Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the prices of Graphics cards that are sold in the India based on there features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Description:- The current data has been scrapped from a AMD Authorized retialer in india.\n",
    "\n",
    "#### The data has the following columns/features \n",
    "\n",
    "#### Name: - It is the name given to the graphic card \n",
    "\n",
    "#### Model: - It is the model name given to the grpahic card \n",
    "\n",
    "#### Price: - It is the price of the graphic card on that particular day \n",
    "\n",
    "#### Date: - It is the date on which the data was extracted \n",
    "\n",
    "#### vram:- It is amount of video memeory a graphic card contains 4GB,8GB,16GB,24GB,48GB......\n",
    "\n",
    "#### vram_type: - (Graphics Double Data Rate) GDDR is double data rate (DDR) memory specialized for fast rendering \n",
    "#### on graphics cards (GPUs) It has various types such as GDDR4,GDDR6,GDDR6X\n",
    "\n",
    "#### feature:- 1 - OC(Overclocked) and 0 represents not overclocked \n",
    "\n",
    "#### Brand:- It is the brand of the graphic card chip that is either Nvidea or AMD\n",
    "\n",
    "#### provider:- It is the company that builds the casing and cooling system over the graphic card\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np \n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data is already cleaned by removeing all the null values and duplicates and it is being extracted from the sql database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('postgresql://akmsvuudvojpti:35e7628d30691d2b61384f7ab2e97a883f60b27c3922790ac79cc03268656843@ec2-3-219-229-143.compute-1.amazonaws.com/dbae31ljqo7na3')\n",
    "query = '''\n",
    "\n",
    "select * from \"GraphicsCardTable\"\n",
    "'''\n",
    "df = pd.read_sql_query(query, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns =['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Removal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here we are checking if there are any outliers present in the Price column\n",
    "#As we can see that there are outliers beyond the 0.75 quantile from the Box plot\n",
    "sns.boxplot(x = df['Price'])\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here we can see clearly that the distribution of the Price column is skewed this confirms that there are outliers \n",
    "sns.distplot(x = df['Price'])\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we are treating the outliers by replacing them with the mean of the column \n",
    "df['Price'] = np.where(df[\"Price\"] >df[\"Price\"].quantile(0.75), df[\"Price\"].mean(),df[\"Price\"])\n",
    "#Here we are normalizing the columns using the Z-score\n",
    "df['Price'] = df['Price']  / df['Price'].abs().max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#As we can see from the box plot the outliers have been removed \n",
    "sns.boxplot(x = df['Price'])\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#As we can see from the below distribtion it is now normalized when we compare it prior to the outlier treatment\n",
    "sns.distplot(x = df['Price'])\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here  we are pre processing the data and prepaing the data for analysis \n",
    "from sklearn import preprocessing \n",
    "def preprocessor(df):\n",
    "    res_df = df.copy()\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    res_df['vram'] = le.fit_transform(res_df['vram'])\n",
    "    res_df['vram_type'] = le.fit_transform(res_df['vram_type'])\n",
    "    res_df['Brand'] = le.fit_transform(res_df['Brand'])\n",
    "    res_df['provider'] = le.fit_transform(res_df['provider'])\n",
    "    return res_df\n",
    "new_df = preprocessor(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping the columns as they have dont contribute to the analysis\n",
    "new_df = new_df.drop(columns = ['Name','Model','Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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>index</th>\n",
       "      <th>Price</th>\n",
       "      <th>vram</th>\n",
       "      <th>vram_type</th>\n",
       "      <th>feature</th>\n",
       "      <th>Brand</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.054363</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.062876</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.111431</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.154506</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.197425</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>0.851710</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>0.851710</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>0.851710</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>0.851710</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>0.851710</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index     Price  vram  vram_type  feature  Brand  provider\n",
       "0       0  0.054363     0          0        0      1         2\n",
       "1       1  0.062876     0          1        0      1         0\n",
       "2       2  0.111431     1          0        0      1         8\n",
       "3       3  0.154506     1          2        0      1         5\n",
       "4       4  0.197425     1          2        0      1         5\n",
       "..    ...       ...   ...        ...      ...    ...       ...\n",
       "62     62  0.851710     4          3        0      1         2\n",
       "63     63  0.851710     7          3        0      1         8\n",
       "64     64  0.851710     7          2        0      1         5\n",
       "65     65  0.851710     7          3        0      1         4\n",
       "66     66  0.851710     8          2        0      1         5\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.metrics \n",
    "import math  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(new_df.drop(['Price'],axis=1),\n",
    "                                                    new_df['Price'],test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.8098202665526664\n",
      "Mean absolute error: 0.09\n",
      "Root mean squared error:  0.10652608172686175\n",
      "Adjusted r2_score:  1.503951923597809\n"
     ]
    },
    {
     "data": {
      "image/png": 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MVwIAgA6EKegeXwsAANoRpqD7fDUAAGgjTEHP+HoAABARwhSkw1cEAABhCtLkawIAUOGEKUifrwoAQAUTpiAzvi4AABVKmILM+coAAFQgYQqyw9cGAKDCCFOQPb46AAAVRJiC7PL1AQCoEMIUZJ+vEABABRCmIDd8jQAAypwwBbnjqwQAUMaEKcgtXycAgDIlTEHu+UoBAJQhYQryo3ehCwAAILuEqSxLkoimFREtqyJqqiPq+0akUoWuiiIhUAEAlBFhKsuWvhnxcmNrmFqnpjpi62ERA/sXri6Khq8XAECZEKaybOmbEfPmtw9TEa3P581v/TsVz1cMAKAMCFNZliStR6a6Mr+xtR0VrWi/ZldeeWWMHDky+vTpE+PGjYuZM2d22f7WW2+NHXfcMTbeeOMYPHhwfOELX4jly5fnqVoAgMIRpnJg3W+murJyVWs7KlpRftXuvPPOmDp1apx77rkxZ86c2GeffWLSpEnR2Nj5vxI89thjceKJJ8bJJ58cf/nLX+Kuu+6Kp556KqZMmZLnygEA8kuYypENhametqNsFeXX7ZJLLomTTz45pkyZEmPGjIlLL700hg4dGldddVWn7Z988skYMWJEnH766TFy5MjYe++945RTTomnn346z5UDAOSPMJVDNdXZbUfZKrqvXEtLS8yePTsmTpzYbvrEiRNj1qxZnc6z5557xl//+teYMWNGJEkSf/vb3+KXv/xlHHbYYetdzsqVK6O5ubndAwCgVAhTOVbfd8Nhqfb9S6hT0Yrua7ds2bJYs2ZNDBo0qN30QYMGxeLFizudZ88994xbb701jj322KipqYktt9wy+vXrF5dffvl6lzN9+vSor69vewwdOjSr7wMAIFeEqTxIpVovjd6VUcPcj4riC1TrpD60ciZJ0mHaOvPmzYvTTz89zj///Jg9e3b8/ve/j1dffTVOPfXU9b7+tGnToqmpqe2xYMGCrNYPAJALwlQeDewfse2ojkeqaqtbp7sPFVGEN/YdMGBAVFVVdTgatWTJkg5HrdaZPn167LXXXvGNb3wjIiJ22GGH2GSTTWKfffaJ7373uzF48OAO89TW1kZtbW323wAAQI4IUwUwsH/EgH4fXPWv5v3T/ByZ4n1F9xWsqamJcePGRUNDQ7vpDQ0Nseeee3Y6z7vvvhu9PrQ1qaqqiojWI1sAAKVOmCqgVCqiX13EFpu3/leY4p8U5dfwrLPOiuuuuy5uuOGGeP755+PMM8+MxsbGtlP4pk2bFieeeGJb+8MPPzzuueeeuOqqq+KVV16Jxx9/PE4//fTYfffdY8iQIYV6GwAAWSFMQfEqulP+IiKOPfbYWL58eVx00UWxaNGiGDt2bMyYMSOGDx8eERGLFi1qd0+qk046KVasWBE/+clP4mtf+1r069cvDjzwwPj+979fqLcAAJAVwhQUt1TinLiIiGhubo76+vpoamqKurq6QpcDACBMQYH0JBv4SgIAFCFhCkqDryUAQJERpqB0+GoCABQRYQpKi68nAECREKag9PiKAgAUAWEKSpOvKQBAgQlTULp8VQEACkiYgtLm6woAUCDCFJQ+X1kAgAIQpqA8+NoCAOSZMAXlw1cXACCPhCkoL76+AAB5IkxB+fEVBgDIA2EKypOvMQBAjglTUL58lQEAckiYgvLm6wwAkCPCFJQ/X2kAgBwQpqAy9C50AQAA5UaY6kKSRDStiGhZFVFTHVHfNyKVKnRVkDaBCgAgi4SpLix9M+LlxtYwtU5NdcTWwyIG9i9cXZABX28AgCwRprqw9M2IefPbh6mI1ufz5rf+HUqQrzgAQBYIU11IktYjU12Z39jarpCSJOKt5ogly1v/W+h6KAlO+QMAyJAwtQHrfjPVlZWrWtv1q8tPTR/mdETS5KsOAJABYaobNhSmetou25yOSAZ83QEA0iRMdVNNdXbbZVOpnI5I0fKVBwBIgzDVA/V9NxyWat+/hHq+9eR0ROiErz0AQA8JUz2USrX+Fqkro4YV5n5UxX46IkXPVx8AoAeEqTQN7B+x7aiOR6pqq1unF+rCD8V8OiIlwVX+AAC6SZjK0MD+EQP6fXCaXc37p/kV4sjUOutOR+zqCFShTkekJNgEAAB0gzCVJalU66XRt9i89b+FDFPr6inW0xEpCTYDAAAbIEyVuWI9HZGS4JQ/AIAuCFMVohhPR6QkCFQAAOshTFWYdacjQg/YJAAAdEKYArrDZgEA4EOEKaC7nPIHAPBPhKkeShK/O6KiCVQAAO8Tpnpo6ZsRLze2v4dTTXXrZchdGY8KYRMBABDCVI8tfTNi3vyON8RtWdU6fembhakL8sxmAgCoeMJUDyVJ65GprsxvbG0HZc6mAgCoaMJUGtb9ZqorK1e1toMyZ3MBAFQsYSpNGwpTPW0HJcwmAwCoSMJUBmqqs9sOSpjNBgBQcYSpDNX33XBYqn3/EupQ5mw6AICKIkxlQSrVemn0rowa5n5UVASbDwCgYghTWTSwf8S2ozoeqaqtbp3uPlRUCDf2BQAqgjCVAwP7Rwzo98FV/2reP83PkSkqiEAFAJQ9YSqHUqmIfnWFrgIKxqYEAChrwhSQSzYnAEDZEqaAXLNJAQDKkjAF5IPNCgBQdoQpIF9sWgCAsiJMAflk8wIAlA1hCsg3mxgAoCwIU0Ah2MwAACVPmAIKxaYGAChpwhRQSDY3AEDJEqaAQutd6AIAANIhTEEJSpKIphURLasiaqoj6vtGpFKFriojAhUAUHKEKShBS9+MeLmxNUytU1MdsfWwiIH9C1dXhmx6AICSIkxBCVr6ZsS8+e3DVETr83nzW/9eomx+AICSIUxBCUqS1iNTXZnf2NquBNkEAQAlQZiCErXuN1NdWbmqtV0JshkCAIqeMAUlbENhqqftioxNEQBQ1IQpKHE11dltV2RsjgCAoiVMQRmo77vhsFT7/iXUS5BNEgBQlIQpKBOpVOul0bsyaljJ3o/KZgkAKDrCFJSZgf0jth3V8UhVbXXr9BK+D5Ub+wIARUWYgjI1sH/EgH4fXPWv5v3T/Er0yNQ6AhUAUDSEKShzqVREv7pCV5FVNlEAQFEQpoBSZDMFABScMAWUKpsqAKCghCmglNlcAQAFI0wBpc4mCwAoCGEKKAc2WwBA3glTQLmw6QIA8kqYAsqJzRcAkDfCFFBubMIAgLwQpoByZDMGAOScMAWUK5syACCnhCmgnBXt5uzKK6+MkSNHRp8+fWLcuHExc+bM9bY96aSTIpVKdXhst912eawYAPgwYQood0W5Sbvzzjtj6tSpce6558acOXNin332iUmTJkVjY2On7S+77LJYtGhR22PBggWx2WabxWc/+9k8Vw4ArCNMAZUglSRJUugiPmz8+PGxyy67xFVXXdU2bcyYMXHkkUfG9OnTNzj/fffdF0cffXS8+uqrMXz48G4ts7m5Oerr66OpqSnq6urSrh0AEKaA0taTbFB0m7aWlpaYPXt2TJw4sd30iRMnxqxZs7r1Gtdff3184hOf6DJMrVy5Mpqbm9s9AIDMCVNAJSm6zduyZctizZo1MWjQoHbTBw0aFIsXL97g/IsWLYrf/e53MWXKlC7bTZ8+Perr69seQ4cOzahuAECYAipP0W7iUqlUu+dJknSY1pkbb7wx+vXrF0ceeWSX7aZNmxZNTU1tjwULFmRSLgBUPGEKqES9C13Ahw0YMCCqqqo6HI1asmRJh6NWH5YkSdxwww0xefLkqKmp6bJtbW1t1NbWZlwvACBMAZWr6DZ1NTU1MW7cuGhoaGg3vaGhIfbcc88u53300Ufj5ZdfjpNPPjmXJQIA/0SYAipZ0R2hiog466yzYvLkybHrrrvGhAkT4tprr43GxsY49dRTI6L1dL2FCxfGTTfd1G6+66+/PsaPHx9jx44tRNkAUHGEKaDSFWWgOvbYY2P58uVx0UUXxaJFi2Ls2LExY8aMtqv2LVq0qMM9qZqamuLuu++Oyy67rBAlA0DFEaYAivQ+VIXgPlQA0H3CFFDOSvo+VABAcROmAD5g8wcAdJswBdCeTSAA0C3CFEBHNoMAwAYJUwCdsykEALokTAGsn80hALBewhRA12wSAYBOCVMAG2azCAB0IEwBdI9NIwDQjjAF0H02jwBAG2EKoGdsIgGAiBCmANJhMwkACFMAabKpBIAKJ0wBpM/mEgAqmDAFkBmbTACoUMIUQOZsNgGgAglTANlh0wkAFUaYAsgem08AqCDCFEB22YQCQIUQpgCyz2YUACqAMAWQGzalAFDmhCmA3Mnp5nTZsmWxZs2aXC4CAOiCMAWQWxltUp9++um46KKLYt68ee2m//rXv47BgwfHoEGDYsCAAfGTn/wkoyIBgJ4TpgByL5UkSZLuzJ///OfjjjvuiIULF8aAAQMiIuL111+P0aNHR0tLSwwePDj+9re/RZIk8cgjj8Q+++yTtcKzrbm5Oerr66OpqSnq6uoKXQ4AZESYAkhfT7JBRpvWJ598Mnbaaae2MBURcf3110dLS0v86Ec/ioULF8ZTTz0VVVVV8V//9V+ZLAoA6CZhCiB/Mtq8/u1vf4thw4a1m/bAAw/EpptuGqeddlpEROy8886x9957x9y5czNZFADQDcIUQH5ltIn98AUnVq5cGXPnzo299torampq2qYPGTIkFi9enMmiAIANEKYA8i+jzezw4cPj2WefbXv+hz/8IVpaWuKggw5q127dOYgAQG4IUwCFkdGm9ogjjoiXXnopzjzzzPj1r38dZ599dvTq1Ss+/elPt2s3Z86cGD58eEaFAgCdE6YACiejze3Xv/71+OhHPxqXXXZZHHXUUfH888/H1KlT42Mf+1hbmz/96U+xcOHC2HfffTMuFgBoT5gCKKzemcy82Wabxdy5c+OXv/xlLFmyJMaNGxcHHnhguzaLFy+OM844I0444YSMCgUA2hOmAAovo/tQlRP3oQKglAhTALmTt/tQAQD5J0wBFI+MTvlb59VXX42ZM2fGokWLYuXKlZ22SaVScd5552VjcQBQsYQpgOKS0Sl/LS0tMWXKlLj11lsjIqKrl0qlUh3uW1VMnPIHQLETpgDyoyfZIKMjVOeff37ccsst0b9//zjhhBNim222iU033TSTlwQAOiFMARSnjALVbbfdFv369Yv/+Z//cZ8pAMgRYQqgeGW0OV6yZEnss88+whQA5IgwBVDcMtokDx8+PN55551s1QIA/BNhCqD4ZbRZPvnkk+PPf/5zLFiwIFv1AAAhTAGUiow2zV//+tfjsMMOi0mTJsUjjzzS5VX+AIDuEaYASkdGF6XYeuutIyLi9ddfj4MOOiiqq6tj8ODBkUqlOrRNpVIxf/78TBYHAGVPmAIoLRkFqtdee63d85aWlnj99dczeUkAqFjCFEDpyShQrV27Nlt1AEBFE6YASpNNNQAUmDAFULpsrgGggIQpgNKWlU32c889F1/+8pdj++23j8033zwGDBgQ22+/fZx22mnx3HPPZWMRAFB2hCmA0pdKMrzW+WWXXRbf+MY3Ys2aNZ1eNr13797xgx/8IM4444xMFpNzzc3NUV9fH01NTVFXV1focgAoc8IUQPHqSTbIaNPd0NAQZ555ZtTU1MSZZ54Zc+bMiTfffDPeeuutmDt3bnzta1+L2traOOuss+LBBx/MZFEAUDaEKYDykdERqkmTJsWDDz4YjzzySOy5556dtnniiSdi3333jYMPPjhmzJiRdqG55ggVAPkgTAEUv7wdofrzn/8c++2333rDVETEhAkTYv/9948//elPmSwKAEqeMAVQfjLajL/77rsxcODADbYbOHBgvPvuu5ksCgBKmjAFUJ4y2pQPHTo0nnjiiVizZs1626xevTqeeOKJGDp0aCaLAoCSJUwBlK+MNuef/vSn4/XXX48pU6ZEc3Nzh783NzfHl770pWhsbIwjjzwyk0UBQEkSpgDKW0YXpfj73/8eu+22W7z22mtRV1cXn/zkJ2PEiBGRSqXi1Vdfjd/+9rfR3NwcH/3oR+Opp56K/v37Z7P2rHJRCgCyraTCVJJENK2IaFkVUVMdUd83IpUqdFUABdGTbJDxfajeeOONOOWUU+K3v/1tp38/7LDD4pprrokhQ4ZkspicE6gAyKaSClNL34x4ubE1TK1TUx2x9bCIgcX7j6EAuZLXQLXOq6++Go899li88cYbERExZMiQ2HvvvWPkyJHZePmcE6gAyJaSC1Pz5q//79uOEqqAitOTbNA7WwsdOXJkyYQnAMiVkgpTSdJ6ZKor8xsjBvRz+h/AehTrJh4ASk5JhamID34z1ZWVq1rbAdCpHh2huummmyIi4qijjoq+ffu2Pe+uE088sUftAaBUlFyYithwmOppO4AK1KPfUPXq1StSqVQ8//zzsc0227Q935AkSSKVSnV5v6pC8xsqANJVkmEqIuKt5ohnXtxwux23iehn3whUjpz9hur888+PVCoVAwYMaPccACpVyYapiNZLo9dUd30Eqvb9S6gD0KmsXeWv1DlCBUBPlXSYWsdV/gA66Ek2KLXNPgAUhbIIUxGtYWnbUa1Hqv5ZbbUwBdANGW36q6qq4uSTT95guy996UvRu3fWrtAOAAVVNmFqnYH9I/bYofW3UmNGtv53/A7CFEA3ZJRykiSJ7p4x6MxCAMpB2YWpdVIpF54ASENedgFNTU1RW1ubj0UBQM6UbZgCIG09PkLV2Nj+jupvv/12h2nrrF69Ol544YV44IEHYtSoUelVCABFQJgCoDM9DlQjRoxod6n0u+++O+6+++4u50mSJL70pS/1vDoAKALCFADr0+NAte+++7YFqkcffTS22GKL+PjHP95p25qamhgyZEgcccQRcdRRR2VWKQAUgDAFQFd6HKgeeeSRtv/v1atXTJo0KW644YZs1gQARUGYAmBDMrrK36uvvhqbbrpptmoBgKIhTAHQHRntGrbccst45513YsWKFetts2LFimhsbIyWlpZMFgUAeSNMAdBdGe0eLrnkkhg5cmQ888wz623zzDPPxMiRI+Oyyy7LZFEAkBfCFAA9kUoyuOPu+PHjY/ny5fHyyy932W7UqFExaNCgmDVrVrqLyrnm5uaor6+PpqamqKtzY0OASiRMARDRs2yQ0W5i/vz5se22226w3XbbbRfz58/PZFEAkFPCFADpyGhX8c4778Qmm2yywXYbb7xxNDc3Z7IoAMgZYQqAdGW0uxg6dGg8/fTTG2w3e/bsGDx4cCaLAoCcEKYAyERGu4yJEyfGK6+8Epdffvl621xxxRUxf/78OOSQQzJZFABknTAFQKYy2m2cc8450bdv35g6dWoceeSRMWPGjHjhhRfixRdfjBkzZsSRRx4Zp59+etTV1cU555zTo9e+8sorY+TIkdGnT58YN25czJw5s8v2K1eujHPPPTeGDx8etbW1MWrUKDccBmC9hCkAsiGjG/sOHTo0fv3rX8dnPvOZ+PWvfx3//d//3e7vSZLEgAED4he/+EWMGDGi26975513xtSpU+PKK6+MvfbaK6655pqYNGlSzJs3L4YNG9bpPMccc0z87W9/i+uvvz623nrrWLJkSaxevTqTtwdAmRKmAMiWjC6bvs5bb70V1157bTz44IOxYMGCiGgNW5/4xCdiypQp0b9//x693vjx42OXXXaJq666qm3amDFj4sgjj4zp06d3aP/73/8+jjvuuHjllVdis802S+s9uGw6QGUQpgDYkJ5kg6wEqmxqaWmJjTfeOO6666446qij2qafccYZMXfu3Hj00Uc7zPPlL385Xnzxxdh1113j5ptvjk022SSOOOKIuPjii2OjjTbqdDkrV66MlStXtj1vbm6OoUOHClQAZUyYAqA7ehKoMjrlLxeWLVsWa9asiUGDBrWbPmjQoFi8eHGn87zyyivx2GOPRZ8+feLee++NZcuWxZe//OX4+9//vt7fUU2fPj0uvPDCrNcPQHESpgDIhaLdlaRSqXbPkyTpMG2dtWvXRiqViltvvTV23333+OQnPxmXXHJJ3HjjjfGPf/yj03mmTZsWTU1NbY91pyoCUH6EKQBypUdHqHr16hW9evWKefPmxTbbbBNVVVXdnjeVSnXrIhEDBgyIqqqqDkejlixZ0uGo1TqDBw+Oj3zkI1FfX982bcyYMZEkSfz1r3+Nj33sYx3mqa2tjdra2m7XD0BpEqYAyKUeBaphw4ZFKpWK6urqiGi98MT6jhqlq6amJsaNGxcNDQ3tfkPV0NAQn/70pzudZ6+99oq77ror3n777dh0000jIuLFF1+MXr16xVZbbZXV+gAoHcIUALlWdBeliGi9bPrkyZPj6quvjgkTJsS1114bP/3pT+Mvf/lLDB8+PKZNmxYLFy6Mm266KSIi3n777RgzZkzsscceceGFF8ayZctiypQpsd9++8VPf/rTbi3TVf4AyoswBUC6SvqiFBERxx57bCxfvjwuuuiiWLRoUYwdOzZmzJgRw4cPj4iIRYsWRWNjY1v7TTfdNBoaGuKrX/1q7LrrrrH55pvHMcccE9/97ncL9RYAKCBhCoB8KcojVIXgCBVAeRCmAMhUzo5Q/fGPf8yosH333Tej+QGgK8IUAPnWo0C1//77Z3QRijVr1qQ9LwB0RZgCoBB6FKhOPPHEDoFq+fLl8Zvf/CZSqVTsvPPOMWzYsIiIaGxsjLlz50aSJHHYYYfF5ptvnr2qAeCfCFMAFEqPAtWNN97Y7vmiRYtijz32iIMPPjguv/zy2Gabbdr9/cUXX4wzzjgjnnnmmXjyySczLhYAPkyYAqCQMtrlfPOb34w1a9bEr371qw5hKiJim222iXvuuSfWrFkT55xzTiaLAoAOhCkACi2j3c79998f++yzT/Tp02e9bTbaaKPYZ5994v77789kUQDQjjAFQDHIaNfT3Nwcy5Yt22C7ZcuWxYoVKzJZFAC0EaYAKBYZ7X7Gjh0bjz76aMycOXO9bR577LF45JFHYuzYsZksCgAiQpgCoLhktAs655xzYvXq1XHIIYfEv//7v8dDDz0UL7/8csyfPz8eeuih+Pd///c45JBDYu3atX5DBUDGhCkAik0qSZIkkxf4yU9+Et/4xjdi5cqVHS6pniRJ1NTUxPe///0444wzMio013pyN2QA8k+YAiBfepINMg5UERGvv/56XH/99fH444/HG2+8EUmSxJAhQ2LvvfeOL3zhCzFy5MhMF5FzAhVA8RKmAMinvAeqciBQARQnYQqAfOtJNrBLAqBoCVMAFLus7Jaee+65mDp1auy1114xevToOPvss9v+9vjjj8ePf/zj+Pvf/56NRQFQIYQpAEpB70xf4D//8z/j29/+dqxevToiIlKpVLt7U7377rtx5plnRm1tbZxyyimZLg6ACiBMAVAqMto9/epXv4pvfvObMXz48Ljvvvti6dKl8eGfZH3iE5+IAQMGxH333ZfJogCoEMIUAKUkoyNU//Vf/xWbbrppNDQ0xIgRIzptk0qlYvTo0fHiiy9msigAKoAwBUCpyWg3NWfOnJgwYcJ6w9Q6H/nIR2LRokWZLAqAMidMAVCKMtpVrV69OjbeeOMNtlu6dGnU1NRksigAypgwBUCpymh3NWrUqJg9e3asWbNmvW3eeeedmDt3bmy77baZLAqAMiVMAVDKMtplfeYzn4m//vWvcd555623zXnnnRdvvvlmHHvssZksCoAyJEwBUOpSyYcvy9cD77zzTuy+++7xf//3f7HXXnvFEUccEWeffXbsu+++8ZnPfCbuu+++eOihh2LHHXeMJ598Mmpra7NZe1b15G7IAGROmAKgWPUkG2QUqCIilixZEieddFL8/ve/j1Qq1XbZ9HX/f9BBB8Wtt94aW2yxRSaLyTmBCiB/hCkAillPskHGN/bdYostYsaMGfHMM89EQ0NDvPbaa7FmzZrYaqut4hOf+ESMHz8+00UAUEaEKQDKSUaB6uijj47BgwfHFVdcETvuuGPsuOOO2aoLgDLUaZhKJRFvrYhoWRVRUx1R3zcilSp0qQDQLRkFqhkzZsSRRx6ZpVIAKGedhqnlb0a83NgaptapqY7YeljEwP6FKxYAuimjkyxGjhwZ77zzTrZqAaBMrTdMzZvfPkxFtD6fNz9i6ZuFKRYAeiCjQHX88cfHo48+GosXL85WPQCUmfWe5vdyY9czzm9snQkAilhGgWratGmxzz77xH777Rf33ntvrFq1asMzAVAx1nsBiqYVHY9MfdjKVa3tAKCIZfQbqtGjR8fatWtjwYIF8ZnPfCZSqVRsscUW0adPnw5tU6lUzJ8/P5PFAVBCurya34bC1DrdbQcABZJRoHrttdfaPU+SxOl/AGz40ug11d17oe62A4ACyShQrV27Nlt1AFAmunWfqfq+rWGpqyNQte9fQh0AiphbKQKQNd2+aW8q1Xpp9K6MGuZ+VAAUvbSOUM2YMSPuu+++WLBgQdTW1sYOO+wQX/jCF2LkyJHZrg+AEtHtMLXOwP4R247qeB+q2urWMOU+VACUgFSS9OyatJ/73OfijjvuiIjW30xFtF5wora2Nu6444444ogjsl9lHjQ3N0d9fX00NTVFXV1docsBKCk9DlP/LEk+uOpfzfun+TkyBUAB9SQb9OgI1fXXXx+333579O7dOyZPnhw777xzrFixIn7zm9/EE088ESeeeGK8/vrrUV9fn9EbAKB0ZBSmIlrDUz//kAVAaepRoPr5z38evXr1it/97ndx0EEHtU2fNm1afOELX4ibbrop7rnnnvjCF76Q9UIBKD4ZhykAKHE92u09++yzsccee7QLU+t861vfiiRJ4tlnn81acQAUL2EKAHoYqJqbm2PUqFGd/m3d9Obm5syrAqCoCVMA0KpHu78kSaKqqqrzF3p/T+reVADlTZgCgA/YBQLQbcIUALTXo8um9+rVK1JpXso2lUrF6tWr05o3H1w2HaBrwhQAlSJnl02P+ODeU/maD4DCE6YAoHM9ClR+HwVQeYQpAFg/u0QA1kuYAoCu2S0C0ClhCgA2zK4RgA6EKQDoHrtHANoRpgCg++wiAWgjTAFAz9hNAhARwhQApMOuEgBhCgDSZHcJUOGEKQBIn10mQAUTpgAgM3abABVKmAKAzNl1AlQgYQoAssPuE6DCCFMAkD12oQAVRJgCgOyyGwWoEMIUAGSfXSlABRCmACA37E4BypwwBQC5Y5cKUMaEKQDILbtVgDIlTAFA7tm1ApQhYQoA8sPuFaDMCFMAkD92sQBlRJgCgPyymwUoE8IUAOSfXS1AGRCmAKAw7G4BSpwwBQCFY5cLUMKEKQAoLLtdgBIlTAFA4dn1ApQgYQoAioPdL0CJEaYAoHjYBQOUEGEKAIqL3TBAiRCmAKD42BUDlABhCgCKk90xQJETpgCgeNklAxQxYQoAipvdMkCREqYAoPjZNQMUIWEKAEpD0e6er7zyyhg5cmT06dMnxo0bFzNnzlxv20ceeSRSqVSHx//93//lsWKA7BCmAKB0FOUu+s4774ypU6fGueeeG3PmzIl99tknJk2aFI2NjV3O98ILL8SiRYvaHh/72MfyVDFAdghTAFBainI3fckll8TJJ58cU6ZMiTFjxsSll14aQ4cOjauuuqrL+bbYYovYcsst2x5VVVV5qhggc8IUAJSeottVt7S0xOzZs2PixIntpk+cODFmzZrV5bw777xzDB48OA466KB4+OGHc1kmQFYJUwBQmnoXuoAPW7ZsWaxZsyYGDRrUbvqgQYNi8eLFnc4zePDguPbaa2PcuHGxcuXKuPnmm+Oggw6KRx55JPbdd99O51m5cmWsXLmy7Xlzc3P23gRADwhTAFC6ii5QrZNKpdo9T5Kkw7R1Ro8eHaNHj257PmHChFiwYEH88Ic/XG+gmj59elx44YXZKxggDcIUAJS2otttDxgwIKqqqjocjVqyZEmHo1Zd2WOPPeKll15a79+nTZsWTU1NbY8FCxakXTNAOoQpACh9RbfrrqmpiXHjxkVDQ0O76Q0NDbHnnnt2+3XmzJkTgwcPXu/fa2tro66urt0DIF+EKQAoD0V5yt9ZZ50VkydPjl133TUmTJgQ1157bTQ2Nsapp54aEa1HlxYuXBg33XRTRERceumlMWLEiNhuu+2ipaUlbrnllrj77rvj7rvvLuTbAOiUMAUA5aMoA9Wxxx4by5cvj4suuigWLVoUY8eOjRkzZsTw4cMjImLRokXt7knV0tISX//612PhwoWx0UYbxXbbbRe//e1v45Of/GSh3gJAp4QpACgvqSRJkkIXUQyam5ujvr4+mpqanP4H5IQwBQCloSfZwK4cIA+EKQAoT3bnADkmTAFA+bJLB8ghYQoAypvdOkCOCFMAUP7s2gFyQJgCgMpg9w6QZcIUAFQOu3iALBKmAKCy2M0DZIkwBQCVx64eIAuEKQCoTHb3ABkSpgCgctnlA2RAmAKAyma3D5AmYQoAsOsHSIMwBQBECFQAPSZMAQDrGAIA9IAwBQD8M8MAgG4SpgCADzMUAOgGYQoA6IzhAMAGCFMAwPoYEgB0QZgCALpiWACwHsIUALAhhgYAnRCmAIDuMDwA+BBhCgDoLkMEgH8iTAEAPWGYAPA+YQoA6ClDBYAQpgCA9BguABVPmAIA0mXIAFQ0YQoAyIRhA1CxhCkAIFOGDkBFEqYAgGwwfAAqjjAFAGSLIQRQUYQpACCbDCOAiiFMAQDZZigBVARhCgDIBcMJoOwJUwBArhhSAGVNmAIAcsmwAihbwhQAkGuGFkBZEqYAgHzoXegCALKtaMNUkkQ0rYhoWRVRUx1R3zcilSp0VQBABgQqoKwUbZha+mbEy42tYWqdmuqIrYdFDOxfuLoAgIwUwzADICuKOkzNm98+TEW0Pp83v/XvAEBJKoahBkDGijZMJUnrkamuzG9sbQcAlJxiGG4AZKRow1TEB7+Z6srKVa3tAICSUyxDDoC0FHWYithwmOppOwCgqBTTsAOgR4o+TEW0Xngim+0AgKJSbEMPgG4piTAV0Xpp9A2Fpdr3L6EOAJScYhx+AHSpZMJUROt9prYe1nWbUcPcjwoASlSxDkEAOlVSYWqdgf0jth3V8UhVbXXrdPehAoCS5ca+QMkoyTC1zsD+EQP6fXDVv5r3T/NzZAoASppABZSEkg5T66RSEf3qCl0FAJBFpTYcASpQWYQpAKAsGZIARU2YAgCKmWEJULSEKQCg2BmaAEVJmAIASoHhCVB0hCkAoFQYogBFRZgCAEqJYQpQNIQpAKDUGKoARUGYAgBKkeEKUHDCFABQqgxZgIISpgCAUmbYAhSMMAUAlDpDF6AghCkAoBwYvgB5J0wBAOXCEAbIK2EKACgnhjFA3ghTAEC5MZQB8kKYAgDKkeEMkHPCFABQrgxpgJwSpgCAcmZYA+SMMAUAlDtDGyAnhCkAoBIY3gBZJ0wBAJXCEAfIKmEKAKgkhjlA1ghTAEClMdQBskKYAgAqkeEOkDFhCgCoVIY8QEaEKQCgkhn2AGkTpgCASmfoA6RFmAIAEKiANAhTAACtDIGAHhGmAAA+YBgEdJswBQDQXtEOha688soYOXJk9OnTJ8aNGxczZ87s1nyPP/549O7dO3baaafcFggVRpgCAOioKIdDd955Z0ydOjXOPffcmDNnTuyzzz4xadKkaGxs7HK+pqamOPHEE+Oggw7KU6VQGYQpAIDOpZIkSQpdxIeNHz8+dtlll7jqqqvapo0ZMyaOPPLImD59+nrnO+644+JjH/tYVFVVxX333Rdz587t9jKbm5ujvr4+mpqaoq6uLpPyoawIUwBApelJNii6YVFLS0vMnj07Jk6c2G76xIkTY9asWeud72c/+1nMnz8/vvOd73RrOStXrozm5uZ2D6A9YQoAoGtFNzRatmxZrFmzJgYNGtRu+qBBg2Lx4sWdzvPSSy/FN7/5zbj11lujd+/e3VrO9OnTo76+vu0xdOjQjGuHciJMAQBsWNEOj1KpVLvnSZJ0mBYRsWbNmvjXf/3XuPDCC2Obbbbp9utPmzYtmpqa2h4LFizIuGYoF8IUAED3dO9wTh4NGDAgqqqqOhyNWrJkSYejVhERK1asiKeffjrmzJkTX/nKVyIiYu3atZEkSfTu3TseeOCBOPDAAzvMV1tbG7W1tbl5E1DChCkAgO4rumFSTU1NjBs3LhoaGtpNb2hoiD333LND+7q6unj22Wdj7ty5bY9TTz01Ro8eHXPnzo3x48fnq3QoecIUAEDPFN0RqoiIs846KyZPnhy77rprTJgwIa699tpobGyMU089NSJaT9dbuHBh3HTTTdGrV68YO3Zsu/m32GKL6NOnT4fpwPoJUwAAPVeUgerYY4+N5cuXx0UXXRSLFi2KsWPHxowZM2L48OEREbFo0aIN3pMK6D5hCgAgPUV5H6pCcB8qKpUwBQDQXknfhwrIH2EKACAzhk5QoYQpAIDMGT5BBRKmAACywxAKKowwBQCQPYZRUEGEKQCA7DKUggohTAEAZJ/hFFQAYQoAIDcMqaDMCVMAALljWAVlTJgCAMgtQysoU8IUAEDuGV5BGRKmAADywxALyowwBQCQP4ZZUEaEKQCA/DLUgjIhTAEA5J/hFpQBYQoAoDAMuaDECVMAAIVj2AUlTJgCACgsQy8oUcIUAEDhGX5BCRKmAACKgyEYlBhhCgCgeBiGQQkRpgAAiouhGJQIYQoAoPgYjkEJEKYAAIqTIRkUOWEKAKB4GZZBEROmAACKm6EZFClhCgCg+BmeQRESpgAASoMhGhQZYQoAoHQYpkEREaYAAEqLoRoUCWEKAKD0GK5BERCmAABKkyEbFJgwBQBQugzboICEKQCA0mboBgUiTAEAlD7DNygAYQoAoDwYwkGeCVMAAOXDMA7ySJgCACgvhnKQJ8IUAED5MZyDPBCmAADKkyEd5JgwBQBQvgzrIIeEKQCA8mZoBzkiTAEAlD/DO8gBYQoAoDIY4kGWCVMAAJXDMA+ySJgCAKgshnqQJcIUAEDlMdyDLBCmAAAqU+9CF0CWJElE04qIllURNdUR9X0jUqlCV1URhCkAgMolUJWDpW9GvNzYGqbWqamO2HpYxMD+haurAghTAACVzdCv1C19M2Le/PZhKqL1+bz5rX8nJ4QpAAAM/0pZkrQemerK/MbWdmSVMAUAQIRAVdrW/WaqKytXtbYja4QpAADWMQwsZRsKUz1txwYJUwAA/DNDwVJWU53ddnRJmAIA4MMMB0tZfd8Nh6Xa9y+hTkaEKQAAOmNIWMpSqdZLo3dl1DD3o8qQMAUAwPoYFpa6gf0jth3V8UhVbXXrdPehyogwBQBAV9zYtxwM7B8xoN8HV/2ref80P0emMiJMAQCwIQJVuUilIvrVFbqKsiFMAQDQHYaI8CHCFAAA3WWYCP9EmAIAoCcMFeF9whQAAD1luAghTAEAkB5DRiqeMAUAQLoMG6lowhQAAJkwdKRiCVMAAGTK8JGKJEwBAJANhpBUHGEKAIBsMYykoghTAABkk6EkFUOYAgAg2wwnqQjCFAAAuWBISdkTpgAAyBXDSsqaMAUAQC4ZWlK2hCkAAHLN8JKyJEwBAJAPhpiUHWEKAIB8McykrAhTAADkk6EmZUOYAgAg3ww3KQvCFAAAhVC0Q84rr7wyRo4cGX369Ilx48bFzJkz19v2sccei7322is233zz2GijjeLjH/94/Nd//Vceq6WQhCkAAAqld6EL6Mydd94ZU6dOjSuvvDL22muvuOaaa2LSpEkxb968GDZsWIf2m2yySXzlK1+JHXbYITbZZJN47LHH4pRTTolNNtkk/u3f/q0A74B8EaYAACikVJIkSaGL+LDx48fHLrvsEldddVXbtDFjxsSRRx4Z06dP79ZrHH300bHJJpvEzTff3K32zc3NUV9fH01NTVFXV5dW3eSXMAUAQC70JBsU3fCzpaUlZs+eHRMnTmw3feLEiTFr1qxuvcacOXNi1qxZsd9++623zcqVK6O5ubndg9IhTAEAUAyKbgi6bNmyWLNmTQwaNKjd9EGDBsXixYu7nHerrbaK2tra2HXXXeO0006LKVOmrLft9OnTo76+vu0xdOjQrNRP7glTAAAUi6IdhqZSqXbPkyTpMO3DZs6cGU8//XRcffXVcemll8btt9++3rbTpk2LpqamtseCBQuyUje5JUwBAFBMiu6iFAMGDIiqqqoOR6OWLFnS4ajVh40cOTIiIrbffvv429/+FhdccEEcf/zxnbatra2N2tra7BRNXghTAAAUm6IbjtbU1MS4ceOioaGh3fSGhobYc889u/06SZLEypUrs10eBSJMAQBQjIruCFVExFlnnRWTJ0+OXXfdNSZMmBDXXnttNDY2xqmnnhoRrafrLVy4MG666aaIiLjiiiti2LBh8fGPfzwiWu9L9cMf/jC++tWvFuw9kD3CFAAAxaooA9Wxxx4by5cvj4suuigWLVoUY8eOjRkzZsTw4cMjImLRokXR2NjY1n7t2rUxbdq0ePXVV6N3794xatSo+N73vhennHJKod4CWSJMAQBQzIryPlSF4D5UxUeYAgCgEEr6PlQQIUwBAFAaDFEpOsIUAAClwjCVoiJMAQBQSgxVKRrCFAAApcZwlaIgTAEAUIoMWSk4YQoAgFJl2EpBCVMAAJQyQ1cKRpgCAKDUGb5SEMIUAADlwBCWvBOmAAAoF4ax5JUwBQBAOTGUJW+EKQAAyo3hLHkhTAEAUI4Mack5YQoAgHJlWEtOCVMAAJQzQ1tyRpgCAKDcGd6SE8IUAACVwBCXrBOmAACoFIa5ZJUwBQBAJTHUJWuEKQAAKo3hLlkhTAEAUIkMecmYMAUAQKUy7CUjwhQAAJXM0Je0CVMAAFQ6w1/SIkwBAIBARRqEKQAAaGUYTI8IUwAA8AFDYbpNmAIAgPYMh+kWYQoAADoyJGaDhCkAAOicYTFdEqYAAGD9DI1ZL2EKAAC6ZnhMp4QpAADYMENkOhCmAACgewyTaUeYAgCA7jNUpo0wBQAAPWO4TEQIUwAAkA5DZoQpAABIk2FzhROmAAAgfYbOFUyYAgCAzBg+VyhhCgAAMmcIXYGEKQAAyA7D6AojTAEAQPYYSlcQYQoAALLLcLpCCFMAAJB9htQVQJgCAIDcMKwuc8IUAADkjqF1GROmAAAgtwyvy5QwBQAAuWeIXYaEKQAAyA/D7DIjTAEAQP4YapcRYQoAAPLLcLtMCFMAAJB/htxlQJgCAIDCMOwuccIUAAAUjqF3CROmAACgsAy/S5QwBQAAhWcIXoKEKQAAKA6G4SVGmAIAgOJhKF5ChCkAACguhuMlQpgCAIDi07vQBbBhWQlTSRLRtCKiZVVETXVEfd+IVCon9QIAQKUQqIpcVsLU0jcjXm5sDVPr1FRHbD0sYmD/rNYLAACVxEljRSxrYWre/PZhKqL1+bz5rX8HAADSIlAVqayd5vdyY9dt5je2tgMAAHpMoCpCWbsAxbrfTHVl5arWdgAAQI/5DVURSqVaH//2bxlezW9DYaqn7QAAgHYEqiL0uc9FjBoVsfvuGV4avaY6u+0AAIB2nPJXpPbYIwv3marvu+GwVPv+JdQBAIAeE6jKWSrVemn0rowa5n5UAACQJoGq3A3sH7HtqI5HqmqrW6e7DxUAAKTNb6gqwcD+EQP6fXDVv5r3T/NzZAoAADIiUFWKVCqiX12hqwAAgLLilD8AAIA0CVQAAABpEqgAAADSJFABAACkSaACAABIk0AFAACQpqINVFdeeWWMHDky+vTpE+PGjYuZM2eut+0999wTBx98cAwcODDq6upiwoQJcf/99+exWgAAoBIVZaC68847Y+rUqXHuuefGnDlzYp999olJkyZFY2Njp+3/+Mc/xsEHHxwzZsyI2bNnxwEHHBCHH354zJkzJ8+VAwAAlSSVJElS6CI+bPz48bHLLrvEVVdd1TZtzJgxceSRR8b06dO79RrbbbddHHvssXH++ed3q31zc3PU19dHU1NT1NW5AS4AAFSqnmSDojtC1dLSErNnz46JEye2mz5x4sSYNWtWt15j7dq1sWLFithss83W22blypXR3Nzc7gEAANATRReoli1bFmvWrIlBgwa1mz5o0KBYvHhxt17jRz/6UbzzzjtxzDHHrLfN9OnTo76+vu0xdOjQjOoGAAAqT9EFqnVSqVS750mSdJjWmdtvvz0uuOCCuPPOO2OLLbZYb7tp06ZFU1NT22PBggUZ1wwAAFSW3oUu4MMGDBgQVVVVHY5GLVmypMNRqw+788474+STT4677rorPvGJT3TZtra2NmprazOuFwAAqFxFd4SqpqYmxo0bFw0NDe2mNzQ0xJ577rne+W6//fY46aST4rbbbovDDjss12UCAAAU3xGqiIizzjorJk+eHLvuumtMmDAhrr322mhsbIxTTz01IlpP11u4cGHcdNNNEdEapk488cS47LLLYo899mg7urXRRhtFfX19wd4HAABQ3ooyUB177LGxfPnyuOiii2LRokUxduzYmDFjRgwfPjwiIhYtWtTunlTXXHNNrF69Ok477bQ47bTT2qZ//vOfjxtvvDHf5QMAABWiKO9DVQjuQwUAAESU+H2oAAAASoVABQAAkCaBCgAAIE0CFQAAQJoEKgAAgDQJVAAAAGkSqAAAANIkUAEAAKSpd6ELKBbr7m/c3Nxc4EoAAIBCWpcJ1mWErghU71uxYkVERAwdOrTAlQAAAMVgxYoVUV9f32WbVNKd2FUB1q5dG2+88Ub07ds3UqlU2q/T3NwcQ4cOjQULFkRdXV0WK+Sf6ef80df5oZ/zQz/nh37OH32dH/o5P4qpn5MkiRUrVsSQIUOiV6+ufyXlCNX7evXqFVtttVXWXq+urq7gK0Il0M/5o6/zQz/nh37OD/2cP/o6P/RzfhRLP2/oyNQ6LkoBAACQJoEKAAAgTQJVltXW1sZ3vvOdqK2tLXQpZU0/54++zg/9nB/6OT/0c/7o6/zQz/lRqv3sohQAAABpcoQKAAAgTQIVAABAmgQqAACANAlU3fT222/H1KlTY8iQIdGnT5/Yaaed4o477tjgfPfcc08cf/zxsfXWW8dGG20UI0aMiM997nPx0ksv5aHq0pNuP//hD3+Igw8+OIYMGRK1tbWxxRZbxIEHHhgzZszIQ9WlKd2+/rBvf/vbkUqlYuzYsTmosvSl28833nhjpFKpTh+LFy/OQ+WlJdP1+Ve/+lXst99+UVdXF5tssklst912ce211+aw4tKUbj/vv//+612frdOdy2Sdfvjhh+Pggw+OLbbYIjbddNPYYYcd4sc//nGsWbMmx1WXnkz6+f7774+99torNtpoo6ivr4/DDz88/vKXv+S44tK0YsWKOPvss2PixIkxcODASKVSccEFF3R7/iVLlsRJJ50UAwYMiI033jgmTJgQDz74YO4K7qmEbjn44IOTfv36JVdffXXy0EMPJVOmTEkiIrn11lu7nG/33XdPjjjiiOSGG25IHnnkkeTmm29OxowZk2y66abJc889l6fqS0e6/XzHHXckZ5xxRnLHHXckjzzySHLPPfckEydOTCIiufnmm/NUfWlJt6//2Zw5c5La2tpk0KBByXbbbZfDaktXuv38s5/9LImI5Gc/+1nyxBNPtHu0tLTkqfrSkcn6PH369KRXr17Jl7/85eR3v/td8oc//CH5yU9+klx++eV5qLy0pNvPf/nLXzqsxw8++GBSXV2d7LHHHnmqvrSk29cNDQ1Jr169kv333z+57777koaGhuSrX/1qEhHJ6aefnqfqS0e6/XzfffclqVQqOfLII5Pf/va3yW233ZaMHj066d+/f/Lyyy/nqfrS8eqrryb19fXJvvvu29bH3/nOd7o173vvvZeMHTs22WqrrZJbbrkleeCBB5JPf/rTSe/evZNHHnkkt4V3k0DVDb/97W+TiEhuu+22dtMPPvjgZMiQIcnq1avXO+/f/va3DtMWLlyYVFdXJyeffHLWay1lmfRzZ1paWpKPfOQjyT777JPNMstCNvp61apVyU477ZScfvrpyX777SdQdSKTfl4XqJ566qlcl1nyMunnp59+OunVq1fy/e9/P9dllrxsb6NvvPHGJCKS6667LptlloVM+vpzn/tcUltbm7z99tvtpk+cODGpq6vLSb2lKpN+Hj16dLLDDjska9eubZv22muvJTU1Ncm//uu/5qzmUrV27dq2vlq6dGmPAtUVV1yRREQya9astmmrVq1Ktt1222T33XfPRbk95pS/brj33ntj0003jc9+9rPtpn/hC1+IN954I/70pz+td94tttiiw7QhQ4bEVlttFQsWLMh6raUsk37uTHV1dfTr1y969+6dzTLLQjb6+nvf+178/e9/j//3//5frsosedlep+lcJv38k5/8JGpra+OrX/1qrsssedlen6+//vrYdNNN49hjj81mmWUhk76urq6Ompqa2GijjdpN79evX/Tp0ycn9ZaqdPt5+fLl8cILL8SkSZMilUq1TR8+fHiMHTs27rvvPqdXfsi603vTce+998bo0aNjwoQJbdN69+4dJ5xwQvz5z3+OhQsXZqvMtAlU3fDcc8/FmDFjOgzMd9hhh7a/98Qrr7wSr7/+emy33XZZq7EcZKOf165dG6tXr4433ngjvvOd78SLL74YX/va13JSbynLtK/nzZsX3/3ud+Oqq66KTTfdNGd1lrpsrNOf+tSnoqqqKjbbbLM4+uije7y9qQSZ9PMf//jHGDNmTNx9990xevToqKqqiq222iq++c1vRktLS07rLjXZ3Be+9NJLMXPmzDjuuONsQzqRSV+feuqp0dLSEqeffnq88cYb8dZbb8XNN98c9957b5x99tk5rbvUpNvP67YNnd18tra2Nt59992YP39+lqutXM8991zbZ/LP1k0rht+t+af7bli+fHl89KMf7TB9s802a/t7d61evTpOPvnk2HTTTePMM8/MWo3lIBv9/MlPfjLuv//+iIioq6uLO++8Mw477LDsFloGMunrtWvXxhe/+MU4+uij45Of/GTOaiwHmfTzlltuGeeee27sscceUVdXF88++2x873vfiz322CMef/zx2HHHHXNWd6nJpJ8XLlwYS5cujdNPPz0uvvji2HbbbePBBx+M733ve7FgwYK49dZbc1Z3qcnmvvD666+PiIiTTz45O8WVmUz6evz48fHQQw/FZz/72bjiiisiIqKqqiqmT5/uHxg/JN1+HjRoUGy22Wbx+OOPt5v+1ltvtYWwnnwf6Nry5cvbPpN/ls62J1cEqm7q6jBldw9hJkkSJ598csycOTPuvvvuGDp0aLbKKxuZ9vPll18eb731VixatChuueWWOPbYY+PnP/95HH/88dkssyyk29eXXHJJvPTSS/HrX/86F2WVnXT7+dBDD41DDz207fm+++4bhx12WGy//fZx/vnnx69+9aus1lnq0u3ntWvXxooVK+L222+P4447LiIiDjjggHjnnXfi0ksvjQsvvDC23nrrrNdbqrKxL1y9enX8/Oc/j+222y722GOPbJVWdtLt69mzZ8dRRx0V48ePj2uuuSY22WSTeOihh+Lb3/52vPfee3HeeeflotySlU4/9+rVK0477bS4+OKL4+KLL45TTjklmpubY+rUqfHuu++2tSF7srHtySWfdjdsvvnmnabfv//97xERnabmD0uSJKZMmRK33HJL3HjjjfHpT38663WWumz088c+9rHYbbfd4ogjjohf/OIXcdBBB8Vpp50Wa9euzXq9pSzdvm5sbIzzzz8/vvOd70RNTU289dZb8dZbb8Xq1atj7dq18dZbb8U//vGPnNZeSrKxTv+zESNGxN577x1PPvlkVuorF5n08+abbx4REYcccki76ZMmTYqIiP/5n//JVpklL1vr84wZM2Lx4sUxZcqUrNZXTjLp69NOOy0GDRoU9957b3zqU5+KAw44IC6++OL45je/GRdccEG88sorOau71GTSz+eff36ceeaZ8d3vfjcGDRoUH/vYxyKi9fdXEREf+chHclBxZcr2vjQXBKpu2H777eP555+P1atXt5v+7LPPRkRs8P4768LUz372s7juuuvihBNOyFmtpSzTfu7M7rvvHm+++WYsXbo0KzWWi3T7+pVXXol//OMfccYZZ0T//v3bHo8//ng8//zz0b9//5g2bVrO6y8VuVinkyTxL58fkkk/d3ZefkRrP0f4V+Z/lq31+frrr4+ampqYPHly1mssF5n09dy5c2PcuHFRVVXVbvpuu+0Wa9eujeeffz77BZeoTPq5d+/ecckll8Ty5cvjf//3f+ONN96I3/zmN9HY2BgjR46MrbbaKqe1V5Ltt9++7TP5Z5nsS7OuoNcYLBEzZsxIIiK544472k0/9NBDN3hZzbVr1yYnn3xykkqlkmuvvTbXpZa0TPq5M2vXrk3222+/pF+/fsmqVauyWWrJS7ev33zzzeThhx/u8Nhxxx2TESNGJA8//HDy0ksv5eMtlIRsr9OvvPJKsummmyZHHnlkNssseZn08zXXXNPpPWdOP/30pFevXslrr72Wk5pLUTbW50WLFiW9e/dOjjnmmFyVWRYy6euRI0cmY8eO7dDmW9/6VhIRydy5c3NScynK9jZ69uzZSVVVVXLppZdms8yy09PLpl955ZVJRCRPPvlk27RVq1Yl2223XTJ+/PgcVdkzAlU3HXzwwUn//v2Ta6+9NnnooYeSL33pS0lEJLfccktbmy9+8YtJVVVVux3wV77ylSQiki9+8Ysdbmr4P//zP4V4K0Ut3X4+4ogjkvPOOy+5++67k0ceeSS57bbb2m7se8UVVxTirRS9dPu6M+5DtX7p9vNBBx2UXHjhhcm9996bPPjgg8mll16aDBkyJOnbt2/y7LPPFuKtFLV0+7mlpSXZZZddkvr6+uSyyy5LGhoaknPOOSepqqpKvvKVrxTirRS1TLcb3/ve95KISB544IF8ll2S0u3rH//4x0lEJJMmTUruu+++5IEHHkjOOeecpHfv3sknPvGJQryVopZuPz/88MPJf/7nfya///3vk9/97nfJhRdemGy88cbJYYcd1uMgVilmzJiR3HXXXckNN9yQRETy2c9+NrnrrruSu+66K3nnnXeSJOm8r997771ku+22S4YOHZrceuutSUNDQ3LUUUe5sW8pWrFiRXL66acnW265ZVJTU5PssMMOye23396uzec///kkIpJXX321bdrw4cOTiOj0MXz48Py+iRKQbj9///vfT3bbbbekf//+SVVVVbL55psnhxxySPKb3/wmz++gdKTb150RqNYv3X6eOnVqsu222yZ9+/ZNevfunQwZMiQ54YQTkhdeeCHP76A0ZLI+L1++PDnllFOSQYMGJdXV1ck222yT/OAHP0jWrFmTx3dQGjLdbmyzzTbJiBEj2t0Mlc5l0td33313svfeeycDBgxINtlkk2S77bZLLr744g43+yX9fn788ceT8ePHJ3V1dUltbW0yduzY5Ic//GHS0tKS53dQOroaE6/r2/Wt04sXL05OPPHEZLPNNkv69OmT7LHHHklDQ0P+38R6pJLk/RPFAQAA6BG/tgUAAEiTQAUAAJAmgQoAACBNAhUAAECaBCoAAIA0CVQAAABpEqgAAADSJFABsEGpVKpHjxEjRhS65PU65phjIpVKxcUXX7zBtn/84x8jlUrF0KFDY+3atT1e1kknnRSpVCoeeeSRNCoFoBT0LnQBABS/z3/+8x2mPfbYYzF//vzYcccdY6eddmr3twEDBuSpsp6bPHly3HXXXXHrrbfGeeed12XbW2+9NSIiPve5z0WvXv4NEoCOBCoANujGG2/sMO2kk06K+fPnx5FHHhkXXHBB3mtK16GHHhoDBgyIF154IZ5++unYddddO23X0tISd911V0REnHDCCfksEYAS4p/bAKgo1dXVcdxxx0XEB0egOjNjxox48803Y6eddoqxY8fmqzwASoxABUBWPfLII5FKpeKkk06KxYsXx5QpU2KrrbaK3r17x6WXXhoREfvvv3+kUql47bXXOsz/2muvRSqViv3337/T1//v//7vOOSQQ2LzzTePPn36xDbbbBPnnXdevP32292ucfLkyRERcccdd8SaNWs6bXPLLbdExAdHp9566624/PLL45BDDonhw4dHbW1tbL755nHooYdGQ0NDt5cdEV3+zuzGG2+MVCrV6VG/lpaWuOyyy2K33XaLvn37xiabbBK77757XH/99ZEkSYf2CxYsiNNOOy1Gjx4dG2+8cWy22Wax3XbbxSmnnBIvvPBCj2oGoHNO+QMgJ5YuXRq77bZbrF69Ovbee+947733YuONN87oNb/2ta/FJZdcEn369Indd989BgwYELNnz47vfve78bvf/S4effTR2GSTTTb4OrvvvnuMHj06XnjhhXjwwQdj4sSJ7f7e1NQUv/3tb6NXr15x/PHHR0TEk08+GaeffnoMHTo0ttlmm5gwYUI0NjbGAw88EA888EBcd9118cUvfjGj99eVd955JyZNmhQzZ86MAQMGxN577x29evWKJ554IqZMmRJPPfVUXH311W3t//rXv8Yuu+wSy5Ytix122CEOP/zweO+99+L111+Pn/70pzFhwoQYPXp0zuoFqBQCFQA5MWPGjDjqqKPitttuiz59+mT8er/4xS/ikksuiZ133jnuueeetiM8q1atiq985Stx7bXXxgUXXBA/+MEPuvV6J5xwQpx33nlxyy23dAhUv/zlL+O9996Lgw8+OIYMGRIREaNHj47HH3889txzz3Zt58yZEwceeGCceeaZccwxx8Smm26a8XvtzDe+8Y2YOXNmTJ48Oa688sq25SxdujQOP/zwuOaaa+Lwww+Pww47LCIirrvuuli2bFn86Ec/irPOOqvda73++uuxevXqnNQJUGmc8gdATtTW1sbll1+elTAVEfEf//EfERFx++23tztdrrq6Oi677LLYcsst47rrruv25c1POOGESKVSce+998a7777b7m/rflu17tTAiIiRI0d2CFMRETvvvHOcdtpp0dzcHA8//HBP31a3LFmyJK677roYOXJk/PSnP20X2gYOHBjXXHNNRETbf9fNExFx4IEHdni94cOHx6hRo3JSK0ClcYQKgJzYZZdd4iMf+UhWXmvJkiXxzDPPxJgxYzo9Ta1Pnz6x6667xm9+85t46aWXunUq24gRI2LvvfeOmTNnxq9+9au2U/sWLlwYjz76aGy88cZx1FFHtZtnzZo18eCDD8asWbNi8eLF8d5770VExEsvvdTuv9n26KOPxqpVq+LQQw+N2traDn/fcccdo2/fvvHUU0+1TRs3blxERJx22mnx3e9+N/bZZ5/o3dtuHyDbbFkByIlhw4Zl7bVef/31iIh4/vnnI5VKddl22bJl3f5t0OTJk2PmzJlx6623tgWq2267LdauXRtHHXVUuyNBf/3rX+NTn/pUPPPMM+t9vRUrVnRruT217uIdV111VVx11VXrbfePf/yj7f9POumkeOCBB+IXv/hFHHjggbHxxhvHrrvuGpMmTYovfvGLscUWW+SkVoBKI1ABkBPpnurX2Sl7667EN3jw4A6/d/qwzTffvNvL+uxnPxtf/epX4/7774+lS5fGwIEDO1zdb50pU6bEM888E0cffXScc845MXr06Ojbt2/06tUrrr322jjllFM6vdJeT3X1/nfeeefYYYcduvU6VVVVceedd8Y3v/nN+NWvfhUPP/xwPPnkk/HHP/4xpk+fHvfff3/sscceGdcLUOkEKgDyrqamJiKi00udL1iwoMO0rbbaKiIittxyy05vMpyufv36xeGHHx6//OUv4xe/+EXsv//+8b//+78xaNCgOPjgg9vavfPOO9HQ0BCDBg2KX/ziF1FVVdXudV555ZUeLbe6unq9l3nv6v3vv//+cckll/RoWTvvvHPsvPPOccEFF0Rzc3NceOGFcckll8QZZ5wRf/rTn3r0WgB05KIUAOTd4MGDIyLixRdf7PC3Bx54oMO0rbbaKkaPHh3/+7//G6+++mpWa1l34Ylbbrklbr755oiIOP7449uFpqampli7dm0MHjy4Q5havXp13HvvvT1a5uDBg2P58uXx97//vcPfOnv/BxxwQFRVVcVvfvOb9d43qzvq6uriP/7jPyKVSsWzzz6b9usA8AGBCoC822+//SIi4kc/+lG7K+z94Q9/aLv574d9+9vfjjVr1sS//Mu/xHPPPdfh7/Pnz48bbrihx7VMmjQpBgwYEE8++WRcd911EdH+6n4REVtssUXU19fHc889F48//njb9DVr1sTZZ5/daTDsyrr3f/HFF7dNS5Ikpk+fHrNmzerQ/iMf+UicdNJJ8dJLL8XkyZNj2bJlHdrMmjUrZsyY0fb85ptv7rSffv/730eSJFn9jRtAJROoAMi7448/PkaPHh2zZs2KMWPGxGc+85kYP358HHLIIfHlL3+503lOOOGEOPvss2POnDmx0047xW677RbHHHNMHHrooTFmzJjYeuut48c//nGPa6muro5jjz02IiKWL18eY8aMiV122aVdm969e8fZZ58dq1evjv322y8mTpwYxx13XGy99dZx9dVXx2mnndajZZ5zzjmx0UYbxaWXXho777xzfOYzn4mPf/zjcfHFF6/3/f/4xz+OAw44IG6//fb46Ec/Gvvuu28cd9xxsf/++8dWW20Ve+21V7ujW3fffXdsv/32sfXWW8dRRx0V//qv/xp77rlnHHXUUVFVVdV2GXoAMiNQAZB3G220UTz44INx/PHHx4oVK2LGjBmxdu3auPPOO7sMJ9///vfjwQcfjCOOOCL++te/xn333Rdz5syJjTfeOL7xjW+kdYQqov0RqQ9fjGKdb33rW/Hzn/88dthhh3j88cfjD3/4Q+y4447x5JNPxq677tqj5W233Xbx0EMPxf777x8vvvhiNDQ0xKhRo+KJJ56I3XbbrdN5Nt5443jggQfiuuuui1122SWee+65uPfee2P+/PkxatSo+M///M/4+te/3tb+rLPOitNOOy369u0bM2fOjHvvvTeWLFkSxx9/fDz11FNx9NFH96hmADqXSrJxSSIAAIAK5AgVAABAmgQqAACANAlUAAAAaRKoAAAA0iRQAQAApEmgAgAASJNABQAAkCaBCgAAIE0CFQAAQJoEKgAAgDQJVAAAAGkSqAAAANIkUAEAAKTp/wP2BccPP7Dl3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model \n",
    "\n",
    "reg_model = linear_model.LinearRegression() # making regression model\n",
    "reg_model.fit(x_train, y_train) #Here we are fitting the data set as an x_train and y_train\n",
    "\n",
    "prediction = reg_model.predict(x_test) #Here we are predicting the test data\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  -2.5022046387735226e+24\n",
      "Mean absolute error: 338333043517.13\n",
      "Root mean squared error:  386398336662.54315\n",
      "Adjusted r2_score:  -4.6469514720079707e+24\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor #SGD regreessor is a build in library which we can import\n",
    "sgd_regressor_model = SGDRegressor(max_iter=100)  # increasing this value leads to over fitting\n",
    "sgd_regressor_model.fit(x_train,y_train)\n",
    "\n",
    "prediction = sgd_regressor_model.predict(x_test)\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.9427290381450518\n",
      "Mean absolute error: 0.04\n",
      "Root mean squared error:  0.058457612527227296\n",
      "Adjusted r2_score:  1.750782499412239\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "random_forest_regressor_model = RandomForestRegressor()\n",
    "random_forest_regressor_model.fit(x_train,y_train)\n",
    "\n",
    "prediction = random_forest_regressor_model.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.9217418280506466\n",
      "Mean absolute error: 0.04\n",
      "Root mean squared error:  0.0683342898145363\n",
      "Adjusted r2_score:  1.711806252094058\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "decision_tree_regressor_model = DecisionTreeRegressor()\n",
    "decision_tree_regressor_model.fit(x_train,y_train)\n",
    "\n",
    "prediction = decision_tree_regressor_model.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.8070560162803159\n",
      "Mean absolute error: 0.09\n",
      "Root mean squared error:  0.10729746373700737\n",
      "Adjusted r2_score:  1.4988183159491582\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "clf = Ridge(alpha=1.0)\n",
    "clf.fit(x_train, y_train)\n",
    "Ridge()\n",
    "prediction = clf.predict(x_test)\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  -0.7684872814905619\n",
      "Mean absolute error: 0.26\n",
      "Root mean squared error:  0.3248439675533222\n",
      "Adjusted r2_score:  -1.427190665625329\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "regr = MLPRegressor(random_state=40).fit(x_train, y_train)\n",
    "prediction = regr.predict(x_test)\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.7370401454123678\n",
      "Mean absolute error: 0.10\n",
      "Root mean squared error:  0.12526177492610277\n",
      "Adjusted r2_score:  1.368788841480112\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "lasso = linear_model.Lasso(alpha=0.1)\n",
    "lasso.fit(x_train, y_train)\n",
    "prediction = lasso.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.64907045e-01, 2.63559014e-02, 4.10819213e-03, 2.13067174e-03,\n",
       "       2.03718021e-04, 2.29447166e-03])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance = random_forest_regressor_model.feature_importances_\n",
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['index', 'vram', 'vram_type', 'feature', 'Brand', 'provider']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = list(x_train.columns)\n",
    "columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.96491, 0.02636, 0.00411, 0.00213, 0.0002, 0.00229]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_value = []\n",
    "for i in range(len(feature_importance)):\n",
    "    feature_importance_value.append(round(feature_importance[i],5))\n",
    "feature_importance_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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>Features</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>index</td>\n",
       "      <td>0.96491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vram</td>\n",
       "      <td>0.02636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vram_type</td>\n",
       "      <td>0.00411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feature</td>\n",
       "      <td>0.00213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brand</td>\n",
       "      <td>0.00020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>provider</td>\n",
       "      <td>0.00229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Features   Values\n",
       "0      index  0.96491\n",
       "1       vram  0.02636\n",
       "2  vram_type  0.00411\n",
       "3    feature  0.00213\n",
       "4      Brand  0.00020\n",
       "5   provider  0.00229"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # making a dataframe\n",
    "feature_importance_df = pd.DataFrame({\"Features\":columns,\n",
    "                                     \"Values\":feature_importance_value })\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "sns.barplot(x=feature_importance_df['Features'], y=feature_importance_df['Values'],\n",
    "            data = feature_importance_df ).set(title='Feature Importance')\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [1,2,3,5,20,50,100,1000,6,8,4,200,250,500,460] # number of trees in the random forest\n",
    "max_features = ['auto', 'sqrt'] # number of features in consideration at every split\n",
    "max_depth = [int(x) for x in np.linspace(10, 400, num = 12)] # maximum number of levels allowed in each decision tree\n",
    "min_samples_split = [1,2, 6, 10,12,16,18,20,100,1000,250,500,360] # minimum sample number to split a node\n",
    "min_samples_leaf = [1, 3, 4,10,20,40,5,1000,100] # minimum sample number that can be stored in a leaf node\n",
    "bootstrap = [True, False] # method used to sample data points\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "\n",
    "'max_features': max_features,\n",
    "\n",
    "'max_depth': max_depth,\n",
    "\n",
    "'min_samples_split': min_samples_split,\n",
    "\n",
    "'min_samples_leaf': min_samples_leaf,\n",
    "\n",
    "'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importing Random Forest Classifier from the sklearn.ensemble\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rf_random = RandomizedSearchCV(estimator = rf,param_distributions = random_grid,\n",
    "               n_iter = 100, cv = 5, verbose=2, random_state=35, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 tasks      | elapsed:   14.1s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:   24.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 45, 80, 116, 151, 187,\n",
       "                                                      222, 258, 293, 329, 364,\n",
       "                                                      400],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 3, 4, 10, 20,\n",
       "                                                             40, 5, 1000, 100],\n",
       "                                        'min_samples_split': [1, 2, 6, 10, 12,\n",
       "                                                              16, 18, 20, 100,\n",
       "                                                              1000, 250, 500,\n",
       "                                                              360],\n",
       "                                        'n_estimators': [1, 2, 3, 5, 20, 50,\n",
       "                                                         100, 1000, 6, 8, 4,\n",
       "                                                         200, 250, 500, 460]},\n",
       "                   random_state=35, verbose=2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random grid:  {'n_estimators': [1, 2, 3, 5, 20, 50, 100, 1000, 6, 8, 4, 200, 250, 500, 460], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 45, 80, 116, 151, 187, 222, 258, 293, 329, 364, 400], 'min_samples_split': [1, 2, 6, 10, 12, 16, 18, 20, 100, 1000, 250, 500, 360], 'min_samples_leaf': [1, 3, 4, 10, 20, 40, 5, 1000, 100], 'bootstrap': [True, False]} \n",
      "\n",
      "Best Parameters:  {'n_estimators': 8, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'auto', 'max_depth': 116, 'bootstrap': False}  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('Random grid: ', random_grid, '\\n')\n",
    "# print the best parameters\n",
    "print ('Best Parameters: ', rf_random.best_params_, ' \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.9298906886225227\n",
      "Mean absolute error: 0.04\n",
      "Root mean squared error:  0.06467876020023387\n",
      "Adjusted r2_score:  1.7269398502989706\n"
     ]
    },
    {
     "data": {
      "image/png": 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S4jdUAADkzAdhKuKUE9+LmSd1EKaameWPEuIIFQAAOdEqTJ0SMfO/Vm86TEWY5Y+S4ggVAABZ1yZMzYzolurVFJY6OgJllj9KjCNUAABkVbthqls0zd63/ZCOHzxiiFn+KCkCFQAAWbPRMNWsX5+Ij49oe1pfTY+m5f365LW/kCmn/AEAkBWbDFPN+vWJ6Nu7aTa/1WuawlVdL0emKEkCFQAAGet0mGqWSkX0rs1b/yBXnPIHAEBGuhymoIz4qAMAkDZhikrn4w4AQFqEKRCoAABIgzAFTXzsAQDoEmEKPuCjDwBApwlT0Jpp0wEA6BRhiowlSdldf0ygAgBgkzoVpsqwWCaL3ngz4qWGps9Hs+oeEdsPabrYc4kSqAAA6FCnwlSZFstkyRtvRjy7oO3y1Wualn98RMl+ThykBQBgozodpp5d0DpMRXxQLL/xZt76SxFKkqaw3ZEFDU3tSpBABQBAuzp9ml8ZF8tkQfNpoB1ZtaapXQkSqAAAaKPTE1CkWywnScRbjRFLlzf9V+AqX5v6fHS1XZHxGyoAAFrp0mx+6RTLfm9VWap7ZLddkXGECgCAFl2eGr2rxbLfW1Weul6b/pzUvD8rZAkSqAAAiIg0rzPVlWLZ760qUyrVdPSxIyOGlOwU+wIVAADpX7S3K8VymU9OQAf69WmaGn3D8F3To6SnTI/wGyoAgIqXdphq1lwsb/i7qJoeTWGquVgu88kJ2IR+fSL69i67iz8LVAAAFSzjMNWsM8VymU9OQCekUhG9awvdi6wSqAAAKlTWwlSzTRXLzb+36ugIVAlPTkBl8hsqAIAKlPUw1RllPjkBlUmgAgCoMAUJU83KeHICKpNT/gAAKkhBw1SzMp2cgMokUAEAVIiiCFPNynByAiqTU/4AACpAUYUpKCO+RgAAZU6YgtzxVQIAKGPCFOSWrxMAQJkSpiD3fKUAAMqQMAX54WsFAFBmhCnIn6L9as2cOTOGDx8ePXv2jLFjx8acOXM6bH/jjTfGzjvvHJtvvnkMGDAgvvjFL8by5cvz1FsAgOIgTEF+FeXXa9asWTF16tT41re+FfPnz4/99tsvJk6cGA0NDe22f/jhh+PEE0+Mk08+Of7617/GrbfeGo8//nhMmTIlzz0HACgcYQryryi/YhdddFGcfPLJMWXKlBg1alRcfPHFMXjw4Lj88svbbf/YY4/FsGHD4vTTT4/hw4fHvvvuG6eccko88cQTee45AEBhCFNQGEX3NVu9enXMmzcvJkyY0Gr5hAkTYu7cue0+Zu+9946//e1vMXv27EiSJP7+97/HbbfdFkccccRG17Nq1apobGxsdQMAKEXCFBRO0X3Vli1bFuvWrYv+/fu3Wt6/f/9YsmRJu4/Ze++948Ybb4xJkyZFdXV1bLvtttG7d++49NJLN7qeGTNmRF1dXctt8ODBWX0dAAD5IExBYRXt1y2VSrW6nyRJm2XNnn322Tj99NPjvPPOi3nz5sUf//jHeOWVV+LUU0/d6PNPnz49VqxY0XJbuHBhVvsPAJBrwhQUXvdCd2BDffv2jaqqqjZHo5YuXdrmqFWzGTNmxD777BPf+MY3IiJip512ii222CL222+/+N73vhcDBgxo85iampqoqanJ/gsAAMgDYQqKQ9F97aqrq2Ps2LFRX1/fanl9fX3svffe7T7m3XffjW4bbEGqqqoiounIFgBAORGmoHgU5Vdv2rRpcfXVV8e1114bzz33XJx55pnR0NDQcgrf9OnT48QTT2xpf+SRR8Ydd9wRl19+ebz88svxyCOPxOmnnx577rlnDBw4sFAvAwAg64QpKC5Fd8pfRMSkSZNi+fLlccEFF8TixYtjzJgxMXv27Bg6dGhERCxevLjVNalOOumkWLlyZfzsZz+Lr33ta9G7d+/4xCc+ET/4wQ8K9RIAALJOmILik0qcExcREY2NjVFXVxcrVqyI2traQncHAKAVYQrypyvZwNcQAKDICVNQvHwVAQCKmDAFxc3XEQCgSAlTUPx8JQEAipAwBaXB1xIAoMgIU1A6fDUBAIqIMAWlxdcTAKBICFNQenxFAQCKgDAFpcnXFACgwIQpKF2+qgAABSRMQWnzdQUAKBBhCkqfrywAQAEIU1AefG0BAPJMmILy4asLAJBHwhSUF19fAIA8Eaag/PgKAwDkgTAF5cnXGAAgx4QpKF++ygAAOSRMQXnzdQYAyBFhCsqfrzQAQA4IU1AZfK0BALJMmILK4asNAJBFwhRUFl9vAIAsEaag8viKAwBkgTAFlcnXHAAgQ8IUVC5fdQCADAhTUNl83QEA0iRMAb7yAABpEKaACIEKAKDLhCmgma8+AEAXCFPAh/n6AwB0kjAFbMgmAACgE4QpoD02AwAAmyBMARtjUwAA0AFhCuiIzQEAwEYIU8Cm2CQAALRDmAI6w2YBAGADwhTQWTYNAAAfIkwBXWHzAADwPmEK6CqbCACAEKaA9NhMAAAVT5gC0mVTAQBUNGEKyITNBQBQsYQpIFM2GQBARRKmgGyw2QAAKo4wBWSLTQcAUFGEKSCbbD4AgIohTAHZZhMCAFQEYQrIBZsRAKDsCVNArtiUAABlTZgCcsnmBAAoW8IUkGs2KQBAWRKmgHywWQEAyo4wBeSLTQsAUFaEKSCfbF4AgLIhTAH5ZhMDAJQFYQooBJsZAKDkCVNAodjUAAAlTZgCCsnmBgAoWcIUUGg2OQBASRKmgGJgswMAlBxhCigWNj0AQEkRpoBiYvMDAJQMYQooNjZBAEBJEKaAYmQzBAAUPWEKKFY2RQBAUROmgGJmcwQAFC1hCih2NkkAQFESpoBSYLMEABQdYQooFTZNAEBREaaAUmLzBAAUDWEKKDU2UQBAURCmgFJkMwUAFJwwBZQqmyoAoKCEKaCU2VwBAAUjTAGlziYLACgIYQooBzZbAEDeCVNAubDpAgDySpgCyknRbr5mzpwZw4cPj549e8bYsWNjzpw5G2170kknRSqVanMbPXp0HnsMAGyKMAWUm6LchM2aNSumTp0a3/rWt2L+/Pmx3377xcSJE6OhoaHd9pdcckksXry45bZw4cLYaqut4rOf/Wyeew4AbIwwBZSjVJIkSaE7saFx48bFbrvtFpdffnnLslGjRsXRRx8dM2bM2OTj77rrrjj22GPjlVdeiaFDh3ZqnY2NjVFXVxcrVqyI2tratPsOALQlTAGlpCvZoOg2ZatXr4558+bFhAkTWi2fMGFCzJ07t1PPcc0118QhhxzSYZhatWpVNDY2troBANknTAHlrOg2Z8uWLYt169ZF//79Wy3v379/LFmyZJOPX7x4cfzhD3+IKVOmdNhuxowZUVdX13IbPHhwRv0GANoSpoByV7SbtFQq1ep+kiRtlrXnuuuui969e8fRRx/dYbvp06fHihUrWm4LFy7MpLsAwAaEKaASdC90BzbUt2/fqKqqanM0aunSpW2OWm0oSZK49tprY/LkyVFdXd1h25qamqipqcm4vwBAW8IUUCmKbtNWXV0dY8eOjfr6+lbL6+vrY++99+7wsQ8++GC89NJLcfLJJ+eyiwBAB4QpoJIU3RGqiIhp06bF5MmTY/fdd4/x48fHVVddFQ0NDXHqqadGRNPpeosWLYrrr7++1eOuueaaGDduXIwZM6YQ3QaAiidMAZWmKAPVpEmTYvny5XHBBRfE4sWLY8yYMTF79uyWWfsWL17c5ppUK1asiNtvvz0uueSSQnQZACqeMAVUoqK8DlUhuA4VAKRPmALKSUlfhwoAKC3CFFDJbO4AgLQJU0Cls8kDANIiTAEIVABAGoQpgCY2fQBAlwhTAB+w+QMAOk2YAmjNJhAA6BRhCqAtm0EAYJOEKYD22RQCAB0SpgA2zuYQANgoYQqgYzaJAEC7hCmATbNZBADaEKYAOsemEQBoRZgC6DybRwCghTAF0DU2kQBARAhTAOmwmQQAhCmANNlUAkCFE6YA0mdzCQAVTJgCyIxNJgBUKGEKIHM2mwBQgYQpgOyw6QSACiNMAWSPzScAVBBhCiC7bEIBoEIIUwDZZzMKABVAmALIje6F7gAAkFtphakkiVixMmL1mojqHhF1vSJSqbz0F6CU5DRQLVu2LPr06RNVVVW5XA0AsBFphak33ox4qaEpTDWr7hGx/ZCIfn1y2l+AUpPRwf4nnngiLrjggnj22WdbLf/tb38bAwYMiP79+0ffvn3jZz/7WUadBAC6Lu0w9eyC1mEqoun+swua/g5Ai4wC1aWXXhr/8R//Edtss03Lstdeey2OO+64+Pvf/x7bbrttrFy5Ms4444yYM2dOxp0FADon7dP8XmrouM2ChqZ2AEREhoHqsccei1122SX69u3bsuyaa66J1atXx09+8pNYtGhRPP7441FVVRX/9V//lXFnAYBNS3sCiubfTHVk1ZqmdgBERIaB6u9//3sMGTKk1bJ77rknttxyyzjttNMiImLXXXeNfffdN5588slMVgUAdEJGs/ltKkx1tR1ABcgoUK1bt67V/VWrVsWTTz4Z++yzT1RXV7csHzhwYCxZsiSTVQEAm5Dx1OjVPbLbDqACZBSohg4dGk8//XTL/T/96U+xevXqOPjgg1u1a2xsjLq6ukxWBQB0ICvXmarrtemwVPP+FOoARESGgeqoo46KF198Mc4888z47W9/G2eddVZ069YtPv3pT7dqN3/+/Bg6dGhGHQUA2pe1i/amUk1To3dkxBDXowL4kIwC1de//vX4yEc+Epdcckkcc8wx8dxzz8XUqVPjox/9aEubP//5z7Fo0aLYf//9M+4sANBa1sJUs359Ij4+ou2RqpoeTctdhwqglYwu7LvVVlvFk08+GbfddlssXbo0xo4dG5/4xCdatVmyZEmcccYZccIJJ2TUUQCgtayHqWb9+kT07f3BrH/V75/m58gUQBupJHExiYgPfue1YsWKqK2tLXR3AKBDOQtTAHQpG9j0AkCJEaYAikdGp/w1e+WVV2LOnDmxePHiWLVqVbttUqlUnHvuudlYHQBULGEKoLhkdMrf6tWrY8qUKXHjjTdGRERHT5VKpdpct6qYOOUPgGInTAHkR1eyQUZHqM4777y44YYbok+fPnHCCSfEDjvsEFtuuWUmTwkAtEOYAihOGQWqm266KXr37h3/8z//4zpTAJAjwhRA8cpoc7x06dLYb7/9hCkAyBFhCqC4ZbRJHjp0aLzzzjvZ6gsA8CHCFEDxy2izfPLJJ8df/vKXWLhwYbb6AwCEMAVQKjLaNH/961+PI444IiZOnBgPPPBAh7P8AQCdI0wBlI6MJqXYfvvtIyLitddei4MPPjh69OgRAwYMiFQq1aZtKpWKBQsWZLI6ACh7whRAackoUL366qut7q9evTpee+21TJ4SACqWMAVQejIKVOvXr89WPwCgoglTAKXJphoACkyYAihdNtcAUEDCFEBpy8om+5lnnol///d/jx133DG23nrr6Nu3b+y4445x2mmnxTPPPJONVQBA2RGmAEpfKslwrvNLLrkkvvGNb8S6devanTa9e/fu8aMf/SjOOOOMTFaTc42NjVFXVxcrVqyI2traQncHgDInTAEUr65kg4w23fX19XHmmWdGdXV1nHnmmTF//vx4880346233oonn3wyvva1r0VNTU1MmzYt7r333kxWBQBlQ5gCKB8ZHaGaOHFi3HvvvfHAAw/E3nvv3W6bRx99NPbff/849NBDY/bs2Wl3NNccoQIgH4QpgOKXtyNUf/nLX+KAAw7YaJiKiBg/fnwceOCB8ec//zmTVQFAyROmAMpPRpvxd999N/r167fJdv369Yt33303k1UBQEkTpgDKU0ab8sGDB8ejjz4a69at22ibtWvXxqOPPhqDBw/OZFUAULKEKYDyldHm/NOf/nS89tprMWXKlGhsbGzz98bGxvjyl78cDQ0NcfTRR2eyKgAoScIUQHnLaFKKf/zjH7HHHnvEq6++GrW1tfHJT34yhg0bFqlUKl555ZX4/e9/H42NjfGRj3wkHn/88ejTp082+55VJqUAINuEKYDS1JVskPF1qF5//fU45ZRT4ve//327fz/iiCPiyiuvjIEDB2aympwTqADIJmEKoHR1JRt0z3RlAwcOjP/+7/+OV155JR5++OF4/fXXW5bvu+++MXz48ExXAQAlRZgCqBwZB6pmw4cPF54AqHjCFEBlsYkHgCwRpgAqT5eOUF1//fUREXHMMcdEr169Wu531oknntil9gBQKoQpgMrUpUkpunXrFqlUKp577rnYYYcdWu5vSpIkkUqlOrxeVaGZlAKAdAlTAOUlZ5NSnHfeeZFKpaJv376t7gNApRKmACpbxtOmlwtHqADoKmEKoDx1JRvY7ANAGoQpACIyDFRVVVVx8sknb7Ldl7/85ejePWsztANAQQlTADTLaPOfJEl09oxBZxYCUA6EKQA+LC+7gBUrVkRNTU0+VgUAOSNMAbChLp+H19DQ0Or+22+/3WZZs7Vr18bzzz8f99xzT4wYMSK9HgJAERCmAGhPlwPVsGHDWk2Vfvvtt8ftt9/e4WOSJIkvf/nLXe8dABQBYQqAjelyoNp///1bAtWDDz4Y22yzTXzsYx9rt211dXUMHDgwjjrqqDjmmGMy6ykAFIAwBUBHuhyoHnjggZb/79atW0ycODGuvfbabPYJAIqCMAXApmQ0l/krr7wSW265Zbb6AgBFQ5gCoDMy2jVsu+228c4778TKlSs32mblypXR0NAQq1evzmRVAJA3whQAnZXR7uGiiy6K4cOHx1NPPbXRNk899VQMHz48LrnkkkxWBQB5IUwB0BUZ7SLuuuuuGD58eOy7774bbbPvvvvGsGHD4s477+zSc8+cOTOGDx8ePXv2jLFjx8acOXM6bL9q1ar41re+FUOHDo2ampoYMWKE33YB0CXCFABdldFvqBYsWBB77733JtuNHj06/vznP3f6eWfNmhVTp06NmTNnxj777BNXXnllTJw4MZ599tkYMmRIu4857rjj4u9//3tcc801sf3228fSpUtj7dq1nV4nAJVNmAIgHRkFqnfeeSe22GKLTbbbfPPNo7GxsdPPe9FFF8XJJ58cU6ZMiYiIiy++OO6+++64/PLLY8aMGW3a//GPf4wHH3wwXn755dhqq60ioul6WQDQGcIUAOnKaHcxePDgeOKJJzbZbt68eTFgwIBOPefq1atj3rx5MWHChFbLJ0yYEHPnzm33Mb/97W9j9913jx/+8Iex3XbbxQ477BBf//rX45///OdG17Nq1apobGxsdQOg8ghTAGQio13GhAkT4uWXX45LL710o20uu+yyWLBgQRx22GGdes5ly5bFunXron///q2W9+/fP5YsWdLuY15++eV4+OGH45lnnok777wzLr744rjtttvitNNO2+h6ZsyYEXV1dS23wYMHd6p/AJQPYQqATGW02zj77LOjV69eMXXq1Dj66KNj9uzZ8fzzz8cLL7wQs2fPjqOPPjpOP/30qK2tjbPPPrtLz51KpVrdT5KkzbJm69evj1QqFTfeeGPsueee8clPfjIuuuiiuO666zZ6lGr69OmxYsWKltvChQu71D8ASpswBUA2ZPQbqsGDB8dvf/vb+MxnPhO//e1v47//+79b/T1Jkujbt2/8+te/7vRvmvr27RtVVVVtjkYtXbq0zVGrZgMGDIjtttsu6urqWpaNGjUqkiSJv/3tb/HRj360zWNqamqipqamU30CoLwIUwBkS8a7j/333z9eeOGF+P73vx+HHHJIjBw5MkaOHBmHHHJI/OAHP4jnn38+DjzwwE4/X3V1dYwdOzbq6+tbLa+vr9/ojIL77LNPvP766/H222+3LHvhhReiW7duMWjQoLReFwDlSZgCIJtSSZIkhe7EhmbNmhWTJ0+OK664IsaPHx9XXXVV/PznP4+//vWvMXTo0Jg+fXosWrQorr/++oiIePvtt2PUqFGx1157xXe/+91YtmxZTJkyJQ444ID4+c9/3ql1NjY2Rl1dXaxYsSJqa2tz+fIAKBBhCoDO6Eo2yOiUv1yZNGlSLF++PC644IJYvHhxjBkzJmbPnh1Dhw6NiIjFixdHQ0NDS/stt9wy6uvr46tf/WrsvvvusfXWW8dxxx0X3/ve9wr1EgAoMsIUALlQlEeoCsERKoDyJUwB0BVdyQZd2p1069YtunfvHi+88EJERFRVVXX61r17UR4MA6DMCVMA5FKXUs6QIUMilUpFjx49IqJplr+NTWUOAIUmTAGQa10KVK+++mqH9wGgWAhTAOSDXQsAZUeYAiBf7F4AKCvCFAD51KVT/h566KGMVrb//vtn9HgA6IgwBUC+dSlQHXjggRlNQrFu3bq0HwsAHRGmACiELgWqE088sU2gWr58efzud7+LVCoVu+66awwZMiQiIhoaGuLJJ5+MJEniiCOOiK233jp7vQaADxGmACiULgWq6667rtX9xYsXx1577RWHHnpoXHrppbHDDju0+vsLL7wQZ5xxRjz11FPx2GOPZdxZANiQMAVAIWW0yznnnHNi3bp18Zvf/KZNmIqI2GGHHeKOO+6IdevWxdlnn53JqgCgDWEKgELLaLdz9913x3777Rc9e/bcaJvNNtss9ttvv7j77rszWRUAtCJMAVAMMtr1NDY2xrJlyzbZbtmyZbFy5cpMVgUALYQpAIpFRrufMWPGxIMPPhhz5szZaJuHH344HnjggRgzZkwmqwKAiBCmACguGe2Czj777Fi7dm0cdthh8W//9m9x3333xUsvvRQLFiyI++67L/7t3/4tDjvssFi/fr3fUAGQMWEKgGKTSpIkyeQJfvazn8U3vvGNWLVqVZsp1ZMkierq6vjBD34QZ5xxRkYdzbXGxsaoq6uLFStWRG1tbaG7A8AGhCkA8qUr2SDjQBUR8dprr8U111wTjzzySLz++uuRJEkMHDgw9t133/jiF78Yw4cPz3QVOSdQARQvYQqAfMp7oCoHAhVAcRKmAMi3rmQDuyQAipYwBUCxy8pu6ZlnnompU6fGPvvsEyNHjoyzzjqr5W+PPPJI/PSnP41//OMf2VgVABVCmAKgFHTP9Al++MMfxre//e1Yu3ZtRESkUqlW16Z6991348wzz4yampo45ZRTMl0dABVAmAKgVGS0e/rNb34T55xzTgwdOjTuuuuueOONN2LDn2Qdcsgh0bdv37jrrrsyWRUAFUKYAqCUZHSE6r/+679iyy23jPr6+hg2bFi7bVKpVIwcOTJeeOGFTFYFQAUQpgAoNRntpubPnx/jx4/faJhqtt1228XixYszWRUAZU6YAqAUZbSrWrt2bWy++eabbPfGG29EdXV1JqsCoIwJUwCUqox2VyNGjIh58+bFunXrNtrmnXfeiSeffDI+/vGPZ7IqAMqUMAVAKctol/WZz3wm/va3v8W555670TbnnntuvPnmmzFp0qRMVgVAGRKmACh1qWTDafm64J133ok999wz/u///i/22WefOOqoo+Kss86K/fffPz7zmc/EXXfdFffdd1/svPPO8dhjj0VNTU02+55VXbkaMgCZE6YAKFZdyQYZBaqIiKVLl8ZJJ50Uf/zjHyOVSrVMm978/wcffHDceOONsc0222SympwTqADyR5gCoJh1JRtkfGHfbbbZJmbPnh1PPfVU1NfXx6uvvhrr1q2LQYMGxSGHHBLjxo3LdBUAlBFhCoByklGgOvbYY2PAgAFx2WWXxc477xw777xztvoFQBkSpgAoNxntxmbPnh3Lly/PVl8AKGPCFADlKKNd2fDhw+Odd97JVl8AKFPCFADlKqPd2fHHHx8PPvhgLFmyJFv9AaDMCFMAlLOMdmnTp0+P/fbbLw444IC48847Y82aNdnqFwBlQJgCoNxlNCnFyJEjY/369bFw4cL4zGc+E6lUKrbZZpvo2bNnm7apVCoWLFiQyeoAKCHCFACVIKNA9eqrr7a6nySJ0/8AEKYAqBgZBar169dnqx8AlAlhCoBKYhcHQNYIUwBUmrSOUM2ePTvuuuuuWLhwYdTU1MROO+0UX/ziF2P48OHZ7h8AJUKYAqASpZIkSbrygM9//vNxyy23RETTb6YimiacqKmpiVtuuSWOOuqo7PcyDxobG6Ouri5WrFgRtbW1he4OQEkRpgAoJ13JBl06QnXNNdfEzTffHN27d4/JkyfHrrvuGitXrozf/e538eijj8aJJ54Yr732WtTV1WX0AgAoHcIUAJWsS4Hql7/8ZXTr1i3+8Ic/xMEHH9yyfPr06fHFL34xrr/++rjjjjvii1/8YtY7CkDxEaYAqHRd2u09/fTTsddee7UKU82++c1vRpIk8fTTT2etcwAUL2EKALoYqBobG2PEiBHt/q15eWNjY+a9AqCoCVMA0KRLu78kSaKqqqr9J3p/T+raVADlTZgCgA/YBQLQacIUALTWpWnTu3XrFqlUKr0VpVKxdu3atB6bD6ZNB+iYMAVApcjZtOkRH1x7Kl+PA6DwhCkAaF+XApXfRwFUHmEKADbOLhGAjRKmAKBjdosAtEuYAoBNs2sEoA1hCgA6x+4RgFaEKQDoPLtIAFoIUwDQNXaTAESEMAUA6bCrBECYAoA02V0CVDhhCgDSZ5cJUMGEKQDIjN0mQIUSpgAgc3adABVImAKA7LD7BKgwwhQAZI9dKEAFEaYAILvsRgEqhDAFANlnVwpQAYQpAMgNu1OAMidMAUDu2KUClDFhCgByy24VoEwJUwCQe3atAGVImAKA/LB7BSgzwhQA5I9dLEAZEaYAIL/sZgHKhDAFAPlnVwtQBoQpACgMu1uAEidMAUDh2OUClDBhCgAKy24XoEQJUwBQeHa9ACVImAKA4mD3C1BihCkAKB52wQAlRJgCgOJStLvhmTNnxvDhw6Nnz54xduzYmDNnzkbbPvDAA5FKpdrc/u///i+PPQbILWEKAIpP90J3oD2zZs2KqVOnxsyZM2OfffaJK6+8MiZOnBjPPvtsDBkyZKOPe/7556O2trblfr9+/fLRXYCcq8gwlSQRK1ZGrF4TUd0joq5XRCpV6F4BQCupJEmSQndiQ+PGjYvddtstLr/88pZlo0aNiqOPPjpmzJjRpv0DDzwQBx10ULz55pvRu3fvtNbZ2NgYdXV1sWLFilahDKDQKjJMvfFmxEsNTWGqWXWPiO2HRPTrU7h+AVARupINim6XvHr16pg3b15MmDCh1fIJEybE3LlzO3zsrrvuGgMGDIiDDz447r///g7brlq1KhobG1vdAIpNxYapZxe0DlMRTfefXdD0dwAoEkW3W162bFmsW7cu+vfv32p5//79Y8mSJe0+ZsCAAXHVVVfF7bffHnfccUeMHDkyDj744HjooYc2up4ZM2ZEXV1dy23w4MFZfR0AmarIMJUkTUemOrKgoakdABSBovwNVUREaoPz5JMkabOs2ciRI2PkyJEt98ePHx8LFy6MH//4x7H//vu3+5jp06fHtGnTWu43NjYKVUDRqMgwFfHBb6Y6smpNU7veTs8GoPCKbvfct2/fqKqqanM0aunSpW2OWnVkr732ihdffHGjf6+pqYna2tpWN4BiULFhKmLTYaqr7QAgx4puF11dXR1jx46N+vr6Vsvr6+tj77337vTzzJ8/PwYMGJDt7gHkVEWHqYimiSey2Q4AcqwoT/mbNm1aTJ48OXbfffcYP358XHXVVdHQ0BCnnnpqRDSdrrdo0aK4/vrrIyLi4osvjmHDhsXo0aNj9erVccMNN8Ttt98et99+eyFfBkCXVHyYimiaGr26R8dHoGren0IdAIpAUQaqSZMmxfLly+OCCy6IxYsXx5gxY2L27NkxdOjQiIhYvHhxNDR88KPl1atXx9e//vVYtGhRbLbZZjF69Oj4/e9/H5/85CcL9RIAukSYel8q1TQ1+rMLNt5mxBDXowKgaBTldagKwXWogEIRptrR3nWoano0hSnXoQIgx7qSDYryCBVApRCmNqJfn4i+vT+Y9a/6/dP8HJkCoMgIVAAFIkxtQiplanQAip5dN0ABCFMAUB7svgHyTJgCgPLhlD+APNpomEoSvxcCgBIkUAHkyUbDVHsz2lX3aJo+3Ix2AFDUnGQCkAcdhqlnF7S9kO3qNU3L33izIP0FADpHoALIsQ5P83upoeMHL2hoagcAFCWBCiCHOpyAovk3Ux1ZtaapHQBQlAQqgBzZ5Gx+mwpTXW0HAOSdQAWQA52aGr26R+eerLPtAIC8E6gAsqzT15mq67XpsFTz/hTqAEBREqgAsqhLF+1NpZqmRu/IiCGuRwUARUygAsiSLoWpZv36RHx8RNsjVTU9mpa7DhUAFDUX9gXIgrTCVLN+fSL69v5g1r/q90/zc2QKAIqeQAWQoYzCVLNUKqJ3bU76BwDkjlP+ADKQlTAFAJQsu32ANAlTAIBdP0AahCkAIEKgAugyYQoAaKYEAOgCYQoA+DBlAEAnCVMAwIaUAgCdIEwBAO1RDgBsgjAFAGyMkgCgA8IUANARZQHARghTAMCmKA0A2iFMAQCdoTwA2IAwBQB0lhIB4EOEKQCgK5QJAO8TpgCArlIqAIQwBQCkR7kAVDxhCgBIl5IBqGjCFACQCWUDULGEKQAgU0oHoCIJUwBANigfgIojTAEA2aKEACqKMAUAZJMyAqgYwhQAkG1KCaAiCFMAQC4oJ4CyJ0wBALmipADKmjAFAOSSsgIoW8IUAJBrSgugLAlTAEA+KC+AsiNMAQD5osQAyoowBQDkkzIDKBvCFACQb0oNoCwIUwBAISg3gJInTAEAhaLkAEqaMAUAFJKyAyhZwhQAUGhKD6AkCVMAQDFQfgAlR5gCAIqFEgQoKcIUAFBMlCFAyRCmAIBioxQBSoIwBQAUI+UIUPSEKQCgWClJgKImTAEAxUxZAhQtYQoAKHZKE6AoCVMAQClQngBFR5gCAEqFEgUoKsIUAFBKlClA0RCmAIBSo1QBioIwBQCUIuUKUHDCFABQqpQsQEEJUwBAKVO2AAUjTAEApU7pAhSEMAUAlAPlC5B3whQAUC6UMEBeCVMAQDlRxgB5I0wBAOVGKQPkhTAFAJQj5QyQc8IUAFCulDRATglTAEA5U9YAOSNMAQDlTmkD5IQwBQBUAuUNkHXCFABQKZQ4QFYJUwBAJSnaMmfmzJkxfPjw6NmzZ4wdOzbmzJnTqcc98sgj0b1799hll11y20GgDWEKAKg0RVnqzJo1K6ZOnRrf+ta3Yv78+bHffvvFxIkTo6GhocPHrVixIk488cQ4+OCD89RToJkwBQBUolSSJEmhO7GhcePGxW677RaXX355y7JRo0bF0UcfHTNmzNjo4z73uc/FRz/60aiqqoq77rornnzyyU6vs7GxMerq6mLFihVRW1ubSfeh4ghTAEA56Uo2KLqSZ/Xq1TFv3ryYMGFCq+UTJkyIuXPnbvRxv/jFL2LBggXxne98J9ddBD5EmAIAKln3QndgQ8uWLYt169ZF//79Wy3v379/LFmypN3HvPjii3HOOefEnDlzonv3zr2kVatWxapVq1ruNzY2pt9pqFDCFABQ6Yq29EmlUq3uJ0nSZllExLp16+Jf//Vf47vf/W7ssMMOnX7+GTNmRF1dXctt8ODBGfcZKokwBQBQhIGqb9++UVVV1eZo1NKlS9sctYqIWLlyZTzxxBPxla98Jbp37x7du3ePCy64IJ566qno3r173Hfffe2uZ/r06bFixYqW28KFC3PyeqAcCVMAAE2K7pS/6urqGDt2bNTX18cxxxzTsry+vj4+/elPt2lfW1sbTz/9dKtlM2fOjPvuuy9uu+22GD58eLvrqampiZqamux2HiqAMAUA8IGiC1QREdOmTYvJkyfH7rvvHuPHj4+rrroqGhoa4tRTT42IpqNLixYtiuuvvz66desWY8aMafX4bbbZJnr27NlmOZAZYQoAoLWiDFSTJk2K5cuXxwUXXBCLFy+OMWPGxOzZs2Po0KEREbF48eJNXpMKyC5hCgCgraK8DlUhuA4VbJwwBQBUkpK+DhVQXIQpAICNUxYBGyVMAQB0TGkEtEuYAgDYNOUR0IYwBQDQOUokoBVhCgCg85RJQAthCgCga5RKQEQIUwAA6VAuAcIUAECalExQ4YQpAID0KZuggglTAACZUTpBhRKmAAAyp3yCCiRMAQBkhxIKKowwBQCQPcooqCDCFABAdimloEIIUwAA2aecggogTAEA5IaSCsqcMAUAkDvKKihjwhQAQG4praBMCVMAALmnvIIyJEwBAOSHEgvKjDAFAJA/yiwoI8IUAEB+KbWgTAhTAAD5p9yCMiBMAQAUhpILSpwwBQBQOMouKGHCFABAYSm9oEQJUwAAhaf8ghIkTAEAFAclGJQYYQoAoHgow6CECFMAAMVFKQYlQpgCACg+yjEoAcIUAEBxUpJBkROmAACKl7IMipgwBQBQ3JRmUKSEKQCA4qc8gyIkTAEAlAYlGhQZYQoAoHQo06CICFMAAKVFqQZFQpgCACg9yjUoAsIUAEBpUrJBgQlTAAClS9kGBSRMAQCUNqUbFIgwBQBQ+pRvUADCFABAeVDCQZ4JUwAA5UMZB3kkTAEAlBelHOSJMAUAUH6Uc5AHwhQAQHlS0kGOCVMAAOVLWQc5JEwBAJQ3pR3kiDAFAFD+lHeQA8IUAEBlUOJBlglTAACVQ5kHWSRMAQBUFqUeZIkwBQBQeZR7kAXCFABAZVLyQYaEKQCAyqXsgwwIUwAAlU3pB2kSpgAAUP5BGoQpAAAiBCroMmEKAIBmykDoAmEKAIAPUwpCJwlTAABsSDkInSBMAQDQHiUhbIIwBQDAxigLoQPCFAAAHVEawkYIUwAAbIryENohTAEA0BlKRNiAMAUAQGcpE+FDhCkAALpCqQjvE6YAAOgq5SKEMAUAQHqUjFQ8YQoAgHQpG6lowhQAAJlQOlKxhCkAADKlfKQiCVMAAGSDEpKKI0wBAJAtykgqijAFAEA2FW0pOXPmzBg+fHj07Nkzxo4dG3PmzNlo24cffjj22Wef2HrrrWOzzTaLj33sY/Ff//VfeewtpUCYAgAg27oXugPtmTVrVkydOjVmzpwZ++yzT1x55ZUxceLEePbZZ2PIkCFt2m+xxRbxla98JXbaaafYYost4uGHH45TTjkltthii/h//+//FeAVUGyEKQAAciGVJElS6E5saNy4cbHbbrvF5Zdf3rJs1KhRcfTRR8eMGTM69RzHHntsbLHFFvGrX/2qU+0bGxujrq4uVqxYEbW1tWn1m+IkTAEA0BVdyQZFV1auXr065s2bFxMmTGi1fMKECTF37txOPcf8+fNj7ty5ccABB+Sii5QQYQoAgFwqulP+li1bFuvWrYv+/fu3Wt6/f/9YsmRJh48dNGhQvPHGG7F27do4//zzY8qUKRttu2rVqli1alXL/cbGxsw6TtERpgAAyLWiLS9TqVSr+0mStFm2oTlz5sQTTzwRV1xxRVx88cVx8803b7TtjBkzoq6uruU2ePDgrPSb4iBMAQCQD0V3hKpv375RVVXV5mjU0qVL2xy12tDw4cMjImLHHXeMv//973H++efH8ccf327b6dOnx7Rp01ruNzY2ClVlQpgCACBfiq7MrK6ujrFjx0Z9fX2r5fX19bH33nt3+nmSJGl1St+Gampqora2ttWN0idMAQCQT0V3hCoiYtq0aTF58uTYfffdY/z48XHVVVdFQ0NDnHrqqRHRdHRp0aJFcf3110dExGWXXRZDhgyJj33sYxHRdF2qH//4x/HVr361YK+B/BOmAADIt6IMVJMmTYrly5fHBRdcEIsXL44xY8bE7NmzY+jQoRERsXjx4mhoaGhpv379+pg+fXq88sor0b179xgxYkR8//vfj1NOOaVQL4E8E6YAACiEorwOVSG4DlXpEqYAAMimkr4OFXSFMAUAQCEpPSlZwhQAAIWm/KQkCVMAABQDJSglR5gCAKBYKEMpKcIUAADFRClKyRCmAAAoNspRSoIwBQBAMVKSUvSEKQAAipWylKImTAEAUMyUphQtYQoAgGKnPKUoCVMAAJQCJSpFR5gCAKBUKFMpKsIUAAClRKlK0RCmAAAoNcpVioIwBQBAKVKyUnDCFAAApUrZSkEJUwAAlDKlKwUjTAEAUOqUrxSEMAUAQDlQwpJ3whQAAOVCGUteCVMAAJQTpSx5I0wBAFBulLPkhTAFAEA5UtKSc8IUAADlSllLTglTAACUM6UtOSNMAQBQ7pS35IQwBQBAJVDiknXCFAAAlUKZS1YJUwAAVBKlLlkjTAEAUGmUu2SFMAUAQCVS8pIxYQoAgEql7CUjwhQAAJVM6UvahCkAACqd8pe0CFMAABDRvdAdoACSJGLFyojVayKqe0TU9YpIpTr9cGEKAACaCFSV5o03I15qaApTzap7RGw/JKJfn00+XJgCAIAPKIUryRtvRjy7oHWYimi6/+yCpr93QJgCAIDWlMOVIkmajkx1ZEFDU7t2CFMAANCWkrhSNP9mqiOr1jS124AwBQAA7VMWV4pNhamNtBOmAABg45TGlaK6R5fbCVMAANAx5XGlqOu16VBV8/4U6iFMAQBAZyiRK0Uq1TQ1ekdGDIlIpYQpAADoJGVyJenXJ+LjI9oeqarp0bS8Xx9hCgAAusCFfStNvz4RfXt/MOtf9fun+TkyBQAAXSZQVaJUKqJ3batFwhQAAHSdkhlhCgAA0qRsrnDCFAAApE/pXMGEKQAAyIzyuUIJUwAAkDkldAUSpgAAIDuU0RVGmAIAgOxRSlcQYQoAALJLOV0hhCkAAMg+JXUFEKYAACA3lNVlTpgCAIDcUVqXMWEKAAByS3ldpoQpAADIPSV2GRKmAAAgP5TZZUaYAgCA/FFqlxFhCgAA8ku5XSaEKQAAyD8ldxkQpgAAoDCU3SVOmAIAgMJRepcwYQoAAApL+V2ihCkAACg8JXgJEqYAAKA4KMNLjDAFAADFQyleQoQpAAAoLsrxEiFMAQBA8ele6A6waXkPU0kSsWJlxOo1EdU9Iup6RaRSOVwhAACUJoGqyOU9TL3xZsRLDU1hqll1j4jth0T065PDFQMAQOlx0lgRK0iYenZB6zAV0XT/2QVNfwcAAFoIVEWqIKf5vdTQcZsFDU3tAACAiBCoilJBJqBo/s1UR1ataWoHAABEhN9QFaVUqun2//5fHmfz21SY6mo7AACoAAJVEfr85yNGjIjYc888To1e3SO77QAAoAIU7Sl/M2fOjOHDh0fPnj1j7NixMWfOnI22veOOO+LQQw+Nfv36RW1tbYwfPz7uvvvuPPY2+/baK8/XmarrtemwVPP+FOoAAEBEFGmgmjVrVkydOjW+9a1vxfz582O//faLiRMnRkND+5MmPPTQQ3HooYfG7NmzY968eXHQQQfFkUceGfPnz89zz0tYKtU0NXpHRgxxPSoAAPiQVJIU37Rt48aNi9122y0uv/zylmWjRo2Ko48+OmbMmNGp5xg9enRMmjQpzjvvvE61b2xsjLq6ulixYkXU1tam1e+y0N51qGp6NIUp16ECAKACdCUbFN1vqFavXh3z5s2Lc845p9XyCRMmxNy5czv1HOvXr4+VK1fGVltttdE2q1atilWrVrXcb2xsTK/D5aZfn4i+vT+Y9a/6/dP8HJkCAIA2iu6Uv2XLlsW6deuif//+rZb3798/lixZ0qnn+MlPfhLvvPNOHHfccRttM2PGjKirq2u5DR48OKN+l5VUKqJ3bcQ2Wzf9V5gCAIB2FV2gapbaoIhPkqTNsvbcfPPNcf7558esWbNim2222Wi76dOnx4oVK1puCxcuzLjPAABAZSm6U/769u0bVVVVbY5GLV26tM1Rqw3NmjUrTj755Lj11lvjkEMO6bBtTU1N1NTUZNxfAACgchXdEarq6uoYO3Zs1NfXt1peX18fe++990Yfd/PNN8dJJ50UN910UxxxxBG57iYAAEDxHaGKiJg2bVpMnjw5dt999xg/fnxcddVV0dDQEKeeempENJ2ut2jRorj++usjoilMnXjiiXHJJZfEXnvt1XJ0a7PNNou6urqCvQ4AAKC8FWWgmjRpUixfvjwuuOCCWLx4cYwZMyZmz54dQ4cOjYiIxYsXt7om1ZVXXhlr166N0047LU477bSW5V/4whfiuuuuy3f3AQCAClGU16EqBNehAgAAIrqWDYruN1QAAAClQqACAABIk0AFAACQJoEKAAAgTQIVAABAmgQqAACANAlUAAAAaRKoAAAA0iRQAQAApEmgAgAASJNABQAAkCaBCgAAIE0CFQAAQJoEKgAAgDQJVAAAAGkSqAAAANIkUAEAAKRJoAIAAEiTQAUAAJCm7oXuQLFIkiQiIhobGwvcEwAAoJCaM0FzRuiIQPW+lStXRkTE4MGDC9wTAACgGKxcuTLq6uo6bJNKOhO7KsD69evj9ddfj169ekUqlcrLOhsbG2Pw4MGxcOHCqK2tzcs6K40xzj1jnHvGOPeMce4Z49wyvrlnjHOvmMY4SZJYuXJlDBw4MLp16/hXUo5Qva9bt24xaNCggqy7tra24B+acmeMc88Y554xzj1jnHvGOLeMb+4Z49wrljHe1JGpZialAAAASJNABQAAkCaBqoBqamriO9/5TtTU1BS6K2XLGOeeMc49Y5x7xjj3jHFuGd/cM8a5V6pjbFIKAACANDlCBQAAkCaBCgAAIE0CFQAAQJoEqhx4++23Y+rUqTFw4MDo2bNn7LLLLnHLLbds8nF33HFHHH/88bH99tvHZpttFsOGDYvPf/7z8eKLL+ah16Ul3TH+05/+FIceemgMHDgwampqYptttolPfOITMXv27Dz0urSkO8Yb+va3vx2pVCrGjBmTg16WtnTH+LrrrotUKtXubcmSJXnoeenI9HP8m9/8Jg444ICora2NLbbYIkaPHh1XXXVVDntcWtId3wMPPHCjn2Gf49Yy+Qzff//9ceihh8Y222wTW265Zey0007x05/+NNatW5fjXpeWTMb47rvvjn322Sc222yzqKuriyOPPDL++te/5rjHpWflypVx1llnxYQJE6Jfv36RSqXi/PPP7/Tjly5dGieddFL07ds3Nt988xg/fnzce++9uetwVyVk3aGHHpr07t07ueKKK5L77rsvmTJlShIRyY033tjh4/bcc8/kqKOOSq699trkgQceSH71q18lo0aNSrbccsvkmWeeyVPvS0O6Y3zLLbckZ5xxRnLLLbckDzzwQHLHHXckEyZMSCIi+dWvfpWn3peGdMf4w+bPn5/U1NQk/fv3T0aPHp3D3pamdMf4F7/4RRIRyS9+8Yvk0UcfbXVbvXp1nnpfGjL5HM+YMSPp1q1b8u///u/JH/7wh+RPf/pT8rOf/Sy59NJL89Dz0pDu+P71r39t89m99957kx49eiR77bVXnnpfGtId4/r6+qRbt27JgQcemNx1111JfX198tWvfjWJiOT000/PU+9LQ7pjfNdddyWpVCo5+uijk9///vfJTTfdlIwcOTLp06dP8tJLL+Wp96XhlVdeSerq6pL999+/ZXy/853vdOqx7733XjJmzJhk0KBByQ033JDcc889yac//emke/fuyQMPPJDbjneSQJVlv//975OISG666aZWyw899NBk4MCBydq1azf62L///e9tli1atCjp0aNHcvLJJ2e9r6UqkzFuz+rVq5Ptttsu2W+//bLZzZKWjTFes2ZNsssuuySnn356csABBwhUG8hkjJsD1eOPP57rbpa0TMb4iSeeSLp165b84Ac/yHU3S1a2t8XXXXddEhHJ1Vdfnc1ulrRMxvjzn/98UlNTk7z99tutlk+YMCGpra3NSX9LUSZjPHLkyGSnnXZK1q9f37Ls1VdfTaqrq5N//dd/zVmfS9H69etbxumNN97oUqC67LLLkohI5s6d27JszZo1ycc//vFkzz33zEV3u8wpf1l25513xpZbbhmf/exnWy3/4he/GK+//nr8+c9/3uhjt9lmmzbLBg4cGIMGDYqFCxdmva+lKpMxbk+PHj2id+/e0b1792x2s6RlY4y///3vxz/+8Y/4j//4j1x1s6Rl+3NMW5mM8c9+9rOoqamJr371q7nuZsnK9mf4mmuuiS233DImTZqUzW6WtEzGuEePHlFdXR2bbbZZq+W9e/eOnj175qS/pSjdMV6+fHk8//zzMXHixEilUi3Lhw4dGmPGjIm77rrLqZUf0nw6bzruvPPOGDlyZIwfP75lWffu3eOEE06Iv/zlL7Fo0aJsdTNtAlWWPfPMMzFq1Kg2xflOO+3U8veuePnll+O1116L0aNHZ62PpS4bY7x+/fpYu3ZtvP766/Gd73wnXnjhhfja176Wk/6WokzH+Nlnn43vfe97cfnll8eWW26Zs36Wsmx8jj/1qU9FVVVVbLXVVnHsscd2eftS7jIZ44ceeihGjRoVt99+e4wcOTKqqqpi0KBBcc4558Tq1atz2u9Skc393Ysvvhhz5syJz33uc7YZH5LJGJ966qmxevXqOP300+P111+Pt956K371q1/FnXfeGWeddVZO+11K0h3j5u1AexegrampiXfffTcWLFiQ5d5Wpmeeeabl/fiw5mXF8Js1/ySfZcuXL4+PfOQjbZZvtdVWLX/vrLVr18bJJ58cW265ZZx55plZ62Opy8YYf/KTn4y77747IiJqa2tj1qxZccQRR2S3oyUskzFev359fOlLX4pjjz02PvnJT+asj6UukzHedttt41vf+lbstddeUVtbG08//XR8//vfj7322iseeeSR2HnnnXPW71KSyRgvWrQo3njjjTj99NPjwgsvjI9//ONx7733xve///1YuHBh3HjjjTnrd6nI5v7ummuuiYiIk08+OTudKxOZjPG4cePivvvui89+9rNx2WWXRUREVVVVzJgxwz8gfki6Y9y/f//Yaqut4pFHHmm1/K233moJYV35DrBxy5cvb3k/PiydbU2uCFQ50NEhzc4e7kySJE4++eSYM2dO3H777TF48OBsda8sZDrGl156abz11luxePHiuOGGG2LSpEnxy1/+Mo4//vhsdrOkpTvGF110Ubz44ovx29/+NhfdKivpjvHhhx8ehx9+eMv9/fffP4444ojYcccd47zzzovf/OY3We1nKUt3jNevXx8rV66Mm2++OT73uc9FRMRBBx0U77zzTlx88cXx3e9+N7bffvus97fUZGN/t3bt2vjlL38Zo0ePjr322itbXSsb6Y7xvHnz4phjjolx48bFlVdeGVtssUXcd9998e1vfzvee++9OPfcc3PR3ZKUzhh369YtTjvttLjwwgvjwgsvjFNOOSUaGxtj6tSp8e6777a0ITuysa3JJe90lm299dbtJuV//OMfERHtJuwNJUkSU6ZMiRtuuCGuu+66+PSnP531fpaybIzxRz/60dhjjz3iqKOOil//+tdx8MEHx2mnnRbr16/Pen9LUbpj3NDQEOedd1585zvfierq6njrrbfirbfeirVr18b69evjrbfein/+85857XupyMbn+MOGDRsW++67bzz22GNZ6V85yGSMt95664iIOOyww1otnzhxYkRE/M///E+2ulmysvUZnj17dixZsiSmTJmS1f6Vg0zG+LTTTov+/fvHnXfeGZ/61KfioIMOigsvvDDOOeecOP/88+Pll1/OWb9LSSZjfN5558WZZ54Z3/ve96J///7x0Y9+NCKafn8VEbHddtvloMeVJ9v7y1wQqLJsxx13jOeeey7Wrl3bavnTTz8dEbHJa/E0h6lf/OIXcfXVV8cJJ5yQs76WqkzHuD177rlnvPnmm/HGG29kpY+lLt0xfvnll+Of//xnnHHGGdGnT5+W2yOPPBLPPfdc9OnTJ6ZPn57z/peCXHyOkyTxL6IfkskYt3e+fkTTGEf4l+eI7H2Gr7nmmqiuro7JkydnvY+lLpMxfvLJJ2Ps2LFRVVXVavkee+wR69evj+eeey77HS5BmYxx9+7d46KLLorly5fH//7v/8brr78ev/vd76KhoSGGDx8egwYNymnfK8WOO+7Y8n58WCb7y6wr6ByDZWj27NlJRCS33HJLq+WHH374JqffXL9+fXLyyScnqVQqueqqq3Ld1ZKVyRi3Z/369ckBBxyQ9O7dO1mzZk02u1qy0h3jN998M7n//vvb3Hbeeedk2LBhyf3335+8+OKL+XgJRS/bn+OXX3452XLLLZOjjz46m90saZmM8ZVXXtnudWhOP/30pFu3bsmrr76akz6Xkmx8hhcvXpx07949Oe6443LVzZKWyRgPHz48GTNmTJs23/zmN5OISJ588smc9LnUZHtbPG/evKSqqiq5+OKLs9nNstLVadNnzpyZRETy2GOPtSxbs2ZNMnr06GTcuHE56mXXCFQ5cOihhyZ9+vRJrrrqquS+++5LvvzlLycRkdxwww0tbb70pS8lVVVVrXbKX/nKV5KISL70pS+1ueDh//zP/xTipRStdMf4qKOOSs4999zk9ttvTx544IHkpptuarmw72WXXVaIl1K00h3j9rgOVfvSHeODDz44+e53v5vceeedyb333ptcfPHFycCBA5NevXolTz/9dCFeStFKd4xXr16d7LbbbkldXV1yySWXJPX19cnZZ5+dVFVVJV/5ylcK8VKKUqbbie9///tJRCT33HNPPrtdUtId45/+9KdJRCQTJ05M7rrrruSee+5Jzj777KR79+7JIYccUoiXUrTSHeP7778/+eEPf5j88Y9/TP7whz8k3/3ud5PNN988OeKII7ocxCrB7Nmzk1tvvTW59tprk4hIPvvZzya33nprcuuttybvvPNOkiTtj/N7772XjB49Ohk8eHBy4403JvX19ckxxxzjwr7lbuXKlcnpp5+ebLvttkl1dXWy0047JTfffHOrNl/4wheSiEheeeWVlmVDhw5NIqLd29ChQ/P7IopcumP8gx/8INljjz2SPn36JFVVVcnWW2+dHHbYYcnvfve7PL+C4pfuGLdHoGpfumM8derU5OMf/3jSq1evpHv37snAgQOTE044IXn++efz/AqKXyaf4+XLlyennHJK0r9//6RHjx7JDjvskPzoRz9K1q1bl8dXUNwy3U7ssMMOybBhw1pdGJXWMhnj22+/Pdl3332Tvn37JltssUUyevTo5MILL2xzsd9Kl+4YP/LII8m4ceOS2trapKamJhkzZkzy4x//OFm9enWeX0Fp6KjObR7XjX2WlyxZkpx44onJVlttlfTs2TPZa6+9kvr6+vy/iI1IJcn7J4QDAADQJX5VCwAAkCaBCgAAIE0CFQAAQJoEKgAAgDQJVAAAAGkSqAAAANIkUAEAAKRJoAJgk1KpVJduw4YNK3SXN+q4446LVCoVF1544SbbPvTQQ5FKpWLw4MGxfv36Lq/rpJNOilQqFQ888EAaPQWgFHQvdAcAKH5f+MIX2ix7+OGHY8GCBbHzzjvHLrvs0upvffv2zVPPum7y5Mlx6623xo033hjnnntuh21vvPHGiIj4/Oc/H926+TdIANoSqADYpOuuu67NspNOOikWLFgQRx99dJx//vl571O6Dj/88Ojbt288//zz8cQTT8Tuu+/ebrvVq1fHrbfeGhERJ5xwQj67CEAJ8c9tAFSUHj16xOc+97mI+OAIVHtmz54db775Zuyyyy4xZsyYfHUPgBIjUAGQVQ888ECkUqk46aSTYsmSJTFlypQYNGhQdO/ePS6++OKIiDjwwAMjlUrFq6++2ubxr776aqRSqTjwwAPbff7//u//jsMOOyy23nrr6NmzZ+ywww5x7rnnxttvv93pPk6ePDkiIm655ZZYt25du21uuOGGiPjg6NRbb70Vl156aRx22GExdOjQqKmpia233joOP/zwqK+v7/S6I6LD35ldd911kUql2j3qt3r16rjkkktijz32iF69esUWW2wRe+65Z1xzzTWRJEmb9gsXLozTTjstRo4cGZtvvnlstdVWMXr06DjllFPi+eef71KfAWifU/4AyIk33ngj9thjj1i7dm3su+++8d5778Xmm2+e0XN+7Wtfi4suuih69uwZe+65Z/Tt2zfmzZsX3/ve9+IPf/hDPPjgg7HFFlts8nn23HPPGDlyZDz//PNx7733xoQJE1r9fcWKFfH73/8+unXrFscff3xERDz22GNx+umnx+DBg2OHHXaI8ePHR0NDQ9xzzz1xzz33xNVXXx1f+tKXMnp9HXnnnXdi4sSJMWfOnOjbt2/su+++0a1bt3j00UdjypQp8fjjj8cVV1zR0v5vf/tb7LbbbrFs2bLYaaed4sgjj4z33nsvXnvttfj5z38e48ePj5EjR+asvwCVQqACICdmz54dxxxzTNx0003Rs2fPjJ/v17/+dVx00UWx6667xh133NFyhGfNmjXxla98Ja666qo4//zz40c/+lGnnu+EE06Ic889N2644YY2geq2226L9957Lw499NAYOHBgRESMHDkyHnnkkdh7771btZ0/f3584hOfiDPPPDOOO+642HLLLTN+re35xje+EXPmzInJkyfHzJkzW9bzxhtvxJFHHhlXXnllHHnkkXHEEUdERMTVV18dy5Yti5/85Ccxbdq0Vs/12muvxdq1a3PST4BK45Q/AHKipqYmLr300qyEqYiI//zP/4yIiJtvvrnV6XI9evSISy65JLbddtu4+uqrOz29+QknnBCpVCruvPPOePfdd1v9rfm3Vc2nBkZEDB8+vE2YiojYdddd47TTTovGxsa4//77u/qyOmXp0qVx9dVXx/Dhw+PnP/95q9DWr1+/uPLKKyMiWv7b/JiIiE984hNtnm/o0KExYsSInPQVoNI4QgVATuy2226x3XbbZeW5li5dGk899VSMGjWq3dPUevbsGbvvvnv87ne/ixdffLFTp7INGzYs9t1335gzZ0785je/aTm1b9GiRfHggw/G5ptvHsccc0yrx6xbty7uvffemDt3bixZsiTee++9iIh48cUXW/032x588MFYs2ZNHH744VFTU9Pm7zvvvHP06tUrHn/88ZZlY8eOjYiI0047Lb73ve/FfvvtF9272+0DZJstKwA5MWTIkKw912uvvRYREc8991ykUqkO2y5btqzTvw2aPHlyzJkzJ2688caWQHXTTTfF+vXr45hjjml1JOhvf/tbfOpTn4qnnnpqo8+3cuXKTq23q5on77j88svj8ssv32i7f/7zny3/f9JJJ8U999wTv/71r+MTn/hEbL755rH77rvHxIkT40tf+lJss802OekrQKURqADIiXRP9WvvlL3mmfgGDBjQ5vdOG9p66607va7Pfvaz8dWvfjXuvvvueOONN6Jfv35tZvdrNmXKlHjqqafi2GOPjbPPPjtGjhwZvXr1im7dusVVV10Vp5xySrsz7XVVR69/1113jZ122qlTz1NVVRWzZs2Kc845J37zm9/E/fffH4899lg89NBDMWPGjLj77rtjr732yri/AJVOoAIg76qrqyMi2p3qfOHChW2WDRo0KCIitt1223YvMpyu3r17x5FHHhm33XZb/PrXv44DDzww/vd//zf69+8fhx56aEu7d955J+rr66N///7x61//Oqqqqlo9z8svv9yl9fbo0WOj07x39PoPPPDAuOiii7q0rl133TV23XXXOP/886OxsTG++93vxkUXXRRnnHFG/PnPf+7ScwHQlkkpAMi7AQMGRETECy+80OZv99xzT5tlgwYNipEjR8b//u//xiuvvJLVvjRPPHHDDTfEr371q4iIOP7441uFphUrVsT69etjwIABbcLU2rVr48477+zSOgcMGBDLly+Pf/zjH23+1t7rP+igg6Kqqip+97vfbfS6WZ1RW1sb//mf/xmpVCqefvrptJ8HgA8IVADk3QEHHBARET/5yU9azbD3pz/9qeXivxv69re/HevWrYt/+Zd/iWeeeabN3xcsWBDXXnttl/syceLE6Nu3bzz22GNx9dVXR0Tr2f0iIrbZZpuoq6uLZ555Jh555JGW5evWrYuzzjqr3WDYkebXf+GFF7YsS5IkZsyYEXPnzm3TfrvttouTTjopXnzxxZg8eXIsW7asTZu5c+fG7NmzW+7/6le/anec/vjHP0aSJFn9jRtAJROoAMi7448/PkaOHBlz586NUaNGxWc+85kYN25cHHbYYfHv//7v7T7mhBNOiLPOOivmz58fu+yyS+yxxx5x3HHHxeGHHx6jRo2K7bffPn760592uS89evSISZMmRUTE8uXLY9SoUbHbbru1atO9e/c466yzYu3atXHAAQfEhAkT4nOf+1xsv/32ccUVV8Rpp53WpXWeffbZsdlmm8XFF18cu+66a3zmM5+Jj33sY3HhhRdu9PX/9Kc/jYMOOihuvvnm+MhHPhL7779/fO5zn4sDDzwwBg0aFPvss0+ro1u333577LjjjrH99tvHMcccE//6r/8ae++9dxxzzDFRVVXVMg09AJkRqADIu8022yzuvffeOP7442PlypUxe/bsWL9+fcyaNavDcPKDH/wg7r333jjqqKPib3/7W9x1110xf/782HzzzeMb3/hGWkeoIlofkdpwMopm3/zmN+OXv/xl7LTTTvHII4/En/70p9h5553jsccei913371L6xs9enTcd999ceCBB8YLL7wQ9fX1MWLEiHj00Udjjz32aPcxm2++edxzzz1x9dVXx2677RbPPPNM3HnnnbFgwYIYMWJE/PCHP4yvf/3rLe2nTZsWp512WvTq1SvmzJkTd955ZyxdujSOP/74ePzxx+PYY4/tUp8BaF8qycaURAAAABXIESoAAIA0CVQAAABpEqgAAADSJFABAACkSaACAABIk0AFAACQJoEKAAAgTQIVAABAmgQqAACANAlUAAAAaRKoAAAA0iRQAQAApEmgAgAASNP/B7RsYm3FPjnCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "random_forest_regressor_model = RandomForestRegressor(n_estimators = 20, min_samples_split = 2, min_samples_leaf= 1, max_features = 'sqrt', max_depth= 116, bootstrap=True)\n",
    "random_forest_regressor_model.fit(x_train,y_train)\n",
    "\n",
    "prediction = random_forest_regressor_model.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the model \n",
    "import pickle\n",
    "pickle.dump(random_forest_regressor_model, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:  0.9298906886225227\n",
      "Mean absolute error: 0.04\n",
      "Root mean squared error:  0.06467876020023387\n",
      "Adjusted r2_score:  1.7269398502989706\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Unpickling the model and testing the model\n",
    "pickled_model = pickle.load(open('model.pkl', 'rb'))\n",
    "prediction = pickled_model.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"r2_score: \",sklearn.metrics.r2_score(y_test,prediction))\n",
    "print(\"Mean absolute error: %.2f\" % sklearn.metrics.mean_absolute_error(y_test,prediction))# To cut the error in 2 decimal places\n",
    "print(\"Root mean squared error: \", math.sqrt(sklearn.metrics.mean_squared_error(y_test,prediction)))\n",
    "r2 = sklearn.metrics.r2_score(y_test,prediction)\n",
    "Adj_r2 = 1 - (1-r2 * (len(y_test)-1)/(len(y_test)-x_test.shape[1]-1))\n",
    "print(\"Adjusted r2_score: \",Adj_r2)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(y_test,prediction, c='pink')\n",
    "\n",
    "p1 = max(max(prediction), max(y_test))\n",
    "p2 = min(min(prediction), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let me know if you have any suggestion \n",
    "# Email: - rmsrinivas199627@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "52634da84371cba311ea128a5ea7cdc41ff074b781779e754b270ff9f8153cee"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}