Analysis/lstm_stock_predictor_closing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Stock Predictor Using Closing Prices\n",
    "\n",
    "In this notebook, you will build and train a custom LSTM RNN that uses a 10 day window of Bitcoin closing prices to predict the 11th day closing price. \n",
    "\n",
    "You will need to:\n",
    "\n",
    "1. Prepare the data for training and testing\n",
    "2. Build and train a custom LSTM RNN\n",
    "3. Evaluate the performance of the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "In this section, you will need to prepare the training and testing data for the model. The model will use a rolling 10 day window to predict the 11th day closing price.\n",
    "\n",
    "You will need to:\n",
    "1. Use the `window_data` function to generate the X and y values for the model.\n",
    "2. Split the data into 70% training and 30% testing\n",
    "3. Apply the MinMaxScaler to the X and y values\n",
    "4. Reshape the X_train and X_test data for the model. Note: The required input format for the LSTM is:\n",
    "\n",
    "```python\n",
    "reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import hvplot.pandas\n",
    "from pathlib import Path\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current working directory:/Users/aalfiler/Sites/hw_deepLearning/Code\n"
     ]
    }
   ],
   "source": [
    "print(f\"Current working directory:{Path.cwd()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the random seed for reproducibility\n",
    "# Note: This is for the homework solution, but it is good practice to comment this out and run multiple experiments to evaluate your model\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import random\n",
    "random.set_seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fng_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-07-29</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-28</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27</th>\n",
       "      <td>47</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-26</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-25</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             fng_value\n",
       "date                  \n",
       "2019-07-29          19\n",
       "2019-07-28          16\n",
       "2019-07-27          47\n",
       "2019-07-26          24\n",
       "2019-07-25          42"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the fear and greed sentiment data for Bitcoin\n",
    "df = pd.read_csv('../Data/btc_sentiment.csv', index_col=\"date\", infer_datetime_format=True, parse_dates=True)\n",
    "df = df.drop(columns=\"fng_classification\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-07-25    9882.429688\n",
       "2019-07-26    9847.450195\n",
       "2019-07-27    9478.320313\n",
       "2019-07-28    9531.769531\n",
       "2019-07-29    9529.889648\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the historical closing prices for Bitcoin\n",
    "df2 = pd.read_csv('../Data/btc_historic.csv', index_col=\"Date\", infer_datetime_format=True, parse_dates=True)['Close']\n",
    "df2 = df2.sort_index()\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fng_value</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-07-25</th>\n",
       "      <td>42</td>\n",
       "      <td>9882.429688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-26</th>\n",
       "      <td>24</td>\n",
       "      <td>9847.450195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27</th>\n",
       "      <td>47</td>\n",
       "      <td>9478.320313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-28</th>\n",
       "      <td>16</td>\n",
       "      <td>9531.769531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-29</th>\n",
       "      <td>19</td>\n",
       "      <td>9529.889648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fng_value        Close\n",
       "2019-07-25          42  9882.429688\n",
       "2019-07-26          24  9847.450195\n",
       "2019-07-27          47  9478.320313\n",
       "2019-07-28          16  9531.769531\n",
       "2019-07-29          19  9529.889648"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the data into a single DataFrame\n",
    "df = df.join(df2, how=\"inner\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fng_value</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>30</td>\n",
       "      <td>9114.719727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>15</td>\n",
       "      <td>8870.820313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-03</th>\n",
       "      <td>40</td>\n",
       "      <td>9251.269531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-04</th>\n",
       "      <td>24</td>\n",
       "      <td>8218.049805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>11</td>\n",
       "      <td>6937.080078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             fng_value        Close\n",
       "2018-02-01          30  9114.719727\n",
       "2018-02-02          15  8870.820313\n",
       "2018-02-03          40  9251.269531\n",
       "2018-02-04          24  8218.049805\n",
       "2018-02-05          11  6937.080078"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function accepts the column number for the features (X) and the target (y)\n",
    "# It chunks the data up with a rolling window of Xt-n to predict Xt\n",
    "# It returns a numpy array of X any y\n",
    "def window_data(df, window, feature_col_number, target_col_number):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(df) - window - 1):\n",
    "        features = df.iloc[i:(i + window), feature_col_number]\n",
    "        target = df.iloc[(i + window), target_col_number]\n",
    "        X.append(features)\n",
    "        y.append(target)\n",
    "    return np.array(X), np.array(y).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Closing Prices using a 10 day window of previous closing prices\n",
    "# Then, experiment with window sizes anywhere from 1 to 10 and see how the model performance changes\n",
    "window_size = 10\n",
    "\n",
    "# Column index 0 is the 'fng_value' column\n",
    "# Column index 1 is the `Close` column\n",
    "feature_column = 1\n",
    "target_column = 1\n",
    "X, y = window_data(df, window_size, feature_column, target_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 70% of the data for training and the remaineder for testing\n",
    "split = int(0.7 * len(X))\n",
    "X_train = X[: split]\n",
    "X_test = X[split:]\n",
    "y_train = y[: split]\n",
    "y_test = y[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Use the MinMaxScaler to scale data between 0 and 1.\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "scaler.fit(y)\n",
    "y_train = scaler.transform(y_train)\n",
    "y_test = scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train sample values:\n",
      "[[[0.60761794]\n",
      "  [0.58242373]\n",
      "  [0.62172321]\n",
      "  [0.51499412]\n",
      "  [0.38267307]\n",
      "  [0.46160996]\n",
      "  [0.4503991 ]\n",
      "  [0.5193988 ]\n",
      "  [0.56445096]\n",
      "  [0.55127638]]\n",
      "\n",
      " [[0.58242373]\n",
      "  [0.62172321]\n",
      "  [0.51499412]\n",
      "  [0.38267307]\n",
      "  [0.46160996]\n",
      "  [0.4503991 ]\n",
      "  [0.5193988 ]\n",
      "  [0.56445096]\n",
      "  [0.55127638]\n",
      "  [0.5012101 ]]\n",
      "\n",
      " [[0.62172321]\n",
      "  [0.51499412]\n",
      "  [0.38267307]\n",
      "  [0.46160996]\n",
      "  [0.4503991 ]\n",
      "  [0.5193988 ]\n",
      "  [0.56445096]\n",
      "  [0.55127638]\n",
      "  [0.5012101 ]\n",
      "  [0.58660203]]\n",
      "\n",
      " [[0.51499412]\n",
      "  [0.38267307]\n",
      "  [0.46160996]\n",
      "  [0.4503991 ]\n",
      "  [0.5193988 ]\n",
      "  [0.56445096]\n",
      "  [0.55127638]\n",
      "  [0.5012101 ]\n",
      "  [0.58660203]\n",
      "  [0.5487353 ]]\n",
      "\n",
      " [[0.38267307]\n",
      "  [0.46160996]\n",
      "  [0.4503991 ]\n",
      "  [0.5193988 ]\n",
      "  [0.56445096]\n",
      "  [0.55127638]\n",
      "  [0.5012101 ]\n",
      "  [0.58660203]\n",
      "  [0.5487353 ]\n",
      "  [0.64593307]]] \n",
      "\n",
      "X_test sample values:\n",
      "[[[0.04675556]\n",
      "  [0.03913222]\n",
      "  [0.03975922]\n",
      "  [0.03856719]\n",
      "  [0.03679562]\n",
      "  [0.03728835]\n",
      "  [0.03974167]\n",
      "  [0.04528668]\n",
      "  [0.04528668]\n",
      "  [0.07024855]]\n",
      "\n",
      " [[0.03913222]\n",
      "  [0.03975922]\n",
      "  [0.03856719]\n",
      "  [0.03679562]\n",
      "  [0.03728835]\n",
      "  [0.03974167]\n",
      "  [0.04528668]\n",
      "  [0.04528668]\n",
      "  [0.07024855]\n",
      "  [0.07145402]]\n",
      "\n",
      " [[0.03975922]\n",
      "  [0.03856719]\n",
      "  [0.03679562]\n",
      "  [0.03728835]\n",
      "  [0.03974167]\n",
      "  [0.04528668]\n",
      "  [0.04528668]\n",
      "  [0.07024855]\n",
      "  [0.07145402]\n",
      "  [0.07659928]]\n",
      "\n",
      " [[0.03856719]\n",
      "  [0.03679562]\n",
      "  [0.03728835]\n",
      "  [0.03974167]\n",
      "  [0.04528668]\n",
      "  [0.04528668]\n",
      "  [0.07024855]\n",
      "  [0.07145402]\n",
      "  [0.07659928]\n",
      "  [0.07277624]]\n",
      "\n",
      " [[0.03679562]\n",
      "  [0.03728835]\n",
      "  [0.03974167]\n",
      "  [0.04528668]\n",
      "  [0.04528668]\n",
      "  [0.07024855]\n",
      "  [0.07145402]\n",
      "  [0.07659928]\n",
      "  [0.07277624]\n",
      "  [0.07757854]]]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the features for the model\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
    "print (f\"X_train sample values:\\n{X_train[:5]} \\n\")\n",
    "print (f\"X_test sample values:\\n{X_test[:5]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and Train the LSTM RNN\n",
    "\n",
    "In this section, you will design a custom LSTM RNN and fit (train) it using the training data.\n",
    "\n",
    "You will need to:\n",
    "1. Define the model architecture\n",
    "2. Compile the model\n",
    "3. Fit the model to the training data\n",
    "\n",
    "### Hints:\n",
    "You will want to use the same model architecture and random seed for both notebooks. This is necessary to accurately compare the performance of the FNG model vs the closing price model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the LSTM model. \n",
    "# The return sequences need to be set to True if you are adding additional LSTM layers, but \n",
    "# You don't have to do this for the final layer. \n",
    "# Note: The dropouts help prevent overfitting\n",
    "# Note: The input shape is the number of time steps and the number of indicators\n",
    "# Note: Batching inputs has a different input shape of Samples/TimeSteps/Features\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "number_units = 5\n",
    "dropout_fraction = 0.2\n",
    "\n",
    "# Layer 1\n",
    "model.add(LSTM(\n",
    "    units=number_units,\n",
    "    return_sequences=True,\n",
    "    input_shape=(X_train.shape[1], 1))\n",
    "    )\n",
    "model.add(Dropout(dropout_fraction))\n",
    "# Layer 2\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "# Layer 3\n",
    "model.add(LSTM(units=number_units))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "# Output layer\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_3 (LSTM)                (None, 10, 5)             140       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 10, 5)             0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 10, 5)             220       \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 10, 5)             0         \n",
      "_________________________________________________________________\n",
      "lstm_5 (LSTM)                (None, 5)                 220       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 5)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 586\n",
      "Trainable params: 586\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summarize the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "372/372 [==============================] - 4s 4ms/step - loss: 0.1370\n",
      "Epoch 2/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0666\n",
      "Epoch 3/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0632\n",
      "Epoch 4/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0521\n",
      "Epoch 5/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0483\n",
      "Epoch 6/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0409\n",
      "Epoch 7/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0361\n",
      "Epoch 8/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0342\n",
      "Epoch 9/10\n",
      "372/372 [==============================] - 2s 4ms/step - loss: 0.0326\n",
      "Epoch 10/10\n",
      "372/372 [==============================] - 1s 4ms/step - loss: 0.0289\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fd89de86a10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "# Use at least 10 epochs\n",
    "# Do not shuffle the data\n",
    "# Experiement with the batch size, but a smaller batch size is recommended\n",
    "model.fit(X_train, y_train, epochs=10, shuffle=False, batch_size=1, verbose=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Performance\n",
    "\n",
    "In this section, you will evaluate the model using the test data. \n",
    "\n",
    "You will need to:\n",
    "1. Evaluate the model using the `X_test` and `y_test` data.\n",
    "2. Use the X_test data to make predictions\n",
    "3. Create a DataFrame of Real (y_test) vs predicted values. \n",
    "4. Plot the Real vs predicted values as a line chart\n",
    "\n",
    "### Hints\n",
    "Remember to apply the `inverse_transform` function to the predicted and y_test values to recover the actual closing prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 3ms/step - loss: 0.0435\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.04350407421588898"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "model.evaluate(X_test, y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make some predictions\n",
    "predicted = model.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recover the original prices instead of the scaled version\n",
    "predicted_prices = scaler.inverse_transform(predicted)\n",
    "real_prices = scaler.inverse_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-20</th>\n",
       "      <td>3924.239990</td>\n",
       "      <td>3745.115234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-21</th>\n",
       "      <td>3974.050049</td>\n",
       "      <td>3747.829834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-22</th>\n",
       "      <td>3937.040039</td>\n",
       "      <td>3756.912842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-23</th>\n",
       "      <td>3983.530029</td>\n",
       "      <td>3768.161377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>4149.089844</td>\n",
       "      <td>3782.000732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Real    Predicted\n",
       "2019-02-20  3924.239990  3745.115234\n",
       "2019-02-21  3974.050049  3747.829834\n",
       "2019-02-22  3937.040039  3756.912842\n",
       "2019-02-23  3983.530029  3768.161377\n",
       "2019-02-24  4149.089844  3782.000732"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame of Real and Predicted values\n",
    "stocks = pd.DataFrame({\n",
    "    \"Real\": real_prices.ravel(),\n",
    "    \"Predicted\": predicted_prices.ravel()\n",
    "}, index = df.index[-len(real_prices): ]) \n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Real</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-20</th>\n",
       "      <td>3924.239990</td>\n",
       "      <td>3745.115234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-21</th>\n",
       "      <td>3974.050049</td>\n",
       "      <td>3747.829834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-22</th>\n",
       "      <td>3937.040039</td>\n",
       "      <td>3756.912842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-23</th>\n",
       "      <td>3983.530029</td>\n",
       "      <td>3768.161377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>4149.089844</td>\n",
       "      <td>3782.000732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Real    Predicted\n",
       "2019-02-20  3924.239990  3745.115234\n",
       "2019-02-21  3974.050049  3747.829834\n",
       "2019-02-22  3937.040039  3756.912842\n",
       "2019-02-23  3983.530029  3768.161377\n",
       "2019-02-24  4149.089844  3782.000732"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the real vs predicted values as a line chart\n",
    "stocks = pd.DataFrame({\n",
    "    \"Real\": real_prices.ravel(),\n",
    "    \"Predicted\": predicted_prices.ravel()\n",
    "    }, index = df.index[-len(real_prices): ])\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stocks.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}