car_prediction.ipynb

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        "id": "VrKtRsu6g2ME"
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      "source": [
        "import pandas as pd\n"
      ]
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        "df=pd.read_csv(\"/content/car data.csv\")"
      ],
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        "df.head()"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
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            ],
            "text/plain": [
              "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
              "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
              "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
              "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
              "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
              "4    swift  2014           4.60           6.87       42450    Diesel   \n",
              "\n",
              "  Seller_Type Transmission  Owner  \n",
              "0      Dealer       Manual      0  \n",
              "1      Dealer       Manual      0  \n",
              "2      Dealer       Manual      0  \n",
              "3      Dealer       Manual      0  \n",
              "4      Dealer       Manual      0  "
            ]
          },
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    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Seller_Type'].unique())\n",
        "print(df['Transmission'].unique())\n",
        "print(df['Owner'].unique())"
      ],
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        "colab": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Dealer' 'Individual']\n",
            "['Manual' 'Automatic']\n",
            "[0 1 3]\n"
          ]
        }
      ]
    },
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        "df.isnull().sum()"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Car_Name         0\n",
              "Year             0\n",
              "Selling_Price    0\n",
              "Present_Price    0\n",
              "Kms_Driven       0\n",
              "Fuel_Type        0\n",
              "Seller_Type      0\n",
              "Transmission     0\n",
              "Owner            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
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          "data": {
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              "      <th>Year</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.627907</td>\n",
              "      <td>4.661296</td>\n",
              "      <td>7.628472</td>\n",
              "      <td>36947.205980</td>\n",
              "      <td>0.043189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.891554</td>\n",
              "      <td>5.082812</td>\n",
              "      <td>8.644115</td>\n",
              "      <td>38886.883882</td>\n",
              "      <td>0.247915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2003.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>32000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>48767.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>92.600000</td>\n",
              "      <td>500000.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
              "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
              "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
              "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
              "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
              "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
              "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
              "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
              "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
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        "df.columns"
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        "outputId": "8b34296e-7382-4c64-d85f-bc6da8cbbdd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
              "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_dataset=df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven','Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
      ],
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              "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
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              "\n",
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              "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
              "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
              "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
              "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
              "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
              "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
              "\n",
              "  Transmission  Owner  current_year  \n",
              "0       Manual      0          2020  \n",
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              "      <td>42450</td>\n",
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              "      <td>0</td>\n",
              "      <td>2020</td>\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
              "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
              "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
              "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
              "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
              "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
              "\n",
              "  Transmission  Owner  current_year  no_of_year  \n",
              "0       Manual      0          2020           6  \n",
              "1       Manual      0          2020           7  \n",
              "2       Manual      0          2020           3  \n",
              "3       Manual      0          2020           9  \n",
              "4       Manual      0          2020           6  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#final_dataset.drop(['Year'],axis=1,inplace=True)\n",
        "final_dataset.drop(['current_year'],axis=1,inplace=True)\n",
        "final_dataset.head()"
      ],
      "metadata": {
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          "height": 206
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        "id": "olSakUarlU9M",
        "outputId": "f5387d42-a985-463d-f590-920b82d5f7d7"
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      "execution_count": null,
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        {
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            "text/plain": [
              "   Selling_Price  Present_Price  Kms_Driven  Owner  no_of_year  \\\n",
              "0           3.35           5.59       27000      0           6   \n",
              "1           4.75           9.54       43000      0           7   \n",
              "2           7.25           9.85        6900      0           3   \n",
              "3           2.85           4.15        5200      0           9   \n",
              "4           4.60           6.87       42450      0           6   \n",
              "\n",
              "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
              "0                 0                 1                       0   \n",
              "1                 1                 0                       0   \n",
              "2                 0                 1                       0   \n",
              "3                 0                 1                       0   \n",
              "4                 1                 0                       0   \n",
              "\n",
              "   Transmission_Manual  \n",
              "0                    1  \n",
              "1                    1  \n",
              "2                    1  \n",
              "3                    1  \n",
              "4                    1  "
            ]
          },
          "metadata": {},
          "execution_count": 36
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    {
      "cell_type": "code",
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        "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
      ],
      "metadata": {
        "id": "sFhhKJVama5-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yZKoSU99mrHF",
        "outputId": "efe11e0d-3d63-46d6-a0f7-3e8e8972aced"
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e97020d5-36ca-4a75-b993-d268859cd441 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e97020d5-36ca-4a75-b993-d268859cd441');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Selling_Price  Present_Price  Kms_Driven  Owner  no_of_year  \\\n",
              "0           3.35           5.59       27000      0           6   \n",
              "1           4.75           9.54       43000      0           7   \n",
              "2           7.25           9.85        6900      0           3   \n",
              "3           2.85           4.15        5200      0           9   \n",
              "4           4.60           6.87       42450      0           6   \n",
              "\n",
              "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
              "0                 0                 1                       0   \n",
              "1                 1                 0                       0   \n",
              "2                 0                 1                       0   \n",
              "3                 0                 1                       0   \n",
              "4                 1                 0                       0   \n",
              "\n",
              "   Transmission_Manual  \n",
              "0                    1  \n",
              "1                    1  \n",
              "2                    1  \n",
              "3                    1  \n",
              "4                    1  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "cMAPFe5hmyGl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(final_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GVBYCl2nnlXD",
        "outputId": "104628bb-20a2-475f-fc1c-28ae38583f9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fb288843710>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QOzMPJ/rdqCh0oK6yEBaL3sZcKdUmyobJJJgLygibzYxVC89ATakTnQNulBc4sPCMQths5kw3jWhKUnnlI/tqItKC1n0w+3wyAuaU9I4ZJb3Q2+DGbQA2ANgW+vkFBAc4iMiAwlde1JTlqT5usZjQcGYxGs7UuGGUcRNlg7mgTLHZzGisnpHpZhCdtsn636lgTSYiLWjdB7PPJyNgTknvmFHSA10NbkgpXQD+NdPtICIiIiIiIiIiIiIi/dLF4IYQ4ntSytuFEM8CkOMfl1JemYFmERERERERERERERGRDulicAPAE6F/H8hoK4gopRRFoq3XFZl/Mdk5vyn7MSukB8whTTfMPBHphdb1iPWPjIA5Jb1jRkkPdDG4IaXcJ4QwA1grpfy7TLeHiE6fokjsaj6B9dv3w+1T4LCasHn1Iiyvq2BnR1GYFdID5pCmG2aeiPRC63rE+kdGwJyS3jGjpBemTDcgTEoZADBHCGGbyuuEEGcKIV4SQrwlhGgWQqwL3T9DCPGCEKIl9G9xWhpORKrael2RTg4A3D4F67fvR1uvK8MtI71hVkgPmEOabph5ItILresR6x8ZAXNKeseMkl7oZnAjpBXA/wghNggh1odvk7zGD+DLUsrzAFwE4EtCiPMQXJj8RSllLYAXwYXKiTTVOeCOdHJhbp+CrkF3wu+hKBKt3UN49b0etHYPQVFiluShLBAvK0d7Xfzbk2ZSUbOIjCTVmWefTUTJ0roPZp9PRsCckt4xo6QXupiWaoz3QjcTgPxEXiCl7ADQEfrvQSHEYQCzAKwE8InQ034O4I8A7kxtc4lovPCciyYh4LCaojo7h9WEmfmOhN+Hlzhmp/Hzcs7Md6hm5a8fnMKWF4/wb0+aiJfDsrzEahaRniQy/3F5gXrmE+2nx2+PfTaFcf5tmiqt+2D2+ZQsLesbc0p6x4ySXujmyg0hxCIAzQC2Syk3jb1N4T2qAZwP4HUA5aGBDwA4AaA8xU0monHCBzcu37IHt2/bj3XLauGwBstM+EBHdYkzoffiJY7ZaWxGbnjkdVy+ZQ/e7x3C5tWLorKyblktfr23HQD/9qQNixnYeEVdVA43XlEHiznDDSOaIrU6u6v5RMyVFNUlzpjaO5V+eiz22RSWaP6IxjKbEPO9Yd2yWpjTdLRC6+1RdtC6vnHflPSOGSW90MWVG0KIuwB8HsA+APcLIb4lpXxkiu+RB2AHgNullANCjI6eSymlEEK1xxFCrAWwFgCqqqqS/A2I0sdIGR17cKOj343HXz2KtZfU4PwzizCnxDmlM1smusSxpiwvHc2n05BoTtUOgN36y79i17oleK5pCboG3RAQuH3bfnT0j17Oyr89pcJEOT3R78GP/nQEay6ugRCAlMCP/nQENaUNmFPC3JE2UtHnxxtomN+0JKqGmkwCy+sqMD9Ue2fmJ38GKvvs6WWinCaaP6Kxwt8bxvbBj796FOdXFaG6NLncTJTTdGyPsl866hv3TUnvmFEyAl0MbgC4HsAiKeWwEKIEwC4ACQ9uCCGsCA5s/EJK+ZvQ3Z1CiEopZYcQohJAl9prpZRbAWwFgMbGRp5SRLpjpIyOP7jR0e/GlheP4FdrPzrlHb5UTpdB6ZdoTuMdADsx4MZFNaWoKctDa/cQ+oa9Uc/h355SYaKcurx+HO0dwcMvHYl6zbDXr10DadpLRZ8/lYEGk0mgpizvtA86s8+eXibKKQe6KBnlBQ70DXuj+uDTrSET5TQd26Psl476xn1T0jtmlIxAL4MbHinlMABIKXuFEAlfECqCl2g8CuCwlHLzmIeeAfD3AP4j9O/TKWwvEalI5cGN6hInHvrc+TjY3g9FAmYBLJhdmNR0GaQfiWQkPFXK+Lnb1f72nNebUmXODKdqNqtmsOaQsWRioIF9NoVxoIuSoXUNYc2iZGhd37hvSnrHjJJe6GVWyRohxDOh27MAzh7z8zOTvPbjAG4EsFQIsT90uxzBQY3LhBAtAC4N/UxEaZTK+bsBwOuX2PpyKx7afQQ/frkVXr+uL1yhBCSSkfBUKc81LcGv1n4UzzUtUV2UlvN6UyrNmZGLe1fVR2Xz3lX1mDMjN8MtI5qaVPfFiWKfTUDm8kfGp3UNYc2iqdK6vnHflPSOGSW90MuVGyvH/fxAoi+UUr4CIN5pusuSbhERTVkq5+/mnM3ZKdGMJDJVCjNCqXSsbxjf390SNWfs93e3YHFVMfNEhpLKvjhRrMcUlon8kfFpXUNYsygZWtc37puS3jGjpBe6GNyQUv4pkecJIXZIKa9Jd3uIKHmpmr+bczZnL2aE9KhzwK06ZyzzREaUqjqbKNZjGkvr/JHxaV1DWLMoWVrWN+6bkt4xo6QXepmWKlE1mW4AEQUpikRr9xBefa8Hrd1DKZ8KKDyn6Vicszl7pCI/zAilEvNE2STdffR4/PwQ0enQuoawZpERMKekd8wo6YXRBjc4ESaRDmix1gHnbM5eqcoPM0KpVFWsPmdsVTHnjCVjycR6RKzHRHQ6tO6D2eeTETCnpHfMKOmFLqalIiJj0WKeWs7ZnL1SlR9mhFKJc8ZStsjEXPKsx0R0OrTug9nnkxEwp6R3zCjphdEGN/gNiUgHtJqnlnM2Z6dU5ocZoVThnLGULTI1lzzrMRElS+s+mH0+GQFzSnrHjJJeGG1w485MN4CIgMpCB5qWzUV4hosd+9rRN+zl3IqUEOaH9Ki8wIE5JTlYsXAWROhUimcPHGcuyXCYZco0RZFo63Whc8CN8gJexUOTC8/bPnZgVos1N7TaHmUPLesb+3PSO9ZSOh2prKe6GtwQQryJ2HU1+gHsBXCvlPL32reKiMZSFIm3Ogax9eVWuH0KHFYT1i2rRW15HufWpkkxP6RXVcW5uG1pLb7+1KFINjlnLBkRs0yZFF7zJTw1Wnj9leV1FRzgoLjC6/aMz0269g213h5lB63rG/tz0jvWUkpWquuprgY3ADwPIADgl6GfPwsgF8AJAI8BuCIzzSKiMLW5vB98sQX/fdsSfmmlSTE/pFfH+oYjXx6BYDa//tQhzhlLhsMsUyZlYs0XMj6t1+3hOkGUDK3rG/tz0jvWUkpWquup3gY3LpVSLh7z85tCiDeklIuFEJ/PWKuIKCLeXN7dQ26cPZM7WTQx5of0KlPrFBClGrNMmcT8UbK0XreH6wTRVGld31hPyQhYSykZqa5vehvcMAshLpRS/gUAhBAXADCHHvNnrllEFJbKeRU5J/P0o8d5OZlDAvSZTaJk6DnLrLfZT8/5I33Tuj6wHtFUcW0YMgot6xtrKSUj1fXNlKqGpcgtAB4VQrwvhGgD8CiALwohnAC+ldGWERGA0XkVHdZg+Uh2XsXwHHuXb9mDGx55HZdv2YNdzSegKOOX3aFskqr8pApzSGFVxbm4d1V9VDY5rzEZkd7qbBjr7fSg1/yRvmldH1iPKBla1zfWU0qGlvWNtZSSler6JqTUX+iEEIUAIKXs13K7jY2Ncu/evVpukqa3KQ9n6yWj4dH505lXsbV7CJdv2RMzUvsc52TWm5TnNBX5SRXmMGucdk5bu4dw82N/wYqFsyAEICWw8+Bx/OwLFzILlAqa9vl6qrNhrLeGkJKc6jF/pG9TrA8p6fNZjygZCda3pAoe6ymlwhTqG2spZVQq66mupqUSQtgBXAOgGoBFiODvIKW8e4LX/BTACgBdUsr60H3/D8AXAXSHnvY1KeVzaWs40TSTinkVOYfo9KWneTmZQwrrHHDjaO8IHn7pSNT9zAIZkZ7qbBjr7fShx/yRvnEtAzIKrg1DeqdlfWMtpdORyvqmt2mpngawEsH1NVxjbhN5DMBylfu/K6VcFLpxYINIZ8Jz7I3FOURJa8whhTELROnFzxgRxaN1fWA9IqJspWV9Yy0lvdDb4MZsKeX1Usr7pZTfCd8meoGU8mUAJzVqHxGlCOcQJS0oikRr9xBefa8Hrd1DMfN/MocUxiwQpVein7HJ6jYRZR+uZUCkjn0iTZWW9Y21lPRCV9NSAfizEGKBlPLNFLzXrUKImwDsBfBlKWVfCt6TaFoJz4HXOeBGeUHq5/i0WQTWXlIDRQImEfyZpp905Sy8wNn67fvh9imRna3ldRWR9zeZBJbXVWB+0xLOZUtw2k144NoGuDx+OB0WOO16OweEKLXS3c+PlUi9TaRuE1F20roPZp9PeqcoErvf6cTB9n4oEjALYMHsQiw9p5x9IsWl9fdbHtOhZKXye4jeBjcuBvAFIcT7ADwILhwipZQLp/g+PwRwDwAZ+vc7AP5B7YlCiLUA1gJAVVVVks0mSp9MZTTdBxjael24Z+dbkcV7FQncs/Mt1JRyTlEjSjanqczZ+M7RJBB5XyA4/+f67fsxf9wCZ5zLdvqYKKfHTrrwzokhbH7h3UgW1182D3NmOFFdymyQNrTs8zMxkDBZvW3rdeG+XYex5uIahJbew327DmN+RT5rtI7w+xOlWjr6YPb5ZAST5bS124WtL7dG5XRumYs5pQml8vvtRBnlMR1KVqq/h+jt9ITPAKgF8CkAVyC4UPgVU30TKWWnlDIgpVQAPALgwgmeu1VK2SilbCwrK0uy2UTpk6mMtvW6VA8Mt/VGL4OT7KWyvS4Prm+swqOvtOKh3Ufwkz2tuL6xCiddnpT/LpR+U8np2My8efxUQjmbTLhzvHzLHtzwyOu4fMsevHHsFIpzbVHPCy9wRtPTRDntGvREDnIAwaxsfuFddA+yJpF2tOzzE+3ntcR9A2OYLKecRoWmKh19MPt8SodU1zfmlPRuooxyv42SlervIboY3BBCFIT+czDObarvVznmx6sAHDrdNhJNN50D7kihCRt/YFjtgPKu5hMJ7eTZzCZs2d0SVcy27G6B1ayLskRpMj4zL77dNWnOEqHWOX7tt2/iusbZUc/jAmcUT/+ITzWL/SO+DLWIKL3i9fOdA5kbAOa+gfGdzr4hTV/x+uBTaeqDtd4eZQet6xv3TUnvuN9GyUr19xC9JO6XoX/3IbhGxr4xt70TvVAI8V8AXgVwjhCiXQixBsD9Qog3hRAHAXwSwB1pazlRliovcEQWhgobf2D4dEZbh70B1WI27A2koPWkV+Mzo0hMmrNExOsc55Xnc4EzSojTZlbNYo7NnKEWEaVXrs2imvncDGae+wbGp8crgkj/ChxW1XpU4LBmxfYoO2hd37hvSnrH/TZKVqq/h+hicENKuSL071lSyprQv+FbzSSvvUFKWSmltEopZ0spH5VS3iilXCClXCilvFJK2aHNb0KUPapLnNi8etGEB4YTubojnniDJ+UFPKs+m43PzI597WhaWnvaAxDx8nRuRQGea1qCX639KJ5rWsJFaSkuu8WMdcuis7huWS0cFn6BpOzkDQRi6m/T0lr4Asokr0wf7hsY3+nsG9L0VV5gV+2DywvsWbE9yg5a1zfum5Lecb+NkpXq7yG6WFBcCLF4osellG9o1RYiCjKZBJbXVWB+0xJ0DboxM9+B6hJn1IHhcGc2dicv0bPuw4Mn4xcQ4ln12W18Zjr63di29xi2rb0II76Aas4SES9PZ5U6IwuqEU2kJM8Gp82MtZfUQJGASQTPmCvJs03+YiIDKnHasW3vscji3VIC2/Yew/L6ioy1ifsGxnc6+4Y0fVXNcKK2PC+qD64tz0PVjPR89rXeHmUHresb901J77jfRslK9fcQXQxuAPjOBI9JAEu1aggRBecTbet1oXPAjfICBy6sLlE92Hw6nVkigyeUfdQyc+fyc7FgVhFMJhGTvUQzwTzR6aqa4cScUhdmOO1wefxwOizId5h5oIOyVnWJExtWnIeD7f1QJGAxARtWnJfRL6Ss5cbHAx2UDJNJ4BO1M1GWZ0dHvxuVhTmoqyxI22df6+1RdtC6vnHflPSO+22UrFR/D9HF4IaU8pOZbgMRBYUXShu/06Y2nc/pdmbhM+p5Vv30MVFmppK9eO/NPNHpcHkU/MuTB6LyR5TNvH6JrS+36irzrOXGxgMdlAxFkfj94c6k9wH1vj3KDpmob9w3Jb3jfhslK5XfQ3Sx5oYQ4uqJbpluH9F0MtWF0sKd2UU1pagpy+MXAppUvMzEy977PVyElNKPi+DSdJNo5hVFopsTrloAACAASURBVLV7CK++14PW7iEoisxEc8lAuG9IU6V1H8w+n5KlZX1jTokoW6W6vuniyg0AV0zwmATwG60aQjTdxVsorXPAzdF4Sqt42Tt8YiCydgZRurD20XQz0cKo4cyf7hV1RESJ0LoPZp9PRsCcElG2SnV908XghpTy5ky3gYiCcm0W1YXScm3mDLaKpoN4i/S92zmI8yoLuBNPacXaR9NNIgujxjuran7TEtZkIkoZrftg9vlkBMwpEWWrVNc3XUxLFSaEKBdCPCqEeD7083lCiDWZbhfRdOINBNC0tBYOa7A8OKwmbFhxHvyK5FQUlFbVJU5886oFMdnLtZlx0uXJcOso23kDAdxx6byo/N1x6Tz4AsokryQypvDCqGMzP35h1M4BN4pzbfjSJ+fi1qXBW3GuDV2D7kw1m4iykNZ9MPt8MgLmlIiyVarrmy6u3BjjMQA/A/DvoZ/fBbANwKOZahDRdKIoEjazCYqU2Ly6AVICUgLtp4bx8rvd6Bv2Yuk55ZyKgk6Loki83+PC0ZMuOG0WlBfYUTUjOO3U4qoirFtWi7I8O471DeOh3UfQN+zFfdcsxGJFMnuUNmV5dlQW2rD1xo/gpMuHGU4rTg17UJpnz3TTiKZMUSTael3oHHCjvEB9wdNEFkatLHTg//5tDXpcXigSMAvg//5tDSoKHOM3SRSRSP6IxirLs6M414IHrm2Ay+OH02HBsMeXtj5Y6+1R9tCyvnHflIyAfT4lI9X9sN4GN0qllNuFEP8GAFJKvxAikOlGEU0HavNq37OyHn0uD37+56PoG/Zi3bJaVBQ4cF5lITssSopaztYtq8VZpU6U5NkwI9eORWcW4ebH/jfqEsU7dxxE/RmFEALodXlgM5sw7A1wB4pSasQn8dUn9kWyuenKukw3iWjKklknQ8a5MHMq9/PLLQFcp4WSIyVwasSPu555K5Kb9ZfNi1uDjLY9yg6ZqG/cNyU9Y59PyZISGPQEovrhr3z6nKT7YV1NSwXAJYQoQXARcQghLgLQn9kmEU0PavNqb3j6EIa8Adx40RwU59rw4IstaOsZxq7mE5yiipKilrMHX2zBWx0D+OM7Pfg/39+Djv7gNChjhRcWv/mxv+B/3+/D9Vtfww2PvI7Lt+xhHiklugc92PhMc1Q2Nz7TjO5BTolGxhJvnYy2XlfU88JfSC/fsiduPT3p8sLlDWDry614aPcR/PjlVri8AZx0eaf8XjQ9vN+jnr/3e1yTvJKms+4hDza/8G5Ubja/8C66h9LTB2u9PcoOWtc37puS3iW6z0k0Xq/Lg2//7p2o7Hz7d++gN8npyPU2uLEewDMAzhZC/A+AxwHcltkmEU0PnQPuqDPlgWCBmVWUg217j+HqxbPh9ilwefzssChp8XKmSCDfYcaai2vQ1uvCv/+fc1FZODrtSXhh8RULZ2HL7hbuQFHK9Qx5VbPZO+4gLpHexauz49fJaOt14b5dh7Hm4hrcunQubllSg/t2HY6qp25/AA++2BIzIO32B2Lei19uCQCOnnSprtNy7CSzQPGdGvap1q1Tw76s2B5lB63rG/dNSe+4Nhsl66RLvb6ddCXXD+tiWiohxAUAPpBSviGE+FsA/wjgGgC/B9Ce0caF3PTFf8aHPadi7j+jtAiPP/KDDLSIKLXKCxxwWE1RBcZhNeH4qRFc31gFkyn4c/eQJ3KQpKYsL4MtJiOKlzOnzYx8hxXf+0NL1HRVj78anBLtm1ctwLd/9w6u+cjsuAftmEc6HQU5FtVs5jt0satElLB4dXZmfvQ6Gb0uD65vrIoMGDusJjQtrcVJlydSTz0+RbXmev3R9000oMLaPL0UOqy46WNzIoNi4f68wGHNdNNIx3JtZtW6lWszZ8X2KDtoXd+4b0p6V1noUP1McG02mozDqt4PhxcYnyq9XLnxYwDh4ee/QXBB8YcB9AHYmqlGjfVhzymUfua2mJvagAeREVWXOLF59aJIMQkf5Pj13nZs2d2C2UW5uOPSefjF68dUD5IQJUItZ+uW1aK2PA/37Hwr5uzg712/CM81LcHiqiL0DXsjrxmLeaRUyLWasW5ZbUw2c6080EHGolZnN69ehOoSZ9TzbGZTzJVwW3a3wGoerbFzSpyqNbdqRvR7hQdUxj+PtXn6sZqF6tU+VjPn3qb4inKtqn1wUW56DhprvT3KDlrXN+6bkt4FFKh+JgLKJC+kaa/Qod4PJztYrJchX7OU8mTov68HsFVKuQPADiHE/oleKIT4KYAVALqklPWh+2YA2AagGkAbgNVSyr40tZ0oK5hMAsvrKjDj5gvxl/dPorrUiQ9PDeOaj8zGjn3t8AUUPPbnNvQNe1UPkhAlwmQS+NS55dj2xYtwvH8EpU47hAl4vfVknOmqgvO1d/S78ciNjXjwxXfQtLQ26kxj5pFSYcSr4Pk3O3D/tQ0Y8fqRa7PgkZffQ8Psokw3jWhKwv35/KYl6Bp0Y2a++uLew94AinNtuHrxbIjQQzv2tWPYOzrl1FmlwYGS8QtFnlUaXXPDAyrjn8faPP244uTK5Q1M/EKa1uaXF+D9HhfWXlIDRQImAcwuzsH88oKs2B5lB63rG/dNSe+6BtWv3O0ecuPsmbxyl+I7t6IAJ0c82HrjR9Dn8qHYaUVAKjivIrl+WDeDG0IIi5TSD2AZgLVjHpusjY8BeAjB9TnC/hXAi1LK/xBC/Gvo5ztT2F6irGQyCZTn22E1C3z1yQNRlxbOK8/Dd69viHuQhCgRiiLx+8OdMQfAPnFOGR566UjMZYm+gMTlW/ZEnnvfNQsxZ0YOPj73Igx7AygvYB4pNSqLHPjMgsqY2jd27RciozCZBGrK8iacEqqiYPKpBBIdKEn0eZT9EskV0XgWiwmfqatE1Yx+nOh3o6LQgbrKQlgs6ZloQuvtUXbQur5x35T0LtGpUInGM5kEegZ92PD0oUh9u2dlfdLfHfTSe/8XgD8JIZ4GMAJgDwAIIeYC6J/ohVLKlwGcHHf3SgA/D/33zwGsSmlribLYoNsX59JCiYtqSlFTlhdVcBRForV7CK++14PW7iEoisxU08kA4i066/IE8MiNjZhTkgMAkYGMDU+/GfXcO3ccREGODQ1nFuNjZ8fmkShZ8WrfoJuLi1J2mmrm5STde3hARW1fIVO4j6I91lJKlskkkO+woiDHinyHNe01ROvtkfFpXd9YT0nvEp0KlWi85o7+yMAGEKxvG54+hOaOCYcA4tLFlRtSym8IIV4EUAng91JGvj6ZANyWxFuWSyk7Qv99AkB5CppJlNUURaKt14UP+kZwy5Ia7NjXjo5+N4BgoTkx4MFCldfsaj4Rcxb+8roKfkEgVfEWnd1zpAc/2dOK712/CCVOGwY9fuTZLfD6ZcxzOwe4QC2l3okBt+pUA2q1jygbfNivnvmOfjcWnhn82cj9vJHbbmQn4vTzrKU0EUWR2P1OJw6290ORgFkAC2YXYuk55Wn5vGq9PcoOWtc37puS3vHKXUpWZ5z61jngSer9dDG4AQBSytdU7ns3Be8rhRBxT9MSQqxFaBqsqqqq090cUcqlM6NebwAHP+zHyWEvXG4//u23b0YOADQtrcUTrx1FR78bDqsJM5y2mNfHOwt/ftMSHnyeZhLJqaJI5NosqpeuSgkU59rwfo8Lt28bPRC1blktHn/1aGSgzWE1wSwEWruHuONEUzZRTsvy7KpTDZTlxdY+onRJtJa29brQOeCOOzWf36+guaMfHf1uVBbmoK6yIGa6ldnFOaqZn1WcE3mOkft5I7dd7ybKaYnTrtrPq+1HEoUdO+lCS+cQtr7cGlWP5pblobo0uc/rRDlNx/Yo+6WjvnHflPRusn3TRKZCJRov1fVNL9NSpVqnEKISAEL/dsV7opRyq5SyUUrZWFZWplkDiRKVrox6vQE8dfBDfP7R13Hgg/7IwAYQPACwZXcLrl48OzLQIRE7RhjvLPzOAXfK2knGMFlOw2fQNv3qDdy5fH7Upat3XDoPv3mjHVcvnq166fV1jbMjz21aWos3PujD5Vv2YFfzCU4xQlMyUU5HfAHV/I34uQguaSfRWnr5lj244ZHXVWuh36/gqQPHcf3W1/BP//kGrt/6Kp46cBx+f3R/bbeYVTNvt5gjz4nXz3cN6r+f5z5K+kyUU7+i4I5L58X084qiqL0VEQDgxIBHtR4lewYnMHFO07E9yn7pqG/cNyW94zFTSodU1zfdXLmRYs8A+HsA/xH69+nMNodIf978sB93hea4s1tMqgcAqktysebiGmzbewwfn1sS8x7xzsLPtZljnkvTW/gM2uJcG0rybFh7SQ0UCZgE4AidTSwEVHM4qzAHty6dCymBbXuPYcXCWTwDl1JuyBNQzZ/LzS+QpB+JXI3Q3NGPrz8VPYft1586hNqZeWg4szjyXu19w6qZb+8bRm15PgBjLxTJfZTMsJlNyLGaovr5HKsJVnO2nlNHqTAw4lOtR/0j6VlbQOvtUXbQur5x35SIslWq65vhBzeEEP8F4BMASoUQ7QA2IjiosV0IsQbAUQCrM9dCIv1RFIkP+0cixeSsUqfqAYAzihzoc3lw+7J5kCoriXoDATQtrcWW3aOXkjUtrYUvwLPzKFr4DNqrF8/GV588GJO1NRfXwCygmkOn3YId+9phswjcufxcHOkawq1L52LHvnZ0DXL9DUqNghz1A6H5OYbfVaIsMtGVFOFa2NEfZ07wfjcazhy9z2lXz7zTNpr5quJcPPjZRfD5JVweP5wOC6xmgari3DT8dqnFfZTMGPEF8Oj/vI8VC2dBCECRwKP/8z6+ddWCTDeNdCzPYVatR3n29AxGar09yg5a1zfumxJRtkp1fTN8VZRS3hDnoWWaNoTIQNp6XRBCYE5JDlYsnAVFUVQPAHztt2/isxdUAQKwWWLPSClx2rFt7zGsubgGQiByZv3y+ooM/FakZ+Gzf+NdnVE1IwceXwAbr6jDpmebIzncuKIOj/25FV+7/FwMefxRC8Pes7IeZxTp/+xhMgYhFGy6sg4bnxnN36Yr6zDBsl1EmkvkSorKwhzV51QURtfLEa9fte8f8fkjz2nvc+Gkyxddl6+oQ3ufC9Vl+VHvl8haIFriPkpmeAMBXN9YFZMrLweVaAIFdqtqH5zvsGbF9ig7aF3fuG9KRNkq1fXN8IMbRDR1vS4PFCWAL31iLu56phm3LKnBsweO4/5rG3CkaxABBZHFxB98sQVrL6lBUY4V8ytk1IGK6hIn7lx+btQB582rF6G6xJnB3470qLrEiYc+d34kJ+MPuh07OQIA2HnweNSBqB+9fAQrFs5CS9dgZNFHIDggsuHpQ/jh3y3GGQW5MQvlEk2VkGb84I9HovL3gz8ewf3XNGS6aUQR1SVObF69aMJ+t66yAPeuqo9MTeWwmnDvqnrUVRZGvZfTblE9+P+d60Yz3zHgiQxsAMHau+nZZvzsCxdEDW6E1wIZ367ldRUZG+DgPkpmOCyWyIE/YHQdt5994YIMt4z0zBtQVPvg765elBXbo+ygdX3jvikRZatU1zcObhBNM4oi8eEpN8zCjLueOQC3T8GOfe248aI5ONI1iC0vHol6vtunQJHAXz84hTklzqgpgEwmgeV1FZjftARdg27MzM/8mZqkX16/xLd/93bMmcLrltXi8VeP4pqPzMbR3hE8/FJ0BsOXfatd8fHXD05hhtMWNY88UTK6hzyq+esZ4uKipB+J9Lsmk0BZvg0PXNsAl9cPp82C/BxzTN/ssJrx2QuqIov5heuxwzo6LUvvkFe19va6vFH3JbIWiNa4j5IZp+KsZTDAtQxoAj1x+2BvnFcYa3uUHbSub9w3JSPQ25W7ZAyprm8c3CCaZtp6Xbhzx0FsuqIOxbk2XL14NoQATCZgfkW+6ln1JgEEFETm9FbrwLjuAU1k7IGvJ147ijUX12DOjBx0DXqQazWjbzj4ZVItf1ICFpP6YwEFMfPIEyVjhtOmmrEZTlsGW0UUy2QSqCnLi9vvvt/jwj8+8UZMlv/7tiU4e+boawaG/Xj81aNRZ0w9/upRNMwuijynvMAeZxose9Q2E1kLBND+C/Bk/68o9cry1DNTkmef4FU03Tms6mtgOKzpuTJX6+1RdtC6vnHflPROUSSeP3QCX/716FWy37luET5Tn7krd8kYUl3f2HsTTTOdA24U59pwRnEOvvypebCYgB372vG9P7SgZ9CDb161ILJjHz6LsyTXhp0Hj2NmviMy9cTlW/bghkdex+Vb9mBX8wkoCuf+pPjGHvjq6HfjN2+0I8dmgTegwK9IPPS581FgN2PDivOi8te0tBY7Dx7H2TPz8I1x2Qw/Nn4eeaJk5NnN2HhFXVTGNl5RhzwbFxclYzl60qU60HDspCvqPrvVBJtl9IunEIDNIqLW2CovtOHuK+ujPhd3X1mPisLoLx4z8x0xBwUdVhPK8kbrM/cfpgezSeLeVdGZuXdVPawm/p0pvny7JXTlWPR3kDx7es7F1Hp7lB20rm/cNyW9a+0ewv2/O4w1F9fg1qVzccuSGtz/u8No7R7KdNNI51Jd39h7E00zlYUO3PSxOfji43ujFkJ74rWj+Obzb+OeK+uw6crzkO+wQlGAD/qG8dM/v487l5+L6hIn2npduG/X4ciZngBw367DmF+RzzMjKa6xi+BWFjpw40Vz8JUnD8RMTQUAay6ugdkEXFA9A4NuH1YumoVvPfc2ZhXZ8YPPLcb+9lMIKMG54W9bWhszjzxRMgQECnMsUVP5mEyAEDzriIzFabOongmVa4ve7R/2+fBPl8zFpp1jFgtfUQe3b3R6ja4BH7bvPYr7r23AiNePHJsFj/+5FTVlTlTNGH0vixnYeEVdzMLjljHfT7j/MD0EFIFhjx9rL6mBIgGTAIY9fvgV1lKKz2IWmFXkiMrNrCIHrOb05Ebr7VF20Lq+cd+U9O74qWFc31gVNeV009JaHD81jLnl+ZO/AU1bigKYhYyqb8NeH5I954mDG0TTTEBBZH5tYHQhtDUX1+Dhl45AkcCdvzmEykIHrl48GwtnFeBnX7gwMnVEr8uj2oGddHl4cILiqi5x4r5rFuLOHQdx9eLZMYvxPfjiaAbD8y4+/Hfn48vbD0Rd8bFpZzPuXlmPE/1ufPuaBiyuKuZi4pQSfSM+rB+TNyB4QPiRmxoz2CqiqXPazVi3rDZmLQ2nPfpMqByrFZt2vhFVizftbMZ/rvlo5Dkn+t3Ye7Qfe4/+Neq1Jwbc0T/3e/CjP0UvCvijPx1BTWkD5pQE9w24/zA9DHv9+Obzb8fW0hs/ksFWkd6dGvHj8VfbcNPf1EQNpN5+6TlZsT3KDlrXN+6bkt7ZLOaY7/VbdrfgZ1+4IMMtI73rGHDju384EpkmX0rgN2+0Y+MV5yGZJcU5uEE0zcSbF1uI0JmdocuxO/rdePSVVjw3bjFQm8mk2oFt++JF2v0SZDgmk8AZRQ6subgG8yvy42YwzGE1QSqxi4gf7R3B/7b14Sd7gtnkwAalypDbr5pLl8efoRYRJWfEF0BJrjV4JpTHD6fDgmG3D25fIOp5Xr+imnlfYPS+0nz1+XBLxs2H6/L6VRcFHPaOfn64/zA9DPsCqrkaGXcf0Vguj191IDVdfbDW26PsoHV9474p6Z3Lw4xScvLsFvQNe6O+OzispqSnh+TgBtE0oigSipSYU5KDFQtnRQ4mP3vgOEwiOKVE/7AHlYUOXNc4G/Nm5kPK4OvCC0KdHPaqdmAnQwtCE8VT4rRj58HjuKD6PDQtmxu55HDHvnb0DXsRXnNsTkkONqyow8CID+uWzcX2ve3o6A+eJRxe4P6+axaiusSZod+EslF5gV21NpZxEVwymLI8O/567BTuevatyBUS6y+bh9JxWS4vcKhmfmb+6DoZhbkWbLqyDhufGZ1uatOVdSjKjf4KMWeGU/W9qmaM1mnuP0wPJU6bahaKc62ZbRjp2sx89YWax9cto26PsoPW9Y37pqR3Z5U4VWspv6fTZIpyLHjw+gb4AoicjGU1Be9PBgc3iKaR93tcaOsZwj/97dyoebE3XVkHjy+AH/3pCO5dWY/1l83DhqcPRR7fvHoRltdVwGQSyE1wLm+i8aqKc/EvnzoH73a6sPXl1qjpUvLsFox4/djy2QZ4/BK3/vKNmPU4+oa9uHdVPboHPZhV5IgMuBGlgs1iwj9/Ym7MQVy7lVcHkbEEFGDzC+9GXSGx+YV3sWx+edTzzCbE7A9svKIO5jGRPznkww/+GD3d1A/+eATfvGpB1HudWZSDL32yFneN2Xe4e2U9zizKiTwn12ZRPUjD/Yfs4rCaVWtpDhfApQl4AwHcfWUd7hqTm7uvrINfCUz+YgNsj7KD1vWN+6akd2eV5eE71y3Cl3+9P5LR71y3iNON0qQCEjg57I/5HlKR5HKqrIpE04SiSLR2D6Ew1x4pIEDwoMfGZ5rROejF0d4RSCAysBF+fP32/WjrdQEInkGy/rJ5cIR2qsJnhJYX8AwSmlj7qWEIiJiDbg++2IL+ER+++fw7EMIUk78HX2zBN1bV45GbGjGryA63L4DSPDsURaKtZwivt/Zi99udeK9rCEqyK1DRtDfg9ke+PAKjtXHQzcuqyVi6BtWnn+weil4n4/ipkZj9gU3PNuP4qZHIc4Y8o9NNPbQ7uCbS0d4RDI2bbuBw50BkYCP8Xnc9fQiHOwciz6kotOP2S+dFBk/MArj90nmoKIzef/D7FRz4oA+7DnXgwAen4PdzOiMjYS2lZFhNZtgtAg9c24D7rlmAB65tgN0iYDGl56Cx1tuj7KB1fWM9pWSFj/28+l4PWrvT9x3ZZBL49Hnl2Lb2Ivzo84uxbe1F+PR55TwJkSbl8gZUv4cMe5M7yYCnShFNA4oisav5BGxmAUWqz7Ftt5hCV2SozyXaOeBGTVkeZhflorLQgbWX1ECRgEkAlYUOzC7K1fJXIoNRFIk3jp1CW69LNV+KBJqWzUVAkbhlSQ127GsHgMgCUy5vAA/sbMatn6zFU/uPY8HsQhzpHkJL51DUorljrzIimoo+V5wpc1ycMoeMZWa+Q/UKy7I8R9TzXB71/t7lGf1SMTM/sSkx4q3n1TngifysKMEFysdfuaeMeZnfr+CpA8fx9adGrwC5d1U9VjXM4hpLBsFaSslw2Ez4sN8TtU+3blkt5pbnZ8X2KDtoXd9YTykZ4WM/67fvT/t3ZEWR+GNLFw6290ORwOGOAXQPebD0HA5w0MRSXd84uEE0DbSfciHfboHL60dZnDlma0qdWLesFkW56ouHWs0mKIrEsb5hfOXJgzGP151RyMsPKa62Xhe+9ts3ccuSGtV81c7Mw3/sOowVC2fBbALuvrIOJwbc+MZzhyM7ZU1La/HQSy1YsXAWDrb3A0DkIBkwepXR/KYlzCJNWUWB+gHhigLHBK8i0h+LObiG1vjLvC3jTkgucar39zPGLBZugsDtl87D+z0uKHL0aguziP7CWhxn32HsPORdg6MHEoHRK/MWVxXjrFDNbu7ox/d3t0SmwQKA7+9uQe3MPDScWZyS/z+UXhWFrKU0dX0un2p9WDAryfkpdLY9yg5a1zfum1Iy2npdkYENIL3fkY+ddKG1O3rK6fWXzcPcMheqS/l9nOIrL1A/Llmen9yMMFk9uCGEaAMwCCAAwC+lbMxsi4i05/UGsP+DfhzpGoIigTybGd+6agH+7bdvRjqge1bWw2ox4fk3OzB3Zh6altZiy+6WqIPKbxw9ia5BD4pzraojrF2DblSXONHW60LngBvlBQ5Ulzg5Yk8ARs/qffmdLmxevQhvnxiAIoNnAK9bNg+PvvIerm+sisrdumW1KM61oaM/+Notu1vw7WsbAAB9wx4U59pxy5IaAMFFycPPC19lRDQVJhPwrasXRB3ErS51Rq0/oGeKIll/CQBwot+DHfuO4f5rGzDi9SPXZsHP/9yKmlIn5pSM1ka/ouBrn5mPHpc3kvkSpw2BMZdSuAMB1astziiKPrAy7PXjjkvn4bt/eDfyvDsunYdh7+jUGS6PH8W5tsgVeUCwdo99Tq/Lg3/4m7PQOzzapn/4m7Nw0uVBMvi50J5ZGLuWUmYMxakPLk96pt/RenuUHbSub0bfN6XMiHc1bddg6r8jdw16VNd5O//MIg5u0IQE1OubSHI3PasHN0I+KaXsyXQjiDLl8IkBHO8biRlNf/Cz5+PN4/2QErBbBJo/7MeKhjNQ5rTh3r3HohYP3bb3GFYsnIXN2/dj29qPxZ3uQqvLH8l4wgvJLq+vjMrIhhXnYdjjw0dryiIDG8DoGXRrLq7Bwy8didz3TucgfrKnFfesrMcDv38bR3tHIgNwT7wWXHTc41fg9yucwoSmxO0LoHvQE1MrKwywnpCWl5+T/nkDASydX4GvPnkg6iQFXyB6DtsRXwAjPiUq83dcOg8jvtHn+fxS9ezmn9wUfb5QgcOKHKspasrKHKsJBY7RKzcqCu24+ePVkS/B4c/YzDGfsRm5drxzYihmMKU4d+qfQ34uMmPEwLWUMqc8346bPjYnZpqomUmewam37VF20Lq+GXnflDKnPM4VPzPzU3/FT/+IT3UgpX/El/JtUXbxK1K1vp1RmFxOeeSHKMsNuP0xByY2v/AuvH4FD+0+gkdfaUVRrh2/3tuOzS+8C7+UuHP5uXj0ldbI49c3VuE3b7TD7VPgCwSwefWiqAXFN69eBLMJqpc/hhcip+ltYMSLDSvqYgYw7tn5Fgpz7TCboLpjNHbk3mE1Qcrg/RuePoTrPnJm5HlbdrfgusbZuOPSedj4zCH8ubWXi4vTlPgVqJ55FDDAWsZtvS7ct+sw1lxcg1uXzsUtS2pw367DrL/TVIHdim2hkxTCedi29xjy7Nao5+VYzZErLYBg5r/7h3fhsI7OXzXk8avW5vELirv9AXzz+bex5cXgwuNbIlaAxQAAIABJREFUXjyCbz7/Ntz+0YGSYa+i+hkb8SpR7602mOJOYlHxeNMy8HORXkaupZQ5I76A6md/7GCrkbdH2UHr+sZ6SsmoLnGqHq+pLnGmfFs5VnNkO2EOqylqX5JIjTcgVeubL5DcMZxsv3JDAvi9EEIC+LGUcmumG0SktXgHJty+ABxWE+6+sh4/e6UVHf1uAEDPkBdl+Tb855qP4uWWbgQU4InXjqKj3x2aP9uOUyO+qLMzbRYRmRJo/HbScfkjGY/ZLHBq0BM3ixfMmaF6hkn45NqxV2eEXzd2QVu3T8GZxbnY/MK76Oh3Y+/Rk5hdnMPsUcJcCR7E1aNelydmWrempbU46fLwMzANDXp8qnkY8kSfRdc3HOdsu+HR5xXkWFRrc74j+ivEkDugOsXLkHv0QGHPkHof0DM0OuXUoEe9TYPu2DMA/X4FzR396Oh3o7IwB3WVBVFX7MVf5Jz7Jelk5FpKmTMYNzfpGWzQenuUHbSub6ynlAyTSWB5XQXmNy1B16AbM/PTNy2n2QSsW1YbcxUcp06jyQy5U1vfsn1w42Ip5XEhxEwALwgh3pZSvjz2CUKItQDWAkBVVVUm2kg0odPNaFm++iKfMwvs2PLZ83GifwQvvdsTud9pt+C6H72GOSU5+NIna3HX04eipnMwm4Bbf/nXmPeLN11VOi5/JP2ZLKd2sxlFOVbVjLT1DuOhl1pw95X1uOuZ0bxturIOFQV2/PDzi3G4YyAyyBZ+Xa7dEvM+4UG4gAIewKIYE+W0LE99UbPSPP1f+m8zm2KuitqyuwXb1l6U4ZbRVKViv9RmNqvm4fGbL4x63sz8OJkfMy1LrsWM9ZfNi5lKKnfcGXkz8myqU7yUjFmcPM+uPlCSN6aWF8bpJwpzoq868fsVPHXgOL7+1Gifce+qeqxqmBUZ4Mi1qW8v18azCU9XttZSyhxnGj6vE+U0Hduj7JeO+sZ6SulgMgnUlOWl5LvwRBnNtVngtJmjTnx12szItWb7oWY6XXG/hyRZ37J6PE1KeTz0bxeA3wK4UOU5W6WUjVLKxrKyMq2bSDSp08mookh4/AF846oFUZcl/sunzsFdTx+CxSzg9gVw69K5WLdsLr72mfmwWYIj+kd7R/DwSy144NoGPPS58/Hfty3B8rqKuFdo+AIBPPS589G0bG7k/R763PlpufyR9GeynJbk2XDS5cHGK+qisti0tBa/eaM9mLc/tuBnN1+AH39+MR67+UIU5lgx4lMwq8iBHKsZNovAlz45F03L5uKhG86H02aKyu6ed7vQtGwu7llZj3x78Mspp6aisSbKqYTEVz59TlQ+v/LpcxC8CFTfhr0B1bo87OUZqEaTiv1Sl1f9TCiXN/pMqPrKQtyzsj4q8/esrMeCysLIc065fSjPt+GBaxtw39UL8MB1DSjPt2Fg3JUU/oD62hy+MTXYahZYt6w2anvrltXCah49k9BuDg6ejH3O+svmwTbuFMC3Ovrx/d0tUVNvfX93C97q6I88xxsIoGlpbUyf40tyPg9FkWjtHsKr7/WgtXtoWvcvE9dSxbC1lDLHHsrJ+NzYrckfrpgop+nYHmW/dNS3bN03pewxUUYtZvWrQSwWrm1GE0t1fcva4TQhhBOASUo5GPrvTwG4O8PNwk1f/Gd82HNK9bEzSovw+CM/0LhFlK3CC2n2DLqR57BEjaYXO6244cIq+AIKNv+hJepM+bGO9o7g7dACzs81LYHJJFBe4MCckhysWDgrMvXEsweOozTPjq5Bb9SCQJtXL8rAb056VDXDiSPdQ2jtGsK3r22AAHD4xGDU1Rhev4TPr2DEp+Ct93qwfW87+oa9uHtlPc6rzMPaS87GPTvfijoreMe+4HO+edUC/P3Hq/Hvvz0UdXZxZaED1aW8eoMm5/L4YTNHL4hsM5vgMsCl/zPztVs4kPSvaoZTNQ9VM6JPNrDZzFi58AycVepE54Ab5QUOLDyjELYxZy4XO63oHvRiw29GFye/Z2U9inKjr6TodXlUp6U66RqdcspmEagodER9xioKHVGDG73DHhQ5LHjg2ga4PH44HRYMu33oG/ZEba8nzlRsvS5v5DklTntk7REhACmBbXuPYXl9Rcz/M0WRaOt1Rf4/jJ++gYuTJ87lCRi2llLm2C0CpXm2qNyU5tlgi3PgzGjbo+ygdX0z8r4pTQ/H+0bwwz+1Rvb/Agrwwz+1YlNhHerOyHTrSM9SXd+ydnADQDmA34rgNywLgF9KKXdltknAhz2nUPqZ29Qfe/77GreGsll4Ic3vrl6EO8YsqAkED3KsvaQGPUNeFOfaIldjbHymGY/c1IiFswpwyyVnY8TrR2WhAy+/k4ejvS5UlzhRVZyL25bWxkwF4Q9I1YU75zct4dRABJNJYOk55agpzcNJlwcCAv/y5IFIXioLHbjpY3Ow9ol9kVxtWHEetv3lGO56+hAeuakxMrABjJ4VvObiGjz80hF87bdvYu0lNTELUjXMLtJkcGOyA2Okf7k2C77x3OGYWvnEP8Rc9Kk7nO+Wxjqr1InvXLcIX/716IH471y3CGeVxl5JabOZ0Vg9I+57eXwSG0LTUwLB2rrh6UN4fNznoqLAoTotVXnB6ACbPwD84rU23PQ3NRjx+JFrt+Dnf27FncvPjTxnZr4DR7pcuOvZt6IGqs+bVRi1vVybRXXqrcduviDynOoSJ+5cfm7MgMT4K0oTGbiItzh5uvdxJltXRI+MXEspc3wB4M4db8bk5hdrPpoV26PsoHV9Yz0lvStx2tE37MXDLx2J3OewmjBjzLSkRGpSXd+ydnBDStkKoCHT7SDKlPBCmvGmp1AkcM/Ot3D/tQ348NQwZhXl4v0eFzy+AG7++Fn46pOjZ2luvKIOx/uGsav5BM4pz48MbITf6+tPHcIP/m4xFxSnCZlMAtUlTrx9YhD37TqMpqW1kYNT1zXOjpnS5J6db+GRGxvx7d+9jd4hr2q+wmcIu30Kcm3/n717D4/rqu9//1mjkTS6W9bdsuVL4jixZMsEEwIkPa0N1HASx0AIlDbcUtI2Dc5pTlsoT0hOQn60AWp+CaSFQIAECiSUNjg0zQ9IgKQthDo3x8rNxrFd25Isy7YuI41Go1nnD2lGGs2MNJK3ZvYevV/PM0+srT2zv6N893df1l5rFejPf+/chKeGh8IL/2QTT/Tmh95gOOWT51OfBHerzr6Q/v2FTn3uynYNhyMqLfLra0/8Vq9ryU7jHtynuNAkPAlVXDi/WtTdn3oS8BP9iT0pQpGxlMNSfePDm+Pr9IVGteX8xoTzi51b1qpvyhBXAX9BfH6P2Ofs+umr2rLukoTtDacZim14ylBsPp/RW9fV6zvXvFFd/SE1VQa0YVlVUl1+7WRQdzz6UryHhyTd8ehLWtdQoXPqyyf+DqmH5FzIc5xM5hVxIy/XUuTOqTTneacWKG+yvT3kh2zXN+op3G7M2oRr+ti5nWXoNMzC6fqWt40bwGIXm0izIc1EPdaOn8R3nRlSNCr91ZSbDTdsXZvQo+PWhzu0673tuvHB59I2YpSlmSiUYVEw1dSnX7/968O65pI1KvBJ65sqU+bVfx8+pfdd1KLGqtR5vK6hQtdvOVe//m2PKgKF+t8/S3xqOBtzvuTqiV44q7q0MOWT59XTht9xo6aqgN6xoSnhpvENW9eqsZL6uxgd6g3q+u8+m1QvH0lRk8LhMe093pdw83/qsFSNlalrb0Nl4mR/g6HUD1IMhiYbG6oChSl7W0x9QqtnMPXwVieDIzpXFfH1Mhl6KxKJavcLx2dtHDjeN6QPXLRSX/zZ5KTpf/HW89TZNxRv3GiozP7Qbx2dfSkfJllbX672FdULtt2z5eVaitypKS9KuY8tLV+Yp3/Tbo+njTGDbNc36incrthfkHII0LecW5Pr0OByTtc39z7243Ef/Nh1euu7PpD0evnV/bkODYtANGrVHxrR375rg/pDkZQTav7LM0cVKPSpcUlp0s2GOx/br3dfuDz+eaHRqAZGIgmNGFPFGlF2XbUpYTuphn/A4hWNWh3uDcZzrbMvpLt/fkB3PXZApUUFKfNqLDrew+hQz2BSHt+wda0++8hL+vqTB/Vnv7s25bBVY1mY8HWmJ3rhHcaYlE+eG+P+3jdjUaWMfZ7zJsPjMq1J4fCYHtp7XH9071O6/rvP6g/vfUoP7T2u8JTeD41LinXb9taE2nvb9lY1Lkls3CgPpD43KC+ebCg5MzSaMq4zQ5M9N+orivXBN63Uvf9xUF9+/IC+/uRBffBNK1VXnri95spAysnQm6c06KVrHOiYMum4NN5bJNawEVvviz97VcX+ydhX1ZRl/Rwn9oDJVKHRqLr63H1s8XItRe74jNEtlyfWmlsub5V/gfLGyqbcnjE8bYz0sl3fqKeYr2jU6mDPoH7125M62DOo6AJdE4fHxvS+zS3x87Z7/+Og3rd5fG5XYCZO1zd6biyQdHNrhDuuy0E0WGx+e2JQRQV+dfUPKDQ6poeeO6brf+9cNVYGdOT0kL7968M6PRTWzi1rdehkMOXF89SaEij0qWRKT5BdV21KGoKnZWmZWpaW6fydl6q7P6TSogKFx6I6NDFXB8PzLG6xoZvKiwtSPilXWGD02Xdt0Kf+9YWELq3f/vVhhUajOto3oh8+fVTXXLJGK5eW6OiZYd3/q8nJyJ8/eiZlHr/cPaA1deWz5t/ZzJmRiyd64byTg6mH3zk5OJLmHe5xYiD1TdCewVD8yXMsHLfNuZNugvm68sSatPd4n26eNp/GzT/apzW1ZfF5OLr6RhQo8umeq1+v00Ojqi4t1JnhsLr7RrSiejK3igsKUs77MrWBoD5trZxsuDg9NJryQucN0+YFefnEgIr9Go8rOKrqskKdGRrRyycGtKllvFfDTI0D7Ssml50ZTt3o0jc82eji8xlta23U+Tsv1YmBkOorzu7/cyY501RVkvLv5fYeWV6upcidwZGIvvLLAwlP/37llwf0v3ZsWJDtDYWjWd0e8kO26xv1FPMRjVo9/kq39h7tU9RKBUbasLxKW9Y1OH5+WlNWrMdf7koYGve+/zqobW2Njm4H+cfp+kbjxll66cUOvfVdH0ha/vKr+3XJO3IQEBa9aNTq4MlBWUnB8Jh++PRRXX3xSt31+H5VlxbpvZuX66+3rVNDRUB/8697dXl7c8qL59hxL1Do0y2Xter+/zqY1IiR6gI/NqfCR77138w/gLjY0E13vHtDynE5nz/ap5+92KV7P7RZT712SmNR6du/Hm+8iA2jFmOtNP3hk6hVyjx+tXtA65sqZxwe6mznzIg90TvbpLVwt6VlqYeoqC51/xAVNLDljhvn3Ml0gvmuND08uvonewYUFfj01/+cPOnuA9denPC+usoiLVsSSJjnY9mSgOoqJ/ef6jK/bt3eqlt2d8TjunV7q6rLJ7ufZ9pbYTQ6pmDY6q9/+HT8s265vFWj0cleJ5k2DlQGClOuVxFI7Bbv8xmtqSs/6+EGM82ZJaV+3XJ5q259uCPhOy4pm193/Ww1wnm5liJ3guGIDvcOJ0xKK0lDU3qSOWlwJPX2BkcWfq42eFe26xv1FPNx5FRQ+7sHdc8TBxPOA8+tK3d8Lr6W6lK9/6KVCUPj3r6jTS3VpY5uB/nH6frGsFRnadT6VPuOjye9wqOcGCE3DvUGNTpmtbSsSAVGOj0Ujs9t8KE3r9LrWpaopz+kvcfO6E9+5xw9/PyxpOF+/vZdG/T29Q36yh9dqO9c80Zd0FSuz105eeEdu8C/eE1t0lPx6eYfONQbzMnfA+4QGybF5/PFx+W8fsu5uuaSNXpgzxENhMa053CfvvjTV9RSXap7/+NgvGHj05et19LSQt182Xr9eO8xfeJfXtDXnzyoqy9eqaaq8ZtUDz9/TLfvSByiZOeWtfrBnqOzDg91tjkbe6L3kZ2X6vvXvlGP7LyUxjwPKi/2pxx+p6LY/c+B5GLIHIxz4zGvsy+k+391OKHO3v+rwwmNFtLkmPNTBQp9qpky5vxQmom7p99wPD04ql0/fTU+FNpYVNr101d1enCy90PnmRH9YM8Rfe7Kdt3xng363JXt+sGeI+o8M/mEVmyOj+kxTZ/jIzKm+E3/WEy3PtyhyJSwYo0pU/eL6Y0p0vhQWDdsTR72cGqPEicd6p2cwPz6Lefqjy9dozsefSkpZ46fCel7Tx1O+Ht976nJHotzEWtQeeddT+oPvvaU3nnXk3q0o2tBhqnwci1F7sQaI6cab4xcmP0wNifh9O1NHwIPmCrb9Y16ivno7h9J2Qu2u9/5Hj9HTg+lHAL0yOkhx7eF/OJ0faMqAnmmuz+kgyeDKvL71FwV0KcvW6/P/PhF3f3zA/rUO9bp5c6B+MFuZU2JPrntAkWiUX3hynaFx6Lq7g/pdS1LtKq2XG3NS+a1/XRjfTO58uIVe7L8a0/8Vh/fsjZhgte/2Xa+BkYiun7LuZKkodGIrrlkjUoKfWptrtL/t3ufDvcOJwxV1dkX0l2P79c1l6yJj+05FI7Enxq2VvHh12Z7et2JnHXqiV7kTjgyJsnqC1e2KxiOqKzIr6HwqMJjC/PUqJOcHjIHmXPjMa+hMqDTQ+GEJ5JT9eSpTtMzoLrUn/BZqScUT/yszv5Qyqegu/pDap/4dzAc0Z7Dfdpz+NmEdYbCkw8EbWiq0meuaNOnfzR5jPjMFW3a0FSV8J7eYDjl3703GJ6M6cyI/uEXicPO/MMvDmhVTbtalk7+vzFGKisqSOh1UlZUoOlD/jrV86E3OKL3bW5J6sF4KjiSkDMNlQG9emJQO783+feab4+sdI1w56eYZP5shUZT19JQxP21FLmzvrFSt+9oSzg/vH1Hm9ZP2/ed0pau1ixbmO0hP2S7vnn53BS5EwxH0jyY4vwD2G48D4Y3OF3faNwA8kg4PCa/z2hVbalqy4t0qDeof3pq/OnNAp90flOlrv320/ED0OHeYf3Fg8/pHz5woZ4+cmb8qfhtF6hl6fyf9mV4FKTSUl2q+z76BgVDYzK+8XHS+4Yj6jwzpFAkqi///ED84vLTEz00br6sVX/2nacTbsbEGjTunlh/5dISffXq1+uTP3xBkuJDsM1leChyFtL4+Ns3734xKQ++/sHNOYwqczSw5YYb68eqmjJ99eoLNTA8puBIRGUBvyoCBUm18NzaSh05FUq4qCgq9OncusqEz8pk2L20Q0BVTf4dVi4tS7nO1HOOoqICLSktTGhoWFJaqKKiybk7pPHeFrPN3zGUdpibxIv7zr6Q/vGXB/XuC5fLmPFeJ//4y4M6r7EiPnyDk8OPFRX44scpafLYNn2oLyeHPMzmzYfQqLdrKXLD7/dpR3uz1taXq6svpMaqgFqbquT3L8xAE0VFBbpi4zKtri2LN1huXFaVVGuAqbJd37x+borcWLm0TG9fX6s/vHi1TgdHtbSsUN/59WtndY8nHTeeB8MbnK5vNG4AeWJ4eFTPHDujk4Mjqqso1khkTLt++qpCo9H4hf3n3rMh5cVtJGp16doavff1zRqLSk+91jvvpxKZfwDTRaNW/3mwR5Exq+FwVMGRiEYiYzqvsULlxX792T8lNmB85scv6psffoOePnw6Zb6aKfPBLC0v1ovH++PDdDyw54geuPZiDY+OZfz0ekt1qe54z0Z94od7ydlFbCjtU07eeDrObZNaLxZuPOZFozZlLkejNiEnfD4jn5FePTEQb0jYuLwqaZ23X9CgB669WJ19ITVN3HCcnlutTZX6/JUbtf/EYHzyynPry9U65anr1bWp/1arayf/Vod6g/r4955NutB5ZFoPg8aqYt22vVU3T5m/47btrWqsmmzcWFqWugFk6bSxfDPp6eJkz4dMh/rKtEdWJvt+Nm8+eL2WInf8fp/aV1SrfUV2tldUVKDNq5ZmZ2PIC9mub9RTzEdzZUAffNM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\n",
            "text/plain": [
              "<Figure size 1620x1620 with 90 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "ASwKjQyHnxFW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corrmat=final_dataset.corr()\n",
        "top_corr_features=corrmat.index\n",
        "plt.figure(figsize=(20,20))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "9VO4ch6GoeT3",
        "outputId": "933012de-20d2-4eef-dd4e-7e915b96b547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g=sns.heatmap(final_dataset[top_corr_features].corr(),annot=True,cmap='RdYlGn')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "6z0qYA5ipB9l",
        "outputId": "58f5c2e6-b677-4e8b-f3ca-3eca6cabc278"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=final_dataset.drop(['Selling_Price'],axis=1)\n",
        "y=final_dataset['Selling_Price']"
      ],
      "metadata": {
        "id": "BKUcx7AEqy5Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KbwnHVQnpu-I",
        "outputId": "6fbd36f2-f458-4af6-8a9e-af5d82f93341"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-40e8fffb-6631-40c5-8841-21e1af9749ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "      <th>no_of_year</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>5.59</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40e8fffb-6631-40c5-8841-21e1af9749ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-40e8fffb-6631-40c5-8841-21e1af9749ee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-40e8fffb-6631-40c5-8841-21e1af9749ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Present_Price  Kms_Driven  Owner  no_of_year  Fuel_Type_Diesel  \\\n",
              "0           5.59       27000      0           6                 0   \n",
              "1           9.54       43000      0           7                 1   \n",
              "2           9.85        6900      0           3                 0   \n",
              "3           4.15        5200      0           9                 0   \n",
              "4           6.87       42450      0           6                 1   \n",
              "\n",
              "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
              "0                 1                       0                    1  \n",
              "1                 0                       0                    1  \n",
              "2                 1                       0                    1  \n",
              "3                 1                       0                    1  \n",
              "4                 0                       0                    1  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVwdLwNyroZC",
        "outputId": "a5e92f97-d6b9-4b64-aeb2-95533cbcda6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3.35\n",
              "1    4.75\n",
              "2    7.25\n",
              "3    2.85\n",
              "4    4.60\n",
              "Name: Selling_Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "model=ExtraTreesRegressor()\n",
        "model.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ag8KL1Zrqjs",
        "outputId": "5bc55fcf-f99d-4353-c586-cbcfa9facad5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OciYCUECs-wD",
        "outputId": "011748a3-8d30-4e6d-86d7-7753171a2a5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.07616357e-01, 4.08750662e-02, 3.73898649e-04, 7.71575746e-02,\n",
              "       2.14361161e-01, 9.79803699e-03, 1.20973480e-01, 1.28844425e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feat_importance=pd.Series(model.feature_importances_,index=x.columns)\n",
        "feat_importance.nlargest(5).plot(kind='barh')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "GEAmuuhktCom",
        "outputId": "3f49d07c-4fa9-4730-d811-599a470a9cf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "G4YcPaqu1wlF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_random=RandomForestRegressor()"
      ],
      "metadata": {
        "id": "zNVJ-zt02l7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "ovPdp9bx4jYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_estimators=[int(x) for x in np.linspace(start=100,stop=1200,num=12)]\n",
        "max_features=['auto','sqrt']\n",
        "max_depth=[int(x) for x in np.linspace(5,30,num=6)]\n",
        "min_samples_split=[2,5,10,15,100,900]\n",
        "min_samples_leaf=[1,2,5,10]"
      ],
      "metadata": {
        "id": "JxTAqSGC3IiI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n"
      ],
      "metadata": {
        "id": "BNR6llzU5CYI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_grid={\n",
        "    \n",
        "     'n_estimators':n_estimators,\n",
        "     'max_features':max_features,\n",
        "     'max_depth':max_depth,\n",
        "     'min_samples_split':min_samples_split,\n",
        "     'min_samples_leaf':min_samples_leaf\n",
        "}\n",
        "print(random_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g86q3FJ65Yr6",
        "outputId": "c52a9f2c-b552-40a8-f676-613978b34af0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100, 900], 'min_samples_leaf': [1, 2, 5, 10]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf=RandomForestRegressor()"
      ],
      "metadata": {
        "id": "IdWorGUE6ows"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random=RandomizedSearchCV(estimator=rf,param_distributions=random_grid,n_iter=10,scoring='neg_mean_squared_error',cv=5,verbose=2,random_state=42,n_jobs=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "y67AnJlW63pw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRlMr5Ak8biF",
        "outputId": "d8040caf-77a0-4df6-feb4-11dcf10e0bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=700; total time=   1.0s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=700; total time=   1.0s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=700; total time=   1.0s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=700; total time=   1.0s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=700; total time=   1.0s\n",
            "[CV] END max_depth=10, max_features=auto, min_samples_leaf=10, min_samples_split=900, n_estimators=900; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=auto, min_samples_leaf=10, min_samples_split=900, n_estimators=900; total time=   1.2s\n",
            "[CV] END max_depth=10, max_features=auto, min_samples_leaf=10, min_samples_split=900, n_estimators=900; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=auto, min_samples_leaf=10, min_samples_split=900, n_estimators=900; total time=   1.2s\n",
            "[CV] END max_depth=10, max_features=auto, min_samples_leaf=10, min_samples_split=900, n_estimators=900; total time=   1.2s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=900, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=900, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=900, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=900, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=900, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
            "[CV] END max_depth=30, max_features=auto, min_samples_leaf=5, min_samples_split=900, n_estimators=900; total time=   1.2s\n",
            "[CV] END max_depth=30, max_features=auto, min_samples_leaf=5, min_samples_split=900, n_estimators=900; total time=   1.1s\n",
            "[CV] END max_depth=30, max_features=auto, min_samples_leaf=5, min_samples_split=900, n_estimators=900; total time=   1.2s\n",
            "[CV] END max_depth=30, max_features=auto, min_samples_leaf=5, min_samples_split=900, n_estimators=900; total time=   1.1s\n",
            "[CV] END max_depth=30, max_features=auto, min_samples_leaf=5, min_samples_split=900, n_estimators=900; total time=   1.1s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.9s\n",
            "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=5, min_samples_split=2, n_estimators=1000; total time=   1.3s\n",
            "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=5, min_samples_split=2, n_estimators=1000; total time=   1.4s\n",
            "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=5, min_samples_split=2, n_estimators=1000; total time=   1.3s\n",
            "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=5, min_samples_split=2, n_estimators=1000; total time=   1.3s\n",
            "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=5, min_samples_split=2, n_estimators=1000; total time=   1.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.4s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.5s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
              "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
              "                                        'min_samples_split': [2, 5, 10, 15, 100,\n",
              "                                                              900],\n",
              "                                        'n_estimators': [100, 200, 300, 400,\n",
              "                                                         500, 600, 700, 800,\n",
              "                                                         900, 1000, 1100,\n",
              "                                                         1200]},\n",
              "                   random_state=42, scoring='neg_mean_squared_error',\n",
              "                   verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=rf_random.predict(x_test)"
      ],
      "metadata": {
        "id": "KPNZ0sLL8b1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDKh5wri-0R2",
        "outputId": "57c42653-8867-4e7d-ffb2-01a0110e1253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 9.21284854,  3.77928617,  0.33369179,  2.41736402, 10.90946295,\n",
              "        0.75474482,  2.65055529,  4.62744555,  7.31816425,  1.13666993,\n",
              "        0.98484197, 20.5370395 ,  0.63799004,  4.3157803 ,  7.10219462,\n",
              "        5.77046329,  5.75318582,  4.22709372,  5.40644696,  0.46092175,\n",
              "        0.43301407, 21.48537958,  3.17939901,  0.65679521,  6.66629415,\n",
              "        7.1465353 ,  0.19480032,  2.5764787 ,  0.24200776,  4.78299658,\n",
              "        0.33632564,  2.69285053,  5.30899576,  2.89637275,  4.43313588,\n",
              "        4.50009742,  2.67902417,  8.33278154,  2.99996133,  4.91066119,\n",
              "        7.11168178,  7.32752268,  0.43929203,  0.44230797,  5.61588353,\n",
              "        5.82242156,  2.59984943,  8.41916772,  0.42899841,  2.6711834 ,\n",
              "        0.4341551 ,  0.76975825,  1.66660231,  4.9201221 ,  0.32917473,\n",
              "        0.49762461,  6.69050913,  5.00848783,  0.59533194,  7.62564763,\n",
              "        0.86162944])"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(y_test-predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "E6MIYc5n-28m",
        "outputId": "96c11715-3069-4be5-c76b-0ef1c76da6d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb284b2df90>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "D5kDUPFI_B0U",
        "outputId": "ff47854e-537e-4291-a221-a9b729f202b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb284a27d10>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "file=open('random_forest_regression_model.pkl','wb')\n",
        "pickle.dump(rf_random,file)"
      ],
      "metadata": {
        "id": "3CNkkAPm_TrU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}