Mobile_Price_Prediction.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "-5MUnpb3SRbc"
      },
      "outputs": [],
      "source": [
        "## Importing necessary library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## To upload file in colab\n",
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "5QbJFLssSdZN",
        "outputId": "51d69d9b-bab0-4fe4-a397-05c576a44293"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
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              "\n",
              "     <input type=\"file\" id=\"files-686b26b8-c0de-47e8-aca1-3c283a26ffc3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-686b26b8-c0de-47e8-aca1-3c283a26ffc3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Mobile_Price_Prediction.csv to Mobile_Price_Prediction.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('Mobile_Price_Prediction.csv')\n",
        "df.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "ORK-AmYMSg1Y",
        "outputId": "b0950f03-0646-41a2-92b7-e69cd928b281"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Unnamed: 0                            Name    Brand  \\\n",
              "0           0  OnePlus 7T Pro McLaren Edition  OnePlus   \n",
              "1           1                   Realme X2 Pro   Realme   \n",
              "2           2               iPhone 11 Pro Max    Apple   \n",
              "3           3                       iPhone 11    Apple   \n",
              "\n",
              "                    Model  Battery capacity (mAh)  Screen size (inches)  \\\n",
              "0  7T Pro McLaren Edition                    4085                  6.67   \n",
              "1                  X2 Pro                    4000                  6.50   \n",
              "2       iPhone 11 Pro Max                    3969                  6.50   \n",
              "3               iPhone 11                    3110                  6.10   \n",
              "\n",
              "  Touchscreen  Resolution x  Resolution y  Processor  ...  Rear camera  \\\n",
              "0         Yes          1440          3120          8  ...         48.0   \n",
              "1         Yes          1080          2400          8  ...         64.0   \n",
              "2         Yes          1242          2688          6  ...         12.0   \n",
              "3         Yes           828          1792          6  ...         12.0   \n",
              "\n",
              "   Front camera  Operating system  Wi-Fi Bluetooth  GPS Number of SIMs   3G  \\\n",
              "0          16.0           Android    Yes       Yes  Yes              2  Yes   \n",
              "1          16.0           Android    Yes       Yes  Yes              2  Yes   \n",
              "2          12.0               iOS    Yes       Yes  Yes              2  Yes   \n",
              "3          12.0               iOS    Yes       Yes  Yes              2  Yes   \n",
              "\n",
              "   4G/ LTE   Price  \n",
              "0      Yes   58998  \n",
              "1      Yes   27999  \n",
              "2      Yes  106900  \n",
              "3      Yes   62900  \n",
              "\n",
              "[4 rows x 22 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Name</th>\n",
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              "      <th>4G/ LTE</th>\n",
              "      <th>Price</th>\n",
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              "      <th>0</th>\n",
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              "      <td>OnePlus 7T Pro McLaren Edition</td>\n",
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              "      <td>Realme X2 Pro</td>\n",
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              "      <td>64.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Android</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>27999</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>iPhone 11 Pro Max</td>\n",
              "      <td>Apple</td>\n",
              "      <td>iPhone 11 Pro Max</td>\n",
              "      <td>3969</td>\n",
              "      <td>6.50</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1242</td>\n",
              "      <td>2688</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>iOS</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>106900</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>iPhone 11</td>\n",
              "      <td>Apple</td>\n",
              "      <td>iPhone 11</td>\n",
              "      <td>3110</td>\n",
              "      <td>6.10</td>\n",
              "      <td>Yes</td>\n",
              "      <td>828</td>\n",
              "      <td>1792</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>iOS</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>62900</td>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Cleaning**"
      ],
      "metadata": {
        "id": "_AcMAlyxS_GV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Dataset is taken from Kaggle.So there is no missing value and it of correct type\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0_K0vMlSwPN",
        "outputId": "059d3897-0b1e-4beb-8b02-07838bb1f49b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1359 entries, 0 to 1358\n",
            "Data columns (total 22 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Unnamed: 0              1359 non-null   int64  \n",
            " 1   Name                    1359 non-null   object \n",
            " 2   Brand                   1359 non-null   object \n",
            " 3   Model                   1359 non-null   object \n",
            " 4   Battery capacity (mAh)  1359 non-null   int64  \n",
            " 5   Screen size (inches)    1359 non-null   float64\n",
            " 6   Touchscreen             1359 non-null   object \n",
            " 7   Resolution x            1359 non-null   int64  \n",
            " 8   Resolution y            1359 non-null   int64  \n",
            " 9   Processor               1359 non-null   int64  \n",
            " 10  RAM (MB)                1359 non-null   int64  \n",
            " 11  Internal storage (GB)   1359 non-null   float64\n",
            " 12  Rear camera             1359 non-null   float64\n",
            " 13  Front camera            1359 non-null   float64\n",
            " 14  Operating system        1359 non-null   object \n",
            " 15  Wi-Fi                   1359 non-null   object \n",
            " 16  Bluetooth               1359 non-null   object \n",
            " 17  GPS                     1359 non-null   object \n",
            " 18  Number of SIMs          1359 non-null   int64  \n",
            " 19  3G                      1359 non-null   object \n",
            " 20  4G/ LTE                 1359 non-null   object \n",
            " 21  Price                   1359 non-null   int64  \n",
            "dtypes: float64(4), int64(8), object(10)\n",
            "memory usage: 233.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Price.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUm0rfvESvWx",
        "outputId": "ef4e7c28-4b94-4202-ed77-85bf1caeb053"
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      "execution_count": 6,
      "outputs": [
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              "174990"
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          "metadata": {},
          "execution_count": 6
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      "outputs": [
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          "execution_count": 7
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              "        Unnamed: 0  Battery capacity (mAh)  Screen size (inches)  \\\n",
              "count  1359.000000             1359.000000           1359.000000   \n",
              "mean    679.000000             2938.489330              5.291310   \n",
              "std     392.453819              873.514133              0.671357   \n",
              "min       0.000000             1010.000000              2.400000   \n",
              "25%     339.500000             2300.000000              5.000000   \n",
              "50%     679.000000             3000.000000              5.200000   \n",
              "75%    1018.500000             3500.000000              5.700000   \n",
              "max    1358.000000             6000.000000              7.300000   \n",
              "\n",
              "       Resolution x  Resolution y    Processor      RAM (MB)  \\\n",
              "count   1359.000000   1359.000000  1359.000000   1359.000000   \n",
              "mean     811.543046   1490.777778     5.551141   2488.777778   \n",
              "std      270.707271    557.780120     2.196562   1664.440386   \n",
              "min      240.000000    320.000000     1.000000     64.000000   \n",
              "25%      720.000000   1280.000000     4.000000   1000.000000   \n",
              "50%      720.000000   1280.000000     4.000000   2000.000000   \n",
              "75%     1080.000000   1920.000000     8.000000   3000.000000   \n",
              "max     2160.000000   3840.000000    10.000000  12000.000000   \n",
              "\n",
              "       Internal storage (GB)  Rear camera  Front camera  Number of SIMs  \\\n",
              "count            1359.000000  1359.000000   1359.000000     1359.000000   \n",
              "mean               30.654864    12.070199      7.037969        1.833701   \n",
              "std                36.950241     8.948337      6.295448        0.374457   \n",
              "min                 0.064000     0.000000      0.000000        1.000000   \n",
              "25%                 8.000000     8.000000      2.000000        2.000000   \n",
              "50%                16.000000    12.200000      5.000000        2.000000   \n",
              "75%                32.000000    13.000000      8.000000        2.000000   \n",
              "max               512.000000   108.000000     48.000000        3.000000   \n",
              "\n",
              "               Price  \n",
              "count    1359.000000  \n",
              "mean    11465.825607  \n",
              "std     13857.497077  \n",
              "min       494.000000  \n",
              "25%      4763.500000  \n",
              "50%      6999.000000  \n",
              "75%     11999.000000  \n",
              "max    174990.000000  "
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              "      <th>mean</th>\n",
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              "      <td>12.070199</td>\n",
              "      <td>7.037969</td>\n",
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              "      <td>11465.825607</td>\n",
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              "      <th>std</th>\n",
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              "      <td>873.514133</td>\n",
              "      <td>0.671357</td>\n",
              "      <td>270.707271</td>\n",
              "      <td>557.780120</td>\n",
              "      <td>2.196562</td>\n",
              "      <td>1664.440386</td>\n",
              "      <td>36.950241</td>\n",
              "      <td>8.948337</td>\n",
              "      <td>6.295448</td>\n",
              "      <td>0.374457</td>\n",
              "      <td>13857.497077</td>\n",
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              "      <th>min</th>\n",
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              "      <td>1010.000000</td>\n",
              "      <td>2.400000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>320.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>494.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>339.500000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>1280.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4763.500000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>679.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>1280.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6999.000000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>3500.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>11999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1358.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>7.300000</td>\n",
              "      <td>2160.000000</td>\n",
              "      <td>3840.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>12000.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>174990.000000</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "## Remove none utilizing column \n",
        "df.drop('Name', axis=1, inplace=True)\n",
        "df.drop('Unnamed: 0', axis=1, inplace=True)\n",
        "df.drop('Resolution x', axis=1, inplace=True)\n",
        "df.drop('Resolution y', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "L3hh0lVeShGb"
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      "execution_count": 9,
      "outputs": []
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        "df.sample(4)"
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      "metadata": {
        "id": "6XJREchFShLN",
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          "height": 340
        },
        "outputId": "1f98eb1b-f5da-4ad3-895e-dc912803a937"
      },
      "execution_count": 10,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          Brand               Model  Battery capacity (mAh)  \\\n",
              "1067  Panasonic            T44 Lite                    2400   \n",
              "1151         Yu           reka Note                    4000   \n",
              "707        Vivo                 Z10                    3225   \n",
              "537   Microsoft  Lumia 540 Dual SIM                    2200   \n",
              "\n",
              "      Screen size (inches) Touchscreen  Processor  RAM (MB)  \\\n",
              "1067                   4.0         Yes          4       512   \n",
              "1151                   6.0         Yes          8      3000   \n",
              "707                    6.0         Yes          8      4000   \n",
              "537                    5.0         Yes          4      1000   \n",
              "\n",
              "      Internal storage (GB)  Rear camera  Front camera Operating system Wi-Fi  \\\n",
              "1067                    8.0          2.0           0.3          Android   Yes   \n",
              "1151                   16.0         13.0           5.0          Android   Yes   \n",
              "707                    32.0         16.0          24.0          Android   Yes   \n",
              "537                     8.0          8.0           5.0          Windows   Yes   \n",
              "\n",
              "     Bluetooth  GPS  Number of SIMs   3G 4G/ LTE  Price  \n",
              "1067       Yes   No               2  Yes      No   2190  \n",
              "1151       Yes  Yes               2  Yes     Yes   6499  \n",
              "707        Yes  Yes               1   No      No   7994  \n",
              "537        Yes  Yes               2  Yes      No   7000  "
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>537</th>\n",
              "      <td>Microsoft</td>\n",
              "      <td>Lumia 540 Dual SIM</td>\n",
              "      <td>2200</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4</td>\n",
              "      <td>1000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Windows</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>7000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49550a33-08e6-4ebd-8739-829fd9f16ada')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-49550a33-08e6-4ebd-8739-829fd9f16ada button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-49550a33-08e6-4ebd-8739-829fd9f16ada');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.available"
      ],
      "metadata": {
        "id": "LIl2lnDgShPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7f1a342-dd55-44a4-8217-3b327b5241b2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Solarize_Light2',\n",
              " '_classic_test_patch',\n",
              " 'bmh',\n",
              " 'classic',\n",
              " 'dark_background',\n",
              " 'fast',\n",
              " 'fivethirtyeight',\n",
              " 'ggplot',\n",
              " 'grayscale',\n",
              " 'seaborn',\n",
              " 'seaborn-bright',\n",
              " 'seaborn-colorblind',\n",
              " 'seaborn-dark',\n",
              " 'seaborn-dark-palette',\n",
              " 'seaborn-darkgrid',\n",
              " 'seaborn-deep',\n",
              " 'seaborn-muted',\n",
              " 'seaborn-notebook',\n",
              " 'seaborn-paper',\n",
              " 'seaborn-pastel',\n",
              " 'seaborn-poster',\n",
              " 'seaborn-talk',\n",
              " 'seaborn-ticks',\n",
              " 'seaborn-white',\n",
              " 'seaborn-whitegrid',\n",
              " 'tableau-colorblind10']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('ggplot')"
      ],
      "metadata": {
        "id": "GCuYXrq9ShUO"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "df.Brand.value_counts().head(10).plot(kind='bar');"
      ],
      "metadata": {
        "id": "RJ3vvbidShY-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "cb91830e-c4ac-45f1-c9bc-daefcb95c42b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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SJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJAASK/rEvLw8pk6dyp49e4iLiyMrK4tevXpx4MABJk2axM6dO2ncuDF33303ycnJVVmzJEmSVOUqHIwTEhIYOHAgaWlpHDp0iNGjR9OhQwcWLlzIBRdcQL9+/ZgzZw5z5szhpptuqsqaJUmSpCpX4aEUjRo1Ii0tDYDatWtz9tlnk5+fT25uLj169ACgR48e5ObmVk2lkiRJUjWq8BXjb9uxYwebNm2idevW7N27l0aNGgHQsGFD9u7de9zn5OTkkJOTA0B2djapqamVqmF7pZ5dNSp7DlXFXkTZiyh7EWUvouxFlL2IshdR9iIqDL2odDA+fPgwEyZM4NZbb6VOnTrlHouLiyMuLu64z8vKyiIrKyvydV5eXmVLCdwP4Ryqir2IshdR9iLKXkTZiyh7EWUvouxFVFX0olmzZid8rFKrUhQVFTFhwgQuu+wyLrnkEgAaNGjA7t27Adi9ezf169evzEtIkiRJNaLCwbi0tJRnn32Ws88+m2uvvTZyvHPnzixatAiARYsW0aVLl8pXKUmSJFWzCg+lWLduHYsXL6Zly5bcf//9APzTP/0T/fr1Y9KkSSxYsCCyXJskSZIU6yocjM8991xef/314z72yCOPVLggSZIkKQjufCdJkiRhMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEQGJ1/eBVq1YxY8YMSkpKuPLKK+nXr191vZQkSZJUadVyxbikpITf/OY3PPjgg0yaNIklS5awZcuW6ngpSZIkqUpUSzDeuHEjZ555Jk2bNiUxMZGMjAxyc3Or46UkSZKkKhFXWlpaWtU/dNmyZaxatYqhQ4cCsHjxYjZs2MBtt90W+Z6cnBxycnIAyM7OruoSJEmSpL9JYJPvsrKyyM7OjqlQPHr06KBLiBn2IspeRNmLKHtRxj5E2YsoexFlL6JOhV5USzBOSUlh165dka937dpFSkpKdbyUJEmSVCWqJRj/5Cc/Ydu2bezYsYOioiKWLl1K586dq+OlJEmSpCqRMGbMmDFV/UPj4+M588wzeeqpp/iv//ovLrvsMrp27VrVL1Mt0tLSgi4hZtiLKHsRZS+i7EUZ+xBlL6LsRZS9iIr1XlTL5DtJkiTpVOPOd5IkSRIGY0mSJAkwGEuSJEkAJAZdgHSqKCoqIjHRXxlJ0skVFRWxdetWAJo1a+bfj1NE6P8vffrpp3To0KHcsYULF9KzZ89gCgrI3Llzv3OsTp06pKWl0apVq5ovKGBjxoxh+PDhNGnSBCjb5vy5557jySefDLiymrdv3z7mzJnD119/TWFhYeT4L37xiwCrCsYf/vAHzj//fOrUqQPAwYMHWb16NRdffHHAldWsgoICXn/9ddauXQtAu3btuOGGGyJ9CYPFixdz+eWXH/e9E+Daa6+t4YqCU1BQwN69eznrrLMA+OijjyLvFR07dqRhw4ZBlheI1atXM3XqVBo3bgxAXl4eI0aMoF27dgFXFowVK1bw1VdfcfTo0cixG264IcCKTiz0wfitt97i448/ZuDAgRw+fJhnn32W0047LXTB+PPPP+eLL77goosuAmD58uX86Ec/4v3336dr16707ds34AprVr9+/Xj88ce55ppryM/PZ9WqVQwbNizosgIxZcoUMjIyWLlyJUOGDGHhwoXUr18/6LIC8cYbb5QLwXXr1uXNN98MXTCeNm0aLVu25O677wbKQuK0adO47777Aq6s5hw5cgSAQ4cOBVxJ8GbOnMk555wTCcavvvoqF154IYWFhaxbt47bb7894Apr3iuvvMLPf/5zmjVrBsDWrVv59a9/zRNPPBFwZTVv+vTpFBYWsnr1ajIzM1m2bBmtW7cOuqwTCn0wHjNmDO+++y4PPPAAAAMGDKB79+4BV1Xz8vPzeeKJJzj99NOBsj6MGzeORx99lFGjRoUuGKenpzNkyBDGjh1LvXr1+NWvfhXKqx4A+/fvJzMzk3nz5tGuXTvatWvHz372s6DLCsTxVrcsLi4OoJJgbd++vVwI7t+/P/fff3+AFdW8n/70p0DZuYfd559/Xi781q5dm8GDBwPw8MMPB1VWoIqLiyOhGMqGUoTxvQJg/fr1jB8/nvvuu4/+/fvTp08fHn/88aDLOqHQB+ODBw+yceNGmjZtSn5+Pnl5eZSWlhIXFxd0aTVq79695cY/JSQksHfvXmrVqsVpp50WYGXBePPNN/noo4949NFH2bx5M2PGjOHmm2+mU6dOQZdW4479u2jUqBErVqygUaNGHDhwIOCqgpGWlsbLL7/M3/3d3wHw+9//PuYXq68OtWrVYu3atZx77rkArF27llq1agVcVTB27NjB/Pnz2blzZ7ngM2rUqACrqlnFxcXl/mbecccdkf8uKCgIoqTApaWl8eyzz3LZZZcB8MEHH4TyvQKIvDckJSWRn59PvXr12L17d8BVnVjog/FDDz1E3759yczMpLCwkFmzZvHwww8zduzYoEurUd27d+ehhx6KbN29fPlyunfvzuHDh2nevHnA1dW8AwcOMG7cOGrVqkXbtm1JT0/n2WefDWUwvv766ykoKGDgwIHMmDGDgoICbrnllqDLCsTgwYN56623mDx5MgAdOnTgtttuC7iqmjdkyBCmTp0aCT1169ZlxIgRAVcVjCeffJIrrriCiy66iPj4cC70FB8fz549eyJ31Vq2bAmU3YkM20WmY4YMGcLvf/975s+fD8C5554b+UAdNp06deLgwYP06dOHUaNGERcXR2ZmZtBlnVDod77Ly8sjNTW13LE1a9aEcoD8xo0bWb9+PQDnnHMOP/nJTwKuSLFg3759oR1TrOMrKSkhPj4+EozDNOnuLz344IMxfVu4JixevJj58+czcOBAfvzjHwOwadMmZs6cydVXX02PHj0CrrDmffzxx3Tq1CmUd1y/z9GjRzl69GhMv2eEPhgfOXKEd999l7y8PIYOHcq2bdvYunVrZBJamJSUlLBnzx5KSkoix/7yQ0NYuBJD1L/927/RuHFjMjIyuPjii0lOTg66pBr30ksvceutt5KdnX3cK2Bhum0OMGzYMNLT08nIyOD8888P7VVBgA8//JBt27bRsWPHcsPRwnbbfNWqVcyePZuvvvqKuLg4mjdvTr9+/bjwwguDLi0Q06ZN449//CPnnXceGRkZpKenk5CQEHRZNerjjz/+3scvueSSGqrkbxP6YDxp0iTS0tJYvHgxEyZM4MiRI/z85z8P3bJc8+fP580336RBgwbEx8dHxlmPHz8+6NICMXbsWDIyMnj33XfLrcRw0003BV1aIDZu3MiSJUvIzc2lefPmZGRkcPnllwddVo354osvSEtLY82aNcd9PGx3mI4cOcLy5ctZunQpmzZtolOnTnTr1i0y5jhMXn31VRYvXkzTpk3LDaUI44fo43nvvffo3bt30GUEoqioiFWrVrF06VLWrl1Lhw4dGDp0aNBl1Zhp06Z97+PDhw+voUr+NqEfY7x9+3buvvtulixZApQNDg+jefPmMXnyZOrVqxd0KTHBlRjKa926Na1bt+bv//7veeWVV5g6dWqogvGxq39hC8AnkpSUREZGBhkZGRw4cICXXnqJX/ziF/zud78LurQa99FHH/H000+7ecMJzJ07N7TBODExkfT0dAAKCwvJzc0NVTCO1eB7MqH/TU5MTKSwsDByK/Cbb74J5RtcampqTI/5qWmuxBBVUFDAH/7wB5YuXcr27dvp0qUL48aNC7qsQKxdu5Y33niDvLw8iouLI3dWnn766aBLq3Fr1qxh6dKlrFq1irS0tMiaxmHTokULDh48SIMGDYIuRTFk5cqVLF26NDJnKTMzM7S/IwUFBbzxxhv86U9/AmJ/Q6DQD6X4v//7P95++222bNlCx44dWbduHcOGDeP8888PurQa9cwzz7B169bvTBYI0+5N37Z8+XLOO+888vLyIisx9O/fP7JqR5iMGDGCLl26kJGRQdu2bYMuJ1AjR47klltuIS0trdxt87DdaRkxYgStWrXi0ksvpXPnzpH1z8NozJgxbN68mdatW5e7qBK2cecnMmzYMJ555pmgy6hxkydPJiMjgwsvvDD0E/DGjx9Py5YtI5MwFy9ezObNm2N2Q6DQB2Mou22+YcMGSktLadOmTShn4L/xxhvHPe7i9Qrjut4n4goEZQoKCmL2ak9Nc9w53Hzzzcd9jygtLaWwsJDXXnstgKqCt2fPHj7//HOgbDhaWO8q3H///d+Zt3W8Y7EifGMG/sK///u/88gjj5Rbn/bYsTAxAJfnov1R+/fv55133mHLli2hX6Gjffv2zJw5k0suuSSUKxC888479O3b94RB59huZ2HSrl270AegV155JegSYs5HH33EzJkzIx+QXnzxRQYOHEjXrl0DrqzmnWobAoU2GBcWFlJYWMj+/fvLjR0tKCggPz8/wMqC8eijjx73eBjDD7ho/7dNmTKFjIwMVqxYUW6FjjDauHEjULZKxbeF5ffk/fff55xzzgnNB4G/xtKlS5k1a5YBSOW8/fbbjBs3LvIhad++fTz22GOh/Hdxqm0IFNpgnJOTw3vvvcfu3bsZPXo0x0aU1KlTh6uvvjrg6mrewIEDI/9dWFjIxx9/HLo1F7/ttNNOo1evXkGXERNcoSMqLAH4RK655hpmzpzJnj17uPTSS+nWrVtkQ4ewmj17tgFI31FSUlLuzkFycnK5PQLCoqSkhMWLF/Pkk0+eMhsChTYY9+rVi169ejF//nyuueaaoMsJ3F9eATr33HNDG36g7N/HG2+8EfpF+8EVOr7tVJtdXdV69+5N79692blzJ0uWLOGZZ56hsLCQ7t27061bN84666ygS6xxBiAdT3p6Or/85S/p1q0bUHZnIYybncTHx7Nu3Tog9gPxMU6+A9atW/edsaRh28Ly20GnpKSEL774ghkzZvDrX/86wKqC46L9Ua7QEXWqza6uCZs2beKZZ55h8+bNoVzHeObMmfz5z38uF4BatmwZ2s2AFLVs2bJIKDzvvPO4+OKLA64oGM8//zz5+flceuml5faKcOe7GPXUU0+xfft2WrVqVS4AhW0SyYgRI4iLi6O0tJSEhASaNGnCDTfcEMqdrADuvPNOJk2aFMo1rf8aYd3N6lSbXV1diouLI+u0fvbZZ7Rv355u3brRpUuXoEsLhAFIOrET7YAXqxuAhP6v/hdffMHEiRNDvxzV1KlTgy4hprho//cL625Wp9rs6qr26aef8uGHH7Jy5Upat25Nt27duP3220O9jjFA165dHVOscj7++GN++9vfsnfvXiC67OXLL78ccGU1L1YD8ImEPhi3aNGCPXv20KhRo6BLCdRHH31Eeno6tWvX5q233mLTpk1cf/31oRxTC2VjSUeOHOmi/Srn27OrS0tLSU5OPuXe9Ctj9uzZdO/enZtvvpnk5OSgy4kJ69evZ8aMGWzZsoWioiJKSko4/fTTQxmAFDVr1ixGjRpF8+bNgy4lcLt27eLFF1+M3FU599xzGTRoEGeccUbAlR1f6IPx/v37ueeee0IfgN566y0uvfRS1q5dy2effcZ1113HCy+8ENrNDAYMGBB0CYpBrVq1OqVmV1e1MI6xP5kXX3yRkSNHMnHiRLKzs1m0aBHbtm0LuiwFrGHDhobi/2/atGl0796de+65B4APPviAadOm8fDDDwdc2fGFPhi7sUWZY+OrV6xYQVZWFp06dQrtbkXgov1w8t2swmjevHn07NmT2rVr89xzz7Fp0yZuvPFGOnbsGHRpCtCZZ55JSUkJ8fHxXHHFFTzwwAPceOONQZelAKWlpTFp0iS6dOlSbkvoWJ1wVp327dvHFVdcEfm6Z8+evPfeewFW9P1CH4zDtG3n90lJSWH69FsLLz8AAAlKSURBVOl8+umn9O3bl6NHjxLmeZku2u9uVsfzv//7v/Tq1YtVq1axf/9+7rjjDp5++mmDcYglJSVRVFREq1atmDVrFg0bNgz1e6fKHDp0iKSkJD799NNyx8MYjOvVq8fixYvp3r07AB9++CH16tULuKoTC20w/r6rYWEcIH/33XezatUq+vTpQ926ddm9e3eolxty0X4dz7HAs3LlSnr06EGLFi0MQSF3xx13UFJSwuDBg3nvvffYtWsX9957b9BlKWBhmntwMsOGDePFF1/k5ZdfJi4ujrZt28Z0f0IbjL0aVl5SUhLt27dn165dke1uY/kTXXVz0X4dT1paGmPHjmXHjh3ceOONHDp0KPQr2oRd48aNgbIVSxyap2O2bt3KCy+8wN69e5kwYQKbN2/mk08+4R/+4R+CLq3G7dq16zvzttauXUtqampAFX2/0AZjlffaa6+xaNEimjZtWu4PfVgn27hrkY5n6NChfPnllzRt2pSkpCT2798f01c+VH0mTpzIPffcw7333lvuPfPYXcfx48cHWJ2C9txzzzFw4ECmT58OwI9+9COmTJkSymA8Y8YMnnjiiZMeixUGYwFly7U99dRTbmhB2R+2a665hs8//5y1a9cCkJWV5aL9Ij4+niZNmrBt27bQTkBUmWuvvRaA0aNHf+exvLy8mi5HMaawsJDWrVuXO/btTcTCYP369axbt459+/Yxd+7cyPGCgoKYvgNrChLghhbfFhcXx7hx45gwYUIoJ0roxP7nf/6HefPmkZ+fT6tWrVi/fj1t27YN7Z2VMJsyZQo//elP6dOnTyTw7Nmzh1deeYWtW7eSnZ0dcIUKUr169fjmm28idxOWLVsWuv0SioqKOHz4MMXFxRw6dChyvE6dOpGl22JR6LeEVpnPP/+cX/3qV7Rs2TLU6zkf8/TTT3P11Vd/5xO/wu3ee+9l3LhxPPTQQzz55JN8/fXX/Md//Af33Xdf0KWphh04cIBXX32VdevWMWjQIP785z8zd+5crrvuOq666qrQXR1Uedu3b2f69OmsW7eOunXr0qRJE+66667ImPQw2blzJ40bN+bw4cMAMb9TpleMBZRtCd23b19atmzpGzqwceNGfv7zn9O4cWOSkpIcNyigbILVsS2gjx49ytlnn83WrVsDrkpBSE5O5vbbb2fevHk89thjNGrUiF/+8pcxu5uXalbTpk15+OGHOXz4MKWlpdSuXZv33nuP3r17B11ajTt06BAPPPAABw4cAMqupo8YMYKWLVsGXNnxGYwFlK1K0atXr6DLiBkPPfRQ0CUoBqWkpHDw4EG6dOnC2LFjqVu3biivAAkOHjzIb3/7WzZs2MCDDz7IypUrefzxxxk0aBDnn39+0OUpRnz76ujcuXNDGYynT5/OzTffHPm9WL16NdOnT2fs2LEBV3Z8BmMBZXuXv/rqq3Tu3LncUIq0tLQAqwrO7t27adGiBbVr1wbKJgt8/fXXhqCQu//++4GyLcPXrFlDQUEB6enpAVelIIwaNYqrrrqK2267jYSEBDp27MiXX37JCy+8QGpqKiNHjgy6RCkmHDlypNyHxfbt23PkyJEAK/p+BmMB8OWXXwKwYcOGcsfDOqnohRdeKLeUzOmnn/6dYwqnkpIS9uzZQ5MmTYCyCVexuh6nqs+jjz76nWETrVq1YuzYseTk5ARUlRR7mjRpwptvvsnll18OwAcffBB5/4xFBmMB4Q3AJ3JsTPEx8fHxFBcXB1iRYsH8+fN58803adCgQeTfh2PPw+n7xhJnZWXVYCWKJd+3q25Yl3gcNmwYr7/+OhMmTADK7lAPGzYs4KpOzGCsiBUrVvDVV19x9OjRyLEbbrghwIqC07RpU+bNm8dVV10FwH//93/H9Cdc1Yx58+YxefLkUO8KKenE3FX3u5KTkxk8eHDQZfzVDMYCygbHFxYWsnr1ajIzM1m2bFmolyobMmQIM2bM4O233yYuLo7zzz+ff/3Xfw26LAUsNTWVOnXqBF2GJMW8kw09jNXlYA3GAsp2qBk/fjz33Xcf/fv3p0+fPjz++ONBlxWYBg0aOHlG39GkSRPGjBlDp06dOO200yLHj+2CJkkqs379elJTU+nWrdspdaHNYCyAyNqsSUlJ5Ofnk5yczO7duwOuqua988479O3blxdffPG4j59Kt4NU9VJTU0lNTaWoqIiioqKgy5GkmPX888/z6aef8uGHH/Lhhx/SqVMnunXrRosWLYIu7XsZjAVAp06dOHjwINdddx2jR48GIDMzM+Cqat7ZZ58NhHeZOn2//v37B12CJJ0S4uPjSU9PJz09naNHj7JkyRLGjBlD//79ufrqq4Mu74TcEjrkNm7cSGpqKg0bNgRg0aJFfPDBBzRr1owBAwaQnJwccIVS7Ni3bx/vvPMOW7ZsKTfD3FVdJOm7jh49yooVK1iyZAk7d+7koosuIjMzk5SUlKBLOyGvGIfc888/z8MPPwzAmjVrePXVVxk0aBBffvklzz33HPfee2/AFdasU3WygGrGlClTyMjIYMWKFQwZMoSFCxdSv379oMuSpJjz9NNP89VXX3HhhRdyww03xOwW0H/JYBxyJSUlkavCS5cu5corr6Rr16507do1sstXmJyqkwVUM/bv309mZibz5s2jXbt2tGvXjp/97GdBlyVJMeeDDz4gKSmJbdu2MX/+/MjxY/sEvPzyywFWd2IG45ArKSmhuLiYhIQE/vjHP3L77beXeyxsTtXJAqoZx7ZLb9SoEStWrKBRo0YcOHAg4KokKfb87ne/C7qECjEYh1y3bt0YM2YM9erVo1atWpx33nkAfPPNN6Fcr/VUnSygmnH99ddTUFDAwIEDmTFjBgUFBdxyyy1BlyVJqiJOvhPr169nz549dOjQgdNPPx2ArVu3cvjw4VCuznAqThZQ9SosLOT999/nm2++oWXLlmRmZpKQkBB0WZKkKmYwlr7l25MFMjIyTpnJAqpekyZNIiEhgfPOO4+VK1fSuHFjBg0aFHRZkqQq5lAK6VtO1ckCql5btmxhwoQJQNn63g8++GDAFUmSqoPBWPqWU3WygKrXsUl3gEMoJOkHzKEUknQS//iP/xgZf19aWkphYSFJSUneSZCkHxiDsSRJkgTEB12AJEmSFAsMxpIkSRIGY0mSJAkwGEuSJEkA/D8/FH5yzhocfgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obervation: Higher number of mobile belong to Intex brand followed by Samsung\n"
      ],
      "metadata": {
        "id": "-uQdDrLqS2o8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style= 'ticks')\n",
        "sns.pairplot(df, hue= 'Price');"
      ],
      "metadata": {
        "id": "Cz-VTDqcShnD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "356ecc53-bf9a-4c63-a0cd-1b709da7865c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1521.47x1440 with 72 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.lineplot(x= 'Price', y= 'Screen size (inches)', data= df);"
      ],
      "metadata": {
        "id": "bZnbOSQ5ShwN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "a6e52cc3-3135-48d0-d03c-ee8b6d2274d5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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jNhm0WLakEJsvXYYV9WY01eTDZKBTO7HwyMk04L6bN+OOR3bgvsd24Yf/ejYaq/JTvawg6F8WETcWcpFQ9+A0Mk06VBRnR9zuUMc4fvLUfhTnZ6C13gxgYb9vQpmeISv+56n9+J5Jh/UtZXPax59fPwmthsFnL2qSHtNoGHzu4qX4yVP7sfvoEM5aWRGvJceN3mEbpmZcWN0U7PI0Vefj9T19GJ10oLQwM0WrOz1gOR69Q1Yc6RrHMd8gkUmbC4AgLFrrC3H5llq01JlRX5m3IApLCUIN+TlG/ODrm3HfY7txsneSRDOxeJm0uhaseHR7WRi56C2VWJYDAIxN2gGY8e5HA7DZPfjEloXRRixRdJ+axuGOcVx1bkOqlxIX7E4vAMDt5eb0+sGxGby1rx9XntMQ8m/inNUVePq1E3hmexs2rShPO9dWzDOv8uWZRRqrhZNXR/8UieY44/GyaOubkjpbHO+ZkL6DxQUZWN1ULIyjritEVQmNoyYWN+a8DPz8/52f6mUoQqKZIAB4vVxMt+HHpoR+pg/9cT8AJE00H+2yoCg/Y96iJd7u+L/95G0AWDSiWRwNzLH8nF7/9GsnoNdr8ZkLm0Ke02o1+OyFTfjFswew/8Ro2k3ZO9g+hvKiLClSIlJbngudVoP2/klsWZ1+DvlCwu704ESPv7NFW98kPL4LtOrSbJxzRiVW1JvRUm8O+TkQBJE6SDQTpz08z8PLzk00J5v/fXo/asvzcPdNG+e1n0TdFeA4flG4YKJoZvnYRXPvkBXvHRjEpy9oQn6OUXGb89dW40+vn8Qzr5/E2mUlaeM2e1kORzrHcf6a6pDn9Dotaity0U6TAWNmyuaSBogc7bage3AaHC/EdRoq8/CJLXVoqRPGUedlK39nCIJIPSSaidMeL8v7WjOpFy7jKRLNXpbDwY4xeLws9Lr0m9DFchw0mvRbV6yIsQyOiz2e8fQ/T8Bk0OGa8xvDbqPXafDpC5rw678ewuHOcaxqLA67bTJp75uCw8VKrebkNFXl492PBhbNxVEi4HkeIxN2X9RCGEc9ODYDADDoNGheUojPXrwUrXVmLKstRIaRTsMEsVCgf63EaY/Hy8IbII5YlsPe4yPY2FoWVkinSjTzPOByszjSacGZadjH0svy0C+C3ypSPIOLzWnuGpzG+4eGcN0lzcjNitzm65INNXjm9ZN45vW2tBHNBzvGwDDAykblVk+N1fl45YMeDFlmURmlaPZ0geN49I/YcKTL7yRbpp0AgKwMPZbXFuKSDTVorTejoSp/zt1YCIJIPYvg9EaE4xfPfIS1y0uxZRXlDyPh8XLwev3i6N0Dg/jfpz/E//3nBagtz1V8TapEs8j+E6NpKprnVjiXbkjxjBjfzlOvnkBWhh5Xnxc9223Qa/GpCxrx2LajON49geV1hXNZalw52D6Guoq8sIK/yVcM2N4/ddqKZi/LoWNgShDIvs4WMw4PAKAw14RWX8FeS70ZS8pyyZEniEUEieZFzOt7+vD6nj689JOrU72UtMbjZcEGOM1i9wCPl5UekxfO2eweOF3e5C1Sxr7jI/jq1StSdvxwLBrRPId4RlvfJPYcG8YXPr4M2Rl6Va/52KZaPLu9Hc++0YZ7vrppTmuNF06XFyd6JnGVb3S2EjWlOTDoNOjon8L5aTycJZ44XV6cDBhHfaJ3UrqoqijKwlkrywWhXG9GaWFm2uTTCYKIPySaidOSGYcHA6M2LFtSCLvTC53Gf8v0cMd4yPZKhXOpKAbkAWg1DAbHZjBsmUWZOSvpa4hEoGO/kJlLPOOPrxxHTqYBV54dXnTKMRl1+OR5DXjylePoGJhKaU/SY90T8LJc2DwzIHT+qK/MQ3v/ZBJXllyss26p9duxbgs6B6aF4UcMUFeRh8s2LRHGUdcVoiDXlOrlEgSRREg0pzlzbQ0WaxYz1n3bnZ6wo1mdbsGBTeepVPf9bheO90zgbw9eCaeLhU4ruEPDllmMTqoTw6mKaLTWm3GoYxz7j4/gEzEItGTAzqFwLh1xe4T3war8d3S0y4KP2sbwlStakGlS5zKLfGJLHf76Vjue3d6GO768Iea1xouD7WPQaRm0RImJNNUU4J+7e8FyPLSLIHowNukI6mzRN2wDAOi0GiytycenLmhES50Zy2sLkaXyDgJBEIuT9FU1BIC5twbzJPA2+Y6Dg3j4LwfwxPc/piiMr73zHwCAFx+6KmFrmC9i2yyO4+FweaWJWkouczhSJZrLi7IwNunAvhOjaSeaPXMcBpJuSE6zypZzL77bifxsIy6fQ7/urAw9rjinHs+83obeYSuWlCnn6BPNwY4xNC8phClKN4fGqny89F4XBkZtKVvrXOF5HgOjMzjWbZEK98SL5AyjDstrC3HemVVorTejqTofBv3C7wRDEET8INGcJrAshxO9k9JoZjVEcqEDxYvN7kZOGFd4LoxPOeBwsXC5WUXRHOhy8z7RkW45P3E5XpaDl+WkYp1DnepFc0p6NfPCZ7l2WQn+uacPbg+bVid2tc5suiMVAqoYbsJyPA51jGPLqoo531256pwGvPhOJ/6yvR3/+YW1c9rHfLDZ3eganMbnL10WdVupGLBvakGIZrvTg+17+gSR3G3B9IwbAJCfbURLfSGuPrcBLfVm1JXnQkvjqAmCiACJ5jThT77WUz/+1tloqVMnnCO50IFFbN/+xXv49e0XxWWdwr4FQa7GhBubdGDG4UF9ZV7cjh8PRAnv8XDCX3hB4B/uGEemSSeNsI1EqpxmBsDa5aX4+85uHOm0YM2y9OmisegKAVV8yXtOTWPW4cHKBvUXvHJyswy4fHMdXninA/9yWTMqktyZ4lDHOHgeOCNCnlmksjgbGUYtOgamcPGGmiSsbu6wLIcf/WEvDrSPobQwE2uXlaKlzowVDWZUFGWl3cU8QRDpDV1Wpwlijm7S6orL/gKdZrGxfryQRDOiCwqXh01ovnrO+E6WbtZ/cTEyYYdl2qnK7ddqmBQVAgqf5crGIhh0Guw/MRKX/b770QCGLbPz3s+iEc1Sy7no393DnRYA4Xsbq+WT5zVAp9XguTfb57WfuXCwfQwZRi2aaqIXImo0DBqq8tGxACYD/m7bERxoH8O/fe4M/O7OS3Db59fgsk1LUFmcTYKZIIiYIdGcJsT797c3gdlSdwxOc7oift6BFxfHeyYAAKtUiB9znil1vZoZwKjXYmVjEfYdj49o/t+nP8Q/d/fO6bV8wBdBTZxhIeD2qu+ecbhjHBVFWfMeS16Qa8Klm5bgzX39GJ2wz2tfsXKwbQyt9UVStj8ajVX56Do1ndYZ9td29eDvO7px9bkNuGTjklQvhyCIRQCJ5jRDjXurhkSezMToxx9fOT7vfb3wTmdY4WeZTpwoFa9RnG4vtL52c8d7JlCYa0JFUfhb4+JPpyg/I+UDTtYuK8Wp8VmcGp/fnQSO48FyPFweNvrGCgS6y4m8WEsmartnsByPo13j83aZRT51fhMYBnj+reS5zWOTDpwan43Yak7O0uoCeLwc+oatCVzZ3DnaZcGv/3oIa5pL8JUrWlK9HIIgFgkkmtMExifj4uXeJlY0C/t+fU8frLPuee1r23udeOfDAcXn4hVViYTQOYMBz/M40T2BlQ1FfkUdgaL8DDjdcxOZ8yHw+7F2uZBl3n98dF77FIXhXL8zroDPwbtoWs6pc5q7T01j1unFiob4iObiggxctL4Gr+/pS+hFYyDiMJ/VTerfQ6OvGLBjIP0iGiMTdvzwD3tQWpiFb9+wjor7CIKIG/TbJF1QEGpjkw7Y5iBKZx0e9I1EdoB4no+YSewcmAqbTw0UV2194YccKAmOSZszyKEVXU5AOAEnK/8sxjMcLhY6rQbjU05Mz7pVO4bF+fO7FT8fxK9KRVE2Koqy5p1rFnsrR3OJnS4v+kdsIY8HOtSpuJBIBGozzWKLwvkUAcr5zIVNYDkeL7zTGbd9RuJgxxjys40xdcIoM2ciO0MvtW5MFxwuL+7//W6wHI+7b9qoejIjQRCEGkg0pxsB5+gb7/8nvvfbD2LexX/8/F389E8fBe9WZmG/89EgbvvZO9hxcFB67MMTo3h2+0kc7hjHv//0Hby8sxu/f+koTskKCQNFs1jAqIRb4Xb/o387jJ88vV/6O8vx8LIcDneM47afvoOXdnSpe5MRUOfQCdLT5fZCq2HQPTQNQF2eGUitaA5k3fJSHO4Yh8vDoqN/ClO22N15MYcsRhLC8coHPfh/P3sn5MImUDRbZ+Z+d8Dj5fDwXw5EvBCbK7G6tuJnEe0i7nDnOCqL559nDqTMnIXzzqzEKx/0YHoen6caeJ7HofYxrGosktouqoFhGDRW56eVaOY4Hv/79H70DVvxnRvWoTLJHUgIglj8kGhOE8TTVTwyzUrdMp569UTQ38Us4uCof9t7fvsBnnzlBPYcGwYA9A5Z8be3O3DfY7uCXhvYzi7SBDib3RPymN3pxUzA4yzLg+N4DPjWrORkxoqaWIfoNPOcIAB6hqwozDWhzJyp6hhFKRLN8vjO2mWlcHs5yfFUulCJhhTPYCO/dtbhgdPNSkVyIoFie2pm7nGd9v5JvLarF9//7Qdx+R4EEmvUR3yPkb7fQp7ZErdoRiCfvWgp3B4WL76bWLe5f8SGCasLq2LIM4s0Veejd8g6p+9cInj6tRPYdWQYN121Amua06cNI0EQiwcSzWmC2P5IFEWRirt6h2Ivvnk7TG5YKRYi3mLPzzECAFwyBzLQaY6UwbZYQ909nueDXs9xHFiOh8d34k3WoA75255xeFBSmKG6DVV+jjFlI4QD17iiwQyDXov98+iiwfpiONEyzeGyzy63v6f1fJxR0WFmGAbf+837GFM5zjwRqMk0dw1Owe70qr47EQvVpTnYvKoCf9/RjRn7/OoGInGwXbjYiiXPLNJYlQ+W49F9ajrey4qZ9z4axDPb23DJhhpceU56TckkCGLxQKI5TQiUXx4vi6//6A3F7d77aBDf+p+38P6hU/M6nih2GQXVLEY5xK4S8mxzsGgOLyos007F4wbuT4xniG3sDLokfyWZwD+qF8EaDQNzStzm4M/boNdiVWMR9p8YBa/yPsWswxMkdCQxHCWeIQpIubMYGM+Yj2g+0TuJksJM3H/LZthdXtzz2/fnXWg6V8TvY6RM8+EOoT9zIpxmALj24qVwuLz4+87uhOwfEIoASwszUWbOivm1TdUFAJDyfs0dA1P42TMfYXltIb7x6VXUf5kgiIRBojnN4BH5RN3lEzuxDiyRn0f8462jv5aNJJoVthcdaqUcKcfzQaKZ44R4hijE9Dp1TnO8OgtoIgjlaJ1MUpVrlq943fJSDFlmVTuzz7/VjoefOwiHS3CI/fGMKKKZV3aaxXiGTstII4rnQlvfJJprClBXkYe7btyIYYsd9/1uF5yu6NMZ440ap1nIM2ejMNeUkDXUVeRhQ0sZtr3bCbszNOo0X1iWw5HOcZyxNPZoBgAU5ZuQn21Eewo7aExanXjg97uRl23AHV/eoPr3B0EQxFwg0ZwuSKFmHkPj4SeziQKnb8SGSWuokxvu5OpweRWLxEYjCC1RWIc6zX5nked4OFzeIBGbkylUrE+odJrZINGs7is573Z0vqsFKWIxhyh5URyLv+bDWt8YbXE4SzRO9k6C43hpgIZ4URQtmxrWafbFeUoKMjE9O7efy4TVibFJB5qXCO7lyoYifPsL69DeP4kfbd2b9CEa0bpnsCyHY92WuPVnDsfnLm6Cze7Bqx/0xH3fnYNCu7zVjXMTzakuBnR7WDzwhz2wOTy46ysbpYt1giCIREGiOU0QowHvHhjEv/3k7bDbiYVJb+8fwH2/3x0Sj/jFswcUXzc948YN339V+rv4slc/6AkR2uJzfmcx+Bhyp/k7//cevnzfP0Neb1EQ9VxAppnneSmeIbqcBn1yvpLiNYpG6/dtY72rW5SfGIcxEizHg5FlqcvMWagqyVYlmlmOR3u/kB0Wx2ar7dPsF82yTLMnQDTPMZ5xsldYU3NNgfTYWSvL8c3PnoEPT47i53/+KGntCIXvpHAsLszths7BaSHPnKBohkjzkkKcsbQYz25vw8BofIsjD7QJ/ZlXzYQTssMAACAASURBVCHPLNJUnY+BEZt01yJZ8DyPXz53ECd7J3Hb59egvjIvqccnCOL0hERzmtE1GLmohosypviDw0OqjhMoBuRiST5OWd5BIKiQj+fRIytMFHet7DTzUj9gUQMJhYDCY8m6vSoK5PkMPiguUNdpI16wHA+706vYe3btslJ0DkxFneo3MGqDwyVsM+xzmr1qCwF9P1h59wzxmMUFGbDOuqP2NlbiZO8EdFomRPxcunEJvnj5crzz0QAe23YkYoY+XngCPsNw/96OdAoFdCvi2J85HN/67BnQajW4//d7MOuIX0zjYPsYastzkZc9d4e2sTofHB/991a8+dvbnXhzXz/+5dJmbFlVkdRjEwRx+kKiOYnwPI+7f/0+dh8ZwhP/OIYnA8ZQq3U5vREEiWXaEUMpm59whTOisyfXKe5AcRXwnChoxP8r5Y4D4xmcT4xzLC8JseQVAgrvWTwaH/JMdJKdaRYFU3ZmqGhet7wEXpbHyShuc7vYoQLC5DQg0GlWF8+QFwyKUYbSwkzwPObU7aGtbwp1FXmK3VM+c2ETrjq3Htve68JzbyZ+vHTg95sNI9IPd1pQVZKNggTlmQMpLczE7V9aj2HLLB764745XZTIcXlYHO+ZiGl0thJNVcJkwGRGNPYdH8EfXj6KLasqcO0lzUk7LkEQBInmJHOgfQz3P74Hf3mjHc9ub/M/oVKpyYvyApm0ulRXjgdqgXCd08Kdm70yp9m/Np9o9v3dMu0McQZ5ngfH+7PMgDB6WXKak9Vybu5RZolk92oWxWhOpiHkudZ6M7QaJmpR1sm+KWSZdCgrypLiGVys8Qy50+wWnWbBeZ+KMaIhRkbEPLMchmFw05UrcP6aKjzxj+N4bVev4nbxIjCzrRQJYVkOR7sSn2cOZGVDEb5+zUrsPzGKJ/9xbN77O9E9AY+Xm3MRoEhBrglFeaakddDoH7HhoT/uQ115Hv79ujNjGshCEAQxX3SpXgABfHRyFO9+JEzmUzK2DraNoakmH5kmfYjL1DNkhdGgRUWRMP1KrWMd1JwszIv+9naH4uNBhYB84OP+rDIg9Ht2ulloAvYvbu9lOUmQsAFOs9pCwPkSj65UyRbN1giiWa/TQqfThB19LtLWN4mmmgKwLIdhS4zxDEk0h2aaNQxgzhNc11hzzX3DVjjdbFCeWY5Gw+DW686E1e7Gr547gNwsPc5amZjb8tFEc+fgNBwuL1YmOM8s5+Ob69B9yorn3+pAbXkuzl9bPed9HewYg1bDoLV+/vGSxup8dAzEf4qjHJvdjR/8fjcMOi3uvHEDTEY6fREEkVzIaU4iSoK4f8SG7z36gXRyVnLp7vrN+/jxE/swaXOGnMT/79kD2Pqy33lSrQUDNbPa1/hwh+nTLApfnofkAMk7dgS2LRNFGMfxIUIsEk6XN36FR+Ly1WZlAzbLUYhJJBJxkmLE40Z4G063Fz1DViytKYA5LwMjE3apGBMIFcNyxJ+dvHuG28PCoNdK3QtibTsnFQEuKYy4nU6rwXe/uB5N1QV46I/7cdiXK443QfEMBdEsTl9MRp5Zztc+uRKt9Wb84tn5jRs/2D6GpTUFyIiD8Gyszsfg2Cxm4pi3lsOyHB58Yh/GJh2448sbUJLkegKCIAiARHPK+dcH3wz6e7gOAR+eHMUXv/8aJmQdKWYdniCHUN5ZIRxzjSXIJ/rxvH+KnzvAac73FRfJLwKkNnZeTopzeDkuKPIRjRvv/yfueGTnHN+BgNitJCjLHIP9zIBJ+hAFWwSnGYh+8dM1OA2O47G0Oh+FuSa4PSymbC4p8uNVmWkO6Z7hZmE0aJGXJYrm2Jzmtr5J5GYZVI0wNxl1+N5XN6GkIAO//MvBmI6jlmhO86HOcVSX5qAgJ/ndU/Q6Db77pfUoyDHigcf3hPw+UMOMw4OO/ql555lFxCEnnQns1/y7bUdwoH0M3/zMKiyvi3xxRRAEkShINCeReNzClItQuTuoVsbxAWIgFgHtlXUT4AEYfaJZ7DrAwz/gJNRpFvfDSV05WJaP2iM4EJs9Do6WlGmeX1FVfrYRjVXJaXcliubsMKIZiPyzbOsTRI3gNAuCb9hi9zv+fOTMPBemYNDlc5pzsgxgmNid5hO9k1haU6D6IiQ3y4A1y0oxZYtdMKoh8Lso7xzjZTkc77ZgZQpcZpG8bCPuunEjZp0e/PAPe2L6twMITjnHY955ZpFGXzFgonLNr+3qwd93dOPqcxtw8YYlCTkGQRCEGkg0J5H+4dim+CkhFzXyE6Za89Muizeo7YErF0w8z8NokDnNHI+8LEHYhcRNgjLN/mPHEs+IB9LHxAf9L2byc4xxucWtBtusBwwDZCm0nFNDW98kigsyUJBrgtk3mGV4YlZy/IHIEQ1/PCM002zUa6HVMMjJNMTkNM86PBgYtYUtAgxHhlEHh8urqgXdZIziWnx/Wg0DmWZG58AUHC42qUWAStRV5OG2z6/Byd5J/Or5gzG14jvUPgajQYulETLksZCbZUBpYWZCOmgc6RzHI88fwprmEnzlipa4758gCCIWSDQvMOROb6jLFF01W6YdeHNfv/8Bno86QllEXizG8wFOs9fvNBv0WmRn6EMEVGCm2cMK23tZLmq7s3gTj+4ZyWbG7kaWSe+fYqhEhDfU1jeJpb5b6eLo55EJe5CbGqkYUBTX8p+V28NKF0552caYume090+C5xGxCFCJTKMOHO/v3BEOy7QDX77vn9IgDzWIfadNRl2I03xIzDPXp1Y0A8CWVRW47pJmvLG3H9ve61L9uoMdY2itN8e16LapOj/u47RHJuz40da9KDNn4ds3rJtXT3WCIIh4QL+FFhjyCWVuD6uqfVwgSiOo1Y4pDhXNvCSa/ZlmQZSa80wh8YzA0dwjFn+fYLl7mXgWnmq22T1h88zRmJ5xYWTCLrmLep0GhbkmDFtmg4rdIl28iN89+QAVl5uFwTeUJj/bCOus+njGSV8xW1OMojnDJLj70QpCLdNC8eyBdvWiWfwMMgzakDswRzotqCnLSZuRzZ+/tBlnrSzH77cdwYcnR6Nub5l2oH9kZs6js8PRVJ2P0Qn7nCdCynG4vLj/97vBcjzuvmmj4kAfgiCIZEOiOQGwHK94Mo+HPpPHM0JSFXMoTuMRfbCFSIhohn/0tdhrWRDGDMx5GbBMO4MLq3j/fkR3m+V4//GTLGLnm2mOJx4vB2cEEWhzuBUHm4gwDBP2/YidFpbW5EuPlZkzMWyxB929iHTxFK6fc6DTnJttCLlQisTJ3klUl2bHLIrESEw00Sx2dDjZOxG1HZ+IO8hp9n82XpbDsW5L0lvNRUKjYXDb59egujQHDz65D6fGIkfADrYLTvnqeYzOVqKx2pdrjoPbzHE8/vfp/egbtuI7N6xDZXH2vPdJEAQRD0g0J4BfPXcQn7vjZdU54VhgI4zRnp5xzWkiIBCL0yzLNHOBmWZ/PEN0mgfHZvBfv9zh3z7Aafb3aeZUHz9eJLnxhSqeevU4vvurHWGfn7G7ozrN4aKtbX1T0DBAQ5VfNJcWZmJkwi5NZgTUiWZ5JEjMNAOC06zWbZxxeHCsy4Lmmti7IYii2e6MLJpnfUWjTjeL492RpyWKiHdMTEZd0J2djoEpON1sWolmQPgs7rpxIzQMg/sf3w27M3yh7MH2MeRkGlBXEd/i1YbK+BUDPv3aCew6Mowbr1qBNc0l894fQRBEvCDRnAC27xEmloXolxiKdcIRyS2zzrpVicHQKX2I2vJNfI28UIzjeRj1goAJdJoZAIV5oS25FEUzx8OV9HiGuCD/H1MtpAdGZzA2FTp6XMQ2O/d4RlvfJGrKcoOKFsvMWbBMO+Bw+UVwpE4MgXn0QFxuv2jOyzZixuFR5eo+90Yb7C4vrji7Lqb3AgCZKuMZMwECcu/xEVX7Fj+DDIMu6MI3lf2Zo1FmzsLtX1qHwbFZ/M9T+xX7S/M8j0PtY1jVVBT3SXpZGXpUFmfPuxjwvY8G8cz2NlyyoQZXnVMfp9URBEHEBxLNCwxvVPc6+slQXkwIRHeavSwPy7QjRFzzgOQ0i4V9QqaZkTo0BG0fEM8IFCSxts2aL+KnpGa2yWPbjuDVD3oSuyAIzmukz8Fmd89poArP80IRoCw3XGbOBM9DGqcNIGJBqGhIy/Pn4nATAMjLFkR9tFzzyIQdL77bhQvWVge532pRG8+Y9cUzastzsU+laPYXAmqDxOfhjnEsKctBXnZ65JnlrGosxs1Xr8DeYyN46tXjIc+fGp/F+LQzbv2Z5TRV589LNHf0T+Fnf/4Qy2sL8Y1Pr0p6H3SCIIhoJE00u1wu3HPPPbj00ktx5ZVX4u67707WoZNOIlOy0SIfGhU/Ubkwkw8sUcLjZTFpdSl2zxAzze5ApzlgrLJ8e0BwtuVFjUklhvYZB9vH4pLVjIZ11h22IJLleMw6PRF7NDNM6F0EABiyCNPa5KK5tDALAHBqLEA0R+qe4VPNboU+zYHdM4DoA06eePkYNBoGN3x8ecTtwuGPZ0Tu2T1jd0OnZbC6qRj9IzaMTNij7tvj4aBhhA4w4r83L8vhWM9E2kUz5Fy+pQ6XbVqCv7zRjnc/Ggh67qCvGDLeeWaRxup8TFidsEyHv1sSjkmrE/c/vht5OUbc8eUN0PsKSwmCINKJ5DSYBfDQQw/BaDTitddeA8MwGB9PzAjcdCYeEjGaaGZUOM1KbmY00SyKOaXuGVqfUvd4ZU5zbqho5qR4Bq98CzlJhXl+p5kPeUzucDndLPRJaHc1Y3eD5XiwLBfSXsvu9IDn5za6u603tAgQgDSBb3DMJj3miRCTCZ9p5oIyzUBk0XyydwLvHhjEtRcvRVF+6N0INWSahM8hqtPs9CI7w4CWukK8+G4n9h0bxifOjnzbXxzWotUw0ne0vW8KLnfq+zNHg2EYfP2aVegfseHnzxxARVG2VKR3oG0MxQUZKDdnJeTYTb7jdA5MK95lCofbw+KBP+zBjMODB791Ttp0JiEIgpCTFKd5dnYWL7zwAm699VZJkBQVpffJJ1Z4no84TU3cZr4oCU1AKKYTHd5oyFuGAf5oRaTXCENI5C61/8/uoO4ZCDlxcpz/M/KwXNTPCwD++lY7jnQm7gJLzU/E5faGvO+4r4PnpUmHShcwNl/cIScrgtMc5vG2/ikYDVrUlOYEPV6QY4Jep8FgkNMcW6aZ5/mgeEauNNRGOZ7B8zwe23YU+TlGfOqCxrDHiobq7hl2N7IydCgpyER5UZaqXLPHy0Gv00KjYeDxchidtGPPsWEAQGt9+uWZ5QijtjcgN8uABx7fjUmrEyzH43DHOM5oKk5Y7KG+Ig8aBmjrVz/5lOd5/PK5gzjZO4nbPr8G9ZXJma5JEAQxF5LiNPf39yM/Px8PP/wwdu/ejaysLNx6661Yt25d0HZWqxVWqzXoseHh4WQscd68vLMbv/nbYdz8yZWSkLTNupGfY8TuI0PYe3wEr+3qTcixOZ7HJ7/zEtY0l0QVzX95oy2kAwfPR3ea//MX72LK5sJ3bgj+mQlCXTio1KcZQlQgVybwnn7tBManhelsXi+rKp7x7PY2nLemCivifFuckYeaI8hnYYBGYvOVTjcrFc+5vRxMMrNNHKEdqRCQYRjFi6q23kk0VuWHuNcaDYPSwkwMjPrblEXONIc6zeLPXIxniC6hNYzT/P7hIRzvmcC3PrtacovngsmgBcOo6J7h8EgTFNcvL8WrH/TA6fbCZAj/q8/tYWHUa2Ay6DA+5cBN978OQMhFp2ueWU5+jhF337gR3/6/9/CjrXtx45WtmHF4sCpBeWZA6DZSXZoTUweNv73diTf39eNfLm3GllUVCVsbQRBEPEiKaGZZFv39/WhpacF//dd/4eDBg7jlllvw+uuvIzvb34Nz69atePjhh5OxpJiwTDui3m58a78wYe/RFw5Lj9kcLuTnGHH/43sSuj5RzBxoG0WBQiQikCf+EVogBEQXzWLvXaVMsyhA/fEMoXuGKMpEwdc3EhADYHkwKnxeJVc8PgiLDlpBmCsOp5uNe7cBOeJnBCi7vaILHalPc05W6Ahrj5dD5+A0rpR1IijIFcSfXDRHGjIjFQIGfAfEiXxirl2cWKg0FdDj5bD178dQU5aDi9fXhD2OGhiGkUZpR2LW6UF2hnChsW55Kba914VDHePY0FIW9jUuDwu9XovrLlmKpdX+SEuso75TTX1lHv79ujPx4JP78KOtwu+g1QmOlzRVF2Dv8eGgi+lw7Ds+gj+8fBRbVlXg2kuaE7ougiCIeJAU0VxeXg6dTocrrrgCALB69WoUFBSgu7sbK1eulLb70pe+hGuuuSbotcPDw7j++uuTscywTFpdMWX0ko5P+XG8MAEt9pdHLwQUkW/H8Tw0PgEqPidmmgGgpjQH3UPTAIAZu79oy+vlEC0mzHG8YqePeOB3miPv3+PlhImF3uDoSbyxBXSbULpQmFHhNBfmmjA+Ffzz7xmahpflQvLM4ve5TJZvjdinmQ91mkXRLLYd1GgY5GYZMK0Qz3h5ZzeGLLP4/tc2xWUkshrRPGP3SAWPKxrMMBm02HdsJKJo9niFjLY5LwOXbFwy73WmknPOqETPkBXPbm9DTVlO1Ivq+dJYnY/te/swNulASWFm2O36R2x46I/7UFcuCPtEX5QSBEHEg6SI5sLCQmzcuBE7d+7E2Wefje7ublgsFixZEnxCys3NRW5ubjKWRMhQOy1NyQXlZWKK45VN2xmHX0gJx4t8okzkwBNxfSOTduw4dAojEw4UF4Se5F1uQZR5PGzCBDMQfEGhVIxnVSWajegcmApy+fxFgMouqVgMKOKNkGn2d8/wr0/Mehv1fhGcpzDgxGZ345nXT+LMpcVYu6w07DFiIcOog12F0yzGM/Q6Lc5YWoy9x0ciOqFCIeDi6cZ5/WXLYHd4kpIXFosB2wemwopmm92NHzy2GwadFnfeuAEmY9Lq0QmCIOZF0n5b3Xvvvbjjjjvw4x//GDqdDg8++CAJ5DhxwNdKas6oyDSLKDnNWpnTDJ5X7OJhC3SaWQ4aJlSYBOpStaO954LYv/exbUeh1TBYVluIz17UFLKd0+2/EAhyveNsjFkD4hlKRYeiqM6KMG66MNcEp5vFjMM/BKWtfwr5OUYUh+lSUSoTNvLhNYFIY7SVnGaDv0WY0lTAZ15vw6zTg69c2Rp2/7GSYdTBESHTzPM8Zh2eoBHd65aXYdeRYfQO21Bbrvz7x+PhFlXLM42Gwdc/tSopx6qryIVOy6Cjf0oxo8yyHH78xF6MTdnxw2+cjRKFC1WCIIh0JWmiubq6Gk8++WSyDpcepLANcSyo6dMsErJdYPcM33McjyBRKQrhWUdgbpeDLsot+kQ6zaKAb64pwPWXLQPH86gqyQnZzun2i7K55KvVZDsBf/wCUM4V2+xuZGUIeeFwiLfeRybskmg+2TuJ5pqCsGuIKZ6hkGkW7y6I8QwAyM02YKTP3w/51PgMXt7ZhYvX18R1fHOmKXI8w+Vm4WX5oAuNdcuFscx7jw2HFc1uDxvx4oQIj16nxZLyXLSH6aDxu21HcLB9HLdeewaW18U+Pp0gCCKVLJ57kMScYefpNPufE0UlD7m287Jc0LjmwDHaStz165247/e7Va1pPqxsKIrYxk10UoU/R44CyPnZnz/Eg0/uU7WtNUoh4IzdE7VHc6FPNI/6BnjMODwYHJtBU034iXtypy9Saz2WD+80B8YZ8rONmJ71O81bXz4GrVaDL8xxkEk4omWaZ32DTwKdZnNeBuor8yJOB3R5WOh19KtxrjRW5aOjfyokzvTarh78fUc3rj63ARdvWNhZcYIgTk/ozED4nGZ1LmrIdgp9mgUtHKyaxTiEfz+RRfPB9uQMv4k2TMUZKJpjdJp7h23YdWQYzii5WyA406zkNFvt7oh5ZkDINAPA6KQgmjt8bt/S6vBdHwJjFXqdJmRMeiBSyzkvJwkilzc0npGXbYTd6YXHy+JYtwXvHxrCp89vlER9vMgw6iJOBJxx+CMtYrcQQGg9d6JnIqhjSSAeLysNayFip6m6ALNOL4YCxrMf6RzHI88fwprmEnzlipYUro4gCGLukGgmwPOIKJYCUeU0Bw5Z8f1fLlC8LCc5l2lBmNRDsNMcm2h2OD3wshwOqxjOYouaaY4umjOMOmQYtdKo6JN9gmhuClMEKMeg00SJZwT+rIXt/E5zoGj2DTixufH7bUdRmGvCNefPfZBJODJN+ohOc2AOPLD7zbqWUnA88OGJUcXXuTxc0PshYkMqBuwT+jWPTNjxo617UWbOwrdvWBeXzikEQRCpgH57JZA0koQR4Xk+4lCLQJT6NIsEZprlEdoZtU5zmn1o88k0i4IunDgLxDbrQYZREGrKmWZPxB7NgG90eV4GRiccAATRUlmcHRRPiIRep1VVCAj4f9b+THOw0wwAf9/RhZN9k7jh48sS0iFBjGeE62qiFM8ABCc0N8sQNqLh8bLQL6LuGcmmpiwHBp0GHQNTsDs9uP/3u8FyPO6+aaPq7yJBEEQ6QmeGOKHULWKhwHJzLwQMjDdwLC8JGPnnERg/AKJnmpNFNLPbOR+n2Sea959UIZrtbhTmCm6ocqY5utMMAOY8E0YmZsHzPE72TYb0Z46EXq+JGNNhOX9WXcw1uxREc75PNL/wbifqKnJxwbr5DTIJR4ZRBy8b/rsrfufkQk2rYbB2WQn2nxhVnKAoTAQkp3mu6LQa1FXmoa1vEj/904foG7biOzesQ2VxdvQXEwRBpDGqRPPQ0BDeeustvPjii3jrrbcwNDSU6HURSUataJZHBwJFJ8tx/r/LnWZ5PMPLqxqjHSuTVicmbbEPeAmHa45OM8fxcLhY5GUbMDQ+i1PjMxG3t9ndMOcJmV+508xyfFAbuUgU5WVgdNKOsSkHpmwuNEeJZgT+mPTaKPEMnofRN37aLYtnBGaac33xDI7jceOVrRE7fsyHDJ97HS6iMesI36Zv/fIy2OxuqY91IC4PR4WA86SpKh/Huiew68gwbrxqBdY0l6R6SQRBEPMm7D1Tj8eDZ555Bs888wz6+/tRU1ODrKwszM7Ooq+vD1VVVbjuuuvwuc99DgZD9JP56Ug6RXYjMo+Wc4G3xjne/3eNLJ8hj2ckymn+n6f2Y9bhwfoIE9/UwvN8kFB2uVkYVIopMdaxeWUFXvmgBx+dGEXF2eGdthm7B42+LKhbJs7tTg94HlG7ZzBgYM43weFisd8XCVGbZwaEQsBomeYMoxYOl1dao/h/g4LTvG55Kc5YmjixlGnyi2YxEhKIGM9QEs1nNhdDo2Gw9/hwUOsz1ve9JKd5fjTV5AM7gUs21OAq2Qh3giCIhUpY0Xz11Vdj06ZNuPfee7F69Wpotf6TCMuyOHToEF566SVcc801ePnll5OyWCIx8Lz6QSIer1AkJYol+YVBuGy0kmhWujVucyh3NFDDrMODI10WlBTEPvI8nHyXxzPUimbR/ayvzEO5OQv7T47iE2criwee5wWn2dddQp4rFosEs1XFM4T3vuPAoHCbvEL9ACG9XhvVaTYZdABc0s/f5WGh1TBBPbczTXrc/sX1aElwH95oTvOM3QOTQavYDzw704DltYXYd3wEX7zc383BpXARQMTOltWVYFke56+tUtWnnCAIYiEQVjQ/+eSTMJvNis9ptVqceeaZOPPMMzExMZGwxRHJQ63T7PVyMOo1QaI5UDiLU/PE86R4upRnmsMVAs7aw7cQkyMfHHKwfQxcDPlsIHrdYWC7OJeHRQ7UFTLZfZPqMow6rFlWgu17+4QCM4VJcw6XFyzHIy/bCK2GCbmAET+7aE4zABT5Ih5HOsfRVF0QfbJdwOcnxDPCXzxxnCiaA7pneNigaIbIltWh0+DijSia7WGmAs46PBGHlKxbXoqtLx/D+JQDRflinlx4X2ovjghljHotLtlIvZgJglhchD0zhBPMAOB0OuF2C+5XYSFNdVro8Igt0xzowslzyWLrOnkhoFLLOSXRHEtgQ+5yf+gruItpkmCUDI3LzUqZXHlsIhKi+5lh0mHtshK43CyOdSlfYFpnhc8mJ1Pvc/E55ecjDGEREZ1mjkfEoSZKGPQaxc4dIizHSwJZzLa73GzKXNnAeIYSs05PxG4N65eXAkBQFw1ymgmCIIhwqLJTfvzjH+PQoUMAgLfffhsbNmzA+vXr8eabbyZ0cQudKZsr+kbpQIwt5wwR3Euvbz/yO7KKw03mGfoOzFPzPC/leOM5ftvpZpHrE6uxFAI6fO5nplGHlQ1F0Gk12HdCucWZ30k2CMI1xGl2S89HI9Okk8RktCJAOULLOVYS6XIEpzm4LV4qO01I8YwwTvOMPbLTXFOWg+KCjCDRLH539CSaCYIgCBmqRPNLL72EpqYmAMAvf/lLPPTQQ3jkkUfw05/+NKGLW+hYZ+aez00mPGIbbhI4MjnEaRbFd4Bo5vnEFAIGvrx/xIbxKQdyswyq89mAiniG24tMkw5aLQOXm1XthNtd/niGyajDinqz5ITLsQZklvU6bYijbQvTOk0JhmGk0dhLYxTNGoZB58A0vnTvq2BlF1Hiz0rstxyYaU6VK5thFD4Pu0s50jPr8CA7I/yFBsMwWLe8FAfax0IKG43Up5kgCIKQoerM4HA4kJGRgcnJSfT39+Oyyy7D5s2bMTg4mOj1LRwUal3eOzCI9w6k/2fE88ChDnVjqz2yeAYvE76iUyfvniGPZ3i8HF7d1Ruyf5blcMt/v6Fy3f5ji4J0Y2sZvCyvXpBH2UzI7Opg0Gljc5p9Qi7D5/quWVaCvmEbLNOOkG39TrJemMoni0iIz6sRzRoNg9LCTGRn6FFelBV1+8Cf0oyvCNPL8hibCl6neHEkOs17jg3jyv94EZZpp2KmORlEi2fYHG5kZUQeqrJ+eSlcbhZHuiwA/HcTombBPzBMdgAAIABJREFUCYIgiNMOVWO6amtrsW3bNvT19WHLli0AgImJCZhMpoQubqHzwZEh9I1YU72MqMQSk/B4uaBuBPJXesN1z7B7oNUwUseMcL2UZxweDI5F7mksErju/SdGUV2agwrfAAW3l5WK1uaDy83CZNDCoNPENNzEH88QhG7zEsH17T5lDRrpDPiLJ/U6rZBpljnlVrsbWRn6qOOHaytyoWEYXP+xZZiwOmPuWnDzJ1fin7t78Y/3ezBsmUWZ2S+6JafZ95m+sbdfej/i2ORkY4oQz3C6vBifcqDcHPnCYWVjEQw6DfYdH8Ga5hLpgoVazhEEQRByVDnN99xzD55++mns2rULt956KwBgx44dkoAmEMGxTP92S7HEJDze4MEPPM8HTQX0Z5rF9hnC/2ccnqBCtukw0ZVwrqEivsM6XV4c6bRg7bISKToSqaBNYRdhcbq9MBl00Ou1MYlme0AhIABUl+YAAPqGbRFfp9dpQlrOzdg9qjpn6LQaaDQM6irysHZZqeq1ijRU5ePTFwoxrCGLPeg5eTwjcL2pEphaDQOjQSt91oH0DFvB80BdZV7EfZgMOqxqKsa+YyNBfbkNFM8gCIIgZKiy4latWoU///nPQY9dddVVuOqqqxKyKCK5xCqag5xm2Uu9XnGMdsBjLAe3h0VpYWbY4kiDTyyOTNgVn1dct+/gR7os8LIc1i4rkV6vvtNF5PfudLMw5/mcZo9fnEW7FHK4vNBoGKl1WU6mAQU5RvSPRBbNBr02JJ5hs7tV9WieC3Iz2pyXAZ1Wg+Hx2aDH5fEMEXeYlnPJIsOoU7zQ6h6cBgDUVUQWzYDQem7f8REMjs1IeXjqnkEQBEHIUW2n7Ny5E3fccQduueUWAMDhw4fxwQcfJGxhi4WF0Nc/XKRCCXeI06y8r8D3LfbRzY3QMq0g14SsDD0GRtVFMwKPvf/ECIwGLVrqzJLYiaU9nIjSj8rpFkShfg7xjEyjLigiUV2aE10065S6Z3iQoyLPHA+0vkz0kCVYNLOyeIZItG4qiSbTqFOMZ3SfsiLLpFM16Caw9ZzLd8FCopkgCIKQo0o0P/nkk/j+97+P2tpa7N27FwBgMpnw85//PKGLI5JDLE6z28PKMs2yQkCpe4YgFhn4hXSkiIFBr0G5ORMTVuWssxJiIeCHJ0axsqEIBr1WEjtqi/aixbndbiEbbdDHVghod3mlaIZITWkO+kZsQQWMcgKnLYpY7W5VPZrjRXlRFoZloln8jhj0GmhkVxcpdZpNOsV4RvepadRW5KnKdZcUZqKmLAd7j41In72ehpsQBEEQMlSdGbZu3YrHH38cN998MzQa4SX19fXo7u5O6OIWAwvBaVYaZx2JiPEMVuyeEfq63Cxj2H3qddqgwrNwBApOjgdGJuw4NT6LNc0lAPwFXGqd5miOopBp9jnNMQ43yZDlf6vLcuBweWGZll8Y+N+TXhc6YGTG7lbVo3kuKInKMnMmhi2zwZ+17zui0TAhPYxTmf9VimdwHI+eIWtMI8TXLy/F0S4LpmeE+BAVAhIEQRByVJ3tZmdnUV5eDsB/kvV6vdDrk3PLeGGT/qo5VtEc6MJxHB88RjtCv+dwTjPDCLEENaI5sDUez/OSyCk1C72J1RYCXn1uPQBg04pyxfUAgpQVx0QbdLEVAjqcCqJZLAYME9FgGF+mOSCewXE8ZhweZKsoBIwX5eYsOFxsULGmmGnWMEzIiOlUCsxMoz4knjE8MQunm1WVZxZZt7wULMdjz9FhABTPIAiCIEJRJZrXr1+PRx99NOixJ554Ahs3bkzIoojkIh9kEQ15MVggId0zAgiXadZqNDDo/U6zVsmm9vGXN9qkPweKdfEVauMZep0WGg0T8VhuDwuehy+eEbvTnGkMjWcAiJhrFlrO+X8edqcHPK9uGmC8KPP1dw6MaIgXVloNE9LD2BiH1n5zRclp7h4U2jzG4jQvry1ElkmHE72TABByYUAQBEEQqs4Md911F15//XVceOGFmJ2dxWWXXYZXXnkFt99+e6LXt3AIo70WYzyjpjwXX7t6BcqLssJOBJTedsD7lwu/pTX5+MqVrdBqGeh1GpT53OJwo4/b+iZxsD3YaRYjBKJIjzWeEQmnz1kW4hmxt5yTZ5rzso3IzTJEFs06DTwBa7fGMEI7Xoi9jQOLAQPjGfI4Riqn5wmZ5uCJgN2npqFhgJoy9aJZq9XgTF/ER6thovbEJgiCIE4/VFlEJSUleP7553Ho0CGcOnUK5eXlWLVqlZRvJhY2sYpmg06Dj59Vi3c/Ggzp2ObxtZxTuoiQi+G1y0qxot4MnYaBQa+VxFqWSQ/rbGgf57+80YasDD1mfSO5Od5fhihenMyne4Ycl0scqawN6zT3DCkPr3E4PSHxDECIaETq1Sxkp/1O84xvhLaaPs3xorQwEwyDoLZzwaJZ5jSnNJ4R2j2j+5QVlSU5Ma9rfUspdhw8RT2aCYIgCEVUnx0YhsHq1atx2WWXYdWqVQAAjovttj6Rnsw108wwodME/YWAwao5K0MfEoUoyBUKAzUaDfQ6Dcz5GdBqGMXRx73DVuw6Mowrz66XHuN5SKKd8al0sf2ZS+Vwk0g43YIYMxl0ktOsdniiw+VFpilU6Nb42s4FFtkF7tOg18LLcpJItaXAaTbotTDnmoKcZvE7opRpTmX+N8Okg9vLBUWMuoemY4pmiKxdVirlygmCIAhCjirRfPToUVx77bU444wz0NraitbWVrS0tKC1tTXR60sZOw+ews0/3I6v/fB17DoyFPL89IxLcjMHR2046ctCyok2AS4d4GLMNPtFMxMyGoQNcyGVnaEHIxfNOcIYdp2WgUGnhVbDoDDXpCg2X3ynEyaDFlee4xfNHM/7RbvkNIuFgH5X+FDHGH74hz1BrfXkrfKUEOMZRqNWEooer7KDPTbp8O+b5xW7ZwCC0zzj8IQd8iJ+tmLrPpvPcU90IaC8bVxZURaGA6YCSoWAipnm1A43AfyTJG12N8YmHTEVAYrkZRuxtLqARDNBEAShiCrRfPvtt2Pjxo14/vnnsX37dmzfvh1vvPEGtm/fnuj1pYziggwsqy3AlM2FTt90sUDGJh1ST+G/vt2Z7OWFsLG1LOSxwlxT2O3/8/q10p/FwrOPb67FzdesjHosUTQZ9BrwPB80HIVllcVodqZecp8ri7Nx/ceWSRnmL1/Rgo9vrgUAXHvJUmmUcyC9w1Ysry0MKibkeUhRjUxfflgp07z32Ag+ODyEGUdw9pVB5G4ffqdZi7wcwRWX55qvvXgpAOBot0V6zOVmwfFQFM01UTpoiOsXc80DozPQaBgUF2SGXed8ueWalfjpv58X9Fi5OStspvmzFzXhgrVV0nMpdZp9n7E4QKfnVOxFgIF8+YoWfOFjy+OzOIIgCGJRoUo0Dw4O4rbbbkNDQwMqKyuD/lusLK0pwP/7l7XIyzZGHEYBIOrzyeCGy0NP9M1LCsJuv6y2UPrzoy8cBiB0EAiMP4RDdEONeh14TpwKJzwWbrpgToZByh3nZOpx3SXNkoi+cF0NltYIa71sU63UczkQ26wnZMAHz/OYsAqObaHPtVbKNIsu8Iw9NCftiSCaXVIhoE66AHHKRPPnL1sGk0GLo11+0Sy6npkmBae5TBDNA2FEs9gDWbyQ6RycRnVJdkJzw584u15qhydSXpSFKZtLei9cQPeM9S1lWN/iv0hLaabZFOw0d59SPz5biRUNRbhwXXV8FkcQBEEsKlSJ5ksuuQQ7duxI9FrSEjXdL17f05f4hUQh0xh6+57jeMUhI0BwnZ4oBOU55HAEZpp58HB7WJh8jp98uqC4x6xMvZQ7jjFCDUC47Z6bKRfNwKRNcPv9+WgGWi0TVLQ3NiXEDKxy0cwAbjZ8waAUz9BrJWEoLwbUahjUVeQpimYlp7kgx4isDH1Yp1m8+BBFf9fgNOor5yYA54PY/k9sOxcYz5CTDk6zXzRbkZ9tREFO+EE6BEEQBDEXwnbP+Pa3vy218XK73fjWt76FtWvXoqioKGi7Bx98MLErTDEMw6gu/kol8vZmgCAq9Xr1rdKUBJESQaKZF9zaDKMO1lk3vOHiGRn6gAuQ2D5QjgdmnR5kh4hmHhNWJ3Iy9UE5W71WE9Tr2O80B8czoiHGM4wGrZTbVfos6yvy8PL73ZiecSEv2yhFBZREM8MwvmLAGcVjioWMbg+LSZsTE1Yn6ivzY1p3PCgPEM11FXlBhYBy0iHTLH7mXaemUVuRq2p8NkEQBEHEQljRvGTJkqC/NzY2Jnwx6chCOfUqCTSO52HQaZRFs8IbUyuaRWEnXFAITrN4/HCFgDmZBpT7hma01plVHUdk1uEb8JEV7KZzPI9JqxMFsuy2XqeVnFqPl8Wkr+hOqY1dJP0uusomgy6s0wwA9VWCE3ys24KzVlZEjGcAQjGgOHkO8HfPYBgGerGQ0cuhy5elb0iF0+z7WQ2NCy59YKZZTmrjGcJ3wuHywsty6Bu2BRWLEgRBEES8CCuav/WtbyVzHWmL4Kamv9WsNNmO53mfA+t3WDUaBp86r1GKSgQyl3gGxwsjqyXRHMFpLjNn4U/3X46hMWWXNRxiBwl52zUhnuGS8syB6xPFrWXaKT0elGn2LVOn1YRtuecKGG7id5q9IdvVlOTAoNPgaNdEkGhWupABBNH8z929kjMdiEEqBPSL5roUiObsDD1yMvVSMaCUafZ9RwK/KqkUzf54hgeDozPwstyciwAJgiAIIhKqMs2PPvooDh06FPTYoUOH8Nvf/jYhi0ovFkY8QwmO40MGNVQUZaGmLEdx+0gjpQPRBbScA3i4vawUD/GGOM3CPsWWaUJMIzb/3uZQFs2i05yfGyw89TqN5DQHtoKzBcQzeN/KsrP0QRP4AnH6xK9BIdMc+B50Og2WLinA0S5hWqHdKRwnnGgWO2gMjIZePEiZZi+LzoFplJkzkR1mQmKiKTNnSQNOImWa0yWeIRYB1s+xCJAgCIIgIqFKND/xxBMh8YyGhgZs3bo1IYtKJ4Rit/CkswvN835XWER0MpV0a8yZZgAcJ0QJMgxRnOZ5DOfwO83B4pHngQmrstPs9g03EYsAAf+gkECyMgxB+edAnG4WBr0WGg0TMdMMAK31ZnQNTsPu9ER1mqtKswEot50L7P6RqiJAkcC2c5HiGelSCNh9ygqdVoPKkuyUref/t3fnYVKVd97/P6e23ptu6AYaupFFQRRRBCVoACWRRQXCqIFI1MRRNI5GHx8SF9yjUdRMcAIJidlMLqI/R0ajCcb8DBqZLA6OoGJc0IgidCObNjRL01Xn+aP6VNd+Ti+nqrrq/bquuUKfU1XnrgPXmU9//d73DQDIX45C85EjR+TzxQYAv9+v1tYkPaJ5xqqmxnOyOUa2mTITN6JIE3CcFoCtnmZPe0/zkZie5rjVM9o/szvV0n2RraRjg/e+lla1BUNJepoTK839+hQnDc2lRb6EX3ys1pXDrUEVt4flIn/4+yXraZakMcP7KWRKb2/ZG9XTnPw711aVqKTIq61JQrP1C8mn+w6rcXdLVkPzwJoy7fz0YPsOheFj1n+NiG7vyWZo9vvCu0kePNymf27/TEMGVsjnZRtsAEDPc/T/XY4//nj95je/iTn22GOP6bjjjnNlULnEWiGiN0pWaY7/OVpne5rVXoVvbQupuCgcnIIp1mnuTmi2Qmj8Os27PwsH4r4J7RneSLjd+elB9SkPqF+f4qSrZxQFvClnex5qbesIzTaV5lFH9ZXHY+jND3brwOE2GYYi741nGIbq+1doa2S3yPA/MEMdAfSdj8I7TI7IwsoZlrp+pQqFTH2y90Bkgmd8pdnn9Thu63FLSZFPBw63acv2ZvqZAQCuSTkRMNpNN92kr3/963r66afV0NCgrVu3aufOnfrFL37h9viyzlD60JzLgToYMiNVYYsVypKN22l7hlXJ80StnlHk98pjJFaaLfFV4s4yjMTKrTXJL77SHPB51NLeV7zz04OqrSpReWkg6eoZAZ9HHsNIuinL4SPByDUjoTlFpbmkyKej68PrNY8Y3EfFAV/a3u2GARXa+O7OhOPWLyRvb9kjKTsrZ1giazXvOtDRnhH3nbLZz2wpKfKpcWeLPt1/mH5mAIBrHIXmY445Rs8995xeeOEFNTU1afr06TrjjDNUVlbm9viyLlV7Rm9gmqb8geTtGcl6sZ2EZp/X6Hhd++oZR9pCkd7fVDsClpd2rdLsab9GeYk/oaJpbWMev114fHtGffuOetujV+2IWuatrMSvXZ8eVLxDrcHIZ1sT9NKteX3csH76/V8+UG1VScrl5ixDBlRo7StbE7b2tv5+PtqxT9UVRQm/EGSStURg4+6WSDU/+u9eyu7KGZbSYp/e/jD8S0ZXdwIEAMCO4+a/srIynXvuubrssst0zjnnFERgluzbM7IZp6+Yd0La86YZ3ugjmrUOcChZaG6vIl457wQNqkn+9xvdI+0xjEg49fs88no8SScCeozUk+LsWJXxZBMJI5XmisTVMw4fCck0Te369EB7pdkfmVBoMaJW9ki2nfbh1jYVt09wNIzwZMBUlWYp3Nd8pC2k19/baft9U22nbVWaTVNZ7WeWpOqKYgV8HjXtbon0NMf/YpULobmkyBfZvZH2DACAWxwlma1bt2rZsmV66623dODAgZhzL774ohvjyhnJ1jPOFf2rStOeD5lmwiStojTtGVYl95zPD9eUk+t14a3PJrwmvifaCs0Bv1feFJXmkqL0rQrp+H1eHWoNJmyhLYUrzUUBb0JAtTY3aTl4RAcPB1VbXaKDh9rUcqhNwWBI3rhfJMqK/Un7uduCZkz7QZHN7orHDe/XPq7D6tenJO33spad+2jHvpitzqP/vkbUZ6+fWQoH5AH9ytS4q0UjG6rDx+JuU660Z0hSTXsbDgAAbnAUmhcvXqyGhgbdcMMNKilJHwbyjpG8KtsbhDc3ST4RsKvtGdGf5zGMSIgM+DzyeIxIT7OVQU8+tn/CRL3OiKy+kaS9Y/dnB9W3ojghkFvtGTvbWy5qq0ojrRz7Dx5J2FCkyO9NOUEyJjQHvJHtmpOpKA3oqIEV+rBpn217Rm11qQL+8AoaR0Wtm+31GJGWlGxXmqXwsnNNu1sUbP/3Yv3CYd3x+HXAs8HqO6fKDABwk6PQvHnzZj366KPyeLL//yAzza5A2rS7c7vb9SibsYWSrdPc3l7R1V8EoicWGlET//y+cKU5fvWME0bU6OgeWAEi2UTCPc2HNHJIdcLxSGhuX26upqpYR9rC4X7fgdaku/D5U4S/4rhK897mw2nHefzwfvqwaZ9te4bXY6i+f7k+2rEvUnWWYW2lHa5oZ3MSoGVgTalee29n5O81YSKgv2ttNz3JutdMAgQAuMlRCj7llFP0j3/8w+2x5CS71TP2H0hdecw2M0l7hhUOk30nJx0UvqgQHl3hLWqfCJhq9Yzuil9uTgq3TySbKOf3eRQyFdmYo7a6NPL+ZMvOeTyGylKsqWz1NEvhSnOqiY6WMcNrJDnr4R4yoCLpWs0Bn1dlxT4N6Ju+/SYT6vqV6XBrMFKpj/+vEblQabbuNZMAAQBuclQmGjx4sC677DKdddZZqqmpiTl37bXXujKwXGHbi5vDrRuhkBlZ9cGSrj3DSf+2PyY0Rx33h9frdS00p1jnOX4SoNQxxo8/2S+f11BVeVGkUt3cvsFJfKU9WSiXEivNdo4b3leSs9DcMKBCL776cWQdakvA71FdTUWX+8B7krXs3Lb2lUdydck5ifYMAIC7HJWJDh48qDPPPFNtbW1qamqK+b98F149I3eC8YnHdPzSYhepTFOaPXl4zLGzTxsmSRpcW540cNr5wikNHdePClABn1eGYUTW83XCMAzHuxBGh9opJw2O/Dl+uTlJkfaL7Tv3q6aqRB6PEemJ3n8gvIvgy5uaIttZS1K/ymKVJgnm0UHZSWju16dEcyYP1+fG1Nm+tqG9LePjT2JbfGZMPErnnD7M9v2ZYC07t31nuGpvdWhZf2+5sHrGhNH9NX3iUZGADwCAGxxVmu+99163x5GzDBkpi8nZqASOG9Vfr23e5ei1IdNM6AW2fvZ6PVq8cLyWrPxrx0kHX2fO5BEdL4+rNHd2oZHqymIdbnXW3hK9KsKs04bqpY3bwp9RkRiarWMff7Jfg2vDwdhafWPfgSP6/1/+UI27W3T7ZZ+LvKc4RWW4KK49w4nLv5R+KUDLkIEdK2hE+8qMYx29PxP6V5fKY0RVmhPaM7Ifmkcd1Vejjuqb7WEAAPJcytD88ccfq76+XlJ4yblUGhoaUp7LCzbrNIcyvFJzTCXXtnMk/djSZX4n+Tf6NfFtIE6Ul/gdb68dveRc9C8rySrNVvVzT/OhSGW+tNgvjyHt+vSgXtrwsY4b1lfjj+1ve11re/Dw5/bspLeBfUvl83r0Uft22rm4vKHf51FNVYk+aZ9U6Y2bDJwL7RkAAGRCyhQwe/ZsbdiwQZJ01llnyWjfMjmaYRh666233B1hloX3A8yd9ozOtD+EUsxZq2xvdYivlHc2skX3twbS7DQYr7oLS9BFLzkXfd1knxU9Oa22OjyZzuMxVFYS0HN/36KDh4P69kWnOPovBcVxS871JK/Xo/r+5drS2Nyjn9vT6mrKIqG5o9Ic/t9caM8AACATUoZmKzBL0ttvv52RweQiwzBkpguquZOnE5immbSabPX8dre9xPAkhuaosynfZ7fxRzIVMZXmjuPJKs3RY6mt6rhWRalf23e1asLoATq+fSOSpKI+P1V7Rk915jQMqMj50DywX1mkJShhcxNCMwCgQGR/vagcZ7eNdqY3PokJbja14c6OrbMhOvrVqTYH6SnREwGtYXo9RtL1m2NCc3V0aA6/9uKzRzu+bmdXz+gsazJgLquLmmCXiz3NAABkQsqkc/XVV+v1119P++bXX39dV199dY8PKpeEg2Tq8PnmP3dnbjCSzji5o4fcrhXCrpOju9XS+NUzkqmpKtHAft1bb9hjSKVRE/Ws61ZVFCXdxbAoRaX58ycN0pe/OLJT6/nGrJ7hQv/ukF4QmgfWdIRma6v1yOoZ9DQDAApEyvaM+fPn684779T+/ft16qmnatiwYSorK1NLS4u2bNmil19+WZWVlbruuusyOd6sSJdN24KZrTRXVRSpf3V4YpZdJdl+ImBcT3MnQ3T061NtclHk93a7QlteGogJx1ZPc7KNTeLHUhMVmr809ehOXztmcxNXKs0dy97lwLLMScVUmhN2BCQ0AwAKQ8rQPHnyZE2ePFlvvPGGXnrpJb322mvat2+fKisrNWrUKH3/+9/Xcccd5/hC06ZNUyAQUFFRuJ928eLFmjx5cve/gcviQ0K8bKzhbK1gYDcp0G5oPVppdjE8VZTGrrBhXbZvkuXmosdSVuJXaYqd/pwqcnEioCTV1ZS7uilMT4j+LwW0ZwAACpXtGlonnHCCTjjB2bqzdv7jP/5DI0eO7JHPyhTDSN6cYQXSbEQdK7jU1ZanfV04VKdOxt1d4sx6t8fo+M/2bojvW7a+f6pVOKzqZ3RrRle5XWn2+zwaVFumrTv22784S0qL/epTHtC+ltbIL0rW3zbtGQCAQsFEQAfSVZOzUmn2Oguo3Vmn2Qnr/X6/NxKm3Lgb5Ukm+0nJV86QOqqf0ZMAuypmnWaXAmJvmAw4sF9Z8v7xFL3sAADkm57drcHG4sWLZZqmxo8fr+uvv16VlZUx55ubm9XcHLv8Vra36k61esa6jdv03tZPtSnDEwGljqpuMJhiIeZ2dnk+vvWk06tntL++KxubdEZ8e0akpznFNuCBHqw0d3Yb7a4Ih+ZGVz67p9T1K9MH2z5LOE6lGQBQKDIWmletWqW6ujq1trbqnnvu0V133aUHH3ww5jWPPPKIli9fnqkhOWLIkC+usvvOh3v01J/fd/W6I4dU6d2PPk167rK5Y/TQYxs0uH+5pk8cEqnu1tWUqXFXS+R1ne2T7epEQCukRrdo9FSzxsTjB2rsMbUxx2qrS3TcsL4aM6Im6Xt8XkOTTqjThNEDunVtjxG7lJ5bAfFzx9fpH//coz7lySvqueBzJ9TFTDwdOqiPRg/tq8H907cIAQCQLzIWmuvq6iRJgUBAF154ob7xjW8kvOaSSy7RvHnzYo41NTVp4cKFGRljMoYR3rkt2p7mwz32+bMmDdVV558oSXpv66f6P8v+LEn63rVTNfv//laS9Mvbputrd/0x8p6xR9fqZ7dMlyRd8+VxkeM/uemLkhR5X3x7xjPfmxvzc3xIjllrOepkqmq7J1JpTgzNPeWb88clHCst9mvp1aknkRqGoZu/dmq3r10U8MVU392qNB/dUKXvXnW6K5/dU04fO0injx0U+XlA31Ldf03uT+QFAKCnOA7Nf/nLX/T73/9ee/bs0cqVK/XGG29o//79mjRpku17Dxw4oGAwqIqKCpmmqTVr1mj06MQNJiorKxNaNrLNSDYTsAf7mJ30RHd15z67T47/VL83eShMuVJ1pKc5P1vji+Mqy9G7AwIAgMLiKAX8+te/1q9+9StdcMEFeu655yRJxcXFuueeexyF5t27d+uaa65RMBhUKBTSiBEjdPvtt3dv5JliJO6sl+mpf12t34ZCybfR7vjg2JOpdvUzUpSaPRnqac6W4riQzJrEAAAULkeh+ZFHHtEvf/lL1dfX6+GHH5YkDR8+XB988IGjizQ0NOipp57q+iizKGmVtZekZrsqdnw3hS9laE7+fuuwP09XUIjvYWbSGwAAhctRibClpSXSk2y1CrS1tcnv797GEb1BsiprVzLzpbOP7/oYupia7bfR7kSlOdnx9tSdbxXYVGsQ59v3BAAAzjkKzaeccop+8pOfxBz71a9+pYkTJ7oyqFySanOTzjqmoSrpcSef3dX1lDu7TrPPmyI0p3oTZmPKAAAfaUlEQVR/+//G9DTn7sZ2nZbY00xoBgCgUDlqz7jlllt05ZVX6j//8z/V0tKiGTNmqKysTD/+8Y/dHl/WGTJkpl8O2dnndHcnkS7oTGj2eIyUq18YqY7HrZ4Re9LZGHNZfE+zz+vJ+S2vAQCAOxyF5v79+2v16tV64403tG3bNtXV1Wns2LHyePJzAli0cKU5YfmMDI+h6+0Z6ecBdpxN1Zohpak05/nqGckqy0UBrw4casvCaAAAQDY5SjvLly/XO++8o7Fjx2rWrFk66aST5PF4Elo28lV8wTbTO2e71p4R9Wd/qtYMI81EwHSV5jwQX2mW6GsGAKBQOQrNP/rRj3TppZfq2WefjTm+cuVKVwaVSzxJWhO6lJm70a7Q1bfahvuoD043CTBVpdu6NYECqzQDAIDC4yjtBAIB/fznP9cDDzygZcuWRY472ZgjHyR8zy587W61+LrUDz2wX8cWyPHLzVlXNIx0Y8/3SnOS0EylGQCAguQoNBuGoWOPPVZPPPGE/vd//1dXXXWVWlpasjK5LdMMI3HptsQe525w8FFu3WWvx9DooX0lpW7P8KSrNLe/pdB6mgEAQOFxlHasSmvfvn31i1/8QrW1tbrgggvU1pb/E6KM1JtId/JzuvFeF98cmcyXoj0jHJpTvltSPleak/U0s5U2AACFyFFo/pd/+ZfIn30+n+68805dfPHFOvHEE10bWM5ItoN0V9ozslyUT3V9q2c73W6Anelp7tEqfJYlbc+g0gwAQEFyVDa79dZbE44tWLBACxYs6PEB5RqPYfTQahnJg6eTkNmtNpj2wadeNi58Jr49w4w6n/LykdCcGCTzoXGniNUzAABAu5Sh+dZbb9V3vvMdSdK3v/3tlB9w//339/yockg4/HV/G+3uj6FrImNNtRV2pD0jNgxaLTkeT7rVM6z2jDztaU4SkKk0AwBQmFKG5vr6+sifhwwZkpHB5KQkEwEz3p7RA2Xb1MXi9vYMb+wrQu1fOt3qGZEqdd72NLN6BgAACEsZmq+44orIn6+++uqMDCYXGTISQrJbq2dUVxZJkmZMPCp2DF1M3PPOODpSDV4wfVTS16SqNIesSnOa1TOMJD3NlrISf1eGHDH26Bo17m7p1md01YC+paooDWhQbXnCuWSVZuvvDQAA5C9HPc1///vfNXjwYDU0NGjnzp168MEH5fF4dP3116u2ttbtMWZVsm20e7I9I/qz+vUp0TPfm5s4hi5+9qWzj5ekpJ9p8URCc1xPc/vA0vU0p6s0l5d2LzTf843Tu/X+7hhUW67ffGdW0nPJKs39+pS4PSQAAJBljppR77zzTnm94bBw3333qa2tTYZhJJ0gmG+MZKtndPWDMv9WB59tBd/YfwpWe4YnzeoZ1tFCalmgpxkAgMLkqNK8Y8cODRo0SG1tbfrv//5vrV27Vn6/X5MnT3Z7fFlnGIarOwI6a71wLzVb1/fFrZ5hVYpnThqq59d/lPa90Zub5PsmkYX0CwIAAOjgKDSXl5dr165d2rx5s0aMGKGysjK1trYWxOYmUs+0Y3SnWuxupTn8v/GV5uKAT9+/bqpqq0v0p1e2pn1vvm5ukgyVZgAACpOj0PzVr35V559/vo4cOaKbb75ZkvTqq69q+PDhrg4uFyRrz+hNS87ZfnaK9gwn10/VD53+Xb0blWYAAAqTo9C8aNEinXXWWfJ6vZHl5wYMGKC7777b1cHlAk/S1OwsNvfrU6zdnx2SlNj+0Ck9XGqOXu3BCr7pxudJcf3+fUtVUuRTdUXhrB4xqLZcAZ9HVeWF850BAIDD0CxJw4YNS/tzPuvqMs2VZYFIaB42qI/DT0/U0zXb6NUenFSaUw1g7NG1+v/uObt7Oxb2MiOHVOuJ+84tqO8MAAAcrp5RyHpqImD3xuDeZ1vfLV1o9qS5fiGGx0L8zgAAFDpCs40eW3KuW2NwL6RZS8ulbx8hJAIAgMJGaHagB3bRzlnBkINKM/9KAABAgXPc0yxJu3fv1oEDB2KONTQ09OiAck14ImDHz6GQqVff+SR7A+phTkJzZyrN+w60dnNEAAAAucdRaH7ppZe0ZMkS7dq1K6a/1zAMvfXWW64NLifEbaP95Ivvad3GbY7e2r+6VB9sb3ZrZD3CdBCanXaHVJYV6a0te9r/HOj22AAAAHKFo9B811136aqrrtK8efNUXFzs9phyiiEpFOr4eUuT8xBc168s5ufvXTtZLQfadPyIfvrD37bo4d9uynq/dNC072n2GIZqq0v04xu/kPaz7lo0SbubD8nv9ah/39IeHScAAEA2OQrNzc3NWrBgQUGuGhD+zj2TbIsDPnkMjwJ+r007ROaEIpXm9Jt2+Lwe29cUF/k0uLa8x8YGAACQKxwlt/POO0+rV692eyw5yVD2V89wUzDoZMk5I+UGJwAAAIXAUaX5tdde069//Ws9/PDDqqmpiTm3atUqVwaWKwyP0fU6c7qc2R5Cs53HQw7aM2RInnSLNQMAAOQ5R6H5ggsu0AUXXOD2WHJSuNLc89E2VyKotXqGz5d6RB5D8hKaAQBAAXMUmufNm+f2OHJXDmxu4iarp9mbbjFmw5Bh5PFNAAAAsOGop9k0TT3++OO6+OKLNXv2bEnS+vXrtWbNGlcHlwsMdWMiYC/ImZHQ7E1faaY9AwAAFDJHofmhhx7SE088ofnz56uxsVGSNHDgQP30pz91dXC5wPVttLNcxg5GKs2pQ7EhJgICAIDC5ig0P/nkk1q5cqXOOeecyLJz9fX12rp1q6uDywWG4dJEwBwRbF+EOt1EQINKMwAAKHCOepqDwaDKysIbdVihuaWlRaWl+b+BhaGOXfMk6XBrMHuDcUHISaXZMHpD/gcAAHCNo0rz1KlTde+996q1tVVSuMf5oYce0plnnunq4HKBYcS2Jrce6ZnQnCvdDh09zVSaAQAAUnEUmm+66Sbt3LlT48eP1759+zRu3Dht375dixcvdnt8OcGNtuOGARWSpGOGVPf8h3fCqKPC168oDSQ9H/B7VVEaUGVZ8vMAAACFwFF7Rnl5uVasWKHdu3dr27ZtqqurU21trdtjywnhCmvPp+bjhvXTLV8/VSeNzO59nDNlhL4y/dikofjohipJ0nULxmV6WAAAADnFUaVZkvbu3at169bp5ZdfVm1trXbs2KGmpiY3x5YzoivNrUdCPfa5/fqURHrEs8XrMVRTVZL2NeWlAZWnqEQDAAAUAkeh+X/+5380c+ZMPfPMM/rhD38oSfrwww91xx13uDm2nGAYRsyOgG+8v8v5e90YEAAAADLOUWj+7ne/q2XLlulnP/uZfL5wR8eJJ56o119/3dXB5QJ3mjMAAADQmzgKzdu2bdOkSZMkdSw55/f7FQzm1/JrSXVjc5PSYkct4wAAAMhxjkLziBEjtG7duphjf/3rXzVy5EhXBpVLrJ3wzC4k59ISf08PBwAAAFngqBR644036oorrtAZZ5yhQ4cO6bbbbtPatWsj/c35zOpLNs3cWVu5p9F+AgAAkJ6jSvPYsWP19NNP6+ijj9Z5552n+vp6PfHEExo7dqzb48s+q9Kc5WEAAAAge2wrzcFgUOPGjdMrr7yiyy+/PBNjyilGdKmZ9TAAAAAKkm2l2ev1aujQodq7d28mxpNzrNBMpRkAAKBwOeppnj17tq688kpdfPHFGjhwYMw5a1WNfGXImgiY5YG4JV8btQEAAHqQo9D86KOPSpJ+8IMfxBw3DEN/+tOfen5UOSRSac7b1AwAAAA7jkLz2rVr3R5HzjMlbd2xL9vDAAAAQBY4Wj3jrbfeUmNjY8yxxsZGvf32264MKpcYUes0/2h153ZANJg4CAAAkBccheZvfetbamtrizl25MgRfetb3+r0BZcvX65Ro0bp3Xff7fR7s8ETWT1DaguGsjoW19B6AgAAkJaj0Lx9+3Y1NDTEHBsyZIi2bdvWqYu9+eab2rhxowYPHtyp92VXxzrNR9ryb9twauEAAAD2HIXmgQMH6s0334w59uabb6p///6OL9Ta2qq77rpLd9xxR6cGmG3REwHbglRkAQAACpGjiYBf+9rXdNVVV+myyy7TkCFD9NFHH+nnP/+5rrzySscXeuihhzRnzhzV19enfE1zc7Oam5tjjjU1NTm+hhs6QnPnK839+hTH/BzwewneAAAAvZCj0PzlL39ZFRUVeuKJJ9TU1KSBAwfqhhtu0MyZMx1dZMOGDdq0aZMWL16c9nWPPPKIli9f7ugzM8UwotszYnuafV6P2oIhlZf4NXfKCLUFQzpjfL127Dkg05TGHxtbiR/Qt5T2YQAAgF7IUWiWpFmzZmnWrFldusj69ev1/vvv6wtf+IKkcPX4X//1X3Xvvffq85//fOR1l1xyiebNmxfz3qamJi1cuLBL1+0JkZ5f00yYCDjrtKF6Zt0/9ZXpozRnyojI8fr+Fck/yzDYSwQAAKAXShuaN23apEAgoJEjR0qS9uzZo3vuuUebN2/WSSedpBtuuEFlZWW2F1m0aJEWLVoU+XnatGlauXJl5HMtlZWVqqys7Mr3cE97yA2ZiZVmC8VjAACA/JZ2IuB3v/td7dq1K/LzkiVLtGXLFs2fP1+bN2/WAw884PoAs61jG+3ESnM+FI2pfAMAANhLW2l+//33NWHCBEnhSXrr1q3TM888o2HDhmnatGlasGBBl1bD6E07DEaHypSV5l5eau7lwwcAAHBd2kpzMBiU3++XJG3cuFE1NTUaNmyYJKmuri5hpYt81LEjoBJXvuhoeM7omAAAAJBZaUPz0UcfrWeffVaStGbNGk2aNClybseOHaqoSD7hLZ9ElpyLCsZ9ygO6dv44tskGAAAoEGlD8+LFi3X77bfr1FNP1YsvvqjLL788cm7NmjU6+eSTXR9gtkV20Y4qJg+qKdfQuhybsAgAAADXpO1pnjBhgl544QVt2bJFQ4cOVXl5eeTc1KlTdfbZZ7s+wKwzOiYCWnze2N81entPMwAAANKzXae5vLxcY8aMSTg+fPhwVwaUa5I1YPi84aP5sPIELSYAAAD20rZnIHYioMWbZ5Xm3j5+AAAAtxGabUQmAkYFS78vv26bx0O1GQAAIB3H22gXqo6JgB2p2ZsQMntvqfaousq8+yUAAACgp5GWbHQsOdfB1x4yk7Vu9DYEZgAAAHskJhtGktUz/O09zTQ1AAAAFAZCs41kK2QkTATM0FgAAACQHYRmW+HUHIpZpzl/lpwDAACAPUKzjUgwjionJ25uQq0ZAAAgnxGabSTJzPJ5PaquLMrGcAAAAJAFhGYb1kTAUCh2G+1+fUoi5wAAAJDfCM02rFzcFgxFjlk9zQAAACgMhGYbRnuDRuuRYORYYk9zRocEAACADCM022kvKh9pi6o0RzY3ycaAAAAAkGmEZhtWMG6NDs1x22ibrNQMAACQ1wjNNqzJfkei2zPYehoAAKCgkP5sWDXl6Eqz1xN327pYaO5XVSw/ARwAACDn+bI9gFxnRHqaEyvNVhW6q80Z1RXF3RkaAAAAMoQyp6329oyoSrO/ffUM5gECAAAUBkKzDWvOX+uRqPaMuHWaWXIOAAAgvxGabUQmAka1ZwR83vaT2RgRAAAAMo2eZjtxS86deEyNThpZG/uaHCs1X3/hyaqpKsn2MAAAAPIGodlGZPWM9iXnvnbu8fK092xYuwXmVmSWzhzfkO0hAAAA5BXaM2x0tGeEK83RS8SxIyAAAEBhIDTbMOK20bZWzgAAAEDhIAHasFowrPaMZLsB5lhLMwAAAHoYodlOfKU5uj0jG+MBAABAxhGabXRsox2uNCdrzzBzbiogAAAAehKh2UZ8T3NMewYzAQEAAAoCodlGZPWMI2kmAlJoBgAAyGuEZhtGZHOToDyG5PUmLjlHZgYAAMhvhGYbkdUz2kLyWdtnR84BAACgEBCa7Vg9zUeC8nuTx2STNecAAADyGqHZhidqR0B/XKWZUjMAAEBhIDQ71NoWki9FpRkAAAD5jdBso2PJuWBCpdnqd6Y7AwAAIL8Rmm10bKMdSthCm2WaAQAACgOh2UZMpTnZGs0AAADIe6RAG1Zobgua8vuS3y5WzwAAAMhvhGYbRlQPRnx7BgAAAAoDKbATaM8AAAAoTKRAG9GT/RInAjITEAAAoBAQmm0YUTuYpO5pztRoAAAAkA2EZhvRxeT49gzrFJkZAAAgvxGabaSbCEh3BgAAQGHwZXsAuS6m0pzhJedmTRqq6spiSdLXzjlOH3+y35XrAAAAID1Ccyckrp7hbqn5qvNPjPz5vGnHuHotAAAApJax0HzVVVfp448/lsfjUWlpqW699VaNHj06U5fvMg/rNAMAABS8jIXmpUuXqqKiQpL0/PPP6+abb9aTTz6Zqct3XZr2DCtPs3oGAABAfstY6dQKzJK0f//+XrPGcfQoffGrZ/SOrwAAAIBuymhP85IlS/SXv/xFpmnqpz/9acL55uZmNTc3xxxramrK1PCSig73qSYCAgAAIL9lNDTfc889kqSnnnpK999/vx5++OGY84888oiWL1+eySF1SsrVM1ipGQAAIK9lZfWML33pS7rtttu0d+9eVVdXR45fcsklmjdvXsxrm5qatHDhwkwPMcLjiZoImOHVMwAAAJAbMhKaW1pa1NzcrLq6OknS2rVr1adPH1VVVcW8rrKyUpWVlZkYkmPRsThlewaFZgAAgLyWkdB88OBBXXvttTp48KA8Ho/69OmjlStX9o7JgFFDZCIgAABAYcpIaK6pqdHjjz+eiUv1OEP2EwEpNAMAAOQ3loOwkW4bbeuUW9toAwAAIDcQmm1Et5AkTASkPQMAAKAgEJptMBEQAAAAhGY76SYCUmoGAAAoCIRmG0wEBAAAAKHZhifqDvl93phzVrszEwEBAADyG6G5E3ze2HYMmjMAAAAKA6HZRvTqGfGV5hNH1kqSpoyrz+iYAAAAkFkZ2dykN4uuJsdPBKzvX6Fnvjc3swMCAABAxlFptpNmcxMAAAAUBlKgDY9hv3oGAAAA8hspsBN8hGYAAICCRAq0kXYbbQAAABQEUqANKzN7PIa8HhaZAwAAKESEZhtWTKafGQAAoHCRBG1Y7Rl+WjMAAAAKFknQhtWewSRAAACAwkUSdIj2DAAAgMJFErRhtWewcgYAAEDhIgnasNozulNpPnN8fQ+NBgAAANngy/YAcl1kImAXQ/NT98+OWesZAAAAvQ+h2YYVd7vanuGlrQMAAKDXI9HZ6In2DAAAAPRuJEFbTAQEAAAodCRBGx4qzQAAAAWPJGinmxMBAQAA0PuRBG10dyIgAAAAej+SoA0mAgIAAIAkaIMdAQEAAEASdMAwqDQDAAAUMpKgA4Ykv8+b7WEAAAAgSwjNDlSWF6lvZVG2hwEAAIAsYRttB5YvPlOlxf5sDwMAAABZQmh2oE85VWYAAIBCRnsGAAAAYIPQDAAAANggNAMAAAA2CM0AAACADUIzAAAAYIPQDAAAANggNAMAAAA2CM0AAACADUIzAAAAYIPQDAAAANggNAMAAAA2fNkegJ1gMChJampqyvJIAAAAkK+srGllz3g5H5p37twpSVq4cGGWRwIAAIB8t3PnTh111FEJxw3TNM0sjMexQ4cOadOmTaqtrZXX63X9ek1NTVq4cKFWrVqlgQMHun69QsP9dQ/31j3cW/dwb93DvXUX99c92bq3wWBQO3fu1JgxY1RcXJxwPucrzcXFxZowYULGrztw4EDV19dn/LqFgvvrHu6te7i37uHeuod76y7ur3uycW+TVZgtTAQEAAAAbBCaAQAAABuEZgAAAMCG94477rgj24PINUVFRZo4caKKioqyPZS8xP11D/fWPdxb93Bv3cO9dRf31z25eG9zfvUMAAAAINtozwAAAABsEJoBAAAAG4TmOB988IHmz5+vGTNmaP78+dqyZUu2h5RT9u7dq8svv1wzZszQ7NmzdfXVV2vPnj2SpFGjRmn27NmaO3eu5s6dq3feeSfyvrVr12rmzJk666yzdN111+ngwYPdPpePpk2bppkzZ0bu4bp16yRJGzdu1Jw5czRjxgxdeuml2r17d+Q9bpzLNx9//HHkns6dO1fTpk3TqaeeKin1PZe4t6ksXbpU06ZN06hRo/Tuu+9Gjqd7fmb6XG+W7P6me/ZKPH+dSvVvN9PPgXx8RiS7t+mevVIvfP6aiHHRRReZTz31lGmapvnUU0+ZF110UZZHlFv27t1r/v3vf4/8fN9995k33XSTaZqmOXLkSHP//v0J79m/f7952mmnmR988IFpmqZ58803mz/4wQ+6dS5fnXnmmeY777wTcywYDJpf/OIXzfXr15umaZorVqwwb7zxRtfOFYK7777bvPPOO03TTH7PTZN7m8769evN7du3J9y7dM/PTJ/rzZLd33TPXtPk+etUqn+7mXwO5OszItW9jRb97DXN3vf8JTRH2bVrlzl+/Hizra3NNE3TbGtrM8ePH2/u3r07yyPLXX/4wx/MSy65xDTN1A/tNWvWmIsWLYr8/Prrr5tnn312t87lq2QPkNdee80855xzIj/v3r3bPOmkk1w7l+8OHz5sTpw40dy0aZNpmqkf2txbe9H3Lt3zM9Pn8kW68BH97DVNnr+d5TQ08/ztvFT3Mv7Zm+61uXpvc34b7UxqbGzUgAED5PV6JUler1f9+/dXY2Oj+vbtm+XR5Z5QKKRHH31U06ZNixy76KKLFAwGNWXKFF1zzTUKBAJqbGzUoEGDIq8ZNGiQGhsbJanL5/LZ4sWLZZqmxo8fr+uvvz7hPvTt21ehUEiffvqpK+eqqqoy80WzZO3atRowYICOP/74yLH4e15ZWcm97aR0z0/TNDN6Lt+f18mevRLP3+7K1HOgUJ8RyZ69Uu96/tLTjC77zne+o9LSUn31q1+VJL344ov6r//6L61atUrvvfeeVqxYkeUR9j6rVq3S008/rdWrV8s0Td11113ZHlLeWb16tc4777zIz9xz9Dbxz16J52938RxwX/yzV+p9953QHKWurk47duxQMBiUJAWDQX3yySeqq6vL8shyz9KlS/Xhhx9q2bJl8njC/4ys+1ReXq4LLrhAr776auT49u3bI+/dvn175LVdPZevrO8XCAR04YUX6tVXX024D3v27JHH41FVVZUr5/LZjh07tH79es2ePTtyLNk9t45zb51L9/zM9Ll8luzZK/H87a5MPgcK8RmR7Nkr9b7nL6E5Sr9+/TR69Gj97ne/kyT97ne/0+jRo/P+P/V11r//+79r06ZNWrFihQKBgCTps88+06FDhyRJbW1teu655zR69GhJ0uTJk/XGG29EZrY/9thjmjVrVrfO5aMDBw5o3759kiTTNLVmzRqNHj1aY8aM0aFDh/TKK69ICt+HmTNnSpIr5/LZk08+qalTp6q6ulpS6nsucW87K93zM9Pn8lWyZ6/E87e7Mv0cKMRnRPyzV+qdz192BIzz/vvv68Ybb1Rzc7MqKyu1dOlSDR8+PNvDyhmbN2/Wueeeq6FDh6q4uFiSVF9fr8suu0y33XabDMNQW1ubxo0bp5tvvlllZWWSpOeff14PPPCAQqGQRo8erfvuu0+lpaXdOpdvtm7dqmuuuUbBYFChUEgjRozQLbfcov79++vVV1/V7bffrsOHD2vw4MF64IEHVFNTI0munMtXM2bM0JIlSzRlyhRJ6e+5xL1N5e6779Yf//hH7dq1S9XV1aqqqtLvf//7tM/PTJ/rzZLd32XLliV99q5YsUIbNmzg+etQsnu7cuXKjD8H8vEZkeq5ICU+e6Xe+fwlNAMAAAA2aM8AAAAAbBCaAQAAABuEZgAAAMAGoRkAAACwQWgGAAAAbBCaASCPjBs3Tlu3bs32MAAg77DkHADkuGnTpmnXrl3yer0qKSnRlClTdOutt0bW4QUAuI9KMwD0AitXrtSGDRv05JNPatOmTfrRj34Uc76trS1LIwOAwkBoBoBeZMCAAZo8ebI2b96sUaNGadWqVZo+fbqmT58uSRo1apQ+/PBDSdKhQ4d033336cwzz9T48eP1la98JbLd8saNG7VgwQJNmDBBc+bM0csvv5y17wQAvYEv2wMAADjX2Niol156SWeddZZefPFFPf/883r88ccjWytHW7p0qd577z099thjqqmp0WuvvSaPx6MdO3boiiuu0P3336/Jkyfrb3/7m775zW/q2WefVd++fbPwrQAg91FpBoBe4N/+7d80YcIEXXjhhTrllFN05ZVXSpIWLVqkqqqqhNAcCoW0evVqLVmyRAMGDJDX69XJJ5+sQCCg3/72t5oyZYqmTp0qj8ej008/XWPGjNGf//znbHw1AOgVqDQDQC+wYsUKnXbaaQnH6+rqkr5+7969Onz4sBoaGhLObd++XX/4wx/0wgsvRI61tbVp4sSJPTdgAMgzhGYA6MUMw0h6vLq6WkVFRdq6dauOPfbYmHN1dXWaO3eu7r777kwMEQDyAu0ZAJCHPB6PzjvvPN17773asWOHgsGgNmzYoNbWVs2ZM0cvvPCC1q1bp2AwqMOHD+vll19WU1NTtocNADmL0AwAeeqGG27QyJEjdf755+vUU0/Vgw8+qFAopLq6Ov3whz/Uj3/8Y02aNElTp07Vz372M4VCoWwPGQByFpubAAAAADaoNAMAAAA2CM0AAACADUIzAAAAYIPQDAAAANggNAMAAAA2CM0AAACADUIzAAAAYIPQDAAAANggNAMAAAA2/h/xnWnfrKdpwwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation: Biggest screen size is 7 inch, which belong in the price range of above 1.60L"
      ],
      "metadata": {
        "id": "cVKF5y0kTbI9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,6))\n",
        "sns.histplot(df.Price, kde=True);"
      ],
      "metadata": {
        "id": "QvAjWBD3Sh0l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "7966637c-b611-4e62-895b-3c58c321a7d7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "JPOdAye0Sh9q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e05a4cb-7dd5-4fe1-db35-534524af69e8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Brand', 'Model', 'Battery capacity (mAh)', 'Screen size (inches)',\n",
              "       'Touchscreen', 'Processor', 'RAM (MB)', 'Internal storage (GB)',\n",
              "       'Rear camera', 'Front camera', 'Operating system', 'Wi-Fi', 'Bluetooth',\n",
              "       'GPS', 'Number of SIMs', '3G', '4G/ LTE', 'Price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Renaming the name of the column\n",
        "col_nams = ['Brand', 'Model', 'Batterycapacity', 'Screensize',\n",
        "       'Touchscreen', 'Processor', 'RAM', 'Internalstorage',\n",
        "       'RearCamera', 'FrontCamera', 'OperatingSystem', 'WiFi', 'Bluetooth',\n",
        "       'GPS', 'NumberofSIMs', 'ThreeG', 'FourG', 'Price']"
      ],
      "metadata": {
        "id": "BS6KzSegSiC5"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = col_nams"
      ],
      "metadata": {
        "id": "9crEmuQ1SiHZ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "EbQEHSoQSiLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7fbee0b-0670-490f-aef3-a2cfd17e5365"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1359 entries, 0 to 1358\n",
            "Data columns (total 18 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Brand            1359 non-null   object \n",
            " 1   Model            1359 non-null   object \n",
            " 2   Batterycapacity  1359 non-null   int64  \n",
            " 3   Screensize       1359 non-null   float64\n",
            " 4   Touchscreen      1359 non-null   object \n",
            " 5   Processor        1359 non-null   int64  \n",
            " 6   RAM              1359 non-null   int64  \n",
            " 7   Internalstorage  1359 non-null   float64\n",
            " 8   RearCamera       1359 non-null   float64\n",
            " 9   FrontCamera      1359 non-null   float64\n",
            " 10  OperatingSystem  1359 non-null   object \n",
            " 11  WiFi             1359 non-null   object \n",
            " 12  Bluetooth        1359 non-null   object \n",
            " 13  GPS              1359 non-null   object \n",
            " 14  NumberofSIMs     1359 non-null   int64  \n",
            " 15  ThreeG           1359 non-null   object \n",
            " 16  FourG            1359 non-null   object \n",
            " 17  Price            1359 non-null   int64  \n",
            "dtypes: float64(4), int64(5), object(9)\n",
            "memory usage: 191.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Changing categorical feature into binary number\n",
        "df.Touchscreen = df.Touchscreen.map({'Yes':1,'No':0}).astype('int64')\n",
        "df.WiFi = df.WiFi.map({'Yes':1,'No':0}).astype('int64')\n",
        "df.Bluetooth = df.Bluetooth.map({'Yes':1,'No':0}).astype('int64')\n",
        "df.GPS = df.GPS.map({'Yes':1,'No':0}).astype('int64')\n",
        "df.ThreeG = df.ThreeG.map({'Yes':1,'No':0}).astype('int64')\n",
        "df.FourG = df.FourG.map({'Yes':1,'No':0}).astype('int64')\n"
      ],
      "metadata": {
        "id": "3vehX_h6SiUn"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## changing object type into integer\n",
        "df.Internalstorage = df.Internalstorage.astype('int64')\n",
        "df.RearCamera = df.RearCamera.astype('int64')\n",
        "df.FrontCamera = df.FrontCamera.astype('int64')\n",
        "df.Screensize = df.Screensize.astype('int64')"
      ],
      "metadata": {
        "id": "nfiw5mHS7exo"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0K4MnauU7FQb",
        "outputId": "653ef97c-d74e-4caf-eaae-02f84fc212e5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1359 entries, 0 to 1358\n",
            "Data columns (total 18 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Brand            1359 non-null   object\n",
            " 1   Model            1359 non-null   object\n",
            " 2   Batterycapacity  1359 non-null   int64 \n",
            " 3   Screensize       1359 non-null   int64 \n",
            " 4   Touchscreen      1359 non-null   int64 \n",
            " 5   Processor        1359 non-null   int64 \n",
            " 6   RAM              1359 non-null   int64 \n",
            " 7   Internalstorage  1359 non-null   int64 \n",
            " 8   RearCamera       1359 non-null   int64 \n",
            " 9   FrontCamera      1359 non-null   int64 \n",
            " 10  OperatingSystem  1359 non-null   object\n",
            " 11  WiFi             1359 non-null   int64 \n",
            " 12  Bluetooth        1359 non-null   int64 \n",
            " 13  GPS              1359 non-null   int64 \n",
            " 14  NumberofSIMs     1359 non-null   int64 \n",
            " 15  ThreeG           1359 non-null   int64 \n",
            " 16  FourG            1359 non-null   int64 \n",
            " 17  Price            1359 non-null   int64 \n",
            "dtypes: int64(15), object(3)\n",
            "memory usage: 191.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## To remove decimal from column Internal Storage, Rear Camera, front Camera & screen size\n",
        "def point(st):\n",
        "    return st.split('.')[0]"
      ],
      "metadata": {
        "id": "82lj1j2uUcdC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Internalstorage.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_R6qShQAqSM",
        "outputId": "93068b7c-c09e-4740-bafe-288e4180b302"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([256,  64, 128,  32,  16,   8,   4,   0,   3, 512,   2,   1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.RearCamera.apply(point)"
      ],
      "metadata": {
        "id": "HGGv7l93SiZS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.RearCamera.unique()"
      ],
      "metadata": {
        "id": "Ll44ebpYSidf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3848333-6667-4765-caad-25246fd0d760"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 48,  64,  12,  16,  40,  13,  23,  21,  32,  24,  20,  19,   2,\n",
              "        18,   8,   5,  10,   3,  41, 108,  25,   0,  15])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.FrontCamera.apply(point)"
      ],
      "metadata": {
        "id": "PvVMaKl5BKzG"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.FrontCamera.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5zUtnCDBUtK",
        "outputId": "65fbf900-25fe-46d8-d9e4-bf49a03ca840"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16, 12, 32, 10, 24, 20, 25, 13,  8,  7,  5,  4, 48,  0,  1,  2,  3,\n",
              "       40])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.Screensize.apply(point)"
      ],
      "metadata": {
        "id": "pi-MP5YtCBAZ"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Screensize.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qgLBq0mCEIg",
        "outputId": "ca05129d-460a-470f-8290-5b0722246b02"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 5, 4, 2, 3, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "h3nKdItbBmix",
        "outputId": "1bfedae4-4d5d-4064-f025-f305b14a995a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Brand              Model  Batterycapacity  Screensize  Touchscreen  \\\n",
              "589    Nokia          Lumia 520             1430           4            1   \n",
              "290   Realme                 C1             4230           6            1   \n",
              "1096    Oppo                A37             2630           5            1   \n",
              "912    Intex  Aqua Crystal Plus             2100           5            1   \n",
              "\n",
              "      Processor   RAM  Internalstorage  RearCamera  FrontCamera  \\\n",
              "589           2   512                8           5            0   \n",
              "290           8  2000               16          13            5   \n",
              "1096          4  2000               16           8            5   \n",
              "912           4  2000               16          13            5   \n",
              "\n",
              "     OperatingSystem  WiFi  Bluetooth  GPS  NumberofSIMs  ThreeG  FourG  Price  \n",
              "589          Windows     1          1    1             1       1      0   3850  \n",
              "290          Android     1          1    0             1       0      0   7990  \n",
              "1096         Android     1          1    1             2       1      1   8666  \n",
              "912          Android     1          1    1             2       0      1   7490  "
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Internal storage & RAM has higest correlation with target variable [Price]\n",
        "df.corr()['Price'].abs().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UP_NtUuCLAK2",
        "outputId": "45db5e1f-c866-445c-f5d4-dec0e74c31e9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price              1.000000\n",
              "Internalstorage    0.644084\n",
              "RAM                0.613446\n",
              "RearCamera         0.378562\n",
              "Screensize         0.325336\n",
              "FrontCamera        0.310439\n",
              "Processor          0.302492\n",
              "Batterycapacity    0.297525\n",
              "NumberofSIMs       0.162491\n",
              "FourG              0.149792\n",
              "GPS                0.066807\n",
              "Touchscreen        0.050460\n",
              "WiFi               0.028506\n",
              "Bluetooth          0.027999\n",
              "ThreeG             0.003769\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Using ohe-hot encoding to convert categorical column into numeric\n",
        "df= pd.get_dummies(data= df, columns=['Brand'],prefix='Brand')\n",
        "df= pd.get_dummies(data= df, columns=['OperatingSystem'],prefix='OS')"
      ],
      "metadata": {
        "id": "y84SGwPJYhiG"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lz77vUV82b1M",
        "outputId": "3f1941f3-7335-4c76-de99-90af535b8fff"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Model', 'Batterycapacity', 'Screensize', 'Touchscreen', 'Processor',\n",
              "       'RAM', 'Internalstorage', 'RearCamera', 'FrontCamera', 'WiFi',\n",
              "       'Bluetooth', 'GPS', 'NumberofSIMs', 'ThreeG', 'FourG', 'Price',\n",
              "       'Brand_10.or', 'Brand_Acer', 'Brand_Alcatel', 'Brand_Apple',\n",
              "       'Brand_Aqua', 'Brand_Asus', 'Brand_Billion', 'Brand_Black Shark',\n",
              "       'Brand_BlackBerry', 'Brand_Blu', 'Brand_Cat', 'Brand_Celkon',\n",
              "       'Brand_Comio', 'Brand_Coolpad', 'Brand_Gionee', 'Brand_Google',\n",
              "       'Brand_HP', 'Brand_HTC', 'Brand_Homtom', 'Brand_Honor', 'Brand_Huawei',\n",
              "       'Brand_InFocus', 'Brand_Infinix', 'Brand_Intex', 'Brand_Itel',\n",
              "       'Brand_Jio', 'Brand_Jivi', 'Brand_Karbonn', 'Brand_Kult', 'Brand_LG',\n",
              "       'Brand_Lava', 'Brand_LeEco', 'Brand_Lenovo', 'Brand_Lephone',\n",
              "       'Brand_Lyf', 'Brand_M-tech', 'Brand_Meizu', 'Brand_Micromax',\n",
              "       'Brand_Microsoft', 'Brand_Mobiistar', 'Brand_Motorola', 'Brand_Nokia',\n",
              "       'Brand_Nubia', 'Brand_Nuu Mobile', 'Brand_OnePlus', 'Brand_Onida',\n",
              "       'Brand_Oppo', 'Brand_Panasonic', 'Brand_Phicomm', 'Brand_Philips',\n",
              "       'Brand_Poco', 'Brand_Razer', 'Brand_Reach', 'Brand_Realme',\n",
              "       'Brand_Samsung', 'Brand_Sansui', 'Brand_Smartron', 'Brand_Sony',\n",
              "       'Brand_Spice', 'Brand_Swipe', 'Brand_TCL', 'Brand_Tambo', 'Brand_Tecno',\n",
              "       'Brand_Videocon', 'Brand_Vivo', 'Brand_Xiaomi', 'Brand_Xolo',\n",
              "       'Brand_Yu', 'Brand_ZTE', 'Brand_Zen', 'Brand_Ziox', 'Brand_Zopo',\n",
              "       'Brand_Zuk', 'Brand_iBall', 'Brand_iVoomi', 'Brand_mPhone',\n",
              "       'OS_Android', 'OS_BlackBerry', 'OS_Cyanogen', 'OS_Sailfish', 'OS_Tizen',\n",
              "       'OS_Windows', 'OS_iOS'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Selecting only numeric column to train the model\n",
        "mobile= df.select_dtypes('number')\n",
        "mobile.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "PkUIKF8O2eFq",
        "outputId": "50f385af-00c9-4b74-bbde-c246a643a660"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Batterycapacity  Screensize  Touchscreen  Processor    RAM  \\\n",
              "0             4085           6            1          8  12000   \n",
              "1             4000           6            1          8   6000   \n",
              "\n",
              "   Internalstorage  RearCamera  FrontCamera  WiFi  Bluetooth  ...  \\\n",
              "0              256          48           16     1          1  ...   \n",
              "1               64          64           16     1          1  ...   \n",
              "\n",
              "   Brand_iBall  Brand_iVoomi  Brand_mPhone  OS_Android  OS_BlackBerry  \\\n",
              "0            0             0             0           1              0   \n",
              "1            0             0             0           1              0   \n",
              "\n",
              "   OS_Cyanogen  OS_Sailfish  OS_Tizen  OS_Windows  OS_iOS  \n",
              "0            0            0         0           0       0  \n",
              "1            0            0         0           0       0  \n",
              "\n",
              "[2 rows x 98 columns]"
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 98 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83c37d06-5fa4-4190-bcdf-231029aa4bf1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-83c37d06-5fa4-4190-bcdf-231029aa4bf1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-83c37d06-5fa4-4190-bcdf-231029aa4bf1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mobile.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yysLc5E_215S",
        "outputId": "89f64e84-c4c8-4371-fad9-f6630c5e0736"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Batterycapacity', 'Screensize', 'Touchscreen', 'Processor', 'RAM',\n",
              "       'Internalstorage', 'RearCamera', 'FrontCamera', 'WiFi', 'Bluetooth',\n",
              "       'GPS', 'NumberofSIMs', 'ThreeG', 'FourG', 'Price', 'Brand_10.or',\n",
              "       'Brand_Acer', 'Brand_Alcatel', 'Brand_Apple', 'Brand_Aqua',\n",
              "       'Brand_Asus', 'Brand_Billion', 'Brand_Black Shark', 'Brand_BlackBerry',\n",
              "       'Brand_Blu', 'Brand_Cat', 'Brand_Celkon', 'Brand_Comio',\n",
              "       'Brand_Coolpad', 'Brand_Gionee', 'Brand_Google', 'Brand_HP',\n",
              "       'Brand_HTC', 'Brand_Homtom', 'Brand_Honor', 'Brand_Huawei',\n",
              "       'Brand_InFocus', 'Brand_Infinix', 'Brand_Intex', 'Brand_Itel',\n",
              "       'Brand_Jio', 'Brand_Jivi', 'Brand_Karbonn', 'Brand_Kult', 'Brand_LG',\n",
              "       'Brand_Lava', 'Brand_LeEco', 'Brand_Lenovo', 'Brand_Lephone',\n",
              "       'Brand_Lyf', 'Brand_M-tech', 'Brand_Meizu', 'Brand_Micromax',\n",
              "       'Brand_Microsoft', 'Brand_Mobiistar', 'Brand_Motorola', 'Brand_Nokia',\n",
              "       'Brand_Nubia', 'Brand_Nuu Mobile', 'Brand_OnePlus', 'Brand_Onida',\n",
              "       'Brand_Oppo', 'Brand_Panasonic', 'Brand_Phicomm', 'Brand_Philips',\n",
              "       'Brand_Poco', 'Brand_Razer', 'Brand_Reach', 'Brand_Realme',\n",
              "       'Brand_Samsung', 'Brand_Sansui', 'Brand_Smartron', 'Brand_Sony',\n",
              "       'Brand_Spice', 'Brand_Swipe', 'Brand_TCL', 'Brand_Tambo', 'Brand_Tecno',\n",
              "       'Brand_Videocon', 'Brand_Vivo', 'Brand_Xiaomi', 'Brand_Xolo',\n",
              "       'Brand_Yu', 'Brand_ZTE', 'Brand_Zen', 'Brand_Ziox', 'Brand_Zopo',\n",
              "       'Brand_Zuk', 'Brand_iBall', 'Brand_iVoomi', 'Brand_mPhone',\n",
              "       'OS_Android', 'OS_BlackBerry', 'OS_Cyanogen', 'OS_Sailfish', 'OS_Tizen',\n",
              "       'OS_Windows', 'OS_iOS'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= mobile.drop('Price', axis=1)\n",
        "y= mobile.Price"
      ],
      "metadata": {
        "id": "Xwmn_Bam6saz"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWkrxeOw7m8U",
        "outputId": "bb12ed35-2ee6-47f7-a91c-5d92768be94e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1359, 97)\n",
            "(1359,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "TEQ8H7X77ut4"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=13)"
      ],
      "metadata": {
        "id": "e6aX9bxF9gG1"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Taken log to reduce the skewness in the data & roundoff the number\n",
        "y_train = np.log(y_train)\n",
        "y_test = np.log(y_test)"
      ],
      "metadata": {
        "id": "Vx-jcdar9-FY"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Linear Regression**"
      ],
      "metadata": {
        "id": "ohMAYS9SRrFe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression, Lasso, Ridge"
      ],
      "metadata": {
        "id": "Qn_1PsEd-XTC"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "TTB9SFi1DRnn"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lm = LinearRegression()"
      ],
      "metadata": {
        "id": "GSt5zn4I-p6R"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lm.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wUkodukhCSdB",
        "outputId": "ef08f9db-2751-4fb5-9983-4a33d2093d9a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## In regression, score mean R suqare value\n",
        "lm.score(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ax_O4-wiCYYS",
        "outputId": "3ef051d1-01ae-43f6-9d2f-54397705d813"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7532144027180391"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lm.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppdl-mtcDv4K",
        "outputId": "fc3b038a-8bb5-4475-92b4-29e6caa48710"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6774023566789664"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = lm.predict(X_train)\n",
        "sns.scatterplot(y_train, y_pred_train);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "0vfI2GHpEBsb",
        "outputId": "af2af250-584e-4715-d570-0de59f865933"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test_lm = lm.predict(X_test)\n",
        "sns.scatterplot(y_test, y_pred_test_lm);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "lDesKgwPEbwc",
        "outputId": "2db8e2e1-cad5-4f0b-c891-4abc97c6ec3e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## MAE\n",
        "mean_absolute_error(y_test, y_pred_test_lm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9b0Frk8EcEF",
        "outputId": "f9bab1f2-0603-4365-ae3a-3beb9669c7aa"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3418615693277487"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## we have taken log of price column, so using exponancial to convert\n",
        "np.exp(0.34186156932774675)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1zzO15PGUSD",
        "outputId": "48c86758-443e-49c7-fb7c-7308c2db524a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.4075654337976151"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## MSE\n",
        "mean_squared_error(y_test, y_pred_test_lm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ijBFiML0pYI",
        "outputId": "5a3197e5-f70c-4c77-a75d-b6f7103c53f7"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.20012010973287594"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## RMSE\n",
        "np.sqrt(mean_squared_error(y_test, y_pred_test_lm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7m5RB1RZGpjB",
        "outputId": "6fe31b99-332b-439f-f15b-3b8fd00e38f1"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.44734786210831046"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## R2 score\n",
        "r2_score(y_test, y_pred_test_lm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKq9GkIi2e2W",
        "outputId": "33ddb0cf-fa38-45b6-cf17-90ea66062604"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6774023566789664"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2Ne5v2zHgSb",
        "outputId": "aad8d4cb-f0cd-4465-ad7e-d66a375e2d8a"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.018285131127636"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Lasso**"
      ],
      "metadata": {
        "id": "f3i6zHOG3BNo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lasso = Lasso(alpha=1)"
      ],
      "metadata": {
        "id": "JzW1XkT_IAKQ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z47iSltrIn7v",
        "outputId": "7db2782e-69d6-470c-89bf-51ce8eef816d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=1)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lasso.score(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjEjTR7kJaZm",
        "outputId": "c4f6ff53-6090-4562-e68b-0ea4e2b0cf7a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.49518491894179373"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lasso.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PD4HnbSCJcu_",
        "outputId": "62000d1b-39fa-4d7a-b57a-6db8279a7612"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4761079081531535"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ridge**"
      ],
      "metadata": {
        "id": "mMuBwNea3Fjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ridge = Ridge()"
      ],
      "metadata": {
        "id": "8wNJSdQlJdBJ"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KX06ik3RJdH3",
        "outputId": "ac392ca1-d614-4842-ddd7-d72bb0f87ead"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge()"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ridge.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfUDbhupJdMq",
        "outputId": "cf8fad10-b5b3-4a7d-ceac-9f87dd3ca4bc"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7484062261450635"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ridge.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSYCXHs5JdRu",
        "outputId": "99beeb30-9686-45af-aae1-ea41f0c70f6e"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.684930557426473"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,lm.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdupVgHqKrq5",
        "outputId": "637227af-b01f-4d31-9e10-8493a3062dd8"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3418615693277487"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,lasso.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJI6QjdjLSsW",
        "outputId": "d1282c22-273c-453b-873e-9fb2a41d3b49"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4161906097267428"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,ridge.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q18MrkRfLS61",
        "outputId": "0c9c8241-0cc8-44b6-ad8c-8a4d12ccb400"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3386278799738206"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ridge is has a minimum error"
      ],
      "metadata": {
        "id": "cqw0p_xQMCbB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Auto ML**"
      ],
      "metadata": {
        "id": "Gqk96lXx-BrT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install lazypredict"
      ],
      "metadata": {
        "id": "FLZmzsZCLyBl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8c160f6-acc1-412e-a134-fa3112fc6a98"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting lazypredict\n",
            "  Downloading lazypredict-0.2.12-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from lazypredict) (7.1.2)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.7/dist-packages (from lazypredict) (0.90)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.0.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from lazypredict) (4.64.1)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.7/dist-packages (from lazypredict) (2.2.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.2.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.3.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from lightgbm->lazypredict) (1.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lightgbm->lazypredict) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->lazypredict) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->lazypredict) (2022.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->lazypredict) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lazypredict) (3.1.0)\n",
            "Installing collected packages: lazypredict\n",
            "Successfully installed lazypredict-0.2.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lazypredict.Supervised import LazyRegressor"
      ],
      "metadata": {
        "id": "AwAH1VRyNyik"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "auto= LazyRegressor(random_state=13, ignore_warnings=True)"
      ],
      "metadata": {
        "id": "DDLM_a4zN-zN"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models= auto.fit(X_train,X_test,y_train,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LiW9VO6cOIpG",
        "outputId": "7fa5bb33-5f1b-4bfa-851e-00c395bc1953"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 90%|█████████ | 38/42 [27:13<05:14, 78.71s/it] "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13:27:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 42/42 [27:14<00:00, 38.92s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "5U7VQwf2y9mG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t7sMzKMyOUai",
        "outputId": "9be14ad0-310a-42c0-e86a-5255d10dfe7e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                              Adjusted R-Squared  \\\n",
              "Model                                                                              \n",
              "GradientBoostingRegressor                                                   0.63   \n",
              "XGBRegressor                                                                0.62   \n",
              "ExtraTreesRegressor                                                         0.60   \n",
              "RandomForestRegressor                                                       0.60   \n",
              "LGBMRegressor                                                               0.57   \n",
              "HistGradientBoostingRegressor                                               0.57   \n",
              "BaggingRegressor                                                            0.57   \n",
              "LassoLarsCV                                                                 0.56   \n",
              "BayesianRidge                                                               0.56   \n",
              "LassoCV                                                                     0.56   \n",
              "ElasticNetCV                                                                0.56   \n",
              "LarsCV                                                                      0.55   \n",
              "LassoLarsIC                                                                 0.55   \n",
              "PoissonRegressor                                                            0.55   \n",
              "RidgeCV                                                                     0.55   \n",
              "Ridge                                                                       0.55   \n",
              "Lars                                                                        0.55   \n",
              "SGDRegressor                                                                0.55   \n",
              "HuberRegressor                                                              0.54   \n",
              "NuSVR                                                                       0.54   \n",
              "SVR                                                                         0.53   \n",
              "TweedieRegressor                                                            0.51   \n",
              "OrthogonalMatchingPursuit                                                   0.51   \n",
              "OrthogonalMatchingPursuitCV                                                 0.51   \n",
              "GammaRegressor                                                              0.50   \n",
              "LinearSVR                                                                   0.50   \n",
              "KNeighborsRegressor                                                         0.47   \n",
              "DecisionTreeRegressor                                                       0.47   \n",
              "ExtraTreeRegressor                                                          0.39   \n",
              "AdaBoostRegressor                                                           0.34   \n",
              "PassiveAggressiveRegressor                                                  0.20   \n",
              "MLPRegressor                                                               -0.00   \n",
              "ElasticNet                                                                 -0.35   \n",
              "DummyRegressor                                                             -0.40   \n",
              "LassoLars                                                                  -0.40   \n",
              "Lasso                                                                      -0.40   \n",
              "KernelRidge                                                              -180.58   \n",
              "GaussianProcessRegressor                                               -19382.41   \n",
              "LinearRegression                                      -9244001882925658275840.00   \n",
              "TransformedTargetRegressor                            -9244001882925658275840.00   \n",
              "RANSACRegressor                                  -200808217776971449418907648.00   \n",
              "QuantileRegressor             -1731933018978392430302313106347675691465881256...   \n",
              "\n",
              "                                                                       R-Squared  \\\n",
              "Model                                                                              \n",
              "GradientBoostingRegressor                                                   0.74   \n",
              "XGBRegressor                                                                0.73   \n",
              "ExtraTreesRegressor                                                         0.72   \n",
              "RandomForestRegressor                                                       0.72   \n",
              "LGBMRegressor                                                               0.70   \n",
              "HistGradientBoostingRegressor                                               0.69   \n",
              "BaggingRegressor                                                            0.69   \n",
              "LassoLarsCV                                                                 0.69   \n",
              "BayesianRidge                                                               0.68   \n",
              "LassoCV                                                                     0.68   \n",
              "ElasticNetCV                                                                0.68   \n",
              "LarsCV                                                                      0.68   \n",
              "LassoLarsIC                                                                 0.68   \n",
              "PoissonRegressor                                                            0.68   \n",
              "RidgeCV                                                                     0.68   \n",
              "Ridge                                                                       0.68   \n",
              "Lars                                                                        0.68   \n",
              "SGDRegressor                                                                0.68   \n",
              "HuberRegressor                                                              0.67   \n",
              "NuSVR                                                                       0.67   \n",
              "SVR                                                                         0.66   \n",
              "TweedieRegressor                                                            0.65   \n",
              "OrthogonalMatchingPursuit                                                   0.65   \n",
              "OrthogonalMatchingPursuitCV                                                 0.65   \n",
              "GammaRegressor                                                              0.65   \n",
              "LinearSVR                                                                   0.65   \n",
              "KNeighborsRegressor                                                         0.62   \n",
              "DecisionTreeRegressor                                                       0.62   \n",
              "ExtraTreeRegressor                                                          0.56   \n",
              "AdaBoostRegressor                                                           0.53   \n",
              "PassiveAggressiveRegressor                                                  0.43   \n",
              "MLPRegressor                                                                0.29   \n",
              "ElasticNet                                                                  0.03   \n",
              "DummyRegressor                                                             -0.00   \n",
              "LassoLars                                                                  -0.00   \n",
              "Lasso                                                                      -0.00   \n",
              "KernelRidge                                                              -128.62   \n",
              "GaussianProcessRegressor                                               -13836.13   \n",
              "LinearRegression                                      -6598962996070823493632.00   \n",
              "TransformedTargetRegressor                            -6598962996070823493632.00   \n",
              "RANSACRegressor                                  -143349819180020917302460416.00   \n",
              "QuantileRegressor             -1236365163990474807952791267203495291028401160...   \n",
              "\n",
              "                                                                RMSE  \\\n",
              "Model                                                                  \n",
              "GradientBoostingRegressor                                       0.40   \n",
              "XGBRegressor                                                    0.41   \n",
              "ExtraTreesRegressor                                             0.42   \n",
              "RandomForestRegressor                                           0.42   \n",
              "LGBMRegressor                                                   0.43   \n",
              "HistGradientBoostingRegressor                                   0.44   \n",
              "BaggingRegressor                                                0.44   \n",
              "LassoLarsCV                                                     0.44   \n",
              "BayesianRidge                                                   0.44   \n",
              "LassoCV                                                         0.44   \n",
              "ElasticNetCV                                                    0.44   \n",
              "LarsCV                                                          0.44   \n",
              "LassoLarsIC                                                     0.44   \n",
              "PoissonRegressor                                                0.44   \n",
              "RidgeCV                                                         0.45   \n",
              "Ridge                                                           0.45   \n",
              "Lars                                                            0.45   \n",
              "SGDRegressor                                                    0.45   \n",
              "HuberRegressor                                                  0.45   \n",
              "NuSVR                                                           0.45   \n",
              "SVR                                                             0.46   \n",
              "TweedieRegressor                                                0.47   \n",
              "OrthogonalMatchingPursuit                                       0.47   \n",
              "OrthogonalMatchingPursuitCV                                     0.47   \n",
              "GammaRegressor                                                  0.47   \n",
              "LinearSVR                                                       0.47   \n",
              "KNeighborsRegressor                                             0.48   \n",
              "DecisionTreeRegressor                                           0.48   \n",
              "ExtraTreeRegressor                                              0.52   \n",
              "AdaBoostRegressor                                               0.54   \n",
              "PassiveAggressiveRegressor                                      0.59   \n",
              "MLPRegressor                                                    0.67   \n",
              "ElasticNet                                                      0.77   \n",
              "DummyRegressor                                                  0.79   \n",
              "LassoLars                                                       0.79   \n",
              "Lasso                                                           0.79   \n",
              "KernelRidge                                                     8.97   \n",
              "GaussianProcessRegressor                                       92.65   \n",
              "LinearRegression                                      63981234805.98   \n",
              "TransformedTargetRegressor                            63981234805.98   \n",
              "RANSACRegressor                                     9430035891624.66   \n",
              "QuantileRegressor             27694157061396879354889362647023616.00   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "GradientBoostingRegressor            0.20  \n",
              "XGBRegressor                         0.65  \n",
              "ExtraTreesRegressor                  0.70  \n",
              "RandomForestRegressor                0.61  \n",
              "LGBMRegressor                        0.09  \n",
              "HistGradientBoostingRegressor        0.81  \n",
              "BaggingRegressor                     0.09  \n",
              "LassoLarsCV                          0.16  \n",
              "BayesianRidge                        0.03  \n",
              "LassoCV                              0.16  \n",
              "ElasticNetCV                         0.17  \n",
              "LarsCV                               0.16  \n",
              "LassoLarsIC                          0.05  \n",
              "PoissonRegressor                     0.03  \n",
              "RidgeCV                              0.03  \n",
              "Ridge                                0.02  \n",
              "Lars                                 0.06  \n",
              "SGDRegressor                         0.02  \n",
              "HuberRegressor                       0.06  \n",
              "NuSVR                                0.18  \n",
              "SVR                                  0.19  \n",
              "TweedieRegressor                     0.02  \n",
              "OrthogonalMatchingPursuit            0.02  \n",
              "OrthogonalMatchingPursuitCV          0.04  \n",
              "GammaRegressor                       0.02  \n",
              "LinearSVR                            0.23  \n",
              "KNeighborsRegressor                  0.03  \n",
              "DecisionTreeRegressor                0.03  \n",
              "ExtraTreeRegressor                   0.03  \n",
              "AdaBoostRegressor                    0.18  \n",
              "PassiveAggressiveRegressor           0.03  \n",
              "MLPRegressor                         1.59  \n",
              "ElasticNet                           0.02  \n",
              "DummyRegressor                       0.02  \n",
              "LassoLars                            0.03  \n",
              "Lasso                                0.02  \n",
              "KernelRidge                          0.06  \n",
              "GaussianProcessRegressor             0.37  \n",
              "LinearRegression                     0.03  \n",
              "TransformedTargetRegressor           0.03  \n",
              "RANSACRegressor                      0.33  \n",
              "QuantileRegressor                 1626.83  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b670f41-224e-4b80-9cbf-491c41971682\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.62</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoissonRegressor</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.54</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.54</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.53</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TweedieRegressor</th>\n",
              "      <td>0.51</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.51</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.51</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GammaRegressor</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.47</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.47</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.39</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.40</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.40</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.40</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-180.58</td>\n",
              "      <td>-128.62</td>\n",
              "      <td>8.97</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-19382.41</td>\n",
              "      <td>-13836.13</td>\n",
              "      <td>92.65</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>-9244001882925658275840.00</td>\n",
              "      <td>-6598962996070823493632.00</td>\n",
              "      <td>63981234805.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>-9244001882925658275840.00</td>\n",
              "      <td>-6598962996070823493632.00</td>\n",
              "      <td>63981234805.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-200808217776971449418907648.00</td>\n",
              "      <td>-143349819180020917302460416.00</td>\n",
              "      <td>9430035891624.66</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuantileRegressor</th>\n",
              "      <td>-1731933018978392430302313106347675691465881256...</td>\n",
              "      <td>-1236365163990474807952791267203495291028401160...</td>\n",
              "      <td>27694157061396879354889362647023616.00</td>\n",
              "      <td>1626.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b670f41-224e-4b80-9cbf-491c41971682')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2b670f41-224e-4b80-9cbf-491c41971682 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2b670f41-224e-4b80-9cbf-491c41971682');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **XG Boost Regressor**"
      ],
      "metadata": {
        "id": "--SIspLvVo_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor"
      ],
      "metadata": {
        "id": "lFiuJuLYVXbC"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb = XGBRegressor(max_depth= 5, random_state=13)"
      ],
      "metadata": {
        "id": "k_mp6trKSyvZ"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0AEC5QXS-CM",
        "outputId": "79a184df-ac19-42cb-c9cd-d7f09e9b591c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13:27:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(max_depth=5, random_state=13)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb.score(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CAC3MbQLTblI",
        "outputId": "fa7edc03-0d04-4980-948d-a926dcd36121"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8494697952398317"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWKVSArxTxa5",
        "outputId": "081bc43d-73b9-47d1-8afa-49aa473bc7f3"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7412711689716822"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test_xgb = xgb.predict(X_test)\n",
        "sns.scatterplot(y_test, y_pred_test_xgb);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "y-mkxCBQ3L2E",
        "outputId": "c8c6ae54-88a8-4991-f5bd-b1cf98e0c32b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## MAE\n",
        "mean_absolute_error(y_test, y_pred_test_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EfX80Sg3MT_",
        "outputId": "7a2bb65c-e32c-4ec1-9c6c-0627f682a26b"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3053292712165711"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Since we have take the log of target column\n",
        "np.exp(0.3190862430059167)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WeqaRin73ltT",
        "outputId": "f0a7ce87-88e3-4810-b5bf-b2fe1aa7b958"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3758699789521605"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## MSE\n",
        "mean_squared_error(y_test, y_pred_test_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gRSombJ3l3N",
        "outputId": "60c0e5ed-4666-4ec9-e3e0-7bc999de002d"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1604997529536193"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## RMSE\n",
        "np.sqrt(mean_squared_error(y_test, y_pred_test_xgb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJLVTGsy3l-1",
        "outputId": "b489bb5c-9c69-4b6d-cfbc-7487f28e2774"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4006242041534926"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## R2 score or coefficient of Determination\n",
        "r2_score(y_test, y_pred_test_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXG7db7w3mGH",
        "outputId": "e3f36596-8bcf-4720-df5f-7d65c4a35c67"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7412711689716822"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Extra Trees Regressor**"
      ],
      "metadata": {
        "id": "UEGz-LfJVhVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import ExtraTreesRegressor"
      ],
      "metadata": {
        "id": "_FS4qINCT22l"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "etr= ExtraTreesRegressor(max_depth=9,n_estimators=500, random_state=13)"
      ],
      "metadata": {
        "id": "1bx2c7zpUNiB"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "etr.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlMUAt1JUTBx",
        "outputId": "d1ae3a3f-9e49-4eae-a9bf-8e11d760752a"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesRegressor(max_depth=9, n_estimators=500, random_state=13)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "etr.score(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YCf5Yz4U_Gk",
        "outputId": "fdac11c2-6299-4337-d23c-6d811c83cbe6"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8449479809374487"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "etr.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1TXorwMVFfP",
        "outputId": "b259c17f-9559-4109-d928-7e218f9f87a8"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7204422735295886"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test_etr = etr.predict(X_test)\n",
        "sns.scatterplot(y_test, y_pred_test_etr);"
      ],
      "metadata": {
        "id": "jqQotTscVKFh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "482e1359-982b-4d14-9472-4f689b0c658d"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## MAE\n",
        "mean_absolute_error(y_test, y_pred_test_etr)"
      ],
      "metadata": {
        "id": "QSMpKBwr4gHW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "452ed657-2b81-40ca-8ccd-ad6efda4b285"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.32001116246885203"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.exp(0.3368904223543692)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBDXyo8Yz0TP",
        "outputId": "26786512-e0cc-48b5-cc4f-c51ad1337e65"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.400585582459"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## MSE\n",
        "mean_squared_error(y_test, y_pred_test_etr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMt2RZsM9kL3",
        "outputId": "b5ff74e7-40c9-4730-b8fa-e1a2b3e7e426"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.17342074269977906"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## RMSE\n",
        "np.sqrt(mean_squared_error(y_test, y_pred_test_etr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sy3bIwag9qL9",
        "outputId": "a78f0f5c-140a-48c5-aebb-0718b58dec2e"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4164381619157628"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred_test_etr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDqsfwqw919t",
        "outputId": "f1dd4726-ee6f-4293-c1c9-9281e941f615"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7204422735295886"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion: XD Boost Regressor is giving the best r2 score.**"
      ],
      "metadata": {
        "id": "lOmtKqq5lGIL"
      }
    }
  ]
}