diff --git a/notebooks/1.0_DataLoading.ipynb b/notebooks/1.0_DataLoading.ipynb
index 4be0e38..a28ca32 100644
--- a/notebooks/1.0_DataLoading.ipynb
+++ b/notebooks/1.0_DataLoading.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -216,7 +216,7 @@
        "[5 rows x 37 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,7 +228,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(87, 37)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +269,7 @@
        "        33.11848833,  2.        ]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -262,7 +282,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(87, 37)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -321,7 +361,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "practice1",
+   "display_name": "che4230",
    "language": "python",
    "name": "python3"
   },
@@ -335,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/2.0_Test_DataLoading.ipynb b/notebooks/2.0_Test_DataLoading.ipynb
index 7c55704..4358ca2 100644
--- a/notebooks/2.0_Test_DataLoading.ipynb
+++ b/notebooks/2.0_Test_DataLoading.ipynb
@@ -2,16 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'c:\\\\Users\\\\Gabi\\\\git\\\\meter_Hw4'"
+       "'/Users/sanyao1/Library/CloudStorage/OneDrive-LouisianaStateUniversity(2)/lsu spring-25/CHE 4230/homeworks/Assignment 4'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,58 +43,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "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"
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'data/Meter_A.txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m preprocessor \u001b[38;5;241m=\u001b[39m DataPreprocessing()\n\u001b[0;32m----> 3\u001b[0m \u001b[43mpreprocessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata/Meter_A.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Library/CloudStorage/OneDrive-LouisianaStateUniversity(2)/lsu spring-25/CHE 4230/homeworks/Assignment 4/meter_Hw4/src/data_preprocess.py:9\u001b[0m, in \u001b[0;36mDataPreprocessing.load_data\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mload_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, path):\n\u001b[0;32m----> 9\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\t\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdropna()\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28mprint\u001b[39m(data\u001b[38;5;241m.\u001b[39mhead())\n\u001b[1;32m     13\u001b[0m     data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy()\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/Meter_A.txt'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.84149871,  1.00936653,  0.99381601, ..., 35.174155  ,\n",
-       "        32.72949   ,  1.        ],\n",
-       "       [ 0.84225022,  1.00658369,  0.99660521, ..., 34.847005  ,\n",
-       "        32.73112167,  1.        ],\n",
-       "       [ 0.84072314,  1.01164657,  0.99815174, ..., 34.68912833,\n",
-       "        32.77181   ,  1.        ],\n",
-       "       ...,\n",
-       "       [ 0.79347112,  1.00955964,  1.00111012, ..., 33.82487   ,\n",
-       "        33.02083167,  2.        ],\n",
-       "       [ 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.        ]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -106,7 +73,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "practice1",
+   "display_name": "che4230",
    "language": "python",
    "name": "python3"
   },
@@ -120,7 +87,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/3.0_Model_built.ipynb b/notebooks/3.0_Model_built.ipynb
index 35cc720..a596c4f 100644
--- a/notebooks/3.0_Model_built.ipynb
+++ b/notebooks/3.0_Model_built.ipynb
@@ -8,7 +8,7 @@
     {
      "data": {
       "text/plain": [
-       "'c:\\\\Users\\\\Gabi\\\\git\\\\meter_Hw4'"
+       "'/Users/sanyao1/Library/CloudStorage/OneDrive-LouisianaStateUniversity(2)/lsu spring-25/CHE 4230/homeworks/Assignment 4/meter_Hw4'"
       ]
      },
      "execution_count": 1,
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,16 +143,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{1.0, 2.0}"
+       "{np.float64(1.0), np.float64(2.0)}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +192,8 @@
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -337,12 +338,21 @@
        "/* Toggleable label */\n",
        "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
@@ -495,7 +505,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -594,13 +605,13 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "DecisionTreeClassifier(criterion='entropy', max_depth=3)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -611,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -630,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -639,7 +650,7 @@
        "0.9285714285714286"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +669,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "practice1",
+   "display_name": "che4230",
    "language": "python",
    "name": "python3"
   },
@@ -672,7 +683,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/4.0_Test_Model_built.ipynb b/notebooks/4.0_Test_Model_built.ipynb
index 7fe4319..fe21833 100644
--- a/notebooks/4.0_Test_Model_built.ipynb
+++ b/notebooks/4.0_Test_Model_built.ipynb
@@ -8,7 +8,7 @@
     {
      "data": {
       "text/plain": [
-       "'c:\\\\Users\\\\Gabi\\\\git\\\\meter_Hw4'"
+       "'/Users/sanyao1/Library/CloudStorage/OneDrive-LouisianaStateUniversity(2)/lsu spring-25/CHE 4230/homeworks/Assignment 4/meter_Hw4'"
       ]
      },
      "execution_count": 1,
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +154,7 @@
        "0.9285714285714286"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,7 +170,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "practice1",
+   "display_name": "che4230",
    "language": "python",
    "name": "python3"
   },
@@ -184,7 +184,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/5.0_ann_model.ipynb b/notebooks/5.0_ann_model.ipynb
new file mode 100644
index 0000000..8eaa493
--- /dev/null
+++ b/notebooks/5.0_ann_model.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/sanyao1/Library/CloudStorage/OneDrive-LouisianaStateUniversity(2)/lsu spring-25/CHE 4230/homeworks/Assignment 4/meter_Hw4'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "os.chdir(\"../\")\n",
+    "\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.data_preprocess import DataPreprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": [
+    "preprocessor = DataPreprocessing()\n",
+    "\n",
+    "data = preprocessor.load_data(\"data/Meter_A.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the dataset into training/validation and test sets\n",
+    "train_validation, test = train_test_split(data, test_size = 0.2, random_state=12)\n",
+    "\n",
+    "# Split the remaining into traning and validation sets\n",
+    "train, validation = train_test_split(train_validation, test_size = 0.2, random_state=99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Isolate the features and target variable of training dataset\n",
+    "X_train = train[:, :-1]\n",
+    "y_train = train[:, -1]\n",
+    "\n",
+    "# Ensure all duplicates are removed\n",
+    "#set(y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Isolate the features and target variable of test dataset\n",
+    "X_test = test[:, :-1]\n",
+    "y_test = test[:, -1]        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standardize the features of training data\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train ANN model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import MLPRegressor from sklearn neural network\n",
+    "from sklearn.neural_network import MLPRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimized ANN Regression - MSE: 0.0452, R² Score: 0.8192\n",
+      "Best Parameters: {'hidden_layer_sizes': (100, 50, 25, 10), 'learning_rate_init': 0.001, 'max_iter': 500}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# Define hyperparameter grid\n",
+    "param_grid = {\n",
+    "    'hidden_layer_sizes': [(50,), (100,), (50, 100), (100, 50), (100, 50, 25, 10)],\n",
+    "    'learning_rate_init': [0.001, 0.01, 0.1],\n",
+    "    'max_iter': [200, 500, 1000]\n",
+    "}\n",
+    "\n",
+    "# Initialize the MLPRegressor\n",
+    "mlp = MLPRegressor(random_state=42)\n",
+    "\n",
+    "# Initialize GridSearchCV\n",
+    "grid_search = GridSearchCV(estimator=mlp, param_grid=param_grid, cv=3, scoring='r2', n_jobs=-1)\n",
+    "\n",
+    "# Fit GridSearchCV\n",
+    "grid_search.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Get the best estimator\n",
+    "best_mlp = grid_search.best_estimator_\n",
+    "\n",
+    "# Fit the best estimator on the training data\n",
+    "best_mlp.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Predict on the test data\n",
+    "y_pred_best_mlp = best_mlp.predict(X_test_scaled)\n",
+    "\n",
+    "# Calculate the MSE and R² score\n",
+    "mse_best_mlp = mean_squared_error(y_test, y_pred_best_mlp)\n",
+    "r2_best_mlp = r2_score(y_test, y_pred_best_mlp)\n",
+    "\n",
+    "print(f\"Optimized ANN Regression - MSE: {mse_best_mlp:.4f}, R² Score: {r2_best_mlp:.4f}\")\n",
+    "print(f\"Best Parameters: {grid_search.best_params_}\")\n",
+    "\n",
+    "# Extract four models from worst to best\n",
+    "results = grid_search.cv_results_\n",
+    "sorted_indices = np.argsort(results['mean_test_score'])\n",
+    "four_indices = [sorted_indices[0], sorted_indices[len(sorted_indices)//3], sorted_indices[2*len(sorted_indices)//3], sorted_indices[-1]]\n",
+    "\n",
+    "mse_values = -results['mean_test_score'][four_indices]\n",
+    "param_labels = [str(results['params'][i]) for i in four_indices]\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.barh(param_labels, mse_values)\n",
+    "plt.xlabel(\"Mean Squared Error\")\n",
+    "plt.ylabel(\"Hyperparameter Combinations\")\n",
+    "plt.title(\"Four ANN Regression Models from Worst to Best\")\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the excercise above, i used the gridsearch to find the best combination of hyperparameters that minimizes the MSE value and optimizes the R2 score. In the above plot, the combination of hidden layer sizes: (100, 50), learning rate init of 0.01 and max iter of 200 initially yielded a good MSE value, however, the R2 score was still low. To improve this, i increased the number of hidden layers to four - (100, 50, 25, 10). This together 'learning_rate_init': 0.001, and 'max_iter': 500 increased the R2 score to an acceptable level. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "che4230",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/6.0_Test_annmodel.ipynb b/notebooks/6.0_Test_annmodel.ipynb
new file mode 100644
index 0000000..e494341
--- /dev/null
+++ b/notebooks/6.0_Test_annmodel.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/sanyao1/Library/CloudStorage/OneDrive-LouisianaStateUniversity(2)/lsu spring-25/CHE 4230/homeworks/Assignment 4/meter_Hw4'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "os.chdir(\"../\")\n",
+    "\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.data_preprocess import DataPreprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": [
+    "preprocessor = DataPreprocessing()\n",
+    "\n",
+    "data = preprocessor.load_data(\"data/Meter_A.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Isolate the features and target variable of training dataset\n",
+    "X_train = train[:, :-1]\n",
+    "y_train = train[:, -1]\n",
+    "\n",
+    "X_val = validation[:, :-1]\n",
+    "y_val = validation[:, -1]\n",
+    "\n",
+    "# Standardize the features of training data\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_val_scaled = scaler.transform(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.model_builder import annModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/che4230/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimized ANN Regression - MSE: 0.0580, R² Score: 0.7633\n",
+      "Best Parameters: {'hidden_layer_sizes': (100, 50, 25, 10), 'learning_rate_init': 0.001, 'max_iter': 500}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.7632979300279283"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_builder = annModel()\n",
+    "\n",
+    "ann_model = model_builder.dt(X_train=X_train_scaled, X_test=X_val_scaled, y_train=train[:, -1], y_test = validation[:, -1])\n",
+    "\n",
+    "model_builder.mse_best_mlp\n",
+    "model_builder.r2_best_mlp"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "che4230",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/model_builder.py b/src/model_builder.py
index 8677c95..576fe4d 100644
--- a/src/model_builder.py
+++ b/src/model_builder.py
@@ -1,6 +1,9 @@
 from sklearn.tree import DecisionTreeClassifier
 import numpy as np
 from sklearn.metrics import accuracy_score
+from sklearn.neural_network import MLPRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.model_selection import GridSearchCV
 
 # Importing the parent: DataPreprocessing class from data_preprocess.py
 from src.data_preprocess import DataPreprocessing 
@@ -29,4 +32,46 @@ def dt(self, X_train, X_test, y_train, y_test):
         #get performance
         self.accuracy = accuracy_score(y_test, DT_predicted)
 
-        return DT_classifier
\ No newline at end of file
+        return DT_classifier
+    
+class annModel(DataPreprocessing):
+    def __init__(self, *args, **kwargs):
+        super(annModel, self).__init__(*args, **kwargs)
+
+    def dt(self, X_train, X_test, y_train, y_test):
+        # Initialize the MLPRegressor
+        mlp = MLPRegressor(random_state=42)
+
+        # Define hyperparameter grid
+        param_grid = {
+            'hidden_layer_sizes': [(50,), (100,), (50, 100), (100, 50), (100, 50, 25, 10)],
+            'learning_rate_init': [0.001, 0.01, 0.1],
+            'max_iter': [200, 500, 1000]
+        }
+        # Initialize GridSearchCV
+        grid_search = GridSearchCV(estimator=mlp, param_grid=param_grid, cv=3, scoring='r2', n_jobs=-1)
+
+        # Fit GridSearchCV
+        grid_search.fit(X_train, y_train)
+
+        # Get the best estimator
+        best_mlp = grid_search.best_estimator_
+
+        # Fit the best estimator on the training data
+        best_mlp.fit(X_train, y_train)
+
+        # Predict on the test data
+        y_pred_best_mlp = best_mlp.predict(X_test)
+
+        # Calculate the MSE and R² score
+        mse_best_mlp = mean_squared_error(y_test, y_pred_best_mlp)
+        r2_best_mlp = r2_score(y_test, y_pred_best_mlp)
+
+        print(f"Optimized ANN Regression - MSE: {mse_best_mlp:.4f}, R² Score: {r2_best_mlp:.4f}")
+        print(f"Best Parameters: {grid_search.best_params_}")
+
+        #get performance
+        self.mse_best_mlp = mean_squared_error(y_test, y_pred_best_mlp)
+        self.r2_best_mlp = r2_score(y_test, y_pred_best_mlp)
+
+        return mlp
\ No newline at end of file