Python(Exploratory_Data_Analytics).ipynb

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      "source": [
        "# **EXPLORATORY DATA ANALYSIS IN PYTHON**"
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      "source": [
        "**What is Exploratory Data Analysis ?**\n",
        "\n",
        " Exploratory Data Analysis (EDA) is the process of examining and summarizing a dataset to understand its characteristics, identify patterns, and make informed decisions. It involves calculating summary statistics, visualizing data through plots and charts, identifying missing or inconsistent data, and exploring relationships between variables. EDA provides insights that guide further analysis and decision-making."
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        "**How to perform Exploratory Data Analysis ?**\n",
        "\n",
        "Performing EDA involves the following concise steps:\n",
        "\n",
        "1. Understand the Data: Get familiar with the dataset's structure, variables, and potential missing values or inconsistencies.\n",
        "\n",
        "2. Clean the Data: Handle missing values and erroneous data appropriately.\n",
        "\n",
        "3. Calculate Summary Statistics: Compute basic statistics like mean, median, and standard deviation for numeric variables, and frequency counts for categorical variables.\n",
        "\n",
        "4. Visualize the Data: Create plots such as histograms, box plots, and scatter plots to visualize the data distribution, outliers, and relationships between variables.\n",
        "\n",
        "5. Analyze Relationships: Identify correlations between numeric variables and visualize them using correlation matrices or scatter plots.\n",
        "\n",
        "6. Identify Outliers and Anomalies: Spot unusual observations that deviate significantly from the norm.\n",
        "\n",
        "7. Handle Categorical Variables: Analyze categorical variables using bar plots or pie charts to understand category distributions.\n",
        "\n",
        "8. Iterate and Explore: Continuously explore the data, generate hypotheses, and delve deeper into specific aspects for further analysis."
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        "**What data are we exploring today ?**\n",
        "\n",
        "\n",
        "\n",
        "We have a very beautiful data-set of cars which contains more of 10, 000 rows and more than 10 columns which contains features of the car such as Engine Fuel Type, Engine HP, Transmission Type, highway MPG, city MPG and many more. "
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "**Task** : Find best features to help predict prices of cars"
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      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
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      "source": [
        "## 1. Importing the required libraries for EDA"
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      "source": [
        "Below are the libraries that are used in order to perform EDA (Exploratory data analysis) in this tutorial."
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns                      \n",
        "import matplotlib.pyplot as plt            \n",
        "#%matplotlib inline     \n",
        "#sns.set(color_codes=True)"
      ]
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      "source": [
        "## 2. Loading the data into the data frame."
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      "source": [
        "Loading the data into the pandas data frame is certainly one of the most important steps in EDA, as we can see that the value from the data set is comma-separated. So all we have to do is to just read the CSV into a data frame and pandas data frame does the job for us."
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              "  Make       Model  Year             Engine Fuel Type  Engine HP  \\\n",
              "0  BMW  1 Series M  2011  premium unleaded (required)      335.0   \n",
              "1  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
              "2  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
              "3  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
              "4  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
              "\n",
              "   Engine Cylinders Transmission Type     Driven_Wheels  Number of Doors  \\\n",
              "0               6.0            MANUAL  rear wheel drive              2.0   \n",
              "1               6.0            MANUAL  rear wheel drive              2.0   \n",
              "2               6.0            MANUAL  rear wheel drive              2.0   \n",
              "3               6.0            MANUAL  rear wheel drive              2.0   \n",
              "4               6.0            MANUAL  rear wheel drive              2.0   \n",
              "\n",
              "                         Market Category Vehicle Size Vehicle Style  \\\n",
              "0  Factory Tuner,Luxury,High-Performance      Compact         Coupe   \n",
              "1                     Luxury,Performance      Compact   Convertible   \n",
              "2                Luxury,High-Performance      Compact         Coupe   \n",
              "3                     Luxury,Performance      Compact         Coupe   \n",
              "4                                 Luxury      Compact   Convertible   \n",
              "\n",
              "   highway MPG  city mpg  Popularity   MSRP  \n",
              "0           26        19        3916  46135  \n",
              "1           28        19        3916  40650  \n",
              "2           28        20        3916  36350  \n",
              "3           28        18        3916  29450  \n",
              "4           28        18        3916  34500  "
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        "df.head()               "
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              "   Make       Model  Year             Engine Fuel Type  Engine HP  \\\n",
              "0   BMW  1 Series M  2011  premium unleaded (required)      335.0   \n",
              "1   BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
              "2   BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
              "3   BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
              "4   BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
              "5   BMW    1 Series  2012  premium unleaded (required)      230.0   \n",
              "6   BMW    1 Series  2012  premium unleaded (required)      300.0   \n",
              "7   BMW    1 Series  2012  premium unleaded (required)      300.0   \n",
              "8   BMW    1 Series  2012  premium unleaded (required)      230.0   \n",
              "9   BMW    1 Series  2013  premium unleaded (required)      230.0   \n",
              "10  BMW    1 Series  2013  premium unleaded (required)      300.0   \n",
              "11  BMW    1 Series  2013  premium unleaded (required)      230.0   \n",
              "12  BMW    1 Series  2013  premium unleaded (required)      300.0   \n",
              "13  BMW    1 Series  2013  premium unleaded (required)      230.0   \n",
              "14  BMW    1 Series  2013  premium unleaded (required)      230.0   \n",
              "\n",
              "    Engine Cylinders Transmission Type     Driven_Wheels  Number of Doors  \\\n",
              "0                6.0            MANUAL  rear wheel drive              2.0   \n",
              "1                6.0            MANUAL  rear wheel drive              2.0   \n",
              "2                6.0            MANUAL  rear wheel drive              2.0   \n",
              "3                6.0            MANUAL  rear wheel drive              2.0   \n",
              "4                6.0            MANUAL  rear wheel drive              2.0   \n",
              "5                6.0            MANUAL  rear wheel drive              2.0   \n",
              "6                6.0            MANUAL  rear wheel drive              2.0   \n",
              "7                6.0            MANUAL  rear wheel drive              2.0   \n",
              "8                6.0            MANUAL  rear wheel drive              2.0   \n",
              "9                6.0            MANUAL  rear wheel drive              2.0   \n",
              "10               6.0            MANUAL  rear wheel drive              2.0   \n",
              "11               6.0            MANUAL  rear wheel drive              2.0   \n",
              "12               6.0            MANUAL  rear wheel drive              2.0   \n",
              "13               6.0            MANUAL  rear wheel drive              2.0   \n",
              "14               6.0            MANUAL  rear wheel drive              2.0   \n",
              "\n",
              "                          Market Category Vehicle Size Vehicle Style  \\\n",
              "0   Factory Tuner,Luxury,High-Performance      Compact         Coupe   \n",
              "1                      Luxury,Performance      Compact   Convertible   \n",
              "2                 Luxury,High-Performance      Compact         Coupe   \n",
              "3                      Luxury,Performance      Compact         Coupe   \n",
              "4                                  Luxury      Compact   Convertible   \n",
              "5                      Luxury,Performance      Compact         Coupe   \n",
              "6                      Luxury,Performance      Compact   Convertible   \n",
              "7                 Luxury,High-Performance      Compact         Coupe   \n",
              "8                                  Luxury      Compact   Convertible   \n",
              "9                                  Luxury      Compact   Convertible   \n",
              "10                Luxury,High-Performance      Compact         Coupe   \n",
              "11                     Luxury,Performance      Compact         Coupe   \n",
              "12                     Luxury,Performance      Compact   Convertible   \n",
              "13                                 Luxury      Compact   Convertible   \n",
              "14                     Luxury,Performance      Compact         Coupe   \n",
              "\n",
              "    highway MPG  city mpg  Popularity   MSRP  \n",
              "0            26        19        3916  46135  \n",
              "1            28        19        3916  40650  \n",
              "2            28        20        3916  36350  \n",
              "3            28        18        3916  29450  \n",
              "4            28        18        3916  34500  \n",
              "5            28        18        3916  31200  \n",
              "6            26        17        3916  44100  \n",
              "7            28        20        3916  39300  \n",
              "8            28        18        3916  36900  \n",
              "9            27        18        3916  37200  \n",
              "10           28        20        3916  39600  \n",
              "11           28        19        3916  31500  \n",
              "12           28        19        3916  44400  \n",
              "13           28        19        3916  37200  \n",
              "14           28        19        3916  31500  "
            ],
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              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
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              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
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              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
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              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>31200</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BMW</td>\n",
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              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
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              "      <th>7</th>\n",
              "      <td>BMW</td>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
              "      <td>BMW</td>\n",
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              "      <td>premium unleaded (required)</td>\n",
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              "      <td>37200</td>\n",
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              "      <th>10</th>\n",
              "      <td>BMW</td>\n",
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              "      <td>BMW</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>BMW</td>\n",
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              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>44400</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2013</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
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              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>37200</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2013</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
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              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>31500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de1232d2-5bd5-4a67-8c83-73d942e55686')\"\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-de1232d2-5bd5-4a67-8c83-73d942e55686 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de1232d2-5bd5-4a67-8c83-73d942e55686');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.tail(15)\n",
        "df.head(15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjQnr4SPzaL5",
        "papermill": {
          "duration": 0.029003,
          "end_time": "2020-11-27T11:52:30.549371",
          "exception": false,
          "start_time": "2020-11-27T11:52:30.520368",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAmC369yTpMF",
        "papermill": {
          "duration": 0.029103,
          "end_time": "2020-11-27T11:52:30.608838",
          "exception": false,
          "start_time": "2020-11-27T11:52:30.579735",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 3. Checking the types of data"
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make                  object\n",
              "Model                 object\n",
              "Year                   int64\n",
              "Engine Fuel Type      object\n",
              "Engine HP            float64\n",
              "Engine Cylinders     float64\n",
              "Transmission Type     object\n",
              "Driven_Wheels         object\n",
              "Number of Doors      float64\n",
              "Market Category       object\n",
              "Vehicle Size          object\n",
              "Vehicle Style         object\n",
              "highway MPG            int64\n",
              "city mpg               int64\n",
              "Popularity             int64\n",
              "MSRP                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beRJyQAezdX8",
        "papermill": {
          "duration": 0.029309,
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      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoHuBhXxT5E9",
        "papermill": {
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          "start_time": "2020-11-27T11:52:30.826672",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 4. Dropping irrelevant columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3cy877Mze4H",
        "papermill": {
          "duration": 0.029217,
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        },
        "tags": []
      },
      "source": [
        "This step is certainly needed in every EDA because sometimes there would be many columns that we never use in such cases dropping is the only solution. In this case, the columns such as Engine Fuel Type, Market Category, Vehicle style, Popularity, Number of doors, Vehicle Size doesn't make any sense to me so I just dropped for this instance."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.unique(df['Engine Fuel Type'])"
      ],
      "metadata": {
        "id": "HFUpACwXoSrp",
        "outputId": "55ce007f-eed8-4eab-8c01-97d300ad4015",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['premium unleaded (required)', 'regular unleaded',\n",
              "       'premium unleaded (recommended)', 'flex-fuel (unleaded/E85)',\n",
              "       'diesel', 'electric',\n",
              "       'flex-fuel (premium unleaded recommended/E85)', 'natural gas',\n",
              "       'flex-fuel (premium unleaded required/E85)',\n",
              "       'flex-fuel (unleaded/natural gas)', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
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          "shell.execute_reply": "2020-11-27T11:52:30.997034Z"
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          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Make       Model  Year  Engine HP  Engine Cylinders Transmission Type  \\\n",
              "0  BMW  1 Series M  2011      335.0               6.0            MANUAL   \n",
              "1  BMW    1 Series  2011      300.0               6.0            MANUAL   \n",
              "2  BMW    1 Series  2011      300.0               6.0            MANUAL   \n",
              "3  BMW    1 Series  2011      230.0               6.0            MANUAL   \n",
              "4  BMW    1 Series  2011      230.0               6.0            MANUAL   \n",
              "\n",
              "      Driven_Wheels  Number of Doors                        Market Category  \\\n",
              "0  rear wheel drive              2.0  Factory Tuner,Luxury,High-Performance   \n",
              "1  rear wheel drive              2.0                     Luxury,Performance   \n",
              "2  rear wheel drive              2.0                Luxury,High-Performance   \n",
              "3  rear wheel drive              2.0                     Luxury,Performance   \n",
              "4  rear wheel drive              2.0                                 Luxury   \n",
              "\n",
              "  Vehicle Size Vehicle Style  highway MPG  city mpg  Popularity   MSRP  \n",
              "0      Compact         Coupe           26        19        3916  46135  \n",
              "1      Compact   Convertible           28        19        3916  40650  \n",
              "2      Compact         Coupe           28        20        3916  36350  \n",
              "3      Compact         Coupe           28        18        3916  29450  \n",
              "4      Compact   Convertible           28        18        3916  34500  "
            ],
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              "      <td>BMW</td>\n",
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              "      <td>3916</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df = df.drop(['Engine Fuel Type'], axis=1)\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "axis=0 (default): If you specify axis=0 or axis='index', it means you want to drop labels from the rows of the DataFrame. This operation removes the specified rows from the DataFrame, effectively reducing the number of rows.\n",
        "\n",
        "axis=1 or axis='columns': If you specify axis=1 or axis='columns', it means you want to drop labels from the columns of the DataFrame. This operation removes the specified columns from the DataFrame, effectively reducing the number of columns."
      ],
      "metadata": {
        "id": "omX-OCCgpqy2"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20OeQBpWz89v",
        "papermill": {
          "duration": 0.029929,
          "end_time": "2020-11-27T11:52:31.057925",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.027996",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caAownWdUZso",
        "papermill": {
          "duration": 0.030223,
          "end_time": "2020-11-27T11:52:31.118132",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.087909",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 5. Renaming the columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UABLiEcyz-2G",
        "papermill": {
          "duration": 0.030318,
          "end_time": "2020-11-27T11:52:31.178829",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.148511",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "In this instance, most of the column names are very confusing to read, so I just tweaked their column names. This is a good approach it improves the readability of the data set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Make       Model  Year     HP  Cylinders Transmission        Drive Mode  \\\n",
              "0  BMW  1 Series M  2011  335.0        6.0       MANUAL  rear wheel drive   \n",
              "1  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "2  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "3  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "4  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "\n",
              "   Number of Doors                        Market Category Vehicle Size  \\\n",
              "0              2.0  Factory Tuner,Luxury,High-Performance      Compact   \n",
              "1              2.0                     Luxury,Performance      Compact   \n",
              "2              2.0                Luxury,High-Performance      Compact   \n",
              "3              2.0                     Luxury,Performance      Compact   \n",
              "4              2.0                                 Luxury      Compact   \n",
              "\n",
              "  Vehicle Style  MPG-H  MPG-C  Popularity  Price  \n",
              "0         Coupe     26     19        3916  46135  \n",
              "1   Convertible     28     19        3916  40650  \n",
              "2         Coupe     28     20        3916  36350  \n",
              "3         Coupe     28     18        3916  29450  \n",
              "4   Convertible     28     18        3916  34500  "
            ],
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              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df = df.rename(columns={\"Engine HP\": \"HP\", \"Engine Cylinders\": \"Cylinders\", \"Transmission Type\": \"Transmission\", \"Driven_Wheels\": \"Drive Mode\",\"highway MPG\": \"MPG-H\", \"city mpg\": \"MPG-C\", \"MSRP\": \"Price\" })\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tcGiOmV0afN",
        "papermill": {
          "duration": 0.030645,
          "end_time": "2020-11-27T11:52:31.323355",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.292710",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiW7x_O4WIDX",
        "papermill": {
          "duration": 0.030638,
          "end_time": "2020-11-27T11:52:31.385353",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.354715",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 6. Dropping the duplicate rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LpR5NW70hXm",
        "papermill": {
          "duration": 0.030478,
          "end_time": "2020-11-27T11:52:31.446542",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.416064",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "This is often a handy thing to do because a huge data set as in this case contains more than 10, 000 rows often have some duplicate data which might be disturbing, so here I remove all the duplicate value from the data-set. For example prior to removing I had 11914 rows of data but after removing the duplicates 10925 data meaning that I had 989 of duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df.duplicated()"
      ],
      "metadata": {
        "id": "TeaxgkYLnFvL",
        "outputId": "fad22817-a1d3-4831-ce90-1012ecc60eb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "11909    False\n",
              "11910    False\n",
              "11911    False\n",
              "11912    False\n",
              "11913    False\n",
              "Length: 11914, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:31.512940Z",
          "iopub.status.busy": "2020-11-27T11:52:31.512151Z",
          "iopub.status.idle": "2020-11-27T11:52:31.516507Z",
          "shell.execute_reply": "2020-11-27T11:52:31.515912Z"
        },
        "id": "1nBN_BCDWSmv",
        "outputId": "03b7b8d9-b6a6-402f-e200-4ef9958c0d72",
        "papermill": {
          "duration": 0.039307,
          "end_time": "2020-11-27T11:52:31.516612",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.477305",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11914, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:31.587137Z",
          "iopub.status.busy": "2020-11-27T11:52:31.586114Z",
          "iopub.status.idle": "2020-11-27T11:52:31.602151Z",
          "shell.execute_reply": "2020-11-27T11:52:31.601648Z"
        },
        "id": "yB8t6o0wH7If",
        "outputId": "c629a5d2-66cb-4e98-9a46-b9f5e4efe650",
        "papermill": {
          "duration": 0.053706,
          "end_time": "2020-11-27T11:52:31.602271",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.548565",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of duplicate rows:  (715, 15)\n"
          ]
        }
      ],
      "source": [
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"number of duplicate rows: \", duplicate_rows_df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chhNvMFCIzqI",
        "papermill": {
          "duration": 0.031075,
          "end_time": "2020-11-27T11:52:31.664844",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.633769",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "Now let us remove the duplicate data because it's ok to remove them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:31.735456Z",
          "iopub.status.busy": "2020-11-27T11:52:31.734753Z",
          "iopub.status.idle": "2020-11-27T11:52:31.741387Z",
          "shell.execute_reply": "2020-11-27T11:52:31.740842Z"
        },
        "id": "kuobmetTV820",
        "outputId": "58c60039-7fc1-44a9-8b0e-0a92bcfdb6f3",
        "papermill": {
          "duration": 0.045289,
          "end_time": "2020-11-27T11:52:31.741503",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.696214",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make               11914\n",
              "Model              11914\n",
              "Year               11914\n",
              "HP                 11845\n",
              "Cylinders          11884\n",
              "Transmission       11914\n",
              "Drive Mode         11914\n",
              "Number of Doors    11908\n",
              "Market Category     8172\n",
              "Vehicle Size       11914\n",
              "Vehicle Style      11914\n",
              "MPG-H              11914\n",
              "MPG-C              11914\n",
              "Popularity         11914\n",
              "Price              11914\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df.count()      # Used to count the number of rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MJKjbzHI40K",
        "papermill": {
          "duration": 0.032123,
          "end_time": "2020-11-27T11:52:31.806242",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.774119",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "So seen above there are 11914 rows and we are removing 989 rows of duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:31.876439Z",
          "iopub.status.busy": "2020-11-27T11:52:31.875501Z",
          "iopub.status.idle": "2020-11-27T11:52:31.896377Z",
          "shell.execute_reply": "2020-11-27T11:52:31.895850Z"
        },
        "id": "OiOsEF6WVTSj",
        "outputId": "5b70fb28-a83d-478f-c22e-a9486d5b2faf",
        "papermill": {
          "duration": 0.058104,
          "end_time": "2020-11-27T11:52:31.896493",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.838389",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Make       Model  Year     HP  Cylinders Transmission        Drive Mode  \\\n",
              "0  BMW  1 Series M  2011  335.0        6.0       MANUAL  rear wheel drive   \n",
              "1  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "2  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "3  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "4  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "\n",
              "   Number of Doors                        Market Category Vehicle Size  \\\n",
              "0              2.0  Factory Tuner,Luxury,High-Performance      Compact   \n",
              "1              2.0                     Luxury,Performance      Compact   \n",
              "2              2.0                Luxury,High-Performance      Compact   \n",
              "3              2.0                     Luxury,Performance      Compact   \n",
              "4              2.0                                 Luxury      Compact   \n",
              "\n",
              "  Vehicle Style  MPG-H  MPG-C  Popularity  Price  \n",
              "0         Coupe     26     19        3916  46135  \n",
              "1   Convertible     28     19        3916  40650  \n",
              "2         Coupe     28     20        3916  36350  \n",
              "3         Coupe     28     18        3916  29450  \n",
              "4   Convertible     28     18        3916  34500  "
            ],
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              "\n",
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              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
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              "      <td>Compact</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
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              "      <td>Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
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              "      <td>3916</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13ba87ba-f38f-448d-b00a-1da247addfcf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13ba87ba-f38f-448d-b00a-1da247addfcf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13ba87ba-f38f-448d-b00a-1da247addfcf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df = df.drop_duplicates()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:31.968742Z",
          "iopub.status.busy": "2020-11-27T11:52:31.968146Z",
          "iopub.status.idle": "2020-11-27T11:52:31.974209Z",
          "shell.execute_reply": "2020-11-27T11:52:31.973613Z"
        },
        "id": "2gMM4lb0Vzor",
        "outputId": "31c77afe-7f04-444a-c83f-d197b741378f",
        "papermill": {
          "duration": 0.045247,
          "end_time": "2020-11-27T11:52:31.974315",
          "exception": false,
          "start_time": "2020-11-27T11:52:31.929068",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make               11199\n",
              "Model              11199\n",
              "Year               11199\n",
              "HP                 11130\n",
              "Cylinders          11169\n",
              "Transmission       11199\n",
              "Drive Mode         11199\n",
              "Number of Doors    11193\n",
              "Market Category     7823\n",
              "Vehicle Size       11199\n",
              "Vehicle Style      11199\n",
              "MPG-H              11199\n",
              "MPG-C              11199\n",
              "Popularity         11199\n",
              "Price              11199\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCLUdZOQ1PDP",
        "papermill": {
          "duration": 0.032655,
          "end_time": "2020-11-27T11:52:32.040143",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.007488",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkXUQtyQW3Dy",
        "papermill": {
          "duration": 0.033049,
          "end_time": "2020-11-27T11:52:32.106118",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.073069",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 7. Dropping the missing or null values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5pKvJi41YCp",
        "papermill": {
          "duration": 0.033171,
          "end_time": "2020-11-27T11:52:32.173467",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.140296",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "This is mostly similar to the previous step but in here all the missing values are detected and are dropped later. Now, this is not a good approach to do so, because many people just replace the missing values with the mean or the average of that column, but in this case, I just dropped that missing values. This is because there is nearly 100 missing value compared to 10, 000 values this is a small number and this is negligible so I just dropped those values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:32.247260Z",
          "iopub.status.busy": "2020-11-27T11:52:32.246296Z",
          "iopub.status.idle": "2020-11-27T11:52:32.251941Z",
          "shell.execute_reply": "2020-11-27T11:52:32.251446Z"
        },
        "id": "Tzdlg-1OWjMz",
        "outputId": "8e91b865-970e-4438-82b4-b53a966c6ac2",
        "papermill": {
          "duration": 0.045703,
          "end_time": "2020-11-27T11:52:32.252054",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.206351",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Make                  0\n",
            "Model                 0\n",
            "Year                  0\n",
            "HP                   69\n",
            "Cylinders            30\n",
            "Transmission          0\n",
            "Drive Mode            0\n",
            "Number of Doors       6\n",
            "Market Category    3376\n",
            "Vehicle Size          0\n",
            "Vehicle Style         0\n",
            "MPG-H                 0\n",
            "MPG-C                 0\n",
            "Popularity            0\n",
            "Price                 0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df.isnull().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWJqTVxTJQnO",
        "papermill": {
          "duration": 0.033238,
          "end_time": "2020-11-27T11:52:32.318801",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.285563",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "This is the reason in the above step while counting both Cylinders and Horsepower (HP) had 10856 and 10895 over 10925 rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:32.393563Z",
          "iopub.status.busy": "2020-11-27T11:52:32.392780Z",
          "iopub.status.idle": "2020-11-27T11:52:32.424245Z",
          "shell.execute_reply": "2020-11-27T11:52:32.423583Z"
        },
        "id": "KbbV0xHPWoad",
        "outputId": "f930f55a-bcc8-471a-c378-a939cc1c2083",
        "papermill": {
          "duration": 0.071586,
          "end_time": "2020-11-27T11:52:32.424366",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.352780",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make               7735\n",
              "Model              7735\n",
              "Year               7735\n",
              "HP                 7735\n",
              "Cylinders          7735\n",
              "Transmission       7735\n",
              "Drive Mode         7735\n",
              "Number of Doors    7735\n",
              "Market Category    7735\n",
              "Vehicle Size       7735\n",
              "Vehicle Style      7735\n",
              "MPG-H              7735\n",
              "MPG-C              7735\n",
              "Popularity         7735\n",
              "Price              7735\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df = df.dropna()    # Dropping the missing values.\n",
        "df.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t7L9l2mJSoX",
        "papermill": {
          "duration": 0.033726,
          "end_time": "2020-11-27T11:52:32.496715",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.462989",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "Now we have removed all the rows which contain the Null or N/A values (Cylinders and Horsepower (HP))."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:32.571710Z",
          "iopub.status.busy": "2020-11-27T11:52:32.571093Z",
          "iopub.status.idle": "2020-11-27T11:52:32.575569Z",
          "shell.execute_reply": "2020-11-27T11:52:32.576056Z"
        },
        "id": "V-DmX1O4Wtox",
        "outputId": "5f6d7945-c73a-48af-e431-6aa51fc1f8b2",
        "papermill": {
          "duration": 0.045646,
          "end_time": "2020-11-27T11:52:32.576197",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.530551",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Make               0\n",
            "Model              0\n",
            "Year               0\n",
            "HP                 0\n",
            "Cylinders          0\n",
            "Transmission       0\n",
            "Drive Mode         0\n",
            "Number of Doors    0\n",
            "Market Category    0\n",
            "Vehicle Size       0\n",
            "Vehicle Style      0\n",
            "MPG-H              0\n",
            "MPG-C              0\n",
            "Popularity         0\n",
            "Price              0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df.isnull().sum())   # After dropping the values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bk8RAHqQJVJK",
        "papermill": {
          "duration": 0.033656,
          "end_time": "2020-11-27T11:52:32.644093",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.610437",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Py3sQc_ZxyU",
        "papermill": {
          "duration": 0.034018,
          "end_time": "2020-11-27T11:52:32.712147",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.678129",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 8. Detecting Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g6CJPofZzHN",
        "papermill": {
          "duration": 0.033631,
          "end_time": "2020-11-27T11:52:32.779927",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.746296",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "An outlier is a point or set of points that are different from other points. Sometimes they can be very high or very low. It's often a good idea to detect and remove the outliers. Because outliers are one of the primary reasons for resulting in a less accurate model. Hence it's a good idea to remove them. The outlier detection and removing that I am going to perform is called IQR score technique. Often outliers can be seen with visualizations using a box plot. Shown below are the box plot of MSRP, Cylinders, Horsepower and EngineSize. Herein all the plots, you can find some points are outside the box they are none other than outliers. The technique of finding and removing outlier that I am performing in this assignment is taken help of a tutorial from[ towards data science](https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![1_0MPDTLn8KoLApoFvI0P2vQ 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)"
      ],
      "metadata": {
        "id": "V5Ctr4HspjvR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:33.179459Z",
          "iopub.status.busy": "2020-11-27T11:52:33.178064Z",
          "iopub.status.idle": "2020-11-27T11:52:33.370107Z",
          "shell.execute_reply": "2020-11-27T11:52:33.369534Z"
        },
        "id": "9HtvQYVHZ8u5",
        "outputId": "6d26bd42-2839-4773-c29d-8ed495675bc8",
        "papermill": {
          "duration": 0.237157,
          "end_time": "2020-11-27T11:52:33.370221",
          "exception": false,
          "start_time": "2020-11-27T11:52:33.133064",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'HP'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-64810ae3a846>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'HP'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'HP'"
          ]
        }
      ],
      "source": [
        "sns.boxplot(x=df['HP'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:32.853465Z",
          "iopub.status.busy": "2020-11-27T11:52:32.852781Z",
          "iopub.status.idle": "2020-11-27T11:52:33.096979Z",
          "shell.execute_reply": "2020-11-27T11:52:33.096361Z"
        },
        "id": "vtxX_y6zZ2ri",
        "outputId": "5034f4df-fe93-446c-a67b-9d42f9f106b0",
        "papermill": {
          "duration": 0.283132,
          "end_time": "2020-11-27T11:52:33.097091",
          "exception": false,
          "start_time": "2020-11-27T11:52:32.813959",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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r77NWv0kRAFh7rXFnEACAzicQAIBEIAAAiUAAAJJOCYTLL788ttpqq6iuro5BgwbFY489ttLxN954Y3z961+P6urq2GWXXeKf//xnB82UpVZnn02aNClKpdJyH9XV1R04W+6///449NBDo76+PkqlUtxyyy1fept77703BgwYEFVVVbHddtvFpEmT2n2e/J/V3Wf33ntvWmelUineeuutjpkwccEFF8Ree+0VPXr0iE033TRGjBgRzz///Jferqsc0zo8EP7+97/HqaeeGuPHj48nn3wydttttzj44IPjnXfeWeH4hx56KI444og4/vjj46mnnooRI0bEiBEjYvbs2R0883XX6u6ziM9/a9ibb75Z/nj11Vc7cMYsXLgwdtttt7j88stXafwrr7wSw4cPjwMOOCBmzpwZY8eOjRNOOCHuvPPOdp4pS63uPlvq+eefX26tbbrppu00Q77ovvvui1GjRsUjjzwS06ZNi08//TS++c1vxsKFC1u8TZc6phUdbODAgcWoUaPKnzc1NRX19fXFBRdcsMLxhx9+eDF8+PDlLhs0aFBx4okntus8+T+ru88mTpxY9OzZs4Nmx5eJiGLKlCkrHXPGGWcUO++883KX/fCHPywOPvjgdpwZLVmVfXbPPfcUEVF8+OGHHTInvtw777xTRERx3333tTimKx3TOvQMwieffBIzZsyIAw88sHxZRUVFHHjggfHwww+v8DYPP/zwcuMjIg4++OAWx9O2WrPPIiIWLFgQW265ZTQ0NMR3vvOdeOaZZzpiurSSddZ17b777rH55pvHQQcdFA8++GBnT2ed9vHHH0dExIYbbtjimK601jo0EN57771oamqK3r17L3d57969W3zd7K233lqt8bSt1uyzHXbYIa6++uq49dZb49prr43m5uYYPHhwzJs3ryOmTCu0tM4aGxtj0aJFnTQrVmbzzTePP/3pT3HTTTfFTTfdFA0NDTF06NB48sknO3tq66Tm5uYYO3Zs7LvvvtGvX78Wx3WlY1qr/5ojtGSfffaJffbZp/z54MGDY8cdd4w///nPce6553bizGDtscMOO8QOO+xQ/nzw4MExd+7cuPTSS+Ovf/1rJ85s3TRq1KiYPXt2PPDAA509lTbToWcQNt5446isrIy33357ucvffvvt2GyzzVZ4m80222y1xtO2WrPPvmi99daL/v37x0svvdQeU6QNtLTO6urqoqamppNmxeoaOHCgddYJRo8eHXfccUfcc8890adPn5WO7UrHtA4NhO7du8cee+wR06dPL1/W3Nwc06dPX+4nzmXts88+y42PiJg2bVqL42lbrdlnX9TU1BSzZs2KzTffvL2myVdkna0dZs6caZ11oKIoYvTo0TFlypS4++67Y+utt/7S23SptdbR74q8/vrri6qqqmLSpEnFs88+W/zkJz8pevXqVbz11ltFURTF0UcfXZx55pnl8Q8++GDRrVu3YsKECcWcOXOK8ePHF+utt14xa9asjp76Omt199k555xT3HnnncXcuXOLGTNmFD/60Y+K6urq4plnnumsL2GdM3/+/OKpp54qnnrqqSIiiksuuaR46qmnildffbUoiqI488wzi6OPPro8/uWXXy7WX3/94vTTTy/mzJlTXH755UVlZWXxr3/9q7O+hHXO6u6zSy+9tLjllluKF198sZg1a1YxZsyYoqKiorjrrrs660tY55x00klFz549i3vvvbd48803yx//+9//ymO68jGtwwOhKIriD3/4Q9G3b9+ie/fuxcCBA4tHHnmkfN3+++9fHHvsscuNv+GGG4rtt9++6N69e7HzzjsX//jHPzp4xqzOPhs7dmx5bO/evYtvfetbxZNPPtkJs153Lf0vcF/8WLqfjj322GL//fdPt9l9992L7t27F9tss00xceLEDp/3umx199mFF15YbLvttkV1dXWx4YYbFkOHDi3uvvvuzpn8OmpF+ysills7XfmY5s89AwCJv8UAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQACANcj9998fhx56aNTX10epVIpbbrlltbdRFEVMmDAhtt9++6iqqootttgizj///NXahkAAyrbaaqu47LLLOnsasE5buHBh7LbbbnH55Ze3ehtjxoyJK6+8MiZMmBDPPfdc3HbbbTFw4MDV2obfpAhrqeOOOy7+8pe/RMTnf1Gzb9++ccwxx8QvfvGL6NZtxX/p/d13343a2tpYf/31O3KqQAtKpVJMmTIlRowYUb5syZIl8ctf/jL+9re/xUcffRT9+vWLCy+8MIYOHRoREXPmzIldd901Zs+evdyfBF9dziDAWuyQQw6JN998M1588cUYN25cnH322fHb3/42jfvkk08iImKTTTYRB7CGGz16dDz88MNx/fXXx9NPPx0/+MEP4pBDDokXX3wxIiJuv/322GabbeKOO+6IrbfeOrbaaqs44YQT4oMPPlit+xEIsBarqqqKzTbbLLbccss46aST4sADD4zbbrstjjvuuBgxYkScf/75UV9fX/4p44svMXz00Udx4oknRu/evaO6ujr69esXd9xxR/n6Bx54IIYMGRI1NTXR0NAQp5xySixcuLCjv0xYZ7z22msxceLEuPHGG2PIkCGx7bbbxmmnnRbf+MY3YuLEiRER8fLLL8err74aN954Y1xzzTUxadKkmDFjRnz/+99frfta8XlGYK1UU1MT77//fkRETJ8+Perq6mLatGkrHNvc3BzDhg2L+fPnx7XXXhvbbrttPPvss1FZWRkREXPnzo1DDjkkzjvvvLj66qvj3XffjdGjR8fo0aPL36iAtjVr1qxoamqK7bfffrnLlyxZEhtttFFEfL52lyxZEtdcc0153FVXXRV77LFHPP/886v8soNAgHVAURQxffr0uPPOO+Pkk08uv9fgyiuvjO7du6/wNnfddVc89thjMWfOnPI3mW222aZ8/QUXXBAjR46MsWPHRkTE1772tfj9738f+++/f1xxxRVRXV3d7l8XrGsWLFgQlZWVMWPGjHKsL7XBBhtERMTmm28e3bp1Wy4idtxxx4j4/AyEQADijjvuiA022CA+/fTTaG5ujiOPPDLOPvvsGDVqVOyyyy4txkFExMyZM6NPnz7pJ5Wl/vOf/8TTTz8dkydPLl9WFEU0NzfHK6+8Uv6GBLSd/v37R1NTU7zzzjsxZMiQFY7Zd99947PPPou5c+fGtttuGxERL7zwQkREbLnllqt8XwIB1mIHHHBAXHHFFdG9e/eor69f7n8v1NbWrvS2NTU1K71+wYIFceKJJ8Ypp5ySruvbt2/rJgzEggUL4qWXXip//sorr8TMmTNjww03jO233z5GjhwZxxxzTFx88cXRv3//ePfdd2P69Omx6667xvDhw+PAAw+MAQMGxI9//OO47LLLorm5OUaNGhUHHXRQi8G/IgIB1mK1tbWx3Xbbteq2u+66a8ybNy9eeOGFFX5TGTBgQDz77LOt3j6wYk888UQccMAB5c9PPfXUiIg49thjY9KkSTFx4sQ477zzYty4cfHf//43Nt5449h7773j29/+dkREVFRUxO233x4nn3xy7LffflFbWxvDhg2Liy++eLXmIRCAFdp///1jv/32i+9973txySWXxHbbbRfPPfdclEqlOOSQQ+JnP/tZ7L333jF69Og44YQTora2Np599tmYNm1a/PGPf+zs6UOXNXTo0FjZryhab7314pxzzolzzjmnxTH19fVx0003faV5+G+OQItuuumm2GuvveKII46InXbaKc4444xoamqKiM/PMNx3333xwgsvxJAhQ6J///5x1llnRX19fSfPGmgLfpMiAJA4gwAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQ/H8pVK2cxylI+wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x=df['Price'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**H.W : How to handle outliers**"
      ],
      "metadata": {
        "id": "RvZ5kmdxyIMz"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqFPRda8eEp_",
        "papermill": {
          "duration": 0.038833,
          "end_time": "2020-11-27T11:52:34.169033",
          "exception": false,
          "start_time": "2020-11-27T11:52:34.130200",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## 9. Plot different features against one another"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-W6Q9-hJosZ",
        "papermill": {
          "duration": 0.037659,
          "end_time": "2020-11-27T11:52:34.244690",
          "exception": false,
          "start_time": "2020-11-27T11:52:34.207031",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "### Histogram\n",
        "\n",
        "Histogram refers to the frequency of occurrence of variables in an interval. In this case, there are mainly 10 different types of car manufacturing companies, but it is often important to know who has the most number of cars. To do this histogram is one of the trivial solutions which lets us know the total number of car manufactured by a different company."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Make.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5))\n",
        "plt.title(\"Number of cars by make\")\n",
        "plt.ylabel('Number of cars')\n",
        "plt.xlabel('Make');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "F6gQUyDxnHhg",
        "outputId": "6551438c-95c7-4c1f-a85f-fbd01b7527e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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tjdDQUGX4ZlL16tVDlixZkJCQgPr166v7ZaRR9erVsWjRIgQHB6NPnz7w9PTEpEmTcPv27Y8mW4mPXbt2LZo0aYI2bdpg1apVaNmyJVxdXREcHIyff/4ZsbGxyJkzJypVqoQOHTp8kmMiIkoLjaRlxjMRERFlCnFxcXB1dUW9evWwaNGijG4OEdFXhXO2iIiIvmAbNmzAo0eP0LZt24xuChHRV4c9W0RERF+go0eP4ty5cxg3bhyyZcuW4mLCRERkWOzZIiIi+gLNnTsXXbt2Rfbs2bFs2bKMbg4R0VeJPVtEREREREQGwJ4tIiIiIiIiA2Dpdx0kJCTg33//ha2tLTQaTUY3h4iIiIiIMoiI4MWLF3B1dU11TUcmWzr4999/4ebmltHNICIiIiKiz8S9e/eQK1euj+7DZEsHtra2AN79Qu3s7DK4NURERERElFGio6Ph5uam5Agfw2RLB4lDB+3s7JhsERERERGRTtOLWCCDiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgAmW0RERERERAbAZIuIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZABMtoiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBmCS0Q3IzDyGbNVpv9vBdQzcEiIiIiIi+tywZ4uIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZABMtoiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyACYbBERERERERkAky0iIiIiIiIDYLJFRERERERkAEy2iIiIiIiIDIDJFhERERERkQEw2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgAmW0RERERERAbAZIuIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZABMtoiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyACYbBERERERERkAky0iIiIiIiIDyNBkKzw8HPXq1YOrqys0Gg02bNigdb+IYOTIkciRIwcsLS1Ro0YNREREaO3z9OlTtGrVCnZ2dnBwcEBQUBBiYmK09jl37hwqVaoECwsLuLm5YfLkyYY+NCIiIiIi+splaLL18uVLFCtWDLNnz07x/smTJ2PGjBmYN28ejh49CmtrawQEBOD169fKPq1atcLFixexY8cObNmyBeHh4ejSpYtyf3R0NGrWrAl3d3ecPHkSP//8M0aPHo0FCxYY/PiIiIiIiOjrpRERyehGAIBGo8H69evRsGFDAO96tVxdXdG/f38MGDAAAPD8+XM4OzsjNDQUzZs3x+XLl+Ht7Y3jx4+jdOnSAIBt27ahdu3a+Pvvv+Hq6oq5c+fixx9/RGRkJMzMzAAAQ4YMwYYNG3DlyhWd2hYdHQ17e3s8f/4cdnZ2ynaPIVt1evzt4Dq6/hqIiIiIiOgz9qHcICWf7ZytW7duITIyEjVq1FC22dvbo1y5cjh8+DAA4PDhw3BwcFASLQCoUaMGjIyMcPToUWWfypUrK4kWAAQEBODq1at49uxZis8dGxuL6OhorRsREREREZEan22yFRkZCQBwdnbW2u7s7KzcFxkZiezZs2vdb2JiAkdHR619UoqR9DneN3HiRNjb2ys3Nze39B8QERERERF9VT7bZCsjDR06FM+fP1du9+7dy+gmERERERFRJvPZJlsuLi4AgAcPHmhtf/DggXKfi4sLHj58qHV/XFwcnj59qrVPSjGSPsf7zM3NYWdnp3UjIiIiIiJS47NNtjw9PeHi4oJdu3Yp26Kjo3H06FH4+fkBAPz8/BAVFYWTJ08q++zevRsJCQkoV66csk94eDjevn2r7LNjxw4UKFAAWbJk+URHQ0REREREX5sMTbZiYmJw5swZnDlzBsC7ohhnzpzB3bt3odFo0KdPH/z000/YtGkTzp8/j7Zt28LV1VWpWFioUCEEBgaic+fOOHbsGA4ePIgePXqgefPmcHV1BQC0bNkSZmZmCAoKwsWLF7FmzRpMnz4d/fr1y6CjJiIiIiKir4FJRj75iRMnUK1aNeXnxASoXbt2CA0NxaBBg/Dy5Ut06dIFUVFRqFixIrZt2wYLCwvlMStXrkSPHj3g7+8PIyMjNGnSBDNmzFDut7e3x/bt29G9e3eUKlUK2bJlw8iRI7XW4iIiIiIiItK3z2adrc8Z19kiIiIiIiLgC1lni4iIiIiIKDNjskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgAmW0RERERERAbAZIuIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZABMtoiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyACYbBERERERERkAky0iIiIiIiIDYLJFRERERERkAEy2iIiIiIiIDIDJFhERERERkQEw2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgAmW0RERERERAbAZIuIiIiIiMgAmGwREREREREZgElGN4D+j8eQranuczu4zidoCRERERERpRd7toiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyACYbBERERERERkAky0iIiIiIiIDYLJFRERERERkAEy2iIiIiIiIDIDJFhERERERkQEw2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgAmW0RERERERAbAZIuIiIiIiMgAmGwREREREREZgElGN+Bj4uPjMXr0aKxYsQKRkZFwdXVF+/btMXz4cGg0GgCAiGDUqFH49ddfERUVhQoVKmDu3LnIly+fEufp06fo2bMnNm/eDCMjIzRp0gTTp0+HjY1NRh2awXkM2arTfreD6xi4JUREREREX6fPumdr0qRJmDt3LmbNmoXLly9j0qRJmDx5MmbOnKnsM3nyZMyYMQPz5s3D0aNHYW1tjYCAALx+/VrZp1WrVrh48SJ27NiBLVu2IDw8HF26dMmIQyIiIiIioq/EZ92zdejQITRo0AB16rzrffHw8MDq1atx7NgxAO96tUJCQjB8+HA0aNAAALBs2TI4Oztjw4YNaN68OS5fvoxt27bh+PHjKF26NABg5syZqF27NqZMmQJXV9dkzxsbG4vY2Fjl5+joaEMfKhERERERfWE+656t8uXLY9euXbh27RoA4OzZszhw4ABq1aoFALh16xYiIyNRo0YN5TH29vYoV64cDh8+DAA4fPgwHBwclEQLAGrUqAEjIyMcPXo0xeedOHEi7O3tlZubm5uhDpGIiIiIiL5Qn3XP1pAhQxAdHY2CBQvC2NgY8fHxGD9+PFq1agUAiIyMBAA4OztrPc7Z2Vm5LzIyEtmzZ9e638TEBI6Ojso+7xs6dCj69eun/BwdHc2Ei4iIiIiIVPmsk621a9di5cqVWLVqFQoXLowzZ86gT58+cHV1Rbt27Qz2vObm5jA3NzdYfCIiIiIi+vJ91snWwIEDMWTIEDRv3hwA4OPjgzt37mDixIlo164dXFxcAAAPHjxAjhw5lMc9ePAAxYsXBwC4uLjg4cOHWnHj4uLw9OlT5fFERERERET69lnP2Xr16hWMjLSbaGxsjISEBACAp6cnXFxcsGvXLuX+6OhoHD16FH5+fgAAPz8/REVF4eTJk8o+u3fvRkJCAsqVK/cJjoKIiIiIiL5Gn3XPVr169TB+/Hjkzp0bhQsXxunTpzFt2jR07NgRAKDRaNCnTx/89NNPyJcvHzw9PTFixAi4urqiYcOGAIBChQohMDAQnTt3xrx58/D27Vv06NEDzZs3T7ESIRERERERkT581snWzJkzMWLECHTr1g0PHz6Eq6srvv/+e4wcOVLZZ9CgQXj58iW6dOmCqKgoVKxYEdu2bYOFhYWyz8qVK9GjRw/4+/srixrPmDEjIw6JiIiIiIi+EhoRkYxuxOcuOjoa9vb2eP78Oezs7JTtHkO26vT428F1dNpPl3j6jKUmHhERERERfTg3SMlnPWeLiIiIiIgos2KyRUREREREZABMtoiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyACYbBERERERERkAky0iIiIiIiIDUJ1s/ffff3j16pXy8507dxASEoLt27frtWFERERERESZmYnaBzRo0ACNGzfGDz/8gKioKJQrVw6mpqZ4/Pgxpk2bhq5duxqinZSBPIZs1Wm/28F1DNwSIiIiIqLMQ3XP1qlTp1CpUiUAwLp16+Ds7Iw7d+5g2bJlmDFjht4bSERERERElBmpTrZevXoFW1tbAMD27dvRuHFjGBkZwdfXF3fu3NF7A4mIiIiIiDIj1cmWl5cXNmzYgHv37iEsLAw1a9YEADx8+BB2dnZ6byAREREREVFmpDrZGjlyJAYMGAAPDw+UK1cOfn5+AN71cpUoUULvDSQiIiIiIsqMVBfI+Pbbb1GxYkXcv38fxYoVU7b7+/ujUaNGem0cERERERFRZqUq2Xr79i0sLS1x5syZZL1YZcuW1WvDiIiIiIiIMjNVwwhNTU2RO3duxMfHG6o9REREREREXwTVc7Z+/PFHDBs2DE+fPjVEe4iIiIiIiL4IqudszZo1C9evX4erqyvc3d1hbW2tdf+pU6f01jgiIiIiIqLMSnWy1bBhQwM0g4iIiIiI6MuiOtkaNWqUIdpBRERERET0RVE9Z4uIiIiIiIhSp7pnKz4+Hr/88gvWrl2Lu3fv4s2bN1r3s3AGERERERFRGnq2xowZg2nTpqFZs2Z4/vw5+vXrh8aNG8PIyAijR482QBOJiIiIiIgyH9XJ1sqVK/Hrr7+if//+MDExQYsWLbBw4UKMHDkSR44cMUQbiYiIiIiIMh3VyVZkZCR8fHwAADY2Nnj+/DkAoG7duti6dat+W0dERERERJRJqU62cuXKhfv37wMA8ubNi+3btwMAjh8/DnNzc/22joiIiIiIKJNSnWw1atQIu3btAgD07NkTI0aMQL58+dC2bVt07NhR7w0kIiIiIiLKjFRXIwwODlb+36xZM7i7u+PQoUPIly8f6tWrp9fGERERERERZVaqk633+fr6wtfXVx9tISIiIiIi+mKoHkY4ceJELF68ONn2xYsXY9KkSXppFBERERERUWanOtmaP38+ChYsmGx74cKFMW/ePL00ioiIiIiIKLNLU+n3HDlyJNvu5OSkVCkkIiIiIiL62qlOttzc3HDw4MFk2w8ePAhXV1e9NIqIiIiIiCizU10go3PnzujTpw/evn2L6tWrAwB27dqFQYMGoX///npvIBERERERUWakOtkaOHAgnjx5gm7duuHNmzcAAAsLCwwePBhDhw7VewOJiIiIiIgyI9XJlkajwaRJkzBixAhcvnwZlpaWyJcvH8zNzQ3RPiIiIiIiokwpzets2djYoEyZMvpsCxERERER0RdDdYEMIiIiIiIiSh2TLSIiIiIiIgNgskVERERERGQAOiVbJUuWxLNnzwAAY8eOxatXrwzaKCIiIiIiosxOp2Tr8uXLePnyJQBgzJgxiImJMWijiIiIiIiIMjudqhEWL14cHTp0QMWKFSEimDJlCmxsbFLcd+TIkXptIBERERERUWakU7IVGhqKUaNGYcuWLdBoNPjrr79gYpL8oRqNhskWERERERERdEy2ChQogN9++w0AYGRkhF27diF79uwGbRgREREREVFmpnpR44SEBEO0g4iIiIiI6IuiOtkCgBs3biAkJASXL18GAHh7e6N3797ImzevXhtHRERERESUWalOtsLCwlC/fn0UL14cFSpUAAAcPHgQhQsXxubNm/HNN9/ovZH0ZfEYsjXVfW4H1/kELSEiIiIiMhzVydaQIUPQt29fBAcHJ9s+ePBgJltERERERETQcZ2tpC5fvoygoKBk2zt27IhLly7ppVFERERERESZnepky8nJCWfOnEm2/cyZM6xQSERERERE9P+pHkbYuXNndOnSBTdv3kT58uUBvJuzNWnSJPTr10/vDSQiIiIiIsqMVPdsjRgxAiNHjsTMmTNRpUoVVKlSBbNmzcLo0aMxfPhwvTfwn3/+QevWrZE1a1ZYWlrCx8cHJ06cUO4XEYwcORI5cuSApaUlatSogYiICK0YT58+RatWrWBnZwcHBwcEBQUhJiZG720lIiIiIiJKpDrZ0mg06Nu3L/7++288f/4cz58/x99//43evXtDo9HotXHPnj1DhQoVYGpqir/++guXLl3C1KlTkSVLFmWfyZMnY8aMGZg3bx6OHj0Ka2trBAQE4PXr18o+rVq1wsWLF7Fjxw5s2bIF4eHh6NKli17bSkRERERElFSa1tlKZGtrq692pGjSpElwc3PDkiVLlG2enp7K/0UEISEhGD58OBo0aAAAWLZsGZydnbFhwwY0b94cly9fxrZt23D8+HGULl0aADBz5kzUrl0bU6ZMgaurq0GPgYiIiIiIvk6qe7Y+pU2bNqF06dJo2rQpsmfPjhIlSuDXX39V7r916xYiIyNRo0YNZZu9vT3KlSuHw4cPAwAOHz4MBwcHJdECgBo1asDIyAhHjx5N8XljY2MRHR2tdSMiIiIiIlLjs062bt68iblz5yJfvnwICwtD165d0atXLyxduhQAEBkZCQBwdnbWepyzs7NyX2RkZLIqiSYmJnB0dFT2ed/EiRNhb2+v3Nzc3PR9aERERERE9IX7rJOthIQElCxZEhMmTECJEiXQpUsXdO7cGfPmzTPo8w4dOlSZj/b8+XPcu3fPoM9HRERERERfHlXJ1tu3b+Hv75+s2p+h5MiRA97e3lrbChUqhLt37wIAXFxcAAAPHjzQ2ufBgwfKfS4uLnj48KHW/XFxcXj69Kmyz/vMzc1hZ2endSMiIiIiIlJDVbJlamqKc+fOGaotyVSoUAFXr17V2nbt2jW4u7sDeFcsw8XFBbt27VLuj46OxtGjR+Hn5wcA8PPzQ1RUFE6ePKnss3v3biQkJKBcuXKf4CiIiIiIiOhrpHoYYevWrbFo0SJDtCWZvn374siRI5gwYQKuX7+OVatWYcGCBejevTuAd2Xo+/Tpg59++gmbNm3C+fPn0bZtW7i6uqJhw4YA3vWEBQYGonPnzjh27BgOHjyIHj16oHnz5qxESEREREREBqO69HtcXBwWL16MnTt3olSpUrC2tta6f9q0aXprXJkyZbB+/XoMHToUY8eOhaenJ0JCQtCqVStln0GDBuHly5fo0qULoqKiULFiRWzbtg0WFhbKPitXrkSPHj3g7+8PIyMjNGnSBDNmzNBbO4mIiIiIiN6nOtm6cOECSpYsCeDdkL6k9L2oMQDUrVsXdevW/eD9Go0GY8eOxdixYz+4j6OjI1atWqX3thEREREREX2I6mRrz549hmgHERERERHRFyXNpd+vX7+OsLAw/PfffwAAEdFbo4iIiIiIiDI71cnWkydP4O/vj/z586N27dq4f/8+ACAoKAj9+/fXewOJiIiIiIgyI9XJVt++fWFqaoq7d+/CyspK2d6sWTNs27ZNr40jIiIiIiLKrFTP2dq+fTvCwsKQK1cure358uXDnTt39NYwIiIiIiKizEx1z9bLly+1erQSPX36FObm5nppFBERERERUWanumerUqVKWLZsGcaNGwfgXen1hIQETJ48GdWqVdN7A4k+xmPIVp32ux1cx8AtISIiIiLSpjrZmjx5Mvz9/XHixAm8efMGgwYNwsWLF/H06VMcPHjQEG0kIiIiIiLKdFQPIyxSpAiuXbuGihUrokGDBnj58iUaN26M06dPI2/evIZoIxERERERUaajumcLAOzt7fHjjz/quy1ERERERERfjDQlW8+ePcOiRYtw+fJlAIC3tzc6dOgAR0dHvTaOiIiIiIgos1I9jDA8PBweHh6YMWMGnj17hmfPnmHGjBnw9PREeHi4IdpIRERERESU6aju2erevTuaNWuGuXPnwtjYGAAQHx+Pbt26oXv37jh//rzeG0lERERERJTZqO7Zun79Ovr3768kWgBgbGyMfv364fr163ptHBERERERUWalOtkqWbKkMlcrqcuXL6NYsWJ6aRQREREREVFmp9MwwnPnzin/79WrF3r37o3r16/D19cXAHDkyBHMnj0bwcHBhmklERERERFRJqNTslW8eHFoNBqIiLJt0KBByfZr2bIlmjVrpr/WERERERERZVI6JVu3bt0ydDuIiIiIiIi+KDolW+7u7oZuBxERERER0RclTYsa//vvvzhw4AAePnyIhIQErft69eqll4YRERERERFlZqqTrdDQUHz//fcwMzND1qxZodFolPs0Gg2TLcq0PIZs1Wm/28F1DNwSIiIiIvoSqE62RowYgZEjR2Lo0KEwMlJdOZ6IiIiIiOiroDpbevXqFZo3b85Ei4iIiIiI6CNUZ0xBQUH4/fffDdEWIiIiIiKiL4bqYYQTJ05E3bp1sW3bNvj4+MDU1FTr/mnTpumtcURERERERJlVmpKtsLAwFChQAACSFcggIiIiIiKiNCRbU6dOxeLFi9G+fXsDNIeIiIiIiOjLoHrOlrm5OSpUqGCIthAREREREX0xVCdbvXv3xsyZMw3RFiIiIiIioi+G6mGEx44dw+7du7FlyxYULlw4WYGM//3vf3prHBERERERUWalOtlycHBA48aNDdEWIiIiIiKiL4bqZGvJkiWGaAcREREREdEXRfWcLSIiIiIiIkqd6p4tT0/Pj66ndfPmzXQ1iIiIiIiI6EugOtnq06eP1s9v377F6dOnsW3bNgwcOFBf7SIiIiIiIsrUVCdbvXv3TnH77NmzceLEiXQ3iIiIiIiI6EugtzlbtWrVwh9//KGvcERERERERJma3pKtdevWwdHRUV/hiIiIiIiIMjXVwwhLlCihVSBDRBAZGYlHjx5hzpw5em0cERERERFRZqU62WrYsKHWz0ZGRnByckLVqlVRsGBBfbWLiIiIiIgoU1OdbI0aNcoQ7SAiIiIiIvqicFFjIiIiIiIiA9C5Z8vIyOijixkDgEajQVxcXLobRfQl8BiyVaf9bgfXMXBLiIiIiCgj6JxsrV+//oP3HT58GDNmzEBCQoJeGkVERERERJTZ6ZxsNWjQINm2q1evYsiQIdi8eTNatWqFsWPH6rVxREREREREmVWa5mz9+++/6Ny5M3x8fBAXF4czZ85g6dKlcHd313f7iIiIiIiIMiVVydbz588xePBgeHl54eLFi9i1axc2b96MIkWKGKp9REREREREmZLOwwgnT56MSZMmwcXFBatXr05xWCERERERERG9o3OyNWTIEFhaWsLLywtLly7F0qVLU9zvf//7n94aR0RERERElFnpnGy1bds21dLvRERERERE9I7OyVZoaKgBm0FEH8M1u4iIiIgynzRVIyQiIiIiIqKPY7JFRERERERkAEy2iIiIiIiIDCBTJVvBwcHQaDTo06ePsu3169fo3r07smbNChsbGzRp0gQPHjzQetzdu3dRp04dWFlZIXv27Bg4cCDi4uI+ceuJiIiIiOhrkmmSrePHj2P+/PkoWrSo1va+ffti8+bN+P3337Fv3z78+++/aNy4sXJ/fHw86tSpgzdv3uDQoUNYunQpQkNDMXLkyE99CERERERE9BXJFMlWTEwMWrVqhV9//RVZsmRRtj9//hyLFi3CtGnTUL16dZQqVQpLlizBoUOHcOTIEQDA9u3bcenSJaxYsQLFixdHrVq1MG7cOMyePRtv3rzJqEMiIiIiIqIvXKZItrp37446deqgRo0aWttPnjyJt2/fam0vWLAgcufOjcOHDwMADh8+DB8fHzg7Oyv7BAQEIDo6GhcvXkzx+WJjYxEdHa11IyIiIiIiUkPndbYyym+//YZTp07h+PHjye6LjIyEmZkZHBwctLY7OzsjMjJS2SdpopV4f+J9KZk4cSLGjBmjh9YTEREREdHX6rNOtu7du4fevXtjx44dsLCw+GTPO3ToUPTr10/5OTo6Gm5ubp/s+YkMTZdFkrlAMhEREVH6fNbDCE+ePImHDx+iZMmSMDExgYmJCfbt24cZM2bAxMQEzs7OePPmDaKiorQe9+DBA7i4uAAAXFxcklUnTPw5cZ/3mZubw87OTutGRERERESkxmedbPn7++P8+fM4c+aMcitdujRatWql/N/U1BS7du1SHnP16lXcvXsXfn5+AAA/Pz+cP38eDx8+VPbZsWMH7Ozs4O3t/cmPiYiIiIiIvg6f9TBCW1tbFClSRGubtbU1smbNqmwPCgpCv3794OjoCDs7O/Ts2RN+fn7w9fUFANSsWRPe3t5o06YNJk+ejMjISAwfPhzdu3eHubn5Jz8mIiIiIiL6OnzWyZYufvnlFxgZGaFJkyaIjY1FQEAA5syZo9xvbGyMLVu2oGvXrvDz84O1tTXatWuHsWPHZmCriYiIiIjoS5fpkq29e/dq/WxhYYHZs2dj9uzZH3yMu7s7/vzzTwO3jIiIiIiI6P981nO2iIiIiIiIMismW0RERERERAbAZIuIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZABMtoiIiIiIiAyAyRYREREREZEBZLpFjYno8+IxZKtO+90OrmPglhARERF9XphsEdFng4kbERERfUk4jJCIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZAAskEFEXyxdCm6w2AYREREZCnu2iIiIiIiIDIA9W0REOtBnWXqWuCciIvo6sGeLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgAmW0RERERERAbARY2JiDI5LpJMRET0eWLPFhERERERkQEw2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNg6XciIlKwjDwREZH+sGeLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgskVERERERGQATLaIiIiIiIgMgMkWERERERGRATDZIiIiIiIiMgCus0VERAajy7pdXLOLiIi+VEy2iIgoU+CCy0RElNlwGCEREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyACYbBERERERERkAky0iIiIiIiIDYOl3IiL66rCMPBERfQrs2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGQCTLSIiIiIiIgNgNUIiIqJ00qW6ISsbEhF9fT7rnq2JEyeiTJkysLW1Rfbs2dGwYUNcvXpVa5/Xr1+je/fuyJo1K2xsbNCkSRM8ePBAa5+7d++iTp06sLKyQvbs2TFw4EDExcV9ykMhIiIiIqKvzGedbO3btw/du3fHkSNHsGPHDrx9+xY1a9bEy5cvlX369u2LzZs34/fff8e+ffvw77//onHjxsr98fHxqFOnDt68eYNDhw5h6dKlCA0NxciRIzPikIiIiIiI6CvxWQ8j3LZtm9bPoaGhyJ49O06ePInKlSvj+fPnWLRoEVatWoXq1asDAJYsWYJChQrhyJEj8PX1xfbt23Hp0iXs3LkTzs7OKF68OMaNG4fBgwdj9OjRMDMzS/a8sbGxiI2NVX6Ojo427IESEREREdEX57NOtt73/PlzAICjoyMA4OTJk3j79i1q1Kih7FOwYEHkzp0bhw8fhq+vLw4fPgwfHx84Ozsr+wQEBKBr1664ePEiSpQokex5Jk6ciDFjxhj4aIiIiJLTZf4XwDlgRESZQaZJthISEtCnTx9UqFABRYoUAQBERkbCzMwMDg4OWvs6OzsjMjJS2SdpopV4f+J9KRk6dCj69eun/BwdHQ03Nzd9HQoREdEnwcSNiChjZZpkq3v37rhw4QIOHDhg8OcyNzeHubm5wZ+HiIiIiIi+XJ91gYxEPXr0wJYtW7Bnzx7kypVL2e7i4oI3b94gKipKa/8HDx7AxcVF2ef96oSJPyfuQ0REREREpG+fdc+WiKBnz55Yv3499u7dC09PT637S5UqBVNTU+zatQtNmjQBAFy9ehV3796Fn58fAMDPzw/jx4/Hw4cPkT17dgDAjh07YGdnB29v7097QERERJkYhyUSEanzWSdb3bt3x6pVq7Bx40bY2toqc6zs7e1haWkJe3t7BAUFoV+/fnB0dISdnR169uwJPz8/+Pr6AgBq1qwJb29vtGnTBpMnT0ZkZCSGDx+O7t27c6ggERFRBmHiRkRfg8862Zo7dy4AoGrVqlrblyxZgvbt2wMAfvnlFxgZGaFJkyaIjY1FQEAA5syZo+xrbGyMLVu2oGvXrvDz84O1tTXatWuHsWPHfqrDICIiIiKir9BnnWyJSKr7WFhYYPbs2Zg9e/YH93F3d8eff/6pz6YRERERERF9VKYokEFERERERJTZMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyAA+6wIZRERERLrQpZQ8y8gT0afGni0iIiIiIiIDYLJFRERERERkAEy2iIiIiIiIDIBztoiIiIiS0GX+F8A5YESUOvZsERERERERGQCTLSIiIiIiIgPgMEIiIiIiA+GQRKKvG3u2iIiIiIiIDIA9W0RERESZBBdvJspc2LNFRERERERkAEy2iIiIiIiIDIDJFhERERERkQEw2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGQDX2SIiIiL6CumyZhfAdbuI0oM9W0RERERERAbAZIuIiIiIiMgAmGwREREREREZAJMtIiIiIiIiA2CyRUREREREZACsRkhERERE6cLKhkQpY88WERERERGRATDZIiIiIiIiMgAmW0RERERERAbAOVtERERE9FnhHDD6UrBni4iIiIiIyADYs0VEREREXyz2klFGYs8WERERERGRAbBni4iIiIhIR7r0lLGXjBKxZ4uIiIiIiMgA2LNFRERERJQBOJ/sy8dki4iIiIgok2Pi9nliskVERERERFo4N00/mGwREREREZHBfM29biyQQUREREREZABMtoiIiIiIiAyAyRYREREREZEBMNkiIiIiIiIyACZbREREREREBsBki4iIiIiIyABY+p2IiIiIiDKFzFZGnskWERERERF9lQydvHEYIRERERERkQEw2SIiIiIiIjIAJltEREREREQGwGSLiIiIiIjIAJhsERERERERGcBXlWzNnj0bHh4esLCwQLly5XDs2LGMbhIREREREX2hvppka82aNejXrx9GjRqFU6dOoVixYggICMDDhw8zumlERERERPQF+mrW2Zo2bRo6d+6MDh06AADmzZuHrVu3YvHixRgyZIjWvrGxsYiNjVV+fv78OQAgOjpaa7+E2Fc6Pff7j/sQXeLpM5au8Xic6mPpOx7/Bupj6RrvazlOXePxb6A+lq7xvpbj1DUe/wbqY+ka72s5Tl3j8W+gPpau8b6W43w/XuL/RSTVx2lEl70yuTdv3sDKygrr1q1Dw4YNle3t2rVDVFQUNm7cqLX/6NGjMWbMmE/cSiIiIiIiyizu3buHXLlyfXSfr6Jn6/Hjx4iPj4ezs7PWdmdnZ1y5ciXZ/kOHDkW/fv2UnxMSEvD06VNkzZoVGo3mg88THR0NNzc33Lt3D3Z2dulqsz5jsW0ZH+tzbtvXcpyfc9t4nBkf72tpG48z4+N9LW3jcWZ8vK+lbRlxnCKCFy9ewNXVNdV4X0WypZa5uTnMzc21tjk4OOj8eDs7O738sfUdS9/xvpa28TgzPt7X0jYeZ8bH+1raxuPM+HhfS9t4nBkf72tp26c+Tnt7e53ifBUFMrJlywZjY2M8ePBAa/uDBw/g4uKSQa0iIiIiIqIv2VeRbJmZmaFUqVLYtWuXsi0hIQG7du2Cn59fBraMiIiIiIi+VF/NMMJ+/fqhXbt2KF26NMqWLYuQkBC8fPlSqU6oD+bm5hg1alSyIYgZHUvf8b6WtvE4Mz7e19I2HmfGx/ta2sbjzPh4X0vbeJwZH+9radvnfJzAV1KNMNGsWbPw888/IzIyEsWLF8eMGTNQrly5jG4WERERERF9gb6qZIuIiIiIiOhT+SrmbBEREREREX1qTLaIiIiIiIgMgMkWERERERGRATDZIiIiIjKAuLg4LFu2LNk6n0T09WCBDCIiIiIDsbKywuXLl+Hu7p7RTfkk3rx5g4cPHyIhIUFre+7cuTOoRUQZiz1b6VS9enVERUUl2x4dHY3q1at/+gYZ0KtXr3DlyhWcO3dO60aZz61btxAREZFse0REBG7fvv3pG0REn723b9/C398/xXPHl+zNmze4evUq4uLi0vT4smXL4syZM/ptlIGICNJ6DT4iIgKVKlWCpaUl3N3d4enpCU9PT3h4eMDT01PPLSXKPL6aRY0NZe/evXjz5k2y7a9fv8b+/ft1ihEdHa3z89nZ2em034MHDzBgwADs2rULDx8+THbyjI+P1/k5Hz16hA4dOuCvv/5K8X41sT5XERERGDlyJObPn5/sd/z8+XN07doVP/30E/LkyZNBLdSv9u3bo2PHjsiXL5/W9qNHj2LhwoXYu3evqngdO3ZElSpV0K5dO63t0dHR6NOnDxYvXvzRx2/atAm1atWCqakpNm3a9NF969evr1ObqlSpAn9/f1StWhV+fn4wNTXV6XGG0rhxY4SGhsLOzg6NGzf+6L7/+9//PlGrUhYVFYVFixbh8uXLAIDChQujY8eOsLe3Vx0rPDz8o/dXrlxZVbwTJ05g7dq1uHv3brJzb0b/3gwpPj4e58+fh7u7O7JkyaL68Xv27EG1atVSvG/27Nno3r17qjFMTU2/qgtsr169Qs+ePbF06VIAwLVr15AnTx707NkTOXPmxJAhQ3SK061bN/Tr1w/37t1DqVKlYG1trXV/0aJF09S+uLg47N27Fzdu3EDLli1ha2uLf//9F3Z2drCxsVEVa9GiRfjll1+URDpfvnzo06cPOnXqpHOM9u3bw8TEBFu2bEGOHDmg0WhUtcHQlixZgmbNmsHKyiqjm6LIkycPjh8/jqxZs+o1blRUFI4dO5ZiD2Pbtm31+lwZTV/vg1OnTsHU1BQ+Pj4AgI0bN2LJkiXw9vbG6NGjYWZmluY2chhhGiV+4BQvXhy7d++Go6Ojcl98fDy2bduG+fPn69RLYGRkpPNJSdfEplatWrh79y569OiR4kmvQYMGOsUBgFatWuHOnTsICQlB1apVsX79ejx48AA//fQTpk6dijp16ugcK6n9+/dj/vz5uHHjBtatW4ecOXNi+fLl8PT0RMWKFXWOEx8fj9DQUCWxfP/Esnv37lRjdOnSBQ4ODpg8eXKK9w8ePBjR0dGYO3euzu1KtG7dug9+OTx16pTqeK9evUoxlpoPbDs7O5w6dQpeXl5a269fv47SpUun2Fv7MUZGRrC0tERQUBBCQkJgZPSu0/zBgwdwdXVN9XVrZGSEyMhIZM+eXXlsSjQajc7vgfbt22Pfvn24c+cOLC0t4efnh2rVqqF69eooW7YsjI2NdYqjr0SwQ4cOmDFjBmxtbdGhQ4ePxlmyZIlObTt37hyKFCkCIyOjVL8E6/r6OHHiBAICAmBpaYmyZcsCAI4fP47//vsP27dvR8mSJXWKkyilv2fS85GaizW//fYb2rZti4CAAGzfvh01a9bEtWvX8ODBAzRq1Ejn31tK0vq+mjFjBrp06QILCwvMmDHjo/v26tVL5/b06dMHPj4+CAoKQnx8PKpUqYJDhw7BysoKW7ZsQdWqVXWOBQBZsmTBzp07UapUKa3t06dPx4gRI3S+6Ne3b1+Ym5sjODhY1fN/SK9eveDl5ZXsdzNr1ixcv34dISEhquLp80Jj7969cfDgQYSEhCAwMBDnzp1Dnjx5sHHjRowePRqnT5/WKc6H3gMiouqcltSdO3cQGBiIu3fvIjY2VkkEe/fujdjYWMybN0/nWCNHjsS0adPQs2dP+Pn5AQAOHz6MWbNmoW/fvhg7dqxOcaytrXHy5EkULFhQ9fF8jL4SB2dnZ/z3339o2rQpgoKCUL58edVtcXR0xLVr15AtWzZkyZLlo9/dnj59mmq8pJ99+rJ582a0atUKMTExsLOz02qjRqPRqV36vDjYr18/jBs3DtbW1ujXr99H9502bVqqbUtKn++DMmXKYMiQIWjSpAlu3ryJwoULo1GjRjh+/Djq1Kmj+lykRShNNBqNGBkZiZGRkWg0mmQ3KysrWbRokU6x9u7dq9xCQ0PFxcVFhgwZIhs3bpSNGzfKkCFDJEeOHBIaGqpz+2xsbOT06dNpPDptLi4ucvToURERsbW1latXr4qIyMaNG6VChQppirlu3TqxtLSUTp06ibm5udy4cUNERGbOnCm1atVSFat79+5ibW0t3333nfTu3Vv69OmjddNF/vz55dixYx+8/8SJE5I/f35V7RIRmT59utjY2EiPHj3EzMxMvv/+e6lRo4bY29vLsGHDVMV6+PCh1KlTR3ndvX9Tw87OTk6dOpVs+4kTJ8TGxkZVLJF374c9e/ZI3rx5pUaNGvL06VMREYmMjFTdNn27deuWLFq0SNq2bSu5c+cWjUYjtra2EhgYKJMnT0718RqNRh48eKD8/0O3jDjO99v2/vko8Wc1batYsaK0b99e3r59q2x7+/attGvXTipVqqS6jVFRUVq3R48eyfbt26VcuXKyc+dOVbF8fHxk1qxZIvLuHHfjxg1JSEiQzp07y8iRI1W3TST97ysPDw95/Pix8v8P3Tw9PVW1K2fOnHL8+HEREVm/fr24urrK1atXZfjw4VK+fHnVx/nrr7+Kk5OTXL58Wdk2ZcoUsbOzk/DwcJ3j9OjRQ+zs7KRUqVLSpUsX6du3r9ZNLVdXVzlx4kSy7SdPnpScOXOqjhcYGCje3t4yZ84cWb9+vWzYsEHrpkbu3Lnl8OHDIvJ/rzcRkYiICLG1tdU5zu3btz96S4sGDRpI69atJTY2Vqtte/bsES8vL1WxsmXLJqtWrUq2fdWqVZI1a1ad45QuXVr279+v6rlTs2nTJrG1tRWNRiP29vbi4OCg3LJkyaIq1tu3b+V///uf1K9fX0xNTaVAgQISHBws9+/f1zlGaGiovH79Wvn/x266SHoO15d8+fJJ79695eXLl2mO0b59e4mOjhYRkXbt2kn79u0/eEtN1apV5dmzZ8r/P3SrVq2a6nbq831gZ2cn169fFxGR4OBgqVmzpoiIHDhwQHLlyqW6bUkx2Uqj27dvy61bt0Sj0cjx48e1Tpz//vuvxMXFpSlu9erVUzzprVy5UqpUqaJznEKFCqX4ZTotbG1t5datWyLy7sPnwIEDIiJy8+ZNsbS0TFPM4sWLy9KlS0VE+0Ps1KlT4uzsrCpW1qxZZevWrWlqRyILC4uPfujdvn07TcdaoEAB5e+Z9DhHjBgh3bt3VxWrZcuWUqFCBTl+/LhYW1vL9u3bZfny5VKgQAHZsmWLqlh169aVpk2bar1O4+LipEmTJhIYGKgqlsj/fWA8fvxYqlSpIl5eXnLp0qU0JVv37t374H2JX3zS48aNG/Ljjz+KnZ1dhieC6XX79m1JSEhQ/q+PL3QWFhZaX8gTXbx4Mc3v95Ts3btXSpYsqeoxVlZWyrnI0dFRzp07JyIily5dEhcXlzS1Q5/vK30yNzdX3gudO3eW3r17i8i7866aL/pJTZo0SXLmzCm3bt2S4OBgsbOzU87nutL3lyVzc3OJiIhItj0iIkLMzc1Vx9PnhUZLS0vlnJ30/H3mzBmxs7PTy3OklaOjo1y5ckVEtNt269Yt1e9Te3t7uXbtWrLtV69eFXt7e53j7Nq1S/z8/GTPnj3y+PFjef78udYtLfSROKQkMjJSpkyZIj4+PmJqair16tWTDRs2SHx8vE6Pf/v2rSxdulQiIyPT1Q6NRiPLli1TLq5/6KaGlZWV8nr40unzfWBra6u8D2rUqCEhISEiInLnzh2xsLBIVzuZbH1mLC0tP3jSU/PCCQsLk5o1aypfTNKjdOnSsm3bNhERqVevnrRp00b+/vtvGTRokOTJkydNMS0tLZW2JX2D3LhxQ/UHbI4cOZTetrRydnaWXbt2ffD+nTt3qk4CRd4dZ+IXXScnJzlz5oyIiFy7dk0cHR1VxdJnD+PFixcla9askjdvXuXqVN68ecXJyUnOnz+vKpaIiJGRkXJ17u3btxIUFCT29vayYMEC1QlNoUKF5MmTJ8m2HzhwQNUHf1K3b9+W0NBQad++vXh4eIiVlZVUr15dxowZoyrO0qVLlauaScXGxioXDz6kePHiUqJECZ1uGSl79uwSFhaWbPu2bdske/bsenuey5cvi7W1tarH5MyZU0mwfHx8lAsZhw4dSvOXX0P03OtD7ty5JSwsTOLi4sTNzU1J/C5cuCAODg5pjjto0CDJmjWrODg46OXiRXoVLlxYZs6cmWz7jBkzpFChQqrj6fNCY6VKlWTGjBki8u5z6ubNmyLyrncvICBAVaxly5ZJ+fLlJUeOHMpnwi+//KK6ty2Rg4ODXLx4UWlb4mfo/v37Vb9Pe/TokWKvZP/+/aVbt246x0nao570lp6ef0MmDkeOHJEuXbqIubm5eHh4iL29vXh4eMiePXt0enzSz/e0+thoibSOmmjUqJGsWbMmXe1Kqlq1akrPVFLPnz9P0wUWfdLn+6BatWrStm1bWbZsmZiamioXgfbu3Svu7u7paicLZOjB8uXLMW/ePNy6dQuHDx+Gu7s7fvnlF+TJk0fV3CgAcHNzw6+//pps7tDChQvh5uamc5xmzZrh1atXyJs3L6ysrJIVB9BlzG6i3r174/79+wCAUaNGITAwECtXroSZmRlCQ0N1jpOUi4sLrl+/Dg8PD63tBw4cUF2Eon///pg+fTpmzZqV5gm5lStXxsyZMz9YQXLGjBmoVKmS6rguLi54+vQp3N3dkTt3bhw5cgTFihXDrVu3VFd8evnypTKuO0uWLHj06BHy588PHx8f1XO/vL29ce7cOcyaNQtnz56FpaUl2rZtix49emjNP9RV0mMxMTHBwoUL4e3tjW7duqmO5evri5o1a2LPnj2wtbUF8K7IQr169TB69Gid4yxbtgx79+7F3r178fjxY5QvXx5VqlRB586dUaZMmTQVzOjQoQMCAwOTja9/8eIFOnTo8NH5Aw0bNlT+//r1a8yZMwfe3t7KHIkjR47g4sWLafqdJXXp0qUU5x7pWlikWbNmCAoKwpQpU5Q5DQcPHsTAgQPRokUL1e15fy6ZiOD+/fsIDg5G8eLFVcWqXLkyduzYAR8fHzRt2hS9e/fG7t27sWPHDvj7+6tuG6Df95U+5o8m6tChA7777jtlzm2NGjUAvCtio+ucmJTmkOXMmRNWVlaoXLkyjh07hmPHjgFQN58MeDe/88aNG6hcuTIsLS2V+Udq9evXDz169MCjR4+U8++uXbswderUNM2RCAkJwZAhQzB//vxkny9qTZgwAbVq1cKlS5cQFxeH6dOn49KlSzh06BD27dunc5y5c+di5MiR6NOnD8aPH6/M0XJwcEBISIjq7wkAULNmTYSEhGDBggUA3s3FiYmJwahRo1C7du1UH5907oxGo8HChQuxfft2+Pr6Anj3Ort7966qOVF79uxReRSpCwgIwIkTJ/RWnOrBgwdYvnw5lixZgps3b6Jhw4bYsmULatSogZcvX2Ls2LFo164d7ty5k2qssmXL4vTp0+ku6a/vOVt16tTBwIEDcenSJfj4+CT7rNP1syCRPgrBJXr58iWCg4M/eI68efOmqnjpfR8kFRISglatWmHDhg348ccflTnt69atS9P8vqRYICOd3j+JXrhwAXny5EFoaCiWLl2q+uTz559/okmTJvDy8kK5cuUAAMeOHUNERAT++OMPnV88idWTPuT9qnFqJJaAz507N7Jly5amGBMnTsSKFSuwePFifPPNN/jzzz9x584d9O3bFyNGjEDPnj11jtWoUSPs2bMHjo6OKFy4cLITiy4Vyk6fPg0/Pz/UrVsXgwYNQoECBQAAV65cweTJk7F161YcOnRIdXGATp06wc3NDaNGjcLs2bMxcOBAVKhQASdOnEDjxo2xaNEinWOVKVMGP/30EwICAlC/fn04ODhg4sSJmDFjBtatW4cbN26oaps+7du3DxUqVICJifb1m507d+LgwYMYNWqUzrESEhLw7bff4unTpwgLC8OhQ4dQv359/PTTT+jdu7fOcYyMjJA7d24MGTIEQUFBeqlGaGRkhAcPHsDJyUlr+9mzZ1GtWjWdL2J06tQJOXLkwLhx47S2jxo1Cvfu3Uu1emNKbt68iUaNGuH8+fPK5Hvg/4pR6DoJ/82bNxg4cCDmzZunlLo2NTVF165dERwcDHNzc1XtSiwA9P5Hja+vLxYvXqxqMv3Tp0/x+vVruLq6IiEhAZMnT8ahQ4eQL18+DB8+PE1V+vT5vurRowdCQ0NRp06dFAsT/fLLL6ratm7dOty7dw9NmzZFrly5ALw7tzs4OOj0BV3XctsajUbnLzlPnjzBd999hz179kCj0SAiIgJ58uRBx44dkSVLFkydOlWnOEnNnTsX48ePx7///gsA8PDwwOjRo9NUNS1Llix49eoV4uLi0n2hEQBu3LiB4OBgnD17FjExMShZsiQGDx6sVCzThbe3NyZMmICGDRvC1tYWZ8+eRZ48eXDhwgVUrVoVjx8/VtUmAPj7778REBAAEUFERARKly6NiIgIZMuWDeHh4al+ef9QZcr3aTQaVRcJ9CFpIaJHjx5h7Nix6NChQ7oTh3r16iEsLAz58+dHp06d0LZt22QXFx8+fAgXF5dkSUBK1q5di6FDh6Jv375prjJpbGyM+/fv6zXZ0leRKX0WgkvUokUL7Nu3D23atEnxHKnmMx5I//tAF69fv4axsXG6vkMw2UonQ51E586dq5RdLlSoEH744QdVPVv6NHbsWAwYMCBZudT//vsPP//8M0aOHKk6pohgwoQJmDhxIl69egUAMDc3x4ABA5J9AU2Nviq7bdmyBR07dsSTJ0+0tmfNmhULFy5UfTUIeJc4JCQkKEnIb7/9pnw5/P7771WVEl2xYgXi4uLQvn17nDx5EoGBgXj69KnSw9isWTPV7dNHZUNDePPmDerUqYNXr17h3LlzmDhxInr06KEqxrx587B3717s27cPr1+/RsWKFVG1alVUqVIFpUqVUnUVvkSJEtBoNDh79iwKFy6slVTGx8fj1q1bCAwMxNq1a3WKZ29vjxMnTiQrvZ/4YfH8+XOd25aoXr16MDY2xsKFC+Hp6Yljx47hyZMn6N+/P6ZMmaK6Z/bVq1dKopHYQ54W718hNjIygpOTEywsLFTFiYuLw6pVqxAQEABnZ+c0tSUl+nxfZcuWDcuWLVN9RTUzadu2LR4+fIiFCxeiUKFCymdeWFgY+vXrh4sXL6Y59qNHj2Bpaam6bHlShrzQmFaWlpa4cuUK3N3dtb4nREREoGjRovjvv//SFDcuLg6//fYbzp07pySCrVq1gqWlpZ6P4MMMURH1Y8lCUmorOQYFBaFTp07KaIKUiAju3r2rU2+VPqpM6lKN8MKFCyhSpEiqsfQtaaXslFIFS0tLzJw5Ex07dtQ5poODA7Zu3YoKFSrorZ1xcXFYs2aN1gWRT/0+SA2TrXTS50n07du3CAwMxLx585J9CUuLGzduYMmSJbhx4wamT5+O7Nmz46+//kLu3LlRuHBhneN86MrLkydPkD179nSts/XmzRtcv34dMTEx8Pb2TteHrD78999/2LZtG65fvw4RQf78+VGzZs3Pal2OROnpYdT32mnpHT6V0of0ixcv0KJFC9SpUwddu3ZVtqclEbx06RL27dunDCuMjY1FhQoVUK1aNQwYMCDVx48ZM0b5t3///lqvUzMzM3h4eKBJkyY6J88uLi4IDg5G+/bttbaHhoZi8ODBePDgge4H9/9ly5YNu3fvRtGiRWFvb49jx46hQIEC2L17N/r3769zmeoVK1agcePGennN6/ucZmVlhcuXL6d72M7HpOd95erqir179yJ//vzpbkdq5bbTcpFLH1xcXBAWFoZixYppfebdvHkTRYsWRUxMTIa0S18Mse6lt7c3Jk6ciAYNGmj9zmbOnIklS5akaQmQz8X7y3ak1IsNqE+MDO3169eqL/i8L7Whhrqcp5IuCZLUixcvsHr1aixcuBAnT57MkN/dnTt3ICLIkycPjh07pjWiw8zMDNmzZ9d5CZVEnp6e+PPPP1GoUCF9NzfdUluGKT1/A87ZSidPT0+cOXMm2Ztq27Ztql9M+lwwct++fahVqxYqVKiA8PBwjB8/HtmzZ8fZs2exaNEirFu3TudYHxqLf/bs2TTN70nKzMwM3t7e6YqhT5aWlmjUqFG6Yqj5G6anB8nKykr1sMZEffr0QVRUFI4ePZri2mlq9e7dWxk+VaRIEdVzN4oXL57sQzrx5/nz52PBggXpWpPG29sb3t7e6Nq1K/7991/MmTMHM2fOxLZt23RKthKHQXp4eKBZs2bp/pDu06cPunbtilOnTilrWR09ehSLFy/GiBEj0hQzPj5e+cDOli0b/v33XxQoUADu7u64evWqznH69u2LH374AfXr10fr1q0REBCg+gM1kb4XwS1btmyK51t9Ss/7Sh/zRxOtX79e6+e3b9/i1q1bMDExQd68eVUnW02aNEHZsmUxePBgre2TJ0/G8ePH8fvvv+sU5+XLlykm4k+fPlU9xDRRetci1GeC5ODgkOrfTu25qF+/fujevTtev34NEcGxY8ewevVqTJw4EQsXLtS57e+LiIjAnj17UrzApfb1kdbFwm/duqV8Cb9165aq59TFsmXL0KxZs2SvrTdv3ijr7ukqISEB48ePx7x58/DgwQNlTaYRI0bAw8MDQUFBqtqmj/PQ+yNvwsPDsWjRIvzxxx9wdXVF48aNMXv2bNVx9+3bhylTpigjpLy9vTFw4EBVIxwSj0+XIZW6GjduHEaOHImlS5fq5YLexIkT4ezsnKx3bfHixXj06FGy893HpHTOPX36NJYuXapccE0r9myl08KFCzF69GhMnToVQUFBWLhwIW7cuKGcRJs3b64qnr4WjPTz80PTpk3Rr18/rStpx44dQ+PGjfH333+nGiNxwb7nz58nWxgvPj4eMTEx+OGHH9J0ImjUqFGKH2gajQYWFhbw8vJCy5YtlblTqUnvh/WyZct0eh5dTuxJr+6l9qGd2od1agsAJqVmMcAcOXJg48aNKFu2LOzs7HDixAnkz58fmzZtwuTJk3HgwAGdYwHpHz6ly2TkRGo/4B4+fIg9e/YovVrXrl2DqakpfH19Ua1aNVXzyfRp7dq1mD59utZw4d69e+O7775LU7xKlSqhf//+aNiwIVq2bIlnz55h+PDhWLBgAU6ePIkLFy7oFCcuLg7btm3D6tWrsXHjRlhZWaFp06Zo1apVmiYJ63MRXH3MkQD0u8jm+wt+Js5tSOv80Y+Jjo5G+/bt0ahRI7Rp00bVY52cnLB79+5kc43Onz+PGjVq6NybWrt2bZQqVQrjxo2Dra0tzp07B3d3dzRv3hwJCQmqLuQB74p4/Pjjj2jfvj0WLFiADh064MaNGzh+/Di6d++O8ePHpxojtSvSgO4JkprCF1WqVNF535UrV2L06NHK0FxXV1eMGTNG9Rf8RL/++iu6du2KbNmywcXFJdnCtWp6y/S1WHh4eDjKly+fbO5uXFwcDh06hMqVK+vcpkT6HFkzduxYLF26FGPHjkXnzp2VOfZr1qxBSEgIDh8+rLp9QPqLEkVGRiI0NBSLFi1CdHQ0vvvuO8ybNw9nz55N08XoFStWoEOHDmjcuLEyXO/gwYNYv349QkND0bJlS9UxgfQfJ/BuSP6NGzcgIvDw8Eh2jlTby+vh4YFVq1Yl+2w6evQomjdvrpcLAKtWrcKaNWuwcePGNMdgsqUH+jyJ9uzZE8uWLUO+fPlS/DKh6xdqGxsbnD9/Hp6enlrJ1u3bt1GwYEG8fv061RhLly6FiKBjx44ICQmBvb29cl/i0KmPjX3+mPbt22PDhg1wcHBAqVKlALx7k0VFRaFmzZo4e/Ysbt++jV27dqU6tldfH9Y2NjYwMTH5YJVAXVdeT5o4nD59GgMGDMDAgQOV39Xhw4cxdepUTJ48WatCXUren8R86tQpxMXFKUnotWvXYGxsjFKlSqmaxGxnZ4dz587Bw8MD7u7uWLVqFSpUqIBbt26hcOHCyjw6Xelz+JS+dOvWDXv37sXVq1dhYmKCsmXLomrVqqhWrRrKly+vc++Uo6Mjrl27hmzZsikXID5E7eR7fQoLC8PLly/RuHFjXL9+HXXr1sW1a9eQNWtWrFmz5oOVNj/m1atXWL9+PVatWoWdO3ciV65cqgux6OucBuhnjgTw7n21fv16ODg4fLRQwIsXL3DixImPxkptzmhSun5p/Zjz58+jXr16qialA+967c+cOZPsAtaVK1dQokQJnYe8X7hwAf7+/ihZsiR2796N+vXr4+LFi3j69CkOHjyIvHnzqmpXwYIFMWrUKLRo0ULrs2rkyJF4+vQpZs2alWoMQyVIhvDq1SvExMSke+K+u7s7unXrpurK/YcULVoU33//Pbp37678DTw9PfH9998jR44cOl/VN8SUA30VJQIALy8vzJ8/H/7+/lqvtStXrsDPzw/Pnj1T1TZ9FCWqV68ewsPDUadOHbRq1QqBgYFKMYa0JluFChVCly5d0LdvX63t06ZNw6+//qpc4NOVvoovAUj1taT24qeFhQUuX76crBjQzZs34e3trdN33dToY4g0hxHqQatWrdCqVSu9nEQvXLigDGG5du2a1n1qhqU4ODjg/v37yV6Ap0+fRs6cOXWKkTiR2NPTE+XLl9dLNbdELi4uaNmyJWbNmqV8gUpISEDv3r1ha2uL3377DT/88AMGDx6cai/LnDlzsGDBArRo0QKhoaEYNGiQ1oe1LgoVKoQHDx6gdevW6NixY7qG9yXteWnatClmzJih1eNTtGhRuLm5YcSIEakmW0mrWU6bNg22trZYunSpUnXt2bNn6NChg+riBwUKFMDVq1fh4eGBYsWKKWWS582bhxw5cqiKBeh3+FRqvYy6Dhs5ffo0GjZsiGrVqqFChQppHrLwyy+/KMPz0lKK+lMJCAhQ/u/l5YUrV67g6dOnqSaIH2NlZYWAgAA8e/YMd+7cUf0hDejvnAbob5jSnj178Msvv6Bv374frBj74sULBAYGphpLHwmUGs+fP09TARUfHx+sWbMm2fCy3377TdUXuiJFiuDatWuYNWsWbG1tERMTg8aNG6N79+5pOnfcvXtXuSptaWmJFy9eAADatGkDX19fnZItfSZQhij48NNPP6FVq1bw9PSElZWVXoZPPXv2DE2bNk13HODd/O46deoAeHch9eXLl9BoNOjbty+qV6+uc7L1odEcT548SXaRJTWJRYk0Gg38/f0/WJRIjX/++Ucp551UQkIC3r59qyoW8G74vKenJ3bt2pViUSJd/PXXX+jVqxe6du2ql3mtwLvkoF69esm2169fH8OGDVMdTx/HmUjfI0nc3Nxw8ODBZN91Dx48CFdX13TH/++//zBjxgydvzd/ULpW6aLPVv/+/aVixYpy//59sbW1lYiICDlw4IDkyZNHRo8erTpeXFycrFu3TsaNGyfjxo2T//3vfxIXF5fm9mXLli3FhYivXr0qWbNmFRGRc+fO6bSIrb4WDk5c4NDe3l5KlSolc+bMSfOq94ksLCzk0qVLybZfunRJ9Yrkrq6ucuHChWTbz58/Lzly5FAVa/ny5bJkyRIRETlx4oRky5ZNjIyMxMLCQn777TdVsUREGjZsKPb29uLp6Sl169aVRo0aad3UcHBw0LpZW1uLRqMRc3NzyZIli+q2fY5SWvgz6S2jvXz5UlasWCG1atUSMzMzyZs3rwwfPlwuX76c0U3TGwsLiw8uRB0TEyMVKlSQAgUKqIr56tUrefnypfLz7du35ZdffklxkejUTJ8+XesWEhIigwcPFldXV2nRooXqeJs2bRITExNp27athIaGSmhoqLRp00ZMTExk/fr1quPpi6enp7IIcalSpWTevHkiIhIWFpbm9/uzZ89kypQpEhQUJEFBQTJt2jSJiorS6bEajUZZoD3xfZrehWaLFi0qRkZG4ufnJ7Nnz5ZHjx6l6biS6tixo8ydOzfdcUTSv1h44nneyMhIateurXXur1+/vnh4eKheBHr06NEyevRo0Wg0MmDAAOXn0aNHy4QJE2TVqlUSGxurKmbJkiVl+fLlIqK9AO6YMWOkYsWKqmKJiGTNmlXOnj0rIiJ2dnZy5coVERHZtWuXFC9eXKcYhw8flk6dOomtra2ULVtWZs6cKY8ePRITExNlsV618ubNq7yPkpo7d654eXmpjqeP4zSUSZMmSdasWWXx4sVy+/ZtuX37tixatEiyZs0qEyZMUBXLwcFBsmTJotwcHBzE2NhYbG1tZePGjelqJ3u20iDxiosu0lNlKHFeVeL6KmpMmDAB3bt3h5ubG+Lj4+Ht7Y34+Hi0bNkSw4cPVxXr+vXrqF27Nv755x9lCMrEiRPh5uaGrVu3qh42Arwbw33lypVkw86uXLmidElbWFjo9HvW18LB5cqVQ7ly5RASEoLff/8dS5YswYABA9CwYUMsXrw4TZO/CxUqpMzfS6xU9+bNG0ycOFF1AZXo6Gg8evQo2fZHjx4pV4N11bp1a+X/pUqVwp07d9K1dpqDg0O6C4skSmkoR0REBLp27YqBAwfqHCc8PFyn/dTOI0hISMD169dTnJSuayx9TcR9f77Qx+g6X6h58+bYsmULrKys8N1332HEiBFpHi6sb/rq9QTeLUbfpk0bODg4aM05iImJQWBgIB4+fIi9e/eqal+DBg3QuHFj/PDDD4iKikLZsmVhZmaGx48fY9q0aVpVNVPz/ppciSXz27Vrh6FDh6pqF/BuuNKGDRswYcIErFu3DpaWlihatCh27tyZas+QIYv+VK9eHZs2bUKJEiXQoUMH9O3bF+vWrVPWIlTrxIkTCAgIgKWlpVJ8Ztq0aRg/fjy2b9+eavETQxR8OHv2LC5evIiVK1diypQp6NOnD7755hu0atUKDRs21LmnK+ki1V5eXhgxYgSOHDmS4vpTahapTu9i4YlTDEQEtra2WiW3zczM4Ovri86dO+vcHkD/RYmAd0VD2rVrh3/++QcJCQn43//+h6tXr2LZsmXYsmWL6nj6KErk6+sLX19fhISEYM2aNVi8eDH69euHhIQE7NixA25ubskqFaamf//+6NWrF86cOaO1MH1oaCimT5+u7iCR/uM05FD8gQMH4smTJ+jWrZsyl8zCwgKDBw9WfZ785ZdftNqWeM4tV65cmtZwTIpzttJAzZchtV2mCQkJSkW4xPGhtra26N+/P3788Ued159IdPfuXVy4cAExMTEoUaJEmrqpa9euDRHBypUrleqDT548QevWrWFkZIStW7eqjtmrVy+sXr0aw4YNQ5kyZQAAx48fx4QJE9CyZUtMnz4dCxcuRGhoaKrDCPW5cHBS4eHhGDVqFMLDw/H48eM0vdmOHTuGevXqQUSULyHnzp2DRqPB5s2blS8Dumjbti3279+PqVOnalWwS6wwlNr6MpndiRMn0Lp1a1y5ckWn/VNbIwRQX474yJEjaNmypVISNz2xUqJ2Im7S+UIigvXr18Pe3h6lS5cGAJw8eRJRUVFo3LixzsPdEodFp6cK4fvSWunsfe+/B9++fYtXr17BzMwMVlZWqj+oFy5ciN69e2Pr1q2oWrUqXr58icDAQERGRmLfvn2qh6Fky5YN+/btQ+HChbFw4ULMnDkTp0+fxh9//IGRI0emaRjm5+BjJb2TSst7QJ9rEQLvCsV4eXnh119/VWLGxcWhU6dOuHnzps4XYQzp4MGDWLVqFX7//Xe8fv1a52qKhlikGtDPYuHy/+d3z5w5M8OXcPmY/fv3Y+zYsVprMo0cORI1a9ZUHUtfRYned/XqVSxatAjLly9HVFQUvvnmG62FnnWxfv16TJ06VasA08CBA3VaDP196T3OpUuXonnz5jA3NzfYOngxMTG4fPkyLC0tkS9fvjRdHL979y7c3NxSTAbv3r2L3Llzp6ltADiM8HMzZMgQcXJykjlz5sjZs2fl7NmzMnv2bHFycpJhw4ZlSJusrKyUIQZJnTlzRqytrdMUMy4uTn766SdxcXFRhmW4uLjI+PHjleGJd+7ckXv37qUaKz4+Xt6+fav8vHr1aunZs6fMmDFD9TCDv//+W8aPHy9eXl6SI0cOGThwYLqHTsXExMj8+fOlb9++0rdvX1mwYIHExMSojvPy5Uvp2rWrmJubK8PNzMzMpGvXrjrFS3x+XW5p8fbtW9mxY4fMmzdPoqOjRUTkn3/+kRcvXqQp3vtOnz4ttra2Ou/v6Ogo7u7uMmrUKLl+/bpERUWleFOjWLFi0rRpU7l06ZI8e/YsXbFScuPGjTS/pwYNGiSdOnXSGt4bFxcnXbp0kQEDBqS7bWm1evVqMTU1lbp164qZmZnUrVtX8ufPL/b29tK+fft0x7927Zr4+/vLtm3b0vT4SZMmiZ2dnezZs0cqVaokefLk0em8kxJLS0u5c+eOiIg0bdpUGbJ99+5dsbS0VBXr4cOHH7wvpfOxISUOz9HlltEsLCxSPGdfvHhR9d9AROTKlSvSvXt3qV69ulSvXl26d++uDKNKq9OnT0v//v0lZ86cqoeTf67i4+PF1NRUrl27pte4n/OQ623btskff/whIiIRERFSoEAB0Wg0ki1bNtm1a1e648fFxcn69eulXr166Y6VHoY+zvRYvHixvHr1Si+xjIyMlCHEST1+/DjdrzX2bOnJyZMnlSsIhQsXRokSJdIUx9XVFfPmzUtWSnPjxo3o1q0b/vnnH53ifKiccdLS6g0aNNBpnSxHR0ds2bIlWWnNgwcPol69eumuwJZ4VU/XBSINYe3atViyZAn27duHgIAAdOjQAXXq1NHblX19evnypVIRLm/evDpPPP5Y1bWkNBqNqsqGwLsKjIGBgbh79y5iY2OV9Ut69+6N2NhYzJs3T+dY71/BExHcv38fs2bNgpub2wcXYn7fmzdvsH79eixevBj79+9H7dq1ERQUhMDAwDQXjLC2tsbZs2dTnGSdXv/99x+GDh2Kv/76S9W6WImcnJxw4MCBZNXmrl69ivLly+PJkycffGzS4UmpUTM8CdBfpbOPUdvr+b4hQ4bg559/hoeHB/bu3Qs3N7c0xSlatCg6deqERo0aoUiRIti2bRv8/Pxw8uRJ1KlTB5GRkTrHcnFxwaJFi5TCBYmmTJmCESNG6FQ98HOtpGmIIhSJnJ2dsXz58mQ9FWFhYWjbtq2qBcP/+OMPNG/eHKVLl1aG0h45cgTHjx/Hb7/9hiZNmugc69atW1i1ahVWrVqFq1evokqVKmjZsiW+/fZbrUq/Ge3169fJep91/WwuXLgwFi1aBF9fX721Z8OGDVqv2/eHXKe1dL6hpLcoUWaRnuPUx1D8RM7Ozvjvv//QtGlTBAUFpWl5kkRJF+hO6s6dO/D29sbLly/THJvJVjo9fPgQzZs3x969e+Hg4AAAiIqKQrVq1fDbb78lK1eaGgsLC5w7dy7ZXKarV6+iePHiOpfnrVatGk6dOoX4+PhkZcILFiyIq1evQqPR4MCBA6lWomrbti1OnTqFRYsWaQ1f69y5M0qVKoXQ0FBVx6hv27Ztg42NDSpWrAgAmD17Nn799Vd4e3tj9uzZOg2BMDIyQu7cudGqVSs4Ozt/cD+1XzQB/S08uWzZMpQpUybZXK/Xr19j7dq1quar6FvDhg1ha2uLRYsWIWvWrEpJ3b1796Jz586IiIjQOdb7Q2U1Gg2cnJxQvXp1TJ06Nc0Vz0JDQ7F06VLExsaiXbt2GDNmTLL1YFJTvXp1DBo0SHUVrPe9/yElInjx4gUsLS2xcuVKVeuWJI0ZGhqabJjIxo0b0b59+4+WNTbU8CTgXYJ68eJFeHh4IGvWrNi7dy98fHxw+fJlVK9eHffv31cVLyVnzpxB5cqVVS1u+/58oD///BPFihVLVnVKzTDHdevWoWXLloiPj4e/vz+2b98O4N0c1/DwcJ0vFADvFhseOXIkOnTogGnTpuHp06do27Ytzp8/j/nz5+s0R9IQw3eWLl2KbNmyKUngoEGDsGDBAnh7e2P16tU6rYOX9EvNx4YopmVYYq9evbB+/XpMmTJFa77KwIED0aRJE1UVRfPmzYtWrVph7NixWttHjRqFFStW6LwMgq+vL44fP46iRYuiVatWaNGiRbqrm+lrkWrgXTn6QYMGYe3atSlelNH1b7B582ZMnjwZc+fORZEiRXR+/rTQdci1moRAzQWHt2/fKssppOdYdZmXqNFo8Mcff3x0H0NdWNHXcSbS91D8uLg4bN68GaGhofjrr7+QJ08edOjQAe3atYOLi4tOMRI7J6ZPn47OnTtrzaGMj4/H0aNHYWxsjIMHD6pqW1JMttKpWbNmuHnzJpYtW6Z8Cb506RLatWsHLy8vrF69WlW8xCIN719p7tmzJ44fP44jR47oFCckJAT79+/HkiVLlKtSz58/R6dOnVCxYkV07twZLVu2xH///YewsLCPxoqKikK7du2wefNmZRJuXFwc6tevj9DQUJ2vyhmqsIiPjw8mTZqE2rVr4/z58yhdujT69++PPXv2oGDBgjrNVfHw8Ei1bWn5oqnPhSeNjIxgbW2N0NBQrSuqDx48gKura7rnC6VH1qxZcejQIRQoUCDZum7e3t6q1+0ylFu3biEoKAj79u3Do0ePdOrZTWr9+vUYPnw4Bg4cmOKkdF2vwr//xTfpRNx//vknTR9q/fr1w7JlyzBs2DCtiyLBwcFo06aNqvWs9ClXrlz466+/4OPjg6JFi2Lo0KFo0aIFDh8+jMDAQFVlzPXV6wnovj6W2tLukZGRuH//PooVK6ZcODh27Bjs7OxQsGBBVbFOnz6NNm3aIDY2Fk+fPkW5cuWwePFinb9EGEKBAgUwd+5cVK9eHYcPH4a/vz9CQkKwZcsWmJiY6JSc3rlzB7lz54ZGo0l1QXO1i5i/efMGAwcOxLx58xAXFwcAMDU1RdeuXREcHKxqLoeVlRXOnTuXrCc7IiICxYoV0/m89uOPP6JVq1ZpWjPpQ/S1SDUAdO/eHXv27MG4cePQpk0bzJ49G//88w/mz5+P4OBgtGrVSqc4WbJkwatXrxAXFwczMzOtQhmAfntPdV37SM1cZrXzhfLkyYP169ejWLFiqh6XlL7OQ0kvrISGhn70+0xGHGei4sWLI3/+/BgzZgxy5MiRrJ3p6eV98OABVqxYgaVLl+LKlSsIDAxEUFAQ6tWr99F6B4mjfvbt2wc/Pz+teaKJa8oOGDAgfaX50zUIkcTOzk6OHTuWbPvRo0d1Klv+vr1794q1tbUUKlRIOnbsKB07dpRChQqJjY2NhIeH6xzH1dU1xbKhFy5cEFdXVxEROXnypFJmXRfXrl2TTZs2yaZNmyQiIkLnxyVKWrp1yJAhYmdnJ76+vso8IT8/P7Gzs5MhQ4aoimttbS23bt0SEZFRo0ZJkyZNROTd8Tk7O6tupz7lzp1bgoOD9RJLo9HI1KlTxdLSUkaNGqVsj4yM1Gk88fvl2D92U8vBwUF5vSUtqbt//37Jnj27qlj79u1Lcdz0mzdvZN++farb9vr1a1m5cqX4+/uLlZWVNG3aVP766y/VcUTkgyWg1ZaCfl90dLTMnz9fypYtm+Y48fHxMmnSJHF1dVXa5urqKpMmTUrzMg0JCQmSkJCQpscmatGihUydOlVERMaOHStOTk7SqVMncXd3V/1aS+l37+zsLC1atJB///03Xe38HEVHR0uzZs3ExMRETExMJDQ0NM2x7ty589GbrpLOSxs0aJC0adNGRN59tmTLlk11u/bt26c15zbR27dv0/R+T/Ty5Us5d+6cnDt3Tqscvxq1atWSxYsXJ9u+ePFiqVmzZppi6uM9JfJublpKc8cuX76seh6Ym5ub7NmzR0REWSpGRGTZsmVSq1YtneMkLinwoZu+vHr1Snr37i358+fXW8y0WLhwodSuXVuePHmS5hg3btyQ+Ph4PbZK//RxnImsrKzS9P1RV4lL+Jibm4uHh4fY29uLh4eH8vr+mPbt26d7uZ8PYen3dEpISEhxsV9TU9NkQ8Y+5ubNm/D09ESVKlVw7do1zJkzR5kD1rhxY3Tr1k1VZaznz5/j4cOHya6kPXr0SBlq4+DgkGxsdkr27NmDatWqIV++fOnK7JNWZuzUqRN69eqFcePGJdvn3r17quKamZkpVxh37typDKdzdHTUeVjR69evsXPnTtStWxcAMHToUMTGxir3m5iYYOzYsarLz+pz4UngXcn28uXLo1GjRrhw4QKWL1+u82MNOS+gZs2aCAkJwYIFCwC867WLiYnBqFGjtBZ01kXVqlXh7OyM9evXa439f/r0KapVq6ZzD96xY8ewZMkS/Pbbb/Dw8ECHDh2wdu1a1b1ZSemrFHSi8PBwLFq0CH/88QdcXV3RuHFjnRZxTYmRkREGDRqEQYMGpXse5LJly/Dzzz8rwz/z58+PgQMHok2bNqpjzZo1C69fvwbw7gq/qakpDh06hCZNmqhehkLNOfVTady4MUJDQ2FnZ5fqkCA1QxIPHjyI1q1bw9HREefOncPBgwfRs2dP/Pnnn5g3b57q6qip9d7r+r6ysbHBkydPkDt3bmzfvl0ZgmNhYaHzMPekqlWrhvv37yebJ/H8+XNV7/ekj4uPj4ejo6NWr8/Tp09hYmKS6nsiae9p/fr1MXjwYJw8eVI5Fx05cgS///676rmG+nxPAfpbpBp497vJkycPgHfnjMQeqIoVK6pariCtleQ+5kNDrq2srLBixQpVsYyNjVN8rT158gTZs2dX/VqbNWsWrl+/DldXV7i7uyebP63LyJV8+fJptalZs2aYMWPGR6czpObPP/+EsbGx1kL3ALB9+3bEx8ejVq1aquLp4zgTlStXDtevX9frvOcHDx5g+fLlWLJkCW7evImGDRtiy5YtqFGjBl6+fImxY8eiXbt2qfaiJ+09TM/SSylhspVO1atXR+/evbF69WolGfrnn3/Qt29fndanSJT0Defq6oqIiAjMmTMnzW+4Bg0aoGPHjpg6dapWafXEdaOAd19G358blpLAwEDkypVLGQeb1snjSf3+++84ceJEsu2tW7dG6dKlsXjxYp1jVaxYEf369UOFChVw7NgxrFmzBsC7OWq6vlFCQ0OxdetWJdmaNWsWChcurAyDuHLlClxcXD5YeORDmjZtiu3bt+OHH35Q9biUJH7g+Pr64ujRo6hfvz7Kly+vc/EJtcOh1Jg6dSoCAgLg7e2N169fo2XLloiIiEC2bNlUD6UF3q315O/vj9mzZ6N9+/bKdlEx6tnX1xe5c+dGr169UKpUKQBIcRkBNfOj1A5pSklkZCRCQ0OxaNEiREdH47vvvkNsbCw2bNigt2FG6Sk2M23aNIwYMQI9evRAhQoVALz7vf3www94/Pgx+vbtqype0uTWyMgIQ4YMSXPbPkf29vbKe1OfFzSqV6+Ovn37Yty4cTA1NUWhQoVQrVo1tG7dGj4+PsqXAV2dPn1a6+fEQgOJa1Dp6ptvvkGnTp1QokQJXLt2TbmYkjgvTy0RSTEJfPLkic7Ff5Jq3rw56tWrh27dumltX7t2LTZt2oQ///zzo49P/HxMas6cOZgzZ47Wtu7du+t8Xtf3ewoARowYgcaNG+PGjRuoXr06AGDXrl1YvXq1qvlawLthYrdu3ULu3LlRsGBBrF27FmXLlsXmzZuVuehqpafQRlLvz7FLz9pHH/r8iI2NVb3EAJDya0Wt99v0559/YuLEiemKOWTIEAQHByfbnpCQgCFDhqhOttJ7nEmL4PTs2RP9+/dHZGRkuobiJ6pXrx7CwsKQP39+dO7cGW3bttX6zLG2tkb//v3x888/pxpL30svaTFIf9lX5O7du1K8eHExNTWVPHnySJ48ecTU1FRKlCihqnxw0lXrRd515ScOxUqLFy9eSKdOncTMzEyrTHjnzp2VMuGnT5+W06dPpxrr0aNHMm3aNClWrJiYmJhIzZo1Zc2aNarLqifl7OwsS5YsSbZ9yZIlqoed3blzR+rUqSNFixaVhQsXKtv79OkjPXv21ClGxYoVZdOmTcrPSYfCiYgsX75cfH19VbVLRGTChAmSLVs2adeunUyZMkWmT5+udVPj/dfIy5cvpWHDhmJra5vhJXBF3g37WbFihQwcOFC6du0qv/76a5pKsiaWX/3jjz/E2tpa+vbtKwkJCToPl0yU0pC/lIahpWbjxo3y5s0b5f8fu6Wmbt26YmdnJy1atJAtW7Yow/tMTExSHParRmRkpLRu3Vpy5MghxsbGaS6R7OHhIUuXLk22PTQ0VDw8PNLUtri4OPn9999l7NixMnbsWFm3bl2KQ8dS07hx4xSH5U6aNEm+/fbbNLXtc7V3794Ut8fHx8vYsWP19jxbtmyRKlWq6Lz/s2fPpHv37lK/fn2t4bgjR46Un376Sec4iUOWjYyMpHbt2lrDmOvXry8eHh4SEBCg5lBERCRLlixy6dKlZNsvX74sjo6OquPpgyHeUyLv/nbly5cXKysryZo1q1SrVu2Dr5uPmTZtmvJ5tGPHDrGwsFCWGAkJCdE5TkxMjHTv3l2cnJw+qzLtiZ+3RkZGMn78eK3P4GnTpknDhg2lePHiqmK+fftWxowZk+ZlIhK9/7n+/nePtLCwsFCmViR169YtsbKySlfstEg63F7fQ/E7duwohw4d+ug+CQkJOi1LYcill1ggQw9EBDt37lTKDhcqVAg1atRQFeP9kpNJiwykR0xMjFLUIU+ePOlebPDUqVNYsmSJ0lvRsmVLBAUFqZ44GRwcjDFjxqBz585ak/kXL16MESNGfPKr3zly5MDhw4eVK7NOTk44fvy48vO1a9dQpkwZVZP5gY9XeVNbcGPMmDEYOHCgVqUcAMrCy3v27Pno40uWLIldu3YhS5YsqRYrUTMsQN+SvhdOnz6NBg0awNvbG9OnT4e3t/cnLwTyfuW0D9GlkpKJiQl69eqFrl27ag3JNTU1xdmzZ9PVs1WrVi3cvXsXPXr0SHHisa6LWVpYWODChQspFgXw8fFRhgTq6uLFi6hfvz4iIyO1KqM6OTlh8+bNqoqB6LMogCE9fPhQKd9foECBZMOW1Lh+/Tpu3LiBypUrw9LS8oM9QemJX6xYsXSVNU6LxMIAS5cuxXfffadVTCFxUnrnzp2RLVs2VXGtra1x5MiRFF8j5cqVy5BiPfp+TxnanTt3cPLkSXh5eanqadBXoY33RUVFYdGiRVrL63Ts2FHnnuTEz+E7d+4gV65cWku6JL7Wxo4di3Llyqlql62tLc6fP5+mHt1ExsbGiIyMVCpX29ra4ty5czpXiE2Ji4sLVq1apfR4Jtq5cydatmyJhw8fqo4ZFRWFdevW4caNGxg4cCAcHR1x6tQpODs7p1pZM7Xhe0npY/RIWulr6aUUpStV+8q9efNGjI2N5fz58+mOZWRkpLWIpY2Njdy8eTPdcQ3hn3/+kVGjRom5ublYW1uLsbGxVKxYUS5cuKAqzpo1a6R8+fKSJUsWyZIli5QvX17WrFmjuj0nT57UWuRzw4YN0qBBAxk6dKjOvW8fmmyc6PLly2Jubq66bZ+T0aNHKxPFkxYrSemm1uPHj5X/3717V0aMGCEDBgxI0wT396/03b9/X8qVKyc5c+ZM05WvlNo2cOBAVQVn9OXw4cPSqVMnsbW1lbJly8rMmTPl0aNHeunZsrGx0amnOjWFCxeW8ePHJ9s+btw4KVKkiOp4vr6+Uq9ePXn69Kmy7enTp1K/fn3x8/NTFUufRQEM4fnz59K6dWsxMTFRrtqamJhIq1atVC96/fjxY6levbpyxTfxaneHDh2kf//+aWpb0ltUVJRcvnxZmjVrJsWKFdM5jru7u4wZM0bu3r2rug0pGT16dJoWef+QqlWrSo8ePZJt79atm1SsWFF1vL1790rdunUlb968kjdvXqlXr57qc4e+31Mi785lSXtVjh49Kr1795b58+frHOPQoUOyefNmrW1Lly4VDw8PcXJyks6dO8vr1691jqevQhtJHT9+XBwdHSVnzpxKz2euXLkka9ascvLkSVWxqlatqnUeSq/69eunu/CHRqPR6tlNHD2UnqJVXbp0ER8fH7l+/bqyLSIiQooWLSpBQUGq23j27FlxcnISLy8vMTExUc5FP/74o1IgJyPp4z0qImJubi5Xr15Ntv3KlSvp/nxhspVOnp6ecubMmXTHMcQb7vjx4zJw4EBp1qxZuqvNvXnzRn7//XepVauWmJiYiK+vr/z6668SExMjt27dklatWkmhQoVUx9WH0qVLy7p160TkXWUfCwsLadGihXh5eUnv3r11iuHl5aXESMmaNWskb9686WqnvqpQXbx4Uf766y+t4WtJh0B+SufOnRN3d3cxMjKSAgUKyOnTp8XZ2VlsbGzEzs5OjI2NZf369apitm/fXqKjo7W2vX79Wtq2batqyI2+2pYlSxZ59OiRiLz7ovt+29IiJiZGFi1aJBUqVBBTU1NluE56YhcqVEhOnTqV5scnXjT6448/xNjYWAICApRhfwEBAWJiYiL/+9//VMe1sLBI8ULM+fPnVX+AlSlTRsaMGZNs+6hRo6RkyZKq26Zv3333neTLl0+2bdumJDXbtm2TAgUKSLNmzVTFatOmjQQEBMi9e/e0hhZt27ZNvL29VbctMWlLetNoNJI7d+5Uh+Ek9csvv0ixYsXE2NhYatSoIatXr1b1hdzQDhw4IBYWFlKpUiXl4lGlSpXEwsJC9Rew5cuXi4mJiXz33XfKsLPvvvtOTE1NZeXKlTrHWbdunV7fUyLvhr4vW7ZMRN5dkLK1tRU/Pz/Jli1biu+RlAQGBmoNyz137pyYmJhIp06dZNq0aeLi4qJV+TY11tbWSqXKnDlzytGjR0VE5ObNm2Jtba1znKQqVqwo7du31xp2/PbtW2nXrp1UqlQpTTH1Ze7cueLi4iL9+/eXVatWqR5WLvLu806XmxpRUVHi6+srJiYm4uHhIR4eHmJsbCzVqlWTZ8+eqT5Of39/GThwoIhoD3M8ePCguLu7q4r1oSH4mzZtku3bt6vuZNDXe1REpGzZsilOPenRo4eULVtWVaz3MdlKJ32VxNT3G2716tViamoqdevWFTMzM6lbt67kz59f7O3tVb9xe/ToIVmzZhVHR0fp3bt3ij159+/fF41GoyquiMiJEydk+fLlsnz58jR/UbSzs1Ou4AQHBysleQ8cOCC5cuXSKUavXr3E29tb/vvvv2T3vXr1Sry9vaVXr15pat/SpUulSJEiYm5uLubm5uLj46N8SKpx48YNKVq0aLLxzxk5Hj4wMFDq1q0rBw4ckO+//15y5swpHTt2lPj4eImPj5du3bpJuXLlMnXbrK2tlQ+X93ug9eHKlSsycOBAcXFxEQsLC6lXr16a4oSFhUnNmjVTHKuvC41GI2XLlpX58+fLvn37pFWrVlKyZEkpWbKktGrVKs3vz6JFi8quXbuSbd+1a5fqq/qbNm0SExMTadu2rVJOuk2bNmlK6g3ByspK9u/fn2x7eHi46rkSzs7OyoW8pF9wbty4kaYvrnv27JG9e/cqt/DwcLl8+XKa5s6JvBtR0LNnT8mWLZtkyZJFunfvrrqnQUR/cw2TOn36tLRs2VK8vb2lVKlS0qFDB7l27ZrqOAULFpRp06Yl2z516lQpWLCgqlgnTpzQ23tK5N1yG4m9vNOnT5fy5cuLyLvzgKenp04xXFxc5Pjx48rPw4YNkwoVKig/r127VtVFVB8fH2XOmL+/v9IDO336dMmZM6fOcZKysLCQy5cvJ9t+8eJFsbS0VBUrLi5OFi5cKC1atBB/f3+pVq2a1k2t9M4HNqSEhAQJCwuTyZMny8yZM9M1kiPpd6yk56Lbt2+rHvHzoflbSedtVa5cWeceSH2+R/W19FJKmGylU/HixcXGxkbMzc0lf/78UqJECa1bRvHx8ZFZs2aJyP+9ORISEqRz584ycuRIVbGqV68uq1at+ujVy7dv36qamPvgwQOpVq2aaDQaZRihRqOR6tWrq/4ya2trq3yQ1qhRQ5nQe+fOHZ2vnEdGRoqLi4vkzp1bJk+eLBs2bJANGzbIpEmTxM3NTXLkyCGRkZGq2iXy7g1vZWUlgwYNUq7gDBw4UKysrFI8QXxM3bp1pUGDBvLo0SOxsbGRS5cuyf79+6Vs2bI6nQgcHByU33VqN11lzZpVzp49KyLvirJoNBo5ceKEcv/ly5dVrze3b9++D97UnPD01bYaNWqIj4+PtG/fXjQajTRv3lw6dOiQ4i094uLiZP369WlOthwcHJSCODY2Nqr/puHh4dKhQwextbUVa2tradu2bZrXOUo6XG3r1q1SuHBh+f333+XevXty7949+f3338XHx0e2bt2qOvb7RQGqV68u+/bt08tw7vRyc3PTGtKc6OzZs6q/bNrY2CjntaRfcBKHVan1oaG+6f0S8ebNGwkJCVEKKhQrVkwWLVqkcy9+YGCgeHt7y5w5c2T9+vXKuTfxlpHMzMxSXBMoIiJCb8PKf//99zQ9Lun6kvXq1VN6qNR87pmbm2sNB61QoYJWkZNbt26JjY2Nzm3SV6GNpLJnzy5hYWHJtm/btk11Ma3u3buLtbW1fPfdd9K7d2/p06eP1i2zS2lYaGhoqLi7u6dpWGgiJycn5cJA0nPR9u3bdb6gnWjnzp1Srlw52blzp0RHR0t0dLTs3LlT/Pz8ZOvWrXLgwAEpXLiwdOzYUad4+n6P/vPPPzJs2DBp3LixNG7cWH788Ue5c+eOdO7cWXWspJhspZO+577oi5WVlXIidnR0VL4AXLp0SVxcXDKsXYm+++47KV26tFbVqIsXL0rp0qWlefPmqmJVq1ZN2rZtK8uWLRNTU1Pljbd3715VXdw3b96UgICAZL1GAQEBaa4OpM8qVEmTBzs7O+Wq5q5du3SqpJR0ccmpU6dKlixZpHnz5krXe/PmzSVLliyqksDUKimprSCYGDOlq15qr3Trq22RkZEyePBg+fbbb8XIyEhq1aolDRs2TPGWkfS1oGhMTIwsXrxYKleuLEZGRpIvXz4JDg6W+/fv6xzj/b/Z+39HfV39ff78ucybN0/KlCmT4VeSRUTmz58vNWrU0Ppd3b9/X2rWrCnz5s1TFatWrVoyfPhwEfm/Obzx8fHStGlTady4sc5xDDHUV+RdkrVmzRoJDAwUY2NjqVChgixevFjGjh2rLDStC33NNUwqPj5erl69Kvv37092wUaNvHnzpvh3mzt3rnh5eekU4+3bt3L+/Plkc0E2bNggRYsWFTMzM1VtSlS2bFkZPHiwhIeHi4WFhdILevjwYZ0T+9y5cyu/k9jYWLG0tJSdO3cq9587d07Vxbf33b59W/744w/lcystevbsKbly5ZLffvtN7t69K3fv3pXVq1dLrly5dJ4mkChr1qxpusCji5RGxXxqKQ0LNTU1lU6dOsnUqVNVDwtNFBQUJA0bNpQ3b94o56I7d+5IiRIlVP8NChcuLAcPHky2/cCBA8rw6B07doibm5tO8fTxHk3NmTNn0v35wmTrC5UzZ04lwfLx8ZFVq1aJyLsrH3Z2dqrjXb9+XXr06CH+/v7i7+8vPXv21Jp8qZadnZ0cO3Ys2fajR4+q7gk5e/asFClSROzs7LQS3B49euj8gZ/UkydP5OjRo3L06NF0Dw81NzdP8arLtWvXVF91cXBwUMYz58mTR3bv3i0i7/42aodTNG7cWGbOnJls+8yZM6VBgwY6x9FoNB8t7JKWZCsqKkrr9ujRI9m+fbtyNSwj2+bh4aHVQ/Cli4iIkGHDhombm5uYmprq3OuWdLhaare02Ldvn7Rt21asra0lX758Mnjw4BTPJ59C8eLFtUYz2NjYiKmpqTJZ29TUVGxsbFSPdDh//rxkz55dAgMDxczMTL799lspVKiQODs7qzr36nuo78mTJ5Wh5U5OTtK/f/9kw7zUzMdL71zD9x0+fFg8PT0/OFRJjTlz5oiZmZn88MMPsmzZMlm2bJl8//33Ym5urlPyfP78eSXRNTIykkaNGklkZKRUrlxZHB0dZfDgwWkuHb5nzx5xcHAQIyMjrV71oUOH6jwv+4cffhA/Pz8JDw+Xfv36SdasWbWKSq1YsUJKly6dpvbpS2xsrPTq1UtrGRtzc3Pp06eP6l6aHDlypFgAIa3i4uJk7Nix4urqKsbGxsrFvOHDh2stQ/Op6HtYaKKoqCipUaOGODg4iLGxsfJ5ULlyZdXFbSwsLFIchXDu3DnlnHH79m2dv9Ok9z2qC30kW1zUOJ06deqE1q1bo2rVqhndFC2VK1fGjh074OPjg6ZNm6J3797YvXs3duzYoWqxZQAICwtD/fr1Ubx4cWVBxoMHD6Jw4cLYvHkzvvnmG9XtS0hISLaYHfCuBHZCQoKqWEWLFsX58+eTbf/555+1SrzqytHRUSlHn15eXl5Yu3Ythg0bprV9zZo1WqW/dVGkSBGcPXsWnp6eKFeuHCZPngwzMzMsWLBA9RIBYWFhmDRpUrLtgYGBqsvut2/fHubm5gDeLWT5ww8/KIuRxsbGqooFpLww7DfffAMzMzP069cPJ0+ezLC23bp1S/VjPpW7d+9+9P7cuXOrjunl5YVhw4bB3d0dQ4cOxdatW3V6XJUqVVQ/V2o+xWLQaaGPhU1TUqRIEVy7dg2zZs2Cra0tYmJi0LhxY3Tp0gU//fQTFixYoFOc48ePY/fu3ShatCiKFSuGBQsWoFu3bsoyBj179oSvr6/O7SpTpgy++eYbzJ07Fw0bNkzxPO7p6YnmzZvrFC8kJARDhgzB/Pnz01VCO9EPP/yA0qVLY+vWrSkugaBG165d4eLigqlTp2Lt2rUA3i3tsmbNGp2WUhg8eDC8vLwwa9YsrF69GqtXr8bly5cRFBSEbdu2aZW7V0NEkCdPHty9exdxcXFai/t26dIl2fIgHzJu3Dg0btwYVapUgY2NDZYuXaq1uO/ixYtRs2bNVOMsW7ZMp+dr27atTvslZWZmhunTp2PixIm4ceMGACBv3rw6H2NS/fv3x/Tp0zFr1iy9LJ8wfvx4LF26FJMnT0bnzp2V7UWKFEFISAiCgoLS/RxqPHv2DM7OzsrP+/bt01rAuEyZMrh3757quPb29tixYwcOHjyIs2fPIiYmBiVLllS9xBEAlCpVCgMHDsSyZcuUcvePHj3CoEGDUKZMGQDvlkRwc3PTKV5636OfCtfZSqcGDRogLCwMTk5OaN68OVq3bq16zSlDePr0KV6/fg1XV1ckJCRg8uTJOHToEPLly4fhw4erWnm9RIkSCAgISLYi+ZAhQ/5fe3ceVnP6/gH8fU6WIiVlkGnRFNXEyDQY+9qqLEOMlBZjrGVkiWEQY/haMmGEaVEoRtlH1lZbQ8raikoNQwlFpXp+f3R1fo62s3ZOuV/X5bo6n885T/dRnc/nfpb7wblz50Tak2ns2LEoLCxEaGgoNDU1AQC5ublwcHCAmpoajh49KnSb8ig8PByTJ0/GqFGj+BLVixcv4vDhwxg/frzAbZ09exbFxcWYMGEC0tPTYWtri7S0NKirqyMsLEyoJFpHRwfu7u7w9PTkO75lyxb4+voKvC9G9V45DQkMDBQ4trqkpKTAzMyMt7N7Q6QV28WLF3Hx4kX8999/NToGAgIChGpLkrhcbr03EMLuTxYbG4uAgACEh4eDy+XC3t4ebm5uQt2YV7dTnyFDhjTYhq2tLWJjY2FjYwMHBwdYWlpCQUFBIvuTNTXJycno06ePwD/PhvZwfPbsGTQ1NQVqr6KiAvv374ednZ1Q15D6qKmp4e3btygvL0ebNm1qJG8FBQVCtde2bVskJyfX2NNKFj777DOcO3cOvXv3xqtXr6CmpoZ9+/bB0dFRrHYrKyuhqKiIe/fuCd1pV5tXr15BWVm5RudkQUEBlJWV+RKw2nC5XCgrK6NFixao65aSw+EI/bOUtPHjxyMqKgodOnTAl19+WeN3LSIiQqj29PX1sXv3bowcOZLv7yolJQXffvstXr58KcnwG6Sjo4OQkBAMGTIEZWVlaN++PU6ePMm7N7hz5w6GDh0qkZ9DYWEh2rdvL/TrUlJSMG7cODx69IiXUOXk5EBPTw/Hjx9H9+7dcezYMbx580bsv5MbN27AzMxMrDYA4T9za0MjW2I6fvw4Xr58ib/++gsHDx7E1q1bYWhoCAcHB0ydOlUiPXWi6NChA+9rLpcr1ibBDx484PUYfMjV1RXbtm0Tqc0dO3bAzs4Ourq6fH9wJiYm2L9/f4Ov79ChA9LS0qChoQE1NbV6bzJl+QH/3Xff4fr16/Dx8cGxY8cAVPW6JCQkwNTUVKi2LCwseF8bGBggJSUFBQUFDb7/2qxZswYzZsxAdHQ0byPH69evIzIyEnv37hW4HUkkUR+7ffs232PGGP79919s2LABvXv3FrgdacS2Zs0aeHt7w8zMTOxec0m7desW3+P379/j1q1b2Lp1K3799VeB2sjLy0NQUBCCgoKQkZGBAQMGwNfXF/b29rwRQWHVNur/4f+bIBewM2fO1LoZNBHMx7+nov7eKigo4Mcff8SQIUMklmyJeg2pS79+/ZCRkSHxZKuoqKhG54qKikq9r3nx4gWvM1FVVRVt27YVurOiNlwuFwYGBsjPz5fI30NdmwN/eB9RHyMjIzx79gzTpk2Dq6urUBshN6SkpATbt29HVFRUrR1cwnT2tm/fXqgOzobk5ubW+ntWWVmJ9+/fS+z7CMra2hpeXl7YuHEjjh07hjZt2mDw4MG887dv38YXX3whdLsbN26Erq4uJk+eDACwt7dHeHg4OnfujL///luoAQZDQ0Pcv38f586dQ1paGoCqjd9Hjx7NG20XZrZAUVERFBQU+EaJk5KSsHLlSvz9998CXV8mTJhQ7/nCwkKB46kLJVsSoKamhpkzZ2LmzJl48uQJQkNDERAQgF9++QXl5eUyicnJyQnDhw/HkCFDRPrj+lDHjh2RlJRU40M9KSmJ11sqLC0tLSQmJuLixYu8XeGNjIwEHpb28fFBu3bteF/L000vALx+/Zr3tYGBAf74449an9PQxRqoSmoFIcyoirOzM4yMjODr68vrzTMyMkJ8fDwv+RJVz5498ffffws8DeBjvXv3BofDqdFD2r9/f5mOHAGAn58fgoKCxO5xk4baLnhmZmbQ1NTEpk2bGrygWFlZ4cKFC9DQ0ICTkxNcXV3Ro0cPseP6uHe3OglcuXKlwElgfHw8/P398fXXX8PIyAiOjo4CT1NrTJIeXZQUSU6nNTExwcOHD9GtWzeJxDZ9+nSJtFNt/vz58PT0xNOnT9GzZ88aoxfCJAKPHj3CvHnzEB0djZKSEt5xxhg4HE6DP08Oh4M3b95AUVGR95p3797xXR+AhpO22mzYsAGLFy/Grl27YGJiIvTrJenevXu4fv06AgICMGTIEOjr68PNzQ0ODg4ivbcPubm54dy5c5g4cSL69u0r1rVe0h1wxsbGiIuLg46ODt/xI0eOCN2ZKgmSmhb6MT8/Pxw4cAAAcP78eZw/fx5nzpzB4cOHsXjxYpw7d06gdt6/fw8lJSUkJSXB0tISlpaWQsdSLScnB/b29khISICCggLmzZuHdevWYdasWTh06BDGjx+PK1euCNRWXZ0NH54XZQrsh2gaoQS9f/8ep0+fxv79+3H69Gl06NABubm5MollxowZiI2NRUZGBrp27YqhQ4di2LBhGDp0qMA9Yd7e3li0aBE2b94MHx8feHl5YcCAAQCqpsJt3LgRCxcuxMqVK4WKrbKyEkFBQYiIiMDjx4/B4XDQrVs3TJw4EY6OjnKXOImioZuuaoLcfHG5XOjo6MDU1LTOKRoA5Gbq5cfTlIT18RRGLpeLjh07QlFRURLhiUVdXR0JCQlid2A0poyMDHz11VcoLi6u93l2dnZwc3PDmDFjRFrrKKyYmBih1+AVFxfj0KFDCAgIQEJCAioqKrB161a4urryOl9k6fjx43yPqxPLffv2Yc2aNRJZwyHslBZJT6eNjIzEsmXLsHbtWnz99dc1RjxFubmuqKjAsWPHeB1vX375Jezs7ET6PazuHf9QdeeNIAnShwYOHAjGGDw8PNCpU6can+kNrU38+DpQHcPHj0VJwj+cftmqVasa679kNaPj3bt3+OuvvxAYGIiEhASMGzcOAQEBvGRfWKqqqvj777950/DFVV5ejujoaGRmZmLq1Klo164d8vLyoKKiAmVlZaHaOn78OKZPn45ly5bB29sba9asQWpqKoKDg3Hq1CmR1rNLgrjTQj+mpKSEtLQ0aGlpwcPDAyUlJdi9ezfS0tLQr18/oaZL6unp4ejRo2Ivt5kyZQpSU1Ph5uaGiIgIxMTEoE+fPujXrx+8vLzw+eefi9W+pFGyJQFRUVE4ePAgwsPDUVlZiQkTJsDBwQEjRoyQeeKQm5uL2NhYxMTEICYmBmlpaejSpQuePHnS4GsVFBTw77//omPHjti2bRu2bNmCvLw8AICmpiYWL14Md3d3od4jYwy2tra8oWdDQ0MwxvDgwQPcuXMHdnZ2vOl2DREkoeFwODIZXYyJieF9zRiDtbU1/vzzT3Tt2pXveYIUEpg7dy5CQ0Oho6MDFxcXTJs2TeDpHYIoKSlBWVkZ3zFxeiNFTbauXr2K/Px8jBkzhncsODgYq1atQnFxMcaNG4ft27eLfNGWhKVLl0JZWVnoDobG8HFvefX0y9WrVyMlJQVJSUmyCawOwq7B+1hqair8/f0REhKCwsJCjB49GidOnJBwlJJx8OBBHDp0qEYyVhtBprTExMTIbJTsw2RGEolDRkYGrK2tkZubyxtJTU1NhZaWFk6fPi10x0ZD600/HoWoj7KyMm7evCnyCO+H14H6iFJQZt++ffWel/SIobBiY2OxatUqxMbG4sWLFyJPOzU2NkZYWJhEpiZmZWXB0tIS2dnZKC0tRVpaGvT09ODh4YHS0lL4+fkJ1E71yC6Hw0FcXBy8vb35Ckf88ssvIo0gyStNTU0cOXIEAwYMQI8ePbBu3TpMmjQJqamp+Oabb2pce+rj7++PiIgIhISEiHUfo6mpiYiICPTv3x///fcfOnfujK1bt2LBggUitylNlGyJqWvXrigoKIClpSUcHBxga2sr05vBj719+xbx8fGIiopCdHQ0EhMTYWxsXGN9R20+XlgNAG/evAEAkXuRAwMD4eHhgePHj2P48OF85y5duoRx48Zhx44dAg3Z1nfjcvXqVfj6+qKyspJv+oesiDvaU1paioiICAQEBODKlSuwsbGBm5sbzM3NRUro3759iyVLluDw4cPIz8+vcV6cGzlra2v4+/ujS5cuQr3OysoKw4YNw9KlSwFULebt06cPb8rjpk2b8OOPP2L16tUixyYuDw8PBAcHo1evXujVq1eNKUpbt26VUWS1dz4wxqClpYWwsDB8++23MomrvjV45eXliI+PF6v9iooKnDx5EgEBAXKbbD18+BC9evUSKLFszKIzomgogRA2cbC2tgZjDAcOHODdfOXn52PatGngcrkCV8CUhuHDh+Pnn38Wqerapyg3Nxf79u1DYGAgiouLeWu4DA0NRW7zzJkz8PX1hZ+fn1CJcm3GjRuHdu3awd/fH+rq6rxrcnR0NH744Qekp6cL1E51R3T1vdHkyZPh6+vLVwmwOZk3bx5OnToFAwMD3Lp1C48fP4aysjLCwsLwv//9T6h1c6ampsjIyMD79++ho6NTY2Rc0LYUFBSQl5fH+z8Xt2NE2ijZEtPevXsxadIkkaqySNPy5csRHR2NW7duwcjIiDeNUJiFzVwuF8+ePeOV55QEc3NzjBgxos6CHevXr0dMTAzOnj0rUvupqanw8vLCyZMn4eDgAG9vb7E/oCVB3GTrQ1lZWQgKCkJwcDDKy8tx7949oac/zJ07F1FRUVi7di0cHR2xc+dO5ObmYvfu3diwYQMcHBzEjlNYXbp0wcmTJ3nVg37++WfExMTwbsb/+usvrFq1Cvfv32/02Kp93EHwsaioqEaKpKbo6Gi+ZKt6+qW+vj5atJDd8tzqJLCuNXji3Ig1Be/evcOyZctw5swZpKamyjocudO2bVtcu3YNPXv25DuenJyMgQMHCpSgnjhxAlZWVmjZsmWDCbednZ3AsWVmZmLWrFmYNm0aTExMxFr/JQ2SnH4pjsOHDyMwMBAxMTGwsLCAi4sLbGxsJBLH8+fPYW9vj9jYWLGrVaqrq+PKlSvo0aMH3zX58ePHMDY2xtu3bwVq5+OOaBUVFSQlJUnk+i6P3r9/j99//x05OTlwdnbmrUerXjs/Y8YMgdtas2ZNvedXrVolUDsKCgp4+vQp7/5URUWFtzWOPKICGWKq3lshIyMDmZmZGDJkCJSUlGrMzW5sGzZsQMeOHbFq1SpMmDAB3bt3F6md7t27N/g+hPmwu337Nv73v//Ved7Kygq+vr4Ct1ctLy8Pq1atwr59+2BhYYGkpCSZLxqWlg9vXkUdgTp58iSCg4MxbNgwuLi4YPDgwdDX14eOjg4OHDggdLK1b98+aGhowMbGBgCwZMkS7NmzB8bGxrwpkA2R1h4hkiTLZKoh33zzjcgVA6Xp473J5GkNnqR9XBmUMYY3b96gTZs2AlVZbSoKCwuRkJBQa3U4YReSt27dmjdj4kNFRUUCry0ZN24c7+a3vkpmwk5zfP78OTIzM/lGG0Vd/yVptU2//O2330SefimOKVOmQFtbGz/99BM6deqEx48fY+fOnTWe5+7uLnTb33//PXJzc7F+/fpa180Jo7Kystaf2ZMnT8Ra89ncxyxatmyJRYsW1Tj+008/Cd2WoMlUQxhjfPenRUVFMDU1rbFmU9bbDVSjZEtM+fn5sLe3R1RUFDgcDtLT06Gnpwc3Nzeoqalhy5YtMonr1q1biImJQXR0NLZs2YJWrVrxRreGDRsmcPK1Zs2aBiu1CKOgoKDeofZOnToJtdjy1atXWL9+PbZv347evXvj4sWLfKVO5Yk4F4kPpxHGx8djzJgx2LFjBywtLWtdEN6QgoICXi+ciooK7wNp0KBBmD17ttDtrV+/Hrt27QJQNYVzx44d2LZtG06dOoWffvpJoP1LOnXqxNt7o6ysDImJiXy9YG/evKl1A9XG0NA6GqDq5xseHt4I0dSuU6dOsLe3h6urKwYNGiSzOKo1hTV4kvZxZdTqxLJfv34SK5Uua9WzBoqKiqCiosL3fjkcjtDJ1pgxYzBz5kz4+/vzNpO/fv06Zs2aJfAo1IcJ38fJX7WcnBx4e3sLFZurqytMTU0RGhoq9o2+pLm7u+OLL77AtWvXaky/dHd3b9Tpl9ra2uBwODh48GCdz+FwOCIlW1euXMHVq1clsn+pubk5tm3bxtsQnMPhoKioCKtWrYK1tbXA7XA4HIltp9CU3L9/H9nZ2TXWeAszWlzt5s2bfCOywlZvlNU0apExIhZHR0dmYWHBcnJymLKyMsvMzGSMMRYZGcmMjY1lHN3/S0pKYtOnT2ctWrRgXC5XoNdwOBz27NkzicbB5XLZf//9V+f5p0+fChzfxo0bWYcOHZixsTE7duyYpEKUiPHjx/P9a9GiBTM3N69xXBCzZ89mampqrFevXmzbtm3s+fPnYsfXs2dPFh0dzRhjbOTIkczT05Mxxtjvv//OunbtKnR7SkpKLCsrizHG2JIlS5ijoyNjjLG7d+8yDQ0NgdqYNWsW+/bbb1lsbCxbuHAhU1dXZ6Wlpbzz+/fvZ2ZmZkLHJgnOzs4C/ZOlo0ePsrFjx7KWLVsyAwMD9ttvv7Hc3FyZxWNpack2bNjAe3z79m3WokULNmPGDLZlyxbWuXNntmrVKpnFR0RjYGDAPDw8WHFxsUTae/nyJRs7dizjcrmsVatWrFWrVozL5bJx48axwsJCiXwPxqqugYJeW6q1adOGpaenSywGSWrTpg27fft2jeNJSUmsbdu2MohIOkxNTdnVq1cl0lZOTg4zNjZmRkZGrEWLFqx///5MXV2dde/eXah7HQ6Hw6ytrSVyfW8KMjMzWa9evRiHw2FcLpdxOBze18L+TT179owNHz6ccTgcpqamxtTU1BiHw2EjRoyo996wqaM1W2Lq3Lkzzp49i6+++opvDrAwC6KlgTGGW7duITo6GtHR0YiPj8fr16/Rq1cvDB06FD4+Pg228fEiUEngcrmwsrKqsze7tLQUkZGRApdEV1JSwqhRo+qdGy7srvCSIMlF7lwuF9ra2jA1Na2390yY9+nj4wMFBQW4u7vjwoULsLW1BWMMZWVl8PHxgYeHh8BtAcBnn32Gs2fPwtTUFKampli4cCEcHR2RmZmJr776SqC/gxcvXmDChAmIj4/n7RHy4QaUI0eORP/+/QXem+lT9fz5c4SEhCAoKAgPHjyAhYUFXF1dYWdn16hrt5rCGjxJi4yMhLKyMm9kcefOndi7dy+MjY2xc+fOZjG61bZtW9y5c0fs9SmVlZXYtGkTTpw4gbKyMmhra2P69OngcDgwMjKS+KbEwpbMBwBbW1s4Ozvju+++k2gsktChQwecOnWKtx1LtcuXL8PW1lZupk+J69y5c1izZg1+/fXXWvdNE7Zybnl5OcLCwnD79m1e9UAHB4capfPrI+9FbCTN1tYWCgoK+PPPP9GtWzckJCQgPz8fnp6e2Lx5s1CziSZPnoyHDx8iODgYRkZGAKpGzKZPnw59fX2EhoaKHOecOXPg7e0NDQ0NkduQFkq2xNSuXTskJibCwMCAL9m6ceMGLCwsaq301hjU1NRQVFSEr776ijd9cPDgwUIV8qitGqG4JPkh5ezsLNDQfVP/wGuM95mVlYWbN2/CwMCgxkJ1QTg4OCAlJYU35SY7Oxvq6uo4ceIEli9fjrt37wrclqT3CPmUbd++HYsXL0ZZWRk0NDQwa9YseHl5oU2bNlL/3oqKikhPT+dtbj1o0CBYWVnh559/BgA8fvwYPXv2rHW9TlPVs2dPbNy4EdbW1rhz5w7MzMzg6emJqKgoGBoaNvnPIqBqSu2UKVNgb28vVjtr167F6tWrMWrUKCgpKeHs2bP4/vvvpbZxuSjJ1p49e7Bu3Tq4urrWeqNf3/QpQaYeVxOlQ9DJyQmJiYk1pl/+8MMP+PrrrxEUFCR0m/Koepp8bZVWhV03l5+fD3V1dQBV00r37t2Ld+/ewc7OTm6XH8gDDQ0NXLp0Cb169YKqqioSEhLQo0cPXLp0CZ6engJVt66mqqqKCxcu4JtvvuE7npCQAHNzcxQWFoocpzwXKqE1W2IaPHgwgoODsXbtWgBVHwiVlZX43//+12DlMmnav38/Bg8eLNZ+SXXNfReHJG82msvFpCGSfJ+XLl3CvHnzcO3aNb7fDR0dHbRv3x4DBgyAn5+f0BeenTt3YsWKFcjJyUF4eDjvgnbz5k18//33QrVV1xpBSe4t1pw9e/YM+/btQ1BQELKysjBx4kS4ubnhyZMn2LhxI65du4Zz585JPQ55XoMnLY8ePYKxsTEAIDw8HLa2tli/fj0SExOFWhMibz6s8GdjY4PFixfj/v37QicgHwoODsYff/yBH3/8EQBw4cIF2NjY4M8//xRpHao0zJo1CwBqXevV0I3+h59jjDEcPXoUqqqqvJHemzdvorCwUKik7EO+vr5wdnbGgAEDeCPW5eXlsLOzw++//y5Sm/Lo0qVLYq+HunPnDmxtbZGTkwMDAwOEhYXB0tISxcXF4HK58PHxwZEjR+otrvIpq6io4BUQ0dDQQF5eHnr06AEdHR2hK6xWVlbW+rnfsmVLse855XnsiEa2xHT37l2MHDkSffr0waVLl2BnZ4d79+6hoKAAly9fbtSKQPV5/fo1Ll26hB49evCGbsmnx87ODsOHD6+zipCvry+ioqJw9OjRRo6MiCsiIgKBgYE4e/YsjI2NMWPGDEybNo1vNDszMxNGRkY1FjhLw+zZs5GcnIyNGzfi2LFj2LdvH/Ly8ngjkwcOHMC2bdvwzz//SD2WxtKhQwfEx8fD2NgYgwYNgpOTE2bOnCl0aWl5I2jyI8xIQ+vWrZGRkcEb+QSqRkMzMjLw+eefCx2jPG8GvXTpUhQUFMDPz483Yl9RUYE5c+ZARUUFmzZtEritxp5+2RxYWVmhRYsW8PLyQkhICE6dOgULCwvs3bsXADB//nzcvHkT165dk3Gk8mnw4MHw9PTEuHHjMHXqVLx8+RIrVqzAnj17cPPmTaFmrowdOxaFhYUIDQ2FpqYmgKr92RwcHKCmpibWvYckt9iROBmtFWtWCgsL2bp169ikSZOYlZUV+/nnn1leXp5MY5o0aRLbvn07Y4yxt2/fMgMDA9ayZUvWokULduTIEZnGRmRHW1ub3b9/v87zDx48YFpaWiK1HRsbyxwcHNi3337Lnjx5whhjLDg4mMXFxYnUHhGOiooKmzlzJktISKjzOW/fvmWrV69ulHieP3/OBg8ezDgcDmvXrh2LiIjgOz9ixAi2fPnyRomlsdja2jILCwvm7e3NWrZsyfs7OHv2LDMwMJBxdPKltmJJysrK7OHDhyK111hFbF6+fCn0azQ0NFhKSkqN4ykpKaxDhw5CteXt7c24XC4zNzdnY8eOZYqKiszFxUXomJoKXV1dtmbNGl4BJlGoq6uz5ORkxhhjb968YRwOh924cYN3/sGDB0xVVVXcUJutyMhIFh4ezhhjLC0tjfXo0YNxOBymoaHBLly4IFRb2dnZrHfv3qxly5ZMT0+P6enpsZYtWzJTU1OWk5MjjfDlAiVbzVSnTp1YUlISY4yxAwcOMH19fVZcXMz++OMP1rt3bxlHR2SldevW9VbXSk9PZ4qKikK3e+TIEaakpMRmzJjBWrduzavKuX37dmZlZSVyvKRhr169Yq9evWJ5eXm8r2v7JyuFhYWsvLy8xvH8/Hy+apPNQVZWFrOxsWG9evVif/75J+/4ggUL2Pz582UYmfguXrzIjIyMav1dKiwsZMbGxiw2Nlbg9j6u6FZXVTdZ2rBhAwsLC+M9njhxIuNwOExTU5N3fRVE+/bta62Ye+zYMda+fXuhYtLX12d+fn68x+fPn2etWrViFRUVQrUjLRUVFSw1NZXFxcWxmJgYvn+i8PHxYV999RVTUFBgo0aNYqGhoaykpESoNj6urPxh5WjGhKuCTKrk5+ezyspKkV5bWVnJzp07x3x9fZmvry87f/68yHHcvHmTryrnsWPH2NixY9myZcvk6vpC0wglQJIbPEqKkpIS0tLSoKWlBScnJ2hqamLDhg3Izs6GsbGxzKokEtn64osvsGXLljrnpkdERGDRokV4+PChUO2amprip59+gpOTE99Q/q1bt2BlZYWnT59KIHpSm+pNruvC5GADVtL0SXoKclOo6NatWzccOHAAAwYMwPnz52Fvb49Dhw7h8OHDyM7OFnjt48KFCxEcHIzly5fzFbPYsGEDHB0dsXXrVoFjkvT0S0m6du0apk6diqysrBrrZ8T9DEpMTERQUBBCQ0NRUVGBqVOnwtXVFX369GnwtVwuF8+ePUPHjh0BVE03u337Nrp16wagap2rpqYmfUZ+xNXVVaDnSauojSC++eYbeHl54bvvvsPDhw/x5ZdfYvz48fjnn39gY2ODbdu2ySy2D1GBDDFJeoNHSdHS0sLVq1fRoUMHREZGIiwsDADw8uVLKCoqyiQmInvW1tZYuXIlLC0ta/wevHv3DqtWreLbhFZQqampGDJkSI3jqqqqYlUXIg2Liorifc0Yg7W1Nf7880907dpVhlF9muraLiM/Px+fffZZk76Zq15/Vxdzc3Ns3rxZ4PaaQmXGp0+f8pKaU6dOwd7eHubm5tDV1UW/fv0Ebmfz5s3o3LkztmzZgn///RdA1dYIixcvhqenp1AxlZeX1/jsbtmyJd6/fy9UO9Iwa9YsmJmZ4fTp0+jSpYtEN/rt06cP+vTpgy1btuCPP/7A0qVLsWvXLvTs2RPu7u5wcXGp9/s5OzvztpwpKSnBrFmz0LZtWwBVW86QmoKCgqCjowNTU1OJFp+4ePEifHx8eJsaGxkZYcGCBRg1apTQbaWlpaF3794AqrYTGTJkCA4ePIjLly9jypQplGw1F56ennB1dcX69esbpZyyoBYsWAAHBwcoKytDR0cHw4YNAwDExsaKVNqbNA8rVqxAREQEunfvjnnz5qFHjx4AgJSUFOzcuRMVFRW80tzC6Ny5MzIyMqCrq8t3PD4+Xj4XqzYjQ4cO5XusoKCA/v370/+7DNR1Q1JaWtrktyx49uxZvdUjW7RogefPnzdiRNKnpqaGnJwcaGlpITIyEuvWrQNQ9XMWJnHmcrlYsmQJlixZgtevXwMQfn+oaowxvsQBqJk8ALLZXzI9PR1HjhyRSpGO9+/f4+jRowgMDMT58+fRv39/XpXV5cuX48KFCzh48GCtr50+fTrf42nTptV4jqw6xuXZ7NmzERoaikePHsHFxQXTpk0TuyrwH3/8AQ8PD0ycOJG3n+e1a9dgbW0NHx8fzJ07V6j2GGO8GWUXLlzgdRZraWnhxYsXYsUqSZRsiSk3Nxfu7u5ylWgBVZu79evXD9nZ2Rg9ejSvmpSenh7vgkE+PZ06dcKVK1cwe/ZsLFu2jHdzyOFwYGFhgZ07d6JTp05Ct/vDDz/Aw8MDAQEB4HA4yMvLw9WrV7Fo0SKsXLlS0m+DELni6+sLoOrv6M8//4SysjLvXEVFBWJjY2FoaCir8CSia9euuHv3bp030rdv30aXLl0aOSrpmjBhAqZOnQoDAwPk5+fDysoKAHDr1i2REwpxtmMBaiYOQO3Jgyz069cPGRkZEk22EhMTERgYiNDQUHC5XDg5OcHHx4fv72n8+PE19m36UFMYRZVHO3fuxNatWxEREYGAgAAsW7YMNjY2cHNzg7m5uUgjl+vXr4ePjw/mzZvHO+bu7o6BAwdi/fr1QidbZmZmWLduHUaNGoWYmBjs2rULQNU2HKLcy0iNjNaKNRvjx49nhw4dknUYNdy5c6fOc0ePHm28QIjcKigoYAkJCez69eusoKBArLYqKyvZunXrWNu2bRmHw2EcDocpKiqyFStWSChaIqiPF38T6dPV1WW6urqMw+EwLS0t3mNdXV3WvXt3Zm5uzq5duybrMMUyb948ZmJiwt69e1fj3Nu3b5mJiUmTLwLysbKyMrZp0ybm7u7OEhMTece3bt3K9u7dK3A7T58+ZdOmTWNdunRhCgoKjMvl8v1rLiIiIpixsTELDAxkN27cYMnJyXz/RMHlcpmFhQU7fPgwKysrq/U5RUVFEqk0Ser3+PFjtnr1aqanp8e0tbXZmzdvhG6jbdu2tRbpSktLY23bthW6veTkZGZiYsJUVFT4Ku3OmzePff/990K3Jy1UIEMEH27w+Pz5c3h7e8PFxUWsDR4lrWvXroiPj+ctAK0WHh4OJycnFBcXyyQu0ryVlZUhIyMDRUVFMDY25uvhJ43j48XfpPEMHz4cERERUFNTk3UoEvfs2TP06dMHCgoKdU5BTkxMlK/eZDlhZWWF7OxszJs3r9a1TGPHjpVRZJJV235sHA5HrCI9WVlZ0NHRkUR4REw5OTkIDAxEUFAQysrKkJKSIvQ1furUqTA1NcXixYv5jm/evBk3btzg1RcQV0lJCRQUFOqd+tyYKNkSgTQ2eJS0VatWYf/+/bh8+TI6d+4MADh06BBcXV0RFBSESZMmySQuQohkfbyZ68mTJzFixAi+9RuAbNZwkOYlKysLs2fPxtmzZ2udgtwcEvwPO1MbImhnart27RAXF8dbyN9cZWVl1Xuekqamp7S0lDeNMD4+HmPGjIGLiwssLS0FvheunmYNAK9fv8bmzZsxcOBAfPvttwCq1mxdvnwZnp6eWLFihVTeh6xRstWMzZ8/H1FRUYiNjUVkZCRmzJiBkJAQfPfdd7IOjTQDH9/k14du9KWnKZTQbs4WLlyItWvXom3btli4cGG9zxWmxLc8e/nyJTIyMsAYg4GBQbMayfv4BrJ6ZObDx9UE7Uw1NjbGgQMHYGpqKpkgmzk1NTWB1wMVFBRIOZpP15w5cxAWFgYtLS24urrCwcEBGhoaQrcjaCcMh8MRaNuZpvj7QQUyRHTp0iXMmzcP165dq7Hg9dWrVxgwYAD8/PwwePBgGUUIbN++HQ4ODujfvz9yc3MRGhrabKYrENlTVVXlfc0Yw9GjR6GqqgozMzMAwM2bN1FYWChUUkaER0mUbN26dYtXdjsxMVGi5a7llZqaWr0FCZqyD/fKvHDhApYuXYr169fzeuGvXr2KFStWYP369QK3uW3bNnh5eWH37t01KrY2N5mZmdi2bRuvrLexsTE8PDzwxRdfCNyGvJTr/tT5+flBW1sbenp6iImJQUxMTK3Pa6gz9dGjRxKNqyn+ftDIlogkvcGjJNQ2/eH9+/f46aefYG5uzjflQVZryUjztHTpUhQUFMDPzw8KCgoAqnp958yZAxUVFWzatEnGERJCiHBMTEzg5+eHQYMG8R2Pi4vDzJkzeQlFQ9TU1PD27VuUl5ejTZs2NdaRyEvvu7jOnj0LOzs79O7dGwMHDgQAXL58GcnJyTh58iRGjx4tcFsVFRXYvHkzTpw4gbKyMowcORKrVq2CkpKStMInH3F2dhao84g6/BpGyZaIdHR0EBkZCSMjo1rPp6SkwNzcHNnZ2Y0WU1NYS0aap44dOyI+Pp63aL5aamoqBgwYgPz8fBlFRoj0ubq6NvgcDocDf3//RoiGSIqSkhL++ecfmJiY8B2/ffs2+vXrh3fv3gnUzr59++o9X1s596bI1NQUFhYW2LBhA99xLy8vnDt3DomJiQK3tXbtWqxevRqjRo2CkpISzp49i++//x4BAQGSDps0IsYYjhw5gqioKPz33398I8mAYEsOqveqE4S4Wy1ICiVbIlJUVKx3z5GMjAz07NlT4A9jQpoyNTU1BAUF1Zimevz4cTg7O+Ply5cyiowQ6eNyudDR0YGpqWmdGxsDaNSZDkR8Q4YMgaKiIkJCQnhVFp89ewYnJyeUlJTUOa3qU6WoqIg7d+7AwMCA73haWhp69eqFkpISgdsyMDDAokWL8OOPPwKomtJpY2ODd+/eCdyxTOSPh4cHdu/ejeHDh6NTp041Rs4EGSXjcrkNjriJUwFTGmjNlog+xQ0eCamLi4sL3NzckJmZib59+wIArl+/jg0bNghcwIGQpmr27NkIDQ3Fo0eP4OLigmnTpqFDhw6yDouIKSAgAOPHj4e2tja0tLQAVJW/NjAwwLFjx0Rqs6SkBGVlZXzH5KX3XVwdO3ZEUlJSjWQrKSkJn332mVBtZWdnw9ramvd41KhR4HA4yMvLw+effy6ReEnjCwkJQUREBN/PVlhRUVESjKhx0MiWiObPn4/o6Gj8888/UFRU5Dv37t079O3bF8OHD+creSltwnwvd3d3KUZCPjWVlZXYvHkzfv/9d/z7778AgC5dusDDwwOenp68dVyENFcflki+cuUKbGxs4ObmBnNz80+iaEZzxRjD+fPnkZKSAgAwMjLi3fgLqri4GEuXLsXhw4drnVItL73v4vL29oaPjw+8vLwwYMAAAFVrtjZu3IiFCxdi5cqVAreloKCAp0+fomPHjrxjtIdg09etWzecOXMGhoaGsg6lUVGyJSJ53OBR0uU1CRFF9Xzq5tJbS4iwsrKyEBQUhODgYJSXl+PevXu0wfcnbO7cuYiKisLatWvh6OiInTt3Ijc3F7t378aGDRvg4OAg6xAlgjGGbdu2YcuWLcjLywMAaGpqYvHixXB3dxcqQeVyubCyskLr1q15x2rbQ5C2FWla9u3bh8jISAQEBEis2ElhYSH8/f15BWu+/PJLuLq68lVMljVKtsTwKWzwSIigysvLER0djczMTEydOhXt2rVDXl4eVFRU6EaTfFJycnIQGBiIoKAglJWVISUlhf4GmghpzBDR1tZGcHAwhg0bBhUVFSQmJkJfXx8hISEIDQ3F33//LWq4cuvNmzcAqkajREH7BzZP7969w/jx43H58mXo6urWqMwpTBEVALhx4wYsLCygpKTEW8Lwzz//4N27dzh37hz69OkjsdjFQcmWBMj7Bo8fJoKESENWVhYsLS2RnZ2N0tJSpKWlQU9PDx4eHigtLYWfn5+sQyREqj6cRhgfH48xY8bAxcUFlpaWtKC/CZHGDBFlZWXcv38f2tra+PzzzxEREYG+ffvi0aNH6NmzJ4qKisQJWW6MGDECERERaN++Pd/x169fY9y4cbh06ZJsAiNyw97eHlFRUZg4cWKtBTJWrVolVHuDBw+Gvr4+9u7dixYtqspQlJeXY8aMGXj48CFiY2MlFrs4qECGBMjrBo/BwcHYtGkT0tPTAQDdu3fH4sWL4ejoKOPISHPj4eEBMzMzJCcnQ11dnXd8/Pjx+OGHH2QYGSHSN2fOHISFhUFLSwuurq4IDQ2FhoaGrMMiIqhtA9bnz5+Dw+GI/DPV09PDo0ePoK2tDUNDQxw+fBh9+/bFyZMnayQmTVl0dHSN4h9AVVGQuLg4GURE5M3p06dx9uzZGnvXierGjRt8iRYAtGjRAkuWLIGZmZlEvockULLVTG3duhUrV67EvHnzeJsLxsfHY9asWXjx4kWdmzETIoq4uDhcuXIFrVq14juuq6uL3NxcGUVFSOPw8/ODtrY29PT0EBMTU2dJcFpf0nQUFhbi559/xqFDh3hbV6ipqWHKlCn49ddfhVoP4uLiguTkZAwdOhReXl6wtbXFjh078P79e2zdulVab6HR3L59m/f1/fv38fTpU97jiooKREZGomvXrrIIjcgZLS0tia7nVlFRQXZ2do2CGzk5OSJPYZUGSraaqe3bt2PXrl1wcnLiHbOzs8OXX36J1atXU7JFJKqysrLWilpPnjyRqw88QqTBycmJpmk3IwUFBfj222+Rm5sLBwcHGBkZAahKJIKCgnDx4kVcuXJF4CUDH15vR40ahZSUFNy8eRP6+vro1auXVN5DY+rduzc4HA44HA5GjBhR47ySkhK2b98ug8iIvNmyZQuWLFkCPz8/6Orqit3e5MmT4ebmhs2bN/NVwFy0aBGmTJkidvuSQmu2mqm6Nl1OT09Hz549hdpckJCGTJ48GaqqqtizZw+vPG/Hjh0xduxYaGtr0yJmQkiTsWDBAly8eBEXLlyoUVH46dOnMDc3x8iRI+Hj4yPW93ny5Am8vb2xZ88esdqRtaysLDDGoKenh4SEBL5y7a1atcJnn31G238QAFWjw2/fvkV5eTnatGlTo0BGQUGBUO2VlZVh8eLF8PPzQ3l5ORhjaNWqFebMmYNff/1VYhUPxUXJVjNlYmKCqVOnYvny5XzH161bh0OHDuHOnTsyiow0R0+ePIGFhQUYY0hPT4eZmRnS09OhoaGB2NhYoTe0JIQQWdHV1cXu3bthYWFR6/nIyEjMmjULjx8/Fuv7JCcno0+fPs1mny1CGrJv3756z0+fPl2kdt++fYvMzEwAwBdffIFdu3Zh06ZNfFNaZYmmETYzd+/ehYmJCby9vWFvb4/Y2Fjemq3Lly/j4sWLOHz4sIyjJM3N559/juTkZBw6dAjJyckoKiqCm5sbHBwc5KZniRBCBPHvv//iyy+/rPO8iYmJ3NzEyZN9+/ZBQ0MDNjY2AIAlS5Zgz549MDY2RmhoKHR0dGQcIZE1UZOpj5WWlmL16tU4f/48WrdujcWLF2PcuHEIDAyEpaUlFBQU5Gq5DI1sNTNcLhfffPMN3NzcYGhoiD179vA2ejMyMoKnpydMTU1lHCUhhBAin7p27YpDhw7VWTEtLi4OkydP5m3cK6rmNrLVo0cP7Nq1CyNGjMDVq1cxcuRIbNu2DadOnUKLFi2oQAzhU1JSUqN6paDFM5YuXYrdu3dj1KhRuHLlCp4/fw4XFxdcu3YNy5cvx6RJk+Rq6iqNbDUzMTExCAwMxKJFi1BZWYnvvvsOPj4+GDJkiKxDI83Yb7/9hk6dOsHV1ZXveEBAAJ4/f46lS5fKKDJCCBGOhYUFfv75Z5w/f75GhdXS0lKsXLkSlpaWMopOfuXk5PDWiR87dgwTJ07EzJkzMXDgQAwbNky2wRG5UFxcjKVLl+Lw4cPIz8+vcV7Qjoe//voLwcHBsLOzw927d9GrVy+Ul5cjOTlZLosV0chWM1VcXIzDhw8jKCgI8fHx+OKLL+Dm5obp06ejc+fOsg6PNDO6uro4ePAgrxpQtevXr2PKlCm17l1DCCHy6MmTJzAzM0Pr1q0xd+5cGBoagjGGBw8e4I8//kBpaSlu3LgBLS2tetuZMGFCvecLCwsRExPTbEa2PvvsM5w9exampqYwNTXFwoUL4ejoiMzMTHz11VfNZvNmIrq5c+ciKioKa9euhaOjI3bu3Inc3Fzs3r0bGzZsgIODg0DttGrVCo8ePeJtKaCkpISEhAT07NlTmuGLjJKtT0BGRgYCAwMREhKCp0+fwtLSEidOnJB1WKQZUVRUxIMHD9CtWze+4w8fPoSxsTFVvySENCmPHj3CnDlzcO7cOVTfJnE4HIwePRo7duyoUem3Ni4uLgJ9r+ZSrdXBwQEpKSkwNTVFaGgosrOzoa6ujhMnTmDZsmW4d++erEMkMqatrY3g4GAMGzYMKioqSExMhL6+PkJCQhAaGoq///5boHYUFBTw9OlTXuXL6irIH9+DyAuaRvgJ0NfXx/Lly6Gjo4Nly5bh9OnTsg6JNDNaWlq4fPlyjQ+6y5cvQ1NTU0ZREUKIaLp164YzZ87g5cuXSE9PB1B1Le3QoYPAbTSXJEpQO3fuxIoVK5CTk4Pw8HCoq6sDAG7evImpU6fKODoiDwoKCqCnpwegan1Wdan3QYMGYfbs2QK3wxiDs7MzWrduDaBq/desWbPQtm1bvufJyzpBSraaudjYWAQEBCA8PBxcLhf29vZwc3OTdVikmfnhhx+wYMECvH//nrep5cWLF7FkyRJ4enrKODpCCBGNmpoa+vbtK+swmoT27dtjx44dNY6vWbMGd+/elUFERN7o6enh0aNH0NbWhqGhIQ4fPoy+ffvi5MmTUFVVFbidj6saTps2TdKhShRNI2yG8vLyEBQUhKCgIGRkZGDAgAFwc3ODvb19jayfEElgjMHLywu+vr686kKKiopYunQpfvnlFxlHRwghpLG9efMGoaGh8Pf3x40bN5rN2jQiOh8fHygoKMDd3R0XLlyAra0tGGN4//49tm7dCg8PD1mHKBWUbDUzVlZWuHDhAjQ0NODk5ARXV1f06NFD1mGRZqyiogKXL19Gz5490bJlSzx48ABKSkowMDDgDfETQgj5NMTGxsLf3x/h4eHQ1NTEhAkT8N133+Gbb76RdWhEzmRlZeHmzZvQ0NDA/v37sWfPHlmHJBWUbDUzdnZ2cHNzw5gxY+RqjwHSvNVVIIMQQkjz9/TpUwQFBcHf3x+vX7+Gvb09/Pz8kJycDGNjY1mHR+Rcc9tz7mNcWQdAJOvEiRMYO3YsJVqkUZmYmODhw4eyDoMQQkgjs7W1RY8ePXD79m1s27YNeXl52L59u6zDIkRuUIEMQojY1q1bh0WLFmHt2rX4+uuva6wNFHRXeEIIIU3LmTNn4O7ujtmzZ8PAwEDW4RAidyjZIoSIzdraGkDVNNYPd29njIHD4TTbqQGEEPKpi4+Ph7+/P77++msYGRnB0dERU6ZMkXVYhMgNWrNFCBFbTExMveeHDh3aSJEQQgiRheLiYhw6dAgBAQFISEhARUUFtm7dCldXV7Rr107W4REZmjBhQr3nCwsLERMT02w7ZinZIoQQQgghEpOamgp/f3+EhISgsLAQo0ePxokTJ2QdFpERFxcXgZ7XXDcCp2SLECIRcXFx2L17Nx4+fIi//voLXbt2RUhICLp164ZBgwbJOjxCCCGNrKKiAidPnkRAQAAlW+STRdUICSFiCw8Ph4WFBZSUlJCYmIjS0lIAwKtXr7B+/XoZR0cIIUQWFBQUMG7cOEq0yCeNki1CiNjWrVsHPz8/7N27Fy1btuQdHzhwIBITE2UYGSGEEEKI7FCyRQgRW2pqKoYMGVLjuKqqKgoLCxs/IEIIIYQQOUDJFiFEbJ07d0ZGRkaN4/Hx8dDT05NBRIQQQgghskfJFiFEbD/88AM8PDxw/fp1cDgc5OXl4cCBA1i0aBFmz54t6/AIIYQQQmSCNjUmhIjNy8sLlZWVGDlyJN6+fYshQ4agdevWWLRoEebPny/r8AghhBBCZIJKvxNCJKasrAwZGRkoKiqCsbExlJWVZR0SIYQQQojM0MgWIURkrq6uAj0vICBAypEQQgghhMgfGtkihIiMy+VCR0cHpqamqO+j5OjRo40YFSGEEEKIfKCRLUKIyGbPno3Q0FA8evQILi4umDZtGjp06CDrsAghhBBC5AKNbBFCxFJaWoqIiAgEBATgypUrsLGxgZubG8zNzcHhcGQdHiGEEEKIzFCyRQiRmKysLAQFBSE4OBjl5eW4d+8eFckghBBCyCeL9tkihEgMl8sFh8MBYwwVFRWyDocQQgghRKYo2SKEiKW0tBShoaEYPXo0unfvjjt37mDHjh3Izs6mUS1CCCGEfNKoQAYhRGRz5sxBWFgYtLS04OrqitDQUGhoaMg6LEIIIYQQuUBrtgghIuNyudDW1oapqWm9xTAiIiIaMSpCCCGEEPlAI1uEEJE5OTlRxUFCCCGEkDrQyBYhhBBCCCGESAEVyCCEEEIIIYQQKaBkixBCCCGEEEKkgJItQgghhBBCCJECSrYIIYQQQgghRAoo2SKEEEJE8PjxY3A4HCQlJck6FEIIIXKKki1CCCGfDGdnZ3A4HMyaNavGublz54LD4cDZ2bnxAyOEENIsUbJFCCHkk6KlpYWwsDC8e/eOd6ykpAQHDx6Etra2DCMjhBDS3FCyRQgh5JPSp08faGlpISIigncsIiIC2traMDU15R2LjIzEoEGD0L59e6irq2PMmDHIzMyss92Kigq4urrC0NAQ2dnZAIDjx4+jT58+UFRUhJ6eHtasWYPy8nLpvTlCCCFyhZItQgghnxxXV1cEBgbyHgcEBMDFxYXvOcXFxVi4cCFu3LiBixcvgsvlYvz48aisrKzRXmlpKSZNmoSkpCTExcVBW1sbcXFxcHJygoeHB+7fv4/du3cjKCgIv/76q9TfHyGEEPnAYYwxWQdBCCGENAZnZ2cUFhZi79690NLSQmpqKgDA0NAQOTk5mDFjBtq3b4+goKAar33x4gU6duyIO3fuwMTEBI8fP0a3bt0QFxeH1atXo7S0FKdOnYKqqioAYNSoURg5ciSWLVvGa2P//v1YsmQJ8vLyGuX9EkIIka0Wsg6AEEIIaWwdO3aEjY0NgoKCwBiDjY0NNDQ0+J6Tnp6OX375BdevX8eLFy94I1rZ2dkwMTHhPe/777/H559/jkuXLkFJSYl3PDk5GZcvX+YbyaqoqEBJSQnevn2LNm3aSPldEkIIkTVKtgghhHySXF1dMW/ePADAzp07a5y3tbWFjo4O9u7dC01NTVRWVsLExARlZWV8z7O2tsb+/ftx9epVjBgxgne8qKgIa9aswYQJE2q0raioKOF3QwghRB5RskUIIeSTZGlpibKyMnA4HFhYWPCdy8/PR2pqKvbu3YvBgwcDAOLj42ttZ/bs2TAxMYGdnR1Onz6NoUOHAqgqxJGamgp9fX3pvhFCCCFyi5ItQgghnyQFBQU8ePCA9/WH1NTUoK6ujj179qBLly7Izs6Gl5dXnW3Nnz8fFRUVGDNmDM6cOYNBgwbhl19+wZgxY6CtrY2JEyeCy+UiOTkZd+/exbp166T63gghhMgHSrYIIYR8slRUVGo9zuVyERYWBnd3d5iYmKBHjx7w9fXFsGHD6mxrwYIFqKyshLW1NSIjI2FhYYFTp07B29sbGzduRMuWLWFoaIgZM2ZI6d0QQgiRN1SNkBBCCCGEEEKkgPbZIoQQQgghhBApoGSLEEIIIYQQQqSAki1CCCGEEEIIkQJKtgghhBBCCCFECijZIoQQQgghhBApoGSLEEIIIYQQQqSAki1CCCGEEEIIkQJKtgghhBBCCCFECijZIoQQQgghhBApoGSLEEIIIYQQQqSAki1CCCGEEEIIkYL/A3H7ER9ds/R/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.histplot(df['Year'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "L7P0oOtCse1q",
        "outputId": "4af55c04-d1cf-40f7-e547-bb8205e9e3a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Year', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ROThOLQfRZw",
        "papermill": {
          "duration": 0.041995,
          "end_time": "2020-11-27T11:52:35.626197",
          "exception": false,
          "start_time": "2020-11-27T11:52:35.584202",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "### Scatterplot\n",
        "\n",
        "We generally use scatter plots to find the correlation between two variables. Here the scatter plots are plotted between Horsepower and Price and we can see the plot below. With the plot given below, we can easily draw a trend line. These features provide a good scattering of points."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.scatterplot(x=df['HP'], y=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "uC4CUI6y24bh",
        "outputId": "2b22165b-65fb-453e-c6ff-0be1b6455307"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'HP'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-8160fa8a38ed>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'HP'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'HP'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.lineplot(x=df['HP'],y=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "z2R-b5WrfVJ1",
        "outputId": "3a7bbb62-2b55-4321-8374-1d64784a8246"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='HP', ylabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c37WtYYWJuAQ",
        "papermill": {
          "duration": 0.04046,
          "end_time": "2020-11-27T11:52:35.081261",
          "exception": false,
          "start_time": "2020-11-27T11:52:35.040801",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "### Heat Maps\n",
        "\n",
        "Heat Maps is a type of plot which is necessary when we need to find the dependent variables. One of the best way to find the relationship between the features can be done using heat maps. In the below heat map we know that the price feature depends mainly on the Engine Size, Horsepower, and Cylinders."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-11-27T11:52:35.171817Z",
          "iopub.status.busy": "2020-11-27T11:52:35.170966Z",
          "iopub.status.idle": "2020-11-27T11:52:35.541204Z",
          "shell.execute_reply": "2020-11-27T11:52:35.540448Z"
        },
        "id": "yhHfLVTj6nmy",
        "outputId": "28d92586-de8d-41a4-8746-94974c7e0b30",
        "papermill": {
          "duration": 0.419422,
          "end_time": "2020-11-27T11:52:35.541316",
          "exception": false,
          "start_time": "2020-11-27T11:52:35.121894",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-845a837895fd>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  c= df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "c= df.corr()\n",
        "sns.heatmap(c,annot=True)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "papermill": {
      "duration": 12.885345,
      "end_time": "2020-11-27T11:52:37.154623",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2020-11-27T11:52:24.269278",
      "version": "2.1.0"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}