Stock Market Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Market Prediction using Numerical and Textual Analysis\n",
    "\n",
    "By KEERTHIVASINI R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stock to analyze and predict: SENSEX (S&P BSE SENSEX) <br>\n",
    "\n",
    "Date: 01-01-2006 to 31-12-2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import lag_plot\n",
    "from pandas import datetime\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-02</td>\n",
       "      <td>9422.490234</td>\n",
       "      <td>9457.089844</td>\n",
       "      <td>9366.089844</td>\n",
       "      <td>9390.139648</td>\n",
       "      <td>9390.139648</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-03</td>\n",
       "      <td>9401.830078</td>\n",
       "      <td>9546.780273</td>\n",
       "      <td>9390.790039</td>\n",
       "      <td>9539.370117</td>\n",
       "      <td>9539.370117</td>\n",
       "      <td>13200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>9523.290039</td>\n",
       "      <td>9664.940430</td>\n",
       "      <td>9523.290039</td>\n",
       "      <td>9648.080078</td>\n",
       "      <td>9648.080078</td>\n",
       "      <td>21400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>9638.269531</td>\n",
       "      <td>9679.719727</td>\n",
       "      <td>9571.309570</td>\n",
       "      <td>9617.740234</td>\n",
       "      <td>9617.740234</td>\n",
       "      <td>19200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>9635.089844</td>\n",
       "      <td>9680.919922</td>\n",
       "      <td>9535.660156</td>\n",
       "      <td>9640.290039</td>\n",
       "      <td>9640.290039</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date         Open         High          Low        Close  \\\n",
       "0  2006-01-02  9422.490234  9457.089844  9366.089844  9390.139648   \n",
       "1  2006-01-03  9401.830078  9546.780273  9390.790039  9539.370117   \n",
       "2  2006-01-04  9523.290039  9664.940430  9523.290039  9648.080078   \n",
       "3  2006-01-05  9638.269531  9679.719727  9571.309570  9617.740234   \n",
       "4  2006-01-06  9635.089844  9680.919922  9535.660156  9640.290039   \n",
       "\n",
       "     Adj Close   Volume  \n",
       "0  9390.139648  14000.0  \n",
       "1  9539.370117  13200.0  \n",
       "2  9648.080078  21400.0  \n",
       "3  9617.740234  19200.0  \n",
       "4  9640.290039  16000.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"BSESN.csv\")\n",
    "df.replace(np.nan, inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1234, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Date','Close']] \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1234 entries, 0 to 1233\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   Date    1234 non-null   datetime64[ns]\n",
      " 1   Close   1234 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 19.4 KB\n"
     ]
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df.Date, format='%Y-%m-%d')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "plt.title('BSESN Stock Prices')\n",
    "plt.plot(df['Close'])\n",
    "plt.xticks(np.arange(0,1500, 300), df['Date'][0:1500:300])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1521b4c4788>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data, test_data = df[0:int(len(df)*0.8)], df[int(len(df)*0.8):]\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.title('BSESN Stock Prices')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Prices')\n",
    "plt.plot(df['Close'], 'blue', label='Training Data')\n",
    "plt.plot(test_data['Close'], 'green', label='Testing Data')\n",
    "plt.xticks(np.arange(0,1500, 300), df['Date'][0:1500:300])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA\n",
    "\n",
    "ARIMA (AutoRegressive Integrated Moving Average) is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.\n",
    "\n",
    "ARIMA models explain a time series based on its own past values, basically its own lags and the lagged forecast errors.\n",
    "\n",
    "An ARIMA model is characterized by 3 terms (p, d, q):\n",
    "\n",
    "- p is the order of the AR term\n",
    "\n",
    "- d is the number of differencing required to make the time series stationary\n",
    "\n",
    "- q is the order of the MA term\n",
    "\n",
    "As we see in the parameters required by the model, any stationary time series can be modeled with ARIMA models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stationarity\n",
    "\n",
    "A stationary time series is one whose properties do not depend on the time at which the series is observed. Thus, time series with trends, or with seasonality, are not stationary — the trend and seasonality will affect the value of the time series at different times.\n",
    "\n",
    "Subtract the previous value from the current value. Now if we just difference once, we might not get a stationary series so we might need to do that multiple times. \n",
    "\n",
    "And the minimum number of differencing operations needed to make the series stationary needs to be imputed into our ARIMA model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADF test\n",
    "\n",
    "We'll use the Augumented Dickey Fuller (ADF) test to check if the price series is stationary.\n",
    "\n",
    "The null hypothesis of the ADF test is that the time series is non-stationary. So, if the p-value of the test is less than the significance level (0.05) then we can reject the null hypothesis and infer that the time series is indeed stationary.\n",
    "\n",
    "So, in our case, if the p-value > 0.05 we'll need to find the order of differencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.448789416529229\n",
      "p-value: 0.558675490721378\n"
     ]
    }
   ],
   "source": [
    "# Check if price series is stationary\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "result = adfuller(df.Close.dropna())\n",
    "print(f\"ADF Statistic: {result[0]}\")\n",
    "print(f\"p-value: {result[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-value > 0.05, therefore the time series is not stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!pipenv install --skip-lock pmdarima\n",
    "\n",
    "from pmdarima.arima.utils import ndiffs\n",
    "ndiffs(df.Close, test=\"adf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore d value is 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### p\n",
    "\n",
    "p is the order of the Auto Regressive (AR) term. It refers to the number of lags to be used as predictors. \n",
    "\n",
    "We can find out the required number of AR terms by inspecting the Partial Autocorrelation (PACF) plot.\n",
    "\n",
    "The partial autocorrelation represents the correlation between the series and its lags. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "\n",
    "diff = df.Close.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title(\"Difference once\")\n",
    "ax2.set_ylim(0, 1)\n",
    "plot_pacf(diff, ax=ax2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### q\n",
    "\n",
    "q is the order of the Moving Average (MA) term. It refers to the number of lagged forecast errors that should go into the ARIMA Model.\n",
    "\n",
    "We can look at the ACF plot for the number of MA terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = df.Close.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title(\"Difference once\")\n",
    "ax2.set_ylim(0, 1)\n",
    "plot_acf(diff, ax=ax2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset=df.copy()\n",
    "dataset.set_index('Date', inplace=True)\n",
    "dataset = dataset[['Close']] \n",
    "from matplotlib import pyplot\n",
    "pyplot.figure()\n",
    "pyplot.subplot(211)\n",
    "plot_acf(dataset, ax=pyplot.gca(),lags=10)\n",
    "pyplot.subplot(212)\n",
    "plot_pacf(dataset, ax=pyplot.gca(),lags=10)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to evaluate the ARIMA model, I decided to use two different error functions: Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE). SMAPE is commonly used as an accuracy measure based on relative errors.\n",
    "\n",
    "SMAPE is not currently supported in Scikit-learn as a loss function I, therefore, had first to create this function on my own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smape_kun(y_true, y_pred):\n",
    "    return np.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))))"
   ]
  },
  {
   "attachments": {
    "1_I3WbbaaUPe9Mn5WcUVEwwg.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1_I3WbbaaUPe9Mn5WcUVEwwg.png](attachment:1_I3WbbaaUPe9Mn5WcUVEwwg.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "Testing Mean Squared Error: 33910.630\n",
      "Symmetric mean absolute percentage error: 8.380\n"
     ]
    }
   ],
   "source": [
    "train_ar = train_data['Close'].values\n",
    "test_ar = test_data['Close'].values\n",
    "history = [x for x in train_ar]\n",
    "print(type(history))\n",
    "predictions = list()\n",
    "for t in range(len(test_ar)):\n",
    "    model = ARIMA(history, order=(2,1,1))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test_ar[t]\n",
    "    history.append(obs)\n",
    "error = mean_squared_error(test_ar, predictions)\n",
    "print('Testing Mean Squared Error: %.3f' % error)\n",
    "error2 = smape_kun(test_ar, predictions)\n",
    "print('Symmetric mean absolute percentage error: %.3f' % error2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SMAPE is commonly used loss function for Time Series problems and can, therefore, provide a more reliable analysis. That showed that our model is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                    D.y   No. Observations:                 1232\n",
      "Model:                 ARIMA(2, 1, 1)   Log Likelihood               -8620.611\n",
      "Method:                       css-mle   S.D. of innovations            264.623\n",
      "Date:                Sun, 13 Dec 2020   AIC                          17251.221\n",
      "Time:                        21:50:41   BIC                          17276.803\n",
      "Sample:                             1   HQIC                         17260.845\n",
      "                                                                              \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          8.8100      7.326      1.203      0.229      -5.549      23.169\n",
      "ar.L1.D.y      0.7589      0.161      4.723      0.000       0.444       1.074\n",
      "ar.L2.D.y     -0.0794      0.029     -2.765      0.006      -0.136      -0.023\n",
      "ma.L1.D.y     -0.6886      0.159     -4.333      0.000      -1.000      -0.377\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            1.5785           +0.0000j            1.5785            0.0000\n",
      "AR.2            7.9743           +0.0000j            7.9743            0.0000\n",
      "MA.1            1.4522           +0.0000j            1.4522            0.0000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(model_fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "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": [
    "residuals=pd.DataFrame(model_fit.resid)\n",
    "residuals.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "residuals.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.004552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>264.730673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1389.414936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-129.448966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.510853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>138.316537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2077.986686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "count  1232.000000\n",
       "mean      0.004552\n",
       "std     264.730673\n",
       "min   -1389.414936\n",
       "25%    -129.448966\n",
       "50%      10.510853\n",
       "75%     138.316537\n",
       "max    2077.986686"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1521fb4a4c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "plt.plot(df['Close'], 'green', color='blue', label='Training Data')\n",
    "plt.plot(test_data.index, predictions, color='green', marker='o', linestyle='dashed', label='Predicted Price')\n",
    "plt.plot(test_data.index, test_data['Close'], color='red', label='Actual Price')\n",
    "plt.title('Microsoft Prices Prediction')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Prices')\n",
    "plt.xticks(np.arange(0,1500, 300), df['Date'][0:1500:300])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LTz6e2odpn23ngfk7+arLDuyLM43+B7OPfz9KUHNXrunR8JeeOBdJBkII25ZxCH592hjR0/cuUIpP7G7h9ZJw5l/jymDTTgi/CewdiAj25rMZfYnPLMLP0/ncn+CVgrH/htxEYxiqize06Mqg5va8M8aXV1cdpDDtDXI8+/D6BkfS8jeRmldKVlEZL1zXDQd722xwUba6K1hkZKSOioqydhhCCGuqLIe5I6AgFe77AzwDSMgqZszbGxjWyZ9PpkZe+BznU5oPn42CzEPnfHmq6TnSvPvQqpkLgd6uhAV4Mm1gWxwbeDJQSkVrrf/0g5E7AyGE7Vr/CpzYC5MXgGcAJpNm9pIYnBzseHFC99qd26UZ3LEK4n8DUxWYKkEbj+XOPszrdq1N9g2cjyQDIYTNMZk0mXvX0mLzO+jeM7DrPA6AhTuS2JaQw2s39LDMFpNuPtD9hj8VX/ZG7Q2YJAMhRMOWsBEOrgB7J4q1EzHpZWxPPsmN5ctJ0AGM/2Mozrt/pbm7E2l5pQxs58stfYMvfF5xBkkGQoiGqbIM1r0EW96jyt6VSpPG1VTGQKUZCFQ4uPJznznc5diZ3OJyckrK6dLSi9ljOzeq5pv6IslACNHwZB42VhU9sZf9gTcx6eg4XNw8uDEykCl9W9K+mR2O9k5c62Q7C8E1dJIMhBANh9bGnIFfnwYnd5iykCdXe9IpCBbdMxAXx4a/Y5itathjoIQQTcua5+Cnx6DtYLjvD3KCRrI3JZ+RXQIkEdQxSQZCCIvRWvPT3rQz1/C5WCYT7P4GOo2D274DzwA2xWWhNTa51o+tkWYiIcTlKysymnPMHbYbYrO4f/5O7BSM7BLA9IEhDO7ge3EduidioDgTuk4AO+Nz6sYjmTRzdbS55aBtkSQDIcTlOfIrfHMTuPpAQDcI6MaJ4170c/Gh74BhLNyRzOoD6bT3d2f6oBAm922Dk8NfNEbErTYe248AjLuMDbGZDOngh72djA6qa5IMhBCXZ/sccG8BncZA+n70znncUlHCLQBtv+ZvI8exMiaNr/5I5Nnl+yk4WcGDI8LOf77YNcZ+Ax4tjKcZRaQXlDG0ozQR1QfpMxBCXLr8ZGOV0D7T4br34O51rLo2imFlb1Dp4A5H1+HiaM+kPkEsf2AwPQKbsTH2L/YTPpkLydsh7Orqog1HMgEYEuZf17URSDIQQlyO3d8AGiJuqy5asecERe4h2IcMgsRN1eVKKfqG+LA7KY+yyqpzny/+N9Am6HBVddGG2Cza+7sT6O1aR5UQNUkyEEJcGpMJdv0PQoeCTygABaUVrDucwfjwVqiQIZB1BArTq9/SL7Q5ZZUm9qXkn/uccWuMheECjcU0Syuq2BafzdCOcldQXyQZCCEuTcLvkHccek+vLvpl3wnKK01cF9EaQq4wCo+dvjuIDPEBYHvCWfsIgzHRLG6tsWuYvdGNuSMxh7JKE0OliajeSDIQQlyaXf8zNnzpPL66aMWeVIJ9XOkV7G3sJezkeUZTkZ+HM+383YlKzPnz+dL3QWHaGf0FG2OzcLK3o387nzqsiKhJkoEQ4uKV5Bj7A4ffAo7GEtGZhWVsOZrNdT1bG/MJ7B2g7cAzkgFAvxAfoo7lYjKdtaFW3BrjsWZ/wZFMIkOa4+YkAx7riyQDIcTFi1kMVeXQe2p10U9706gyaSZEBJ4+7hz9BpEhPuSfrOBIRuGZ54xdAwE9wLMlABkFpRw6USj9BfVMkoEQ4oJMJk1ecRnsnGfMBWjZo/q1FXtS6dzSk44BnqffEDLEeEzcWF3Uz9xvsCOhRlNRaQEkbYWw03cFp4agyhIU9UuSgRDiTNs+ge/vgZwEAApLK5jx5Q5mvjoHMvZT1ev0XUFSTgnRx3K5tmfrM8/R8s/9BsE+rgR4ObM9sUYncsLvxnaSZwwpzcTPw5kuLb3qpn7inKRBTghx2q75sOqfgIL9Synqcy9TDw9mb2YVn3hv4WSxEzO3BvNSSCEdWnjyQ0wqANednQzO0W9war7BjoQctNZG/0LsanD2guD+gHEHsik2i6Ed/bGTJSjqldwZCCEMcWvgh79Du2Hw0G7y212Dx/Z3mJN3D6uGxHNV1Say2o7lUJ5i3LubmLsxnhW7U+ndxptgH7c/ny/kCsiOhcIT1UX9Qn04UVBKcu7JGkNKrwR7RwAOpBWQXVwuTURWIMlACAGpu2HRNGjRBW7+Hxsy3Rl8ZDJ3OryKZ4u2dNz+FJQVEDziHn55ZChDw/x5eeVBDp0o/PNdwSnV/QY15hu0NfcbJOZA5iEoSP5TExHAEEkG9U6aiYRo6nITYf5N4OYDt37Lz3HFPPDNLsJaePDyHXfg6nkvxCwyRge1HUQLpfh0Wh+W7Ezhhz2pZ44iqqlluNEElLgJetwIQKeWnni6OLAjMYcbSn83jjMng/JKE99FJ9M90IsWni71UHFRkyQDIZqykhz4+kaoKoMZP4JXKz76fTOhfu58e+9APF2M5hsippzxNqUUN/YJ4sY+Qec/t70DtDmz38DeThHZtjnb47MhbyX4d4Fmxjnm/ZFIfGYxn8+ItHg1xYVJM5EQTdnyB4ylJaYsBP9O5JdUsDc5j3E9Wp1OBLURMuRP/QZ9Q30Ymzsfjv9hrHoKZBSW8vaaWIZ38mdE54DaX1dcMkkGQjRVpQUQ+ysMuBfaDgJgy9EsTJbcZvIc/Qaj+YPHHL8ltc110P9eAP7z82HKKqt4ZnxXy1xXXDJJBkI0VYkbzWP8a6wJFJeFh7MDEcHelrlGq57mfgPz5LOUnbTb9DjRuiNf+T0KSrE7KY9vo5OZOSSUdv4elrmuuGTSZyBEUxW3Fpw8qsf4A2yKzWJAOx8c7S30OdHO3rjrSNwEBamwYArK3Z+PnF4k83gxJpPmuRX78fd05m9/tQuaqHMX/BdXSn2ulMpQSu2rUeajlFqtlIo1PzY3lw9TSuUrpXabv56t8Z4xSqnDSqk4pdTsGuWhSqlt5vJFSiknS1dSCHEWrY15BSFXgIPxX+5YdjHHc0q4wtLLRocMgew4+N/1UF4Ety6kU4dQ9qUW8PW2Y+xJyuPJsZ3xcJbPptZ0Men/S2DMWWWzgbVa6zBgrfn5KRu11hHmrxcBlFL2wAfAWKArMEUpdapx8HXgLa11ByAXuPNyKyOEuEg58ZB3DDqMrC46tSaQxcf4n+o3yDoCN34OAd3oG+JDlUnz4g8H6N3Gm4nnG54q6s0Fk4HWegNw9iLkE4CvzN9/BUy8wGn6AXFa63itdTmwEJiglFLACOC7SziXEKK24tYajzWSwabYLFo3c6Gdn7tlr9UyHNoMgnH/gY6jAejdtjlKQZXWPH9dN1l6ogG43PuyAK11mvn7E0DNsWADlVJ7gFTgMa31fiAQSKpxTDLQH/AF8rTWlTXKz/sRQSk1C5gF0KZNm8sMXQjB0bXQPBR82gFQZdJsOZrFmO4tjTWDLMnOHmauOqPIy8WR0V1bEuzjSniQt2WvJy5LrRvptNZaKXVqt4qdQFutdZFSahywDLBYr5DWeg4wByAyMlJf4HAhxLlUlkHCxjMmksUk51FQWsmQetxm8uOpfertWuLCLnfIQLpSqhWA+TEDQGtdoLUuMn//E+ColPIDUoDgGu8PMpdlA95KKYezyoUQdeX4VqgoPmNNoE2xWSgFQzrImkBN1eUmgxXAqd2wpwPLAZRSLc39ACil+pnPnw3sAMLMI4ecgMnACq21BtYDN559LiFEHTm6FuwcT29cj9F53K21Fz7uMpivqbqYoaULgD+ATkqpZKXUncBrwNVKqVjgKvNzMP6o7zP3GbwLTNaGSuBB4BfgILDY3JcA8ATwqFIqDqMP4TPLVU8I8Sdx66DNAHA2JngVlVWy83guQzrINpNN2QX7DLTWU87z0sizC7TW7wPvn+c8PwE/naM8HmO0kRCirhWmQ/peGPlcddG2+GwqTVr2EGjiZDkKIZqSo+uMx7PmFzg72NGnbXMrBSUaAkkGQjQlcWvAvQUEnN7QflNcFv3b+eLiaG/FwIS1STIQoqkwmSB+PbQfAXbGf/20/JPEZRRxhYwiavIkGQjRVKTthpLs+lmCQtgcSQZCNBVHzUtQtBteXbQpNgs/D2c6t/S0UlCioZBkIERTEbcOWkWAhzGENDa9kFX70hjdLcDyS1AImyPJQIimICXa2Gay01gATCbNk9/vxcPZgUev7mjl4ERDIMlAiMauqhJ+eBg8AmDA/QAs3JFE1LFc/m9cF3w9nK0bn2gQZDcJIRq7HXPhRAzc9CW4eJFRWMqrqw4ysJ0vN/YJsnZ0ooGQOwMhGrOCNFj3MrQfCV0nAvDiDwcoqzTxyvXdpa9AVJNkIERj9suTUFUO1/wXlGL94Qx+jEnjweEdZPN5cQZJBkI0VrFrYP9SGPo4+LSjpLySp5fuo72/O/dc2c7a0YkGRvoMhGiMKk7CT/8A3zAY/HcA3lkTS0reSRbfMxBnB1l6QpxJkoEQjdHGNyA3EaatAAdnjqQXMndTApP7BtMv1Mfa0YkGSJqJhGhsMg7Cprch/BZodyVaa174YT8ezg78c0xna0cnGihJBkI0JlWVsOw+cPGCUa8A8Mv+E2yOy+YfozrKTmbivKSZSAgbp7U+PUR089uQusuYU+Dhz8nyKl768SCdW3pya7821gxTNHCSDISwBVrDib1QkIIOG8X+tCLWHsxg7aF04jKKeHJcF6aGFsNvrxnzCbpdD8AnG46SkneShbMG4GAvDQHi/CQZCNGQ5afA3sWwZxFkHgRgo11fHii5hyLlRq9gb7oHNuOFZbu5yudftHRphrrmDQCSc0v46LejjA9vxYB2vtashbABkgyEaIiOb4X1r0DCRkBT2rIP7zrMoqr8JP/kGzb7vkLVzf+jedseVFaZWP/pP2l14hBzWz7P7U7NcQFeWXkQO6X4v3FdrF0bYQMkGQjR0BzbAl9PAtfmcOUTxLYcx+TvMlAK/ndff+zLpuD17XSYPwau/wQHn1CuyviS+BZX83JiR1bN3caMQSGs2neCx0Z1pLW3q7VrJGyA0lpbO4bLEhkZqaOioqwdhhCWlRwN8yaAZ0u44yeisx2544vteDg78PVd/U8vIZGfAotuh9Sdxmqkpip4YBs/xVfwyKLdlFWaaOPjxq+PDJW9jcUZlFLRWuvIs8ulR0mIhiJtD3x9Pbj7wvQVbD5hx9TPtuHj7sS39w06cy2hZoFwxyqIuB2K0mH8m+Dux7gerVgwawDdWnvxyvXdJRGIiyZ3BkKcx5H0Qg6mFTAhIrDuL5Z+AL68Bpzc4Y6f2JDhyl3zogj1ded/d/WjhafLud+nNZTkGAlEiItwvjsD6TMQAmDn/6CyFPrdDUB8ZhGT52wlp7icoOau9Glbh0s4ZMUaTUP2TjBtOXlOLXl08e+083Nn4awBeLv9xUQxpSQRCIuQZiIh9iyCFQ/CT49B0nYyC8uY/sV2APw9nXl55UHq7A66sgwWTAFtgukrwLc9L688SF5JBW/eHPHXiUAIC5JkIJq2hA2w/AFoOwS8gjCt+Dt3f/EHWYXlfD6jL4+P6sSu43ms3JtWN9ff8i5kx8L1H4N/JzYcyeS76GTuvbI9XVt71c01hTgHSQai6co4BAtvB9/2MHk+lWNexy7zIIMyFvD+rb2ICPZmUp8gOrf05PWfD1FWWWXZ6+ckwIb/QpfrIOxqissqefL7vbTzd+fBER0sey0hLkCSgWiaCk/A/JvA0QVu+xbt0own9wezqqovjzotZWRAMQD2doqnrulCUs5J5m05dt7TVVVWkpBVzM/70nhnTSz3z4/mmnc3suVo1rnfoDWs+icoexjzGgD//fUwqfkn+fekcBkFJOqddCCLpqesCL65GUqy4Y6V4N2G99fG8m10MmFDnschZjL8+ChMXQpKcUWYP8M6+fPeulhu7BNEc/uTxjDQlJ3o1F0UJezAtSSNR8ufY5cOQylo6+NGVlE5X2xOZFB7vz/HcGglxP4Ko16GZoFEH8vlyy2JTB3QlsgQ2W9A1D9JBqJpMVXBkjuNRd+mLILWvdiRmMNba44wMaI1d18TAf7PwqrHYe93EH4TAP83rguPvDOPtE8/pHne74DRoZzl0IptZW0Y6ZDLW+2jyR89g7AAD9ycHHj5xwN89UcieSXlZ3YElxXBqiegRTfofy9llVXMXhJDKy8X2W9AWI00E4lGzWQ6axTQ2hfhyM8w9t/QcRQFpcaM3aDmbrx8fQ9jKei+d0JgH/h5tjGGPyWajmvvZqXT/xGcu4283vexd/jnXOXwBYNPvkX66E9w6XUzIelr6NnCHjcn4zPWhIhAKqo0q/adODOGDf+GgmRjopi9Ix+uP0psRhGv3NADD2f5fCasQ5KBaDxMpjOepheUMui1dTz5fQwVVSZjCOnmtyHyzur5BM8v309afilv3RJx+g+xnT2MfxtO5sLHQ+DTEXD8D4oGPcHV+gPGH7yaa1e5oNx8WfbAYO4cEorqdTtUlBgb0Jt1D/Sinb87y3en1AjqAPzxAfS6HdoMIP9kBXM3xjOuR0uGd2pRxz8gIc5PkoFoHNJi4LVg+OFhKCtCa80zy/aRVVTGgu1JvPrp1+gVf4OQK2Ds6wCs2JPK97tSeHB4B/q0bX7m+VqFw9DHwFQJI5+Dh/fiMer/mDq8J8m5J5k6oC0rHhxyevhnUCT4dYRd86tPoZRiQs9AtiXkkJZ/0ug0/ukxcPaEq14EYOH24xSXV3H/MBk9JKxL7klF47DmOaM/IPpLiP+NP3r+i18PaGaP7UwAuQxadz8Z9t44jpuDj70jKXkneWrpXnq18eZv5xvGOfz/jK8a7h/WngkRrQlq7nbmsUpBxG1GHFlx4Gecc0JEa95ac4Qf9qQyy/8AHNsM17wJ7r6UV5rMHcy+dA9sVgc/FCEuntwZCNsX/zscXQcjnoIZP2KqqqT/b7fxevNl3NXXl+sPP46vYzl3lf+DSfOOkJhVzKOLdmMyad6+JeKSdgBTSv05EZzSc7IxVHT319VFIX7u9Az25sedx2D1s+DXCXpPB+DHmFROFJRy99B2taq+EJYgyUDYNq1h7QvgFQh974aQITzTeg5LqoZyy8nFOLzTA1J34jDpU56/+yZyS8oZ9fYGtiXk8Nx13Wjr6265WDxbQoerYM9C4y7FbELP1vTKXAY58TDqJbB3QGvNnA3xhLXwYFhHf8vFIMRlkmQgbNvBHyAlGoY9CY4ubIzNZP7uXBIHvw6TF4CrN1z9InQZT5+2Pnx37yBaNXNhYkRrbuoTZPl4et0GhWnGnYrZtZ3cechhCceaRULYKAA2x2Vz6EQhdw9td3ozeyGs6ILJQCn1uVIqQym1r0aZj1JqtVIq1vzY3FyulFLvKqXilFIxSqneNd4z3Xx8rFJqeo3yPkqpveb3vKvkf4a4WFWVxlBRv07Qcwol5eblHPzc+fvIMOg8Dh7eC4Mfqn5LhxYerP/HMN66JaJu/gh3HAuuPrDrdFOR/+4P8FFFvFQ2hVMDXedsjMff05kJEa0tH4MQl+Fi7gy+BMacVTYbWKu1DgPWmp8DjAXCzF+zgI/ASB7Ac0B/oB/w3KkEYj7m7hrvO/taQpzbnm+MRd5GPgP2Drzx6xGSc0/y6g09/nI5Bzs7VXefxh2cIPxmOPyTMUch7zhs/YjEwGtZk9eKXUl5HEwrYMORTGYMCsHZQZadEA3DBZOB1noDkHNW8QTgK/P3XwETa5TP04atgLdSqhUwGlittc7RWucCq4Ex5te8tNZbtbFG8Lwa5xLi/CpOwvpXITASOo/neHYJX2xO4Lb+bejfzsrr+0fcBlXlxgzmdS+DUvhe9xJODnas2J3K3I0JuDnZc1v/NtaNU4gaLndoaYDW+tSavieAAPP3gUBSjeOSzWV/VZ58jvJzUkrNwrjjoE0b+Y/UpG3/FApT4YY5oBQ7EnMwaZg+KMTakRlzFFr2MCa4FaTAkEfwDAhlZOcclu1Oobisktv6t5W9CkSDUusOZPMn+nrZO1NrPUdrHam1jvT3lxEYTdbJPNj4hjFyJ/QKAPam5OPmZE/7mvsEW1PE7UYicPOFIY8AxpyDvJIKqkyaO4eEWjlAIc50uckg3dzEg/kxw1yeAgTXOC7IXPZX5UHnKBfi/A4sh9I8GP5UddGe5Dy6t26GvV0DGX8QfjO4+8NVL4CLMaFsWKcWNHdzZFyPVgT7nGeughBWcrnJYAVwakTQdGB5jfJp5lFFA4B8c3PSL8AopVRzc8fxKOAX82sFSqkB5lFE02qcS4hzS9oGbn7QuhcAFVUmDqQW0COoAc3idfOBx2Kh99TqIhdHe1b+/QpenxRuxcCEOLcL9hkopRYAwwA/pVQyxqig14DFSqk7gWPAzebDfwLGAXFACXAHgNY6Ryn1ErDDfNyLWutTndL3Y4xYcgVWmb+EOL/jWyG4v7EEBBCbXkRZpYnwhpQMoDq+mlp7u1ohECEu7ILJQGs95TwvjTzHsRp44Dzn+Rz4/BzlUUD3C8UhBADFWZBzFPpUT1UhJjkPgPAgb+vEJEQjIDOQhVUczSwyVvK8VEnbjMfg/tVFMSn5eLo4EOIr7fBCXC5JBqJumUzGpu/Ht1UXpeadZOL7m7n+gy1kFZVd2vmStoG9E7SKqC6KSc4jPKiZLOsgRC1IMhB1a9ObsO4l+HoSpMWgtWb293upNGlyS8r5+4JdVJ29G9lfOb7NSASOLgCUVlRx+EQhPQK96yR8IZoKSQai7sT/DutfgU7jjOGV829k5YY/2HAkkyfGdOKlid3ZcjSbN1cfvrjzVZZB6i4I7ldddPhEIRVVmp4NrfNYCBsjyUBYjNaaNQfSySkuh4I0Y+N53zC44VO4fQmmijK6r5vJVW3tmTYwhJsjg5nSL5gP1h9l9YH0C18gbQ9UlUGbAdVFpzqPG9SwUiFskCQDYTE7j+dx17woxr+9joKvb4fyYrh5Hjh7oP078Yr3c7Qkiw94HbvKEgCeu7YbPQKb8eji3RzLLv7rC5yr8zg5H193JwJlyKYQtSLJQFyetBh4ozMsvQ+StoPWrDuUjr2d4u8sxCsjip9Cn6TKrxMAi3Yk8dmxAP7o9W+cM3bDt3dAVSUujvZ8eFtv7JTi3q93UlpRdf5rHt8KzUPB4/TG8THJ+fSQzmMhak32QBaXZ+9iKM6EgyuMpaRbdMO9cAgP+3syOX8pm5tP4P6Y9vQr3MoTYzrz8sqDDGjnw5XXjTMWHfnxEfjsaugynuAOV/P2LT2Z+VUUt8/dxqD2vrTz96Cdvzuhfu54ujgaO5olbYf2I6pDKCmvJDajkNHdAs4fpxDiokgyEJfnyK8QMgRu+Rr2LaF822fcf/ITOAm07sXgmZ/yxp5Mnlm+j0kfbcHNyZ7/3NgTOzsFkTMBBVGfG5vTrH2R4R4BrA7tz5s5g3h/fRtqDjCKCPZm0U0BOBdnQJvTTUT7UwswaZlsJoQlSDIQly43EbIOQ58Z4OwJfWbwXeUIvlm2nC8HZuJ35T3g4MykPkH0auPN8z8cYFLvwDMXZ4u8w/gqTIejayFuDR3i1vKhaR1ls6M5XurO0cxiYpLz+PC3o/zxWzTD4E/9BUDDW4ZCCBskyUBcuiO/Go8dR1cXrTuUQW6zbvheO/yMNXna+Xswb2a/s89wmmcARNxqfGXFwQf9cN78JmHj/k1YgCdjurdkd1Ie2Qe/QDt7ovy7VL91b3IeLb1caOHlYvEqCtHUSAeyuHSxv4BPe/BtDxgTvzbHZTG8s3/tOnL9OhirfEZ9DjkJ1cX/GNWRrlWHSHLrDnanf2VPdR4LIWpPkoG4NOXFkLDxjLuCbQk5nKyoYkTnFn/xxot05WywczAmq5n1aWFHJ7tkfshrQ2FpBQAFpRXEZxUTHijJQAhLkGQgLk3CBmPiV9io6qL1hzJwdrBjYDu/2p/fqxUMuA/2fmsMXwVIicIOzeay9nyxORGAfaf6C4K9a39NIYQkA3GJjvwMTh7QdjBgzDpedyiDQe19cXWyt8w1Bj8ELt6w9gXj+fFtoOzw6TiITzfGk19SQUyKkQx6yJ2BEBYhyUBcPK0hdjW0Hw4OxmbuRzOLOZ5TYpkmolNcveGKf0DcGuNOJGkbBHTngdE9KSytZO6meGKS8wj2ccXHXTaVF8ISJBmIi5e+z9jkPex0f8H6Q8b218MtmQwA+s0CryBY/SykRENwf7q08uKaHq34fFMC2xNyCZeVSoWwGEkG4uId+cV4rNFfsO5QBh0DPAhqbuGNZRxdYPiTxiql5UXVi9M9cnUYJyuqyCoqk5FEQliQJANx8WJ/NfYS8DSWfygorWBHYo7l7wpO6TkF/Dsb35uXre7QwpMJEYGATDYTwpJk0pm4OCU5kLwDhj5eXbQpNotKk2ZEpzpKBnb2MOEDOLQSmgVXF88e25kALxci2/rUzXWFaIIkGYiLE7cGtOmM/oJ1hzLwcnGgT9vmdXfdoEjjq4YALxdmj+1cd9cUogmSZiJxcY78Au7+0LoXACaT5rfDGQzt6I+DvfwaCWHr5H+xuLCqSohbbXQcm5eDiEnJJ6uo3LJDSoUQViPJQFxY8nYozT9jFNGS6GScHOwkGQjRSEgyEBe2fynYOxmTzYCT5VUs253CuO4t8XaTSV9CNAaSDMRfKy+BPYug6wRwMYZyrtybRmFpJZP7tbFycEIIS5FkcB5LopOZ+MFmDqQWWDsU6zqwDMryjY1szBZsP047P3f6h8rQTiEaC0kGAFs/hm9nQOZhwJhM9fLKA+xOyuP6DzfzbVTS6WMrSs9468bYTP62YBdxGYX1GHA9ivoCfMOqF6Y7kl5I9LFcJvcLlk3ohWhEZJ5BYTqseR4qT8KBFdBnBl9zE7klFXwxoy9zNsTz9HdR6D0LuLFqFXbp++DmeezzGMRrqw6xKS4LgJ3Hcll6/6DGtetW+n6j83jUK9W7ly3cnoSjvWJS7yArByeEsCRJBlveNdbnn/kr7P0WHfU500zf0Drodob7duDKtispzfgKt6R8kuyC8PEKxWnhVF4te4wDLr15ZnxXIoK9mfrZNu74cgeL7hmIh3Mj+bFGf2l0HEfcChg7mn2/K5lR3Vri6+Fs3diEEBbVSP5qXabCdNjxGYTfAm36Q5v+vFs0nG7732Bi1qfwwafYKXvcOl9DdItJzPjdBbsT+Sx2fpkvXN6iYsp3uIeFAvDBbb2566soHpi/k7nTI3G09YlYNTuO3Yy+gV/2nyCvpIIpfaXjWIjGpmkng1N3Beb1do5lF/PeHsUtfd/hqog8OBEDXSdCs0D6ACvDS1ixJ4XmnVfitOQGnL67FaYth8DeDO/UgpcndufJ7/fyzLJ9vHpDD9tuUz9Px3GwjyuD2vtaLSwhRN2w8Y+vtVDzrsC8sfsbvx7B0d6Oh0aGQegVMPABaBZY/ZY2vm48OCKMFq3bGEnA1Ru+vsFoWwem9GvDg8M7sHBHEu+vi7NGrSznrI7j+MwitsbnMLlvG+zsbDjJCSHOqekmg7PuCval5LNiTyozh4RcXCdws0CYtgIcXGHeRMhPAeAfozpyfa9A3lh9hDUH0uuwAnXoVMdxnxnVHceLdiRhb6e4qY90HAvRGDXNZHCOu4J//3KYZq6OzBra/uLP4xMK05ZBSTZsnwOAUorXJ4XTxseNuZvi6yD4enBWx3F5pYnvopMZ2blF4xotJYSo1jSTwVl3BVuOZrHhSCYPDG9PM1fHSzuXfyfoOAZ2fwNVFQA4Odhxc2QQW+NzOJZdbOno69Z5Oo6zi8uZ0l86joVorJpeMjjHXcHHv8fT0suFaQNDLu+cvadBcQYc+bm6aFKfIOwUfBuVbIGg69H+pWd0HFdUmXhrzRHa+7szNMzfurEJIepM00sGZ90VZBeVsTkui0l9AnFxtL+8c3a4CjxbQ/RX1UWtmrkytKM/30UnU2XSloi87pUVwW+vQYuu1R3HC7cfJz6zmCfHdsFeOo6FaLSaVjIwVcHRdWfcFazad4Iqk2Z8eOvLP6+9A/S63dgNLP/0ncAtkcGcKChlQ2xmbSO3OJNJ886aWOZurNGvsf5fkH8crnkTlKKwtIK318QyoJ0PI7vIUtVCNGa1SgZKqYeUUvuUUvuVUg+by55XSqUopXabv8bVOP5JpVScUuqwUmp0jfIx5rI4pdTs2sT0l+zs4Z4NMObV6qIf9qTSoYUHnVt61u7cvW43Hnd9XV00sksAPu5OLN6RdJ431RNTFax6AmK+NZ6aNE8t28dba47w8sqDLIlOhpRo2PYRRM6EtgMB+Pj3o2QXl/PUuK62PWdCCHFBlz3pTCnVHbgb6AeUAz8rpX40v/yW1vq/Zx3fFZgMdANaA2uUUh3NL38AXA0kAzuUUiu01gcuN7a/ZO8IrsaevekFpWxPzOGhkWG1/2PXvK2x3v/O/xlNUHb2ODnYcX2vQOb9kUh2UZn1lnD47VXY9jGgMJkqeSK2K99GJ3Pvle3Zk5THM9/vYmyLl3HzCICrngcgNe8kczcmMDGiNT2CmlknbiFEvanNnUEXYJvWukRrXQn8DtzwF8dPABZqrcu01glAHEYi6QfEaa3jtdblwELzsXVuZUwaWlO7JqKaek+HgmQ4ur666Ja+wVRUaZbuSrHMNS5V7GrY8B8In4wOHQrL7qdk13c8NDKMJ8Z04sPbevOQ28+45R4iZ9i/qvcs+O+vh9HAY6M7WSduIUS9qk0y2AdcoZTyVUq5AeOAYPNrDyqlYpRSnyulmpvLAoGa7SXJ5rLzlf+JUmqWUipKKRWVmVn7dvgfY1Lp0sqLDi08an0uADqNAzc/2PlldVHHAE8igr1ZHJWE1vXckZyXBN/fDS26UTnuDR5zmM0OU0fec/qAR4KOoJSieWkSd5u+5Vfdn+lbWlBaUcW+lHyW7kph5uBQgpq71W/MQgiruOxkoLU+CLwO/Ar8DOwGqoCPgPZABJAGvFHbIGtcc47WOlJrHenvX7thjsm5Jew8nsf48FYWig5wcIKIKXB4FRRlVBffHBnMkfQidiflWe5aF1JZbuzRUFUJN8/jnyviWLI3l71D52AX2Mt47cgv8MND2Dm44HLdG+xLzefx72J4ddVBvF0duX/4JUzAE0LYtFp1IGutP9Na99FaDwVygSNa63StdZXW2gR8itEMBJDC6TsHgCBz2fnK69TKmDQArrVUE9EpvaeDqdKYhGZ2bc9WuDjasbg+5xysfgZSomDC++S7t2XprhRmDArhrqt6wu1LIKArfHMLJG6EUS8ytE8PHhvViR/2pLI5LpuHRobh5XKJE/CEEDartqOJWpgf22D0F3yjlKr5Uft6jOYkgBXAZKWUs1IqFAgDtgM7gDClVKhSygmjk3lFbeK6GD/EpNIzqBltfC3cDOIXBm0Gwc55YG4W8nRxZFyPVvywJ5WS8krLXu9c9i81Ooz73wfdJrLzeC5aw6iuAcbrrt4wdRm06mnMkeg1DYD7h7Vnct9gegZ7c2v/tnUfpxCiwajtEtZLlFK+QAXwgNY6Tyn1nlIqAtBAInAPgNZ6v1JqMXAAqDQfXwWglHoQ+AWwBz7XWu+vZVx/KSGrmH0pBTx9TZe6uUCf6bD0HkjYAO2uBIw5B9/vTGFlTBo3RQZf4AS1UJoPy/8GQX3h6hcBiE7Mxd5OEdHG+/Rxbj4w6zcjYdkZnwmUUrw2KRyttQwlFaKJqVUy0FpfcY6yqX9x/CvAK+co/wn4qTaxXIof96QCMK6HBfsLauo6wdhKc/mDcPc68PCnX6gPnQI8eenHA3Ru6VV3wzUTN0N5IYx8zujDAKKO5dCttRduTmf9cytVvSrpmcWSCIRoaprWDGSzH2PSiGzbnNbernVzAUdXmPwNFGfCwluhohSlFHOnR+Lp4sjtn21jX0p+3Vw7cSM4uECw0VVTUWVid1Iefdo2v8AbhRBNWZNLBkfSCzmcXsi1PS3ccXy2wN5wwyfGvgArHgStCfZxY+GsAXg4O3D7Z9vYn1oHCSFhAwT3Bwdjgtv+1AJKK0z0DfGx/LWEEI1Gk0sGP+5JxU7B2B4t6/5iXSfAiGdg77fGxC8g2MeNBXcPwM3RntvnbuNgWoHlrlecDen7IHRodVFUYg4AkXJnIIT4C00qGWit+SEmjQHtfGnhWU+btFzxDwifDOtfgX3fA8b2mQtmDcDF0Z7b5m5j7cF0YpLzOJJeyLHsYtILSimvNF36tRI3Go9nJINcgn1cZVMaIcRfqu1oIpvz7xvDqdfuUaXgunchNxGW3WfsiubXkbbebVgwszeTP9vJnV9F/eltgd6uLLlvEC2bXcIf8cSN4OQBrXsBRvKLOpbL0DA/C1VGCNFYNalkoJSyTtu5gzNMng+fj4GfHqsuDkGxxbMVaRFjONjjCUqrTJRVmCgsreDfvxzmvvnRLJw1AGeHi9xnIWEDtBloLMYHHMsuIauojD4h0kQkhPhrTSoZWJW7H9y/FfKTIO949aPdib0EHvqcwAB/GP5/1YcHeLlw3/ydPL/iAK/e0OPC5y88AVlHoNfpkb1Rx3IBpPNYCHFBkgzqk70D+IQaX6dobYw2+v11aB5SvQn92B6tuH9Yez787Sg9Aptx64X2H0441V9weupHVGIOXi4OdPC30EJ8QohGq0l1IDdISsH4tyH0Sljxd6Opx+wfozoxtKM/z63YR7T5U/55JW4wlp9uGV5dFHUsl8gQH+xku0ohxAVIMmgI7B3h5nnGVpyLbofMw0axneLdyRG0aubK/fOjySgsBYyO4fSCUrYczWLFnlQqqkxGEmk7xNjNDcgtLicuo0gmmwkhLoo0EzUUrt5w62KYexXMvwnuWgse/ni7OfHJbT2Z8fE6Zny8HkdXT45mFlNUdnrBu2eGeHBnbqKxMJ3ZqTsJmV8ghLgYkgwakuZt4daF8MU18EFfsHeCskK6VJSwzR5Kil15xf0NInr3pH0LD9r7e/DN9uMc2brI+Jes2V9wLBdHe0XPYG+rVUcIYTskGTQ0gX2MhLBrPji5GfMGnL3A2QO3Le/xiukdGPub8RrQuaUnW2IPkkczPP06c2oQalRiDt0Dm+HieJHDUoUQTZokg4ao3TDj62wtusL/Jhob11xjbCDn6+7EVS6HWVvUmRNbjnHXFe0oragiJjmfGYND6jFoIYQtkw5kW9J+OAx8EHbMhcM/G2U58biePEFuiwH899fDHMsuZl9KPuVVJuk8FkJcNEkGtmbksxDQHZY/YOyzbB6KOnr8TTja2TF7yV52JBqdx5IMhBAXS5KBrXFwhklzobzISAgJv4NnK1qEdOfJcV34Iz6bj38/Sjs/d/w8nK0drRDCRkgysEUtusDVL0Hsr3BgOYRcAUoxuW8wA9r5kH+yQu4KhBCXRJKBrep3N4SNAm2qXrLazk7x2g3h+Lo7MbJLgJUDFELYEhlNZKuUggkfGpvmdLm2ujjEz52op6+SfYyFEJdEkoEt8/CHcf/+U7EkAiHEpZJmIiGEEJIMhBBCSDIQQgiBJAMhhBBIMhBCCIEkAyGEEEgyEEIIgSQDIYQQgNJaWzuGy6KUygSOXebb/YAsC4ZjbY2pPo2pLtC46tOY6gKNqz6XUpe2Wmv/swttNhnUhlIqSmsdae04LKUx1acx1QUaV30aU12gcdXHEnWRZiIhhBCSDIQQQjTdZDDH2gFYWGOqT2OqCzSu+jSmukDjqk+t69Ik+wyEEEKcqaneGQghhKhBkoEQQoimlQyUUmOUUoeVUnFKqdnWjudSKaU+V0plKKX21SjzUUqtVkrFmh9tZvNjpVSwUmq9UuqAUmq/Uuohc7nN1Ukp5aKU2q6U2mOuywvm8lCl1Dbz79wipZSTtWO9WEope6XULqXUj+bntlyXRKXUXqXUbqVUlLnM5n7PTlFKeSulvlNKHVJKHVRKDaxtfZpMMlBK2QMfAGOBrsAUpVRX60Z1yb4ExpxVNhtYq7UOA9aan9uKSuAfWuuuwADgAfO/iS3WqQwYobXuCUQAY5RSA4DXgbe01h2AXOBO64V4yR4CDtZ4bst1ARiutY6oMR7fFn/PTnkH+Flr3RnoifHvVLv6aK2bxBcwEPilxvMngSetHddl1CME2Ffj+WGglfn7VsBha8dYi7otB6629ToBbsBOoD/GrFAHc/kZv4MN+QsIMv9BGQH8CChbrYs53kTA76wym/w9A5oBCZgHAFmqPk3mzgAIBJJqPE82l9m6AK11mvn7E0CANYO5XEqpEKAXsA0brZO5WWU3kAGsBo4CeVrrSvMhtvQ79zbwT8Bkfu6L7dYFQAO/KqWilVKzzGU2+XsGhAKZwBfmZry5Sil3almfppQMGj1tfCSwubHCSikPYAnwsNa6oOZrtlQnrXWV1joC41N1P6CzdSO6PEqp8UCG1jra2rFY0BCtdW+MZuIHlFJDa75oS79ngAPQG/hIa90LKOasJqHLqU9TSgYpQHCN50HmMluXrpRqBWB+zLByPJdEKeWIkQjma62/NxfbdJ201nnAeoymFG+llIP5JVv5nRsMXKeUSgQWYjQVvYNt1gUArXWK+TEDWIqRrG319ywZSNZabzM//w4jOdSqPk0pGewAwswjIpyAycAKK8dkCSuA6ebvp2O0u9sEpZQCPgMOaq3frPGSzdVJKeWvlPI2f++K0fdxECMp3Gg+zCbqorV+UmsdpLUOwfh/sk5rfRs2WBcApZS7Usrz1PfAKGAfNvh7BqC1PgEkKaU6mYtGAgeobX2s3RlSzx0v44AjGG25T1k7nsuIfwGQBlRgfDq4E6Mtdy0QC6wBfKwd5yXUZwjGrWwMsNv8Nc4W6wSEA7vMddkHPGsubwdsB+KAbwFna8d6ifUaBvxoy3Uxx73H/LX/1P99W/w9q1GnCCDK/Pu2DGhe2/rIchRCCCGaVDOREEKI85BkIIQQQpKBEEIISQZCCCGQZCCEEAJJBkIIIZBkIIQQAvh/CVCO+8e37EsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual vs Fitted\n",
    "model_fit.plot_predict(\n",
    "    start=1,\n",
    "    end=60,\n",
    "    dynamic=False,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1521e7e97c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "plt.plot(test_data.index, predictions, color='green', marker='o', linestyle='dashed',label='Predicted Price')\n",
    "plt.plot(test_data.index, test_data['Close'], color='red', label='Actual Price')\n",
    "plt.legend()\n",
    "plt.title('BSESN Stock Prices Prediction')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Prices')\n",
    "plt.xticks(np.arange(1000,1259,100), df['Date'][1000:1259:100])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above image is a zoomed in version. From this can be noticed how the two curves closely follow each other. However, the predicted price seems to look like a “noisy” version of the actual price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis using ARIMA lead overall to appreciable results. This model demonstrated in fact to offer good prediction accuracy and to be relatively fast compared to other alternatives such as RRNs (Recurrent Neural Networks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NLTK's VADER module\n",
    "VADER is an NLTK module that provides sentiment scores based on words used (\"completely\" boosts a score, while \"slightly\" reduces it), on capitalization & punctuation (\"GREAT!!!\" is stronger than \"great.\"), and negations (words like \"isn't\" and \"doesn't\" affect the outcome).\n",
    "<br>To view the source code visit https://www.nltk.org/_modules/nltk/sentiment/vader.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #Importing the PANDAS python library\n",
    "import numpy as np #importing Numpy\n",
    "%matplotlib inline \n",
    "#from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer #initiating VADER instance\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "analyser = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "headlines= pd.read_csv(\"C:\\\\Users\\\\Kv\\\\Desktop\\\\The Sparks Foundation\\\\stock price prediction\\\\india-news-headlines.csv\")\n",
    "headlines.head()\n",
    "# cleaning dataset\n",
    "#Drop rows with missing values\n",
    "headlines.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>headline_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>643711</th>\n",
       "      <td>20101231</td>\n",
       "      <td>Leopard cub found dead near Ballarpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643712</th>\n",
       "      <td>20101231</td>\n",
       "      <td>Massive rally by students for T state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643713</th>\n",
       "      <td>20101231</td>\n",
       "      <td>Men on bike cut elderly woman's earlobes; make...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643714</th>\n",
       "      <td>20101231</td>\n",
       "      <td>3 security guards arrested for chain-snatchings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643715</th>\n",
       "      <td>20101231</td>\n",
       "      <td>Modala Sala</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date                                      headline_text\n",
       "643711  20101231              Leopard cub found dead near Ballarpur\n",
       "643712  20101231              Massive rally by students for T state\n",
       "643713  20101231  Men on bike cut elderly woman's earlobes; make...\n",
       "643714  20101231    3 security guards arrested for chain-snatchings\n",
       "643715  20101231                                        Modala Sala"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 643716 entries, 0 to 643715\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   Date           643716 non-null  datetime64[ns]\n",
      " 1   headline_text  643716 non-null  object        \n",
      "dtypes: datetime64[ns](1), object(1)\n",
      "memory usage: 14.7+ MB\n"
     ]
    }
   ],
   "source": [
    "headlines[\"Date\"] = pd.to_datetime(headlines[\"Date\"],format='%Y%m%d')\n",
    "headlines.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(643716, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>headline_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>Hit list cited to shoot down CM's website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-02</td>\n",
       "      <td>20 dancing girls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-03</td>\n",
       "      <td>Furore over road model patent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>Dual citizenship for Pravasi Indians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>Sania sizzles in doubles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>2010-12-27</td>\n",
       "      <td>PM's heart surgeon injured in road mishap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>2010-12-28</td>\n",
       "      <td>Sangeetha 's fun-filled B'day bash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>2010-12-29</td>\n",
       "      <td>Diwali Gala Charity Banquet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>2010-12-30</td>\n",
       "      <td>Watch: Top news in one minute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>The argument guide</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1826 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                              headline_text\n",
       "0    2006-01-01  Hit list cited to shoot down CM's website\n",
       "1    2006-01-02                           20 dancing girls\n",
       "2    2006-01-03              Furore over road model patent\n",
       "3    2006-01-04       Dual citizenship for Pravasi Indians\n",
       "4    2006-01-05                   Sania sizzles in doubles\n",
       "...         ...                                        ...\n",
       "1821 2010-12-27  PM's heart surgeon injured in road mishap\n",
       "1822 2010-12-28         Sangeetha 's fun-filled B'day bash\n",
       "1823 2010-12-29                Diwali Gala Charity Banquet\n",
       "1824 2010-12-30              Watch: Top news in one minute\n",
       "1825 2010-12-31                         The argument guide\n",
       "\n",
       "[1826 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping the headlines for each day\n",
    "#headlines['headline_text'] = headlines.groupby(['Date']).transform(lambda x : ' '.join(x)) \n",
    "headlines = headlines.drop_duplicates(subset='Date', keep='first', inplace=False) \n",
    "headlines.reset_index(inplace = True, drop = True)\n",
    "headlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1826, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1826"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating score for each news headline in the dataframe/dataset\n",
    "i=0 #counter\n",
    "compval1 = [ ]  #empty list to hold our computed 'compound' VADER scores\n",
    "while i<len(headlines):\n",
    "    \n",
    "    k = analyser.polarity_scores(headlines.iloc[i]['headline_text'])\n",
    "    compval1.append(k['compound'])\n",
    "    \n",
    "    i = i+1\n",
    "    \n",
    "#converting sentiment values to numpy for easier usage\n",
    "compval1 = np.array(compval1)\n",
    "len(compval1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "headlines['VADER score'] = compval1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>VADER score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>Hit list cited to shoot down CM's website</td>\n",
       "      <td>-0.3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-02</td>\n",
       "      <td>20 dancing girls</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-03</td>\n",
       "      <td>Furore over road model patent</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>Dual citizenship for Pravasi Indians</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>Sania sizzles in doubles</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>20 action babes</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-01-07</td>\n",
       "      <td>Caught after 4 uncertain days</td>\n",
       "      <td>-0.2960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006-01-08</td>\n",
       "      <td>20 swimsuit sirens</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006-01-09</td>\n",
       "      <td>The Mahatma's tale</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006-01-10</td>\n",
       "      <td>Amar Singh moves SC; Advani; Mamata join tap d...</td>\n",
       "      <td>0.2960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2006-01-11</td>\n",
       "      <td>Sharon's arch rival plays a friendly card</td>\n",
       "      <td>0.6369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006-01-12</td>\n",
       "      <td>Pretty but bad</td>\n",
       "      <td>-0.5647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006-01-13</td>\n",
       "      <td>Ash + Abhishek?</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2006-01-14</td>\n",
       "      <td>Relief for car owners</td>\n",
       "      <td>0.4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2006-01-15</td>\n",
       "      <td>Read My Lips: TN Shanbhag;Proprietor; Strand B...</td>\n",
       "      <td>-0.2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006-01-16</td>\n",
       "      <td>Babes you should see</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006-01-17</td>\n",
       "      <td>Sweet 17 for Barcelona</td>\n",
       "      <td>0.4588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2006-01-18</td>\n",
       "      <td>IIM-B issue: Arjun goes 'yes-no-yes'</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2006-01-19</td>\n",
       "      <td>Babes; sing for us!</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2006-01-20</td>\n",
       "      <td>An opinion worth thousands of crores</td>\n",
       "      <td>0.2263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date                                      headline_text  VADER score\n",
       "0  2006-01-01          Hit list cited to shoot down CM's website      -0.3400\n",
       "1  2006-01-02                                   20 dancing girls       0.0000\n",
       "2  2006-01-03                      Furore over road model patent       0.0000\n",
       "3  2006-01-04               Dual citizenship for Pravasi Indians       0.0000\n",
       "4  2006-01-05                           Sania sizzles in doubles       0.0000\n",
       "5  2006-01-06                                    20 action babes       0.0000\n",
       "6  2006-01-07                      Caught after 4 uncertain days      -0.2960\n",
       "7  2006-01-08                                 20 swimsuit sirens       0.0000\n",
       "8  2006-01-09                                 The Mahatma's tale       0.0000\n",
       "9  2006-01-10  Amar Singh moves SC; Advani; Mamata join tap d...       0.2960\n",
       "10 2006-01-11          Sharon's arch rival plays a friendly card       0.6369\n",
       "11 2006-01-12                                     Pretty but bad      -0.5647\n",
       "12 2006-01-13                                    Ash + Abhishek?       0.0000\n",
       "13 2006-01-14                              Relief for car owners       0.4767\n",
       "14 2006-01-15  Read My Lips: TN Shanbhag;Proprietor; Strand B...      -0.2023\n",
       "15 2006-01-16                               Babes you should see       0.0000\n",
       "16 2006-01-17                             Sweet 17 for Barcelona       0.4588\n",
       "17 2006-01-18               IIM-B issue: Arjun goes 'yes-no-yes'       0.0000\n",
       "18 2006-01-19                                Babes; sing for us!       0.0000\n",
       "19 2006-01-20               An opinion worth thousands of crores       0.2263"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "predicted_value = [ ] #empty series to hold our predicted values\n",
    "while(i<len(headlines)):\n",
    "    if ((headlines.iloc[i]['VADER score'] >= 0.1)):\n",
    "        predicted_value.append('positive')\n",
    "        i = i+1\n",
    "    elif ((headlines.iloc[i]['VADER score'] > -0.1) & (headlines.iloc[i]['VADER score'] < 0.1)):\n",
    "        predicted_value.append('neutral')\n",
    "        i = i+1\n",
    "    elif ((headlines.iloc[i]['VADER score'] <= -0.1)):\n",
    "        predicted_value.append('negative')\n",
    "        i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>VADER score</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>Hit list cited to shoot down CM's website</td>\n",
       "      <td>-0.340</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-02</td>\n",
       "      <td>20 dancing girls</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-03</td>\n",
       "      <td>Furore over road model patent</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>Dual citizenship for Pravasi Indians</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>Sania sizzles in doubles</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>20 action babes</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-01-07</td>\n",
       "      <td>Caught after 4 uncertain days</td>\n",
       "      <td>-0.296</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006-01-08</td>\n",
       "      <td>20 swimsuit sirens</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006-01-09</td>\n",
       "      <td>The Mahatma's tale</td>\n",
       "      <td>0.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006-01-10</td>\n",
       "      <td>Amar Singh moves SC; Advani; Mamata join tap d...</td>\n",
       "      <td>0.296</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date                                      headline_text  VADER score  \\\n",
       "0 2006-01-01          Hit list cited to shoot down CM's website       -0.340   \n",
       "1 2006-01-02                                   20 dancing girls        0.000   \n",
       "2 2006-01-03                      Furore over road model patent        0.000   \n",
       "3 2006-01-04               Dual citizenship for Pravasi Indians        0.000   \n",
       "4 2006-01-05                           Sania sizzles in doubles        0.000   \n",
       "5 2006-01-06                                    20 action babes        0.000   \n",
       "6 2006-01-07                      Caught after 4 uncertain days       -0.296   \n",
       "7 2006-01-08                                 20 swimsuit sirens        0.000   \n",
       "8 2006-01-09                                 The Mahatma's tale        0.000   \n",
       "9 2006-01-10  Amar Singh moves SC; Advani; Mamata join tap d...        0.296   \n",
       "\n",
       "  sentiment  \n",
       "0  negative  \n",
       "1   neutral  \n",
       "2   neutral  \n",
       "3   neutral  \n",
       "4   neutral  \n",
       "5   neutral  \n",
       "6  negative  \n",
       "7   neutral  \n",
       "8   neutral  \n",
       "9  positive  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines['sentiment'] = predicted_value\n",
    "headlines.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neutral     1046\n",
       "positive     496\n",
       "negative     284\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines.sentiment.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hybrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>VADER score</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-02</td>\n",
       "      <td>9390.139648</td>\n",
       "      <td>20 dancing girls</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-03</td>\n",
       "      <td>9539.370117</td>\n",
       "      <td>Furore over road model patent</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>9648.080078</td>\n",
       "      <td>Dual citizenship for Pravasi Indians</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>9617.740234</td>\n",
       "      <td>Sania sizzles in doubles</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>9640.290039</td>\n",
       "      <td>20 action babes</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>2010-12-24</td>\n",
       "      <td>20073.660160</td>\n",
       "      <td>The Tourist</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>2010-12-27</td>\n",
       "      <td>20028.929690</td>\n",
       "      <td>PM's heart surgeon injured in road mishap</td>\n",
       "      <td>-0.4019</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2010-12-28</td>\n",
       "      <td>20025.419920</td>\n",
       "      <td>Sangeetha 's fun-filled B'day bash</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2010-12-29</td>\n",
       "      <td>20256.029300</td>\n",
       "      <td>Diwali Gala Charity Banquet</td>\n",
       "      <td>0.4215</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2010-12-30</td>\n",
       "      <td>20389.070310</td>\n",
       "      <td>Watch: Top news in one minute</td>\n",
       "      <td>0.2023</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1234 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date         Close                              headline_text  \\\n",
       "0    2006-01-02   9390.139648                           20 dancing girls   \n",
       "1    2006-01-03   9539.370117              Furore over road model patent   \n",
       "2    2006-01-04   9648.080078       Dual citizenship for Pravasi Indians   \n",
       "3    2006-01-05   9617.740234                   Sania sizzles in doubles   \n",
       "4    2006-01-06   9640.290039                            20 action babes   \n",
       "...         ...           ...                                        ...   \n",
       "1229 2010-12-24  20073.660160                                The Tourist   \n",
       "1230 2010-12-27  20028.929690  PM's heart surgeon injured in road mishap   \n",
       "1231 2010-12-28  20025.419920         Sangeetha 's fun-filled B'day bash   \n",
       "1232 2010-12-29  20256.029300                Diwali Gala Charity Banquet   \n",
       "1233 2010-12-30  20389.070310              Watch: Top news in one minute   \n",
       "\n",
       "      VADER score sentiment  \n",
       "0          0.0000   neutral  \n",
       "1          0.0000   neutral  \n",
       "2          0.0000   neutral  \n",
       "3          0.0000   neutral  \n",
       "4          0.0000   neutral  \n",
       "...           ...       ...  \n",
       "1229       0.0000   neutral  \n",
       "1230      -0.4019  negative  \n",
       "1231       0.0000   neutral  \n",
       "1232       0.4215  positive  \n",
       "1233       0.2023  positive  \n",
       "\n",
       "[1234 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge = pd.merge(df, headlines, how='left', on='Date')\n",
    "df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9390.139648</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9539.370117</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9648.080078</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9617.740234</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9640.290039</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>20073.660160</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>20028.929690</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>20025.419920</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>20256.029300</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>20389.070310</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1234 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close sentiment\n",
       "0      9390.139648   neutral\n",
       "1      9539.370117   neutral\n",
       "2      9648.080078   neutral\n",
       "3      9617.740234   neutral\n",
       "4      9640.290039   neutral\n",
       "...            ...       ...\n",
       "1229  20073.660160   neutral\n",
       "1230  20028.929690  negative\n",
       "1231  20025.419920   neutral\n",
       "1232  20256.029300  positive\n",
       "1233  20389.070310  positive\n",
       "\n",
       "[1234 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df=df_merge[['Close','sentiment']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>negative</th>\n",
       "      <td>14631.007866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>14775.091689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive</th>\n",
       "      <td>14533.227952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close\n",
       "sentiment              \n",
       "negative   14631.007866\n",
       "neutral    14775.091689\n",
       "positive   14533.227952"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.groupby(['sentiment']).mean()"
   ]
  }
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