From 8ebb0b0d28a049e4cd89665a9542fb738923e22d Mon Sep 17 00:00:00 2001
From: PRIYANSHU TIWARI <techarena955@gmail.com>
Date: Sun, 21 Jul 2024 00:21:48 +0530
Subject: [PATCH 1/3] HERE si my TCS stock preddition by using LSTM

---
 .../TCS by LSTM-checkpoint.ipynb              | 1380 +++++
 ... stock prediction pycaret-checkpoint.ipynb | 1075 ++++
 NYSE/TCS stocks/TCS by LSTM.ipynb             | 1380 +++++
 NYSE/TCS stocks/TCS_stock_action.csv          |   71 +
 NYSE/TCS stocks/TCS_stock_history.csv         | 4464 +++++++++++++++++
 NYSE/TCS stocks/TCS_stock_info.csv            |  151 +
 NYSE/TCS stocks/logs.log                      |    0
 7 files changed, 8521 insertions(+)
 create mode 100644 NYSE/TCS stocks/.ipynb_checkpoints/TCS by LSTM-checkpoint.ipynb
 create mode 100644 NYSE/TCS stocks/.ipynb_checkpoints/tcs stock prediction pycaret-checkpoint.ipynb
 create mode 100644 NYSE/TCS stocks/TCS by LSTM.ipynb
 create mode 100644 NYSE/TCS stocks/TCS_stock_action.csv
 create mode 100644 NYSE/TCS stocks/TCS_stock_history.csv
 create mode 100644 NYSE/TCS stocks/TCS_stock_info.csv
 create mode 100644 NYSE/TCS stocks/logs.log

diff --git a/NYSE/TCS stocks/.ipynb_checkpoints/TCS by LSTM-checkpoint.ipynb b/NYSE/TCS stocks/.ipynb_checkpoints/TCS by LSTM-checkpoint.ipynb
new file mode 100644
index 00000000..efc4f27e
--- /dev/null
+++ b/NYSE/TCS stocks/.ipynb_checkpoints/TCS by LSTM-checkpoint.ipynb	
@@ -0,0 +1,1380 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "10aa62ad",
+   "metadata": {
+    "_kg_hide-output": true,
+    "papermill": {
+     "duration": 0.007239,
+     "end_time": "2024-05-30T15:39:49.831700",
+     "exception": false,
+     "start_time": "2024-05-30T15:39:49.824461",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# TCS Stock Prediction - LSTM\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "59111c23",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_kg_hide-output": true,
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:39:49.859978Z",
+     "iopub.status.busy": "2024-05-30T15:39:49.859234Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.511494Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.510665Z"
+    },
+    "papermill": {
+     "duration": 12.662365,
+     "end_time": "2024-05-30T15:40:02.513835",
+     "exception": false,
+     "start_time": "2024-05-30T15:39:49.851470",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import LSTM\n",
+    "from keras.layers import Dropout\n",
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb4999ec",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "9de88d689da1a279477470f6cef999c0a45bf98a",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.543056Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.542482Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.584817Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.583916Z"
+    },
+    "papermill": {
+     "duration": 0.052249,
+     "end_time": "2024-05-30T15:40:02.586967",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.534718",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Dividends</th>\n",
+       "      <th>Stock Splits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2002-08-12</td>\n",
+       "      <td>28.794172</td>\n",
+       "      <td>29.742206</td>\n",
+       "      <td>28.794172</td>\n",
+       "      <td>29.519140</td>\n",
+       "      <td>212976</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2002-08-13</td>\n",
+       "      <td>29.556316</td>\n",
+       "      <td>30.030333</td>\n",
+       "      <td>28.905705</td>\n",
+       "      <td>29.119476</td>\n",
+       "      <td>153576</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2002-08-14</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>26.563503</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>822776</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2002-08-16</td>\n",
+       "      <td>26.972458</td>\n",
+       "      <td>28.255089</td>\n",
+       "      <td>26.582090</td>\n",
+       "      <td>27.046812</td>\n",
+       "      <td>811856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4458</th>\n",
+       "      <td>2021-09-24</td>\n",
+       "      <td>3890.000000</td>\n",
+       "      <td>3944.399902</td>\n",
+       "      <td>3855.000000</td>\n",
+       "      <td>3871.300049</td>\n",
+       "      <td>2320754</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4459</th>\n",
+       "      <td>2021-09-27</td>\n",
+       "      <td>3900.000000</td>\n",
+       "      <td>3904.000000</td>\n",
+       "      <td>3802.899902</td>\n",
+       "      <td>3836.949951</td>\n",
+       "      <td>1673362</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4460</th>\n",
+       "      <td>2021-09-28</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3751.250000</td>\n",
+       "      <td>3779.149902</td>\n",
+       "      <td>2253075</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4461</th>\n",
+       "      <td>2021-09-29</td>\n",
+       "      <td>3759.800049</td>\n",
+       "      <td>3806.000000</td>\n",
+       "      <td>3722.149902</td>\n",
+       "      <td>3791.899902</td>\n",
+       "      <td>2489161</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4462</th>\n",
+       "      <td>2021-09-30</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3765.000000</td>\n",
+       "      <td>3773.199951</td>\n",
+       "      <td>640479</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4463 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date         Open         High          Low        Close   Volume  \\\n",
+       "0     2002-08-12    28.794172    29.742206    28.794172    29.519140   212976   \n",
+       "1     2002-08-13    29.556316    30.030333    28.905705    29.119476   153576   \n",
+       "2     2002-08-14    29.184536    29.184536    26.563503    27.111877   822776   \n",
+       "3     2002-08-15    27.111877    27.111877    27.111877    27.111877        0   \n",
+       "4     2002-08-16    26.972458    28.255089    26.582090    27.046812   811856   \n",
+       "...          ...          ...          ...          ...          ...      ...   \n",
+       "4458  2021-09-24  3890.000000  3944.399902  3855.000000  3871.300049  2320754   \n",
+       "4459  2021-09-27  3900.000000  3904.000000  3802.899902  3836.949951  1673362   \n",
+       "4460  2021-09-28  3850.000000  3850.000000  3751.250000  3779.149902  2253075   \n",
+       "4461  2021-09-29  3759.800049  3806.000000  3722.149902  3791.899902  2489161   \n",
+       "4462  2021-09-30  3805.000000  3805.000000  3765.000000  3773.199951   640479   \n",
+       "\n",
+       "      Dividends  Stock Splits  \n",
+       "0           0.0           0.0  \n",
+       "1           0.0           0.0  \n",
+       "2           0.0           0.0  \n",
+       "3           0.0           0.0  \n",
+       "4           0.0           0.0  \n",
+       "...         ...           ...  \n",
+       "4458        0.0           0.0  \n",
+       "4459        0.0           0.0  \n",
+       "4460        0.0           0.0  \n",
+       "4461        0.0           0.0  \n",
+       "4462        0.0           0.0  \n",
+       "\n",
+       "[4463 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset=pd.read_csv(\"TCS_stock_history.csv\")\n",
+    "display(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b904b654",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.602979Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.602733Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.624846Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.624057Z"
+    },
+    "papermill": {
+     "duration": 0.032102,
+     "end_time": "2024-05-30T15:40:02.626758",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.594656",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Dividends</th>\n",
+       "      <th>Stock Splits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2002-08-12</td>\n",
+       "      <td>28.794172</td>\n",
+       "      <td>29.742206</td>\n",
+       "      <td>28.794172</td>\n",
+       "      <td>29.519140</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2002-08-13</td>\n",
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+       "      <td>30.030333</td>\n",
+       "      <td>28.905705</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>29.184536</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>26.563503</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2002-08-16</td>\n",
+       "      <td>26.972458</td>\n",
+       "      <td>28.255089</td>\n",
+       "      <td>26.582090</td>\n",
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+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4458</th>\n",
+       "      <td>2021-09-24</td>\n",
+       "      <td>3890.000000</td>\n",
+       "      <td>3944.399902</td>\n",
+       "      <td>3855.000000</td>\n",
+       "      <td>3871.300049</td>\n",
+       "      <td>2320754</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4459</th>\n",
+       "      <td>2021-09-27</td>\n",
+       "      <td>3900.000000</td>\n",
+       "      <td>3904.000000</td>\n",
+       "      <td>3802.899902</td>\n",
+       "      <td>3836.949951</td>\n",
+       "      <td>1673362</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4460</th>\n",
+       "      <td>2021-09-28</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3751.250000</td>\n",
+       "      <td>3779.149902</td>\n",
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+       "    <tr>\n",
+       "      <th>4461</th>\n",
+       "      <td>2021-09-29</td>\n",
+       "      <td>3759.800049</td>\n",
+       "      <td>3806.000000</td>\n",
+       "      <td>3722.149902</td>\n",
+       "      <td>3791.899902</td>\n",
+       "      <td>2489161</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4462</th>\n",
+       "      <td>2021-09-30</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3765.000000</td>\n",
+       "      <td>3773.199951</td>\n",
+       "      <td>640479</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4463 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Date         Open         High          Low        Close   Volume  \\\n",
+       "0    2002-08-12    28.794172    29.742206    28.794172    29.519140   212976   \n",
+       "1    2002-08-13    29.556316    30.030333    28.905705    29.119476   153576   \n",
+       "2    2002-08-14    29.184536    29.184536    26.563503    27.111877   822776   \n",
+       "3    2002-08-15    27.111877    27.111877    27.111877    27.111877        0   \n",
+       "4    2002-08-16    26.972458    28.255089    26.582090    27.046812   811856   \n",
+       "...         ...          ...          ...          ...          ...      ...   \n",
+       "4458 2021-09-24  3890.000000  3944.399902  3855.000000  3871.300049  2320754   \n",
+       "4459 2021-09-27  3900.000000  3904.000000  3802.899902  3836.949951  1673362   \n",
+       "4460 2021-09-28  3850.000000  3850.000000  3751.250000  3779.149902  2253075   \n",
+       "4461 2021-09-29  3759.800049  3806.000000  3722.149902  3791.899902  2489161   \n",
+       "4462 2021-09-30  3805.000000  3805.000000  3765.000000  3773.199951   640479   \n",
+       "\n",
+       "      Dividends  Stock Splits  \n",
+       "0           0.0           0.0  \n",
+       "1           0.0           0.0  \n",
+       "2           0.0           0.0  \n",
+       "3           0.0           0.0  \n",
+       "4           0.0           0.0  \n",
+       "...         ...           ...  \n",
+       "4458        0.0           0.0  \n",
+       "4459        0.0           0.0  \n",
+       "4460        0.0           0.0  \n",
+       "4461        0.0           0.0  \n",
+       "4462        0.0           0.0  \n",
+       "\n",
+       "[4463 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset['Date']=pd.to_datetime(dataset['Date'])\n",
+    "dataset=dataset.sort_values('Date',ascending=True).reset_index(drop=True)\n",
+    "#dataset['Date']=dataset['Date'].astype(str)\n",
+    "display(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a1d8576",
+   "metadata": {
+    "_uuid": "99cca4c05240bd82c4492c6c3c200925c5c096e5",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.643344Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.643075Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.648066Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.647199Z"
+    },
+    "papermill": {
+     "duration": 0.015127,
+     "end_time": "2024-05-30T15:40:02.649865",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.634738",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "trainset=dataset.iloc[-1000:-100,1:2].values #Open\n",
+    "testset=dataset.iloc[-100:,1:2].values #Open \n",
+    "real_stock_price = testset.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8beae7ae",
+   "metadata": {
+    "_uuid": "1abc6fe47fb5c4443767a2e2053cf9999c8a9a5c",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.682140Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.681516Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.687744Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.686822Z"
+    },
+    "papermill": {
+     "duration": 0.016649,
+     "end_time": "2024-05-30T15:40:02.689856",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.673207",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.00863905]\n",
+      " [0.00920529]\n",
+      " [0.01467811]\n",
+      " [0.01090375]\n",
+      " [0.01446842]]\n",
+      "900\n"
+     ]
+    }
+   ],
+   "source": [
+    "sc = MinMaxScaler(feature_range = (0,1))\n",
+    "training_scaled = sc.fit_transform(trainset)\n",
+    "print(training_scaled[0:5])\n",
+    "print(len(training_scaled))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e27fae4c",
+   "metadata": {
+    "_uuid": "d0d549e9a4af9ab516c3304fb44d650a815dcedc",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.706979Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.706741Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.710325Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.709473Z"
+    },
+    "papermill": {
+     "duration": 0.014132,
+     "end_time": "2024-05-30T15:40:02.712229",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.698097",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x_train = []\n",
+    "y_train = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d2d3f2f3",
+   "metadata": {
+    "_uuid": "4c4f8ba0c635e7da0ba1bc52abf0a42068acdc89",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.744586Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.743917Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.750575Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.749753Z"
+    },
+    "papermill": {
+     "duration": 0.017017,
+     "end_time": "2024-05-30T15:40:02.752512",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.735495",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(840, 60) (840,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(60,len(training_scaled)):\n",
+    "    x_train.append(training_scaled[i-60:i, 0])\n",
+    "    y_train.append(training_scaled[i,0])\n",
+    "x_train,y_train = np.array(x_train),np.array(y_train)\n",
+    "print(x_train.shape,y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3fe1f380",
+   "metadata": {
+    "_uuid": "316c5775b5c0a984b432f138792d2c0e9a484491",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.784806Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.784541Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.789019Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.788206Z"
+    },
+    "papermill": {
+     "duration": 0.014923,
+     "end_time": "2024-05-30T15:40:02.790963",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.776040",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(samples, time steps, features) = (840, 60, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n",
+    "print('(samples, time steps, features) =',x_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2beb16da",
+   "metadata": {
+    "_uuid": "ed24d733423254641f134c0e84f44a8ce175589f",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.807891Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.807627Z",
+     "iopub.status.idle": "2024-05-30T15:40:04.002484Z",
+     "shell.execute_reply": "2024-05-30T15:40:04.001714Z"
+    },
+    "papermill": {
+     "duration": 1.205862,
+     "end_time": "2024-05-30T15:40:04.004829",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.798967",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "regressor = Sequential()\n",
+    "regressor.add(LSTM(units = 50,return_sequences = True,input_shape = (x_train.shape[1],1)))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(LSTM(units = 50,return_sequences = True))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(LSTM(units = 50,return_sequences = True))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(LSTM(units = 50))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(Dense(units = 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9b2d9863",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "6681ff55d9a29b1ad0f37cdc82318fb2c8fbc0bd",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:04.023680Z",
+     "iopub.status.busy": "2024-05-30T15:40:04.023094Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.196836Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.195796Z"
+    },
+    "papermill": {
+     "duration": 8.185984,
+     "end_time": "2024-05-30T15:40:12.199621",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:04.013637",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "27/27 [==============================] - 3s 31ms/step - loss: 0.0389\n",
+      "Epoch 2/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0057\n",
+      "Epoch 3/10\n",
+      "27/27 [==============================] - 1s 30ms/step - loss: 0.0052\n",
+      "Epoch 4/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0047\n",
+      "Epoch 5/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0043\n",
+      "Epoch 6/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0046\n",
+      "Epoch 7/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0044\n",
+      "Epoch 8/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0045\n",
+      "Epoch 9/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0045\n",
+      "Epoch 10/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0050\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x163018e50>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')\n",
+    "regressor.fit(x_train,y_train,epochs =10, batch_size = 32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "822f38ef",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "31aa33ed70b08e71d0e8bfe4c1abae7901ab947d",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.287242Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.286898Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.292836Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.291964Z"
+    },
+    "papermill": {
+     "duration": 0.023133,
+     "end_time": "2024-05-30T15:40:12.294775",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.271642",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3723,)\n",
+      "[184.63800167 185.63356023 184.87500324 182.97887664 185.06463021\n",
+      " 184.3061393  185.06466558 183.92695682 184.11657917 184.87501269]\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = dataset.iloc[len(trainset)-len(testset)-60:,1].values\n",
+    "print(inputs.shape)\n",
+    "print(inputs[0:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59a8e2dc",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013847,
+     "end_time": "2024-05-30T15:40:12.323670",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.309823",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "In the context of a LSTM model, this reshaping may be necessary if the input data has only one feature (i.e., one time series) and the LSTM model expects a 3D input shape of (n_samples, n_time_steps, n_features). In this case, since there is only one feature, the shape does not change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7aa4e312",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "7d74568c439acc8dda77dc4365d4b413095508e1",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.353568Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.353252Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.358747Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.357855Z"
+    },
+    "papermill": {
+     "duration": 0.022762,
+     "end_time": "2024-05-30T15:40:12.360690",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.337928",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3723, 1)\n",
+      "(3723, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = inputs.reshape(-1,1)\n",
+    "print(inputs.shape)\n",
+    "\n",
+    "#sc = MinMaxScaler(feature_range = (0,1))\n",
+    "inputs = sc.transform(inputs)\n",
+    "print(inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3c962cd2",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "ef076f0fdbe6ec72d85e7326c3e8aa73fcbad667",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.390465Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.390192Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.396836Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.395965Z"
+    },
+    "papermill": {
+     "duration": 0.023947,
+     "end_time": "2024-05-30T15:40:12.398827",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.374880",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3663, 60)\n",
+      "(3663, 60, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test = []\n",
+    "for i in range(60,len(inputs)):\n",
+    "    x_test.append(inputs[i-60:i,0])\n",
+    "x_test = np.array(x_test)\n",
+    "print(x_test.shape)\n",
+    "\n",
+    "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n",
+    "print(x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23808ef3",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "24730042bdad3a140e033a78ae9b170304f8cefb",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.428521Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.428246Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.116866Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.116047Z"
+    },
+    "papermill": {
+     "duration": 0.70562,
+     "end_time": "2024-05-30T15:40:13.118807",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.413187",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "115/115 [==============================] - 1s 8ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_price = regressor.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8c507e69",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "4b06462fa06e7399373d8a552cd159176fbeef33",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.149441Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.149155Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.154197Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.153273Z"
+    },
+    "papermill": {
+     "duration": 0.022316,
+     "end_time": "2024-05-30T15:40:13.156092",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.133776",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3663, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_price = sc.inverse_transform(predicted_price)\n",
+    "print(predicted_price.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fee58a4f",
+   "metadata": {
+    "_kg_hide-output": false,
+    "_uuid": "e8b30d578fafb1564da4448efb1f994537f6f588",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.186273Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.185980Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.440139Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.439281Z"
+    },
+    "papermill": {
+     "duration": 0.271652,
+     "end_time": "2024-05-30T15:40:13.442276",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.170624",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(real_stock_price[-100:],color = 'red', label = 'Real Price')\n",
+    "plt.plot(predicted_price[-100:], color = 'blue', label = 'Predicted Price')\n",
+    "plt.title('TCS Stock Price Prediction')\n",
+    "plt.xlabel('days')\n",
+    "plt.ylabel('Price')\n",
+    "plt.xlim(0,100)  \n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "52971fdb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.474377Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.474096Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.899104Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.898213Z"
+    },
+    "papermill": {
+     "duration": 0.444389,
+     "end_time": "2024-05-30T15:40:13.902296",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.457907",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(18,10))\n",
+    "plt.plot(dataset.iloc[-1000:-100,0],trainset,'-o',label='train actual')\n",
+    "#plt.plot(dataset.iloc[-1000:-100,0],train_pred,'--',label='Prophet')\n",
+    "plt.plot(dataset.iloc[-100:,0],real_stock_price[-100:],'-o',label='test actual')\n",
+    "plt.plot(dataset.iloc[-100:,0],predicted_price[-100:],'-o',label='test predicted')\n",
+    "plt.title('TCS Stock Price',size=24)\n",
+    "plt.xlabel('Date',size=16)\n",
+    "plt.ylabel('Price',size=16)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "162992af",
+   "metadata": {
+    "_kg_hide-output": true,
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.973595Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.973249Z",
+     "iopub.status.idle": "2024-05-30T15:40:19.892150Z",
+     "shell.execute_reply": "2024-05-30T15:40:19.891239Z"
+    },
+    "papermill": {
+     "duration": 5.939242,
+     "end_time": "2024-05-30T15:40:19.894142",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.954900",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = np.array(trainset[-60:])\n",
+    "new_predicted_price=[]\n",
+    "for i in range(100):\n",
+    "    x = np.reshape(inputs, (1,60,1))\n",
+    "    pred = regressor.predict(x)\n",
+    "    inputs = np.append(inputs, pred)\n",
+    "    inputs = inputs[1:]\n",
+    "    new_predicted_price+=[pred.reshape(-1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ab12649e",
+   "metadata": {
+    "_kg_hide-output": true,
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:19.961103Z",
+     "iopub.status.busy": "2024-05-30T15:40:19.960774Z",
+     "iopub.status.idle": "2024-05-30T15:40:19.966508Z",
+     "shell.execute_reply": "2024-05-30T15:40:19.965587Z"
+    },
+    "papermill": {
+     "duration": 0.041324,
+     "end_time": "2024-05-30T15:40:19.968515",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:19.927191",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_predicted_price = sc.inverse_transform(np.array(new_predicted_price))\n",
+    "print(new_predicted_price.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "69ff2e6d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:20.035126Z",
+     "iopub.status.busy": "2024-05-30T15:40:20.034829Z",
+     "iopub.status.idle": "2024-05-30T15:40:20.309774Z",
+     "shell.execute_reply": "2024-05-30T15:40:20.308818Z"
+    },
+    "papermill": {
+     "duration": 0.310487,
+     "end_time": "2024-05-30T15:40:20.311902",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:20.001415",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(real_stock_price,color = 'red', label = 'Real Price')\n",
+    "plt.plot(new_predicted_price, color = 'blue', label = 'New Predicted Price')\n",
+    "plt.title('TCS Stock Price Prediction')\n",
+    "plt.xlabel('days')\n",
+    "plt.ylabel('Price')\n",
+    "plt.xlim(0,100)  \n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fe0703c5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:20.380186Z",
+     "iopub.status.busy": "2024-05-30T15:40:20.379342Z",
+     "iopub.status.idle": "2024-05-30T15:40:20.854541Z",
+     "shell.execute_reply": "2024-05-30T15:40:20.853544Z"
+    },
+    "papermill": {
+     "duration": 0.511082,
+     "end_time": "2024-05-30T15:40:20.856663",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:20.345581",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(18,10))\n",
+    "plt.plot(dataset.iloc[-1000:-100,0],trainset,'-o',label='train actual')\n",
+    "#plt.plot(dataset.iloc[-1000:-100,0],train_pred,'--',label='Prophet')\n",
+    "plt.plot(dataset.iloc[-100:,0],real_stock_price[-100:],'-o',label='test actual')\n",
+    "plt.plot(dataset.iloc[-100:,0],new_predicted_price[-100:],'-o',label='test predicted')\n",
+    "plt.title('TCS',size=24)\n",
+    "plt.xlabel('Date',size=16)\n",
+    "plt.ylabel('Price',size=16)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "datasetId": 1624347,
+     "sourceId": 3358601,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 36.200134,
+   "end_time": "2024-05-30T15:40:23.293779",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-05-30T15:39:47.093645",
+   "version": "2.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NYSE/TCS stocks/.ipynb_checkpoints/tcs stock prediction pycaret-checkpoint.ipynb b/NYSE/TCS stocks/.ipynb_checkpoints/tcs stock prediction pycaret-checkpoint.ipynb
new file mode 100644
index 00000000..b6117efd
--- /dev/null
+++ b/NYSE/TCS stocks/.ipynb_checkpoints/tcs stock prediction pycaret-checkpoint.ipynb	
@@ -0,0 +1,1075 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "498a6e70",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:12:52.315362Z",
+     "iopub.status.busy": "2021-10-06T04:12:52.314561Z",
+     "iopub.status.idle": "2021-10-06T04:13:28.718965Z",
+     "shell.execute_reply": "2021-10-06T04:13:28.717881Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:05.617981Z"
+    },
+    "papermill": {
+     "duration": 36.430388,
+     "end_time": "2021-10-06T04:13:28.719210",
+     "exception": false,
+     "start_time": "2021-10-06T04:12:52.288822",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting pycaret\n",
+      "  Obtaining dependency information for pycaret from https://files.pythonhosted.org/packages/3e/6f/b3d59fac3869a7685e68aecdd35c336800bce8c8d3b45687bb82cf9a2848/pycaret-3.3.2-py3-none-any.whl.metadata\n",
+      "  Downloading pycaret-3.3.2-py3-none-any.whl.metadata (17 kB)\n",
+      "Requirement already satisfied: ipython>=5.5.0 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (8.15.0)\n",
+      "Requirement already satisfied: ipywidgets>=7.6.5 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (8.0.4)\n",
+      "Requirement already satisfied: tqdm>=4.62.0 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (4.65.0)\n",
+      "Requirement already satisfied: numpy<1.27,>=1.21 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (1.24.3)\n",
+      "Requirement already satisfied: pandas<2.2.0 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (2.0.3)\n",
+      "Requirement already satisfied: jinja2>=3 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (3.1.2)\n",
+      "Requirement already satisfied: scipy<=1.11.4,>=1.6.1 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (1.11.1)\n",
+      "Requirement already satisfied: joblib<1.4,>=1.2.0 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (1.2.0)\n",
+      "Requirement already satisfied: scikit-learn>1.4.0 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pycaret) (1.5.0)\n",
+      "Collecting pyod>=1.1.3 (from pycaret)\n",
+      "  Downloading pyod-2.0.1.tar.gz (163 kB)\n",
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+      "Requirement already satisfied: ptyprocess>=0.5 in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from pexpect>4.3->ipython>=5.5.0->pycaret) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=5.5.0->pycaret) (0.2.5)\n",
+      "Requirement already satisfied: executing in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from stack-data->ipython>=5.5.0->pycaret) (0.8.3)\n",
+      "Requirement already satisfied: asttokens in /Users/shikarichacha/anaconda3/lib/python3.11/site-packages (from stack-data->ipython>=5.5.0->pycaret) (2.0.5)\n",
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+      "\u001b[?25hUsing cached scikit_learn-1.4.2-cp311-cp311-macosx_12_0_arm64.whl (10.5 MB)\n",
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+      "\u001b[?25hDownloading dash_core_components-2.0.0-py3-none-any.whl (3.8 kB)\n",
+      "Downloading dash_html_components-2.0.0-py3-none-any.whl (4.1 kB)\n",
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+      "\u001b[?25hDownloading tsdownsample-0.1.3-cp311-cp311-macosx_11_0_arm64.whl (1.1 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading retrying-1.3.4-py3-none-any.whl (11 kB)\n",
+      "Building wheels for collected packages: pyod\n",
+      "  Building wheel for pyod (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for pyod: filename=pyod-2.0.1-py3-none-any.whl size=193267 sha256=cebe66403243aa589ea48393a97e0ead6257f96d2099140d2d60567c576bea63\n",
+      "  Stored in directory: /Users/shikarichacha/Library/Caches/pip/wheels/60/c9/a5/1c4c4f7da35f31ae2f79002aa0d84cf79ffb9fa17bc2b4ff15\n",
+      "Successfully built pyod\n",
+      "Installing collected packages: dash-table, dash-html-components, dash-core-components, tsdownsample, scikit-base, schemdraw, retrying, plotly, deprecation, scikit-learn, lightgbm, yellowbrick, sktime, scikit-plot, pyod, imbalanced-learn, dash, plotly-resampler, category-encoders, tbats, pycaret\n",
+      "  Attempting uninstall: plotly\n",
+      "    Found existing installation: plotly 5.9.0\n",
+      "    Uninstalling plotly-5.9.0:\n",
+      "      Successfully uninstalled plotly-5.9.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pycaret "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf4e6014",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:28.888368Z",
+     "iopub.status.busy": "2021-10-06T04:13:28.885583Z",
+     "iopub.status.idle": "2021-10-06T04:13:32.563378Z",
+     "shell.execute_reply": "2021-10-06T04:13:32.562740Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.832094Z"
+    },
+    "papermill": {
+     "duration": 3.758694,
+     "end_time": "2021-10-06T04:13:32.563516",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:28.804822",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pycaret.internal.pycaret_experiment import TimeSeriesExperiment\n",
+    "from sktime.utils.plotting import plot_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "682e4d7e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:32.703893Z",
+     "iopub.status.busy": "2021-10-06T04:13:32.700703Z",
+     "iopub.status.idle": "2021-10-06T04:13:32.757344Z",
+     "shell.execute_reply": "2021-10-06T04:13:32.756708Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.838602Z"
+    },
+    "papermill": {
+     "duration": 0.126406,
+     "end_time": "2021-10-06T04:13:32.757500",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:32.631094",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data0=pd.read_csv('TCS_stock_history.csv')\n",
+    "data0.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2a8b38a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:32.898117Z",
+     "iopub.status.busy": "2021-10-06T04:13:32.897552Z",
+     "iopub.status.idle": "2021-10-06T04:13:32.921339Z",
+     "shell.execute_reply": "2021-10-06T04:13:32.920839Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.880318Z"
+    },
+    "papermill": {
+     "duration": 0.095103,
+     "end_time": "2021-10-06T04:13:32.921481",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:32.826378",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data1=data0[['Date','Close']]\n",
+    "data1.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b770f843",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:33.063951Z",
+     "iopub.status.busy": "2021-10-06T04:13:33.063220Z",
+     "iopub.status.idle": "2021-10-06T04:13:33.077985Z",
+     "shell.execute_reply": "2021-10-06T04:13:33.078447Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.907696Z"
+    },
+    "papermill": {
+     "duration": 0.089222,
+     "end_time": "2021-10-06T04:13:33.078605",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:32.989383",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data1 = data1.sort_values('Date',ascending=True)\n",
+    "data1.index = pd.DatetimeIndex(data1[\"Date\"])\n",
+    "data1.drop([\"Date\"],axis=1,inplace=True)\n",
+    "data1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21b43c72",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:33.226771Z",
+     "iopub.status.busy": "2021-10-06T04:13:33.225956Z",
+     "iopub.status.idle": "2021-10-06T04:13:33.244230Z",
+     "shell.execute_reply": "2021-10-06T04:13:33.244622Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.936125Z"
+    },
+    "papermill": {
+     "duration": 0.091114,
+     "end_time": "2021-10-06T04:13:33.244808",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:33.153694",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data1 = data1.asfreq('d')\n",
+    "data1.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2deb1e59",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:33.404352Z",
+     "iopub.status.busy": "2021-10-06T04:13:33.393197Z",
+     "iopub.status.idle": "2021-10-06T04:13:33.407238Z",
+     "shell.execute_reply": "2021-10-06T04:13:33.407651Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.958552Z"
+    },
+    "papermill": {
+     "duration": 0.091398,
+     "end_time": "2021-10-06T04:13:33.407842",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:33.316444",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data2=data1.fillna(method='ffill')\n",
+    "data2.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a1fc27e",
+   "metadata": {
+    "_kg_hide-output": true,
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:33.558161Z",
+     "iopub.status.busy": "2021-10-06T04:13:33.557035Z",
+     "iopub.status.idle": "2021-10-06T04:13:33.563153Z",
+     "shell.execute_reply": "2021-10-06T04:13:33.563734Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.975191Z"
+    },
+    "papermill": {
+     "duration": 0.085245,
+     "end_time": "2021-10-06T04:13:33.563898",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:33.478653",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# data must be univariate\n",
+    "data=data2.Close\n",
+    "print(type(data))\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8457014f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:33.710956Z",
+     "iopub.status.busy": "2021-10-06T04:13:33.710153Z",
+     "iopub.status.idle": "2021-10-06T04:13:34.507088Z",
+     "shell.execute_reply": "2021-10-06T04:13:34.507549Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:13.999678Z"
+    },
+    "papermill": {
+     "duration": 0.872022,
+     "end_time": "2021-10-06T04:13:34.507723",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:33.635701",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# plot_series\n",
+    "_ =plot_series(data)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12b36ad5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:34.671271Z",
+     "iopub.status.busy": "2021-10-06T04:13:34.670596Z",
+     "iopub.status.idle": "2021-10-06T04:13:35.952813Z",
+     "shell.execute_reply": "2021-10-06T04:13:35.952366Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:14.770661Z"
+    },
+    "papermill": {
+     "duration": 1.369368,
+     "end_time": "2021-10-06T04:13:35.952948",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:34.583580",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#TimeSeriesExperiment from pycarat\n",
+    "exp = TimeSeriesExperiment()\n",
+    "exp.setup(data=data,session_id=42,fh=50) # fh:days of prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9270e904",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:36.105072Z",
+     "iopub.status.busy": "2021-10-06T04:13:36.104197Z",
+     "iopub.status.idle": "2021-10-06T04:13:42.429993Z",
+     "shell.execute_reply": "2021-10-06T04:13:42.430514Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:16.377663Z"
+    },
+    "papermill": {
+     "duration": 6.403599,
+     "end_time": "2021-10-06T04:13:42.430683",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:36.027084",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#ARIMA\n",
+    "model = exp.create_model(\"arima\")\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf473f88",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:42.582726Z",
+     "iopub.status.busy": "2021-10-06T04:13:42.581827Z",
+     "iopub.status.idle": "2021-10-06T04:13:42.590984Z",
+     "shell.execute_reply": "2021-10-06T04:13:42.590468Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:23.415977Z"
+    },
+    "papermill": {
+     "duration": 0.085201,
+     "end_time": "2021-10-06T04:13:42.591110",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:42.505909",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_train = exp.get_config(\"y_train\")\n",
+    "y_train[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2c181b0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:42.748439Z",
+     "iopub.status.busy": "2021-10-06T04:13:42.747777Z",
+     "iopub.status.idle": "2021-10-06T04:13:43.996080Z",
+     "shell.execute_reply": "2021-10-06T04:13:43.995618Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:23.430863Z"
+    },
+    "papermill": {
+     "duration": 1.331752,
+     "end_time": "2021-10-06T04:13:43.996267",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:42.664515",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_predict = exp.predict_model(model)\n",
+    "plot_series(data, y_train, y_predict, labels=['All', 'Train', 'Test Prediction'])\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe7e4f61",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:44.207093Z",
+     "iopub.status.busy": "2021-10-06T04:13:44.200398Z",
+     "iopub.status.idle": "2021-10-06T04:13:44.509627Z",
+     "shell.execute_reply": "2021-10-06T04:13:44.510221Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:24.636284Z"
+    },
+    "papermill": {
+     "duration": 0.408704,
+     "end_time": "2021-10-06T04:13:44.510411",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:44.101707",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plot_series(data[-100:],  y_predict, labels=['All','Test Prediction'])\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdfb4733",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:13:44.698743Z",
+     "iopub.status.busy": "2021-10-06T04:13:44.697765Z",
+     "iopub.status.idle": "2021-10-06T04:14:22.825514Z",
+     "shell.execute_reply": "2021-10-06T04:14:22.825965Z",
+     "shell.execute_reply.started": "2021-10-06T04:11:24.933472Z"
+    },
+    "papermill": {
+     "duration": 38.226906,
+     "end_time": "2021-10-06T04:14:22.826217",
+     "exception": false,
+     "start_time": "2021-10-06T04:13:44.599311",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#SMAPE\n",
+    "tuned_model = exp.tune_model(model,optimize = 'SMAPE')\n",
+    "tuned_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b22f9444",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:14:22.983674Z",
+     "iopub.status.busy": "2021-10-06T04:14:22.983064Z",
+     "iopub.status.idle": "2021-10-06T04:14:23.172881Z",
+     "shell.execute_reply": "2021-10-06T04:14:23.173336Z",
+     "shell.execute_reply.started": "2021-10-06T04:12:00.857271Z"
+    },
+    "papermill": {
+     "duration": 0.269753,
+     "end_time": "2021-10-06T04:14:23.173519",
+     "exception": false,
+     "start_time": "2021-10-06T04:14:22.903766",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "exp.predict_model(tuned_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b194587a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:14:23.332768Z",
+     "iopub.status.busy": "2021-10-06T04:14:23.332169Z",
+     "iopub.status.idle": "2021-10-06T04:14:31.003633Z",
+     "shell.execute_reply": "2021-10-06T04:14:31.004199Z",
+     "shell.execute_reply.started": "2021-10-06T04:12:01.137231Z"
+    },
+    "papermill": {
+     "duration": 7.753098,
+     "end_time": "2021-10-06T04:14:31.004371",
+     "exception": false,
+     "start_time": "2021-10-06T04:14:23.251273",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# y_pred_future for Future Prediciton\n",
+    "final_model = exp.finalize_model(tuned_model)\n",
+    "y_pred_future = exp.predict_model(final_model, verbose=False)\n",
+    "\n",
+    "_=plot_series(data, y_train,y_predict, y_pred_future, labels=['All', 'Train','Test Prediction', 'Future Prediciton'])\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c9c03ca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:14:31.170185Z",
+     "iopub.status.busy": "2021-10-06T04:14:31.169603Z",
+     "iopub.status.idle": "2021-10-06T04:14:31.459994Z",
+     "shell.execute_reply": "2021-10-06T04:14:31.459522Z",
+     "shell.execute_reply.started": "2021-10-06T04:12:08.446240Z"
+    },
+    "papermill": {
+     "duration": 0.373676,
+     "end_time": "2021-10-06T04:14:31.460149",
+     "exception": false,
+     "start_time": "2021-10-06T04:14:31.086473",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "_=plot_series(data[-100:], y_predict, y_pred_future, labels=['All', 'Test Prediction',  'Future Prediciton'])\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57958c54",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-10-06T04:14:31.626343Z",
+     "iopub.status.busy": "2021-10-06T04:14:31.625756Z",
+     "iopub.status.idle": "2021-10-06T04:14:31.634032Z",
+     "shell.execute_reply": "2021-10-06T04:14:31.633306Z",
+     "shell.execute_reply.started": "2021-10-06T04:12:08.774196Z"
+    },
+    "papermill": {
+     "duration": 0.092014,
+     "end_time": "2021-10-06T04:14:31.634248",
+     "exception": false,
+     "start_time": "2021-10-06T04:14:31.542234",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(data[-10:])\n",
+    "print(y_pred_future[0:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c843a7e2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.081429,
+     "end_time": "2021-10-06T04:14:31.798261",
+     "exception": false,
+     "start_time": "2021-10-06T04:14:31.716832",
+     "status": "completed"
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+   },
+   "outputs": [],
+   "source": []
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+ ],
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+   "end_time": "2021-10-06T04:14:32.992574",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
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+     }
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+}
diff --git a/NYSE/TCS stocks/TCS by LSTM.ipynb b/NYSE/TCS stocks/TCS by LSTM.ipynb
new file mode 100644
index 00000000..efc4f27e
--- /dev/null
+++ b/NYSE/TCS stocks/TCS by LSTM.ipynb	
@@ -0,0 +1,1380 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "10aa62ad",
+   "metadata": {
+    "_kg_hide-output": true,
+    "papermill": {
+     "duration": 0.007239,
+     "end_time": "2024-05-30T15:39:49.831700",
+     "exception": false,
+     "start_time": "2024-05-30T15:39:49.824461",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# TCS Stock Prediction - LSTM\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "59111c23",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_kg_hide-output": true,
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:39:49.859978Z",
+     "iopub.status.busy": "2024-05-30T15:39:49.859234Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.511494Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.510665Z"
+    },
+    "papermill": {
+     "duration": 12.662365,
+     "end_time": "2024-05-30T15:40:02.513835",
+     "exception": false,
+     "start_time": "2024-05-30T15:39:49.851470",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import LSTM\n",
+    "from keras.layers import Dropout\n",
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb4999ec",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "9de88d689da1a279477470f6cef999c0a45bf98a",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.543056Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.542482Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.584817Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.583916Z"
+    },
+    "papermill": {
+     "duration": 0.052249,
+     "end_time": "2024-05-30T15:40:02.586967",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.534718",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Dividends</th>\n",
+       "      <th>Stock Splits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2002-08-12</td>\n",
+       "      <td>28.794172</td>\n",
+       "      <td>29.742206</td>\n",
+       "      <td>28.794172</td>\n",
+       "      <td>29.519140</td>\n",
+       "      <td>212976</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2002-08-13</td>\n",
+       "      <td>29.556316</td>\n",
+       "      <td>30.030333</td>\n",
+       "      <td>28.905705</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2002-08-14</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>26.563503</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>822776</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2002-08-16</td>\n",
+       "      <td>26.972458</td>\n",
+       "      <td>28.255089</td>\n",
+       "      <td>26.582090</td>\n",
+       "      <td>27.046812</td>\n",
+       "      <td>811856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4458</th>\n",
+       "      <td>2021-09-24</td>\n",
+       "      <td>3890.000000</td>\n",
+       "      <td>3944.399902</td>\n",
+       "      <td>3855.000000</td>\n",
+       "      <td>3871.300049</td>\n",
+       "      <td>2320754</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4459</th>\n",
+       "      <td>2021-09-27</td>\n",
+       "      <td>3900.000000</td>\n",
+       "      <td>3904.000000</td>\n",
+       "      <td>3802.899902</td>\n",
+       "      <td>3836.949951</td>\n",
+       "      <td>1673362</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4460</th>\n",
+       "      <td>2021-09-28</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3751.250000</td>\n",
+       "      <td>3779.149902</td>\n",
+       "      <td>2253075</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4461</th>\n",
+       "      <td>2021-09-29</td>\n",
+       "      <td>3759.800049</td>\n",
+       "      <td>3806.000000</td>\n",
+       "      <td>3722.149902</td>\n",
+       "      <td>3791.899902</td>\n",
+       "      <td>2489161</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4462</th>\n",
+       "      <td>2021-09-30</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3765.000000</td>\n",
+       "      <td>3773.199951</td>\n",
+       "      <td>640479</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4463 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date         Open         High          Low        Close   Volume  \\\n",
+       "0     2002-08-12    28.794172    29.742206    28.794172    29.519140   212976   \n",
+       "1     2002-08-13    29.556316    30.030333    28.905705    29.119476   153576   \n",
+       "2     2002-08-14    29.184536    29.184536    26.563503    27.111877   822776   \n",
+       "3     2002-08-15    27.111877    27.111877    27.111877    27.111877        0   \n",
+       "4     2002-08-16    26.972458    28.255089    26.582090    27.046812   811856   \n",
+       "...          ...          ...          ...          ...          ...      ...   \n",
+       "4458  2021-09-24  3890.000000  3944.399902  3855.000000  3871.300049  2320754   \n",
+       "4459  2021-09-27  3900.000000  3904.000000  3802.899902  3836.949951  1673362   \n",
+       "4460  2021-09-28  3850.000000  3850.000000  3751.250000  3779.149902  2253075   \n",
+       "4461  2021-09-29  3759.800049  3806.000000  3722.149902  3791.899902  2489161   \n",
+       "4462  2021-09-30  3805.000000  3805.000000  3765.000000  3773.199951   640479   \n",
+       "\n",
+       "      Dividends  Stock Splits  \n",
+       "0           0.0           0.0  \n",
+       "1           0.0           0.0  \n",
+       "2           0.0           0.0  \n",
+       "3           0.0           0.0  \n",
+       "4           0.0           0.0  \n",
+       "...         ...           ...  \n",
+       "4458        0.0           0.0  \n",
+       "4459        0.0           0.0  \n",
+       "4460        0.0           0.0  \n",
+       "4461        0.0           0.0  \n",
+       "4462        0.0           0.0  \n",
+       "\n",
+       "[4463 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset=pd.read_csv(\"TCS_stock_history.csv\")\n",
+    "display(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b904b654",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.602979Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.602733Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.624846Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.624057Z"
+    },
+    "papermill": {
+     "duration": 0.032102,
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+     "start_time": "2024-05-30T15:40:02.594656",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Dividends</th>\n",
+       "      <th>Stock Splits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>28.794172</td>\n",
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+       "      <td>28.794172</td>\n",
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+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2002-08-13</td>\n",
+       "      <td>29.556316</td>\n",
+       "      <td>30.030333</td>\n",
+       "      <td>28.905705</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2002-08-14</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>29.184536</td>\n",
+       "      <td>26.563503</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>822776</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>27.111877</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2002-08-16</td>\n",
+       "      <td>26.972458</td>\n",
+       "      <td>28.255089</td>\n",
+       "      <td>26.582090</td>\n",
+       "      <td>27.046812</td>\n",
+       "      <td>811856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4458</th>\n",
+       "      <td>2021-09-24</td>\n",
+       "      <td>3890.000000</td>\n",
+       "      <td>3944.399902</td>\n",
+       "      <td>3855.000000</td>\n",
+       "      <td>3871.300049</td>\n",
+       "      <td>2320754</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4459</th>\n",
+       "      <td>2021-09-27</td>\n",
+       "      <td>3900.000000</td>\n",
+       "      <td>3904.000000</td>\n",
+       "      <td>3802.899902</td>\n",
+       "      <td>3836.949951</td>\n",
+       "      <td>1673362</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4460</th>\n",
+       "      <td>2021-09-28</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3850.000000</td>\n",
+       "      <td>3751.250000</td>\n",
+       "      <td>3779.149902</td>\n",
+       "      <td>2253075</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>4461</th>\n",
+       "      <td>2021-09-29</td>\n",
+       "      <td>3759.800049</td>\n",
+       "      <td>3806.000000</td>\n",
+       "      <td>3722.149902</td>\n",
+       "      <td>3791.899902</td>\n",
+       "      <td>2489161</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4462</th>\n",
+       "      <td>2021-09-30</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3805.000000</td>\n",
+       "      <td>3765.000000</td>\n",
+       "      <td>3773.199951</td>\n",
+       "      <td>640479</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4463 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Date         Open         High          Low        Close   Volume  \\\n",
+       "0    2002-08-12    28.794172    29.742206    28.794172    29.519140   212976   \n",
+       "1    2002-08-13    29.556316    30.030333    28.905705    29.119476   153576   \n",
+       "2    2002-08-14    29.184536    29.184536    26.563503    27.111877   822776   \n",
+       "3    2002-08-15    27.111877    27.111877    27.111877    27.111877        0   \n",
+       "4    2002-08-16    26.972458    28.255089    26.582090    27.046812   811856   \n",
+       "...         ...          ...          ...          ...          ...      ...   \n",
+       "4458 2021-09-24  3890.000000  3944.399902  3855.000000  3871.300049  2320754   \n",
+       "4459 2021-09-27  3900.000000  3904.000000  3802.899902  3836.949951  1673362   \n",
+       "4460 2021-09-28  3850.000000  3850.000000  3751.250000  3779.149902  2253075   \n",
+       "4461 2021-09-29  3759.800049  3806.000000  3722.149902  3791.899902  2489161   \n",
+       "4462 2021-09-30  3805.000000  3805.000000  3765.000000  3773.199951   640479   \n",
+       "\n",
+       "      Dividends  Stock Splits  \n",
+       "0           0.0           0.0  \n",
+       "1           0.0           0.0  \n",
+       "2           0.0           0.0  \n",
+       "3           0.0           0.0  \n",
+       "4           0.0           0.0  \n",
+       "...         ...           ...  \n",
+       "4458        0.0           0.0  \n",
+       "4459        0.0           0.0  \n",
+       "4460        0.0           0.0  \n",
+       "4461        0.0           0.0  \n",
+       "4462        0.0           0.0  \n",
+       "\n",
+       "[4463 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset['Date']=pd.to_datetime(dataset['Date'])\n",
+    "dataset=dataset.sort_values('Date',ascending=True).reset_index(drop=True)\n",
+    "#dataset['Date']=dataset['Date'].astype(str)\n",
+    "display(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a1d8576",
+   "metadata": {
+    "_uuid": "99cca4c05240bd82c4492c6c3c200925c5c096e5",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.643344Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.643075Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.648066Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.647199Z"
+    },
+    "papermill": {
+     "duration": 0.015127,
+     "end_time": "2024-05-30T15:40:02.649865",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.634738",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "trainset=dataset.iloc[-1000:-100,1:2].values #Open\n",
+    "testset=dataset.iloc[-100:,1:2].values #Open \n",
+    "real_stock_price = testset.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8beae7ae",
+   "metadata": {
+    "_uuid": "1abc6fe47fb5c4443767a2e2053cf9999c8a9a5c",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.682140Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.681516Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.687744Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.686822Z"
+    },
+    "papermill": {
+     "duration": 0.016649,
+     "end_time": "2024-05-30T15:40:02.689856",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.673207",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.00863905]\n",
+      " [0.00920529]\n",
+      " [0.01467811]\n",
+      " [0.01090375]\n",
+      " [0.01446842]]\n",
+      "900\n"
+     ]
+    }
+   ],
+   "source": [
+    "sc = MinMaxScaler(feature_range = (0,1))\n",
+    "training_scaled = sc.fit_transform(trainset)\n",
+    "print(training_scaled[0:5])\n",
+    "print(len(training_scaled))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e27fae4c",
+   "metadata": {
+    "_uuid": "d0d549e9a4af9ab516c3304fb44d650a815dcedc",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.706979Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.706741Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.710325Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.709473Z"
+    },
+    "papermill": {
+     "duration": 0.014132,
+     "end_time": "2024-05-30T15:40:02.712229",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.698097",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x_train = []\n",
+    "y_train = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d2d3f2f3",
+   "metadata": {
+    "_uuid": "4c4f8ba0c635e7da0ba1bc52abf0a42068acdc89",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.744586Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.743917Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.750575Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.749753Z"
+    },
+    "papermill": {
+     "duration": 0.017017,
+     "end_time": "2024-05-30T15:40:02.752512",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.735495",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(840, 60) (840,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(60,len(training_scaled)):\n",
+    "    x_train.append(training_scaled[i-60:i, 0])\n",
+    "    y_train.append(training_scaled[i,0])\n",
+    "x_train,y_train = np.array(x_train),np.array(y_train)\n",
+    "print(x_train.shape,y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3fe1f380",
+   "metadata": {
+    "_uuid": "316c5775b5c0a984b432f138792d2c0e9a484491",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.784806Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.784541Z",
+     "iopub.status.idle": "2024-05-30T15:40:02.789019Z",
+     "shell.execute_reply": "2024-05-30T15:40:02.788206Z"
+    },
+    "papermill": {
+     "duration": 0.014923,
+     "end_time": "2024-05-30T15:40:02.790963",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.776040",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(samples, time steps, features) = (840, 60, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n",
+    "print('(samples, time steps, features) =',x_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2beb16da",
+   "metadata": {
+    "_uuid": "ed24d733423254641f134c0e84f44a8ce175589f",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:02.807891Z",
+     "iopub.status.busy": "2024-05-30T15:40:02.807627Z",
+     "iopub.status.idle": "2024-05-30T15:40:04.002484Z",
+     "shell.execute_reply": "2024-05-30T15:40:04.001714Z"
+    },
+    "papermill": {
+     "duration": 1.205862,
+     "end_time": "2024-05-30T15:40:04.004829",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:02.798967",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "regressor = Sequential()\n",
+    "regressor.add(LSTM(units = 50,return_sequences = True,input_shape = (x_train.shape[1],1)))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(LSTM(units = 50,return_sequences = True))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(LSTM(units = 50,return_sequences = True))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(LSTM(units = 50))\n",
+    "regressor.add(Dropout(0.2))\n",
+    "regressor.add(Dense(units = 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9b2d9863",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "6681ff55d9a29b1ad0f37cdc82318fb2c8fbc0bd",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:04.023680Z",
+     "iopub.status.busy": "2024-05-30T15:40:04.023094Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.196836Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.195796Z"
+    },
+    "papermill": {
+     "duration": 8.185984,
+     "end_time": "2024-05-30T15:40:12.199621",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:04.013637",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "27/27 [==============================] - 3s 31ms/step - loss: 0.0389\n",
+      "Epoch 2/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0057\n",
+      "Epoch 3/10\n",
+      "27/27 [==============================] - 1s 30ms/step - loss: 0.0052\n",
+      "Epoch 4/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0047\n",
+      "Epoch 5/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0043\n",
+      "Epoch 6/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0046\n",
+      "Epoch 7/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0044\n",
+      "Epoch 8/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0045\n",
+      "Epoch 9/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0045\n",
+      "Epoch 10/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0050\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x163018e50>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')\n",
+    "regressor.fit(x_train,y_train,epochs =10, batch_size = 32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "822f38ef",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "31aa33ed70b08e71d0e8bfe4c1abae7901ab947d",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.287242Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.286898Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.292836Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.291964Z"
+    },
+    "papermill": {
+     "duration": 0.023133,
+     "end_time": "2024-05-30T15:40:12.294775",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.271642",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3723,)\n",
+      "[184.63800167 185.63356023 184.87500324 182.97887664 185.06463021\n",
+      " 184.3061393  185.06466558 183.92695682 184.11657917 184.87501269]\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = dataset.iloc[len(trainset)-len(testset)-60:,1].values\n",
+    "print(inputs.shape)\n",
+    "print(inputs[0:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59a8e2dc",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013847,
+     "end_time": "2024-05-30T15:40:12.323670",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.309823",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "In the context of a LSTM model, this reshaping may be necessary if the input data has only one feature (i.e., one time series) and the LSTM model expects a 3D input shape of (n_samples, n_time_steps, n_features). In this case, since there is only one feature, the shape does not change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7aa4e312",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "7d74568c439acc8dda77dc4365d4b413095508e1",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.353568Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.353252Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.358747Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.357855Z"
+    },
+    "papermill": {
+     "duration": 0.022762,
+     "end_time": "2024-05-30T15:40:12.360690",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.337928",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3723, 1)\n",
+      "(3723, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = inputs.reshape(-1,1)\n",
+    "print(inputs.shape)\n",
+    "\n",
+    "#sc = MinMaxScaler(feature_range = (0,1))\n",
+    "inputs = sc.transform(inputs)\n",
+    "print(inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3c962cd2",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "ef076f0fdbe6ec72d85e7326c3e8aa73fcbad667",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.390465Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.390192Z",
+     "iopub.status.idle": "2024-05-30T15:40:12.396836Z",
+     "shell.execute_reply": "2024-05-30T15:40:12.395965Z"
+    },
+    "papermill": {
+     "duration": 0.023947,
+     "end_time": "2024-05-30T15:40:12.398827",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.374880",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3663, 60)\n",
+      "(3663, 60, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test = []\n",
+    "for i in range(60,len(inputs)):\n",
+    "    x_test.append(inputs[i-60:i,0])\n",
+    "x_test = np.array(x_test)\n",
+    "print(x_test.shape)\n",
+    "\n",
+    "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n",
+    "print(x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23808ef3",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "24730042bdad3a140e033a78ae9b170304f8cefb",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:12.428521Z",
+     "iopub.status.busy": "2024-05-30T15:40:12.428246Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.116866Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.116047Z"
+    },
+    "papermill": {
+     "duration": 0.70562,
+     "end_time": "2024-05-30T15:40:13.118807",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:12.413187",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "115/115 [==============================] - 1s 8ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_price = regressor.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8c507e69",
+   "metadata": {
+    "_kg_hide-output": true,
+    "_uuid": "4b06462fa06e7399373d8a552cd159176fbeef33",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.149441Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.149155Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.154197Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.153273Z"
+    },
+    "papermill": {
+     "duration": 0.022316,
+     "end_time": "2024-05-30T15:40:13.156092",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.133776",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3663, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_price = sc.inverse_transform(predicted_price)\n",
+    "print(predicted_price.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fee58a4f",
+   "metadata": {
+    "_kg_hide-output": false,
+    "_uuid": "e8b30d578fafb1564da4448efb1f994537f6f588",
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.186273Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.185980Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.440139Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.439281Z"
+    },
+    "papermill": {
+     "duration": 0.271652,
+     "end_time": "2024-05-30T15:40:13.442276",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.170624",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(real_stock_price[-100:],color = 'red', label = 'Real Price')\n",
+    "plt.plot(predicted_price[-100:], color = 'blue', label = 'Predicted Price')\n",
+    "plt.title('TCS Stock Price Prediction')\n",
+    "plt.xlabel('days')\n",
+    "plt.ylabel('Price')\n",
+    "plt.xlim(0,100)  \n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "52971fdb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.474377Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.474096Z",
+     "iopub.status.idle": "2024-05-30T15:40:13.899104Z",
+     "shell.execute_reply": "2024-05-30T15:40:13.898213Z"
+    },
+    "papermill": {
+     "duration": 0.444389,
+     "end_time": "2024-05-30T15:40:13.902296",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.457907",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(18,10))\n",
+    "plt.plot(dataset.iloc[-1000:-100,0],trainset,'-o',label='train actual')\n",
+    "#plt.plot(dataset.iloc[-1000:-100,0],train_pred,'--',label='Prophet')\n",
+    "plt.plot(dataset.iloc[-100:,0],real_stock_price[-100:],'-o',label='test actual')\n",
+    "plt.plot(dataset.iloc[-100:,0],predicted_price[-100:],'-o',label='test predicted')\n",
+    "plt.title('TCS Stock Price',size=24)\n",
+    "plt.xlabel('Date',size=16)\n",
+    "plt.ylabel('Price',size=16)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "162992af",
+   "metadata": {
+    "_kg_hide-output": true,
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:13.973595Z",
+     "iopub.status.busy": "2024-05-30T15:40:13.973249Z",
+     "iopub.status.idle": "2024-05-30T15:40:19.892150Z",
+     "shell.execute_reply": "2024-05-30T15:40:19.891239Z"
+    },
+    "papermill": {
+     "duration": 5.939242,
+     "end_time": "2024-05-30T15:40:19.894142",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:13.954900",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 12ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = np.array(trainset[-60:])\n",
+    "new_predicted_price=[]\n",
+    "for i in range(100):\n",
+    "    x = np.reshape(inputs, (1,60,1))\n",
+    "    pred = regressor.predict(x)\n",
+    "    inputs = np.append(inputs, pred)\n",
+    "    inputs = inputs[1:]\n",
+    "    new_predicted_price+=[pred.reshape(-1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ab12649e",
+   "metadata": {
+    "_kg_hide-output": true,
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:19.961103Z",
+     "iopub.status.busy": "2024-05-30T15:40:19.960774Z",
+     "iopub.status.idle": "2024-05-30T15:40:19.966508Z",
+     "shell.execute_reply": "2024-05-30T15:40:19.965587Z"
+    },
+    "papermill": {
+     "duration": 0.041324,
+     "end_time": "2024-05-30T15:40:19.968515",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:19.927191",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_predicted_price = sc.inverse_transform(np.array(new_predicted_price))\n",
+    "print(new_predicted_price.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "69ff2e6d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:20.035126Z",
+     "iopub.status.busy": "2024-05-30T15:40:20.034829Z",
+     "iopub.status.idle": "2024-05-30T15:40:20.309774Z",
+     "shell.execute_reply": "2024-05-30T15:40:20.308818Z"
+    },
+    "papermill": {
+     "duration": 0.310487,
+     "end_time": "2024-05-30T15:40:20.311902",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:20.001415",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+ffuKhw8fCiGyTsiZlpYmxo8fL0xMTMSKFSuyPZ/sEnK+THP32MmTJ7Pdp7mzTGPXrl2idevWwsbGRlhbW4s6deqIBQsW6JQ5e/as6Nu3r3B0dBTm5ubC2dlZtGvXTixbtizH+giRdULOl70qWenhw4dFt27dhL29vTA3Nxfly5cX3bp1y1R++/btwsvLS1hYWIiKFSuK+fPnZ5mQ8+W79oSQd9pNmDBBVKxYUZibmwtHR0fRrVs3cfnyZW2Z7L4n2X22K1eu1NZHpVKJnj17igsXLuiUye7fS0ElEiUqSAohXnGbDhERERFliWOkiIiIiPKJgRQRERFRPjGQIiIiIsonBlJERERE+cRAioiIiCifGEgRERER5RMTcuZSWloaHjx4AFtb21xNNUFERESGJ4RAXFwcXF1dYWJS8O1HDKRy6cGDB3BzczN0NYiIiCgfwsPDcz2Rel4wkMolzdxZ4eHhsLOzM3BtiIiIKDdiY2Ph5uamtzkwGUjlkqY7z87OjoEUERFREaOvYTkcbE5ERESUTwYNpGbPng2FQqGzODs7a/cPHTo00/6mTZvqHCMpKQnjxo1D2bJlYWNjgx49euDevXs6ZaKjo+Hn5weVSgWVSgU/Pz/ExMQUxikSERFRMWbwFqm6desiIiJCu4SFhens79y5s87+Xbt26eyfOHEitmzZgoCAABw5cgTx8fHw9fVFamqqtszAgQMRGhqKwMBABAYGIjQ0FH5+foVyfkRERFR8GXyMlJmZmU4r1MuUSmW2+9VqNX755ResW7cO7du3BwCsX78ebm5u2L9/Pzp16oRLly4hMDAQx44dQ5MmTQAAK1asgLe3N65cuYKaNWsW/EkRERmJ1NRUpKSkGLoaRHpjbm4OU1NTg72/wQOpa9euwdXVFUqlEk2aNMGXX36JKlWqaPcfOnQIjo6OKF26NFq3bo0vvvgCjo6OAICQkBCkpKSgY8eO2vKurq7w8PBAUFAQOnXqhODgYKhUKm0QBQBNmzaFSqVCUFBQtoFUUlISkpKStM9jY2ML+tSJiPRGCIHIyEgOY6ASoXTp0nB2djZInkeDBlJNmjTB2rVrUaNGDTx8+BDz5s1Ds2bNcOHCBTg4OKBLly7o06cP3N3dcevWLcycORPt2rVDSEgIlEolIiMjYWFhgTJlyugc18nJCZGRkQCAyMhIbeCVkaOjo7ZMVvz9/TFnzpyCPWEiokKiCaIcHR1hbW3NRMJULAkh8Pz5c0RFRQEAXFxcCr0OBg2kunTpol339PSEt7c3qlatijVr1mDSpEno16+fdr+HhwcaNWoEd3d37Ny5E7169cr2uEIInf80svoP5OUyL5s+fTomTZqkfa7JQ0FEZOxSU1O1QZSDg4Ohq0OkV1ZWVgCAqKgoODo6Fno3n8EHm2dkY2MDT09PXLt2Lcv9Li4ucHd31+53dnZGcnIyoqOjdcpFRUXByclJW+bhw4eZjvXo0SNtmawolUptzijmjiKiokQzJsra2trANSEqHJrvuiHGAxpVIJWUlIRLly5l2zT35MkThIeHa/c3bNgQ5ubm2Ldvn7ZMREQEzp8/j2bNmgEAvL29oVarceLECW2Z48ePQ61Wa8sQERVH7M6jksKQ33WDdu1NmTIF3bt3R8WKFREVFYV58+YhNjYWQ4YMQXx8PGbPno3evXvDxcUFt2/fxowZM1C2bFm8/fbbAACVSoXhw4dj8uTJcHBwgL29PaZMmQJPT0/tXXy1a9dG586dMXLkSCxfvhwAMGrUKPj6+vKOPSIiInotBm2RunfvHgYMGICaNWuiV69esLCwwLFjx+Du7g5TU1OEhYWhZ8+eqFGjBoYMGYIaNWogODhYZ76cxYsX46233kLfvn3RvHlzWFtbY8eOHTp9pBs2bICnpyc6duyIjh07wsvLC+vWrTPEKRMRkRGYPXs26tevX+DHPXToEBQKBe+WLEEUQghh6EoUBbGxsVCpVFCr1RwvRURGLTExEbdu3ULlypVhaWlp6OrkydChQ7FmzRoAgKmpKVxdXdGtWzd8+eWXme7Qfh2zZ8/G1q1bERoamuX+27dvo3LlytrnpUuXhqenJ+bOnYvWrVtne9zk5GQ8ffoUTk5O7FotRDl95/X9+21UY6SoaElKAsLDgchI4OlTIC5ObmNoTkSvQzOjxe3bt7Fy5Urs2LEDY8eONUhd9u/fj4iICBw+fBh2dnbo2rUrbt26lWXZlJQUWFhYGCyfERkGAynKlZQU4Ngx4KefgBEjgAYNgFKlgIoVARcXwMEBsLMDLC0BW1ugbVtg+nRg2zYZaBER5ZZmRosKFSqgY8eO6NevH/bu3atTZtWqVahduzYsLS1Rq1Yt/PTTTzr7p02bhho1asDa2hpVqlTBzJkz83VHl4ODA5ydneHl5YXly5fj+fPn2rooFAosW7YMPXv2hI2NDebNm5dl197Ro0fRunVrWFtbo0yZMujUqZP2bnMhBBYuXIgqVarAysoK9erVw6ZNm/JcTzIcg2c2J+MWFQUsXw4sXQpERGTeb2YGpKbqtkI9ewYcOiQXjRo1gCFDgKFDAVdXPVeaiDITAnj+3DDvbW0N5LOF5ubNmwgMDIS5ubl224oVKzBr1iwsWbIEDRo0wJkzZzBy5EjY2NhgyJAhAABbW1usXr0arq6uCAsLw8iRI2Fra4upU6e+xmlkvsV+1qxZ8Pf3x+LFi2FqapqptSo0NBQ+Pj4YNmwYvv/+e5iZmeHgwYPa+WA//fRTbN68GUuXLkX16tXx77//4t1330W5cuVy7EIkIyIoV9RqtQAg1Gq1oatSKE6fFmLoUCGUSiHk/8BC2NsL0aWLEJ98IsTmzULcvi1EWpos/+KFEM+fCxETI0RYmBArVwoxYoQQHh5CKBTpxzAxEcLXV4gtW4RITjboKRIVWwkJCeLixYsiISEhfWN8fPo/xMJe4uNzXfchQ4YIU1NTYWNjIywtLQUAAUB888032jJubm7it99+03nd3Llzhbe3d7bHXbhwoWjYsKH2+axZs0S9evWyLX/r1i0BQJw5c+b/P7548d577wlTU1Nx7tw5IYQQAMTEiRN1Xnfw4EEBQERHRwshhBgwYIBo3rx5lu8RHx8vLC0tRVBQkM724cOHiwEDBmRbN8osy+/8/9P37zdbpEjHw4fAmDHAli3p2xo3BiZMAN55B7CwyPp1pqaAlZVcVCrAwwMYPlzuU6vl8X75BThyBPj7b7mULw/MmgX873+yZYuICADatm2LpUuX4vnz51i5ciWuXr2KcePGAZDJlMPDwzF8+HCMHDlS+5oXL15ApVJpn2/atAnffvstrl+/jvj4eLx48SJfA42bNWsGExMTPH/+HC4uLli9ejU8PT21+xs1apTj60NDQ9GnT58s9128eBGJiYno0KGDzvbk5GQ0aNAgz3Ulw+DPF2n9+acMop48kYFN377A+PFAhvme80Wlkl16Q4cCly8Dv/4KrFkD3L8PjBoFfPUVMG8e0Ls3YMJRe0T6YW0NxMcb7r3zwMbGBtWqVQMAfP/992jbti3mzJmDuXPnIi0tDYDs3mvy0n9OmrQ3x44dQ//+/TFnzhx06tQJKpUKAQEB+Prrr/Nc9Y0bN6JOnTooXbp0ltPt2NjY5Ph6zfQlWdGcy86dO1G+fHmdfUqlMs91JcNgIEV48gT44AMgIEA+r19fBjpeXgX/XrVqAQsXAp9/DixbBnzxBXD1qgzaGjYEFiwAfHwK/n2JSjyFAnjFj76xmjVrFrp06YIxY8bA1dUV5cuXx82bNzFo0KAsyx89ehTu7u745JNPtNvu3LmTr/d2c3ND1apV8/VaAPDy8sKBAwcwZ86cTPvq1KkDpVKJu3fvcjxUEca//0u4f/6R3XABAbJ7buZM4Phx/QRRGVlaAhMnAjduyO69UqWAkBCgfXtg8GAZ3BERAUCbNm1Qt25dfPnllwBkDih/f3989913uHr1KsLCwrBq1Sp88803AIBq1arh7t27CAgIwI0bN/D9999jS8bxCoVo+vTpOHnyJMaOHYtz587h8uXLWLp0KR4/fgxbW1tMmTIFH374IdasWYMbN27gzJkz+PHHH7W5tMj4MZAqwTZtAjp3lukJatcGgoNlS1F246D0wc4OmD0buHlTtoopFMC6dbI+GzcyJxURSZMmTcKKFSsQHh6OESNGYOXKldrxSq1bt8bq1au1CTR79uyJDz/8EB988AHq16+PoKAgzJw50yD1rlGjBvbu3YuzZ8+icePG8Pb2xrZt22D2/wND586di88++wz+/v6oXbs2OnXqhB07dugkAyXjxszmuVTcMpuvXAm89x6Qlia71daska1EhnbsmMxTdeGCfN6jh8xd9dLwASLKQVHObE6UH8xsToVq0SJg5EgZRI0aBfz2m3EEUQDQtClw+rRspTI3B7Zvl12Pf/1l6JoRERFlxkCqBBFCZhvX5KObNk0O+M4wv7NRsLCQ46bOnAHefBOIiZGpF8aOBRISDF07IiKidAykSpBp04D58+X6ggVy3Zing6pbFzh6VNYbkNnVmzQBLl0ybL2IiIg0GEiVECtWyC49QE758hqzJBQqc3MZ8O3ZAzg6AmFhQKNGwKpVhq4ZERERA6kS4eBB2S0GyLvyRo0ybH3yo2NH4OxZoEMHOV3YsGEyeWhysqFrRkREJRkDqWLu6lWZMfzFC2DgQODTTw1do/xzdgYCA2UWdIVCju9q2zbryZSJiIgKAwOpYuzpU8DXF4iOBry95Vx3xjwmKjdMTIBPPpFz9alUQFCQzIgeFGTomhERUUnEQKqYSkmRd7pduwZUrCgnDTaWFAcFoWtX4NQpOSA9IgJo0wb4+WdD14qIiEoaBlLF1EcfybFRpUrJ1hsnJ0PXqOBVqyYTeL7zjgwc33sPmDBBdmMSEREVBgZSxdC+fcB338n1334DPD0NWx99KlUK+OMPOW4KAL7/XnZnqtWGrRcRUV5UqlQJ3377rfa5QqHA1q1bC70es2fPRv369Qv8uIcOHYJCoUBMTEyBH9vQGEgVM9HRwP/+J9fHjgW6dzdsfQqDQiHHTW3aBFhZyVQJ3t5y/j4iKlqGDh0KhUKB+Zqkd/9v69atUBTyIE+FQqFdbG1t0ahRI2zevLlQ3jsiIgJdunTJVVl9BT9ZuX37ts7nUqZMGbRq1QqHDx/O8XXNmjVDREQEVCpVodSzMDGQKmY++AC4fx+oXh1YuNDQtSlcvXsDR44Arq4yaWfjxsB//xm6VkSUV5aWlliwYAGio6MNXRWsWrUKEREROHnyJOrVq4c+ffogODg4y7LJBZiPxdnZGUqlssCOV9D279+PiIgIHD58GHZ2dujatStu3bqVZdmUlBRYWFjA2dm50IPhwsBAqhjZuFF25ZmaAuvWATY2hq5R4XvjDeDkSXkn35MnQPv2wIYNhq4VEeVF+/bt4ezsDH9//xzLBQUFoVWrVrCysoKbmxvGjx+PZ8+eAQB++OEHeGYY16Bp0frxxx+12zp16oTp06fn+B6lS5eGs7MzatWqhWXLlsHS0hLbt28HILvj5s2bh6FDh0KlUmHkyJGvrBcAREVFoXv37rCyskLlypWxIYv/pF7u2rt37x769+8Pe3t72NjYoFGjRjh+/DhWr16NOXPm4OzZs9pWotWrVwMA1Go1Ro0aBUdHR9jZ2aFdu3Y4e/aszvvMnz8fTk5OsLW1xfDhw5GYmJjj56Hh4OAAZ2dneHl5Yfny5Xj+/Dn27t2rrfuyZcvQs2dP2NjYYN68eVl27R09ehStW7eGtbU1ypQpg06dOmmDZyEEFi5ciCpVqsDKygr16tXDpk2bclW3wsZAqpi4f18mqASAGTPkVCollasr8O+/soUqORl4912ZiFQIQ9eMyHCEAJ49M8yS1397pqam+PLLL/HDDz/g3r17WZYJCwtDp06d0KtXL5w7dw4bN27EkSNH8MEHHwAA2rRpgwsXLuDx48cAgMOHD6Ns2bLaLqgXL14gKCgIrVu3znW9zM3NYWZmhpSUFO22RYsWwcPDAyEhIZg5c+Yr6wXI7svbt2/jn3/+waZNm/DTTz8hKioq2/eNj49H69at8eDBA2zfvh1nz57F1KlTkZaWhn79+mHy5MmoW7cuIiIiEBERgX79+kEIgW7duiEyMhK7du1CSEgI3njjDfj4+ODp06cAgD/++AOzZs3CF198gVOnTsHFxQU//fRTrj8PDWtrawDQ+VxmzZqFnj17IiwsDMOGDcv0mtDQUPj4+KBu3boIDg7GkSNH0L17d6SmpgIAPv30U6xatQpLly7FhQsX8OGHH+Ldd999ZReiQQjKFbVaLQAItVpt6KpkkpYmRMeOQgBCNGwoRHKyoWtkHFJThZg6VX4ugBB+fkIkJhq6VkT6l5CQIC5evCgSEhK02+Lj0/8tFPYSH5/7ug8ZMkT07NlTCCFE06ZNxbBhw4QQQmzZskVk/Mny8/MTo0aN0nntf//9J0xMTERCQoJIS0sTZcuWFZs2bRJCCFG/fn3h7+8vHB0dhRBCBAUFCTMzMxEXF5dtXQCILVu2CCGESExMFHPnzhUAxK5du4QQQri7u4u33npL5zWvqteVK1cEAHHs2DHt/kuXLgkAYvHixVm+9/Lly4Wtra148uRJlvWcNWuWqFevns62AwcOCDs7O5H40n96VatWFcuXLxdCCOHt7S1Gjx6ts79JkyaZjpXRrVu3BABx5swZIYQQ8fHx4r333hOmpqbi3Llz2rpPnDhR53UHDx4UAER0dLQQQogBAwaI5s2bZ/ke8fHxwtLSUgQFBelsHz58uBgwYECWr8nqO6+h799vMwPFb1SAli8H9u6VeaLWrZPz05FM3rlgAVC1qhx4v24dcPcusHkzYG9v6NoR0assWLAA7dq1w+TJkzPtCwkJwfXr13W6xYQQSEtLw61bt1C7dm20atUKhw4dgo+PDy5cuIDRo0fjq6++wqVLl3Do0CG88cYbKFWqVI51GDBgAExNTZGQkACVSoWvvvpKZxB4o0aN8lSvq1evwszMTOd1tWrVQunSpbOtQ2hoKBo0aAD7PPzHFRISgvj4eDg4OOhsT0hIwI0bNwAAly5dwujRo3X2e3t74+DBg688frNmzWBiYoLnz5/DxcUFq1ev1ulKfflzeVloaCj69OmT5b6LFy8iMTERHTp00NmenJyMBg0avLJuhY2BVBH3+LHsygMAf3+gdm3D1scYjRoFVKok800dPgw0awbs3CkDLKKSwtoaiI833HvnR6tWrdCpUyfMmDEDQ4cO1dmXlpaG9957D+PHj8/0uooVKwKQ3Xs///wz/vvvP9SrVw+lS5fW3mF26NAhtGnT5pV1WLx4Mdq3bw87Ozs4Ojpm2m/z0mDUV9XrypUrAJCnQddWVla5LpuxHi4uLjh06FCmfTkFbbm1ceNG1KlTB6VLl84UrAGZP5eX5XROaWlpAICdO3eifPnyOvuMcQA+A6ki7pNPZMqDevXkHXuUtY4dgaNHgW7dgCtXgKZNgR075CNRSaBQFM0bUObPn4/69eujRo0aOtvfeOMNXLhwAdWqVcv2tW3atMGECROwadMmbdDUunVr7N+/H0FBQZgwYcIr39/Z2TnH93jZq+pVu3ZtvHjxAqdOnULjxo0BAFeuXMkxv5KXlxdWrlyJp0+fZtkqZWFhoR1blLEekZGRMDMzQ6VKlbKty7FjxzB48GDttmPHjr3iDCU3NzdUfY2/Rr28vHDgwAHMmTMn0746depAqVTi7t27eRrDZigcbF6EnToFrFgh13/4ATBjWJwjT0/g+HF5Z9/jx3LC47/+MnStiCgnnp6eGDRoEH744Qed7dOmTUNwcDDef/99hIaG4tq1a9i+fTvGjRunLePh4QEHBwds2LBBG0i1adMGW7duRUJCAlq0aFHg9X1VvWrWrInOnTtj5MiROH78OEJCQjBixIgcW2gGDBgAZ2dnvPXWWzh69Chu3ryJv/76S5uGoVKlSrh16xZCQ0Px+PFjJCUloX379vD29sZbb72FPXv24Pbt2wgKCsKnn36KU6dOAQAmTJiAX3/9Fb/++iuuXr2KWbNm4cKFCwX+mWRl+vTpOHnyJMaOHYtz587h8uXLWLp0KR4/fgxbW1tMmTIFH374IdasWYMbN27gzJkz+PHHH7FmzZpCqV9eMJAqotLSgHHj5FDOgQOBli0NXaOiwcVFdu/5+gKJiUCfPsBXX/GOPiJjNnfuXIiX/pF6eXnh8OHDuHbtGlq2bIkGDRpg5syZcHFx0ZZRKBTaFo2W//+fpJeXF1QqFRo0aAA7O7sCr2tu6rVq1Sq4ubmhdevW6NWrlzZFQXYsLCywd+9eODo6omvXrvD09MT8+fNhamoKAOjduzc6d+6Mtm3boly5cvj999+hUCiwa9cutGrVCsOGDUONGjXQv39/3L59G07/P2dYv3798Nlnn2HatGlo2LAh7ty5gzGa27/1rEaNGti7dy/Onj2Lxo0bw9vbG9u2bYPZ/7cIzJ07F5999hn8/f1Ru3ZtdOrUCTt27EDlypULpX55oRAvfzspS7GxsVCpVFCr1Xr5x5dXa9YAQ4fKpvorV4CXupHpFVJTgYkTgSVL5PPRo9mqR8VHYmIibt26hcqVK8OyOM1WTpSNnL7z+v79ZotUEaRWA9OmyfWZMxlE5YepqZyXb/FiOXZk2TLZShUba+iaERFRUcJAqgj6/HPg4UM5DczEiYauTdGlUMjPb/NmeVfRnj1AixYyRQIREVFuMJAqYi5dki0pAPDdd4AR3gla5Lz1lsyE7uIChIXJOfpOnjR0rYiIqChgIFXETJsGvHgBdO8O5HJicMqFhg3lHX1eXrK1r3Vr2VJFRESUEwZSRcixYzL3kYkJsGiRoWtT/Li5AUeOyAA1IUHO1efvzzv6qOjivURUUhjyu85Aqgj55BP5OHQoULOmQatSbNnaAtu3y9QSgMwaP3iwTJVAVFSY//88Uc+fPzdwTYgKh+a7bm6AOdJ4s3cRceAA8M8/gIUF8Nlnhq5N8WZmJseh1a4tA6r164EbN4AtW4D/T79CZNRMTU1RunRpREVFAQCsra3zNCUJUVEhhMDz588RFRWF0qVLa3NrFSYGUkWAEOnz6Y0eDbi7G7Y+JcWYMfLOyD59gOBgOQh9xw45jorI2Dk7OwOANpgiKs5Kly6t/c4XNibkzCVDJuTctk3eWWZtDdy8yVaRwnblihzcf+2aTIC6Zo0cP0VUFKSmpiIlJcXQ1SDSG3Nz8xxbovT9+80WKSOXmgp8+qlcnziRQZQh1KwpB/r36wfs3w+88468JnPmyIH/RMbM1NTUIN0dRCUFfwaMXEAAcP48ULo0MGWKoWtTctnbA7t3A5Mmyefz5slWQmZCJyIq2RhIGbGUlPSB5VOnAmXKGLY+JZ2ZGfD118DatTIR6o4dQJMmsuuPiIhKJgZSRmzVKjkmytERGD/e0LUhDT8/mW+qQgXg8mXgzTeZvJOIqKRiIGWkUlJkMkhA3rFnY2PY+pCuRo2AU6eAli2BuDg5+HzqVJl1noiISg4GUkZqwwbg9m3ZGjVypKFrQ1lxcpL5vTRj1xYtAtq3ByIjDVsvIiIqPAykjFBqKvDll3J9yhSZ9oCMk7m5DKA2bZJZ0Q8fBt54A/jvP0PXjIiICgMDKSP0xx8yZ5G9vUzAScavd2/g5Emgbl0gIgJo00be2ZeaauiaERGRPjGQMjJpacAXX8j1Dz+UrRxUNNSsCRw/LufmS0sDZs4EOnaUgRURERVPDKSMzNatwIULgJ0d8MEHhq4N5ZUm8/maNXL9n3+AevWAPXsMXTMiItIHBlJGRAjZHQTIdAelSxu0OvQaBg8GQkJkEPXoEdC5sxzvlpRk6JoREVFBYiBlRHbvBs6ckS0ZEyYYujb0ujRTy7z/vnz+9dcy51RYmGHrRUREBYeBlJEQApg7V66PHQuULWvY+lDBsLQEliyRE0+XKyeDqEaNZFCVlmbo2hER0etiIGUk/vlHtl5YWqbP50bFR48eMojy9QWSk2U3n48PcOeOoWtGRESvg4GUkdCMjRo5EnB2NmxdSD+cnIDt24Hly2VusEOHAA8PYNkytk4RERVVDKSMQFCQ/FE1N5fTjFDxpVAAo0YBoaFAs2ZAfDwwZozMiH7zpqFrR0REecVAygho8kYNGSInwqXir3p14N9/gW+/BaysgIMHAU9P4Icf2DpFRFSUMJAysNOngV27ABMTYNo0Q9eGCpOpqbw7MywMaN0aeP5cpr1o3hw4e9bQtSMiotxgIGVgmjn1+vcHqlUzbF3IMKpWlTcb/PSTzGR/7BjQsKEckB4fb+jaERFRTgwaSM2ePRsKhUJncc4w0loIgdmzZ8PV1RVWVlZo06YNLly4oHOMpKQkjBs3DmXLloWNjQ169OiBe/fu6ZSJjo6Gn58fVCoVVCoV/Pz8EBMTUxinmKNLl4DNm+X69OmGrQsZlomJHCt16RLwzjtyjr6vvwbq1JED1ImIyDgZvEWqbt26iIiI0C5hGbIVLly4EN988w2WLFmCkydPwtnZGR06dEBcXJy2zMSJE7FlyxYEBATgyJEjiI+Ph6+vL1IzzBY7cOBAhIaGIjAwEIGBgQgNDYWfn1+hnmdW/P1l/qi335Z3bxGVLw/8+SewcydQqRIQHg707CnTJly7ZujaERFRJsKAZs2aJerVq5flvrS0NOHs7Czmz5+v3ZaYmChUKpVYtmyZEEKImJgYYW5uLgICArRl7t+/L0xMTERgYKAQQoiLFy8KAOLYsWPaMsHBwQKAuHz5cq7rqlarBQChVqvzcorZunFDCFNTIQAhTp0qkENSMfPsmRAffyyEubn8npibC/HRR0IU0FeQiKhEKOjf75cZvEXq2rVrcHV1ReXKldG/f3/c/P97wG/duoXIyEh07NhRW1apVKJ169YICgoCAISEhCAlJUWnjKurKzw8PLRlgoODoVKp0KRJE22Zpk2bQqVSactkJSkpCbGxsTpLQVqwQHbfdOokx8MQvczaWrZahoUBXboAKSnAokVy6pnVq3l3HxGRMTBoINWkSROsXbsWe/bswYoVKxAZGYlmzZrhyZMniIyMBAA4OTnpvMbJyUm7LzIyEhYWFihTpkyOZRwdHTO9t6Ojo7ZMVvz9/bVjqlQqFdzc3F7rXDO6f1/+EALAJ58U2GGpmKpZU97Z+fffMm1CZCTwv//JqWYOHDB07YiISjaDBlJdunRB79694enpifbt22Pnzp0AgDVr1mjLKBQKndcIITJte9nLZbIq/6rjTJ8+HWq1WruEh4fn6pxyw99fThPSsqVciHKjWzfg/Hlg4ULAzk5OcN2+vWytOnfO0LUjIiqZDN61l5GNjQ08PT1x7do17d17L7caRUVFaVupnJ2dkZycjOjo6BzLPHz4MNN7PXr0KFNrV0ZKpRJ2dnY6S0G4fRv4+We5PmdOgRySShALC+Cjj4AbN2QOKnNzIDAQqF8fGDpUfr+IiKjwGFUglZSUhEuXLsHFxQWVK1eGs7Mz9u3bp92fnJyMw4cPo1mzZgCAhg0bwtzcXKdMREQEzp8/ry3j7e0NtVqNEydOaMscP34carVaW6Ywff65HOvi4wO0bVvob0/FRNmyMiv6pUtAv37y7s81a4AaNWQahZcygBARkb7oZQh7Lk2ePFkcOnRI3Lx5Uxw7dkz4+voKW1tbcfv2bSGEEPPnzxcqlUps3rxZhIWFiQEDBggXFxcRGxurPcbo0aNFhQoVxP79+8Xp06dFu3btRL169cSLFy+0ZTp37iy8vLxEcHCwCA4OFp6ensLX1zdPdS2IUf+XLglhYiLvwMpwEyHRazt+XIj27eV3CxBCqRRiwgQhIiIMXTMiIsPS9117Bg2k+vXrJ1xcXIS5ublwdXUVvXr1EhcuXNDuT0tLE7NmzRLOzs5CqVSKVq1aibCwMJ1jJCQkiA8++EDY29sLKysr4evrK+7evatT5smTJ2LQoEHC1tZW2NraikGDBono6Og81bUgLkTfvvJHrkePfB+CKEeHDgnRsmV6QGVlJcTEiULcu2fomhERGYa+AymFEEIYtk2saIiNjYVKpYJarc7XeKnQUKBBA0ChkOteXgVeRSIAMoTavx+YORM4flxus7CQY6imTQOqVDFo9YiICtXr/n6/ilGNkSrOZs6Uj/36MYgi/VIogA4dgOBgYM8eoFUreZfozz/LMVR+frzLj4iooDCQKgTBwTIHkKkp79SjwqNQAB07AocPA//+K5O/pqYC69cD9erJ5/v2yRYsIiLKHwZShUCTdHPIENkiQFTYWraUaRJOngT69pWTJO/dKwOt+vWBtWuBpCRD15KIqOhhIKVne/YABw/KMSqffWbo2lBJ16gRsHEjcP06MH48YGMju/mGDAEqVpTf0QcPDF1LIqKig4GUHj1/LnP6AMDYsYC7u2HrQ6RRuTLw3XdAeLjMtF++PBAVBcydK7+n/foBR46w24+I6FUYSOnRrFnArVtAhQoyESeRsSlTBvj4Y/k9/eMP2QX44kX6uqcn8P33wEuTBxAR0f9jIKUnISHAN9/I9aVLAVtbw9aHKCfm5kCfPnJQemgoMHw4YGUFXLggp6JxdZXdf0ePspWKiCgjBlJ6kJICjBgBpKUB/fsDvr6GrhFR7tWrB6xcKcdKLVkiW6USE+WA9BYtgNq1gfnzOZaKiAhgIKUXixfLv+rt7eU4FKKiqHRp4P33gbNnZQqPoUMBa2vgyhVg+nTAzQ3o2lV2AyYkGLq2RESGwczmuZTbzKjXr6f/Bb96tewOISou4uKAP/8EVq2Sg9E17OyA3r2BQYOANm1kzjQiImOg78zmDKRyKTcXQgigfXvgn3/k4969MikiUXF0/br8Y2H9euDOnfTtrq7AgAHyzr9GjfhvgIgMi4GUkcjNhZg5E5g3Tw7SDQsDqlYt5EoSGUBamhyEvn69bK3KeIdf5coyAWi/fjLxJ4MqIipsDKSMxKsuxLx56fPpLV8OjBpVyBUkMgJJScDu3UBAALBjh8ylplGtGtCrl1zefFNmVyci0jcGUkYipwuxaBEwdapc/+orYPJkA1SQyMg8fw7s3Ckzqe/cKccNapQvD7z9tlxatpTpF4iI9IGBlJHI7kJ89x0wcaJcnzcvfV49IkoXHy9bqjZvlhN4x8en7ytdWt7917070LmzfE5EVFAYSBmJrC7EsmXpU8DMnMns5US5kZgIHDgA/PWXDKoePUrfZ2YGtGolA6uuXYFatTiuioheDwMpI6G5ENHRahw9aodvvpF35wGyW2/+fP6HT5RXqanA8ePA9u1yuXRJd3+lSjKg6tJFplUoVcoQtSSiooyBlJHQXIhq1dS4fl1eCFNTGUR98QWDKKKCcP06sGuXXA4dkoPXNczNgebNgY4d5dKgAQesE9GrMZAyEpoLAaihUtlh1Cjggw+AihUNXTOi4unZM+DgQTlQfc8eObFyRg4OQNu2gI8P0K4dUL06/6AhoswYSBkJzYVYsECNMWPsOAkxUSG7cUMGVHv3ym71uDjd/W5uMrBq00YulSoxsCIiBlJGQ98XgohyLyUFOHlSDlo/cEDOBZicrFumYkUZULVqJVMssMWKqGRiIGUkGEgRGa/nz+Xcf4cPy7FVJ04AL17olnF0BFq0kEFV8+Yy0zrzVxEVfwykjAQDKaKi49kzIChIBlX//ScDq4wD1wE5ldObbwLNmsmlaVOgXDmDVJeI9IiBlJFgIEVUdCUlAadOyaDqv/9kV2DGOQE1qlQBmjRJX+rXBywtC726RFSAGEgZCQZSRMVHWhpw5YpstQoOlpMuX76cuZy5OeDpKVuuNEudOjJxKBEVDQykjAQDKaLiLSZGDmA/fhw4dkw+Pn6cuZylJVCvHvDGG3Jp2FAGV0ploVeZiHKBgZSRYCBFVLIIAdy5I4Orkydl1+CpU5nTLgCy5apOHdkVWL++TBbq5QWUKVPYtSailzGQMhIMpIgoLU3mswoJAU6fTl+yGm8FyNxWXl5y8fSUS40agIVF4dabqCRjIGUkGEgRUVaEAO7eBUJDdZfbt7Mub2YG1KwJeHjIpW5d2ZpVtSrHXhHpAwMpI8FAiojyQq0Gzp8Hzp2Ty9mzwIULQGxs1uXNzWWAVbu2XGrVkkvNmoC1deHWnag4YSBlJBhIEdHrEgK4d08GWJrl4kXg0iWZ+yo7FSvKgKpmTdk1qHl0c5OTpxNR9hhIGQkGUkSkL2lpQHi4DKouXJCpGC5flgHW06fZv06plLmvqleXS7VqcqlaVQZZ7CokYiBlNBhIEZEhPH4sA6qrV+Vy5Yp8vH5dzjmYHTMzoHJlGVRVqaK7VK4M8L8xKikYSBkJBlJEZExevJCtWNeuyaDq2jW53LgB3LyZeRLnl9nbA5UqyaCqUiW5VKwIuLvLpXRp/Z8DUWFgIGUkGEgRUVGRmgrcvy+DquvXgVu35HLzplyySjT6Mjs7GVBVrCi7CStWTF+vUAEoX55JSKloYCBlJBhIEVFxERcn0zNkXG7dkglI79wBnjzJ3XEcHWVQpQmsXl5cXQGVClAo9HcuRK/CQMpIMJAiopLi2TMZUN29K7sPMz7evStbuxITc3csKysZULm6Ai4umRdnZ7k4OAAmJvo9LyqZ9P37zXs6iIhIh42NTBJap07W+4WQdxOGh8t0DvfuyeBKs9y7Bzx4IOcvTEiQXYw3buT8nqamsoXL2RlwcpKLo2P6upMTUK6c3FaunMy7RWQMGEgREVGeKBSyBcnBQc4tmJ2EBCAiQgZV9+/L9ZeXhw/lmK3U1PRtuVGmjAyoslrKlk1fHBzkY6lS7GIk/WAgRUREemFllZ5yIScpKUBUFBAZKZeHD+USFaW7HhUlg660NDm/YXS0TAWRG+bm6YFVxsXeXi4ODjI4s7fXfbSxYQBGOWMgRUREBmVunj5A/VXS0mS3YlQU8OhR5uXxY93l0SMgKUkGa3lp8cpYt9KlZVCVcdFsK106fVGpdNdVKsDSMo8fBhU5DKSIiKjIMDFJ77bLDSGA58/lnYhPnsjg6skTGYxpHjXr0dHpz58+lbm6UlLSg7T8sLBID6pUKplWQvOoWbe1TX+uWbe11V3YMma8GEgREVGxpVDIIMTGRubByi0hgPj49C7EjItaLQfSx8TI5zEx6ds0j7Gx8hjJya8XiGU8j1Kl0gMrzXrGR81iY5N5XfMZvLxYWLxevYiBFBERUSYKRXrQkpcATCMtTebrUqtlUKVWpy+a55rHuDi5Hhubvh4Xl74IIRfN84JkZgZYW6cHVtbW6c816y8vVlaZHzMuGbdZWqY/FtcWNQZSREREBczEJL0773UIIfN6xcXJFjJNMKVZj4/XXeLiZPn4+PRHzXrG5cULefwXL9KDOH1TKnUDrIxB1svrSmXm9aweX15XKmULXO3a+j8fDQZSRERERkrTpVeqVMEeNzlZBlTPn+sGWAkJ6ds1+xIS0p9rFs22hITM6xm3paWlv2dSklxiYgr2XF5WubKcCqmwMJAiIiIqYSws5FKmjH7fJyVFZsHXBFea9cTEzOsZtyUlpT/XrL/8qFl/eXF11e85vYyBFBEREemFublcbG0NXRP94cxGRERERPnEQIqIiIgonxhIEREREeUTAykiIiKifGIgRURERJRPDKSIiIiI8omBFBEREVE+MZAiIiIiyicGUkRERET5xECKiIiIKJ8YSBERERHlk9EEUv7+/lAoFJg4caJ229ChQ6FQKHSWpk2b6rwuKSkJ48aNQ9myZWFjY4MePXrg3r17OmWio6Ph5+cHlUoFlUoFPz8/xOh7+mkiIiIq9owikDp58iR+/vlneHl5ZdrXuXNnREREaJddu3bp7J84cSK2bNmCgIAAHDlyBPHx8fD19UVqaqq2zMCBAxEaGorAwEAEBgYiNDQUfn5+ej8vIiIiKt7MDF2B+Ph4DBo0CCtWrMC8efMy7VcqlXB2ds7ytWq1Gr/88gvWrVuH9u3bAwDWr18PNzc37N+/H506dcKlS5cQGBiIY8eOoUmTJgCAFStWwNvbG1euXEHNmjX1d3JERERUrBm8Rer9999Ht27dtIHQyw4dOgRHR0fUqFEDI0eORFRUlHZfSEgIUlJS0LFjR+02V1dXeHh4ICgoCAAQHBwMlUqlDaIAoGnTplCpVNoyWUlKSkJsbKzOQkRERJSRQVukAgICcPr0aZw8eTLL/V26dEGfPn3g7u6OW7duYebMmWjXrh1CQkKgVCoRGRkJCwsLlClTRud1Tk5OiIyMBABERkbC0dEx07EdHR21ZbLi7++POXPmvMbZERERUXFnsEAqPDwcEyZMwN69e2FpaZllmX79+mnXPTw80KhRI7i7u2Pnzp3o1atXtscWQkChUGifZ1zPrszLpk+fjkmTJmmfx8bGws3NLcdzIiIiopLFYIFUSEgIoqKi0LBhQ+221NRU/Pvvv1iyZAmSkpJgamqq8xoXFxe4u7vj2rVrAABnZ2ckJycjOjpap1UqKioKzZo105Z5+PBhpvd/9OgRnJycsq2fUqmEUql8rXMkIiKi4s1gY6R8fHwQFhaG0NBQ7dKoUSMMGjQIoaGhmYIoAHjy5AnCw8Ph4uICAGjYsCHMzc2xb98+bZmIiAicP39eG0h5e3tDrVbjxIkT2jLHjx+HWq3WliEiIiLKD4O1SNna2sLDw0Nnm42NDRwcHODh4YH4+HjMnj0bvXv3houLC27fvo0ZM2agbNmyePvttwEAKpUKw4cPx+TJk+Hg4AB7e3tMmTIFnp6e2sHrtWvXRufOnTFy5EgsX74cADBq1Cj4+vryjj0iIiJ6LQZPf5AdU1NThIWFYe3atYiJiYGLiwvatm2LjRs3wtbWVltu8eLFMDMzQ9++fZGQkAAfHx+sXr1ap0Vrw4YNGD9+vPbuvh49emDJkiWFfk5ERERUvCiEEMLQlSgKYmNjoVKpoFarYWdnZ+jqEBERUS7o+/fb4HmkiIiIiIoqBlJERERE+cRAioiIiCifGEgRERER5RMDKSIiIqJ8YiBFRERElE8MpIiIiIjyiYEUERERUT4xkCIiIiLKJwZSRERERPnEQIqIiIgonxhIEREREeUTAykiIiKifGIgRURERJRPDKSIiIiI8omBFBEREVE+vVYgdf36dezZswcJCQkAACFEgVSKiIiIqCjIVyD15MkTtG/fHjVq1EDXrl0REREBABgxYgQmT55coBUkIiIiMlb5CqQ+/PBDmJmZ4e7du7C2ttZu79evHwIDAwusckRERETGzCw/L9q7dy/27NmDChUq6GyvXr067ty5UyAVIyIiIjJ2+WqRevbsmU5LlMbjx4+hVCpfu1JERERERUG+AqlWrVph7dq12ucKhQJpaWlYtGgR2rZtW2CVIyIiIjJm+eraW7RoEdq0aYNTp04hOTkZU6dOxYULF/D06VMcPXq0oOtIREREZJTy1SJVp04dnDt3Do0bN0aHDh3w7Nkz9OrVC2fOnEHVqlULuo5ERERERkkhmPwpV2JjY6FSqaBWq2FnZ2fo6hAREVEu6Pv3O18tUqtWrcKff/6Zafuff/6JNWvWvHaliIiIiIqCfAVS8+fPR9myZTNtd3R0xJdffvnalSIiIiIqCvIVSN25cweVK1fOtN3d3R1379597UoRERERFQX5CqQcHR1x7ty5TNvPnj0LBweH164UERERUVGQr0Cqf//+GD9+PA4ePIjU1FSkpqbin3/+wYQJE9C/f/+CriMRERGRUcpXHql58+bhzp078PHxgZmZPERaWhoGDx7MMVJERERUYrxW+oOrV6/i7NmzsLKygqenJ9zd3QuybkaF6Q+IiIiKHn3/fuerRUqjRo0aqFGjRkHVhYiIiKhIyXUgNWnSJMydOxc2NjaYNGlSjmW/+eab164YERERkbHLdSB15swZpKSkAABOnz4NhUKRZbnsthMREREVN5wiJpc4RoqIiKjoMbopYl68eAEzMzOcP3++wCtDREREVJTkOZAyMzODu7s7UlNT9VEfIiIioiIjXwk5P/30U0yfPh1Pnz4t6PoQERERFRn5Sn/w/fff4/r163B1dYW7uztsbGx09p8+fbpAKkdERERkzPIVSL311ltQKBTgOHUiIiIqyfIUSD1//hwfffQRtm7dipSUFPj4+OCHH35A2bJl9VU/IiIiIqOVpzFSs2bNwurVq9GtWzcMGDAA+/fvx5gxY/RVNyIiIiKjlqcWqc2bN+OXX35B//79AQCDBg1C8+bNkZqaClNTU71UkIiIiMhY5alFKjw8HC1bttQ+b9y4MczMzPDgwYMCrxgRERGRsctTIJWamgoLCwudbWZmZnjx4kWBVoqIiIioKMhT154QAkOHDoVSqdRuS0xMxOjRo3VSIGzevLngakhERERkpPIUSA0ZMiTTtnfffbfAKkNERERUlOQpkFq1apW+6kFERERU5ORrihgiIiIiYiBFRERElG8MpIiIiIjyiYEUERERUT4xkCIiIiLKJwZSRERERPnEQIqIiIgonxhIEREREeUTAykiIiKifGIgRURERJRPRhNI+fv7Q6FQYOLEidptQgjMnj0brq6usLKyQps2bXDhwgWd1yUlJWHcuHEoW7YsbGxs0KNHD9y7d0+nTHR0NPz8/KBSqaBSqeDn54eYmJhCOCsiIiIqzowikDp58iR+/vlneHl56WxfuHAhvvnmGyxZsgQnT56Es7MzOnTogLi4OG2ZiRMnYsuWLQgICMCRI0cQHx8PX19fpKamassMHDgQoaGhCAwMRGBgIEJDQ+Hn51do50dERETFlDCwuLg4Ub16dbFv3z7RunVrMWHCBCGEEGlpacLZ2VnMnz9fWzYxMVGoVCqxbNkyIYQQMTExwtzcXAQEBGjL3L9/X5iYmIjAwEAhhBAXL14UAMSxY8e0ZYKDgwUAcfny5VzXU61WCwBCrVa/zukSERFRIdL377fBW6Tef/99dOvWDe3bt9fZfuvWLURGRqJjx47abUqlEq1bt0ZQUBAAICQkBCkpKTplXF1d4eHhoS0THBwMlUqFJk2aaMs0bdoUKpVKWyYrSUlJiI2N1VmIiIiIMjIz5JsHBATg9OnTOHnyZKZ9kZGRAAAnJyed7U5OTrhz5462jIWFBcqUKZOpjOb1kZGRcHR0zHR8R0dHbZms+Pv7Y86cOXk7ISIiIipRDNYiFR4ejgkTJmD9+vWwtLTMtpxCodB5LoTItO1lL5fJqvyrjjN9+nSo1WrtEh4enuN7EhERUcljsEAqJCQEUVFRaNiwIczMzGBmZobDhw/j+++/h5mZmbYl6uVWo6ioKO0+Z2dnJCcnIzo6OscyDx8+zPT+jx49ytTalZFSqYSdnZ3OQkRERJSRwQIpHx8fhIWFITQ0VLs0atQIgwYNQmhoKKpUqQJnZ2fs27dP+5rk5GQcPnwYzZo1AwA0bNgQ5ubmOmUiIiJw/vx5bRlvb2+o1WqcOHFCW+b48eNQq9XaMkRERET5YbAxUra2tvDw8NDZZmNjAwcHB+32iRMn4ssvv0T16tVRvXp1fPnll7C2tsbAgQMBACqVCsOHD8fkyZPh4OAAe3t7TJkyBZ6entrB67Vr10bnzp0xcuRILF++HAAwatQo+Pr6ombNmoV4xkRERFTcGHSw+atMnToVCQkJGDt2LKKjo9GkSRPs3bsXtra22jKLFy+GmZkZ+vbti4SEBPj4+GD16tUwNTXVltmwYQPGjx+vvbuvR48eWLJkSaGfDxERERUvCiGEMHQlioLY2FioVCqo1WqOlyIiIioi9P37bfA8UkRERERFFQMpIiKiku7GDSAgAHjwwNA1KXKMeowUERER6dnDh0CLFoAm3ZCXF9C5s1yaNwcsLAxbPyPHFikiIqKSKi0N8POTQZSdHaBQAOfOAQsXAu3aAe7uMtCibDGQIiIiKqkWLAD27QOsrIDgYCAqCvjtN2DwYEClkgHWH38YupZGjYEUERFRSXTkCDBzplxfsgSoUwcoWxYYMABYswb47DO5b/PmwquTEMCtW8DTp4X3nq+JgRQREVFJ8+SJDJhSU4FBg4D//S9zmbfflo///gs8fqy/usTHAzt2AO+/D1SrBlSpAnh6ytaxIoCBFBERUUkihAyc7t0DqlcHli6VY6NeVrky0KCBHEe1fXvB1+PCBaBnT8DeHujRA/jpJ+DmTbnvwQNg+HBZVyPHQIqIiKgkWbhQtgAplXL8U4bZQjLRtEoVZPdeVBQwZoy8O3D7diAlRQZtY8fK50ePyjsF//4bWLas4N5XTxhIERERlQRCALNmAR9/LJ9//TVQv37Or+nVSz7u2wfExr7e+ycmysHt1arJACktTR7//HmZx+rHH4Hu3YFmzWQ5AJg8Gbh06fXeV88YSBERERmL1FQgPFw/xx09Gvj8c/n8s89kC9Cr1KkD1KgBJCcDu3bl7T0fPAC2bgVmzAB8fABHRxnExcUBDRsChw8Df/0F1K2buWtx/HigY0cgIUGO4UpOztt7FyIGUkRERIb24AEwd64caF2xomwtKiiJiUCfPsDPP8uAZelSYM6crMdFvUyhSG+Vyq577+5d2Zr00UfAO+8AjRoBDg5A+fKya9DfH/jnHxlAlS8v7wg8cQJo1Sr79zUxAVavlsc5cyb97kIjxEmLc4mTFhMRUYESAtizB1i+XI5ZSk1N32dhIQOIOnVe7z1iYuSA7n//lcf87Tegd++8HePkSaBxY8DGBnj0SOac0rhzR3YPxsRkfp2JCeDhIV/bpIl8rFMHMMvDpCpbt8pgTKEA9u+XSULzSN+/3wykcomBFBERFZj79+Wdc/v2pW9r2RJ47z1gwwZg924ZfBw9Cpia5v99evUCtmyRWcu3bQPatMn7MYSQrWT37snB4N27y+0vXgCtWwNBQUDNmkCXLnLQeKVK8rFKFRl8va733pOtaZUqAdev5/nz0PfvN7v2iIiICtPGjTJPkiaj+PjxMhXAv//K8UA//ywDn+PHge++y//7XLsmgyiFAjhwIH9BFCBfn9Xde3PnyiDKzk4GfosXy3Pp0UOeX0EEUQDwzTeyi+/2bdmCZ2QYSBERERWG6GgZKPXvL9cbNQJOn5bBUsYuvAoV0sdIffKJDIjy48cf5WOXLvK9XodmnJQmXcG//wLz5slty5bJFih9sbGR8wECwIoV+nuffGIgRUREpG+XL8u8Sb/9JrumPvtMtubUqpV1+eHD5Z1uiYlyPS0tb+8XFwesWiXXx49/vboDQIsWcvqYp0/luKVBg2Sdhg6VGdL1beRI+bhjBxARof/3ywMGUkRERPo2a5YcY1Stmpzjbs4cwNw8+/IKhWx9sbEB/vtP3mmXF2vXyrxPNWoAHTq8Xt0BOUC8Z0+57ueXnhX9hx9e/9i5UaeOzC+Vmirv+jMiDKSIiIj06fFjOVYJAP78E2jaNHevq1wZmD9frk+bJlMG5EZampyEGADGjZN3zxUETfdeUpIMAgMCgFKlCubYuTFihHxcuTL7FrobN/LeeveaGEgRERHp07p1clxRw4avziT+srFjZb6lZ89kADZs2Ku7tvbvl12JtrbAkCH5rnYmPj6ASiXX588H3nij4I6dG337yoHtN24Ahw5l3n/njrzT8e23Xz8Lex4wkCIiIsqvR4/k7fknT2a9XwjZggKkt6jkhYkJsGkTMHCgPNaqVbK7zt9fjp/Kiqa7bejQnOfRyyulUt6199NPwMSJBXfc3LKxkZ8DkHnQeUKCbDF78kQmN7WwKLRqMY9ULjGPFBERZTJunOxGc3eXrUCWlrr7g4Pl2B4rK9mSpGnRyY/gYBnAaLr4KlWS792tW3qZGzfk2CUhgCtXZNBVnISEyDsQLSxkwOTgIM91+HAZZDo4yDLu7tqXMI8UERGRMYqPl4O6AdmttHhx5jK//CIf+/Z9vSAKALy9ZTC1bp2cauX2bcDXV7bSREXJMj/+KAOLzp2LXxAFyO7RBg3k3Hvr1sltP/8sgygTEzluK0MQVRgYSBEREeXH77/LsTiaVqgvvtAdvxQXJ3/Ygfx162XFxAR4913Z2jR5snz+++9A7doyoNAEbgWR8sBYaVIhrFghk5aOGyeff/EF0L59oVeHgRQREVFeCZGekuDzz+VA8GfPZAJNjY0b5baaNYHmzQv2/W1sgK++koFEvXoyv9N778nArnp1oFOngn0/YzJwoOwqvXhRnmdKihxgPm2aQarDQIqIiCivTp6UkworlfJOum+/ldtXr5ZjdID0QebDh8u8UPrQqJGsy/z56S1j48cXXMoDY6RSya5SAFCrZaC6erX+PuNXKMafNBERkZ5oWqP69pUDnJs0kV1uQsgB4WFhsrXIzAwYPFi/dTE3l60xFy/Ku+ref1+/72cM3ntPPpYqJc/ZgDeB8a69XOJde0REBEB2o5UvL9MPBAXJQeCAzPZdo4a8Fd/DAzh/Xt6S/9dfhq1vcbVrl5yX0Msrx2K8a4+IiMiYrFkjg6h69XSzlFeokD5O5/x5+VhQg8wps65dXxlEFQYGUkRERLklBLBsmVwfMybzuJyPPpIBFSAfO3Ys3PpRoWMgRURElFv//ANcvSrH5miybGdkbS1zOVlaAjNmAKamhV9HKlRmhq4AERGR0REC2LNHZtCuVQtwcZGtT5rWKD+/7Kdf6dEDeP7cYHeRUeFiIEVERPSyRYt08xLZ2cmA6vRp+XzMmJxfzyCqxGDXHhERUUYnTqQn1nR3lzmZYmPl9hcvgBYtAE9Pw9aRjAZbpIiIiDRiY4EBA2TA1LevnOIlORm4fl1OSnz3rsyiTfT/GEgRERFpvP8+cPOmbIlavlx20SmVQN26ciF6Cbv2iIiIAGDdOmD9enmn3W+/AaVLG7pGVAQwkCIiIrp+HRg7Vq7PmgU0a2bY+lCRwa49IiIqmVJSZAB14QLg7w/ExwOtWsn8T0S5xECKiIiKh1u3ZHdcmTLZl3n2DJg+HTh4ELhyRQZTGmXKpHftEeUSAykiIir6Ll4EGjQAXF2BkBDA3j7rch9+CKxYkf7cxiZ9IPn48YCbW+HUl4oNBlJERFT0LVwo0xTcvg0MGQJs2ybzP2W0dasMohQKYOVKoF07oGLFzOWI8oDfHiIiKtru3QM2bJDr5ubA338DX32lW+bBA2DECLk+ZQowbBhQqRKDKHpt/AYREVHR9u23MoFm69bAkiVy24wZwJEjcj0tDRg6FHjyRHb/zZ1rqJpSMcRAioiIiq6YGJk4EwCmTgVGjgQGDQJSU4F+/YBHj4Dvvwf27QOsrGTLlVJp0CpT8cIxUkREVHQtWybTFnh4AF26yPFPy5bJAeeXLwPduwNnzsiy33wD1K5t2PpSscMWKSIiKpqSkoDvvpPrH30kgygAKFUK+PNP2QJ1/LgchN69O/Dee4arKxVbDKSIiKhoWr8eiIwEKlQA+vfX3efhAfz0k1x3cpJ36WkCLaICxK49IiIqetLSgEWL5PqHHwIWFpnLDB0qJx+uVAlwdCzM2lEJwkCKiIiKnh07ZGZylUoOMM9O27aFVycqkdi1R0RERc/ChfJx7FjA1tawdaESjYEUEREZj59/Bn79Necy//0HBAXJ7rzx4wunXkTZYNceEREZh3Pn0u+s8/QE3nwz63JffCEf//c/wNm5cOpGlA22SBERkXH45Zf09U8+ybrMqVPAnj2AqSkwbVrh1IsoBwykiIjI8JKSZDoDjX37gIMHM5f78kv5OHAgULly4dSNKAcMpIiIyPC2bgWePpU5oUaPlts++QQQIr3MhQvAli1y/eOPC72KRFlhIEVERIan6dYbOhT47DOZlTw4GNi5M73M/PnysVcvoE6dQq8iUVYYSBERkWHduQPs3y/X//c/wMUl/W68Tz6RyTdv3gR+/11umzHDMPUkyoJBA6mlS5fCy8sLdnZ2sLOzg7e3N3bv3q3dP3ToUCgUCp2ladOmOsdISkrCuHHjULZsWdjY2KBHjx64d++eTpno6Gj4+flBpVJBpVLBz88PMTExhXGKRET0KqtWyS48Hx+gShW5bepUwM5O3sn3xx/AggVAairQuTPQsKFh60uUgUEDqQoVKmD+/Pk4deoUTp06hXbt2qFnz564cOGCtkznzp0RERGhXXbt2qVzjIkTJ2LLli0ICAjAkSNHEB8fD19fX6SmpmrLDBw4EKGhoQgMDERgYCBCQ0Ph5+dXaOdJRETZSE2VgRQADB+evt3eXk5EDMjxUKtXy3W2RpGRUQiRcSSf4dnb22PRokUYPnw4hg4dipiYGGzdujXLsmq1GuXKlcO6devQr18/AMCDBw/g5uaGXbt2oVOnTrh06RLq1KmDY8eOoUmTJgCAY8eOwdvbG5cvX0bNmjVzVa/Y2FioVCqo1WrY2dkVyLkSEZV4e/cCnToBZcoADx4Alpbp++LiZAvV48fyecuWwL//GqaeVGTp+/fbaMZIpaamIiAgAM+ePYO3t7d2+6FDh+Do6IgaNWpg5MiRiIqK0u4LCQlBSkoKOnbsqN3m6uoKDw8PBAUFAQCCg4OhUqm0QRQANG3aFCqVSluGiIgMZOVK+ThokG4QBcipXzK2QGWXW4rIgAye2TwsLAze3t5ITExEqVKlsGXLFtT5/7sxunTpgj59+sDd3R23bt3CzJkz0a5dO4SEhECpVCIyMhIWFhYoU6aMzjGdnJwQGRkJAIiMjIRjFrN+Ozo6astkJSkpCUlJSdrnsbGxBXG6RESk8fixTHsA6HbrZTRmjGy1KlcOyPBHM5GxMHggVbNmTYSGhiImJgZ//fUXhgwZgsOHD6NOnTra7joA8PDwQKNGjeDu7o6dO3eiV69e2R5TCAGFQqF9nnE9uzIv8/f3x5w5c/J5VkRE9Err1wMpKcAbbwD162ddxtISyHATEpGxMXjXnoWFBapVq4ZGjRrB398f9erVw3fffZdlWRcXF7i7u+PatWsAAGdnZyQnJyM6OlqnXFRUFJycnLRlHj58mOlYjx490pbJyvTp06FWq7VLeHh4fk+RiIheFh4O/PSTXM+uNYqoCDB4IPUyIYROl1pGT548QXh4OFxcXAAADRs2hLm5Ofbt26ctExERgfPnz6NZs2YAAG9vb6jVapw4cUJb5vjx41Cr1doyWVEqldq0DJqFiIhe04sXwLffyoSa164BpUvL6V6IiiiDdu3NmDEDXbp0gZubG+Li4hAQEIBDhw4hMDAQ8fHxmD17Nnr37g0XFxfcvn0bM2bMQNmyZfH2228DAFQqFYYPH47JkyfDwcEB9vb2mDJlCjw9PdG+fXsAQO3atdG5c2eMHDkSy5cvBwCMGjUKvr6+ub5jj4iICsCpU8CoUcCZM/J5s2bAzz/LYIqoiDJoIPXw4UP4+fkhIiICKpUKXl5eCAwMRIcOHZCQkICwsDCsXbsWMTExcHFxQdu2bbFx40bY2tpqj7F48WKYmZmhb9++SEhIgI+PD1avXg1TU1NtmQ0bNmD8+PHau/t69OiBJUuWFPr5EhGVWHPnArNnyyzlpUsDCxfKLj0To+sYIcoTo8sjZayYR4qIKJ9u3wYqV5brgwYBX38N5DBGlagg6fv32+B37RERUTH322/ysW1beaceUTHCNlUiItIfIdKDp3ffNWxdiPSAgRQREelPaChw6RKgVAK9exu6NkQFjoEUERHpz4YN8rF7d0ClMmxdiPSAgRQREelHamr6+Ch261ExxUCKiIj049AhICICsLcHunQxdG2I9IKBFBER6YdmkHmfPoCFhWHrQqQnDKSIiKjgJSQAf/0l19mtR8UYAymi4kAIID7e0LUgSrdjBxAXB7i7y6lgiIopBlJERVlKirwr6o035B1Rq1YZukZEkuZuvUGDOA0MFWv8dhMVRXFxwOLFQLVqstskNFTOYfb++zJnD5EhPXkC7Nol1wcNMmxdiPSMgRRRUXP4MFCxIjBpEnD3LuDoKCeEbd9ejksZMABISjJ0Lakk+/NP4MULoEEDoE4dQ9eGSK8YSBEVNVOnAjExQI0awM8/A3fuAJ9+CqxdC5QtC5w9C0yfbuhaUkmVnAysXCnX2RpFJQADKaKiJCwMOHECMDcH/vsPGDkSsLSU+1xc0sdILV4MBAa++nhpafI4a9fKH0CinNy/Dxw9Km9uyEpSkkx1EBIiv5cDBhRu/YgMgIEUUUFKS5OZnCMi9HP8X36Rjz16yC69l/n6ynFSADB0KBAVlXUdjx4FJkwAKlQAWrUChgyRj3fv6qfeVPSdOgV4egItWsg5817+biUmAr16Adu3y3n1tmwBXF0NU1eiQqQQIrs/LSij2NhYqFQqqNVq2NnZGbo6ZKy+/14GKG3bAv/8U7DHTkwEypcHnj4Fdu8GOnfOulxCAvDmm8CFC0DHjsDgwUB4uFzu3QNOn5aPGpr5z9RqmYF67VqgW7fMx42OlmUqVSrY8yLjFxwsv2+xsenbypYFli2TQdXz58BbbwH79gFWVjKYat/eYNUlykjfv98MpHKJgRS9UloaUKsWcO2afH7+PFC3bsEdPyBAdpW4uQG3bgGmptmXDQuTwVR2g87t7ICePYF+/eQPXkQE0LcvcPKk3P/xx3IA+/37wLZtwNatwL//ynOcN0+OwVIoCu7cyHj9+68MrOPjZavlF1/IVs9z5+T+AQOAhw/lHw42NsDOnUDr1oatM1EGDKSMBAMpeqW9e4FOndKfv/8+sGRJ7l8fFwccOAB06CB/kF7Wvr3c/9lnwJw5rz7e+vXA/PlAuXIy+NIsVarIH0TN2CqNpCTgo4+AH36Qzx0ds+4aBGTQ9euvWdeTip4tW4BNm4CGDWXXXYMGchze/v2yGzkhQX7/tm0DrK3leLrPP5ffr9RUeYxSpWRLaYsWhj0XopcwkDISDKTolXr2lF0aDRvKwba2trJFx9b21a8NDpb5oG7eBNq0kV0kZmbp+2/dkgGQQiHL6LN77Y8/gBEjZGBnYiJ/GHv2lMv+/cAHH8hb2+vXlz+sFSvqry6kf5cuyYSuiYnp26ysgMaNgWPHZIDdtauc7uXl4PvkSeB//5MB9/btQNOmhVt3olzQ9+83B5sTFYQ7d4C//5bra9cCNWvKQEST3Tk7KSnArFkyWLl5U247dEh2rWX066/ysUMH/Y9R6ttXdtv88Yfssjl8WOasqloVeO892YVTrpxMAtqokbzrj4qmlBTAz08GUW++CXTvLsfJJSTI656UJMc+bd6cOYgC5GvCwuQfDAyiqIRiIEVUEJYvl+OH2rWTCQjHjJHbf/op+1vFr12TAdTnn8vXvvtuevqCr78GNm6U6y9epG8fMUK/56FRqZK8jb1s2cz7WraUd3A1aAA8egT4+MguHTKshAQZ5Kak5P41c+fK1lN7ezkObvt2eU0vXpTf6e++kwG1Upn9MRQK2Q1IVEKxay+X2LVXwqSkyK613AyoTkqSY48ePZLdH716yYSZrq7yx+2//zKPGwkMTL/bqXRpYOlSoH9/uW/6dDn2xNpadq3cvSvTGjg4yL/8c/pRK0zPn8vWjM2bZVfQnj0yyMqKEHIsTcbuSipYb78tg6E335Tj42rUyLn88eNA8+byumzcKFsiiYohdu0RFaYnT+SAazs7OXD82bNXv2bTJhlElS8vB+YCMjjSZHX+6Sfd8sHBMth6/lyOhzp3Lj2IAuRdce3by/1vvw18+63cPniw8QRRgAz0AgLkHV0JCTLYO306c7mDB+XdjFWqALdvF3o1S4RTp2QQBchxSw0ayKz32f2d/OyZDIJTU4GBAxlEEb0OQbmiVqsFAKFWqw1dFdKH+HghvvhCCJVKCPnzI5e2bYV49izn1zZrJst+/rnu9pAQud3cXIjISLnt/HkhypSR27t0ESI5OetjPnokhLu7bl3On3/ds9SP58+FaNVK1rFsWSEuXZLbo6OFGDFC9xyaNs3+nCn/fH3l59ujhxDt2qV/3j16CBEVlbn82LFyf/nyQjx9Wvj1JSpE+v79ZiCVSwykirFffxXC2Tn9x8fLS4gffhDC1lY+9/GRwUJWzpyRZczMhIiIyLy/aVO5/4svhLhzR/5waQKK+Pic6xUSIoSlpSzv7f3ap6lXarUQDRvKulaoIMSKFUK4uKR/psOHC1G6tFyfOtXQtS1eTp6Un6uJiRBXrwqRmirE118LYWEht6tU8tp06CBEv35CDBmSfl327jV07Yn0joGUkWAgVUxt25b+o1K5shDr18sfIiGEOHpUiFKl5L4OHbIOpjQtLv36ZX38tWvTg4tateR67dpCPH6cu/pt3ChEpUpCBAbm7/wK06NH8twytkDVqCHE4cNy/19/pW/fvduwdS1ONK1Rgwfrbg8NFaJuXd3rkXH54APD1JeokOn795uDzXOJg82LodRUoF49OZXKyJEyeaaFhW6ZI0fk1BjPnsnpVhYvljlzIiKAyEjgk0/k+KB//816oHViopzP7skT+dzNTc5z5+am//MzhPv3ZbLPO3eAqVNl8tCMt82PGyc/57JlgbNnORfb6zp1Sg4uNzEBLl8GqlfX3Z+SIu/Ke/JETi0UHS0flUrgww+zTmlAVMwwIaeRYCBVDK1bJwdwly4tcziVKZN1uX//Bbp0kYO/s+LpKYOC7O7wmzYNWLhQ3mJ+5AhQu3aBVN9oPX8uc2g5OWXel5gIeHvLHFStW8tM7TlNdUM5695d5i8bPBhYs8bQtSEySvr+/ea9yFQyJSfLRJiADHSyC6IA2cKya5f8sVKrAWdnwMVFPrq6AsOG5Zwm4ZNP5G3//fsX/yAKkHfzWVtnvc/SUt5q37ChTPj46ady7jYT3kCcZ6dOySDKxER+jkRkEGyRyiW2SBUzP/4opzpxdgZu3Mj+hz8jIThRb0H57bf09BBVqgBjx8qpRuztDVuvooStUUS5wjxSRAXt2TOZ0RkAZs7MXRAFMIgqSAMHyu5OTbfqlClyLNnIkcD58/k75r17ckobfYmPl3U+flx/75FbbI0iMhoMpKjk+f57+YNbuXLhTblCmX30kRyc/vPPgJeXHLS/cqVcHz06fYB+bpw4AVSrJpOi9u4tx14VZGN7SgrwzjuyG7hFi/S5D/XhVfVOSZGtqYCcVujlAeZEVKgYSFHJEh0tWxUAOcfdy3fpUeGytpatUKGhclD/22/LQGL5cjnFydKl8u7KnMTEAP36yal6UlPllDXt28ts6osXy/2vQwhg1Cg5BY5CIec+HD5c3pWYlvZ6x87o6lV5HjY2co677MyaJVvFSpdOb1klIoNhIEUly6JF8ofVwwMYMMDQtSENhUKmj9i8GTh0SN4J+fSpHDvVqBEQFJT164SQg/1v35YtjEePytfY2srAZNIkmeLi6tX8123WLGD1anl34fbtMqUDIL9LvXvnbhqhnNy7JwO1OnXkBMEJCcDEicDatZnL/vOPnIcRAFasACpWfL33JqLXxsHmucTB5kXE48dyLrvgYDnhb0SE/KGLj5ePycmy3LZt6fPikfF58QJYtkyOYYuJkWOBPv9cTuic8Q6/H34Axo8HzM1lsNWokdweFycHtC9YANy6BZQrJ1uUGjTIWz1+/hl47z25vmJFelfwhg0ygEtOlsccM0Z3DJ2pKdCsGVCzZvbHvnlTzsO4ZIlsTQPkvIXOzsAvv8hjbNsmtwHyu+3lJb/TI0fKuhHRK+n991svaT6LIWY2NyLPn8tpMTZvFuK774T46COZWbxGjeyzOGdcOncWIi3N0GdBuREVJYSfn+61e/RI7jt1Kn0alG+/zfr1Dx8K0aCBLGNnJ8S//+b+vbdvl9OuAEJ89lnm/UePClGuXM7ftXr15PRA16/L19y/L8TixUI0aaJbrmVLIf77T5ZJTU0/Zysr+T5paUJ07y631ar16umFiEiLmc2NBFukjMTDh0DTprIrJzu1a8ukj97ecgByqVJy3IlmsbfnHXhFzapVsstOkyl+5Ur5/OZN4K23ZJdgdtdUrZatj//+K/NYbdqU3sojhGzpuXlTdv9duSKXq1eBixdly9iwYfL9sjr+rVtynNLjx7rbY2NlN+OLF+nbqlaV76P5L9fEBGjXTmYY79JF9/gpKfK8du2SOc7+9z/gm2/kmL4TJ2R3JRHlCjObG4kSHUg9ewZMmCAHuK5fb7j/xIWQY1K2bAFUKjmY2M1N/rC6ucnnTZsyF1Fxde4c0KeP7ngnd3fgzJmcE6oCctxR374yZYCZGdChAxAeLgOhnMY4vfWWHLdkbp73+j55Ir+rf/whxzZpBs03by7H573zTtbZ3zWePZOD5o8dS9/23XeyK5OIco2BlJEosYHUlSsyeLlwQT53dpZjUSpXLvy6/P67zD9kZibz6PCv8pInNlYOzN64UX4PjhwBmjTJ3WtTUmTLzoYNutsVCpmhvnp1OaYp41KlSsG0Xj56JMft1a+ftwHiT57IQfiXLslWtB072JpKlEcMpIxEiQyk/vpL/vDExckAqkwZ+R969eqy26Jcuaxfl5ZW8FN+REQAdevK9AVz5qTfOUUljxDAzp2y5bFZs7y9Ni1Nfq8fP5Z/DFSpIlu1lEr91LUgPH4sz7d3b9lNTUR5wkDKSJSoQOrFC+Djj4Gvv5bPW7aULQBCyHFHd+8CjRvL7gobG1lG8+M2c6YcvzRhgryFu3Tp16+PEEDPnvKv8QYNZBdjfrpaiIioxOEUMVS4UlPl2A1NEDVliswS7eIiuz/27AEcHOSA1z59ZHfJoUNy3Ef37jKxYkyMbDWqVAmYPTvrhIia5Im5sX69DKLMzeWcYgyiiIjISLBFKpdKTIvUpEkyG7RSKQOYd97JXObYMXm3UUKC7Ba5c0dut7ICxo2TY5e+/DJ9XJWdnQyynj6VU4I8eCC7K5yc5N1Qvr7Z1+fBA9mlFxMDfPEFMGNGgZ8yEREVX+zaMxIlIpBavlzOcQYAAQFyuors7Nwpu9tSU2UL0ahRwCefyJYrQI5F2bxZtky9ahLaKVNk4PVyS9O1azLx4OHDMtFicLAcYExERJRLDKSMRLEPpPbtk7lsUlNlBumZM1/9mp07ZZAzZkz2d/GlpclpNS5cSO8eLF9eDlT395cTCAMybUFAgGzhOntW7vvzT/l6pRIICZEtU0RERHnAQMpIaC/Ew4ewc3Q0dHUK1qVLchC5Wi1nk1+7tvBusd68WSY8VKvlXYFvvgns3Zu+39dXjrNq2LBw6kNERMUKB5sbm8aNga1b07MTF3WPHsn8NGq1HDCeXQZnfenVSyZUfPNNmdpg716ZOqF/fzlwfccOBlFERGS02CKVS9qIFoAdIDMjf/ednI6kqLp8WQYyly7JfDrHjmWfG0rfkpNld96TJ3LAevXqhqkHEREVK+zaMxLaCzFlCuy+/17+8JuZyRxL1aqlL9WrA3XqyJnbjdmWLcCQITLZpqsrsH9/0Q4KiYiIssBAykjoXIhHj4DJk4Ft27Iu7OkJ/PijDLKMTWoq8OmnwPz58nnr1jLZZk5zfhERERVRDKSMRJYX4uxZOZHq9etyuXFD3uqvmQR18GBg4ULjCVIeP5Zz1e3bJ59PmiQDKia4JCKiYoqBlJHI9YV48kQmjVyxQg5It7MD5s4Fxo7NOQfSixcywGnYENDHXYEHDsjA7sEDwNoa+OUXOaCbiIioGONde0WNg4NMbHnsmEwiGRsr553r10/mRMqKEDKhZdeucmb4kSPlAPCCkJwMTJsmB8c/eADUqiXrxiCKiIjotTGQ0pfGjWXAsmyZTCi5ebPMh5SVZcuAVavkelKSTEFQp44MrA4cyH+qhatXgWbNZPeiEMB778nElp6e+TseERER6WAgpU+mpjJ4+fln+XzuXOCPP3TLBAXJFisAWLAAOHIEePttmctp926gfXuZY2nTptxP8puSIlMzvPGGDJzs7WUgt2yZ7NYjIiKiAsFAqjAMHiznkwOAoUOB06flekQE0Lu3DHz69gU++kgmxdy8Wc4z98EHMvAJCQH69JGtVL/+KrvrsrN7N+DlBUycKAe9t20rB8S//ba+z5KIiKjE4WDzXHrtwWqpqUD37jLQqVABOHoUGDBAtkh5eMgJeUuVyvy6x4+BH36QS3S03ObqCrRpI+ee8/CQS2KiDNZ275ZlypUD5s0Dhg83/pxWREREesK79oxEgVwItRpo0gS4cgWwtZXJMFUq4NQpmcwzJ3Fxsovw669lS1Z2zM1lV+Gnn8pjExERlWAMpIxEgV2Iq1dlMBUTI8dB7dgh57rLraQkmYU8LEzmrDp/Xk71kpQE9OgBfPUVp1chIiL6f8U6/cHSpUvh5eUFOzs72NnZwdvbG7s1XVMAhBCYPXs2XF1dYWVlhTZt2uDChQs6x0hKSsK4ceNQtmxZ2NjYoEePHrh3755OmejoaPj5+UGlUkGlUsHPzw8xMTGFcYqZ1agB/PWXTEPw/fd5C6IAeQdgt27Axx8D69fLiX3j44GnT2WmdQZRREREhcaggVSFChUwf/58nDp1CqdOnUK7du3Qs2dPbbC0cOFCfPPNN1iyZAlOnjwJZ2dndOjQAXFxcdpjTJw4EVu2bEFAQACOHDmC+Ph4+Pr6IjXDHW4DBw5EaGgoAgMDERgYiNDQUPj5+RX6+Wq1ayfzRH3wQcEcz8wMKFOmYI5FREREuSeMTJkyZcTKlStFWlqacHZ2FvPnz9fuS0xMFCqVSixbtkwIIURMTIwwNzcXAQEB2jL3798XJiYmIjAwUAghxMWLFwUAcezYMW2Z4OBgAUBcvnw51/VSq9UCgFCr1a97ikRERFRI9P37bTTpD1JTUxEQEIBnz57B29sbt27dQmRkJDp27Kgto1Qq0bp1awQFBQEAQkJCkJKSolPG1dUVHh4e2jLBwcFQqVRo0qSJtkzTpk2hUqm0ZYiIiIjyI4fJ3wpHWFgYvL29kZiYiFKlSmHLli2oU6eONshxemnCXycnJ9y5cwcAEBkZCQsLC5R5qVvLyckJkZGR2jKOWcxd5+joqC2TlaSkJCQlJWmfx8bG5u8EiYiIqNgyeItUzZo1ERoaimPHjmHMmDEYMmQILl68qN2vUCh0ygshMm172ctlsir/quP4+/trB6erVCq4ubnl9pSIiIiohDB4IGVhYYFq1aqhUaNG8Pf3R7169fDdd9/B2dkZADK1GkVFRWlbqZydnZGcnIxoTaLKbMo8fPgw0/s+evQoU2tXRtOnT4dardYu4eHhr3WeREREVPwYPJB6mRACSUlJqFy5MpydnbFv3z7tvuTkZBw+fBjNmjUDADRs2BDm5uY6ZSIiInD+/HltGW9vb6jVapw4cUJb5vjx41Cr1doyWVEqldq0DJqFiIiIKCODjpGaMWMGunTpAjc3N8TFxSEgIACHDh1CYGAgFAoFJk6ciC+//BLVq1dH9erV8eWXX8La2hoDBw4EAKhUKgwfPhyTJ0+Gg4MD7O3tMWXKFHh6eqJ9+/YAgNq1a6Nz584YOXIkli9fDgAYNWoUfH19UbNmTYOdOxERERV9Bg2kHj58CD8/P0REREClUsHLywuBgYHo0KEDAGDq1KlISEjA2LFjER0djSZNmmDv3r2wtbXVHmPx4sUwMzND3759kZCQAB8fH6xevRqmGeaX27BhA8aPH6+9u69Hjx5YsmRJ4Z4sERERFTucIiaX9J1inoiIiApesZ4ihoiIiKgoYyBFRERElE8MpIiIiIjyiYEUERERUT4xkCIiIiLKJ4PPtVdUaG5u5Jx7RERERYfmd1tfSQoYSOXSkydPAIBz7hERERVBT548gUqlKvDjMpDKJXt7ewDA3bt39XIhKPdiY2Ph5uaG8PBw5vQyMF4L48FrYVx4PYyHWq1GxYoVtb/jBY2BVC6ZmMjhZCqViv8ojATnQDQevBbGg9fCuPB6GA/N73iBH1cvRyUiIiIqARhIEREREeUTA6lcUiqVmDVrFpRKpaGrUuLxWhgPXgvjwWthXHg9jIe+rwUnLSYiIiLKJ7ZIEREREeUTAykiIiKifGIgRURERJRPDKSIiIiI8omBVC789NNPqFy5MiwtLdGwYUP8999/hq5Ssefv748333wTtra2cHR0xFtvvYUrV67olBFCYPbs2XB1dYWVlRXatGmDCxcuGKjGJYe/vz8UCgUmTpyo3cZrUbju37+Pd999Fw4ODrC2tkb9+vUREhKi3c/rUThevHiBTz/9FJUrV4aVlRWqVKmCzz//HGlpadoyvBb68e+//6J79+5wdXWFQqHA1q1bdfbn5nNPSkrCuHHjULZsWdjY2KBHjx64d+9e3isjKEcBAQHC3NxcrFixQly8eFFMmDBB2NjYiDt37hi6asVap06dxKpVq8T58+dFaGio6Natm6hYsaKIj4/Xlpk/f76wtbUVf/31lwgLCxP9+vUTLi4uIjY21oA1L95OnDghKlWqJLy8vMSECRO023ktCs/Tp0+Fu7u7GDp0qDh+/Li4deuW2L9/v7h+/bq2DK9H4Zg3b55wcHAQf//9t7h165b4888/RalSpcS3336rLcNroR+7du0Sn3zyifjrr78EALFlyxad/bn53EePHi3Kly8v9u3bJ06fPi3atm0r6tWrJ168eJGnujCQeoXGjRuL0aNH62yrVauW+Pjjjw1Uo5IpKipKABCHDx8WQgiRlpYmnJ2dxfz587VlEhMThUqlEsuWLTNUNYu1uLg4Ub16dbFv3z7RunVrbSDFa1G4pk2bJlq0aJHtfl6PwtOtWzcxbNgwnW29evUS7777rhCC16KwvBxI5eZzj4mJEebm5iIgIEBb5v79+8LExEQEBgbm6f3ZtZeD5ORkhISEoGPHjjrbO3bsiKCgIAPVqmRSq9UA0iePvnXrFiIjI3WujVKpROvWrXlt9OT9999Ht27d0L59e53tvBaFa/v27WjUqBH69OkDR0dHNGjQACtWrNDu5/UoPC1atMCBAwdw9epVAMDZs2dx5MgRdO3aFQCvhaHk5nMPCQlBSkqKThlXV1d4eHjk+dpw0uIcPH78GKmpqXByctLZ7uTkhMjISAPVquQRQmDSpElo0aIFPDw8AED7+Wd1be7cuVPodSzuAgICcPr0aZw8eTLTPl6LwnXz5k0sXboUkyZNwowZM3DixAmMHz8eSqUSgwcP5vUoRNOmTYNarUatWrVgamqK1NRUfPHFFxgwYAAA/tswlNx87pGRkbCwsECZMmUylcnr7zsDqVxQKBQ6z4UQmbaR/nzwwQc4d+4cjhw5kmkfr43+hYeHY8KECdi7dy8sLS2zLcdrUTjS0tLQqFEjfPnllwCABg0a4MKFC1i6dCkGDx6sLcfroX8bN27E+vXr8dtvv6Fu3boIDQ3FxIkT4erqiiFDhmjL8VoYRn4+9/xcG3bt5aBs2bIwNTXNFJ1GRUVlinRJP8aNG4ft27fj4MGDqFChgna7s7MzAPDaFIKQkBBERUWhYcOGMDMzg5mZGQ4fPozvv/8eZmZm2s+b16JwuLi4oE6dOjrbateujbt37wLgv43C9NFHH+Hjjz9G//794enpCT8/P3z44Yfw9/cHwGthKLn53J2dnZGcnIzo6Ohsy+QWA6kcWFhYoGHDhti3b5/O9n379qFZs2YGqlXJIITABx98gM2bN+Off/5B5cqVdfZXrlwZzs7OOtcmOTkZhw8f5rUpYD4+PggLC0NoaKh2adSoEQYNGoTQ0FBUqVKF16IQNW/ePFMqkKtXr8Ld3R0A/20UpufPn8PERPdn1NTUVJv+gNfCMHLzuTds2BDm5uY6ZSIiInD+/Pm8X5t8DZEvQTTpD3755Rdx8eJFMXHiRGFjYyNu375t6KoVa2PGjBEqlUocOnRIREREaJfnz59ry8yfP1+oVCqxefNmERYWJgYMGMDbigtJxrv2hOC1KEwnTpwQZmZm4osvvhDXrl0TGzZsENbW1mL9+vXaMrwehWPIkCGifPny2vQHmzdvFmXLlhVTp07VluG10I+4uDhx5swZcebMGQFAfPPNN+LMmTPa1ES5+dxHjx4tKlSoIPbv3y9Onz4t2rVrx/QH+vLjjz8Kd3d3YWFhId544w3tLfikPwCyXFatWqUtk5aWJmbNmiWcnZ2FUqkUrVq1EmFhYYardAnyciDFa1G4duzYITw8PIRSqRS1atUSP//8s85+Xo/CERsbKyZMmCAqVqwoLC0tRZUqVcQnn3wikpKStGV4LfTj4MGDWf5GDBkyRAiRu889ISFBfPDBB8Le3l5YWVkJX19fcffu3TzXRSGEEPluPyMiIiIqwThGioiIiCifGEgRERER5RMDKSIiIqJ8YiBFRERElE8MpIiIiIjyiYEUERERUT4xkCIiIiLKJwZSRFSstWnTBhMnTjR0NYiomGIgRURERJRPDKSIiIiI8omBFBEVG8+ePcPgwYNRqlQpuLi44Ouvv9bZv379ejRq1Ai2trZwdnbGwIEDERUVBQAQQqBatWr46quvdF5z/vx5mJiY4MaNGwCA2bNno2LFilAqlXB1dcX48eML5+SIyCgxkCKiYuOjjz7CwYMHsWXLFuzduxeHDh1CSEiIdn9ycjLmzp2Ls2fPYuvWrbh16xaGDh0KAFAoFBg2bBhWrVqlc8xff/0VLVu2RNWqVbFp0yYsXrwYy5cvx7Vr17B161Z4enoW5ikSkZHhpMVEVCzEx8fDwcEBa9euRb9+/QAAT58+RYUKFTBq1Ch8++23mV5z8uRJNG7cGHFxcShVqhQiIiLg5uaGoKAgNG7cGCkpKShfvjwWLVqEIUOG4JtvvsHy5ctx/vx5mJubF/IZEpExYosUERULN27cQHJyMry9vbXb7O3tUbNmTe3zM2fOoGfPnnB3d4etrS3atGkDALh79y4AwMXFBd26dcOvv/4KAPj777+RmJiIPn36AAD69OmDhIQEVKlSBSNHjsSWLVvw4sWLQjpDIjJGDKSIqFh4VeP6s2fP0LFjR5QqVQrr16/HyZMnsWXLFgCyy09jxIgRCAgIQEJCAlatWoV+/frB2toaAODm5oYrV67gxx9/hJWVFcaOHYtWrVohJSVFfydGREaNgRQRFQvVqlWDubk5jh07pt0WHR2Nq1evAgAuX76Mx48fY/78+WjZsiVq1aqlHWieUdeuXWFjY4OlS5di9+7dGDZsmM5+Kysr9OjRA99//z0OHTqE4OBghIWF6ffkiMhomRm6AkREBaFUqVIYPnw4PvroIzg4OMDJyQmffPIJTEzk34sVK1aEhYUFfvjhB4wePRrnz5/H3LlzMx3H1NQUQ4cOxfTp01GtWjWdrsLVq1cjNTUVTZo0gbW1NdatWwcrKyu4u7sX2nkSkXFhixQRFRuLFi1Cq1at0KNHD7Rv3x4tWrRAw4YNAQDlypXD6tWr8eeff6JOnTqYP39+plQHGsOHD0dycnKm1qjSpUtjxYoVaN68Oby8vHDgwAHs2LEDDg4Oej83IjJOvGuPiOglR48eRZs2bXDv3j04OTkZujpEZMQYSBER/b+kpCSEh4dj1KhRcHFxwYYNGwxdJSIycuzaIyL6f7///jtq1qwJtVqNhQsXGro6RFQEsEWKiIiIKJ/YIkVERESUTwykiIiIiPKJgRQRERFRPjGQIiIiIsonBlJERERE+cRAioiIiCifGEgRERER5RMDKSIiIqJ8YiBFRERElE//B5V60v3DRl35AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(real_stock_price,color = 'red', label = 'Real Price')\n",
+    "plt.plot(new_predicted_price, color = 'blue', label = 'New Predicted Price')\n",
+    "plt.title('TCS Stock Price Prediction')\n",
+    "plt.xlabel('days')\n",
+    "plt.ylabel('Price')\n",
+    "plt.xlim(0,100)  \n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fe0703c5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-30T15:40:20.380186Z",
+     "iopub.status.busy": "2024-05-30T15:40:20.379342Z",
+     "iopub.status.idle": "2024-05-30T15:40:20.854541Z",
+     "shell.execute_reply": "2024-05-30T15:40:20.853544Z"
+    },
+    "papermill": {
+     "duration": 0.511082,
+     "end_time": "2024-05-30T15:40:20.856663",
+     "exception": false,
+     "start_time": "2024-05-30T15:40:20.345581",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(18,10))\n",
+    "plt.plot(dataset.iloc[-1000:-100,0],trainset,'-o',label='train actual')\n",
+    "#plt.plot(dataset.iloc[-1000:-100,0],train_pred,'--',label='Prophet')\n",
+    "plt.plot(dataset.iloc[-100:,0],real_stock_price[-100:],'-o',label='test actual')\n",
+    "plt.plot(dataset.iloc[-100:,0],new_predicted_price[-100:],'-o',label='test predicted')\n",
+    "plt.title('TCS',size=24)\n",
+    "plt.xlabel('Date',size=16)\n",
+    "plt.ylabel('Price',size=16)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "datasetId": 1624347,
+     "sourceId": 3358601,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 36.200134,
+   "end_time": "2024-05-30T15:40:23.293779",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-05-30T15:39:47.093645",
+   "version": "2.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/NYSE/TCS stocks/TCS_stock_action.csv b/NYSE/TCS stocks/TCS_stock_action.csv
new file mode 100644
index 00000000..75bf3f07
--- /dev/null
+++ b/NYSE/TCS stocks/TCS_stock_action.csv	
@@ -0,0 +1,71 @@
+Date,Dividends,Stock Splits
+2004-10-28,0.375,0.0
+2005-02-03,0.4375,0.0
+2005-07-06,0.625,0.0
+2005-08-18,0.375,0.0
+2005-10-18,0.375,0.0
+2006-01-18,0.375,0.0
+2006-06-16,0.5625,0.0
+2006-07-28,0.375,2.0
+2006-10-23,0.75,0.0
+2007-01-22,0.75,0.0
+2007-06-15,1.0,0.0
+2007-07-27,0.75,0.0
+2007-10-22,0.75,0.0
+2008-01-23,0.75,0.0
+2008-06-18,1.25,0.0
+2008-07-31,0.75,0.0
+2008-10-29,0.75,0.0
+2009-01-28,0.75,0.0
+2009-06-16,1.25,2.0
+2009-07-27,1.0,0.0
+2009-10-28,1.0,0.0
+2010-01-27,1.0,0.0
+2010-06-15,5.0,0.0
+2010-07-29,1.0,0.0
+2010-11-01,1.0,0.0
+2011-01-27,1.0,0.0
+2011-06-08,4.0,0.0
+2011-07-28,1.5,0.0
+2011-10-25,1.5,0.0
+2012-01-25,1.5,0.0
+2012-06-07,4.0,0.0
+2012-07-23,1.5,0.0
+2012-10-31,1.5,0.0
+2013-01-23,1.5,0.0
+2013-06-06,6.5,0.0
+2013-07-29,2.0,0.0
+2013-10-25,2.0,0.0
+2014-01-27,2.0,0.0
+2014-06-06,10.0,0.0
+2014-07-28,20.0,0.0
+2014-10-29,2.5,0.0
+2015-01-27,2.5,0.0
+2015-06-05,12.0,0.0
+2015-07-20,2.75,0.0
+2015-10-21,5.5,0.0
+2015-10-23,2.75,0.0
+2016-01-21,2.75,0.0
+2016-06-06,13.5,0.0
+2016-07-25,3.25,0.0
+2016-10-24,3.25,0.0
+2017-01-23,3.25,0.0
+2017-06-13,13.75,0.0
+2017-07-24,3.5,0.0
+2017-10-25,3.5,0.0
+2018-01-22,3.5,0.0
+2018-05-31,14.5,2.0
+2018-07-17,4.0,0.0
+2018-10-23,4.0,0.0
+2019-01-17,4.0,0.0
+2019-06-04,18.0,0.0
+2019-07-16,5.0,0.0
+2019-10-17,40.0,0.0
+2020-01-23,5.0,0.0
+2020-03-19,12.0,0.0
+2020-06-03,6.0,0.0
+2020-07-16,5.0,0.0
+2020-10-14,12.0,0.0
+2021-01-14,6.0,0.0
+2021-05-25,15.0,0.0
+2021-07-15,7.0,0.0
diff --git a/NYSE/TCS stocks/TCS_stock_history.csv b/NYSE/TCS stocks/TCS_stock_history.csv
new file mode 100644
index 00000000..82144611
--- /dev/null
+++ b/NYSE/TCS stocks/TCS_stock_history.csv	
@@ -0,0 +1,4464 @@
+Date,Open,High,Low,Close,Volume,Dividends,Stock Splits
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diff --git a/NYSE/TCS stocks/TCS_stock_info.csv b/NYSE/TCS stocks/TCS_stock_info.csv
new file mode 100644
index 00000000..daec5d8b
--- /dev/null
+++ b/NYSE/TCS stocks/TCS_stock_info.csv	
@@ -0,0 +1,151 @@
+zip,400001
+sector,Technology
+fullTimeEmployees,509058
+longBusinessSummary,"Tata Consultancy Services Limited provides information technology (IT) and IT enabled services worldwide. The company operates through Banking, Financial Services and Insurance; Manufacturing; Retail and Consumer Business; Communication, Media and Technology; and Others segments. It offers CHROMA, a cloud-based talent management solution; ignio, a cognitive automation software product; TCS iON, an assessment platform; TAP, a procurement offering; TCS MasterCraft, a platform to automate and manage IT processes; Quartz, a blockchain solution; and TCS OmniStore, a commerce platform. The company also provides customer intelligence and insight solutions; Intelligent Urban Exchange, an integrated software to accelerate smart city programs; OPTUMERA, a digital merchandising suite; TCS BaNCS, a financial platform; and Jile, an agile DevOps product. In addition, it offers advanced drug development and connected intelligent platforms; ERP on cloud, an enterprise solution; and HOBS, a platform for subscription based digital business. Further, the company provides cognitive business, consulting, analytics, automation and artificial intelligence, Internet of Things, cloud applications, blockchain, cloud infrastructure, cyber security, interactive, industrial and quality engineering, and enterprise services. It serves banking, financial, and public services; consumer goods and distribution; insurance; life sciences and healthcare; manufacturing, retail; hi-tech; travel, transportation, and hospitality industries; communications, media, and technology industries; and energy, resource, and utility industries. It has strategic partnerships with Bharti Airtel Limited for implementing 5G networks in India; Google LLC for launching Google Garages; and NICE Ltd. to expand markets for financial crime solutions. The company was founded in 1968 and is headquartered in Mumbai, India. Tata Consultancy Services Limited is a subsidiary of Tata Sons Private Limited."
+city,Mumbai
+phone,91 22 6778 9999
+country,India
+companyOfficers,[]
+website,http://www.tcs.com
+maxAge,1
+address1,TCS House
+industry,Information Technology Services
+address2,Raveline Street Fort
+ebitdaMargins,0.27843
+profitMargins,0.20103
+grossMargins,0.43512002
+operatingCashflow,399649996800
+revenueGrowth,0.185
+operatingMargins,0.26288
+ebitda,476859990016
+targetLowPrice,2750
+recommendationKey,buy
+grossProfits,709010000000
+freeCashflow,320431259648
+targetMedianPrice,3650
+currentPrice,3773.2
+earningsGrowth,0.304
+currentRatio,2.931
+returnOnAssets,0.21420999
+numberOfAnalystOpinions,45
+targetMeanPrice,3663.8
+debtToEquity,8.998
+returnOnEquity,0.38166
+targetHighPrice,4578
+totalCash,423680016384
+totalDebt,81639997440
+totalRevenue,1712660021248
+totalCashPerShare,114.537
+financialCurrency,INR
+revenuePerShare,459.555
+quickRatio,2.426
+recommendationMean,2.4
+exchange,NSI
+shortName,TATA CONSULTANCY S
+longName,Tata Consultancy Services Limited
+exchangeTimezoneName,Asia/Kolkata
+exchangeTimezoneShortName,IST
+isEsgPopulated,False
+gmtOffSetMilliseconds,19800000
+quoteType,EQUITY
+symbol,TCS.NS
+messageBoardId,finmb_6411769
+market,in_market
+annualHoldingsTurnover,
+enterpriseToRevenue,7.994
+beta3Year,
+enterpriseToEbitda,28.711
+52WeekChange,0.50266504
+morningStarRiskRating,
+forwardEps,94.86
+revenueQuarterlyGrowth,
+sharesOutstanding,3699049984
+fundInceptionDate,
+annualReportExpenseRatio,
+totalAssets,
+bookValue,243.476
+sharesShort,
+sharesPercentSharesOut,
+fundFamily,
+lastFiscalYearEnd,1617148800
+heldPercentInstitutions,0.16299999
+netIncomeToCommon,344299995136
+trailingEps,92.385
+lastDividendValue,
+SandP52WeekChange,0.28745568
+priceToBook,15.497215
+heldPercentInsiders,0.72203004
+nextFiscalYearEnd,1680220800
+yield,
+mostRecentQuarter,1625011200
+shortRatio,
+sharesShortPreviousMonthDate,
+floatShares,1013059200
+beta,0.671152
+enterpriseValue,13691073331200
+priceHint,2
+threeYearAverageReturn,
+lastSplitDate,
+lastSplitFactor,
+legalType,
+lastDividendDate,
+morningStarOverallRating,
+earningsQuarterlyGrowth,0.285
+priceToSalesTrailing12Months,8.149461
+dateShortInterest,
+pegRatio,
+ytdReturn,
+forwardPE,39.776512
+lastCapGain,
+shortPercentOfFloat,
+sharesShortPriorMonth,
+impliedSharesOutstanding,
+category,
+fiveYearAverageReturn,
+previousClose,3791.9
+regularMarketOpen,3805
+twoHundredDayAverage,3322.348
+trailingAnnualDividendYield,0.010548802
+payoutRatio,0.35720003
+volume24Hr,
+regularMarketDayHigh,3805
+navPrice,
+averageDailyVolume10Day,2006803
+regularMarketPreviousClose,3791.9
+fiftyDayAverage,3727.4443
+trailingAnnualDividendRate,40
+open,3805
+toCurrency,
+averageVolume10days,2006803
+expireDate,
+algorithm,
+dividendRate,40
+exDividendDate,1626307200
+circulatingSupply,
+startDate,
+regularMarketDayLow,3765
+currency,INR
+trailingPE,40.842125
+regularMarketVolume,640471
+lastMarket,
+maxSupply,
+openInterest,
+marketCap,13957255397376
+volumeAllCurrencies,
+strikePrice,
+averageVolume,2397369
+dayLow,3765
+ask,3771.85
+askSize,0
+volume,640471
+fiftyTwoWeekHigh,3981.75
+fromCurrency,
+fiveYearAvgDividendYield,1.47
+fiftyTwoWeekLow,2492.3
+bid,3771.45
+tradeable,False
+dividendYield,0.0105
+bidSize,0
+dayHigh,3805
+regularMarketPrice,3773.2
+logo_url,https://logo.clearbit.com/tcs.com
diff --git a/NYSE/TCS stocks/logs.log b/NYSE/TCS stocks/logs.log
new file mode 100644
index 00000000..e69de29b

From 393cb760c8b483c64e727ebb835802b1aa701832 Mon Sep 17 00:00:00 2001
From: PRIYANSHU TIWARI <techarena955@gmail.com>
Date: Thu, 25 Jul 2024 14:13:29 +0530
Subject: [PATCH 2/3] updated

---
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 rename {NYSE/TCS stocks => Nifty /TCS stocks by LSTM}/.ipynb_checkpoints/tcs stock prediction pycaret-checkpoint.ipynb (100%)
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From c08e6fe23a0fbbae136cbeec2e43b54ed9122a6d Mon Sep 17 00:00:00 2001
From: PRIYANSHU TIWARI <techarena955@gmail.com>
Date: Sat, 27 Jul 2024 19:07:59 +0530
Subject: [PATCH 3/3] Done sir

---
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