Coordinate_Control_MSU_Team_1_FINAL_SUBMISSION.ipynb

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  "metadata": {
    "colab": {
      "name": "Coordinate_Control_MSU_Team_1_FINAL_SUBMISSION.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "P-Rddg6_byp4"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3qJSpEX9tMn"
      },
      "source": [
        "*Include all necessary imports*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUdgFJ87hC-n"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import seaborn as sns\n",
        "from math import radians, cos, sin, asin, sqrt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fNArGgi91Fq"
      },
      "source": [
        "*Unzip all uploaded files so we can access them*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKF_f5Da1U0y",
        "outputId": "e60fb626-2dca-4d6a-8cc7-bdb96bd35c29"
      },
      "source": [
        "!unzip baseline_locations_test.csv.zip\n",
        "!unzip baseline_locations_train.csv.zip\n",
        "!unzip sample_submission.csv.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  baseline_locations_test.csv.zip\n",
            "  inflating: baseline_locations_test.csv  \n",
            "Archive:  baseline_locations_train.csv.zip\n",
            "  inflating: baseline_locations_train.csv  \n",
            "unzip:  cannot find or open sample_submission.csv.zip, sample_submission.csv.zip.zip or sample_submission.csv.zip.ZIP.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dh6FU0EJo1Q",
        "outputId": "ddeeed22-9005-43ca-d78f-722d90ef3b39"
      },
      "source": [
        "!unzip sample3.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  sample3.zip\n",
            "   creating: sample3/Mi8/\n",
            "  inflating: sample3/Mi8/ground_truth.csv  \n",
            "  inflating: sample3/Mi8/Mi8_derived.csv  \n",
            "  inflating: sample3/Mi8/Mi8_GnssLog.txt  \n",
            "   creating: sample3/Mi8/supplemental/\n",
            "  inflating: sample3/Mi8/supplemental/Mi8_GnssLog.20o  \n",
            "  inflating: sample3/Mi8/supplemental/SPAN_Mi8_10Hz.nmea  \n",
            "   creating: sample3/Pixel4/\n",
            "  inflating: sample3/Pixel4/ground_truth.csv  \n",
            "  inflating: sample3/Pixel4/Pixel4_derived.csv  \n",
            "  inflating: sample3/Pixel4/Pixel4_GnssLog.txt  \n",
            "   creating: sample3/Pixel4/supplemental/\n",
            "  inflating: sample3/Pixel4/supplemental/Pixel4_GnssLog.20o  \n",
            "  inflating: sample3/Pixel4/supplemental/SPAN_Pixel4_10Hz.nmea  \n",
            "   creating: sample3/Pixel4Modded/\n",
            "  inflating: sample3/Pixel4Modded/ground_truth.csv  \n",
            "  inflating: sample3/Pixel4Modded/Pixel4Modded_derived.csv  \n",
            "  inflating: sample3/Pixel4Modded/Pixel4Modded_GnssLog.txt  \n",
            "   creating: sample3/Pixel4Modded/supplemental/\n",
            "  inflating: sample3/Pixel4Modded/supplemental/Pixel4Modded_GnssLog.21o  \n",
            "  inflating: sample3/Pixel4Modded/supplemental/SPAN_Pixel4Modded_10Hz.nmea  \n",
            "   creating: sample3/Pixel4XL/\n",
            "  inflating: sample3/Pixel4XL/ground_truth.csv  \n",
            "  inflating: sample3/Pixel4XL/Pixel4XL_derived.csv  \n",
            "  inflating: sample3/Pixel4XL/Pixel4XL_GnssLog.txt  \n",
            "   creating: sample3/Pixel4XL/supplemental/\n",
            "  inflating: sample3/Pixel4XL/supplemental/Pixel4XL_GnssLog.20o  \n",
            "  inflating: sample3/Pixel4XL/supplemental/SPAN_Pixel4XL_10Hz.nmea  \n",
            "   creating: sample3/Pixel4XLModded/\n",
            "  inflating: sample3/Pixel4XLModded/ground_truth.csv  \n",
            "  inflating: sample3/Pixel4XLModded/Pixel4XLModded_derived.csv  \n",
            "  inflating: sample3/Pixel4XLModded/Pixel4XLModded_GnssLog.txt  \n",
            "   creating: sample3/Pixel4XLModded/supplemental/\n",
            "  inflating: sample3/Pixel4XLModded/supplemental/Pixel4XLModded_GnssLog.20o  \n",
            "  inflating: sample3/Pixel4XLModded/supplemental/SPAN_Pixel4XLModded_10Hz.nmea  \n",
            "   creating: sample3/Pixel5/\n",
            "  inflating: sample3/Pixel5/Pixel5_derived.csv  \n",
            "  inflating: sample3/Pixel5/Pixel5_GnssLog.txt  \n",
            "   creating: sample3/Pixel5/supplemental/\n",
            "  inflating: sample3/Pixel5/supplemental/Pixel5_GnssLog.21o  \n",
            "   creating: sample3/SamsungS20Ultra/\n",
            "  inflating: sample3/SamsungS20Ultra/SamsungS20Ultra_derived.csv  \n",
            "  inflating: sample3/SamsungS20Ultra/SamsungS20Ultra_GnssLog.txt  \n",
            "   creating: sample3/SamsungS20Ultra/supplemental/\n",
            "  inflating: sample3/SamsungS20Ultra/supplemental/SamsungS20Ultra_GnssLog.21o  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFfyxTIhM3no"
      },
      "source": [
        "*Load the csv files into the respective dataframes*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "ESCI62YG9KH_",
        "outputId": "f13dff3c-0275-49da-a20a-c02707bcf601"
      },
      "source": [
        "trainfile = pd.read_csv(\"baseline_locations_train.csv\")\n",
        "trainfile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>collectionName</th>\n",
              "      <th>phoneName</th>\n",
              "      <th>millisSinceGpsEpoch</th>\n",
              "      <th>latDeg</th>\n",
              "      <th>lngDeg</th>\n",
              "      <th>heightAboveWgs84EllipsoidM</th>\n",
              "      <th>phone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529463442</td>\n",
              "      <td>37.423575</td>\n",
              "      <td>-122.094091</td>\n",
              "      <td>-34.06</td>\n",
              "      <td>2020-05-14-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529464442</td>\n",
              "      <td>37.423578</td>\n",
              "      <td>-122.094101</td>\n",
              "      <td>-33.29</td>\n",
              "      <td>2020-05-14-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529465442</td>\n",
              "      <td>37.423573</td>\n",
              "      <td>-122.094111</td>\n",
              "      <td>-30.99</td>\n",
              "      <td>2020-05-14-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529466442</td>\n",
              "      <td>37.423583</td>\n",
              "      <td>-122.094121</td>\n",
              "      <td>-32.83</td>\n",
              "      <td>2020-05-14-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529467442</td>\n",
              "      <td>37.423579</td>\n",
              "      <td>-122.094114</td>\n",
              "      <td>-34.49</td>\n",
              "      <td>2020-05-14-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131337</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760315000</td>\n",
              "      <td>37.334460</td>\n",
              "      <td>-121.899600</td>\n",
              "      <td>-8.09</td>\n",
              "      <td>2021-04-29-US-SJC-2_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131338</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760316000</td>\n",
              "      <td>37.334472</td>\n",
              "      <td>-121.899583</td>\n",
              "      <td>-7.59</td>\n",
              "      <td>2021-04-29-US-SJC-2_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131339</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760317000</td>\n",
              "      <td>37.334491</td>\n",
              "      <td>-121.899597</td>\n",
              "      <td>-8.35</td>\n",
              "      <td>2021-04-29-US-SJC-2_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131340</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760318000</td>\n",
              "      <td>37.334495</td>\n",
              "      <td>-121.899583</td>\n",
              "      <td>-8.73</td>\n",
              "      <td>2021-04-29-US-SJC-2_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131341</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760319000</td>\n",
              "      <td>37.334485</td>\n",
              "      <td>-121.899570</td>\n",
              "      <td>-7.64</td>\n",
              "      <td>2021-04-29-US-SJC-2_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131342 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             collectionName  ...                                phone\n",
              "0       2020-05-14-US-MTV-1  ...           2020-05-14-US-MTV-1_Pixel4\n",
              "1       2020-05-14-US-MTV-1  ...           2020-05-14-US-MTV-1_Pixel4\n",
              "2       2020-05-14-US-MTV-1  ...           2020-05-14-US-MTV-1_Pixel4\n",
              "3       2020-05-14-US-MTV-1  ...           2020-05-14-US-MTV-1_Pixel4\n",
              "4       2020-05-14-US-MTV-1  ...           2020-05-14-US-MTV-1_Pixel4\n",
              "...                     ...  ...                                  ...\n",
              "131337  2021-04-29-US-SJC-2  ...  2021-04-29-US-SJC-2_SamsungS20Ultra\n",
              "131338  2021-04-29-US-SJC-2  ...  2021-04-29-US-SJC-2_SamsungS20Ultra\n",
              "131339  2021-04-29-US-SJC-2  ...  2021-04-29-US-SJC-2_SamsungS20Ultra\n",
              "131340  2021-04-29-US-SJC-2  ...  2021-04-29-US-SJC-2_SamsungS20Ultra\n",
              "131341  2021-04-29-US-SJC-2  ...  2021-04-29-US-SJC-2_SamsungS20Ultra\n",
              "\n",
              "[131342 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "XdvJv3in9KeD",
        "outputId": "19e058b9-9557-4e29-d435-9bc8dd3b33f1"
      },
      "source": [
        "testfile = pd.read_csv(\"baseline_locations_test.csv\")\n",
        "testfile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>collectionName</th>\n",
              "      <th>phoneName</th>\n",
              "      <th>millisSinceGpsEpoch</th>\n",
              "      <th>latDeg</th>\n",
              "      <th>lngDeg</th>\n",
              "      <th>heightAboveWgs84EllipsoidM</th>\n",
              "      <th>phone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-15-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273608785432</td>\n",
              "      <td>37.416628</td>\n",
              "      <td>-122.082053</td>\n",
              "      <td>-30.69</td>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-05-15-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273608786432</td>\n",
              "      <td>37.416646</td>\n",
              "      <td>-122.082040</td>\n",
              "      <td>-31.76</td>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-05-15-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273608787432</td>\n",
              "      <td>37.416653</td>\n",
              "      <td>-122.082039</td>\n",
              "      <td>-31.65</td>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-15-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273608788432</td>\n",
              "      <td>37.416607</td>\n",
              "      <td>-122.082063</td>\n",
              "      <td>-31.52</td>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-15-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
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              "      <td>37.416609</td>\n",
              "      <td>-122.082073</td>\n",
              "      <td>-28.95</td>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
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              "    <tr>\n",
              "      <th>91481</th>\n",
              "      <td>2021-04-29-US-SJC-3</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303763185000</td>\n",
              "      <td>37.334539</td>\n",
              "      <td>-121.899383</td>\n",
              "      <td>-8.39</td>\n",
              "      <td>2021-04-29-US-SJC-3_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91482</th>\n",
              "      <td>2021-04-29-US-SJC-3</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303763186000</td>\n",
              "      <td>37.334545</td>\n",
              "      <td>-121.899380</td>\n",
              "      <td>-7.36</td>\n",
              "      <td>2021-04-29-US-SJC-3_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91483</th>\n",
              "      <td>2021-04-29-US-SJC-3</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303763187000</td>\n",
              "      <td>37.334551</td>\n",
              "      <td>-121.899371</td>\n",
              "      <td>-4.08</td>\n",
              "      <td>2021-04-29-US-SJC-3_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91484</th>\n",
              "      <td>2021-04-29-US-SJC-3</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303763188000</td>\n",
              "      <td>37.334540</td>\n",
              "      <td>-121.899371</td>\n",
              "      <td>-5.70</td>\n",
              "      <td>2021-04-29-US-SJC-3_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91485</th>\n",
              "      <td>2021-04-29-US-SJC-3</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303763189000</td>\n",
              "      <td>37.334562</td>\n",
              "      <td>-121.899354</td>\n",
              "      <td>-0.90</td>\n",
              "      <td>2021-04-29-US-SJC-3_SamsungS20Ultra</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>91486 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            collectionName  ...                                phone\n",
              "0      2020-05-15-US-MTV-1  ...           2020-05-15-US-MTV-1_Pixel4\n",
              "1      2020-05-15-US-MTV-1  ...           2020-05-15-US-MTV-1_Pixel4\n",
              "2      2020-05-15-US-MTV-1  ...           2020-05-15-US-MTV-1_Pixel4\n",
              "3      2020-05-15-US-MTV-1  ...           2020-05-15-US-MTV-1_Pixel4\n",
              "4      2020-05-15-US-MTV-1  ...           2020-05-15-US-MTV-1_Pixel4\n",
              "...                    ...  ...                                  ...\n",
              "91481  2021-04-29-US-SJC-3  ...  2021-04-29-US-SJC-3_SamsungS20Ultra\n",
              "91482  2021-04-29-US-SJC-3  ...  2021-04-29-US-SJC-3_SamsungS20Ultra\n",
              "91483  2021-04-29-US-SJC-3  ...  2021-04-29-US-SJC-3_SamsungS20Ultra\n",
              "91484  2021-04-29-US-SJC-3  ...  2021-04-29-US-SJC-3_SamsungS20Ultra\n",
              "91485  2021-04-29-US-SJC-3  ...  2021-04-29-US-SJC-3_SamsungS20Ultra\n",
              "\n",
              "[91486 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "R1TnNDiD9Kzt",
        "outputId": "a52c89b8-2995-4ec3-8b09-49a379cc8422"
      },
      "source": [
        "submission = pd.read_csv(\"sample_submission.csv\")\n",
        "submission"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>phone</th>\n",
              "      <th>millisSinceGpsEpoch</th>\n",
              "      <th>latDeg</th>\n",
              "      <th>lngDeg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "      <td>1273608785432</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "      <td>1273608786432</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "      <td>1273608787432</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "      <td>1273608788432</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-15-US-MTV-1_Pixel4</td>\n",
              "      <td>1273608789432</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51685</th>\n",
              "      <td>2021-03-16-US-RWC-2_SamsungS20Ultra</td>\n",
              "      <td>1299971642000</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51686</th>\n",
              "      <td>2021-03-16-US-RWC-2_SamsungS20Ultra</td>\n",
              "      <td>1299971643000</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51687</th>\n",
              "      <td>2021-03-16-US-RWC-2_SamsungS20Ultra</td>\n",
              "      <td>1299971644000</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51688</th>\n",
              "      <td>2021-03-16-US-RWC-2_SamsungS20Ultra</td>\n",
              "      <td>1299971645000</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51689</th>\n",
              "      <td>2021-03-16-US-RWC-2_SamsungS20Ultra</td>\n",
              "      <td>1299971646000</td>\n",
              "      <td>37.904611</td>\n",
              "      <td>-86.481078</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>51690 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     phone  ...     lngDeg\n",
              "0               2020-05-15-US-MTV-1_Pixel4  ... -86.481078\n",
              "1               2020-05-15-US-MTV-1_Pixel4  ... -86.481078\n",
              "2               2020-05-15-US-MTV-1_Pixel4  ... -86.481078\n",
              "3               2020-05-15-US-MTV-1_Pixel4  ... -86.481078\n",
              "4               2020-05-15-US-MTV-1_Pixel4  ... -86.481078\n",
              "...                                    ...  ...        ...\n",
              "51685  2021-03-16-US-RWC-2_SamsungS20Ultra  ... -86.481078\n",
              "51686  2021-03-16-US-RWC-2_SamsungS20Ultra  ... -86.481078\n",
              "51687  2021-03-16-US-RWC-2_SamsungS20Ultra  ... -86.481078\n",
              "51688  2021-03-16-US-RWC-2_SamsungS20Ultra  ... -86.481078\n",
              "51689  2021-03-16-US-RWC-2_SamsungS20Ultra  ... -86.481078\n",
              "\n",
              "[51690 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EFioNTvOFLB"
      },
      "source": [
        "*Check the code for any missing information*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XstrDKTPOKzK",
        "outputId": "2ea50ff4-07b4-412e-b7b7-5ec7eff5dee5"
      },
      "source": [
        "trainfile.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "collectionName                False\n",
              "phoneName                     False\n",
              "millisSinceGpsEpoch           False\n",
              "latDeg                        False\n",
              "lngDeg                        False\n",
              "heightAboveWgs84EllipsoidM    False\n",
              "phone                         False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVHl3joZNGLR"
      },
      "source": [
        "*Now that we see there is no missing data, we move on to establishing the path to get the ground truth data from. We use the .rglob method to get all ground truth datafiles, and store them in a list.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQOb0Aaq-HwI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbbf0ab5-fdd5-4e2c-9e36-fed14b0ac5f9"
      },
      "source": [
        "truth_path = Path(\"/content/sample3\")\n",
        "data_truth = (truth_path).rglob('ground_truth.csv')\n",
        "\n",
        "data_truth\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<generator object Path.rglob at 0x7f968c8f09d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55QvxUegbSW2"
      },
      "source": [
        "# Model 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDYZA9xdNRcs"
      },
      "source": [
        "*Instantiate the column names for the model data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bt8Z63B-2qTA"
      },
      "source": [
        "cols = ['collectionName', 'phoneName', 'millisSinceGpsEpoch', 'latDeg',\n",
        "       'lngDeg']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVi24xSyNcJy"
      },
      "source": [
        "*Create a truth list containting all of the ground truth files in the training data, which will be populated by going through the truths list and creating datafiles from all of the ground truth csv files*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3aC5agBdtEhZ",
        "outputId": "e10ef351-c7e0-4362-9067-1fe7eb033066"
      },
      "source": [
        "truths = []\n",
        "\n",
        "for filepath in data_truth:\n",
        "    f = pd.read_csv(filepath, usecols=cols)\n",
        "    truths.append(f)\n",
        "truths"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[           collectionName phoneName  millisSinceGpsEpoch     latDeg      lngDeg\n",
              " 0     2020-05-14-US-MTV-1    Pixel4        1273529463442  37.423576 -122.094132\n",
              " 1     2020-05-14-US-MTV-1    Pixel4        1273529464442  37.423576 -122.094132\n",
              " 2     2020-05-14-US-MTV-1    Pixel4        1273529465442  37.423576 -122.094132\n",
              " 3     2020-05-14-US-MTV-1    Pixel4        1273529466442  37.423576 -122.094132\n",
              " 4     2020-05-14-US-MTV-1    Pixel4        1273529467442  37.423576 -122.094132\n",
              " ...                   ...       ...                  ...        ...         ...\n",
              " 1735  2020-05-14-US-MTV-1    Pixel4        1273531198442  37.645823 -122.405608\n",
              " 1736  2020-05-14-US-MTV-1    Pixel4        1273531199442  37.645823 -122.405608\n",
              " 1737  2020-05-14-US-MTV-1    Pixel4        1273531200442  37.645823 -122.405608\n",
              " 1738  2020-05-14-US-MTV-1    Pixel4        1273531201442  37.645823 -122.405608\n",
              " 1739  2020-05-14-US-MTV-1    Pixel4        1273531202442  37.645823 -122.405608\n",
              " \n",
              " [1740 rows x 5 columns],\n",
              "            collectionName     phoneName  ...     latDeg      lngDeg\n",
              " 0     2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424351 -122.091984\n",
              " 1     2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424351 -122.091984\n",
              " 2     2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424351 -122.091984\n",
              " 3     2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424351 -122.091984\n",
              " 4     2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424351 -122.091984\n",
              " ...                   ...           ...  ...        ...         ...\n",
              " 1713  2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424373 -122.091969\n",
              " 1714  2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424373 -122.091969\n",
              " 1715  2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424373 -122.091969\n",
              " 1716  2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424373 -122.091969\n",
              " 1717  2021-04-15-US-MTV-1  Pixel4Modded  ...  37.424373 -122.091969\n",
              " \n",
              " [1718 rows x 5 columns],\n",
              "            collectionName       phoneName  ...     latDeg     lngDeg\n",
              " 0     2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.423575 -122.09413\n",
              " 1     2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.423575 -122.09413\n",
              " 2     2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.423575 -122.09413\n",
              " 3     2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.423576 -122.09413\n",
              " 4     2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.423576 -122.09413\n",
              " ...                   ...             ...  ...        ...        ...\n",
              " 1741  2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.645823 -122.40561\n",
              " 1742  2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.645823 -122.40561\n",
              " 1743  2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.645823 -122.40561\n",
              " 1744  2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.645823 -122.40561\n",
              " 1745  2020-05-14-US-MTV-1  Pixel4XLModded  ...  37.645823 -122.40561\n",
              " \n",
              " [1746 rows x 5 columns],\n",
              "            collectionName phoneName  millisSinceGpsEpoch     latDeg     lngDeg\n",
              " 0     2020-08-06-US-MTV-2       Mi8        1280789552000  37.525215 -122.35334\n",
              " 1     2020-08-06-US-MTV-2       Mi8        1280789553000  37.525215 -122.35334\n",
              " 2     2020-08-06-US-MTV-2       Mi8        1280789554000  37.525215 -122.35334\n",
              " 3     2020-08-06-US-MTV-2       Mi8        1280789555000  37.525215 -122.35334\n",
              " 4     2020-08-06-US-MTV-2       Mi8        1280789556000  37.525215 -122.35334\n",
              " ...                   ...       ...                  ...        ...        ...\n",
              " 1739  2020-08-06-US-MTV-2       Mi8        1280791291000  37.427345 -122.06991\n",
              " 1740  2020-08-06-US-MTV-2       Mi8        1280791292000  37.427345 -122.06991\n",
              " 1741  2020-08-06-US-MTV-2       Mi8        1280791293000  37.427345 -122.06991\n",
              " 1742  2020-08-06-US-MTV-2       Mi8        1280791294000  37.427345 -122.06991\n",
              " 1743  2020-08-06-US-MTV-2       Mi8        1280791295000  37.427345 -122.06991\n",
              " \n",
              " [1744 rows x 5 columns],\n",
              "            collectionName phoneName  millisSinceGpsEpoch     latDeg      lngDeg\n",
              " 0     2020-08-06-US-MTV-2  Pixel4XL        1280789548433  37.525216 -122.353338\n",
              " 1     2020-08-06-US-MTV-2  Pixel4XL        1280789549433  37.525216 -122.353338\n",
              " 2     2020-08-06-US-MTV-2  Pixel4XL        1280789550433  37.525216 -122.353338\n",
              " 3     2020-08-06-US-MTV-2  Pixel4XL        1280789551433  37.525216 -122.353338\n",
              " 4     2020-08-06-US-MTV-2  Pixel4XL        1280789552433  37.525216 -122.353338\n",
              " ...                   ...       ...                  ...        ...         ...\n",
              " 1743  2020-08-06-US-MTV-2  Pixel4XL        1280791291433  37.427345 -122.069913\n",
              " 1744  2020-08-06-US-MTV-2  Pixel4XL        1280791292433  37.427345 -122.069913\n",
              " 1745  2020-08-06-US-MTV-2  Pixel4XL        1280791293433  37.427345 -122.069913\n",
              " 1746  2020-08-06-US-MTV-2  Pixel4XL        1280791294433  37.427345 -122.069913\n",
              " 1747  2020-08-06-US-MTV-2  Pixel4XL        1280791295433  37.427345 -122.069913\n",
              " \n",
              " [1748 rows x 5 columns]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wm0caAUtXBma"
      },
      "source": [
        "*Now that we have our truths, we concatenate the data to the truth dataframe*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llDfDikdtNQR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "de2c9c63-9652-4246-92f5-0fe62488e379"
      },
      "source": [
        "truth_data = pd.concat(truths, ignore_index=True)\n",
        "truth_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>collectionName</th>\n",
              "      <th>phoneName</th>\n",
              "      <th>millisSinceGpsEpoch</th>\n",
              "      <th>latDeg</th>\n",
              "      <th>lngDeg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529463442</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529464442</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529465442</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529466442</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529467442</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8691</th>\n",
              "      <td>2020-08-06-US-MTV-2</td>\n",
              "      <td>Pixel4XL</td>\n",
              "      <td>1280791291433</td>\n",
              "      <td>37.427345</td>\n",
              "      <td>-122.069913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8692</th>\n",
              "      <td>2020-08-06-US-MTV-2</td>\n",
              "      <td>Pixel4XL</td>\n",
              "      <td>1280791292433</td>\n",
              "      <td>37.427345</td>\n",
              "      <td>-122.069913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8693</th>\n",
              "      <td>2020-08-06-US-MTV-2</td>\n",
              "      <td>Pixel4XL</td>\n",
              "      <td>1280791293433</td>\n",
              "      <td>37.427345</td>\n",
              "      <td>-122.069913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8694</th>\n",
              "      <td>2020-08-06-US-MTV-2</td>\n",
              "      <td>Pixel4XL</td>\n",
              "      <td>1280791294433</td>\n",
              "      <td>37.427345</td>\n",
              "      <td>-122.069913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8695</th>\n",
              "      <td>2020-08-06-US-MTV-2</td>\n",
              "      <td>Pixel4XL</td>\n",
              "      <td>1280791295433</td>\n",
              "      <td>37.427345</td>\n",
              "      <td>-122.069913</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8696 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           collectionName phoneName  millisSinceGpsEpoch     latDeg      lngDeg\n",
              "0     2020-05-14-US-MTV-1    Pixel4        1273529463442  37.423576 -122.094132\n",
              "1     2020-05-14-US-MTV-1    Pixel4        1273529464442  37.423576 -122.094132\n",
              "2     2020-05-14-US-MTV-1    Pixel4        1273529465442  37.423576 -122.094132\n",
              "3     2020-05-14-US-MTV-1    Pixel4        1273529466442  37.423576 -122.094132\n",
              "4     2020-05-14-US-MTV-1    Pixel4        1273529467442  37.423576 -122.094132\n",
              "...                   ...       ...                  ...        ...         ...\n",
              "8691  2020-08-06-US-MTV-2  Pixel4XL        1280791291433  37.427345 -122.069913\n",
              "8692  2020-08-06-US-MTV-2  Pixel4XL        1280791292433  37.427345 -122.069913\n",
              "8693  2020-08-06-US-MTV-2  Pixel4XL        1280791293433  37.427345 -122.069913\n",
              "8694  2020-08-06-US-MTV-2  Pixel4XL        1280791294433  37.427345 -122.069913\n",
              "8695  2020-08-06-US-MTV-2  Pixel4XL        1280791295433  37.427345 -122.069913\n",
              "\n",
              "[8696 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzSbkuouBYes"
      },
      "source": [
        "*Using the necessary column data, we create a dataframe that contains relevant information in the trainfile*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1vFqD7L9ZgS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "587f94eb-e5fe-4be6-9048-92ba58e6f7d9"
      },
      "source": [
        "train = trainfile[cols]\n",
        "train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>collectionName</th>\n",
              "      <th>phoneName</th>\n",
              "      <th>millisSinceGpsEpoch</th>\n",
              "      <th>latDeg</th>\n",
              "      <th>lngDeg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529463442</td>\n",
              "      <td>37.423575</td>\n",
              "      <td>-122.094091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529464442</td>\n",
              "      <td>37.423578</td>\n",
              "      <td>-122.094101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529465442</td>\n",
              "      <td>37.423573</td>\n",
              "      <td>-122.094111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529466442</td>\n",
              "      <td>37.423583</td>\n",
              "      <td>-122.094121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-14-US-MTV-1</td>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1273529467442</td>\n",
              "      <td>37.423579</td>\n",
              "      <td>-122.094114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131337</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760315000</td>\n",
              "      <td>37.334460</td>\n",
              "      <td>-121.899600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131338</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760316000</td>\n",
              "      <td>37.334472</td>\n",
              "      <td>-121.899583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131339</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760317000</td>\n",
              "      <td>37.334491</td>\n",
              "      <td>-121.899597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131340</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760318000</td>\n",
              "      <td>37.334495</td>\n",
              "      <td>-121.899583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131341</th>\n",
              "      <td>2021-04-29-US-SJC-2</td>\n",
              "      <td>SamsungS20Ultra</td>\n",
              "      <td>1303760319000</td>\n",
              "      <td>37.334485</td>\n",
              "      <td>-121.899570</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131342 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             collectionName        phoneName  ...     latDeg      lngDeg\n",
              "0       2020-05-14-US-MTV-1           Pixel4  ...  37.423575 -122.094091\n",
              "1       2020-05-14-US-MTV-1           Pixel4  ...  37.423578 -122.094101\n",
              "2       2020-05-14-US-MTV-1           Pixel4  ...  37.423573 -122.094111\n",
              "3       2020-05-14-US-MTV-1           Pixel4  ...  37.423583 -122.094121\n",
              "4       2020-05-14-US-MTV-1           Pixel4  ...  37.423579 -122.094114\n",
              "...                     ...              ...  ...        ...         ...\n",
              "131337  2021-04-29-US-SJC-2  SamsungS20Ultra  ...  37.334460 -121.899600\n",
              "131338  2021-04-29-US-SJC-2  SamsungS20Ultra  ...  37.334472 -121.899583\n",
              "131339  2021-04-29-US-SJC-2  SamsungS20Ultra  ...  37.334491 -121.899597\n",
              "131340  2021-04-29-US-SJC-2  SamsungS20Ultra  ...  37.334495 -121.899583\n",
              "131341  2021-04-29-US-SJC-2  SamsungS20Ultra  ...  37.334485 -121.899570\n",
              "\n",
              "[131342 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQNpzWRCtekQ"
      },
      "source": [
        "*Merge the data up to the third column*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWP0szba-NmX"
      },
      "source": [
        "train = train.merge(truth_data, on=cols[:3], suffixes=(\"_current\",\"_truth\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8N-cKhTtn7P"
      },
      "source": [
        "*Create a copy of the test file*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlaf9Ql4-YdZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "da030d93-ee4e-4ab2-e314-94d50b73bfb5"
      },
      "source": [
        "test = testfile.copy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-263b96d7f3fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtestfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'testfile' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amcxGjpRt0QB"
      },
      "source": [
        "*Get the unique names from the train and test data for the collection name and phone name columns*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQ9r3vnD-c3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "241c91eb-8682-41a5-ffdd-29ee3894c4f6"
      },
      "source": [
        "print(\"############### collectionName unique values ##############################\")\n",
        "print(\"train: {}\".format(train.collectionName.nunique()))\n",
        "print(train.collectionName.unique())\n",
        "print(\"----------------------------------------------\")\n",
        "print(\"test: {}\".format(test.collectionName.nunique()))\n",
        "print(test.collectionName.unique())\n",
        "print(\"----------------------------------------------\")\n",
        "\n",
        "print(\"\\n\")\n",
        "\n",
        "print(\"############### phoneName unique values ##############################\")\n",
        "print(\"train: {}\".format(train.phoneName.nunique()))\n",
        "print(train.phoneName.unique())\n",
        "print(\"----------------------------------------------\")\n",
        "print(\"test: {}\".format(test.phoneName.nunique()))\n",
        "print(test.phoneName.unique())\n",
        "print(\"----------------------------------------------\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "############### collectionName unique values ##############################\n",
            "train: 3\n",
            "['2020-05-14-US-MTV-1' '2020-08-06-US-MTV-2' '2021-04-15-US-MTV-1']\n",
            "----------------------------------------------\n",
            "test: 19\n",
            "['2020-05-15-US-MTV-1' '2020-05-28-US-MTV-1' '2020-05-28-US-MTV-2'\n",
            " '2020-06-04-US-MTV-2' '2020-06-10-US-MTV-1' '2020-06-10-US-MTV-2'\n",
            " '2020-08-03-US-MTV-2' '2020-08-13-US-MTV-1' '2021-03-16-US-MTV-2'\n",
            " '2021-03-16-US-RWC-2' '2021-03-25-US-PAO-1' '2021-04-02-US-SJC-1'\n",
            " '2021-04-08-US-MTV-1' '2021-04-21-US-MTV-1' '2021-04-22-US-SJC-2'\n",
            " '2021-04-26-US-SVL-2' '2021-04-28-US-MTV-2' '2021-04-29-US-MTV-2'\n",
            " '2021-04-29-US-SJC-3']\n",
            "----------------------------------------------\n",
            "\n",
            "\n",
            "############### phoneName unique values ##############################\n",
            "train: 5\n",
            "['Pixel4' 'Pixel4XLModded' 'Mi8' 'Pixel4XL' 'Pixel4Modded']\n",
            "----------------------------------------------\n",
            "test: 7\n",
            "['Pixel4' 'Pixel4XL' 'Pixel4XLModded' 'Mi8' 'Pixel4Modded'\n",
            " 'SamsungS20Ultra' 'Pixel5']\n",
            "----------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W98wd3TBt-hf"
      },
      "source": [
        "*Use the get dummies method to create dummy data, and assign it to the train and test phone variables*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N351tb3f-c0e"
      },
      "source": [
        "train_phone = pd.get_dummies(train.loc[:,\"phoneName\"])\n",
        "test_phone = pd.get_dummies(test.loc[:,\"phoneName\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75rkYZ300x_J"
      },
      "source": [
        "*Check the shape of the test phone and train phone data files*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bm9ECwWC-01q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93ba76af-9a9c-4ce2-8665-de837acb7205"
      },
      "source": [
        "print(\"train_phone shape:{}\".format(train_phone.shape))\n",
        "print(\"test_phone shape:{}\".format(test_phone.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_phone shape:(8696, 5)\n",
            "test_phone shape:(91486, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKr8JlhRueaA"
      },
      "source": [
        "*take the data from train phone, align it to be the same format with the columns of the test phone dataframe, and have that data saved to the train_phone1 and test_phone1 dataframes*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOe6C7ET-7r3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36771a7f-e543-4813-ad55-48414572df15"
      },
      "source": [
        "train_phone1, test_phone1 = train_phone.align(test_phone, join=\"outer\",axis=1, fill_value=0)\n",
        "print(\"Updated train shape {}\".format(train_phone.shape))\n",
        "print(\"Updated test shape {}\".format(test_phone.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated train shape (8696, 5)\n",
            "Updated test shape (91486, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbdwWywRu8q-"
      },
      "source": [
        "*concatenate the training and testing data from the train_phone1 and test_phone1 datafiles*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8ri1BIK_EIM"
      },
      "source": [
        "train1 = pd.concat([train.iloc[:,3:], train_phone1], axis=1, ignore_index=False)\n",
        "test1 = pd.concat([test.iloc[:,3:5], test_phone1], axis=1, ignore_index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0pqln5AvSxJ"
      },
      "source": [
        "*Check the shape of the train1 and test1 datafiles while previewing the train1 and test1 columns*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WaJF0nm_MlN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48688dad-68c8-4a5d-8940-8100d97a3bd3"
      },
      "source": [
        "print(\"train1_shape:\",train1.shape)\n",
        "train1.columns\n",
        "print(\"test1-shape:\",test1.shape)\n",
        "test1.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train1_shape: (8696, 11)\n",
            "test1-shape: (91486, 9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['latDeg', 'lngDeg', 'Mi8', 'Pixel4', 'Pixel4Modded', 'Pixel4XL',\n",
              "       'Pixel4XLModded', 'Pixel5', 'SamsungS20Ultra'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKjY4i2lvbzf"
      },
      "source": [
        "*Plot the figure that shows our current data vs the predicted data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkGyBgP0_V0l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "a5e92b0e-8e13-4166-a8db-6aacdd9ae92d"
      },
      "source": [
        "plt.figure(figsize=[10,5])\n",
        "plt.plot(train1[\"latDeg_current\"][50:100],train1[\"lngDeg_current\"][50:150],\"bo\",label=\"current\")\n",
        "plt.plot(train1[\"latDeg_truth\"][50:150],train1[\"lngDeg_truth\"][50:150],\"r*\",label=\"truth\")\n",
        "plt.title(\"current vs truth\", fontweight=\"bold\")\n",
        "plt.xlabel(\"latDeg\")\n",
        "plt.ylabel(\"lngDeg\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff0c7d7bb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYbHZNK8vhne"
      },
      "source": [
        "*Plot the figures showing the individual longitudes and latitudes*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgYj1fj7_eB_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "7fbc3e89-cf64-422f-9cea-9aaa90136e5d"
      },
      "source": [
        "plt.figure(figsize=[15,5])\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(train1[\"latDeg_current\"][:100],train1[\"latDeg_truth\"][:100],\"bo\")\n",
        "plt.title(\"lat (current vs truth)\", fontweight=\"bold\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'lat (current vs truth)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Ih7ef7T_nH9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "efe8f210-5292-4b77-f2d8-64aecfa7a15d"
      },
      "source": [
        "plt.subplot(1,2,2)\n",
        "plt.plot(train1[\"lngDeg_current\"][:100],train1[\"lngDeg_truth\"][:100],\"bo\")\n",
        "plt.title(\"lng (current vs truth)\", fontweight=\"bold\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'lng (current vs truth)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNFjW9H2_t72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "21c50b71-3826-4c23-9109-a947fd505f38"
      },
      "source": [
        "# for latitude\n",
        "columns1 = list(train1.columns[4:])\n",
        "X1,y1 = train1.loc[:,[train1.columns[0]]+columns1],train1[\"latDeg_truth\"].values\n",
        "  \n",
        "#for longitude\n",
        "columns2 = list(train1.columns[4:])\n",
        "X2,y2 = train1.loc[:,[train1.columns[1]]+columns2],train1[\"lngDeg_truth\"].values\n",
        "train1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>latDeg_current</th>\n",
              "      <th>lngDeg_current</th>\n",
              "      <th>latDeg_truth</th>\n",
              "      <th>lngDeg_truth</th>\n",
              "      <th>Mi8</th>\n",
              "      <th>Pixel4</th>\n",
              "      <th>Pixel4Modded</th>\n",
              "      <th>Pixel4XL</th>\n",
              "      <th>Pixel4XLModded</th>\n",
              "      <th>Pixel5</th>\n",
              "      <th>SamsungS20Ultra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37.423575</td>\n",
              "      <td>-122.094091</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37.423578</td>\n",
              "      <td>-122.094101</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37.423573</td>\n",
              "      <td>-122.094111</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.423583</td>\n",
              "      <td>-122.094121</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37.423579</td>\n",
              "      <td>-122.094114</td>\n",
              "      <td>37.423576</td>\n",
              "      <td>-122.094132</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8691</th>\n",
              "      <td>37.424363</td>\n",
              "      <td>-122.092011</td>\n",
              "      <td>37.424373</td>\n",
              "      <td>-122.091969</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8692</th>\n",
              "      <td>37.424380</td>\n",
              "      <td>-122.092007</td>\n",
              "      <td>37.424373</td>\n",
              "      <td>-122.091969</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8693</th>\n",
              "      <td>37.424379</td>\n",
              "      <td>-122.092010</td>\n",
              "      <td>37.424373</td>\n",
              "      <td>-122.091969</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8694</th>\n",
              "      <td>37.424369</td>\n",
              "      <td>-122.091993</td>\n",
              "      <td>37.424373</td>\n",
              "      <td>-122.091969</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8695</th>\n",
              "      <td>37.424392</td>\n",
              "      <td>-122.092003</td>\n",
              "      <td>37.424373</td>\n",
              "      <td>-122.091969</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8696 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      latDeg_current  lngDeg_current  ...  Pixel5  SamsungS20Ultra\n",
              "0          37.423575     -122.094091  ...       0                0\n",
              "1          37.423578     -122.094101  ...       0                0\n",
              "2          37.423573     -122.094111  ...       0                0\n",
              "3          37.423583     -122.094121  ...       0                0\n",
              "4          37.423579     -122.094114  ...       0                0\n",
              "...              ...             ...  ...     ...              ...\n",
              "8691       37.424363     -122.092011  ...       0                0\n",
              "8692       37.424380     -122.092007  ...       0                0\n",
              "8693       37.424379     -122.092010  ...       0                0\n",
              "8694       37.424369     -122.091993  ...       0                0\n",
              "8695       37.424392     -122.092003  ...       0                0\n",
              "\n",
              "[8696 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTsx0xrl_1pM"
      },
      "source": [
        "Xt1 = test1.loc[:,[test1.columns[0]]+columns1] #latitude\n",
        "Xt2 = test1.loc[:,[test1.columns[1]]+columns2] #longitude"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMDUiiAX_-5L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3a10800-9462-49c9-b3ec-ddfe9fca4385"
      },
      "source": [
        "print(\"X1 columns:\", X1.columns.tolist())\n",
        "print(\"Xt1 columns:\", Xt1.columns.tolist())\n",
        "print(\"\\n\")\n",
        "print(\"X2 columns:\", X2.columns.tolist())\n",
        "print(\"Xt2 columns:\", Xt2.columns.tolist())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X1 columns: ['latDeg_current', 'Mi8', 'Pixel4', 'Pixel4Modded', 'Pixel4XL', 'Pixel4XLModded', 'Pixel5', 'SamsungS20Ultra']\n",
            "Xt1 columns: ['latDeg', 'Mi8', 'Pixel4', 'Pixel4Modded', 'Pixel4XL', 'Pixel4XLModded', 'Pixel5', 'SamsungS20Ultra']\n",
            "\n",
            "\n",
            "X2 columns: ['lngDeg_current', 'Mi8', 'Pixel4', 'Pixel4Modded', 'Pixel4XL', 'Pixel4XLModded', 'Pixel5', 'SamsungS20Ultra']\n",
            "Xt2 columns: ['lngDeg', 'Mi8', 'Pixel4', 'Pixel4Modded', 'Pixel4XL', 'Pixel4XLModded', 'Pixel5', 'SamsungS20Ultra']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0CJMaDkwVU_"
      },
      "source": [
        "*Create the train test split with 70% of the data being used for training and 30% of the data being used for testing*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLBwu0mLAJTi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c84d9f63-1d1e-404c-e412-8b75649b8c63"
      },
      "source": [
        "xtr1,xval1,ytr1,yval1 = train_test_split(X1, y1, test_size=0.3, random_state=10)\n",
        "xtr2,xval2,ytr2,yval2 = train_test_split(X2, y2, test_size=0.3, random_state=10)\n",
        "\n",
        "print(\"xtr1 shape:{}; xval1 shape:{}\".format(xtr1.shape,xval1.shape))\n",
        "print(\"xtr2 shape:{}; xval2 shape:{}\".format(xtr2.shape,xval2.shape))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "xtr1 shape:(6956, 8); xval1 shape:(1740, 8)\n",
            "xtr2 shape:(6956, 8); xval2 shape:(1740, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DjQLgGS_wv8X"
      },
      "source": [
        "*Use the haversine formula to get the difference from truth values and current values*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugFFmoIRAQa-"
      },
      "source": [
        "def lat_lon_dist(df):\n",
        "    \n",
        "    distance_list = []\n",
        "    for i in range(df.shape[0]):\n",
        "        lat1 = df[\"latDeg_truth\"][i]\n",
        "        lon1 = df[\"lngDeg_truth\"][i]\n",
        "        lat2 = df[\"latDeg_pred\"][i]\n",
        "        lon2 = df[\"lngDeg_pred\"][i]\n",
        "        # convert decimal degrees to radians \n",
        "        lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n",
        "        # haversine formula \n",
        "        dlon = lon2 - lon1 \n",
        "        dlat = lat2 - lat1 \n",
        "        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
        "        c = 2 * asin(sqrt(a)) \n",
        "        # The radius of earth in kilometers is 6371\n",
        "        mdist = 6371* c*1000\n",
        "        distance_list.append(mdist)\n",
        "    \n",
        "    return distance_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3HmukAOw1T1"
      },
      "source": [
        "*initialize the linear regression model with the training data, and fit the data to the linear regression to make the prediction for longitude and latitude, while checking the score*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMncj5IJAVp1"
      },
      "source": [
        "lr1 = LinearRegression() \n",
        "model_lat = lr1.fit(xtr1,ytr1)\n",
        "pred_yval1 = model_lat.predict(xval1) # prediction for values of data latitude\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGAEOb5kj55c",
        "outputId": "f1d17506-95a4-4729-b4c6-5d6bb9d57d38"
      },
      "source": [
        "print(format(mean_squared_error(yval1,pred_yval1), \".16f\")) #latitude prediction accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0000000001755435\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcqGZWDnAcoD"
      },
      "source": [
        "lr2 = LinearRegression()\n",
        "model_lng = lr2.fit(xtr2,ytr2)\n",
        "pred_yval2 = model_lng.predict(xval2) # prediction for values of longitude "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESOVb-AYxF9X",
        "outputId": "ac296f68-2869-4064-af5d-e190791dd5b3"
      },
      "source": [
        "print(format(mean_squared_error(yval2, pred_yval2), '.16f')) # longitude prediction accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0000000002558310\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "on9_gSxWB9YJ"
      },
      "source": [
        "val_df = pd.concat([xval1[[\"latDeg_current\"]], xval2], ignore_index=False, axis=1).reset_index(drop=[\"index\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6gnrsZ9yD1M"
      },
      "source": [
        "*Define the truth data and prediction data as columns in the dataframe, then get the distance from the truth data by using the function containing the haversine formula to get the distance differences*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFc1SyYhCCuf"
      },
      "source": [
        "#truths\n",
        "val_df[\"latDeg_truth\"] = yval1\n",
        "val_df[\"lngDeg_truth\"] = yval2\n",
        "\n",
        "#predictions\n",
        "val_df[\"latDeg_pred\"] = pred_yval1\n",
        "val_df[\"lngDeg_pred\"] = pred_yval2\n",
        "val_df[\"dist\"] = lat_lon_dist(val_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kEWNOl_7xta3"
      },
      "source": [
        "*Reverse the one-hot decoding for phoneName*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpi_giP_CHTi"
      },
      "source": [
        "phone = val_df.iloc[:,2:-5].idxmax(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWTOSL2aCQRU"
      },
      "source": [
        "val_df1 = pd.concat([val_df.iloc[:,:2],val_df.iloc[:,-3:]], axis=1, ignore_index=False)\n",
        "val_df1[\"phoneName\"] = phone\n",
        "val_df1 = val_df1[val_df1.columns[-1:].tolist()+val_df1.columns[:-1].tolist()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okRxfLpoxeSR"
      },
      "source": [
        "*Create the dataframe showing the data for each phone's distance difference*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brDL8kGMCQKb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "87f59396-741b-4adc-9788-40916d8f14d9"
      },
      "source": [
        "val_df2 = pd.DataFrame()\n",
        "val_df2[\"phoneName\"] =  val_df1.phoneName.unique().tolist()\n",
        "val_df2[\"dist_50\"] = [np.percentile(val_df1[val_df1.phoneName==ph][\"dist\"],50) for ph in val_df2[\"phoneName\"].tolist()]\n",
        "val_df2[\"dist_95\"] = [np.percentile(val_df1[val_df1.phoneName==ph][\"dist\"],95) for ph in val_df2[\"phoneName\"].tolist()]\n",
        "val_df2[\"avg_dist_50_95\"] = np.mean(np.array(val_df2.iloc[:,1:]),axis=1)\n",
        "val_df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>phoneName</th>\n",
              "      <th>dist_50</th>\n",
              "      <th>dist_95</th>\n",
              "      <th>avg_dist_50_95</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pixel4</td>\n",
              "      <td>1.125461</td>\n",
              "      <td>2.860650</td>\n",
              "      <td>1.993056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pixel4Modded</td>\n",
              "      <td>1.194256</td>\n",
              "      <td>4.229832</td>\n",
              "      <td>2.712044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pixel4XLModded</td>\n",
              "      <td>1.432765</td>\n",
              "      <td>3.745192</td>\n",
              "      <td>2.588979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mi8</td>\n",
              "      <td>1.389676</td>\n",
              "      <td>3.568231</td>\n",
              "      <td>2.478953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pixel4XL</td>\n",
              "      <td>1.140884</td>\n",
              "      <td>2.994377</td>\n",
              "      <td>2.067630</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        phoneName   dist_50   dist_95  avg_dist_50_95\n",
              "0          Pixel4  1.125461  2.860650        1.993056\n",
              "1    Pixel4Modded  1.194256  4.229832        2.712044\n",
              "2  Pixel4XLModded  1.432765  3.745192        2.588979\n",
              "3             Mi8  1.389676  3.568231        2.478953\n",
              "4        Pixel4XL  1.140884  2.994377        2.067630"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-Rddg6_byp4"
      },
      "source": [
        "# Model 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JoqczD8xB0p"
      },
      "source": [
        "*In order to decrease the mean squared error, change the hyperparameters of fit intercept and normailze to true in order to reflect the lower mean squared error*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7i8KYV7V0k3I"
      },
      "source": [
        "slr1 = LinearRegression(fit_intercept=True, normalize=True) \n",
        "smodel_lat = slr1.fit(xtr1,ytr1)\n",
        "spred_yval1 = smodel_lat.predict(xval1) # prediction for values of data latitude\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOAfTbvt1F1M",
        "outputId": "6998251c-f9c4-4dc7-861b-723e1e92c76a"
      },
      "source": [
        "print(format(mean_squared_error(yval1,spred_yval1), \".16f\")) #latitude prediction accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0000000001755424\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CucW9H3t0jP7"
      },
      "source": [
        "slr2 = LinearRegression(fit_intercept=True, normalize=True)\n",
        "smodel_lng = slr2.fit(xtr2,ytr2)\n",
        "spred_yval2 = smodel_lng.predict(xval2) # prediction for values of longitude "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBzs6FkD1XMQ",
        "outputId": "1e3cb01d-05a4-43c4-eba1-f7a8a0c9190c"
      },
      "source": [
        "print(format(mean_squared_error(yval2,spred_yval2), \".16f\")) #longitude prediction accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0000000002558310\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}