From c809472a1438d8b112cc393eb37f840b760583b0 Mon Sep 17 00:00:00 2001
From: PRIYANSHU TIWARI <techarena955@gmail.com>
Date: Sat, 20 Jul 2024 21:02:19 +0530
Subject: [PATCH] here is my AMD

---
 .../AMD by LSTM-checkpoint.ipynb              |  1355 ++
 NYSE/AMD stock prediction/AMD by LSTM.ipynb   |  1355 ++
 NYSE/AMD stock prediction/AMD.csv             | 10638 ++++++++++++++++
 3 files changed, 13348 insertions(+)
 create mode 100644 NYSE/AMD stock prediction/.ipynb_checkpoints/AMD by LSTM-checkpoint.ipynb
 create mode 100644 NYSE/AMD stock prediction/AMD by LSTM.ipynb
 create mode 100644 NYSE/AMD stock prediction/AMD.csv

diff --git a/NYSE/AMD stock prediction/.ipynb_checkpoints/AMD by LSTM-checkpoint.ipynb b/NYSE/AMD stock prediction/.ipynb_checkpoints/AMD by LSTM-checkpoint.ipynb
new file mode 100644
index 00000000..24490404
--- /dev/null
+++ b/NYSE/AMD stock prediction/.ipynb_checkpoints/AMD by LSTM-checkpoint.ipynb	
@@ -0,0 +1,1355 @@
+{
+ "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": [
+    "# AMD 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",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1980-03-17</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.302083</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>3.145833</td>\n",
+       "      <td>3.145833</td>\n",
+       "      <td>219600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1980-03-18</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>2.937500</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>727200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1980-03-19</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.083333</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>3.041667</td>\n",
+       "      <td>3.041667</td>\n",
+       "      <td>295200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1980-03-20</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.062500</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>159600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1980-03-21</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>2.906250</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>130800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10632</th>\n",
+       "      <td>2022-05-16</td>\n",
+       "      <td>93.860001</td>\n",
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+       "      <td>93.080002</td>\n",
+       "      <td>94.239998</td>\n",
+       "      <td>94.239998</td>\n",
+       "      <td>131065700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10633</th>\n",
+       "      <td>2022-05-17</td>\n",
+       "      <td>97.879997</td>\n",
+       "      <td>103.199997</td>\n",
+       "      <td>97.540001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>164227200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10634</th>\n",
+       "      <td>2022-05-18</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>104.220001</td>\n",
+       "      <td>95.709999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>142603200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10635</th>\n",
+       "      <td>2022-05-19</td>\n",
+       "      <td>96.690002</td>\n",
+       "      <td>100.690002</td>\n",
+       "      <td>95.089996</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>141506200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10636</th>\n",
+       "      <td>2022-05-20</td>\n",
+       "      <td>98.389999</td>\n",
+       "      <td>98.639999</td>\n",
+       "      <td>88.260002</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>163996500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10637 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date        Open        High        Low       Close   Adj Close  \\\n",
+       "0      1980-03-17    0.000000    3.302083   3.125000    3.145833    3.145833   \n",
+       "1      1980-03-18    0.000000    3.125000   2.937500    3.031250    3.031250   \n",
+       "2      1980-03-19    0.000000    3.083333   3.020833    3.041667    3.041667   \n",
+       "3      1980-03-20    0.000000    3.062500   3.010417    3.010417    3.010417   \n",
+       "4      1980-03-21    0.000000    3.020833   2.906250    2.916667    2.916667   \n",
+       "...           ...         ...         ...        ...         ...         ...   \n",
+       "10632  2022-05-16   93.860001   98.169998  93.080002   94.239998   94.239998   \n",
+       "10633  2022-05-17   97.879997  103.199997  97.540001  102.470001  102.470001   \n",
+       "10634  2022-05-18  100.000000  104.220001  95.709999   96.279999   96.279999   \n",
+       "10635  2022-05-19   96.690002  100.690002  95.089996   96.669998   96.669998   \n",
+       "10636  2022-05-20   98.389999   98.639999  88.260002   93.500000   93.500000   \n",
+       "\n",
+       "          Volume  \n",
+       "0         219600  \n",
+       "1         727200  \n",
+       "2         295200  \n",
+       "3         159600  \n",
+       "4         130800  \n",
+       "...          ...  \n",
+       "10632  131065700  \n",
+       "10633  164227200  \n",
+       "10634  142603200  \n",
+       "10635  141506200  \n",
+       "10636  163996500  \n",
+       "\n",
+       "[10637 rows x 7 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset=pd.read_csv(\"AMD.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>Close</th>\n",
+       "      <th>Adj Close</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1980-03-17</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <td>3.125000</td>\n",
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+       "      <td>3.145833</td>\n",
+       "      <td>219600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1980-03-18</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>2.937500</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>727200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1980-03-19</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <td>3.020833</td>\n",
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+       "      <td>3.041667</td>\n",
+       "      <td>295200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1980-03-20</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.062500</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>159600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1980-03-21</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>2.906250</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>130800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>94.239998</td>\n",
+       "      <td>131065700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10633</th>\n",
+       "      <td>2022-05-17</td>\n",
+       "      <td>97.879997</td>\n",
+       "      <td>103.199997</td>\n",
+       "      <td>97.540001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>164227200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10634</th>\n",
+       "      <td>2022-05-18</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>104.220001</td>\n",
+       "      <td>95.709999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>142603200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10635</th>\n",
+       "      <td>2022-05-19</td>\n",
+       "      <td>96.690002</td>\n",
+       "      <td>100.690002</td>\n",
+       "      <td>95.089996</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>141506200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10636</th>\n",
+       "      <td>2022-05-20</td>\n",
+       "      <td>98.389999</td>\n",
+       "      <td>98.639999</td>\n",
+       "      <td>88.260002</td>\n",
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+       "      <td>93.500000</td>\n",
+       "      <td>163996500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10637 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date        Open        High        Low       Close   Adj Close  \\\n",
+       "0     1980-03-17    0.000000    3.302083   3.125000    3.145833    3.145833   \n",
+       "1     1980-03-18    0.000000    3.125000   2.937500    3.031250    3.031250   \n",
+       "2     1980-03-19    0.000000    3.083333   3.020833    3.041667    3.041667   \n",
+       "3     1980-03-20    0.000000    3.062500   3.010417    3.010417    3.010417   \n",
+       "4     1980-03-21    0.000000    3.020833   2.906250    2.916667    2.916667   \n",
+       "...          ...         ...         ...        ...         ...         ...   \n",
+       "10632 2022-05-16   93.860001   98.169998  93.080002   94.239998   94.239998   \n",
+       "10633 2022-05-17   97.879997  103.199997  97.540001  102.470001  102.470001   \n",
+       "10634 2022-05-18  100.000000  104.220001  95.709999   96.279999   96.279999   \n",
+       "10635 2022-05-19   96.690002  100.690002  95.089996   96.669998   96.669998   \n",
+       "10636 2022-05-20   98.389999   98.639999  88.260002   93.500000   93.500000   \n",
+       "\n",
+       "          Volume  \n",
+       "0         219600  \n",
+       "1         727200  \n",
+       "2         295200  \n",
+       "3         159600  \n",
+       "4         130800  \n",
+       "...          ...  \n",
+       "10632  131065700  \n",
+       "10633  164227200  \n",
+       "10634  142603200  \n",
+       "10635  141506200  \n",
+       "10636  163996500  \n",
+       "\n",
+       "[10637 rows x 7 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": [
+      "[[0.00161334]\n",
+      " [0.00221834]\n",
+      " [0.00369723]\n",
+      " [0.00880613]\n",
+      " [0.        ]]\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 [==============================] - 2s 30ms/step - loss: 0.0222\n",
+      "Epoch 2/10\n",
+      "27/27 [==============================] - 1s 30ms/step - loss: 0.0045\n",
+      "Epoch 3/10\n",
+      "27/27 [==============================] - 1s 31ms/step - loss: 0.0036\n",
+      "Epoch 4/10\n",
+      "27/27 [==============================] - 1s 34ms/step - loss: 0.0040\n",
+      "Epoch 5/10\n",
+      "27/27 [==============================] - 1s 35ms/step - loss: 0.0040\n",
+      "Epoch 6/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0037\n",
+      "Epoch 7/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0033\n",
+      "Epoch 8/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0035\n",
+      "Epoch 9/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0032\n",
+      "Epoch 10/10\n",
+      "27/27 [==============================] - 1s 34ms/step - loss: 0.0029\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x161484250>"
+      ]
+     },
+     "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": [
+      "(9897,)\n",
+      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\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": [
+      "(9897, 1)\n",
+      "(9897, 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": [
+      "(9837, 60)\n",
+      "(9837, 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": [
+      "308/308 [==============================] - 3s 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": [
+      "(9837, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_price = sc.inverse_transform(predicted_price)\n",
+    "print(predicted_price.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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('AMD 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": 24,
+   "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('AMD 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 11ms/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 8ms/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 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 8ms/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 8ms/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 8ms/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 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 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 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 8ms/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 8ms/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 9ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/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": 23,
+   "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('AMD 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": 22,
+   "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('AMD Stock Price',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/AMD stock prediction/AMD by LSTM.ipynb b/NYSE/AMD stock prediction/AMD by LSTM.ipynb
new file mode 100644
index 00000000..24490404
--- /dev/null
+++ b/NYSE/AMD stock prediction/AMD by LSTM.ipynb	
@@ -0,0 +1,1355 @@
+{
+ "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": [
+    "# AMD 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",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1980-03-17</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.302083</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>3.145833</td>\n",
+       "      <td>3.145833</td>\n",
+       "      <td>219600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1980-03-18</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>2.937500</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>727200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1980-03-19</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.083333</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>3.041667</td>\n",
+       "      <td>3.041667</td>\n",
+       "      <td>295200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1980-03-20</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.062500</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>159600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1980-03-21</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>2.906250</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>130800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10632</th>\n",
+       "      <td>2022-05-16</td>\n",
+       "      <td>93.860001</td>\n",
+       "      <td>98.169998</td>\n",
+       "      <td>93.080002</td>\n",
+       "      <td>94.239998</td>\n",
+       "      <td>94.239998</td>\n",
+       "      <td>131065700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10633</th>\n",
+       "      <td>2022-05-17</td>\n",
+       "      <td>97.879997</td>\n",
+       "      <td>103.199997</td>\n",
+       "      <td>97.540001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>164227200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10634</th>\n",
+       "      <td>2022-05-18</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>104.220001</td>\n",
+       "      <td>95.709999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>142603200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10635</th>\n",
+       "      <td>2022-05-19</td>\n",
+       "      <td>96.690002</td>\n",
+       "      <td>100.690002</td>\n",
+       "      <td>95.089996</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>141506200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10636</th>\n",
+       "      <td>2022-05-20</td>\n",
+       "      <td>98.389999</td>\n",
+       "      <td>98.639999</td>\n",
+       "      <td>88.260002</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>163996500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10637 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date        Open        High        Low       Close   Adj Close  \\\n",
+       "0      1980-03-17    0.000000    3.302083   3.125000    3.145833    3.145833   \n",
+       "1      1980-03-18    0.000000    3.125000   2.937500    3.031250    3.031250   \n",
+       "2      1980-03-19    0.000000    3.083333   3.020833    3.041667    3.041667   \n",
+       "3      1980-03-20    0.000000    3.062500   3.010417    3.010417    3.010417   \n",
+       "4      1980-03-21    0.000000    3.020833   2.906250    2.916667    2.916667   \n",
+       "...           ...         ...         ...        ...         ...         ...   \n",
+       "10632  2022-05-16   93.860001   98.169998  93.080002   94.239998   94.239998   \n",
+       "10633  2022-05-17   97.879997  103.199997  97.540001  102.470001  102.470001   \n",
+       "10634  2022-05-18  100.000000  104.220001  95.709999   96.279999   96.279999   \n",
+       "10635  2022-05-19   96.690002  100.690002  95.089996   96.669998   96.669998   \n",
+       "10636  2022-05-20   98.389999   98.639999  88.260002   93.500000   93.500000   \n",
+       "\n",
+       "          Volume  \n",
+       "0         219600  \n",
+       "1         727200  \n",
+       "2         295200  \n",
+       "3         159600  \n",
+       "4         130800  \n",
+       "...          ...  \n",
+       "10632  131065700  \n",
+       "10633  164227200  \n",
+       "10634  142603200  \n",
+       "10635  141506200  \n",
+       "10636  163996500  \n",
+       "\n",
+       "[10637 rows x 7 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset=pd.read_csv(\"AMD.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",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1980-03-17</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.302083</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>3.145833</td>\n",
+       "      <td>3.145833</td>\n",
+       "      <td>219600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1980-03-18</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>2.937500</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>3.031250</td>\n",
+       "      <td>727200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1980-03-19</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.083333</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>3.041667</td>\n",
+       "      <td>3.041667</td>\n",
+       "      <td>295200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1980-03-20</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.062500</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>3.010417</td>\n",
+       "      <td>159600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1980-03-21</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.020833</td>\n",
+       "      <td>2.906250</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>130800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10632</th>\n",
+       "      <td>2022-05-16</td>\n",
+       "      <td>93.860001</td>\n",
+       "      <td>98.169998</td>\n",
+       "      <td>93.080002</td>\n",
+       "      <td>94.239998</td>\n",
+       "      <td>94.239998</td>\n",
+       "      <td>131065700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10633</th>\n",
+       "      <td>2022-05-17</td>\n",
+       "      <td>97.879997</td>\n",
+       "      <td>103.199997</td>\n",
+       "      <td>97.540001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>102.470001</td>\n",
+       "      <td>164227200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10634</th>\n",
+       "      <td>2022-05-18</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>104.220001</td>\n",
+       "      <td>95.709999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>96.279999</td>\n",
+       "      <td>142603200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10635</th>\n",
+       "      <td>2022-05-19</td>\n",
+       "      <td>96.690002</td>\n",
+       "      <td>100.690002</td>\n",
+       "      <td>95.089996</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>96.669998</td>\n",
+       "      <td>141506200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10636</th>\n",
+       "      <td>2022-05-20</td>\n",
+       "      <td>98.389999</td>\n",
+       "      <td>98.639999</td>\n",
+       "      <td>88.260002</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>163996500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10637 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date        Open        High        Low       Close   Adj Close  \\\n",
+       "0     1980-03-17    0.000000    3.302083   3.125000    3.145833    3.145833   \n",
+       "1     1980-03-18    0.000000    3.125000   2.937500    3.031250    3.031250   \n",
+       "2     1980-03-19    0.000000    3.083333   3.020833    3.041667    3.041667   \n",
+       "3     1980-03-20    0.000000    3.062500   3.010417    3.010417    3.010417   \n",
+       "4     1980-03-21    0.000000    3.020833   2.906250    2.916667    2.916667   \n",
+       "...          ...         ...         ...        ...         ...         ...   \n",
+       "10632 2022-05-16   93.860001   98.169998  93.080002   94.239998   94.239998   \n",
+       "10633 2022-05-17   97.879997  103.199997  97.540001  102.470001  102.470001   \n",
+       "10634 2022-05-18  100.000000  104.220001  95.709999   96.279999   96.279999   \n",
+       "10635 2022-05-19   96.690002  100.690002  95.089996   96.669998   96.669998   \n",
+       "10636 2022-05-20   98.389999   98.639999  88.260002   93.500000   93.500000   \n",
+       "\n",
+       "          Volume  \n",
+       "0         219600  \n",
+       "1         727200  \n",
+       "2         295200  \n",
+       "3         159600  \n",
+       "4         130800  \n",
+       "...          ...  \n",
+       "10632  131065700  \n",
+       "10633  164227200  \n",
+       "10634  142603200  \n",
+       "10635  141506200  \n",
+       "10636  163996500  \n",
+       "\n",
+       "[10637 rows x 7 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": [
+      "[[0.00161334]\n",
+      " [0.00221834]\n",
+      " [0.00369723]\n",
+      " [0.00880613]\n",
+      " [0.        ]]\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 [==============================] - 2s 30ms/step - loss: 0.0222\n",
+      "Epoch 2/10\n",
+      "27/27 [==============================] - 1s 30ms/step - loss: 0.0045\n",
+      "Epoch 3/10\n",
+      "27/27 [==============================] - 1s 31ms/step - loss: 0.0036\n",
+      "Epoch 4/10\n",
+      "27/27 [==============================] - 1s 34ms/step - loss: 0.0040\n",
+      "Epoch 5/10\n",
+      "27/27 [==============================] - 1s 35ms/step - loss: 0.0040\n",
+      "Epoch 6/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0037\n",
+      "Epoch 7/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0033\n",
+      "Epoch 8/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0035\n",
+      "Epoch 9/10\n",
+      "27/27 [==============================] - 1s 32ms/step - loss: 0.0032\n",
+      "Epoch 10/10\n",
+      "27/27 [==============================] - 1s 34ms/step - loss: 0.0029\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x161484250>"
+      ]
+     },
+     "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": [
+      "(9897,)\n",
+      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\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": [
+      "(9897, 1)\n",
+      "(9897, 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": [
+      "(9837, 60)\n",
+      "(9837, 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": [
+      "308/308 [==============================] - 3s 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": [
+      "(9837, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_price = sc.inverse_transform(predicted_price)\n",
+    "print(predicted_price.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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('AMD 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": 24,
+   "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('AMD 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 11ms/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 8ms/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 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 8ms/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 8ms/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 8ms/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 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 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 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 8ms/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 8ms/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 9ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "1/1 [==============================] - 0s 9ms/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": 23,
+   "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('AMD 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": 22,
+   "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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n9o174tFfPFZ8a3b064lelJ+lh684Xbcv8Ib/C6Y59N4d8/S/V5+p/GxzwWY8W7ugn6AtTyJoa5JqPnxZ+ut98ltR39Xi/d5PJklVL5jfaDOcfuPJ0ic+xWUmegGxYLH0vVtlGMaAx/oLNeb222/X9773vZ6vm5qaCMsBAAAAIMm4PYYqahu0v7ldowtyNaOkKCYVs+VVLi1fW92nJYTDlqtli5xJGTYCPmWlDp005mNt39Ok/zy7RF84eUzM/i7ipazUoflOe8i/ZWuGRXMmj9JPvjxN1z+9JeicDpt3Dgwi52Lp5IWxbWuSCjxu6S8/CD2urUH605V9Hysc663G738jwddvvH/w3rRXevbr3n7rvZ9jtv97fY25cUNUSgfldrtdkrdq3OE4+n9U9u/f31Nlbrfb1dnZqYMHD/apKt+/f79mzw6862xOTo5ycnLitHIAAAAAQLRe2ebSnS9WqaGls+exovws3XtxqRZMGxvxvOVVLi1dXTmgLrCusV1LV1cmbWUu4NPYdkSSdGGpXWeMT42w2JphMd1PfMG0sbr200N69M1av9+3KPnazAwZvrYmQ8nujVKzK7Ln+jba7F1173F7e5777ffebe2N3psSvpsQLfXmjlf5pHTOLel/8yJCKd16paSkRHa7XevWret5rLOzU2+88UZPCH7GGWcoKyurzxiXy6WqqqqgQTkAAAAAIHnd/0q1rn+6sk9ILkkNLV26/uktuv+V6ojmdXsMLV9b7Tee8D22fG01bViQtNweQwe6e3PXHmhN23P19gVOv33LHUncZgZpKqp2Jt1/n+W3HW3DErLfuLzV6W/+1Pu5xy29eru5w9GnPKikryg/fPiwPv74456va2trtXXrVhUVFen444/XTTfdpPvuu0+TJ0/W5MmTdd999ykvL09XXHGFJMlms+nqq6/W97//fRUXF6uoqEi33HKLpk6dqnnz5iXqxwIAAAAAROiVbXsDVpL6PPpmrU49dqQWTAsvLKuobejTbqU/Q5KrsV0VtQ2mq1+BwVJe5dLda6rV2uWRJN3yx7/rv1/9R9q2DFowzaELS0O3bAHiymzbk4B6bbRZcrb54P3dR7zV4WaC9d7oUx5Q0gflf/vb3/SFL3yh52tf3/Arr7xSTzzxhH7wgx+ora1N119/vQ4ePKgzzzxTr776qgoKCnqe8+CDDyozM1OXXXaZ2tradP755+uJJ56Q1crbDAAAAAAglbg9hu58scrU2LterNKFpfawQrO6psAheW/7m82NAwbLUG0ZFE7LFiAuxs+WChyRt1/x8QXYZoP3toaj/eDDEXWwn74shmGk53twYqypqUk2m02NjY0qLCxM9HIAAAAAYEjaVFOvyx/bbHr8M9fMNB2ilVe59MPnt6uhpSum8wLx5vYYOmvFhoDvhrBIstty9fatc6m2BuKheo13k81oXPmSt6Lc45Z+coLUdij0c770G2/w/eRF5o5hsUp31EmZ2aHHprhIstyU7lEOAAAAAEhvbo+hTTX1enHrHm2qqTdd8e1jtvLbV40bKiS3yNsDeUZJamyQiOTX/xyPpKd4OC2DAMSBc7F02e+kYSMje37hOG9luuTdaPPM6809b/gY7/MKTW5gbbilf70b2RqHgKRvvQIAAAAAGJrKq1xavra6TwA4Mi8rrDlGF+SGHBNsA8/efHW4yxY5/Vbluj0GvZIRFl9P8d43gOyFubp7cXg9xc3eEKJlEBBHzsXSyQulN34svfXfkif0u5N6lH7JG5D7nHOLtwd5W6CbWxZvOD5+tvd5ZSvMV7TTozwggnIAAAAAQNIJ1G/5YKv54KEoP0tnjA9d3ReqGvfofNn60RdL+wSYvnB8XXWdXti6Vw0tnT3fiyTwxNBRXuXSdasrBzxe19Su61ZX6pEweoqbuSEUzjgAEfrwZemNFVLIW6/9bPyldOznvWG75A2/Fz0kPfuNAHMZUtkDR8N152LpvB9Kf70v9LHoUR4QrVcAAAAAIEZi0UJhqHN7DL2z84Bu+/P2cGOGARpaunTuT15XeVXwDdbMVtneufCUPsFleZVLZ63YoMsf26zfvrOrT0guHQ08Qx3fH86l9OT7vT5f+alu+eO2oGNvf2676d/7jJIiOWy5CvT+BVoGAYPA45bKb1XYIblP+W3eOXyci6XLnpIsfuLbYX7+ls+6WbIG6z1u6dviBQNQUQ4AAAAAMeCvTYjDlqtli6goNsvfaxgtV2Po6lyzVbb/bGjr+TxQxbs/tz+3XfOddtNtWDiX0lO45/fB1i5t/qRecyaNCjnWmmHRskVOLfVToR6qZRCAGNm9UWraG+GTDalpj3eOkrP7fcszcHjbQW+1+WVPeQP16jXekN7dOXCspJ4rQe8qdAxARTkAAAAARMkXmvYPwOoa27U0worioSbQaxgrP/jT3/XOxwf8VmiHqsb1eXD9R3pl217TPc19fIGnGZxL6SnS83tTjbnzRpI8Hik/Z2A95Ii8LK0Ko40LgAjFovd37zl6KtT96f5foPLbpB0veEPzYCF94dijoToCoqIcAAAAAKIQLDQ15K3hWr62OqyK4qEm3OA5Ek3tbn3t1+/2fN27QttXjeuvX3R/NzyzRd+deziiwDNQZbDbY2hzTb3eqflMT23azbmUZqI7v8096/5XqvXom7V+vxdOX38AUYhF7+/ec4SsUO+uQn/5ewp6rcgbJX13q5QZrC0LJCrKAQAAACAqoTaCNORt/1FR2zB4i0oxZjfTjKX+FdplpQ7dPG9yyOd5DOnnr+2M4Ij+Q4zyKpfOuHedvvabd/XwXz/R4Q6333G+GTiXUk805/esE0K3XXll296AIbnP8rXV9LkH4m38bG/ldsj3J/njp3+42Qr11hDvPGk9IP3r3eBjIImgHAAAAACiYnYjSLPjhqJfv1Uz6Mc0uv/d/tw2vfHhfv30/z7Ujr1NcTuev8CzvMql61ZX6lCYFb+cS6kl0t/XiLwszZxYHHSM22PozherQs7FDRZgEGRYpbIV3V+EG5YbA/uHx6JC3ScWbWGGAFqvAAAAAEAUzG4EaXbcUPPKtr167cPPEnb8g61HdOUT78X1GP4CT7fH0N1rdkQ0H+dSaon09/XApVNDttjZ/Em9GlrM3WjhBgswCJyLvb3Ay28Nb2PPmdcP7B8+frY03C4drot+XbEM3dMYFeUAAAAAEIWDLR0hxzhsuZpRUjQIq0ktZqthU52/wLOitkF1TaHPnd4s4lxKRTNKijRiWJbp8fbCHD18xemyDcv2u/msT3mVS9c89TfT83KDBRgkzsXSTVXSlS9Jp1xs7jknLRj4WIZVOvv7US7GT0sXBERFOQAAAABEyO0xdM/LH4Qcd9dCJ5sv+lFR22C6GjYZDcvKkGFI7Uc8AceMyPMfkK6vDr9C0JC0bBHnUqqxZlh01ZwJenB96N72xwzP1v930RTd83J1n77mI4Zl6ao5E3TD3MmyZlh62vaYVZyfzQ0WYDBlWL3h9HPXhB4bLMgeP6v7E4vMbu57VPf/VvRv6YKAqCgHAAAAgAiZ3aRvZH72IKwm9aR6K4i2Lk9PSJ4Z4L+uG1u7+mwaKnlvsDy3ZU/YxwsUuiP5TR49XBYT9zeOeAx9++nKAdeVQ21denD9Tp1x7zq9sm2vlq+tDuv491xcyg0WYLDt3ig1u0KPm35l4CC7q837Mf+Y8I9fONbbBqZ/SxcEREU5AAAAAESIjTyjk06tIAIVlfvq/5avrdZ8p13WDIsqaht0MMwNPCXpUGuXrltdqUeWTFdZqSPyxWJQlVe59O2nt5iqBT3U2hV03KHWLl3/9Jawjj/35GO0YBrnCzDozG6geaRN2rhSOrRbGjlB+vw1Umb3Dfb27k2mMzKlvFFSa738V5b3qji/+GFpxPHeKnUqycNCUA4AAAAAEWIjz+jMKCmSw5Zrqio/1bka21VR26BZE4ujvnFy23Pbe0J3JDe3x9DytdVBw+8Mi/Tvnz9Oz1T8K+zGCmZcc/bEOMwKICSzG2i+/WDfr1+9U5p1g3Ts56W1N3ofaw62MWivkHzUidLpXwt3pehG6xUAAAAAiJAv6A0UV7L5YnDWDIuWLXIGfP0iMSIvK6bzxdK67r7k0d44OdTapZUbPo7FkhBnZtozeQzpmYp/xeX4XH+ABBo/29v+JNz/VTI80sZfSM9+XWprCD0+r1gq/bL3c/u0sJeJowjKAQAAACBCvqBXCvyfwWy+GFxZqUOrlkzXmIKcPo8X5Fp11ezxuvH8yabmcdhy9ciS6Xrg0qmSwo4lBsWf3v9Uz2/ZI4/HGPDzhuvxjbVye+JRf5xYbo+hTTX1enHrHm2qqU/5nzGRbZcs4voDJFSGVSpb0f1FHP8OWw9IH77k/dxBUB4NWq8AAAAAQBR8Qe/ytdV9KkctklYNci9pt8dQRW2D9je3a3SBt5I0FUKyslKHJo0u0LyfvaGczAw9cdWMnirYs1ZsCPn8m86frO+cP7nnZ/X3+0gGTe1HdPMftkqScgLt/mnSodYuba6p15zJo2KwsuRQXuUa8Htz2HK1bJEzZXuyJ6rt0si8LN1/6dSUfd2AtOFc7N1Qs/xWqSlY+5QoHem+bnY0xe8YQwBBOQAAAABEqazUoflOu2Y/8Jr2NXVI8nYLLczNkttjDEpYnYohY+9gv6nNu7nlqOE5mjWxWJK0qabeVNh95gnFfV5j3+/DN/eo/Bx9+5lKHQpzA80RwzIli0WNITZYjERHoN0/w3D905Va8aX0CEPLq1xaurpywOtc19iupasrB/2mU6z42jPVNbbHpf94ICsvn55WN1GAlOZcLJ28UHr9fumtn8T3WG/9TBozVZpySXyPk6YIygEAAAAgBqwZFrV2uvs8dsWv3x2UsDoVQ0Z/wb4kdbrdPTcXzLat8DfOmmHpCdwl6YFLp/p9jXzx+v9ccbpsw7K16ZMDkrzPnXlCsdZV12np6sowfrLozSwZqc21B0OOa2zrStrfbziCbXhpyPs7Wr62OiU3MPW1Z/J3DvXafi/mDrR0xGlmABHJsEonnBv/oNzwSH+8UrL8zhvQIyz0KAcAAACAMPnro1xe5VJz+5EBY31hdXmVK25rCRYySt6QMZl6PfuCfX/V4p81d+qMe9epvMplum2FmXG+FjkOW9+xdluuVi2ZrgXTxmrO5FG65cKTdcuFJ2nOpFGyZlh6nleUn2Xuh4uSRdIHruawnrN8bbU6j3hStrd3qA0vDUmuxnZV1JrY1C5J2fIGnj8j8rJ0zdkT4nK8RLV8ARBEpJt7RqL8NsnjDj0OfVBRDgAAAGDIiaaXt79KaHthjtoDtNKId0VsOCFj7wrrRAkW7PscavVWSv/PFacHbVthkTfo9vUzD6V/Sxazv/uyUofmnjxGM+9/TQ0tnaaOFSlDUqOfGy7Bxrsa2wesLdnb7vQWzTsHkl2gd3tI0sHWLv3x/T0xPV64fxMABpFvc89nv6H4vqdEUtMeafdGqeTs+B0jDVFRDgAAAGBIKa9y6awVG3T5Y5t14++36vLHNuusFRtMVXwHqoSua+oI2v86nhWxqRYyhgr2fQxJ97z8ge5a6JQ0sP7O9/WyRc6wbj74WrJcfNo4zZpYbPq52ZkZuu+LpYNRByhJGjEsvAr2/gF+vN/JEEuxfOdAMuk84tEPn68KeVMolgyF/zcBYBD5NvcsjPQmZhh/24f3RXiMoYugHAAAAMCQETDoNhEqmqmEDiUeYfWuAy2mxu3cdzgpWnLUNZl/DVyN7RqZn61VS6bLHqBlymBWTAdq3+ITy/YsV80piSqUT9a2O/74NrwM9PNa5K2QT6Uq6fIql2bevz7u70Dob0ReluY77YN6TABhci6WbqqSrnxJ+tJvpJMuMve8c/6fdOa15o8zfExk6xvCaL0CAAAAYEiIdsNAs5XQwcS6Ira8yqUH1+80NXbl6x9r5esfJ7QlxyvbXLr9uW1hPWd/c7suPm1cRC1T4qF3+5a6xjY1tHSqaHiO7IW5qmtq181/2BrV/L7WGTfMnaST7MN195odqmuKbGPGZGu7E0ioDS+l1KqSDtZuJd4OtXYl/e8bgLxtWErO9vYR/8sPzD1n/Fnej+8+Enps3ihvT3SEhaAcAAAAwJAQbS/vaKrB49E32O0xdPeaHWE/z1c9P5jV2G6PoRt/v0UvbQu/DYjv5oKvZUoyCLSWTTX1Uc3bPxT2hfIrN3ysB9d/FPG8ydJ2JxRbXtaAViQj8rJ0/6VTU6LXuhSbd55EK1V+3wDk7SPeavJ/O15cKp32DXNjP/8tbxiPsNB6BQAAAMCQ8Ou3akyNW1dd5/fxSKvB41URW1HbEFGl8WC35Civcmn6Pa+GHZKnYruNUC1EQvHXTsaaYdGN8ybrkSXTI27tkuy9vX0V2P76dR9s7ZInyVvH9BbpO09iWSuf7L9vAL2E00e8ySW9+YC5saMmR7aeIY6KcgAAAABp75Vte/Xah5+ZGvvbd3ZpRknRgArWgy0dskhhV4rGqyI2mqrRWLXkcHuMoO1Qyqtcus5PO41QUrHdhhS8hUggdyw4RaMLc0K2kykrdaityxN2a5dkv9lgpgL7hme2aKUsWjAtOavKe/8d7Nx3OOzn/8ecCfpLVV2fgN1hy9XiUx361Zu1ksxfd5L99w2gn7D6iIfx/0DoTx4RgnIAAAAAac3tMXTni1Wmx/vrVV5e5dK3n94SUTuFnMyMuGyuF4uq0WjC9vIql5avrR4Q7vn6n3ce8eiHz5t/3XuzJ7CPerR8G36G6i3ua8fzH2eVmL4ZYC8M/3d+xviRSX2zwUwFtseQrn+6Uo9kDO7mrWb4+zsI13ynXXcsdPq96XT68SPD6lOfajeXgCFv/GxpWJHU1hCjCS1S4Vj6k0eIoBwAAABAWquobVBDy8CWDoH0r7b29gKPvOdwXVNHXDbXm1FSJHthTsQbPUrSzn2HtammPqyNMd0eI2DPbF//8/88p0R/fP/TsF53n7sWnqJvzjEfHiejUL3FI62YP9gS/u/6pW0uXTTNNSBgDvVugEgEmzPQ98K5WRNss91EiHbTzt57FwTqe19W6lBBbpa+9ut3Q85387wTk+5GAoAQPnw5ipC8//vcuq+NZQ/QnzxCBOUAAAAA0lqkVdN1jW2SpJUbdqquKbrN8fY3t8c0mPTNtWCqQ799Z1fE61r5+sda+frHfSrBg3llm0t3vLBdB/30kpaO/uf6o93tIsLlsOWmfEju4+stfpJ9+ICK40gq5t0eQz98IbIKfX/vkAj2boBIBJvTtwZ/3wvnnRGxaBcUK9Fu2hnOzZIDh83dIJkwKi/C1QBICI9bKr81suee90Op8gmpae/RxwrHekNy5+KYLG8oIigHAAAAkNYibVHyXy9V67UP94e9CaU/uw606qwVG2ISTMai1UN/vkrw/7nidI3Mz/Eb5t//SnXEAbhZ6dg2wlddHu1NkpUbdvrd7NKM3gFzoCpo3znQfzNRMwLN6WpsD9ij3ne8b509IaxjRdMuKJYi3bTTJ5ybJWavYWziCaSY3Rv7Bt2mdLdWOecW77/dG70bgg4f4223QiV5VAjKAQAAAKS1GSVFcthyww61DrZ2xSQkH5GXpZ+v/ygmwWS0rR4C8c13wzNb5Ok1uS/M93iMuIbk+TlW/fdXTk3bthGB2mqY5fYYejyKdw5IR9/VEKgK2pD//vxm1hZJZbVv/G/e3hXW83YdaA3zSPERTWB/wxcm6ub5J5l+jX3XsLrGdr+vc+8WLgBSyOF9YT7BT2uVkrNjuqShLiPRCwAAAACAeLJmWLRskVOJrFMOFExK3mDS7QkdM0bb6sGM/svwVQT/vz9vi8vxcjItuun8Sdq27MK0DcljoaK2QYfaIqsm9xldkBuyCrp3f36zNn9SH1VltYlTv49nKnab+nuJFbfH0Kaaer24dY821dT3HDua6u05k44J6x0FvmuYpAHXsUj73QNIAsPHhDe+cKx02VO0VokjKsoBAAAApL2yUodWLZmu//fHbWruODJox822WoK2y+i/cWgw0bZ6iEZLhzvmc848oUj/+62ZhHsmRFO93Lva+KVt5t7ib/Z45VUu3fqn+NxECaSuqUMrN3ysG+dNjvuxXtnm0p0vVqmhpbPnsaL8LF186ljZbbnKz7GG/bdRnJ8dUeW37xoWi373AJLE+Nne8LvJJf+31CXljZLK7pcKHLRWGQQE5QAAAACGhLJSh7Z92qiH/1qjsyeP0vXnTZLrUJu+98e/x/xYedlWtXa61ek2V/lqJphcX10X7bKSyod1zYleQsqItHq5f7XxrgMtpp5npr1JvNoAmfHg+o90kn14TMNht8fQ5pp6vVPzmfYcbNOOvU36+LOBr1dDS5ce37g74uPcc3FpxDeHYtXvHkCSyLBKZSukZ78h7xW79xW1++/6ogepIB9EBOUAAAAAhozWTm/159RxNs2aWKxNNfUxnX9EXpYOtXb1HMesUEGo22Po+a17olla0jnU2mWqkh6he1QH0rva2O0x9EzFP0097/GNtbph7qSAAexgtAEKJdxe6sGUV7l023PbI94s1ayLpjm0YFp04X60/e4BJBnnYm87lfJb+27sWTjW24uckHxQEZQDAAAAGDJautuu5Od4/1PoYEtHzOa+8fxJ+sN7n0oKL2xzmNiEr6K2QQ0t8Q3xEiGaliJDia9H9dLVlQNqDgOZMaFIN86brJkneEPVitoG1TWZO98PtXbp1j9t09knjhpQtez2GHrindqEtQHyMduyKJTyKpeuW10Zo1UFZhuWqYe+enrcjwMgBTkXSycvlHZv9G7wOXwMbVYShKAcAAAAwJDR0ukNyofnZKq8yqVvP70l6jkd3VW7tmHZeui1j8N+/lc/f3zIqthIAuUMS/gbJQ62aDZEHGoC9ajubURelto73Wo/4lHFrgZ97dfvymHL1V0LT1G1K7xWN3+q/FR/qvxU0tFzXFLQ4w+2aG+0uD2G7l6zI0arCW7Fl6bRIgVAYBlWqeTsRK9iyCMoBwAAADBkHO7eeG9YtjXq1hE3fGGS5kwa1VNt+2KErVEOtnZoU0190J7D4QbKFkkrLz9dI/NztL+5XaPyc3TE7dG1//u+2rs8Ea0zlnpvMAnz+veoHpWfI1mkA4c7tOtAq36+/qMB57SrsV3XR3lDyNXYPihV1+GK9kZLOFX2kXKw2SYApAyCcgAAAABDhq/1iutQW9RVsU5HYZ+2D5GGdk9s3K0nem0O6C9Ym1FS1NP/PJRgwdzPvnJq1KFprPg2mER4/PWodnsMnbViQ0J7hseaxSIZQX4ge2FO1Dda4t36566Fp+ibc0o4zwEgRWQkegEAAAAAEA9uj6FNNfV6cesebaqpl9tj9ATlHUeir6q+5+VquXv1NvFtuBhtJFbX2K6lqytVXuXqeezH5R+EDMmvmj1ez1wzU2/fOjdg9eqCaWN17TklUa4wOsX52Vq1ZDoVtjFUUduQNO1QYsUXkgf6e2pqP6KVG3b2+Rv0x991wPf4geb4VpOPKsghJAeAFEJFOQAAAIC0U17lGtBLuSg/S53dAfnogpyoj9F/M8FINlz0x/e8Hz6/XW1dHu060KJH36wN+pwReVm686IppkK5H5Sdot+/9y81th0xtZ5ofpb+ivKztOn285WdSc1WLKXrpqj/MWeCntuyx+9NotZOtx5cv1O/eusTffVzx2me0z6gbZG/64DDlqvFpzr04laX6pri+7rRgx8AUgv/7wQAAABAWimvcmnp6soBFbYNLV09Pcr/+9V/aEReVtTV3/0DSt+Gi3Zb9AFZQ0uXbv7DVj302s6QYw+1dqmitsHUvBW1DaZDcik2Ibml+999X5xKSB4H6RrInn/yGOVmWoOOaelw6zfv7NLlj23WWSs29LwTI9B1wNXYrkffrI17SO6gBz8ApBwqygEAAACkDbfHMLVJZ3OHW5I76uP5Cyj7b7i4fO0ONbSE7i0eLbNVxYmoPrazoWFczSgpUlF+1qCcZ4PFYcuVLAor0Pa1LfqfK07XPS9/kNCe7fTgB4DUQ1AOAAAAIG0MVq9mi7zhb6CKUd+Gi+VVrkELL81WFQ929TEbGsafNcOiL542Tr95Z9egH9vXmufmeZM1YVS+RuXn6HvPbtW+KPt/L1vk1IYP9oX1HKN7PXe+WBXV393sE4o0ujBXjhG5+sN7n+pgS6fp0H1kXpbuv3QqN4UAIAURlAMAAABIG4NRLe2Le0NVjPqq2wdDcX626TYPvk1H6xrbw664zbBIvfdO7P91b76bCYTkg2Oe056QoNzfuwWWXzxF162ujHjOm+edqPlOu374/Pawn2tIUd+cmj6+SLdceJIk6dRjR/jdd8D39Y3nT+reINR7c2zmCcWc7wCQomgOBwAAACBtxKNaesSwrD5f2225WrVkesiK0cGqbpekey4uNR3O+TYdlRR2j3ZfKH7tuSfomWtmauXl03v6j/dm9mYCYsd3A2SwNbZ2DnisrNShR5ZM14i8rAHfC3U22AtzdMPcSaqobUhgK5mjkXigfQfstlw9smS6bp5/km658GTdcuFJmjNpFOc7AKQwKsoBAAAApI1oqqUD+Z8rpisjw6L9ze0aXeBtt2ImDBusXuDXnlOiBdPCa/PgC/+Wr62OKMz/wkmjNfOEYknSqoyB89CTfPD5boCYreTOsVo0LCdTja1dUf2ttHZ5dN3qSj3S7+aRr1f/5pp6bfrkgHwV142tXfr209419q/QlqS7F0+RtfvvLVFmnTCqz9f99x0I5zoAAEgdFsMwErm/RcpoamqSzWZTY2OjCgsLE70cAAAAAAGUV7m0dHVl1EG5r3XI27fOjSgQ21RTr8sf2xzlKgIbnmPVj780TQumjY14DrfH6An/PvmsRQ+9ttPU82zDsrTiS0f7MPeehxAxsR5a/5EeXB/691iUl6V7L5nqN7SOhCOMv5XyKteAmyuOfjdXovn7CdYSKJThOZn6+7ILOH8BIMVFkuVSUQ4AAAAg7QzLtqq10216vL/+w1J0rUPiUd3e2/CcTF0YZcW2b9NRyRtMmg3KG9u6+lQR954HiXXD3Ml6YuMuHWwN3rakobVLI/Ozo3pnQW+uxnZV1DaYOg/MVGj7/n4iWVekIbkk/fhL0wjJAWCIokc5AAAAgLRRXuXSdasrTYfkFnlbl/jrP2ymD3kw0fQCN6OuqUMVtQ0xmy+SVhe3Pbe9eyNDJAtrhkWXnj7O1Nj9ze0qK3Xo7Vvn6n+vPlO23Ohq6cI5h3w3Vy4+bZxmTRy4AWbvv5/Bcs3Z4bcxAgCkD4JyAAAAAGnB7TF095odpsc7usPw2xc49fatc/XMNTP10FdP0zPXzNTbt86NSX/tQBsBxkos+zhHshHqodYurdzwcczWgNiY57SbGuf7nVszLJozeZT+46ySqI4b6810g20KGmvXnD1Bdywc3GAeAJBcaL0CAAAAIC1U1DaorqnD1Ni7Fp6ib84p6alijWfrkN5tJuoa23TPyx/oYEtnTNqxxDKYjLTVxeMba3XD3Em0q0giodr++Prvzygp6vP4hFH5ER/T4We+WPD9/azc8LEef6dWh9qCt5QJVyx6/QMA0gNBOQAAAIC0EE519aiCnEENdnsH8cOyrVq6ujLqOWMdTPpaXYS7Eeqh1i7TvakxOHr/LsPpvx/NjZdo+vmHYs2w6MZ5k3XD3EmqqG3QOx9/ppWv10Q8X9mUMZo0ukCzJhZr5gkD274AAIYmWq8AAAAASAvhhHyxbhERDl87lhHDomsn8dXPHx/zgM+3NkeYrWJi2QIGsRGo7U+w/vu+SvRwzqqReVk9m7rGm++G0+QxBVHNc+XsEt1y4UmaM2kUITkAoAcV5QAAAADSwoySItkLc0K2X4lXi4hwlJU6VJCbpa/9+t2I55gwKi+GKzqqd6uYpzbV6i9V+0I+J5E3HhBY79/l/uZ2jS7wnvuBwuFgleg+N54/qXsDV0vCKrJ3HWiJ6vnc2AEA+ENQDgAAACAtWDMsunvxFF0Xoq1JPFtEhGPmCcVB+0iHEs9w2le5O6OkSGfcu06HWv33hQ7U6xrJI9z++75K9OVrq/v0q3fYcrVskXNQKseDcXsMPVPxz6jm2HWgNUarAQCkE1qvAAAAAEgbZaUOPbJkurKtA/9TZzBbRJjhq94Nl0WDVxVvzbDogUun+m3FEazXNVJbWalDb986V89cM1MPffU0PXPNTL1969yk+NsJZ9PeQH7/3j+7q+IBADiKinIAAAAAaaWs1KF5zr16ZXudzp48SqceOyJpN+3zVe/evWaHqfAvEeF0oApje5JUGCM+wq1EHyyxaJviamxnA1oAwAAE5QAAAADSTlunW5K0aNpYXfb54xK8muB8faRXbvhYD67/KOjYRIXT4fa6BuIlVi2H6FMOAOiPoBwAAABA2mnpDsrzc1LjP3msGRbdOG+yTrIPH1C5XZSfpS+eNk7znPaEhtPJWmGMoWVGSVFUvf192IAWANBfavy/RgAAAAAIQ2vnEUlSXo41wSsJD5XbQHC+3v5LV1fKIoUdlrMBLQAgEIJyAAAAAGmntaO7ojw79f6Th8ptILhAffN9774oHJatn3e3MeodpLMBLQAgmNT7f40AAAAAEIDbY6iitkEHWrwbY+ZkZiR4RQDiIdS7L/y1MWIDWgBAMBbDMKJp6zVkNDU1yWazqbGxUYWFhYleDgAAAIB+yqtcA4KxY4bn6J5LphCMAUOQ78YZbYwAYOiJJMslKDeJoBwAAABIXuVVLi1dXem3X7FF0qol0wnLAQAAhohIslzehwgAAAAgpbk9hpavrQ64qZ8hafnaark91AgBAADAP4JyAAAAACmtorahT7sVf1yN7aqobRikFQEAACDVEJQDAAAASGn7m4OH5D7rquvivBIAAACkKoJyAAAAACltdEGuqXEvbt1L+xUAAAD4RVAOAAAAIKXNKClSUX5WyHH1LZ20XwEAAIBfBOUAAAAAUpo1w6IvnjbO1FizbVoAAAAwtBCUAwAAAEh585x2U+PMtmkBAADA0EJQDgAAACDlzSgpUnF+dsDvWyQ5bLmaUVI0eIsCAABAyiAoBwAAAJDyrBkWfeHk0X6/Z+n+uGyRU9YMi98xAAAAGNoIygEAAACkhS63R5I0PCezz+N2W65WLZmuslJHIpYFAACAFJAZeggAAAAAJC+3x1BFbYM21tRLkn5+2anKz83S/uZ2jS7wtluhkhwAAADBEJQDAAAASFnlVS4tX1stV2N7z2N3vFCl5RdP0cWnjUvgygAAAJBKaL0CAAAAICWVV7m0dHVln5BckvY3d2jp6kqVV7kStDIAAACkGoJyAAAAACnH7TG0fG21DD/f8z22fG213B5/IwAAAIC+CMoBAAAApJyK2oYBleS9GZJcje2qqG0YvEUBAAAgZRGUAwAAAEg5+5sDh+SRjAMAAMDQRlAOAAAAIOWMLsiN6TgAAAAMbQTlAAAAAFLOjJIijcjLCvh9iySHLVczSooGb1EAAABIWQTlAAAAAFLOuuo6HWrtCvh9Q9KyRU5ZMyyDtygAAACkLIJyAAAAACnF7TG0fG110DEj8rI032kfpBUBAAAg1RGUAwAAAEgpFbUNcjUG36TzUGuXKmobBmlFAAAASHUE5QAAAABSyv7m4CF5uOMAAAAAgnIAAAAAKWV0QW5MxwEAAAAE5QAAAABSyoySIjlsuQq0TadFksOWqxklRYO5LAAAAKQwgnIAAAAkPbfH0Kaaer24dY821dTL7TESvSQkkDXDomWLnJI0ICz3fb1skVPWjEBROgAAANBXZqIXAAAAAATzyjaX7nyxSg0tnT2POWy5WrbIqbJSRwJXhkQqK3Vo1ZLpuntttep6bexp59wAAABABAjKAQAAkLTuf6Vaj75ZO+BxV2O7lq6u1Kol0wlEh7D5TruyMjJ09VN/kyT99srP6dyTRlNJDgAAgLARlAMAACApvbJtr9+Q3MeQtHxtteY77SkTjLo9hipqG7S/uV2jC7w9tPuv3cyYRBmstZk5TnmVS8vXVsvVq5r8hy9U6W6qyQEAABABgnIAAAAkHbfH0J0vVoUc52psV0Vtg2ZNLB6EVUXHX7BrL8zR5TOO14RR+RpdkKuDLZ265+W+Y4rys3TvxaVaMG1sIpbdw9/649ECx8xxyqtcWrq6Uv071dc1tuu61ZV6hHcaAAAAIEwWwzDYCcmEpqYm2Ww2NTY2qrCwMNHLAQAASGubaup1+WObTY196Kun6eLTxsV5RdEJFOyG49pzSnT7AmfYz3N7DG2uqdemTw5IsmjWxGLNPKE4rErwQOv3zRCrFjihXqeb503W0vMm6dyfvN4nSO9vRF6W3r9zftJU4gMAAGBwRZLlUlEOAACApLO/OXAI2t/ogtw4riR6bo+h5WurowrJJenRN2t16rEjtWDawEA6UKuS8iqXbntuuw61dvWMXfn6xxqRl6X7LinVyPyckG1Ugq3fkDcsj0ULHDOv04Prd+qJjbt0sNfP48+h1i6t3PCxbpw3OeL1AAAAYGghKAcAAEDSCSf8PtjSGceVRK+itiFo9XM47nqxSheW9g2kA7UqWXyqI2CP90OtXbr+6S19HgvURiXU+g15W+A8uO4jzZk0KuK+5WZfp1Ahuc/jG2t1w9xJVJUDAADAlIxELwAAAADoze0x5PEYGjEsy9T4e16ultuTvN0Ew6mOD6W+pVMVtQ09X/talfQPmF2N7UE3QvWnrrFdS1dXqrzK1edxs+tf+frHuvyxzTprxYYBc5gRy9dJ8t4M6P1aAQAAAMFQUQ4AAICk4a86OpRk39Bz14GWmM7nC5Rj1dLFJ1AblXBb27i6N9R8+IrTTbV28YlHC51Yh+8AAABIXwTlAAAASAqvbHPp+qcrI3pusgaibo+hZyr+GdM5fYFyLFu6+PjaqPS+8TCjpEj2whzVNXWENZfZ1i4+M0qK5LDlqq6xPWbhf7L3rwcAAEDyoPUKAAAAEu6VbXt1wzORheRS8gaiFbUNYQfMwYzIy9KMkiJJ8b050Htua4ZFl884Puo5XQFau/Q+zrJFzqiPI3kr4x223J7XCgAAAAiFoBwAAAAJVV7l0vVPb1EkbcaTPRCNR9/tddV1kuJ7c2BUfo421dTrxa17tKmmXseOzIvJvIa8rV0C9ZQvK3Vo1ZLpys+2Rn2cZYucbOQJAAAA02i9AgAAgITx9dmOVLIHovEIs3/4/Ha1dXk0eniO7IW5qmuKbRifn52h/1z9N7V0uHseG54TXXDdW6Ce8m6PoYraBrV1unWKvUB/++ehiI/xH3MmBGzxAgAAAPhDUA4AAICEibbPdrIHojNKijQiL0uHWrtiNmdDS5du/sNWSdKwrNi/QbSl0zPgscO9QvNYWFddpxklRaqobdD+5nbVftaipzbvVkNLZ0zmn++0x2QeAAAADB0E5QAAAEiY9d1tRCI11APRtq6BoXYqePa9f+mFrXtjFoz7WCTZk7gVDwAAAJIXQTkAAABiwtc6Y39zu0YXeMPKYC1R3B5Dz2/dE/HxxhTmJH0gWlHbENNq8nRxuNMtdca2St13piVzKx4AAAAkL4JyAAAARMXtMbRyw8d6/J1aHWo7Ggo7bLlatsgZsDVKRW2DGloiD5G/MWt80geisd7ME4HZQ5xvAAAAQDAE5QAAAIhYeZVLtz233W/VdF1ju5aurtSqJdP9hpeRhsjZVos63YaOHZkX0fMH064DLYleQto798RRuu7cSSHfwQAAAAAEQ1AOAACAiJRXuXTd6sqA3zfkbYexfG215jvtA0LM0QW5YR3vhi9M0pxJo/THv/1Lz23Zo382tGpTTb3pVi+Dze0x9EzFP0OOsxfm6CdfPlXv1tZLssg2LEs/euWDuKxpWFZGyvY1D+S6cydp1sTiRC8DAAAAKY6gHAAAAGFzewzd9tz2kOMMSa7GdlXUNgwIM2eUFMlhy5WrMXhluW+DxpvnnyhrhkWbP6mXJD381xq1dX7UMy5Uq5fBVlHboLqmjpDjLp8xXmefeIzOPvEYSd7X9rfv1KqusV1GDNdTmJupd384T3NWvBZVy5tk4mDjTgAAAMRIRqIXAAAAgNTi9hi69U/bwtqksq6xrc/zN9XU66Vte/XVzx8vMzXgvTdobGjtlCS19dsM0tfqpbzKZXpd8WS2tcyEUX1byFgzLFq2yBnTkFySvnzGsRqWbdUXTxsX45kHn6X7Hxt3AgAAIFZSPii/++67ZbFY+vyz2+093zcMQ3fffbfGjh2rYcOG6bzzztOOHTsSuGIAAIDUVV7l0pwHXtOfKj8N63n3vPyByqtcKq9y6awVG3T5Y5t14++36sH1H8mWl6UReVl+n+ew5fbpce72GHp5m/8g3BcsL19bLbcn1jGzeb4bATv3NZsa768FzXynPeBrEqn5Tu//R57ntIcYGR2LvO1kbMNit/78bGufr+39zgsAAAAgWmnRemXKlClav359z9dW69H/I/3jH/9YP/vZz/TEE0/oxBNP1L333qv58+frH//4hwoKChKxXAAAgKTl9hjeliGNbWpo6VTR8BzZC73tLdZV12np6sqIKp0PtnQG7Gfe2NrVZ84lZx6v6cePlGPEsAF9xytqG9TQ0hnwOL1bvcwoKVJFbcOg9jAvr3Jp+drqkO1kpKMtZfy1DqmobQirYj+U3i1KZpQUyV6YY6otTLh8r+7di6foH3XNenD9zqjmK8rP0r0Xl+rCUseg/y4BAAAwtKRFUJ6ZmdmnitzHMAz9/Oc/1x133KFLL71UkvTkk09qzJgxevrpp3XttdcO9lIBAACSVrCQ116Yo+b2IxG3Awn2vP7fW/3uP/Xah/v9ttUw287kqU21Wrr6fR1qOxo2x7uHeXmVy/SNBN9PFah1iNmfM5LjWDMsunvxlKAbsfZXlJ+l/1o0RcUFuT1h9cGWTt3zct/zxd7rNZ7vtOvxjbvCCvyvP2+iivOz+9yg8a2bDTsBAAAQT2kRlO/cuVNjx45VTk6OzjzzTN1333064YQTVFtbq7q6Ol1wwQU9Y3NycnTuuedq48aNQYPyjo4OdXQcrbJpamqK688AAACQSKFC3nhUHwfj6zfev72GvzYl/vylap/pOWPBt7mp2RsJ9hChvdmfszdfq5bewXSg45SVOvTIkum67bntA4LskXlZ+tElpRqZnxOygvvCUnvASm9rhkUPXDrVdCBfnJ+t719wEpXiAAAASIiUD8rPPPNMPfXUUzrxxBO1b98+3XvvvZo9e7Z27Nihuro6SdKYMWP6PGfMmDHavXt30Hnvv/9+LV++PG7rBgAASAZuj6HNNfW67c/mQ97BYMhbDb18bbXmO+094emMkiI5bLmmWpv4m1N+5oyFlRt2mq6cLsrP0l0LTwka1of7c9618BR9c06JJJluUeKr+t5cU69NnxyQZNGsicWaeUKx6dfGmmEJWuldVurQ1XMm6Dfv7Ao518WnjSUkBwAAQMKkfFD+b//2bz2fT506VbNmzdLEiRP15JNPaubMmZIki6Xv/+E2DGPAY/3dfvvt+t73vtfzdVNTk4477rgYrhwAACCxwumnnQi9+437wlhrhkWLT3Xo0TdrI563/5zh8vVx94XRZ4wfqcdNBME+B1u69O2nt2hVhiVgWG7NsGjZIqfpauxRBTkRtSixZlg0Z/IozZk8yvRzwjXPaTcVlM+P8yajAAAAQDApH5T3l5+fr6lTp2rnzp265JJLJEl1dXVyOI7+R8j+/fsHVJn3l5OTo5ycnHguFQAAIGHC6aedaL37dZdXufSrKEJyn7qmyG4OlFe5dPea6j7PH5mX1acXeiiBquX7Kyt16OZ5k01tiBlJq5bB4quOr2tsD3i+OQJsagoAAAAMloxELyDWOjo69MEHH8jhcKikpER2u13r1q3r+X5nZ6feeOMNzZ49O4GrBAAAGDxuj6F3dh7QT//vQ/30//6htz76THevqU6JkFw6GgK7PYaWr43NuhsOm+u57vYY2lRTrxe37tFD63fqutWVA0L2g2FsVunTu1o+mBvmTpa9MHAIblHyh8y+6njp6OaiPpbuf4E2NQUAAAAGS8pXlN9yyy1atGiRjj/+eO3fv1/33nuvmpqadOWVV8piseimm27Sfffdp8mTJ2vy5Mm67777lJeXpyuuuCLRSwcAAIi78irXgA0bV74e++PkZGao44gnpnNa5N2M0hcCV9Q2xKxNTFF+dsgxg9Gapne1vD/WDIvuXuzU0u4WLL1vEvhi5VQImctKHVq1ZPqA1zPUpqYAAADAYEn5oPzTTz/V5ZdfrgMHDuiYY47RzJkztXnzZo0fP16S9IMf/EBtbW26/vrrdfDgQZ155pl69dVXVVBQkOCVAwAAxNcr21y6/mlzPa6jNffkY/SXqn0xm89fCBwqVA6H3TYs6PcHqzWNmZYp6RIy+zYPNbvZKAAAADCYLIZhpMq7bhOqqalJNptNjY2NKiwsTPRyAAAAgnpl2159++ktg9Ze5X+vPlO3/OnvMau+dvgJgTfV1OvyxzZHPXdxfrYq7pgXMKB1ewydtWJDXCvJfdXyb98613RQ3H8TUUJmAAAAwL9IstyUrygHAABAX+VVLl3/9JZBO57DlquZE4u1bJFT162OvIJ9eI5V//654zTPafcbAvs2hYw2wF586lhV1DYEDJpj2eLFn0hbplgzLJo1sTg+iwIAAACGuLTbzBMAAGAo8214OVh6b8RYVurQw1ecPmDDRjPuWHCK/r7sQt21aIpmTSz2GyD33hQyGo9v3KXLH9uss1ZsUHmVa8D3Y9nixR+7LVerlkxPmZYpAAAAwFBARTkAAEAaiWc1dG5mhtp7bdjprz3Kgmlj9c1dB/X4xl1hzT26MMdUdXVZqUOPLJmu7z37d7V2usM6Rn91je1aurpyQGhtpm+4P3nZ1gFrcthyddfCUzQyP4eWKQAAAEASIygHAABII7GohrZIfXqb+wJxsxsxXjDFHn5QHkY4Pd9pV2FuddRBue9nXL62WvOd9p6fZUZJkYrys9TQ0hXWfF+fOV6PvvmJJGnpuSforMnHaOYJ/qvjAQAAACQXgnIAAIA0Emk1dG+//OrpKi7wXwFtpke2r5d4XWN7yM1EfZtazigpMr2+itoG1TWZuyFgJvB2Nbarorah52ezZlh0+nEj9NqHn5lek6SekFySVr3xiV7YundAxT0AAACA5ESPcgAAgDRy2nEjZImygLm4IEezJhbr4tPGBewXHkzvXuLBnhnpppZmq+Zv+MJE3XXRFFNj11XX9XxeXuUKOyT3x9faxV8fdAAAAADJhaAcAAAgDbg9hh5av1Ofu3edjFBl3CHEon1LWalDq5ZMl90WuMI90k0tzVbNz5l0jOyF5sa+uHWv3B4jppuh9m7t4vZE+UsBAAAAEFe0XgEAAEhBbo/R0y+89rMW/frtWh3uOBKTuWPRvkXyhuW9+5qPys+RLNKBwx1RbWppprWLo1c7l/wcq1o6gvczr2/pVEVtgyTFdDNUQwNbuwAAAABIPgTlAAAAKaa8yqXla6tjGuhKkfULD8WaYYl5QOxr7bJ0deWAjUd9fO1cyqtcIUNyn1hU0idibgAAAADRo/UKAABACimvcmnp6sqoQ/L+ddyR9gtPlGCtXa4+q0RlpY6w26jsOtAas2r6/uI1LwAAAIDYICgHAABIEb7gN5pu1xZJ155TMiBgjrRfeCKVlTr09q1z9fAVp/d5/MtnHCtJqqhtCOuGwoPrP1J9c7uK8rNjtkaL+raBAQAAAJCcaL0CAAAwCHr3FI+0P3e4wW9/xfnZ+tEXS1VW6tAPyk6Jej3JYF11nf7rpQ/6PHbV4+/p7sVOdRzxhD3fd/+wVYH23QzU5iWQVKvSBwAAAIYygnIAAIA489dTvCg/S/deXKoF08aanieaPtf5OVZtuv18ZWd631AYj97hg83XhqZ/eL2vqV1LV1fqpnmTw54zUEgueavu71p4in74QpUOtXaFnMtuy9WyRc6UqtIHAAAAhiqCcgAAgDgKFOY2tHTp+qe36NpPD+n2BU6/z3V7DG2uqdemTw5Issg2LCvidfzn2RN7QvJ0EKwNjSFvNfczFf+UvTBX+5rao2pXo+757vi3U7Rg2lhlZFj8/k59rp4zQfOc9pSt0gcAAACGIoJyAACAOHF7DN323PagIe2jb9bq1GNHasG0vlXH5VUu3fbc9gGVyxaLZISZ+o7Iy9INcyeF96QkF6oNjSGprqlDN887UT9f/1HUxzMk3fD7LcrMtPRsJNr/XQIOKsgBAACAlEVQDgAAECcrN+w01aLjpj9sUdWeRs2ZPEqfn1CkVX+t0YMBwt1wQ3JJeuDSqWlX2Wy2Dc2EUXlatWS67l6zQ3VNHVEfd/naas132lVW6tB8pz0t+rwDAAAAICgHAACIOV/LlEff+MTU+E63oYffqNHDb9TEdB3pXOE8uiDX9LhZE4s132nXyg0fB7wBYZarsV0VtQ2aNbE4Lfq8AwAAAPAiKAcAAIghfxt3JsLN8ybrhrmT07bCeUZJkRy2XNU1+u8/bpF3M80ZJUWSvJuX3jhvsk6yD/fb0iYc0WyqCgAAACA5pc+OTgAAAAnm27gzkSG5w5arR5ZM143zTkzbkFzyBt/LFnk3Qe3/U/q+XrbIOeA1KCt16P0756tsij3iY5utZgcAAACQOgjKAQAAYsDtMbR8bXXQjTsHw0+/fGpatlrxx7eppt3WN7i223K1asn0oK9Dxa76sI9nkfdGhK9KHQAAAED6oPUKAABADFTUNiS83YokHWiJfsPKVBLJppoVtQ1qaIms9Yq/KnUAAAAAqY+gHAAAIAbWV9clegmShmZbkHA31Yykx3g6b4wKAAAAgKAcAAAgLG6PoYraBtU1tqmhpVNFw3M0eniOntuyJ9FLoy2ISeHeTEj3jVEBAAAAEJQDAACYVl7l0vK11UnRYsUf2oKYM6OkSA5bruoa24P2lB+Zl6X7L51KFTkAAAAwBLCZJwAAgAnlVS4tXV2ZlCH5yLwsPRJi80ocZc2waNkipyTvBp3+3HT+JP3tzvm8pgAAAMAQQUU5AABACG6PoeVrq4NWH4frxvMn6XPji7TpkwP6266D+vunh9RxJPQR5p8yWnnZVlksFo0bOUyzJ47SzBOKqSQPU1mpQ6uWTB/wDgF6kQMAAABDE0E5AABACBW1DTGrJO8fxJ594jGSpHc+PqCv/frdkM//j7NOCGvjSgRWVurQfKddFbUN2t/crtEF3h7v3HQAAAAAhh6CcgAAUoRvE0kCvcFX1xSbkPyGL0zUzfNP8vt7m3lCcdC+2RZJdjbrjDlrhoUbDwAAAAAIygEASFa9g/FdB1r1TMU/+wS2RflZuvfiUi2YNrZn/Oaaem365IAki84sKVJGhkUHDneYCtYJ4v0rr3Lpv9buiMlccyYdE/A19fXNXrq6UhapT1juewabdQIAAABAfFgMw4hlu8201dTUJJvNpsbGRhUWFiZ6OQCABBmsMPmVbS7d+WKVGlo6Q4699pwSnXrsSP3gz9t0uONIwHHBei+XV7no1dyP22No5YaP9eD6j2Iyn8OWq7dvnRvyfOF3AQAAAADRiSTLJSg3iaAc8WI2dKPSE0g8fwFm/6ruWLj/lWo9+mZtzObr75El03sC12BhsO8Ks6rX+KHilW0u3fHCdh1s7YrZnI+E8TpyzQcAAACAyBGUxxFBOeLBX+hmL8zR5TOO14RR+T3hyLrqOqoLgQQrr3Jp6epKv72jJW9V9+0LnFEf55Vte3X901uinieYEXlZev/O+VpXXae71+xQXVNHwLG+vthmKqHTRaxvVOTnWPXfXzmV6zUAAAAADBKC8jgiKEeshQrdfEbkZelQkIrGcCoUAUTG7TF01ooNfW5W+fPwFdO1YFrkf49uj6HP/2idGlpiV8UcyEXTHHp5myvkNcjnmWtmDokND2N5oyI/x6przirRd84/ccjcZAAAAACAZBBJlstmnkACuD2Glq+tNhVQBQvJJen7f/y75p48RtmZGbFZHIABNn9SHzIkl6Qf/HmbLiy1RxyKVtQ2DEpILkkvbzcfkkvSOx9/lrZtQHxtTuoa2/T/rYl8084bz5+kGSXFpjdPBQAAAAAkD4JyIAEqahtMhW5mtHS4Nf2edfrpV6ZRWQ7EQXmVS7f9ebupsYc7jmjlho9147zJIcf660G9vzk21wUzwn0/2crXa3o+L8rP0hdPG6d5TnufMNjfzyRJm2vqtemTA5IsmjWxWDNPKE6aADmcTVMDoRUWAAAAAKQ+Wq+YROsVxNKLW/foxt9vjemcFg3NDfeAeDLbIqk327BMPfy1M/xWFfuC5HXVdXph694+4eyIYVk62VGgzZ80xPiniC/fZqaSZUDgPGJYptq7PGo/4unznBF5WXrg0qkJu175fg+PvVWjDR9+FtVcX55+rFZ8eVrSBP8AAAAAAHqUxxVBOWJpU029Ln9sc8znzc+x6pGvnaHZk0YR2gBRMtuXPBRftbGkAZvyDnUPX3G6Fkwb2/N15xGPfrdpl3Y3tGp8UZ6+PmtCTNtKuT2GVm74WI+/U6tDbbFpcfPQV0/TxaeNi8lcAAAAAIDYICiPI4JyxFKsArhAEl2tCaSDWN3QskhhVaQPJRZJN54/Wd85f7Ie+MsH+vXbtQNawiycOkYP/vt0vb/7YFQ90surXLrtue0h930I11DZ5BQAAAAAUglBeRwRlCPWXtq6Vzf8fktcj/EIrViAiMWjRRL8C/dmQrg9wcurXLpudWVEawu1jrdvncs7eAAAAAAgyUSS5cbu/cwATCuvcunW57fF/TjL11bL7eFeGAaH22NoU029Xty6R5tq6lP+3Nt1oCXRSxgywj1TXI3tum51pcqrXCHHuj2Glq+tjmxhISxb5CQkBwAAAIA0kZnoBQBDgW/juP3N7dp1oFUPrv9oUI7ramxXRW0DbQEQd+VVrgH9t8Ot+k0m5VUuPbh+Z6KX4Vdud8/u/htk9jamIFvtRzxqbDsyWMtKiJv/sFXznfagYXVFbUPM21yNzMvS/bS3AgAAAIC0QlAOxJm/AHEw7W9m40DEV3mVS0tXVw6oCvZV/fbfsDGY3jeVeveiDvR4rPSef1R+jpa9uCNmc8dSUX6WNt8+T9YMizbX1GvTJwckWXRmSZEyMiw6cLij5/VZuWFn0ob9sdLW5dGcB17TpdOP1ci8bI0qyNHo4TmSRdrf1K6Glk799aPPYnKssiljNGl0gWZNLNbME4qpJAcAAACANENQDsRRoABxMI0uyPX7eLyDRwwNbo+h257bHvQcv+GZLVopixZMC159G6gqffGpDq35u6vP40X5Wbr34lLTAXy4x01W931xqrK7K8rnTB6lOZNHBRw7YVT+YC0roeqaOvTwX2vieoxrzi7RHQudcT0GAAAAACCxCMqBOPH1xU1kSG4blqkZJUUDHk+3NhlInJUbdupQa1fQMR5Duv7pSl29e4LmOe06Y/xIvb/7YJ+bNOuq6wJWpT/6Zu2AORtaunT901t07aeHdPuCyAPMZLiZZUYkrT4C3SRDeK45ewIhOQAAAAAMAQTlQJxE2xfX6SjQnkPtamzzH0IOy8pQW1fgHsWS1NR2RP9XVdenkjdQMFjX2K6lqyu1asl0wnJICv2uA7fH0KNvfmJ6vt+8s0u/eWeXLOq7eePIvEy1dnoiCqsffbNWpx47MmC1erCfIRluZplx0/mT9J3zTwz7HR8zSoo0YliWDgW4hiC4WL5rAQAAAACQ/AjKgTiJpje4vTBHa79ztiT16Zssi3p6EHsMQ1/79btB5zHkreS9ef9kTRiVr1H5Obp7jf9g0PfY8rXVITfHQ/oz866DlRt2qrXTHfbc/c+/g63RbTj5gz//Xba8rAF9o1/Z5tKdL1apoaWz57GReZmadUKxTjimQLZhWVHdzBqRl6Uca4b2NXdEtf5Aon2XhzXDoqvmTEiaPuX9b5AkmxHDMnXVnBJNGJVPOyoAAAAAGIIshmEk83+3Jo2mpibZbDY1NjaqsLAw0ctBCthUU6/LH9sc0XMfMVHV/eLWPbrx91sjmj+UZ66ZqVkTi+MyN5JfqHYkN8+brKXnTdKMH61Pqmrl/Gyrzp48SmeML9I7Hx+I2SaOgTyyZLrmO+36xWs79dBrkYfRM08o0uUzjh9wMywWQa3bY+iMe9eFbI/jT/9g2yJp4dQxmjBquCSLMjMs+tVbnwS9WXLzvMk9wfPBlk59++nka3NjkXTj+ZP1nfMnE4wDAAAAQJqIJMulohyIkxklRSrKz1JDS3gB1c3zTjRVQRrP/sPrqusIyocot8cI+K4DnwfX79QTG3clVUguSS2dbpXv2KfyHfvifqx/K7X3/J3ePP9EtXcd8dtLPRTbsEz977dmxi2gtWZY9MClUyPqw/7TL0/TobYu7W5o1fiiPH191oSejUR9vnP+ZP3ytZ369duf6HDH0cA8UDX8qozpQTdOHZ5j1VfOOFZjRwzTobYuffJZizZ8sE8d7vjF6/9zxem0VwEAAAAAEJQD8WLNsOiLp43Tb97ZFdbzJozKMzUunv2Hn/3bp7pjoZPqyiFo5YadqmsK3Y7kYAQVyulkyczxfb6+fYFTpx47ckCrl1BWfGla3P/OykodWrVkum778/awrhdjR+bpS58LfsPMmmHRTfNP1HfOnxy0n33vtcx32lVR26C6xjYdONyhQ21dssiiWROLB7TPkbw3by57dKPe333I9NrNyLBIKy+fHrC/PQAAAABgaCEoB2LE36aB85z2sINys5Xi8ew/fLjjiDbX1GvO5FExnxvJq7zKlTT9rJPZyO5+6P0tmObQhaVHQ+CGlk59eqhNf/zbpzrccWTAHPdfOnXQNs4tK3WoIDcr5L4GPg6b9xpmljXDYvpdKOGM9Y3/89I5Wvv3vfrh89vV3B5dT3ufX/z76YTkAAAAAIAeBOVADPjb+HDEsCzNmVQc1gZ24YZTN8ydrN++U6vGttgER71t+uQAQfkQ4vY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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('AMD Stock Price',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/AMD stock prediction/AMD.csv b/NYSE/AMD stock prediction/AMD.csv
new file mode 100644
index 00000000..e4c5310f
--- /dev/null
+++ b/NYSE/AMD stock prediction/AMD.csv	
@@ -0,0 +1,10638 @@
+Date,Open,High,Low,Close,Adj Close,Volume
+1980-03-17,0.000000,3.302083,3.125000,3.145833,3.145833,219600
+1980-03-18,0.000000,3.125000,2.937500,3.031250,3.031250,727200
+1980-03-19,0.000000,3.083333,3.020833,3.041667,3.041667,295200
+1980-03-20,0.000000,3.062500,3.010417,3.010417,3.010417,159600
+1980-03-21,0.000000,3.020833,2.906250,2.916667,2.916667,130800
+1980-03-24,0.000000,2.916667,2.635417,2.666667,2.666667,436800
+1980-03-25,0.000000,2.750000,2.552083,2.604167,2.604167,645600
+1980-03-26,0.000000,2.604167,2.427083,2.447917,2.447917,466800
+1980-03-27,0.000000,2.375000,2.239583,2.375000,2.375000,1129200
+1980-03-28,0.000000,2.583333,2.458333,2.541667,2.541667,666000
+1980-03-31,0.000000,2.583333,2.520833,2.541667,2.541667,430800
+1980-04-01,0.000000,2.770833,2.583333,2.739583,2.739583,492000
+1980-04-02,0.000000,2.739583,2.614583,2.625000,2.625000,283200
+1980-04-03,0.000000,2.635417,2.604167,2.635417,2.635417,226800
+1980-04-07,0.000000,2.614583,2.531250,2.541667,2.541667,355200
+1980-04-08,0.000000,2.625000,2.500000,2.593750,2.593750,765600
+1980-04-09,0.000000,2.666667,2.583333,2.666667,2.666667,134400
+1980-04-10,0.000000,2.739583,2.656250,2.739583,2.739583,174000
+1980-04-11,0.000000,2.770833,2.635417,2.635417,2.635417,109200
+1980-04-14,0.000000,2.645833,2.500000,2.562500,2.562500,267600
+1980-04-15,0.000000,2.562500,2.520833,2.562500,2.562500,505200
+1980-04-16,0.000000,2.572917,2.343750,2.354167,2.354167,358800
+1980-04-17,0.000000,2.427083,2.270833,2.427083,2.427083,696000
+1980-04-18,0.000000,2.468750,2.312500,2.322917,2.322917,342000
+1980-04-21,0.000000,2.364583,2.281250,2.291667,2.291667,498000
+1980-04-22,0.000000,2.604167,2.375000,2.562500,2.562500,880800
+1980-04-23,0.000000,2.729167,2.593750,2.614583,2.614583,588000
+1980-04-24,0.000000,2.812500,2.614583,2.781250,2.781250,733200
+1980-04-25,0.000000,2.760417,2.687500,2.708333,2.708333,343200
+1980-04-28,0.000000,2.791667,2.718750,2.729167,2.729167,372000
+1980-04-29,0.000000,2.729167,2.635417,2.666667,2.666667,270000
+1980-04-30,0.000000,2.625000,2.541667,2.552083,2.552083,273600
+1980-05-01,0.000000,2.541667,2.458333,2.458333,2.458333,505200
+1980-05-02,0.000000,2.468750,2.416667,2.468750,2.468750,282000
+1980-05-05,0.000000,2.604167,2.500000,2.604167,2.604167,302400
+1980-05-06,0.000000,2.729167,2.583333,2.687500,2.687500,386400
+1980-05-07,0.000000,2.708333,2.656250,2.687500,2.687500,138000
+1980-05-08,0.000000,2.687500,2.604167,2.604167,2.604167,225600
+1980-05-09,0.000000,2.583333,2.531250,2.541667,2.541667,128400
+1980-05-12,0.000000,2.614583,2.520833,2.614583,2.614583,180000
+1980-05-13,0.000000,2.708333,2.656250,2.687500,2.687500,213600
+1980-05-14,0.000000,2.781250,2.697917,2.770833,2.770833,345600
+1980-05-15,0.000000,2.770833,2.729167,2.729167,2.729167,292800
+1980-05-16,0.000000,2.739583,2.645833,2.666667,2.666667,135600
+1980-05-19,0.000000,2.708333,2.666667,2.687500,2.687500,66000
+1980-05-20,0.000000,2.750000,2.677083,2.750000,2.750000,453600
+1980-05-21,0.000000,2.802083,2.739583,2.781250,2.781250,512400
+1980-05-22,0.000000,2.916667,2.812500,2.916667,2.916667,499200
+1980-05-23,0.000000,2.989583,2.895833,2.989583,2.989583,411600
+1980-05-27,0.000000,3.062500,3.000000,3.010417,3.010417,247200
+1980-05-28,0.000000,3.145833,3.000000,3.145833,3.145833,458400
+1980-05-29,0.000000,3.208333,3.072917,3.083333,3.083333,618000
+1980-05-30,0.000000,3.166667,3.031250,3.166667,3.166667,298800
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