diff --git a/combined_app.py b/combined_app.py
index 22c1d5e2..fbca9171 100644
--- a/combined_app.py
+++ b/combined_app.py
@@ -27,7 +27,7 @@
 st.pyplot(fig)
 
 
-st.subheader('Closing Price vs Time Chart (MOVING AVERAGE 100 (R) AND 200 (G))')
+st.subheader('MOVING AVERAGE 100 (R) AND 200 (G)')
 
 ma100 = df.Close.rolling(100).mean()
 ma200 = df.Close.rolling(200).mean()
@@ -42,97 +42,44 @@
 testing = pd.DataFrame(df['Close'][int(len(df) * 0.7):int(len(df))])
 
 scaler = MinMaxScaler(feature_range=(0,1))
-
 training_array = scaler.fit_transform(training)
 
-# Splitting training data into x_train and y_train
-
-# x_train = []
-# y_train = []
+model = load_model('/Users/aaditroychowdhury/Documents/CS 222/Main branch/main-project-shmoney/keras_model.h5')
 
-# for i in range(100, training_array.shape[0]):
-    
-#     x_train.append(training_array[i - 100 : i])
-#     y_train.append(training_array[i, 0])
-    
-# x_train, y_train = np.array(x_train), np.array(y_train)
 
-filename = 'random_forest_model.joblib'
-model = joblib.load(filename)
+past_100_days = training.tail(100)
+final_df = pd.concat([past_100_days, testing], ignore_index=True)
+input_data = scaler.fit_transform(final_df)
 
-# Ensure 'Tomorrow' column is created by shifting 'Close' by -1
-df["Tomorrow"] = df["Close"].shift(-1)
 
-# Check if 'Tomorrow' column was successfully created
-if "Tomorrow" in df.columns:
-    # Drop rows where 'Tomorrow' has NaN values (last row will have NaN after shift)
-    df = df.dropna(subset=["Tomorrow"])
+x_test = []
+y_test = []
 
-    # Now safely compare 'Tomorrow' with 'Close'
-    df["Target"] = (df["Tomorrow"] > df["Close"]).astype(int)
-else:
-    st.error("The 'Tomorrow' column was not created.")
+for i in range(100, input_data.shape[0]):
     
-
-
-if "Target" in df.columns:
-    predictions = model.predict(df)
-
-    st.subheader("Predictions vs Original")
-    fig2 = plt.figure(figsize=(12, 6))
-    plt.plot(df["Target"], 'b', label='Original Price')
-    plt.plot(predictions, 'r', label='Predicted Price')
-    plt.legend()
-    st.pyplot(fig2)
-else:
-    st.error("The 'Target' column was not created. Unable to make predictions.")
+    x_test.append(input_data[i - 100 : i])
+    y_test.append(input_data[i, 0])
+   
     
-    
-if "Target" in df.columns:
-    # Exclude 'Tomorrow' and 'Target' columns for prediction
-    features = df.drop(["Tomorrow", "Target"], axis=1)
-    predictions = model.predict(features)
-    # ... rest of the plotting code ...
-    st.subheader("Predictions vs Original")
-    fig2 = plt.figure(figsize= (12, 6))
-    plt.plot(df["Target"], 'b', label='Original Price')
-    plt.plot(predictions, 'r', label='Predicted Price')
-
-    st.pyplot(fig2)
-    
-st.write("DataFrame columns:", df.columns)
-
-# past_100_days = training.tail(100)
-# final_df = pd.concat([past_100_days, testing], ignore_index=True)
-# input_data = scaler.fit_transform(final_df)
+x_test, y_test = np.array(x_test), np.array(y_test)
 
 
-# x_test = []
-# y_test = []
-
-# for i in range(100, input_data.shape[0]):
-    
-#     x_test.append(input_data[i - 100 : i])
-#     y_test.append(input_data[i, 0])
-    
-# x_test, y_test = np.array(y_test), np.array(y_test)
-# x_test = x_test.reshape(1, -1)
-# y_test = y_test.reshape(1, -1)
-# y_predicted = model.predict(x_test)
-# scaler = scaler.scale_
+# Now you can make predictions
+y_predicted = model.predict(x_test)
+scaler = scaler.scale_
 
-# scale_factor = 1 / scaler[0]
-# y_predicted = y_predicted * scale_factor
-# y_test = y_test * scale_factor
+scale_factor = 1 / scaler[0]
+y_predicted = y_predicted * scale_factor
+y_test = y_test * scale_factor
 
 # final figure
 
-# st.subheader("Predictions vs Original")
-# fig2 = plt.figure(figsize= (12, 6))
-# plt.plot(y_test, 'b', label='Original Price')
-# plt.plot(y_predicted, 'r', label='Predicted Price')
+st.subheader("Predictions vs Original")
+fig2 = plt.figure(figsize= (12, 6))
+plt.plot(y_test, 'b', label='Original Price')
+plt.plot(y_predicted, 'r', label='Predicted Price')
 
-# plt.xlabel('Time')
-# plt.ylabel('Price')
-# plt.legend()
-# st.pyplot(fig2)
\ No newline at end of file
+plt.xlabel('Time')
+plt.ylabel('Price')
+plt.legend()
+st.pyplot(fig2)
diff --git a/new_model.ipynb b/new_model.ipynb
new file mode 100644
index 00000000..04b64c46
--- /dev/null
+++ b/new_model.ipynb
@@ -0,0 +1,1610 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import yfinance as yf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed\n"
+     ]
+    },
+    {
+     "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Price</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticker</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2010-01-04 00:00:00+00:00</th>\n",
+       "      <td>6.447412</td>\n",
+       "      <td>7.643214</td>\n",
+       "      <td>7.660714</td>\n",
+       "      <td>7.585000</td>\n",
+       "      <td>7.622500</td>\n",
+       "      <td>493729600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010-01-05 00:00:00+00:00</th>\n",
+       "      <td>6.458558</td>\n",
+       "      <td>7.656429</td>\n",
+       "      <td>7.699643</td>\n",
+       "      <td>7.616071</td>\n",
+       "      <td>7.664286</td>\n",
+       "      <td>601904800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010-01-06 00:00:00+00:00</th>\n",
+       "      <td>6.355828</td>\n",
+       "      <td>7.534643</td>\n",
+       "      <td>7.686786</td>\n",
+       "      <td>7.526786</td>\n",
+       "      <td>7.656429</td>\n",
+       "      <td>552160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010-01-07 00:00:00+00:00</th>\n",
+       "      <td>6.344079</td>\n",
+       "      <td>7.520714</td>\n",
+       "      <td>7.571429</td>\n",
+       "      <td>7.466071</td>\n",
+       "      <td>7.562500</td>\n",
+       "      <td>477131200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010-01-08 00:00:00+00:00</th>\n",
+       "      <td>6.386254</td>\n",
+       "      <td>7.570714</td>\n",
+       "      <td>7.571429</td>\n",
+       "      <td>7.466429</td>\n",
+       "      <td>7.510714</td>\n",
+       "      <td>447610800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Price                     Adj Close     Close      High       Low      Open  \\\n",
+       "Ticker                         AAPL      AAPL      AAPL      AAPL      AAPL   \n",
+       "Date                                                                          \n",
+       "2010-01-04 00:00:00+00:00  6.447412  7.643214  7.660714  7.585000  7.622500   \n",
+       "2010-01-05 00:00:00+00:00  6.458558  7.656429  7.699643  7.616071  7.664286   \n",
+       "2010-01-06 00:00:00+00:00  6.355828  7.534643  7.686786  7.526786  7.656429   \n",
+       "2010-01-07 00:00:00+00:00  6.344079  7.520714  7.571429  7.466071  7.562500   \n",
+       "2010-01-08 00:00:00+00:00  6.386254  7.570714  7.571429  7.466429  7.510714   \n",
+       "\n",
+       "Price                         Volume  \n",
+       "Ticker                          AAPL  \n",
+       "Date                                  \n",
+       "2010-01-04 00:00:00+00:00  493729600  \n",
+       "2010-01-05 00:00:00+00:00  601904800  \n",
+       "2010-01-06 00:00:00+00:00  552160000  \n",
+       "2010-01-07 00:00:00+00:00  477131200  \n",
+       "2010-01-08 00:00:00+00:00  447610800  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start = '2010-01-01'\n",
+    "end = '2024-10-31'\n",
+    "\n",
+    "df = yf.download('AAPL', start, end)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Price</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticker</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-10-24 00:00:00+00:00</th>\n",
+       "      <td>230.316620</td>\n",
+       "      <td>230.570007</td>\n",
+       "      <td>230.820007</td>\n",
+       "      <td>228.410004</td>\n",
+       "      <td>229.979996</td>\n",
+       "      <td>31109500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-10-25 00:00:00+00:00</th>\n",
+       "      <td>231.155685</td>\n",
+       "      <td>231.410004</td>\n",
+       "      <td>233.220001</td>\n",
+       "      <td>229.570007</td>\n",
+       "      <td>229.740005</td>\n",
+       "      <td>38802300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-10-28 00:00:00+00:00</th>\n",
+       "      <td>233.143494</td>\n",
+       "      <td>233.399994</td>\n",
+       "      <td>234.729996</td>\n",
+       "      <td>232.550003</td>\n",
+       "      <td>233.320007</td>\n",
+       "      <td>36087100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-10-29 00:00:00+00:00</th>\n",
+       "      <td>233.413193</td>\n",
+       "      <td>233.669998</td>\n",
+       "      <td>234.330002</td>\n",
+       "      <td>232.320007</td>\n",
+       "      <td>233.100006</td>\n",
+       "      <td>35417200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-10-30 00:00:00+00:00</th>\n",
+       "      <td>229.847122</td>\n",
+       "      <td>230.100006</td>\n",
+       "      <td>233.470001</td>\n",
+       "      <td>229.550003</td>\n",
+       "      <td>232.610001</td>\n",
+       "      <td>47070900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Price                       Adj Close       Close        High         Low  \\\n",
+       "Ticker                           AAPL        AAPL        AAPL        AAPL   \n",
+       "Date                                                                        \n",
+       "2024-10-24 00:00:00+00:00  230.316620  230.570007  230.820007  228.410004   \n",
+       "2024-10-25 00:00:00+00:00  231.155685  231.410004  233.220001  229.570007   \n",
+       "2024-10-28 00:00:00+00:00  233.143494  233.399994  234.729996  232.550003   \n",
+       "2024-10-29 00:00:00+00:00  233.413193  233.669998  234.330002  232.320007   \n",
+       "2024-10-30 00:00:00+00:00  229.847122  230.100006  233.470001  229.550003   \n",
+       "\n",
+       "Price                            Open    Volume  \n",
+       "Ticker                           AAPL      AAPL  \n",
+       "Date                                             \n",
+       "2024-10-24 00:00:00+00:00  229.979996  31109500  \n",
+       "2024-10-25 00:00:00+00:00  229.740005  38802300  \n",
+       "2024-10-28 00:00:00+00:00  233.320007  36087100  \n",
+       "2024-10-29 00:00:00+00:00  233.100006  35417200  \n",
+       "2024-10-30 00:00:00+00:00  232.610001  47070900  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Price</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticker</th>\n",
+       "      <th></th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-01-04 00:00:00+00:00</td>\n",
+       "      <td>6.447412</td>\n",
+       "      <td>7.643214</td>\n",
+       "      <td>7.660714</td>\n",
+       "      <td>7.585000</td>\n",
+       "      <td>7.622500</td>\n",
+       "      <td>493729600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-01-05 00:00:00+00:00</td>\n",
+       "      <td>6.458558</td>\n",
+       "      <td>7.656429</td>\n",
+       "      <td>7.699643</td>\n",
+       "      <td>7.616071</td>\n",
+       "      <td>7.664286</td>\n",
+       "      <td>601904800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-01-06 00:00:00+00:00</td>\n",
+       "      <td>6.355828</td>\n",
+       "      <td>7.534643</td>\n",
+       "      <td>7.686786</td>\n",
+       "      <td>7.526786</td>\n",
+       "      <td>7.656429</td>\n",
+       "      <td>552160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-01-07 00:00:00+00:00</td>\n",
+       "      <td>6.344079</td>\n",
+       "      <td>7.520714</td>\n",
+       "      <td>7.571429</td>\n",
+       "      <td>7.466071</td>\n",
+       "      <td>7.562500</td>\n",
+       "      <td>477131200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-01-08 00:00:00+00:00</td>\n",
+       "      <td>6.386254</td>\n",
+       "      <td>7.570714</td>\n",
+       "      <td>7.571429</td>\n",
+       "      <td>7.466429</td>\n",
+       "      <td>7.510714</td>\n",
+       "      <td>447610800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Price                       Date Adj Close     Close      High       Low  \\\n",
+       "Ticker                                AAPL      AAPL      AAPL      AAPL   \n",
+       "0      2010-01-04 00:00:00+00:00  6.447412  7.643214  7.660714  7.585000   \n",
+       "1      2010-01-05 00:00:00+00:00  6.458558  7.656429  7.699643  7.616071   \n",
+       "2      2010-01-06 00:00:00+00:00  6.355828  7.534643  7.686786  7.526786   \n",
+       "3      2010-01-07 00:00:00+00:00  6.344079  7.520714  7.571429  7.466071   \n",
+       "4      2010-01-08 00:00:00+00:00  6.386254  7.570714  7.571429  7.466429   \n",
+       "\n",
+       "Price       Open     Volume  \n",
+       "Ticker      AAPL       AAPL  \n",
+       "0       7.622500  493729600  \n",
+       "1       7.664286  601904800  \n",
+       "2       7.656429  552160000  \n",
+       "3       7.562500  477131200  \n",
+       "4       7.510714  447610800  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.reset_index()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/8r/p04lrk5s6q1849wrcqsvc_w40000gn/T/ipykernel_13119/4261181942.py:1: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
+      "  df = df.drop(['Date', 'Adj Close'], axis=1)\n"
+     ]
+    },
+    {
+     "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Price</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticker</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.643214</td>\n",
+       "      <td>7.660714</td>\n",
+       "      <td>7.585000</td>\n",
+       "      <td>7.622500</td>\n",
+       "      <td>493729600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.656429</td>\n",
+       "      <td>7.699643</td>\n",
+       "      <td>7.616071</td>\n",
+       "      <td>7.664286</td>\n",
+       "      <td>601904800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.534643</td>\n",
+       "      <td>7.686786</td>\n",
+       "      <td>7.526786</td>\n",
+       "      <td>7.656429</td>\n",
+       "      <td>552160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.520714</td>\n",
+       "      <td>7.571429</td>\n",
+       "      <td>7.466071</td>\n",
+       "      <td>7.562500</td>\n",
+       "      <td>477131200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.570714</td>\n",
+       "      <td>7.571429</td>\n",
+       "      <td>7.466429</td>\n",
+       "      <td>7.510714</td>\n",
+       "      <td>447610800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Price      Close      High       Low      Open     Volume\n",
+       "Ticker      AAPL      AAPL      AAPL      AAPL       AAPL\n",
+       "0       7.643214  7.660714  7.585000  7.622500  493729600\n",
+       "1       7.656429  7.699643  7.616071  7.664286  601904800\n",
+       "2       7.534643  7.686786  7.526786  7.656429  552160000\n",
+       "3       7.520714  7.571429  7.466071  7.562500  477131200\n",
+       "4       7.570714  7.571429  7.466429  7.510714  447610800"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop(['Date', 'Adj Close'], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot.line(y=\"Close\", use_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ma100 = df.Close.rolling(100).mean()\n",
+    "ma200 = df.Close.rolling(200).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x11e4c56c0>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(df.Close)\n",
+    "plt.plot(ma100, 'r')\n",
+    "plt.plot(ma200, 'g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3732, 5)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1120, 1)\n",
+      "(2612, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "training = pd.DataFrame(df['Close'][0:int(len(df) * 0.7)])\n",
+    "testing = pd.DataFrame(df['Close'][int(len(df) * 0.7):int(len(df))])\n",
+    "\n",
+    "print(testing.shape)\n",
+    "print(training.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Ticker</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.643214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.656429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.534643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.520714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.570714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Ticker      AAPL\n",
+       "0       7.643214\n",
+       "1       7.656429\n",
+       "2       7.534643\n",
+       "3       7.520714\n",
+       "4       7.570714"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.01046536],\n",
+       "       [0.01064169],\n",
+       "       [0.0090166 ],\n",
+       "       ...,\n",
+       "       [0.93498218],\n",
+       "       [0.95916785],\n",
+       "       [0.95309649]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "\n",
+    "training_array = scaler.fit_transform(training)\n",
+    "training_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2612, 1)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train = []\n",
+    "y_train = []\n",
+    "\n",
+    "training_array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(100, training_array.shape[0]):\n",
+    "    \n",
+    "    x_train.append(training_array[i - 100 : i])\n",
+    "    y_train.append(training_array[i, 0])\n",
+    "    \n",
+    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/aaditroychowdhury/Documents/CS 222/Main branch/main-project-shmoney/env/bin/python\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "print(sys.executable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: keras\n",
+      "Version: 3.6.0\n",
+      "Summary: Multi-backend Keras.\n",
+      "Home-page: https://github.com/keras-team/keras\n",
+      "Author: Keras team\n",
+      "Author-email: keras-users@googlegroups.com\n",
+      "License: Apache License 2.0\n",
+      "Location: /Users/aaditroychowdhury/Documents/CS 222/Main branch/main-project-shmoney/env/lib/python3.10/site-packages\n",
+      "Requires: absl-py, h5py, ml-dtypes, namex, numpy, optree, packaging, rich\n",
+      "Required-by: tensorflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip show keras\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: keras in ./env/lib/python3.10/site-packages (3.6.0)\n",
+      "Requirement already satisfied: absl-py in ./env/lib/python3.10/site-packages (from keras) (2.1.0)\n",
+      "Requirement already satisfied: numpy in ./env/lib/python3.10/site-packages (from keras) (2.0.2)\n",
+      "Requirement already satisfied: rich in ./env/lib/python3.10/site-packages (from keras) (13.9.4)\n",
+      "Requirement already satisfied: namex in ./env/lib/python3.10/site-packages (from keras) (0.0.8)\n",
+      "Requirement already satisfied: h5py in ./env/lib/python3.10/site-packages (from keras) (3.12.1)\n",
+      "Requirement already satisfied: optree in ./env/lib/python3.10/site-packages (from keras) (0.13.0)\n",
+      "Requirement already satisfied: ml-dtypes in ./env/lib/python3.10/site-packages (from keras) (0.4.1)\n",
+      "Requirement already satisfied: packaging in ./env/lib/python3.10/site-packages (from keras) (24.2)\n",
+      "Requirement already satisfied: typing-extensions>=4.5.0 in ./env/lib/python3.10/site-packages (from optree->keras) (4.12.2)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in ./env/lib/python3.10/site-packages (from rich->keras) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./env/lib/python3.10/site-packages (from rich->keras) (2.18.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in ./env/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install keras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: tensorflow in ./env/lib/python3.10/site-packages (2.18.0)\n",
+      "Requirement already satisfied: absl-py>=1.0.0 in ./env/lib/python3.10/site-packages (from tensorflow) (2.1.0)\n",
+      "Requirement already satisfied: astunparse>=1.6.0 in ./env/lib/python3.10/site-packages (from tensorflow) (1.6.3)\n",
+      "Requirement already satisfied: flatbuffers>=24.3.25 in ./env/lib/python3.10/site-packages (from tensorflow) (24.3.25)\n",
+      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in ./env/lib/python3.10/site-packages (from tensorflow) (0.6.0)\n",
+      "Requirement already satisfied: google-pasta>=0.1.1 in ./env/lib/python3.10/site-packages (from tensorflow) (0.2.0)\n",
+      "Requirement already satisfied: libclang>=13.0.0 in ./env/lib/python3.10/site-packages (from tensorflow) (18.1.1)\n",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in ./env/lib/python3.10/site-packages (from tensorflow) (3.4.0)\n",
+      "Requirement already satisfied: packaging in ./env/lib/python3.10/site-packages (from tensorflow) (24.2)\n",
+      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in ./env/lib/python3.10/site-packages (from tensorflow) (5.28.3)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in ./env/lib/python3.10/site-packages (from tensorflow) (2.32.3)\n",
+      "Requirement already satisfied: setuptools in ./env/lib/python3.10/site-packages (from tensorflow) (74.1.2)\n",
+      "Requirement already satisfied: six>=1.12.0 in ./env/lib/python3.10/site-packages (from tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in ./env/lib/python3.10/site-packages (from tensorflow) (2.5.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.6 in ./env/lib/python3.10/site-packages (from tensorflow) (4.12.2)\n",
+      "Requirement already satisfied: wrapt>=1.11.0 in ./env/lib/python3.10/site-packages (from tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in ./env/lib/python3.10/site-packages (from tensorflow) (1.67.1)\n",
+      "Requirement already satisfied: tensorboard<2.19,>=2.18 in ./env/lib/python3.10/site-packages (from tensorflow) (2.18.0)\n",
+      "Requirement already satisfied: keras>=3.5.0 in ./env/lib/python3.10/site-packages (from tensorflow) (3.6.0)\n",
+      "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in ./env/lib/python3.10/site-packages (from tensorflow) (2.0.2)\n",
+      "Requirement already satisfied: h5py>=3.11.0 in ./env/lib/python3.10/site-packages (from tensorflow) (3.12.1)\n",
+      "Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in ./env/lib/python3.10/site-packages (from tensorflow) (0.4.1)\n",
+      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in ./env/lib/python3.10/site-packages (from tensorflow) (0.37.1)\n",
+      "Requirement already satisfied: wheel<1.0,>=0.23.0 in ./env/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.45.0)\n",
+      "Requirement already satisfied: rich in ./env/lib/python3.10/site-packages (from keras>=3.5.0->tensorflow) (13.9.4)\n",
+      "Requirement already satisfied: namex in ./env/lib/python3.10/site-packages (from keras>=3.5.0->tensorflow) (0.0.8)\n",
+      "Requirement already satisfied: optree in ./env/lib/python3.10/site-packages (from keras>=3.5.0->tensorflow) (0.13.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in ./env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.4.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in ./env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in ./env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2.2.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in ./env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in ./env/lib/python3.10/site-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.7)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./env/lib/python3.10/site-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in ./env/lib/python3.10/site-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in ./env/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in ./env/lib/python3.10/site-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./env/lib/python3.10/site-packages (from rich->keras>=3.5.0->tensorflow) (2.18.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in ./env/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tensorflow\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "\n",
+    "# Initialize the model\n",
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2512, 100, 1)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/aaditroychowdhury/Documents/CS 222/Main branch/main-project-shmoney/env/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
+    "               input_shape= (x_train.shape[1], 1)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "\n",
+    "model.add(LSTM(units=60, activation='relu', return_sequences=True))\n",
+    "model.add(Dropout(0.3))\n",
+    "\n",
+    "\n",
+    "\n",
+    "model.add(LSTM(units=80, activation='relu', return_sequences=True))\n",
+    "model.add(Dropout(0.4))\n",
+    "\n",
+    "\n",
+    "model.add(LSTM(units=80, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "model.add(Dense(units=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">26,640</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">45,120</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">51,520</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">81</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │        \u001b[38;5;34m10,400\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)        │        \u001b[38;5;34m26,640\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)        │        \u001b[38;5;34m45,120\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m)             │        \u001b[38;5;34m51,520\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m81\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">133,761</span> (522.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m133,761\u001b[0m (522.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">133,761</span> (522.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m133,761\u001b[0m (522.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 125ms/step - loss: 0.0476\n",
+      "Epoch 2/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0078\n",
+      "Epoch 3/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 125ms/step - loss: 0.0050\n",
+      "Epoch 4/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 130ms/step - loss: 0.0047\n",
+      "Epoch 5/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0045\n",
+      "Epoch 6/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 130ms/step - loss: 0.0042\n",
+      "Epoch 7/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0039\n",
+      "Epoch 8/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 126ms/step - loss: 0.0041\n",
+      "Epoch 9/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0035\n",
+      "Epoch 10/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 129ms/step - loss: 0.0029\n",
+      "Epoch 11/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 130ms/step - loss: 0.0033\n",
+      "Epoch 12/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 135ms/step - loss: 0.0030\n",
+      "Epoch 13/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0030\n",
+      "Epoch 14/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0032\n",
+      "Epoch 15/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 132ms/step - loss: 0.0022\n",
+      "Epoch 16/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 126ms/step - loss: 0.0025\n",
+      "Epoch 17/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 129ms/step - loss: 0.0027\n",
+      "Epoch 18/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 132ms/step - loss: 0.0023\n",
+      "Epoch 19/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 134ms/step - loss: 0.0024\n",
+      "Epoch 20/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 134ms/step - loss: 0.0022\n",
+      "Epoch 21/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 132ms/step - loss: 0.0021\n",
+      "Epoch 22/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 130ms/step - loss: 0.0022\n",
+      "Epoch 23/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 129ms/step - loss: 0.0024\n",
+      "Epoch 24/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 126ms/step - loss: 0.0022\n",
+      "Epoch 25/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0022\n",
+      "Epoch 26/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 131ms/step - loss: 0.0021\n",
+      "Epoch 27/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0021\n",
+      "Epoch 28/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 135ms/step - loss: 0.0020\n",
+      "Epoch 29/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 135ms/step - loss: 0.0019\n",
+      "Epoch 30/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 134ms/step - loss: 0.0026\n",
+      "Epoch 31/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 136ms/step - loss: 0.0020\n",
+      "Epoch 32/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 133ms/step - loss: 0.0019\n",
+      "Epoch 33/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 131ms/step - loss: 0.0017\n",
+      "Epoch 34/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 129ms/step - loss: 0.0025\n",
+      "Epoch 35/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 134ms/step - loss: 0.0019\n",
+      "Epoch 36/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 131ms/step - loss: 0.0019\n",
+      "Epoch 37/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0021\n",
+      "Epoch 38/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0022\n",
+      "Epoch 39/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 131ms/step - loss: 0.0020\n",
+      "Epoch 40/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0019\n",
+      "Epoch 41/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0018\n",
+      "Epoch 42/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0018\n",
+      "Epoch 43/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0018\n",
+      "Epoch 44/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0016\n",
+      "Epoch 45/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 128ms/step - loss: 0.0022\n",
+      "Epoch 46/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0020\n",
+      "Epoch 47/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 130ms/step - loss: 0.0018\n",
+      "Epoch 48/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 127ms/step - loss: 0.0020\n",
+      "Epoch 49/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 133ms/step - loss: 0.0017\n",
+      "Epoch 50/50\n",
+      "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 133ms/step - loss: 0.0018\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x139721840>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "model.fit(x_train, y_train, epochs=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save('keras_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Ticker</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2612</th>\n",
+       "      <td>79.807503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2613</th>\n",
+       "      <td>79.212502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2614</th>\n",
+       "      <td>79.722504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2615</th>\n",
+       "      <td>79.182503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2616</th>\n",
+       "      <td>79.527496</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Ticker       AAPL\n",
+       "2612    79.807503\n",
+       "2613    79.212502\n",
+       "2614    79.722504\n",
+       "2615    79.182503\n",
+       "2616    79.527496"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_100_days = training.tail(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df = pd.concat([past_100_days, testing], ignore_index=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Ticker</th>\n",
+       "      <th>AAPL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>72.477501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>72.449997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>72.879997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>73.412498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>75.087502</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Ticker       AAPL\n",
+       "0       72.477501\n",
+       "1       72.449997\n",
+       "2       72.879997\n",
+       "3       73.412498\n",
+       "4       75.087502"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.09083225],\n",
+       "       [0.09067978],\n",
+       "       [0.09306354],\n",
+       "       ...,\n",
+       "       [0.98292564],\n",
+       "       [0.98442244],\n",
+       "       [0.96463175]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_data = scaler.fit_transform(final_df)\n",
+    "input_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1220, 1)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1120, 100, 1)\n",
+      "(1120,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test = []\n",
+    "y_test = []\n",
+    "\n",
+    "for i in range(100, input_data.shape[0]):\n",
+    "    x_test.append(input_data[i - 100 : i])\n",
+    "    y_test.append(input_data[i, 0])\n",
+    "\n",
+    "x_test, y_test = np.array(x_test), np.array(y_test)\n",
+    "\n",
+    "print(x_test.shape)\n",
+    "print(y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_predicted = model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1120, 1)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00554362])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler.scale_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scale_factor = 1 / 0.00554362"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predicted = y_predicted *  scale_factor\n",
+    "y_test = y_test * scale_factor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig2 = plt.figure(figsize= (12, 6))\n",
+    "plt.plot(y_test, 'b', label='Original Price')\n",
+    "plt.plot(y_predicted, 'r', label='Predicted Price')\n",
+    "\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "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": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}