ9bPfBfkuPmqq7JzQ9lb1KqKpEImlF2ezx7ZIEqiKTzIrADpuMy6Hi9zpp7QpjmNZGFEAiqWNiBVHAsnU0welQqHLZCEWShKJJZGBwTLc23Kpoqq0iEI7jcNiGvJ+DnWESmo5dlZnW4Cn6vd9/ywfLNj9OICiGoksKO3fuxO/3U19fj8Ph4L/+67+QZZmbbroJl6vwiG/BkTNas5poXEPNqtSng6phmhiGhKpIaKmsN+c5Q34NS0iSRF8wjqYbqMrAL9v0U7MDtKYbJCMGkZhGtcdBo1MlnJJkZV/aMKEvlECSpFT2qhEIJ3E7BhQBSc2wsvOcXwDFGfWIceOCSqLoTbOvfe1rBAKWN+qKFSvYsGEDb731FnfccceYLU4wwGjNalwOFT0rwqVvSmRJsmq2koRNkXE5FNKduJIEmmFk5pjJksSMJg9VLpWkbqAqMkb2NVN/2lQZWSZjXA6gGyY9gRjBSIJanxO/z4HbqXJsszcTtIORJIe6wuzvDOG025g9rZrsmyebKqOnaswjee8CQaVRdMA9ePAgs2bNwjRN/vznP/PDH/6QH/3oR/ztb38by/UJUozWrGbp+bPAtAKoaQ4EQo9bpdpjxzCs3fvqKjvuVKaYLi+kY2q1x6rRO2wKqiLhdqoYholuGBiGdU1ZArdTZbgKVTSu09oTBtPk4wuP54oPzaa6yk6VM12GMOkNxFFkmH98HaoiY1Otyq/bqYI5cP3RGvUIBONN0SUFu91OKBRi165dNDc3U1tbi6ZpGfcwwdhyuF3y/5tZw8tvHcJI6U7PP3Vqzu10ehff7bRlVAodPRGcdoVwTKOtJwpAg99BJG4QjiaRJHA7LE+C91oDKLJEc73lhZCtUqivcZBI6PQEYpimlfFKspUZG6ZVrjBNcNhUTjmhgU27eojGkzTVVREMJ1DVBNG4TiKp88a2Tt7Z1Z0adOnA57Zx7BQfi8+pYdOu7vy12BGoB4rxQxCeCYKxouiAu2TJEq6//noikQjXXXcdAJs3b54QG1JHC8PVI1eu3cpf3zqEJIFdldBN+Otbh5jasJVrLp6X+RrMfz6xkZfeOJhzrLMvzgWnT+NDp8/gh795g2AkmSk1aLpJIJQA4L9uvRCwgtMPf/MGoaiGKkuWCgKQkWjwu6hy2Yadd6bIEr4qOx9beALHz/Dz2JrNbNjSTkIzQANJSiBj8k+L5nDW+6ZwzcX5N2mLrdMWavTI5zo23HMEgtFSdEnh9ttv56tf/Sp33XVXJuBKkpTR5QrGj9Uv7wYJVFlGkmRLkSCljhfg5bcOAVYJIf2VPr6gpQm/14mqyJhI2FSFxlo3VS41p1Hh0affpT+cQDfMVFeXdVw3THqD8Zxb/MHNFIYJoWiC17a0UeW0EYomqfU5MrXaSEyjsy/G4/+7la7eCNF48og+p2KaOYRngmAsKSrD1XWdxYsX88wzz+RobufPnz9mCxMUz2AlAlia22hcK3ieYeSvt6aPR+Ia0xurcuR/pmlmFAAbt7Szvz2UUTikSweyZKkPEpqRyWwXtDTx4Kq3hygKVEVm1/5+6qqdIFmbg1VOa6JEIJxEN0z2tQf53Ys7uOzcWVR7DLxVdpRReO0WM6ZHjPIRjCVFZbiKoqAoiqjXViiDlQgAujmgix2O4QzC08cLKQDSHrTZQTsdl02sIHXSrDruv+WDmVvxQteTJAm7KgMmjbVuGvwummtdGSXDc+v2ct+jr/Hunm66+6LED/PLJB/FKBqE6kEwlhRdUrj++uv58pe/zGuvvca+ffvYv39/5kswvgxWImiG1Xiw9PxZBc87/9SpwEBmms5U08eHUwDMn13HT1e9TSyuoeTR+JopRcFgpcDhFAVXfOh4egMJeoMxarwOvFV2/FV2FrQ0IksSrV1hVvxqA799YTttvRH6Q/Fhs/R8FKNoEKoHwVhSdKfZvHlDN13AquNu2bKl4LkrVqzgueee4+DBgzz11FPMmTMHgD179nDbbbfR19eH3+9nxYoVzJw587CPFcNE6zSDwrvnt//kr2za1ZN57vzZtdz/ufMOe96n7/1fOvsG7lwa/A4e+fYlea9Z5VS59boFrHppJ3tb+wmE89dUbarM7TeeNewG32Dfg+wNvfRaO3sizJzq44QZft7Y1sn+9iCxhJbpjJtSX8UNl57ICdP9VHsdQ6wTD/cZFlI0VEp3mlBLTDzKMvFh48aNTJs2jWuvvZaHHnooE3Cvv/56Pvaxj7F06VJWr17NH/7wB371q18d9rFimGgBt5CXwmC/BD3VKbbsojmcMKNm2PP+7439Q1QKABecPo3u/mhOsE0zf3Ytuw8FCEfz39IrsjTsRIeRTk3YuKWdXz71Dn6vA1036eqLEIgkiSUG2pMvPudYlpx7HLVeBx63vaLbzUeCmDAxMSm6pJDm0KFDvPnmm7S2thZ9zoIFC5gyZUrOse7ubjZv3sySJUsAS3a2efNmenp6Cj6Wj0AgwIEDB3K+2traRvrWKppCu+eFVAqFziukUsgXbIGUhlbPnDeYxhoXHrct767+SBUAq17aiWGaRGMaqioxvclLfbWTGY1eptZXYZgm//vqe9z/6Gts2t1NV1+MxKD669GKUEtMTIrW4XZ0dPDVr36Vt956C7/fT19fH6eccgrf+973aGoa+W/c1tZWmpqaUBTrVlBRFBobG2ltbcU0zWEfq62tHXKtxx57jAceeGDEaziaKLR7XkilUOi8w6kUhiP9eL57oyqXLUfJMPg9yJJlRpO2S6z22IdVAKTXbgLhqIZNlWmqddMdiPP/ffIsnn5lD2vX7eVQZ5gVj23kkvcfy6XnHkeN5+jPdoVaYmJSdMC96667mDdvHj/72c9wu91EIhG+973vceedd/LQQw+N5RoPyw033MCVV16Zc6ytrY1rr712nFZUegpNHAhFk5YjWFZ8SasUCp3X2RfFMMy8gbMQVS4bkZhVv80+N+0eNtyuvtuhsrc1AKnWYd0wiHVrHDvFBwytWbodKvGkjm5AZ28kYwNZ7XGw/t1WrvzQbE6b08BjT2/mUFeYZ/7+Hv/Y0cUNqcnB1R47NrVwbbdS66RiwsTEpOiA+/rrr/PDH/4Qm82yz3O73Xz961/nvPPOG9ULT5kyhfb2dnRdR1EUdF2no6ODKVOmYJrmsI/lw+fz4fP5RrWOo4V0l1YMLaemd9UFx2dquJph5NRwl54/K1PDzXdeIBRjb1toyGvZ1bSV41CD3GObPXzwlGmZmjGmmRmbXu2xFdzVD8cSlj9DlpohfTxfh1cokiSpm0SiyYyvg2laAyJ/98IObIrMB0+eyr/deBZr/rabtev3crAzxP2PbsDpUDhhRg1XnD+LM09szpvtVkpXWb6gX+j7LTh6KbqGW11dza5du3KO7d69e9SBrq6ujpaWFtasWQPAmjVraGlpoba2tuBjk5UFLU189qqTqfG5CEWtVtn0Bso1F89j2UVzcNpVNAOcdpVlF83hmovn5T1v4ZkzWPXSTva1Dw22YLXwKsrQ/xqyDD6Pk2sunsd5p05NZanWY6oi0R9O0htMsPDMGQBDBlD2BRMMlv7KEvQFE6x6aSehWIJDnWF2HQxwqDOMZhhomj7UKRKIJ3RWPr+N+ho3Gza3sv7dtpxsOxbX2bKnmx//7h/87R+H8tZ2s+ukkbhOd3+czr4o333i9SMeblks6aDfG4jmBn0Y9vstOHopWqXw29/+lu9973t8/OMfZ+rUqRw8eJD/+Z//4Utf+hLLli0reO53vvMd1q5dS1dXFzU1Nfj9fp5++ml27drFbbfdRiAQwOfzsWLFCmbNsrSjhR4rhommUigV2VndgY7wsM9TZAk9VauVUhtyJiZVLhtfvvr0zDU0zaCzLwZAQ43VqBCKJJEkiSqXmpOdtfdG8pYvFEXCocpE4kOD4uBJEYNZvngu//OXnVR77cQTBn3B+JDn1/qcfOdf3o9/UG33pvuex+uygm1nbzQ1wcLK2Bv85QlwhYzl77/lg2P62oLyMyJZ2Lp163jqqafo7OyksbGRJUuWcM4554zl+kaNCLj5yf4B33mgf9jnZQc6VZZQFBnNMHDaVWZNq85c42BnCE2zLMJURWZaQxX724OA5aGbJpbQMrPSBmNTZXQjNeI8KwMu5n9m2hxHlWUcdgWXQ6UvFCee0DPm6mANtrzh0hM5frqfao8du03JfBbd/ZapuixJGIaJqkrUVTvLEvTSQX9w+3QoqvHwNy8a09cWlJ+iSwqJRIK9e/eiqirV1dUkEglWrVrF17/+9bFcn6DEtPdEMk0Cw9kRyOlhZVhlBEWxbBYxwabIOddIagaSZF0rPZFBN8wcE3KwdtiHC6C6YQz46GaKtcW9n2hcIz18IhhJzT7zOPB7HEytc+OwWf/Frdruazy46h+094QJRhJ8/EKrqyyR1JGwAr6Jid/jKJsiQLQSTy6K3jS77bbb2Lp1KxdeeCENDQ1juSbBGJK9++2wWyqAtMxLliTcLpXjplYDDJl4665SmZqaKZa+hk2V0TTLkjHt8mUZy+RG83hSzwydTE8JTv9bkWXsNoVoPJlpMU4HcZfDRjyezDvg0mGTUVUlpZiwmiHiSZ2O3gjVVY7UObnr2LC5nT0H+/mXq05m9nQ/t3zsZP7zv98gHE1it0n4PU6QrHKLYZpDpgmXmkrbHKtU1cZEoeiSwplnnskLL7xw1KgBREkhP9k1XE036exNG487UVPBs5hpuD968k0iMS0zQFKWoLHWXbCGa5gm3f0xZFnKOIoZhsmMJi8fOHnKsN1yhzpDw3bEAXkfA3DYlcyU3/5BtV1ZlvjoB2by0Q/MpK0rwq+f3UJS1wt+JmMVeCqplVh0t40tRQfcyy+/nEceeYT6+vqxXlNJEAF3eLJ/wMGkLxgnqZtDvA1Wrt3KH17cQTzlX2BTZdxOlRqvk9auUOZ4GrdDZfYMP/Nn1/HK24c41GltyqXrpzAQqDU9NTNNAo/LzoxmL3U+Bxu2dAzxWfjCd//CvrYAg/sxqlw26v1OegMxQhEtM0k4PXU4kdRBAq/bjtup0h9KEItrQ6oVLqfKeadMIRjVeGNrB4lErjJCAlRVZt7M2mGDYTkyw7F+DbGBN/YULCm8+uqrmb9fccUVfO5zn+P666+nrq4u53nvf//7x2Z1gjEhPSEhndE01LgyGc2LG/ZzwowaAJ55ZY8lEZMt+VdSMwhFkgTDiSHBD6wR7PNn1/Hihv2oqsQxTR7iSZ1YykpxQUsTX1x2miVJawsQiWn4quz4PXZ6A1E6eyLceu0ZOUFk45b2vMEWIBpLsr9do9HvpLFmoOb5XmsATTcymt9AOEE0rlHjdeCwK/QF44Ouo/H8+v1c8aHZNPqddAfiRGIDXhEm1nsfTqdbDj1vOV5DdLeNPQUD7je/+c0hx773ve/l/FuSJF544YXSrkpQFrJ1qGDpd2NomX79aFxDliQ0w8xUQvVBkS/jgWta+t3VL++mxmvPe810oF/Q0jQkmxr8vOw1Dn8LZk0c7g3G8bgtY/xw1DItH3zfltQMOvui+KrsNNW6CYTjJDQDPaViMIE/vrwLWZKo9TkttUMwnvN+C61xuM+xVMGwHK8hutvGnoIB98UXXyzXOgTjQHtPhGRS41BnJHM7Xu2xkUxtgiU047DSrMGPR+MazbWunGP5sqR9bQHiCR1Ntzbk/B4HJrD1vR5uuu/5zC1ze09kWMWCYZoYpoGmmexvD5LUzcwGYLaOOHut/aEEMZuO3+sgntDpC8VzHtdNk86+KG6nSkONi2AkSTg6YEOZ772UIzPM9xqaZgz5vLJnsw1XfhjusUrbwJuIFK1SEEw8JEx6g4nU360A1htM0FSrgDm8DrZQM4JNkegLxXPVDU6VqQ3ezHM2bmknErNqrookoWkmHb2Wr4PdJufcMrsdKnabQlLXMQYpFSTJyrhNyKknSxL43Db6w0lLzpa1bknKVTI01brpC8aHSLMiMY14QqPG58TtUOkNWs0d+TK+cmSGg18jHE3S2RdDVeQhJQZg2PJDoccONxlacOSIgDuJya5lmoOPF3DakqThg7HLodIbiGdqrpquE0/oLD5nJkBmNI+up7JnxWqsSCatExKawa6DgYxEzeFQcDtVAmEDWTJzarmyLGVKAtmYJgSjSZrq3HT0RNANE5ti+U+aJhip1+4LxS0lQ1a2a1PlLD0xdPXFcDsUGmpcSJKZN+MrR2Y4+DV6AtYvgNpqZ8a+MbscVKhUVKg0UewEZMHoGLEfrmDikExtiKVjqySBIlvHk5pBHjsFABRFHhKPJSxpWDB1+519TYC/v92a2fiJxTVURUJWJAzdzHSDwUAgN0yTUCRJZ0+ULy47jRlN3oy/g6XdlTImO/nQdJMqp4rf60BKSdBURaahJrfcEU9Y2S5YWaSvyjbkWpG4Tnt3hBqfi1s+NnSTqpDPRakY/BqGabVSVzkHcqZ0GSO7MWUkjwnGHpHhTkLSNTzTtDJGVZZQUsFLMwxcqewnGk9mstm0QbnLYUmx9rUFcy+aejxfxolkdXqlN37sNkvbqsoShmSiKnJmwvDg1t5owsq+InGNeTNrCYTj6LqRydAKtSfHEho2RebYZh+arufc8meTne1OqXNb49sjCXR9IKM2TJhS56Gh1kUwHB/it3u4zLAUkq7s10hvOmaTXcYoVOIYbflDNEUcOSLgTjKy5UV+j53eYALNMDHRU7UCy9ZxcLNBugNs1lQvBzqGeiJYG05DX29gdLqZ2fjxexwpL16QJDPXySvPdbOdtMJRLdMGfLg5ZukR7UDmdlzL17KWIpHQ6eyN4LCrNPrd9IfiRONa5n2tfnkXr246RJXThqxINNdVcdFZx3DGvMKbUYeTdI0mkB2ujFHosR/+5g06e6PohokiS7gcKjddPr/g61WKleXRjgi4k4xseVF6fEtvII5uQJVroNng9gdfweO2EYkONBS4XSq7DwWp8doJx5KZgY5pCtV2FVnKbPxUuazb9r5QnETSxOlQ0XTD6lobdL4kkVNvBFAVBW+VNSmiqdZFe09upicB1yyemzOckqtO5tGn36W1KzLsOu02mYRmIsk6umHgrbLjdqskEhpIln63ozcKRJGAjp4IHd3WFAvdGH4z6nDyu2ID2eDAvPDMGWza1Z1/g2uYza+NW9pTmbmU+sUlFTUZoxyytMmACLiTjMHyolqfkxqvY4g7VXtPhKYaF1JtrovVntYgzbUuDIOUMYz1g5vQDGyKREIbGslkCZwONScrcztVFEU67EBLt2NovTEU1fivWy/MHDvcJGCwbsdXvbSTKfUG4ahG76DmBwC304YsSwTDCRprXThtCqoqI0kSF511LP/93Fa6+q3NKhMIRpJEon2sXLudumonLqeCIlulmeyA1N4TQcbkYH9oYLRQ6hdGsYEsX4b54ob9w9aKhytxrHppJ1UulbpqZ+ZYLHH4wCmaIkqD2DSbZBTrTjXc81ypsTc2Vc7UN62SgVVSkCUJRZEytVhZghqfg2OafQU3l7oDcTxuW2ZMjyxZfgtaagnhmGXv+F5rkFA0mWMQfsKMGmZNq6be72LWtOpMp9xg0htGsYSOPOh/vixDLKHj99hxO23U+FwEoxo2m8rVF83jg6dMRTdMZCnXZU03YdveHnYe6KPG68CdZxPL5VDo7IuhadadgqaZdPbFcDqUojexSjVUcrSbZsLVrDSIDHeSUayEabjnLT1/Fi9u2I/baXViGZLVheZ2qURjOh63SjiqIcsSElhzxRQlc/3hMq98GXUokqCzL0ZvMJ6SqqVLDHLxutIs0iWNpGZgU2QSppHZELQplhwsntSZ0ezN6x2gqhJNNS76wkkSmp7ZIDRSY380TWdKvQe/x0E4liQYSdBY6yYYtrTO6fWnW44lpKI1vKXKMEerGRZNEaVBBNxJRrHi9kLPO2FGDate2omumyR1A1WROKbZx/zZdWza1c3+tmDm+NQGb1GbQPkCgarKTG/y0JWaKGFPdaSlTcfT2V1SN+gP5zZapG+Rs+ueLodCOKqlutAsw3E95fObbpxo74ly/HQ/n//ui0PMd9xOG919Mep8DsLRJIFwIrOhFkvoxBI6PcEEdlWiwe9GliSuuuB4Hkz5VfSHEgMlhWo70bjGDZeeWFQgK0VzxcYt7QTCcQ51RbApMjU+h2XSXkTgFE0RpWFEEx+OJoRb2NFFIWvAB1e9PexUhHhCJxhJDLF89LrtfOnq0/jhb96wlAapHXlFlvBW2ensi2FTJBQZInEr2NpVGbtdIRQZaOVNv6Lf6+AjH5jJM6/sIZ7U8bptKLJENK5zwRkzeGHDfoKRROY8mwqNNVXcvHQ+T/1tNx094WFduIqxZzxS68QcW07NoDflEHdMk5cbLj1RBM4yITLcSU6laCsXtDSx48wZQza/FrQ0Fczudh/sB4ms2i8YkpV5P/r0uwSynM003arBNtSqfPvTZ7PqpZ1sfa8Hh02httpJlVNl7yB9cTobCYYTbNrVzUc/eByrX95NV3+cOp+DpR+azQdOnso7u7rRdJ1oai5bUoPu/ijfX/k69dUuYnErA66usg/JYovp7iqUYRbzPczZnLODx20nltDwVtlFsC0jIuBOYipJW7lxSzsvbthPjddOc60rxyqyUP3wB795w/KBMCwjG8OwgmQsobGvLWjVaLNexzDhQEcoE+QGzxTL1unmOKEZJvvbgnT2RHLW+MJr+zhuqh/TNGnwO4klrOm/umESSxjEEgZVDhWXQ8U0TcIxjYaaI298yP7civkeCpVBZSBUCpOYUu18j/VaCqkbjmn2Ue2xg2RN2zWxFAeqLA9obaWsL8g4ikH+3ffhSOrGkDUiwR/+soN6v5NqjwNflZ0pde4cFURrT5RgJIFhmsyc4uPuz7x/TGwbC30PhcqgMhAZ7iRmPLKe4W5/D7eW4W6709mvLEnYFDIGNbXVTlq7UmPgLX1/pj6gZOm6BmfPiiIPDMA0syYXKxKqIg0rqfqXq07moVX/wKbIhGPJjLNZusmiL5SgP5ygL5Sgqy+Cx23H47IV1XRQiGK/h0eDyqBSyltjiQi4k5hyG04Xuv093FqGa25I135Xrt2W0QP7vXaqnCo2Vcp4+2a3DE9rHBjfPrg2Oq2xikOdYZKakTlFluCfPjyHTbu6hw7WdA4M1owndPoTcTwuG401LnoClu9DGtO0bB+/9L2XqHLZmDWtmo9+YBZnzGvMBJt9bQE03cSmyMxozq/wyA5M4WgSXdep8Q40MuT7Hma/z+zXSGfC4x3YKqm8NZaIgDuJKXfWU6irqtBaVq7dmhkwqcpWffbJ57cDVtPDixv2Y1dlq1QgSQTCSRx2FVWRSWpDywXHTckdhJqdPa9cu5WVz23LeTxdmpg/u47Nu7szgy4Tmk4iqLP4nGNZ9dJOPG4b9X7LjUzXjUzXWjhrXA9ANK4TTxr0Bdt5Z1c3x07xEkqNAQpHk1YN2rSmJv/oyTfxeexE4zpNte6cEUZel4quGxmbzfTAzOG+h+n3+KMn30TTrNFH2/f28qMn3+SLy04b18A2WVqHRQ13ElMOW8FsCnU5FVrL6pd3p4KtjCTJqLIMEqx+eXfmB7XW58zKZE16+mPEUoqBbKtIWYYNWzqGXePql3cjp0oH6S9ZkVj98m427erG77Vbwd1M6YK9djbt6h7y3hRFpicQw+20UVftRJZzSweGYaLp1oSMrXt6CUU1HHYFl11FlWUrWEcSBMIJ2rrCmazv9y/sIJlyS5MkiRqvA7/XQSxhFPU9fOzpzSnlhomqSBimSSCc4LGnN4/021lSJottpMhwjyKK8QwYKWNhOD1cLa6p1l3wlny4tUTjGoOtbxXJOp6uYUp2K6D1heIkUyPZTcCuSkiSdbJhWHKxcDTJ7Q++kvd2/XCv5fc4cm7fTdNMmegMLYlIQGdflFqfnWn1VXT1xzI2lAPnW38GQnHCUZk6n4O4ZtIXimMYJnJqgGd6Q0w3TMLRJDVeR+Yafo99iBfGcBzsDFm/eLJkdKZkcrAzdNhzx5LJMk9NZLhHCenb6lhCy7mtXrl263gvLYd0LS7bUvGnq95m45Z25s+uoy8YJ6HpyJJJQtPpC8aZP7uu4DVdDnWI9aNuWsezd9+rXDamNXhornMzb2YtVS5b5rx0sAUryGSvazSvlSYdFK664Hg0zSSWsOwjYwnrl6KqSPSHk/QEYnjdtowhenod2a+R0Axae6IkNZ3GGhdVLhuGQU52nD2RYvAaimXwNt2RbduVhnyfX6Vt6pUCEXCPEgrdVlcShWRKhW7JC7H0/FlgWubopmmgpcS2S8+fxVUXHE8okmR/e5D3WgPsbw8SiiS56oLjc85LZm1eVXscmXU9+vS73P7gK9x03/Pc/uArnNnSWPC1BgeF9Nj4B1e9jdOhoihy5tb+S1efzj99eA6KJBFL6PSH4mBCY40bR5b5z2CCkSRdfVGcdmu0j5o1eqPKpSLL0qgD09SGqkw3nmmaKf2ydXw8KXd5a7wQJYWjhEK3upVEIZmSCcPekhciXTbJV04p5O96zcXzONQZ4uW3DmWu5cmyJtQ0g9auCFPqjUw23tkT4bxTp7JhS0fe0s3/zazh5bcOZbS8TruCput4XSp9Iavmmu0Yds3F8zLeEx09EZwOhWgsid/nJBrXCIQH2oGz0XST/mAMu02l2mPH7VA41BUhEE5iUyR6gwlMwyqdJHWD7/xyPaos4XLYhlU3ANx46Un8x683EE3oGeMel13hxktPKvg9KJYjkXZNhnlqIuAeJbgcqlVOGHQb6nJU1rfwcLW40dbprrl4Xt56dSF/V4C3tndaPr2p6b7RuE44plHlVOkNxlEVecjOeHcgzm++89Ehr7Vy7Vb++tYhJMmqDSc0k1hCJxLT0A1rBLuJSTyhD5E1ZQeS2x98hdauIDZFpsHvoicQGzLSHUCSZRQF+sMJZFnGaVdw2J047Sp9oTi9gZSnrwSGYY03SiTjKJ3SEHVDduBz2FV0AzTdQFVkHHlGD40mcE4WadeRIEoKRwmFbqsriUK1uLGo0xXa3X706XcJRpKpOqj1mG6YdPVFiCU0krpJjc+R99x8DC7rpOkJxGnrCqPpBrpuptp7YyR1PRP4N25pz5Qutr7Xg6aZ9ARiROIaDTWuvL84k5pBMKJxxrxG3E4VE2ioqaLaY00ZNsy0Wc/AOZYx+lB1Q7penf4FNaPJw3FTfcxo8lDlUnPW+YXv/oV7H1nP9r09yJjD1rsHU0mdi5WKCLhHCddcPI9lF83BaVfRDCsbW3bRnCNWKZSaQrW4sajTFdrIOtQZtgzDZQlVUVBTHr1JzaTG5+KYJi+qIuU9Nx/RuIYyzA7T4PxU00z6Qwn2tQWGbCSapklfKIEJhKNJuvqGbqhl89e3DrHnUD+GYdDTHyUSS1LtsWdGFQ0mkTSsgZ6GOSTwFfoFlV5na5f1uRkGdPXH0HWzqMA5WaRdR4KwZxQc1RSyLbz/0dfQdatjLDN5GEsju2rFZSO2PLz6W8+kVCJWnpJI6kMC7WDSHWV7W/sJRaz5cPmQJKiucmC3yZZ1ombQXOdm1rRq/v52a+Z5NV6rJbizN0qV244sS/Slnp+NTZFQVZlpKcld2s6ycVDJJxzT6OmPYZgmdpuC067QH0pg7dNJGIYVbKfWV9HaFUaS5WFlielJwsPZUI4HldYuXFkFQIEgi2J+WArZFtZ4HamhjxYpr3HqUxrWkZpqLz1/Fk8+vx3NMFCkXJ8FLd/IYkDTdHYf7CMcTY2BZ2g2nF5bXyiOy2HVo0ORJJFYktPnNnLa3EYeXbOZcDRJbzBBMJJE001i/bHM82NxnUA4njPW3e8ZKJdky9d+uupteuNxAmFLswyWljcYSRKLayiKhJFqk5Ykq7TR3hshEtdRFGNIt1866I60c3Gsg2El1pRFwBVUJCP5YRlud9vttCFL0SEZrttpO+y5+RislpBlCY9LpbHGzc4D/XnPSeomSX2g5DE42NpVmURWdhqNa1ZwrLGGe/75tX18+ZrTufez7+fnf9zElvd6M8FdSj8/5WvbWOsmEE4Qi2s4bDKKYik3sgNf2nvi9y/sIKlZ45EkGUJRK9BqukEyaeasU1Wk1NgkMtm9Kln7Catf3p35XEbyC2zl2q387oUdGIaJTZXRdaPkwfBI2oXH6peBCLiCiqQUvfWRuHULnTPaxmM/Iildtloi/Ushlhj+esZwYtsUiUGlgPQ5nb0x6qudGKY1kNPttPGlq09n064ufvL7f2SydbCy2f5QgnBUo8broMHvJBLTMprgwYFv065uGmtdtHVHkCVLRmeYJppu5GzApdF0E0kCdVB7crYscXCA+pcCgXPjlnZ+/8KOTHuxblj17mqPvaTeCaN1wxvLzFgEXEFFUgrryLREbVqWqD9dUywF2RndgY5w3ueku8QOF3gHk7Z0rPe7qfM5rV8ausH82fXMmlbNwY4QsUTuZqGmG3QHomiagxnNHpwOlW9/+pwh105/tjZVRtPMVKsvDI796TsCmyqT1E10k7yyxJEGqFUv7cyMPILUaCSsluVSbrCNtl14LI10hEpBUJGUwjC7HO2iC1qauP+WD1LlyvVQGMDEHCbYSkOem3WWaaamJM/GYVep87twp6RjH3n/TMtLN8/1MKE3FGfznh72HOzn9TxSrvRn6/c4MLG6zXTDzKlJO2wKdlVBVWR0w8RpkzENqzyR/jINa4rzSOVg7T0RbGqWQTxWwE9qRkm9E0b7/R9LtYUIuIKKpBTBspztog1+N+kO3LTfuYQ1ft1hV3LkXrIEHreK22XDblOQZYbIwdwuW85aFVmixufE73Vw8vH1XH3xXJwONSfompDZNDMNywDnZ3/cxKtvH8rx5U1/tooiUV/tRJat85x2BVUZ6NSDlLmOaQXk7N8b1rh6hRNm1Iw4QDXVuqlyqZlgb5ommmEiy1LJfxmO5vs/ltMxRElBUJGUaix3OdpFN25ppy8YA6ypEyYDAytrfU6QJDp7o5hYpt/1fieaZrLwzBk8+/f36A/pOdme26Fw67Vn5F13ldOGXVU4Y24T0uWwcu02NF0nEE7mBETdhGqXDd0w+P1fdnLcNB9et50ql33IZzvn2NpMoPvRk28SCCcwJWtDLV2/xbCkZumJGg01LhTZuv0e6a17Ws1Q7bETjlrOcYos8fFFJ5T8ezWa7/9Y+kSLgCuoWIb7YSnFDnIpd6HT5uMuh5qyh7SySUWWBpoTalz0BmIkNIManyvn9X775+0YKeWBKkt5W22zsakydX4n57xvCrIs8eyre9GMUM54d4DeoDU+vr0nyn88/gaLzzmG0+c2Ue2xZzLj7GLHgpYmvrjsNB57enPGrtFul/G4bPSH4phYxuqmCe3dERprXZnxQiMJUKX6ZTpWjOX6ROOD4KhipM0KY3WNbAZP/gVrMnBSMzhu6sB0iXxNAEfaLBCPa/SF4miGyff++w36Q5Yvg9XMYD1HkqDO5wAkrr54Lqoi8/z6vSQ1Hd0wC77/9Hvb3xGyOthSx02sXygzmrz8160XZn6BVWIArSREhis4qijFDnKpd6Hz3VJXuWwEwwliicJZ35GqMRwOlXqbQiAU55JzjuW/n9uKokhW80KqqcE0oTtgzVp7bt1eJEkiGI7T4HdhtymEY0mCkUTe959+b+m0bHB2lt5qmwxOX6VABFzBUUUp5GKlnlacr+ZnU2Q+vugENu3qLpj1lWLSQXpD7QOnTEWRJZ7++3t09Eax2yyHsVBUwzBMq5MsEcCuyiR1g47eKB6XjWlNHup8zpyuvMHvzTBMFNnyV7AmacjU+hyZMUaV1kJbqYiAKziqKEWAKvU4l0I1v2sOc24pN2iqnDbOO20a849v4N9/vYHeYAyHTcHrttMbjGdGGyU1y9xGkSEcS7J9by/NdW5mT6/GNM2c0kj6vX33ideJxTWcDhm/x0GVy5YpfVRiC22lImRhgqOKUsjFxkKfm9bjPvzNi7j/lg+OyHS7lNI1m6pQ73fysQuPR5Vl4kkdWQKv24bXbcvMMjNN0HXrL4Zp0tYd4bS5DXT3xUgMkkQtaGni1mvPoMHvoq7aidup5nxmwpaxeESGKziqKMUOcqXtkpe6/ilJEufMn4pNVfjT33aztzVAXbWLTyw6hsf/dyuJhEYkbjmdaQYoioTDrnDSrHrimk5Xf5Qqpw1vyo0svcbhPrMHV71d0hLNREYEXMFRRykC1GTY5DmjpYlT5zTQH05kPA8aa9z0h2K4nSY9QWsysK6bmCb0BGLWuHkTQlHLOcznseNyWNK24T6zyTJxtxSIkoKgYsieinD7g68cdsKA4PAoikytz0m1x44swcVnH4OuW11mzbUunHarQywS07jnF+t45R+HSCtFNcOkJxCnJxAb4rebzWSZuFsKRIYrqAjExkt+Vq7dmnd45kjxuOzYVYXT5jYCsHb9Prr7oxzT7GP2NB9/+8chgpEkv352C29s6+C6S+ZR47PmxKUtID0uGx63PWdTDSqvRFPJiMYHQUVQidMC8jFa+dNozvvPJzby0hsHgQGvBVmSjmi0kmGYBMIJwrHcrrRQJMFvnt/Gxi0dADgdCv+0aA7vnz8lJ8DaFJnqKjuOChtemqbS5WmipCCoCI6GeViDZ5MVO1xxNOdt3NLO/6WCLaSmVZhgmKY1zHKUyLKE3+ugxusg297W47Zz89L5fOaK9+F124jFdX71zBZ+/Pt/0BuMZZ6X1A26AzF6A7EcQ5xKYLTfn3JSEQF34cKFXHLJJSxdupSlS5fy17/+FYA9e/awbNkyFi9ezLJly3jvvffGd6GCMWMsHZpKxWjlT6M5b9VLOwdcx6RcN7EjMVBP43baqPe7h/ySO2NeE3fcfA5nzLNKD+/s6uaeh9fz97cHarsmlrl7Z1+EUDRBpdwkHw3ytIoIuAA/+tGPWL16NatXr+a8884D4M4772T58uU899xzLF++nDvuuGOcVykYK46GjZfRZuGjOa+9J5J3/pmZMv0uBTZVpq7aiXeQt67XbeczV1jZrsdlIxrX+NUzW/jJoGxXN6xJE119sSG/LMeDo+EuqWIC7mC6u7vZvHkzS5YsAWDJkiVs3ryZnp6ecV6ZYCwop3ftaGmqddMXSnCwM8x7rUEOdobpCyUOm4WPJntvqnXjrbLkWJYn7cBjS8+fNer3MBhJkvB5HNT6nENG6KSz3dNTG22bUtnuq5tac7LahKbT3R+lPxhHH+Fki1JyNNwlVUzl+9Zbb8U0Tc444wy++tWv0traSlNTE4pi/cZSFIXGxkZaW1upra3NOTcQCBAIBHKOtbW1lW3t5aLSNwSOlErXxs6fXcfm3d0gWfO8EppOIqiz+JxjC543mvbd9DkAwXAyU1740OnTRr1hVginwxq5k63ZBfBV2fnnK+fz+tZ2Vj63jVA0yWNPb+aNre1ce0kL/tQEZNOEUCxJLDXQMntQ51iQ/lnY1xZA0y2f4WqvPTMdWdNNegNxkrqBoshs3NJeEf+3KiLgPvHEE0yZMoVEIsF9993HPffcw4033lj0+Y899hgPPPDA2C2wAhCyqfFn065u/F57xpPArsq4nSqbdnUX9EwYjWxqPKRWac1uKJogGE7kGJqfMa+JE2bUsHLtVt7c1pnKdtfxTx+ew9nva84oGTTDzPg2VHvs2FRlmFcbPemfhaSuE45av4xiJpkpxUnNoLMvhk2RaPQ7x2Qi8GipOFnYtm3buOWWW/jd737H4sWLWb9+PYqioOs6Z599NmvXri06w7322msnjCzsaJFNVRql0rFCft9b0zQJRTUe/uZFpVpyRZBI6vQFrQxxMBu3tLNy7TbCUUtadvLx9SxfPC+T7aaRJPC6bFS5BlqES0H6Z6G7P4amWaN5rAnAMnXVDnqDCWq89or8WRn3Gm4kEiEYDALWf95nnnmGlpYW6urqaGlpYc2aNQCsWbOGlpaWIcEWwOfzMX369Jyv5ubmsr6PseZo2BCoNFau3cqTz28nltBQZeuH7snnt7Ny7dZRXe9oqBGWCrtNod7voso5dFjlgpYm7rz5bE6b0wDA2zu7uOfhdax7J7e2a5oQiCTp6osSjScpFemfhbTrGQwMoXTYFKJxrWJ/VsY94HZ3d/PJT36Syy67jCVLlrBnzx7uvPNOAO666y4ef/xxFi9ezOOPP87dd989zqsdPybTD3upWP3ybpBAlWUkSUaVZZAYtY71aFBSlJK0ZtfvdWQGZKbxVTn45yvnc9PlJ1HlVInENR5ds5kH//A2/aF4znOTukFvIE5vIIZWAu1u+mche/KvYVqqi3hSx+VQK/ZnpeJKCqVionWalXoszGRg6f/3J1QZJGkgWpimgWbA6v+4fFTXnKyjZJKaQX8onlf+FQjH+e/ntvHW9k4A3E6VZRfN5awTm4a0AcsSeKvsVuY8eFRxkWTXcPtDCUvAYYLf68CmyCw8cwYvbthfkT8rFbFpJjg8ol995Lgclm+rmvVzrR+hjrXSlRRjRVqzG4okCEaTOVOGfVUOPnvlfDZuaec3a7cRjmn88ql3eWNrO8sXz6PaM1DbNUxLuxuN6fg89iG3/sWQ/bOg6QMqhakNnszPxAkzairyZ0VkuIIJS7qGm5Zx6Sk965F4EQhyB1cOpj9kZbv/2HH4bFeSoMphw1NlRynhplolM+41XIFgrLjm4nksu2gOTruKZljDIkWwPXIcDpV6vyvvnUK1x8G/XDWfT192Em6nSiSV7T60atOQ2m5au9vVGyESK92mWiUjMlyBQDBq8ml201jZ7lb+saMLgCqnytUXz2VBy9BsF6xfiL4qOzZ14uaBE/edCQSCMcfjslNX7cKep8HBynZP5lNLTsTtVAnHNH7xp3f56f9sIhCOD3l+LKHR1RchGI5XjCFOqREBVyAQHBF2m0JdtROPa6hmV5Ikzn7fFO68+RxOPr4egLe2d3L3w+vZsLltSGA1Utrdzt4o8RK4olUaIuAKBIIjRpYlqocxwQEr273lYydz45ITcTtUwtEkv/jTu/zsfzYRCCeGPD/tu9sXjFec7+6RIAKuQCAoGc4CG2qSJHHO+6Zwx83nMH+2le2+ub2Tux9el9ck3ATCsSSdfZEhEyqOVkTAFQgEJWXw4MrB+L0OPvfxk7nx0oFs9+HV7wyb7eoG9AXjdPVFSWrj77t7JIiAKxAIxoT0hpptcF8wqWx3/hS+ffPZzJ9dB8Ab2zq45+F1vL41/0iceFKnsy9KMGyNdz8aEQFXIBCMGYVMcABqvE4+9/FTuP6jLbgcKqFokp//8R1+9sdNBCNDs92xMsQpFyLgCgSCMSUzuNI31AQHrGz3AydP5Y6bz+Z96Wx3awd3/3z4bLfUhjjlQgRcgUBQFlyO/IMr09R4nfxrnmz353/cRChftktqmGVvhGCkcoZZFkIEXIFAUDYsk3AnPreNfGZhmWz3prM5aZaV7b6+tYO7H17Hm9s68l7TMCEQTg2zTFS2dlcEXIFAUFYkScJb5aDO58y7oQZQ43Py+U9Y2a7ToRCMJPnp/2zi4dXv5M12IT3MMkZvIFax2l0RcAUCwbjgsKvU+V24h7HLHMh2z+HE46xJLxu3tBfMdjNlhr4I4WjllRlEwBUIBOOGIkvU+Jz4h9HsAtT6nHzhn07luo/Mw2kfyHZ/8ad3CEXzKxV0A/pCCbr7YyTymKaPFyLgCgSCcafKZafen98EB6xs99xTpuVkuxs2t3P3z1/NTJrIRzyp09UfpT8UR68A7a4IuAKBoCKwqSkTnGE0uwC11UOz3YdWvV0w2zVNCEUt393x1u6KgCsQCCoGWZaoLqDZhdxst2XmQLZ7z8PrCma7mmHSE4jT0x8jqY3PppoIuAKBoOJwOWzUVQ+v2QUr2/3islO59pJ5OOwKgXCCh1a9zSNPvUt4mGwXIJrlu1vuFmERcAUCQUWSHlzpzeOzm0aSJM47dRp33HQ281LZ7mvvtnH3w+syc9XyYWS1CMfK6LsrAq5AIKhYJEnCV8BnN01dtYsvDcp2H/zD2/zyMNluUjfoCZRPuysCrkAgqHjSPrtO+/Aj7nOy3WNrAFj/bhv3/GIdbxfIdgdrd8cSEXAFAsFRgZJuC64aXrMLqWz36tNYvnguDrtCfyjBT9LZbgEj87R2t6svOmbaXRFwBQLBUYXXPbzPbhpJkjj/tOl8+9NnMzc72314PZt2dhW8flq7GwiVflNNBFyBQHDUcTif3TT1fivbvfriuThsCv2hOD/+/T94dM3mgtmuaUIwmqQ/NHS68JEgAq5AIDgqyfbZLVRikCWJC06fzrdvOps5x/gBWPdOa1HZrshwBQKBIIvD+eymqfe7+PI1pw/Jdh97ejORMg2pFAFXIBAc9aQ1u54Cml0YyHa/lZXtvrqplbsfXs+mXYWz3VIgAq5AIJgQSJJEdRGaXYCGdLZ70RzsNtnKdn/3D341xtmuCLgCgWBCUYxmF1LZ7hkz+PZN53DCDD8Af9/Uyj2/WM+7u7vHZG0i4AoEgglHtmY33yifbBr8Lr6y/HSWpbLdvmCc//rtW/z6mS0lz3ZFwBUIBBOWYjS7YGW7F54xg29/+uxMtvvK24f49s9epS9YOmmYCLgCgWBC40hpdocb5ZNNQ42bryw/nX/68Bxsqkx3f4z97cGSreXwKxAIBIKjHDk1yscRs5oZCslrZUli4YIZnDangUA4wfzj60u2DhFwBQLBpMHttGFTLQ1u/DB+CTU+J1Pqqkr6+qKkIBAIJhVpza7PbTvshlqpEQFXIBBMOiRJwlvloM7rRJXLFwZFwBUIBJMWh0Olvqa4DbVSIAKuQCCY1CipDTW/p7DPbikQAVcgEAiAKldKs6uOXVgUAVcgEAhS2G0K9dUuPIfx2R0tQhYmEAgEWciyRLXXgd0uE0uUdtSOCLgCgUCQB5fDhsNW2hApSgoCgUAwDHKJd9FEwBUIBIIyIQKuQCAQlAkRcAUCgaBMiIArEAgEZUIEXIFAICgTIuAKBAJBmaj4gLtnzx6WLVvG4sWLWbZsGe+99954L0kgEAhGRcUH3DvvvJPly5fz3HPPsXz5cu64447xXpJAIBCMiooOuN3d3WzevJklS5YAsGTJEjZv3kxPT884r0wgEAhGTkW39ra2ttLU1ISiKAAoikJjYyOtra3U1tZmnhcIBAgEAjnntrW1lXWtAoFAcDgqOuAWy2OPPcYDDzyQ9zEReAUCQblpbm5GVYeGV8k0zQLzK8eX7u5uFi9ezPr161EUBV3XOfvss1m7du1hM9xNmzbx5S9/ucwrFggEAnjhhReYPn36kOMVneHW1dXR0tLCmjVrWLp0KWvWrKGlpSUn2AL4fD58Pl/Osfr6ep544gkaGhoyJYkjoa2tjWuvvZYnnniC5ubmI77eREB8JkMRn0l+JtvnMtx7rOiAC3DXXXdx22238ZOf/ASfz8eKFSuKOs/pdLJgwYKSr6e5uTnvb67JjPhMhiI+k/xM9s+l4gPu7Nmz+d3vfjfeyxAIBIIjpqJlYQKBQDCREAFXIBAIyoQIuEXi8/n4/Oc/P2RzbjIjPpOhiM8kP+JzsahoWZhAIBBMJESGKxAIBGVCBFyBQCAoEyLg5mHFihUsXLiQuXPnsn379szxyWwV2dvby2c+8xkWL17MZZddxuc///mMidBk/lw+97nPcfnll3PFFVewfPlytmzZAkzuzyTNAw88kPMzJD4TwBQMYcOGDeahQ4fMCy+80Ny2bVvm+Cc/+Unzj3/8o2mapvnHP/7R/OQnPzleSyw7vb295rp16zL//n//7/+Z//Zv/2aa5uT+XAKBQObvzz//vHnFFVeYpjm5PxPTNM133nnHvOmmm8wLLrgg8zM02T8T0zRNkeHmYcGCBUyZMiXn2GS3ivT7/Zx99tmZf5966qkcOnRo0n8uXq838/dQKIQkSZP+M0kkEtxzzz3ceeedSJIEiJ+fNBXfaVYpFGsVORkwDIOVK1eycOFC8bkA3/zmN3nllVcwTZOHH3540n8mP/zhD7n88suZMWNG5thk/0zSiAxXMGLuvfde3G4311133XgvpSK47777eOmll/jKV77Cv//7v4/3csaVN998k02bNrF8+fLxXkpFIgJukUyZMoX29nZ0XQdA13U6OjqGlB4mOitWrGDv3r384Ac/QJZl8blkccUVV7B+/Xqam5sn7WeyYcMGdu/ezaJFi1i4cCFtbW3cdNNN7Nu3b9J+JtmIgFsk2VaRwLBWkROZ73//+7zzzjv8+Mc/xm63A5P7cwmHw7S2tmb+/eKLL1JdXT2pP5N//ud/5m9/+xsvvvgiL774Is3NzfziF7/gox/96KT9TLIRnWZ5+M53vsPatWvp6uqipqYGv9/P008/za5du7jtttsIBAIZq8hZs2aN93LLwo4dO1iyZAkzZ87E6XQCMH36dH784x9P2s+lq6uLz33uc0SjUWRZprq6mm984xucdNJJk/YzGczChQt56KGHmDNnjvhMEAFXIBAIyoYoKQgEAkGZEAFXIBAIyoQIuAKBQFAmRMAVCASCMiECrkAgEJQJEXAFAoGgTIiAKxAIBGVCBFyBQCAoEyLgCiYFCxcu5OGHH+ayyy7j1FNP5fbbb6erq4ubb76Z0047jRtvvJH+/n4OHDjA3LlzefLJJzn33HM599xzeeSRRzLXicVifOMb3+DMM8/kIx/5CD//+c85//zzx/GdCY4mhD2jYNKwdu1afvnLX6LrOldccQVbtmzhvvvu4/jjj+fmm2/m17/+NVdccQUA69evZ+3atezfv58bbriBefPm8YEPfIAHHniAgwcP8uc//5loNMpnPvOZ8X1TgqMKkeEKJg3XXXcd9fX1NDU1sWDBAk4++WROPPFE7HY7F110EZs3b84891//9V9xu93MnTuXq666KmO68uyzz/LZz36W6upqmpubuf7668fr7QiOQkTAFUwa6uvrM393OBw5/3Y6nUQikcy/s20Dp02bRkdHB8AQS8Hm5uaxXLJggiECrkCQh2zbxUOHDtHY2AhAQ0MDbW1tmcey/y4QHA4RcAWCPPzkJz8hGo2yY8cOVq1axUc/+lEAPvKRj/DTn/6U/v5+2tvbefzxx8d5pYKjCRFwBYI8nHXWWVx00UXceOONfPrTn+bcc88FrNpuc3MzixYt4sYbb2Tx4sUZM3aB4HAIP1yBIIsDBw6waNEi3n33XVT18CKe//7v/+aZZ54Rma6gKESGKxCMgI6ODl5//XUMw2D37t388pe/5MMf/vB4L0twlCB0uALBCEgmk9x5550cOHAAr9fLpZdeKibUCopGlBQEAoGgTIiSgkAgEJQJEXAFAoGgTIiAKxAIBGVCBFyBQCAoEyLgCgQCQZkQAVcgEAjKxP8PaIg5KuUx8SEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "\n",
    "# Chart of the original dataset\n",
    "sns.lmplot(x=\"mpg\", y=\"horsepower\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dd7ceab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12.0]</td>\n",
       "      <td>167</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[12.0]</td>\n",
       "      <td>198</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[13.0]</td>\n",
       "      <td>140</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[13.0]</td>\n",
       "      <td>145</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[14.0]</td>\n",
       "      <td>148</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  features  horsepower  mpg\n",
       "0   [12.0]         167   12\n",
       "1   [12.0]         198   12\n",
       "2   [13.0]         140   13\n",
       "3   [13.0]         145   13\n",
       "4   [14.0]         148   14"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_test_df = test_df.toPandas()\n",
    "original_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5bde8c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f93dc230d60>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "\n",
    "# Chart of original test dataset\n",
    "sns.lmplot(x=\"mpg\", y=\"horsepower\", data=original_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "97fb3775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>mpg</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12.0]</td>\n",
       "      <td>167</td>\n",
       "      <td>12</td>\n",
       "      <td>145.244238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[12.0]</td>\n",
       "      <td>198</td>\n",
       "      <td>12</td>\n",
       "      <td>145.244238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[13.0]</td>\n",
       "      <td>140</td>\n",
       "      <td>13</td>\n",
       "      <td>141.040360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[13.0]</td>\n",
       "      <td>145</td>\n",
       "      <td>13</td>\n",
       "      <td>141.040360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[14.0]</td>\n",
       "      <td>148</td>\n",
       "      <td>14</td>\n",
       "      <td>136.836483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  features  horsepower  mpg  prediction\n",
       "0   [12.0]         167   12  145.244238\n",
       "1   [12.0]         198   12  145.244238\n",
       "2   [13.0]         140   13  141.040360\n",
       "3   [13.0]         145   13  141.040360\n",
       "4   [14.0]         148   14  136.836483"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_predictions = predictions.toPandas()\n",
    "pd_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "707ec47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f93d84ab400>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "\n",
    "# Show the results of a linear regression within each dataset\n",
    "sns.lmplot(x=\"mpg\", y=\"prediction\", data=pd_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "26ccf8df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+------------+----------+------+------------+----------+------+---------+------------------------------------------------------------------+-----+\n",
      "|mpg|cylinders|displacement|horsepower|weight|acceleration|model year|origin|carName  |features                                                          |label|\n",
      "+---+---------+------------+----------+------+------------+----------+------+---------+------------------------------------------------------------------+-----+\n",
      "|15 |8        |350         |165       |3693  |11.5        |70        |1     |buick    |(44,[0,1,2,3,4,5,6,14],[8.0,350.0,165.0,3693.0,11.5,70.0,1.0,1.0])|15.0 |\n",
      "|18 |8        |318         |150       |3436  |11.0        |70        |1     |plymouth |(44,[0,1,2,3,4,5,6,9],[8.0,318.0,150.0,3436.0,11.0,70.0,1.0,1.0]) |18.0 |\n",
      "|16 |8        |304         |150       |3433  |12.0        |70        |1     |amc      |(44,[0,1,2,3,4,5,6,10],[8.0,304.0,150.0,3433.0,12.0,70.0,1.0,1.0])|16.0 |\n",
      "|17 |8        |302         |140       |3449  |10.5        |70        |1     |ford     |(44,[0,1,2,3,4,5,6,7],[8.0,302.0,140.0,3449.0,10.5,70.0,1.0,1.0]) |17.0 |\n",
      "|15 |8        |429         |198       |4341  |10.0        |70        |1     |ford     |(44,[0,1,2,3,4,5,6,7],[8.0,429.0,198.0,4341.0,10.0,70.0,1.0,1.0]) |15.0 |\n",
      "|14 |8        |454         |220       |4354  |9.0         |70        |1     |chevrolet|(44,[0,1,2,3,4,5,6,8],[8.0,454.0,220.0,4354.0,9.0,70.0,1.0,1.0])  |14.0 |\n",
      "|14 |8        |440         |215       |4312  |8.5         |70        |1     |plymouth |(44,[0,1,2,3,4,5,6,9],[8.0,440.0,215.0,4312.0,8.5,70.0,1.0,1.0])  |14.0 |\n",
      "|14 |8        |455         |225       |4425  |10.0        |70        |1     |pontiac  |(44,[0,1,2,3,4,5,6,15],[8.0,455.0,225.0,4425.0,10.0,70.0,1.0,1.0])|14.0 |\n",
      "|15 |8        |390         |190       |3850  |8.5         |70        |1     |amc      |(44,[0,1,2,3,4,5,6,10],[8.0,390.0,190.0,3850.0,8.5,70.0,1.0,1.0]) |15.0 |\n",
      "|26 |4        |133         |115       |3090  |17.5        |70        |2     |citroen  |(44,[0,1,2,3,4,5,6,38],[4.0,133.0,115.0,3090.0,17.5,70.0,2.0,1.0])|26.0 |\n",
      "+---+---------+------------+----------+------+------------+----------+------+---------+------------------------------------------------------------------+-----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#select features from dataframe and transform data\n",
    "supervised_mpg = RFormula(formula=\"mpg ~ .\")\n",
    "fittedRF_mpg = supervised_mpg.fit(car_df)\n",
    "preparedDF_mpg = fittedRF_mpg.transform(car_df)\n",
    "preparedDF_mpg.show(10, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "250923d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create an 80/20 train/test split\n",
    "train_df, test_df = preparedDF_mpg.randomSplit([0.8, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "37d4e34a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta Coefficients: [-0.11239501593374156,0.0,-0.0121124910081065,-0.0035878475312511177,0.0,0.2413957112651524,0.0,-0.5232534534164858,0.0,0.0,-0.28386805974023865,0.0,0.0,0.16941084030788423,0.0,0.0,0.0,0.0,0.0,-0.4948880689360844,0.0,0.3911724427033135,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.8371296147204934,-2.899020991701557,2.003798135078389,0.0,0.0,1.6845144686239457,0.0,0.0,5.0770499301988545,0.0,0.0]\n",
      "Intercept: 15.898499856026469\n"
     ]
    }
   ],
   "source": [
    "#Create an instance of linear regression and fit the model\n",
    "lr = LinearRegression(featuresCol = 'features', labelCol='mpg', maxIter=20, regParam=0.3, elasticNetParam=0.8)\n",
    "lr_model = lr.fit(train_df)\n",
    "# Retrieve coeffients and intercept\n",
    "print(\"Beta Coefficients: \" + str(lr_model.coefficients))\n",
    "print(\"Intercept: \" + str(lr_model.intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "27dcea24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 3.994696\n",
      "r2: 0.552513\n"
     ]
    }
   ],
   "source": [
    "#Retrieve the RMSE and Rsquare values, which both indicate that this is a model that we can possibly work with\n",
    "\n",
    "trainingSummary = lr_model.summary\n",
    "print(\"RMSE: %f\" % trainingSummary.rootMeanSquaredError)\n",
    "print(\"r2: %f\" % trainingSummary.r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9391fda6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+---+--------------------------------------------------------------------------------+\n",
      "|prediction        |mpg|features                                                                        |\n",
      "+------------------+---+--------------------------------------------------------------------------------+\n",
      "|13.237935197674929|11 |(44,[0,1,2,3,4,5,6,18],[8.0,429.0,208.0,4633.0,11.0,72.0,1.0,1.0])              |\n",
      "|12.473207210824407|12 |(44,[0,1,2,3,4,5,6,7],[8.0,400.0,167.0,4906.0,12.5,73.0,1.0,1.0])               |\n",
      "|16.556579500107762|13 |(44,[0,1,2,3,4,5,6,8],[8.0,350.0,145.0,3988.0,13.0,73.0,1.0,1.0])               |\n",
      "|11.577212991332974|13 |(44,[0,1,2,3,4,5,6,15],[8.0,400.0,175.0,5140.0,12.0,71.0,1.0,1.0])              |\n",
      "|14.21299586491483 |13 |(44,[0,1,2,3,4,5,6,24],[8.0,400.0,190.0,4422.0,12.5,72.0,1.0,1.0])              |\n",
      "|17.010499215534878|14 |(44,[0,1,2,3,4,5,6,9],[8.0,340.0,160.0,3609.0,8.0,70.0,1.0,1.0])                |\n",
      "|24.9098533069135  |15 |(44,[0,1,2,3,4,5,6,16],[4.0,97.0,48.0,1978.0,20.0,71.0,2.0,1.0])                |\n",
      "|20.603032795619267|15 |(44,[0,1,2,3,4,5,6,7],[6.0,250.0,72.0,3158.0,19.5,75.0,1.0,1.0])                |\n",
      "|18.329894743111826|15 |(44,[0,1,2,3,4,5,6,10],[6.0,258.0,110.0,3730.0,19.0,75.0,1.0,1.0])              |\n",
      "|16.602380518195876|15 |(44,[0,1,2,3,4,5,6,18],[8.0,302.0,130.0,4295.0,14.899999618530273,77.0,1.0,1.0])|\n",
      "+------------------+---+--------------------------------------------------------------------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display predictions based on test data\n",
    "lr_predictions = lr_model.transform(test_df)\n",
    "lr_predictions.select(\"prediction\",\"mpg\",\"features\").show(10, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "147da942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared (R2) on test data = 0.456423\n"
     ]
    }
   ],
   "source": [
    "# use regression evaluator to get the r2 value which appears to be something that we can work with\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "lr_evaluator = RegressionEvaluator(predictionCol=\"prediction\", \\\n",
    "                 labelCol=\"mpg\",metricName=\"r2\")\n",
    "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5f2aef8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE) on test data = 3.98246\n"
     ]
    }
   ],
   "source": [
    "#perform model evaluation to get the RMSE which also appears to be something that we can work with\n",
    "test_result = lr_model.evaluate(test_df)\n",
    "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % test_result.rootMeanSquaredError)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7b0d30a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#After plotting our data it appears that the most important features are displacement, horsepower, weight and acceleration\n",
    "\n",
    "from sklearn import preprocessing\n",
    "# Let's scale the columns before plotting them against MEDV\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "column_sels = ['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin']\n",
    "x = data.loc[:,column_sels]\n",
    "y = data['mpg']\n",
    "x = pd.DataFrame(data=min_max_scaler.fit_transform(x), columns=column_sels)\n",
    "fig, axs = plt.subplots(ncols=4, nrows=2, figsize=(20, 10))\n",
    "index = 0\n",
    "axs = axs.flatten()\n",
    "for i, k in enumerate(column_sels):\n",
    "    sns.regplot(y=y, x=x[k], ax=axs[i],scatter_kws={\"s\": 60} )\n",
    "plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "de08e6ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instatiate an instance of Decision Tree Regressor\n",
    "from pyspark.ml.regression import DecisionTreeRegressor\n",
    "\n",
    "# fit the model\n",
    "dt = DecisionTreeRegressor(featuresCol ='features', labelCol = 'mpg')\n",
    "dt_model = dt.fit(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b004e444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE) on test data = 3.4363\n"
     ]
    }
   ],
   "source": [
    "#Check predictions and calculate RMSE. The RMSE appears to be slightly lower than the one for linear regression \n",
    "#which is a good indication \n",
    "\n",
    "dt_predictions = dt_model.transform(test_df)\n",
    "dt_evaluator = RegressionEvaluator(\n",
    "    labelCol=\"mpg\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
    "rmse = dt_evaluator.evaluate(dt_predictions)\n",
    "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "19d10fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SparseVector(44, {0: 0.4609, 1: 0.0804, 2: 0.0077, 3: 0.0952, 4: 0.0012, 5: 0.326, 7: 0.0013, 9: 0.0266, 25: 0.0007})"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.featureImportances"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}