Youtube_adview_prediction (1).ipynb

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  "metadata": {
    "colab": {
      "name": "Youtube_adview_prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mostNaXpM1dq"
      },
      "source": [
        "Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8r4PUtu5Fh9"
      },
      "source": [
        "import datetime\n",
        "import time\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import joblib\n",
        "import keras\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from google.colab import files"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHor-2aTNGGQ"
      },
      "source": [
        "Read CSV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iidLoRSt5nAc"
      },
      "source": [
        "data = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "gcux3PL3KzbO",
        "outputId": "001c6a71-f2e8-466a-faa1-0a4f0ba670f0"
      },
      "source": [
        "data"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VID_18655</td>\n",
              "      <td>40</td>\n",
              "      <td>1031602</td>\n",
              "      <td>8523</td>\n",
              "      <td>363</td>\n",
              "      <td>1095</td>\n",
              "      <td>2016-09-14</td>\n",
              "      <td>PT7M37S</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>56</td>\n",
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              "      <td>2016-10-01</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VID_2187</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-07-02</td>\n",
              "      <td>PT2M16S</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VID_23096</td>\n",
              "      <td>6</td>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2016-07-27</td>\n",
              "      <td>PT4M22S</td>\n",
              "      <td>H</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VID_10175</td>\n",
              "      <td>1</td>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-06-29</td>\n",
              "      <td>PT31S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>VID_31</td>\n",
              "      <td>2</td>\n",
              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>2015-05-18</td>\n",
              "      <td>PT6M10S</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>VID_5861</td>\n",
              "      <td>1</td>\n",
              "      <td>665673</td>\n",
              "      <td>3849</td>\n",
              "      <td>156</td>\n",
              "      <td>569</td>\n",
              "      <td>2015-10-20</td>\n",
              "      <td>PT3M56S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>VID_805</td>\n",
              "      <td>4</td>\n",
              "      <td>3479</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-08-23</td>\n",
              "      <td>PT3M13S</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010-10-02</td>\n",
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              "      <td>G</td>\n",
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              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>VID_8534</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>PT1M1S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14999 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           vidid  adview    views likes  ... comment   published duration category\n",
              "0      VID_18655      40  1031602  8523  ...    1095  2016-09-14  PT7M37S        F\n",
              "1      VID_14135       2     1707    56  ...       6  2016-10-01  PT9M30S        D\n",
              "2       VID_2187       1     2023    25  ...       2  2016-07-02  PT2M16S        C\n",
              "3      VID_23096       6   620860   777  ...     153  2016-07-27  PT4M22S        H\n",
              "4      VID_10175       1      666     1  ...       0  2016-06-29    PT31S        D\n",
              "...          ...     ...      ...   ...  ...     ...         ...      ...      ...\n",
              "14994     VID_31       2   525949  1137  ...      86  2015-05-18  PT6M10S        A\n",
              "14995   VID_5861       1   665673  3849  ...     569  2015-10-20  PT3M56S        D\n",
              "14996    VID_805       4     3479    16  ...       1  2013-08-23  PT3M13S        B\n",
              "14997  VID_19843       1      963     0  ...       0  2010-10-02    PT26S        G\n",
              "14998   VID_8534       1    15212    22  ...       4  2016-02-19   PT1M1S        D\n",
              "\n",
              "[14999 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niJ7A7kwNLp2"
      },
      "source": [
        "Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pN-GdQ_16Y3e"
      },
      "source": [
        "categories = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"H\": 8}\n",
        "\n",
        "data[\"category\"] = data[\"category\"].map(categories)\n",
        "test[\"category\"] = test[\"category\"].map(categories)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NH4_fNxc7CnX"
      },
      "source": [
        "for e in [\"views\", \"likes\", \"dislikes\", \"comment\"]:\n",
        "    data = data[data[e] != \"F\"]\n",
        "    for i in range(len(test)):\n",
        "        if test[e][i]=='F':\n",
        "            test.loc[i, e] = '0'\n",
        "\n",
        "for e in [\"views\", \"likes\", \"dislikes\", \"comment\", \"adview\"]:\n",
        "    data[e] = pd.to_numeric(data[e])\n",
        "    if e != 'adview':\n",
        "        test[e] = pd.to_numeric(test[e])\n",
        "\n",
        "data = data[data[\"adview\"] < 2000000]\n",
        "\n",
        "data[\"published\"] = LabelEncoder().fit_transform(data[\"published\"])\n",
        "test[\"published\"] = LabelEncoder().fit_transform(test[\"published\"])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyUY6suE7_If"
      },
      "source": [
        "\n",
        "def checki(x):\n",
        "    y = x[2:]\n",
        "    h, m, s, mm = \"\", \"\", \"\", \"\"\n",
        "    P = [\"H\", \"M\", \"S\"]\n",
        "    for i in y:\n",
        "        if i not in P:\n",
        "            mm += i\n",
        "        else:\n",
        "            if i == P[0]:\n",
        "                h = mm\n",
        "                mm = \"\"\n",
        "            elif i == P[1]:\n",
        "                m = mm\n",
        "                mm = \"\"\n",
        "            else:\n",
        "                s = mm\n",
        "                mm = \"\"\n",
        "    if h == \"\":\n",
        "        h = \"00\"\n",
        "    if m == \"\":\n",
        "        m = \"00\"\n",
        "    if s == \"\":\n",
        "        s = \"00\"\n",
        "    bp = h + \":\" + m + \":\" + s\n",
        "    return bp"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Lcoho1y9EEz"
      },
      "source": [
        "\n",
        "def func_sec(time_str):\n",
        "    h, m, s = time_str.split(\":\")\n",
        "    return int(h) * 3600 + int(m) * 60 + int(s)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtXAeLk2LVru"
      },
      "source": [
        "data[\"duration\"] = data[\"duration\"].apply(checki).apply(func_sec)\n",
        "test[\"duration\"] = test[\"duration\"].apply(checki).apply(func_sec)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aal2cra8NRUx"
      },
      "source": [
        "Visualize Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "SXBx6nzs9jiO",
        "outputId": "32b659ab-63a8-4a12-c88d-e21e5f06da3f"
      },
      "source": [
        "fig, axs = plt.subplots(2, 3, figsize=(12, 10))\n",
        "axs[0, 0].scatter(data[\"duration\"], data[\"adview\"], s=2)\n",
        "axs[0, 1].scatter(data[\"views\"], data[\"adview\"], s=2)\n",
        "axs[0, 2].scatter(data[\"likes\"], data[\"adview\"], s=2)\n",
        "axs[1, 0].scatter(data[\"dislikes\"], data[\"adview\"], s=2)\n",
        "axs[1, 1].scatter(data[\"comment\"], data[\"adview\"], s=2)\n",
        "axs[1, 2].scatter(data[\"category\"], data[\"adview\"], s=2)\n",
        "plt.plot()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GFQ2gOvIK5AuZBdLniEhlxR0dHdHZ2ZnKuoEsst0VER1p1zEWMgsMI69AvoyXWa5ABwAAgMKiGQYAAEBh0QwDAACgsGiGAQAAUFg0wwAAACgsmmEAAAAUFs0wAAAACotmGAAAAIVFMwwAAIDCohkGAABAYdEMAwAAoLBohgEAAFBYmW6Gu3r6dcsD29XV0592KQBqIK9AfpBXYFimm+G1W7u1bc8hrd3anXYpAGogr0B+kFdgWKab4ZVL52nO7JlauXRe2qUAqIG8AvlBXoFhmW6GN+88oP5jJ7V554G0SwFQA3kF8oO8AsMy3QzzyhXIj9Ur2nXl/At09K0BxiECGbd6Rbs+uniuVq9oH3c+xhajCDLbDHf19OveLT/mlSuQIz2vHtOO/YcZhwi0CMYWowhmpF3AWO55fJf6j53UeWdNr/nKFUD6yCyQH/c8vks7eo/o6Jsn9dhtHxlzvsEsk2m0srqODNteaft523ttrxlnvl+xHbY7plyZLUn66befp2UL50x5cUBRpJLX0gIlkVlgotLcxw7djmHZwjnasGo5mUZLq9kM254u6T5J10paIulm20uqzHeepNWStidR2B0fX6KPLp6rOz5+xqoAjCGtvEpkFpgM9rFA+uo5MvxBSXsj4oWIOCHpEUk3VJnvHklflfRWEoXxahSYlFTyKpFZYJLYxwIpq6cZvkTS/orHveVpQ2y/X9KCiPjb8RZk+1bbnbY7+/r6JlwsgJoSy2t5XjILNBb7WCBlUz6bhO1pkv5I0m/Xmjci1kVER0R0tLW1TXXVACZoInmVyCyQNvaxQOPV0wy/JGlBxeP55WmDzpO0VNI/2P43SR+StDGxD+UAmAjyCuQLmQVSVk8z/LSkxbYvsz1L0k2SNg4+GRFHImJuRCyKiEWSnpJ0fUR0NqRiAOMhr0C+kFkgZTWb4YgYkHSbpC2SnpP0aETssn237esbXSCA+pFXIF/ILJC+ui66ERGbJG0aNe3OMea9eupllTy0fZ/u3fJj3X7N5frs8kuTWizQ0tLKa1dPv+55fJdk646PL+FT6kCd0sqsVMrt2q3dWr2iXcsWzjnjMVAEmb0cs6ShyzHfu+XHaZcCoIa1W7u1o/cIl2MGcmT05Za5/DKKKLOXY5ak26+5fOjIMIBsW72iXUffPCnZXLoVyInRl1vm8ssoIkdEKivu6OiIzk7G/wODbHdFRGY/IU5mgWHkFciX8TKb6WESAAAAQCPRDAMAAKCwaIYBAABQWDTDAAAAKKxMN8NdPf265YHt6urpT7sUADWQVyA/yCswLNPN8D2P79K2PYdKJ/IHkGlTOT8pO2aguaZ6PmEyi1aS2Wa4q6df3f/+miTpjROnUq4GQC1L5p2vGdOsJfPOn/D3cqJ/oLlWLp2nObNnauXSeZNqbMksWklmL7qxdmu3jp08LUk6Z9b0lKsBUMs3O/dr4HTom537tea6d0/oeznRP9Bcm3ceUP+xk9q884A27zygbXsOSZI2rFpe1/eTWbSSzDbDq1e068DhN/Xy0eO68QOXpl0OgBo+07FA3/jnF/WZjgUT/t5lC+fUvRMGMHWDV4w8+taAbixndiKNLZlFK8nsMIllC+fonLNn6rXjA3q0c/+I5xirBGTPUy++qoHTob/cvo9sAjnQ8+ox7dh/WJt3HhhqbNm3oogy2wxLkgYvFT3qktGMVQIyqJzT144PkE0g49Zu7Vb/sZOaM3vm0BFh9q0oqswOk5CkOz7xHq3d2n3GWzeMVQKy545PvKd05hebbAIZV7kfXbZwzhnTgCJxjDrq2iwdHR3R2dmZyrqBLLLdFREdadcxFjILDCOvQL6Ml9lsD5MAAAAAGiizzTAfkgPyhcwC+UFegWGZbYYZyA/kC5kF8oO8AsMy2wyvXtGujy6eq5VL5/HqFciBwcyuXtHOUScg4yrzOhr5RdHU1QzbXmn7edt7ba+p8vxv2d5t+xnb37O9cKqFDZ7Qe/DKOLx6BeqTRl6l4cwuWziHo07ABKS5jx08k0Ql8ouiqdkM254u6T5J10paIulm20tGzfZDSR0R8T5JfyXpD6da2EPb9+m9d23RC32v68oFF3KqF6AOaeVVKmX2qru/q4e27+OdHaBOae5jB/M6WrWjxhwtRiur58jwByXtjYgXIuKEpEck3VA5Q0Q8GRHHyg+fkjR/qoV9ZdNuvXZ8QL2H35Iiqr56BXCGVPIqSf/9iV3qP3ZS//2JXbyzA9QvtX1s/7GTuuOxZ89ocKsdNeZoMVpZPc3wJZIqr4fcW542llWSvlPtCdu32u603dnX1zfuSi+YPWvo/r/2vcGrUaA+ieVVmlhmT56OEbfS+OMSAUhKeR97KqQv/PnTNfexZBmtLNEP0Nn+VUkdku6t9nxErIuIjojoaGtrG3dZc889a+g+l3cFklcrr9LEMjvNHnErjT8uEcDEJLmPfdvM6ZKkaZb6j52suY8ly2hl9TTDL0laUPF4fnnaCLZXSPpdSddHxPGpFrbo4tlD9887awavRoH6pJJXSZo13SNuAdQllcwefvNEabkSn8tB4dXTDD8tabHty2zPknSTpI2VM9i+StL/UimkB5Mo7IlnDgzd/+m2c3g1CtQnlbxK0mvHT424BVCXVDL76uulZvhUSOefPYN9LAqtZjMcEQOSbpO0RdJzkh6NiF2277Z9fXm2eyWdK+lbtnfY3jjG4ur2hY9cpumW2s6dpTdOnNIn7/s+44aBGtLKqyRdOf+CEbcAaksrs5+44p2SpJnTpZVL5011cUCuzahnpojYJGnTqGl3VtxfkXBdWnPdu7Xmunfrlge2a9ueQ5JKn2bdsGp50qsCWkoaeZWknlePjbgFUJ80MvvqG6UjwydPSZt3HtBnl1+a9CqA3MjsFej+YNNz+pn/Z5MuOmeWrpx/AWOagIx77yUXjLgFkF0XnVM6m0TbebPYt6LwMtsM/9k/vaCB06HHdvxEN37gUj32mx9mTBOQYf9Ufgdn8BZAdm3c8RNJ0qHXTmjt1m6GIaLQMtsMX1xxarU7/2YnQQUyLkbdAsiu6RVnfan3YhpchQ6tKrPN8AVvmzl0f+B0cJ5hIOOmTxt5CyC7fur8syVJM6ZZV86/oK6hElyFDq2qrg/Qpe2s6WZME5BxcXrkLYDsev34SUmlK0ae/7aZdQ1DHNwPsz9Gq8nsMZxzZk0fun/iVOmNV96eAbLrdMUtOQWy7fCxgaH7R98aqCuz1a5Cx9AJtILMNsN3fOI9Q/enmbdngKyr3JiQUyDbLpw9/Mbwjv2HJ51Z9s1oBZkdJvH8y68N3Z8xfRpvzwAZN22adfp06V0ccgpk26zp0yWVjg7PmT1z0pll34xWkNkjw/du+fHQ/RMDp/X8y6+d8fZMvXgbB2i8gdPD55GofDELIHsOvXZ86P7cirM3SRPbZ1YbOgHkTWab4duvuVyDJ34JSV/ZtHvSy+JtHKDxpg2fqWnEi1kA2XNBxTCJPQdf1z1PDO9j2WeiaDLbDH92+aV628zh8k6dnvyHclavaNdHF8/lbRyggaLiBMO3X3N5eoUAqOnYiVGnfakIMPtMFE1mm+Gunn6dODUc1mMnT036VSpv4wDNY5VezALIrtOnh/evs2dO040fGM4s+0wUTWab4TXffkYDFS9cLenAkbcY9wtkVOUV6MgpkG2V+9djJ09r884DU14mn89BXmW2GX6p/9iIx6HSuKaxjg4TQiA7KscfAsieysumz7/w7BFDIia7P2WsMfIqs83wRaM+3SpJ5501fcwxTPWEkIYZaJLKAcQtiG0JWskbJ06NeDzZprbaWGOygjzIbDP8tpnTz5j2ueULxxzDVM+Af161As1RedGcVsS2BK2k/9hJ/do3nhpqWCf7AbpqY43JCvIgs83wy0fePGPaX27vkSQ9tH2frrr7u3po+76h5+oZ8M8nZIHmaPUP3rAtQas5dvL0UMOa5AfoyAryILPN8GvHT50x7R0XvE1S6Rym/cdOjnsu02pvzfAJWaA5Wv0tUbYlaDWL337uUMPa1dOvT37tn/XJ+74/5SyTFeRBZpvhat48MaCunn7dfs3lmjN7pj7TsUC/9Ef/qPfetVkPbd83ogHO01szjKlCqxnMHf/bQPZNt/R3v/ULQw3r2q3d2tF7RDv2H87FPhSYqrqaYdsrbT9ve6/tNVWeP8v2N8vPb7e9KOlCJan38Fta8+1ntHnnAX3j1z+g3QeOas/B1/Xa8VO6d8uPRzTAK5fO05zZM7Vy6bxGlDIhtRqCPDXuyL408vqRP/jeiMcXnTNLV939Xa359jP8bwM1pL2PPRUaOhL80PZ9OvrWgBa3naMrF1x4xvAGXuCiFc2oNYPt6ZLuk/RLknolPW17Y0RUnjtplaT+iPgZ2zdJ+qqkzzSi4D0HX9eeg6/r6FsDuuPjS3TgyFt6+cibQ1e8evalI1q5dJ427zyg/mMntXnngdQvADDY7ErShlXLz3h+cGPDmCpMVVp57T381ojHj+34iSTp+MlTjBcExpGVfeyO3iOSpGd7D+tUSOedNUPrP3XFGcMbau3Pahl853b1inaGTiAzHDVOgWT75yT9fkRcU378ZUmKiK9UzLOlPM8PbM+Q9LKkthhn4R0dHdHZ2Tnmehet+duJ/ByZc9Z0S5JOS5om69KL3qZffPdP6S+379P06dJrbw7oE1e8U7t+clQv9b+pi86Zqbnnna0bOxbo0af3SbZu7FigzTsPaOXSeXq0c7/eOD5QOmWVrXNmTdcdn3jPuBuT0RudejdCXT39pfPERgydFWD091Vb1ljLz+rG711r/rb895Fe+INfHnO+h7bv071bfqzbr7l8Ui+s6v35bXdFRMeEVzByGQ3JqzR+ZhuV1xnTpC985F3afeCoLjpnljbu+Ik8TZpma+a0abrl5xbqe8/9u146/JYuufBs/cZH3jWUmcrb1Sva9fzLr+krm3brbbNm6M0TA/rydUv0s+84b8y/zegcVHt+8F2owXVIOiNz9zyxW28cH6ia2VqZqfZzTDRDE81fVvPaTPX8DpLIa3k5hd3HWqXzHQ/ejjZn9ky9/tZJDZyWXNql6vor3qnOnn4dOPymPnHFO/Vvh96QbH3osov0/37/33Ti1GnNPW+WLrlwtu74+BI9//JrZ2y/K/++UvX92+jsd/X0a823n9HLR94c2nbc8/guvXHi1JjZvufxXZKtOz6+pOZ+Uqq+r5nMfjWJDDdzOzDVdSWxj615ZFjSJZL2VzzulTT65eDQPBExYPuIpIslHRpVyK2SbpWkSy9t7cu1Hj9VGe3QoTdO6Jud+/Xa8YGhqYNHzyTp2OFT6j38lnpeeUP9x05K0tD9Z186MjSt0tqt3eO+Mh/9Cr7eV/Rrt3Zrx/7DQ/clnfF91ZY11vKneiShUU6Puh1L5Qc2J9MMN/nnTyyvUvqZHTgtfeOfX9TA6Yo8nZZOKXTy1KkRz+3pe2PobzWYmcrsPPvSEb12/NTQh3Pv3fJjvfeSC8b824zOQbXnt+05dEY+R2ducBnVllMrM9V+jon+D030/y+reW2mvGY27bxOVIy6Ha0yV4Ntf+V+s/L+zpeODG0L+l47ob7XTmjt1u6h7FRuvyv/vlL1/dvo7K/d2q09B1+XNLztGDyaXjlf5ePB5yufG+9/q9q+ZjL71ST+f5uZgamuK4la62mGExMR6yStk0qvWpu57marPDI8c9q0oWEcX9n0XGJHhmu99Tx6+EW9wzFWr2jX0bdK66qct9r9WtMmst5mmyYNHRkez+3XXD70an0ysvrz1yPtzJaODF/WkCPDt19zuX72HedJqv63GSsHlc9LOuPIcOVzg8sYPDJcbzZGL7vaOuo10f+/PP+/JiWvv4O08zpRjT4yPJj70dvv8fZfg/dHZ3/1ivYRwzJ/9h3n6eibJ4eODFfL8NE3T0p2XftJqfq+ZjL71ST+f5uZgamuK4laMztMAiiaPA+TAIom78MkgKIZL7P1nE3iaUmLbV9me5akmyRtHDXPRkm/Xr7/KUl/X2vHCqAhyCuQL2QWSFnNYRLl8Um3SdoiabqkByNil+27JXVGxEZJD0j6C9t7Jb2qUpgBNBl5BfKFzALpq2vMcERskrRp1I2AaKoAACAASURBVLQ7K+6/JenTyZYGYDLIK5AvZBZIV66uQAcAAAAkiWYYAAAAhUUzDAAAgMKiGQYAAEBh0QwDAACgsGpedKNhK7b7JPXUmG2uqlwiNoPyUqeUn1qLWOfCiGhLaFmJqyOzefmbNRK/g+L8DvKeV6k4f6tq+NmLZ8zMptYM18N2ZxJX+Gm0vNQp5adW6swffhf8DiR+B3lS5L8VP3sxf/axMEwCAAAAhUUzDAAAgMLKejO8Lu0C6pSXOqX81Eqd+cPvgt+BxO8gT4r8t+Jnx5BMjxkGAAAAGinrR4YBAACAhqEZBgAAQGFlthm2vdL287b32l7TpHU+aPug7Z0V0y6y/Xe295Rv55Sn2/aflOt7xvb7K77n18vz77H96xXTl9l+tvw9f2Lbk6xzge0nbe+2vcv26izWavts2//b9o/Kdf638vTLbG8vL/ubtmeVp59Vfry3/PyiimV9uTz9edvXVExP7P/E9nTbP7T9RJbrTFutn2W830+rqON38HnbfbZ3lL++kEadjVJtWznq+TG3OUhfnrdH1f73mrHvG2sdzeQU9/1Z+PkbKiIy9yVpuqR/lfQuSbMk/UjSkias96OS3i9pZ8W0P5S0pnx/jaSvlu9fJ+k7kizpQ5K2l6dfJOmF8u2c8v055ef+d3lel7/32knWOU/S+8v3z5PULWlJ1motf++55fszJW0vL/NRSTeVp98v6f8s3/+/JN1fvn+TpG+W7y8p/w+cJemy8v/G9KT/TyT9lqSHJD1RfpzJOtP8qudnGev30ypfdf4OPi/pa2nX2sDfwRnbylHPV93m8JX+V963R9X+99SEfd9Y62jyz57avj8LP38jv7J6ZPiDkvZGxAsRcULSI5JuaPRKI2KbpFdHTb5B0p+X7/+5pE9WTN8QJU9JutD2PEnXSPq7iHg1Ivol/Z2kleXnzo+Ip6L037ShYlkTrfNARPx/5fuvSXpO0iVZq7W8vtfLD2eWv0LSxyT91Rh1Dtb/V5J+sfyq9AZJj0TE8Yh4UdJelf5HEvs/sT1f0i9L+kb5sbNYZwbU87OM9ftpFa3095yUMbaVlcba5iB9uf7/TXE/PdY6miblfX/qP38jZbUZvkTS/orHveVpafipiDhQvv+ypJ8q3x+rxvGm91aZPiXlt6CvUumoa+ZqdWnowQ5JB1UK3L9KOhwRA1WWPVRP+fkjki6eRP2T8ceS/quk0+XHF2e0zrTV87OM9ftpFfX+PX+l/NbkX9le0JzSMqOV/udbTSv+bZqx7xtrHalIYd+fqZ8/aVlthjOp/EopM+eis32upG9L+r8j4mjlc1mpNSJORcSVkuardETi8pRLOoPtj0s6GBFdadeClvG4pEUR8T6VXgT+eY35ASSgGfu+tPevae/70/75GyGrzfBLkiqPpMwvT0vDvw++vVe+PViePlaN402fX2X6pNieqVIY/jIi/jrLtUpSRByW9KSkn1PprZoZVZY9VE/5+QskvTKJ+ifqw5Kut/1vKr1l+DFJazNYZxbU87OM9ftpFTV/BxHxSkQcLz/8hqRlTaotK1rpf77VtOLfphn7vrHW0VQp7vsz8fM3Slab4aclLXbp0/yzVPoQzsaUatkoafCTlr8u6W8qpt9S/rTmhyQdKb+FsEXSf7Q9p/xpy/8oaUv5uaO2P1QeP3lLxbImpPz9D0h6LiL+KKu12m6zfWH5/tsk/ZJKY5yelPSpMeocrP9Tkv6+/Ap0o6SbXDpLwWWSFqs0yD+R/5OI+HJEzI+IReVl/H1EfC5rdWZEPT/LWL+fVlHzdzBqfOz1Kv3fF8lY2xykr5W2R4Oase8bax1Nk/K+P/Wfv6EiA5/iq/al0qcgu1UaY/q7TVrnw5IOSDqp0liZVSqNdfyepD2Stkq6qDyvJd1Xru9ZSR0Vy/k/VPrw1F5Jv1ExvUPSzvL3fE3lKwBOos6PqPQWxTOSdpS/rstarZLeJ+mH5Tp3SrqzPP1dKjWJeyV9S9JZ5elnlx/vLT//ropl/W65ludVcWaLpP9PJF2t4bNJZLbONL+q/SyS7pZ0fa3fT6t81fE7+IqkXSp9Uv9JSZenXXPCP3+1beV/lvSfy8+Puc3hK/2vPG+Pxvjfa/i+b6x1NPlnT23fn4Wfv5FfXI4ZAAAAhZXVYRIAAABAw9EMAwAAoLBohgEAAFBYNMMAAAAoLJphAECh2H7Q9kHbO+uY91LbT9r+YfmKgtc1o0YAzUMzDAAomvWSVtY57+9JejQirlLpnLxfb1RRANJBMwwAKJSI2Cbp1cpptn/a9mbbXbb/yfbgpeND0vnl+xdI+kkTSwXQBDNqzwIAQMtbp9JFQ/bYXq7SEeCPSfp9Sd+1/SVJ50hakV6JABqBZhgAUGi2z5X085K+VboKrSTprPLtzZLWR8T/sP1zkv7C9tKIOJ1CqQAagGYYAFB00yQdjogrqzy3SuXxxRHxA9tnS5or6WAT6wPQQIwZBgAUWkQclfSi7U9LkkuuKD+9T9Ivlqe/W9LZkvpSKRRAQzgi0q4BAICmsf2wpKtVOsL775LukvT3kv5U0jxJMyU9EhF3214i6c8knavSh+n+a0R8N426ATQGzTAAAAAKi2ESAAAAKCyaYQAAABQWzTAAAAAKi2YYAAAAhUUzDAAAgMKiGQYAAEBh0QwDAACgsGiGAQAAUFg0wwAAACgsmmEAAAAUVqrNsO0HbR+0vbPO+W+0vdv2LtsPNbo+ACORWSA/yCtQH0dEeiu3PyrpdUkbImJpjXkXS3pU0sciot/22yPiYDPqBFBCZoH8IK9AfVI9MhwR2yS9WjnN9k/b3my7y/Y/2b68/NQXJd0XEf3l7yWkQJORWSA/yCtQnyyOGV4n6UsRsUzSf5H09fL0dknttr9v+ynbK1OrEEAlMgvkB3kFRpmRdgGVbJ8r6eclfcv24OSzyrczJC2WdLWk+ZK22X5vRBxudp0ASsgskB/kFaguU82wSkeqD0fElVWe65W0PSJOSnrRdrdKwX26mQUCGIHMAvlBXoEqMjVMIiKOqhTCT0uSS64oP/2YSq9YZXuuSm/pvJBGnQBKyCyQH+QVqC7tU6s9LOkHkn7Wdq/tVZI+J2mV7R9J2iXphvLsWyS9Ynu3pCcl3R4Rr6RRN1BUZBbID/IK1CfVU6sBAAAAacrUMAkAAACgmVL7AN3cuXNj0aJFaa0eyJyurq5DEdGWdh1jIbPAMPIK5Mt4mU2tGV60aJE6OzvTWj2QObZ70q5hPGQWGEZegXwZL7MMkwAAAEBh0QwDAACgsGiGAQAAUFg0wwAAACisms2w7QdtH7S9c5x5rra9w/Yu2/+YbIkAJoLMAvlBXoH01XNkeL2klWM9aftCSV+XdH1EvEfSp5MpDcAkrReZBfJivcgrkKqazXBEbJP06jizfFbSX0fEvvL8BxOqDcAkkFkgP8grkL4kxgy3S5pj+x9sd9m+ZawZbd9qu9N2Z19fXwKrBjAJZBbID/IKNFgSzfAMScsk/bKkayTdYbu92owRsS4iOiKio62tcRfu6erp1y0PbFdXT3/D1gHkWOYyO4jsAmfIbF6RPLaB6UiiGe6VtCUi3oiIQ5K2SboigeVO2tqt3dq255DWbu1OswwgqzKX2UFkFzhDZvOK5LENTEcSl2P+G0lfsz1D0ixJyyX9zwSWO2mrV7SPuAUwQuYyO4jsAmfIbF6RPLaB6ajZDNt+WNLVkuba7pV0l6SZkhQR90fEc7Y3S3pG0mlJ34iIMU8R0wzLFs7RhlXL0ywBSE0eMzuI7KJo8pxXJI9tYDpqNsMRcXMd89wr6d5EKgIwJWQWyA/yCqSPK9ABAACgsGiGAQAAUFg0wwAAACgsmmEAAAAUFs0wAAAACotmGAAAAIVFMwwAAIDCynQzzDW6gfwgrwBaGdu41pXpZphrdAP5QV4BtDK2ca2r5hXo0sQ1uoH8IK8AWhnbuNaV6WaYa3QD+UFeAbQytnGtK9PDJAAAAIBGohkGAABAYdEMAwAAoLBohgEAAFBYNMMAAAAoLJphAAAAFBbNMAAAAAqLZhgAAACFVbMZtv2g7YO2d9aY7wO2B2x/KrnyAEwUmQXyg7wC6avnyPB6SSvHm8H2dElflfTdBGoCMDXrRWaBvFgv8gqkqmYzHBHbJL1aY7YvSfq2pINJFAVg8sgskB/kFUjflMcM275E0n+S9Kd1zHur7U7bnX19fVNdNYBJILNAfpBXoPGS+ADdH0v6nYg4XWvGiFgXER0R0dHW1pbAqgFMApkF8oO8Ag02I4FldEh6xLYkzZV0ne2BiHgsgWUDSB6ZBfKDvAINNuVmOCIuG7xve72kJwgpkF1kFsgP8go0Xs1m2PbDkq6WNNd2r6S7JM2UpIi4v6HVAZgwMgvkB3kF0lezGY6Im+tdWER8fkrVAJgyMgvkB3kF0scV6AAAAFBYNMMAAAAorMw2w109/brlge3q6ulPuxQANZBXAK2O7VzrymwzvHZrt7btOaS1W7vTLgVADeQVQKtjO9e6kjjPcEOsXtE+4hZAdpFXAK2O7VzrymwzvGzhHG1YtTztMgDUgbwCaHVs51pXZodJAAAAAI1GMwwAAIDCohkGAABAYdEMAwAAoLBohgEAAFBYNMMAAAAoLJphAAAAFBbNMAAAAAqLZhgAAACFRTMMAACAwqIZBgAAQGHRDAMAAKCwaIYBAABQWDWbYdsP2j5oe+cYz3/O9jO2n7X9L7avSL5MAPUis0B+kFcgffUcGV4vaeU4z78o6Rci4r2S7pG0LoG6AEzeepFZIC/Wi7wCqZpRa4aI2GZ70TjP/0vFw6ckzZ96WQAmi8wC+UFegfQlPWZ4laTvjPWk7Vttd9ru7OvrS3jVACaBzAL5QV6BBkisGbb9H1QK6u+MNU9ErIuIjojoaGtrS2rVACaBzAL5QV6Bxqk5TKIett8n6RuSro2IV5JYJoDGIbNAfpBXoLGmfGTY9qWS/lrSr0VE99RLAtBIZBbID/IKNF7NI8O2H5Z0taS5tnsl3SVppiRFxP2S7pR0saSv25akgYjoaFTBAMZHZoH8IK9A+uo5m8TNNZ7/gqQvJFYRgCkhs0B+kFcgfVyBDgAAAIVFMwwAAIDCohkGAABAYdEMAwAAoLBohgEAAFBYNMMAAAAoLJphAAAAFBbNMAAAAAqLZhgAAACFRTMMAACAwqIZBgAAQGHRDAMAAKCwaIYBAABQWDTDAAAAKCyaYQAAABQWzTAAAAAKi2YYAAAAhUUzDAAAgMKiGQYAAEBh0QwDAACgsGo2w7YftH3Q9s4xnrftP7G91/Yztt+ffJkA6kVmgfwgr0D66jkyvF7SynGev1bS4vLXrZL+dOplAZiC9SKzQF6sF3kFUlWzGY6IbZJeHWeWGyRtiJKnJF1oe15SBQKYGDIL5Ad5BdKXxJjhSyTtr3jcW552Btu32u603dnX1zfpFXb19OuWB7arq6d/0ssACqwpmSWnQCKavo9NCtsA5EVTP0AXEesioiMiOtra2ia9nLVbu7VtzyGt3dqdYHUARptKZskp0FxJ7WOTwjYAeTEjgWW8JGlBxeP55WkNs3pF+4hbABPSlMySUyARTd/HJoVtAPIiiWZ4o6TbbD8iabmkIxFxIIHljmnZwjnasGp5I1cBtLKmZJacAolo+j42KWwDkBc1m2HbD0u6WtJc272S7pI0U5Ii4n5JmyRdJ2mvpGOSfqNRxQKojcwC+UFegfTVbIYj4uYaz4ek30ysIgBTQmaB/CCvQPq4Ah0AAAAKi2YYAAAAhUUzDAAAgMKiGQYAAEBh0QwDAACgsGiGAQAAUFg0wwAAACgsmmEAAAAUFs0wAAAACotmGAAAAIVFMwwAAIDCohkGAABAYdEMAwAAoLBohgEAAFBYNMMAAAAoLJphAAAAFBbNMAAAAAqLZhgAAACFRTMMAACAwqqrGba90vbztvfaXlPl+UttP2n7h7afsX1d8qUCqAd5BfKFzALpqtkM254u6T5J10paIulm20tGzfZ7kh6NiKsk3STp60kXCqA28grkC5kF0lfPkeEPStobES9ExAlJj0i6YdQ8Ien88v0LJP0kuRIBTAB5BfKFzAIpq6cZvkTS/orHveVplX5f0q/a7pW0SdKXqi3I9q22O2139vX1TaJcADUklleJzAJNwD4WSFlSH6C7WdL6iJgv6TpJf2H7jGVHxLqI6IiIjra2toRWDWCC6sqrRGaBjGAfCzRQPc3wS5IWVDyeX55WaZWkRyUpIn4g6WxJc5MoEMCEkFcgX8gskLJ6muGnJS22fZntWSoN3t84ap59kn5Rkmy/W6Wg8h4N0HzkFcgXMgukrGYzHBEDkm6TtEXScyp9onWX7bttX1+e7bclfdH2jyQ9LOnzERGNKhpAdeQVyBcyC6RvRj0zRcQmlQbtV067s+L+bkkfTrY0AJNBXoF8IbNAurgCHQAAAAqLZhgAAACFRTMMAACAwqIZBgAAQGHRDAMAAKCwaIYBAABQWDTDAAAAKKzMN8NdPf265YHt6urpT7sUAOMgqwCAQXnaJ2S+GV67tVvb9hzS2q3daZcCYBxkFQAwKE/7hLquQJem1SvaR9wCyCayCgAYlKd9Quab4WUL52jDquVplwGgBrIKABiUp31C5odJAAAAAI1CMwwAAIDCohkGAABAYdEMAwAAoLBy0Qzn6Vx1AIaRXQCtgu1Z68pFM5ync9UBGEZ2AbQKtmetK/OnVpPyda46AMPILoBWwfasdeWiGc7TueoADCO7AFoF27PWVdcwCdsrbT9ve6/tNWPMc6Pt3bZ32X4o2TIB1Iu8AvlCZoF01TwybHu6pPsk/ZKkXklP294YEbsr5lks6cuSPhwR/bbf3qiCAYyNvAL5QmaB9NVzZPiDkvZGxAsRcULSI5JuGDXPFyXdFxH9khQRB5MtE0CdyCuQL2QWSFk9zfAlkvZXPO4tT6vULqnd9vdtP2V7ZbUF2b7Vdqftzr6+vslVDGA8ieVVIrNAE7CPBVKW1KnVZkhaLOlqSTdL+jPbF46eKSLWRURHRHS0tbUltGoAE1RXXiUyC2QE+1iggepphl+StKDi8fzytEq9kjZGxMmIeFFSt0rBBdBc5BXIFzILpKyeZvhpSYttX2Z7lqSbJG0cNc9jKr1ile25Kr2l80KCdQKoD3kF8oXMAimr2QxHxICk2yRtkfScpEcjYpftu21fX55ti6RXbO+W9KSk2yPilUYVDaA68grkC5kF0ueISGXFHR0d0dnZmcq6gSyy3RURHWnXMRYyCwwjr0C+jJfZpD5ABwAAAOQOzTAAAAAKi2YYAAAAhUUzDAAAgMKiGQYAAEBh0QwDAACgsGiGAQAAUFg0wwAAACgsmmEAAAAUFs0wAAAACotmGAAAAIVFMwwAAIDCynQz3NXTr1se2K6unv60SwEwDrIKAFPHtjQdmW6G127t1rY9h7R2a3fapQAYB1kFgKljW5qOTDfDq1e068oFF+romyd5lQRkGFkFgKlbuXSe5syeqZVL56VdSqFkuhletnCOzj97hnb0HuFVEpBhZBUApm7zzgPqP3ZSm3ceSLuUQsl0MyzxKgnIi9Ur2vXRxXO1ekW7JMa+AUXHNmDiRm9H0RyZb4Z5lQTkw7KFc7Rh1XItWzhHEmPfgKJjGzBxo7ejaI4ZaRcwlq6efq3d2j10RJhXSUB2DeZ19Yr2oY34YGbJLlBMrbYNqLadQ2vIbDN8z+O7tKP3iI6+eVKP3faRtMsBMI5qeR08wgGgmFptG0Bf0rrqGiZhe6Xt523vtb1mnPl+xXbY7phyZfbIWwB1Ia9AvqSSWUwc27mWVbMZtj1d0n2SrpW0RNLNtpdUme88SaslbU+isDs+vkQfXTxXd3z8jFUBGAN5BfIlrcxi4tjOta56jgx/UNLeiHghIk5IekTSDVXmu0fSVyW9lURhDCIHJoW8AvmSSmYxcWznWlc9zfAlkvZXPO4tTxti+/2SFkTE3463INu32u603dnX1zfhYgHUlFhey/OSWaCx2McCKZvyqdVsT5P0R5J+u9a8EbEuIjoioqOtrW2qqwYwQRPJq0RmgbSxjwUar55m+CVJCyoezy9PG3SepKWS/sH2v0n6kKSNDPAHUkFegXwhs0DK6mmGn5a02PZltmdJuknSxsEnI+JIRMyNiEURsUjSU5Kuj4jOhlQMYDzkFcgXMgukrGYzHBEDkm6TtEXSc5IejYhdtu+2fX2jCuvq6dcnv/bP+uR93+dSjkCd0sqrNHzp1Ye27+MSrECd0swsiqsZl8rO0+W467roRkRskrRp1LQ7x5j36qmXVbqM447eI0P3W+nE3UAjpZFXafjSq8++dET9x05KErkF6pBWZlFcg9trqXHb6WasIymZvQLd6hXtOvrmSclumUs5Aq1sMKcrl87T5p0HyC0AZFQzLpWdp8txOyJSWXFHR0d0djLkCRhkuysiMvuhGDILDCOvQL6Ml9kpn1oNAAAAyCuaYQAAABQWzTAAAAAKi2YYAAAAhZXpZjhP56gDio68AsDUsB1NR6ab4cFz1K3d2p12KQBqIK8AMDVsR9OR6WZ49Yp2fXTx3KFz1PGKCciuyrySVQCYuNF9T57laT+Q2Wa4q6df9zy+S0ffGhiaxismIJtG55WsAmg1zWjuli2cow2rlmvZwjkNW0ez3PPEbm3bc0j3PLE77VJqyuwV6KpdjjlPVzMBimR0XskqgFaTp8sLZ8LgRd1SurjbRGT2yPDqFe2af+HZmm5pybzzJbXWKyaglaxe0a7FbefovLNmaMm884caYrIKFFee3iavRysNYWiGGz9wqebMnqkbP3Bp2qXUlNlmWJJePnpcp0L6Zuf+tEsBMI5lC+fonLNn6rXjA/rL7fsYIgGA4VIFt3nnAfUfO6nNOw+kXUpNmR4mMXA6NGOadfs1l6ddDoBaym+FveP8s3TVhRdy9AQouFYbLsUwiYnJ098/s81w5S+Rt1qB7LvjE+9heASAIYNDG1tFnpq7LMjT3z+zzXCefokAyCyA1sY2rnVldsxwqw28B1oZeQXQ6tjOta7MNsMMvAfyg7wCaHVs51pXZpthrj4H5MdYpxwitwBaxcql8zRn9kytXDov7VKQsLqaYdsrbT9ve6/tNVWe/y3bu20/Y/t7thdOtbDR5xTmFRlQnyzkdRC5BWpLI7OYuDydKgwTU7MZtj1d0n2SrpW0RNLNtpeMmu2Hkjoi4n2S/krSH061sIe279NVd39XD23fJ6l05OnKBRfq6JsnOcoEjCEreR1U7YgxR4uBYWllthlaLevNODLcar+zvKjnyPAHJe2NiBci4oSkRyTdUDlDRDwZEcfKD5+SNH+qhd275cfqP3ZSd/7NTnX19GvZwjk6/+wZ2tF7hKNMwNgykddB1Y4Yc7QYGCGVzDZDq2W9GUeG73l8l7btOaR7Ht/VsHXgTPU0w5dIqrwEXG952lhWSfpOtSds32q703ZnX1/fuCv9TMcCSdLA6RgKEpdCBGpKLK9S/ZmtltexkGNghFT2sc3Qallvyphhe+QtmiLRD9DZ/lVJHZLurfZ8RKyLiI6I6Ghraxt3WU+98MrQ/SXzzpc09rhEABNXK69S/ZkdzOt0a2hHMdbbfeQYmJwk97GYuEef3qf+Yyf16NP7as88STd2LNCc2TN1Y/kAA5qjnmb4JUmVf5X55Wkj2F4h6XclXR8Rx6da2BsnTg3d/2bn/nHmBFAhlbwOHsU4FdKj5by22lukQIOkk9kmaLltQBOO2jbrQ3qMTR6pnivQPS1pse3LVAroTZI+WzmD7ask/S9JKyPiYBKFHT52Yuj+7ddcnsQigSJIJa+LLp6tHfsPlx5ESOLSpUCdUslsM7TaNmBwO7fo4tkNW0ezfmeDL1QkcVU91dEMR8SA7dskbZE0XdKDEbHL9t2SOiNio0pv2Zwr6VsuvWLaFxHXT6WwQ6+XmmFL+uzyS6eyKKAw0srrE88MH8X40LsulsSlS4F6pJXZZmi1bcDGHT8Zuv3jm65qyDqa9TtrtRcqU1XPkWFFxCZJm0ZNu7Pi/oqE69KsGdN0fOC0Zs3I7HVBgExKI69f+Mhlun/bC5JKw5rWXPfupFcBtKw0MouJm1nuS2a2QF/Sai9Upiqzf9Frl75jxC2A7Lr04nM0e+Y0zZ45nWFNAFoSfUnrymwz/J1nD4y4BZBd/23jTh07eVqnTp9mWBOApmrWh8G+99y/j7htBD7Ylo7MNsMnTsWIWwDZdfJ0KafHTwUbcQBN1ayzVrzjgreNuG2EVjoDR54a+8w2wxfMLg1nDumMS7wCyJaP/MzcofuffzAfGz8AjdWsZqhZF/d4zzvPH3HbCK10oZI8NfaZbYbfPD58nuGvbNqdYiUAann2pSND9187fioXGz8AjdWsSws360I+j//oJyNuG6GVLkrUlCv2JSSzzXDlSa1P8NYrkGmfGXW1pKNvDZBZoOAGL55VeRGtPLv43LNG3DZCnoYW1NKsC4gkIbPN8PlnD5/17fjAaY40ARm2+8DRofvTLe3Yf5jMAmgp77zg7P+/vfuPkaO87zj+/t4v4zsb+/wj5Php0zglh0WB2DGEFFUJlQ1xbSRaSpuqhBIhNWlFW4nKSUSAJBJJqNoQQUCIHwopBFwnRBalGEiTQkxzcC4Gzj/Ah43tsw9sw/l89p19v57+Mc8eu+vd27u9nZ2Z3c9LGs3ss3Mz32dnvnvPzjwzkzEOQ5K6FhSiI8MlcGRgeGz6wrNmV0T/GZFKlf5lV1dTUzF93kSkeE0NtRnjpLt26dk0N9Zz7dLw7piTpAZkIToyXAIjbnRs+pKFc7j7hbcr4rSBSCVa++pHF7k21Bk3X/FJ5axIlStH47Gc7vtNJz39Q9z3m87Q1pGkBmQhrS2nUldjtLaEd8FhqcS2MTz6UVuYB17aWTGnDUQqUlof/74TI3zlkqaWtAAADoRJREFUJ68qZ0WqXCU17AD29QxkjMNQSXeTeKxtN8OjjsfadkcdSkGxbQyvuvD0sel6nXYVibVbV7ZS679Namugp3+I5sZ65axIFaukhh3A3BkNGeMwVNLdJMpxX+ZSiW1jeMv+4IKcGoPbVp1fMTuHSCX69DnNtPgvvDlNDVy+aB4PXr9UOStSxSqpYQdwRnNjxjgM5bqbRDnW871rLuDyRfP43jUXhLaOUoltY7jzwFEARh2sbd+rvociMfZ42x66/KnDg32DrFjcoj7DIlXu8bY9XPTt50J/cFa5GpAL5jZmjMNQrnszl2s9SRHbxnD6Q5h1myaReLtjfUfG67s2bFefYZEYK0cD8q4N2+npH+KuDdtDWweU73Zkv9y8P2McitT1F2nXYYShHPeATtJt4mLbGE73sZkNHBkY0lEmkZgaHHEZr+fNmKZbIorEWDkaKn++5Czqauykh/KUWiXdjuzWla1cvmget65sDXU9A0MjGeMwzGlqyBjHWSIaw4f6Btnc1ZuIXxcTVUlPmRFpqM08irHjwFFwrmBfQeWBSDTKcXHb1u4jDI+6jIfyhKFcd62YXleTMQ5DufpZv987kDEOw3p/BH19mEfSSyQRjeH6uuBuEq0tp5al/1E5JOn0gUgh82fmeDzpBE7zKQ9EKle57iZRrvvZDgyPZozDUK5+1qkqhFgV6v2PhvoQfzyUSvwjBG747AIAHmvbU5b+R+VQabeckerWdfj4SWXXTuDUqPJAJBrl+CFarqOcSbqfbSF3PrONnv4h7nxmW6jrSZ3Nyz6rV0qf+vjMjHGcJaIx/PQb+3lxxyFGRkeZOa2OW5afF3VIU1Zpt5wRyTaRU5bKA5FolKOfbbm6QSXpfraFnFJfkzEOy22rFtPcWM9tqxaHto7NXb0Z4zhLRGN43+HjNDfW0z80yu/Nb+LZjm71MRSJsUUfm8GR48PKU5GY+rF/tPCPQ3y0cLlu33XDZQtpbqznhssWhrqecvjg2GDGOCx7PjhG3/Fh9nxwLNT1JMWEGsNmtsLM3jKzTjNbk+P9aWb2pH+/zcwWlDJIA25Zfh7NjfUcGxzJe2pHF+OIRJ+vAO/1DrB572G+8/TWUi9apOJEkbOp+4J3hfho4XLcvgvgkY276Okf4pGNu0JdTzmMusxxWO5/cSfDo477X9wZ7ooSomBj2MxqgXuBK4FW4C/MLPueHzcCPc65TwD/Bny/lEGOAt946k16+ofo6ukHYGPnobEO5pt293D1Pb/lyw+/MvYrtFyN4qk2wCutAV9p9UmaOOQrQN+J4J/fln29PN62h79+qG1svGl3z1jO/vG//g9X37tRuSpVKy45G4Yd/uFZqXHS1yPxtPS7z7NgzX+y9LvPF72MugnM8xmg0zm3E8DMngBWA+mHfFYDt/vpdcA9ZmbOuZL/thkYCi59HHFBA/kbT7150jyp/ikv7jiUdzn1NcEy5jY1MLuxgd6BIQ70ncCAT8xvYsfBYxjwh4vm8dqew8yaXgdm9PYP8qVl57C1+wg3X/FJvvP0VjbvPczGzkP8yR+czq+2vc+sxgbmNTVwyblzeaxtNx+fNZ0bLlvIsx3drFjcwtr2veAc1y49e+ym5ACP3rgsb7ybdvdw9wtvj11olGs6Dv0uUxdlQO76pNdjvHgnOl8SlLkuscrXoVE3lqOp/SJXXl5z38uTWm5tDYyOBmeNHDBvRpDHAIcHBjnUN0hdrTG7sZ7jgyN8adk5PNm+l57+Idrf7QEcc2ZMY3p9LU3T6rh1ZetJ2ya13VYsbuHZjm66e4+z48BRunuP0zLrlLHtmW/7FipvbTmVJ9v3csvy8/jLZWfnrWsl5UJSVHPOiiTNwaODGeNiTKQxfAawN+11F5Ddyhmbxzk3bGa9wFwg47+emd0E3ARw9tn5v/zLwbepOXh0MOMDdMCOg8fGplP/uPtODI/N8+BvdzGcOofhv4tG3EdPpek7MUBXzwAd+4N7LPYdODrW6H1zX+9Y43f3h0FZc2N9wavp0xuZQM7p8RrT5ZKqR776FGosT3a+JChzXUqWrxCvnE034vM31RLIzmOAoRHHwb6gLJWzdTVGv7/JfH/a6eG7X3j7pG2T2m6pnJ05rRYIuoCkjkA9euOyvNu3UPnL73zA8Kjjrg3bx20MV1IuJEVSczau+SoSdxNpDJeMc+4B4AGAJUuWRPqLtlRHhgHW/PwN3usd4AufOm3SR4af7eie0NGHXI3MfNNRSt0dIJ9CjeXJzpcESa5LnHI2XTFHhrd2H2HF4hYe2biLfT39GUeGc22bVFnqyHD2OHu7Zi+jUHn6keHxJHn/SaqkfuZxzVeRuLNCZ1nM7FLgdufccv/66wDOuTvT5tng5/lfM6sD3gPmj3cKZ8mSJa69vb0EVRCpDGa2yTm3ZIrLCCVfQTkrkq4U+eqXo/+xImUwXs5O5G4SrwKLzGyhmTUA1wHrs+ZZD1zvp/8U+G/1ZRKJhPJVJFmUsyIRK9hNwvdP+jtgA1ALPOyc22Jm3wbanXPrgYeAn5pZJ/AhQTKLSJkpX0WSRTkrEr0J9Rl2zj0DPJNV9q206ePAn5U2NBEphvJVJFmUsyLRSsQT6EREREREwqDGsIiIiIhULTWGRURERKRqqTEsIiIiIlVLjWERERERqVoFH7oR2orNDgK7C8w2jxyPiI1Q3OKB+MWkeMY3XjznOOfmlzOYyZhAzsbts54sxR+tpMWf9Hwtl6Rt10IqqT7VVpe8ORtZY3gizKy9FE/4KZW4xQPxi0nxjC9u8ZRS0uum+KOV9Pglt0rbrpVUH9XlI+omISIiIiJVS41hEREREalacW8MPxB1AFniFg/ELybFM764xVNKSa+b4o9W0uOX3Cptu1ZSfVQXL9Z9hkVEREREwhT3I8MiIiIiIqFRY1hEREREqlZsG8NmtsLM3jKzTjNbU+JlP2xmB8ysI61sjpk9b2Y7/LjZl5uZ/cjH8YaZXZz2N9f7+XeY2fVp5Z82szf93/zIzKxAPGeZ2a/NbKuZbTGzm6OMycxOMbNXzOx1H88dvnyhmbX5ZTxpZg2+fJp/3enfX5C2rK/78rfMbHla+aS3r5nVmtlrZvZ01PGY2bv+89xsZu2+LLJ9KGrFbM+Q4ohVLk2hHrHZ14uIfbaZrTOz7Wa2zcwuTdrnL1OXLxeTLDsvkypXjkYdU7HM7B/9/tVhZj8zs1OKWpBzLnYDUAu8A5wLNACvA60lXP7lwMVAR1rZD4A1fnoN8H0/fRXwX4ABlwBtvnwOsNOPm/10s3/vFT+v+b+9skA8LcDFfnom8DbQGlVMfp4ZfroeaPN/uxa4zpffD/ytn/4qcL+fvg540k+3+m03DVjot2ltsdsX+CfgceBp/zqyeIB3gXlZZZHtQ5Wcr5OMJVa5NIV6xGZfLyL2nwBf8dMNwOykff4aSrIf5MzFqOOaYp0y8jKpQ64cjTqmIutxBrALmO5frwW+XMyy4npk+DNAp3Nup3NuEHgCWF2qhTvnXgQ+zCpeTbCD4MdXp5U/6gK/A2abWQuwHHjeOfehc64HeB5Y4d871Tn3OxdsnUfTlpUvnm7n3P/56T5gG8FGjiQmv9yj/mW9HxzweWBdnnhSca4DvuCP1qwGnnDOnXDO7QI6CbbtpLevmZ0JfBF40L+2KOPJI7J9KGKh5utkxC2XipGQfT1f7LMIDjY8BOCcG3TOHSZBn7+Uxji5mEjZeZlU4+RoUtUB082sDmgE9hezkLg2hs8A9qa97iL8JDrNOdftp98DTisQy3jlXTnKJ8Sf5ryI4GhsZDH500GbgQME/4jeAQ4754ZzLGNsvf79XmBuEXGO54fAPwOj/vXciONxwHNmtsnMbvJlsdiHIhBFvhYUl1wqQtz29clYCBwEHvGnkx80syaS9flLiWXlYlJl52VS5cvRxHHO7QP+BdgDdAO9zrnnillWXBvDkfJHHMp+zzkzmwH8HPgH59yRKGNyzo045y4EziQ4mnReudadzcxWAgecc5uiiiGHzznnLgauBL5mZpenvxnVPiSBOOXSZMR0X5+MOoIuaPc55y4CjhF0ixgT589fSm+8XEyKCsjLdAVzNCn8tQerCRr4pwNNZvZXxSwrro3hfcBZaa/P9GVhet+fgsOPDxSIZbzyM3OUj8vM6gm+MB5zzv0iDjEB+NMnvwYuJTiFWZdjGWPr9e/PAj4oIs58LgNWmdm7BKd1Pw/cHWE8qV+kOOcOAE8R/GCIfHtFJIp8zSuuuTRBsdvXJ6kL6HLOpY4AriP4x5uUz19KKE8uJtFJeWlm/x5tSEXLl6NJdAWwyzl30Dk3BPwC+GxRS3Ix6ASdPRD8ctlJ0NpPXeRxfonXsYDMC+juIvMCjx/46S+SeYHHK758DkHH7WY/7ALm+PeyL/C4qkAsRtD37YdZ5ZHEBMzHd6gHpgMvASuB/yDzIp6v+umvkXkRz1o/fT6ZF/HsJLiAp+jtC/wRH11UFEk8QBMwM236ZWBFlPtQpefrJGKJVS5NsS6R7+tFxv0S8Pt++nb/2Sfu89cw5f0gZy4mfUjPy6QOuXI06piKrMcyYAtBX2EjuB7h74taVtSVGaeSVxFcffoO8M0SL/tnBP1Lhgh+Jd1I0M/uV8AO4IW0L14D7vVxvAksSVvO3xBcmNIJ3JBWvgTo8H9zD/5Jf+PE8zmC04ZvAJv9cFVUMQEXAK/5eDqAb/nyc/0/ok6Cf87TfPkp/nWnf//ctGV906/zLdKu+i52+5LZQIgkHr/e1/2wJTV/lPtQ1EOx2zOEOGKVS1OsS+T7epFxXwi0+23wS4LGbOI+fw1T3g9y5mLUcZWgXmN5mdQhV45GHdMU6nIHsN1/J/w09d042UGPYxYRERGRqhXXPsMiIiIiIqFTY1hEREREqpYawyIiIiJStdQYFhEREZGqpcawiIiIiFQtNYZFREREpGqpMSwiIiIiVev/AdA9lbIrCnFSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "LMt3abZg-SPX",
        "outputId": "8971bb1f-6ae1-405b-8f32-1d2a13c8fab7"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(10, 8))\n",
        "corr = data.corr()\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
        "square=True, ax=ax,annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCpz8j2wNYX_"
      },
      "source": [
        "Prepare for input to model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kg_nQRlS9px1"
      },
      "source": [
        "Y = pd.DataFrame(data.iloc[:, 1].values, columns=[\"target\"])\n",
        "test_y = pd.DataFrame(test.iloc[:, 0].values, columns=['vidid'])\n",
        "data = data.drop([\"adview\", \"vidid\"], axis=1)\n",
        "test = test.drop(['vidid'], axis=1)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7ZwZGxW-r6V"
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(\n",
        "    data, Y, test_size=0.1, random_state=42\n",
        ")\n",
        "y_train = y_train.to_numpy().ravel()\n",
        "y_val = y_val.to_numpy().ravel()"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlDunpWP-0CZ"
      },
      "source": [
        "sc_X = MinMaxScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_val = sc_X.transform(X_val)\n",
        "test = sc_X.transform(test)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Z6E5bWbNdMM"
      },
      "source": [
        "Test on Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqwjmqMm_Him"
      },
      "source": [
        "def evaluate(X_val, y_val, model):\n",
        "    y_preds = model.predict(X_val)\n",
        "    print('Mean Absolute Error: ',mean_absolute_error(y_val, y_preds))\n",
        "    print('Mean Squared Error: ',mean_squared_error(y_val, y_preds))\n",
        "    print('Root Mean Squared Error: ',np.sqrt(mean_squared_error(y_val, y_preds)))\n",
        "    print(\"R^2 : \", r2_score(y_val, y_preds))\n",
        "    plt.figure(figsize=(16, 8))\n",
        "    plt.scatter(range(len(y_preds)), y_preds, color='blue', s=1)\n",
        "    plt.scatter(range(len(y_val)), y_val, color='red', s=1)\n",
        "    plt.show()"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xihch7LNiHf"
      },
      "source": [
        "Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "lVR80LWx_OOx",
        "outputId": "beb5cf56-bdf2-4d1e-ec14-d3ab2c9ced85"
      },
      "source": [
        "\n",
        "linear_regression = LinearRegression()\n",
        "linear_regression.fit(X_train, y_train)\n",
        "print(\"Lineasr Regression\")\n",
        "evaluate(X_val, y_val, linear_regression)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Lineasr Regression\n",
            "Mean Absolute Error:  2936.7332975552\n",
            "Mean Squared Error:  104797222.51814939\n",
            "Root Mean Squared Error:  10237.05145626168\n",
            "R^2 :  -0.022721187595377\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsM8JpK0Nmk4"
      },
      "source": [
        "SVR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "fyWt40bP_djh",
        "outputId": "23dda4aa-ac71-4414-dcb8-e9291e289c7f"
      },
      "source": [
        "svr_model = SVR()\n",
        "svr_model.fit(X_train, y_train)\n",
        "print(\"SVR\")\n",
        "evaluate(X_val, y_val, svr_model)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVR\n",
            "Mean Absolute Error:  1046.4203232323523\n",
            "Mean Squared Error:  103562575.41575138\n",
            "Root Mean Squared Error:  10176.569923886505\n",
            "R^2 :  -0.010672206520453864\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6b0N0XFVNroL"
      },
      "source": [
        "Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "B7pgWtEgMBM_",
        "outputId": "0f7a3fb4-7672-4db7-ed65-0c83a5b22fb3"
      },
      "source": [
        "dt_model = DecisionTreeRegressor()\n",
        "dt_model.fit(X_train, y_train)\n",
        "print(\"Decision Tree\")\n",
        "evaluate(X_val, y_val, dt_model)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Decision Tree\n",
            "Mean Absolute Error:  2126.0389344262294\n",
            "Mean Squared Error:  232536011.23155737\n",
            "Root Mean Squared Error:  15249.131491057364\n",
            "R^2 :  -1.269330234627576\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bz1d_MPmNvvF"
      },
      "source": [
        "Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "caFB5fSTMHfZ",
        "outputId": "897788fc-864b-4436-e8e7-d3218e7469b3"
      },
      "source": [
        "rf_model = RandomForestRegressor()\n",
        "rf_model.fit(X_train, y_train)\n",
        "print(\"Random Forest\")\n",
        "evaluate(X_val, y_val, rf_model)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest\n",
            "Mean Absolute Error:  2462.594959016393\n",
            "Mean Squared Error:  194682126.56578568\n",
            "Root Mean Squared Error:  13952.853706886834\n",
            "R^2 :  -0.8999123345131743\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryt3d1HVN1b4"
      },
      "source": [
        "Random Forest (Hyperparameter Tuning)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqCD3u8YMLuY"
      },
      "source": [
        "n_estimators = [int(x) for x in np.linspace(50, 300, num = 10)]\n",
        "max_features = ['auto', 'sqrt']\n",
        "max_depth = [int(x) for x in np.linspace(1, 60, num = 5)]\n",
        "min_samples_split = [5, 10]\n",
        "\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split}"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "QWKfcCT5MO1j",
        "outputId": "5dd4c4c5-9280-4f0d-e950-45f7a98b31db"
      },
      "source": [
        "\n",
        "rf_random = RandomizedSearchCV(estimator=rf_model, param_distributions = random_grid, n_iter = 20, cv = 10, n_jobs=-1, verbose=2, scoring='neg_mean_squared_error')\n",
        "rf_random.fit(X_train, y_train)\n",
        "evaluate(X_val, y_val, rf_random.best_estimator_)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 20 candidates, totalling 200 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  6.2min\n",
            "[Parallel(n_jobs=-1)]: Done 158 tasks      | elapsed: 19.3min\n",
            "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed: 24.6min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error:  2519.575582965499\n",
            "Mean Squared Error:  129789845.24153392\n",
            "Root Mean Squared Error:  11392.534627620576\n",
            "R^2 :  -0.2666254073694869\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWdWQJNUN_C8"
      },
      "source": [
        "Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRzAUPJjMVo8"
      },
      "source": [
        "nn_model = keras.models.Sequential([\n",
        "                keras.layers.Dense(16, input_shape=(7,), activation='relu'),\n",
        "                keras.layers.Dropout(0.2),\n",
        "                keras.layers.Dense(16, activation='relu'),\n",
        "                keras.layers.Dropout(0.2),\n",
        "                keras.layers.Dense(8, activation='relu'),\n",
        "                keras.layers.Dense(units=1)\n",
        "])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBrkH_IDMY0w"
      },
      "source": [
        "\n",
        "nn_model.compile(optimizer='rmsprop', loss='mse', metrics=['mse'])"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "_IzfRVo3MbkN",
        "outputId": "2a17a081-5987-4c56-b21a-5daef9b004ff"
      },
      "source": [
        "\n",
        "nn_model.fit(X_train, y_train, epochs=300, verbose=0)\n",
        "evaluate(X_val, y_val, nn_model)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error:  1718.288444581579\n",
            "Mean Squared Error:  101494464.02284402\n",
            "Root Mean Squared Error:  10074.446090125453\n",
            "R^2 :  0.009510593071085371\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvXVirgSOGAx"
      },
      "source": [
        "Save Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApYyNQHOMfOT",
        "outputId": "2ba4d946-fbc8-4d15-f5d9-1b385c0c8618"
      },
      "source": [
        "joblib.dump(rf_random.best_estimator_, \"ml_model.pkl\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ml_model.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z10joU3qMjat"
      },
      "source": [
        "nn_model.save('nn_model.h5')"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zyzj2HF2OJvZ"
      },
      "source": [
        "Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4xWfUzfMl14"
      },
      "source": [
        "model = joblib.load('ml_model.pkl')"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-j8wQ6fMoUU"
      },
      "source": [
        "\n",
        "model = keras.models.load_model('nn_model.h5')"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGGft5VHMqec"
      },
      "source": [
        "preds = model.predict(test)\n",
        "preds = preds.astype(np.int16)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i768TEzwMtwB"
      },
      "source": [
        "\n",
        "preds = preds.reshape(1, -1)[0].tolist()\n",
        "d = {'adview': preds}\n",
        "pd.DataFrame(d).to_csv('predictions.csv', index=False)"
      ],
      "execution_count": 58,
      "outputs": []
    }
  ]
}