WGU_C964_Capstone.ipynb

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      "source": [
        "# **LAPTOP PRICE PREDICTION MODEL**"
      ],
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      }
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        "### **IMPORT LIBRARIES AND READING IN DATA**"
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        "\n",
        "*   NumPy: Supports array and matrix operations, complementing Pandas for numerical computations.\n",
        "*   Pandas: Enables the creation of DataFrames and facilitates loading CSV data for analysis.\n",
        "*   Matplotlib: Provides a variety of plotting tools (e.g., histograms, heatmaps, bar graphs, box plots) for data visualization.\n",
        "*   Seaborn: Enhances Matplotlib's functionality with a more intuitive and aesthetically pleasing interface for statistical visualization.\n",
        "*   IPyWidgets: Introduces interactive elements like buttons and sliders to enhance the Jupyter Notebook experience.\n",
        "*   SciPy: Assists in building our supervised machine learning model, specifically Linear Regression."
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import ipywidgets as widgets\n",
        "from scipy import stats\n",
        "from IPython.display import display\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "l4RqlmyFETgn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the pandas library, we will read in our CSV file containing the dataset. This data is in its raw, uncleaned form."
      ],
      "metadata": {
        "id": "YgyRsPpjE0e8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path=\"laptop_price.csv\" #update file path as needed\n",
        "CSV = pd.read_csv(path, encoding='latin-1')\n",
        "sns.set_palette(\"GnBu_d\")\n",
        "sns.set_style(\"whitegrid\")"
      ],
      "metadata": {
        "id": "-7LZ64APFeeX"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **DATA EXPLORATION**"
      ],
      "metadata": {
        "id": "a5pyTiDS3eFt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will examine key details of the dataset to gain valuable insights that will be useful to know."
      ],
      "metadata": {
        "id": "TRn5rEWM4XDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CSV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "RDkglNab5kK6",
        "outputId": "1c59bdbc-100b-41ab-c6e6-046f6dc0b66b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      laptop_ID Company                              Product  \\\n",
              "0             1   Apple                          MacBook Pro   \n",
              "1             2   Apple                          Macbook Air   \n",
              "2             3      HP                               250 G6   \n",
              "3             4   Apple                          MacBook Pro   \n",
              "4             5   Apple                          MacBook Pro   \n",
              "...         ...     ...                                  ...   \n",
              "1298       1316  Lenovo                       Yoga 500-14ISK   \n",
              "1299       1317  Lenovo                       Yoga 900-13ISK   \n",
              "1300       1318  Lenovo                   IdeaPad 100S-14IBR   \n",
              "1301       1319      HP  15-AC110nv (i7-6500U/6GB/1TB/Radeon   \n",
              "1302       1320    Asus  X553SA-XX031T (N3050/4GB/500GB/W10)   \n",
              "\n",
              "                TypeName  Inches                            ScreenResolution  \\\n",
              "0              Ultrabook    13.3          IPS Panel Retina Display 2560x1600   \n",
              "1              Ultrabook    13.3                                    1440x900   \n",
              "2               Notebook    15.6                           Full HD 1920x1080   \n",
              "3              Ultrabook    15.4          IPS Panel Retina Display 2880x1800   \n",
              "4              Ultrabook    13.3          IPS Panel Retina Display 2560x1600   \n",
              "...                  ...     ...                                         ...   \n",
              "1298  2 in 1 Convertible    14.0   IPS Panel Full HD / Touchscreen 1920x1080   \n",
              "1299  2 in 1 Convertible    13.3  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
              "1300            Notebook    14.0                                    1366x768   \n",
              "1301            Notebook    15.6                                    1366x768   \n",
              "1302            Notebook    15.6                                    1366x768   \n",
              "\n",
              "                                       Cpu   Ram               Memory  \\\n",
              "0                     Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
              "1                     Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
              "2               Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
              "3                     Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
              "4                     Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
              "...                                    ...   ...                  ...   \n",
              "1298            Intel Core i7 6500U 2.5GHz   4GB            128GB SSD   \n",
              "1299            Intel Core i7 6500U 2.5GHz  16GB            512GB SSD   \n",
              "1300  Intel Celeron Dual Core N3050 1.6GHz   2GB   64GB Flash Storage   \n",
              "1301            Intel Core i7 6500U 2.5GHz   6GB              1TB HDD   \n",
              "1302  Intel Celeron Dual Core N3050 1.6GHz   4GB            500GB HDD   \n",
              "\n",
              "                               Gpu       OpSys  Weight  Price_dollars  \n",
              "0     Intel Iris Plus Graphics 640       macOS  1.37kg        1339.69  \n",
              "1           Intel HD Graphics 6000       macOS  1.34kg         898.94  \n",
              "2            Intel HD Graphics 620       No OS  1.86kg         575.00  \n",
              "3               AMD Radeon Pro 455       macOS  1.83kg        2537.45  \n",
              "4     Intel Iris Plus Graphics 650       macOS  1.37kg        1803.60  \n",
              "...                            ...         ...     ...            ...  \n",
              "1298         Intel HD Graphics 520  Windows 10   1.8kg         638.00  \n",
              "1299         Intel HD Graphics 520  Windows 10   1.3kg        1499.00  \n",
              "1300             Intel HD Graphics  Windows 10   1.5kg         229.00  \n",
              "1301            AMD Radeon R5 M330  Windows 10  2.19kg         764.00  \n",
              "1302             Intel HD Graphics  Windows 10   2.2kg         369.00  \n",
              "\n",
              "[1303 rows x 13 columns]"
            ],
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              "      <th></th>\n",
              "      <th>laptop_ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 2.3GHz</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>Apple</td>\n",
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              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>1440x900</td>\n",
              "      <td>Intel Core i5 1.8GHz</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>250 G6</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel HD Graphics 620</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86kg</td>\n",
              "      <td>575.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>15.4</td>\n",
              "      <td>IPS Panel Retina Display 2880x1800</td>\n",
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              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
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              "      <td>2537.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 3.1GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37kg</td>\n",
              "      <td>1803.60</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>1298</th>\n",
              "      <td>1316</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Yoga 500-14ISK</td>\n",
              "      <td>2 in 1 Convertible</td>\n",
              "      <td>14.0</td>\n",
              "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>4GB</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.8kg</td>\n",
              "      <td>638.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>1317</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Yoga 900-13ISK</td>\n",
              "      <td>2 in 1 Convertible</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.3kg</td>\n",
              "      <td>1499.00</td>\n",
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              "    <tr>\n",
              "      <th>1300</th>\n",
              "      <td>1318</td>\n",
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              "      <td>229.00</td>\n",
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              "      <td>1319</td>\n",
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              "      <td>369.00</td>\n",
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              "<p>1303 rows × 13 columns</p>\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c7081c0c-238f-41ed-97cd-b57306ca08d8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cf9f2720-fefa-4f90-a038-33b85a367c91\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('CSV')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cf9f2720-fefa-4f90-a038-33b85a367c91 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('CSV');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "CSV",
              "summary": "{\n  \"name\": \"CSV\",\n  \"rows\": 1303,\n  \"fields\": [\n    {\n      \"column\": \"laptop_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 381,\n        \"min\": 1,\n        \"max\": 1320,\n        \"num_unique_values\": 1303,\n        \"samples\": [\n          486,\n          1036,\n          303\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Apple\",\n          \"Lenovo\",\n          \"Xiaomi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"GL72M 7RDX\",\n          \"GS73VR 7RF\",\n          \"E402WA-GA007T (E2-6110/4GB/64GB/W10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TypeName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Ultrabook\",\n          \"Notebook\",\n          \"Workstation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4263040545691665,\n        \"min\": 10.1,\n        \"max\": 18.4,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          13.3,\n          15.6,\n          13.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenResolution\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"IPS Panel 4K Ultra HD 3840x2160\",\n          \"4K Ultra HD / Touchscreen 3840x2160\",\n          \"IPS Panel Full HD 2160x1440\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cpu\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 118,\n        \"samples\": [\n          \"Intel Celeron Dual Core 3855U 1.6GHz\",\n          \"AMD A6-Series 7310 2GHz\",\n          \"Intel Core i5 3.1GHz\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ram\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"24GB\",\n          \"16GB\",\n          \"6GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Memory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"240GB SSD\",\n          \"1.0TB HDD\",\n          \"500GB HDD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gpu\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 110,\n        \"samples\": [\n          \"AMD Radeon R7\",\n          \"AMD Radeon Pro 555\",\n          \"Intel Iris Plus Graphics 650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OpSys\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Chrome OS\",\n          \"No OS\",\n          \"Android\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 179,\n        \"samples\": [\n          \"4.42kg\",\n          \"1.35kg\",\n          \"2.03kg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_dollars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 699.0090425337411,\n        \"min\": 174.0,\n        \"max\": 6099.0,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          1292.0,\n          389.0,\n          1798.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A preview of the dataset loaded from the CSV file tells us that we are dealing with 1,303 rows and 13 columns of data."
      ],
      "metadata": {
        "id": "ggwGo0_86Mwx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use .info() to inspect the DataFrame, identifying NULL values and displaying each column's data type."
      ],
      "metadata": {
        "id": "kSM4IaSg60od"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CSV.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTh1l72a7VR1",
        "outputId": "e238c06b-dfb1-49da-caea-4aadbfdf1178"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1303 entries, 0 to 1302\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   laptop_ID         1303 non-null   int64  \n",
            " 1   Company           1303 non-null   object \n",
            " 2   Product           1303 non-null   object \n",
            " 3   TypeName          1303 non-null   object \n",
            " 4   Inches            1303 non-null   float64\n",
            " 5   ScreenResolution  1303 non-null   object \n",
            " 6   Cpu               1303 non-null   object \n",
            " 7   Ram               1303 non-null   object \n",
            " 8   Memory            1303 non-null   object \n",
            " 9   Gpu               1303 non-null   object \n",
            " 10  OpSys             1303 non-null   object \n",
            " 11  Weight            1303 non-null   object \n",
            " 12  Price_dollars     1303 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(10)\n",
            "memory usage: 132.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use .describe() to generate key mathematical information for each numeric column, excluding object-type columns."
      ],
      "metadata": {
        "id": "ENJ-LoMc88mu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CSV.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Y2F6zRYu9DTk",
        "outputId": "79856036-7e2f-4920-9a81-ae16ce62f084"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         laptop_ID       Inches  Price_dollars\n",
              "count  1303.000000  1303.000000    1303.000000\n",
              "mean    660.155794    15.017191    1123.686992\n",
              "std     381.172104     1.426304     699.009043\n",
              "min       1.000000    10.100000     174.000000\n",
              "25%     331.500000    14.000000     599.000000\n",
              "50%     659.000000    15.600000     977.000000\n",
              "75%     990.500000    15.600000    1487.880000\n",
              "max    1320.000000    18.400000    6099.000000"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>laptop_ID</th>\n",
              "      <th>Inches</th>\n",
              "      <th>Price_dollars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1303.000000</td>\n",
              "      <td>1303.000000</td>\n",
              "      <td>1303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>660.155794</td>\n",
              "      <td>15.017191</td>\n",
              "      <td>1123.686992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>381.172104</td>\n",
              "      <td>1.426304</td>\n",
              "      <td>699.009043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.100000</td>\n",
              "      <td>174.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>331.500000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>659.000000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>977.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>990.500000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>1487.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1320.000000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>6099.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1c3fdead-76e6-46a5-8c6f-821cbc1e5ad1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"CSV\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"laptop_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472.29591261766643,\n        \"min\": 1.0,\n        \"max\": 1320.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          660.1557943207981,\n          659.0,\n          1303.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 456.1568872675214,\n        \"min\": 1.4263040545691665,\n        \"max\": 1303.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1303.0,\n          15.017191097467379,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_dollars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1881.86130562464,\n        \"min\": 174.0,\n        \"max\": 6099.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1123.6869915579432,\n          977.0,\n          1303.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's display the maximum and minimum values for each column."
      ],
      "metadata": {
        "id": "34-jWHlz9O8f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Maximum Values:\\n\\n\",CSV.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ueG_wyA9Wav",
        "outputId": "b53ef475-c5a7-44da-be25-226fc838c7ae"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Values:\n",
            "\n",
            " laptop_ID                                    1320\n",
            "Company                                    Xiaomi\n",
            "Product                    Zenbook UX510UW-FI095T\n",
            "TypeName                              Workstation\n",
            "Inches                                       18.4\n",
            "ScreenResolution            Touchscreen 2560x1440\n",
            "Cpu                 Samsung Cortex A72&A53 2.0GHz\n",
            "Ram                                           8GB\n",
            "Memory                                    8GB SSD\n",
            "Gpu                           Nvidia Quadro M620M\n",
            "OpSys                                       macOS\n",
            "Weight                                        4kg\n",
            "Price_dollars                              6099.0\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Minimum Values:\\n\\n\",CSV.min())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCRc-_0W97RT",
        "outputId": "6a44d73c-aac1-4416-8464-97c1e66d6cfe"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum Values:\n",
            "\n",
            " laptop_ID                                           1\n",
            "Company                                          Acer\n",
            "Product             110-15ACL (A6-7310/4GB/500GB/W10)\n",
            "TypeName                           2 in 1 Convertible\n",
            "Inches                                           10.1\n",
            "ScreenResolution                             1366x768\n",
            "Cpu                       AMD A10-Series 9600P 2.4GHz\n",
            "Ram                                              12GB\n",
            "Memory                                      1.0TB HDD\n",
            "Gpu                                AMD FirePro W4190M\n",
            "OpSys                                         Android\n",
            "Weight                                         0.69kg\n",
            "Price_dollars                                   174.0\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the data is in its raw state and some columns are of type 'object' rather than numerical, the max and min values may not be accurate for all columns."
      ],
      "metadata": {
        "id": "xlNjvTMz-Ic9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **DATA CLEANING**"
      ],
      "metadata": {
        "id": "nl2Czxuf-_pQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have an understanding of the data, we need to clean it to enhance readability for both the end user and the ML model."
      ],
      "metadata": {
        "id": "7yMqjWNn-_3Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we will eliminate any potential duplicate rows to ensure data consistency."
      ],
      "metadata": {
        "id": "kNL2kzg5AmPT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CSV.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "IeFFPl3rAyQ8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CSV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "z41vBAeDBBng",
        "outputId": "f259eb1a-e82e-4480-bc27-b9d67feea017"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      laptop_ID Company                              Product  \\\n",
              "0             1   Apple                          MacBook Pro   \n",
              "1             2   Apple                          Macbook Air   \n",
              "2             3      HP                               250 G6   \n",
              "3             4   Apple                          MacBook Pro   \n",
              "4             5   Apple                          MacBook Pro   \n",
              "...         ...     ...                                  ...   \n",
              "1298       1316  Lenovo                       Yoga 500-14ISK   \n",
              "1299       1317  Lenovo                       Yoga 900-13ISK   \n",
              "1300       1318  Lenovo                   IdeaPad 100S-14IBR   \n",
              "1301       1319      HP  15-AC110nv (i7-6500U/6GB/1TB/Radeon   \n",
              "1302       1320    Asus  X553SA-XX031T (N3050/4GB/500GB/W10)   \n",
              "\n",
              "                TypeName  Inches                            ScreenResolution  \\\n",
              "0              Ultrabook    13.3          IPS Panel Retina Display 2560x1600   \n",
              "1              Ultrabook    13.3                                    1440x900   \n",
              "2               Notebook    15.6                           Full HD 1920x1080   \n",
              "3              Ultrabook    15.4          IPS Panel Retina Display 2880x1800   \n",
              "4              Ultrabook    13.3          IPS Panel Retina Display 2560x1600   \n",
              "...                  ...     ...                                         ...   \n",
              "1298  2 in 1 Convertible    14.0   IPS Panel Full HD / Touchscreen 1920x1080   \n",
              "1299  2 in 1 Convertible    13.3  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
              "1300            Notebook    14.0                                    1366x768   \n",
              "1301            Notebook    15.6                                    1366x768   \n",
              "1302            Notebook    15.6                                    1366x768   \n",
              "\n",
              "                                       Cpu   Ram               Memory  \\\n",
              "0                     Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
              "1                     Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
              "2               Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
              "3                     Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
              "4                     Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
              "...                                    ...   ...                  ...   \n",
              "1298            Intel Core i7 6500U 2.5GHz   4GB            128GB SSD   \n",
              "1299            Intel Core i7 6500U 2.5GHz  16GB            512GB SSD   \n",
              "1300  Intel Celeron Dual Core N3050 1.6GHz   2GB   64GB Flash Storage   \n",
              "1301            Intel Core i7 6500U 2.5GHz   6GB              1TB HDD   \n",
              "1302  Intel Celeron Dual Core N3050 1.6GHz   4GB            500GB HDD   \n",
              "\n",
              "                               Gpu       OpSys  Weight  Price_dollars  \n",
              "0     Intel Iris Plus Graphics 640       macOS  1.37kg        1339.69  \n",
              "1           Intel HD Graphics 6000       macOS  1.34kg         898.94  \n",
              "2            Intel HD Graphics 620       No OS  1.86kg         575.00  \n",
              "3               AMD Radeon Pro 455       macOS  1.83kg        2537.45  \n",
              "4     Intel Iris Plus Graphics 650       macOS  1.37kg        1803.60  \n",
              "...                            ...         ...     ...            ...  \n",
              "1298         Intel HD Graphics 520  Windows 10   1.8kg         638.00  \n",
              "1299         Intel HD Graphics 520  Windows 10   1.3kg        1499.00  \n",
              "1300             Intel HD Graphics  Windows 10   1.5kg         229.00  \n",
              "1301            AMD Radeon R5 M330  Windows 10  2.19kg         764.00  \n",
              "1302             Intel HD Graphics  Windows 10   2.2kg         369.00  \n",
              "\n",
              "[1303 rows x 13 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>laptop_ID</th>\n",
              "      <th>Company</th>\n",
              "      <th>Product</th>\n",
              "      <th>TypeName</th>\n",
              "      <th>Inches</th>\n",
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              "      <th>0</th>\n",
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              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 2.3GHz</td>\n",
              "      <td>8GB</td>\n",
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              "      <td>Intel Iris Plus Graphics 640</td>\n",
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              "      <td>1339.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Apple</td>\n",
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              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>1440x900</td>\n",
              "      <td>Intel Core i5 1.8GHz</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>HP</td>\n",
              "      <td>250 G6</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel HD Graphics 620</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86kg</td>\n",
              "      <td>575.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>15.4</td>\n",
              "      <td>IPS Panel Retina Display 2880x1800</td>\n",
              "      <td>Intel Core i7 2.7GHz</td>\n",
              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83kg</td>\n",
              "      <td>2537.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 3.1GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37kg</td>\n",
              "      <td>1803.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1298</th>\n",
              "      <td>1316</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Yoga 500-14ISK</td>\n",
              "      <td>2 in 1 Convertible</td>\n",
              "      <td>14.0</td>\n",
              "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>4GB</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.8kg</td>\n",
              "      <td>638.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>1317</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Yoga 900-13ISK</td>\n",
              "      <td>2 in 1 Convertible</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.3kg</td>\n",
              "      <td>1499.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1300</th>\n",
              "      <td>1318</td>\n",
              "      <td>Lenovo</td>\n",
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              "      <td>Notebook</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
              "      <td>2GB</td>\n",
              "      <td>64GB Flash Storage</td>\n",
              "      <td>Intel HD Graphics</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.5kg</td>\n",
              "      <td>229.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>1319</td>\n",
              "      <td>HP</td>\n",
              "      <td>15-AC110nv (i7-6500U/6GB/1TB/Radeon</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
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              "      <td>AMD Radeon R5 M330</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.19kg</td>\n",
              "      <td>764.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1302</th>\n",
              "      <td>1320</td>\n",
              "      <td>Asus</td>\n",
              "      <td>X553SA-XX031T (N3050/4GB/500GB/W10)</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
              "      <td>4GB</td>\n",
              "      <td>500GB HDD</td>\n",
              "      <td>Intel HD Graphics</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.2kg</td>\n",
              "      <td>369.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1303 rows × 13 columns</p>\n",
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              "\n",
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              "      .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('CSV')\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "CSV",
              "summary": "{\n  \"name\": \"CSV\",\n  \"rows\": 1303,\n  \"fields\": [\n    {\n      \"column\": \"laptop_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 381,\n        \"min\": 1,\n        \"max\": 1320,\n        \"num_unique_values\": 1303,\n        \"samples\": [\n          486,\n          1036,\n          303\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Apple\",\n          \"Lenovo\",\n          \"Xiaomi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"GL72M 7RDX\",\n          \"GS73VR 7RF\",\n          \"E402WA-GA007T (E2-6110/4GB/64GB/W10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TypeName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Ultrabook\",\n          \"Notebook\",\n          \"Workstation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4263040545691665,\n        \"min\": 10.1,\n        \"max\": 18.4,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          13.3,\n          15.6,\n          13.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenResolution\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"IPS Panel 4K Ultra HD 3840x2160\",\n          \"4K Ultra HD / Touchscreen 3840x2160\",\n          \"IPS Panel Full HD 2160x1440\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cpu\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 118,\n        \"samples\": [\n          \"Intel Celeron Dual Core 3855U 1.6GHz\",\n          \"AMD A6-Series 7310 2GHz\",\n          \"Intel Core i5 3.1GHz\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ram\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"24GB\",\n          \"16GB\",\n          \"6GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Memory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"240GB SSD\",\n          \"1.0TB HDD\",\n          \"500GB HDD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gpu\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 110,\n        \"samples\": [\n          \"AMD Radeon R7\",\n          \"AMD Radeon Pro 555\",\n          \"Intel Iris Plus Graphics 650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OpSys\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Chrome OS\",\n          \"No OS\",\n          \"Android\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 179,\n        \"samples\": [\n          \"4.42kg\",\n          \"1.35kg\",\n          \"2.03kg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_dollars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 699.0090425337411,\n        \"min\": 174.0,\n        \"max\": 6099.0,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          1292.0,\n          389.0,\n          1798.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of rows remains unchanged, indicating that no duplicate rows were found or removed."
      ],
      "metadata": {
        "id": "Oo1rfNdqBF2k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, let's remove the 'ID' and 'product' columns as they do not contribute to our laptop price prediction based on key hardware specifications. For better readability, we'll also move the 'price' column to the front of the DataFrame."
      ],
      "metadata": {
        "id": "ohOaSIXHLuft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CSV.drop(\"laptop_ID\", axis = 1,inplace=True)\n",
        "CSV.drop(\"Product\", axis = 1,inplace=True)\n",
        "first_column = CSV.pop(\"Price_dollars\")\n",
        "CSV.insert(0,\"Price_dollars\",first_column)"
      ],
      "metadata": {
        "id": "_394YsESNMRk"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CSV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "bWJk_GYhNZ6W",
        "outputId": "1f086d04-3e8f-479b-930e-392c1baa7c65"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price_dollars Company            TypeName  Inches  \\\n",
              "0           1339.69   Apple           Ultrabook    13.3   \n",
              "1            898.94   Apple           Ultrabook    13.3   \n",
              "2            575.00      HP            Notebook    15.6   \n",
              "3           2537.45   Apple           Ultrabook    15.4   \n",
              "4           1803.60   Apple           Ultrabook    13.3   \n",
              "...             ...     ...                 ...     ...   \n",
              "1298         638.00  Lenovo  2 in 1 Convertible    14.0   \n",
              "1299        1499.00  Lenovo  2 in 1 Convertible    13.3   \n",
              "1300         229.00  Lenovo            Notebook    14.0   \n",
              "1301         764.00      HP            Notebook    15.6   \n",
              "1302         369.00    Asus            Notebook    15.6   \n",
              "\n",
              "                                ScreenResolution  \\\n",
              "0             IPS Panel Retina Display 2560x1600   \n",
              "1                                       1440x900   \n",
              "2                              Full HD 1920x1080   \n",
              "3             IPS Panel Retina Display 2880x1800   \n",
              "4             IPS Panel Retina Display 2560x1600   \n",
              "...                                          ...   \n",
              "1298   IPS Panel Full HD / Touchscreen 1920x1080   \n",
              "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
              "1300                                    1366x768   \n",
              "1301                                    1366x768   \n",
              "1302                                    1366x768   \n",
              "\n",
              "                                       Cpu   Ram               Memory  \\\n",
              "0                     Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
              "1                     Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
              "2               Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
              "3                     Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
              "4                     Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
              "...                                    ...   ...                  ...   \n",
              "1298            Intel Core i7 6500U 2.5GHz   4GB            128GB SSD   \n",
              "1299            Intel Core i7 6500U 2.5GHz  16GB            512GB SSD   \n",
              "1300  Intel Celeron Dual Core N3050 1.6GHz   2GB   64GB Flash Storage   \n",
              "1301            Intel Core i7 6500U 2.5GHz   6GB              1TB HDD   \n",
              "1302  Intel Celeron Dual Core N3050 1.6GHz   4GB            500GB HDD   \n",
              "\n",
              "                               Gpu       OpSys  Weight  \n",
              "0     Intel Iris Plus Graphics 640       macOS  1.37kg  \n",
              "1           Intel HD Graphics 6000       macOS  1.34kg  \n",
              "2            Intel HD Graphics 620       No OS  1.86kg  \n",
              "3               AMD Radeon Pro 455       macOS  1.83kg  \n",
              "4     Intel Iris Plus Graphics 650       macOS  1.37kg  \n",
              "...                            ...         ...     ...  \n",
              "1298         Intel HD Graphics 520  Windows 10   1.8kg  \n",
              "1299         Intel HD Graphics 520  Windows 10   1.3kg  \n",
              "1300             Intel HD Graphics  Windows 10   1.5kg  \n",
              "1301            AMD Radeon R5 M330  Windows 10  2.19kg  \n",
              "1302             Intel HD Graphics  Windows 10   2.2kg  \n",
              "\n",
              "[1303 rows x 11 columns]"
            ],
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              "  <div id=\"df-64f10534-ac85-487b-a288-ea837699c314\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price_dollars</th>\n",
              "      <th>Company</th>\n",
              "      <th>TypeName</th>\n",
              "      <th>Inches</th>\n",
              "      <th>ScreenResolution</th>\n",
              "      <th>Cpu</th>\n",
              "      <th>Ram</th>\n",
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              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1339.69</td>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 2.3GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 640</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>898.94</td>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>1440x900</td>\n",
              "      <td>Intel Core i5 1.8GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>128GB Flash Storage</td>\n",
              "      <td>Intel HD Graphics 6000</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.34kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>575.00</td>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel HD Graphics 620</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2537.45</td>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>15.4</td>\n",
              "      <td>IPS Panel Retina Display 2880x1800</td>\n",
              "      <td>Intel Core i7 2.7GHz</td>\n",
              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1803.60</td>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 3.1GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298</th>\n",
              "      <td>638.00</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>2 in 1 Convertible</td>\n",
              "      <td>14.0</td>\n",
              "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>4GB</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.8kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>1499.00</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>2 in 1 Convertible</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.3kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1300</th>\n",
              "      <td>229.00</td>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
              "      <td>2GB</td>\n",
              "      <td>64GB Flash Storage</td>\n",
              "      <td>Intel HD Graphics</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.5kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>764.00</td>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
              "      <td>6GB</td>\n",
              "      <td>1TB HDD</td>\n",
              "      <td>AMD Radeon R5 M330</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.19kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1302</th>\n",
              "      <td>369.00</td>\n",
              "      <td>Asus</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
              "      <td>4GB</td>\n",
              "      <td>500GB HDD</td>\n",
              "      <td>Intel HD Graphics</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.2kg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1303 rows × 11 columns</p>\n",
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              "    async function quickchart(key) {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('CSV')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "CSV",
              "summary": "{\n  \"name\": \"CSV\",\n  \"rows\": 1303,\n  \"fields\": [\n    {\n      \"column\": \"Price_dollars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 699.0090425337411,\n        \"min\": 174.0,\n        \"max\": 6099.0,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          1292.0,\n          389.0,\n          1798.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Apple\",\n          \"Lenovo\",\n          \"Xiaomi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TypeName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Ultrabook\",\n          \"Notebook\",\n          \"Workstation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4263040545691665,\n        \"min\": 10.1,\n        \"max\": 18.4,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          13.3,\n          15.6,\n          13.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenResolution\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"IPS Panel 4K Ultra HD 3840x2160\",\n          \"4K Ultra HD / Touchscreen 3840x2160\",\n          \"IPS Panel Full HD 2160x1440\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cpu\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 118,\n        \"samples\": [\n          \"Intel Celeron Dual Core 3855U 1.6GHz\",\n          \"AMD A6-Series 7310 2GHz\",\n          \"Intel Core i5 3.1GHz\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ram\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"24GB\",\n          \"16GB\",\n          \"6GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Memory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"240GB SSD\",\n          \"1.0TB HDD\",\n          \"500GB HDD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gpu\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 110,\n        \"samples\": [\n          \"AMD Radeon R7\",\n          \"AMD Radeon Pro 555\",\n          \"Intel Iris Plus Graphics 650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OpSys\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Chrome OS\",\n          \"No OS\",\n          \"Android\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 179,\n        \"samples\": [\n          \"4.42kg\",\n          \"1.35kg\",\n          \"2.03kg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we are using a supervised regression algorithm, we need to convert object-type columns into numeric format. We will go column by column cleaning up and converting them."
      ],
      "metadata": {
        "id": "1TH-mwF6BNtH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This one-hot encodes the Company, TypeName, and OpSys columns by creating separate binary columns for each unique value in these categories, then removes the original columns from the dataset."
      ],
      "metadata": {
        "id": "d_xBXZPO2aYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Company\n",
        "CSV = CSV.join(pd.get_dummies(CSV.Company))\n",
        "CSV = CSV.drop(\"Company\", axis=1)"
      ],
      "metadata": {
        "id": "mjCI7UzBeT26"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#TypeName\n",
        "CSV = CSV.join(pd.get_dummies(CSV.TypeName))\n",
        "CSV = CSV.drop(\"TypeName\", axis=1)"
      ],
      "metadata": {
        "id": "bg8-F9RbmZfh"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#OpSys\n",
        "CSV = CSV.join(pd.get_dummies(CSV.OpSys))\n",
        "CSV = CSV.drop(\"OpSys\", axis=1)"
      ],
      "metadata": {
        "id": "if_hT4tB3Ia-"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This series of transformations extracts key features from multiple columns and applies one-hot encoding where necessary. These transformations ensure that categorical variables are converted into numerical representations suitable for machine learning models while preserving essential information."
      ],
      "metadata": {
        "id": "Bgke00xc2fz4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ScreenResoluton\n",
        "CSV[\"ScreenResolution\"] = CSV.ScreenResolution.str.split(\" \").apply(lambda x: x[-1])\n",
        "CSV[\"Screen Width\"] = CSV.ScreenResolution.str.split(\"x\").apply(lambda x: x[0])\n",
        "CSV[\"Screen Height\"] = CSV.ScreenResolution.str.split(\"x\").apply(lambda x: x[1])\n",
        "CSV = CSV.drop(\"ScreenResolution\", axis=1)\n",
        "CSV[\"Screen Width\"] = CSV[\"Screen Width\"].astype(\"int\")\n",
        "CSV[\"Screen Height\"] = CSV[\"Screen Height\"].astype(\"int\")"
      ],
      "metadata": {
        "id": "ouiHTH4Emmlt"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Cpu\n",
        "CSV[\"CPU Brand\"] = CSV.Cpu.str.split(\" \").apply(lambda x: x[0])\n",
        "CSV[\"CPU Frequency\"] = CSV.Cpu.str.split(\" \").apply(lambda x: x[-1])\n",
        "CSV = CSV.drop(\"Cpu\", axis=1)\n",
        "CSV[\"CPU Frequency\"] = CSV[\"CPU Frequency\"].str[:-3]\n",
        "CSV[\"CPU Frequency\"] = CSV[\"CPU Frequency\"].astype(\"float\")\n",
        "cpu_categories = pd.get_dummies(CSV[\"CPU Brand\"])\n",
        "cpu_categories.columns = [col + \"_CPU\" for col in cpu_categories.columns]\n",
        "CSV = CSV.join(cpu_categories)\n",
        "CSV = CSV.drop(\"CPU Brand\", axis=1)\n"
      ],
      "metadata": {
        "id": "hC_-Fm4KnFgW"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Memory\n",
        "CSV[\"Memory Amount\"] = CSV.Memory.str.split(\" \").apply(lambda x: x[0])\n",
        "CSV[\"Memory Type\"] = CSV.Memory.str.split(\" \").apply(lambda x: x[1])\n",
        "def turn_memory_into_MB(value):\n",
        "    if \"GB\" in value:\n",
        "        return float(value[:value.find(\"GB\")]) * 1000\n",
        "    elif \"TB\" in value:\n",
        "        return float(value[:value.find(\"TB\")]) * 1000000\n",
        "CSV[\"Memory Amount\"] = CSV[\"Memory Amount\"].apply(turn_memory_into_MB)\n",
        "CSV = CSV.drop(\"Memory\", axis=1)\n",
        "CSV = CSV.drop(\"Memory Type\", axis=1)"
      ],
      "metadata": {
        "id": "gN7VedTn3cNe"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Gpu\n",
        "CSV[\"GPU Brand\"] = CSV.Gpu.str.split(\" \").apply(lambda x: x[0])\n",
        "CSV = CSV.drop(\"Gpu\", axis=1)\n",
        "gpu_categories = pd.get_dummies(CSV[\"GPU Brand\"])\n",
        "gpu_categories.columns = [col + \"_GPU\" for col in gpu_categories.columns]\n",
        "CSV = CSV.join(gpu_categories)\n",
        "CSV = CSV.drop(\"GPU Brand\", axis=1)"
      ],
      "metadata": {
        "id": "JgP9r-Pt3dAN"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This converts the Ram and Weight columns into numerical values by removing their unit suffixes (GB for RAM and kg for weight) and changing their data types to integers and floats, respectively."
      ],
      "metadata": {
        "id": "QlFs38M6263p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Ram\n",
        "CSV[\"Ram\"] = CSV[\"Ram\"].str[:-2]\n",
        "CSV[\"Ram\"] = CSV[\"Ram\"].astype(\"int\")"
      ],
      "metadata": {
        "id": "B0iAdF7CnsqZ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Weight\n",
        "CSV[\"Weight\"] = CSV[\"Weight\"].str[:-2]\n",
        "CSV[\"Weight\"] = CSV[\"Weight\"].astype(\"float\")"
      ],
      "metadata": {
        "id": "BV8aBDcx3ClK"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **DATA VISUALIZATION**"
      ],
      "metadata": {
        "id": "gM2zYNIQ0Yzj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that the data is cleaner, let's start visualizing the data to explore its relationships. Let's begin by examining the cleaned columns."
      ],
      "metadata": {
        "id": "XwxA6y0F6x3A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_correlations = CSV.corr()['Price_dollars'].apply(abs).sort_values()"
      ],
      "metadata": {
        "id": "1Rz-9je4384n"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_correlations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x53JCKSIpsET",
        "outputId": "d2f5712b-7553-4dd4-e152-dd2575312c95"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Xiaomi                0.000776\n",
              "Asus                  0.010376\n",
              "Mac OS X              0.015656\n",
              "Huawei                0.016851\n",
              "Windows 10 S          0.018312\n",
              "ARM_GPU               0.018431\n",
              "Samsung_CPU           0.018431\n",
              "Fujitsu               0.027135\n",
              "Lenovo                0.029007\n",
              "Samsung               0.034584\n",
              "Google                0.038086\n",
              "Android               0.038700\n",
              "Toshiba               0.040339\n",
              "HP                    0.041291\n",
              "Microsoft             0.047562\n",
              "Dell                  0.048509\n",
              "Chuwi                 0.055646\n",
              "LG                    0.067053\n",
              "Inches                0.068197\n",
              "Vero                  0.071972\n",
              "2 in 1 Convertible    0.072675\n",
              "Apple                 0.080688\n",
              "Mediacom              0.087161\n",
              "macOS                 0.089928\n",
              "Netbook               0.097572\n",
              "Chrome OS             0.118684\n",
              "Memory Amount         0.122365\n",
              "Windows 10            0.137048\n",
              "Windows 7             0.152381\n",
              "Linux                 0.162060\n",
              "No OS                 0.177094\n",
              "MSI                   0.180100\n",
              "AMD_CPU               0.180111\n",
              "Intel_CPU             0.181127\n",
              "Intel_GPU             0.184205\n",
              "AMD_GPU               0.199415\n",
              "Acer                  0.208349\n",
              "Weight                0.210370\n",
              "Razer                 0.233756\n",
              "Workstation           0.249752\n",
              "Ultrabook             0.255658\n",
              "Nvidia_GPU            0.348797\n",
              "Gaming                0.375789\n",
              "CPU Frequency         0.430293\n",
              "Notebook              0.549248\n",
              "Screen Height         0.552809\n",
              "Screen Width          0.556529\n",
              "Ram                   0.743007\n",
              "Price_dollars         1.000000\n",
              "Name: Price_dollars, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price_dollars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Xiaomi</th>\n",
              "      <td>0.000776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asus</th>\n",
              "      <td>0.010376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mac OS X</th>\n",
              "      <td>0.015656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Huawei</th>\n",
              "      <td>0.016851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Windows 10 S</th>\n",
              "      <td>0.018312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARM_GPU</th>\n",
              "      <td>0.018431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Samsung_CPU</th>\n",
              "      <td>0.018431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fujitsu</th>\n",
              "      <td>0.027135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lenovo</th>\n",
              "      <td>0.029007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Samsung</th>\n",
              "      <td>0.034584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>0.038086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Android</th>\n",
              "      <td>0.038700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toshiba</th>\n",
              "      <td>0.040339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP</th>\n",
              "      <td>0.041291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Microsoft</th>\n",
              "      <td>0.047562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dell</th>\n",
              "      <td>0.048509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chuwi</th>\n",
              "      <td>0.055646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LG</th>\n",
              "      <td>0.067053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inches</th>\n",
              "      <td>0.068197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vero</th>\n",
              "      <td>0.071972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2 in 1 Convertible</th>\n",
              "      <td>0.072675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>0.080688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mediacom</th>\n",
              "      <td>0.087161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macOS</th>\n",
              "      <td>0.089928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netbook</th>\n",
              "      <td>0.097572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chrome OS</th>\n",
              "      <td>0.118684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Memory Amount</th>\n",
              "      <td>0.122365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Windows 10</th>\n",
              "      <td>0.137048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Windows 7</th>\n",
              "      <td>0.152381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Linux</th>\n",
              "      <td>0.162060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No OS</th>\n",
              "      <td>0.177094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSI</th>\n",
              "      <td>0.180100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMD_CPU</th>\n",
              "      <td>0.180111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Intel_CPU</th>\n",
              "      <td>0.181127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Intel_GPU</th>\n",
              "      <td>0.184205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMD_GPU</th>\n",
              "      <td>0.199415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acer</th>\n",
              "      <td>0.208349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>0.210370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Razer</th>\n",
              "      <td>0.233756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Workstation</th>\n",
              "      <td>0.249752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ultrabook</th>\n",
              "      <td>0.255658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nvidia_GPU</th>\n",
              "      <td>0.348797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gaming</th>\n",
              "      <td>0.375789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU Frequency</th>\n",
              "      <td>0.430293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Notebook</th>\n",
              "      <td>0.549248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Screen Height</th>\n",
              "      <td>0.552809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Screen Width</th>\n",
              "      <td>0.556529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ram</th>\n",
              "      <td>0.743007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price_dollars</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_features = list(target_correlations.index[1:])"
      ],
      "metadata": {
        "id": "GXKEy6Q_sq2A"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's visualize the distribution of laptop prices."
      ],
      "metadata": {
        "id": "dp9qE7qr7X-j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data = CSV, x = \"Price_dollars\", color='g', bins=40, kde=True, linewidth = 1, edgecolor = \"black\", alpha = 0.65, legend=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "jRIvHItltR9u",
        "outputId": "4db54603-085b-4652-84df-cfba41022672"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7909c6a078d0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the histogram shows, most laptops are priced between $500 - $1,500."
      ],
      "metadata": {
        "id": "Es6q_n_H7lDN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's examine the average laptop prices by brand."
      ],
      "metadata": {
        "id": "9hpdvdfW9AXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the list of computer brands from the slecte features list\n",
        "computer_brands = [\n",
        "    \"Xiaomi\", \"Asus\", \"Huawei\", \"Fujitsu\", \"Lenovo\",\n",
        "    \"Samsung\", \"Google\", \"Toshiba\", \"HP\", \"Microsoft\",\n",
        "    \"Dell\", \"Chuwi\", \"LG\", \"Vero\", \"Apple\",\n",
        "    \"Mediacom\", \"MSI\", \"Acer\", \"Razer\"\n",
        "]\n",
        "\n",
        "# Ensure the brand columns exist in the modified dataset\n",
        "brand_columns = [brand for brand in computer_brands if brand in CSV.columns]\n",
        "\n",
        "# Compute total price per brand (sum of prices for each brand)\n",
        "total_price_per_brand = CSV[brand_columns].mul(CSV['Price_dollars'], axis=0).sum()\n",
        "\n",
        "# Compute total number of laptops per brand (count of occurrences)\n",
        "total_laptops_per_brand = CSV[brand_columns].sum()\n",
        "\n",
        "# Compute the average price per brand\n",
        "average_prices = total_price_per_brand / total_laptops_per_brand\n",
        "\n",
        "# Sort values for better visualization\n",
        "average_prices = average_prices.sort_values()\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(12, 6))\n",
        "average_prices.plot(kind='bar', color='#ff34b3', edgecolor='black')\n",
        "\n",
        "# Titles and labels\n",
        "plt.title('Average Prices by Brand', fontsize=16, fontweight='bold')\n",
        "plt.xlabel('Brands', fontsize=12)\n",
        "plt.ylabel('Average Price ($)', fontsize=12)\n",
        "plt.xticks(rotation=45, fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "\n",
        "# Add grid for better readability\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "# Adjust layout for better spacing\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "8Ny-vcl1wLiK",
        "outputId": "c50b36f2-da01-4906-91c7-57d0ed069aa2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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n6sEHH9TgwYP15JNPatWqVefU8VFmZqYyMzM1ZswY/frrr/rvf/+rW2+91fz8+iOXy2Wu34ULF2rmzJl6+umnlZGR4VfrtrCwUEVFReratateeeUVrVmzRm+//XbANKa2bNmie+65Rxs3blR2drbCwsIk+Wfj8Gx4Hs8+//zz6t69u+rUqWNrAvBDS5YsMTp37mzcdNNNRt++fY3k5GRj2rRp5nSXy+XD7KqH5cuXG23btjVuv/12o2vXrkb//v2NX375xSgqKvJ1amWyfv16M9eUlBSjbdu2xoIFC3yc1dnZv3+/0adPH6NNmzZGSkqKYRiGUVhY6OOs7Pfbb78Z1157rXHllVcax44dMwyjer8PGRkZRtu2bY2ZM2easSNHjhg33nijMWTIEOPkyZM+zM533PvZL774wujVq5fRp08fo0+fPkbv3r2NtLQ0r3n8xerVq42LLrrI+Pjjj70+k+fCOvZcVytXrjRuueUW4+9//7vx448/+jCrsktLSzN69+5tTJgwwYytXr3aGDVqlHHLLbcYy5Yt811yPpKRkWFcffXVxnvvvWcYhmEUFBQY3bp1My655BLj9ttvN1asWOHjDKtOSkqKcfHFFxufffaZMWvWLOPee+81EhMTjSVLlhiG4X/7JsPwzvnbb781OnfubDz22GNGXl6eD7Mqv+Lv/QsvvGD85S9/MRYvXmx8+eWXxk033WT07NnTcDqdPsrQXo8//rgxfPhwY9++fcaNN95oXHHFFUbbtm2Njz/+2DAMw2+O20szbdo0469//asxevRoY8yYMUabNm2Mzz77zCgoKPB1ameUmZlpvPnmm8aqVauM/Px8wzAMY9WqVUafPn2McePGGb///ruPMzw7f/zxh3HRRRcZL7zwgnk8bhiG8dVXXxnff/+9X6yjyvTbb78Zr7/+uvHPf/7TKCwstPW7CU0pP7R161ajZ8+expw5cwyXy2Xs3bvX+PDDD42kpCRj+vTpvk6v2khJSTHmzJljGIZh5OXlGUOHDjX69OljrFmzptoPcNnZ2cagQYOMxx57zIw9//zzft+YOn78uPHpp58aV111lXHLLbeY8erckDlbpX3W0tPTjUGDBhmXXXZZtW5MuVwuIyUlxWjTpo158OE+MJk4caIxcuTIc27Q9rR+/XqjU6dOxocffmjs2rXL2LhxozFmzBijS5cuxsaNGw3D8K8D6hdeeMEYNWqUYRiGkZOTY6xZs8aYOHGi8cgjjxjffPONj7OrGu4viMePHzcM43/ra9WqVcbQoUP9pjF17Ngx45133jEGDhxoTJw40Yx7Nqa+/fZbH2Zov4yMDONf//qXceLECWPPnj1G3759jaeeespIS0sz/vKXvxh33HGH2aQJJPv37zcGDBhgfPLJJ2bswIEDxuTJk43ExERzW/aXxpQ7T/fY4x5zvvvuO6N9+/Z+2Zhy72cyMzON66+/3li9erVhGKf+oNq5c2fjgw8+MAyjeh4XVKaVK1caycnJxi+//GIYxql12qVLF6NPnz7GDz/8YK5zf/msFvfll18aycnJ5vHAmjVrjDZt2hhffPGFOU91rW3Tpk3GZZddZtx7773G0qVLDcP4X66B0JjKzs42br/9duPxxx/3ir/66qtGmzZtjEsuucRYuXJlwG+Dnu68806jTZs2xsCBA80/Rtr1+eTyPT906NAhRUZG6qqrrpLD4VCjRo30t7/9TRMnTtSXX36p9evX+zpFnzD+/9MOd+3apW3btskwDLVo0UKSVKNGDb3//vtq1KiRJkyYoF9//bVaXTJlFLtkr0aNGrrqqquUlZWlDRs2SJLGjRunO++8U4888ojfXMrnWVNBQYEiIyPVv39/jR49Wlu2bNH9998vSX5zCVtFuE/dXrBggde9BS644AI988wzioqK0uDBg3XixIlq9z7s2rVLx48f19///ncNGDBAN998s9asWaPQ0FDt3r1bX375pbp3766QkBBfp+oz27Zt04UXXqhrr71WcXFxuvDCC/Xcc8+pR48euv/++5WTk1Ot74dhFDs93TAMHTp0SIsWLdLEiRP1xhtvaPv27crJydGMGTO0Z88eH2VadRwOh5YtW6Z77rlHf//73/Xmm28qNzdX3bt319ixY3X48GHNmTOnWl/KZxiGatWqpeuvv14DBw7U77//rn/84x+SpG7duum2225TVFSU3n77bS1btszH2dqnSZMmuuGGG1SzZk3NmDFDXbp00YMPPqj27dsrKSlJGzdu1FdffeU3l/eXVWFhoXbt2qXQ0FAzFhMTozvvvFOtW7fW2LFjtWzZMjkcjmp//zvj/7+s+IcfftDkyZN111136aWXXtKmTZvUu3dvvfTSS1qwYIGeeeaZan8p3+TJk/Xaa69J+t+xQX5+vvbs2aPExER9++23Gjt2rB566CHdeOONysvL0/z587Vv3z5fpl2lTpw4oZMnT+qiiy7S0qVL9fjjj2vq1Klq3ry5XnzxRS1fvty8B6fneFUdP7dffvllibycTqeSk5N14YUXavHixRoxYoSeeOIJXXXVVTp27Jiys7Or5SW127Zt02233aarrrpKkydPVt++fSX97zYx3bt319NPP61ff/1Vb731ll/+cFF2drb27dunPn36mLGlS5fqpZde0rvvvqvWrVtrwoQJ+umnn1RQUODDTO3z2muvqX///srKytKCBQt08uRJ2z6f1fdIGSb3B2HFihX69ttvVbt2bTmdTm3ZssWcp0aNGurRo4dOnjwZkF8aysLhcOjLL7/U0KFDdffdd+uNN97Qr7/+6rUjef/99xUXF6eRI0dWq+ad+xfpNm7cqIyMDIWFhWnw4MFyOp2aM2eOOd+DDz6o//u//9NDDz2kL774wocZn1nxA8lbb71Vc+fO1ZYtW9SvXz898cQTSk1N1YMPPigpsBtTO3fu1HPPPaebb77Z64CldevW+sc//iGn06nbb7/dvMdUdVBQUKB//OMf6tevnwoKCjRlyhT17dtXI0eO1Jdffqnbb79d11xzjYYPHy7p3Ln2vnidhw4d0qZNm8wvf0VFRQoLC9Ntt92mwsJC/fnnn75Is8wcDodWrlxp3rfD/ceOF154QTVq1NAtt9yiefPm6ZprrlHNmjVVs2ZNH2dc+dLS0vTQQw+pY8eOkqRvv/1WEyZMUE5OjtmYOnbsmF599VWtWbPGx9laczcXIiMjNXDgQF1//fXauHGjZWNq9uzZ+uqrr3ycceXKz883t82jR48qJydHhmEoIiJCTZo0MX9dMT4+XpGRkZKkRo0a6f7779e4cePMmD+y+nLepEkT9erVS//973+9GhqxsbFq3bq1WrdurdGjR2vt2rXVumku/e++YPfee6/q1aunTp06acOGDRo+fLj279+v3r176+WXX9Z///tfPfLII9W2MeV0OnXy5El99tlnXsd1NWrUUMuWLfX+++/r4Ycf1sMPP6ybbrpJ0qnGwI8//qisrCxfpV3lunfvrlatWqlv37667777NH78eF122WV66aWXFBkZqZkzZ+r7779XYWGhHA6H+QMr1e1zu2rVKt1///2aOXOm1zZ5+PBh7d+/X999950ee+wxjR8/3ly/n3/+uZ577jnl5+dXq3tM5efn6/XXX9cVV1yhcePGqV69epKkvLw8ZWZmasOGDTp+/Lh69uypp556Sr/99pv+9a9/afPmzT7OvHy2b9+uHTt2qHnz5masbdu2+vjjj9W1a1e98cYbOv/88/Xggw8qMzPTd4lWEfeYeeDAAR0+fFiZmZkKCQnR9OnTlZSUpDlz5mjp0qXm57Oqj/Or1xYNSw6HQ2vXrjX/6h4VFaW2bdtq0aJFysjIMOdr2LChGjdurMLCQh9maz/3RvLnn3/qrbfe0p133qnp06fr4osv1qJFi/T11197/ZLCu+++q06dOql+/fq+StnS1q1bNXjwYN1www1avHixIiIiNH36dH377bf66KOPzPnuv/9+jR49Wq1bt/ZhtmfmPpAcPXq0wsLC1KNHD33yySeaNGmS9u7dq759+2r8+PFKTU3VyJEjJanaNGTOlucBycmTJxUfH6+pU6cqNzdXt912mzk9KChIF1xwgc4//3xt2LBBjzzyiK9SLiE0NFSPPfaYGjZsqGHDhik3N1dTpkzRpZdeqvvvv1+tWrXSU089ZZ7lV50OqKqSw+HQmjVrNHnyZElSnz591KRJE7388svKyckxP8Pug7jq/tc1l8ulhQsX6q677tKPP/6oCy+8UP/v//0/vfvuu3r22Wd1ySWXSJJSU1MVFBQUMOvZ8+DqyJEjGj58uB566CH961//0g033KA9e/bo4YcfNhtTI0aMUFhYmBISEnyYtTV3LUFBQSosLFStWrU0aNAgDRkyRBs2bPBqTA0fPlwul0uffPJJQJwdtGDBArMR7B5zbrvtNg0ZMkSDBw/Wd999p6NHj6qgoEA1atTQjh07tGjRIqWkpGjZsmW64oor1KRJE1+XUWGeN1LeuXOntm/fbk7r27evDh48qHfeecf8Fc3c3FydOHFC99xzjy655BLNnTtXJ0+erNZ/VDh06JDefPNNPfTQQ3rggQc0ZMgQbd68Wf369VPDhg0lSb169dKzzz6rn376SdnZ2T7O2FpMTIxGjhypv/71r5ozZ47effddSVJ8fLzq16+vF198UcOGDTMbFrm5uZoxY4Zyc3PNhnkgqlWrli677DJlZWUpJiZGAwYMkCRFRkbq1VdfVa1atfT666/rnXfe0QsvvKAJEyZUyz/29OjRQ08//bReeeUVzZw50/wja+/evZWdna17771XY8aM0c033yxJysnJ0Q8//KDg4GCvMxqrg7CwMO3Zs0exsbFm7LvvvtO0adN07bXX6t5779WQIUN04MABJScn65///KeysrJUt25d3yVdAe4/srlP8jAMQ40bN1ZiYqJ57Pb3v/9dTZo0CZjvJ27u4/alS5dq9OjRuvXWWzVy5Ei9+uqrCgkJ0csvv6zGjRvr9ddf17fffmtL49RhVOeRCNL/196dx9WU/38Af5VljH1szWAsw8xFSkiWbJFooVLabaGIskeosZUoEWOJ7JE9g1CWyb5kLWQrLUpFC+3bff/+6HfPtzsxM2bo3vJ+/jMP556az+mce87nvD/vz/sD4PXr1zhy5AgAYNq0aQCA06dPY926dVBVVYWOjg7at2+PAwcO4NixYzh06BBatGghyyZXuMePHyM4OBgZGRlYunSpMJ1o8uTJSEpKwuTJk6GtrS2sqCCvHB0dce3aNfzyyy/o0KEDfvnlF+Tl5eHu3buYPn06fv75Z1k38R9LSUmBg4MDTExMYGVlhYKCAmhqasLMzAzOzs4ASqcYnDx5Ev7+/ti2bRuUlJRk3Or/ruxLwq5du1BYWIgRI0agadOmuHr1KlatWoUGDRoII6W5ubn49ddfYWlpCTU1NbkY/ZM8rMRiMV6+fImFCxeiqKgIO3bsQLVq1eDp6YkTJ05g+/bt6Natm9QxV3VisRh79uzB77//jk2bNqFhw4ZYvXo1Hj9+DHV1dUyZMgUFBQXYtm0bzpw5g71796JJkyaybvZfysvLw4oVK3D8+HFs2LABmpqawmfh4eG4cOECDh48iL1796JDhw4ybOnnIbm+7927h9evX+Pu3buoVasW5syZA6B0lPjEiRPYv38/mjdvDg8PD9SpUwd5eXlylykmOZZr164hLCwMz58/x9ChQ6GpqYnmzZtj//79OHjwIDp16oQVK1YAAO7cuYMWLVrg+++/l3Hr/5uEhARYWVmhRYsW2L9/P+Lj46Gnp4fJkyejWbNmCA8Px8WLFzFlyhSMHz8e165dg5eXF3JzcwEAa9asQadOnWR8FJ+Ht7c3QkJCkJ6ejj59+mD27Nlo06YNtm/fjlOnTgmBjSdPnoCIEBQUBDc3N8THx2Pnzp2ybv5fSkxMxOjRoxEQEIBq1arBzMwM/fv3FwYGzp07BzU1NTRp0gS5ubmoXbu2jFv81xISErBv3z788ccfsLCwELKNx48fjxcvXsDAwAA1atTA/fv3kZ6ejqCgINSoUaNKPmdLSkpQUFAAHx8fNG7cGOfOnUNOTg6CgoKEe21eXh5cXFyQmpqKrKwsrFq1Ch07dpRxyz/u0KFDcHNzw7Rp0zB16lQUFBRg/fr1uHDhAgYPHgwzMzOkpKTAz88Pb968weHDh1G9enW5GNxLTExEVFQUtLW1MXr0aNSsWROOjo64fPkyfv/9d3Tp0gV9+vRBw4YNsXHjRvz0009wd3dHzZo15fL5+E9YWFggJycHe/fuRf369VFcXCxVksLT0xOxsbHw8vJCvXr1ZNjSz+/SpUuYNm0a5s2bB3V1dVy8eBE+Pj7w9/dH3759UVxcDEdHRzx9+hQLFiyAtrb2l23QF61Yxf4TsVhMCQkJ1K9fP+rVqxetX79e6vPTp0/ThAkTqEuXLjR06FAaPHiwUEjvayIWi2nevHmkpqZGw4cPlyq6XFJSQpMnTyZjY2M6evSoXK4glZKSIhRyvH37Ni1evJj8/f1p586dZG5uTn379iV9fX3au3evjFv6aSSFVt+8eUNxcXHUr18/WrRokfD5jRs36N27d1RQUCC14kVVsXLlSurVqxcFBQVRcnIyEZUWab1y5QoNGzaMhgwZQhs3biQLCwuysrISip7KsqBi2UKxkmuSqHTlGJFIREZGRpSZmUkFBQU0a9YsUldXF4qzfk2io6Opd+/etGPHDiIqLTDt6elJw4cPJxUVFTIxMaHevXvTw4cPZdvQv5Cbm0tE/ytgmZeXRwsXLqQuXboIRb1TU1Np1qxZZGVlRU+ePJFZW7+EkJAQUlVVpYEDB1KvXr3IwMBAqphnQUEBBQUFkY6ODs2ZM4fEYrHcFqMNDQ2lrl27kpubG61du5b69etHEyZMoLdv31JWVhbt3r2bjI2NydHRUdZN/awKCgooLCyMRowYQTY2NhQSEkJr1qyR2mft2rXUrVs3unTpEhERvXr1ipKTkyktLU0GLf58yi6ecOLECRo0aBAFBwfT2bNnSUtLiywsLCgqKoqIiG7evEmrV6+mqVOn0vLly4V+0Ny5c2nhwoVUUFAgV9e2pC3p6enCf21tbenMmTOkpaVFrq6uwnMyPj6eFixYQNeuXZNZe/+O5FyVPWcxMTHk6elJOjo6wnOEiMjd3Z3s7e3J3t6eVq5cKfRnq9piIh+73iIiImjEiBE0dOhQysvLE7YXFRVRamoqZWZmVlQT/7Gy51VyXAcPHqQOHToI723Z2dnk6elJRkZG1KlTJxo5ciTZ2toK/Sx5KKSdnJxMPXv2JB0dHQoLC6PExETq168fDRgwgDQ0NOjw4cOUkJAg7O/k5CQsikIkv8XaJf68EILkvJ09e5Z69epFxsbGlJiYKHyelpZGXl5epK6uTk+fPq3QtlYEsVhMrq6uwjMzMTGRtLW1hQW2JH+fwsJCmj59OsXHx3/xNnFQSk6V/XLv3LmTunbtShMnTix3Ubx7946io6Pp0aNHX83SsR+Sl5dHy5YtowEDBpCfn5/wwkVU+sWysbEhS0tLuQp+FBcXU1xcHHXo0IE8PT0pPDyciIh+/fVX8vb2JqLSl8KpU6eSSCQiHR0duV5d5s8PJMlS3NevXydtbW1asGCBcJN79uwZOTs70927d2XR1C/u0KFDpKmpKfUin5eXRxkZGUREFBcXRw4ODjR69GhycnISOiayXKUtOTmZnJyc6Pr161Lbt2zZQhoaGnTw4EEyNjYmAwMDITBlZ2dH/fr1k+o8VjUf62ht376ddHR06MWLF0RU+oKclJRER44cofPnz9OrV68qspmfJDIykjQ0NOj58+dE9L9jzM3NJWdnZ+rWrRvdvHmTiEqD5pX9BV5Ccpz5+fk0f/58Onr0KKWlpdHp06dJR0eHrK2tpV4OCgoK6Pjx4xXSGfu3kpKSaPjw4bRv3z4iKj3Grl270qpVq6TO69atW8nS0pJSUlJk2dzPwsPDg86ePUtEpS+rFy9eJCMjI1JWVhZWHSw7AOXk5ETm5uYyaeuXFhYWRn5+frR//35hW2pqKg0bNowsLCwoMjJS2C55vrx//55WrVpFPXr0EO5f8ubBgwc0adIkITBlZ2dHIpGIZs2aJbXfqlWryNDQUBj4kTdln+mJiYn0+vVr4Xv56tUrWrFiRbnAVH5+vtRzRx4CFp+T5Nju379PO3bsoO3btwvPGyKiR48e0YgRI2jYsGFC30JeV7Ate572799Pt2/fFs6XJDC1bt06Iiq9V2VnZ9Pt27fp9evXwjHJS8Dxxo0b1KFDBzIxMSF7e3sKCwujoqIiio+PF76HRP875rlz55KHhwcVFxfLfUDqQ/1byd8/Pz+fjhw5QlpaWqShoUGOjo40depUGjNmDPXv37/KJnvk5ubS8OHD6ciRI5SVlUX9+vUjV1dX4Vzu3bu3wgedOSglR8qOxP75IbRr1y7q06cP+fj4UFJSkrBdXm/UX5Lkb5SZmUn5+fnCzTIvL48WLFhAo0aNoh07dkgFcMRisdTfTZYk7Zf898CBA2Rvb08mJia0Y8cOio2NJQ0NDQoKCiKi0nMcHBxMcXFxsmry35Icy40bN6Q6V3PnziWRSERz586V2t/b25uMjY3ltiP5qf78QF6zZo2QlRAbG0uBgYGkp6dH5ubmtHHjRmE/yRL0RLLvmMTHx5O5uTlNmjRJWJrZz8+PNDQ0hMyZFy9ekJGRERkaGlJGRgYVFBRUmXP4V8LCwmjv3r0UHR0tbHv06BEZGxvT8ePHiUj+RwnLSk1NJWtra+rbt6/wUip5lkRGRlLHjh1JJBLJdfbBvxUeHk76+vpkb29Pz549I6LS796lS5fIwMCArK2t5fK5+rHrKykpiYyMjCgnJ4devnxZLiP13r17VFJSQtnZ2XKZZfCpCgoKyNvbW+pFQZIxZWJiQjo6OkJASvJfPz8/Mjc3r1Iv9yUlJZSWlkYikYhEIhH5+vpKfZ6amkq6urpkY2Mj9WKRnJxMS5YsoeHDh8v1Mu6JiYmkqqoqjOIXFxeTmZkZDRs2jA4cOEBHjx6lxYsXU9euXYWMMHnm4+NDgwYNooEDB5Kuri6dO3eOSkpKKDk5mVasWEHDhg2jXbt2ybqZFebMmTPUrVs3MjMzI0NDQxKJRLR27VqhTxQZGUkjR46k3r17y+1gbNnnRGZmJnXp0oWsra3pwYMHwmdlM6Y+9FyRt2eNi4sLGRoakqOjI9nY2NCJEyfK7ZOfn08+Pj7Up08fqT6RPJP0b+3s7IT+LdH/3rULCgroxYsX9Ouvv9KECRPI1taWNm3aJNfvXZ+ibHyhbBLLmjVryNnZmfr27Utubm7C9ZiXl0fOzs7k5+dHRUVFFda/5aCUHJGc9GvXrtHChQtp5syZtHjxYuHznTt3Ur9+/WjNmjX0+vVrWTVTpiR/o3PnzpGNjQ3p6+uTlZUVHTlyhIhKv0guLi5kampKu3btktuH2Z07d6Qyn549e0aBgYHUrVs3mj17Njk5OdGoUaOEl6bK4MyZM6ShoUEuLi5CZzc2NpYmTZpEffr0obNnz9LRo0dp+fLllaYj+U+UDcpIAom+vr5kYGBAS5cuJWNjY3JyciJPT0/y8vIiPT29cpkX8hLQePnyJU2YMIGmTJlCixYtol69etHly5el9nnx4gVpaWmRubm53HWovhQ/Pz/q0aMHWVhYkLu7O717946IiLy8vEhLS0sIKMrLefwzSbuePXsmTClMSUmhSZMmUa9evaSyJWJjY8nZ2ZmWLVsmt1kU/8Xjx49pxIgRpKysLJXJKAlMGRoa0vDhw+Xq2pa0JTc3l9LS0uj69euUnJxM79+/p+TkZOrXr5+Qkbpo0SJh/6ioKJoxY4ZUtkxVIDm+sLAwOnPmDBGVvlRcunSJhgwZQqamplLP/kWLFpGVlZVUBnVlVPb+kpOTQ0Sl9+y+ffuSubl5uRfEN2/ekIaGhlQ/kqh06pi8ZcxJji0rK0s4d8HBwTRw4EC6cOGC8NnUqVPJ2NiY9PT0aMqUKXLbjyh7/zh16hRpaGhQcHAw/fHHHzRnzhzq2bMnBQQEEFHpC/PKlStJXV2dTp8+LasmV5iYmBjq27cvHTx4kEpKSoSp0srKysJ0t+LiYrp//z5ZW1vLfWBg5cqV5OLiQiYmJtSlSxcyNDQsF5hSVlamlStXym0fQRLADwsLo/nz59Ply5dp2rRpZG1tTb///ruwX0BAAC1dupQ0NTUrXQaRpH9ra2v7wcCURGxsbEU37YuTXHcXLlwgFRUVcnd3J6LSd5b+/fuTmZmZEFcoKioiHx8f0tLSqvDvHgel5EBaWprwpQgJCSE1NTVatmwZ+fv7U9++fcnY2Fh4CdqxYwdpaWmRh4fHV5Gh8CFhYWGkrKxMfn5+5O/vT+7u7iQSiYSHWW5uLi1cuJB0dHTktg5TREQEDRo0iAwMDKSmGaSmptLkyZNp+PDhJBKJaNeuXXL1cvQxDx8+FKZ4/dmLFy/I2dmZtLS0aPjw4WRnZye3HclPdeXKFTIzM6P79+8L12FGRgZlZWWRm5sb2draUkBAgPByf+XKFRo1ahS9efNGxi3/uJiYGBo/fjypqqrStm3bhO1/rochz1Oa/qsPdRyfP39O+/fvJy0tLTI2NqYVK1bQnTt3yMzMTG7vM0T/O5aQkBDS0tKirVu3Cs+O169f08SJE6lXr1508+ZNSkhIoPXr15Otra3cBvT/q6KiIoqKiiJ9fX0yNjaWClQUFRXR+fPnydzcXG6mX0q+dzExMeTs7EzDhg0jFRUV6t69O82aNYuePn1Kq1atIpFIVK5m1OrVq8nExETuAhCfQ3FxMS1fvpxEIhGFhoYSkXRgavDgwTRt2jTy8PAgNTW1KvPMISrNrvb19aXU1FQiKn3Gdu/enezt7YWXCMn3PiMjQ+hfyusLscStW7dIW1ubduzYQfHx8VRcXEyzZs2ixYsXS5WnyMjIoPfv31eKaeMnTpyg3bt3l3tGeHp6Uvfu3enBgwdEVHoOAwICqlQ238dERESQjo4OxcfHS12TR44coQ4dOtC9e/eEbfJYB7asgIAAUldXp4iICIqLi6Pnz5/TsGHDyMDAQCowtXPnTrK0tJSr72BSUpJw75RIS0ujYcOGUUBAAKWlpdG0adPIxsaGjh07RpmZmTR9+nRycnKqtANWHwtMicViys/PJ3d3d3JycqK8vDy5Olf/VtkMqeDgYOrQoQMNGjSIpk6dKuyzZcsWGjx4MI0dO5bmzp1Ljo6OpKGhIZOgIwelZOzdu3dSU7UMDQ2F6U+SEVA3Nzepn9mwYQPp6elVmTofn6KoqIhmzZpFS5Yskdp++PBhEolEQkQ/NzeXli1bJlWUT56IxWKKjIyk4cOHk66urtSDt6CggEJCQsjR0bHSpMYGBQXRmDFjKD8/X6o4Xlnx8fGUm5srNWWtspJ0kN++fUvGxsY0cOBA6t69u9SLT0lJidQLb35+vlDAVN4fdnFxcWRra0sTJ04Uap0RyV+q+ZcgOTcPHjygo0eP0o4dO6RGi/Lz82nz5s1ka2tLnTp1IpFIRLNnzy53vcuTixcvkpqaGgUEBJSrq/f+/XuaPn26ULdOQ0NDrqf1fArJuYyPj6eoqCiKiYkRruGnT5+Sjo4OmZiYlCuoK8lCkbWyGU+amprk5uZGR48epRcvXpCXlxdpa2vTsGHDyN/fn5ydnWnIkCF09epVOnPmDHl4eFSpjNQPyczMJE9PT+rYsSOFhIQQ0f8CU5aWliQSiejKlStVLrPc1dWVhg4dSlu3bv1gYOpDAwbyHOwQi8VUUlJChw8fJmVlZbK1tSUjIyO6desWhYSE0IABA4SpxJXpGRQbG0sDBw4kkUhEmzZtIiLpYss2NjY0bdq0cj8nz+fqc7h79y6JRCIhg1OSafzu3TvS0dERZj5UBsuWLSs3GJCVlUU6OjpkampK9+/fF67ZP5fukKWkpCTS0NAgkUhEkyZNouDgYIqJiSEiovPnz5OVlRWlpaXRixcvaNq0aTR27Fg6ceIEFRYWVvo+/IcCUwUFBbR06VLq2LFjlen/lBUcHEzKysoUHBxMoaGhNGrUKKnPT506RT4+PmRvb0/r1q2T2bsnB6VkrKCggBwcHMjJyYkeP35MOjo6lJeXJwSkJFXwiUgo7ElEQsHkr01ubi4ZGxuTh4eHsE3yAHd1dSU7Ozu5KmZeVkREhNS/JYGpESNGkL6+frkRIXl+ySWS7hz6+flRv379yq3oRVQ6VbEqmTdvHm3btk3oSPn6+pKysjIZGxtTeHh4uVHprKwsCgwMpEmTJtHw4cPloqj5P/GxEaWvwenTp0ldXZ2MjIxIW1tbCOhIXgCJSs/vkSNHpGoTyRuxWEy5ublkb29PXl5eRFRax+zly5fk7+9Pu3fvFvYNCwujK1euyE2G0H9VNkNswIABNGTIEOrYsSPNmjVLqJH29OlTGjp0KJmbm8vd1K6yAakuXbrQ6tWry9WdO3nyJJmamtKoUaPo4MGDNH/+fOrevTsZGBjQuHHjqlRASnJfTU9Pl8oSz8nJoeXLl5cLTJ09e5asra0r/fX8seeEp6cn6evrk5+fn1RgSkNDg8zNzStVJr0kUJOSkkI2Nja0cOFCOn36NPXu3Zt27dpFQ4cOJX19fbmvifbnYEN+fj6dPXuWRowYQUZGRsJ2SR/A1dWVpk+fXpFNrHBl69hcvXpVuIdNmTKFxowZI5Vxk5+fT4aGhsIgvTyTHNecOXOkFlGQXMtBQUEkEonI0tJSCPaUlJTIRUCKqLTI/siRI8nc3JyMjY1p4cKFpKWlRfv376fg4GCh0DlR6bT/cePGyfX71acq27+9ceMGrVq1ilRVVSvdlMR/IioqikQiER0+fJiISmMJ/fv3p5ycnHIZ8bIOiHNQSg7s2bOHevXqRU+ePCFbW1sKDAwUlr2VPLwSEhLI3t5e6EzLy43tSyu7NLAkaOPl5UXGxsbCvF/JPj4+PmRmZiaXf5u0tDTS0dEptwJQSUkJhYeHk4aGBllZWcl9qvKLFy/Ix8eHXr16JfV3Pnv2LA0aNIhCQ0OFm1xJSQmVlJSQo6Oj1MpAld3BgweF72VhYSFFRERQeHg4mZmZkbW1NV26dKnc0vI+Pj7k4uJS6ZZ3fvnyJdnb25OZmZlUSn1V9uzZM+rTpw8dPXpU6ID5+PhQr169hBXO/rxCmzwq+zI7ffp0WrBgAT19+pQWL15MY8eOpUGDBlG/fv3KrWZVldy5c0cIKL58+ZLOnTtHY8aMIVtbWyHzIioqivr06UNjxoyRcWvLS0pKop49e5KTk5OwTSwWS90/AgMDSUNDgw4cOEBEpdkZOTk5VeLl4fjx4/Ts2TPhfnrmzBkyMDAgLS0tcnBwEILBeXl5QmCq7FQ+ecl4+xyePn1a7nhWrFhB+vr6tGXLFiFzPioqimxtbeV+0EMiMjKSHB0dhQDq48ePqXfv3vTgwQN6/PgxLV26VMh627Bhg1z274jKBw8lz4XCwkIKCwujAQMGCH28goICEovFZG5uTi4uLrJoboUoOzCgqalJfn5+QtZxSEgIjR8/nqytrSk8PJweP35MPj4+1Lt3b7mc4fCx79Pt27epa9eu5OfnJ7X97NmztHTpUtLR0aGJEydWRBM/2cuXL2natGnk4OBAoaGhdPbsWbKxsSEHBwcSiUQ0atQo4TqOjo6uchmnkv5tjx49SFlZWai3WdUUFRVJJUVcu3aN+vXrJxWQCg4OlpoiLSsclJKhsg/XESNGkL29Pc2cOZO6d+9OU6ZMkdrXy8uLRo4cWSVrQ3xM2cJsTk5OdPLkSRKLxfTHH3+QhYUFLV26VGpazdKlS2nKlClyWWdAMnKrr69P48ePL/fZ2LFjSSQSkampqYxa+PcKCwvJxMREmObj6elJwcHBwuf29vakra1NwcHBlJGRQRkZGeTj40N9+/ally9fyq7hn4lk1Ehi//795OrqKozEJycnk6mpKVlZWdGVK1eE/SSBDAlZj0R8qhcvXpCjoyMlJibKuikV4ubNm6Sjo0OvXr2S6oiuXr2aunfvLmQgyOPL0ceCxr6+vmRmZkYdO3YU7qVZWVm0bt06mjJlilwey38hOZ4NGzbQ2LFjpT67c+cOWVtbCy+DJSUl9PTpU7ksppuQkEAmJiY0efJkqWm0RNLXn6WlpVAjorLdXz5ELBZTVlYWdezYkWxsbCguLo4iIyNJU1OT1q9fT0FBQaStrU2mpqbC3yUvL49WrFhBIpGIzp8/L+Mj+LxCQ0NJU1OTjhw5Ui6jz9XVldTV1aVqxUlUhsDUiRMnyM7OjlRVVWnHjh2UkJBAgYGBtHDhQsrMzKSMjAy6c+cOjR49ulLUsdmyZQtNnTpVqDUo6R9cvHiRBgwYQFpaWjR69GhydnamoUOHCgNcVe0eLHHlyhVSU1Ojffv2lcvKuH79Ok2ePJlEIhENGzaMdHR05DJTpez36MaNG/T7779TeHi4EDxbv349aWlp0fr16yknJ4eSkpJo0qRJ5O/vT+Hh4aSqqkp3796VVfP/UnR0NE2cOJFsbW0pJiaGcnJy6N69e2Rvb0/Hjh0joqp7bRKVHv/kyZPlNtv9c5L0DWJjY2nAgAFCn97Hx4fU1dXlok6sAhERWIUpLCxEzZo1hX8XFxejevXq2Lp1K65fvw4LCwusX78ejRs3xuDBg9G8eXNcvnwZJ06cwN69e9GhQwcZtr7inTt3DjNnzsT06dMxaNAg/PTTTwCAffv24cSJE8jKyoKKigpycnJw5coV7Nu3Ty7+RmKxGIqKiigoKIBYLMa3336LwsJCXL9+HR4eHmjRogW2b98u7L906VL06dMHnTp1QvPmzWXY8r/m7++P6tWr4+eff8bdu3exZ88e9O3bF4MGDYKBgQGcnJyQkJCA2NhY/Pzzz0hKSsKWLVvQqVMnWTf9P1m3bh2SkpKwYsUKAICCggLWrl2L8+fPo2/fvrCyssKPP/6I1NRUODo6olq1aujfvz/u37+Pmzdv4vbt26hWrRqICAoKCjI+mk/35/tWVXbu3DnMnj0bf/zxBxo1aoT8/HzUqlULhYWFGDJkCGbNmgVDQ0NZN7OcoqIiWFpa4uHDh2jdujUGDRoEFRUV6OnpAQBevnyJ169fo0+fPsL9ydXVFe/fv4eXl1eVPL+bNm3ChQsXEBAQIByfgoICTp06BWdnZ4SGhsr1/RYAYmNjsXz5chARpkyZAnV1dQCQupeMHj0aSkpK8Pb2lmVTPxvJsb1+/RpmZmbo1KkTdHV1ER8fDycnJwBAdnY2bGxsUL16dcyfPx/q6urIz8/Hxo0bYWhoiHbt2sn4KP49yfezLEdHR8TGxmLChAkYOnQovv32WwBAWloa9PX1Ub16dcydOxeGhoaV7jkjFouxZ88e7N27F506dRKOTVdXF/3795dx6/5a2XO1fv16BAQEYMSIEcjNzcW5c+fQt29fjB8/Hp07d8bFixfx22+/4fXr1/D39xf6qpL3gKqEiFBSUoL58+ejTp06WLJkCbKzs/Hq1SsEBwejevXqsLe3R61atRAVFYVvv/0W9erVQ+PGjWXd9I9atWoVTpw4gVq1aqG4uBgNGzaEi4sLevToge3bt2Pjxo2oVasWqlevjgYNGiAoKAiPHj3CzJkzsX37drRu3VrWh/BBsbGxWLZsGQDAwcEB3bt3l3GLKlZRURFq1Kgh62Z8FpJ7f2RkJJ48eYLi4mKoq6vj559/FvZJTEyEnp4e9u3bh0uXLmHjxo0IDAxE586dZdjyUhyUqkAJCQnw9PTEoEGDoK+vj1q1agmfvX79GgYGBpg9ezYGDhyI1atX4969e6hXrx6aNGmCuXPnykWwpSKlpKRgypQpGDFiBMaNG1fu8+vXr+Pu3bu4e/cuWrVqBSsrK6kvXkW7d+8efvrpJzRo0AAAcP78eRw5cgTp6ekwNTWFnp4eateujYsXL8LDwwN169bF2LFjce/ePVy5cgUBAQFQUlKSWfv/iZs3b8LBwQE7d+6EiooKUlNTcfDgQWzevBk9e/bEsGHDUK1aNdSuXRvVq1eX+yDbP/Xy5Uv8+OOPqF69Op4+fQqRSAQA2LJlC06fPo2ePXvC2toaP/74I968eYNly5bh/fv3qFGjBjZu3IgaNWp88EWDydaHXt7EYjFMTU3x3XffYdOmTahZsyaICBkZGbCxscHs2bMxePBgGbX4r30oaNyvXz9oaGjAzMxMONZXr15h7969OHz4MPbu3YtffvlFxi3/MkJCQjBjxgxs2bIF/fr1E7ZHRERg/vz52LJlC1q2bCnDFv4zZQNTZV8axGIxUlNT4erqCj09PRgbG1e6gMTHSF7Uk5KSYGJigoyMDBgbGwsDAwCQlZWF0aNHo1atWpg+fTp69+4twxZ/HmWfE0FBQWjYsCG0tLQAADNnzsSzZ88wadIk6Orq4ptvvsHTp0+xf/9+NG/eHLa2tqhWrZosm/+XJNfmkydPkJqaiuzsbOjo6AgBmdu3b+PixYsIDQ1FXFwcfvjhBwQHB6N27doybvnfS0pKQmBgIPr06SNch1euXIGPjw9EIhHc3NygoKCAq1evwsfHB0pKSsLAZElJiVyft//Czc0Nb968wfjx43Hs2DGkpKQgOTkZioqKqFu3Lnbt2lUpBkSOHTsGT09P/Pbbb+jcuTMiIyMRFBSEq1evYvXq1VBXV8ebN29w+/Zt1KtXD71790a1atXg5eWFGzduYOvWrWjUqJGsD+OjPjb4wSoPyf01NDQUixYtgrKyMuLi4tCmTRvo6OjAwsICAJCZmYmxY8eidu3aiIqKQkBAgFwEpACAp+9VoBcvXpC9vT116tSJrK2tafXq1ZSVlSXM2d20aRPp6enR27dvqaCggN6/f0/v37+XuwKsFSUpKYkGDhxIFy9eFLaVTSOVpCIWFhbKNL1ULBZTREQEiUQi2rhxIxUWFlJ4eDipqamRq6srzZkzhzp06EDu7u5CQdKIiAiytramYcOGkZGRkVymLH+Mp6cnzZ49W0jFnjFjBg0bNoxmz55NY8eOJWVlZdqzZ4+MW/llhIaGkr6+vtTqMJs3byYjIyNasWKFkP6al5dHmZmZwnVZWWpIfU0k5yY8PJzWrVtHBw4cEKaHhIWFkbGxMY0dO5YSEhLo2bNntG7dOtLU1JTrwsk3btygbt26CfUDUlJSaP369aSqqkqjRo2iAwcO0I4dO8jT05P09PSqzCozknOZmZlJKSkpUlPYFi5cSN26daOwsDDKzMykkpISWrlyJenq6lJ6erqsmvzJyhZmLTuVz8vLi0aMGFGl6n1Izqek75OcnExaWlqkq6tLT548kXreZ2VlkZaWFo0dO1Yup+5/irLHtWrVKmFKUNlaH05OTmRgYECrV6+my5cvk52dHc2fP1/4XF6nb0qOLTQ0lPr06UPGxsbUpUsXmjRpEl27dk34/N27dxQVFUUjR46krl27UlJSkiyb/Y+cO3eORCIR9e7dW6hVJ3Hx4kVSVlamGzduEFFpuYY//viD9PX1ycTERBbN/WI+1A8/ePAgWVpakoqKCs2cOZNCQ0OpoKCA9u7dS+PGjZPbmox/5unpWW6VvRcvXtD06dPJ0dGx3Ip0z549Izc3t3KrMsuzr7GGaFVz69Yt0tTUFGpM3r9/n9TU1Gj48OG0Y8cOYT8bGxtSUVGRu2uzauWLyrl27dph8+bNePLkCfbt24fTp0/jxIkTGDZsGIyNjdG3b18EBgYiIiICWlpaqFGjRpUY8fy3MjMzkZubK2SUlZ1CFBUVhejoaAwdOlSmaZf0/5FpFRUVLFy4EB4eHqhVqxYUFBQwffp0IcNLS0sLrq6uEIvFmDRpElRUVBAQEICUlBTUqVMHdevWldkxfKouXbpg586dqFGjBhYuXIhbt25h586d+PnnnxETE4MrV66gZ8+esm7mZ/Hn7KZmzZqhXbt2CAoKgqKiIoyMjGBvbw+gNCtDQUEBlpaWaNWqlXDdElGVS82vChQUFBAWFoZp06ahe/fuiIiIgLq6OiwsLDB48GBUq1YNvr6+0NfXx/fff4+SkhL4+fmhRYsWsm76R/Xs2RNmZmbYtWsX3N3d0axZM0RHR6N58+b46aefcObMGdy6dQsjR47E7t275XqqxD8luQefP38e27dvR1xcHLp16wZNTU2Ym5tjyZIlUFRUxNSpU9G6dWvUqVMHcXFx2LFjB7777jtZN/8fa9OmDRYtWoTly5dj06ZNmD17Nq5evYq9e/ciMDAQ33//vayb+FlIzuetW7dw//596Ovro0WLFggMDISJiQk8PDzg5uYmTM+rW7cujh8/joyMDKns88pI0t/z9/fHkSNH4O/vL4xgS7JpfH194eXlhYsXL+LEiRNo2bIlfvvtNwClfzt5zbhRUFDA9evX4erqitmzZ2PUqFF48uQJjIyMUFhYiOLiYvTt2xf169dH/fr1sW/fPrx//x5NmzaVddPLkfQLJP/t3LkzrKyssG/fPiQnJwP4X6Zf//790aZNGzx8+BA9e/ZEzZo1oampicLCQmzZsgVJSUlVIptc8r2NiIhAXFwc0tLSoKuri1GjRmHw4MFITEyEioqKsF9MTAyqVauGkpISWTf9H/n222/x6tUrZGdnC/31du3aQUNDA+vXr0dBQQHq1KkDoPT6iI+PBxFh3759lSYTuU2bNpg7dy58fX3RrFkzWTeHfSKxWIzw8HBoa2vDzMwMCQkJwuwrBQUF7N69GzVr1oSVlRXmz5+PRo0a4YcffpB1s6XJLh72dSsoKKB3796Rp6cnWVhYkLKyMq1bt4569uxJRkZG5aLuX6tJkybR4MGDy6064+HhQfPnz5dpFpmk+GFqaipFRERQWloa/f777yQSiah///60c+dOqf2Dg4Opa9eu5OHhIawcWFlZW1tThw4dqG/fvnIXaf8SwsLChFH4hw8f0qxZs8jS0lJq6WI/Pz/q168f7d69W0atZP+EZDQ3OTmZXF1dhZUhHz16RJMnTyYbGxthBS+i0pVKoqKiKs0iE6dPnyZzc3MqKSmhBQsWUJ8+fYQini9evKCAgIBKXdTzQ6PxFy5cIDU1Ndq8eTOFh4fTjBkzSEdHR2pFpAsXLtC+ffsoICBALgp6/luS0ezevXuTsrIyRUZGyrpJn03ZVfa6detG69evp6ioKGF7UlIS9e7dm8aMGUPR0dGybOoXk52dTdOmTRMWyIiPj6ezZ8+Sra0tubm5CRlxr1+/ppiYGKEfIu/ZuHl5ebR69Wry9vYmIqK4uDjS1tamWbNmkZ6eHhkaGtLly5flvqjyyZMnaf78+UJRaInU1FRydnYmVVVVqWyprKwsGjx4MAUGBhLR/67xwsLCKrU6JFHps6dbt25kYWFB3bt3Jx0dHfL19aX3798L+0RHR5Onpyepq6vTkydPZNjaD/vY4gDBwcGkpaVFv//+u9T72a1bt8jQ0LDcQjAlJSWVNnOzsmSvfe2SkpLo4MGDdODAASF7Ojk5mR49ekS5ublkbm4uZNG+ePGCevToQQMGDKCAgABZNvsvcU0pOZCeno6wsDAEBQUhMjISNWvWxJkzZ+R6/vHnRP8/cvLs2TMkJyeDiKCmpoYGDRrg2bNnmDdvHjIzM+Hq6or8/HxERETg0KFD2Ldvn1Dbp6JJRshevHgBV1dXfPvtt6hduzZ+++03HDx4EG5ubjAxMYGzs7NQYwoAzpw5gxkzZsDOzg5OTk6VLoNGcq4kdbHmzp0LbW3tKlPH5EMiIiIwd+5cdOvWDYsXL8Y333yDhw8fYseOHUIhXiMjIwCldQeGDx8ut6PVrFRERAS2b9+O1NRULF68WBjJfPLkCdatW4esrCxYWFhAX19fxi39d2xsbHDnzh00adIEW7durVL1CF+9eiVVByoxMREzZszAiBEjMHr0aOTk5GDYsGH47rvvQEQwMDAQshmripiYGHh5eWHWrFkyraP4Jdy7dw+TJ0/GnDlzMGrUKGF7ZmYmGjZsKNxzmzZtCh8fH7Rp00Z2jf0MPvTsHDduHAoKCmBvb489e/agsLAQLVu2xIULF9CvX79yBe3ltV5h2WMrKirC/fv30bhxYzRt2hS2trb45Zdf4O7ujkePHsHCwgLKyspwcnJCnz59ZNzyD8vOzoaxsTGys7PRpEkTqKqqonv37hg5ciQAIC8vD4sWLcLZs2dhbm6OZs2a4c6dO0hMTERQUFCl6+99iufPn8PW1hbTp0+HgYEBatWqBW9vb9y7dw+9evWCvb09oqKisGvXLsTFxcHd3V3unktlv0dXr15FXl4eCgsLhcVC3NzccOHCBUyZMgXdu3fHd999hwULFkAsFmP79u1Vtg/M5M+TJ0/g4OCAxo0bIz4+HvXr18ecOXMwdOhQAMDdu3fx66+/Yu3atWjXrh2ePHmCVatW4ZdffsGYMWPkNztTZuEwVm5E6O3bt/TgwYNKPYr7qSR/g5CQEOrduzeNHDmS1NTUaMaMGcJoU2JiIjk6OpKWlhbp6OiQpaWlTLNzJG1+9uwZqaurk4+PDyUmJgpL+xIRBQQEkEgkIj8/P6lRIqLSmgqVfZT3zZs3NGTIEFqzZo2sm/LZ/fl7mZeXR/7+/mRubk4LFiwQamlFRkYKGVNHjx6V+hl5revBSl27do0MDAyoS5cudPLkSanPnjx5Qo6OjmRsbEwhISEyauG/I7l2w8LCSEdHh86ePSu1vbILCgqi3r17U05OjnBMWVlZ5O/vT69evaKUlBQaMmQILV68mDIyMsjCwoIGDBhAq1evlnHLP7+yz5uqZPv27TRmzBgiKs0aOn/+PDk5OZGxsbGQmZqQkEA6OjpyXd/tnyiblVH2O3rz5k0aNWoU9ejRg9auXUu3b98mIqLdu3eTnZ1dpcpkCA8PF2pmSp6dYWFhZGRkRC9fviSi0vvx6NGjycbGRq7PaXFxMa1evZoCAwPp4cOH5O/vT+rq6jRr1izy8/OjwsJCSktLIw8PDxKJROTo6EjBwcHC+ZL3bLZP8ednyrVr10hLS0sqY6igoIA8PT1JX1+fMjIyiIjo7t27cp917OnpSZqamjR06FDq2rUrmZqa0oMHD4iIaPny5TR8+HDq3LkzDR8+nIyNjYV78ceyrBj7nKKioqhLly7k7e1Nubm5dPXqVerXrx/Z2dkJs4fCw8Opf//+dPz4cSIiWrt2Lc2ePZvevXsny6b/LQ5KMZko+9J+7do10tDQENKbJYUhx40bR2FhYcJ+cXFxlJ6eXi7IIwsZGRlkaWlJy5Ytk9pettOxa9cuEolEtHnzZsrKyqroJn5xx44dIzU1NeFhXRV8rLOUn59P27dvJ1NT03KBqZkzZ5KVlRWdOXOmIpvK/qM7d+6QmZkZ2dralitO++jRI5o9e7ZcvyD9laoWNJZ09lNSUoTi5JLOlVgsFu6vXl5e5OjoKHy2cuVKGjx4ME2ePJnS0tJk0HL2qYKCgmjQoEHk5+dHEyZMIHt7e3JwcCB3d3cSiUTC1NPKHvgv+1IfEBBAc+bMIT8/P6nFB/5c5Hvs2LHk6upaYW38r96/f0/29vblpvkHBQWRtra2sG3t2rXk7e0tPFflWVhYGHXt2lVoe35+Pq1du5ZEIhEZGxvTli1bKCwsjDw8PEhNTY3u3LlDRFVvSpTk+r127Ro9evSIwsLCaODAgcKAqyRQU1BQQCoqKlKLw8izw4cPU69evejRo0f05s0bSk1NJVNTUxoxYoSwEMrz58/p6tWrdOPGDeE+VJUCjkx+JSUlUc+ePcnJyUlqu4mJCeno6Ajvx1lZWeTg4EA6Ojqko6NDPXr0qBQL28hfvi+r0rZu3YqsrCxhelNBQQEuXrwIExMTWFhYICEhAcuWLUP//v2RmZmJDRs24I8//gAAtGrVCt999x3q1asny0MAALx9+xZv3rzB0KFDIRaLhe3Vq1eHWCwGEWHMmDFYuHAhfH19sXXrVmRnZ8uwxZ9fr1690Llz5ypTEHH37t0wNzdHUVERjh49ipUrVwqfffPNN7C0tISenh7u3bsHT09PFBYWonPnzpg4cSIaN26MoKAgvHnzRoZHwD6E/n+G+qNHj3D69GkEBATg7du36NatG+bNm4ecnBwEBATg+vXrws906tQJHh4ecl3U/K80adIEU6dOxa5duxARESHr5vxnioqKiI+PR2BgIL777js8fPgQffv2RXh4OBQUFITCs69evQIRoX79+gBKF8ewsbGBu7v7VzMdvrIgIqlnp+R72r17d2hpaeHo0aP44YcfMHHiRGzYsAHm5ubo3LkzvvnmGwCQy+lqn0Iy1cfPzw/r1q1DSUkJDhw4AC8vL5w6dQoA8MMPPyA7OxuXLl3CxIkTkZaWBldXVwD/+3vJI0nb6tWrh4kTJ6Jr166YMWMGoqKiAJSe4/z8fMyePRsmJiYICAiAnp6ecG7l2YABA2BoaIgDBw4AKO0bhIaGYvDgwejZsydu3rwJe3t7NG7cGNra2rC3t8fNmzeFRXqqCgUFBYSHh2P8+PFITU2FmpoaxGIx1q1bBwDCAkTZ2dlo3749mjRpIsvmftSfv0dxcXFQU1NDp06d0LBhQzRt2hQBAQEoLi7GqlWrAADt27dHnz590LNnT6FYe1WemsnkR0lJCVq2bInCwkLcuXMHQOkz5OHDh6hfvz7mzp2L+fPnIywsDGPGjMHkyZMxceJEHDx4EB07dpRx6/8ef4tYhYmPj8fly5cxePBgIbCkqKgIPT091KlTB9nZ2Zg+fTp69OgBDw8PXL9+HQ4ODvDz8wNQuoKdvIiKikJSUhLU1dWhoKAgNRdd8t+8vDzo6uqiVq1aWLVqlbASX1WhpKQEf3//StGR/Dv79++Hl5cXVq5ciRo1aiAiIgIRERFYt24dnJycAAC1atXC+PHjcfv2bfz+++9IT0+Hl5cXOnXqBFNTUyxevBi5ubkyPhL2ZwoKCggJCYG7u7swj3716tVwd3eHnp4eZs+eDR8fHwQGBqKoqAj9+/cHgEr/ElHVgsZHjx7FyZMnMX36dDRv3hwDBw4Ung/dunVDYWEhWrVqhbt372LlypUoLCzE8ePHERQUxAEpOSF5ThYVFQmrC1+7dg2XLl1CbGwsdHR00KNHDyxatAgZGRlSqyMeP34cBQUFQgCystZv+XP9p9evX2PdunXo2bMnIiIisG3bNuzduxcAoKenh+fPnyMkJAQ1a9YU6hJJVnaTB2WPR9KuwsJCoV+grq6OatWqYevWrZgxYwZWr16Nzp07Y/fu3Th+/DiqVasGXV1dYTXFyqBTp044evQo3r17h3HjxqF+/fpYuXIl6tati+TkZNy9exc6OjooLCzEnDlzMHfuXISGhlb61SHLio+Px/v37zFr1iwMHDgQALBmzRpMmTIFTk5OmDRpEmrXro0TJ04gNTVVLs9veHg4IiMjoaCgAAMDAzRt2hRv3rxBRkYGgNJB5vz8fNSqVQvOzs5YsGABEhIS0KJFC6nvMNcQZRWlZcuW8Pb2xvLly+Hv74+goCCcP38ea9euhaqqKh4/foxnz57B3d0ddevWRYcOHbB+/XpZN/ufk12SFvvaFBcXC6tWhIeHU2ZmJhGRsALJuXPnyNjYWJiTfvnyZTIzM6OpU6cKK87Iizt37pCKispfTtnauXMnjR8/nohImE/P5M+BAwdIWVlZqL9DVFpHasmSJTRmzBjy9fWVqhWwfv16srKyIg8PDyFl+8SJE9SlSxdKTU2t8PazvxYZGUk9e/akw4cPExFRWlqaUO9NMgXh5s2bpK+vT3PmzJHpip6fW2WYDvN3JOfo4sWLpKurK0yDSUtLozlz5pCamppQcyc2NpZcXFzIzMyMzMzMvoqVQSsLyT302bNntH79eiIqra+ooqJCzs7ONH78eDIwMCBra2up6bS3bt2iX3/9tdJMP/grZZ8jklpL06ZNk1oNMzIykpycnMja2prOnTtHRKU1tOR5lb2EhARh2si9e/fI0tKyXG3Uu3fvkq2tLQ0bNkxYda0y1+AxMTEhkUhENjY2H+3fFRUVUXp6OiUnJ1ds476w5ORkUlZWJmVlZeG7LPHgwQPS1tamAQMGkJaWFmlra9PDhw9l1NKPCwoKIh0dHXJ3dxdW4CUqvU579OhB27Ztk9r/3LlzpK+vT2/fvq3opjJWTkxMDI0fP55UVFTI39+/3Ofp6el06tQpoW5fZSEfQy2sStu8eTNq1aqFcePGoU6dOsjMzISnpycyMjIQFBQkTLXIy8tDTk4OUlJS0Lx5c9y+fRs9e/aEnZ2dMDoqL1q0aIG6devi2LFj6Ny5szDNh8qsNpOcnIwOHTpALBZLrcDH5MfNmzfh5uaGadOmQVtbW9ju7e2NGjVqoGPHjrhy5QpKSkowbdo0EBFevnwJIyMjmJqaCllyKioqOHbsGJo2bSrDo2Ef8vr1a6irq8PExASxsbEYP348zMzMYGdnB6B0CrGGhgYWL16M77//Ht9++62MW/z5VNYsxrLZF5L7acuWLZGYmIg7d+6gd+/eaNSoEVxcXKCgoIAJEybA398f6urqWLBgAb755huprBomW5Lz+eTJExgZGWHWrFnIzMzExo0bMWfOHIwZMwYAcP36dRw+fBgbN25E06ZN0bBhQ1y+fBkpKSkICAgQVsmsjIhIuKY9PT1x5MgRAKX9nl69egmrKHbu3BmTJk3C9u3b4eXlhbp166Jnz54ASv+O8pIhJVFUVIQFCxYgJiYGp06dQl5eHgoKCrBo0SK4u7sLK2V27doVBgYGcHFxga2tLbZt2yZ3q6/9E5I+3ujRo+Hv74958+ahYcOGH1xFsXr16lLZfpWZ5PjEYjGUlJSwfPlyeHt74/nz51L7qKqq4vjx43j+/DnEYjFatGghd/2iY8eO4ddff8WqVaugpaUlZEXv3LkT9erVg62tLQ4cOICCggLY2Njg/fv3OHToEJSUlDjrlsmFtm3bYvHixViyZAlu3LiBLl26QF1dHUDpPfm7776Drq6ujFv56Sr3pHwm94qLi5GbmwtPT08cOnQIQGmdgdmzZ0NJSUm44QOlNaNq1KiBZcuWYdSoUdi9ezf09PTk8sVCSUkJixcvxpUrV+Dr64sXL14AKH2BysvLg4+PD0JCQjBq1CgoKipW2qkGVZ2SkhK6d++OR48eITIyEgDg6OiIy5cvw9HREdOmTUPv3r1x4cIFDB48GJaWloiKisLIkSOlpm22bt260i9NXlXFx8cLNeBsbW3Rr18/LF68GABw6tQpeHh4oLCwEOrq6sILFJMtRUVFvHr1CocPH0ZCQgLevHmDli1bonXr1sIUWSJCo0aNMG/ePAwePBiTJ0/GjRs3ULduXdSoUUMunxtfI8k98sWLFzA3N8fUqVNhZ2eH/Px8vHnzBkpKSsK+vXv3homJCdLS0hATE4MmTZpgzJgxwlLWlVXZgEV8fDyuXr0Kf39/eHt7Y/jw4di5c6cQpAJKA1NjxoyBrq6u8KIByGcdrRo1amDRokX4/vvvMXbsWCgrK2Pu3LkQi8WYP38+EhIShH3btm0LTU1NDB48GLVr15Zhq/89yXns1asXMjIycO3aNantVZHk+r1z5w6OHj2K9PR0GBoaYt68efjjjz+wYsUKAKV/g6KiInz77bdQVVWFmpqa3AWkoqOjsW3bNri4uGDo0KFCQMrJyQmenp74448/UKdOHVhZWWHnzp0YMmQIxo8fj7dv32Lz5s1Cv48xWWvVqhVcXV1BRNi0aZNQY0pSz60yUiCS42qJrErIzc3Frl274Ovri8WLF8PCwgIlJSW4e/cuvL29kZubiz179qBhw4a4desW7ty5g7y8PBgaGsrlPHQJsViMgwcPYtmyZWjVqhXU1NTwzTffICUlBQ8ePIC/vz86deok62ayvxEbG4vly5ejWrVqyMrKQn5+PtatWycEKHJzc/H48WNcv35dqCtVvXp1lJSUcC0BOVJQUPDBzKCYmBgsWrQIUVFR0NXVhYeHh/CivHLlSsTFxWHlypVysYACK30BKioqgpOTEx49egRFRUUUFBRAU1MTwcHBGDx4MJydnVG9enUhQ7WwsBDz5s1DeHg4zp07V6Vqt1Rmku/Zs2fPMGbMGDRq1Ego4p2UlISpU6fC1NQU1tbWUtlxZmZmaNeunfCyW1Vs374djx49QqNGjbBw4UIApS/J+/btw9WrVzFp0iSYmJiU+zl5fdaUzZ55+fIlXFxcoKioiC1btuDRo0fYuHEjiouL4enpiZYtW2LDhg1ISUmBi4tLlQga79mzB7/99hv27t2L9u3by7o5X1RISAgWLFiA8ePHY8iQIRCJRCguLsapU6ewcOFC2NjYYN68eQDwwawxeXHlyhW4ublh69ataNu2LRQVFbFkyRJcvXoVCxYswK5du1CvXj0YGBhAQ0MD9+7dQ+3atYXaaPJUz40xoPQdRjL7yMXFBWpqarJu0r/GQSn2xZTtZCYkJODAgQPw9/fHypUrYWho+NHAVGUTEREBf39/xMfHo06dOujatStMTU05c6YSiY2NxZIlSxAZGYlly5YJaa8fexmQ15eEr1VKSgo8PDxgaWmJXr16SX2WnZ2NdevW4eLFixg5ciTs7e2RmJiIgwcPYv/+/QgICBCmzjD5kZ2djbp16+Lx48eIiYlBcnIygoKCEB0dDSUlJZSUlKB9+/b44YcfoKysjLZt26JDhw5o3LixrJvOID1lz8LCAioqKoiNjcXQoUOxaNEiAMCiRYtw/vx5bNy4EV27dgVQ+kI7efJkqKmpYcqUKbI8hM8qJycHv/32G/bv3w91dXVs3bpV+EwSmLpx4wasrKxgbW0tw5Z+nOSclh0AkBStB4CVK1dix44dUFFRwbZt2/D06VNs3LgR169fR+fOnREdHY39+/dDJBLJ8jA+m/j4eGzYsAErVqyQyyy2/6JsYCkyMhKTJk3CrFmzYGpqKnWsksDUr7/+iuHDh2Pp0qWyavI/smnTJuzcuRM3b94UtqWmpkIsFuP777/Hixcv4OrqiuLiYmzZskVq+iX3+5i8io6Ohq+vL+bPny8s6FMZcVCKfXFnz57Fhg0b0KZNG1y4cAGFhYVYsmQJzM3NhcDUmjVrkJiYiJMnT1bKjAV+WFV+8fHxWLJkCRQVFWFvby9Mm5DcIuV15I+VBr3nzp2LBg0awM7ODt27dwfwv+9leno6vL29ce/ePSQnJ+Onn37C+/fv4evry9mMcupDo+3+/v54+vQpbG1tkZ6ejlu3buHhw4fIycmBp6cnDwTImcjISFhaWmLy5MmYMmUKDh8+jDVr1kBPTw9ubm4AADs7O0RERMDW1haNGzfG8+fPcejQIRw8eFCuM6X/juT6LXsdJyYm4ujRo9iwYQPc3NxgZWUl7B8TE4NNmzZBLBbD29tbbp83HxsA2Lp1K/z9/TFnzhwEBgaCiLBr1y7UqlULx48fR3FxMfr06YNWrVrJsPWfn+T8VpU+4O3bt6GmpiaVDbRv3z6cPHkS27dvF7JQ/7yK5KFDh7BmzRqcOHFCrgcGTp06hQULFmDDhg3Q1NSU+kxyTFu3bkV4eDjWrl1baaeZsq9PYWFhpV81moNS7IuKioqChYUFFixYgKFDhyIlJQXHjh3Djh07pAJTt27dwtatW7FkyRL8+OOPsm72Jyvb8ZTn1GX21yRT+QBgypQpQnCDyT/JuSMiODg4COdOMpKfk5ODgoICHDlyBFpaWqhXr55UPRsm/06fPg03NzecPHlS6tzl5ubyy4McCg8PR0hIiJAZlZWVhVOnTpULTC1duhRRUVFIS0uDkpISFixYgI4dO8qy6f9J2Rf23NxcVK9eXXhZSE5ORmBgIHbv3o158+bBwsJC+LnExET88MMPUFRUlNt+hGQAoH79+rC3t0f37t2xZcsWbNu2DWvWrEGfPn0QHR2N2bNnAygtHl0ZM+C/RseOHUNQUBDWrl0rlSHk6+uLS5cuCXXPyl6b9+/fR6tWrdCoUSMhu1WeJSQkwMjICJqampg3b54wBVwiOzsbc+bMQevWreHi4iKjVjL2deKgFPuiLly4AB8fH+zfv194WL1//x4bNmzArl274OXlheHDh0MsFqOwsJBrgTCZi42NxYoVK/D27Vu4u7tXyhWCvlYfC0yVlJSgpKQE69atQ3x8PLy9vSv9iNLXhogQExODCRMmYPfu3WjVqpWQnSCvL/DsfyTnKDs7G8HBweUCU+np6VBUVET16tXl/sX2r5QNSO3cuROXL1+GWCxG69athQUWJIGpgIAAzJs3D2ZmZh/9HfJIcp+tWbMmGjdujHPnzsHLywt9+/YV9omOjoadnR0aN26M/fv3Q0FBgb+jckpyvWVnZyMnJwdKSkpISkpCs2bNUL16dZw+fRozZ87Erl27hJUggdLMjNWrV0NVVRV6enqV5vyePHkSLi4u0NHRwcSJE4UAeGJiIlxdXZGWloYjR46gevXq/GxhrALJ71OPVQm1atVCdHQ0kpKSAJR2TOvXrw99fX1Uq1YNc+fOxeHDh6GoqMgBKSYX2rRpA2dnZ/To0aNSr/j0NWrTpg0WLVoEBQUFbNy4UViNpKSkBCtXrsS2bdswefJkDkhVQgoKCmjXrh1q1aol1AORTJfhlwb5JzlHdevWhb6+PmbOnIlTp04JmamNGjVCw4YNK3VACvjfCnmrV6+Gv78/evbsCR0dHZw5cwYODg4oLi7G999/D0tLS4wZMwZubm44d+7cB3+HvGrTpg0WLlyI/Px8HD9+HJMmTRICUpKVydq1awd/f3+sXr2aVyCWY5KAVHx8PG7fvg0lJSVER0dj2rRp2LNnD4qKiqCrqwt9fX04ODjg2rVrKCgoEAaXT5w4AVVV1Up1fnV1deHm5oaQkBBMnjwZEydOxPjx4zFz5kxkZ2fj8OHDwmI2lem4GKvsOFOKfTYfGlHIycnB5MmT0bx5c0yZMkWo+ZGSkoJVq1ahQ4cOGDRoUKWuHcGqNnkftWbllc2YsrOzw6VLlxAQEIDAwECuIVVJSZ4vRkZGGDBgAGbOnCnrJrH/IDs7G6dPn4arqysmTZokTPeqrMrW8zh37hzWrFmD5cuXo2vXrjh//jzmzJkDIkKnTp2we/duVK9eHYmJibhy5QpMTEwq5YpeH6vDyM/MyiUlJQWGhoZo3Lgxpk6dCm1tbcyfPx9JSUkYPnw4LC0t8ebNG6xfvx6HDx/GTz/9hG+++QZv376Fn59fpX2mRkVF4dChQ4iNjcUPP/yATp06wcLCglfZY0xGOCjFPgvJC8Pt27dx//59JCcno0+fPujbty8uXLiAHTt2oGXLlhg3bhyaNWuGvXv34vr169ixY0elHxlljMkfyTK5d+/eRW5uLg4cOABlZWVZN4v9R/v27YO6ujpnMVYBWVlZOHfuHNTU1NC2bVtZN+dfu3LlCp48eQINDQ2oqqrizJkziI2NxeTJk3Hx4kU4OzvDyckJ7dq1w8SJEzFgwACsXbtWWLUOQKV9Cf7YlGlWedy8eRPjxo2DsrIyGjduDHNzc/Tt2xe//vornj17BlNTU5iZmaFatWq4dOkSXr9+jVq1akFdXb1cTaaqoKoUrWessuGgFPtsQkNDMX/+fOjp6SElJQVpaWlo1aoV1q5di0OHDiE0NBSXL19G27ZtkZ6ejp07d1bqYqaMMfkWExMDLy8vzJo1Cz///LOsm8M+A67xUbVU9vN55MgR+Pr6YtCgQRg5ciRUVVUBAElJSahfvz4mTJiAAQMGwMHBAampqRgzZgxiY2MxatQoLFu2TMat/zwkAwAZGRlwcXGBmpqarJvEPtGCBQvw+PFjtGrVCunp6Zg4cSL69OkjBKZGjhwJMzMzqUBqVVDZ7z+MVSWVb1iGydyHUrPj4uLg7e0NZ2dnWFhYIDExEQYGBkJRxFGjRsHIyAgPHjwAEeHHH3/E999/L4vmM8a+Ej/99BPWrVtX5TrSXzN+gahaKvP5DA4OxrJly+Dh4YH+/ftLZX03b94cCQkJSE5OxsCBAwGUHquqqiq8vLwq7ZSnD2nTpg3mzp0LX19fNGvWTNbNYX/hz/13ybTToUOHgoigr6+PAwcOYPPmzVBQUMCSJUuwePFinDhxAvn5+Rg3blyVyiKqzPcfxqoanvTNPonkgfbq1SucP39e2P727VvUqFEDFhYWSEhIgI2NDQwMDDBv3jwApcvGEhHU1dXRo0cPDkgxxioEB6QYY59beno69u/fj7lz50JPT08ISOXk5CAiIgIRERGoX78+6tevjw0bNiA8PBzOzs5ITU2FsrIyqlWrhpKSEhkfxefTrl07eHt7o3nz5rJuCvsISf/99evXOHv2LAAIddBUVFRw//59xMXFYcmSJWjatCn8/Pxw/fp1LF68GEpKSrh8+TKys7NleQiMsSqMM6XYJ1FUVERKSgpMTU3RqFEjZGdnw9DQELVq1cJ3332H6OhoTJgwAf369ROWP46IiMCpU6fQoEGDSl03gjHGGGMMKA1MKSkpCf/et28fbty4gdDQUDRp0gStW7eGo6Mj1q5di0WLFqFJkybYuXMnFBUVIRaLq1TGCQBe1VTOSQJSRkZGePfuHfr37w8jIyN07NgRbdu2xdy5c7Ft2zbo6upixowZWLt2Lfz9/VFYWAhvb29kZGSgQYMGsj4MxlgVxUEp9sliY2Px7t07tGzZEiEhIVBUVMSwYcOQlJQEfX19WFtbw9XVVdg/ODgYUVFR/DBjjDHGWJWQnZ2NsLAw1KlTB4GBgXj58iW6d++Obdu2ISsrC2vXrkV0dDQOHjyIlJQUtG3bFoqKipW2qDmr/MRiMVq2bIm2bdvi7du3uHbtGry9vWFvb4969eqhXr16iIyMxIABAzB9+nS4u7vj4MGD0NTU5KmZjLEviguds3/lz0UR7ezs0LRpUzg4OEBNTQ0TJ05EQUEBzp49i0OHDmHv3r0QiUSybjZjjDHG2H92/fp1ODo6omHDhqhTpw7mz5+PDh064LvvvsO7d+8wZswYDBo0CNOnTxd+5kM1ORmrSLGxsVi9ejXEYjGMjIygoKCAXbt2oX79+jh//jxUVVUREBCAmjVrIiYmBrVr1+aSG4yxL46DUuwvfawo4sWLF3HmzBmhKGJ6ejpMTU3x448/YsGCBcjLy0Pt2rXRoEEDLFmyhFfZY4wxxliVkp6ejpycHPz4449S29+9ewcHBweMGDEC5ubmvMoXkysxMTFYsWIFxGIxFi1aBCUlJTx79gybN2+Grq4uDA0N+ZpljFUoDkqxjypbFPHhw4cYMmSI8Fl6ejqsra1hY2ODYcOGYfHixcjMzMTEiROhqamJuLg4KCgooHHjxjxtjzHGGGNfhfT0dLi4uCAjIwOBgYFVrnYUqxpiY2OxbNkyAICDgwO6d+8u4xYxxr5mHJRif+mviiJeuHAB27Ztw/r165GRkYG1a9fi3bt3MDY2hrGxsaybzhhjjDFWIdLT03H48GHcuXMHaWlpCAwMRI0aNVBSUsKBKSaXYmNjsXz5chARpkyZAnV1dVk3iTH2leKJ7ewvSYoiqqmpCUURJ0yYgAMHDiA/P18oitiuXTtMnz4d1apVQ0hICLKysmTddMYYY4yxCpGSkoK7d++iVatW2L9/P2rUqIHi4mIOSDG51aZNGyxatAg1atSAl5cX7t+/L+smMca+Upwpxf4WF0VkjDHGGPtr79+/R7169aCgoMAZUqzSiI6Ohq+vL+bPn4/mzZvLujmMsa8QB6XYP8JFERljjDHG/h73h1hlI1nIiDHGZIGDUuwf46KIjDHGGGOMMcYY+1w4KMU+CRdFZIwxxhhjjDHG2OfAhc7ZJ+GiiIwxxhhjjDHGGPscOCjFPlmbNm0wd+5cKCkpoVmzZrJuDmOMMcYYY4wxxiohnr7H/jUuisgYY4wxxhhjjLF/i4NSjDHGGGOMMcYYY6zC8fQ9xhhjjDHGGGOMMVbhOCjFGGOMMcYYY4wxxiocB6UYY4wxxhhjjDHGWIXjoBRjjDHGGGOMMcYYq3AclGKMMcYYY4wxxhhjFY6DUowxxhhjjDHGGGOswnFQijHGGGOMAQDmz5+PQYMGyboZjDHGGPtKVJd1AxhjjDHGqqqjR4/CxcVFalujRo3Qvn17TJw4EQMGDJBRyxhjjDHGZI+DUowxxhhjX5iTkxNatmwJIkJaWhqCgoJgZ2eHzZs3Q0tLS9bNY4wxxhiTCQ5KMcYYY4x9Yf3794eKiorwb1NTU2hqauLkyZMfDUoVFxdDLBajZs2aFdVMxhhjjLEKxTWlGGOMMcYqWP369fHNN9+gevXS8cFXr15BJBJh27Zt2LlzJ7S1taGiooLo6GgUFhbC19cXI0eORPfu3aGmpgYrKyvcuHFD6neW/R0HDhyAtrY2OnfuDBMTE0RERJRrw7lz52BgYAAVFRUYGBjg7NmzH2xrcHAwRo4cia5du6Jbt24YPnw4du3a9fn/KIwxxhj76nCmFGOMMcbYF5adnY309HQAQFpaGvbs2YPc3FyMGDFCar+jR4+ioKAAZmZmqFmzJho0aIDs7GwcOnQIBgYGGDVqFHJycnD48GFMnDgRhw4dQseOHaV+x8mTJ5GTkwNzc3MoKCjA398fjo6OOHfuHGrUqAEAuHLlChwdHdG+fXvMnj0bGRkZcHFxwffffy/1u65evYpZs2ahd+/emDNnDgAgJiYGd+/exdixY7/Un4sxxhhjXwkOSjHGGGOMfWHjxo2T+nfNmjXh4eEBTU1Nqe3Jyck4e/YsGjVqJGwrKSnBhQsXpKbxmZmZQVdXF3v27IGHh4fU70hKSkJoaCgaNGgAAGjbti0cHBxw5coVYaqgt7c3GjdujH379qFevXoAAA0NDdja2qJFixbC7woLC0PdunWxbds2VKtW7b//IRhjjDHGyuCgFGOMMcbYF+bm5oa2bdsCAN6+fYvjx49j0aJFqFOnDnR0dIT9dHR0pAJSAFCtWjUhICQWi/H+/XuIxWJ07twZjx8/Lvf/0tPTEwJSAKCurg4ASEhIAACkpqYiKioKdnZ2QkAKADQ1NdG+fXvk5eUJ2+rXr4+8vDxcvXoV/fv3/69/BsYYY4wxKRyUYowxxhj7wlRVVaUKnRsYGMDIyAhLly7FwIEDhe0tW7b84M8HBQVh+/btePnyJYqKiv5y/x9++EHq35IA1fv37wGUZlIBQOvWrcv9bNu2baUCXVZWVjh9+jQmTZoEJSUlaGpqQldXlwNUjDHGGPssuNA5Y4wxxlgFU1RURM+ePfHmzRvExcUJ22vVqlVu399//x3z589Hq1atsHz5cvj7+2PHjh3o1asXiKjc/h+bZvehff9O48aNcezYMWzatAmDBg3CzZs3MWnSJMybN++TfxdjjDHG2J9xphRjjDHGmAyUlJQAAHJzc/Htt99+dL+QkBD8+OOP+O2336CgoCBsX7du3b/6/zZv3hwApIJhEi9fviy3rWbNmhg0aBAGDRoEsViMxYsX48CBA3BwcPhgthVjjDHG2D/FmVKMMcYYYxWsqKgIV69eRY0aNdCuXbu/3FeS+VQ20+nBgwe4f//+v/p/N2vWDB07dkRQUBCysrKE7VevXsWLFy+k9s3IyJD6t6KiIkQiEQCgsLDwX/3/GWOMMcYkOFOKMcYYY+wLu3TpEmJiYgAA6enpOHHiBGJjY2FnZ4e6desiMzPzoz87cOBAhIaGYurUqRg4cCBevXqF/fv3o3379sjNzf1X7Zk1axbs7e1hZWUFExMTZGZmIiAgAD///LPU71y0aBHevXuHXr16QUlJCUlJSQgICEDHjh3/NpjGGGOMMfZ3OCjFGGOMMfaFlZ1q98033+Cnn37C4sWLYWFh8bc/O3LkSLx9+xYHDhzAlStX0L59e3h5eeHMmTO4devWv2pP//794evri7Vr12L16tVo1aoVVqxYgfPnz0v9zhEjRuDgwYPYt28f3r9/j6ZNm0JXVxeOjo5QVOSEe8YYY4z9Nwr0b6peMsYYY4wxxhhjjDH2H/AQF2OMMcYYY4wxxhircByUYowxxhhjjDHGGGMVjoNSjDHGGGOMMcYYY6zCcVCKMcYYY4wxxhhjjFU4DkoxxhhjjDHGGGOMsQrHQSnGGGOMMcYYY4wxVuE4KMUYY4wxxhhjjDHGKhwHpRhjjDHGGGOMMcZYheOgFGOMMcYYY4wxxhircByUYowxxhhjjDHGGGMVjoNSjDHGGGOMMcYYY6zCcVCKMcYYY4wxxhhjjFU4DkoxxhhjjDHGGGOMsQr3f22Q81KglT5vAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the bar graph shows, Razer and LG laptops tend to fall on the higher end of the price range."
      ],
      "metadata": {
        "id": "QDH7UyVY9CUk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's examine the average laptop prices by CPU brand."
      ],
      "metadata": {
        "id": "mSihdueU9MJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the list of CPU brands\n",
        "cpu_list = [\"Samsung_CPU\", \"AMD_CPU\", \"Intel_CPU\"]\n",
        "\n",
        "# Ensure the CPU columns exist in the modified dataset\n",
        "cpu_columns = [cpu for cpu in cpu_list if cpu in CSV.columns]\n",
        "\n",
        "# Compute total price per CPU brand (sum of prices for each CPU type)\n",
        "total_price_per_cpu = CSV[cpu_columns].mul(CSV['Price_dollars'], axis=0).sum()\n",
        "\n",
        "# Compute total number of laptops per CPU brand (count of occurrences)\n",
        "total_laptops_per_cpu = CSV[cpu_columns].sum()\n",
        "\n",
        "# Compute the average price per CPU brand\n",
        "average_prices_cpu = total_price_per_cpu / total_laptops_per_cpu\n",
        "\n",
        "# Sort values for better visualization\n",
        "average_prices_cpu = average_prices_cpu.sort_values()\n",
        "\n",
        "# Plot the bar graph\n",
        "plt.figure(figsize=(12, 6))\n",
        "average_prices_cpu.plot(kind='bar', color='#34a8eb', edgecolor='black')\n",
        "\n",
        "# Titles and labels\n",
        "plt.title('Average Prices by CPU Brand', fontsize=16, fontweight='bold')\n",
        "plt.xlabel('CPU Brand', fontsize=12)\n",
        "plt.ylabel('Average Price ($)', fontsize=12)\n",
        "plt.xticks(rotation=0, fontsize=10)  # No rotation needed for few labels\n",
        "plt.yticks(fontsize=10)\n",
        "\n",
        "# Add grid for better readability\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "# Adjust layout for better spacing\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "zLYFCpdRzJiZ",
        "outputId": "f1b334af-5616-4a43-c309-58b4736b61a1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the bar graph shows Intel CPU's point towrads a higher price."
      ],
      "metadata": {
        "id": "fzhzUBJp9UeM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, Lets look at how ram sizes relate to price"
      ],
      "metadata": {
        "id": "62J-4GhA903a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "average_prices = CSV.groupby('Ram')['Price_dollars'].mean().sort_values()\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "average_prices.plot(kind='line', marker='o', color='#ff34b3', linestyle='-', linewidth=2, markersize=8)\n",
        "#average_prices.plot(kind='bar', color='#ff34b3', edgecolor='black')\n",
        "\n",
        "plt.title('Average Prices by Ram', fontsize=16)\n",
        "plt.xlabel('Ram', fontsize=12)\n",
        "plt.ylabel('Average Price', fontsize=12)\n",
        "plt.xticks(rotation=45, fontsize=10)\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "dT9fEk58zh-c",
        "outputId": "01226693-76cf-4e3d-dff6-f6f920f88a25"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the line graph the larger the ram size the higher the price."
      ],
      "metadata": {
        "id": "idn123xU-GA-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **CREATING THE LAPTOP PRICE PREDICTION MODEL**"
      ],
      "metadata": {
        "id": "AY17K26H-SUi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CSV.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnGnU5RX_m1A",
        "outputId": "a8f39563-4074-4e2a-a262-1741f1d8dd42"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Price_dollars', 'Inches', 'Ram', 'Weight', 'Acer', 'Apple', 'Asus',\n",
              "       'Chuwi', 'Dell', 'Fujitsu', 'Google', 'HP', 'Huawei', 'LG', 'Lenovo',\n",
              "       'MSI', 'Mediacom', 'Microsoft', 'Razer', 'Samsung', 'Toshiba', 'Vero',\n",
              "       'Xiaomi', '2 in 1 Convertible', 'Gaming', 'Netbook', 'Notebook',\n",
              "       'Ultrabook', 'Workstation', 'Android', 'Chrome OS', 'Linux', 'Mac OS X',\n",
              "       'No OS', 'Windows 10', 'Windows 10 S', 'Windows 7', 'macOS',\n",
              "       'Screen Width', 'Screen Height', 'CPU Frequency', 'AMD_CPU',\n",
              "       'Intel_CPU', 'Samsung_CPU', 'Memory Amount', 'AMD_GPU', 'ARM_GPU',\n",
              "       'Intel_GPU', 'Nvidia_GPU'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "FooFbUfAAIfc"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define categorical feature lists\n",
        "computer_brands = [\n",
        "    \"Xiaomi\", \"Asus\", \"Huawei\", \"Fujitsu\", \"Lenovo\",\n",
        "    \"Samsung\", \"Google\", \"Toshiba\", \"HP\", \"Microsoft\",\n",
        "    \"Dell\", \"Chuwi\", \"LG\", \"Vero\", \"Apple\",\n",
        "    \"Mediacom\", \"MSI\", \"Acer\", \"Razer\"\n",
        "]\n",
        "\n",
        "cpu_list = [\"Samsung_CPU\", \"AMD_CPU\", \"Intel_CPU\"]\n",
        "operating_systems = [\n",
        "    \"Mac OS X\", \"Windows 10 S\", \"Windows 10\",\n",
        "    \"Windows 7\", \"macOS\", \"Chrome OS\",\n",
        "    \"Linux\", \"No OS\", \"Android\"\n",
        "]\n",
        "\n",
        "gpu_list = [\"ARM_GPU\", \"Intel_GPU\", \"AMD_GPU\", \"Nvidia_GPU\"]\n",
        "device_types = [\"Netbook\", \"2 in 1 Convertible\", \"Workstation\", \"Ultrabook\", \"Gaming\", \"Notebook\"]\n",
        "\n",
        "# Ensure training and prediction use the SAME features\n",
        "all_features = [\n",
        "    \"Memory Amount\", \"CPU Frequency\", \"Ram\", \"Inches\",\n",
        "    \"Screen Height\", \"Screen Width\", \"Weight\"\n",
        "] + computer_brands + cpu_list + operating_systems + gpu_list + device_types\n",
        "\n",
        "# Prepare dataset\n",
        "y = CSV[\"Price_dollars\"]\n",
        "X = CSV[all_features]  # Ensure consistency in feature selection\n",
        "\n",
        "# Train-Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.6, random_state=42)\n",
        "\n",
        "# Train Linear Regression Model\n",
        "LR_model = LinearRegression()\n",
        "LR_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "WU-Up41GXvt1",
        "outputId": "22dbfa7d-4392-4877-efdc-a9a457e12bfd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that our Linear Regression model is created and trained, let's obtain its prediction results and compare them to the actual values."
      ],
      "metadata": {
        "id": "bfii6MJzB0Sh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LR_model_prediction = LR_model.predict(X_test)\n",
        "plt.scatter(x=y_test, y=LR_model_prediction, color=\"g\", edgecolor=\"black\")\n",
        "a, b = np.polyfit(y_test, LR_model_prediction, 1)\n",
        "plt.plot(y_test, a * y_test + b, color=\"black\")\n",
        "plt.xlabel(\"Actual Prices (y_test)\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Laptop Price Prediction Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cH9nTv9vCMXq",
        "outputId": "77d92829-9ac8-42d0-e869-3b94c3c79dc8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we have is a scatter plot, where the closer the points are to the line, the more accurate the predictions. As you can see, my laptop price prediction model performs exceptionally well. Lets take a dive into what the scatter plot describes by displaying the MAE and RMSE."
      ],
      "metadata": {
        "id": "sXv_vdkEDRx1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "import math\n",
        "\n",
        "print(\"Mean Absolute Error (MAE): \", metrics.mean_absolute_error(y_test, LR_model_prediction))\n",
        "print(\"Root Mean Squared Error (RMSE): \", math.sqrt(metrics.mean_squared_error(y_test, LR_model_prediction)))"
      ],
      "metadata": {
        "id": "IB6sDIXCLpJK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "490ba1c0-6e3e-43d1-b97c-4365ccf0eaaa"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error (MAE):  251.74939408063602\n",
            "Root Mean Squared Error (RMSE):  350.21369950787545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The RMSE value indicates the average deviation of a prediction from the actual value. In this case, it shows that laptop price predictions were off by approximately $350, which is reasonably accurate. This aligns with the scatter plot, as most data points remain close to the trend line."
      ],
      "metadata": {
        "id": "L702AlWVQpj_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **USER INTERFACE**"
      ],
      "metadata": {
        "id": "UCsDkjmAF7RY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To use the laptop price prediction user interface, simply run the cell below, adjust the sliders and dropdowns to match the specifications of the laptop you want to evaluate. Select values for memory (MB), CPU frequency (GHz), RAM (GB), screen size (inches), resolution (height & width), and weight (KG) using the provided sliders. Choose a computer brand, CPU brand, operating system, GPU brand, and device type from the dropdown menus. Once all selections are made, click the \"Predict Price\" button, and the estimated laptop price will be displayed below."
      ],
      "metadata": {
        "id": "kMJdnFwEcDbb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create UI Widgets for Prediction\n",
        "memory_amount = widgets.IntSlider(min=1000, max=64000, step=1000, description=\"Memory (MB)\")\n",
        "cpu_frequency = widgets.FloatSlider(min=1.0, max=5.0, step=0.1, description=\"CPU (GHz)\")\n",
        "ram = widgets.IntSlider(min=2, max=64, step=2, description=\"RAM (GB)\")\n",
        "inches = widgets.FloatSlider(min=10.0, max=20.0, step=0.1, description=\"Screen Size\")\n",
        "screen_height = widgets.IntSlider(min=600, max=4000, step=100, description=\"Height (px)\")\n",
        "screen_width = widgets.IntSlider(min=800, max=8000, step=100, description=\"Width (px)\")\n",
        "weight = widgets.FloatSlider(min=0.5, max=5.0, step=0.1, description=\"Weight (KG)\")\n",
        "\n",
        "computer_brand = widgets.Dropdown(options=computer_brands, description=\"Brand\")\n",
        "cpu_brand = widgets.Dropdown(options=cpu_list, description=\"CPU Brand\")\n",
        "operating_system = widgets.Dropdown(options=operating_systems, description=\"OS\")\n",
        "gpu_brand = widgets.Dropdown(options=gpu_list, description=\"GPU Brand\")\n",
        "device_type = widgets.Dropdown(options=device_types, description=\"Device Type\")\n",
        "\n",
        "predict_button = widgets.Button(description=\"Predict Price\")\n",
        "output = widgets.Output()\n",
        "\n",
        "# Function to Predict Laptop Price\n",
        "def predict_laptop_price(b):\n",
        "    with output:\n",
        "        output.clear_output()\n",
        "\n",
        "        # Prepare input dictionary with default values\n",
        "        input_data = {feature: 0 for feature in all_features}\n",
        "\n",
        "        # Set numeric values\n",
        "        input_data[\"Memory Amount\"] = memory_amount.value\n",
        "        input_data[\"CPU Frequency\"] = cpu_frequency.value\n",
        "        input_data[\"Ram\"] = ram.value\n",
        "        input_data[\"Inches\"] = inches.value\n",
        "        input_data[\"Screen Height\"] = screen_height.value\n",
        "        input_data[\"Screen Width\"] = screen_width.value\n",
        "        input_data[\"Weight\"] = weight.value\n",
        "\n",
        "        # Set one-hot encoded values\n",
        "        input_data[computer_brand.value] = 1\n",
        "        input_data[cpu_brand.value] = 1\n",
        "        input_data[operating_system.value] = 1\n",
        "        input_data[gpu_brand.value] = 1\n",
        "        input_data[device_type.value] = 1\n",
        "\n",
        "        # Convert to DataFrame and ensure column order matches training\n",
        "        input_df = pd.DataFrame([input_data])[all_features]\n",
        "\n",
        "        # Make Prediction\n",
        "        predicted_price = LR_model.predict(input_df)[0]\n",
        "\n",
        "        # Display Result\n",
        "        print(f\"Estimated Laptop Price: ${predicted_price:,.2f}\")\n",
        "\n",
        "# Attach Function to Button\n",
        "predict_button.on_click(predict_laptop_price)\n",
        "\n",
        "# Display UI\n",
        "display(\n",
        "    memory_amount, cpu_frequency, ram, inches,\n",
        "    screen_height, screen_width, weight,\n",
        "    computer_brand, cpu_brand, operating_system,\n",
        "    gpu_brand, device_type, predict_button, output\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429,
          "referenced_widgets": [
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        "id": "_eBAKAplPX6e",
        "outputId": "93307384-0eb4-45c5-c4ec-0e87ec3a1a08"
      },
      "execution_count": 36,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "IntSlider(value=1000, description='Memory (MB)', max=64000, min=1000, step=1000)"
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              "IntSlider(value=2, description='RAM (GB)', max=64, min=2, step=2)"
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}