1) Regression Models/Linear Regression/Linear_Regression.ipynb

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            "--2024-12-10 14:58:29--  https://media.githubusercontent.com/media/pradeep-016/ML_Course/refs/heads/main/1)%20Regression%20Models/Linear%20Regression/laptop_prices.csv\n",
            "Resolving media.githubusercontent.com (media.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to media.githubusercontent.com (media.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 191364 (187K) [text/plain]\n",
            "Saving to: ‘laptop_prices.csv’\n",
            "\n",
            "laptop_prices.csv   100%[===================>] 186.88K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2024-12-10 14:58:29 (5.33 MB/s) - ‘laptop_prices.csv’ saved [191364/191364]\n",
            "\n"
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      "source": [
        "!wget \"https://media.githubusercontent.com/media/pradeep-016/ML_Course/refs/heads/main/1)%20Regression%20Models/Linear%20Regression/laptop_prices.csv\""
      ]
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        "import numpy as np\n",
        "import pandas as pd"
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        "df = pd.read_csv('/content/laptop_prices.csv')\n",
        "df.head()"
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              "  Company      Product   TypeName  Inches  Ram     OS  Weight  Price_euros  \\\n",
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              "1   Apple  Macbook Air  Ultrabook    13.3    8  macOS    1.34       898.94   \n",
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              "3   Apple  MacBook Pro  Ultrabook    15.4   16  macOS    1.83      2537.45   \n",
              "4   Apple  MacBook Pro  Ultrabook    13.3    8  macOS    1.37      1803.60   \n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TypeName</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inches</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ram</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OS</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price_euros</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Screen</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenW</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenH</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Touchscreen</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IPSpanel</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RetinaDisplay</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU_company</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU_freq</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU_model</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PrimaryStorage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SecondaryStorage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PrimaryStorageType</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SecondaryStorageType</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GPU_company</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GPU_model</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "hhY4QwaI9f4V",
        "outputId": "6ee75d65-8241-47a6-bfc3-33a648dcebf6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Inches          Ram       Weight  Price_euros      ScreenW  \\\n",
              "count  1275.000000  1275.000000  1275.000000  1275.000000  1275.000000   \n",
              "mean     15.022902     8.440784     2.040525  1134.969059  1900.043922   \n",
              "std       1.429470     5.097809     0.669196   700.752504   493.346186   \n",
              "min      10.100000     2.000000     0.690000   174.000000  1366.000000   \n",
              "25%      14.000000     4.000000     1.500000   609.000000  1920.000000   \n",
              "50%      15.600000     8.000000     2.040000   989.000000  1920.000000   \n",
              "75%      15.600000     8.000000     2.310000  1496.500000  1920.000000   \n",
              "max      18.400000    64.000000     4.700000  6099.000000  3840.000000   \n",
              "\n",
              "           ScreenH     CPU_freq  PrimaryStorage  SecondaryStorage  \n",
              "count  1275.000000  1275.000000     1275.000000       1275.000000  \n",
              "mean   1073.904314     2.302980      444.517647        176.069020  \n",
              "std     283.883940     0.503846      365.537726        415.960655  \n",
              "min     768.000000     0.900000        8.000000          0.000000  \n",
              "25%    1080.000000     2.000000      256.000000          0.000000  \n",
              "50%    1080.000000     2.500000      256.000000          0.000000  \n",
              "75%    1080.000000     2.700000      512.000000          0.000000  \n",
              "max    2160.000000     3.600000     2048.000000       2048.000000  "
            ],
            "text/html": [
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              "      <th>Inches</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1275.000000</td>\n",
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              "      <td>1275.000000</td>\n",
              "      <td>1275.000000</td>\n",
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              "      <td>1275.000000</td>\n",
              "      <td>1275.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15.022902</td>\n",
              "      <td>8.440784</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>10.100000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.690000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>1366.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>609.000000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>256.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.600000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.040000</td>\n",
              "      <td>989.000000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.600000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.310000</td>\n",
              "      <td>1496.500000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.400000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>6099.000000</td>\n",
              "      <td>3840.000000</td>\n",
              "      <td>2160.000000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>2048.000000</td>\n",
              "      <td>2048.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 446.2575963637752,\n        \"min\": 1.4294698446247902,\n        \"max\": 1275.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1275.0,\n          15.022901960784312,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ram\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 446.22170839681405,\n        \"min\": 2.0,\n        \"max\": 1275.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1275.0,\n          8.44078431372549,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 450.07777142385805,\n        \"min\": 0.6691959759708271,\n        \"max\": 1275.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0405254901960785,\n          2.04,\n          1275.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_euros\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1880.659968304601,\n        \"min\": 174.0,\n        \"max\": 6099.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1134.9690588235292,\n          989.0,\n          1275.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 954.632811576843,\n        \"min\": 493.3461863720428,\n        \"max\": 3840.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1275.0,\n          1900.0439215686274,\n          3840.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 525.4080496963254,\n        \"min\": 283.8839398555495,\n        \"max\": 2160.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1275.0,\n          1073.9043137254903,\n          2160.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 450.0489474584384,\n        \"min\": 0.5038457085709569,\n        \"max\": 1275.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.302980392156863,\n          2.5,\n          1275.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PrimaryStorage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 677.1471819125817,\n        \"min\": 8.0,\n        \"max\": 2048.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1275.0,\n          444.5176470588235,\n          512.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SecondaryStorage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 766.0679191248988,\n        \"min\": 0.0,\n        \"max\": 2048.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          176.06901960784313,\n          2048.0,\n          415.96065537272585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 805
        },
        "id": "1Rs20dqK9nch",
        "outputId": "dcf633ea-018c-429f-a33b-cff09d044595"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company                  object\n",
              "Product                  object\n",
              "TypeName                 object\n",
              "Inches                  float64\n",
              "Ram                       int64\n",
              "OS                       object\n",
              "Weight                  float64\n",
              "Price_euros             float64\n",
              "Screen                   object\n",
              "ScreenW                   int64\n",
              "ScreenH                   int64\n",
              "Touchscreen              object\n",
              "IPSpanel                 object\n",
              "RetinaDisplay            object\n",
              "CPU_company              object\n",
              "CPU_freq                float64\n",
              "CPU_model                object\n",
              "PrimaryStorage            int64\n",
              "SecondaryStorage          int64\n",
              "PrimaryStorageType       object\n",
              "SecondaryStorageType     object\n",
              "GPU_company              object\n",
              "GPU_model                object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TypeName</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inches</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ram</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OS</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price_euros</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Screen</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenW</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenH</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Touchscreen</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IPSpanel</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RetinaDisplay</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU_company</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU_freq</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPU_model</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PrimaryStorage</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SecondaryStorage</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PrimaryStorageType</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SecondaryStorageType</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GPU_company</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GPU_model</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_reduced = df.drop(columns=[\n",
        "    'Product', 'CPU_model', 'GPU_model', 'PrimaryStorageType', 'SecondaryStorageType',\n",
        "    'Screen', 'TypeName', 'Touchscreen', 'IPSpanel', 'RetinaDisplay'\n",
        "])"
      ],
      "metadata": {
        "id": "Ysl4Sw5tGvp_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "oWS1bADO900R"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.histplot(df_reduced['Price_euros'], kde = True,bins = 28)\n",
        "plt.title('Distribution of Laptop Prices')\n",
        "plt.xlabel('Price (Euros)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "g1zgzZeV9-zv",
        "outputId": "21b3beee-ad40-4b4f-8f11-caf49680007f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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l1VOBVS9qtVoEBgaK22+/Xbz//vsWU05Vu34qsB07doi7775bBAcHC7VaLYKDg8WkSZPE+fPnLd73448/ip49ewqVSmUx9daIESNEr169aq2vrqnAvvrqK7Fw4ULh7+8vtFqtmDBhgsU0VdXeeecd0a5dO6HRaMTQoUPFkSNHauyzvtqunwpMCCEKCwvFvHnzRHBwsHBychJdunQRb7/9tnn6p2oAxJw5c2rUVNcUZddLT08Xjz76qPD19RVqtVr07t271unKbnQqsGs/39cuM2fOFEIIcfbsWREVFSXc3NyEr6+vmDVrlnkKs2uPXz291sWLF8WYMWOEi4uLCAgIEIsWLRJGo9HiuH/88YcYOHCgUKvVNaYF2759uxg6dKjQarVCp9OJO++8U5w9e9bi/dVfc2fPnhX333+/cHd3F15eXmLu3LmipKSkwfOurrUx16eua1nb5y07O1vMnTtXtGvXTqjVatG+fXsxffp0kZWVZW5TXl4u3nzzTdGrVy+h0WiEl5eXGDhwoHjttddEfn6+EKLx30NEVDdJCBu9m4GIiOzCjBkz8O2330Kv17f4sRYvXozXXnsNmZmZ8PX1bfHjEZH94ZhbIiIiInIYDLdERERE5DAYbomIiIjIYXDMLRERERE5DPbcEhEREZHDYLglIiIiIofBhzig8rGRKSkpcHd3t/qjQYmIiIio+YQQKCwsRHBwsMXjyK/HcAsgJSUFISEhcpdBRERERA1ISkpC+/bt69wua7hdunQpvv/+e5w7dw5arRa33HIL3nzzTXTr1s3cprS0FM8//zzWr1+PsrIyjB07Fh9//DECAgLMbRITE/HUU09h165dcHNzw/Tp07F06VKoVI07verHVyYlJUGn01n3JImIiIio2QoKChASEtLgY8dlDbd79uzBnDlzMHjwYBgMBrz00ksYM2YMzp49C1dXVwDAvHnz8NNPP2HDhg3w8PDA3Llzcd9992Hfvn0AAKPRiAkTJiAwMBB//PEHUlNTMW3aNDg5OeGNN95oVB3VQxF0Oh3DLREREZENa2gIqU1NBZaZmQl/f3/s2bMHw4cPR35+Pvz8/PDll1/i/vvvBwCcO3cOPXr0wP79+3HzzTdjy5YtuOOOO5CSkmLuzV21ahVefPFFZGZmQq1WN3jcgoICeHh4ID8/n+GWiIiIyAY1Nq/Z1GwJ+fn5AABvb28AwNGjR1FRUYGoqChzm+7duyM0NBT79+8HAOzfvx+9e/e2GKYwduxYFBQU4MyZM7Uep6ysDAUFBRYLEREREdk/mwm3JpMJzz33HIYOHYqIiAgAQFpaGtRqNTw9PS3aBgQEIC0tzdzm2mBbvb16W22WLl0KDw8P88KbyYiIiIgcg82E2zlz5uD06dNYv359ix9r4cKFyM/PNy9JSUktfkwiIiIiank2MRXY3LlzsXnzZuzdu9diaofAwECUl5cjLy/Povc2PT0dgYGB5jaHDh2y2F96erp5W200Gg00Go2Vz4KIiIiI5CZrz60QAnPnzsUPP/yAnTt3okOHDhbbBw4cCCcnJ+zYscO8Li4uDomJiRgyZAgAYMiQITh16hQyMjLMbbZt2wadToeePXu2zokQERERkU2Qted2zpw5+PLLL/Hjjz/C3d3dPEbWw8MDWq0WHh4emDlzJubPnw9vb2/odDo8/fTTGDJkCG6++WYAwJgxY9CzZ0888sgjeOutt5CWloZXXnkFc+bMYe8sERERURsj61Rgdc1Ttnr1asyYMQPAnw9x+Oqrrywe4nDtkIOEhAQ89dRT2L17N1xdXTF9+nQsW7as0Q9x4FRgRERERLatsXnNpua5lQvDLREREZFts8t5bomIiIiImoPhloiIiIgcBsMtERERETkMhlsiIiIichgMt0RERETkMGziCWXUfImJicjKyrLa/nx9fREaGmq1/RERERG1BoZbB5CYmIjuPXqgpLjYavvUurjgXGwsAy4RERHZFYZbB5CVlYWS4mJMefFtBIR2avb+0hMv4os3FyArK4vhloiIiOwKw60DCQjthPZdesldBhEREZFseEMZERERETkMhlsiIiIichgMt0RERETkMBhuiYiIiMhhMNwSERERkcNguCUiIiIih8FwS0REREQOg/PcUp1iY2Otti8+zpeIiIhaA8Mt1VCQkwkAmDp1qtX2ycf5EhERUWtguKUaSvQFAIAJT7yMbn0GNnt/fJwvERERtRaGW6qTT3AYH+dLREREdoU3lBERERGRw2C4JSIiIiKHwXBLRERERA6D4ZaIiIiIHAZvKKPaSQqkGlxw+fhV6MsMKCozQCFJCPHSItTHBR18XeGi5pcPERER2RamE6ohH1oETX8X58s9gZxii23nM/Q4n6GHSiFhULgXBoZ6QaXkHwCIiIjINjDckoUjV3JwCuFQBwBKmBDZyQ8BOme4aVQorTAiIbsYl7L0yNKX48ClHJxJKcDIrn7o6Ocmd+lEREREDLf0p7i0Quy7mA0AKIzZijFD+mFweDeLNsGeWtzc0Rvn0/X4PT4LhaUG/O9kKgaHe+Hmjj5QSJIcpRMREREB4A1lVOVqXgm2nU0HALRDNnJ++RBqyVRrW0mS0C3QHdOGhKFfiCcA4PCVXGw6kYLSCmNrlUxERERUA8MtoaCkAptPpsAoBDr5uSIcGY16n5NSgRFd/TC2VwBUCgkJ2cX49mgyisoMLVwxERERUe0Ybgm/x2ehtMIEf3cNxvYKxI0OLOgeqMMDg9rDVa1EdlE5vj2WjMLSihaplYiIiKg+DLdtXFpBKS5k6AEAUT0C4NTEmQ/83Z1x/8D2cHdWIa+4At8eTUZBCQMuERERtS6G2zZMCIF98VkAgO6B7vBz1zRrf54uatw/oD08tE4oKDXg++NXOUSBiIiIWhXDbRuWkFOM5NwSKCUJQzr6WGWfOq0T7h/QHjpnFfJLKvBjTAoqar8vjYiIiMjqGG7bKNM1vbZ9Qjyg0zpZbd9uzirc078dtE5KZOrL8EemCpJKbbX9ExEREdWF4baNSsguRpa+HGqVAoPDva2+fy8XNe7t3w5qpQJZZQr4jH8OJiGsfhwiIiKia8kabvfu3Ys777wTwcHBkCQJGzdutNguSVKty9tvv21uEx4eXmP7smXLWvlM7M+ZlHwAQM8gHbROyhY5hp+7Bnf2DYIEAdcew7H+tL5FjkNERERUTdYnlBUVFaFv37547LHHcN9999XYnpqaavF6y5YtmDlzJiZOnGixfsmSJZg1a5b5tbu7e8sU7CBKyo24nFUEoDLctqT2Xi4Y4G3E0RwVvo3VI+j73zEq3KXZ+/X19UVoaKgVKiQiIiJHImu4jY6ORnR0dJ3bAwMDLV7/+OOPGDVqFDp27Gix3t3dvUZbqtu5tAKYBODvrmn2DAmN4V2ejvw/fofHLQ/hg/1ZePmZl1B2NbZZ+9S6uOBcbCwDLhEREVmQNdzeiPT0dPz000/47LPPamxbtmwZXn/9dYSGhmLy5MmYN28eVKq6T62srAxlZWXm1wUFBS1Ssy0SQuBsauX5tnSvbbUSfQHyfluHkEFRKFD7IHzaW7gtsALaJn71pSdexBdvLkBWVhbDLREREVmwm3D72Wefwd3dvcbwhWeeeQYDBgyAt7c3/vjjDyxcuBCpqalYvnx5nftaunQpXnvttZYu2SZlFpYhS18OpSShW2BrDt8Q6ONRjlilGtn6chzT6zBxYDuoFLynkYiIiKzHbpLFp59+iilTpsDZ2dli/fz58zFy5Ej06dMHTz75JN555x2sWLHComf2egsXLkR+fr55SUpKaunybUZ1r21HP1c4t9CNZHVRSgJ39A6CRqVAWkEpdsdlturxiYiIyPHZRbj97bffEBcXh8cff7zBtpGRkTAYDLhy5UqdbTQaDXQ6ncXSFhhNAufSCgEAvYLlOWdPFzWiIwIhATiTUmCetYGIiIjIGuwi3H7yyScYOHAg+vbt22DbmJgYKBQK+Pv7t0Jl9iUlrwRlBhO0TkqEeDd/xoKmCvNxxc1VT0TbFZeJzMK6e9mJiIiIboSsY271ej3i4+PNry9fvoyYmBh4e3ubbxQqKCjAhg0b8M4779R4//79+3Hw4EGMGjUK7u7u2L9/P+bNm4epU6fCy8ur1c7DXiRkFwMAwnxcoJAkWWsZHO6FlPwSJGQX4+dTqXj4phBoVK07TIKIiIgcj6w9t0eOHEH//v3Rv39/AJXjZ/v3749XX33V3Gb9+vUQQmDSpEk13q/RaLB+/XqMGDECvXr1wj//+U/MmzcP//73v1vtHOzJlezKuW3DfVxlrqTyAR1jewXCTaNCXkkFdsRmQPAJZkRERNRMsvbcjhw5ssFAM3v2bMyePbvWbQMGDMCBAwdaojSHU1hageyickgAQn3kG5JwLa2TEhN6B2HD0SRcyNAjNKUAEe085C6LiIiI7JhdjLml5rtSNSQh0MO5xR632xSBHs64pZMvAGDP+UzkFJXLXBERERHZM4bbNiKhakhCmI302l5rQKgnQr1dYDAJbDmdCoPRJHdJREREZKcYbtsAo0kgMaey59YWxtteT5IkjOkZAK2TEln6cvwenyV3SURERGSnGG7bgJS8ElQYBbROSvi7a+Qup1auGhXG9AoAAJxIzjeHcSIiIqIbwXDbBvw5S4ILJJmnAKtPuI8r+lTdULbtbDrKKowyV0RERET2huG2DfhzflvbG5JwvVu7+MJD6wR9mQG7z/PxvERERHRjGG4dXGmFEdlVMxCEeGtlrqZhTkoFxvYKgATgXFohLmQUyl0SERER2RGGWweXll8KAPDUOsFFLeu0xo0W5KHFwLDKJ8ztjstEKYcnEBERUSMx3Dq4lPwSAECQp7PMldyYyI7e8HJxQnG5Eb9d4OwJRERE1DgMtw4utarnNsjD9ockXEulUCCqR+XsCWdTC8zz9BIRERHVh+HWgZlMwjwsIcjDvnpuASDYU4u+7StnT9h5LgMVfLgDERERNYDh1oFl6ctgMAmoVQr4uKrlLqdJbunkC3dnFQpKDTh4OUfucoiIiMjGMdw6sNRrem1teX7b+qhVCozs5gcAOJ6Yi5yqmR+IiIiIasNw68DMN5PZ4ZCEa3X0dUMHX1eYBLA7LgNCyF0RERER2SqGWwdmrzeT1WZEVz8oFRKScktwtZhftkRERFQ7pgQHpS81oLDUAAlAoM6+e24BwEPrhEFVc9+ezFNCcrL/cyIiIiLrY7h1UKlVQxJ83TRQqxzj0zwozAs6ZxVKjBJ0N90rdzlERERkgxwj9VANKXY8BVhdVEoFbu3sCwDQ3TQROSV8chkRERFZYrh1UOkFleE20IHCLQB09neDt9oEhdoZX58plLscIiIisjEMtw5ICIEsfRkAwN9dI3M11iVJEnp7VfbY7rhcgvPpDLhERET0J4ZbB5RXUoEKo4BSIcHLxT4f3lAfX41AUdw+mASw9OdYucshIiIiG8Jw64CyCit7bX3d1FAo7PPhDQ3J2/MZlBKwKy4TR67wyWVERERUieHWAWVWDUnwc3OsIQnXMuSmYFR45fy9724/L3M1REREZCtUchdA1pdZ3XPrYONtr9dPnY7dCh32xWdj3S8H0NOv6UMwfH19ERoaasXqiIiISA4Mtw7I0XtuC3IyAQBPPzYZ3mPmwL1/NP66ZifS17/c5H1qXVxwLjaWAZeIiMjOMdw6mOJyA4rKKmcT8HXQcFuiLwAATHjiZYT0HIitKQLOYX3xyDs/wM9Z3PD+0hMv4os3FyArK4vhloiIyM4x3DqY6iEJnlonh3kyWV18gsPQtUcvJEsZOHU1H5cqdOjfu73cZREREZGMHDv9tEFZ+nIAgJ+Dj7e91uBwLygkIDm3BGlVD68gIiKitonh1sG0lZvJruXu7IRuAe4AgGMJuTJXQ0RERHJiuHUw1TeT+TvoeNu6DAjzAgDEZ+iRV1wuczVEREQkF4ZbB2I0AblFbW9YAlB581yYjwsEgOOJeXKXQ0RERDJhuHUg+RUSBACtkxIuaqXc5bS6QVW9t2dTC1BcbpC5GiIiIpIDw60Dya+ofNSun7sGkuSYj92tTztPLfzdNTCYBE4m58tdDhEREcmA4daB5JdXhds2Nt62miRJGFjVe3vqaj6Mphuf85aIiIjsG8OtAymo6rn1dm36Y2jtXSc/N7hqlCguNyI+Qy93OURERNTKGG4dSKGB4VapkBAR7AEAOJmcJ28xRERE1OoYbh2EpHZBqbEy3Hq5Oslcjbx6t/OAQgJS8kvN8/4SERFR2yBruN27dy/uvPNOBAcHQ5IkbNy40WL7jBkzIEmSxTJu3DiLNjk5OZgyZQp0Oh08PT0xc+ZM6PVt78/RTr4hAAA3jQoaVdubKeFarhoVOvm5AWDvLRERUVsja7gtKipC37598dFHH9XZZty4cUhNTTUvX331lcX2KVOm4MyZM9i2bRs2b96MvXv3Yvbs2S1dus1x8gkFwF7ban3bewIAzqUVoqzCKG8xRERE1GpUch48Ojoa0dHR9bbRaDQIDAysdVtsbCy2bt2Kw4cPY9CgQQCAFStWYPz48fi///s/BAcHW71mW+Xk0x4A4O3SdsfbXivY0xk+rmpkF5XjbGoB+od6yV0SERERtQKbH3O7e/du+Pv7o1u3bnjqqaeQnZ1t3rZ//354enqagy0AREVFQaFQ4ODBg3Xus6ysDAUFBRaLvXPyqRyW0JZvJruWJEno3b7yxrIzqQUQgtOCERERtQU2HW7HjRuHtWvXYseOHXjzzTexZ88eREdHw2is/DNzWloa/P39Ld6jUqng7e2NtLS0Ove7dOlSeHh4mJeQkJAWPY/WwHBbU/cAdygVErL15cjgjWVERERtgqzDEhry8MMPmz/u3bs3+vTpg06dOmH37t0YPXp0k/e7cOFCzJ8/3/y6oKDArgNumUFA5RkAgOH2WhonJTr7uSEuvRBnUgoQoHOWuyQiIiJqYTbdc3u9jh07wtfXF/Hx8QCAwMBAZGRkWLQxGAzIycmpc5wuUDmOV6fTWSz2LKXQAElSQK0Q0Dq17ZkSrtczuPJzG5deCIPRJHM1RERE1NLsKtwmJycjOzsbQUFBAIAhQ4YgLy8PR48eNbfZuXMnTCYTIiMj5Sqz1SUXGgAA7k4CkiTJXI1tCfHSwt1ZhXKDCfGZbW+KOCIiorZG1nCr1+sRExODmJgYAMDly5cRExODxMRE6PV6LFiwAAcOHMCVK1ewY8cO3H333ejcuTPGjh0LAOjRowfGjRuHWbNm4dChQ9i3bx/mzp2Lhx9+uE3NlJBcUBVuVbxp6nqSJKFnUGXv7dkU+79xkIiIiOona7g9cuQI+vfvj/79+wMA5s+fj/79++PVV1+FUqnEyZMncdddd6Fr166YOXMmBg4ciN9++w0ajca8jy+++ALdu3fH6NGjMX78eNx6663497//LdcpycIcbp0YbmtTHW6TcktQUFIhczVERETUkmS9oWzkyJH1TtH0yy+/NLgPb29vfPnll9Ysy+5Uh1sdw22tdFonhHhpkZRbgnNphbipg7fcJREREVELsasxt1STwWhCqp49tw3pXtV7ey6Nc94SERE5MoZbO5eQUwyDCTCVl8KFEyXUqZOfK5QKCbnFFcjknLdEREQOi+HWzsVnVM4AUJGTDE6UUDeNSomOvq4AgHPphTJXQ0RERC2F4dbOXc4qAgAYcpJlrsT2dQt0BwCcTyuEiUMTiIiIHBLDrZ1LyK4MtxW5KTJXYvvCfVyhUSlQVG5Ecm6J3OUQERFRC2C4tXN/9twy3DZEqZDQxd8NABCXxqEJREREjojh1s4lZBcDACryUmWuxD50D6ycNSE+Q8/H8RIRETkghls7VlJuRGp+KQD23DZWsKcz3DQqlBtNuFL1iwERERE5DoZbO5aYUxnOXJ0kmEr5Z/bGkCQJXQIqhyZcyOA1IyIicjQMt3aserxtkLusD5qzO9Xjbi9nFXFoAhERkYNhuLVj1TMlBLnx6Q03IlBXOTShwiiQkMOhCURERI6E4daOXTGHW/bc3ohrhyac5wMdiIiIHArDrR27klXZ6xjIntsbdu3QBI5MICIichwMt3bM3HPLMbc3LFDnDHfnyqEJaaV8bjEREZGjYLi1U6UVf04DxmEJN06SJHSu6r29WsxvAyIiIkfBn+p2qvrhDTpnFdzV7HlsiuqhCaklCkDpJHM1REREZA0Mt3aqekhCuK8rJInhtimqZ00wCAnOYX3lLoeIiIisgOHWTl2pmuM23MdV5krslyRJ6OhXef1cutwsczVERERkDQy3dqr60bHhPi4yV2LfOvlVDk1w6RIJo0nIXA0RERE1F8OtnTL33Pqy57Y52nlq4SQJKF29cCGnQu5yiIiIqJkYbu1U9dPJwjgsoVmUCgmB2sqJbg9dLZW5GiIiImouhls7VFphRErVNGAd2HPbbMEuleH24NVSCMGhCURERPaM4dYOJeZUjrd1d1bBy4VTWDVXgLOAMJQjVW9EfIZe7nKIiIioGRhu7VBi1c1kYT4unAbMCpwUQMmVGADAr2fT5S2GiIiImoXh1g5V99yGeHGmBGspuXAAAPDrmTSZKyEiIqLmYLi1Q0m5leE21Jvh1lqK4w9BAnAiOR+p+SVyl0NERERNxHBrh5JyKsNXe4ZbqzEV56Gbb+X45e0cmkBERGS3GG7tUJJ5WIJW5kocy03BzgA47paIiMieMdzaGSEEhyW0kJvaVYbb/RezkV/CBzoQERHZI4ZbO5NTVI7iciMkCWjHnlurCnZXoWuAGwwmgd1xGXKXQ0RERE3AcGtnqmdKCHB3hkallLkaxzOmZyAA4NczHJpARERkjxhu7UxSbuXNZCHe7LVtCWN6BQAAdsdloLTCKHM1REREdKMYbu2M+WYyjrdtEb3beSBQ54yiciP2X8yWuxwiIiK6QQy3diaJD3BoUZIkmXtvfz3LBzoQERHZG4ZbO1M9UwJ7blvO7T0rw+322AyYTELmaoiIiOhGMNzameoHOHCO25YT2cEHbhoVMgvLcPJqvtzlEBER0Q2QNdzu3bsXd955J4KDgyFJEjZu3GjeVlFRgRdffBG9e/eGq6srgoODMW3aNKSkpFjsIzw8HJIkWSzLli1r5TNpHQajCSl5leE21Ic9ty1FrVJgRFc/AMCOWM6aQEREZE9kDbdFRUXo27cvPvrooxrbiouLcezYMfz973/HsWPH8P333yMuLg533XVXjbZLlixBamqqeXn66adbo/xWl5pfCoNJQK1UIMDdWe5yHFpUT38AwDY+rYyIiMiuqOQ8eHR0NKKjo2vd5uHhgW3btlms+/DDD3HTTTchMTERoaGh5vXu7u4IDAxs0VptQfV423ZeWigUkszVOLZR3fyhVEg4l1aI5NxitOcNfERERHbBrsbc5ufnQ5IkeHp6WqxftmwZfHx80L9/f7z99tswGAz17qesrAwFBQUWiz1Irhpv257jbVucp4saA8O8AAA7Yvm0MiIiInthN+G2tLQUL774IiZNmgSdTmde/8wzz2D9+vXYtWsXnnjiCbzxxht44YUX6t3X0qVL4eHhYV5CQkJaunyrqH46WShnSmgVt/eonjWBQxOIiIjshV2E24qKCjz44IMQQmDlypUW2+bPn4+RI0eiT58+ePLJJ/HOO+9gxYoVKCsrq3N/CxcuRH5+vnlJSkpq6VOwCk4D1rqiqqYEO3ApG4WlFTJXQ0RERI1h8+G2OtgmJCRg27ZtFr22tYmMjITBYMCVK1fqbKPRaKDT6SwWe8AHOLSuDr6u6OjnigqjwN7zWXKXQ0RERI1g0+G2OtheuHAB27dvh4+PT4PviYmJgUKhgL+/fytU2LoSq+e49eaY29bCoQlERET2RdbZEvR6PeLj482vL1++jJiYGHh7eyMoKAj3338/jh07hs2bN8NoNCItrfJxqN7e3lCr1di/fz8OHjyIUaNGwd3dHfv378e8efMwdepUeHl5yXVaLaKk3IgsfeVQC/bctp6ongH4195L2HkuAwajCSqlTf8+SERE1ObJGm6PHDmCUaNGmV/Pnz8fADB9+nQsXrwYmzZtAgD069fP4n27du3CyJEjodFosH79eixevBhlZWXo0KED5s2bZ96PI7la9fAGV7USni5OMlfTdgwI9YKXixNyiytwJCEXN3ds+K8HREREJB9Zw+3IkSMhhKhze33bAGDAgAE4cOCAtcuySdXhtp2XFpLEOW5bi1IhYVR3f3x/7Cp2xKYz3BIREdk4/o3VTlzNrQq3nhxv29qqx91uO5ve4C9cREREJC+GWzuRck3PLbWuYV39oFYqcCW7GBczi+Quh4iIiOrBcGsnqoclBLPnttW5aVS4uVPlcIQdnDWBiIjIpjHc2gkOS5DX7T0qp5bjlGBERES2jeHWTlT33LbnsARZ3FY17vZoQi5yisplroaIiIjqwnBrBwxGE9IKSgEA7Tw5x60c2nlq0TNIB5MAdp3LkLscIiIiqkOTwu2lS5esXQfVI72wDEaTgEohwc9dI3c5bVZUTz6tjIiIyNY1Kdx27twZo0aNwrp161BaWmrtmug61eNtgzydoVRwjlu5RFWNu91zPhOlFUaZqyEiIqLaNCncHjt2DH369MH8+fMRGBiIJ554AocOHbJ2bVTlal4xAN5MJreIYA8E6DQoLjfiwKVsucshIiKiWjQp3Pbr1w/vv/8+UlJS8OmnnyI1NRW33norIiIisHz5cmRmZlq7zjYtJY/jbW2BQiFhdNWNZTtiOe6WiIjIFjXrhjKVSoX77rsPGzZswJtvvon4+Hj89a9/RUhICKZNm4bU1FRr1dmmJefyAQ62ovppZdtj+bQyIiIiW9SscHvkyBH85S9/QVBQEJYvX46//vWvuHjxIrZt24aUlBTcfffd1qqzTaueBqydp7PMldCQTj7QOimRml+KMykFcpdDRERE11E15U3Lly/H6tWrERcXh/Hjx2Pt2rUYP348FIrKrNyhQwesWbMG4eHh1qy1zbqaWz3mlsMS5ObspMSwLr749Ww6dsRmIKKdh9wlERER0TWa1HO7cuVKTJ48GQkJCdi4cSPuuOMOc7Ct5u/vj08++cQqRbZlQog/e245LMEmcEowIiIi29WkntsLFy402EatVmP69OlN2T1dI7e4AqUVJgBAkAeHJdiC27r7Q5KAU1fzkZpfgiAP/tJBRERkK5rUc7t69Wps2LChxvoNGzbgs88+a3ZR9KfqOW593TRwdlLKXA0BlZ+LAaFeAIBtZ9l7S0REZEuaFG6XLl0KX1/fGuv9/f3xxhtvNLso+pN5jlsOSbAp0RGBAIAtp9JkroSIiIiu1aRhCYmJiejQoUON9WFhYUhMTGx2UW1BYmIisrKyGmx3ME4PAHAVpTh27FitbWJjY61aGzVsbK9A/OOnWBy8nI2conJ4u6rlLomIiIjQxHDr7++PkydP1pgN4cSJE/Dx8bFGXQ4tMTER3Xv0QElxcYNtvW57HLrB92DLd1/gq6dX19tWr9dbq0RqQIi3C3oF63AmpQDbz6bjwcEhcpdEREREaGK4nTRpEp555hm4u7tj+PDhAIA9e/bg2WefxcMPP2zVAh1RVlYWSoqLMeXFtxEQ2qnetvszVUgpAYaPuxudH7iz1jaxh/Zgy2fvo7S0tCXKpTqM6xWIMykF2HomjeGWiIjIRjQp3L7++uu4cuUKRo8eDZWqchcmkwnTpk3jmNsbEBDaCe279Kq3jSE3EUAZQkNC0N7PrdY26YkXW6A6asi4iEC8s+08fr+QhcLSCrg7O8ldEhERUZvXpHCrVqvx9ddf4/XXX8eJEyeg1WrRu3dvhIWFWbu+Nq+gtAIAGJxsUGd/N3T0c8WlzCLsisvEXX2D5S6JiIiozWtSuK3WtWtXdO3a1Vq10HUqjCbzHLc652Z9qqgFSJKEcb0C8fHui/jldBrDLRERkQ1oUmIyGo1Ys2YNduzYgYyMDJhMJovtO3futEpxbV1hqQEAoFYqoOEctzZpXERluN0Vl4HSCiPnIiYiIpJZk8Lts88+izVr1mDChAmIiIiAJEnWrotw7ZAE9traqt7tPBDs4YyU/FL8diELt1c9mpeIiIjk0aTUtH79enzzzTcYP368teuhaxSWVPbcMtzaLkmSMDYiEKv3XcHW02kMt0RERDJr0hPK1Go1OnfubO1a6DrVPbc63kxm08b1qnxa2fbYdFQYTQ20JiIiopbUpHD7/PPP4/3334cQwtr10DUKy6p6brXsubVlg8K94eumRn5JBQ5cypa7HCIiojatSanp999/x65du7Blyxb06tULTk6WPYvff/+9VYpr6wpL2HNrD5QKCbf3DMRXhxKx9XQahnXxk7skIiKiNqtJ4dbT0xP33nuvtWuh6xSUcsytvRgXURlufzmTjiV3R0Cp4E2WREREcmhSalq9erW166DrGE0CRdXDEthz2ypiY2Ob/F5no4CLk4QsfRnWbz+IHr5q+Pr6IjQ01IoVEhERUUOa3CVoMBiwe/duXLx4EZMnT4a7uztSUlKg0+ng5lb7Y2Kp8YrKDBAAlJIEVzXnTm1JBTmZAICpU6c2az8+E+bDLeI2PL3sv8jd9Qm0Li44FxvLgEtERNSKmhRuExISMG7cOCQmJqKsrAy333473N3d8eabb6KsrAyrVq2ydp1tTvUDHNycVZxHuIWV6AsAABOeeBnd+gxs8n6uFks4kAUEDrkH0Td1x5dvLkBWVhbDLRERUStq8kMcBg0ahBMnTsDHx8e8/t5778WsWbOsVlxbxgc4tD6f4DC079Krye8PNJpw9LdLKDYCTgGcKo+IiEgOTUpOv/32G/744w+o1WqL9eHh4bh69apVCmvrCnkzmd1RKRXo5OeGc2mFSCpq0ix7RERE1ExN+glsMplgNBprrE9OToa7u3uziyKgkA9wsEtdAyq//q8WKwCJAZeIiKi1Nemn75gxY/Dee++ZX0uSBL1ej0WLFvGRvFbCacDsU6i3C5xVCpSaJDiHRMhdDhERUZvTpHD7zjvvYN++fejZsydKS0sxefJk85CEN998s9H72bt3L+68804EBwdDkiRs3LjRYrsQAq+++iqCgoKg1WoRFRWFCxcuWLTJycnBlClToNPp4OnpiZkzZ0Kv1zfltGwKe27tk1IhobN/5WwhLj2Gy1wNERFR29OkcNu+fXucOHECL730EubNm4f+/ftj2bJlOH78OPz9/Ru9n6KiIvTt2xcfffRRrdvfeustfPDBB1i1ahUOHjwIV1dXjB07FqWlpeY2U6ZMwZkzZ7Bt2zZs3rwZe/fuxezZs5tyWjZDCMExt3asemiCS7ehqDDyEdVEREStqcnJSaVSNXte0OjoaERHR9e6TQiB9957D6+88gruvvtuAMDatWsREBCAjRs34uGHH0ZsbCy2bt2Kw4cPY9CgQQCAFStWYPz48fi///s/BAcHN6s+uZRUGGEwVYYiN4Zbu9POSwtnhUCp1h0n0ssQKXdBREREbUiTktPatWvr3T5t2rQmFXOty5cvIy0tDVFRUeZ1Hh4eiIyMxP79+/Hwww9j//798PT0NAdbAIiKioJCocDBgwfrfERwWVkZysrKzK8LCgqaXa81VffauqqVUCl4U5K9UUgS2rmYcFGvxG+JJbDvvyMQERHZlybPc3utiooKFBcXQ61Ww8XFxSrhNi0tDQAQEBBgsT4gIMC8LS0trcYwCJVKBW9vb3Ob2ixduhSvvfZas2tsKX/OccvxtvYq1LUy3B68Wgp9mQFuGvbAExERtYYmdQvm5uZaLHq9HnFxcbj11lvx1VdfWbtGq1u4cCHy8/PNS1JSktwlWeB4W/vnpRaoyE5CuRH4+VSq3OUQERG1GVb7m3eXLl2wbNmyGr26TRUYGAgASE9Pt1ifnp5u3hYYGIiMjAyL7QaDATk5OeY2tdFoNNDpdBaLLSksqQy3nCnBfkkSoD+9EwDw/bFkmashIiJqO6w6oFOlUiElJcUq++rQoQMCAwOxY8cO87qCggIcPHgQQ4YMAQAMGTIEeXl5OHr0qLnNzp07YTKZEBlpv7fxFJbx0buOoOjMbkgADlzKQVJOsdzlEBERtQlNSk+bNm2yeC2EQGpqKj788EMMHTq00fvR6/WIj483v758+TJiYmLg7e2N0NBQPPfcc/jHP/6BLl26oEOHDvj73/+O4OBg3HPPPQCAHj16YNy4cZg1axZWrVqFiooKzJ07Fw8//LDdzpQAXPMABy3DrT0zFmYiwl+NUxnl2Hj8Kp4e3UXukoiIiBxek9JTdbisJkkS/Pz8cNttt+Gdd95p9H6OHDmCUaNGmV/Pnz8fADB9+nSsWbMGL7zwAoqKijB79mzk5eXh1ltvxdatW+Hs7Gx+zxdffIG5c+di9OjRUCgUmDhxIj744IOmnJbNqH6Ag7uGwxLs3chwLU5llOP741cx97bOkCRJ7pKIiIgcWpPCrclkssrBR44cCSHqnuRekiQsWbIES5YsqbONt7c3vvzyS6vUYwsqjCaUVlReXx17bu3eze2c8Ylaj8tZRTiWmIeBYV5yl0REROTQOImqjameKUGtVECjUspcDTWX1kmBcRGVNzd+c9i2ZuUgIiJyRE3qGqwePtAYy5cvb8oh2izzkAT22jqMhweH4vtjV/G/kyl45Y4enL+YiIioBTUpQR0/fhzHjx9HRUUFunXrBgA4f/48lEolBgwYYG7H8YU3znwzGSf9dxiDw73Q2d8N8Rl6bIxJwSM3h8ldEhERkcNqUoK688474e7ujs8++wxeXpVjCHNzc/Hoo49i2LBheP75561aZFtS3XPLOW4dhyRJmHRTKF7ffBZfHkzE1MhQ/uJHRETUQpo05vadd97B0qVLzcEWALy8vPCPf/zjhmZLoJr4dDLHNHFAO6hVCsSmFuBEcr7c5RARETmsJoXbgoICZGZm1lifmZmJwsLCZhfVlv0Zbtlz60g8XdSY0DsIAPDVwUSZqyEiInJcTQq39957Lx599FF8//33SE5ORnJyMr777jvMnDkT9913n7VrbFMKSvl0Mkc16aZQAMCmEynmzzMRERFZV5PC7apVqxAdHY3JkycjLCwMYWFhmDx5MsaNG4ePP/7Y2jW2GSYhoC/jsARHNTjcC1383VBSYcR3R5PlLoeIiMghNSncuri44OOPP0Z2drZ55oScnBx8/PHHcHV1tXaNbUZRmQFCAAoJcOVsCQ5HkiRMuyUcALB63xUYTXU/wISIiIiaplkPcUhNTUVqaiq6dOkCV1fXep82Rg2rHm/rplFBwbvpHdLEAe3goXVCYk4xtsemy10OERGRw2lSuM3Ozsbo0aPRtWtXjB8/HqmpqQCAmTNnchqwZuDNZI7PRa3C5MjKsbef/HZZ5mqIiIgcT5PC7bx58+Dk5ITExES4uLiY1z/00EPYunWr1Ypra3gzWdswfUg4VAoJh67k4BSnBSMiIrKqJoXbX3/9FW+++Sbat29vsb5Lly5ISEiwSmFtUXXPLR/g4NgCPZxxR5/KacE++f2SzNUQERE5liaF26KiIose22o5OTnQaDTNLqqtKmTPbZsx89aOAIDNJ1ORklciczVERESOo0nhdtiwYVi7dq35tSRJMJlMeOuttzBq1CirFdfW8OlkbUfv9h64uaM3DCaBlbsvyl0OERGRw2hSinrrrbcwevRoHDlyBOXl5XjhhRdw5swZ5OTkYN++fdausU0QQvCGsjbm2dFdceDSAXx9OAl/GdUJQR5auUsiIiKye00KtxERETh//jw+/PBDuLu7Q6/X47777sOcOXMQFBRk7RrbhHKDCeVGEwD23DqS2NjYOrdpAPT0U+NsZjmWbDiAWQM86t2Xr68vQkNDrVwhERGRY7nhFFVRUYFx48Zh1apVePnll1uipjapoKrXVuukhJOyWdMPkw0oyMkEAEydOrXeds6hfRAw6Q38fC4P/5l3P4z6nDrbal1ccC42lgGXiIioHjccbp2cnHDy5MmWqKVN481kjqVEXwAAmPDEy+jWZ2Cd7YQA9mSYkA01Rry4Gv28jbW2S0+8iC/eXICsrCyGWyIiono0KUlNnToVn3zyCZYtW2btetos3kzmmHyCw9C+S6962wz3LcYPx6/iSrEKw/t0gk7LMddERERN1aQkZTAY8Omnn2L79u0YOHAgXF1dLbYvX77cKsW1JbyZrO0K8dKivZcWybkl2BefhejeHLdORETUVDcUbi9duoTw8HCcPn0aAwYMAACcP3/eoo0kSdarrg3hsIS2S5IkDO/ihy8PJeJ8hh5980oQ7MmZE4iIiJrihpJUly5dkJqail27dgGofNzuBx98gICAgBYpri0p4LCENs3PXYNewTqcSSnA3guZeGhQCH9RJCIiaoIbui1fCGHxesuWLSgqKrJqQW1VYVllzy0fvdt2DenoAyelhPSCMsSlF8pdDhERkV1q1pxT14ddahqjSaCorPIuefbctl2uGhUGh3sDAH67kIXSitpnTiAiIqK63VC4lSSpxp9K+afT5tOXVQ5JUCokaJ2UMldDcuof4gkvFycUlxvx24UsucshIiKyOzfUTSiEwIwZM6DRaAAApaWlePLJJ2vMlvD9999br8I24NqbyfjLQtumUioQ1SMAG44m42xqAboGuCHMx7XhNxIRERGAGwy306dPt3jd0NOXqHF4MxldK9hTi37tPRGTnIcd5zIwNTJM7pKIiIjsxg2lqdWrV7dUHW1adc8tbyajakM6+eBSlh4FpQb8diET3fh7DxERUaM064Yysg7zAxw0TDBUSa2qHJ4AAKdTCpBYxG9VIiKixuBPTBtgDrd87CpdI8TbBTdVzZ5wLEcJlXd7mSsiIiKyfQy3NqCg+oYy9tzSdSI7eqO9lxZGIcHvnoUoM3D6PSIiovow3MpMCGHuudWx55auo5AkjOsVCI1CQO0Xhg8O5cFkYsAlIiKqC8OtzEoqjDBWhRU39txSLVw1KkT6GiCMFdifXIq3fomTuyQiIiKbxXArs+peW1eNEkoF57il2vk5C2T//D4AYNWei/jqUKLMFREREdkmhluZ/TlTAockUP2Kzu7GQ73cAACvbDyN7WfTZa6IiIjI9th8uA0PDzc/9vfaZc6cOQCAkSNH1tj25JNPylx14xWY57jlkARq2IM93XDfgHYwmgT+8uUx/M5H9BIREVmw+XB7+PBhpKammpdt27YBAB544AFzm1mzZlm0eeutt+Qq94aZe275AAdqBEmS8NbEPhjbKwDlBhNmrT2Cw1dy5C6LiIjIZth8uPXz80NgYKB52bx5Mzp16oQRI0aY27i4uFi00el0MlZ8Y6qfTsZH71JjqZQKfDCpP0Z09UNJhRGPrT6Mk8l5cpdFRERkE2w+3F6rvLwc69atw2OPPQZJ+vPmqy+++AK+vr6IiIjAwoULUVxcXO9+ysrKUFBQYLHI5c+eW4ZbajyNSolVUwcisoM3CssMmPbpIZxLk+/rmIiIyFbYVbjduHEj8vLyMGPGDPO6yZMnY926ddi1axcWLlyIzz//HFOnTq13P0uXLoWHh4d5CQkJaeHK68ZhCdRUWrUSn8wYjH4hnsgrrsDU/x7CpUy93GURERHJyq7C7SeffILo6GgEBweb182ePRtjx45F7969MWXKFKxduxY//PADLl68WOd+Fi5ciPz8fPOSlJTUGuXXYDBVznML8IYyaho3jQqfPXoTegTpkKUvw5T/HkRSTv1/uSAiInJkdhNuExISsH37djz++OP1touMjAQAxMfH19lGo9FAp9NZLHIorsy1UCsVUKvs5lNBNsbDxQmfz7wJnfxckZpfiin/PYj0glK5yyIiIpKF3XQXrl69Gv7+/pgwYUK97WJiYgAAQUFBrVBV85QYKscNuzurLMYQE9UlNja2zm1/i3TFK7tKkZhTjIkf7sHrI73h4ayss72vry9CQ0NbokwiIiLZ2EW4NZlMWL16NaZPnw6V6s+SL168iC+//BLjx4+Hj48PTp48iXnz5mH48OHo06ePjBU3TrHxz3BLVJ+CnEwAaHA8uVLnj8ApbyIZfpj8r31I/+pvEBVltbbVurjgXGwsAy4RETkUu0hV27dvR2JiIh577DGL9Wq1Gtu3b8d7772HoqIihISEYOLEiXjllVdkqvTGFFX13Oq0vJmM6leir5wJYcITL6Nbn4H1ti2sAHanCyCoCyJf3oAhvgZc/4eB9MSL+OLNBcjKymK4JSIih2IX4XbMmDEQQtRYHxISgj179shQkXUUV06UAB1nSqBG8gkOQ/suvRps5xFcgu+PX0VqiQKX4I8RXfxaoToiIiL58S4mGZl7bjksgaws2FOLMT0DAAAxSXl8yAMREbUZDLcyqh5zy2EJ1BK6Brjjlk4+AIA95zORls8ZFIiIyPEx3MpF6YRShltqYYPCvNDZzw0mAfx8OhWlVfMqExEROSqGW5moPPwBAE5KCc6c45ZaiCRJiOrpDw+tEwpLDfj1bHqt49eJiIgcBVOVTFQeleMhdVonznFLLUqjUmJC7yAoFRIuZxXheFKe3CURERG1GIZbmZjDLWdKoFbg567B8C6+AIA/LmajsELmgoiIiFoIw61MqoclcKYEai2923kg1NsFRpPAkWwVIPHbn4iIHA9/usnk2mEJRK1BkiSM7uEPtVKBnHIF3AfdLXdJREREVsdwKxMOSyA56JydMKxqeILnsKlIKTTIXBEREZF1MdzKxDwsQcthCdS6egXr4O9sgsJJg0+OF3D2BCIicigMtzIoMwgoXb0AAB7suaVWJkkS+nkZIIwVOJ5Whu2xGXKXREREZDUMtzLIKKr8U7CTJKBxUspcDbVF7k5AwaGNAIAlm8/w4Q5EROQwGG5lkFFcGSRcVPxzMMknf//X8NYqkJRTgn/vvSR3OURERFbBcCuDjKLKcOvK4bYkI1FRihl9dQCAj3bFIyWvROaKiIiImo/hVgbV4ZY9tyS3oSHOuCncG2UGE97bfl7ucoiIiJqN4VYG5nCrZLgleUmShBejuwMAvj2ajAvphTJXRERE1DwMtzL4c1gCwy3Jb2CYF8b0DIBJAG//Eid3OURERM3CcCuD6tkSOOaWbMUL47pBIQG/nk3H0YRcucshIiJqMobbVqYvM6CwvLLHlmNuyVZ09nfH/QPbAwDe3HqOD3YgIiK7xb7DVnY1t/KOdGNJAZwUzjJXQ21dbGys+eOoQCN+UACHLudg7S8H0Ntfc0P78vX1RWhoqLVLJCIiuiEMt62sqNyAIDclLl+9CqCT3OVQG1WQkwkAmDp1qsV6r6gnoRt4B178bBfS1798Q/vUurjgXGwsAy4REcmK4baVDQj1wkfj/THw7wuAId/LXQ61USX6AgDAhCdeRrc+A83riw3A1hQB57C+mLb8B/hqGjc8IT3xIr54cwGysrIYbomISFYMt0RtmE9wGNp36WWxLgnpOJ1SgCsVHugX0U6myoiIiJqGN5QRkYVB4d6QJCAhpxhp+aVyl0NERHRDGG6JyIKH1gndA90BAIeu5MhcDRER0Y1huCWiGgaHe0MCcDmrCBmF7L0lIiL7wXBLRDV4uajRNaCq9/Yye2+JiMh+MNwSUa0Gh3sBAC5mFiFLXyZzNURERI3DcEtEtfJx06CzvxsA4DDH3hIRkZ1guCWiOt0U7g0AOJ+uR25RuczVEBERNYzhlojq5OeuQUdfVwDsvSUiIvvAcEtE9bqpQ2Xv7bn0QuSXVMhcDRERUf0YbomoXgE6Z4R6u0AI4GhCrtzlEBER1YvhlogaVD329mxKAfRlBpmrISIiqhvDLRE1qJ2XFsEezjAKgWOJ7L0lIiLbxXBLRI0yuGrs7ankfJSUG2WuhoiIqHYMt0TUKGHeLvB318BgEohJypO7HCIiolrZdLhdvHgxJEmyWLp3727eXlpaijlz5sDHxwdubm6YOHEi0tPTZayYyHFJkoTBVWNvY5LzUGZg7y0REdkemw63ANCrVy+kpqaal99//928bd68efjf//6HDRs2YM+ePUhJScF9990nY7VEjq2Tnyu8XdUoN5hwIjlf7nKIiIhqUMldQENUKhUCAwNrrM/Pz8cnn3yCL7/8ErfddhsAYPXq1ejRowcOHDiAm2++ubVLJXJ4kiRhcJgXfjmbjpjEPPQP8YST0uZ/RyYiojbE5n8qXbhwAcHBwejYsSOmTJmCxMREAMDRo0dRUVGBqKgoc9vu3bsjNDQU+/fvr3efZWVlKCgosFiIqHG6BrjDQ+uEkgojTl9l7y0REdkWmw63kZGRWLNmDbZu3YqVK1fi8uXLGDZsGAoLC5GWlga1Wg1PT0+L9wQEBCAtLa3e/S5duhQeHh7mJSQkpAXPgsixKBQSBoZ5AQCOJubCYDLJXBEREdGfbHpYQnR0tPnjPn36IDIyEmFhYfjmm2+g1WqbvN+FCxdi/vz55tcFBQUMuEQ3oEeQOw5dzoG+zIAzKQXwkbsgIiKiKjbdc3s9T09PdO3aFfHx8QgMDER5eTny8vIs2qSnp9c6RvdaGo0GOp3OYiGixlMpFBhU1Xt7+EoOjOy8JSIiG2FX4Vav1+PixYsICgrCwIED4eTkhB07dpi3x8XFITExEUOGDJGxSqK2oVc7Hdw0KhSVGXFJb1f/lRARkQOz6WEJf/3rX3HnnXciLCwMKSkpWLRoEZRKJSZNmgQPDw/MnDkT8+fPh7e3N3Q6HZ5++mkMGTKEMyUQtQKVQoHIDt7YcS4DcQVKSE4auUsiIiKy7XCbnJyMSZMmITs7G35+frj11ltx4MAB+Pn5AQDeffddKBQKTJw4EWVlZRg7diw+/vhjmasmajt6BOlwJCEX+SUVcB9wh9zlEBER2Xa4Xb9+fb3bnZ2d8dFHH+Gjjz5qpYqI6FpKhYTIDt749Ww6dJETUVTOwbdERCQvDpQjombpFugOd5WAUqvDd7F6ucshIqI2juGWiJpFIUno7WUAAGy+UISknGKZKyIioraM4ZaImi3QWaDkygkYTMBbv8TJXQ4REbVhDLdE1GySBOTu+i8kAP87kYLjiblyl0RERG0Uwy0RWUVFxmWMDK98cuDrm8/CZBIyV0RERG0Rwy0RWc3kCHe4qJU4lpiHb44kyV0OERG1QQy3RGQ1Pi5KzL+9KwDgjZ9jkVlYJnNFRETU1jDcEpFVzbglHBHtdCgoNeAfP52VuxwiImpjGG6JyKpUSgWW3tsHCgn4MSYFe85nyl0SERG1IQy3RGR1vdt7YMYtHQAAC787ifziCpkrIiKitoLhlohaxPNjuiLcxwUp+aV4aeMpCMHZE4iIqOUx3BJRi3DVqPD+w/2hUkj46WQqNhxJlrskIiJqAxhuiajF9A3xxPNjugEAFm06g4uZepkrIiIiR8dwS0Qt6onhHXFLJx+UVBjxxOdHUVDK8bdERNRyGG6JqEUpFBLee6gfAnXOiM/QY84Xx2AwmuQui4iIHBTDLRG1OH+dM/47fRC0Tkr8diELSzZz/lsiImoZDLdE1Coi2nngvYf7QZKAtfsT8PHueLlLIiIiB8RwS0StZmyvQCyM7g4AeGtrHP6156LMFRERkaNRyV0AEbUts4d3Qkm5Ce9uP4+lW85BIUmYNbxjjXaJiYnIysqy2nF9fX0RGhpqtf0REZFtYrglolb3bFQXmITA+zsu4J8/xyK/pALPj+kKSZIAVAbb7j16oKS42GrH1Lq44FxsLAMuEZGDY7glIlk8F9UFCknCu9vP48Nd8UjMKcZb9/eBs5MSWVlZKCkuxpQX30ZAaKdmHys98SK+eHMBsrKyGG6JiBwcwy0RyUKSJDwb1QVBns546ftT2HQiBSl5Jfh4ygBzm4DQTmjfpZeMVRIRkb3hDWVEJKsHB4Vg7WM3wd1ZhSMJuRj/wW84lV4md1lERGSnGG6JSHa3dPbFj3OGonugO7L05Vi8JwceQyfDJOSujIiI7A3DLRHZhI5+btg4ZygeGhQCAcDz1snYk65CbnG53KUREZEdYbglIpvh7KTEm/f3wbORnjCV6pFTrsCXBxNxKjkfQrAbl4iIGsZwS0Q2Z0SYFimfPg0/jQkGk8DOuAxsOpGCojKD3KUREZGNY7glIptkLMzEMH8DhnXxhVIh4Up2MdYdTEB8hl7u0oiIyIZxKjAisprY2Fir7keSgAGhXgj1dsEvZ9KQpS/HT6dS0TNIh+FdfaFRKa1yPCIichwMt0TUbAU5mQCAqVOnWnW/en1lL62vmwYPDQ7BwUs5OJKQi7OpBUjOLcaYnoFo56W16jGJiMi+MdwSUbOV6AsAABOeeBnd+gxs9v5iD+3Bls/eR2lpqXmdSqHA0M6+CPdxxa9n01BQasC3x5IxMMwLN3f0hkrBUVZERMRwS0RW5BMcZpUniqUnXqxzWzsvLSZHhmLv+SycTS3A0YRcJGQXYXzvIHi5qJt9bCIism/s6iAiu6NRKXF7zwBM6B0ErZMSWfpyrD+UxJvNiIiI4ZaI7FdnfzdMiQxFsKczyo0m/HQqFb/HZ8HER5sREbVZDLdEZNdcNSrc1789+od4AgCOJuTih5irKC7nnLhERG0Rwy0R2T2lQsLwrn6IjgiEk1JCcm4JvjqUhNT8ErlLIyKiVmbT4Xbp0qUYPHgw3N3d4e/vj3vuuQdxcXEWbUaOHAlJkiyWJ598UqaKiUhOXQPc8dCgEHi5OEFfZsB3R6/ibGqB3GUREVErsulwu2fPHsyZMwcHDhzAtm3bUFFRgTFjxqCoqMii3axZs5Cammpe3nrrLZkqJiK5+VTNidvJzxVGIbDtbDpO5SoByab/uyMiIiux6anAtm7davF6zZo18Pf3x9GjRzF8+HDzehcXFwQGBrZ2eURkozQqJSb0DsKBSzk4dCUH5wuV8LvvFRRXmOQujYiIWphddWXk5+cDALy9vS3Wf/HFF/D19UVERAQWLlyI4uLievdTVlaGgoICi4WIHIskSRjSyQfjegVCIQm4dL4JC3dkIyG7qOE3ExGR3bKbcGsymfDcc89h6NChiIiIMK+fPHky1q1bh127dmHhwoX4/PPPG3wE6NKlS+Hh4WFeQkJCWrp8IpJJt0B3jPA3wFCYjaQCA+7+aB/+uJgld1lERNRC7CbczpkzB6dPn8b69est1s+ePRtjx45F7969MWXKFKxduxY//PADLl6s+wlHCxcuRH5+vnlJSkpq6fKJSEbeGoG0tfPQ2dsJecUVmPbJIXxxMEHusoiIqAXYRbidO3cuNm/ejF27dqF9+/b1to2MjAQAxMfH19lGo9FAp9NZLETk2Iz6HLw+0gd39Q2GwSTw8g+n8eqPp1Fh5DhcIiJHYtPhVgiBuXPn4ocffsDOnTvRoUOHBt8TExMDAAgKCmrh6ojI3mhUEt5/uB8WjO0GAFi7PwEzVh9CXnG5zJUREZG12HS4nTNnDtatW4cvv/wS7u7uSEtLQ1paGkpKKidmv3jxIl5//XUcPXoUV65cwaZNmzBt2jQMHz4cffr0kbl6IrJFkiRhzqjO+NcjA+GiVmJffDbu+Wgf4jP0cpdGRERWYNPhduXKlcjPz8fIkSMRFBRkXr7++msAgFqtxvbt2zFmzBh0794dzz//PCZOnIj//e9/MldORLZubK9AfPfULWjnqcWV7GLc+9E+/HImTe6yiIiomWx6nlshRL3bQ0JCsGfPnlaqhogcTY8gHX6cOxRPrTuKw1dy8cTnRzF7eEcsGNsNTkqb/t2fiIjqwP+9iahN83XT4MtZN2PmrZVj+v+99xIm/fsAknPrny+biIhsE8MtEbV5TkoF/n5HT6ycMgDuGhWOJOQi+v3f8GPMVblLIyKiG8RwS0RUJbp3EH56Zhj6h3qisNSAZ9fH4Nn1x5FbxNkUiIjsBcMtEdE1Qn1csOGJIXh2dBcoJODHmBTc/u4ebDmVKndpRETUCAy3RETXUSkVmHd7V3z71C3o7O+GLH05nvriGGatPYLEbI7FJSKyZQy3RER1GBDqhZ+euRVzR3WGUiFh29l0RL27B2//cg5FZQa5yyMiolow3BIR1UOjUuKvY7thy7PDcGtnX5QbTPho10Xc9s5u/HA8ucEpC4mIqHUx3BIRNULXAHd8PvMm/OuRgQjx1iK9oAzzvj6BiSv/wOErOXKXR0REVRhuiYgaSZIkjO0ViG3zRmDB2G5wUStxLDEPD6zaj2mfHsKJpDy5SyQiavNs+gllRETWFBsba7V9DXAuw/tjfPDNWT12Xi7G3vOZ2Hs+E4ODNXg4wh0dPJ1ueJ++vr4IDQ21Wo1ERG0Rwy0RObyCnEwAwNSpU624VwlA5XhblUcAPIZOgmuvUTicUobDKWUoOvcb8vd9hYqsxEbvUevignOxsQy4RETNwHBLRA6vRF8AAJjwxMvo1mdgs/cXe2gPtnz2fo39FVQYEZsPJBcr4Np9GFy734p2Lib00Jngoa7/xrP0xIv44s0FyMrKYrglImoGhlsiajN8gsPQvkuvZu8nPfFinfvrCSBLX4ZDl3NwIUOPq8VKXC1Woou/G27q4A1fN02zj09ERHVjuCUisjJfNw3G9w6yCLnVS2d/N0Qy5BIRtRiGWyKiFlJbyI2vWjr7Vfbk+rkz5BIRWRPDLRFRC6s15GZWLtUhl4iIrIPhloiolVSH3OyqkHv+mpAbrFXBya+D3CUSEdk9PsSBiKiV+bhpEN07CFMjQ9E1wA0AkFKiQPBjK/DmvhycScmXuUIiIvvFcEtEJBMfNw2iIypDbnsXI4Qw4eDVMkz44HfMXnuEIZeIqAkYbomIZObjpkGkrxGpn8zBsFBnSBLw69l0c8g9fZUhl4iosRhuiYhsREV2Eubd7IVt84bj7n7B5pB7x4rfMYshl4ioURhuiYhsTGd/d7z/cH+LkLutKuQ+/tlhnEpmyCUiqgvDLRGRjfoz5I7APf2CoZCA7bEZuPPD3/HYmsM4kZQnd4lERDaH4ZaIyMZ19nfDew/3x/b5I3Bf/3ZQSMDOcxm4+6N9mLH6EI4n5spdIhGRzWC4JSKyEx393LD8oX7Y8fxITBzQHkqFhN1xmbj34z8w7dNDOJrAkEtExIc4EBHZkNjY2Ea1m9IZuC3AF9/F6rE7oQR7z2di7/lM9A1Q48Fe7ujhq4avry9CQ0NbuGIiItvCcEtEZAMKcjIBAFOnTr3h96o8A+Ex5EG49roNJ9KBE+nZKE06jdKYzYj5eR3Cw8KsXS4Rkc1iuCUisgEl+gIAwIQnXka3PgObtI8igwnn8o1IKFLAOSQCziEReOSLc3h2rBJ39Q2GWsWRaETk+BhuiYhsiE9wGNp36dXk93cDoC814LeT8TiXVYakAhf8dcMJvPNrHB4dGo4HB4XA00VtvYKJiGwMf40nInIwbs4q9PYyInnlo5ja2x1+7hqk5pfijZ/PIfKNHViw4QTnyiUih8WeWyIiByXKitADSbhjTHfsTSjBlvgiXM4zYMPRZGw4mowu3k6I7uyCIe210KikBvfHG9SIyB4w3BIROaC6blDTtOsO9/53wKX7UFzIAS4cysd7v6Wi+MIBFJ3djdKEE4DJWOs+tS4uOBcby4BLRDaN4ZaIyAE1dINaqVHgit6Ay3olijUucIu4DW4Rt0GtEGjvYkKIiwneGgFFVYdueuJFfPHmAmRlZTHcEpFNY7glInJg9d2g1hmAEAKp+aWISy/EhXQ9SiqMuKRX4pJeCY1KgVBvF4T5uMA1qHXrJiJqKoZbIqI2TJIkBHtqEeypxYgufkjKLUZceiEuZRahzGDChQw9LmToAajR7i+f4a0/cjEsPx4R7TzQ2d8NgTpnKBUNj9dtDYmJicjKyrLa/jjGmMg+MdwSEREAQKGQEObjijAfV5iEQHpBKa5kFSMptxhp+SVQufvgQHIpDiTHmd+jVirQ3kuLUB8XhHm7oL2XC7xd1fB0capa1PDUOsHNWQW1UgFJapkgnJiYiO49eqCkuNhq++QYYyL7xHBLREQ1KCQJQR5aBHloMQQ+SIg7g1VvLcILyz5EnuSO0yn5SMopRrnRhEtZRbiUVdTgPiUJ0KgU0KgqhzwoJROUEHBSAE5KCUqp8rgKCVXLtR/X3GbeL4D8/Hy4jJyFiN6DoXV1R20R+vpcLV3zr1ICVAoBlQSoFEBRTjr2bvgP9selQHLzhberGi5qZYuFcyKyHocJtx999BHefvttpKWloW/fvlixYgVuuukmucsiInIISgVQlnQK93R3w4AB/QEARpNAan4JErOLkZhTjIScYlzNLUFeSQXyisuRV1yB3OJyFJYaAABCAKUVJpRWmFqgQjXcet+ODABoOGc3QjsEPLAYL+7IBnbsAlAZzH1c1fB2U8PbVVP5cdVS/bFP1TZvVzV0ziqGYSIZOES4/frrrzF//nysWrUKkZGReO+99zB27FjExcXB399f7vKIiBxGbGxsjXXOALqqgK7+APyByh8tf/54MZoESg0CFSaBcqNAhRGIi7+Il19djDHTnoXOLxgmAQgAQkiV/6IyDNf3cTUBICPpEuKO7MOAMRMRFNrBvP7aDwQq33j9eyEAg8mEcoMJFUaBcqMJ+sJCJCdcQruOXVFYDpQZTCgzmJCSX4qU/NJGXSuVQoKnixruziq4apRQCSNUwgCtkwS1UoJKIUGlAJRS1b8KqaoH+c/15p5qReXH1b3bSgXgodMh0N+v8n3XLCqFAiqlBKfqf5XXrFMqqvZf+bq6naKR46aFEDBVXS+jSaDCKGAwmmAwCVQYTTAYBQymyutoNAkkp6QiOycPBiFgNAEmISBJkrm3XKpaFJIEBSo/Vladv6rquqgUlefrpJAQ4OeLDuGhUCmkVv3FwWSq/LqoMF7zdWIwodxoRLnhz23JKWnIzcuHQqr8i0D1+UmQKs/zunN0uu5zXvlv5ccKCfDz87PqsJiGxqWbqj5PBpOAwQRUmIT5Y4PJcpvBJODirsPAbmHo6OdmtRqtwSHC7fLlyzFr1iw8+uijAIBVq1bhp59+wqeffoq//e1vMldHRGT/6po3t7mCvd3RvU/PZu/naN4FHD70HUInjEW/cO9m7y/5whksX/Icfjp6FP3790dxuRHZ+nJkF5Uhp6gc2UXlyKlasvXlyC2uXleGHH05isqNMJgEsvRlyNKXNbue2uUBSLTKnhQSoFIq4KSQoFIqzCHWaBIwCgGTScBUtU5e6QDOAKgcyuKkVFQtf36sUkpQX/Oxk1IBtVIBpUKqCm+iKqhV/1sZyqtfV4bW6hBb+a9BhhMXwgSYkuDifAYaJ2Xl+SkkOKkUtZx35b8KSar8nFUvovKcTCaBktIynIs7DyEpIClVgFIFSaH682OlEySF8garzMGMtGIsvn9wi1yDprL7cFteXo6jR49i4cKF5nUKhQJRUVHYv39/re8pKytDWdmf/9nk51c+hrKgoKBli62i1+sBVP7nWVbS/Jsf0hMvAgDSrpzHRVcX7k/m/bXEPrk/x95fS+zT2vu7cvY4AGBw9ENo36FLs/eXeP4Ujm7/EVfOnYSTsvlPgrf2+WYmXwYAHD161Px/djUnAIEAAiUAblWLWWWvdYVRoKDcBH1ZZa91/JVErFj1H/S85Xao3b1hggJCqu6lliAq+/lgqvq4+rWQYG6Da9pWlFcgLzsdnTp1gdrZ2Rw8jVWBtLJ3tep1VW+c0dx7WvN8TQAMABrXJ12TuVf5ml5nYTQgNycLWq0rFColFKK67x2V5wZAXNP7+ud5//mxeZFqfo2UVS1yUEqo7Pmu7m2VAGGsQFrKVbjqPKFUOQGoeX7Xfmy5KCzaXqtIX2GdkTYAFO6+tW8wmSBMZajxpSHM1VVV+udXqMlQAX1OJozd+7Zafqo+jhAN/LIh7NzVq1cFAPHHH39YrF+wYIG46aaban3PokWLzH/d4sKFCxcuXLhw4WI/S1JSUr3Z0O57bpti4cKFmD9/vvm1yWRCTk4OfHx8GhzDU1BQgJCQECQlJUGn07V0qXQNXnt58frLh9dePrz28uG1l4+tXnshBAoLCxEcHFxvO7sPt76+vlAqlUhPT7dYn56ejsDAwFrfo9FooNFoLNZ5enre0HF1Op1NfcLbEl57efH6y4fXXj689vLhtZePLV57Dw+PBts0f6CTzNRqNQYOHIgdO3aY15lMJuzYsQNDhgyRsTIiIiIiam1233MLAPPnz8f06dMxaNAg3HTTTXjvvfdQVFRknj2BiIiIiNoGhwi3Dz30EDIzM/Hqq68iLS0N/fr1w9atWxEQEGD1Y2k0GixatKjGsAZqebz28uL1lw+vvXx47eXDay8fe7/2khANzadARERERGQf7H7MLRERERFRNYZbIiIiInIYDLdERERE5DAYbomIiIjIYTDc3qCPPvoI4eHhcHZ2RmRkJA4dOiR3SXZl7969uPPOOxEcHAxJkrBx40aL7UIIvPrqqwgKCoJWq0VUVBQuXLhg0SYnJwdTpkyBTqeDp6cnZs6cWePZ7ydPnsSwYcPg7OyMkJAQvPXWWy19ajZv6dKlGDx4MNzd3eHv74977rkHcXFxFm1KS0sxZ84c+Pj4wM3NDRMnTqzxgJTExERMmDABLi4u8Pf3x4IFC2AwGCza7N69GwMGDIBGo0Hnzp2xZs2alj49m7Zy5Ur06dPHPCH6kCFDsGXLFvN2XvfWs2zZMkiShOeee868jte/ZSxevBiSJFks3bt3N2/ndW9ZV69exdSpU+Hj4wOtVovevXvjyJEj5u0O/fO23ofzkoX169cLtVotPv30U3HmzBkxa9Ys4enpKdLT0+UuzW78/PPP4uWXXxbff/+9ACB++OEHi+3Lli0THh4eYuPGjeLEiRPirrvuEh06dBAlJSXmNuPGjRN9+/YVBw4cEL/99pvo3LmzmDRpknl7fn6+CAgIEFOmTBGnT58WX331ldBqteJf//pXa52mTRo7dqxYvXq1OH36tIiJiRHjx48XoaGhQq/Xm9s8+eSTIiQkROzYsUMcOXJE3HzzzeKWW24xbzcYDCIiIkJERUWJ48ePi59//ln4+vqKhQsXmttcunRJuLi4iPnz54uzZ8+KFStWCKVSKbZu3dqq52tLNm3aJH766Sdx/vx5ERcXJ1566SXh5OQkTp8+LYTgdW8thw4dEuHh4aJPnz7i2WefNa/n9W8ZixYtEr169RKpqanmJTMz07yd173l5OTkiLCwMDFjxgxx8OBBcenSJfHLL7+I+Ph4cxtH/nnLcHsDbrrpJjFnzhzza6PRKIKDg8XSpUtlrMp+XR9uTSaTCAwMFG+//bZ5XV5entBoNOKrr74SQghx9uxZAUAcPnzY3GbLli1CkiRx9epVIYQQH3/8sfDy8hJlZWXmNi+++KLo1q1bC5+RfcnIyBAAxJ49e4QQldfayclJbNiwwdwmNjZWABD79+8XQlT+cqJQKERaWpq5zcqVK4VOpzNf7xdeeEH06tXL4lgPPfSQGDt2bEufkl3x8vIS//3vf3ndW0lhYaHo0qWL2LZtmxgxYoQ53PL6t5xFixaJvn371rqN171lvfjii+LWW2+tc7uj/7zlsIRGKi8vx9GjRxEVFWVep1AoEBUVhf3798tYmeO4fPky0tLSLK6xh4cHIiMjzdd4//798PT0xKBBg8xtoqKioFAocPDgQXOb4cOHQ61Wm9uMHTsWcXFxyM3NbaWzsX35+fkAAG9vbwDA0aNHUVFRYXH9u3fvjtDQUIvr37t3b4sHpIwdOxYFBQU4c+aMuc21+6huw++TSkajEevXr0dRURGGDBnC695K5syZgwkTJtS4Rrz+LevChQsIDg5Gx44dMWXKFCQmJgLgdW9pmzZtwqBBg/DAAw/A398f/fv3x3/+8x/zdkf/ectw20hZWVkwGo01nnoWEBCAtLQ0mapyLNXXsb5rnJaWBn9/f4vtKpUK3t7eFm1q28e1x2jrTCYTnnvuOQwdOhQREREAKq+NWq2Gp6enRdvrr39D17auNgUFBSgpKWmJ07ELp06dgpubGzQaDZ588kn88MMP6NmzJ697K1i/fj2OHTuGpUuX1tjG699yIiMjsWbNGmzduhUrV67E5cuXMWzYMBQWFvK6t7BLly5h5cqV6NKlC3755Rc89dRTeOaZZ/DZZ58BcPyftw7x+F0iujFz5szB6dOn8fvvv8tdSpvRrVs3xMTEID8/H99++y2mT5+OPXv2yF2Ww0tKSsKzzz6Lbdu2wdnZWe5y2pTo6Gjzx3369EFkZCTCwsLwzTffQKvVyliZ4zOZTBg0aBDeeOMNAED//v1x+vRprFq1CtOnT5e5upbHnttG8vX1hVKprHEnZ3p6OgIDA2WqyrFUX8f6rnFgYCAyMjIsthsMBuTk5Fi0qW0f1x6jLZs7dy42b96MXbt2oX379ub1gYGBKC8vR15enkX7669/Q9e2rjY6na5N/0BTq9Xo3LkzBg4ciKVLl6Jv3754//33ed1b2NGjR5GRkYEBAwZApVJBpVJhz549+OCDD6BSqRAQEMDr30o8PT3RtWtXxMfH8+u+hQUFBaFnz54W63r06GEeFuLoP28ZbhtJrVZj4MCB2LFjh3mdyWTCjh07MGTIEBkrcxwdOnRAYGCgxTUuKCjAwYMHzdd4yJAhyMvLw9GjR81tdu7cCZPJhMjISHObvXv3oqKiwtxm27Zt6NatG7y8vFrpbGyPEAJz587FDz/8gJ07d6JDhw4W2wcOHAgnJyeL6x8XF4fExESL63/q1CmL//C2bdsGnU5n/o90yJAhFvuobsPvE0smkwllZWW87i1s9OjROHXqFGJiYszLoEGDMGXKFPPHvP6tQ6/X4+LFiwgKCuLXfQsbOnRojakez58/j7CwMABt4OetrLez2Zn169cLjUYj1qxZI86ePStmz54tPD09Le7kpPoVFhaK48ePi+PHjwsAYvny5eL48eMiISFBCFE5NYmnp6f48ccfxcmTJ8Xdd99d69Qk/fv3FwcPHhS///676NKli8XUJHl5eSIgIEA88sgj4vTp02L9+vXCxcVF9qlJ5PbUU08JDw8PsXv3boupeYqLi81tnnzySREaGip27twpjhw5IoYMGSKGDBli3l49Nc+YMWNETEyM2Lp1q/Dz86t1ap4FCxaI2NhY8dFHH7X5qXn+9re/iT179ojLly+LkydPir/97W9CkiTx66+/CiF43VvbtbMlCMHr31Kef/55sXv3bnH58mWxb98+ERUVJXx9fUVGRoYQgte9JR06dEioVCrxz3/+U1y4cEF88cUXwsXFRaxbt87cxpF/3jLc3qAVK1aI0NBQoVarxU033SQOHDggd0l2ZdeuXQJAjWX69OlCiMrpSf7+97+LgIAAodFoxOjRo0VcXJzFPrKzs8WkSZOEm5ub0Ol04tFHHxWFhYUWbU6cOCFuvfVWodFoRLt27cSyZcta6xRtVm3XHYBYvXq1uU1JSYn4y1/+Iry8vISLi4u49957RWpqqsV+rly5IqKjo4VWqxW+vr7i+eefFxUVFRZtdu3aJfr16yfUarXo2LGjxTHaoscee0yEhYUJtVot/Pz8xOjRo83BVghe99Z2fbjl9W8ZDz30kAgKChJqtVq0a9dOPPTQQxbzrPK6t6z//e9/IiIiQmg0GtG9e3fx73//22K7I/+8lYQQQp4+YyIiIiIi6+KYWyIiIiJyGAy3REREROQwGG6JiIiIyGEw3BIRERGRw2C4JSIiIiKHwXBLRERERA6D4ZaIiIiIHAbDLRERERE5DIZbIqJWEB4ejvfee6/FjzN8+HB8+eWXLX6c5tq6dSv69esHk8kkdylE5GAYbomIbsCMGTMgSRIkSYJarUbnzp2xZMkSGAyGet93+PBhzJ49u0Vr27RpE9LT0/Hwww+b14WHh5vrvXZZtmxZi9bSkHHjxsHJyQlffPGFrHUQkeNRyV0AEZG9GTduHFavXo2ysjL8/PPPmDNnDpycnLBw4cIabcvLy6FWq+Hn59fidX3wwQd49NFHoVBY9lssWbIEs2bNsljn7u7e5ONUn1NzzZgxAx988AEeeeSRZu+LiKgae26JiG6QRqNBYGAgwsLC8NRTTyEqKgqbNm0CUBnY7rnnHvzzn/9EcHAwunXrBqDmsIS8vDw88cQTCAgIgLOzMyIiIrB582bz9t9//x3Dhg2DVqtFSEgInnnmGRQVFdVZU2ZmJnbu3Ik777yzxjZ3d3cEBgZaLK6urgCANWvWwNPT06L9xo0bIUmS+fXixYvRr18//Pe//0WHDh3g7OwMAEhMTMTdd98NNzc36HQ6PPjgg0hPTze/78SJExg1ahTc3d2h0+kwcOBAHDlyxLz9zjvvxJEjR3Dx4sWGLjkRUaOx55aIqJm0Wi2ys7PNr3fs2AGdTodt27bV2t5kMiE6OhqFhYVYt24dOnXqhLNnz0KpVAIALl68iHHjxuEf//gHPv30U2RmZmLu3LmYO3cuVq9eXes+f//9d7i4uKBHjx7WP0EA8fHx+O677/D9999DqVTCZDKZg+2ePXtgMBgwZ84cPPTQQ9i9ezcAYMqUKejfvz9WrlwJpVKJmJgYODk5mfcZGhqKgIAA/Pbbb+jUqVOL1E1EbQ/DLRFREwkhsGPHDvzyyy94+umnzetdXV3x3//+t84/3W/fvh2HDh1CbGwsunbtCgDo2LGjefvSpUsxZcoUPPfccwCALl264IMPPsCIESOwcuVKc8/ptRISEhAQEFBjSAIAvPjii3jllVcs1m3ZsgXDhg1r9LmWl5dj7dq15uEV27Ztw6lTp3D58mWEhIQAANauXYtevXrh8OHDGDx4MBITE7FgwQJ0797dfB7XCw4ORkJCQqPrICJqCMMtEdEN2rx5M9zc3FBRUQGTyYTJkydj8eLF5u29e/eud0xqTEwM2rdvbw621ztx4gROnjxpcbOVEAImkwmXL1+utXe2pKSk1tALAAsWLMCMGTMs1rVr166eM6wpLCzMYtxwbGwsQkJCzMEWAHr27AlPT0/ExsZi8ODBmD9/Ph5//HF8/vnniIqKwgMPPFCjh1ar1aK4uPiGaiEiqg/DLRHRDRo1ahRWrlwJtVqN4OBgqFSW/5VWj2eti1arrXe7Xq/HE088gWeeeabGttDQ0Frf4+vri9zc3Dq3de7cudZtCoUCQgiLdRUVFTXaNXROtVm8eDEmT56Mn376CVu2bMGiRYuwfv163HvvveY2OTk5rXKzHRG1HQy3REQ3yNXVtc6w2Bh9+vRBcnIyzp8/X2vv7YABA3D27NkbOkb//v2RlpaG3NxceHl5Nfp9fn5+KCwsRFFRkTnAxsTENPi+Hj16ICkpCUlJSebe27NnzyIvLw89e/Y0t+vatSu6du2KefPmYdKkSVi9erU53JaWluLixYvo379/o+slImoIZ0sgImplI0aMwPDhwzFx4kRs27YNly9fxpYtW7B161YAlWNk//jjD8ydOxcxMTG4cOECfvzxR8ydO7fOffbv3x++vr7Yt29fjW2FhYVIS0uzWAoKCgAAkZGRcHFxwUsvvYSLFy/iyy+/xJo1axo8h6ioKPTu3RtTpkzBsWPHcOjQIUybNg0jRozAoEGDUFJSgrlz52L37t1ISEjAvn37cPjwYYshFQcOHIBGo8GQIUNu8AoSEdWN4ZaISAbfffcdBg8ejEmTJqFnz5544YUXYDQaAVT27O7Zswfnz5/HsGHD0L9/f7z66qsIDg6uc39KpRKPPvporQ9FePXVVxEUFGSxvPDCCwAAb29vrFu3Dj///DN69+6Nr776ymL8cF0kScKPP/4ILy8vDB8+HFFRUejYsSO+/vprcz3Z2dmYNm0aunbtigcffBDR0dF47bXXzPv46quvMGXKFLi4uNzIpSMiqpckrh9sRUREdiktLQ29evXCsWPHEBYWJnc59crKykK3bt1w5MgRdOjQQe5yiMiBsOeWiMhBBAYG4pNPPkFiYqLcpTToypUr+Pjjjxlsicjq2HNLRERERA6DPbdERERE5DAYbomIiIjIYTDcEhEREZHDYLglIiIiIofBcEtEREREDoPhloiIiIgcBsMtERERETkMhlsiIiIichgMt0RERETkMP4fSw7RNcTIHo4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.scatterplot(x = df_reduced['Inches'], y = df_reduced['Price_euros'])\n",
        "plt.title('Laptop Prices vs Screen Size')\n",
        "plt.xlabel('Screen Size (Inches)')\n",
        "plt.ylabel('Price (Euros)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "m3Jgr9Dk-SDd",
        "outputId": "bad84653-8f03-4371-a268-3b2730662919"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df_reduced['Inches'].quantile(0.25)\n",
        "Q3 = df_reduced['Inches'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lb = Q1 - 1.5 * IQR\n",
        "up = Q3 + 1.5 * IQR\n",
        "\n",
        "df_reduced['Inches'] = df_reduced['Inches'].apply(lambda x: lb if x < lb else (up if x > up else x))"
      ],
      "metadata": {
        "id": "pHF63FVqCsn0"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "plt.scatter(df_reduced['Ram'], df_reduced['Price_euros'])\n",
        "plt.title('Ram vs Price')\n",
        "plt.xlabel('RAM (GB)')\n",
        "plt.ylabel('Price in Euros')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "uiF7Nh-L_Dhs",
        "outputId": "768ec9ce-3baa-4ea6-ab74-5684907fcdc8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df_reduced['Ram'].quantile(0.25)\n",
        "Q3 = df_reduced['Ram'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lb = Q1 - 1.5 * IQR\n",
        "up = Q3 + 1.5 * IQR\n",
        "\n",
        "df_reduced['Ram'] = df_reduced['Ram'].apply(lambda x: lb if x < lb else (up if x > up else x))"
      ],
      "metadata": {
        "id": "b5ULtm6jAa_x"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "plt.scatter(df_reduced['Ram'], df_reduced['Price_euros'])\n",
        "plt.title('Ram vs Price')\n",
        "plt.xlabel('RAM (GB)')\n",
        "plt.ylabel('Price in Euros')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0sDGxwdqBP-R",
        "outputId": "15ca1298-d3e8-4e7f-d9c8-36e0cf55d618"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.scatterplot(x = df_reduced['Weight'], y =df_reduced['Price_euros'])\n",
        "plt.title('Weight vs Price')\n",
        "plt.xlabel('Weight (kg)')\n",
        "plt.ylabel('Price in Euros')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "IOkG291L_b7a",
        "outputId": "581b1901-2a11-49c2-f96a-1cb884af472a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df_reduced['Weight'].quantile(0.25)\n",
        "Q3 = df_reduced['Weight'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lb = Q1 - 1.5 * IQR\n",
        "up = Q3 + 1.5 * IQR\n",
        "\n",
        "df_reduced['Weight'] = df_reduced['Weight'].apply(lambda x: lb if x<lb else (up if x > up else x))"
      ],
      "metadata": {
        "id": "X3_ZSL3C_o0y"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_reduced.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZogyiC8D_oA",
        "outputId": "3e31c109-ab46-404d-cbd1-da264439fb6f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1275 entries, 0 to 1274\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Company           1275 non-null   object \n",
            " 1   Inches            1275 non-null   float64\n",
            " 2   Ram               1275 non-null   float64\n",
            " 3   OS                1275 non-null   object \n",
            " 4   Weight            1275 non-null   float64\n",
            " 5   Price_euros       1275 non-null   float64\n",
            " 6   ScreenW           1275 non-null   int64  \n",
            " 7   ScreenH           1275 non-null   int64  \n",
            " 8   CPU_company       1275 non-null   object \n",
            " 9   CPU_freq          1275 non-null   float64\n",
            " 10  PrimaryStorage    1275 non-null   int64  \n",
            " 11  SecondaryStorage  1275 non-null   int64  \n",
            " 12  GPU_company       1275 non-null   object \n",
            "dtypes: float64(5), int64(4), object(4)\n",
            "memory usage: 129.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Categories = df_reduced.columns[df_reduced.dtypes == 'object']"
      ],
      "metadata": {
        "id": "l-kbXRx6EcZ3"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in Categories:\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdIMwvgYEh4S",
        "outputId": "0443b89e-5f79-4d73-cf1c-c2df943a2bdc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Company\n",
            "OS\n",
            "CPU_company\n",
            "GPU_company\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "for col in Categories:\n",
        "  df_reduced[col] = encoder.fit_transform(df_reduced[col])"
      ],
      "metadata": {
        "id": "Vn5i5ZhDFB9d"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_reduced.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1KWOoTOMFbV_",
        "outputId": "109f21e4-f9e4-46ac-b0ef-f7c6ef29a13e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Company  Inches   Ram  OS  Weight  Price_euros  ScreenW  ScreenH  \\\n",
              "0        1    13.3   8.0   8    1.37      1339.69     2560     1600   \n",
              "1        1    13.3   8.0   8    1.34       898.94     1440      900   \n",
              "2        7    15.6   8.0   4    1.86       575.00     1920     1080   \n",
              "3        1    15.4  14.0   8    1.83      2537.45     2880     1800   \n",
              "4        1    13.3   8.0   8    1.37      1803.60     2560     1600   \n",
              "\n",
              "   CPU_company  CPU_freq  PrimaryStorage  SecondaryStorage  GPU_company  \n",
              "0            1       2.3             128                 0            2  \n",
              "1            1       1.8             128                 0            2  \n",
              "2            1       2.5             256                 0            2  \n",
              "3            1       2.7             512                 0            0  \n",
              "4            1       3.1             256                 0            2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47b8b5fe-fa12-4627-afb6-b77c59e7648e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Inches</th>\n",
              "      <th>Ram</th>\n",
              "      <th>OS</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Price_euros</th>\n",
              "      <th>ScreenW</th>\n",
              "      <th>ScreenH</th>\n",
              "      <th>CPU_company</th>\n",
              "      <th>CPU_freq</th>\n",
              "      <th>PrimaryStorage</th>\n",
              "      <th>SecondaryStorage</th>\n",
              "      <th>GPU_company</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>13.3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.37</td>\n",
              "      <td>1339.69</td>\n",
              "      <td>2560</td>\n",
              "      <td>1600</td>\n",
              "      <td>1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.34</td>\n",
              "      <td>898.94</td>\n",
              "      <td>1440</td>\n",
              "      <td>900</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>15.6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.86</td>\n",
              "      <td>575.00</td>\n",
              "      <td>1920</td>\n",
              "      <td>1080</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>256</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>15.4</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2537.45</td>\n",
              "      <td>2880</td>\n",
              "      <td>1800</td>\n",
              "      <td>1</td>\n",
              "      <td>2.7</td>\n",
              "      <td>512</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>13.3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.37</td>\n",
              "      <td>1803.60</td>\n",
              "      <td>2560</td>\n",
              "      <td>1600</td>\n",
              "      <td>1</td>\n",
              "      <td>3.1</td>\n",
              "      <td>256</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_reduced",
              "summary": "{\n  \"name\": \"df_reduced\",\n  \"rows\": 1275,\n  \"fields\": [\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 18,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1,\n          10,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4143477946323397,\n        \"min\": 11.600000000000001,\n        \"max\": 18.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          13.3,\n          15.6,\n          11.600000000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ram\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4122826456430655,\n        \"min\": 2.0,\n        \"max\": 14.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8.0,\n          14.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1,\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5952427029858284,\n        \"min\": 0.69,\n        \"max\": 3.5250000000000004,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          2.6,\n          1.95,\n          2.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_euros\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 700.7525041749287,\n        \"min\": 174.0,\n        \"max\": 6099.0,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          1292.0,\n          389.0,\n          1798.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 493,\n        \"min\": 1366,\n        \"max\": 3840,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          2736,\n          2160,\n          2560\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 283,\n        \"min\": 768,\n        \"max\": 2160,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2160,\n          900,\n          1440\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5038457085709569,\n        \"min\": 0.9,\n        \"max\": 3.6,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          2.0,\n          1.1,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PrimaryStorage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 365,\n        \"min\": 8,\n        \"max\": 2048,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          8,\n          180,\n          128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SecondaryStorage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 415,\n        \"min\": 0,\n        \"max\": 2048,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1024,\n          512\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GPU_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaled = scaler.fit_transform(df_reduced)"
      ],
      "metadata": {
        "id": "XXAP9LsQF5uC"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df = pd.DataFrame(scaled, columns = df_reduced.columns)\n",
        "scaled_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NvmSPIvEGQI-",
        "outputId": "4540aa91-cce0-4073-af60-3d87770e6b86"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Company    Inches       Ram        OS    Weight  Price_euros   ScreenW  \\\n",
              "0 -1.333546 -1.221911  0.050587  3.081382 -1.085809     0.292259  1.338239   \n",
              "1 -1.333546 -1.221911  0.050587  3.081382 -1.136229    -0.336954 -0.932863   \n",
              "2  0.131784  0.404918  0.050587 -0.787414 -0.262293    -0.799410  0.040466   \n",
              "3 -1.333546  0.263455  1.809631  3.081382 -0.312712     2.002178  1.987125   \n",
              "4 -1.333546 -1.221911  0.050587  3.081382 -1.085809     0.954536  1.338239   \n",
              "\n",
              "    ScreenH  CPU_company  CPU_freq  PrimaryStorage  SecondaryStorage  \\\n",
              "0  1.853934     0.216459 -0.005918       -0.866236         -0.423449   \n",
              "1 -0.612830     0.216459 -0.998674       -0.866236         -0.423449   \n",
              "2  0.021481     0.216459  0.391185       -0.515929         -0.423449   \n",
              "3  2.558724     0.216459  0.788288        0.184684         -0.423449   \n",
              "4  1.853934     0.216459  1.582493       -0.515929         -0.423449   \n",
              "\n",
              "   GPU_company  \n",
              "0    -0.039845  \n",
              "1    -0.039845  \n",
              "2    -0.039845  \n",
              "3    -2.201665  \n",
              "4    -0.039845  "
            ],
            "text/html": [
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              "      <th>Company</th>\n",
              "      <th>Inches</th>\n",
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              "      <th>0</th>\n",
              "      <td>-1.333546</td>\n",
              "      <td>-1.221911</td>\n",
              "      <td>0.050587</td>\n",
              "      <td>3.081382</td>\n",
              "      <td>-1.085809</td>\n",
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              "      <td>1.338239</td>\n",
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              "      <td>-0.423449</td>\n",
              "      <td>-0.039845</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>-1.333546</td>\n",
              "      <td>-1.221911</td>\n",
              "      <td>0.050587</td>\n",
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              "      <td>0.216459</td>\n",
              "      <td>-0.998674</td>\n",
              "      <td>-0.866236</td>\n",
              "      <td>-0.423449</td>\n",
              "      <td>-0.039845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.131784</td>\n",
              "      <td>0.404918</td>\n",
              "      <td>0.050587</td>\n",
              "      <td>-0.787414</td>\n",
              "      <td>-0.262293</td>\n",
              "      <td>-0.799410</td>\n",
              "      <td>0.040466</td>\n",
              "      <td>0.021481</td>\n",
              "      <td>0.216459</td>\n",
              "      <td>0.391185</td>\n",
              "      <td>-0.515929</td>\n",
              "      <td>-0.423449</td>\n",
              "      <td>-0.039845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.333546</td>\n",
              "      <td>0.263455</td>\n",
              "      <td>1.809631</td>\n",
              "      <td>3.081382</td>\n",
              "      <td>-0.312712</td>\n",
              "      <td>2.002178</td>\n",
              "      <td>1.987125</td>\n",
              "      <td>2.558724</td>\n",
              "      <td>0.216459</td>\n",
              "      <td>0.788288</td>\n",
              "      <td>0.184684</td>\n",
              "      <td>-0.423449</td>\n",
              "      <td>-2.201665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.333546</td>\n",
              "      <td>-1.221911</td>\n",
              "      <td>0.050587</td>\n",
              "      <td>3.081382</td>\n",
              "      <td>-1.085809</td>\n",
              "      <td>0.954536</td>\n",
              "      <td>1.338239</td>\n",
              "      <td>1.853934</td>\n",
              "      <td>0.216459</td>\n",
              "      <td>1.582493</td>\n",
              "      <td>-0.515929</td>\n",
              "      <td>-0.423449</td>\n",
              "      <td>-0.039845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-6ef6cd88-288a-412f-a8da-a4e1d6252cdc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        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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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-455bc1cc-b16c-446f-9288-17c639e3b781')\"\n",
              "            title=\"Suggest charts\"\n",
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              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "scaled_df",
              "summary": "{\n  \"name\": \"scaled_df\",\n  \"rows\": 1275,\n  \"fields\": [\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941203,\n        \"min\": -1.5777679743820783,\n        \"max\": 2.8182226790194873,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          -1.3335462714153246,\n          0.8644490552854583,\n          2.8182226790194873\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inches\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941285,\n        \"min\": -2.424350181150795,\n        \"max\": 2.1024792914449106,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          -1.2219111024925609,\n          0.4049182392215205,\n          -2.424350181150795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ram\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941268,\n        \"min\": -1.7084568325219536,\n        \"max\": 1.809630588418274,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.05058687794816021,\n          1.809630588418274,\n          -0.5357610255418777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000392387694128,\n        \"min\": -4.656209212291248,\n        \"max\": 3.081381854077394,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -3.6890103289951677,\n          -0.7874136791069268,\n          -4.656209212291248\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941276,\n        \"min\": -2.228648906158607,\n        \"max\": 2.53598307485767,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          0.9813853385472444,\n          -0.11103469237359515,\n          0.3763527060372413\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_euros\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000392387694128,\n        \"min\": -1.3718768402954835,\n        \"max\": 7.086637202499958,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          0.2241769487079432,\n          -1.064943419333286,\n          0.9465411859492079\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000392387694119,\n        \"min\": -1.0829180169818473,\n        \"max\": 3.9337839167163287,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1.6951263036190019,\n          0.5271310272203967,\n          1.3382388580527615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ScreenH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941254,\n        \"min\": -1.0779910514469253,\n        \"max\": 3.8273452785293904,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.8273452785293904,\n          -0.6128298477422747,\n          1.2901023492312962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941232,\n        \"min\": -4.461251471462974,\n        \"max\": 4.894168966226651,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.21645874738183835,\n          -4.461251471462974,\n          4.894168966226651\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000392387694126,\n        \"min\": -2.7856363178692845,\n        \"max\": 2.575249764568669,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          -0.6015716176167848,\n          -2.3885336450961026,\n          -0.0059176084570125576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PrimaryStorage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00039238769413,\n        \"min\": -1.1946480488790219,\n        \"max\": 4.388361105811104,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -1.1946480488790219,\n          -0.723923747601227,\n          -0.8662357456619557\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SecondaryStorage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941414,\n        \"min\": -0.4234489600143115,\n        \"max\": 4.502025561516328,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.4234489600143115,\n          2.0392883007510085,\n          0.8079196703683484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GPU_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0003923876941225,\n        \"min\": -2.201665090670261,\n        \"max\": 1.041064586578006,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -2.201665090670261,\n          -1.120755198254172,\n          -0.03984530583808304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = scaled_df.drop(columns='Price_euros')\n",
        "y = scaled_df[['Price_euros']]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "R-HzDv97IPHd"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "tmxI-F6JIep7"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "c-W4ztSOKMN7",
        "outputId": "1cc786c5-78d3-455d-9ce9-8eb501f8310f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
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              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
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              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
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              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
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              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
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              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
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              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
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              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
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              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "vfxTWdXDKa_p"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "rxKyNSSFK6ft"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "EaM0aTHLLU-q"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R² Score: {r2}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjk4wuYsLikw",
        "outputId": "7793919a-f175-4acd-914c-883450a6f5c2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 0.38734978287349175\n",
            "R² Score: 0.6170754284382859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge"
      ],
      "metadata": {
        "id": "A2djlntNLltt"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_model = Ridge(alpha= 0.01)\n",
        "\n",
        "ridge_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_ridge = ridge_model.predict(X_test)\n",
        "\n",
        "mse_ridge = mean_squared_error(y_test, y_pred_ridge)\n",
        "r2_ridge = r2_score(y_test, y_pred_ridge)\n",
        "\n",
        "print(f\"Ridge Mean Squared Error: {mse_ridge}\")\n",
        "print(f\"Ridge R² Score: {r2_ridge}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eqwix-jKMEy6",
        "outputId": "9183b0c9-4462-4d9e-b04d-a05ddcd03c1a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge Mean Squared Error: 0.38735101368548003\n",
            "Ridge R² Score: 0.6170742116874985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "las = Lasso(alpha=0.001)\n",
        "\n",
        "las.fit(X_train, y_train)\n",
        "\n",
        "y_pred_las = las.predict(X_test)\n",
        "\n",
        "mse_las = mean_squared_error(y_test, y_pred_las)\n",
        "r2_las = r2_score(y_test, y_pred_las)\n",
        "\n",
        "print(f\"Lasso Mean Squared Error: {mse_las}\")\n",
        "print(f\"Lasso R² Score: {r2_las}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4hIJ-fzMXUF",
        "outputId": "a0d7f42d-94a1-4647-a0e2-1cebff8a6a31"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lasso Mean Squared Error: 0.38817611550107894\n",
            "Lasso R² Score: 0.6162585361064019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "lJL-1Je1NC_y"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('LinearRegressionModel.pkl', 'wb') as file:\n",
        "  pickle.dump(lr, file)"
      ],
      "metadata": {
        "id": "9h8FiEgXPBDZ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zHAC6sXPQF6B"
      },
      "execution_count": 35,
      "outputs": []
    }
  ]
}