diff --git a/notebooks/1.0_DataLoading.ipynb b/notebooks/1.0_DataLoading.ipynb
index 4be0e38..369519d 100644
--- a/notebooks/1.0_DataLoading.ipynb
+++ b/notebooks/1.0_DataLoading.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -216,19 +216,21 @@
        "[5 rows x 37 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "data = pd.read_csv('../data/Meter_A.txt', sep='\\t', header=None).dropna()\n",
-    "data.head()"
+    "data.head()\n",
+    "\n",
+    "#Reading data from csv file"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -246,10 +248,10 @@
        "       [ 0.79672987,  1.01057037,  0.99902897, ..., 33.81673167,\n",
        "        33.01106667,  2.        ],\n",
        "       [ 0.79019427,  1.00419541,  0.99553749, ..., 33.66862167,\n",
-       "        33.11848833,  2.        ]])"
+       "        33.11848833,  2.        ]], shape=(87, 37))"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -257,12 +259,14 @@
    "source": [
     "data = data.to_numpy()\n",
     "\n",
-    "data"
+    "data\n",
+    "\n",
+    "#turning data into a numpy array"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,12 +274,14 @@
     "    data = pd.read_csv(path, sep='\\t', header=None).dropna()\n",
     "    print(data.head())\n",
     "    data = data.to_numpy()\n",
-    "    return data"
+    "    return data\n",
+    "\n",
+    "#loads file into a tab-separated file into a pandas DataFrame, then removes any row with one missing value but it doesn't seem to have removed any "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -321,7 +327,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "practice1",
+   "display_name": "myenv",
    "language": "python",
    "name": "python3"
   },
@@ -335,7 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/3.0_Model_built.ipynb b/notebooks/3.0_Model_built.ipynb
index 35cc720..13f8bb3 100644
--- a/notebooks/3.0_Model_built.ipynb
+++ b/notebooks/3.0_Model_built.ipynb
@@ -27,11 +27,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from src.data_preprocess import DataPreprocessing"
+    "from meter_Hw4.src.data_preprocess_split import DataPreprocessing"
    ]
   },
   {
@@ -658,7 +658,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "practice1",
+   "display_name": "myenv",
    "language": "python",
    "name": "python3"
   },
@@ -672,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/MLPClassifier Further Analysis.ipynb b/notebooks/MLPClassifier Further Analysis.ipynb
new file mode 100644
index 0000000..17cc66e
--- /dev/null
+++ b/notebooks/MLPClassifier Further Analysis.ipynb	
@@ -0,0 +1,485 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b6268d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'c:\\\\Users\\\\ldiaz\\\\OneDrive\\\\Desktop\\\\CHE 4230\\\\meter_Hw4'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "os.chdir(\"../\")\n",
+    "\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8cc31c11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         0         1         2         3          4          5         6   \\\n",
+      "0  0.841499  1.009367  0.993816  8.469805  10.278727  10.037759  8.501365   \n",
+      "1  0.842250  1.006584  0.996605  7.531891   9.139924   8.951618  7.612213   \n",
+      "2  0.840723  1.011647  0.998152  6.641699   7.975464   7.857692  6.593117   \n",
+      "3  0.841119  1.017807  0.996812  5.687524   6.824334   6.689885  5.615428   \n",
+      "4  0.840358  1.016534  0.996221  5.660385   6.829560   6.675628  5.623977   \n",
+      "\n",
+      "         7          8          9   ...         27         28         29  \\\n",
+      "0  8.581726  10.247763  10.058822  ...  32.451173  34.568685  33.082683   \n",
+      "1  7.623325   9.106345   8.945142  ...  32.428385  34.441732  33.081055   \n",
+      "2  6.681572   7.964596   7.814698  ...  32.428385  34.275715  33.113605   \n",
+      "3  5.763315   6.801051   6.686639  ...  32.485350  34.080403  33.170573   \n",
+      "4  5.736818   6.813453   6.672377  ...  32.503255  34.122720  33.164062   \n",
+      "\n",
+      "          30         31         32         33         34         35  36  \n",
+      "0  36.722005  36.969403  36.075847  36.051432  35.174155  32.729490   1  \n",
+      "1  36.687825  36.933595  36.054688  35.979818  34.847005  32.731122   1  \n",
+      "2  36.661785  36.873370  36.002605  35.963542  34.689128  32.771810   1  \n",
+      "3  36.673177  36.811525  35.974935  35.955403  34.500328  32.849935   1  \n",
+      "4  36.673177  36.826173  35.996095  35.968425  34.474283  32.853190   1  \n",
+      "\n",
+      "[5 rows x 37 columns]\n",
+      "Train: {np.float64(1.0), np.float64(2.0)}\n",
+      "Validation: {np.float64(1.0), np.float64(2.0)}\n",
+      "Test: {np.float64(1.0), np.float64(2.0)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from src.data_preprocess import DataPreprocessing\n",
+    "\n",
+    "preprocess = DataPreprocessing()\n",
+    "\n",
+    "data = preprocess.load_data(\"data/Meter_A.txt\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3838c661",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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V2Rx+Nc9GmgO0sazfZf8DQWtJY0k/c9L7aOh5q/m5kLIm0/VJ2Y7pc5+e+xS4T5mNKWtzZZ73xn62NZatmgJ4ywajUz/pHtRkPC6rJrCb+kpzSKY5O9Pcn+l+pYVHfvrTn2bbmpr/FACgOQQGAWhUWjk4LRSQMspSGWdTttpqq+yP3pQRU5OJlbz99ttZcC7tX1UpmJek4N7KZs7VSCuIphLHVHKYAh41alaNXZWgZs17SQuoLCuVJqdgUd1swbaUrtUzzzyTBf2aej8p2JAy9FL2VN2Mv7qll829Ls2VykNTkDYFPepmQaVAVUMaKm1NC6qkTL2mFglJ9ytl06Xns26gpqaMfHWezVW1ss9QCiim535FAfp031N24cp8PlIAOJUmp5auScoiTKuCp+y/moBeKm9Oi2Okls6bPjtpUZIUGEyLpSQpiLui/tL7bOi+NfS+V0V63ynYnDIya4KfDUkL8KRnJQU/6y5+01BJdWPPe02Wb/p5VldzMk7TeNOiM2nhlpUJFqcy7NTSAispWJ5KsNMCPek+AAC0BHMMAtColH2UghPpj9AU4FtWyipKq8om++67b/ZvzeqnNWoy15ZdYbY50vxb6Q/qtLJnClIsK5WRNqYmi6huplpa/TZlqC0rvdeVKSlMmW0pczEFGeoGCVLwJmVY1VyLPKSy0JQFdfXVVy+3L616m8pTG7su6b03FJRL12XZYEhLSGNIQZi6GVdp5dW6KyfXlQLUdeceTKv/pqy2VA7bWNZiku5HWh03rWhcd8XYtCJtyrxKGaVtbWWfoRTITCWkKZCbVgZeVs39S/c9XZ8UZF1WOn96v0kKkteVzl9TIlxTyr3sMekapYBhzf6U8ZZKW1MwMc2V2NTnMb2PlOGWVgCuu7+xjLnm+uIXv5jd+1Ryu2z2bXpd814aet7T1zU/v+qqCeov+8ynIGc6T818jTUa+lnSmHSf0vOeVl5fVrpHNX2mUudl309NtnTdknsAgNUlYxCARqVgXMpSOfTQQ7MswJRpk+YcS4G1xx57LJtIP5XhJWnhhTQHYMowrCnfTcGAFPhIgY3Pfvazq3ylU/AilTOmedG22267LIspzV+WAmApwy1lVKXASUPS4gIp0yeNLS02kQJRv/vd7xos4U0ByBQ8SguofOpTn8oCIimrqiGprDmNJ2VSpkU2UtAtBZrSPHopsyovaS64NIdcWuwkXZuUmZQCESkLLW1PgaO0iEUKptVkjh177LFZwDUFE1PQZ9lgT7ouV155ZTaXXgoQpWNqFjNZHSlYnALHaQ67NHdaml/t8ssvz/pIGX7LSs9eKl1N9zFlGNYEZFJQqCnf/OY3syBWelYnTZoUW2+9dVammeZYTIHslV1gp6Wt7DP04x//OAsWps9Uei/ps5juUfr8TZgwIZvTcOzYsVn2Z8ryTe8z3bMUBE6Zcum9poBrykJMQf5UTpvuX5r/LmW7pT5T0Kkm03fAgAFZ4C+dI2UOpoBkOseJJ55YO6Z0n3bbbbesbH306NFZFmH6jwcpOPnWW29lWas1/3Ehfd7SPT755JOzoFv6GVGTxdkSP6PSc1lRUZG9x/SzJt3PlBF8++23Z9crlTyn0uF0bPo6/dxIPzNSxuqycw0mNQvcpOcsPW8pGHjYYYdl9+WQQw7Jrlf6OZLOl+bIrDtv54qke5g+b5WVlVlJd/ocpszLlFWZ7mcKVH7pS1/Kfm6m5/uggw7K+klzvabPZxp3nv/hAQBYA7XoGscArJFefvnl4ujRo4tbb711sWvXrsUePXoUd9111+Ivf/nL4sKFC2uPW7x4cfGcc84pbrPNNsUuXboUt9hii2JFRUW9Y5KtttqquN9++y3Xz/jx41O0rviHP/yhwXE89dRTxS9+8YvFDTbYoFheXp6d58tf/nLxwQcfrD3m2muvzc7x+uuv12579NFHi5/+9KeLa621VnHTTTctnn766cV77703Oy71WePDDz8sfuUrXymuu+662b50/iSdK71O567rgQceyK5DOm/Pnj2L+++/f/GFF16od8zZZ5+dfe8777xTb3tD42zIUUcdVezevXuj+9M5TjjhhHrbFi1aVLzggguK2223XXad1ltvveKQIUOyezNnzpza4+64447i9ttvX+zWrVt2b9P3XHPNNcuNa+bMmdn9Svc97Rs2bFi99/Cvf/1rpd5zQ+/lt7/9bfGjH/1oNs5+/fpl56z5/obe5w033FB7/Cc+8Yl6968pb7/9dnHUqFHF3r17Z8/woEGDlrufTT2bDVmZY2uenYsuuqjB/SvzDCVvvPFG8cgjjyxuuOGG2Xvfdttts+tRVVVVe8wHH3yQfd769u2bvcf0Xj/zmc8UL7744uyZSP74xz8Whw8fXtxoo42yY7bccsviscceW5wxY0btec4777ziTjvtlH0O0rjSfTn//PNrz1Hjtddey8a0ySabZJ/3zTbbrPiFL3wh66OuZ599Nntm0nOWjjn33HOz+74yz39jz9Ky/vSnPxV322237PlKLY05XZ8pU6bUHpOu6957711cZ511smuTfqY988wzy322lyxZUjzppJOya10oFOo9i2kcBx98cHHttdfOPlfp2j333HPLnWNFn9tf//rX2WcyXd/0uUrPY/q5NH369Gz/5MmTi4cffnh2f9L9TvcrXduJEyc2eR0AAJqrkP4n7+AkAEBTUobWCSecEJdddpkLBQAALcQcgwAAAABQggQGAQAAAKAECQwCAAAAQAkSGAQA2r00JbL5BQEAaC8eeeSR2H///WPTTTfN5sP+85//vMLvefjhh+OTn/xklJeXR9++feO6665b7pjLL788tt566+jWrVvsvPPO8eSTT0ZrEhgEAAAAgGaYN29e7LDDDlkgb2W8/vrrsd9++8VnP/vZePrpp+OUU06Jb3zjG3HvvffWHnPrrbfGmDFj4uyzz47Jkydn5x8xYkTMmjUrWotViQEAAAAoaVVVVVmrK2X2pbYiKWPw9ttvj5EjRzZ6zBlnnBF//etf47nnnqvddthhh8X7778f99xzT/Y6ZQh+6lOfqq2Uqa6uji222CJOOumkOPPMM6M1dI524q9dPp5Lv3vdfkou/b7Zf98273Pj91+KPCwu75FLv8Uo5NJvl0Xzcum3EMVc+l3ctXsu/Zba/X2r53a59At0TFu+/0wu/b5/zdW59Lvh8GG59Fu9/ia59Pufn/0ql363/Urb//6aLN6qX5v32WnBB5GHJeusl0u/C9bunUu/cd2luXTb8/Ofz6XfpS/k87N50f/m5NJvlxEH5tLvvB59cul3k36fyKXf9iiv+M7q+tf3Do9zzjmn3raUuffDH/6wRc7/+OOPx957711vW8oGTJmDyaJFi2LSpElRUVFRu7+srCz7nvS9raXdBAYBAAAAIA8VFRVZGW9dK5MtuLJmzpwZG2+8cb1t6fXcuXNjwYIF8b///S+WLl3a4DEvvdR6iV4CgwAAAACUtPKVLBte0wgMAgAAAEAr2mSTTeLtt9+uty297tmzZ6y11lrRqVOnrDV0TPre1mJVYgAAAABoRbvssks8+OCD9bbdf//92faka9euMWTIkHrHpMVH0uuaY1qDwCAAAAAANMOHH34YTz/9dNaS119/Pft62rRptXMWHnnkkbXHH3fccTF16tQ4/fTTszkDr7jiivj9738fp556au0xaY7Dq6++OsaNGxcvvvhifOtb34p58+bFqFGjorUoJQYAAACAZpg4cWJ89rOfrX1ds3DJUUcdFdddd13MmDGjNkiYbLPNNvHXv/41CwT+/Oc/j8033zx+85vfZCsT1zj00EPjnXfeibPOOitbrGTw4MFxzz33LLcgSUsSGAQAAACAZthjjz2iWCw2uj8FBxv6nqeeeqrJ85544olZaysCgwAAAAC0iEKXgivZgZhjEAAAAABKkMAgAAAAAJQgpcQAAAAAtIiyzkqJ1+jA4LvvvhvXXHNNPP7449kKKckmm2wSn/nMZ+Loo4+ODTfccIXnqKqqylpdi4vV0aUggREAAAAA2kKzInH/+te/4mMf+1j84he/iF69esXuu++etfR12tavX79sueYVqayszL6nbvt99ezVeR8AAAAAQGtlDJ500klxyCGHxFVXXRWFQv3U0LRE83HHHZcdk7IJm1JRURFjxoypt+2h9Yc0ZygAAAAAQFsFBp955pm47rrrlgsKJmnbqaeeGp/4xCdWeJ7y8vKs1aWMGAAAAKBjK3QxTVxH0qy7leYSfPLJJxvdn/ZtvPHGLTEuAAAAAKC9ZAyedtpp8c1vfjMmTZoUe+21V20Q8O23344HH3wwrr766rj44otba6wAAAAAQB6BwRNOOCF69+4dP/vZz+KKK66IpUuXZts7deoUQ4YMycqMv/zlL7fU2AAAAADoQMo6Lz/9HGtIYDA59NBDs7Z48eJ49913s20pWNilS5fWGB8AAAAA0B4CgzVSILBPnz4tOxoAAAAAoE1YKgYAAAAAStAqZwwCAAAAQF2FLuYY7EhkDAIAAABACRIYBAAAAIASJDAIAAAAACWo3cwxuNftp+TS74MHXZpLv3vduqjN+7yo6oTIw+JF1bn0W13MpdvoVJbPfAqdc5rHodTub5fOpTVfRqFQWu+3WMzpwSqhe1tK1zhP3boNz6Xfb+/2VC79fjj5mVz6HffJk3Lp94TjPsil3993+3ou/c6e2fY/Nw558LDIww1Db86l31FPHZNLv9WjxuTS769fGZxLvx/f9ZBc+l2/24Jc+p0wpWcu/R7x2JG59BsX/C6fftuhshL7m6mjkzEIAAAAACVIYBAAAAAASlC7KSUGAAAAoGMr5DTNFatGxiAAAAAAlCCBQQAAAAAoQQKDAAAAAFCCzDEIAAAAQIso62yOwY5ExiAAAAAAlCCBQQAAAAAoQUqJAQAAAGgRhU5KiTsSGYMAAAAAUIJaPDD45ptvxjHHHNPkMVVVVTF37tx6rWrx4pYeCgAAAADQVoHB2bNnx7hx45o8prKyMnr16lWvXfSHe1t6KAAAAABAS80xeMcddzS5f+rUqSs8R0VFRYwZM6betuKD1zV3KAAAAAC0I2XmGFyzA4MjR46MQqEQxWKx0WPS/qaUl5dnra6FXbo0dygAAAAAQFuVEvfp0yduu+22qK6ubrBNnjx5VccCAAAAALTXwOCQIUNi0qRJje5fUTYhAAAAANABS4nHjh0b8+bNa3R/3759Y/z48as7LgAAAAA6mEJZ09PL0cEDg0OHDm1yf/fu3WPYsGGrMyYAAAAAoL2VEgMAAAAAJZgxCAAAAAANKXSSg9aRuFsAAAAAUIIEBgEAAACgBAkMAgAAAEAJMscgAAAAAC2irFPBlexA2k1g8M3+++bS7163Lsql3wcPvaLN+3z34mPavE/aTnWxmMvlLiv4od8W+vfrEXlwe1tfTh9d93YNv78vvvRBLv0uHrpjLv2uvWBBLv2+9da8XPp9aq9Dcun3hQkLc+l34cIlbd5nl7XLIw8zZ87Ppd/qRW1/jZN/Vw3Ipd8338rnZ8bCqm659Nu5c89c+n3zzXx+Ri7N6XmGjkopMQAAAACUoHaTMQgAAABAx1YoU1XWkcgYBAAAAIASJDAIAAAAACVIYBAAAAAASpA5BgEAAABoEWWdzDHYkcgYBAAAAIASJDAIAAAAACVIYBAAAAAASpA5BgEAAABoEQVzDHYouQQGq6qqslbXoqqq6FpensdwAAAAAKDkNLuUeMGCBTFhwoR44YUXltu3cOHCuP7661d4jsrKyujVq1e9dtVVVzR3KAAAAABAWwQGX3755ejfv3/svvvuMWjQoBg2bFjMmDGjdv+cOXNi1KhRKzxPRUVFdmzddtxxx6/aOwAAAACgXSiUlXXIVqqa9c7POOOMGDhwYMyaNSumTJkSPXr0iF133TWmTZvWrE7Ly8ujZ8+e9ZoyYgAAAABop4HBxx57LCsD7t27d/Tt2zfuvPPOGDFiRAwdOjSmTp3aeqMEAAAAAPILDKb5BTt3/r/1SgqFQlx55ZWx//77Z2XFqdQYAAAAAFjDViXu169fTJw4MZtnsK7LLrss+/eAAw5o2dEBAAAA0GEUygp5D4HWyhg86KCD4uabb25wXwoOHn744VEsFptzSgAAAACgvQcG02rCd999d6P7r7jiiqiurm6JcQEAAAAArah012MGAAAAgBLWrDkGAQAAAKAxZZ3MMdiRyBgEAAAAgBIkMAgAAAAAJUgpMQAAAAAtolCmlLgjaTeBwY3ffymXfi+qOiGXft+9+Jg273P4aTu2eZ9Ay9jh2O3zuZSFEvs/9WIxSoZ7u0Zf5y2ueiaXfqc890Qu/d5dvkcu/e593MBc+v3EnWNz6ffD71+aS7+LZi9u8z6PPfauyMM3Th2SS79f/dKNufR7x8Tzc+l36Rl/yKXfDbbvlUu/a2+wdi79/ufu/+bS79cO/l0u/T6YS6+w+pQSAwAAAEAJEhgEAAAAgBLUbkqJAQAAAOjYCmVy0DoSdwsAAAAASpDAIAAAAACUIKXEAAAAALSIQlnBlexAZAwCAAAAQAkSGAQAAACAEiQwCAAAAAAlyByDAAAAALSIsk7mGOxIZAwCAAAAQAnKJWOwqqoqa/W2LVoU5V275jEcAAAAACg5zc4YfPHFF+Paa6+Nl156KXud/v3Wt74VxxxzTDz00EMrdY7Kysro1atXvXbJtbc2f/QAAAAAQOtnDN5zzz1x4IEHxjrrrBPz58+P22+/PY488sjYYYcdorq6OoYPHx733Xdf7Lnnnk2ep6KiIsaMGVNvW9Xzf1+1dwAAAABAu1AoM8fgGpsx+KMf/SjGjh0b7733XpY1+JWvfCVGjx4d999/fzz44IPZvp/85CcrPE95eXn07NmzXlNGDAAAAADtNDD4/PPPx9FHH519/eUvfzk++OCD+NKXvlS7/4gjjohnn3225UcJAAAAAOS7+Eih8P+nhJaVlUW3bt2y+QFr9OjRI+bMmdOyIwQAAACgQyiUNXs5C3LUrLu19dZbxyuvvFL7+vHHH48tt9yy9vW0adOiT58+LTtCAAAAACDfjMG0+vDSpUtrXw8cOLDe/r/97W8rXHgEAAAAAOhggcHjjjuuyf0//vGPV3c8AAAAAEB7nGMQAAAAABpSKPv/16agYzAjJAAAAACUIIFBAAAAAChBSokBAAAAaBFKiTsWGYMAAAAAUILaTcbg4vIe+fS7qDqXfgGao3rJ0lwuWKn9175idTFKhXvrOreGqqVdIg+Ll+Tz2S10yednZLF8rVz67bRWWclc58VVi6OUnqmlS3P6PaO8Wz79dsnnWe5Uns+f35275fOzOS9Lc/q9GToqGYMAAAAAUILaTcYgAAAAAB1bqVWmdHQyBgEAAACgBAkMAgAAAEAJEhgEAAAAgBJkjkEAAAAAWkShTA5aR+JuAQAAAEAJEhgEAAAAgBKklBgAAACAFlHWqeBKllrGYLFYbInTAAAAAAAdKTBYXl4eL774YkucCgAAAADavcsvvzy23nrr6NatW+y8887x5JNPNnrsHnvsEYVCYbm233771R5z9NFHL7f/85//fPspJR4zZkyD25cuXRo/+clPYoMNNsheX3LJJU2ep6qqKmv1ti1aFOVduzZnOAAAAADQ5m699dYsTnbVVVdlQcFLL700RowYEVOmTImNNtpoueNvu+22WLRoUe3r9957L3bYYYc45JBD6h2XAoHXXnttvWS8dhMYTG8yDXrdddddrpQ4ZQx27949i2auSGVlZZxzzjn1to391jFxxvFfb85wAAAAAGhHCmWlMcfgJZdcEqNHj45Ro0Zlr1OA8K9//Wtcc801ceaZZy53/Prrr1/v9S233BJrr732coHBFAjcZJNNoq00KzD44x//OH7961/HT3/609hzzz1rt3fp0iWuu+66GDBgwEqdp6KiYrnsww9fndycoQAAAABAi6hqoLo1BekaythLmX+TJk3K4ls1ysrKYu+9947HH398pfr77W9/G4cddliWZFfXww8/nGUcrrfeelns7bzzzqut0M19jsEU8Uypkt/61rfitNNOi8WLF69Sp+mi9uzZs15TRgwAAABAHiorK6NXr171WtrWkHfffTebVm/jjTeutz29njlz5gr7SnMRPvfcc/GNb3xjuTLi66+/Ph588MG44IIL4u9//3vss88+WV/tImMw+dSnPpVFRU844YTYcccd48Ybb1yp8mEAAAAA1myFshZZ57bNVTRQ3dpa8/ulbMFBgwbFTjvtVG97yiCskfZvv/328ZGPfCTLItxrr71aZSyrdLfWWWedGDduXHbRUppka0YuAQAAAKA1lTdU3dpIYLB3797RqVOnePvtt+ttT69XND/gvHnzsvkFv/71Fa+zse2222Z9vfrqq9FaViuMmyKZEydOzFZW2WqrrVpuVAAAAADQDnXt2jWGDBmSlfzWqK6uzl7vsssuTX7vH/7wh2wuw69+9asr7Oett97KVi/u06dPtJtS4mVtvvnmWQMAAACAUjBmzJg46qijsmn2UknwpZdemmUD1qxSfOSRR8Zmm2223DyFqYx45MiRyy0o8uGHH8Y555wTBx98cJZ1+Nprr8Xpp58effv2jREjRrTfwCAAAAAAJIWy0liH4tBDD4133nknzjrrrGzBkcGDB8c999xTuyDJtGnTspWK65oyZUpMmDAh7rvvvuXOl0qTn3322Wzqvvfffz823XTTGD58eJx77rmtNtdhIjAIAAAAAM104oknZq0hacGQZX384x+PYrHY4PFrrbVW3HvvvdHWOuZSMQAAAADAahEYBAAAAIASpJQYAAAAgBZRKnMMrilkDAIAAABACWo3GYPFyCeiXN3wnI8AAKutmNMvGoUupfV7VV7XOTeFspJ6rmBNUdYpn8+Q7C2gQwQGAQAAAOjYCmWKUzsSdwsAAAAASpDAIAAAAACUIIFBAAAAAChB5hgEAAAAoEVY8KZjkTEIAAAAACVIYBAAAAAASpBSYgAAAABaRKFMDlpH4m4BAAAAQAkSGAQAAACAEpRLKXFVVVXW6m1btCjKu3bNYzgAAAAAUHJWK2Nw3rx5ce2118b3vve9uOyyy+K9995bqe+rrKyMXr161Ws//831qzMUAAAAAPJWKHTMVqKalTE4YMCAmDBhQqy//vrx5ptvxu677x7/+9//4mMf+1i89tprce6558Y///nP2GabbZo8T0VFRYwZM6betg9efWrV3gEAAAAA0LoZgy+99FIsWbKkNri36aabxhtvvBFPPvlk9u/222+fZQ+uSHl5efTs2bNeU0YMAAAAAB2glPjxxx+PH/7wh1kZcLLOOuvEOeeck2UUAgAAAABr2OIjhf9Xd71w4cLo06dPvX2bbbZZvPPOOy03OgAAAAA6jEJZ6c7XVxKBwb322is6d+4cc+fOjSlTpsTAgQNr96Vy4g022KClxwgAAAAA5BkYPPvss+u9TuXDdd15550xdOjQlhkZAAAAANA+A4PLuuiii1Z3PAAAAAB0UIWyVV7Oghy4WwAAAABQggQGAQAAAKAECQwCAAAAQAlq9qrEAAAAANCQQlnBhelAZAwCAAAAQAlqNxmDXRbNy6XfTiLZQAeQ1391K70VxaqjVLi3a/Z1Li4u5tJvt05Lcum3c+euJXWdC1Xzc+l36YLqkrnOnbt0LqlnqlOnTrn0W1xUlU+/i/N5lpdU5fMzcsnCxVFKSu93HFg9PjEAAAAAUILaTcYgAAAAAB2brM2ORcYgAAAAAJQggUEAAAAAKEFKiQEAAADo0AsnsmpkDAIAAABACRIYBAAAAIASJDAIAAAAACXIHIMAAAAAtAhzDHYsMgYBAAAAoAQJDAIAAABACcqllLiqqipr9bYtWhTlXbvmMRwAAAAAWkKZHLSOpFl3a/LkyfH666/Xvv7d734Xu+66a2yxxRax2267xS233LJS56msrIxevXrVa5dcu3LfCwAAAAC0cWBw1KhR8dprr2Vf/+Y3v4ljjz02dtxxx/je974Xn/rUp2L06NFxzTXXrPA8FRUVMWfOnHptzKjDVv1dAAAAAACtV0r8yiuvxEc/+tHs6yuuuCJ+/vOfZ8HAGik4eP7558cxxxzT5HnKy8uzVldRGTEAAAAAtM/A4Nprrx3vvvtubLXVVvHf//43dtppp3r7d95553qlxgAAAACUjkKhkPcQaK1S4n322SeuvPLK7Othw4bFH//4x3r7f//730ffvn2bc0oAAAAAoL1nDF5wwQXZYiMpKJjmFvzpT38aDz/8cPTv3z+mTJkS//znP+P2229vvdECAAAAAG2fMbjpppvGU089Fbvsskvcc889USwW48knn4z77rsvNt9883j00Udj3333bZmRAQAAAADtI2MwWXfddeMnP/lJ1gAAAACgRqGsWTlo5MzdAgAAAIASJDAIAAAAACWo2aXEAAAAANCQQlnBhelAZAwCAAAAQAkSGAQAAACAEtRuSokLUcyl385d8klxrS7m836Bjim3lb0KpVUGUFIrqLm3tMpjlc/vN6X00c3k9HtkcXHp/P6qDK6NlNjfRMWlOX12q0vrOpcpY4WOGRgEAAAAoIMruf9i17G5WwAAAABQggQGAQAAAKAEKSUGAAAAoEWYp7VjkTEIAAAAACVIYBAAAAAASpDAIAAAAACUIHMMAgAAANAiCgU5aB2JuwUAAAAAJUhgEAAAAABKkMAgAAAAAJSgZs0xeNJJJ8WXv/zlGDp06Gp1WlVVlbV62xYtivKuXVfrvAAAAADkqKzg8q+pGYOXX3557LHHHvGxj30sLrjggpg5c+YqdVpZWRm9evWq1y659pZVOhcAAAAA0AalxPfdd1/su+++cfHFF8eWW24ZBx54YNx1111RXV290ueoqKiIOXPm1GtjRh3W3KEAAAAAAG0VGBw0aFBceumlMX369LjhhhuykuCRI0fGFltsEd/73vfi1VdfXeE5ysvLo2fPnvWaMmIAAACAjq1QVtYhW6la5XfepUuXbL7Be+65J6ZOnRqjR4+OG2+8MT7+8Y+37AgBAAAAgBbXIiHRVFL8wx/+MF5//fUsUAgAAAAArEGBwa222io6derU6P5CoRCf+9znWmJcAAAAAEAr6tycg1NGIAAAAAA0pFBWcGE6kNKdXREAAAAASpjAIAAAAACUoGaVEgMAAABAowpy0DoSdwsAAAAASpDAIAAAAACUIIFBAAAAAChB7WaOwcVdu+fT76LqXPotK1i+G1h51UuW5nK5CmWl9bOqWF2MUuHeus6toWppl8jD4iX5fHYLXfL5GVksXyuXfjutVVYy13lx1eIopWdq6dKcfs8o75ZPv13yeZY7lefz53fnbvn8bM7L0px+b6Z0f8/s6GQMAgAAAEAJEhgEAAAAgBIkMAgAAAAAJajdzDEIAAAAQAdXJgetI3G3AAAAAKAECQwCAAAAQAlSSgwAAABAiygUCq5kByJjEAAAAABKkMAgAAAAAJQggUEAAAAAKEG5zDFYVVWVtXrbFi2K8q5d8xgOAAAAAC2hTA5aR9Lsu3XZZZfFkUceGbfcckv2+ne/+10MGDAg+vXrF9/97ndjyZIlKzxHZWVl9OrVq1679De/W7V3AAAAAAC0bsbgeeedFxdeeGEMHz48Tj311HjjjTfioosuyr4uKyuLn/3sZ9GlS5c455xzmjxPRUVFjBkzpt62D1+d3PzRAwAAAACtHxi87rrrsvbFL34xnnnmmRgyZEiMGzcujjjiiGx/yho8/fTTVxgYLC8vz1pdi5URAwAAAHRohbJC3kOgtUqJp0+fHjvuuGP29Q477JBlCQ4ePLh2/yc/+cnsGAAAAABgDQoMbrLJJvHCCy9kX7/yyiuxdOnS2tfJ888/HxtttFHLjxIAAAAAyK+UOJUMp4VHDjzwwHjwwQezsuHTTjst3nvvvSgUCnH++efHl770pZYdIQAAAACQb2AwzR241lprxeOPPx6jR4+OM888MyspTgHC+fPnx/777x/nnntuy48SAAAAgPav0KziVDpSYDDNKfjd73633rbDDjssawAAAABAxyGMCwAAAAAlSGAQAAAAAEpQs0qJAQAAAKBRZQUXpwORMQgAAAAAJUhgEAAAAABKULspJS5GPqmm1cVcugUASkAxp180Cl1K6/eqvK5zbgplJfVcwZqirFM+n6GCsk7a+pnL6f+nWDXuFgAAAAA00+WXXx5bb711dOvWLXbeeed48sknGz32uuuui0KhUK+l76urWCzGWWedFX369Im11lor9t5773jllVeiNQkMAgAAAEAz3HrrrTFmzJg4++yzY/LkybHDDjvEiBEjYtasWY1+T8+ePWPGjBm17Y033qi3/8ILL4xf/OIXcdVVV8UTTzwR3bt3z865cOHCaC0CgwAAAACUtKqqqpg7d269lrY15pJLLonRo0fHqFGjYsCAAVkwb+21145rrrmm0e9JWYKbbLJJbdt4443rZQteeuml8f3vfz8OPPDA2H777eP666+P6dOnx5///OdoLQKDAAAAALRQpKnQIVtlZWX06tWrXkvbGrJo0aKYNGlSVupb+7bLyrLXjz/+eKOX5sMPP4ytttoqtthiiyz49/zzz9fue/3112PmzJn1zpnGkEqUmzrn6hIYBAAAAKCkVVRUxJw5c+q1tK0h7777bixdurRexl+SXqfgXkM+/vGPZ9mEf/nLX+KGG26I6urq+MxnPhNvvfVWtr/m+5pzzjVqVWIAAAAAyEN5eXnWWssuu+yStRopKNi/f//41a9+Feeee27kRcYgAAAAAKyk3r17R6dOneLtt9+utz29TnMHrowuXbrEJz7xiXj11Vez1zXftzrnXBUCgwAAAAC0iEJZWYdszdG1a9cYMmRIPPjgg7XbUmlwel03K7ApqRT53//+d/Tp0yd7vc0222QBwLrnTAugpNWJV/acq0IpMQAAAAA0w5gxY+Koo46KHXfcMXbaaadsReF58+ZlqxQnRx55ZGy22Wa1C5j86Ec/ik9/+tPRt2/feP/99+Oiiy6KN954I77xjW/Urlh8yimnxHnnnRcf/ehHs0DhD37wg9h0001j5MiR0VoEBgEAAACgGQ499NB455134qyzzsoWBxk8eHDcc889tYuHTJs2LVupuMb//ve/GD16dHbseuutl2UcPvbYYzFgwIDaY04//fQsuPjNb34zCx7utttu2Tm7desWrUVgEAAAAICWUSiUzJU88cQTs9aQhx9+uN7rn/3sZ1lrSsoaTJmFqbWVZgcGZ8yYEVdeeWVMmDAh+zpFP7fddtssrfHoo4/OJl9ckaqqqqzV27ZoUZR37drc4QAAAAAAq6BZsytOnDgxW0r57rvvjsWLF8crr7ySpT527949TjvttNh9993jgw8+WOF5Un11r1696rWf/+b6VRk/AAAAANDagcE0CeKpp56aBQj/8Y9/xHXXXRcvv/xy3HLLLTF16tSYP39+fP/731/heSoqKmLOnDn12snfOHJVxg8AAAAAtHZgcPLkyfG1r32t9vVXvvKVbNvbb7+dTZx44YUXxh//+McVnqe8vDx69uxZrykjBgAAAOjg0oIbHbGVqGa984022iibV7BGCgguWbIkC+wlaTnl2bNnt/woAQAAAID8AoNpgZHjjjsuWyp5/PjxccQRR8SwYcNirbXWyvZPmTIlNttss5YdIQAAAACQ76rE5513XpYxuP/++8fSpUtjl112iRtuuKHessppYREAAAAASlChkPcIaK3A4DrrrBO33nprLFy4MCshTq/rGj58eHNOBwAAAAB0hMBgjW7durX8SAAAAACANlO6y64AAAAAQAlbpYxBAAAAAFhWoUwOWkfibgEAAABACRIYBAAAAIASJDAIAAAAACWo3cwx2GXRvHz67VzIpV+A5iiU5fOzqvTmB6mOUuHertnXubi4mEu/a3VenEu/Xbt2LanrXKian0u/SxdUl8x17tylc0k9U506dcql3+Kiqnz6XZzPs7ykakk+/S7M52dzXkrvd5x2qOAedCTuFgAAAACUIIFBAAAAAChB7aaUGAAAAIAOLqdpkFg1MgYBAAAAoAQJDAIAAABACRIYBAAAAIASJDAIAAAAACVIYBAAAAAAStAqrUq8aNGi+POf/xyPP/54zJw5M9u2ySabxGc+85k48MADo2vXri09TgAAAAAgz8Dgq6++GiNGjIjp06fHzjvvHBtvvHG2/amnnoqrrroqNt988/jb3/4Wffv2bclxAgAAANDOFQqKU9fowOC3vvWtGDRoUBYI7NmzZ719c+fOjSOPPDJOOOGEuPfee1tynAAAAABAnoHBRx99NJ588snlgoJJ2nbuuedmmYRNqaqqylq9bYsWRbkSZAAAAABoE83O71x33XXjP//5T6P70750TFMqKyujV69e9dol197S3KEAAAAAAG2VMfiNb3wjKxf+wQ9+EHvttVftHINvv/12PPjgg3HeeefFSSed1OQ5KioqYsyYMfW2LXxhQnOHAgAAAEB7UlbIewS0ZmDwRz/6UXTv3j0uuuii+M53vhOFwv9/w4vFYrYy8RlnnBGnn356k+coLy/PWl1FZcQAAAAA0H4Dg0kK/qX2+uuvx8yZM7NtKSi4zTbbtPT4AAAAAIBWsFprSKdA4C677JK1mqDgm2++Gcccc0xLjQ8AAAAAaG+BwYbMnj07xo0b19KnBQAAAKC9K5R1zFaiml1KfMcddzS5f+rUqaszHgAAAACgPQYGR44cmS04khYbaUzNgiQAAAAAQPvU7FzJPn36xG233RbV1dUNtsmTJ7fOSAEAAABo31KyWEdsJarZgcEhQ4bEpEmTGt2/omxCAAAAAKADlhKPHTs25s2b1+j+vn37xvjx41d3XAAAAABAewoMDh06tMn93bt3j2HDhq3OmAAAAACA9hYYBAAAAIAGlTV71jpy1G4Cg2/13C5KSf9+Pdq8zx2O3T7yUL1kaZSSQlk+k5YWcvrh6/62jWevfq6NegLWBIOPH5xLv68sLs+l3x49OpfUdX6j7/Bc+v3UyU/n0u/8me+1eZ8bbrBB5GH70QNz6bdf34/m0u+iT+RTabbD6Fdy6XfdwQNy6bfQba1c+l2/71O59Pvx3h/LpV/oqIRxAQAAAKAEtZuMQQAAAAA6uIIctI7E3QIAAACAEiQwCAAAAAAlSGAQAAAAAEqQOQYBAAAAaBllBVeyA5ExCAAAAAAlqMUDg2+//Xb86Ec/aunTAgAAAADtOTA4c+bMOOecc1r6tAAAAABAnnMMPvvss03unzJlyuqMBwAAAICOqmDWujU6MDh48OAoFApRLBaX21ezPf3blKqqqqzVtaiqKrqWlzd3OAAAAADAKmh2GHf99dePq6++Ol5//fXl2tSpU+Ouu+5a4TkqKyujV69e9drVv/rlqowfAAAAAGiLjMEhQ4bE9OnTY6uttmpw//vvv99gNmFdFRUVMWbMmHrbXntzdnOHAgAAAEB7soIqUjp4YPC4446LefPmNbp/yy23jGuvvbbJc5SXl2etrq7ljZ8TAAAAAMg5MHjQQQc1uX+99daLo446anXGBAAAAAC0shZfKubNN9+MY445pqVPCwAAAAC058Dg7NmzY9y4cS19WgAAAADau7KyjtlKVLNLie+4444m96eViQEAAACANSwwOHLkyCgUCk2uPJz2AwAAAADtV7NzJfv06RO33XZbVFdXN9gmT57cOiMFAAAAAPILDA4ZMiQmTZrU6P4VZRMCAAAAsIZKVaQdsZWoZpcSjx07NubNm9fo/r59+8b48eNXd1wAAAAAQHsKDA4dOrTJ/d27d49hw4atzpgAAAAAgPYWGAQAAACABhWaPWsdOSr5wGBeKyjn0m1e77WstGr1C2U5/RB0f9fs+wvQDH5WtY2Su845/aGXx3Uuy+n317yeqdx+Xy+hZ+r/dZxPvyX2XJXa35+wukrstxkAAAAAIBEYBAAAAIASVPKlxAAAAAC0kFKbaqODc7cAAAAAoAQJDAIAAABACVJKDAAAAEDLKFgZuiORMQgAAAAAJUhgEAAAAABK0CoHBt9666348MMPl9u+ePHieOSRR1Z3XAAAAABAewoMzpgxI3baaafYaqutYt11140jjzyyXoBw9uzZ8dnPfralxwkAAABAe1co65itRDX7nZ955plRVlYWTzzxRNxzzz3xwgsvZIHA//3vf7XHFIvFlh4nAAAAAJDnqsQPPPBA3H777bHjjjtmrx999NE45JBDYs8994wHH3ww21ZYwQo0VVVVWatrUVVVdC0vb+5wAAAAAIC2yBicM2dOrLfeerWvy8vL47bbboutt946yxycNWvWCs9RWVkZvXr1qteu/tUvmz96AAAAAKBtAoPbbrttPPvss/W2de7cOf7whz9k+77whS+s8BwVFRVZgLFuG33sSc0dCgAAAADtSaoi7YitRDU7MLjPPvvEr3/96+W21wQHBw8evMI5BlOWYc+ePes1ZcQAAAAA0I7nGDz//PNj/vz5DZ+sc+f405/+FP/9739bYmwAAAAAQHvJGEzBv5Th15gZM2bEOeecs7rjAgAAAKCjKSvrmK1Etfg7nz17dowbN66lTwsAAAAA5FlKfMcddzS5f+rUqaszHgAAAACgPQYGR44cGYVCockFRtJ+AAAAAGANKiXu06dP3HbbbVFdXd1gmzx5cuuMFAAAAIB2rZiSyTpgK1XNDgwOGTIkJk2a1Oj+FWUTAgAAAAAdsJR47NixMW/evEb39+3bN8aPH7+64wIAAAAA2lNgcOjQoU3u7969ewwbNmx1xgQAAABAR1RodnEqOXK3AAAAAKAENTtjEAAAAGimYrVLBrQ7MgYBAAAAoATJGAQAAACgZZhjsEORMQgAAAAAJUhgEAAAAABKkMAgAAAAAJQgcwwCAAAA0CKKhYIr2YHIGAQAAACAEiQwCAAAAAAlaJVKid9777149tlnY4cddoj1118/3n333fjtb38bVVVVccghh0T//v1bfqQAAAAAtG8FOWhrdGDwySefjOHDh8fcuXNj3XXXjfvvvz8LBnbu3Dmqq6vjJz/5SUyYMCE++clPts6IAQAAAIDV1uww7ve+970sEDhnzpz47ne/GyNHjoy99torXn755Xj11VfjsMMOi3PPPXf1RwYAAAAAtJ/A4KRJk2LMmDHRo0ePOPnkk2P69OkxevTo2v0nnnhi/Otf/2ryHKnkOGUc1m2LqqpW7R0AAAAAAK0fGFy0aFGstdZa2dddunSJtddeO3r37l27P32d5iBsSmVlZfTq1ateu/pXv2z+6AEAAABoPwqFjtlKVLMDg1tssUVMnTq19vUtt9wSffr0qX09Y8aMeoHChlRUVGSlyHXb6GNPau5QAAAAAIC2WnwkzSE4a9as2tf77bdfvf133HFH7LTTTk2eo7y8PGt1dS2f19yhAAAAAABtlTF49tlnZ8HBphYnuemmm1Z1PAAAAAB0VGVlHbOtgssvvzy23nrr6NatW+y8887x5JNPNnrs1VdfHUOHDo311lsva3vvvfdyxx999NFRKBTqtc9//vPRmlbtnTchzS/4rW99q6VPCwAAAADtwq233potzpsS6CZPnhw77LBDjBgxol6VbV0PP/xwHH744TF+/Ph4/PHHs6n6hg8fHv/973/rHZcCgWmavpp28803d6zA4OzZs2PcuHEtfVoAAAAAaBVVVVUxd+7cei1ta8wll1wSo0ePjlGjRsWAAQPiqquuyhboveaaaxo8/sYbb4zjjz8+Bg8eHP369Yvf/OY3UV1dHQ8++GC949LUe5tsskltS9mF7WqOwTSHYFPqLkwCAAAAAO1dZWVlnHPOOfW2pWzAH/7wh8sdu2jRopg0aVK2uG6NsrKyrDw4ZQOujPnz58fixYtj/fXXXy6zcKONNsoCgnvuuWecd955scEGG0S7CQyOHDkyq3EuFouNHpP2AwAAAFBaih00JlRRUZGVBte17MK5Nd59991YunRpbLzxxvW2p9cvvfTSSvV3xhlnxKabbpoFE+uWEX/xi1+MbbbZJl577bX47ne/G/vss08WbOzUqVO0i8Bgnz594oorrogDDzywwf1PP/10DBkypCXGBgAAAACtrry8vNFAYEv7yU9+ErfcckuWHZgWLqlRd7HfQYMGxfbbbx8f+chHsuP22muv9jHHYAr6pXTJxqwomxAAAAAAOqrevXtnGXxvv/12ve3pdZoXsCkXX3xxFhi87777ssBfU7bddtusr1dffTVaS7MDg2PHjo3PfOYzje7v27dvtsIKAAAAAKxpunbtmiXO1V04pGYhkV122aXR77vwwgvj3HPPjXvuuSd23HHHFfbz1ltvxXvvvZdV77aWZpcSDx06tMn93bt3j2HDhq3OmAAAAADoiArNzkHrkMaMGRNHHXVUFuDbaaed4tJLL4158+ZlqxQnRx55ZGy22WbZoibJBRdcEGeddVbcdNNNsfXWW8fMmTOz7euss07WPvzww2zxk4MPPjjLOkxzDJ5++ulZAt6IESPaT2AQAAAAAErZoYceGu+8804W7EtBvsGDB2eZgDULkkybNi1bqbjGlVdema1m/KUvfanBlY9TafKzzz4b48aNi/fffz9bmGT48OFZhmFrzn1Y8oHBkpoPMaf3WqwuoWucqc6l10KdHzhtyf0FaH+K1fn8f1GpKbnrXKwumetcndPvr3k9U9VLSutZzus659VvIbf36+9P1nwnnnhi1hqSFgyp6z//+U+T51prrbXi3nvvjbZW8oFBAAAAAFpGsURKidcU7hYAAAAAlCCBQQAAAAAoQQKDAAAAAFCCzDEIAAAAQMsoFFzJDkTGIAAAAACUIIFBAAAAAChBLRYY3HbbbeOVV15pqdMBAAAA0MEUC2UdspWqZs8x+Itf/KLB7dOmTYtrr702Ntlkk+z1t7/97dUfHQAAAADQPgKDp5xySmy22WbRuXP9b62uro7rr78+unTpEoVCQWAQAAAAANakwOA3v/nNeOKJJ+Kmm26K/v37125PAcH77rsvBgwYsMJzVFVVZa2uRVVV0bW8vLnDAQAAAABWQbOLqK+66qo466yzYsSIEXHZZZetSp9RWVkZvXr1qteu/tUvV+lcAAAAALQThULHbCVqlWZXPOigg+Lxxx+P22+/PfbZZ5+YOXNms76/oqIi5syZU6+NPvakVRkKAAAAALAKVnnZlTTP4AMPPBC77757fOITn4hisbjS31teXh49e/as15QRAwAAAEA7nmOwrrTISMr+Gz58eEyYMCH69OnTciMDAAAAANpfxmBdQ4YMiZNPPjnWW2+9ePPNN+OYY45pidMCAAAA0JEUyjpmK1Et/s5nz54d48aNa+nTAgAAAAB5lhLfcccdTe6fOnXq6owHAAAAAGiPgcGRI0dmcws2tdhI2g8AAABAaSmKCa3ZpcRpgZHbbrstqqurG2yTJ09unZECAAAAAPkFBtNCI5MmTWp0/4qyCQEAAACADlhKPHbs2Jg3b16j+/v27Rvjx49f3XEBAAAAAO0pMDh06NAm93fv3j2GDRu2OmMCAAAAoCMqNLs4lRy5WwAAAABQgpqdMUjLMA0jAADQHhTKCvl0XKyOUlIok5cDtD9+MgEAAABACZIxCAAAAECLKEZOWcisEhmDAAAAAFCCBAYBAAAAoAQpJQYAAACgRRQLctA6EncLAAAAAEqQwCAAAAAAlCCBQQAAAAAoQas9x2CxWIyHH344Xn311ejTp0+MGDEiunTp0jKjAwAAAKDjMMfgmh0Y3HfffePmm2+OXr16xezZs7PXTz75ZPTu3Tvee++9+NjHPhaPPPJIbLjhhq0zYgAAAACg7UuJ77nnnqiqqsq+/v73vx8ffPBBvPbaazFr1qx44403onv37nHWWWet/sgAAAAAgPZZSvzQQw/FhRdeGNtss032evPNN48LLrggRo8e3eT3pcBiTXCxxqKqquhaXr46wwEAAAAgR8VCwfVf0xcfKfy/m/y///0vPvKRj9Tb17dv35g+fXqT319ZWZmVItdtV//ql6syFAAAAACgrTIGjz766CgvL4/FixfH66+/Htttt13tvpkzZ8a6667b5PdXVFTEmDFj6m177c3ZqzIUAAAAAKAtAoNHHXVU7dcHHnhgzJ8/v97+P/3pTzF48OAmz5GCiqnV1bV8XnOHAgAAAAC0VWDw2muvbXL/2WefHZ06dVrV8QAAAADQQRULqzRrHTlp8bs1e/bsOP7441v6tAAAAABAew8Mjhs3rqVPCwAAAADkWUp8xx13NLl/6tSpqzMeAAAAAKA9BgZHjhwZhUIhisVio8ek/QAAAACUGDGhNbuUuE+fPnHbbbdFdXV1g23y5MmtM1IAAAAAIL/A4JAhQ2LSpEmN7l9RNiEAAAAA0AFLiceOHRvz5s1rdH/fvn1j/PjxqzsuAAAAADqYYqHF17mlPQUGhw4d2uT+7t27x7Bhw1ZnTAAAAABAKxPGBQAAAIAS1OyMwTVNXiso59JtXu+1rLRWqS6U5RRvd3/X7PsL0Ax+VrWNkrvOOZWG5XGdy3L6/TWvZyq339dL6Jn6fx3n02+JPVel9vcnrK6SDwwCAAAA0DKKITjbkZTYf+YEAAAAABKBQQAAAAAoQUqJAQAAAGgRxbzm02SVuFsAAAAAUIIEBgEAAACgBAkMAgAAAEAJMscgAAAAAC2jUHAl1+SMwbfeeivefffd2tf/+Mc/4ogjjoihQ4fGV7/61Xj88cdbeowAAAAAQN6BwYMPPjj++c9/Zl//5S9/iT322CM+/PDD2HXXXWP+/PkxbNiwuOuuu1p6nAAAAABAnqXEzz//fGy33XbZ15WVlfHjH/84zjjjjNr9l112WZx11lnxhS98oSXHCQAAAADkGRjs3LlzfPDBB9nXr7/+euyzzz719qfXdQOFDamqqspaXYuqqqJreXlzhwMAAABAO1G0zu2aXUqcSoVvvvnm7OtPfOIT8fDDD9fbP378+Nhss82aPEfKNOzVq1e9dvWvftncoQAAAAAAbZUx+JOf/CRbaGT69Omx2267xfe+973417/+Ff37948pU6bErbfeGldddVWT56ioqIgxY8bU2/bam7ObP3oAAAAAoG0CgykA+MQTT8T3v//9uPDCC2PevHlx4403ZiXGn/rUp+KWW26JkSNHNnmO8vLyrNXVtXxe80cPAAAAQLtRLBTyHgKtGRhMPvKRj2TlxMViMWbNmhXV1dXRu3fv6NKly6qcDgAAAABo73MM1lUoFGLjjTeOPn361AYF33zzzTjmmGNaanwAAAAAQHsLDDZk9uzZMW7cuJY+LQAAAACQZynxHXfc0eT+qVOnrs54AAAAAOigioUWz0GjPQUG08IiqYQ4zS/YmLQfAAAAAGi/mh3GTfMJ3nbbbdmCIw21yZMnt85IAQAAAID8AoNDhgyJSZMmNbp/RdmEAAAAAKyZilHokK1UNbuUeOzYsTFv3rxG9/ft2zfGjx+/uuMCAAAAANpTYHDo0KFN7u/evXsMGzZsdcYEAAAAALQyS8UAAAAAQAlqdsbgmsZ8iAAAAAAtFGcpyEHrSNwtAAAAAChBAoMAAAAAUIIEBgEAAACgBJX8HIMAAAAAtIxioeBSdiAyBgEAAACgBAkMAgAAAEAJUkoMAAAAQIsohlLiNTpj8Kc//Wm88cYbrTMaAAAAAKB9BgbHjh0bH/nIR+Jzn/tc3HrrrbFo0aLWGRkAAAAA0L7mGPzNb34T3bt3j6997Wux6aabximnnBLPPfdcy48OAAAAAGg/gcF99903/vznP8dbb70Vp59+etx7772xww47xE477RRXX311fPDBB01+f1VVVcydO7deW1RVtarvAQAAAIB2oFgo65CtVK3WO99oo42ywOCLL74YDz/8cAwYMCBOPfXU6NOnT5PfV1lZGb169arXrv7VL1dnKAAAAABAawYGC4WGV5cZOnRoXHfddTF9+vT42c9+1uQ5KioqYs6cOfXa6GNPau5QAAAAAIBV1Lm531AsFpvc37Nnzxg9enSTx5SXl2etrq7l85o7FAAAAADakWI0nFDGGhIYrK6ubp2RAAAAAABtpsVnV3zzzTfjmGOOaenTAgAAAADtOTA4e/bsGDduXEufFgAAAADIs5T4jjvuaHL/1KlTV2c8AAAAAHRQxUKL56DRngKDI0eOzFYmbmoRksZWLgYAAAAA2odmh3H79OkTt912W7YISUNt8uTJrTNSAAAAACC/wOCQIUNi0qRJje5fUTYhAAAAANABS4nHjh0b8+bNa3R/3759Y/z48as7LgAAAAA6mGKYXm6NDgwOHTq0yf3du3ePYcOGrc6YAAAAAIBWZqkYAAAAAChBzc4YpAPLae7HYnWpzTlZnUuvhbJ84vzuL0D7U6zO5/+LSk3JXedidclc5+qcfn/N65kqtd/ncvvs5vQZihJ7rkrteW6PigU5aB2JuwUAAAAAJUhgEAAAAABKkMAgAAAAAJQgcwwCAAAA0CKKUXAlOxAZgwAAAADQTJdffnlsvfXW0a1bt9h5553jySefbPL4P/zhD9GvX7/s+EGDBsXdd99db3+xWIyzzjor+vTpE2uttVbsvffe8corr0RrEhgEAAAAgGa49dZbY8yYMXH22WfH5MmTY4cddogRI0bErFmzGjz+sccei8MPPzy+/vWvx1NPPRUjR47M2nPPPVd7zIUXXhi/+MUv4qqrroonnngiunfvnp1z4cKF0VoEBgEAAACgGS655JIYPXp0jBo1KgYMGJAF89Zee+245pprGjz+5z//eXz+85+PsWPHRv/+/ePcc8+NT37yk3HZZZfVZgteeuml8f3vfz8OPPDA2H777eP666+P6dOnx5///OdoLQKDAAAAALSIYqHQIVtVVVXMnTu3XkvbGrJo0aKYNGlSVupbG2ArK8teP/744w1+T9pe9/gkZQPWHP/666/HzJkz6x3Tq1evrES5sXPmFhi86667sprnRx99NHv90EMPxb777ptFPn/961+39BgBAAAAoNVUVlZmgbi6LW1ryLvvvhtLly6NjTfeuN729DoF9xqStjd1fM2/zTlnLoHBX/3qV3HQQQdlEySmYOANN9yQ1URvttlm2YSLp5xySpYeCQAAAAAdQUVFRcyZM6deS9vWdJ2b+w1pEsQrrrgiq6MeP358Fhz86U9/Gscff3y2/9Of/nQ2WeLJJ5/cGuMFAAAAoJ0qFgvREZWXl2dtZfTu3Ts6deoUb7/9dr3t6fUmm2zS4Pek7U0dX/Nv2pZWJa57zODBg6PdZAymmudUA5189rOfzVInd99999r9e+yxR7zxxhtNnqOhuu1FjdRtAwAAAEB70bVr1xgyZEg8+OCDtduqq6uz17vsskuD35O21z0+uf/++2uP32abbbLgYN1jUrwsrU7c2DlzCQxusMEGtYG/tDLKkiVLYtq0abX7077111+/2XXbV//ql6syfgAAAABoU2PGjImrr746xo0bFy+++GJ861vfinnz5mWrFCdHHnlkvVLkVFl7zz33ZFW3L730Uvzwhz+MiRMnxoknnpjtLxQK2fR85513Xtxxxx3x73//OzvHpptumk3h125KidOSyV//+tfjqKOOygaaBvmd73wnW30lvYm07PLw4cObPEe6MOkC1vXam7ObP3oAAAAAaGOHHnpovPPOO9nivGlxkFTumwJ/NYuHpCS6FCur8ZnPfCZuuumm+P73vx/f/e5346Mf/Wj8+c9/joEDB9Yec/rpp2fBxW9+85vx/vvvx2677Zads1u3bu0nMHjBBRdkyzLfcsst2Zv65S9/mc07mAKGixcvjmHDhjW6aktTddtdy+c1f/QAAAAAtBvF5hendlgnnnhibcbfsh5++OHlth1yyCFZa0xKuPvRj36UtbbS7MBg9+7d49e//nW9baeddlp2IVJgsEePHi05PgAAAACgFbRYGDelNaag4JtvvhnHHHNMS50WAAAAAGgFLZ7fOXv27GziRQAAAABKSzEKHbKVqmaXEqcFR5oyderU1RkPAAAAANAeA4NpieQ0GWKxWGz0mLQfAAAAAFiDSon79OkTt912W1RXVzfYJk+e3DojBQAAAADyCwwOGTIkJk2a1Oj+FWUTAgAAALBmynuuQHMMtnIp8dixY2PevHmN7u/bt2+MHz++uacFAAAAANpzYHDo0KFN7u/evXsMGzZsdcYEAAAAALS3UmIAAAAAoAQzBmkZuUzDmNNq0YWyfPotVucz12WhrLTi7e4vALCm/W5VltPvr7SNUvt9vdT+TiB/aY4/Og4/EQEAAACgBAkMAgAAAEAJUkoMAAAAQItQStyxyBgEAAAAgBIkMAgAAAAAJUhgEAAAAABKkDkGAQAAAGgRxWLBlexAZAwCAAAAQAlapYzBBQsWxM033xwTJkyIGTNmRFlZWWy77bYxcuTI2GuvvVp+lAAAAABAvoHBV199Nfbee+8sOFheXh5vvfVW7LvvvvGvf/0rrrzyyvjiF78YN910U3TurEoZAAAAoJQUQynxGl1K/O1vfzs+//nPx8yZM2PatGlRWVkZ1dXV8c9//jNefPHFLEB43nnntc5oAQAAAIB8AoN///vf4zvf+U4UCv9/BPjUU0+NBx54IN5777346Ec/GpdeemmMGzeuyXNUVVXF3Llz67VFVVWr/i4AAAAAgNYNDK677rrxwQcf1L6eP39+LFmyJLp27Zq93n777bN5B5uSsgx79epVr139q182dygAAAAAQFsFBj/3uc/FmDFj4qWXXorXX389jjvuuBg8eHD06NEj25/KizfaaKMmz1FRURFz5syp10Yfe9KqvgcAAAAA2skcgx2xlapmrxBy4YUXxoEHHhgDBgzIyom32GKLuP3222v3v/POOzF27Ngmz5EWLUmtrq7l85o7FAAAAACgrQKDKRvw8ccfj1deeSWbK7Bfv371ViD+0pe+tKpjAQAAAADaaylxjbTQyMCBA+sFBZM333wzjjnmmJYYGwAAAADQ3gKDjZk9e/YKVyUGAAAAYM2T91yB5hhs5VLiO+64o8n9U6dObe4pAQAAAID2HhgcOXJktuhIsVhs9Ji0HwAAAABYg0qJ+/TpE7fddltUV1c32CZPntw6IwUAAACgXSsWCx2ylapmBwaHDBkSkyZNanT/irIJAQAAAIAOWEo8duzYmDdvXqP7+/btG+PHj1/dcQEAAAAA7SkwOHTo0Cb3d+/ePYYNG7Y6YwIAAAAA2ltgEAAAAAAaUh2lO19fR9RuAoNbvv9MLv126zY8l35ffOmDNu9zi6vyucZ5KXTJ54dRcbE5NttCqd3fwccPzqXfQlmzp6Lt0IrV1VEq3Ns129NXPJ1Lv1uf9H4u/XYq26SkrvNeu96aS79//8lDufS7ZO6SNu9z7qkfRh6evfq5XPqd+rVpufRbFn/Opd9Jl/4rl37X22FKLv1261WeS78zHnknl35f/8obufQbsV5O/cLqKa2/+AAAAACA9pUxCAAAAEDHVlRK3KHIGAQAAACAEiQwCAAAAAAlSGAQAAAAAEqQOQYBAAAAaBHFYsGVXNMDg08++WQ8/vjjMXPmzOz1JptsErvsskvstNNOLT0+AAAAACDvwOCsWbPi4IMPjkcffTS23HLL2HjjjbPtb7/9dpx66qmx6667xp/+9KfYaKONWmOsAAAAAEAecwwef/zxsXTp0njxxRfjP//5TzzxxBNZS1+nbdXV1XHCCSe01NgAAAAAgPaQMXjvvffGI488Eh//+MeX25e2/eIXv4g99tijJccHAAAAAOQdGCwvL4+5c+c2uv+DDz7IjlmRqqqqrNW1aNGiKO/atTnDAQAAAADaopT40EMPjaOOOipuv/32egHC9HXaNmrUqDj88MNXeJ7Kysro1atXvfbT636/au8AAAAAAGjdjMFLLrkkm0fwsMMOiyVLlkTX/5fhl7L9OnfuHF//+tfj4osvXuF5KioqYsyYMfW2LXru4eaOHQAAAIB2pBiFvIdAa5YSX3nllXHBBRfEpEmTYubMmdn2TTbZJIYMGRI9e/Zc6fMsW3L8gTJiAAAAAGifgcEaKQD42c9+tuVHAwAAAAC0vzkGkwULFsSECRPihRdeWG7fwoUL4/rrr2+psQEAAAAA7SEw+PLLL0f//v1j9913j0GDBsWwYcNi+vTptfvnzJmTLUACAAAAQOkpFgsdspWqZgUGzzjjjBg4cGDMmjUrpkyZEj169Ijddtstpk2b1nojBAAAAADyDQw+9thjUVlZGb17946+ffvGnXfeGSNGjIihQ4fG1KlTW350AAAAAED+gcE0v2Dnzv+3XkmhUMhWKd5///2zsuJUagwAAABAaSpGoUO2UtWsVYn79esXEydOzOYZrOuyyy7L/j3ggANadnQAAAAAQP4ZgwcddFDcfPPNDe5LwcHDDz88isViS40NAAAAAGgPgcGKioq4++67G91/xRVXRHV1dUuMCwAAAABoL6XEAAAAANCYYrF05+vriNpNYPD9a67Opd9v7/ZULv0uHrpjm/c55bknIg9VS7vk0m91TlXt3TotyaXfQiGfN1xq93etzotz6feVxeW59At0TFuf9H4u/f6n/x659PvF0QNz6Xf+Sw/k0u+si07Opd9P/GNcLv3O6rp5m/d51e3nRx7eevHhXPr905PX5dLvhy/MyaXfgS/ekUu/vWc9n0u/nebOzqXfsqsOzKXfv0y+Npd+Iwbn1C+0YSkxAAAAALBmEBgEAAAAgBLUbkqJAQAAAOjYLEnbscgYBAAAAIASJDAIAAAAACVIKTEAAAAALaJYLLiSHYiMQQAAAAAoQS0aGPzf//4X119/fUueEgAAAABo74HBadOmxahRo1rylAAAAABA3nMMzp07t8n9H3zwweqOBwAAAIAOqhjmGFxjA4PrrrtuFAqN3+BisdjkfgAAAACgAwYGe/ToEd/73vdi5513bnD/K6+8Escee+wKz1NVVZW1etuWLo3yTp2aMxwAAAAAoC0Cg5/85Cezf4cNG9ZoRmHKGlyRysrKOOecc+ptO2XHfjHmUwOaMxwAAAAAoC0WH/nKV74S3bp1a3T/JptsEmefffYKz1NRURFz5syp10745MebMxQAAAAA2plisdAhW6lqVsbg6NGjm9y/8cYbr1RgsLy8PGt1zVFGDAAAAADtM2MQAAAAACjRwOCCBQtiwoQJ8cILLyy3b+HChXH99de31NgAAAAA6ECKUeiQrVQ1KzD48ssvR//+/WP33XePQYMGZYuQzJgxo3Z/mitw1KhRrTFOAAAAACCvwOAZZ5wRAwcOjFmzZsWUKVOiR48eseuuu8a0adNackwAAAAAQHsKDD722GNRWVkZvXv3jr59+8add94ZI0aMiKFDh8bUqVNbb5QAAAAAQH6BwTS/YOfO/7eQcaFQiCuvvDL233//rKw4lRoDAAAAUJqqix2zlar/i/KthH79+sXEiROzeQbruuyyy7J/DzjggJYdHQAAAACQf8bgQQcdFDfffHOD+1Jw8PDDD49isYTDrAAAAACwJgYGKyoq4u677250/xVXXBHV1dUtMS4AAAAAOphiFDpkK1XNCgwCAAAAACU4x2Br2nD4sFz6/XDyM7n0u/aCBW3e593le0QeFi/Jp7y8mNPsoZ07d82l37Kcwvyldn+7ds3n/vbo0W5+XAMdQKeyTXLp94ujB+bS77NXP5dLv69/YfNc+v1GTr833zZ7SC79zni37fv8/ITn277TiHhoQD6f3W73TMil380OOzCXfu968+O59Lt17y1z6Xe9DT/Mpd/7nt0gl357/u2RXPrd9tDTc+kXVpeMQQAAAAAoQVJQAAAAAGgRxWLpztfXEckYBAAAAIASJDAIAAAAACVIYBAAAAAASpA5BgEAAABoEcWiC9mRyBgEAAAAgBK0SoHB6urqRrdPmzZtdccEAAAAALSyZgUG586dG1/+8peje/fusfHGG8dZZ50VS5curd3/zjvvxDbbbNMa4wQAAACgnauOQodspapZcwz+4Ac/iGeeeSZ+97vfxfvvvx/nnXdeTJ48OW677bbo2rVrdkxRMTkAAAAArFkZg3/+85/jV7/6VXzpS1+Kb3zjGzFx4sQsS3D//fePqqqq7JhCoXSjrAAAAACwRgYGUxBwq622qn3du3fveOCBB+KDDz6IfffdN+bPn79S50lBxFSWXLdVLV7S/NEDAAAAAK0fGNxyyy3jxRdfrLetR48ecd9998WCBQvioIMOWqnzVFZWRq9eveq1i/50f/NGDgAAAEC7UiwWOmQrVc0KDA4fPjyuvfba5bavs846ce+990a3bt1W6jwVFRUxZ86cem3swZ9rzlAAAAAAgLZafOScc86J6dOnN7gvZQ7ef//92WIkK1JeXp61uhZ2adZQAAAAAIDV0Kxo3HrrrZe1xqTg4LBhw1ZnPAAAAAB0UMVi3iOg1UqJkzSX4IQJE+KFF15Ybt/ChQvj+uuvb+4pAQAAAID2HBh8+eWXo3///rH77rvHoEGDsuzAGTNm1O5PcwWOGjWqNcYJAAAAAOQVGDzjjDNi4MCBMWvWrJgyZUpWOrzrrrvGtGnTWnJMAAAAAEB7Cgw+9thjUVlZGb17946+ffvGnXfeGSNGjIihQ4fG1KlTW2+UAAAAALR7xSh0yNZaZs+eHUcccUT07Nkz1l133fj6178eH374YZPHn3TSSfHxj3881lprrdhyyy3j29/+dlalW1ehUFiu3XLLLa0bGEzzC3bu/H/rlaROr7zyyth///2zsuJUagwAAAAARBYUfP755+P++++Pu+66Kx555JH45je/2eilmT59etYuvvjieO655+K6666Le+65JwsoLuvaa6/NpviraSNHjmzdVYn79esXEydOzOYZrOuyyy7L/j3ggAOaPQAAAAAAyFNVVVXW6iovL8/aqnrxxRezoN6//vWv2HHHHbNtv/zlL2PffffNAn+bbrrpct+TpvD705/+VPv6Ix/5SJx//vnx1a9+NZYsWVIvYS9lIG6yySaxOpqVMXjQQQfFzTff3OC+FBw8/PDDo2hdagAAAAA6kMrKyujVq1e9lratjscffzwL3tUEBZO99947ysrK4oknnljp86Qy4lSKXDcomJxwwgnZdH877bRTXHPNNasUk2tWxmBFRUXWGnPFFVdkDQAAAIDSU9382FS7UFFREWPGjKm3bXWyBZOZM2fGRhttVG9bCu6tv/762b6V8e6778a55567XPnxj370o9hzzz1j7bXXjvvuuy+OP/74bO7CNB9hqwUGAQAAAGBNU96MsuEzzzwzLrjgghWWEa+uuXPnxn777RcDBgyIH/7wh/X2/eAHP6j9+hOf+ETMmzcvLrrooo4bGKxef/VqolfVuE+elEu/b701r8373Pu4gZGHQpfWW92nPSou7qD/eWQVub9tY/DxgyMPhbJmzTjR4RWrq6NUuLdrtqeveDqXfue/9EAu/b7+hc1z6XebA/vl0m/csPzk421h/X0H5NJvrwVt/7P5/O88GHk4YmQ+z9S3j/q/uaza0g3P5FNtttFZH8+l364D18ml36Xrdsul3+0mvJtLvycc8Ydc+v1bLr2Sl+985ztx9NFHN3nMtttum83/N2vWrHrb0zyBaeXhFc0N+MEHH8TnP//56NGjR9x+++3RpUuXJo/feeeds8zCNE9iczId201gEAAAAICOrVhc85ODNtxww6ytyC677BLvv/9+TJo0KYYMGZJte+ihh6K6ujoL5DWVKThixIgswHfHHXdEt24rDvA//fTTsd566zW7/FlgEAAAAABaWP/+/bOsv9GjR8dVV10VixcvjhNPPDEOO+yw2hWJ//vf/8Zee+0V119/fbaISAoKDh8+PObPnx833HBD9jq1JAUjO3XqFHfeeWe8/fbb8elPfzoLGt5///3x4x//OE477bRmj1FgEAAAAABawY033pgFA1PwL61GfPDBB8cvfvGL2v0pWDhlypQsEJhMnjy5dsX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+      "text/plain": [
+       "<Figure size 1400x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\umap\\umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+      "  warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['X', 'X_scaled', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'data', 'plot_heatmap', 'plot_umap', 'scaler', 'y']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"..\")\n",
+    "from src.preprocess_visualize import DataVisualizer\n",
+    "\n",
+    "raw_data = data[\"raw_data\"]   # from your preprocessing class\n",
+    "\n",
+    "pre_viz = DataVisualizer(raw_data)\n",
+    "\n",
+    "pre_viz.plot_heatmap()\n",
+    "pre_viz.plot_umap()\n",
+    "print(dir(pre_viz))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d4173082",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================\n",
+      "   Testing Model 1\n",
+      "   Config: {'hidden_layer_sizes': (45, 11), 'learning_rate_init': 0.0015, 'max_iter': 250}\n",
+      "======================================\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7777777777777778\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         1.0       0.73      0.89      0.80         9\n",
+      "         2.0       0.86      0.67      0.75         9\n",
+      "\n",
+      "    accuracy                           0.78        18\n",
+      "   macro avg       0.79      0.78      0.78        18\n",
+      "weighted avg       0.79      0.78      0.77        18\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================\n",
+      "   Testing Model 2\n",
+      "   Config: {'hidden_layer_sizes': (84, 45), 'learning_rate_init': 0.0015, 'max_iter': 250}\n",
+      "======================================\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7777777777777778\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         1.0       0.86      0.67      0.75         9\n",
+      "         2.0       0.73      0.89      0.80         9\n",
+      "\n",
+      "    accuracy                           0.78        18\n",
+      "   macro avg       0.79      0.78      0.78        18\n",
+      "weighted avg       0.79      0.78      0.77        18\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================\n",
+      "   Testing Model 3\n",
+      "   Config: {'hidden_layer_sizes': (84, 45), 'learning_rate_init': 0.0007, 'max_iter': 430}\n",
+      "======================================\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (430) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.6666666666666666\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         1.0       0.71      0.56      0.62         9\n",
+      "         2.0       0.64      0.78      0.70         9\n",
+      "\n",
+      "    accuracy                           0.67        18\n",
+      "   macro avg       0.68      0.67      0.66        18\n",
+      "weighted avg       0.68      0.67      0.66        18\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================\n",
+      "   Testing Model 4\n",
+      "   Config: {'hidden_layer_sizes': (128, 84), 'learning_rate_init': 0.0015, 'max_iter': 250}\n",
+      "======================================\n",
+      "Accuracy: 1.0\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         1.0       1.00      1.00      1.00         9\n",
+      "         2.0       1.00      1.00      1.00         9\n",
+      "\n",
+      "    accuracy                           1.00        18\n",
+      "   macro avg       1.00      1.00      1.00        18\n",
+      "weighted avg       1.00      1.00      1.00        18\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AACRBpgQAAJewyJQAAABkHJkSAADcwhJHIygBAMAlLMo3AAAAGUemBAAAl7AcnikhKAEAwCUshwclrFMCAAACApkSAABcwnJ4poSgBAAAt7DE0SjfAACAgECmBAAAl7AcXr4hUwIAAAJCwAYlhw8flk6dOtk9DAAAHJUpsfx43GoBG5ScOnVKZs+ebfcwAABwDMvhQYltPSUfffTRDZ8/cODALRsLAACwn21BSfPmzU0U5vF4XNuwAwDALWU5+37bVr6Jjo6WRYsWSXx8fKrH1q1b7RoaAACOZDm8fGNbUFKlShXZsmVLms/fLIsCAADcxbbyzYABA+T8+fNpPl+qVClZtWrVLR0TAABOZjm87cG2oKR27do3fD5HjhxSp06dWzYeAACcznJ4UBKwU4IBAEBwYZl5AABcwiJTAgAAkHGUbwAAcAvLz4cPihcvnuq04u7du3t9Dco3AAC4hGVj+WbTpk1y7dq1xMffffedPPTQQ9K6devADkputsR8Uk2bNs3UsQAAgIwrUKBAssdjxoyRkiVL+jSTNotdS8x7G/EljboAAMCty5TExcWZI6nw8HBz3Mjly5fl3Xfflb59+/o0Jlt6StJaWj7lQUACAIB9y8yPHj1aIiMjkx167maWLFkif/zxhzzxxBM+jJ6eEgAAkIZBgwaZbEdSN8uSqLffflsaNmwohQsXFl8ERKOrLje/evVqOXTokEn5JNWzZ0/bxgUAgKNY/r2cN6WalH755RdZsWKF2XTXV7YHJdu2bZNGjRrJhQsXTHCSN29eOXnypGTPnl0KFixIUAIAgIPMnDnTfH43btzYeeuU9OnTR5o0aSKnT5+WiIgI2bBhg4mydBfhcePG2T08AACCtqfEV9oPqkFJhw4dJEuWLM4LSrZv3y79+vWTkJAQCQ0NNV2+RYoUkbFjx8pzzz1n9/DgZzmzh8sr/VvJns9GyKn1r8qqWX2lSmxR7jOQDgvmzZWGDz0g91YuL+0ebS27du7kPgY5y+agRMs22orRqVOndI3f9qAkLCzMBCRK0z36wyjt8D18+LDNo4O/TR3ymDxQvbR0emG2VG0zSlas3y2fTntGCheI5GYDPvji889k3NjR0vXp7rLgg8USE1NaunXtLL///jv3EbZ5+OGHxePxyN133+3MoKRy5cpmFTilC6wMGTJE5s6dK71795Zy5crZPTz4UbbwMGn+YCV5fsIS+Wbrfjlw+KSMfOMz2X/4hHRpXZt7DfhgzuyZ0vJvbaR5i1ZSslQpeWHocMmWLZssWfQh9zGIWTZnSjLK9qBk1KhREh0dbb4eOXKkREVFSbdu3eTEiRMyffp0u4cHP8oSGiJZsoTKpctXkp2/FHdFalYuyb0GvHTl8mX58YfvpXqNmonnNONcvXpN2bljG/cxiFkOD0psn31TtWrVxK+1fPPFF1/YOh5knnMX4mTDjgMyqEtD2XPwv/Lf389Km0eqSrUKJUy2BIB3Tv9x2iwumS9fvmTn9fHBgwe4jXAs24OSzFgC1xN/TayQUNvGhLR1euEdeWNYOznw5Ui5evWabN99WN7/YrNULkOzKwBkmH378bkjKClRosQNU0QHDtw46tflbocPH57sXOht90pY9H1+GyP85+CRk/Lwk69J9mxZJXfObHLs5FmZM6ajHPz1JLcZ8FJUnigzWzFlU6s+zp8/P/cxiFk27hLsiqBEG1qTunLlillQTcs4AwYMSNcSuAVrD/T7OOFfFy5dNkeeXBFSv2YZeX7CUm4x4KWwrFmlTGxZ2bhhvTzwYP3E9SE2blwvj7Z9nPsIx7I9KOnVq1eq56dMmSKbN29O1xK4lG4CV/0aZUQD+b0/H5eSRQrIqD7NZe/B/8o7H623e2iAo/yjQ0cZ/NxAKVu2nJQrX0HenTNbLl68KM1btLR7aLCRRaYkc+hGPpoF0ZXh4B6RObPJiGeayu235ZFTZy7I0pXbZeiUj+Xq1Xi7hwY4yiMNG8npU6fk9ckT5eTJExJTuoy8/sZbko/yDRzM9kxJWhYuXGj2wYG7fLh8mzkAZFzbdo+bA0jg8ESJ/UGJLp6WNN2kK8EdO3bMrFPy+uuv2zo2AACcxHJ4VGJ7UNKsWbNkN1EXACpQoIDUrVtXSpcubevYAABAEAUlw4YNs3sIAAC4guXsRIn9y8zrXPvjx49fd17n2+tzAAAgOJaZtz0o0R6S1OgqrVmzZr3l4wEAAEFWvpk4caL5UyOxt956S3LmzJn4nO7psGbNGnpKAAAIovKNbUHJ+PHjEzMl06ZNS1aq0QxJ8eLFzXkAAOCdkBBnRyW2BSUHDx40f9arV08WLVokUVFRdg0FAAAEANtn36xatcruIQAA4AqWsxMl9je6tmrVSl5++eXrzo8dO1Zat25ty5gAAEAQBiXa0NqoUaNU977R5wAAQHBMCba9fHPu3LlUp/6GhYXJ2bNnbRkTAABOZFG+yZjy5cvLe++9d935BQsWSGxsbAavDgAAnML2TMngwYOlZcuWsn//fnnggQfMuZUrV8r8+fPlgw8+sHt4AAA4huXwVIntQUmTJk1kyZIlMmrUKFm4cKFERERIhQoVZMWKFVKnTh27hwcAgGNYBCUZ17hxY3Ok9N1330m5cuX88A4AACDQ2T77JqU///xTpk+fLvfdd59UrFjR7uEAAOAYluXfI2iDEp3+2759e4mOjpZx48aZ/pINGzbYPSwAABAMPSXHjh2TWbNmydtvv22m/7Zp08bsDqw9Jsy8AQAguHpKQuxscI2JiZGdO3fKhAkT5LfffpNJkybZNRwAABzPcnj5xrZMyeeffy49e/aUbt26yV133WXXMAAAQLBnStauXWuaWqtUqSLVqlWTyZMny8mTJ+0aDgAAjmc5fJl524KS6tWry5tvvilHjx6Vrl27mhVcCxcuLPHx8bJ8+XITsAAAgOAp39g++yZHjhzSqVMnkznZtWuX9OvXT8aMGSMFCxaUpk2b2j08AAAQLEFJUtr4OnbsWDly5IhZZh4AAHiP8k0mCA0NlebNm8tHH32UGZcHAMCVLJvLN7/++qs8/vjjki9fPrNtjG66u3nzZufsfQMAAJzv9OnTUqtWLalXr56ZYVugQAH56aefJCoqyutrEJQAAOASlo2Lp7388stSpEgRmTlzZuK5EiVKOLenBAAABA5dZV1XXE966LnUaMtF1apVpXXr1maySuXKlc0sW18QlAAA4BKWn3tKRo8eLZGRkckOPZeaAwcOyNSpU82CqMuWLTOLo+oiqbNnz/Z+/B6PxyMuE1G5h91DAFzh9KbJdg8BcIVst6hZosbLa/x6va97V7suMxIeHm6OlLJmzWoyJevWrUs8p0HJpk2bZP369V69Hz0lAAAgVWkFIKmJjo6+bjPdMmXKyIcffijeIigBAMAlLBs3CdaZN3v27El2bu/evVKsWDGvr0FQAgCAS1g2RiV9+vSRmjVryqhRo6RNmzby7bffyvTp083hLRpdAQBAht17772yePFisyJ7uXLl5MUXX5QJEyZIu3btvL4GmRIAAFzCsrF8o/7617+aI73IlAAAgIBApgQAAJew7E6VZBBBCQAALmE5PCihfAMAAAICmRIAAFzC4YkSghIAANzCcnhUQvkGAAAEBMo3AAC4hOXsRAlBCQAAbmE5PCqhfAMAAAIC5RsAAFzCcnaihEwJAAAIDGRKAABwiRCHp0oISgAAcAnL2TEJ5RsAABAYyJQAAOASlsNTJQQlAAC4RIizYxLKNwAAIDCQKQEAwCUsyjcAACAQWJRvAAAAMo7yDQAALmGJs1MlbMgHAAACApkSAABcIsTZiRKCEgAA3MJyeKcr5RsAAOCc8s3OnTu9vmCFChUyMh4AAJBODk+UeBeUVKpUyaSEPB5Pqs8nPKd/Xrt2zd9jBAAAXghxeFTiVVBy8ODBzB8JAAAIal4FJcWKFcv8kQAAgAxxeKIkfY2uc+bMkVq1aknhwoXll19+MecmTJggS5cu9ff4AABAkPA5KJk6dar07dtXGjVqJH/88UdiD0mePHlMYAIAAOxhWZZfj4APSiZNmiRvvvmmPP/88xIaGpp4vmrVqrJr1y5/jw8AAHhJ4wh/HgEflGjTa+XKla87Hx4eLufPn/fXuAAAQJDxOSgpUaKEbN++/brzX3zxhZQpU8Zf4wIAAOmYEuzPwxfDhg27rvxTunTpzN37RvtJunfvLpcuXTJrk3z77bcyf/58GT16tLz11lu+Xg4AAPiJZfOdLFu2rKxYsSLxcZYsWTI3KHnyySclIiJCXnjhBblw4YI89thjZhbOa6+9Jo8++qivlwMAAC6RJUsWKVSoUPq/Pz3f1K5dO3NoUHLu3DkpWLBgugcAAAD8w98zZuLi4syRsodUj9T89NNPJlGRLVs2qVGjhqmiFC1aNPM35Dt+/Lhs2bJF9uzZIydOnEjvZQAAgJ+EWP49NKiIjIxMdui51FSrVk1mzZplekx1+RCdGFO7dm35888/vR6/5UlrQ5s06MWffvpp00cSHx9vzunU4L///e8yZcoUM2C7RVTuYfcQAFc4vWmy3UMAXCFbuuoSvms35/qJKBkxo00ZnzIlSelaZroi/KuvviqdO3fOnEyJ9pRs3LhRPv30U/OGenzyySeyefNm6dq1q6+XAwAAAbp4Wnh4uOTOnTvZ4U1AkrCo6t133y379u3zevw+x24agCxbtkz+8pe/JJ5r0KCBWVDtkUce8fVyAADAhc6dOyf79++Xf/zjH15/j8+Zknz58qVaotFzUVFRvl4OAAC4YEXX/v37y+rVq+Xnn3+WdevWSYsWLUx7R9u2bTMvKNGpwLpWybFjxxLP6dcDBgyQwYMH+3o5AADggr1vjhw5YgKQmJgYadOmjUlibNiwQQoUKODf8o0uK590cDrlR6f4JEzzOXTokKkx6Swc+koAAAg+CxYsyPA1vApKmjdvnuE3AgAAmSvE7iVdb0VQMnTo0MwfCQAACKjF0261dC+eBgAA4E8+Twm+du2ajB8/Xt5//33TS3L58uVkz586dcqf4wMAAF5ydp4kHZmS4cOHm9XZdAXXM2fOmJk4LVu2lJCQELNtMQAAsEeIZfn1uOXj9/Ub5s6daxZK69evn9kNUKf/vPXWWzJkyBAz9QcAAOCWBCW6Jkn58uXN1zlz5jTZEvXXv/7VLD0PAACCb/E0W4KSO+64Q44ePWq+LlmypHz55Zfm602bNnm9Hj4AAECGgxJdNnblypXm62eeecas4nrXXXdJ+/btpVOnTr5eDgAAuGBFV1tm34wZMybxa2121W2JdY17DUyaNGnil0EBAADfOXyZkoyvU1K9enUzA6datWoyatQo/4wKAAAEHb8tnqZ9JmzIBwCAfUIcPiXY5/INAAAITFawl28AAAD8gUwJAAAuYTk8VeJ1UKLNrDdy4sQJf4wHAAAEKa+Dkm3btt30Nffff78EgtObJts9BMAVou7tYfcQAFe4uO3WfC6FSJAEJatWrcrckQAAgKAu3zg9qAIAAC5BoysAAC4R4uxECUEJAABuEeLwoITyDQAACAiUbwAAcAkrGBtd//Of/8jjjz8uNWrUkF9//dWcmzNnjqxdu9bf4wMAAD6Ub/x5BHxQ8uGHH0qDBg0kIiLCrF0SFxdnzp85c4ZdggEAwK0LSl566SWZNm2avPnmmxIWFpZ4vlatWrJ169b0jwQAAGSIVm/8eQR8ULJnz55UV26NjIyUP/74w1/jAgAAQcbnoKRQoUKyb9++685rP8mdd97pr3EBAAAfhViWX4+AD0q6dOkivXr1ko0bN5ou399++03mzp0r/fv3l27dumXOKAEAgFcf6v48An5K8LPPPivx8fHy4IMPyoULF0wpJzw83AQlzzzzTOaMEgAAuJ7PQYlmR55//nkZMGCAKeOcO3dOYmNjJWfOnJkzQgAA4BWHL1OS/sXTsmbNaoIRAAAQGEIcHpX4HJTUq1fvhivGffXVVxkdEwAACEI+97FUqlRJKlasmHhotuTy5ctmjZLy5ctnzigBAICj1ikZM2aMSWL07t078zIl48ePT/X8sGHDTH8JAAAI7l2CN23aJG+88YZUqFDBp+/z24wf3QtnxowZ/rocAABwIE1QtGvXzqz8HhUVZU9Qsn79esmWLZu/LgcAABy4eFr37t2lcePGUr9+fZ+/1+fyTcuWLZM99ng8cvToUdm8ebMMHjzY5wEAAIDApJvuJmy8m0DXJtMjNQsWLDA9plq+SQ+fMyW6x03SI2/evFK3bl357LPPZOjQoekaBAAACLxG19GjR1/3ua/nUnP48GGz4ruu8p7eyonl0VSHl65duybffPONmWXja53oVrp01e4RAO4QdW8Pu4cAuMLFbZNvyfuMXHn93nQZ0f8vRbzOlCxZskRatGghoaGhyeIGnYETEhJirpP0uQyXb/RiDz/8sPz4448BHZQAAICMu1GpJiXdfmbXrl3JznXs2FFKly4tAwcOvGlAkq6eknLlysmBAwekRIkSvn4rAADIRJbYNyc4V65cJkZIKkeOHJIvX77rzvutp+Sll14ym+998sknpsH17NmzyQ4AAGDfOiUhfjxuNa8zJSNGjJB+/fpJo0aNzOOmTZsmW25eW1P0sdaPAAAAvv7668wJSoYPHy5PPfWUrFq1irsMAEAACgmQFV3Ty+ugJGGSTp06dTJzPAAAIEj51Oh6o92BAQCAvSyHf077FJTcfffdN/2BT506ldExAQCAdAia8k1CX4mu5gYAAGBrUPLoo49KwYIF/T4IAACQcQ6v3ngflDi9TgUAgNuFOPyz2uvF03zYIgcAACDzMiXx8fG+Xx0AANwyIc5OlPi+9w0AAAhMlsODEp/3vgEAAMgMZEoAAHCJEBt3CfYHMiUAACAgkCkBAMAlLGcnSghKAABwixCHByWUbwAAQECgfAMAgEuEOLx+Q1ACAIBLWM6OSSjfAACAwECmBAAAlwhxeKqEoAQAAJewnB2TUL4BAACBgUwJAAAuESLO5vTxAwAAlyBTAgCAS1gObyohKAEAwCUscTbKNwAAICCQKQEAwCVCKN8AAIBAYImzUb4BAAABgfINAAAuYTk8VUKmBAAABAQyJQAAuITl8FQJQQkAAC4RIs7m9PEDAIAAMHXqVKlQoYLkzp3bHDVq1JDPP//cp2uQKQEAwCUsG8s3d9xxh4wZM0buuusu8Xg8Mnv2bGnWrJls27ZNypYt69U1CEoAAHAJy8b3btKkSbLHI0eONNmTDRs2EJQAAAB7XLt2TT744AM5f/68KeN4i0wJAAAuYfm5fBMXF2eOpMLDw82Rml27dpkg5NKlS5IzZ05ZvHixxMbGev1+NLoCAOASIX4+Ro8eLZGRkckOPZeWmJgY2b59u2zcuFG6desmHTp0kB9++MHr8Vse7UZxmUtX7R4B4A5R9/awewiAK1zcNvmWvM+iHUf9er3GpfP6lClJqX79+lKyZEl54403vHo95RsAAFzC8nP5xpcAJDXx8fHXBTU3QlACAAAybNCgQdKwYUMpWrSo/PnnnzJv3jz5+uuvZdmyZV5fg6AEAACXsGx87+PHj0v79u3l6NGjpvdEF1LTgOShhx7y+hoEJQAAuIRlY1Ty9ttvZ/gazL4BAAABwdag5OLFi7J27dpUpwvpHOd33nnHlnEBAOBEIWL59bj147fJ3r17pUyZMnL//fdL+fLlpU6dOqYOleDMmTPSsWNHu4YHAIAjyzeWH4+gCUoGDhwo5cqVM40xe/bskVy5ckmtWrXk0KFDdg0JAADYyLZG13Xr1smKFSskf/785vj444/l6aefltq1a8uqVaskR44cdg0NAABHsmydf+PgTIn2k2TJkiXZgi+6m6DuMqilHC3vAACA4GFbpqR06dKyefNm01eS1OTJ/78Ub9OmTW0aGQAAzmQ5O1FiX6akRYsWMn/+/FSf08Ckbdu24sJteQAAyDQhDp99w4Z8ANLEhnyAszbk++L7E3693iNlC8itxIquAAC4hOXw8g1BCQAALmE5PChhmXkAABAQyJQAAOASlsPXKSEoAQDAJUKcHZPYE5R89NFHXr+W9UoAAAgOtgQlzZs39+p1usrrtWvXMn08AAC4gUX5xnfx8fGZ8FcBAACcjJ4SAABcwqKnJOPOnz8vq1evlkOHDsnly5eTPdezZ08/vAMAAO5nUb7JmG3btkmjRo3kwoULJjjJmzevnDx5UrJnzy4FCxYkKAEAIEjYvnhanz59pEmTJnL69GmJiIiQDRs2yC+//CJVqlSRcePG2T08AAAcNSU4xI/HLR+/2Gz79u3Sr18/CQkJkdDQUImLi5MiRYrI2LFj5bnnnrN7eAAAOKp8Y/nxP0EXlISFhZmARGm5RvtKVGRkpBw+fNjm0SEzLJg3Vxo+9IDcW7m8tHu0tezauZMbDfgoZ/ZweaV/K9nz2Qg5tf5VWTWrr1SJLcp9hKPZHpRUrlxZNm3aZL6uU6eODBkyRObOnSu9e/eWcuXK2T08+NkXn38m48aOlq5Pd5cFHyyWmJjS0q1rZ/n999+514APpg55TB6oXlo6vTBbqrYZJSvW75ZPpz0jhQtEch+DfPaN5ccj6IKSUaNGSXR0tPl65MiREhUVJd26dZMTJ07I9OnT7R4e/GzO7JnS8m9tpHmLVlKyVCl5YehwyZYtmyxZ9CH3GvBStvAwaf5gJXl+whL5Zut+OXD4pIx84zPZf/iEdGldm/sYxCw/H0G3TknVqlUTv9byzRdffGHreJB5rly+LD/+8L107tI18ZyW7qpXryk7d2zj1gNeyhIaIlmyhMqly1eSnb8Ud0VqVi7JfYRj2Z4pQfA4/cdps21Avnz5kp3XxzoNHIB3zl2Ikw07DsigLg0lukCkhIRY8mije6VahRJSKH9ubmMQC7Esvx5BlykpUaKE2eMmLQcOHLjh9+tsHT2S8oSGS3h4uN/GCACBptML78gbw9rJgS9HytWr12T77sPy/hebpXIZml3hXLYHJdrQmtSVK1fMgmpaxhkwYMBNv3/06NEyfPjwZOeeHzxUXhgyzO9jRcZE5Yky075TNrXq4/z583N7AR8cPHJSHn7yNcmeLavkzplNjp08K3PGdJSDv5J1DGaWOJvtQUmvXr1SPT9lyhTZvHnzTb9/0KBB0rdv3+syJQg8YVmzSpnYsrJxw3p54MH6iZszbty4Xh5t+7jdwwMc6cKly+bIkytC6tcsI89PWGr3kGAny9m33/agJC0NGzY0AcfMmTNv+Dot06Qs1Vy6msmDQ7r9o0NHGfzcQClbtpyUK19B3p0zWy5evCjNW7TkrgI+qF+jjJmyuffn41KySAEZ1ae57D34X3nno/XcRzhWwAYlCxcuNPvgwF0eadhITp86Ja9PnignT56QmNJl5PU33pJ8lG8An0TmzCYjnmkqt9+WR06duSBLV26XoVM+lqtX47mTQcxyeKrE8ng8HrsXT0va6KrDOXbsmFmn5PXXX5d//vOfPl+TTAngH1H39uBWAn5wcdvkW3Ifvz1wxq/Xu+/OyODKlDRr1ixZUKLrVhQoUEDq1q0rpUuXtnVsAAAgiIKSYcOYJQMAgD/YWbzR2bCLFi2S3bt3S0REhNSsWVNefvlliYmJcc7iaTpF9Pjx49ed12mi+hwAAAh8q1evlu7du8uGDRtk+fLlZomPhx9+WM6fP++cTElaLS26IFrWrFlv+XgAAHAsy763TrlNzKxZs8z2MVu2bJH7778/sIOSiRMnmj+1n+Stt96SnDlzJj6nS5GvWbOGnhIAABw6++bMmf9vuvVlJq1tQcn48eMTMyXTpk1LVqrRDEnx4sXNeQAAYI/UtnJJbX2wlHRhTF2xvVatWlKuXLnAD0oOHjxo/qxXr55pjImKirJrKAAAuILl50RJalu5DB069KaTVLS35LvvvpO1a9c6a52SzMA6JYB/sE4J4Kx1Srb+fNav1ysbHe5zpqRHjx6ydOlS04ahm+76wvbZN61atTJThlIaO3astG7d2pYxAQAAMcFH7ty5kx1pBSSa49CAZPHixfLVV1/5HJAERFCikVSjRo1S3ftGnwMAAF6y/Hz4QEs27777rsybN09y5cplVmfXQ/c3c0xQcu7cuVSn/oaFhcnZs/5NQwEA4PbZN5Yf/+OLqVOnmhk3uiJ7dHR04vHee+85JygpX758qgNesGCBxMbG2jImAADgGy3fpHY88cQTzlk8bfDgwdKyZUvZv3+/PPDAA+bcypUrZf78+fLBBx/YPTwAAIJ29s2tZntQ0qRJE1myZImMGjVKFi5caNbLr1ChgqxYsULq1Klj9/AAAECwBCWqcePG5khJ5zj7sugKAADBzBJns72nJKU///xTpk+fLvfdd59UrFjR7uEAAOAcln2zb1wVlOj03/bt25tO3XHjxpn+Et1pEAAABAdbyzc6f1l3EXz77bfN9N82bdqYleO0x4SZNwAAOHdDPkdlSrTBNSYmRnbu3CkTJkyQ3377TSZNmmTXcAAAcMXsG8uPR9BkSj7//HPp2bOndOvWTe666y67hgEAAII9U6I7B2pTa5UqVaRatWoyefJkOXnypF3DAQDA8Sxn97naF5RUr15d3nzzTTl69Kh07drVrOBauHBhiY+Pl+XLl5uABQAABE9UYvvsmxw5ckinTp1M5mTXrl3Sr18/GTNmjBQsWFCaNm1q9/AAAECwBCVJaePr2LFj5ciRI2aZeQAA4IwN+VwXlCQIDQ2V5s2by0cffWT3UAAAQDAtMw8AADKODfkAAEBAsMTZArJ8AwAAgg/lGwAA3MISRyMoAQDAJSyHRyWUbwAAQEAgUwIAgEtYzk6UkCkBAACBgUwJAAAuYYmzEZQAAOAWljgaja4AACAgkCkBAMAlLIenSghKAABwCcvZMQnlGwAAEBjIlAAA4BKWOBtBCQAAbmGJozH7BgAABAQyJQAAuITl8FQJmRIAABAQyJQAAOASlrMTJQQlAAC4hSXORvkGAABk2Jo1a6RJkyZSuHBhsSxLlixZ4vM1CEoAAHBTqsTy4+GD8+fPS8WKFWXKlCnpHj49JQAAuIRlYwGnYcOG5sgIMiUAACAgkCkBAMAlLD8nSuLi4syRVHh4uDkyA5kSAABcwvLzMXr0aImMjEx26LnMQqYEAACkatCgQdK3b99k5zIrS6IISgAAcAnLz+WbzCzVpIagBAAAZNi5c+dk3759iY8PHjwo27dvl7x580rRokW9ugZBCQAArmHZ9s6bN2+WevXqJT5OKPt06NBBZs2a5dU1CEoAAHAJy8Z15uvWrSsejydD12D2DQAACAhkSgAAcAlLnI2gBAAAl7AcHpVQvgEAAAGBTAkAAC5hObyAQ6YEAAAEBDIlAAC4hSWORlACAIBLWOJslG8AAEBAIFMCAIBLWA5PlRCUAADgEpbDCziUbwAAQEAgUwIAgFtY4mgEJQAAuIQlzkb5BgAABAQyJQAAuITl8FQJmRIAABAQyJQAAOASlsO7SghKAABwCcvZMQnlGwAAEBjoKQEAAAGB8g0AAC5hUb4BAADIODIlAAC4hOXw2Tf0lAAAgIBApgQAAJewnJ0oISgBAMAtLHE2yjcAACAgUL4BAMAtLHE0ghIAAFzCcnhUQvkGAAAEBDIlAAC4hOXsRAlBCQAAbmGJs1G+AQAAAYGgBAAAN6VKLD8e6TBlyhQpXry4ZMuWTapVqybffvut199LUAIAAPzivffek759+8rQoUNl69atUrFiRWnQoIEcP37cq+8nKAEAwEVTgi0//sdXr776qnTp0kU6duwosbGxMm3aNMmePbvMmDHDq+8nKAEAwEWzbyw/Hr64fPmybNmyRerXr594LiQkxDxev369V9dgSjAAAEhVXFycOZIKDw83R0onT56Ua9euyW233ZbsvD7evXu3BG1Qks2VP5W76P/IR48eLYMGDUr1f9wIDBe3TbZ7CLgBfo+Q2Z9/w14aLcOHD092TvtFhg0bJpnB8ng8nky5MnADZ8+elcjISDlz5ozkzp2bewWkA79HCKRMiZZvtH9k4cKF0rx588TzHTp0kD/++EOWLl160/ejpwQAAKRKgw/9h2PSI63sdtasWaVKlSqycuXKxHPx8fHmcY0aNcQbFDoAAIBf6HRgzYxUrVpV7rvvPpkwYYKcP3/ezMbxBkEJAADwi7///e9y4sQJGTJkiBw7dkwqVaokX3zxxXXNr2khKIEtNP2nzVI0uQL8HsFdevToYY70oNEVAAAEBBpdAQBAQCAoAQAAAYGgBH7zxBNPJJubXrduXendu/ctv8Nff/21WJZl5sUDTsTvEoIVQUkQ/J+bfkDroXPIS5UqJSNGjJCrV69m+nsvWrRIXnzxxYAMJKZPn26CJp1zTwADb/C7dL1Tp07JM888IzExMRIRESFFixaVnj17mkURgfQgKAkCjzzyiBw9elR++ukn6devn1ke+JVXXkn1tboin7/kzZtXcuXKJYHowoUL5r4899xzdg8FDsLvUnK//fabOcaNGyffffedzJo1y0z/7Ny5s01/Q3A6gpIgoNNuCxUqJMWKFZNu3bqZHRs/+uijZGnikSNHSuHChc2/eNThw4elTZs2kidPHhNcNGvWTH7++efEa+qmS7pIjj6fL18++de//iUpdyxIWb7RpYoHDhwoRYoUMWPSrM3bb79trluvXj3zmqioKJO50HElrAaoe+SUKFHC/EusYsWKZgnjpD777DO5++67zfN6naTjTIuO69lnn5Xq1atn6N4iuPC7lFy5cuXkww8/lCZNmkjJkiXlgQceMP9f8vHHH9+SbCzch6AkCOmHd9KMiC4BvGfPHlm+fLl88skncuXKFWnQoIHJcvznP/+Rb775RnLmzGn+lZjwff/+97/Nv4pmzJgha9euNWncxYsX3/B927dvL/Pnz5eJEyfKjz/+KG+88Ya5rgYp+n9sSsehWZ3XXnvNPNaA5J133pFp06bJ999/L3369JHHH39cVq9enRg8tWzZ0vyf4vbt2+XJJ580wQZwK/C7dL2E/ayyZGEZLKSDbsgH9+rQoYOnWbNm5uv4+HjP8uXLPeHh4Z7+/fsnPn/bbbd54uLiEr9nzpw5npiYGPP6BPp8RESEZ9myZeZxdHS0Z+zYsYnPX7lyxXPHHXckvpeqU6eOp1evXubrPXv2aBrFvH9qVq1aZZ4/ffp04rlLly55smfP7lm3bl2y13bu3NnTtm1b8/WgQYM8sbGxyZ4fOHDgdddKS2rvC6SG36WbO3HihKdo0aKe5557jv8RIV0IZYOAZj80I6EZEC2HPPbYY8m2nS5fvrxpgk2wY8cO2bdv33X9IJcuXZL9+/ebfwlpNqNatWqJz+m/inSvg7Q2ndYsRmhoqNSpU8frcesYtPfjoYceSnZeszWVK1c2X2vGJek4lLcbPwG+4nfpxjsWN27cWGJjYzNtW3u4H0FJENA+i6lTp5rAQ/tGUqZVc+TIkezxuXPnzE6Pc+fOve5aBQoUSHea21c6DvXpp5/K7bffnuw5lqeHHfhdSt2ff/5pyrv6Dxkt44aFhd3ivxm4BUFJENCgQ5tKvXXPPffIe++9JwULFjS14dRER0fLxo0b5f777zePtalty5Yt5ntTo9kYzdJoL4g22qaUkKnRBtoE+i8uDT4OHTqUZoalTJkyiU27CTZs2OD1zwr4gt+l1DMk2oOmv6v6u5gtWzb+R4V0o9EV12nXrp3kz5/fzLjRRteDBw+adUR0/YEjR46Y1/Tq1UvGjBkjS5Yskd27d8vTTz99wzVGihcvbraz7tSpk/mehGu+//775nmdGaSzbjQ9rjtMapZE/9XVv39/09w6e/ZsUzraunWrTJo0yTxWTz31lJnqPGDAANMkO2/ePNOAezO6e6WWlLREpHbt2mUea8Mu4C9u/13SgOThhx82W9PrTDp9rL9beiT9BwbgtfS1osCJzXm+PH/06FFP+/btPfnz5zeNsXfeeaenS5cunjNnziQ2tmoTa+7cuT158uTx9O3b17w+rUZXdfHiRU+fPn1Mk2zWrFk9pUqV8syYMSPx+REjRngKFSrksSzLjEtps+2ECRNM421YWJinQIECngYNGnhWr16d+H0ff/yxuZaOs3bt2uaaN2teHTp0qHlNymPmzJk+3V8ED36X0m4UT+04ePCgDX9LcDp2CQYAAAGB8g0AAAgIBCUAACAgEJQAAICAQFACAAACAkEJAAAICAQlAAAgIBCUAACAgEBQAgAAAgJBCeBATzzxhDRv3jzxcd26daV37963fBy6vLkuaX6jZdH9/bMG6jgBZBxBCeDHD0/94NNDNxjUTRBHjBhhNivMbIsWLZIXX3wxID+gda+WCRMm3JL3AuBs7BIM+JFu3z5z5kyJi4uTzz77TLp37262cR80aNB1r718+XLi7sgZlTdvXr9cBwDsRKYE8CPdvr1QoUJmp9Zu3bpJ/fr1zXbuScsQI0eOlMKFC0tMTIw5f/jwYWnTpo3kyZPHBBe6o+zPP/+ceE3dbbVv377m+Xz58sm//vUv3Ugz2fumLN9oUDRw4EApUqSIGZNmbXQXV71uvXr1zGuioqJMxkTHpeLj42X06NFSokQJiYiIkIoVK8rChQuTvY8GWnfffbd5Xq+TdJzpoT9b586dE99T78lrr72W6muHDx8uBQoUkNy5c5sdbTWoS+DN2AEEPjIlQCbSD8jff/898fHKlSvNh+ry5cvN4ytXrkiDBg2kRo0aZmv7LFmyyEsvvWQyLjt37jSZlH//+99mC/kZM2ZImTJlzOPFixfLAw88kOb7tm/fXtavXy8TJ040H9C6vf3JkydNkPLhhx9Kq1atzPb0OhYdo9IP9XfffVemTZsmd911l6xZs0Yef/xxEwjUqVPHBE8tW7Y02Z9//vOfsnnzZunXr1+G7o8GE3fccYd88MEHJuBat26duXZ0dLQJ1JLet2zZspnSkwZCHTt2NK/XAM+bsQNwCLu3KQbcuLV9fHy8Z/ny5Z7w8HBP//79E5+/7bbbPHFxcYnfM2fOHE9MTIx5fQJ9PiIiwrNs2TLzODo62jN27NjE569cueK54447Et9L1alTx9OrVy/z9Z49e8zW8fr+N9pu/vTp04nnLl265MmePbtn3bp1yV7buXNnT9u2bc3XgwYN8sTGxiZ7fuDAgdddK6VixYp5xo8f7/FW9+7dPa1atUp8rPctb968nvPnzyeemzp1qidnzpyea9eueTX21H5mAIGHTAngR5988onkzJnTZEA0C/DYY4/JsGHDEp8vX758sj6SHTt2yL59+yRXrlzJrnPp0iXZv3+/nDlzRo4ePSrVqlVLfE6zKVWrVr2uhJNg+/btEhoa6lOGQMdw4cIFeeihh5Kd1xJJ5cqVzdc//vhjsnEozfBk1JQpU0wW6NChQ3Lx4kXznpUqVUr2Gs32ZM+ePdn7njt3zmRv9M+bjR2AMxCUAH6kfRZTp041gYf2jWgAkVSOHDmSPdYP1CpVqsjcuXOvu5aWHtIjoRzjCx2H+vTTT+X2229P9pz2pGSWBQsWSP/+/U1JSgMNDc5eeeUV2bhxY8CPHYD/EZQAfqRBhzaVeuuee+6R9957TwoWLGj6O1Kj/RX6IX3//febxzrFeMuWLeZ7U6PZGM3SrF692jTappSQqdEm0wSxsbHmA1yzFWllWLSfJaFpN8GGDRskI7755hupWbOmPP3004nnNEOUkmaUNIuSEHDp+2pGSntktDn4ZmMH4AzMvgFs1K5dO8mfP7+ZcaONrtqQqs2cPXv2lCNHjpjX9OrVS8aMGSNLliyR3bt3mw/wG60xouuCdOjQQTp16mS+J+Ga77//vnleZwbprBstNZ04ccJkGjRDoRmLPn36yOzZs01gsHXrVpk0aZJ5rHTGy08//SQDBgwwTbLz5s0zDbje+PXXX01ZKelx+vRp05SqDbPLli2TvXv3yuDBg2XTpk3Xfb+WYnSWzg8//GBmAA0dOlR69OghISEhXo0dgEPY3dQCuLHR1Zfnjx496mnfvr0nf/78pjH2zjvv9HTp0sVz5syZxMZWbWLNnTu3J0+ePJ6+ffua16fV6KouXrzo6dOnj2mSzZo1q6dUqVKeGTNmJD4/YsQIT6FChTyWZZlxKW22nTBhgmm8DQsL8xQoUMDToEEDz+rVqxO/7+OPPzbX0nHWrl3bXNObRld9TcpDm3y1SfWJJ57wREZGmp+tW7dunmeffdZTsWLF6+7bkCFDPPny5TMNrnp/9HsT3GzsNLoCzmDpf9kdGAEAAFC+AQAAAYGgBAAABASCEgAAEBAISgAAQEAgKAEAAAGBoAQAAAQEghIAABAQCEoAAEBAICgBAAABgaAEAAAEBIISAAAQEAhKAACABIL/A5tMT6wRuZSXAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from src.mlpclassifier_test import MLPClassificationModel\n",
+    "from src.mlpclassifier_visualization import ConfusionMatrixVisualizer   # adjust if needed\n",
+    "\n",
+    "# Extract data\n",
+    "X_train_scaled = data[\"X_train_scaled\"]\n",
+    "y_train = data[\"y_train\"]\n",
+    "\n",
+    "X_test_scaled = data[\"X_test_scaled\"]\n",
+    "y_test = data[\"y_test\"]\n",
+    "\n",
+    "# ---------------------------------------------------------\n",
+    "# Define 4 hyperparameter combinations to test\n",
+    "# ---------------------------------------------------------\n",
+    "configs = [\n",
+    "    {\"hidden_layer_sizes\": (45, 11), \"learning_rate_init\": 0.0015, \"max_iter\": 250},\n",
+    "    {\"hidden_layer_sizes\": (84, 45), \"learning_rate_init\": 0.0015, \"max_iter\": 250},\n",
+    "    {\"hidden_layer_sizes\": (84, 45), \"learning_rate_init\": 0.0007, \"max_iter\": 430},\n",
+    "    {\"hidden_layer_sizes\": (128, 84), \"learning_rate_init\": 0.0015, \"max_iter\": 250},\n",
+    "]\n",
+    "\n",
+    "# ---------------------------------------------------------\n",
+    "# Loop through each configuration and evaluate + visualize\n",
+    "# ---------------------------------------------------------\n",
+    "for i, cfg in enumerate(configs, start=1):\n",
+    "    print(f\"\\n======================================\")\n",
+    "    print(f\"   Testing Model {i}\")\n",
+    "    print(f\"   Config: {cfg}\")\n",
+    "    print(f\"======================================\")\n",
+    "\n",
+    "    # Create model\n",
+    "    clf = MLPClassificationModel(\n",
+    "        hidden_layer_sizes=cfg[\"hidden_layer_sizes\"],\n",
+    "        learning_rate_init=cfg[\"learning_rate_init\"],\n",
+    "        max_iter=cfg[\"max_iter\"]\n",
+    "    )\n",
+    "\n",
+    "    # Train\n",
+    "    clf.train(X_train_scaled, y_train)\n",
+    "\n",
+    "    # Evaluate\n",
+    "    y_pred = clf.evaluate(X_test_scaled, y_test)\n",
+    "\n",
+    "    # Visualize\n",
+    "    viz = ConfusionMatrixVisualizer(y_test, y_pred)\n",
+    "    viz.plot_confusion_matrix()\n",
+    "    viz.plot_performance_scatter()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ef12c1e",
+   "metadata": {},
+   "source": [
+    "Model\tHidden Layers\tLearning Rate\tIterations\tAccuracy\n",
+    "Model 1\t(45, 11)\t    0.0015\t            250\t      77.8%\n",
+    "Model 2\t(84, 45)\t    0.0015\t            250     \t77.8%\n",
+    "Model 3\t(84, 45)\t    0.0007\t            430\t      66.7%\n",
+    "Model 4\t(128, 84)\t    0.0015\t            250\t       100%\n",
+    "\n",
+    "Observations)\n",
+    "\n",
+    "Model 4 performed the best, achieving perfect classification accuracy (100%).\n",
+    "Model 3 performed the worst.\n",
+    "\n",
+    "Effect of Network Architecture)\n",
+    "\n",
+    "Increasing network size improved performance:\n",
+    "Small network: (45,11) → 77.8%\n",
+    "Medium network: (84,45) → 77.8%\n",
+    "Large network: (128,84) → 100%\n",
+    "\n",
+    "A larger neural network  resulted in improved classification performance.\n",
+    "\n",
+    "A smaller learning rate reduced performance in this case.\n",
+    "The optimizer likely updated weights too slowly, leading to suboptimal convergence.\n",
+    "\n",
+    "More iterations in Model 3 did not lead to a better result.\n",
+    "\n",
+    "It is strange that Model 4 has perfect accuracy. Either I got lucky with th ehyperparameters, or an error was made. \n",
+    "\n",
+    "Shown in Test.ipynb is the MLPClassifier ANN using \"adam\" as a solver instead of \"sgd\". With \"adam\" I was getting the same answer for all iterations so the solver was changed. The first \"adam\" iteration had an accuracy of 94%. This suggests that in addition to changing hyperparamters, it is also worth looking into different solvers.\n",
+    "\n",
+    "Matrix\tAccuracy\tClass 1 Recall\tClass 2 Recall\tMain Bias\n",
+    "1\t77.8%\t0.89\t0.67\tPredicts Class 1\n",
+    "2\t77.8%\t0.67\t0.89\tPredicts Class 2\n",
+    "3\t66.7%\t0.56\t0.78\tWeak overall\n",
+    "\n",
+    "Its also interesting to see how the hyperparameter differences affect how good the model is at detection labels. The 1st iteraction is better at category 1 detection vs the matrix 2 & 3 which are better for Class 2. \n",
+    "\n",
+    "It is important to note that With such a small dataset: 1–2 misclassifications change accuracy significantly so results may vary due to random variation."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb
new file mode 100644
index 0000000..d65d937
--- /dev/null
+++ b/notebooks/Test.ipynb
@@ -0,0 +1,474 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a9ae00d1",
+   "metadata": {},
+   "source": [
+    "# Data Loading & Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efbacb87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'c:\\\\Users\\\\ldiaz\\\\OneDrive\\\\Desktop\\\\CHE 4230\\\\meter_Hw4'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "os.chdir(\"../\")\n",
+    "\n",
+    "os.getcwd()\n",
+    "\n",
+    "# ^^^makes sure current working directory is the correct one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee5e97a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         0         1         2         3          4          5         6   \\\n",
+      "0  0.841499  1.009367  0.993816  8.469805  10.278727  10.037759  8.501365   \n",
+      "1  0.842250  1.006584  0.996605  7.531891   9.139924   8.951618  7.612213   \n",
+      "2  0.840723  1.011647  0.998152  6.641699   7.975464   7.857692  6.593117   \n",
+      "3  0.841119  1.017807  0.996812  5.687524   6.824334   6.689885  5.615428   \n",
+      "4  0.840358  1.016534  0.996221  5.660385   6.829560   6.675628  5.623977   \n",
+      "\n",
+      "         7          8          9   ...         27         28         29  \\\n",
+      "0  8.581726  10.247763  10.058822  ...  32.451173  34.568685  33.082683   \n",
+      "1  7.623325   9.106345   8.945142  ...  32.428385  34.441732  33.081055   \n",
+      "2  6.681572   7.964596   7.814698  ...  32.428385  34.275715  33.113605   \n",
+      "3  5.763315   6.801051   6.686639  ...  32.485350  34.080403  33.170573   \n",
+      "4  5.736818   6.813453   6.672377  ...  32.503255  34.122720  33.164062   \n",
+      "\n",
+      "          30         31         32         33         34         35  36  \n",
+      "0  36.722005  36.969403  36.075847  36.051432  35.174155  32.729490   1  \n",
+      "1  36.687825  36.933595  36.054688  35.979818  34.847005  32.731122   1  \n",
+      "2  36.661785  36.873370  36.002605  35.963542  34.689128  32.771810   1  \n",
+      "3  36.673177  36.811525  35.974935  35.955403  34.500328  32.849935   1  \n",
+      "4  36.673177  36.826173  35.996095  35.968425  34.474283  32.853190   1  \n",
+      "\n",
+      "[5 rows x 37 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from meter_Hw4.src.data_preprocess import DataPreprocessing \n",
+    "\n",
+    "# Using preprocessing parent class which will load the data and preprocess it by dropping N/A's and turning to numpy fuctionality\n",
+    "\n",
+    "preprocessor = DataPreprocessing()\n",
+    "\n",
+    "data = preprocessor.load_data(\"data/Meter_A.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e41347",
+   "metadata": {},
+   "source": [
+    "# Visualizing preprocessed data to assess any possible relationships/trends"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "1079d0b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ldiaz\\anaconda3\\envs\\myenv\\Lib\\site-packages\\umap\\umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+      "  warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import umap\n",
+    "\n",
+    "X = data[:, :-1]      # features\n",
+    "y = data[:, -1]       # labels (0,1,2)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "#heatmap\n",
+    "plt.figure(figsize=(14, 10))\n",
+    "sns.heatmap(pd.DataFrame(X_scaled).corr(), cmap=\"coolwarm\", center=0)\n",
+    "plt.title(\"Correlation Heatmap of Processed Features\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "#umap\n",
+    "umap_model = umap.UMAP(\n",
+    "    n_neighbors=30,\n",
+    "    min_dist=0.1,\n",
+    "    metric=\"euclidean\",\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "embedding = umap_model.fit_transform(X)\n",
+    "\n",
+    "plt.figure(figsize=(10, 7))\n",
+    "scatter = plt.scatter(\n",
+    "    embedding[:, 0],\n",
+    "    embedding[:, 1],\n",
+    "    c=y,\n",
+    "    cmap=\"viridis\",\n",
+    "    s=40,\n",
+    "    alpha=0.9\n",
+    ")\n",
+    "plt.colorbar(scatter, label=\"Label\")\n",
+    "plt.title(\"UMAP Projection of Processed Data\")\n",
+    "plt.xlabel(\"UMAP-1\")\n",
+    "plt.ylabel(\"UMAP-2\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc03fb05",
+   "metadata": {},
+   "source": [
+    "# Going to add training,testing, and validation data splitting to preprocessing parent class seen in 3.0_Model_built.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3b9a91ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train: {np.float64(1.0), np.float64(2.0)}\n",
+      "Validation: {np.float64(1.0), np.float64(2.0)}\n",
+      "Test: {np.float64(1.0), np.float64(2.0)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train_validation, test = train_test_split(data, test_size = 0.2, random_state=12)\n",
+    "\n",
+    "train, validation = train_test_split(train_validation, test_size = 0.2, random_state=99)\n",
+    "\n",
+    "# Check data spread in training, validation, and testing\n",
+    "\n",
+    "print(\"Train:\", set(train[:, -1]))\n",
+    "print(\"Validation:\", set(validation[:, -1]))\n",
+    "print(\"Test:\", set(test[:, -1]))\n",
+    "\n",
+    "# result demonstrates each split contains the same two classes\n",
+    "# this ensures no other labels exist and the analysis will be thorough\n",
+    "\n",
+    "X_train = train[:, :-1]\n",
+    "y_train = train[:, -1]\n",
+    "\n",
+    "X_val = validation[:, :-1]\n",
+    "y_val = validation[:, -1]\n",
+    "\n",
+    "X_test = test[:, :-1]\n",
+    "y_test = test[:, -1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12ab9638",
+   "metadata": {},
+   "source": [
+    "# Going to add standardization process seen in 3.0_Model_built.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "081ac78e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler().fit(X_train)\n",
+    "\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "X_val_scaled   = scaler.transform(X_val)\n",
+    "X_test_scaled  = scaler.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb05b87b",
+   "metadata": {},
+   "source": [
+    "# Retrieving scikit learn MLPRegressor class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a877012f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08858551771298075 0.6456579291480771\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neural_network import MLPRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "hidden_layer_sizes = (64, 32)\n",
+    "learning_rate_init = 0.001\n",
+    "max_iter = 300\n",
+    "\n",
+    "model = MLPRegressor(\n",
+    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    learning_rate_init=learning_rate_init,\n",
+    "    max_iter=max_iter,\n",
+    "    activation='relu',\n",
+    "    solver='adam',\n",
+    "    random_state=42\n",
+    ")\n",
+    "model\n",
+    "\n",
+    "# Training model here\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Predicted values\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "\n",
+    "# Checking accuracy\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(mse, r2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8757b8f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualizing\n",
+    "import matplotlib.pyplot\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_test, y_pred, alpha=0.6)\n",
+    "plt.xlabel(\"Actual Values\")\n",
+    "plt.ylabel(\"Predicted Values\")\n",
+    "plt.title(\"MLPRegressor Performance\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46da2684",
+   "metadata": {},
+   "source": [
+    "# Retrieve MLPClassifier to test it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ce7fbfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9444444444444444\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         1.0       0.90      1.00      0.95         9\n",
+      "         2.0       1.00      0.89      0.94         9\n",
+      "\n",
+      "    accuracy                           0.94        18\n",
+      "   macro avg       0.95      0.94      0.94        18\n",
+      "weighted avg       0.95      0.94      0.94        18\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "hidden_layer_sizes = (64, 32)\n",
+    "learning_rate_init = 0.001\n",
+    "max_iter = 300\n",
+    "\n",
+    "model = MLPClassifier(\n",
+    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    learning_rate_init=learning_rate_init,\n",
+    "    max_iter=max_iter,\n",
+    "    activation='relu',\n",
+    "    solver='adam',\n",
+    "    random_state=42\n",
+    ")\n",
+    "model\n",
+    "\n",
+    "# Training model here\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Predicted values\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "\n",
+    "print(\"\\nClassification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "\n",
+    "# the MLPClassifier will be used for analysis because as begun in the provided code in test_model_built, we - \n",
+    "# are considering our final column as \"labels\"; the MLPRegressor tried to give predictions with continuous data which I don't think is what we're looking for"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59e8343b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/+yX7OMvEx8ebzkdnbsvKNRdmnaUFGmR9GWb1Eoa6P8KsfWg/+9GG9qMN7Ub72S/ZT1kmOyWhDAADAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAIBMnTl3UXrGbDf39VYfBxK/htmxY8fKHXfcIUWLFpXSpUtLhw4d5Keffrrq6+bPny/Vq1eXggULSu3atWXp0qU+OV4AAIBrSauJ66Xu6NXy1aE/zWO91ce6PlD4NcyuX79e+vTpI1999ZWsWrVKkpOTpVWrVpKQkJDpa7Zs2SJdunSRHj16yK5du0wA1mXPnj0+PXYAAIC/s1YT18vPJ89n+JyuD5RAm9+fO1++fLnb45iYGNNDu2PHDrn77rszfM2kSZPknnvukUGDBpnHI0eONEF48uTJMm3aNJ8cNwAAwN/ZmXMXMw2yTvq8blesaEG5ZsNsWmfPnjW3xYsXz3SbrVu3yoABA9zWtW7dWpYsWZLh9klJSWZxio+PN7faC6yLLzj346v9wbtoP/vRhvajDe1G+9mn39wdEprP4XocGuxwu0293buP1/f6/j3JTEEOh8P9qPwkJSVF7rvvPjlz5oxs2rQp0+0KFCggH3zwgSk1cHr77bclOjpaTp48mW77ESNGmOfSmj17thQqVMiLPwEAAAC8ITExUbp27Wo6OsPDw+3omdXaWa17zSrI5sTQoUPdenK1ZzYyMtLU5l7t5Hjz04WWQrRs2VJCQkJ8sk94D+1nP9rQfrSh3Wg/+/SM2e4a9OXskR1ZL0X+ExssSSlBrvX/rHxdnvTMOr9Jz46ACLN9+/aVzz//XDZs2CAVKlTIctsyZcqk64HVx7o+I6GhoWZJS0Olr4OlP/YJ76H97Ecb2o82tBvtZ49JD99uZi1IS4Ns0pUgt+3yItt48p5+nc1AKxw0yC5evFjWrFkjVapUueprGjZsKKtXu59c7fXU9QAAAMg9HdRV7foiWW6jz/t78Jffw6yWFnz44YemflXnmj1x4oRZLly44NqmW7duplTAqV+/fmYWhAkTJsjevXtNTWxsbKwJxQAAAPCOlf0bZxpodb0+Hwj8WmYwdepUc9ukSRO39TNmzJDHH3/c3I+Li5Pg4P9l7jvvvNOE3xdffFFeeOEFqVq1qpnJoFatWj4+egAAgL+3lf0bm+m3dNYCkdOmRlZLCwKhRzYgwmx2JlJYt25dunUPPvigWQAAAJC3NLjqIC+94qreBtr4H7+WGQAAAAC5QZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACs5dcwu2HDBmnfvr2UK1dOgoKCZMmSJVd9zUcffSR16tSRQoUKSdmyZaV79+7y+++/++R4AQAAEFj8GmYTEhJMMJ0yZUq2tt+8ebN069ZNevToId9//73Mnz9ftm/fLr169crzYwUAAEDgye/Pnbdp08Ys2bV161apXLmyPPvss+ZxlSpV5Mknn5RXX301D48SAAAAgcqvYdZTDRs2lBdeeEGWLl1qQvCpU6dkwYIF0rZt20xfk5SUZBan+Ph4c5ucnGwWX3Dux1f7g3fRfvajDe1HG9qN9rNfso+zjCf7CXI4HA4JAFozu3jxYunQoUOW22lpgdbJXrx4US5fvmxqbhcuXCghISEZbj9ixAiJjo5Ot3727Nmm7hYAAACBJTExUbp27Spnz56V8PDwv0+Y/eGHH6RFixbSv39/ad26tRw/flwGDRokd9xxh7z33nvZ7pmNjIyU06dPX/XkePPTxapVq6Rly5aZhm4ELtrPfrSh/WhDu9F+9kv2cZbRvFayZMlshVmrygzGjh0rjRo1MgFW3XLLLVK4cGG56667ZNSoUWZ2g7RCQ0PNkpY2hK+DpT/2Ce+h/exHG9qPNrQb7We/EB9lGU/2EWxbl3NwsPsh58uXz9wGSAczAAAAfMivYfb8+fOye/dus6iDBw+a+3Fxcebx0KFDzVRcTlofu2jRIpk6daocOHDATNWlMxvUr1/fzFULAACAa4tfywxiY2OladOmrscDBgwwt1FRURITE2NqYp3BVj3++ONy7tw5mTx5sjz//PNSrFgxadasGVNzAQAAXKP8GmabNGmSZXmABtq0nnnmGbMAAAAAVtXMAgAAAKkRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArp0we/jwYTly5Ijr8fbt2+W5556T6dOne/vYAAAAAO+G2a5du8ratWvN/RMnTkjLli1NoB02bJi8/PLLnr4dAAAA4Lswu2fPHqlfv765P2/ePKlVq5Zs2bJFPvroI4mJicn5kQAAAAB5HWaTk5MlNDTU3P/yyy/lvvvuM/erV68ux48f9/TtAAAAAN+F2ZtvvlmmTZsmGzdulFWrVsk999xj1h87dkxKlCiR8yMBAAAA8jrMvvrqq/Lf//5XmjRpIl26dJE6deqY9Z9++qmr/AAAAAAIyDCrIfb06dNmef/9913r//Wvf5keW09s2LBB2rdvL+XKlZOgoCBZsmTJVV+TlJRkBptVqlTJlDtUrlzZ7TgAAABw7cjv6QsuXLggDodDrrvuOvP4119/lcWLF0uNGjWkdevWHr1XQkKC6dnt3r27dOzYMVuv6dy5s5w8eVLee+89ufHGG02dbkpKiqc/BgAAAK7FMPt///d/Jnj27t1bzpw5Iw0aNJCQkBDTU/v666/LU089le33atOmjVmya/ny5bJ+/Xo5cOCAFC9e3KzTnlkAAABcmzwOszt37pSJEyea+wsWLJDrr79edu3aJQsXLpThw4d7FGY9pXW59erVk3HjxsmsWbOkcOHCZjaFkSNHSlhYWKZlCbo4xcfHu2Zl0MUXnPvx1f7gXbSf/WhD+9GGdqP97Jfs4yzjyX48DrOJiYlStGhRc3/lypWmlzY4OFj++c9/mpKDvKQ9sps2bZKCBQua0gbtDX766afl999/lxkzZmT4mrFjx0p0dHS69XrshQoVEl/S2R9gL9rPfrSh/WhDu9F+9lvloyyjeTPPwqzWqepArfvvv19WrFgh/fv3N+tPnTol4eHhkpe0NlYHiukFGiIiIsw6LW144IEH5O23386wd3bo0KEyYMAAt57ZyMhIadWqVZ4fb+pPF9r4erU0LcmAXWg/+9GG9qMN7Ub72S/Zx1nG+U16noRZLSXQS9pqiG3evLk0bNjQ1dN56623Sl4qW7aslC9f3hVklQ480wFpR44ckapVq6Z7jc544LzIQ2raEL4Olv7YJ7yH9rMfbWg/2tButJ/9QnyUZTzZh8dTc2kvaFxcnMTGxpoBWU4abJ21tHmlUaNG5uIM58+fd637+eefTZlDhQoV8nTfAAAACDweh1lVpkwZ0wurIdJJL5igl7T1hIbS3bt3m0UdPHjQ3New7CwR6Natm2t77RHWq4w98cQT8sMPP5h5agcNGmSm9spsABgAAAD+vjwuM1DaKztv3jwTOi9duuT23KJFizx6n6ZNm7oeO2tbo6KiJCYmxswh6wy2qkiRIqZe45lnnjGzGmiw1XlnR40alZMfAwAAANdamJ07d67pLdULJGidrA6k0q/69UIGOijM06uJab1rZjTQpqW9v4yGBAAAQI7KDMaMGWNqYz/77DMpUKCATJo0Sfbu3Wt6SCtWrMhZBQAAQOCG2V9++UXatWtn7muY1UvS6nRZOrvB9OnT8+IYAQAAAO+E2euuu07OnTtn7us0WXv27DH39dK2nkxwCwAAAPi8Zvbuu+82Nau1a9eWBx98UPr16ydr1qwx63R6LgAAACBgw+zkyZPl4sWL5v6wYcPMpLZbtmyRTp06yYsvvpgXxwgAAAB4J8wWL17cdV/nmR0yZIinbwEAAAD4Lsx6cn3c8PDw3BwPAAAA4N0wW6xYMTNjQVZ0vljd5sqVK9nfOwAAAJDXYXbt2rW52QcAAADgvzDbuHFjTj8AAADsnWd237590qVLlwzrZ8+ePStdu3aVAwcOePv4AAAAgNyH2fHjx0tkZGSGA7wiIiLMc7oNAAAAEHBhdv369eYiCZnp3LmzuXgCAAAAEHBhNi4uTkqXLp3p8yVLlpTDhw9767gAAAAA74VZLSX45ZdfMn1+//79zDELAACAwAyzd999t7z11luZPv/mm2/KXXfd5a3jAgAAALwXZocOHSrLli2TBx54QLZv325mMNBl27Zt0qlTJ1mxYoXZBgAAAAioeWbVrbfeKgsWLJDu3bvL4sWL3Z4rUaKEzJs3T2677ba8OEYAAAAgd2FW3XvvvfLrr7/K8uXLTY2sXsK2WrVq0qpVKylUqJAnbwUAAAD4NsyqsLAwuf/++3O/ZwAAAMBXNbMAAABAoCHMAgAAwFqEWQAAAFiLMAsAAIC/9wCw+Pj4bL9heHh4bo4HAAAA8G6YLVasmAQFBWXrDa9cuZL9vQMAAAB5HWbXrl3run/o0CEZMmSIPP7449KwYUOzbuvWrfLBBx/I2LFjc3MsAAAAgPfDbOPGjV33X375ZXn99delS5curnX33Xef1K5dW6ZPny5RUVGeHQEAAADgqwFg2gtbr169dOt13fbt23N6HAAAAEDeh9nIyEh555130q1/9913zXMAAABAwF7OduLEidKpUydZtmyZNGjQwKzTHtl9+/bJwoUL8+IYAQAAAO/0zLZt21Z+/vlnad++vfzxxx9m0fu6Tp8DAAAAArZnVmk5wZgxY7x/NAAAAEBeXwFs48aN8uijj8qdd94pR48eNetmzZolmzZtysnbAQAAAL4Js1oX27p1awkLC5OdO3dKUlKSWX/27Fl6awEAABDYYXbUqFEybdo0M6NBSEiIa32jRo1MuAUAAAACNsz+9NNPcvfdd6dbHxERIWfOnPHWcQEAAADeD7NlypSR/fv3p1uv9bI33HCDp28HAAAA+C7M9urVS/r16yfbtm2ToKAgOXbsmHz00UcycOBAeeqpp3J+JAAAAEBeT801ZMgQSUlJkebNm0tiYqIpOQgNDTVh9plnnvH07QAAAADfhVntjR02bJgMGjTIlBucP39eatasKUWKFMn5UQAAAAC+KDPo3r27nDt3TgoUKGBCbP369U2QTUhIMM8BAAAAARtmP/jgA7lw4UK69bpu5syZ3jouAAAAwHtlBvHx8eJwOMyiPbMFCxZ0PXflyhVZunSplC5dOrtvBwAAAPguzBYrVszUy+pSrVq1dM/r+ujo6NwfEQAAAODtMLt27VrTK9usWTNzSdvixYu7ntP62UqVKkm5cuWy+3YAAACA78Js48aNze3BgwelYsWKpicWAAAAsGoA2Jo1a2TBggXp1s+fP98MDgMAAAACNsyOHTtWSpYsmW69Dv4aM2aMt44LAAAA8H6YjYuLkypVqqRbrzWz+hwAAAAQsGFWe2C//fbbdOu/+eYbKVGihLeOCwAAAPB+mO3SpYs8++yzZnYDnV9WF62j7devnzz88MOevh0AAACQ97MZOI0cOVIOHTokzZs3l/z5/3p5SkqKdOvWjZpZAAAABHaY1TllP/74YxNqtbQgLCxMateubWpmAQAAgIAOs056FbCMrgQGAAAABFSYHTBggOmJLVy4sLmflddff91bxwYAAADkPszu2rVLkpOTXfczw1XBAAAAEHBhVmcuyOg+AAAAYNXUXAAAAIBVPbMdO3bM9hsuWrQoN8cDAAAAeLdnNiIiwrWEh4fL6tWrJTY21vX8jh07zDp9HgAAAAiontkZM2a47v/73/+Wzp07y7Rp0yRfvnxmnV4F7OmnnzZBFwAAAAjYmtn3339fBg4c6AqySu/rlF36HAAAABCwYfby5cuyd+/edOt1nV7WFgAAAAjYK4A98cQT0qNHD/nll1+kfv36Zt22bdvklVdeMc8BAAAAARtmX3vtNSlTpoxMmDBBjh8/btaVLVtWBg0aJM8//3xeHCMAAADgnTAbHBwsgwcPNkt8fLxZx8AvAAAAWHPRBK2b/fLLL2XOnDmuS9geO3ZMzp8/7+3jAwAAALzXM/vrr7/KPffcI3FxcZKUlCQtW7aUokWLyquvvmoe65RdAAAAQED2zPbr10/q1asnf/75p4SFhbnW33///ebCCQAAAEDA9sxu3LhRtmzZIgUKFHBbX7lyZTl69Kg3jw0AAADwbs+sziWrV/xK68iRI6bcAAAAAAjYMNuqVSt54403XI91AJgO/HrppZekbdu23j4+AAAAwHthVueZ3bx5s9SsWVMuXrwoXbt2dZUY6CAwT2zYsEHat28v5cqVM6F4yZIl2X6tHkP+/Pmlbt26nv4IAAAAuFZrZiMjI+Wbb76Rjz/+2Nxqr6xeEeyRRx5xGxCWHQkJCVKnTh3p3r27dOzYMduvO3PmjHTr1k2aN28uJ0+e9PRHAAAAwLUYZpOTk6V69ery+eefm/CqS260adPGLJ7q3bu36RHOly+fR725AAAAuIbDbEhIiCkt8KcZM2bIgQMH5MMPP5RRo0ZddXud+1YXJ+dVyzSY6+ILzv34an/wLtrPfrSh/WhDu9F+9kv2cZbxZD8elxn06dPH1Ma+++67pmbVl/bt2ydDhgwx04Nld99jx46V6OjodOtXrlwphQoVEl9atWqVT/cH76L97Ecb2o82tBvtZ79VPsoyiYmJ2d7W4zT69ddfm4sjaBisXbu2FC5c2O35RYsWSV7Q6cC0tECDabVq1bL9uqFDh8qAAQPcema17ldnZQgPDxdffbrQxterpWnvNuxC+9mPNrQfbWg32s9+yT7OMs5v0vMkzBYrVkw6deokvnbu3DmJjY2VXbt2Sd++fV1z3jocDtNLq+G6WbNm6V4XGhpqlrS0IXwdLP2xT3gP7Wc/2tB+tKHdaD/7hfgoy3iyj/w5qVn1B+1F/e6779zWvf3227JmzRpZsGCBVKlSxS/HBQAAAP/JdpjVXtDx48fLp59+KpcuXTLTYumFEjydjis1ndZr//79rscHDx6U3bt3S/HixaVixYqmREDnr505c6YEBwdLrVq13F5funRpKViwYLr1AAAAuDZk+6IJo0ePlhdeeEGKFCki5cuXl0mTJpnBYLmhZQO33nqrWZTWtur94cOHm8fHjx+XuLi4XO0DAAAAf1/Z7pnV3lH9Wv/JJ580j7/88ktp166dmdVAe01zokmTJqbmNTMxMTFZvn7EiBFmAQAAwLUp2ylUe0jbtm3retyiRQtzCdpjx47l1bEBAAAA3gmzly9fNvWpaUeacSEAAAAABHyZgZYDPP74427TXOnVwPTSsqnnms2reWYBAACAHIfZqKiodOseffTR7L4cAAAA8F+Y9df8sgAAAEBmcjYNAQAAABAACLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBafg2zGzZskPbt20u5cuUkKChIlixZkuX2ixYtkpYtW0qpUqUkPDxcGjZsKCtWrPDZ8QIAACCw+DXMJiQkSJ06dWTKlCnZDr8aZpcuXSo7duyQpk2bmjC8a9euPD9WAAAABJ78/tx5mzZtzJJdb7zxhtvjMWPGyCeffCKfffaZ3HrrrXlwhAAAAAhkfg2zuZWSkiLnzp2T4sWLZ7pNUlKSWZzi4+PNbXJysll8wbkfX+0P3kX72Y82tB9taDfaz37JPs4ynuwnyOFwOCQAaM3s4sWLpUOHDtl+zbhx4+SVV16RvXv3SunSpTPcZsSIERIdHZ1u/ezZs6VQoUK5OmYAAAB4X2JionTt2lXOnj1rxkn9LcOshtFevXqZMoMWLVp41DMbGRkpp0+fvurJ8eani1WrVpl635CQEJ/sE95D+9mPNrQfbWg32s9+yT7OMprXSpYsma0wa2WZwdy5c6Vnz54yf/78LIOsCg0NNUta2hC+Dpb+2Ce8h/azH21oP9rQbrSf/UJ8lGU82Yd188zOmTNHnnjiCXPbrl07fx8OAAAA/MivPbPnz5+X/fv3ux4fPHhQdu/ebQZ0VaxYUYYOHSpHjx6VmTNnukoLoqKiZNKkSdKgQQM5ceKEWR8WFiYRERF++zkAAADgH37tmY2NjTVTajmn1RowYIC5P3z4cPP4+PHjEhcX59p++vTpcvnyZenTp4+ULVvWtfTr189vPwMAAACu0Z7ZJk2aSFbjz2JiYtwer1u3zgdHBQAAAFtYVzMLAAAAOBFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizCbx/6IvyCPTt9q7uutPgYAALBFSopDDp1OMPf1Vh8HEr+G2Q0bNkj79u2lXLlyEhQUJEuWLLnqa9atWye33XabhIaGyo033igxMTESqO4et0ZuG7NGdh+LN4/1Vh/regAAgEC35+hZGfnFDzJm2Y/msd7qY10fKPwaZhMSEqROnToyZcqUbG1/8OBBadeunTRt2lR2794tzz33nPTs2VNWrFghgUYDa9wfGffC6noCLQAACGR7jp6VN1fvk++OnJWIggXMOr3Vx7o+UAJtfn/uvE2bNmbJrmnTpkmVKlVkwoQJ5nGNGjVk06ZNMnHiRGndurUECi0lyCzIOunzul3x8DCfHRcAAEB2aCnBwp1H5I+ES3Jj6SKSP+iv0oIiBfPLjaEhsv/UeVm086jULBsuwcFBcs2GWU9t3bpVWrRo4bZOQ6z20GYmKSnJLE7x8X995Z+cnGyWvPDUrK8lNN//6klCgx1ut6m3+/BfDfPkGOA9zt+TvPp9Qd6jDe1HG9qN9rPPodMJcui3eKkQEWqCbLCkmPV6mz8o2Kw/+NtZ+eXkWalcsrDX9+/J31yrwuyJEyfk+uuvd1unjzWgXrhwQcLC0vdyjh07VqKjo9OtX7lypRQqVChPjrNrhb+WtEbW++sX4X/+lKVLl+bJMcD7Vq1axWm1HG1oP9rQbrSfXTqUSL/u9nxxf93R/FpY5Iftp+SHPNh3YmLi3zPM5sTQoUNlwIABrscafCMjI6VVq1YSHh6eJ/vUWQucg76cPbIaZP8TGyxJKf/riq9bLpyeWQvop0P9H3DLli0lJCTE34eDHKAN7Ucb2o32s7NndsyyH02NrJYWaI+sBtkdVypKigTL+YuX5ezFS/JCmxp50jPr/Cb9bxdmy5QpIydPnnRbp481lGbUK6t01gNd0tJQklfBZOpjd5hZC9LSIJt0JchtO8KRPfLydwa+QRvajza0G+1nj39cHyGVS4WbwV5aI6ulBUqD7GVHkBw5myS3VChmtsuLmllP/t5aNc9sw4YNZfXq1W7rtMdM1wcSHdRVsXjWA7v0eQZ/AQCAQBQcHCSdbqsgxQsXMIO9tCdW6a0+1vUdbyvv98Ff5lj9ufPz58+bKbZ0cU69pffj4uJcJQLdunVzbd+7d285cOCADB48WPbu3Stvv/22zJs3T/r37y+BZsPgZpkGWl2vzwMAAASqWuUj5NnmVaV2hQhTUqD0Vntkdb0+Hwj8WmYQGxtr5ox1cta2RkVFmYshHD9+3BVslU7L9cUXX5jwOmnSJKlQoYK8++67ATUtV2oaWHX6LZ21QAd7aY2slhbQIwsAAGxQq3yEmX5LZy3QwV5aI5tXpQVWhtkmTZqIw5H5JdEyurqXvmbXrl1iCw2uOv2Wzlqgt9RcAgAAmwQHB5lBXjprgd4GUpC1rmYWAAAASI0wCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC18ss1xuFwmNv4+Hif7TM5OVkSExPNPkNCQny2X3gH7Wc/2tB+tKHdaD/7Jfs4yzhzmjO3ZeWaC7Pnzp0zt5GRkf4+FAAAAFwlt0VERGS1iQQ5shN5/0ZSUlLk2LFjUrRoUQkKCvLJPvXThYbnw4cPS3h4uE/2Ce+h/exHG9qPNrQb7We/eB9nGY2nGmTLlSsnwcFZV8Vecz2zekIqVKjgl31r4xNm7UX72Y82tB9taDfaz37hPswyV+uRdWIAGAAAAKxFmAUAAIC1CLM+EBoaKi+99JK5hX1oP/vRhvajDe1G+9kvNICzzDU3AAwAAAB/H/TMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcJsLm3YsEHat29vrlChVxRbsmTJVV+zbt06ue2228yIwBtvvFFiYmJyexjwYRsuWrRIWrZsKaVKlTITRzds2FBWrFhBG1j279Bp8+bNkj9/fqlbt26eHiO8235JSUkybNgwqVSpkvl/aeXKleX999/nNFvUhh999JHUqVNHChUqJGXLlpXu3bvL77//7pPjhbuxY8fKHXfcYa6OWrp0aenQoYP89NNPcjXz58+X6tWrS8GCBaV27dqydOlS8QfCbC4lJCSYf4xTpkzJ1vYHDx6Udu3aSdOmTWX37t3y3HPPSc+ePQlDFrWh/k9bw6z+o92xY4dpS/2f+K5du/L8WOGdNnQ6c+aMdOvWTZo3b86ptaz9OnfuLKtXr5b33nvP/NGdM2eO3HTTTXl6nPBeG+qHSP2316NHD/n+++9NKNq+fbv06tWL0+wH69evlz59+shXX30lq1atkuTkZGnVqpVp18xs2bJFunTpYtpQ//5pANZlz5494nM6NRe8Q0/n4sWLs9xm8ODBjptvvtlt3UMPPeRo3bo1zWBJG2akZs2ajujo6Dw5JuRdG+q/vRdffNHx0ksvOerUqcOptqT9li1b5oiIiHD8/vvvPjsueLcNx48f77jhhhvc1r355puO8uXLc6oDwKlTp0w7rl+/PtNtOnfu7GjXrp3bugYNGjiefPJJh6/RM+tjW7dulRYtWrita926tVkPO6WkpMi5c+ekePHi/j4UeGDGjBly4MABMwk47PLpp59KvXr1ZNy4cVK+fHmpVq2aDBw4UC5cuODvQ0M2aXnW4cOHzTdcmn9PnjwpCxYskLZt23IOA8DZs2fNbVZ/1wIpz+T3+R6vcSdOnJDrr7/ebZ0+jo+PN/8jDgsL89uxIWdee+01OX/+vPnaE3bYt2+fDBkyRDZu3GjqZWEX/RCyadMmU6e3ePFiOX36tDz99NOm3lI/pCDwNWrUyNTMPvTQQ3Lx4kW5fPmyKdfytFQI3qcdNFoCqW1Uq1Ytj/OMrvc1emaBXJg9e7ZER0fLvHnzTNE8At+VK1eka9eupt20Rw92/rHVQUYahurXr296815//XX54IMP6J21xA8//CD9+vWT4cOHm7EHy5cvl0OHDknv3r39fWjXvD59+pi617lz51pzLuiS8LEyZcqYr1NS08c6Kp5eWbvoP3QdvKcDF9J+1YLApSUhsbGxZsBC3759XeFIv+rUXtqVK1dKs2bN/H2YyIKOfNfygoiICNe6GjVqmDY8cuSIVK1alfNnweh57fkbNGiQeXzLLbdI4cKF5a677pJRo0aZNobv9e3bVz7//HMz0LlChQo5yjO63tfomfVDnZCOwE1NRw7qethDR04/8cQT5lZnp4A99IPjd999Z2YTcS7aG6Qj4fV+gwYN/H2IuAoNQceOHTPlPU4///yzBAcHX/UPMAJDYmKiaa/U8uXLZ27/GkMGX3I4HCbIatnOmjVrpEqVKlblGXpmc0n/Z7p//363qbf0D6IWTVesWFGGDh0qR48elZkzZ5rn9Y/m5MmTZfDgwWZOPf2l0a+ov/jii9weCnzUhlpaEBUVJZMmTTLBx1kfpD3rqXuKEJhtqH9A09aBaYmI1l9mVR+GwPk3qGUiI0eONB8otVxEa2a1h0//n8o3XHa0odbH6jRcU6dONYOGjh8/buo0tWxE56qF70sLZs+eLZ988omZa9b5d03/pjn/TelUavqNiPaqKy0Tady4sUyYMMF06ui3lfqt1/Tp033ffD6fP+FvZu3atWb6irRLVFSUeV5vGzdunO41devWdRQoUMBMTTJjxgw/HT1y0oZ6P6vtYce/w9SYmsu+9vvxxx8dLVq0cISFhTkqVKjgGDBggCMxMdFPPwFy0oY6FZdOa6htWLZsWccjjzziOHLkCCfTDySDttMldT7R9kv7d27evHmOatWqmTyj045+8cUXfjh6hyNI/+P7CA0AAADkHjWzAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAYLmgoCBZsmRJwLwPAPgSYRYAsmnr1q2SL18+cx1yT1WuXFneeOMNv51rvdb6M888IzfccIOEhoZKZGSktG/fXlavXu23YwIAbyDMAkA2vffeeyYQbtiwQY4dO2bNeTt06JDcfvvtsmbNGhk/frx89913snz5cmnatKn06dPH34cHALlCmAWAbDh//rx8/PHH8tRTT5me2ZiYmHTbfPbZZ3LHHXdIwYIFpWTJknL//feb9U2aNJFff/1V+vfvb77K10WNGDFC6tat6/Ye2nurvbhOX3/9tbRs2dK8X0REhDRu3Fh27tzpUZs9/fTTZp/bt2+XTp06SbVq1eTmm2+WAQMGyFdffZXp6/7973+bbQsVKmR6dP/zn/9IcnKy6/lvvvnGBOKiRYtKeHi4CcyxsbHmOf15tef3uuuuk8KFC5v9LV261KPjBoDsIMwCQDbMmzdPqlevLjfddJM8+uij8v7774vD4XA9/8UXX5jw2rZtW9m1a5f5+r5+/frmuUWLFkmFChXk5ZdfluPHj5slu86dOydRUVGyadMmEzyrVq1q9qHrs+OPP/4wvbDaA6uhMq1ixYpl+loNqRraf/jhB5k0aZK88847MnHiRNfzjzzyiPm5NHDv2LFDhgwZIiEhIeY53V9SUpLpxdae4FdffVWKFCmS7Z8bALIrf7a3BIBrvMRAQ6y655575OzZs7J+/XrT66pGjx4tDz/8sERHR7teU6dOHXNbvHhxU2ur4bBMmTIe7bdZs2Zuj6dPn24CqO773nvvverr9+/fb0K3BnFPvfjii6772ls8cOBAmTt3rgwePNisi4uLk0GDBrneW4O2kz6nvcC1a9c2j7VnFwDyAj2zAHAVP/30k/mKvkuXLuZx/vz55aGHHjIB12n37t3SvHlzr5/LkydPSq9evUxQ1DID/TpfSx40LGZH6t5jT2lZRaNGjUwA115VDbep96tlCj179pQWLVrIK6+8Ir/88ovruWeffVZGjRplXv/SSy/Jt99+m+PjAICsEGYB4Co0tF6+fFnKlStngqwuU6dOlYULF5oeWhUWFubxeQwODk4XNlPXpCotMdCgrF/zb9myxdwvUaKEXLp0KVv70BCs9bJ79+71eOYGLSPQkobPP//clE4MGzbMbb9a8/v999+bGmIdXFazZk1ZvHixeU5D7oEDB+Sxxx4zZQb16tWTt956y6NjAIDsIMwCQBY0xM6cOVMmTJhggqRz0cFPGm7nzJljtrvllluynOaqQIECcuXKFbd1pUqVMlNmpQ60+t6pbd682fRyaqjUQVQ6rdbp06ez3WZa4tC6dWuZMmWKJCQkpHv+zJkzGb5Og3OlSpVMgNUgqqFYB3WlpQPEdGDbypUrpWPHjjJjxgzXczr9V+/evU3N8PPPP29qbgHA2wizAJAF7ZX8888/pUePHlKrVi23RWtCnaUG+lW6Blu9/fHHH12DnlLXnOpgqKNHj7rCqNbb/vbbbzJu3DjzFb0GzmXLlrntX0PkrFmzzHtu27bN9JZ62gus76tBWgekaW/yvn37zPu9+eab0rBhwwxfo/vVkgKtkdVj022dva7qwoUL0rdvX1m3bp0JuRq6dSBYjRo1zPPPPfecrFixQg4ePGhmX1i7dq3rOQDwKgcAIFP33nuvo23bthk+t23bNu1SdXzzzTfm8cKFCx1169Z1FChQwFGyZElHx44dXdtu3brVccsttzhCQ0PNa5ymTp3qiIyMdBQuXNjRrVs3x+jRox2VKlVyPb9z505HvXr1HAULFnRUrVrVMX/+fPP8xIkTXdvo+y1evDjLVjx27JijT58+5rV6fOXLl3fcd999jrVr12b6PoMGDXKUKFHCUaRIEcdDDz1k9hkREWGeS0pKcjz88MPm2PX9ypUr5+jbt6/jwoUL5nm9/49//MP8vKVKlXI89thjjtOnT/ObBsDrgvQ/3o3HAAAAgG9QZgAAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAADEVv8fbcNznhwmaEsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute confusion matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(\n",
+    "    cm,\n",
+    "    annot=True,\n",
+    "    fmt=\"d\",\n",
+    "    cmap=\"Blues\",\n",
+    "    xticklabels=[\"Predicted 1\", \"Predicted 2\"],\n",
+    "    yticklabels=[\"Actual 1\", \"Actual 2\"]\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Confusion Matrix\")\n",
+    "plt.ylabel(\"True Label\")\n",
+    "plt.xlabel(\"Predicted Label\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Visualization\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_test, y_pred, alpha=0.6)\n",
+    "plt.xlabel(\"Actual Class\")\n",
+    "plt.ylabel(\"Predicted Class\")\n",
+    "plt.title(\"MLPClassifier Performance\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/data_preprocess.py b/src/data_preprocess.py
index c6417c0..3c1896c 100644
--- a/src/data_preprocess.py
+++ b/src/data_preprocess.py
@@ -1,15 +1,86 @@
 import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
 
-
-class DataPreprocessing():
+class DataPreprocessing:
     def __init__(self):
         pass
 
     def load_data(self, path):
-        data = pd.read_csv(path, sep="\t", header=None).dropna()
+        # Load raw data
+        df = pd.read_csv(path, sep="\t", header=None).dropna()
+        print(df.head())  # <-- print BEFORE converting to numpy
+
+        # Convert to numpy
+        data = df.to_numpy()
+
+        # ---------------------------------------------------------
+        # 1. TRAIN / VALIDATION / TEST SPLIT
+        # ---------------------------------------------------------
+        train_validation, test = train_test_split(
+            data, test_size=0.2, random_state=12
+        )
+
+        train, validation = train_test_split(
+            train_validation, test_size=0.2, random_state=99
+        )
+
+        # ---------------------------------------------------------
+        # 2. CHECK CLASS DISTRIBUTION
+        # ---------------------------------------------------------
+        print("Train:", set(train[:, -1]))
+        print("Validation:", set(validation[:, -1]))
+        print("Test:", set(test[:, -1]))
+
+        # ---------------------------------------------------------
+        # 3. SEPARATE FEATURES AND LABELS
+        # ---------------------------------------------------------
+        X_train = train[:, :-1]
+        y_train = train[:, -1]
+
+        X_val = validation[:, :-1]
+        y_val = validation[:, -1]
+
+        X_test = test[:, :-1]
+        y_test = test[:, -1]
+
+        # ---------------------------------------------------------
+        # 4. SCALE USING ONLY TRAINING DATA
+        # ---------------------------------------------------------
+        scaler = StandardScaler().fit(X_train)
+
+        X_train_scaled = scaler.transform(X_train)
+        X_val_scaled   = scaler.transform(X_val)
+        X_test_scaled  = scaler.transform(X_test)
+
+        # ---------------------------------------------------------
+        # 5. RETURN EVERYTHING CLEANLY
+        # ---------------------------------------------------------
+        return {
+            "X_train": X_train,
+            "y_train": y_train,
+            "X_val": X_val,
+            "y_val": y_val,
+            "X_test": X_test,
+            "y_test": y_test,
+            "X_train_scaled": X_train_scaled,
+            "X_val_scaled": X_val_scaled,
+            "X_test_scaled": X_test_scaled,
+            "scaler": scaler,
+            "raw_data": data
+        }
+
+'''
+***How to Use: 
+
+from data_preprocess import DataPreprocessing
 
-        print(data.head())
+pre = DataPreprocessing()
+data_dict = pre.load_data("data/Meter_A.txt")
 
-        data = data.to_numpy()
+X_train = data_dict["X_train"]
+y_train = data_dict["y_train"]
 
-        return data
\ No newline at end of file
+X_train_scaled = data_dict["X_train_scaled"]
+'''
\ No newline at end of file
diff --git a/src/mlpclassifier_test.py b/src/mlpclassifier_test.py
new file mode 100644
index 0000000..7ea87c3
--- /dev/null
+++ b/src/mlpclassifier_test.py
@@ -0,0 +1,46 @@
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score, classification_report
+
+class MLPClassificationModel:
+    def __init__(self, hidden_layer_sizes, learning_rate_init, max_iter):
+        self.model = MLPClassifier(
+            hidden_layer_sizes=hidden_layer_sizes,
+            learning_rate_init=learning_rate_init,
+            max_iter=max_iter,
+            solver="sgd",              # <---Now using sgd b/c "adam" gave all same answers for all iteration
+            momentum=0.9,              
+            learning_rate="adaptive",  
+            random_state=42
+        )
+
+    def train(self, X_train, y_train):
+        self.model.fit(X_train, y_train)
+
+
+    def predict(self, X_test):
+        return self.model.predict(X_test)
+
+
+    def evaluate(self, X_test, y_test):
+        y_pred = self.predict(X_test)
+
+        accuracy = accuracy_score(y_test, y_pred)
+        print("Accuracy:", accuracy)
+
+        print("\nClassification Report:")
+        print(classification_report(y_test, y_pred))
+
+        return y_pred
+    
+'''
+***How to use:
+
+clf = MLPClassificationModel(
+    hidden_layer_sizes=(64, 32),
+    learning_rate_init=0.001,
+    max_iter=300
+)
+
+clf.train(X_train_scaled, y_train)
+y_pred = clf.evaluate(X_test_scaled, y_test)
+'''
\ No newline at end of file
diff --git a/src/mlpclassifier_visualization.py b/src/mlpclassifier_visualization.py
new file mode 100644
index 0000000..e0298fa
--- /dev/null
+++ b/src/mlpclassifier_visualization.py
@@ -0,0 +1,56 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+
+class ConfusionMatrixVisualizer:
+    def __init__(self, y_test, y_pred):
+        self.y_test = y_test
+        self.y_pred = y_pred
+        self.cm = confusion_matrix(y_test, y_pred)
+
+    def plot_confusion_matrix(self):
+        plt.figure(figsize=(6, 5))
+        sns.heatmap(
+            self.cm,
+            annot=True,
+            fmt="d",
+            cmap="Blues",
+            xticklabels=["Predicted 1", "Predicted 2"],
+            yticklabels=["Actual 1", "Actual 2"]
+        )
+        plt.title("Confusion Matrix")
+        plt.ylabel("True Label")
+        plt.xlabel("Predicted Label")
+        plt.tight_layout()
+        plt.show()
+
+    def plot_performance_scatter(self):
+        plt.figure(figsize=(8, 6))
+        plt.scatter(self.y_test, self.y_pred, alpha=0.6)
+        plt.xlabel("Actual Class")
+        plt.ylabel("Predicted Class")
+        plt.title("MLPClassifier Performance")
+        plt.grid(True)
+        plt.show()
+
+'''
+***How to use:
+
+# Create model with your chosen hyperparameters
+clf = MLPClassificationModel(
+    hidden_layer_sizes=(64, 32),
+    learning_rate_init=0.001,
+    max_iter=300
+)
+
+# Train
+clf.train(X_train_scaled, y_train)
+
+# Evaluate
+y_pred = clf.evaluate(X_test_scaled, y_test)
+
+# Visualize
+viz = ConfusionMatrixVisualizer(y_test, y_pred)
+viz.plot_confusion_matrix()
+viz.plot_performance_scatter()
+'''
\ No newline at end of file
diff --git a/src/model_builder.py b/src/model_builder.py
index 8677c95..fee55c6 100644
--- a/src/model_builder.py
+++ b/src/model_builder.py
@@ -3,7 +3,7 @@
 from sklearn.metrics import accuracy_score
 
 # Importing the parent: DataPreprocessing class from data_preprocess.py
-from src.data_preprocess import DataPreprocessing 
+from meter_Hw4.src.data_preprocess import DataPreprocessing 
 
 
 class ModelBuilder(DataPreprocessing):
diff --git a/src/preprocess_visualize.py b/src/preprocess_visualize.py
new file mode 100644
index 0000000..174fcfe
--- /dev/null
+++ b/src/preprocess_visualize.py
@@ -0,0 +1,62 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import StandardScaler
+import umap
+
+class DataVisualizer:
+    def __init__(self, data):
+        """
+        data: numpy array where last column is the label
+        """
+        self.data = data
+        self.X = data[:, :-1]      # features
+        self.y = data[:, -1]       # labels
+
+        # Scale features immediately
+        self.scaler = StandardScaler()
+        self.X_scaled = self.scaler.fit_transform(self.X)
+
+    # ---------------------------------------------------------
+    # 1. Correlation Heatmap
+    # ---------------------------------------------------------
+    def plot_heatmap(self):
+        plt.figure(figsize=(14, 10))
+        sns.heatmap(
+            pd.DataFrame(self.X_scaled).corr(),
+            cmap="coolwarm",
+            center=0
+        )
+        plt.title("Correlation Heatmap of Processed Features")
+        plt.tight_layout()
+        plt.show()
+
+    # ---------------------------------------------------------
+    # 2. UMAP Projection
+    # ---------------------------------------------------------
+    def plot_umap(self, n_neighbors=30, min_dist=0.1, metric="euclidean"):
+        umap_model = umap.UMAP(
+            n_neighbors=n_neighbors,
+            min_dist=min_dist,
+            metric=metric,
+            random_state=42
+        )
+
+        embedding = umap_model.fit_transform(self.X_scaled)
+
+        plt.figure(figsize=(10, 7))
+        scatter = plt.scatter(
+            embedding[:, 0],
+            embedding[:, 1],
+            c=self.y,
+            cmap="viridis",
+            s=40,
+            alpha=0.9
+        )
+        plt.colorbar(scatter, label="Label")
+        plt.title("UMAP Projection of Processed Data")
+        plt.xlabel("UMAP-1")
+        plt.ylabel("UMAP-2")
+        plt.tight_layout()
+        plt.show()
\ No newline at end of file