 modify_miniproject.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/naveen-98/ML_Mini_Project/blob/main/%20modify_miniproject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "OIBtAvoxSxVm"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vLfPYBrocOr7"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/ps5.csv')"
      ],
      "metadata": {
        "id": "Uo30nYXlTkNi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFW07L5ZTxGP",
        "outputId": "995fad20-9e9e-4421-be4f-e971fd455093"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                 url  \\\n",
            "0  https://store.playstation.com/en-us/product/UP...   \n",
            "1  https://store.playstation.com/en-us/product/UP...   \n",
            "2  https://store.playstation.com/en-us/product/UP...   \n",
            "3  https://store.playstation.com/en-us/product/UP...   \n",
            "4  https://store.playstation.com/en-us/product/UP...   \n",
            "\n",
            "                                     id                       publisherName  \\\n",
            "0  UP0101-PPSA19225_00-0159266583099383  Konami Digital Entertainment, Inc.   \n",
            "1  UP2565-PPSA04379_00-0242096879154610         astragon Entertainment GmbH   \n",
            "2  UP4040-PPSA04721_00-HUNDREDHEROESDLX                           505 Games   \n",
            "3  UP4040-PPSA04721_00-EIYUDENHEROESPS5                           505 Games   \n",
            "4  UP9000-PPSA13196_00-STELLARBLADEDDE0      Sony Interactive Entertainment   \n",
            "\n",
            "            releaseDate                                               name  \\\n",
            "0  2024-03-28T04:00:00Z                                      Felix the Cat   \n",
            "1  2024-04-04T15:00:00Z              Construction Simulator - Gold Edition   \n",
            "2  2024-04-21T13:00:00Z  Eiyuden Chronicle: Hundred Heroes - Digital De...   \n",
            "3  2024-04-23T13:00:00Z                  Eiyuden Chronicle: Hundred Heroes   \n",
            "4  2024-04-26T04:00:00Z              Stellar Blade™ Digital Deluxe Edition   \n",
            "\n",
            "   isAgeRestricted                                        activeCtaId  \\\n",
            "0            False  PREORDER:BUY_NOW:UP0101-PPSA19225_00-015926658...   \n",
            "1            False  PREORDER:BUY_NOW:UP2565-PPSA04379_00-024209687...   \n",
            "2            False  PREORDER:BUY_NOW:UP4040-PPSA04721_00-HUNDREDHE...   \n",
            "3            False  PREORDER:BUY_NOW:UP4040-PPSA04721_00-EIYUDENHE...   \n",
            "4            False  PREORDER:BUY_NOW:UP9000-PPSA13196_00-STELLARBL...   \n",
            "\n",
            "   starRating/averageRating  starRating/totalRatingsCount  \n",
            "0                      5.00                             1  \n",
            "1                      3.55                          1526  \n",
            "2                      4.79                            29  \n",
            "3                      4.79                            29  \n",
            "4                      0.00                             0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get summary statistics\n",
        "print(df.describe())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Syz1SaPU1CM",
        "outputId": "6909c352-b1c3-40d6-e165-61a805c08a62"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       starRating/averageRating  starRating/totalRatingsCount\n",
            "count               2001.000000                  2.001000e+03\n",
            "mean                   3.934218                  1.364253e+04\n",
            "std                    0.882803                  2.587326e+05\n",
            "min                    0.000000                  0.000000e+00\n",
            "25%                    3.640000                  1.700000e+01\n",
            "50%                    4.160000                  7.300000e+01\n",
            "75%                    4.500000                  5.010000e+02\n",
            "max                    5.000000                  6.661979e+06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get information about the dataset\n",
        "print(df.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oy6lLVsKU5AG",
        "outputId": "68f27f76-fe39-4051-e6af-e891ab7dee8e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2001 entries, 0 to 2000\n",
            "Data columns (total 9 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   url                           2001 non-null   object \n",
            " 1   id                            2001 non-null   object \n",
            " 2   publisherName                 2000 non-null   object \n",
            " 3   releaseDate                   2001 non-null   object \n",
            " 4   name                          2001 non-null   object \n",
            " 5   isAgeRestricted               2001 non-null   bool   \n",
            " 6   activeCtaId                   2001 non-null   object \n",
            " 7   starRating/averageRating      2001 non-null   float64\n",
            " 8   starRating/totalRatingsCount  2001 non-null   int64  \n",
            "dtypes: bool(1), float64(1), int64(1), object(6)\n",
            "memory usage: 127.1+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWO50zoIVjSe",
        "outputId": "c4b5ec2e-f82c-4c7b-c295-111eebe06605"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "url                             0\n",
            "id                              0\n",
            "publisherName                   1\n",
            "releaseDate                     0\n",
            "name                            0\n",
            "isAgeRestricted                 0\n",
            "activeCtaId                     0\n",
            "starRating/averageRating        0\n",
            "starRating/totalRatingsCount    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop or fill missing values\n",
        "df = df.dropna()  # or use df.fillna(method='ffill') for forward filling"
      ],
      "metadata": {
        "id": "sHQHekflVsz7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "df = df.drop_duplicates()"
      ],
      "metadata": {
        "id": "zYdruJtdWGtR"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the releaseDate column\n",
        "print(df['releaseDate'].head())\n",
        "print(df['releaseDate'].dtype)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okxCckrFYSDn",
        "outputId": "65a1be86-4857-48e0-95e1-2686678e1a73"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    2024-03-28T04:00:00Z\n",
            "1    2024-04-04T15:00:00Z\n",
            "2    2024-04-21T13:00:00Z\n",
            "3    2024-04-23T13:00:00Z\n",
            "4    2024-04-26T04:00:00Z\n",
            "Name: releaseDate, dtype: object\n",
            "object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert releaseDate to datetime\n",
        "df['releaseDate'] = pd.to_datetime(df['releaseDate'], errors='coerce')\n",
        "\n",
        "# Check for any conversion issues\n",
        "print(df['releaseDate'].isnull().sum())  # This will show how many entries could not be converted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gR4Yi1PYVW1",
        "outputId": "7201fe1e-030f-4f57-ec7b-f14f5a1b30a9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract year and month from releaseDate\n",
        "df['releaseYear'] = df['releaseDate'].dt.year\n",
        "df['releaseMonth'] = df['releaseDate'].dt.month\n",
        "\n",
        "# Verify the new columns\n",
        "print(df[['releaseDate', 'releaseYear', 'releaseMonth']].head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1SVGCOCYgYw",
        "outputId": "35d1b6ee-c8dc-4638-8a93-171667a57385"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                releaseDate  releaseYear  releaseMonth\n",
            "0 2024-03-28 04:00:00+00:00         2024             3\n",
            "1 2024-04-04 15:00:00+00:00         2024             4\n",
            "2 2024-04-21 13:00:00+00:00         2024             4\n",
            "3 2024-04-23 13:00:00+00:00         2024             4\n",
            "4 2024-04-26 04:00:00+00:00         2024             4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot user ratings over the years\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.lineplot(x='releaseYear', y='starRating/averageRating', data=df)\n",
        "plt.title('User Ratings Over the Years')\n",
        "plt.xlabel('Release Year')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.show()\n",
        "\n",
        "# Distribution of user ratings by release month\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='releaseMonth', y='starRating/averageRating', data=df)\n",
        "plt.title('User Ratings by Release Month')\n",
        "plt.xlabel('Release Month')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IZdWZWjoY5PR",
        "outputId": "c2e43d36-8027-4ded-9467-42d790f3c02e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Top publishers by average rating\n",
        "top_publishers = df.groupby('publisherName')['starRating/averageRating'].mean().sort_values(ascending=False).head(10)\n",
        "print(top_publishers)\n",
        "\n",
        "# Plot average ratings for top publishers\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=top_publishers.index, y=top_publishers.values)\n",
        "plt.title('Top Publishers by Average Rating')\n",
        "plt.xlabel('Publisher')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "id": "VQv_2jsKZBIA",
        "outputId": "549fac8c-2e03-4101-9574-62b1d57be269"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "publisherName\n",
            "SUNSOFT (SUNCORPORATION)        5.0\n",
            "502 Studios, SA                 5.0\n",
            "Flux Game Studio                5.0\n",
            "Means Interactive, LLC          5.0\n",
            "Digerati Distribution           5.0\n",
            "Megalithic Mainframe, L.L.C.    5.0\n",
            "ARTE FRANCE SA                  5.0\n",
            "GUMBO MACHINE, LLC              5.0\n",
            "LLC SHINYUDEN                   5.0\n",
            "Battle Geek Plus L.L.C.         5.0\n",
            "Name: starRating/averageRating, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Example DataFrame\n",
        "data = {\n",
        "    'url': ['http://example.com', 'http://example2.com'],\n",
        "    'id': ['1', '2'],\n",
        "    'publisherName': ['Publisher1', 'Unknown'],\n",
        "    'releaseDate': ['2022-01-01', '2022-02-01'],\n",
        "    'name': ['Item1', 'Item2'],\n",
        "    'isAgeRestricted': [True, False],\n",
        "    'activeCtaId': ['cta1', 'cta2'],\n",
        "    'starRating/averageRating': [4.5, 3.8],\n",
        "    'starRating/totalRatingsCount': [100, 50]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Average rating by age restriction\n",
        "age_restrictions = df.groupby('isAgeRestricted')['starRating/averageRating'].mean()\n",
        "print(age_restrictions)\n",
        "\n",
        "# Optionally, if you want to visualize the result:\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "age_restrictions.plot(kind='bar', color=['blue', 'orange'])\n",
        "plt.xlabel('Is Age Restricted')\n",
        "plt.ylabel('Average Star Rating')\n",
        "plt.title('Average Star Rating by Age Restriction')\n",
        "plt.xticks(ticks=[0, 1], labels=['False', 'True'], rotation=0)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "0Vd3WTMIZ2-b",
        "outputId": "75ca3578-5566-4e1e-a46b-325c95ce9e6e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "isAgeRestricted\n",
            "False    3.8\n",
            "True     4.5\n",
            "Name: starRating/averageRating, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qS9xlBrlaEuZ",
        "outputId": "4f31667e-ea7d-44e0-a01b-407da96e43dd"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['url', 'id', 'publisherName', 'releaseDate', 'name', 'isAgeRestricted',\n",
            "       'activeCtaId', 'starRating/averageRating',\n",
            "       'starRating/totalRatingsCount'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for string values in features\n",
        "print(df.select_dtypes(include=['object']).columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRTxXJlQaOHG",
        "outputId": "002d8f39-0463-4d0b-f1d2-6ff685fc5e1b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['url', 'id', 'publisherName', 'releaseDate', 'name', 'activeCtaId'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for string values in target variable\n",
        "print(df['starRating/averageRating'].dtype)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBx7FUeBaj9I",
        "outputId": "f8da94e1-0dab-4d46-f30d-bef205778646"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Assume df is your DataFrame\n",
        "# df = pd.read_csv('your_data.csv')  # Uncomment and modify this line to load your data\n",
        "\n",
        "# Drop non-numeric columns (if any)\n",
        "non_numeric_cols = df.select_dtypes(include=['object']).columns\n",
        "df.drop(columns=non_numeric_cols, inplace=True)\n",
        "\n",
        "# Define features and target variable\n",
        "X = df.drop(columns=['starRating/averageRating', 'starRating/totalRatingsCount'])\n",
        "y = df['starRating/averageRating']\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and train the model\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')\n"
      ],
      "metadata": {
        "id": "lEQ1JCB1-fSB",
        "outputId": "5ab32842-d18c-4b7b-9216-fc61f73934d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 0.49000000000000027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# Calculate R-squared\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(f'R-squared: {r2}')\n",
        "\n",
        "# Feature importance\n",
        "feature_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feature_importances.sort_values(ascending=False, inplace=True)\n",
        "\n",
        "# Plot feature importance\n",
        "plt.figure(figsize=(10, 6))\n",
        "feature_importances.plot(kind='bar')\n",
        "plt.title('Feature Importance')\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Importance')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "3Y-_L0YKa2wx",
        "outputId": "572257e6-0831-4862-c749-f0367d58cf7b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:918: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
            "  warnings.warn(msg, UndefinedMetricWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-squared: nan\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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AFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFKzJhdi1116bddddN61bt84222yTJ554YrHH33HHHfnc5z6X1q1bp1evXrn33ns/8thvfvObKZVKufLKK5fx1AAAAP/RpEJs5MiRGTx4cM4777w8/fTT2XzzzTNgwIBMmzZtkcc/9thjOfjgg3PUUUflmWeeyb777pt99903L7744kLH/va3v83f//73dO/e/dO+DAAAYCXXpELsiiuuyDHHHJMjjjgiG2+8cW644Ya0bds2N9988yKPv+qqq7L77rvn9NNPz0YbbZQLL7wwW265ZYYPH97guNdffz0nnnhibr311rRo0aKISwEAAFZiTSbE5s6dmzFjxqR///71a1VVVenfv39Gjx69yHNGjx7d4PgkGTBgQIPjFyxYkK9//es5/fTTs8kmmyzRLHPmzEltbW2DBwAAwJJqMiE2ffr01NXVpWvXrg3Wu3btmilTpizynClTpnzs8ZdeemmaN2+ek046aYlnGTZsWKqrq+sfPXr0WIorAQAAVnZNJsQ+DWPGjMlVV12VESNGpFQqLfF5Z555ZmpqauofkydP/hSnBAAAVjRNJsRWX331NGvWLFOnTm2wPnXq1HTr1m2R53Tr1m2xx//1r3/NtGnTsvbaa6d58+Zp3rx5Xn311Zx66qlZd911P3KWVq1apUOHDg0eAAAAS6rJhFjLli3Tp0+fPPDAA/VrCxYsyAMPPJB+/fot8px+/fo1OD5J7r///vrjv/71r+f555/Ps88+W//o3r17Tj/99Pzxj3/89C4GAABYqTVv7AGWxuDBg3PYYYelb9++2XrrrXPllVdm1qxZOeKII5IkgwYNypprrplhw4YlSU4++eTstNNOufzyy7PXXnvlV7/6VZ566qn86Ec/SpKsttpqWW211Rr8jhYtWqRbt27ZcMMNi704AABgpdGkQuzAAw/MW2+9lSFDhmTKlCnZYostMmrUqPoNOSZNmpSqqv/c5Ntuu+1y22235ZxzzslZZ52Vnj175q677sqmm27aWJcAAACQUrlcLjf2EE1dbW1tqqurU1NT4/NiAACwElvSNmgynxEDAABYUQgxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAggkxAACAgjW5ELv22muz7rrrpnXr1tlmm23yxBNPLPb4O+64I5/73OfSunXr9OrVK/fee2/9c/Pmzct3v/vd9OrVK+3atUv37t0zaNCgvPHGG5/2ZQAAACuxJhViI0eOzODBg3Peeefl6aefzuabb54BAwZk2rRpizz+sccey8EHH5yjjjoqzzzzTPbdd9/su+++efHFF5Mks2fPztNPP51zzz03Tz/9dH7zm99k/Pjx2WeffYq8LAAAYCVTKpfL5cYeYklts8022WqrrTJ8+PAkyYIFC9KjR4+ceOKJOeOMMxY6/sADD8ysWbNyzz331K9tu+222WKLLXLDDTcs8nc8+eST2XrrrfPqq69m7bXXXqK5amtrU11dnZqamnTo0KGCKwMAAFYES9oGTeaO2Ny5czNmzJj079+/fq2qqir9+/fP6NGjF3nO6NGjGxyfJAMGDPjI45OkpqYmpVIpHTt2/Mhj5syZk9ra2gYPAACAJdVkQmz69Ompq6tL165dG6x37do1U6ZMWeQ5U6ZMWarj33///Xz3u9/NwQcfvNh6HTZsWKqrq+sfPXr0WMqrAQAAVmZNJsQ+bfPmzcvAgQNTLpdz/fXXL/bYM888MzU1NfWPyZMnFzQlAACwImje2AMsqdVXXz3NmjXL1KlTG6xPnTo13bp1W+Q53bp1W6LjP4ywV199NQ8++ODHfs6rVatWadWqVQVXAQAA0ITuiLVs2TJ9+vTJAw88UL+2YMGCPPDAA+nXr98iz+nXr1+D45Pk/vvvb3D8hxE2YcKE/PnPf85qq6326VwAAADA/9dk7oglyeDBg3PYYYelb9++2XrrrXPllVdm1qxZOeKII5IkgwYNypprrplhw4YlSU4++eTstNNOufzyy7PXXnvlV7/6VZ566qn86Ec/SvJBhB1wwAF5+umnc88996Surq7+82OdOnVKy5YtG+dCAQCAFVqTCrEDDzwwb731VoYMGZIpU6Zkiy22yKhRo+o35Jg0aVKqqv5zk2+77bbLbbfdlnPOOSdnnXVWevbsmbvuuiubbrppkuT111/P3XffnSTZYostGvyuhx56KDvvvHMh1wUAAKxcmtT3iC2vfI8YAACQrIDfIwYAALCiEGIAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFE2IAAAAFqzjEfv7zn2f77bdP9+7d8+qrryZJrrzyyvzud79bZsMBAACsiCoKseuvvz6DBw/OnnvumRkzZqSuri5J0rFjx1x55ZXLcj4AAIAVTkUhds011+THP/5xzj777DRr1qx+vW/fvnnhhReW2XAAAAAroopCbOLEiendu/dC661atcqsWbM+8VAAAAArsopCbL311suzzz670PqoUaOy0UYbfdKZAAAAVmjNKzlp8ODBOf744/P++++nXC7niSeeyC9/+csMGzYsP/nJT5b1jAAAACuUikLs6KOPTps2bXLOOedk9uzZOeSQQ9K9e/dcddVVOeigg5b1jAAAACuUUrlcLn+SF5g9e3bee++9dOnSZVnN1OTU1tamuro6NTU16dChQ2OPAwAANJIlbYOK7ohNnDgx8+fPT8+ePdO2bdu0bds2STJhwoS0aNEi6667bkVDAwAArAwq2qzj8MMPz2OPPbbQ+uOPP57DDz/8k84EAACwQqsoxJ555plsv/32C61vu+22i9xNEQAAgP+oKMRKpVJmzpy50HpNTU3q6uo+8VAAAAArsopC7POf/3yGDRvWILrq6uoybNiw7LDDDstsOAAAgBVRRZt1XHrppfn85z+fDTfcMDvuuGOS5K9//Wtqa2vz4IMPLtMBAQAAVjQV3RHbeOON8/zzz2fgwIGZNm1aZs6cmUGDBmXcuHHZdNNNl/WMAAAAK5RP/D1i+B4xAADgA5/q94glyYwZM/LEE09k2rRpWbBgQYPnBg0aVOnLAgAArPAqCrHf//73OfTQQ/Pee++lQ4cOKZVK9c+VSiUhBgAAsBgVfUbs1FNPzZFHHpn33nsvM2bMyLvvvlv/eOedd5b1jAAAACuUikLs9ddfz0knnZS2bdsu63kAAABWeBWF2IABA/LUU08t61kAAABWChV9RmyvvfbK6aefnpdeeim9evVKixYtGjy/zz77LJPhAAAAVkQVbV9fVfXRN9JKpVLq6uo+0VBNje3rAQCA5FPevv5/t6sHAABgyVX0GTEAAAAqV/EXOs+aNSsPP/xwJk2alLlz5zZ47qSTTvrEgwEAAKyoKgqxZ555JnvuuWdmz56dWbNmpVOnTpk+fXratm2bLl26CDEAAIDFqOitid/+9rfzpS99Ke+++27atGmTv//973n11VfTp0+f/OAHP1jWMwIAAKxQKgqxZ599NqeeemqqqqrSrFmzzJkzJz169Mhll12Ws846a1nPCAAAsEKpKMRatGhRv4V9ly5dMmnSpCRJdXV1Jk+evOymAwAAWAFV9Bmx3r1758knn0zPnj2z0047ZciQIZk+fXp+/vOfZ9NNN13WMwIAAKxQKrojdskll2SNNdZIklx88cVZddVVc9xxx+Wtt97KjTfeuEwHBAAAWNFUFGJ9+/bNF77whSQfvDVx1KhRqa2tzZgxY7LFFlssy/kWcu2112bddddN69ats8022+SJJ55Y7PF33HFHPve5z6V169bp1atX7r333gbPl8vlDBkyJGussUbatGmT/v37Z8KECZ/mJQAAACu5ikJsl112yYwZMxZar62tzS677PJJZ/pII0eOzODBg3Peeefl6aefzuabb54BAwZk2rRpizz+sccey8EHH5yjjjoqzzzzTPbdd9/su+++efHFF+uPueyyy3L11VfnhhtuyOOPP5527dplwIABef/99z+16wAAAFZupXK5XF7ak6qqqjJlypR06dKlwfq0adOy5pprZt68ectswP+2zTbbZKuttsrw4cOTJAsWLEiPHj1y4okn5owzzljo+AMPPDCzZs3KPffcU7+27bbbZosttsgNN9yQcrmc7t2759RTT81pp52WJKmpqUnXrl0zYsSIHHTQQUs0V21tbaqrq1NTU5MOHTosgysFAACaoiVtg6XarOP555+v//dLL72UKVOm1P9cV1eXUaNGZc0116xg3I83d+7cjBkzJmeeeWb9WlVVVfr375/Ro0cv8pzRo0dn8ODBDdYGDBiQu+66K0kyceLETJkyJf37969/vrq6Ottss01Gjx79kSE2Z86czJkzp/7n2traSi8LAABYCS1ViG2xxRYplUoplUqLfAtimzZtcs011yyz4f7b9OnTU1dXl65duzZY79q1a8aNG7fIc6ZMmbLI4z8MyA//7+KOWZRhw4Zl6NChS30NAAAAyVKG2MSJE1Mul7P++uvniSeeSOfOneufa9myZbp06ZJmzZot8yGXN2eeeWaDO221tbXp0aNHI04EAAA0JUsVYuuss07mzZuXww47LKuttlrWWWedT2uuhay++upp1qxZpk6d2mB96tSp6dat2yLP6dat22KP//D/Tp06tX47/g9/Xtzuj61atUqrVq0quQwAAICl3zWxRYsW+e1vf/tpzLJYLVu2TJ8+ffLAAw/Ury1YsCAPPPBA+vXrt8hz+vXr1+D4JLn//vvrj19vvfXSrVu3BsfU1tbm8ccf/8jXBAAA+KQq2r7+y1/+cv2GF0UaPHhwfvzjH+enP/1pXn755Rx33HGZNWtWjjjiiCTJoEGDGmzmcfLJJ2fUqFG5/PLLM27cuJx//vl56qmncsIJJyRJSqVSTjnllFx00UW5++6788ILL2TQoEHp3r179t1338KvDwAAWDks1VsTP9SzZ89ccMEFefTRR9OnT5+0a9euwfMnnXTSMhnufx144IF56623MmTIkEyZMiVbbLFFRo0aVb/ZxqRJk1JV9Z+23G677XLbbbflnHPOyVlnnZWePXvmrrvuyqabblp/zHe+853MmjUrxx57bGbMmJEddtgho0aNSuvWrT+VawAAAKjoe8TWW2+9j37BUin//ve/P9FQTY3vEQMAAJJP6XvEPjRx4sSKBwMAAFjZVfQZsf9WLpdTwU01AACAlVbFIfazn/0svXr1Sps2bdKmTZtsttlm+fnPf74sZwMAAFghVfTWxCuuuCLnnntuTjjhhGy//fZJkr/97W/55je/menTp+fb3/72Mh0SAABgRVLxZh1Dhw7NoEGDGqz/9Kc/zfnnn7/SfYbMZh0AAECy5G1Q0VsT33zzzWy33XYLrW+33XZ58803K3lJAACAlUZFIbbBBhvk9ttvX2h95MiR6dmz5yceCgAAYEVW0WfEhg4dmgMPPDCPPPJI/WfEHn300TzwwAOLDDQAAAD+o6I7Yvvvv38ef/zxrL766rnrrrty1113ZfXVV88TTzyRr3zlK8t6RgAAgBVKRZt10JDNOgAAgGTJ26CityYmSV1dXX7729/m5ZdfTpJsvPHG+fKXv5zmzSt+SQAAgJVCRdU0duzY7LPPPpkyZUo23HDDJMmll16azp075/e//3023XTTZTokAADAiqSiz4gdffTR2WSTTfLaa6/l6aefztNPP53Jkydns802y7HHHrusZwQAAFihVHRH7Nlnn81TTz2VVVddtX5t1VVXzcUXX5ytttpqmQ0HAACwIqrojthnP/vZTJ06daH1adOmZYMNNvjEQwEAAKzIKgqxYcOG5aSTTsqdd96Z1157La+99lruvPPOnHLKKbn00ktTW1tb/wAAAKChiravr6r6T7+VSqUkyYcv898/l0ql1NXVLYs5l2u2rwcAAJJPefv6hx56qOLBAAAAVnYVhdhOO+20rOcAAABYaVT87cvvv/9+nn/++UybNi0LFixo8Nw+++zziQcDAABYUVUUYqNGjcqgQYMyffr0hZ5bWT4XBgAAUKmKdk088cQT89WvfjVvvvlmFixY0OAhwgAAABavohCbOnVqBg8enK5duy7reQAAAFZ4FYXYAQcckL/85S/LeBQAAICVQ0XfIzZ79ux89atfTefOndOrV6+0aNGiwfMnnXTSMhuwKfA9YgAAQPIpf4/YL3/5y/zpT39K69at85e//KX+S5yTDzbrWNlCDAAAYGlUFGJnn312hg4dmjPOOCNVVRW9uxEAAGClVVFFzZ07NwceeKAIAwAAqEBFJXXYYYdl5MiRy3oWAACAlUJFb02sq6vLZZddlj/+8Y/ZbLPNFtqs44orrlgmwwEAAKyIKgqxF154Ib17906SvPjii8t0IAAAgBVdRSH20EMPLes5AAAAVhpLFWL77bffxx5TKpXy61//uuKBAAAAVnRLFWLV1dWf1hwAAAArjaUKsVtuueXTmgMAAGCl4YvAAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACibEAAAACtZkQuydd97JoYcemg4dOqRjx4456qij8t577y32nPfffz/HH398VltttbRv3z77779/pk6dWv/8c889l4MPPjg9evRImzZtstFGG+Wqq676tC8FAABYyTWZEDv00EMzduzY3H///bnnnnvyyCOP5Nhjj13sOd/+9rfz+9//PnfccUcefvjhvPHGG9lvv/3qnx8zZky6dOmSX/ziFxk7dmzOPvvsnHnmmRk+fPinfTkAAMBKrFQul8uNPcTHefnll7PxxhvnySefTN++fZMko0aNyp577pnXXnst3bt3X+icmpqadO7cObfddlsOOOCAJMm4ceOy0UYbZfTo0dl2220X+buOP/74vPzyy3nwwQeXeL7a2tpUV1enpqYmHTp0qOAKAQCAFcGStkGTuCM2evTodOzYsT7CkqR///6pqqrK448/vshzxowZk3nz5qV///71a5/73Oey9tprZ/To0R/5u2pqatKpU6fFzjNnzpzU1tY2eAAAACypJhFiU6ZMSZcuXRqsNW/ePJ06dcqUKVM+8pyWLVumY8eODda7du36kec89thjGTly5Me+5XHYsGGprq6uf/To0WPJLwYAAFjpNWqInXHGGSmVSot9jBs3rpBZXnzxxXz5y1/Oeeedl912222xx5555pmpqampf0yePLmQGQEAgBVD88b85aeeemoOP/zwxR6z/vrrp1u3bpk2bVqD9fnz5+edd95Jt27dFnlet27dMnfu3MyYMaPBXbGpU6cudM5LL72UXXfdNccee2zOOeecj527VatWadWq1cceBwAAsCiNGmKdO3dO586dP/a4fv36ZcaMGRkzZkz69OmTJHnwwQezYMGCbLPNNos8p0+fPmnRokUeeOCB7L///kmS8ePHZ9KkSenXr1/9cWPHjs0uu+ySww47LBdffPEyuCoAAIDFaxK7JibJHnvskalTp+aGG27IvHnzcsQRR6Rv37657bbbkiSvv/56dt111/zsZz/L1ltvnSQ57rjjcu+992bEiBHp0KFDTjzxxCQffBYs+eDtiLvssksGDBiQ73//+/W/q1mzZksUiB+yayIAAJAseRs06h2xpXHrrbfmhBNOyK677pqqqqrsv//+ufrqq+ufnzdvXsaPH5/Zs2fXr/3whz+sP3bOnDkZMGBArrvuuvrn77zzzrz11lv5xS9+kV/84hf16+uss05eeeWVQq4LAABY+TSZO2LLM3fEAACAZAX7HjEAAIAViRADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAomBADAAAoWJMJsXfeeSeHHnpoOnTokI4dO+aoo47Ke++9t9hz3n///Rx//PFZbbXV0r59++y///6ZOnXqIo99++23s9Zaa6VUKmXGjBmfwhUAAAB8oMmE2KGHHpqxY8fm/vvvzz333JNHHnkkxx577GLP+fa3v53f//73ueOOO/Lwww/njTfeyH777bfIY4866qhsttlmn8boAAAADZTK5XK5sYf4OC+//HI23njjPPnkk+nbt2+SZNSoUdlzzz3z2muvpXv37gudU1NTk86dO+e2227LAQcckCQZN25cNtpoo4wePTrbbrtt/bHXX399Ro4cmSFDhmTXXXfNu+++m44dOy7xfLW1tamurk5NTU06dOjwyS4WAABospa0DZrEHbHRo0enY8eO9RGWJP37909VVVUef/zxRZ4zZsyYzJs3L/37969f+9znPpe11147o0ePrl976aWXcsEFF+RnP/tZqqqW7M8xZ86c1NbWNngAAAAsqSYRYlOmTEmXLl0arDVv3jydOnXKlClTPvKcli1bLnRnq2vXrvXnzJkzJwcffHC+//3vZ+21117ieYYNG5bq6ur6R48ePZbuggAAgJVao4bYGWeckVKptNjHuHHjPrXff+aZZ2ajjTbK1772taU+r6ampv4xefLkT2lCAABgRdS8MX/5qaeemsMPP3yxx6y//vrp1q1bpk2b1mB9/vz5eeedd9KtW7dFntetW7fMnTs3M2bMaHBXbOrUqfXnPPjgg3nhhRdy5513Jkk+/Ljc6quvnrPPPjtDhw5d5Gu3atUqrVq1WpJLBAAAWEijhljnzp3TuXPnjz2uX79+mTFjRsaMGZM+ffok+SCiFixYkG222WaR5/Tp0yctWrTIAw88kP333z9JMn78+EyaNCn9+vVLkvz617/O//3f/9Wf8+STT+bII4/MX//613zmM5/5pJcHAACwSI0aYktqo402yu67755jjjkmN9xwQ+bNm5cTTjghBx10UP2Oia+//np23XXX/OxnP8vWW2+d6urqHHXUURk8eHA6deqUDh065MQTT0y/fv3qd0z839iaPn16/e9bml0TAQAAlkaTCLEkufXWW3PCCSdk1113TVVVVfbff/9cffXV9c/Pmzcv48ePz+zZs+vXfvjDH9YfO2fOnAwYMCDXXXddY4wPAABQr0l8j9jyzveIAQAAyQr2PWIAAAArEiEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQMCEGAABQsOaNPcCKoFwuJ0lqa2sbeRIAAKAxfdgEHzbCRxFiy8DMmTOTJD169GjkSQAAgOXBzJkzU11d/ZHPl8ofl2p8rAULFuSNN97IKqusklKp1NjjANAIamtr06NHj0yePDkdOnRo7HEAaCTlcjkzZ85M9+7dU1X10Z8EE2IAsAzU1tamuro6NTU1QgyAj2WzDgAAgIIJMQAAgIIJMQBYBlq1apXzzjsvrVq1auxRAGgCfEYMAACgYO6IAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFKx5Yw8AAE1J7969UyqVlujYp59++lOeBoCmSogBwFLYd9996//9/vvv57rrrsvGG2+cfv36JUn+/ve/Z+zYsfnWt77VSBMC0BSUyuVyubGHAICm6Oijj84aa6yRCy+8sMH6eeedl8mTJ+fmm29upMkAWN4JMQCoUHV1dZ566qn07NmzwfqECRPSt2/f1NTUNNJkACzvbNYBABVq06ZNHn300YXWH3300bRu3boRJgKgqfAZMQCo0CmnnJLjjjsuTz/9dLbeeuskyeOPP56bb7455557biNPB8DyzFsTAeATuP3223PVVVfl5ZdfTpJstNFGOfnkkzNw4MBGngyA5ZkQAwAAKJjPiAHAJzBjxoz85Cc/yVlnnZV33nknyQffH/b666838mQALM/cEQOACj3//PPp379/qqur88orr2T8+PFZf/31c84552TSpEn52c9+1tgjArCcckcMACo0ePDgHH744ZkwYUKDXRL33HPPPPLII404GQDLOyEGABV68skn841vfGOh9TXXXDNTpkxphIkAaCqEGABUqFWrVqmtrV1o/R//+Ec6d+7cCBMB0FQIMQCo0D777JMLLrgg8+bNS5KUSqVMmjQp3/3ud7P//vs38nQALM9s1gEAFaqpqckBBxyQp556KjNnzkz37t0zZcqU9OvXL/fee2/atWvX2CMCsJwSYgDwCT366KN57rnn8t5772XLLbdM//79G3skAJZzQgwAKvSzn/0sBx54YFq1atVgfe7cufnVr36VQYMGNdJkACzvhBgAVKhZs2Z5880306VLlwbrb7/9drp06ZK6urpGmgyA5Z3NOgCgQuVyOaVSaaH11157LdXV1Y0wEQBNRfPGHgAAmprevXunVCqlVCpl1113TfPm//mf07q6ukycODG77757I04IwPJOiAHAUtp3332TJM8++2wGDBiQ9u3b1z/XsmXLrLvuuravB2CxfEYMACr005/+NAcddNBCm3UAwMfxGTEAqNDGG2+cZ599dqH1xx9/PE899VTxAwHQZAgxAKjQ8ccfn8mTJy+0/vrrr+f4449vhIkAaCqEGABU6KWXXsqWW2650Hrv3r3z0ksvNcJEADQVQgwAKtSqVatMnTp1ofU333yzwU6KAPC/hBgAVGi33XbLmWeemZqamvq1GTNm5KyzzsoXv/jFRpwMgOWdXRMBoEKvv/56Pv/5z+ftt99O7969k3ywpX3Xrl1z//33p0ePHo08IQDLKyEGAJ/ArFmzcuutt+a5555LmzZtstlmm+Xggw9OixYtGns0AJZjQgwAAKBgPkkMAEvh7rvvzh577JEWLVrk7rvvXuyx++yzT0FTAdDUuCMGAEuhqqoqU6ZMSZcuXVJV9dF7XpVKpdTV1RU4GQBNiRADAAAomO3rAaAC8+bNy6677poJEyY09igANEFCDAAq0KJFizz//PONPQYATZQQA4AKfe1rX8tNN93U2GMA0ATZNREAKjR//vzcfPPN+fOf/5w+ffqkXbt2DZ6/4oorGmkyAJZ3NusAgAp94QtfWOzzDz30UEGTANDUCDEAAICC+YwYAFToyCOPzMyZMxdanzVrVo488shGmAiApsIdMQCoULNmzfLmm2+mS5cuDdanT5+ebt26Zf78+Y00GQDLO5t1AMBSqq2tTblcTrlczsyZM9O6dev65+rq6nLvvfcuFGcA8N+EGAAspY4dO6ZUKqVUKuWzn/3sQs+XSqUMHTq0ESYDoKnw1kQAWEoPP/xwyuVydtlll/z6179Op06d6p9r2bJl1llnnXTv3r0RJwRgeSfEAKBCr776atZee+2USqXGHgWAJsauiQBQoZdffjmPPvpo/c/XXntttthiixxyyCF59913G3EyAJZ3QgwAKnT66aentrY2SfLCCy9k8ODB2XPPPTNx4sQMHjy4kacDYHlmsw4AqNDEiROz8cYbJ0l+/etf50tf+lIuueSSPP3009lzzz0beToAlmfuiAFAhVq2bJnZs2cnSf785z9nt912S5J06tSp/k4ZACyKO2IAUKEddtghgwcPzvbbb58nnngiI0eOTJL84x//yFprrdXI0wGwPHNHDAAqNHz48DRv3jx33nlnrr/++qy55ppJkvvuuy+77757I08HwPLM9vUAAAAFc0cMAD6Bf/3rXznnnHNy8MEHZ9q0aUk+uCM2duzYRp4MgOWZEAOACj388MPp1atXHn/88fzmN7/Je++9lyR57rnnct555zXydAAsz4QYAFTojDPOyEUXXZT7778/LVu2rF/fZZdd8ve//70RJwNgeSfEAKBCL7zwQr7yla8stN6lS5dMnz69ESYCoKkQYgBQoY4dO+bNN99caP2ZZ56p30ERABZFiAFAhQ466KB897vfzZQpU1IqlbJgwYI8+uijOe200zJo0KDGHg+A5Zjt6wGgQnPnzs3xxx+fESNGpK6uLs2bN09dXV0OOeSQ3HLLLWnevHljjwjAckqIAcAnNHny5Lzwwgt577330rt37/Ts2bOxRwJgOSfEAGAZ+81vfpPzzz8/zz//fGOPAsByymfEAKACN954Yw444IAccsghefzxx5MkDz74YHr37p2vf/3r2X777Rt5QgCWZ+6IAcBS+t73vpchQ4Zks802y7hx41Iul3P22Wfnmmuuycknn5xvfOMbWXXVVRt7TACWYz5FDABL6ZZbbsmPf/zjHHbYYfnrX/+anXbaKY899lj++c9/pl27do09HgBNgDtiALCU2rRpk3/84x/p0aNHkqRVq1Z57LHH0qdPn0aeDICmwmfEAGApzZkzJ61bt67/uWXLlunUqVMjTgRAU+OtiQBQgXPPPTdt27ZN8sH3iV100UWprq5ucMwVV1zRGKMB0AR4ayIALKWdd945pVJpsceUSqU8+OCDBU0EQFMjxAAAAArmM2IA8AnNnTs348ePz/z58xt7FACaCCEGABWaPXt2jjrqqLRt2zabbLJJJk2alCQ58cQT873vfa+RpwNgeSbEAKBCZ555Zp577rn85S9/abCLYv/+/TNy5MhGnAyA5Z1dEwGgQnfddVdGjhyZbbfdtsHmHZtsskn+9a9/NeJkACzv3BEDgAq99dZb6dKly0Lrs2bN+thdFQFYuQkxAKhQ375984c//KH+5w/j6yc/+Un69evXWGMB0AR4ayIAVOiSSy7JHnvskZdeeinz58/PVVddlZdeeimPPfZYHn744cYeD4DlmDtiAFChHXbYIc8++2zmz5+fXr165U9/+lO6dOmS0aNHp0+fPo09HgDLMV/oDAAAUDBvTQSACtXW1i5yvVQqpVWrVmnZsmXBEwHQVLgjBgAVqqqqWuzuiGuttVYOP/zwnHfeeamq8mkAAP7DHTEAqNCIESNy9tln5/DDD8/WW2+dJHniiSfy05/+NOecc07eeuut/OAHP0irVq1y1llnNfK0ACxP3BEDgArtuuuu+cY3vpGBAwc2WL/99ttz44035oEHHsjPf/7zXHzxxRk3blwjTQnA8kiIAUCF2rRpk+effz49e/ZssD5hwoRsvvnmmT17diZOnJhNNtkks2fPbqQpAVgeecM6AFSoR48euemmmxZav+mmm9KjR48kydtvv51VV1216NEAWM75jBgAVOgHP/hBvvrVr+a+++7LVlttlSR56qmnMm7cuNx5551JkieffDIHHnhgY44JwHLIWxMB4BN45ZVXcuONN2b8+PFJkg033DDf+MY3su666zbuYAAs14QYAHwKXnzxxWy66aaNPQYAyymfEQOAZWTmzJn50Y9+lK233jqbb755Y48DwHJMiAHAJ/TII4/ksMMOyxprrJEf/OAH2WWXXfL3v/+9sccCYDlmsw4AqMCUKVMyYsSI3HTTTamtrc3AgQMzZ86c3HXXXdl4440bezwAlnPuiAHAUvrSl76UDTfcMM8//3yuvPLKvPHGG7nmmmsaeywAmhB3xABgKd1333056aSTctxxxy30Zc4AsCTcEQOApfS3v/0tM2fOTJ8+fbLNNttk+PDhmT59emOPBUATYvt6AKjQrFmzMnLkyNx888154oknUldXlyuuuCJHHnlkVllllcYeD4DlmBADgGVg/Pjxuemmm/Lzn/88M2bMyBe/+MXcfffdjT0WAMspIQYAy1BdXV1+//vf5+abbxZiAHwkIQYAy0htbW0efPDBbLjhhtloo40aexwAlmM26wCACg0cODDDhw9Pkvzf//1f+vbtm4EDB2azzTbLr3/960aeDoDlmRADgAo98sgj2XHHHZMkv/3tb1MulzNjxoxcffXVueiiixp5OgCWZ0IMACpUU1OTTp06JUlGjRqV/fffP23bts1ee+2VCRMmNPJ0ACzPhBgAVKhHjx4ZPXp0Zs2alVGjRmW33XZLkrz77rtp3bp1I08HwPKseWMPAABN1SmnnJJDDz007du3z9prr52dd945yQdvWezVq1fjDgfAcs2uiQDwCYwZMyaTJk3Kbrvtlnbt2iVJ/vCHP2TVVVfNdttt18jTAbC8EmIAsBQGDx6cCy+8MO3atcvgwYMXe+wVV1xR0FQANDXemggAS+GZZ57JvHnz6v/9UUqlUlEjAdAEuSMGAABQMLsmAgAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAQAAFEyIAbDCOvzww1MqlRZ6/POf//zErz1ixIh07Njxkw8JwErJ94gBsELbfffdc8sttzRY69y5cyNNs2jz5s1LixYtGnsMAArkjhgAK7RWrVqlW7duDR7NmjXL7373u2y55ZZp3bp11l9//QwdOjTz58+vP++KK65Ir1690q5du/To0SPf+ta38t577yVJ/vKXv+SII45ITU1N/V22888/P8kHX+R81113NZihY8eOGTFiRJLklVdeSalUysiRI7PTTjuldevWufXWW5MkP/nJT7LRRhuldevW+dznPpfrrruu/jXmzp2bE044IWussUZat26dddZZJ8OGDfv0/nAAfKrcEQNgpfPXv/41gwYNytVXX50dd9wx//rXv3LssccmSc4777wkSVVVVa6++uqst956+fe//51vfetb+c53vpPrrrsu2223Xa688soMGTIk48ePT5K0b99+qWY444wzcvnll6d37971MTZkyJAMHz48vXv3zjPPPJNjjjkm7dq1y2GHHZarr746d999d26//fasvfbamTx5ciZPnrxs/zAAFEaIAbBCu+eeexpE0h577JF33303Z5xxRg477LAkyfrrr58LL7ww3/nOd+pD7JRTTqk/Z911181FF12Ub37zm7nuuuvSsmXLVFdXp1QqpVu3bhXNdcopp2S//far//m8887L5ZdfXr+23nrr5aWXXsqNN96Yww47LJMmTUrPnj2zww47pFQqZZ111qno9wKwfBBiAKzQvvCFL+T666+v/7ldu3bZbLPN8uijj+biiy+uX6+rq8v777+f2bNnp23btvnzn/+cYcOGZdy4camtrc38+fMbPP9J9e3bt/7fs2bNyr/+9a8cddRROeaYY+rX58+fn+rq6iQfbDzyxS9+MRtuuGF233337L333tltt90+8RwANA4hBsAKrV27dtlggw0arL333nsZOnRogztSH2rdunVeeeWV7L333jnuuONy8cUXp1OnTvnb3/6Wo446KnPnzl1siJVKpZTL5QZr8+bNW+Rc/z1Pkvz4xz/ONtts0+C4Zs2aJUm23HLLTJw4Mffdd1/+/Oc/Z+DAgenfv3/uvPPOj/kLALA8EmIArHS23HLLjB8/fqFA+9CYMWOyYMGCXH755amq+mBfq9tvv73BMS1btkxdXd1C53bu3Dlvvvlm/c8TJkzI7NmzFztP165d07179/z73//OoYce+pHHdejQIQceeGAOPPDAHHDAAdl9993zzjvvpFOnTot9fQCWP0IMgJXOkCFDsvfee2fttdfOAQcckKqqqjz33HN58cUXc9FFF2WDDTbIvHnzcs011+RLX/pSHn300dxwww0NXmPdddfNe++9lwceeCCbb7552rZtm7Zt22aXXXbJ8OHD069fv9TV1eW73/3uEm1NP3To0Jx00kmprq7O7rvvnjlz5uSpp57Ku+++m8GDB+eKK67IGmuskd69e6eqqip33HFHunXr5rvMAJoo29cDsNIZMGBA7rnnnvzpT3/KVlttlW233TY//OEP6zfA2HzzzXPFFVfk0ksvzaabbppbb711oa3it9tuu3zzm9/MgQcemM6dO+eyyy5Lklx++eXp0aNHdtxxxxxyyCE57bTTlugzZUcffXR+8pOf5JZbbkmvXr2y0047ZcSIEVlvvfWSJKusskouu+yy9O3bN1tttVVeeeWV3HvvvfV37ABoWkrl/30jOwAAAJ8q/99oAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABRNiAAAABft/IWbkfb+puXkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AgmqkM9Ka-Bm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "# Save the model for future use\n",
        "joblib.dump(model, 'ps5_game_rating_predictor.pkl')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dF4zwllsbDa6",
        "outputId": "9be29dc4-6109-49af-fb2b-4c3a45c8e0bb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ps5_game_rating_predictor.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import pandas as pd\n",
        "\n",
        "# Load the pre-trained model\n",
        "model = joblib.load('ps5_game_rating_predictor.pkl')\n",
        "\n",
        "# Example new data with sample values\n",
        "new_data = {\n",
        "    'url': ['https://store.playstation.com/en-us/product/UP0101-PPSA19225_00-0159266583099383'],\n",
        "    'id': ['1'],\n",
        "    'publisherName': ['Sony Interactive Entertainment'],  # Sample publisher\n",
        "    'releaseDate': ['2030-10-25'],  # Sample release date\n",
        "    'name': ['New Sample Game'],  # Sample game name\n",
        "    'isAgeRestricted': [True],  # Sample age restriction\n",
        "    'activeCtaId': ['cta2'],  # Sample activeCtaId\n",
        "    'starRating/totalRatingsCount': [50]  # Sample total ratings count\n",
        "}\n",
        "\n",
        "# Convert new data to DataFrame\n",
        "new_df = pd.DataFrame(new_data)\n",
        "\n",
        "# Drop non-numeric columns that were not used in training\n",
        "non_numeric_cols = ['url', 'id', 'publisherName', 'name', 'releaseDate', 'activeCtaId']\n",
        "new_df.drop(columns=non_numeric_cols, inplace=True)\n",
        "\n",
        "# Ensure the correct dtype for isAgeRestricted\n",
        "new_df['isAgeRestricted'] = new_df['isAgeRestricted'].astype(int)  # Convert boolean to int\n",
        "\n",
        "# Manually specify the features used during training\n",
        "# Ensure this list matches exactly the features used during training\n",
        "training_features = [\n",
        "    'isAgeRestricted',\n",
        "\n",
        "]\n",
        "\n",
        "# Ensure new_df has the same columns as used during training\n",
        "new_df = new_df[training_features]\n",
        "\n",
        "# Make prediction\n",
        "prediction = model.predict(new_df)\n",
        "\n",
        "# Print the prediction\n",
        "print(f'Predicted Average Rating: {prediction[0]}')"
      ],
      "metadata": {
        "id": "yJdgJxCfbFhh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0bd104a-05f5-4cb0-f149-0b808ca13203"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Average Rating: 4.5\n"
          ]
        }
      ]
    }
  ]
}