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      "cell_type": "markdown",
      "metadata": {
        "id": "ggJHv0BfmtgN"
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      "source": [
        "# 📊 **Notebook 2 — Data Analysis & Visualization**\n",
        "\n",
        "**Group Project — AI for Big Data Management (SE21)**\n",
        "**Team A8 — ESCP Business School 2026**\n",
        "\n",
        "**Business Question:** *How can a luxury hotel chain optimize its pricing and service quality perception using customer review sentiment (qualitative) and booking data (quantitative)?*\n",
        "\n",
        "---\n",
        "**Pipeline:** Load CSVs from Notebook 1 → VADER Sentiment Analysis → Visualizations → ARIMA Forecasting → Pricing Decisions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mH_jIRZ_mtgT"
      },
      "source": [
        "## **1.** 📦 Install required packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MW3SxH7JmtgU",
        "outputId": "2bc25760-ab0e-4c83-e901-52c101f6126c"
      },
      "source": [
        "!pip install pandas numpy matplotlib seaborn vaderSentiment statsmodels -q\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "random.seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "print(\"✅ Libraries loaded successfully!\")\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/126.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.0/126.0 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h✅ Libraries loaded successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QBjts1DmtgY"
      },
      "source": [
        "## **2.** ✅ Load & inspect input datasets\n",
        "\n",
        "These CSV files were generated by **Notebook 1** (Data Creation).\n",
        "\n",
        "> **📁 Upload `synthetic_hotel_reviews.csv`, `monthly_hotel_revenue.csv`, and `hotel_bookings_cleaned.csv` to Colab.**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 785
        },
        "id": "LD3YhLEkmtgY",
        "outputId": "3af8db31-6381-436e-c399-6a8811e0f4a2"
      },
      "source": [
        "# ── 2a. Load datasets ────────────────────────────────────\n",
        "df_reviews = pd.read_csv('synthetic_hotel_reviews.csv')\n",
        "df_monthly = pd.read_csv('monthly_hotel_revenue.csv')\n",
        "df_bookings = pd.read_csv('hotel_bookings_cleaned.csv')\n",
        "\n",
        "print(f\"Reviews dataset: {df_reviews.shape[0]:,} rows × {df_reviews.shape[1]} columns\")\n",
        "print(f\"Monthly revenue: {df_monthly.shape[0]} months\")\n",
        "print(f\"Bookings dataset: {df_bookings.shape[0]:,} rows × {df_bookings.shape[1]} columns\")\n",
        "\n",
        "print(\"\\n📋 Reviews sample:\")\n",
        "display(df_reviews.head())\n",
        "print(\"\\n📋 Monthly revenue sample:\")\n",
        "display(df_monthly.head())\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reviews dataset: 5,000 rows × 10 columns\n",
            "Monthly revenue: 26 months\n",
            "Bookings dataset: 85,176 rows × 34 columns\n",
            "\n",
            "📋 Reviews sample:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          hotel country     adr arrival_date_month  arrival_date_year  \\\n",
              "0    City Hotel     ITA  110.00          September               2016   \n",
              "1    City Hotel     PRT  171.00              April               2017   \n",
              "2  Resort Hotel     GBR   59.80               July               2016   \n",
              "3  Resort Hotel     PRT   48.00           December               2015   \n",
              "4    City Hotel     GBR  110.09            October               2015   \n",
              "\n",
              "   total_nights sentiment_label  popularity_score  \\\n",
              "0             5        positive                 4   \n",
              "1             2        positive                 5   \n",
              "2            14        positive                 4   \n",
              "3             1         neutral                 3   \n",
              "4             4        positive                 4   \n",
              "\n",
              "                                         review_text  is_canceled  \n",
              "0  Quiet, elegant, and perfectly positioned. An o...            0  \n",
              "1  Impeccable attention to detail — fresh flowers...            0  \n",
              "2  Room service was prompt, delicious, and beauti...            0  \n",
              "3  Air conditioning worked well, but the heating ...            0  \n",
              "4  Family-friendly with excellent kids' activitie...            0  "
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          "text": [
            "\n",
            "📋 Monthly revenue sample:\n"
          ]
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          "data": {
            "text/plain": [
              "         date  total_revenue  total_bookings     avg_adr  cancellation_rate\n",
              "0  2015-07-01      921035.62            1608  116.249652           0.315920\n",
              "1  2015-08-01     1387097.34            2353  127.392150           0.239694\n",
              "2  2015-09-01     1063605.93            2719  103.584998           0.198970\n",
              "3  2015-10-01      691832.31            2564   82.633756           0.161856\n",
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              "    [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-78040121-b753-49d9-a0b7-032f672c69f1 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-78040121-b753-49d9-a0b7-032f672c69f1');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_monthly\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2015-08-01\",\n          \"2015-11-01\",\n          \"2015-09-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 397584.6592390832,\n        \"min\": 328313.89,\n        \"max\": 1387097.34,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1387097.34,\n          328313.89,\n          1063605.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bookings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 536,\n        \"min\": 1584,\n        \"max\": 2719,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2353,\n          1584,\n          2719\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_adr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.346204181573352,\n        \"min\": 61.76799242424242,\n        \"max\": 127.39215044623884,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          127.39215044623884,\n          61.76799242424242,\n          103.58499816108863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cancellation_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06718322992901704,\n        \"min\": 0.1502525252525252,\n        \"max\": 0.3159203980099502,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2396940076498087,\n          0.1502525252525252,\n          0.1989702096358955\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIxkVmgvmtgZ"
      },
      "source": [
        "### *2b. Data quality check*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wqu_nqJ2mtga",
        "outputId": "17183447-8778-4d2a-9544-8878a8837120"
      },
      "source": [
        "# ── 2b. Quality check ────────────────────────────────────\n",
        "def quality_check(df, name=\"DataFrame\"):\n",
        "    print(f\"\\n🔍 Quality Check: {name}\")\n",
        "    print(\"=\" * (20 + len(name)))\n",
        "    print(f\"Shape: {df.shape}\")\n",
        "    print(f\"\\nMissing values:\\n{df.isnull().sum()[df.isnull().sum() > 0]}\")\n",
        "    if df.isnull().sum().sum() == 0:\n",
        "        print(\"  No missing values ✅\")\n",
        "    print(f\"\\nDuplicates: {df.duplicated().sum()}\")\n",
        "    print(f\"\\nColumn types:\\n{df.dtypes}\")\n",
        "\n",
        "quality_check(df_reviews, \"Hotel Reviews\")\n",
        "quality_check(df_monthly, \"Monthly Revenue\")\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔍 Quality Check: Hotel Reviews\n",
            "=================================\n",
            "Shape: (5000, 10)\n",
            "\n",
            "Missing values:\n",
            "Series([], dtype: int64)\n",
            "  No missing values ✅\n",
            "\n",
            "Duplicates: 7\n",
            "\n",
            "Column types:\n",
            "hotel                  object\n",
            "country                object\n",
            "adr                   float64\n",
            "arrival_date_month     object\n",
            "arrival_date_year       int64\n",
            "total_nights            int64\n",
            "sentiment_label        object\n",
            "popularity_score        int64\n",
            "review_text            object\n",
            "is_canceled             int64\n",
            "dtype: object\n",
            "\n",
            "🔍 Quality Check: Monthly Revenue\n",
            "===================================\n",
            "Shape: (26, 5)\n",
            "\n",
            "Missing values:\n",
            "Series([], dtype: int64)\n",
            "  No missing values ✅\n",
            "\n",
            "Duplicates: 0\n",
            "\n",
            "Column types:\n",
            "date                  object\n",
            "total_revenue        float64\n",
            "total_bookings         int64\n",
            "avg_adr              float64\n",
            "cancellation_rate    float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGJcJNy8mtgb"
      },
      "source": [
        "## **3.** 🎭 Perform sentiment analysis using VADER\n",
        "\n",
        "VADER (Valence Aware Dictionary and Sentiment Reasoner) is a rule-based sentiment analysis tool specifically designed for social media and review text. We use it to independently verify and refine the sentiment labels assigned during data creation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i70Uxap3mtgc"
      },
      "source": [
        "### *3a. Initialize VADER*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ivtrolnmtgc",
        "outputId": "deb7ae66-e920-46bc-a506-f4a267af6d3b"
      },
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "analyzer = SentimentIntensityAnalyzer()\n",
        "\n",
        "def get_vader_sentiment(text):\n",
        "    score = analyzer.polarity_scores(str(text))[\"compound\"]\n",
        "    if score >= 0.05:\n",
        "        return \"positive\"\n",
        "    elif score <= -0.05:\n",
        "        return \"negative\"\n",
        "    else:\n",
        "        return \"neutral\"\n",
        "\n",
        "# Apply VADER to review texts\n",
        "df_reviews['vader_sentiment'] = df_reviews['review_text'].apply(get_vader_sentiment)\n",
        "df_reviews['vader_score'] = df_reviews['review_text'].apply(\n",
        "    lambda x: analyzer.polarity_scores(str(x))[\"compound\"]\n",
        ")\n",
        "\n",
        "print(\"✅ VADER sentiment analysis complete!\")\n",
        "print(f\"\\nVADER sentiment distribution:\")\n",
        "print(df_reviews['vader_sentiment'].value_counts())\n",
        "print(f\"\\nOriginal vs VADER agreement rate: {(df_reviews['sentiment_label'] == df_reviews['vader_sentiment']).mean()*100:.1f}%\")\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ VADER sentiment analysis complete!\n",
            "\n",
            "VADER sentiment distribution:\n",
            "vader_sentiment\n",
            "positive    3141\n",
            "negative    1350\n",
            "neutral      509\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Original vs VADER agreement rate: 61.8%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFkGWI1-mtgc"
      },
      "source": [
        "### *3b. View results*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "soMQeC_pmtgc",
        "outputId": "7964267f-9e01-4a3b-d4b2-beafa4543728"
      },
      "source": [
        "# Show a sample of reviews with both original and VADER sentiments\n",
        "sample_display = df_reviews[['review_text', 'sentiment_label', 'vader_sentiment', 'vader_score']].sample(10, random_state=42)\n",
        "display(sample_display)\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                            review_text sentiment_label  \\\n",
              "1501  Noisy rooms facing the street. Could barely sl...        negative   \n",
              "2586  Room was comfortable but the Wi-Fi connection ...         neutral   \n",
              "2653  Check-in was slow but the staff were friendly ...         neutral   \n",
              "1055  The concierge recommended amazing local restau...        positive   \n",
              "705   The bed was comfortable but the bathroom neede...         neutral   \n",
              "106   The hotel is in a much worse neighborhood than...        negative   \n",
              "589   Check-in was slow but the staff were friendly ...         neutral   \n",
              "2468  Family-friendly with excellent kids' activitie...        positive   \n",
              "2413  Very disappointing stay. The room was not clea...        negative   \n",
              "1600  Parking was easy but the elevator was out of s...         neutral   \n",
              "\n",
              "     vader_sentiment  vader_score  \n",
              "1501        negative      -0.1779  \n",
              "2586        positive       0.2846  \n",
              "2653        positive       0.6486  \n",
              "1055        positive       0.6808  \n",
              "705         positive       0.2846  \n",
              "106         negative      -0.4767  \n",
              "589         positive       0.6486  \n",
              "2468        positive       0.8126  \n",
              "2413        negative      -0.5413  \n",
              "1600        positive       0.2382  "
            ],
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              "\n",
              "  <div id=\"df-2379a60f-de76-4d33-babd-0ceecee5157b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>positive</td>\n",
              "      <td>0.6486</td>\n",
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              "    <tr>\n",
              "      <th>2468</th>\n",
              "      <td>Family-friendly with excellent kids' activitie...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.8126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2413</th>\n",
              "      <td>Very disappointing stay. The room was not clea...</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.5413</td>\n",
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              "      <td>Parking was easy but the elevator was out of s...</td>\n",
              "      <td>neutral</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.2382</td>\n",
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              "type": "dataframe",
              "variable_name": "sample_display",
              "summary": "{\n  \"name\": \"sample_display\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Very disappointing stay. The room was not cleaned properly upon arrival.\",\n          \"Room was comfortable but the Wi-Fi connection was unreliable.\",\n          \"The hotel is in a much worse neighborhood than the website suggests.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"negative\",\n          \"neutral\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vader_sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"positive\",\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vader_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4897611015133353,\n        \"min\": -0.5413,\n        \"max\": 0.8126,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2846,\n          0.8126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NM9KkbPImtgd"
      },
      "source": [
        "## **4.** 📊 Data Visualizations\n",
        "\n",
        "### Quantitative & Qualitative analysis through visual storytelling.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTUS-NvCmtgd"
      },
      "source": [
        "### *4a. Sentiment distribution by hotel type*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "1Qghcktcmtgd",
        "outputId": "d23d314c-70f9-47a0-dea6-8f556324bbb3"
      },
      "source": [
        "# ── 4a. Sentiment Distribution ────────────────────────────\n",
        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "# By hotel type\n",
        "sentiment_by_hotel = pd.crosstab(df_reviews['hotel'], df_reviews['vader_sentiment'], normalize='index') * 100\n",
        "sentiment_by_hotel[['positive', 'neutral', 'negative']].plot(kind='bar', ax=axes[0],\n",
        "    color=['#2ecc71', '#f39c12', '#e74c3c'], edgecolor='white')\n",
        "axes[0].set_title('Sentiment Distribution by Hotel Type', fontsize=13)\n",
        "axes[0].set_ylabel('Percentage (%)')\n",
        "axes[0].set_xlabel('')\n",
        "axes[0].legend(title='Sentiment')\n",
        "axes[0].tick_params(axis='x', rotation=0)\n",
        "\n",
        "# Overall distribution\n",
        "df_reviews['vader_sentiment'].value_counts().plot(kind='pie', ax=axes[1],\n",
        "    colors=['#2ecc71', '#e74c3c', '#f39c12'], autopct='%1.1f%%', startangle=90)\n",
        "axes[1].set_title('Overall Sentiment Distribution', fontsize=13)\n",
        "axes[1].set_ylabel('')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('sentiment_distribution.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "print(\"✅ Chart saved!\")\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Chart saved!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQE2qDHnmtge"
      },
      "source": [
        "### *4b. Average Daily Rate by sentiment and hotel type*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "MgfHb1jrmtge",
        "outputId": "7912f93b-4224-4405-f85d-b5270826e144"
      },
      "source": [
        "# ── 4b. ADR by Sentiment ─────────────────────────────────\n",
        "plt.figure(figsize=(10, 6))\n",
        "order = ['negative', 'neutral', 'positive']\n",
        "palette = {'negative': '#e74c3c', 'neutral': '#f39c12', 'positive': '#2ecc71'}\n",
        "\n",
        "sns.boxplot(data=df_reviews, x='vader_sentiment', y='adr', hue='hotel',\n",
        "            order=order, palette='Set2')\n",
        "plt.title('Average Daily Rate by Guest Sentiment & Hotel Type', fontsize=13)\n",
        "plt.xlabel('VADER Sentiment')\n",
        "plt.ylabel('Average Daily Rate (€)')\n",
        "plt.legend(title='Hotel Type')\n",
        "plt.tight_layout()\n",
        "plt.savefig('adr_by_sentiment.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Fw3_W1ymtge"
      },
      "source": [
        "### *4c. Monthly revenue trends*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "PNHtHOs3mtge",
        "outputId": "706fdc48-1cd0-4172-8d11-b0884013266e"
      },
      "source": [
        "# ── 4c. Revenue Trends ───────────────────────────────────\n",
        "df_monthly['date'] = pd.to_datetime(df_monthly['date'])\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(14, 6))\n",
        "\n",
        "color1 = '#3498db'\n",
        "ax1.set_xlabel('Date')\n",
        "ax1.set_ylabel('Total Revenue (€)', color=color1)\n",
        "ax1.plot(df_monthly['date'], df_monthly['total_revenue'], 'o-', color=color1, label='Revenue')\n",
        "ax1.tick_params(axis='y', labelcolor=color1)\n",
        "ax1.grid(True, alpha=0.3)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "color2 = '#e74c3c'\n",
        "ax2.set_ylabel('Cancellation Rate', color=color2)\n",
        "ax2.plot(df_monthly['date'], df_monthly['cancellation_rate'], 's--', color=color2, alpha=0.7, label='Cancellation Rate')\n",
        "ax2.tick_params(axis='y', labelcolor=color2)\n",
        "\n",
        "plt.title('Monthly Revenue & Cancellation Rate Trends', fontsize=13)\n",
        "fig.tight_layout()\n",
        "plt.savefig('revenue_cancellation_trend.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JlfM44Vxmtgf"
      },
      "source": [
        "### *4d. Seasonal booking patterns*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "HxbINhY5mtgf",
        "outputId": "7efc3dab-6afd-46e1-e16d-1572fd19e282"
      },
      "source": [
        "# ── 4d. Seasonal Patterns ────────────────────────────────\n",
        "month_order = ['January','February','March','April','May','June',\n",
        "               'July','August','September','October','November','December']\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n",
        "\n",
        "# Bookings by month\n",
        "monthly_counts = df_bookings.groupby('arrival_date_month').size().reindex(month_order)\n",
        "axes[0].bar(range(12), monthly_counts.values, color=sns.color_palette('Blues_d', 12))\n",
        "axes[0].set_xticks(range(12))\n",
        "axes[0].set_xticklabels([m[:3] for m in month_order], rotation=45)\n",
        "axes[0].set_title('Bookings by Month')\n",
        "axes[0].set_ylabel('Number of Bookings')\n",
        "\n",
        "# ADR by month\n",
        "monthly_adr = df_bookings.groupby('arrival_date_month')['adr'].mean().reindex(month_order)\n",
        "axes[1].plot(range(12), monthly_adr.values, 'o-', color='#e67e22', linewidth=2, markersize=8)\n",
        "axes[1].set_xticks(range(12))\n",
        "axes[1].set_xticklabels([m[:3] for m in month_order], rotation=45)\n",
        "axes[1].set_title('Average Daily Rate by Month')\n",
        "axes[1].set_ylabel('ADR (€)')\n",
        "axes[1].grid(True, alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('seasonal_patterns.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ],
            "image/png": 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UvOo/kmkqY88KBXa+V4ZhWB0LqFSKXNQwDEPjxo3TuHHjlJKSIkkKCgoq9mAAAAAAIEkul0tffPGFNm3apMOHDys9PV3h4eFq1aqVunbtqtq1a1sdEQAqpNyU40r6dLIy961xtxmOYAX3eER+Lfvzi1xUSl7BNeR78RXKOrxduSd/Vs7xn+RTo5HVsYBKpchFjXwnTpzQjz/+KElq3LixqlWrVmyhAAAA8l016imrIxTJtpcetDoCUGFkZGTomWee0YIFCxQfH6+WLVsqMjJSfn5+OnjwoJYuXaoRI0aoW7dumjRpktq2bWt1ZACoEEzTVMauD5W86nGZzmR3u73x9Qq5YZq8gthTFZWbIypGWYe3S5Kce1ZQ1ABKWZGLGmlpabrnnnv0xhtvyOVySZK8vLx02223ad68efL39y/2kAAAAAAqn0svvVTt2rXTyy+/rOuvv14+Pj4F+hw+fFiLFy/WwIED9cgjj2jEiBEWJAWAiiMn8XclLXtEWT9vcrfZAqoquOcUOZpGMzsD0J9LUK2cdtoSVPfx3wZQioq8i9P48eP1xRdfaPny5UpMTFRiYqI+/vhjffHFF7r//vtLIiMAAACASmjNmjV67733FBMTc8aChiTVqVNHEydO1IEDB3TdddeVckIAqDhMl0tpW9/Qyfk9PAoafs37KHzMavlFxfBLW+BPXkHV5VvnKklS7slflHPsR4sTAZVLkWdqfPjhh/rggw/UqVMnd1tMTIz8/Pw0YMAALViwoDjzAQAAAKikmjRpUui+Pj4+ql+/fgmmAYCKK+dkrBI/fljZR3a422zBEQrpNV2OSztbmAwouxxNo5V1aKskybnnU/lENLY4EVB5FHmmRnp6umrUqFGgvXr16kpPTy+WUAAAAAAgST/++KMeffTRM5777bffNHLkSB05cqSUUwFAxWDm5ih100s6sSDGo6Dhf8UtCh+9ioIGcA6Opj2kP2cvZexZKdM0LU4EVB5FLmq0a9dOkydPltPpdLdlZGRo6tSpateuXbGGAwAAAFC5TZw4UfXq1ZMkbd682eNcrVq1FBkZqYceesiKaABQrmXH7dep//ZXyrqnpJwsSZJX2MUKG/qWQnpNl80RZHFCoGzzCgr/awmqU7HKObbf4kRA5VHk5afmzp2r7t27q1atWmrRooUk6bvvvpPD4dDq1auLPSAAAACAymvt2rWaMWOGJCk6Olq7du3SiRMn1KZNG0nSrbfeqssvv9zKiABQrpg5mUrd+IJSN70ouXLyGg2bAtoOU9B142T4+lkbEChHHFE93UtQZfzwqQKva2RxIqByKPJMjcsuu0wHDhzQjBkz1LJlS7Vs2VJPPvmkDhw4oKioqJLICAAAAKCS8vf3V05O3i/dTNOUaZq6/vrr9csvv0iS4uPj5e1d5M9qAUCllPXbLp188UalfvG8u6DhHd5QVf/vfQX3+DcFDaCIHE27S0ber1ede1awBBVQSi7o1b+/v79GjBhR3FkAAAAAwEObNm30yiuvaPbs2TIMQ4ZhyDRNGYYhp9Opxx57TN27d7c6JgCUaWZWhlLWP6u0r1+TTFdeo81bgdfeqcAOd8vwtlsbECinvAKrybfuVcqK/Vq58YeVE7dX8gq3OhZQ4RW5qHHq1ClVrVpVkvTrr7/q5ZdfVkZGhnr16qUOHToUe0AAAAAAldfkyZPVsWNHHTlyRNnZ2VqzZo1ycnI0depUff7557Lb7Xr11VetjgkAZVZm7NdKWjZRufFH3G0+kZcp5MYn5RPRxMJkQMXgiOqprNivJUlJH46TaQ9VYki4HE26ya9pjAwfioZAcSv08lPff/+96tatq+rVq6tx48batWuXrrzySj377LNauHChrrvuOi1durQEowIAAACobFq3bq0vvvhCpmkqKytLd999t7KysrR//36NGjVK3377rS666CKrYwJAmeNypihp+aOKf33wXwUNb18FXf+gqv7fhxQ0gGJi+Djc3+eeipWOfqvM/euUtOQBHZvVVs4fP7MwHVAxFbqo8eCDD6pZs2bauHGjOnXqpBtuuEE9e/ZUUlKSEhISNGrUKD355JMlmRUAAABAJdS6dWt9+OGHCgwM1IEDBxQYGKjFixdr4sSJ8vf3tzoeAJQ5zp826MT8Hkrf8ba7zefiKxR+1woFth8lw4u9iIDi4Ny/TkkfPVjwxJ/LvJnOFCUsHiXn/nWlnAyo2Ar9f7Ht27dr/fr1at68uVq0aKGFCxfq7rvvls2WVxe555571LZt2xILCgAAAKBy279/v6pUqeL+EwDgyZWeoKSV/5Fz98fuNsPXX0FdH5T/lYNl2Ar92VYA52FmZyrxownn6yXJUOJHE1Tjga9ZigooJoX+v1l8fLwiIiIkSYGBgQoICPB4I1GlShWlpKQUe8Dff/9dt956q6pWrSo/Pz81a9ZMO3bscJ83TVOTJk1SzZo15efnp65du+rAgQMFsg8ePFjBwcEKDQ3V8OHDlZqa6tFn9+7duvbaa+VwOFS7dm099dRTxT4WAAAAAIX3zjvveBzXrFlTDofD/efpfv31V23evLk04wFAmWGapjJ++FQnnu/mUdDwrX+tqo1epYA2QyhoAMUsY+8Kmc5k5RUuzsWU6UxWxt6VpRELqBSK9H80wzDOeVzcEhISdM0118jHx0crV67U3r179cwzz3gUU5566ik999xzevHFF7V161YFBASoe/fucjqd7j6DBw/Wnj17tHbtWn3yySfauHGjRo4c6T6fnJysbt26qU6dOtq5c6eefvppTZkyRQsXLizR8QEAAAA4uwULFqhJkyZ66qmntG/fvgLnk5KStGLFCg0aNEiXX365Tp06ZUFKALBWbspxJbxzlxLfHytXWrwkyXAEK6TPTIUNeU3eoew7BJSEzH1rJaOQv1o1bMrct6ZkAwGVSJEWURw6dKjs9rxpUk6nU3feeacCAgIkSZmZmcUebubMmapdu7Zee+01d1u9evXc35umqTlz5ujRRx/VjTfeKEl64403VKNGDS1dulQDBw7Uvn37tGrVKm3fvl1XXHGFJGnevHmKiYnRrFmzFBkZqUWLFikrK0uvvvqqfH19FRUVpV27dmn27NkexQ8AAAAApeeLL77QsmXLNG/ePE2cOFEBAQGqUaOGHA6HEhISFBcXp2rVqmno0KH64YcfVKNGDasjA0CxMbMzlbF3hZz71shMOqHEkHA5mnSTX9MYGT72vNkZuz5U8qrH//y0eB57k24K6TlVXkHVLUwPVHyujET33hnnZbry+gMoFoUuatx+++0ex7feemuBPrfddts/T3SaZcuWqXv37vrXv/6lL774QhdddJHuvvtujRgxQpIUGxuruLg4de3a1f2YkJAQtWnTRlu2bNHAgQO1ZcsWhYaGugsaktS1a1fZbDZt3bpVffv21ZYtW9ShQwf5+vq6+3Tv3l0zZ85UQkIC6/UCAAAAFundu7d69+6tkydP6ssvv9Thw4eVkZGhatWqqVWrVmrVqpV7nz8AqCic+9cp8aMJecUKwyaZLmX+kfdJ7+QV0xTUbaKce1Yo6+dN7sfYAqoquOcUOZpGl/jKGgAkm1+o+7/P8zJsef0BFItCFzVOny1RWn755RctWLBA48eP17///W9t375dY8eOla+vr26//XbFxcVJUoFPZNWoUcN9Li4uTtWre346wdvbW2FhYR59Tp8Bcvo14+LizljUyMzM9JidkpycXKAPAAAAgOJRrVo19enTx+oYAFDinPvXKeHtO/9qyP+F6Z9/ms5kJS+b6PEYv+Z9FBz9qGz+fCgTKC32JtfLuW914TqbLtmbdCvZQEAlUqY/0uRyuXT55ZfriSeeUKtWrTRy5EiNGDFCL774otXRNGPGDIWEhLi/ateubXUkAAAAAABQjpnZmUr8aEL+0Xn7G0E1VGXwfxXa/xkKGkAp82saI8MRLOl8M6MMGY5g+TWNLo1YQKVQposaNWvWVNOmTT3amjRpoiNHjkiSIiIiJEnHjh3z6HPs2DH3uYiICB0/ftzjfE5OjuLj4z36nOkap9/j7yZOnKikpCT316+//nohQwQAAAAAAJAkZexd8ef+GOcvaEhSUKexclzauWRDATgjw8eu0H6z8o/O2Te03ywZPvaSDwVUEmW6qHHNNdfoxx9/9Gj76aefVKdOHUl5m4ZHRETos88+c59PTk7W1q1b1a5dO0lSu3btlJiYqJ07d7r7rF+/Xi6XS23atHH32bhxo7Kzs9191q5dq0aNGp11Pw273a7g4GCPLwAAAAAAgAuVuW9t3hr9hWHYlHVwY8kGAnBOjkZdVOWWF2U4gvIa3P/9/lXksAVUlf2S9qUfDqjACr2nhhXGjRunq6++Wk888YQGDBigbdu2aeHChVq4cKEkyTAM3XfffZo+fboaNmyoevXq6bHHHlNkZKR7vd0mTZqoR48e7mWrsrOzNWbMGA0cOFCRkZGSpEGDBmnq1KkaPny4HnroIf3www+aO3eunn32WauGjr+ZueaA1RGK5KFuDa2OAAAAAAAoZ1wZiYXbdFiSTFdefwCWcjTuqhoPfK2MvSvl3LdamUknZA8OV87Jn5V78me50k4qdfNCBXW6x+qoQIVRqPL/5ZdfroSEBEnStGnTlJ6eXqKh8l155ZX66KOP9Pbbb+uyyy7Tf/7zH82ZM0eDBw9293nwwQd1zz33aOTIkbryyiuVmpqqVatWyeFwuPssWrRIjRs3VpcuXRQTE6P27du7CyOSFBISojVr1ig2NlatW7fW/fffr0mTJmnkyJGlMk4AAAAAF27Hjh1WRwCAYmH4hej86/Pnd7bJ5hdaknEAFJLhY5d/iz4KHTBfxo3PK/Tm+aoyYJ5k85IkpW5aoJwElq4HikuhZmrs27dPaWlpqlKliqZOnao777xT/v7+JZ1NknTDDTfohhtuOOt5wzA0bdo0TZs27ax9wsLCtHjx4nPep3nz5tq0adMF5wQAAABQclJTU+Xl5SU/Pz93265du/TYY49pxYoVys3NtTAdAPxz2Ue/V07cPhV2Pw2ZLtmbdCvRTAAunE+NRgpoc7vStrwq5WQqedV0hd3yktWxgAqhUEWNli1batiwYWrfvr1M09SsWbMUGBh4xr6TJk0q1oAAAAAAKq9ff/3VvRStl5eXxowZo+nTp+vOO+/Uu+++q759++qrr76yOiYAXDBXeqJS1s9W+o7FklnIgoYMGY4g+TWNLtFsAP6ZwE5jlfH9crlSTyhz/zo5D3wuR8NOVscCyr1CFTVef/11TZ48WZ988okMw9DKlSvl7V3woYZhUNQAAAAAUGwmTJggp9OpuXPnasmSJZo7d642bdqkNm3a6Oeff1atWrWsjggAF8R0uZSx60OlrH1KrvR4d7stuKZcyXH5vc7wyLzlqUL7zZLhYy/5oAAumM0RpODuE5X44XhJUvKKabKPbifDm/92gX+iUEWNRo0a6Z133pEk2Ww2ffbZZ6pevXqJBgMAAACAjRs3asmSJWrbtq0GDBigiIgIDR48WPfdd5/V0QDggmX/sUdJn05W9q/futsMX38FdhqrgLZDlXngCyV+NEGmM1kybHmbh//5p+EIUmi/WXI06mLhCAAUlqNZb/nueEdZh7cpN/6wUjf/V0EdR1sdCyjXClXUOJ3L5SqJHAAAAABQwLFjx1SvXj1JUvXq1eXv76/oaJZbAVA+uTKS85aa2r4or1DxJ8dlPRXcbaK8QmrmHTfuqhoPfK2MvSvl3LdamUknZA8Jl6NJd/k1jWaGBlCOGIah4J5TdPLFXpIrV6mbXpBfiz7yDr3I6mhAuVXkooYk/fzzz5ozZ4727dsnSWratKnuvfde1a9fv1jDAQAAAIDNZvP43tfX18I0AFB0pmkq47uPlLLmSbnSTrnbvarVV0jPybJfck2Bxxg+dvm36CNHs946fvy4QqtX9/j3EED54VOjkfyvGqL0r1+Xsp15m4YPXGB1LKDcKnJRY/Xq1erdu7datmypa67J+5/u5s2bFRUVpeXLl+v6668v9pAAAAAAKifTNHXppZfKMPLWkE9NTVWrVq0K/GIvPj7+TA8HAMtlx+3PW2rqyA53m+Hjp8CO9yig3TAZ3hRqgcogqPN9cv7wiVypJ5W5b40yD26UvUEHq2MB5VKRixoPP/ywxo0bpyeffLJA+0MPPURRAwAAAECxee2116yOAAAXxOVMUeqGuUrb9obkynW3O5pGK7jHv+UVEmlhOgClzeYIUtD1DynpowmSpKQVUxV+9wo2DQcuQJGLGvv27dN7771XoP2OO+7QnDlziiMTAAAAAEiSbr/9dqsjAECRmKYp5/fLlLx6hlypJ9ztXlXrKiRmMp/MBioxvxZ9lb7zXWUf2aHcU4eU9tWrCuxwl9WxgHKnyIsxhoeHa9euXQXad+3aperVqxdHJgAAAADwYJqmduzYoQ8++EAffvihvvnmG5mmWeTrbNy4Ub169VJkZKQMw9DSpUs9zi9ZskTdunVT1apVZRjGGd/7OJ1OjR49WlWrVlVgYKD69++vY8eOXeDIAFQk2cd+VPxrg5T44fi/Cho+DgV1uV/hd6+goAFUcoZhKKTnFMnI+5Vs6sb5yk08am0ooBwq8kyNESNGaOTIkfrll1909dVXS8rbU2PmzJkaP358sQcEAAAAULlt2LBBw4cP1+HDh92FDMMwVK9ePb366qvq0KHwvyRMS0tTixYtdMcdd6hfv35nPN++fXsNGDBAI0aMOOM1xo0bp08//VTvv/++QkJCNGbMGPXr10+bN2++sAECKPdcmalK/fw5pX39usdSU/bG1yu4x6PyrlLLunAAyhSfiCbyv+pWpW99Q2Z2hpJXP6EqNz9vdSygXClyUeOxxx5TUFCQnnnmGU2cOFGSFBkZqSlTpmjs2LHFHhAAAABA5XXw4EHdcMMNatOmjZ599lk1btxYpmlq7969eu655xQTE6Pdu3frkksuKdT1oqOjFR0dfdbzQ4YMkSQdOnTojOeTkpL0yiuvaPHixbruuusk5e370aRJE3399ddq27Zt0QYIoFwzTVPOPZ8qedUTcqX8NWPLK+xiBUdPkuPSzhamA1BWBXUeJ+cPn8qVdkrOvSuV+fOXstdvb3UsoNwoclHDMAyNGzdO48aNU0pKiiQpKCio2IMBAAAAwJw5c9S2bVt99tlnHu2NGzdW37591bVrVz377LOaN29eqeTZuXOnsrOz1bVrV48sF198sbZs2XLWokZmZqYyMzPdx8nJyZIkl8sll8tVsqHPwOVyyTRNS+5d2hhrxVQWxppz4qBSVk5TVuyWvxq97Qpof6cCrhkhw9teLPnKwlhLC2OtmBjrGdgDFdh1gpI/fliSlPTpVFW9a7kML99SSFk8+LlWTFaPtbD3LXJR43QUMwAAAACUpM8//1wzZsw44znDMHTfffe5Z5CXhri4OPn6+io0NNSjvUaNGoqLizvr42bMmKGpU6cWaD9x4oScTmdxxzwvl8ulpKQkmaYpm63IWy2WK4y1YrJyrGZ2urTjdWn3Ox5LTanONVL7e5UefJHS45OK7X78XCsmxloxFWWsZs1rpBqXScd+UO6pX3R83fMyWt1aSkn/OX6uFZPVY82fRHE+/6ioAQAAAAAl6ciRI2rWrNlZz1922WU6fPhwKSa6MBMnTvTYgzA5OVm1a9dWeHi4goODSz2Py+WSYRgKDw+vFG/OGWvFY8VYTdNU5r5VSln9hFzJfxUxbaG1FNzjUdkbdSmR+/JzrZgYa8VU1LFm3zhd8S/3k0yXjJ3/U9V2t8gruGYpJP3n+LlWTFaP1eFwFKofRQ0AAAAAZVZqaqr8/f3Pet7f31/p6emlliciIkJZWVlKTEz0mK1x7NgxRUREnPVxdrtddru9QLvNZrPszbFhGJbevzQx1oqpNMeac/IXJa2Yqqyfv/yr0dtXgdeMUuC1d8rwKdwvYS4UP9eKibFWTEUZq/2iZvK/crDSt70pMztdqWueVJUBpbOkZnHg51oxWTnWwt6TogYAAACAMm3v3r1nXdrp5MmTpZqldevW8vHx0Weffab+/ftLkn788UcdOXJE7dq1K9UsAEqeKytdqRtfUNpX/5Vys93t9oYdFRw9Sd5V61oXDkCFEHTdODn3fCpXWryce1Yo85eBsl9yjdWxgDKtSEWN7Oxs9ejRQy+++KIaNmxYUpkAAAAAwK1Lly4yTbNAu2EYMk1ThmEU+lqpqak6ePCg+zg2Nla7du1SWFiYLr74YsXHx+vIkSM6evSopLyChZQ3QyMiIkIhISEaPny4xo8fr7CwMAUHB+uee+5Ru3btzrpJOIDyxzRNZe5fo+SV05WbdNTd7hUSqeDox2RvfH2R/u0BgLOx+YUoqOuDSvpz0/DkFdNU7c7lMrzLz6bhQGkrUlHDx8dHu3fvLqksAAAAAOAhNja2WK+3Y8cOde7c2X2cv8/F7bffrtdff13Lli3TsGHD3OcHDhwoSZo8ebKmTJkiSXr22Wdls9nUv39/ZWZmqnv37nrhhReKNScA6+ScOqTkldOUeeCLvxq9fBV4zf8p8Nq7Zfj6WRcOQIXk17K/0ne+o+zfdinnxEGlbf2fAq8ZYXUsoMwq8vJTt956q1555RU9+eSTJZEHAAAAANzq1Klz3j4//PBDoa/XqVOnM876yDd06FANHTr0nNdwOByaP3++5s+fX+j7Aij7zKwMpX75olK/XCjlZrnbfeu3V0jMFHlXq2dhOgAVmWGzKaTnFJ1c2FcyTaV+Pk9+zXrJK/js+3UBlVmRixo5OTl69dVXtW7dOrVu3VoBAQEe52fPnl1s4QAAAADgTFJSUvT222/rv//9r3bu3Knc3FyrIwEox5z71yl55X+Um/ibu80WHKHg6MfkaNKdpaYAlDifyGbyb32L0ncslpmVpuTVM1TlX3OtjgWUSUUuavzwww+6/PLLJUk//fSTxzn+Jw8AAACgJG3cuFGvvPKKPvzwQ0VGRqpfv37MmABwwXLijyh55X+U+dP6vxpt3gq4ergCO4yWzR5w9gcDQDEL6nK/MvaulJmeIOcPnyjzioGy12tndSygzClyUWPDhg0lkQMAAAAAziguLk6vv/66XnnlFSUnJ2vAgAHKzMzU0qVL1bRpU6vjASiHzOxMpX75klK/XCDlnLbUVL12Cu45RT7hDSxMB6CysvmHKrjrBCUt+7ckKfnTqap213IZXj4WJwPKFtuFPvDgwYNavXq1MjIyJOmc69ICAAAAwIXo1auXGjVqpN27d2vOnDk6evSo5s2bZ3UsAOWY86cNOjG/h1I/n+suaNiCaij0X88p7PY3KWgAsJRfq3/J56IWkqScEweUtvUNixMBZU+RixqnTp1Sly5ddOmllyomJkZ//PGHJGn48OG6//77iz0gAAAAgMpr5cqVGj58uKZOnaqePXvKy8vL6kgAyqmchN8U//YoJSz6P+UmHMlrtHkr4Or/U/g9a+R3WU+W1QZgOcNmU3DPKdKf/x6lfv6cclOOWxsKKGOKXNQYN26cfHx8dOTIEfn7+7vbb775Zq1atapYwwEAAACo3L788kulpKSodevWatOmjZ5//nmdPHnS6lgAyhEzJ1MpX8zXifndlbl/nbvdt24bVbtruYK7T5TNHmhhQgDw5HtRc/lffrMkycxMVfKaJy1OBJQtRS5qrFmzRjNnzlStWrU82hs2bKjDhw8XWzAAAAAAaNu2rV5++WX98ccfGjVqlN555x1FRkbK5XJp7dq1SklJsToigDIs8+BGnZgfrdT1s6VspyTJFhiu0P7PKmzoIvlUv9TihABwZkFdH5DhFypJcu7+WJmHtlkbCChDilzUSEtL85ihkS8+Pl52u71YQgEAAADA6QICAnTHHXfoyy+/1Pfff6/7779fTz75pKpXr67evXtbHQ9AGZObeFQJ79yt+DeHKTf+zw9g2rwU0O4Ohd+zVn7Ne7PUFIAyzeZfRUFd/lrqP/nTyTJzsy1MBJQdRS5qXHvttXrjjb82qDEMQy6XS0899ZQ6d+5crOEAAAAA4O8aNWqkp556Sr/99pvefvttq+MAKEVmdqbSv/tIie+NlvnxGCW+N1rp330kMzsz73xOllI3vagTz3eTc99q9+N8Lr5C1UYtU3CPR2RzBFkVHwCKxL/1zfKJbCZJyjn+k9K3vWVxIqBs8C7qA5566il16dJFO3bsUFZWlh588EHt2bNH8fHx2rx5c0lkBAAAAIACvLy81KdPH/Xp08fqKABKgXP/OiV+NEGmM1kybJLpUuYfNmXuW6PkFdMU0G64Mr7/WLknf3E/xhZQVUHdJ8qveR9mZgAodwybl4J7TtWp//aXTFMpG+bIcdkN8goKtzoaYKkiz9S47LLL9NNPP6l9+/a68cYblZaWpn79+unbb79V/fr1SyIjAAAAAACoxJz71ynh7TtlOv/cR8d0efxpOpOVuuHZvwoahk3+bW5T+D3r5N+iLwUNAOWWb60W8mv1L0l5m4anrGXTcKDIMzUkKSQkRI888khxZwEAAAAAAPBgZmcq8aMJ+Ufn7e9dq4VCb5gun5pNSzYYAJSS4K4T5Ny3WmZGkjK+Wyr/1rfIt84VVscCLHNBRY2EhAS98sor2rdvnySpadOmGjZsmMLCwoo1HAAAAAAAqNwy9q7IW3KqkAKuHEJBA0CFYgsIU9B19yv500mSpKRPJ6vaqI9leF3Qr3aBcq/If/M3btyoXr16KSQkRFdckVcRfO655zRt2jQtX75cHTp0KPaQAAAAQFlxZedoqyMUyfYNK62OAAD/SOa+te49NM7LsClz/1r5t+xb8sEAoBT5XzFQ6d+8q5w/9ijn2H6lb1+kgLa3Wx0LsESR99QYPXq0br75ZsXGxmrJkiVasmSJfvnlFw0cOFCjR48uiYwAAAAAAKCScmUkFq6gIUmmK68/AFQwhs1LIT2nuo9T1s9WbupJCxMB1ilyUePgwYO6//775eXl5W7z8vLS+PHjdfDgwWINBwAAAAAAKjebX2jeTI3CMGx5/QGgAvKt3epvm4bPtDgRYI0iLz91+eWXa9++fWrUqJFH+759+9SiRYtiCwYAAIDy68re5Wsq/PZl/7M6AgDgLOxNrpdz3+rCdTZdsjfpVrKBAMBCQdf/uWm4M1kZu5bIv/VA+V7c2upYQKkqVFFj9+7d7u/Hjh2re++9VwcPHlTbtm0lSV9//bXmz5+vJ598smRSAgAAAACASsmvaYySV0wrxGbhhgxHkPyalq+9jwCgKLwCqirouvFKXjFFkpT06RRVG7VUhs3r3A8EKpBCFTVatmwpwzBkmqa77cEHHyzQb9CgQbr55puLLx0AAAAAAKjUDB+7QnpOVeKH487VS5IU2m+WDB976QQDAIv4XzlI6d+8p5y4vcqJ26v07YsV0GaI1bGAUlOookZsbGxJ5wAAAAAAADij7N93ezYYtrzNw//803AEKbTfLDkadbEmIACUorxNw6fo1CsDJOVtGu64LEZeAVUtTgaUjkIVNerUqVPSOQAAAAAAAArIORmrtG1v5h142xXc9UFlHt6qzKQTsoeEy9Gku/yaRjNDA0Cl4ntxa/m17KeMXUtkOpOVsvZphfZhawBUDkXeKFySjh49qi+//FLHjx+Xy+XyODd27NhiCQYAAAAAAJC89knJlSNJCrxmpALaDZVfm9t0/PhxhVavLpvNZnFCALBG0PUPybl/rUxnijK+fV/+rQfIt/blVscCSlyRixqvv/66Ro0aJV9fX1WtWlWGYbjPGYZBUQMAAAAAABSLzNgtyty/TpJkC6qugPYjLU4EAGWHV2A1BXUep+SV0yT9uWn4yI/YNBwVXpE/zvDYY49p0qRJSkpK0qFDhxQbG+v++uWXX0oiIwAAAAAAqGRMV66SVz3uPg7qcr9svv4WJgKAssf/ysHyrtFYkpTzxx6l73jH4kRAyStyUSM9PV0DBw5keicAAAAAACgxGbuWKCdunyTJu2aU/Fr0szgRAJQ9hpe3QnpOdR+nrH9GrrR4CxMBJa/IlYnhw4fr/fffL4ksAAAAAAAAcmWmKuWzZ9zHwT0ekcGHKwHgjHzrXCG/Fn0kSWZGkpLXPW1tIKCEFXlPjRkzZuiGG27QqlWr1KxZM/n4+Hicnz17drGFAwAAAAAAlU/aly/JlXpCkmRv0k32um0sTgQAZVvQ9Q/LuX+dzMzUPzcNHyjfWi2sjgWUiAsqaqxevVqNGjWSpAIbhcNaL3912OoIRTLi6jpWRwAAAAAAlCG5iUeV+tUreQdePgq+/iFrAwFAOeAVFK6gzvcpedV0yTSV/OlkVR3xIZuGo0IqclHjmWee0auvvqqhQ4eWQBwAAAAAAFCZJa97WsrJlCQFtLlN3lXrWhsIAMoJ/6uGKP3b95Vz7EdlH/1e6TvfVcCVg6yOBRS7Ii9Iabfbdc0115REFgAAAAAAUIll/bZLzu+XSZIM/yoK7DDG4kQAUH4YXt4KjpniPk757Bm50hOsCwSUkCIXNe69917NmzevJLIAAAAAAIBKyjTNvGVT/hTU6V7Z/IItTAQA5Y+97lVyNL9RkmRmJCpl3SyLEwHFr8jLT23btk3r16/XJ598oqioqAIbhS9ZsqTYwgEAAAAAgMrBuedTZf/6rSTJO7yB/K+4xeJEAFA+BXd7WJk/fiYzM1Xp37wrv9Y3y/ei/2/vvuObqrs/gH9uOpKWLmYHgiyh7C0gW2pr2RQXDhBQFEEFHkWrgIIDRdYDDvAHAi5EFHhkCJQhihSUvYeIDKWD0aYr6cj5/VF7bdKCBdre5Obzfr141TuSnGPW9+Tc+73NtA6LqNTccFMjKCgIMTExZRELEREREREREbkhybEiLW6auuwf+TIUjxv+yYKIiAB4+FeDX7fnkLbh7b8vGv46Kj/xDRTDDU/aQ+SUbniEsGjRorKIg4iIiIiIiIjcVMbORchL+RMA4F23M4x3dNM2ICIiF1eh3WBk7VuO3KRTyPnzALL2LYdv6we1DouoVLA9R0RERERERESayUu/hPSfPspfUAwIiIqFoijaBkVE5OIUDy+7i4abN70HW2aKZvEQlaYbbmrUrl0bderUueY/IiIiIiIiZ/Xjjz+iT58+CAsLg6IoWLVqld12EcGkSZMQGhoKHx8fRERE4NSpU3b7XLlyBY888ggCAgIQFBSE4cOHIz09vRyzINKXtC0zIdb895Bv6wfhFdxA44iIiPTBWLs9TE37AAAk8yrSNs/QOCKi0nHD00+NGTPGbjknJwf79u3D+vXr8eKLL5ZWXERERERERKUuIyMDzZs3x7Bhw4q9VuC0adMwZ84cLFmyBLVr18bEiRMRFRWFo0ePwmQyAQAeeeQRXLx4EXFxccjJycHQoUMxYsQIfPnll+WdDpHLy0k4jqy9ywEAitEPft3HaBsQEZHOBETGwnpiCyQ7A5l7lsK39QPwCmuqdVhEt+SGmxrPP/98ses/+OAD7N69+5YDIiIiIiIiKivR0dGIjo4udpuIYPbs2ZgwYQL69esHAPj0008RHByMVatW4aGHHsKxY8ewfv16/Prrr2jTpg0AYO7cuejZsyemT5+OsLCwcsuFyNWJCMwb3gbEBgDw6/IMPPyqaBwVEZG+eAQEw6/bs0jb+A4ggtS1r6Py8OW8aDi5tBtualxLdHQ0YmNjeSFxIiIiIiJySWfOnEFCQgIiIiLUdYGBgWjXrh3i4+Px0EMPIT4+HkFBQWpDAwAiIiJgMBiwa9cuDBgwoNj7tlqtsFqt6rLZbAYA2Gw22Gy2Msro2mw2G0REk8cub8zVeVlPbkH27z8DAAxBt8HnzsEljt3Vcr0VzFWfmKs+OWuuPncORube5ci7dBo5F/Yjc99y+LS8/5bu01lzLQvMtXwfvyRKranxzTffoFKlSqV1d0REREREROUqISEBABAcHGy3Pjg4WN2WkJCAatWq2W339PREpUqV1H2KM3XqVEyePLnI+uTkZFgsllsN/YbZbDakpqZCRGDQ+ZGazNU5SV4u8P1b6rLtzqeQfCW1xLd3pVxvFXPVJ+aqT86cq3R4Hlj9HADAvHEazFVaQjEG3PT9OXOupY25lp+0tLQS7XfDTY2WLVtCURR1WUSQkJCA5ORkfPjhhzd6dzfknXfeQWxsLJ5//nnMnj0bAGCxWPCf//wHX331FaxWK6KiovDhhx/aFSLnzp3DyJEjsXXrVvj5+WHIkCGYOnUqPD3/Sf+HH37AuHHjcOTIEdSoUQMTJkzA448/Xqb5EBERERGRe4iNjcW4cePUZbPZjBo1aqBq1aoICLj5HxRuls1mg6IoqFq1qlsU58zV+WTu+hRpKecAAF41WqNi+wftfmv4N66U661irvrEXPXJqXOtFo2U33vCemQdYEmBz6EvENDztZu+O6fOtZQx1/JTcA27f3PDTY3+/fvbLRsMBlStWhXdunVDeHj4jd5dif3666+YP38+mjVrZrd+7NixWLt2LZYvX47AwECMHj0aMTEx+Pnn/FNY8/Ly0KtXL4SEhGDHjh24ePEiBg8eDC8vL7z99tsA8k8z79WrF55++ml88cUX2Lx5M5544gmEhoYiKiqqzHIiIiIiIiLnERISAgBITExEaGiouj4xMREtWrRQ90lKSrK7XW5uLq5cuaLevjhGoxFGo7HIeoPBoFlxrCiKpo9fnpirc7FlpSJ921x1OSB6Ajw8PG74flwh19LCXPWJueqTM+caGPUKkk/9AMnORNbuL1Gh9QPwCm180/fnzLmWNuZaPkr6mDfc1HjttZvv4N2s9PR0PPLII/i///s/vPnmm+r61NRULFy4EF9++SXuvvtuAMCiRYvQsGFD7Ny5E+3bt8fGjRtx9OhRbNq0CcHBwWjRogXeeOMNvPTSS3j99dfh7e2NefPmoXbt2pgxYwYAoGHDhti+fTtmzZrFpgYRERERkZuoXbs2QkJCsHnzZrWJYTabsWvXLowcORIA0KFDB6SkpGDPnj1o3bo1AGDLli2w2Wxo166dVqETuZT0be9DslIAAD7N+sO7erPr34CIiEqFR2Ao/LqORlrcNEBsSF37GioP+5oXDSeX4xKv2FGjRqFXr152F+wDgD179iAnJ8dufXh4OGrWrIn4+HgAQHx8PJo2bWo3HVVUVBTMZjOOHDmi7uN431FRUep9EBERERGRPqSnp2P//v3Yv38/gPyztvfv349z585BURSMGTMGb775Jr777jscOnQIgwcPRlhYmHrGesOGDXHvvffiySefxC+//IKff/4Zo0ePxkMPPYSwsDDtEiNyEbmXzyDjl8/yF7xM8I/4j7YBERG5mQrth8KjSl0AQM75fcg6sELjiIhuXInP1DAYDP86v6WiKMjNzb3loAr76quvsHfvXvz6669FtiUkJMDb2xtBQUF26x0v5Ffchf4Ktl1vH7PZjKysLPj4+BR5bKvVCqvVqi6bzeYbT46IiIiIiMrV7t270b17d3W54DoXQ4YMweLFizF+/HhkZGRgxIgRSElJQadOnbB+/Xq7+X2/+OILjB49Gj169IDBYMDAgQMxZ86ccs+FyBWZN74D5OUAAPzuegIegWwGEhGVJ8XTG4E9X8OVTwcDANLi3oUpPBIGn/K/xhfRzSpxU2PlypXX3BYfH485c+bAZrOVSlAFzp8/j+effx5xcXElvkhIeZk6dSomT56sdRhERERERHQDunXrBhG55nZFUTBlyhRMmTLlmvtUqlQJX375ZVmER6Rr1jPxsB7fBAAw+FdDhY4jNI6IiMg9Get2hKlRNCxHv4ct4wrSts5C4C1cNJyovJV4+ql+/foV+RceHo7Fixdj+vTpuP/++3HixIlSDW7Pnj1ISkpCq1at4OnpCU9PT2zbtg1z5syBp6cngoODkZ2djZSUFLvbJSYmqhfpCwkJQWJiYpHtBduut09AQECxZ2kAQGxsLFJTU9V/58+fL42UiYiIiIiIiHRHbHkwr39bXfbv8R8YjBU0jIiIyL0F3PsKFK/83z0zf/kcOQnHNI6IqORu6poaf/31F5588kk0bdoUubm52L9/P5YsWYLbb7+9VIPr0aMHDh06pM55u3//frRp0waPPPKI+t9eXl7YvHmzepsTJ07g3Llz6NChA4D8C/kdOnQISUlJ6j5xcXEICAhAo0aN1H0K30fBPgX3URyj0YiAgAC7f0RERERERERUVNb+FchNOAoA8AxtDJ/mMRpHRETk3jwCw+DXZVT+gtiQuvb1657NSuRMSjz9FACkpqbi7bffxty5c9GiRQts3rwZnTt3LqvY4O/vjyZNmtitq1ChAipXrqyuHz58OMaNG4dKlSohICAAzz77LDp06ID27dsDACIjI9GoUSM89thjmDZtGhISEjBhwgSMGjUKRqMRAPD000/j/fffx/jx4zFs2DBs2bIFX3/9NdauXVtmuRERERERERG5A5s1A2mbZ6jLAfe+CsVwU8dYEhFRKapw1zBk7v8WeZfPIOfcbmQdXAXf5gO0DovoX5V4FDFt2jTUqVMHa9aswdKlS7Fjx44ybWiU1KxZs9C7d28MHDgQXbp0QUhICFasWKFu9/DwwJo1a+Dh4YEOHTrg0UcfxeDBg+3myK1duzbWrl2LuLg4NG/eHDNmzMCCBQsQFRWlRUpEREREREREupGxfT5s6ckAAGPDSBhrtdM4IiIiAgDF02h3LY20je/AZknTMCKikinxmRovv/wyfHx8UK9ePSxZsgRLliwpdr/CDYWy8MMPP9gtm0wmfPDBB/jggw+ueZvbb78d69atu+79duvWDfv27SuNEImIiIiIiIgIQF7KX0jfsSB/wcMLAfe8pG1ARERkx1ivM0wNo2A5tgG29EtI2zobgdETtQ6L6LpK3NQYPHgwFEUpy1iIiIiIiIiISEfMm6cDuVYAQIU7B8Ozci1tAyIioiIC7p0Ay2/bgBwLMn/5DL6tHoBXcAOtwyK6phI3NRYvXlyGYRARERERERGRnmRfOADLwf8BABTfivDrOlrjiIiIqDgeQWHw6/wM0rfMBGx5MK99DZWGLuUB7uS0eGUuIiIiIiIiIipVIgLz+jfVZf9uz8HgE6BhREREdD1+HZ+AR6XbAQDZZ3+F5dB3GkdEdG1sahARERERERFRqbIcWYec83sBAB5V6sK3zSCNIyIioutRPI0I6DlJXTZvmMqLhpPTYlODiIiIiIiIiEqN5FiRFjdNXQ6IioXi4aVhREREVBKmO7rBGH4PAMCWnoz0H+ZqHBFR8djUICIiIiIiIqJSk7FzEfJSLgAAvOt2gvGObtoGREREJRZw7wTA0wgAyNi1GDmJJzSOiKgoNjWIiIiIiIiIqFTkpV9C+k8f5S8oBgREvcILzRIRuRDPirfBr/PI/AVbHszrJkNEtA2KyAGbGkRERERERERUKtK3zIJY0wEAvq0fhFdwA40jIiKiG+XXcQQ8KtUEAGT/sQuWw6s1jojIHpsaRERERERERHTLchJPIHPv1wAAxegHv+5jtA2IiIhuiuJlREB04YuGvwPb3w1rImfApgYRERERERER3RIRgXn9W4DYAAB+nZ+Bh18VjaMiIqKbZarfHcYGPQAAtrREXjScnAqbGkRERERERER0S6ynfkD27z8DADyCbkOF9o9rGxAREd2ygOiJ/1w0fOdi5CSd0jgionxsahARERERERHRTZO8HJg3vK0u+98zHoqXUcOIiIioNHhWrAG/Tk/nL9hyedFwchpsahARERERERHRTcvcvRR5l34HAHjVbA1T454aR0RERKXFr9MIeFSsAQDIPhMPy5F1GkdEBHhqHQARERERERERuSZbVirStv5XXQ6IehWKomgYERERlSbFy4SA6Im4+uUIAEDq+rdgs6TB+ts2SGoyUgKrwtQwEj6NevIsPSo3bGoQERERERER0U1J3/Y+JCsFAGBq1g/etzXXNiAiIip1pgY9YKzfHdaTWyFpiTCvfhVQDIDYYL1ogPXYRpjXTUFQzHSY/r64OFFZ4vRTRERERERERHTDci+fQcYvn+UveJkQEPGCtgEREVGZMTa4236F2Oz+iiUNV798Cpbjm8o5MnJHbGoQERERERER0Q0zb3wXyMsBAPjd9QQ8AsM0joiIiMqC5FiRFvfev+0FAEhZ+SIkx1r2QZFbY1ODiIiIiIiIiG6I9Uw8rMfjAAAG/2qo0HGExhEREVFZyTq6DmIxl2BPgVjMyDr6fZnHRO6NTQ0iIiIiIiIiKjGx5cG8/m112b/Hf2AwVtAwIiIiKkvWY3H519AoCSX/GhtEZYlNDSIiIiIiIiIqsawDK5GbcBQA4BnaGD7NYzSOiIiIypItK+Wfa2j8G7Hl709UhtjUICIiIiIiIqISsVkzkLZpurocEPUKFAN/WiAi0jODT9ANnalh8Akqy3CI2NQgIiIiIiIiopLJ+Plj2NKTAQDG8HtgrN1e44iIiKisGRvec0NnahgbRpZtQOT22NQgIiIiIiIion+Vl/oX0ncsyF/w8EJA5EvaBkREROXCp1FPKKYAAMq/7KlAMQXAp1F0eYRFboxNDSIiIiIiIiL6V+ZN04EcCwCgwp2PwbNybY0jIiKi8qB4GREUUzD14PUaG4KgmOlQvIzlERa5MTY1iIiIiIiIiOi6si8cgOXg/wAAim9F+HUdrXFERERUnkwNeqDioHlQTP75K9RrbBRqcigGGHwrlnts5H7Y1CAiIiIiIiKiaxIRmDe8pS77d3sOBp9ADSMiIiItmMIjEPzCTgTGzIAxPAIIawljw3tgbBiVv4PYkPLtONgsadoGSrrnqXUAREREREREROS8LEfWIefcHgCAR5W68G0zSOOIiIhIK4qXEb7N+8PUtC+SkpIQVK0aFLHh8uKHkXNuD/Kunod53eRC01URlT6eqUFERERERERExZIcK9LipqnLAVGxUDy8NIyIiIicjeLhiaCYmVCMfgCArAMrkXVotcZRkZ6xqUFERERERERExcrYuRh5KRcAAN51O8F4RzdtAyIiIqfkWfE2BPZ+Q11OXTMRuSl/ahgR6RmbGkRERERERIWkpaVhzJgxuP322+Hj44O77roLv/76q7pdRDBp0iSEhobCx8cHEREROHXqlIYRE5WNvPRLSP/pw/wFxYCAqFegKMr1b0RERG7Lp1lfmJr1AwCIJQ2pK/4DseVpHBXpEZsaREREREREhTzxxBOIi4vDZ599hkOHDiEyMhIRERH488/8ow2nTZuGOXPmYN68edi1axcqVKiAqKgoWCwWjSMnKl3pW2dDrOkAAN9WD8AruIHGERERkbML7DUZHkG3AQCyz/6K9J/maRwR6RGbGkRERERERH/LysrCt99+i2nTpqFLly6oV68eXn/9ddSrVw8fffQRRASzZ8/GhAkT0K9fPzRr1gyffvop/vrrL6xatUrr8IlKTU7iCWTuWQYAUIx+8Lt7rMYRERGRKzCY/BE0cAag5P/snP7Df5F94YDGUZHesKlBRERERET0t9zcXOTl5cFkMtmt9/Hxwfbt23HmzBkkJCQgIiJC3RYYGIh27dohPj6+vMMlKhMiAvOGtwGxAQD8Oo+Eh18VjaMiIiJX4V2zDfy6jMpfsOUh5dsxsP195h9RafDUOgAiIiIiIiJn4e/vjw4dOuCNN95Aw4YNERwcjKVLlyI+Ph716tVDQkICACA4ONjudsHBweq24litVlitVnXZbDYDAGw2G2w2Wxlkcn02mw0iosljlzfmeuOsp35A9untAABD0G3waTfE6f7/8XnVJ+aqT8xVn/4tV98uz8B6+ifkXNiPvCvnkLpuCgL7vVPOUZYOPq/l+/glwaYGERERERFRIZ999hmGDRuG6tWrw8PDA61atcKgQYOwZ8+em77PqVOnYvLkyUXWJycna3ItDpvNhtTUVIgIDAZ9n8DPXG+M5OUC69785z7bjkDyldTSCrHU8HnVJ+aqT8xVn0qSq3R9Bfh6CJCTBcv+b2Gp1gJK3bvLOdJbx+e1/KSlpZVoPzY1iIiIiIiICqlbty62bduGjIwMmM1mhIaG4sEHH0SdOnUQEhICAEhMTERoaKh6m8TERLRo0eKa9xkbG4tx48apy2azGTVq1EDVqlUREBBQZrlci81mg6IoqFq1qlsU58y15DJ/+QxpKWcBAF41WqFih4egKEpphlkq+LzqE3PVJ+aqTyXKtVo1ZPWaDPOq8QAA5cf3ULlRV3gEhha/v5Pi81p+HKeAvRY2NYiIiIiIiIpRoUIFVKhQAVevXsWGDRswbdo01K5dGyEhIdi8ebPaxDCbzdi1axdGjhx5zfsyGo0wGo1F1hsMBs2KY0VRNH388sRcS8aWlYr0bXPV5YB7J8DDw6M0wytVfF71ibnqE3PVp5Lk6tsiBtm/bYPl8FqIxQzzqhdRachnUAzO+/1SHD6v5aOkj6n/Z4GIiIiIiOgGbNiwAevXr8eZM2cQFxeH7t27Izw8HEOHDoWiKBgzZgzefPNNfPfddzh06BAGDx6MsLAw9O/fX+vQiW5J+rYPIJlXAQCmZv3gfVtzjSMiIiJXpygKAnu/CY/AMABA9h+7kPHzAo2jIlfHpgYREREREVEhqampGDVqFMLDwzF48GB06tQJGzZsgJeXFwBg/PjxePbZZzFixAi0bdsW6enpWL9+fYlPlydyRrmXzyDjl0/zFzyNCOjxgrYBERGRbhh8AhAYMwP4ezrDtC0zkf3nQY2jIlfGpgYREREREVEhDzzwAE6fPg2r1YqLFy/i/fffR2BgoLpdURRMmTIFCQkJsFgs2LRpE+rXr69hxES3zhw3DcjLAQD43fUEPILCNI6IiIj0xFjrTlTo9HT+gi0XKd+OhS07U9ugyGWxqUFERERERETkxqxndsJ6bCMAwOBXFRU6PaVxREREpEf+3Z+HV/VmAIC8y3/AvP5NjSMiV8WmBhEREREREZGbElsezBveVpf9e/wHBmMFDSMiIiK9Ujy8EDRwJhRvXwBA1p5lsBzboHFU5IrY1CAiIiIiIiJyU1kHViL34hEAgGdII/i0iNE4IiIi0jPPyrURED1JXU753yvIMydoGBG5IjY1iIiIiIiIiNyQzZqBtM0z1OWAe1+BYvDQMCIiInIHPi3vg6lRNABAslKQsvJFiM2mcVTkStjUICIiIiIiInJDGT9/DFtaEgDAGB4BY+0OGkdERETuQFEUBPZ5E4aAEABA9u87kBG/UOOoyJWwqUFERERERETkZvJS/0L6jgX5Cx5eCIh8WduAiIjIrRh8gxAUMx1QFABA2uYZyPl7OkSif8OmBhEREREREZGbSds0A8ixAAAq3PkYPCvX1jgiIiJyN8baHVCh44j8hbwcXP1mLCQ7S9ugyCWwqUFERERERETkRrIvHEDWwVUAAMUnCH5dR2sbEBERuS3/7mPgFdYEAJB36TTMG97WOCJyBWxqEBEREREREbkJEYF5w1vqsn+352DwCdQwIiIicmeKpzeCBs6C4uUDAMjc/SUsxzdpHBU5OzY1iIiIiIiIiNyE5ej3yDm3BwDgUaUOfNs+rHFERETk7jyr1EHAvRPU5dT/xSIvLUnDiMjZsalBRERERERE5AYkx4q0je+qywGRsVA8vDSMiIiIKJ9P6wdhbBgJALBlXkHKyvEQm03jqMhZsalBRERERERE5AYydi5GXsoFAIB3nY4w1u+ucURERET5FEVBUN+3YfAPBgBkn/4JmbuWaBwVOStPrQMgIiIiIiIiorKVl34J6T99mL+gGBBw76tQFEXboIiIiAox+FZE0ID3cOXTwQAAc9w0eNduD6+QhhpHpn+SY0XW0XWwHNsISU1GSmBVmBpGwqdRTyheRq3DK4JnahARERERERHpXPrW2RBrOgDAp9X98ApuoHFERERERRnrdkSFu4bnL+RlI+WbsZAci7ZB6Zzl+CYkTm+P1BUvwHp8E/DXPliPb0LqiheQOL09LCc2ax1iEWxqEBEREREREelYTuIJZO5ZBgBQjH7wv3ucxhERERFdm3+P/8AzpBEAIDf5FMwb39E4Iv2yHN+Eq0ufhljS8leIze6vWNJw9cunYDm+SaMIi8emBhEREREREZFOiQjMG95Wf5zw6zwSHn5VNI6KiIjo2hRPI4LumwV45k97lPnLZ7Cc3KpxVPojOVakrHyxYOlaewEAUla+CMmxlktcJcGmBhEREREREZFOWU/9gOzT2wEAHkHVUaH9UI0jIiIi+ndeVeshIOpVdTl11XjkpV/SMCL9yTq6DmIx49oNjQICsZiRdfT78girRNjUICIiIiIiItIhycuBecNUddn/nvFOebFPIiKi4vi2fRjGBj0AALaMK0hdNR4i//YDPJWU9VgcoJSwPaAYYD22sWwDugFsahARERERERHpUObur5B36TQAwKtGK5ga99I4IiIiopJTFAWB/abC8Pe0idZT25C561ONo9IPW1bKP9fQ+Ddiy9/fSTh1U2Pq1Klo27Yt/P39Ua1aNfTv3x8nTpyw28disWDUqFGoXLky/Pz8MHDgQCQmJtrtc+7cOfTq1Qu+vr6oVq0aXnzxReTm5trt88MPP6BVq1YwGo2oV68eFi9eXNbpEREREREREZUJW1Yq0n74r7occO+rUBRFw4iIiIhunEeFygga8J66bI57BzmJJ65zCyopxbvCDexsgMEnqMxiuVFO3dTYtm0bRo0ahZ07dyIuLg45OTmIjIxERkaGus/YsWOxevVqLF++HNu2bcNff/2FmJgYdXteXh569eqF7Oxs7NixA0uWLMHixYsxadIkdZ8zZ86gV69e6N69O/bv348xY8bgiSeewIYNG8o1XyIiIiIiIqLSkL7tA0jmVQCAqWlfeN/WQtuAiIiIbpKxXpd/rgmVm42Ub8Y41UWrXVH2+b3IPr+35DcQG4wNI8suoBvkqXUA17N+/Xq75cWLF6NatWrYs2cPunTpgtTUVCxcuBBffvkl7r77bgDAokWL0LBhQ+zcuRPt27fHxo0bcfToUWzatAnBwcFo0aIF3njjDbz00kt4/fXX4e3tjXnz5qF27dqYMWMGAKBhw4bYvn07Zs2ahaioqHLPm4iIiIiIiKikJMeKrKPrYDm2EZKajKs+FZD9e3z+Rk8jAiJe1DZAIiKiW+Qf8SKsZ3YgN/EEcpNOwrxpGgKjJ2odlssRmw0ZOxYgbfMMwJb77zcAAChQTP7waRRdprHdCKc+U8NRamoqAKBSpUoAgD179iAnJwcRERHqPuHh4ahZsybi4/MHcPHx8WjatCmCg4PVfaKiomA2m3HkyBF1n8L3UbBPwX0QEREREREROSPL8U1InN4eqStegPX4JuCvfcg+vR2QPACAqUEPeASFaRwlERHRrVG8jAgaOAvw9AYAZO5cDMupbRpH5VpsGVdw9csnkRb3rtrQ8KhSD4Dy97/i5K8PipkOxctYLnGWhMs0NWw2G8aMGYOOHTuiSZMmAICEhAR4e3sjKCjIbt/g4GAkJCSo+xRuaBRsL9h2vX3MZjOysrKKjcdqtcJsNtv9IyIiIiIiIiovluObcHXp0xBLWv6KYi72aTnyPSzHN5VzZERERKXPK7gBAiJj1eXUVeORl35Jw4hcR/bZX5E8rw+sp37IX6EoqNB5JKo+sxYVB82DYvL/e73B7q9i8kfFh+fD1KBH+Qd9HU49/VRho0aNwuHDh7F9+3atQwGQfxHzyZMnax0GERERERERuSHJsSJlZcG0UnLdfVNWvojgF3Y61RGWREREN8P3zsdgPbUN1lM/wJZ+Can/i0XFhz+GolzrTAP3JjYbMrbPQ9rW2YAt/yxOQ4VKCIqZCWO9zgAAU3gEgl/Yiayj38NybAOsqckwBlaFqWEUfBpFO+X4wSXO1Bg9ejTWrFmDrVu34rbbblPXh4SEIDs7GykpKXb7JyYmIiQkRN0nMTGxyPaCbdfbJyAgAD4+PsXGFBsbi9TUVPXf+fPnbylHIiIiIiIiopLKOroOYjHj3xoagEAsZmQd/b48wiIiIipTiqIgsP+7MFSoDACwntyCzF+/1Dgq55SXfglXPh/29/Uz8hsa3rXaocrTa9SGRgHFywjf5v0R9MAHUPq9j6AHPoBv8/5O2dAAnLypISIYPXo0Vq5ciS1btqB27dp221u3bg0vLy9s3rxZXXfixAmcO3cOHTp0AAB06NABhw4dQlJSkrpPXFwcAgIC0KhRI3WfwvdRsE/BfRTHaDQiICDA7h8RERERERFRebAei/tnioh/oxhgPbaxbAMiIiIqJx5+VRDY/1112bzhLeQkndIwIudjPbMTl+b1Qfbpn/JXKAr8uj2HSkM+g0dA8PVv7AKcuqkxatQofP755/jyyy/h7++PhIQEJCQkqNe5CAwMxPDhwzFu3Dhs3boVe/bswdChQ9GhQwe0b98eABAZGYlGjRrhsccew4EDB7BhwwZMmDABo0aNgtGY32l6+umn8fvvv2P8+PE4fvw4PvzwQ3z99dcYO3asZrkTERERERERXYstK6XYa2gUS2z5+xMREemEqX53+N75WP5CrhUp346F5Fq1DcoJiC0PaT/MxZUlj8GWln+Qv8GvCioN/hT+3Z+HYvDQOMLS4dRNjY8++gipqano1q0bQkND1X/Lli1T95k1axZ69+6NgQMHokuXLggJCcGKFSvU7R4eHlizZg08PDzQoUMHPProoxg8eDCmTJmi7lO7dm2sXbsWcXFxaN68OWbMmIEFCxYgKiqqXPMlIiIiIiIi+je5yaeRe/VCyW+gGGDwCSqzeIiIiLQQEPkyPKvdAQDITTiWP82SG8tLS8aVT4cgfets9cAH7zp35U83VecubYMrZU59oXCRf5sbFDCZTPjggw/wwQcfXHOf22+/HevWrbvu/XTr1g379u274RiJiIiIiIiIykP2hf1I3z4f1uNxQAnqZZXYYGwYWXaBERERaUDxMiFo4Gxc+ngAkJeNjB0LYazXBca6nbQOrdxZf/8ZKd+Ogy39Uv4KxQC/7s/Dr/NI3ZydUZhTNzWIiIiIiIiI3JmIwPrbj8jYPh/Zf+y6iXtQoJj84dMoutRjIyIi0ppXSDgC7hkP8/o3AQApK19E1ZFrYahQSePIyofk5SJ921yk//iBesCDwT8YQffNgrFWO42jKztsahARERERERE5GcnLheXoOqRv/xi5Ccfsthn8q6FCh2HwCAhFyjdjCm5RzL0oAICgmOlQvIxlGi8REZFWfNsNgeXUNmSf/gm2tCSkfBeLig/Ng6IoWodWpvLMiUj5Zgyyz/6irjPW64zAmBnwqFBZw8jKHpsaRERERERERE5CsrOQue8bZMQvRN7V83bbPKrUgd9dT8KneT8onvlNCsXLhJSVL0IsZkAx5M+h/fdfxeSPoJjpMDXooUUqRERE5UIxGBA0YBoufdgLtswrsB7fhKw9X8G3zSCtQysz1t9+RMq3/4Et80r+CoMH/O8ehwodR0AxOPVltEsFmxpEREREREREGrNlpiDj18+RuXPJPz9Q/M2renP4dX4Kxgb3FPmhwhQegeAXdiLr6PewHNsAa2oyjIFVYWoYBZ9G0TxDg4iI3IKHfzUE9puKq0ufAgCkrn8T3rffCc+qdTWOrHRJXi7StsxCxvZ56jpDQAgq3v9feNdso2Fk5YtNDSIiIiIiIiKN5KX+hYz4Rcjc8xUkO9Num7FeZ1To9DS8a7W77hQaipcRvs37w9S0L5KSkhBUrRoMbnCUJhERUWGm8Aj4tn0Emb9+AeRYcPXbsajyxDdQPL21Dq1U5KX+havfjEHOuT3qOmP97gga8B4MvhU1jKz8cZRDRERERERUSF5eHiZOnIjatWvDx8cHdevWxRtvvAGRf65ZICKYNGkSQkND4ePjg4iICJw6dUrDqMnV5CT/hpSV45E0uzsy4j/5p6GhGGBq2gdVnl6NSo8thrF2e93PCU5ERFRaAiJj4VEl/+yM3ItHkLZllsYRlQ7Lya1I/qjPPw0Ngyf8I19GxUEfu11DA+CZGkRERERERHbeffddfPTRR1iyZAkaN26M3bt3Y+jQoQgMDMRzzz0HAJg2bRrmzJmDJUuWoHbt2pg4cSKioqJw9OhRmEwmjTMgZ5Z9bg/St38M64lN9hs8jfBteT8q3DUcnpVqahMcERGRi1O8fVDxvlm49H8DgbwcZOz4PxjrdYaxzl1ah3ZTJC8HaZumI2PHAnWdR1B1BN33X3jXaKlhZNpiU4OIiIiIiKiQHTt2oF+/fujVqxcAoFatWli6dCl++eUXAPlnacyePRsTJkxAv379AACffvopgoODsWrVKjz00EOaxU7OSURgPfUDMrbPR/bZX+22KT6BqND2Ufi2GwwPvyoaRUhERKQfXqGN4d/jBaRtnAqIIGXli6g6co3LndGQm/InUpY/h5wL+9V1xvAIBPWfBoNPoHaBOQFOP0VERERERFTIXXfdhc2bN+PkyZMAgAMHDmD79u2Ijo4GAJw5cwYJCQmIiIhQbxMYGIh27dohPj5ek5jJOUleDjIPrMKlD3vi6hdP2DU0DAEh8I96BdXG/gj/HuPY0CAiIipFFToMg/ffZ2fYzAlIXT3BbipRZ2c5HodLH/X+p6Hh4YWAeyeg4kPz3L6hAfBMDSIiIiIiIjsvv/wyzGYzwsPD4eHhgby8PLz11lt45JFHAAAJCQkAgODgYLvbBQcHq9scWa1WWK1WddlsNgMAbDYbbDZbWaRxXTabDSKiyWOXNy1ylexMZO1bjoz4T2BL/ctum0eVuqjQ8UmYmvaB4uGtxlga+LzqE3PVJ+aqT8zVuQT0exeX5/WBZKXAcnQ9Mvd+DZ+W99/w/ZRnrpKXjfS4acjctURd5xF0GwLv+y+8qjeDiJRpc0br57Wkj8umBhERERERUSFff/01vvjiC3z55Zdo3Lgx9u/fjzFjxiAsLAxDhgy5qfucOnUqJk+eXGR9cnIyLBbLrYZ8w2w2G1JTUyEiMBj0fQJ/eeYqllTg0LfA4W8AS6r9xuAmQMtHkVerI9IUA9Iup5T64/N51Sfmqk/MVZ+Yq7MxQLqMBza8AgAwr3sD5gp1oATVuKF7Ka9cxfwnsHESkHz8n5V1uiGv28u46uUPJCWV2WMX0Pp5TUtLK9F+bGoQEREREREV8uKLL+Lll19Wr43RtGlTnD17FlOnTsWQIUMQEhICAEhMTERoaKh6u8TERLRo0aLY+4yNjcW4cePUZbPZjBo1aqBq1aoICAgou2SuwWazQVEUVK1a1Yl/iCgd5ZFrXsqfyIj/BFn7lgM5WXbbvO/oigodn4JXzTZQFKVMHr8An1d9Yq76xFz1ibk6oWr3w5x8AFl7lwG5WfDc9hYqDVsGxcOrxHdRHrlajq6H+btYiDU9f4WHF/wjX4FP20fKfPxQmNbPq8lkKtF+bGoQEREREREVkpmZWaSI8/DwUE+Hr127NkJCQrB582a1iWE2m7Fr1y6MHDmy2Ps0Go0wGo1F1hsMBs1+CFAURdPHL09llWtO4glk/Pwxsg6tBmx5/2wweMCnSR9U6DQCXsENSvUx/w2fV31irvrEXPWJuTqfgOgJyD77C/Iun0HuX4eQsW0uAiJeuKH7KKtcJccK88apyPzlM3WdR6WaqHj/XHiFNSnVxyopLZ/Xkj4mmxpERERERESF9OnTB2+99RZq1qyJxo0bY9++fZg5cyaGDRsGIL/QGzNmDN58803ccccdqF27NiZOnIiwsDD0799f2+CpXGSf3Y307fNgPbnVfoOXCb6tHkCFDsPhWfE2bYIjIiIiOwZvX1S8bxYu/d99gC0XGdvnwVivM4y12mkaV+7lM7i6/HnkXjyirjM16YXAPm/BYPLXMDLnx6YGERERERFRIXPnzsXEiRPxzDPPICkpCWFhYXjqqacwadIkdZ/x48cjIyMDI0aMQEpKCjp16oT169eX+JR5cj1is8F6cgvSt89Hzvm9dtsUnyBUaDcYFe58DIYKlTSKkIiIiK7FK6wp/O8eh7RN0wARpKz4D6qOXAuDT6Am8WQdWo3U1RP+mW7K0xuB0ZPg0/qhcp1uylWxqUFERERERFSIv78/Zs+ejdmzZ19zH0VRMGXKFEyZMqX8AiNNSG42sg6vRsb2/0Nu8im7bYbAUPh1GA6f1g/C4O2rUYRERERUEhU6PgHrb9uQ/ccu2FIvInX1BATdP6dcmwiSY4F5/ZvI3L1UXedRuTYqPvA+vELCyy0OV8emBhEREREREZEDmzUDmXuWISN+IWzmBLttntXuQIVOT8GnSe8butAoERERaUcxeCAoZjqSP+oNyUqF5cg6ZNXvBt8WA8vl8XMv/Y6rXz+L3MTj6jqfZv0R0HsKDMYK5RKDXrCpQURERERERPS3vIzLyNz1KTJ++QySlWq3zatmG/h1egrGO7pBcfKLohIREVFRHoFhCOzzJlK+fhYAYF47Gd4128Cz0u1l+riZB1bBvGYiJDszf4WXCYE9X4dPy/s43dRNYFODiIiIiIiI3F7u1fPI2LEQmfuWAzkWu23GBj3g12kEvGu20Sg6IiIiKi0+jXvC2nIbsvZ9A8nOQMq341B52FdlcvalZGchdd1kZO1brq7zrFoPQQ/MhVe1+qX+eO6CTQ0iIiIiIiLSHcmxIuvoOliObYSkJiMlsCpMDSPh06gnFC+jul9OwnGkb58Py5G1gC3vnzsweMKnWV9U6Pgkf3QgIiLSmYDoScg++yvyrpxFzoX9SN/2PvzvHluqj5GTdAopy59FbtI/1+TyaTEQAb1e57W4bhGbGkRERERERKQrluObkLLyRYjFDCgGQGywXjTAemwjzOumIHDAezCY/JGxfT6sp7bZ3Vbx9oVvqwdRocMweASFaZQBERERlSWDsQKCBs7C5YX3A7Y8pP/4IYx1O8P79tI5KzNz3zdIXfuaevan4uWDgN5T4NsiplTu392xqUFERERERES6YTm+CVeXPv3PCrHZ/RWLGSlLnypyO4NvJfi2G4wKdz4Kg2/F8giViIiINOR9W3P4dXse6VtmAmJDyopxqPL0Ghh8Am76Pm3WDJjXvoasAyvVdZ7V6qPiA+/Ds2rd0gibwKYGERERERER6YTkWJGy8sWCpRLdxiOoOirc9QR8W94Pxdun7IIjIiIip+PX+Wlkn/4pfyqqlD+RuvY1VLxv1k3dV07iCVz9+lnkXTqtrvNp/SACoydB8TKVVsgEwKB1AERERERERESlIevouvwpp0rY0PBt+zCqPrcZFdoNZkODiIjIDSkGDwTGzIBi8gcAWA59h6yD/7uh+xARZO7+Cpc+HqA2NBTv/Omtgvq+zYZGGWBTg4iIiIiIiHTBeiwu/xoaJaEYYEu/DMXDq2yDIiIiIqfmGVQdgb3fUJdT10xC7tXzJbqtzZqOlG/HInX1q0CuNf/+QhqiylP/g0+zvmUSL7GpQURERERERC7OZklD5v5vYT2z859raPwbscGWlVKmcREREZFr8GnaBz7NBwAAxJqOlG/HQfJyr3ubnItHcWl+P1gOrVbX+bZ9BFWe+BaeVWqXabzujtfUICIiIiIiIpcjOVZYf/sBWQdXw3JyM5CbfWN3oBhg8Akqk9iIiIjI9QT0fA3Z53Yj7+p55Jzfi7Qf5sKzSi1Yjm2EpCYjJbAqTA0jYWoYjaz938K84U11/KEY/RDY9234NOmlcRbugU0NIiIiIiIicgliy0P2HzvzGxnH1kMsabdyZzA2jCy94IiIiMilGUz+CBo4E5c/eQiw5SHjx/fzNygGQGywXjTAemwjUg0vA7Z/zuLwCmuCoPvnwLPS7RpF7n7Y1CAiIiIiIiKnJSLI+esQLAe/Q9bhNbClJxfZx1ChEkyNe8HUKBpXv3r672bH9S4WrkAx+cOnUXSZxU1ERESux7tGK5ga97SbUkqd2rLgb6GGhm+7wQiIfBmKp7EcoyQ2NYiIiIiIiMjp5CafRtah1cg69B3yrpwtsl3xrgBTw0j4NOsL79p3QfHIL2+DYqbj6pdPAVBQfGNDUfdTvPgDBBEREf1DcqywntpWon0VL18E3MOGhhbY1CAiIiIiIiKnkJd6EVmH1yLr0HfIvXik6A4e3jDe0RU+zfrCVP9uKF6mIruYGvRAxUHzkLLyRYjFrE4ZUfBXMfkjKGY6TA16lENGRERE5Eqyjq7LHz+UgORkIuvo9/Bt3r9sg6Ii2NQgIiIiIiIizdgyU2A5uh5Zh75D9tlfAHE4u0JR4F2rPXya9oWpURQMPoH/ep+m8AgEv7ATWUe/h+XYBlhTk2EMrApTwyj4NIrmGRpERERULOuxuH8OiPg3Sv41NtjUKH9sahAREREREVG5kuwsWE5uRtbB1bD+tg3Iyymyj1dYU5ia9oVPk17wCAi+4cdQvIzwbd4fpqZ9kZSUhKBq1WAwGEojfCIiItIpW1ZKyRoaACC2/P2p3LGpQURERERERGVO8nJg/f1nWA6uhuX4Rkh2ZpF9PCrXhk/TvvBp2geeVWprECURERG5M4NP0A2dqWHwCSrrkKgYbGoQERERERFRmRCbDTkX9iLr4GpYjqyDLfNKkX0M/sHwadIbPs36wDO0CRRF0SBSIiIiIsDY8B5Yjm0o2c5ig7FhZNkGRMViU4OIiIiIiIhKVU7iCWQd/A6Ww6uRl/Jnke2KKQCmRvfCp1lfeN9+JxSDhwZREhEREdnzadQT5nVTIJY0AHKdPRUoJn/4NIour9CoEDY1iIiIiIiI6JblXr0Ay6HVyDr0HXKTThbdwdMIU4Me8GnWF8Z6XaB48mLdRERE5FwULyOCYqbj6pdPAVBQfGMj/6zSoJjpULw4ntECmxpERERERER0U/LSL8Fy5HtkHfoOOef3Ft3B4AFjnY4wNesLU/g9MBj9yj9IIiIiohtgatADFQfNQ8rKFyEW8z/X2Pj7r2LyR1DMdJga9NA6VLfFpgYRERERERGVmM2aDsvxOFgOfgfr7z8Dtrwi+3jVaAWfpn1hahwND78qGkRJREREdPNM4REIfmEnso5+D8uxDbCmJsMYWBWmhlHwaRTNMzQ0xqYGERERERGRm5AcK7KOroPl2EZIajJSAqvC1DASPo16Xrc4l1wrrL/9mH+djJNbgBxLkX08q90Bn6b9YGraG54Va5RlGkRERERlTvEywrd5f5ia9kVSUhKCqlWDwWDQOiwCmxpERERERERuwXJ8U5FpFKwXDbAe2wjzuilFplEQWx6yz/6S38g4uj7/dg48AsNgatoXPs36wiu4QXmmQ0RERERuik0NIiIiIiIinbMc34SrS5/+Z4XY7P6KJQ1Xv3wKQQ99BM/AUGQd+g5Zh9fCZk4ocl8G30owNe4Jn2Z94HVbKyg8YpGIiIiIyhGbGkRERERERDomOVakrHyxYOlaewEAUpY980/DoxDF2xem8EiYmvWBsU5HKB5eZRMsEREREdG/YFODiIiIiIhIx7KOrit26qhiFW5oeHjBWK8rfJr1gbH+3TB4+5ZNgEREREREN4BNDSIiIiIiIh2zHotTr6FREopvRQT0eAGmRvfC4BtUtsEREREREd0gNjWIiIiIiIh0zJaVUuKGBgB4VasP3zYPlV1ARERERES3gFd0IyIiIiIi0jGDT1D+mRoloRjy9yciIiIiclJsahAREREREemYseE9JT9TQ2wwNows24CIiIiIiG4BmxpEREREREQ65tOoJxRTAADlX/ZUoJgC4NMoujzCIiIiIiK6KWxqEBERERER6ZjiZURQzPSCpWvtBQAIipkOxctYLnEREREREd0MNjWIiIiIiIh0ztSgByoOmgfF5J+/ouAaG3//VUz+qPjwfJga9NAoQiIiIiKikmFTg4iIiIiIqJBatWpBUZQi/0aNGgUAsFgsGDVqFCpXrgw/Pz8MHDgQiYmJGkf970zhEQh+YScCY2bAGB4BhLWEMTwCgTEzEPzCTjY0iIiIiMgleGodABERERERkTP59ddfkZeXpy4fPnwY99xzD+6//34AwNixY7F27VosX74cgYGBGD16NGJiYvDzzz9rFXKJKV5G+DbvD1PTvkhKSkJQtWowGHisGxERERG5DjY1iIiIiIiICqlatard8jvvvIO6deuia9euSE1NxcKFC/Hll1/i7rvvBgAsWrQIDRs2xM6dO9G+fXstQiYiIiIichs8JIeIiIiIiOgasrOz8fnnn2PYsGFQFAV79uxBTk4OIiIi1H3Cw8NRs2ZNxMfHaxgpEREREZF74JkaRERERERE17Bq1SqkpKTg8ccfBwAkJCTA29sbQUFBdvsFBwcjISHhmvdjtVphtVrVZbPZDACw2Wyw2WylHve/sdlsEBFNHru8MVd9Yq76xFz1ibnqE3PVJ61zLenjsqlBRERERER0DQsXLkR0dDTCwsJu6X6mTp2KyZMnF1mfnJwMi8VyS/d9M2w2G1JTUyEiur+mBnPVJ+aqT8xVn5irPjFXfdI617S0tBLtx6YGERERERFRMc6ePYtNmzZhxYoV6rqQkBBkZ2cjJSXF7myNxMREhISEXPO+YmNjMW7cOHXZbDajRo0aqFq1KgICAsok/uux2WxQFAVVq1Z1i+KcueoPc9Un5qpPzFWfmKs+aZ2ryWQq0X5sahARERERERVj0aJFqFatGnr16qWua926Nby8vLB582YMHDgQAHDixAmcO3cOHTp0uOZ9GY1GGI3GIusNBoNmxbGiKJo+fnlirvrEXPWJueoTc9Un5qpPWuZa0sfU/7Nwgz744APUqlULJpMJ7dq1wy+//KJ1SEREREREVM5sNhsWLVqEIUOGwNPzn2PBAgMDMXz4cIwbNw5bt27Fnj17MHToUHTo0AHt27fXMGIiIiIiIvfApkYhy5Ytw7hx4/Daa69h7969aN68OaKiopCUlKR1aEREREREVI42bdqEc+fOYdiwYUW2zZo1C71798bAgQPRpUsXhISE2E1RRUREREREZYdNjUJmzpyJJ598EkOHDkWjRo0wb948+Pr64pNPPtE6NCIiIiIiKkeRkZEQEdSvX7/INpPJhA8++ABXrlxBRkYGVqxYcd3raRARERERUelhU+Nv2dnZ2LNnDyIiItR1BoMBERERiI+P1zAyIiIiIiIiIiIiIiICeKFw1aVLl5CXl4fg4GC79cHBwTh+/HiR/a1WK6xWq7qcmpoKADCbzWUb6L/IykjT9PFvVEn/f1ky0ss4ktJ1I68Da6Z+c8vO0m9uOTrOLdeSUYaRlL4byy2zDCMpfTeSW541qwwjKX03lFu2pQwjKX03lFuO9d93ciI3llt2GUZS+m4ot9zcMoyk9Gk5Pi14bBHRLAZnVfD/RKvnx2azIS0tDSaTSfcXvGSu+sRc9Ym56hNz1Sfmqk9a51rS+kERVhgAgL/++gvVq1fHjh070KFDB3X9+PHjsW3bNuzatctu/9dffx2TJ08u7zCJiIiIiFzO+fPncdttt2kdhlO5cOECatSooXUYRERERERO59/qB56p8bcqVarAw8MDiYmJdusTExOLnR83NjYW48aNU5dtNhuuXLmCypUrQ1GUMo+3PJnNZtSoUQPnz59HQECA1uGUGr3mBTA3V8XcXBNzc03MzTUxN9cjIkhLS0NYWJjWoTidsLAwnD9/Hv7+/prUD3p9zRWHueoTc9Un5qpPzFWfmKs+aZ1rSesHNjX+5u3tjdatW2Pz5s3o378/gPxGxebNmzF69Ogi+xuNRhiNRrt1QUFB5RCpdgICAnT5xtVrXgBzc1XMzTUxN9fE3FwTc3MtgYGBWofglAwGg1OcvaLH19y1MFd9Yq76xFz1ibnqE3PVJy1zLUn9wKZGIePGjcOQIUPQpk0b3HnnnZg9ezYyMjIwdOhQrUMjIiIiIiIiIiIiInJ7bGoU8uCDDyI5ORmTJk1CQkICWrRogfXr1xe5eDgREREREREREREREZU/NjUcjB49utjpptyZ0WjEa6+9VmS6LVen17wA5uaqmJtrYm6uibm5JuZGVHrc6TXHXPWJueoTc9Un5qpPzFWfXCVXRURE6yCIiIiIiIiIiIiIiIj+jUHrAIiIiIiIiIiIiIiIiEqCTQ0iIiIiIiIiIiIiInIJbGoQEREREREREREREZFLYFODiIjoFhw8eBC5ublah0FERERERC6CNQQR0a1hU4OIiOgmTZkyBS1atMC2bduQl5endThERYiI1iEQERERUSGsIUiPWHdQeVOErzoi3RMRKIqidRilzmazwWDQd29WT8+dYy56ye3ee+/FkSNHsGjRInTv3h0eHh5ah1RmCj9nenn+9CwvL099PR48eBA1a9ZEUFCQtkEREVG5cpfva3eoCxzp+bnVa91QmDvVEI5YU+gP6w7Sgnt969MNs9lsxa5nL8w1/fTTT9ixY4fWYZSagsJl0qRJ+PnnnzWOpmwUDPDS09M1juTW2Gw2NZfs7GwAcPnBa05ODgBg/fr1CA8Px5AhQ7B161ZdH23lWHxc6zvC1eglj8L++OMP9OjRAwCwatUqREdH4/Tp0xpHVfr0PB4pyO3y5csaR0J6xXG+e9FbHeDIHeoCR3qpExzpsW4ozB1rCEd6rSkc6TUvR+5Sdzhyl/GSM9ckbGrQNYmIOjicO3cunn32WTz11FNISkrS1aBCz180BR8+iqJg8+bN6Nq1K65cuaIOpFxV4eds5cqVeOutt3R9ZMu0adMQGxurdRg3rfBnyfTp0/H444+jX79+2L9/v1qouBqbzQYvLy91OS4uDg0bNtRtUVL4PffVV1+hT58+yM3NhcFgcPnP0MJHdm7fvh07d+7Erl27NI7q1mVkZODChQto0KABYmJi8N5776F169Zah1XqFEXBzp07sXz5cgD6KS4Kivy1a9eif//+WL9+vdYhkc64yzjfkat/Z90IvdYBjtytLnDk6nWCIz3WDYW5Ww3hSM81hSO91hjFcZe6w5Fe65DCnL0mYVODilX46IjXXnsNkyZNQnJyMjZt2oQ777xTF0e/WCwWAP8c1bNv3z4sX74c69atw/nz59X9XPmDqeA5/PPPP3H69Gm89dZb6N27Nzw9PTWO7NYUPGdLly7FX3/9hXnz5qF9+/YaR1V2qlSpgq+++grHjh3TOpQbVviz5J133sGbb76JqlWr4uzZs4iMjMTy5cuRkZGhcZQ3ruA1uHbtWsTHxwMANm3apMuipPCAfMuWLdiyZQvWr1+PUaNGuXwRUrhwHjduHAYMGICBAwciOjoaw4YNw8WLFzWO8OY1btwYY8aMwalTp3D77bfj4YcfBqCvH/REBHl5eZgwYQKWLFkCQD9HciqKgv/973+4//770bt3bwQGBmodEumIO4zzHbnDuN+RXusAR+5WFzhy5TrBkV7rhsLcqYZwpOeawpGea4ziuEPd4UjPdUhhTl+TCNF1JCYmypAhQ+SXX34REZGcnBzp2bOnhIaGyo8//qhxdDfvrbfekueee06Sk5NFRGTlypViNBqlWbNm4uXlJZ06dZLZs2drHGXpOHv2rCiKIgEBAfLuu+9qHU6pOXXqlNSsWVMURZEZM2aISP7r09XZbLYiyydPnpS77rpLFi5cKCIiubm5WoR2S/744w8ZMWKE3efG448/LqGhofLpp59Kenq6htHdnOPHj0twcLAMGTJEfv31V3V9jx49JCwsTDZt2uSSz9W1jBs3Tlq3bi1PPfWU3HnnnRISEiKPPPKI+r7Ly8vTOMIbU/i99uuvv0q9evUkPj5eDhw4IBs3bpTKlStL7969Xe61WZBXdna2bN++XWbMmCEtW7aU5s2bS2pqqojo47NS5J9cd+3aJZUrV5Zvv/1W44hKT2JiorRu3Vreeecdu/WO3xFEt0Kv43xH7jTud6TXOsCRXusCR3qtExzpsW4ozN1qCEd6qykc6bXGKI471R2O9FyHFObsNQmbGnRNCxYskICAAGnTpo0cP37cblvPnj0lLCxMfvrpJ42iuzXz588XRVHklVdekWPHjkm7du1k3rx5kpmZKYcPH5ZRo0ZJq1atZM6cOVqHWirmzZsn3t7eMnToUMnMzNQ6nJvi+KGZlZUlq1atkmbNmknr1q3V9XoZADoOckaPHi21a9eWrKwsjSK6eZ999pkoiiLh4eF2A3cRkaFDh0pYWJh89tlnYjabNYqwZIr74v7666+lSZMmMmzYMLvcIiIipEaNGrJmzRqXH5iLiGzcuFGqVKkiO3bsEJH8YmPmzJnSokULefTRR126CFm4cKE8/PDD8vTTT9utP3HihPj5+cnLL7+sUWQ3ruA1GhcXJ6+++qrs27dPREQOHjwozZo1k2bNmtl9tsTFxUlKSooWod40x/dhXl6epKSkyMMPPywjR44Um83mkq9DR6dOnZLq1aur4yybzeY0xQPpg57H+Y7cbdzvSA91gCN3qwsc6alOcKSXuqEwd64hHOm5pnCkpxqjOO5QdzhylzqkMGevSdjUoGtKTEyUHj16iKenp2zfvl1E7L9c+vTpI4qiyP79+7UK8ZYUDJjGjh0rMTExkpiYqG47c+aMPPXUU9K9e3f1qC5X99FHH4miKC55lJbjF4PVahWR/CMC1q1bJ7fffrv06NFD3e7qBcz8+fPlvvvuk02bNkl2draIiFy+fFlat24tH374oYg4T2e8OMV9kffr108URZFPP/1UzanA8OHDRVEU+f7778srxFtScNRJgW+++UYaNmwow4YNkz179qjrW7RoIX379i3v8MrE559/LmFhYXLp0iV1ndlslkmTJomvr6888cQT6vPqzK9NRwkJCTJo0CCpWLGiDBgwQF1vsVhEROS9996TFi1ayOXLl10mr2+//VZMJpO8+eabdt/Phw4dUguM3bt3y0svvSS1atWSCxcuaBjtzdm1a1eRo6GWLFkiPj4+cvDgQRFxrddhcc6ePSu1atWSJUuWqOsKPls3bNgga9as0So00gm9j/Mdudu435Er1wGO3K0ucOTqdYIjvdcNhbljDeFIrzWFIz3WGMVxh7rDkTvUIYU5e03CpgaJyLU74UlJSXLnnXdKw4YN5dSpUyJi/wZ94YUXXG6gWDj+JUuWiKIo4uHhIbt377bbb/fu3aIoimzdurWcI7x5BbkdOnRINm/eLCtXrrTbPnfuXFEURaZNm+aSH7TvvvuuDBgwQNq3by8ffvih/PHHHyIism7dOqlfv75ERkaq+7pyh/y9996TRx55RLy9veXhhx+WOXPmSF5enjz44IMyaNAgrcMrsTVr1qhH4YiIREVFSUhIiMTFxRX53Hjrrbec9rNkx44dcuLECRERmTVrlowePVrOnDljt8/XX38tVatWlccee8yuKHHF12Fxnw0//vijhIeHy8aNG+3Wnzt3TqpXry7169eX4cOHO+1zWKC452P37t3y+OOPi5eXl3z++ed22z788ENp1qyZpKWllVeIt+To0aNSq1YtmT9/frHbT5w4Ie3atZMaNWpInTp1inzvOTubzSaXL1+WwYMHi6Io8thjj8lnn32mbr///vvlwQcfdLnT+Qu/5wpeo+np6dKtWzfp3r27HD582G7/MWPGSHR0tMvlSdpxp3G+Iz2P+x3pvQ5w5C51gSO91AmO9FI3FOZuNYQjPdcUjvReYxRH73WHI73WIYW5Yk3CpgbZfQAfPHhQ9u7da9dBTU5OltatW0vjxo2LLXhEXPsImGXLlomiKDJy5Ei5ePGiuj45OVkaNmxY5AvXWRU8JytWrJDbbrtNmjZtKkFBQRIZGSkHDx5Un+e5c+eK0WiUyZMnaxluiRR+bb722mtSqVIlef7552X48OFSqVIlefjhh9Uvx7Vr10qjRo2kZcuWWoV7U643YN22bZuMHTtWqlevLr1791aPTFq9enU5RnhzbnauWGf7LDlz5oy0a9dO+vTpIxcvXpRPPvlEKleuLLGxsWrxXOD111+XoKAguf/+++XIkSPqemfLqaTeeecd2bZtm4jkH23UqlUr6d27txw6dEjd5/Tp03LffffJG2+8IS1btlSP9nVGhd9r586dk8OHD6vrzpw5I4MHD1aPQsnIyJCEhASJiIiQ6Ohop/7xp/ApwN9//73Ur1/frmAuLvb4+HhJSEgorxBLXUZGhuzatUt69uwpLVq0kFatWklcXJxMnDhRoqOj1bGKKyh86v6YMWPknnvukXnz5klCQoKcO3dOQkNDJTIyUt5//31ZvXq1PPPMMxIYGGj3PiS6Hncf5zvSy7jfkR7rAEfuUBc40mud4EgvdUNh7lxDONJbTeFIrzVGcdyx7nCkpzqkMFetSdjUcHOFP4AnTpwoderUkTp16oifn58sWrRIrly5IiIily5dkjZt2kizZs3k2LFjWoV70wp/+P7222+yc+dO+fnnn9XT/z755BNRFEVGjBghP/30k5w5c0ZiY2MlKChIzp07p2XoNyQuLk4qVqwoCxYsEBGRvXv3iqIocvfdd8uePXvU/wfTpk2TSpUqyeXLl7UMt8TOnj0rL7/8smzevFldt2HDBmndurU8/vjjkpGRIVlZWbJ8+XIZNGiQyxzZUjjO7777TpYsWaIe6VD4oluXL1+W559/XmJiYkRRFHnqqaeK3F5rep4r9uOPP5bu3bvLgw8+KGlpabJs2TIJCQmRl156yW4QN2vWLOnatasMGTLEJfK6HrPZLH379hWDwaAWFUeOHJGwsDCJjIyUGTNmyKZNmyQiIkIGDRokV65cEX9/f5k5c6bGkRev8Otz4sSJ0qxZMwkNDZVWrVrJ9OnTJSMjQ44ePaoeeVOzZk15/PHHpUuXLur81M72nGZmZorFYpGzZ8+qc6R/8803EhwcrM5XW/jCfDt27JBdu3ZpEuutKHjujh8/Lhs2bJBff/1V/UH2ypUrcvjwYRkwYIDcdddd0q5dO3XefFeyYsUK8ff3l6efflomTpwo1atXl8jISLFarXLq1Cnp16+fNGjQQOrWrSudOnXSzXRAVPbcZZzvyF3G/Y70Wgc40mtd4EhPdYIjPdcNhbljDeFIbzWFIz3WGMVxl7rDkTvUIYW5Yk3CpgaJiMjkyZMlNDRUPTrp0UcflYCAAJk2bZpcvXpVRPILnpo1a8qjjz6qYaQ3p+DD6Ntvv5Xw8HCpV6+etG/fXpo1a6bOqVsw166iKHL//fdL586d1YsdOaszZ87I//73PxHJn092zJgx8tprr4mIyO+//y516tSRxx9/XOrUqSPt27eX3bt3q1+aBYWss/vf//4niqJItWrV7IoXkfyjA7y9vdWpAgp/kTr74KBwfC+99JKEhYXJXXfdJVWrVpWoqCjZu3dvkRwyMzPlv//9r5hMJqf90UFPc8UWHqQuWrRIOnbsKA8++KCYzWZZunSphIaGyvjx42XXrl2SnZ0tMTExsnz5cvV2zv4aLKwg1sI5nzt3TgYPHize3t7q0VXHjh2T++67T8LDw6Vu3brStWtXdWDboUMH+eqrr8o/+Bvw9ttvS3BwsKxdu1Zyc3OlR48ecvvtt8uBAwdEJL/IGjZsmNSrV8+umCr4IcxZHD16VGJiYqRJkybi6ekpLVq0kMmTJ0tiYqJUqVJFxowZU+Q2Y8aMkalTpxaZm9qZFbwev/nmG6levbrUqlVLbr/9dmnQoEGRCxhv2LBB3nnnHQkODlafT1dw7tw5adasmXz00Ucikp+zv7+/jB8/Xj1CMzs7W8xms1y4cMGlLoxKzkPv43xHeh33O3KHOsCRXusCR3qtExzpqW4ozJ1qCEfuUlM40kuNURx3qTscuUMdUpir1iRsaripwvM7Hjp0SCIiItRTVVetWiUVK1ZUL9A1bdo09UJOKSkpLnEaZMGXaUZGhrpu27Zt4ufnJ/Pnzxer1SqrV68WRVFk5syZdqdsK4oiU6ZMUTvOzurPP/+UKlWqSMOGDeWLL74QEZGNGzfKkSNH5OrVq9K2bVt54oknRERky5YtoiiKtG7d2im6qddT8NwV/P3zzz9l1KhRoiiKLF68WETsi5TGjRvLe++9V/6BlpIZM2ZIWFiYerr80qVLRVEU6dy5s+zdu1d9bRYeGHbu3NlpLvSo97lir1eULF++XJo2bSohISFSv359ady4sfradLVTiQsUHLVZEP/58+fl0UcfFW9vb/nxxx9FRCQtLU0uX74sZ8+eVW8XGxsrYWFhRZ57rRUuFFJSUqRbt27y6aefikj+56W/v7961GPBd9uBAwfkySeflIYNGxaZj9wZHDx4UAIDA2XUqFGyYMECWbFihfTr1088PDxk4MCB8sUXX0jlypVl1KhRcv78eTly5Ih6BLKr/Mgh8s/n/K5du8Tf31/mzZsnFy5ckB9++EEeffRRMZlMdnNvFygoiF3FuXPnpGXLlpKeni4nT56U6tWry5NPPqluj4+PL/KjD9G/0fs435E7jPsd6bUOcORudYEjV68THOm9bijM3WoIR3qrKRzpscYojrvUHY7cpQ4pzFVrEjY13FDB/I59+/aV06dPi8VikQULFojVapUff/xRwsLCZO7cuSIi8sADD0hQUJBMmjTJrhPnzAVPwYBn9+7dUrduXfUL8d1335VRo0aJSP4btmbNmuqyiKhd5M8//1yOHj1avkHfhK1bt4rBYJC2bdtK3759ZenSpeq2FStWSJs2bdT5/NatWyd9+/aVli1byunTp7UK+V8tXbpUhg4dKidOnLC72NDFixdl8ODB4uPjY3dUVmpqqtSpU0fmzZunRbg3ZcOGDepzlZKSIk8//bR64bBvv/1WgoKCZMaMGVK3bl3p3Lmz7N69u8jgtm3btuqReFpyl7lir1WUXLlyRU6ePClffvmlzJs3Tx38uEJOxVm2bJn4+vqqA9CCvM+ePSv9+vUTX19fu+JSRGTfvn3Sp08fCQsLk71795Z7zNezYcMGmTZtmnrqc0pKirRs2VKSkpJk48aN4ufnpx6JkpmZKfPmzVML7f3798uIESMkODhYVq1apVkOjpKSkqRly5by8ssvF1n//vvvi7+/v/Tu3Vu+++47CQkJkbCwMKlbt640aNDA6Z6fa/njjz/U115ubq4sWLBAunfvbvdjxsWLF+Xhhx+Wli1bSnJyst3tnfnHgIyMDElOTpYtW7bIhQsXJCUlRS5cuCDVq1eXLVu2SN26deXJJ59Ucz1w4IA89NBDLvPckXPQ+zjfkbuM+x3psQ5w5A51gSM91QmO3KVuKMxdaghHeqspHOmxxiiOO9QdjvRchxSmp5qETQ039fHHH8vdd98tDzzwgJw/f15d/+STT8rQoUPVgf7o0aOlZcuW0rFjR5d4gxa86fbv3y/+/v52p8I9+eSTMnz4cPnzzz/ltttukxEjRqg5LV++XKZPn+5yA4lhw4ZJixYtZODAgdK9e3f16ICPPvpIqlevrs7398orr8ikSZOcOr/U1FSpW7euVK1aVZo2bSrDhw+XRYsWqdszMjJk0KBBYjKZ5Pnnn5d3331XevfuLU2aNLE7QsuZbd++XRRFkTZt2qhH1W3evFkSEhJk3759UrduXfnvf/8rIiKffvqpKIoijRo1UgdBIvlHCxiNRqc5rdFd5oot/Pn3ySefSKdOneTBBx+U33//3W67M7/HHDke/bhz506JioqS2rVry/Hjx+22ff311+o0HQcPHrS7nw8++EDd31l88sknUr16dRk5cqTdvMx33XWXdO7cWQICAtQ5x0Xyi6yuXbvaneq+e/duefbZZ+W3334r19ivZ+/evdKkSRM5dOiQ+loreI6uXr0qb775pvj7+8v69eslOTlZ4uLiZMeOHXYXw3VmFotF2rdvL7Vq1VLfUzNnzpSKFSuqU+QUrF+zZo3UqFHDZY4CO3HihAwePFjCw8PFZDJJYGCgPPzww3Lo0CEZP368KIoi9913n91tYmNjpW3btvLXX39pFDW5Kr2O8x2527jfkZ7qAEfuUBc40mOd4Mhd6obC9FhDONJzTeFIrzVGcfRedzjScx1SmN5qEjY13Izj0QKdO3eWBx54QB0MdenSxe4opgEDBsj+/fuLPbXV2RTuIvr6+ha5QM9HH30k9913n4SGhqqnY9tsNsnOzpaRI0fKmDFjXOZ0sYLTHdeuXSuPP/64bNiwQWJiYqRLly6ycuVKuXr1qlSvXl3q1q0rHTt2lMDAQKefJzg3N1diY2Nl3rx5smfPHnnvvfckKChIBg0apM7HmJycLGPHjlU/aJctW6b+v3CFAmbVqlWiKIp06dJF+vTpYze4mTNnjtx9992SlJQkIvlzPY8aNUoeeughu0Hu1atXnWKg4I5zxToWJV26dJFHH33U7gcjV1H46Me0tDR1/b59+yQ6OrrIIO2nn36SESNGyMyZM53+vbZ06VLx9fWVZcuWqafIFjx3a9eulfDwcOncubO6f3p6uvTs2VO6detWpKB0tnluFy1aJCaTSV12/E4+ffq0BAYGytSpU8s7tFJhs9nkp59+kiZNmkiLFi3EZrPJ6dOnpVGjRjJz5ky1oBDJH5DXqVPHJS5CeODAAQkNDZWnn35aFi9eLMeOHZOXXnpJ6tatK+Hh4fLee+/J4MGDpV69ehIXFyfffPONjB07Vvz9/V1uqhjSlp7H+Y7cadzvSI91gCN3qAsc6alOcOSOdUNheqohHOm5pnCk5xqjOHqvOxzptQ4pTI81CZsabshxUNG1a1d54IEH5NKlS/L++++LwWCQQYMGScuWLaVRo0YuNb/juXPnpEqVKvLAAw/YrZ8/f74MGjRImjZtKlWrVlXnv0tLS5NXXnlFQkNDnf6ogHPnzsmKFSvs1iUlJUl4eLi8//77kpSUJDExMdKxY0dZvXq1JCYmygsvvCCxsbEuc1r9unXrxN/fXz26KCsrSyZOnKjOA/zuu+/KunXrZOzYseLn5yc///yziLjGoKDAY489Jl27dpWYmBi7uTdjY2Olfv36cvHiRUlNTZXevXvL+++/r97OGY/ecce5YgvHvnDhQmnXrp0sXLiwyDZnVtzRj5988om6/cSJExIVFSUhISGyadMmOXLkiAwYMEBGjhyp7uOsRUhSUpJ069bN7r0jkv9Zv3fvXlm7dq1MnTpVGjVqJG3atJEBAwZIp06dpFmzZuqRy874Xivw008/iclkkm+++eaa+7Rs2VI9WtlVXpOF5eXlSXx8vDRo0EDatm0rIiKvvvqqNG3aVKZNmyYJCQmSlpYmL730ktSrV0+96K+zKvjBNTY2tsj7ZunSpXLnnXdKu3bt5P/+7/9k6NChEhgYKE2bNpWIiAinPdKWnJuex/mO9Dzud+QOdYAjd6gLHOmpTnDkjnVDYXqoIRzpuaZwpPcaozjuUHc40lsdUpheaxI2NdyU46Ci4DTIixcvyvz58+WBBx6Qp556yuU+gM+cOaPOLbt9+3YREXn77bfF19dXDh8+LBcuXJBatWpJq1atpH79+hIdHS2hoaFOOTdcYefOnZPKlSuLoijSs2dPWbZsmXrU3XfffSedO3eWpKQkOXr0qDoIXr58ucZR35xnnnlGnnnmGXW5UaNG0r9/f/nPf/4j9957ryiKIu+88448+uijEhQUJFu3btUu2BtQUGB9/vnn8uSTT8rOnTslJiZGOnfurBafoaGhEhoaKnXq1JGmTZuq7z9n5o5zxRbOuVevXtKvXz/tgrkJ1zr68aGHHpJp06ZJTk6OHDlyREaMGCGKokjdunWlefPm6uvRmQesSUlJ0qhRI7uL73344Ydy3333iaIocscdd0izZs0kPj5enn32WXn++edl+vTp6uvT2Qur8+fPS7Vq1aRv3752808XHMF45coVueuuu+Szzz7TKsQbdvHiRYmPj7dbl52dLbt27ZLatWtLly5dRERk4sSJ0qRJEzGZTNK+fXupWrWqS3x3V6lSRe6//351nc1ms3udzZs3TypXriwff/yxiIicOnVK0tPT7a5vQHSj9DrOd6TXcb8jd6oDHOm1LnCk1zrBkTvWDYW5eg3hSM81hSO91xjF0WPd4UjPdUhheq5J2NRwY9ea3zEhIUFE/vmwcrUP4JMnT8q9994rffv2lSeffFKqVasmGzZsULdfvHhRli5dKrGxsfLFF1+o81k6sz/++EPatGkjHTp0kFatWskTTzwht99+u8yfP1+WLVsmvXv3lnXr1omIyJEjRyQiIkJ69+6tnhbpShYsWCAdO3aUK1euqPM8F+Rx/vx5WbZsmeTk5Eh6err069dPqlev7rTTB2zZssVuTk0Rkb/++kuqV68un3zyiVy8eFFiYmKkU6dOsmbNGrly5YrMmTNHPvzwQ5caALnDXLGOCnJ65pln5KGHHhKr1apxRDfmekc/tm3bVt599105fvy4HDhwQOLj49Xnztlfj0lJSXLbbbfJE088IZs3b5aBAwdK06ZNZeTIkbJhwwb5+uuvpV69ejJr1qwit3WV1+e3334r3t7e8thjj8nhw4fttk2YMEFq1apV5IKbzqrwD3XdunWT2NhY2bx5s/qZ/8svv0jTpk2lY8eOIpL//b1w4UJZsWKFS+RY+AfXn376yW5b4c/NTp06yYABA0TEdV6H5Pz0Os53pMdxvyN3qgMc6akucOQudYIjd6wbCnP1GsKRXmsKR+5QYxRHT3WHI73XIYXpuSZhU8PNOQ4qOnfuLI899pg6v6Mrdc8LO3HihNxzzz3i4+Mj06dPV9e72pdnYSdPnpSYmBjp37+/rFixQlauXCndunWT/v37i6Io0q5dO3VQdPz4cZeeo7Nt27aiKIp07dpVLl++XOw+OTk5cunSJfnzzz/LObqS2bJli3oRtKioKPnoo4/k0KFDIpJ/el+fPn0kLS1NDh8+LAMHDpSuXbuqFwUs4CpfJCL6niv2WpKTk6Vjx47q8+pqrnf04z333COKosiSJUvU7a7yety0aZMEBgZKnTp1pHnz5rJ582a5dOmSiOQfUdSiRQuZMGGCxlHevNzcXJk3b554enpKgwYNZNiwYfLqq6/Kww8/LBUrVnSpo4b++OMPadGihTRo0EDatGkjQ4YMEZPJJC1atJDHHntMli1bJl9//bXUrVtX7rnnHpcckxT84BoVFWVXRBTOpVu3bvLII49oER7pnF7H+Y70OO535E51gCM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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mB7uyBhUmtgf"
      },
      "source": [
        "### *4e. Popularity score vs. revenue heatmap*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "iTbYEdbbmtgf",
        "outputId": "8d785a3b-dd05-40d5-90e6-2398216ce1e3"
      },
      "source": [
        "# ── 4e. Popularity vs Revenue ─────────────────────────────\n",
        "# Merge reviews with booking data for deeper analysis\n",
        "review_stats = df_reviews.groupby('hotel').agg(\n",
        "    avg_score=('popularity_score', 'mean'),\n",
        "    avg_adr=('adr', 'mean'),\n",
        "    positive_pct=('vader_sentiment', lambda x: (x=='positive').mean()*100),\n",
        "    negative_pct=('vader_sentiment', lambda x: (x=='negative').mean()*100),\n",
        "    total_reviews=('review_text', 'count')\n",
        ").round(2)\n",
        "\n",
        "print(\"📊 Hotel Performance Summary:\")\n",
        "display(review_stats)\n",
        "\n",
        "# Correlation matrix\n",
        "numeric_cols = ['adr', 'popularity_score', 'vader_score', 'total_nights', 'is_canceled']\n",
        "corr_matrix = df_reviews[numeric_cols].corr()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='RdYlGn', center=0, fmt='.2f', square=True)\n",
        "plt.title('Correlation Matrix: Key Hotel Metrics', fontsize=13)\n",
        "plt.tight_layout()\n",
        "plt.savefig('correlation_heatmap.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📊 Hotel Performance Summary:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              avg_score  avg_adr  positive_pct  negative_pct  total_reviews\n",
              "hotel                                                                      \n",
              "City Hotel         3.07   112.61         60.99         28.41           3094\n",
              "Resort Hotel       3.21   100.14         65.79         24.71           1906"
            ],
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              "      <td>3.07</td>\n",
              "      <td>112.61</td>\n",
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              "        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",
              "\n",
              "\n",
              "  <div id=\"id_1a03710b-aeb9-4166-a5ec-ede2b96f7278\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('review_stats')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1a03710b-aeb9-4166-a5ec-ede2b96f7278 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('review_stats');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "review_stats",
              "summary": "{\n  \"name\": \"review_stats\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"hotel\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Resort Hotel\",\n          \"City Hotel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09899494936611673,\n        \"min\": 3.07,\n        \"max\": 3.21,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.21,\n          3.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_adr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.817621561396248,\n        \"min\": 100.14,\n        \"max\": 112.61,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100.14,\n          112.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"positive_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.394112549695431,\n        \"min\": 60.99,\n        \"max\": 65.79,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          65.79,\n          60.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"negative_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.616295090390225,\n        \"min\": 24.71,\n        \"max\": 28.41,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          24.71,\n          28.41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 840,\n        \"min\": 1906,\n        \"max\": 3094,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1906,\n          3094\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIWIT0X1mtgg"
      },
      "source": [
        "## **5.** 🔮 Forecast hotel revenue with ARIMA\n",
        "\n",
        "ARIMA (AutoRegressive Integrated Moving Average) is used to forecast future monthly revenue based on historical patterns. We try different parameter combinations and select the best model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBAH5A02mtgg"
      },
      "source": [
        "### *5a. Prepare time series data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cq1EAdhmtgg",
        "outputId": "37d7ccbd-27ee-4f73-f21e-3b83c0aa3b5e"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "from itertools import product\n",
        "import matplotlib.dates as mdates\n",
        "\n",
        "# Prepare the time series\n",
        "ts = df_monthly.set_index('date')['total_revenue']\n",
        "ts.index = pd.DatetimeIndex(ts.index, freq='MS')\n",
        "\n",
        "print(f\"Time series: {len(ts)} data points\")\n",
        "print(f\"Start: {ts.index.min()}\")\n",
        "print(f\"End: {ts.index.max()}\")\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time series: 26 data points\n",
            "Start: 2015-07-01 00:00:00\n",
            "End: 2017-08-01 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZvAFDdEmtgg"
      },
      "source": [
        "### *5b. Find best ARIMA parameters*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rb6YdMZ0mtgg",
        "outputId": "dcfac888-5c22-4e07-e76b-ffa0917b70e1"
      },
      "source": [
        "# ── 5b. Auto-select best ARIMA parameters ────────────────\n",
        "def find_best_arima(series, p_range=(0, 4), d_range=(0, 2), q_range=(0, 2)):\n",
        "    best_aic = float(\"inf\")\n",
        "    best_order = None\n",
        "    best_model = None\n",
        "\n",
        "    for p, d, q in product(range(p_range[0], p_range[1]+1),\n",
        "                           range(d_range[0], d_range[1]+1),\n",
        "                           range(q_range[0], q_range[1]+1)):\n",
        "        try:\n",
        "            model = sm.tsa.ARIMA(series, order=(p, d, q))\n",
        "            result = model.fit()\n",
        "            if result.aic < best_aic:\n",
        "                best_aic = result.aic\n",
        "                best_order = (p, d, q)\n",
        "                best_model = result\n",
        "        except:\n",
        "            continue\n",
        "\n",
        "    return best_model, best_order, best_aic\n",
        "\n",
        "best_model, best_order, best_aic = find_best_arima(ts)\n",
        "print(f\"✅ Best ARIMA order: {best_order}\")\n",
        "print(f\"   AIC: {best_aic:.2f}\")\n",
        "print(f\"\\nModel summary:\")\n",
        "print(best_model.summary().tables[1])\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Best ARIMA order: (1, 2, 1)\n",
            "   AIC: 696.54\n",
            "\n",
            "Model summary:\n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ar.L1          0.3219      0.474      0.679      0.497      -0.608       1.251\n",
            "ma.L1         -0.9763      0.365     -2.676      0.007      -1.691      -0.261\n",
            "sigma2      1.998e+11   2.44e-12   8.19e+22      0.000       2e+11       2e+11\n",
            "==============================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDz41aymmtgg"
      },
      "source": [
        "### *5c. Forecast & visualize*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "qmJGVBmOmtgg",
        "outputId": "e9a6539d-e742-4508-f63c-c816fa705489"
      },
      "source": [
        "# ── 5c. Forecast next 6 months ────────────────────────────\n",
        "forecast_steps = 6\n",
        "forecast = best_model.get_forecast(steps=forecast_steps)\n",
        "forecast_mean = forecast.predicted_mean\n",
        "forecast_ci = forecast.conf_int()\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(ts.index, ts.values, 'b-o', markersize=4, label='Historical Revenue', linewidth=2)\n",
        "plt.plot(forecast_mean.index, forecast_mean.values, 'r--o', markersize=6,\n",
        "         label=f'Forecast (ARIMA {best_order})', linewidth=2)\n",
        "plt.fill_between(forecast_ci.index,\n",
        "                 forecast_ci.iloc[:, 0],\n",
        "                 forecast_ci.iloc[:, 1],\n",
        "                 color='red', alpha=0.1, label='95% Confidence Interval')\n",
        "\n",
        "plt.axvline(x=ts.index[-1], color='gray', linestyle=':', alpha=0.5)\n",
        "plt.title(f'Hotel Revenue Forecast — ARIMA{best_order}', fontsize=14)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Total Monthly Revenue (€)')\n",
        "plt.legend(fontsize=11)\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.savefig('arima_forecast.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "print(f\"\\n📈 Revenue Forecast (next {forecast_steps} months):\")\n",
        "for date, val in zip(forecast_mean.index, forecast_mean.values):\n",
        "    print(f\"  {date.strftime('%B %Y')}: €{val:,.0f}\")\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📈 Revenue Forecast (next 6 months):\n",
            "  September 2017: €3,307,303\n",
            "  October 2017: €3,446,675\n",
            "  November 2017: €3,574,289\n",
            "  December 2017: €3,698,119\n",
            "  January 2018: €3,820,731\n",
            "  February 2018: €3,942,951\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlBylF-Hmtgg"
      },
      "source": [
        "## **6.** 🏷️ Rule-based pricing decisions\n",
        "\n",
        "Based on sentiment analysis and revenue data, we create a **pricing recommendation engine** that suggests price adjustments per hotel segment.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcUXy3Efmtgg"
      },
      "source": [
        "### *6a. Calculate key metrics per segment*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "VSa6uQUpmtgh",
        "outputId": "c7eab9e7-29d2-479b-a28e-15863b8c1fca"
      },
      "source": [
        "# ── 6a. Aggregate metrics for pricing decisions ──────────\n",
        "# Average ADR by hotel type and sentiment\n",
        "avg_metrics = df_reviews.groupby(['hotel', 'vader_sentiment']).agg(\n",
        "    avg_adr=('adr', 'mean'),\n",
        "    booking_count=('review_text', 'count'),\n",
        "    avg_popularity=('popularity_score', 'mean')\n",
        ").reset_index()\n",
        "\n",
        "# Overall metrics per hotel\n",
        "hotel_metrics = df_reviews.groupby('hotel').agg(\n",
        "    avg_adr=('adr', 'mean'),\n",
        "    total_bookings=('review_text', 'count'),\n",
        "    positive_ratio=('vader_sentiment', lambda x: (x == 'positive').mean()),\n",
        "    negative_ratio=('vader_sentiment', lambda x: (x == 'negative').mean()),\n",
        "    avg_popularity=('popularity_score', 'mean')\n",
        ").reset_index()\n",
        "\n",
        "print(\"📊 Hotel-level metrics for pricing:\")\n",
        "display(hotel_metrics)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📊 Hotel-level metrics for pricing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          hotel     avg_adr  total_bookings  positive_ratio  negative_ratio  \\\n",
              "0    City Hotel  112.609577            3094        0.609890        0.284098   \n",
              "1  Resort Hotel  100.144402            1906        0.657922        0.247114   \n",
              "\n",
              "   avg_popularity  \n",
              "0        3.074661  \n",
              "1        3.205142  "
            ],
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-d1199aa6-ccd7-40c0-9050-1dbf6fd7efe7 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-d1199aa6-ccd7-40c0-9050-1dbf6fd7efe7');\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",
              "\n",
              "\n",
              "  <div id=\"id_7a95b22b-4895-4c73-90c5-dd0e85b819a7\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
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              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('hotel_metrics')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_7a95b22b-4895-4c73-90c5-dd0e85b819a7 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('hotel_metrics');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hotel_metrics",
              "summary": "{\n  \"name\": \"hotel_metrics\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"hotel\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Resort Hotel\",\n          \"City Hotel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_adr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.814209566892751,\n        \"min\": 100.14440188877231,\n        \"max\": 112.60957659987072,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100.14440188877231,\n          112.60957659987072\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bookings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 840,\n        \"min\": 1906,\n        \"max\": 3094,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1906,\n          3094\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"positive_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.033963923031174646,\n        \"min\": 0.6098901098901099,\n        \"max\": 0.657922350472193,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.657922350472193,\n          0.6098901098901099\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"negative_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02615155165716706,\n        \"min\": 0.24711437565582373,\n        \"max\": 0.28409825468648997,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.24711437565582373,\n          0.28409825468648997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09226401719641004,\n        \"min\": 3.074660633484163,\n        \"max\": 3.2051416579223506,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.2051416579223506,\n          3.074660633484163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xL0wgpyXmtgh"
      },
      "source": [
        "### *6b. Pricing decision function*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXarSY9Smtgh",
        "outputId": "f67689c6-3031-4315-8318-f37004f7fec6"
      },
      "source": [
        "# ── 6b. Rule-based pricing engine ─────────────────────────\n",
        "def pricing_decision(row):\n",
        "    \"\"\"\n",
        "    Rule-based pricing decision based on sentiment and demand.\n",
        "    - High satisfaction + high demand → Increase price\n",
        "    - Low satisfaction + low demand → Decrease price\n",
        "    - Otherwise → Keep price\n",
        "    \"\"\"\n",
        "    if row['positive_ratio'] >= 0.5 and row['avg_popularity'] >= 3.5:\n",
        "        return \"⬆️ Increase Price\"\n",
        "    elif row['negative_ratio'] >= 0.3 and row['avg_popularity'] <= 2.5:\n",
        "        return \"⬇️ Decrease Price\"\n",
        "    else:\n",
        "        return \"➡️ Keep Price\"\n",
        "\n",
        "hotel_metrics['pricing_action'] = hotel_metrics.apply(pricing_decision, axis=1)\n",
        "\n",
        "print(\"\\n🏷️ PRICING RECOMMENDATIONS:\")\n",
        "print(\"=\" * 50)\n",
        "for _, row in hotel_metrics.iterrows():\n",
        "    print(f\"\\n{row['hotel']}:\")\n",
        "    print(f\"  Current avg ADR: €{row['avg_adr']:.2f}\")\n",
        "    print(f\"  Positive sentiment: {row['positive_ratio']*100:.1f}%\")\n",
        "    print(f\"  Negative sentiment: {row['negative_ratio']*100:.1f}%\")\n",
        "    print(f\"  Avg popularity: {row['avg_popularity']:.1f}/5\")\n",
        "    print(f\"  → Recommendation: {row['pricing_action']}\")\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🏷️ PRICING RECOMMENDATIONS:\n",
            "==================================================\n",
            "\n",
            "City Hotel:\n",
            "  Current avg ADR: €112.61\n",
            "  Positive sentiment: 61.0%\n",
            "  Negative sentiment: 28.4%\n",
            "  Avg popularity: 3.1/5\n",
            "  → Recommendation: ➡️ Keep Price\n",
            "\n",
            "Resort Hotel:\n",
            "  Current avg ADR: €100.14\n",
            "  Positive sentiment: 65.8%\n",
            "  Negative sentiment: 24.7%\n",
            "  Avg popularity: 3.2/5\n",
            "  → Recommendation: ➡️ Keep Price\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dxcHTpHmtgh"
      },
      "source": [
        "### *6c. Monthly pricing recommendations*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "EOzl40xxmtgh",
        "outputId": "b6b7a5bb-5e35-404d-d671-b06d38b3d117"
      },
      "source": [
        "# ── 6c. Monthly pricing with sentiment + revenue trends ──\n",
        "# Merge monthly data with sentiment trends\n",
        "month_order_map = {'January':1, 'February':2, 'March':3, 'April':4, 'May':5, 'June':6,\n",
        "                   'July':7, 'August':8, 'September':9, 'October':10, 'November':11, 'December':12}\n",
        "\n",
        "# Get sentiment by month from reviews\n",
        "monthly_sentiment = df_reviews.groupby('arrival_date_month').agg(\n",
        "    positive_ratio=('vader_sentiment', lambda x: (x == 'positive').mean()),\n",
        "    negative_ratio=('vader_sentiment', lambda x: (x == 'negative').mean()),\n",
        "    avg_adr=('adr', 'mean'),\n",
        "    avg_popularity=('popularity_score', 'mean')\n",
        ").reset_index()\n",
        "\n",
        "monthly_sentiment['pricing_action'] = monthly_sentiment.apply(pricing_decision, axis=1)\n",
        "\n",
        "# Visualization\n",
        "fig, ax = plt.subplots(figsize=(14, 6))\n",
        "months_sorted = sorted(monthly_sentiment['arrival_date_month'].unique(),\n",
        "                       key=lambda x: month_order_map.get(x, 0))\n",
        "ms = monthly_sentiment.set_index('arrival_date_month').reindex(months_sorted)\n",
        "\n",
        "colors = {'⬆️ Increase Price': '#2ecc71', '⬇️ Decrease Price': '#e74c3c', '➡️ Keep Price': '#f39c12'}\n",
        "bar_colors = [colors.get(a, '#95a5a6') for a in ms['pricing_action']]\n",
        "\n",
        "ax.bar(range(len(ms)), ms['avg_adr'], color=bar_colors, edgecolor='white', linewidth=0.5)\n",
        "ax.set_xticks(range(len(ms)))\n",
        "ax.set_xticklabels([m[:3] for m in months_sorted], rotation=45)\n",
        "ax.set_ylabel('Average Daily Rate (€)')\n",
        "ax.set_title('Monthly Pricing Recommendations Based on Sentiment & Revenue', fontsize=13)\n",
        "\n",
        "# Legend\n",
        "from matplotlib.patches import Patch\n",
        "legend_elements = [Patch(facecolor=c, label=l) for l, c in colors.items()]\n",
        "ax.legend(handles=legend_elements, loc='upper right')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('pricing_recommendations.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSXY3SL4mtgh"
      },
      "source": [
        "## **7.** 📝 Key Findings Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIYQsbVAmtgh",
        "outputId": "ea5431ff-7256-4c2f-f579-4287b67efe79"
      },
      "source": [
        "# ── FINAL SUMMARY ─────────────────────────────────────────\n",
        "print(\"=\" * 60)\n",
        "print(\"   NOTEBOOK 2 — ANALYSIS SUMMARY\")\n",
        "print(\"=\" * 60)\n",
        "print(f\"\"\"\n",
        "SENTIMENT ANALYSIS (VADER):\n",
        "  Reviews analysed: {len(df_reviews):,}\n",
        "  Positive: {(df_reviews['vader_sentiment']=='positive').sum():,} ({(df_reviews['vader_sentiment']=='positive').mean()*100:.1f}%)\n",
        "  Neutral: {(df_reviews['vader_sentiment']=='neutral').sum():,} ({(df_reviews['vader_sentiment']=='neutral').mean()*100:.1f}%)\n",
        "  Negative: {(df_reviews['vader_sentiment']=='negative').sum():,} ({(df_reviews['vader_sentiment']=='negative').mean()*100:.1f}%)\n",
        "  VADER vs original agreement: {(df_reviews['sentiment_label'] == df_reviews['vader_sentiment']).mean()*100:.1f}%\n",
        "\n",
        "ARIMA FORECASTING:\n",
        "  Best model: ARIMA{best_order}\n",
        "  AIC: {best_aic:.2f}\n",
        "  Forecast horizon: {forecast_steps} months\n",
        "\n",
        "PRICING INSIGHTS:\n",
        "  Higher-priced bookings correlate with more positive sentiment.\n",
        "  Seasonal patterns show peak pricing opportunities in summer months.\n",
        "  City hotels show higher ADR but also higher cancellation sensitivity.\n",
        "\n",
        "CHARTS EXPORTED:\n",
        "  ✅ sentiment_distribution.png\n",
        "  ✅ adr_by_sentiment.png\n",
        "  ✅ revenue_cancellation_trend.png\n",
        "  ✅ seasonal_patterns.png\n",
        "  ✅ correlation_heatmap.png\n",
        "  ✅ arima_forecast.png\n",
        "  ✅ pricing_recommendations.png\n",
        "\"\"\")\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "   NOTEBOOK 2 — ANALYSIS SUMMARY\n",
            "============================================================\n",
            "\n",
            "SENTIMENT ANALYSIS (VADER):\n",
            "  Reviews analysed: 5,000\n",
            "  Positive: 3,141 (62.8%)\n",
            "  Neutral: 509 (10.2%)\n",
            "  Negative: 1,350 (27.0%)\n",
            "  VADER vs original agreement: 61.8%\n",
            "\n",
            "ARIMA FORECASTING:\n",
            "  Best model: ARIMA(1, 2, 1)\n",
            "  AIC: 696.54\n",
            "  Forecast horizon: 6 months\n",
            "\n",
            "PRICING INSIGHTS:\n",
            "  Higher-priced bookings correlate with more positive sentiment.\n",
            "  Seasonal patterns show peak pricing opportunities in summer months.\n",
            "  City hotels show higher ADR but also higher cancellation sensitivity.\n",
            "\n",
            "CHARTS EXPORTED:\n",
            "  ✅ sentiment_distribution.png\n",
            "  ✅ adr_by_sentiment.png\n",
            "  ✅ revenue_cancellation_trend.png\n",
            "  ✅ seasonal_patterns.png\n",
            "  ✅ correlation_heatmap.png\n",
            "  ✅ arima_forecast.png\n",
            "  ✅ pricing_recommendations.png\n",
            "\n"
          ]
        }
      ]
    }
  ]
}