Time_series_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Time series analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1W5YDeNMp6DW",
        "outputId": "843ba5fa-b847-4d14-bc57-62774c2786ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P83r88mF5lJn"
      },
      "source": [
        "# !pip install jupyterlab ipywidgets"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scxPdbOtp9i_"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "from matplotlib import pyplot as plt\n",
        "import glob\n",
        "from bs4 import BeautifulSoup\n",
        "import datetime\n",
        "import re"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6caSMaYGrCt7"
      },
      "source": [
        "def percentage(part, whole):\n",
        "    return 100 * float(part)/float(whole)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7fgoenFqACo"
      },
      "source": [
        "tweets_path = '/content/drive/My Drive/database/Tweets/bitcoin/'\n",
        "bitcoin_file = '/content/drive/My Drive/database/bitcoinHistoricalData/bitcoin.csv'\n",
        "crypto_name = 'bitcoin'"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWFeLiixqAiF"
      },
      "source": [
        "# initializing dataframes\n",
        "df_noOfTweets = pd.DataFrame(columns=['date','positive', 'neutral', 'negative', 'total']) # for the barchart of no. of tweets\n",
        "df_polarity_line = pd.DataFrame(columns=['date','polarity']) # for the polarity line chart\n",
        "\n",
        "# Tweets about each crypto is taken from saved txt files with dates ranging from 2017-09-01 to 2017-11-30\n",
        "# Tweets are collected by selenium automated script written in python\n",
        "for f in glob.glob( str(tweets_path) + crypto_name + '*.txt'):\n",
        "    df_datawise = pd.read_csv(f, sep='\\t', encoding='utf-8', parse_dates=True)\n",
        "    polarity = 0.00\n",
        "    neutral = positive = negative = 0\n",
        "    for idx,row in df_datawise.iterrows():\n",
        "        polarity += float(row[\"polarity\"])\n",
        "        \n",
        "        # adding reaction of how people are reacting to find average later\n",
        "        if (float(row[\"polarity\"]) >= -0.1 and float(row[\"polarity\"]) <= 0.1):  \n",
        "            neutral += 1\n",
        "        elif (float(row[\"polarity\"]) > 0.1):\n",
        "            positive += 1\n",
        "        elif (float(row[\"polarity\"]) <-0.1):\n",
        "            negative += 1\n",
        "    positive = percentage(positive, len(df_datawise.index))\n",
        "    negative = percentage(negative, len(df_datawise.index))\n",
        "    neutral = percentage(neutral, len(df_datawise.index))\n",
        "    # below df format is for barchart with sentiments\n",
        "    df_temp_df_noOfTweets = pd.DataFrame({'date': df_datawise.iloc[0]['date'], 'positive': ((positive/100)*len(df_datawise.index)),'neutral': ((neutral/100)*len(df_datawise.index)), 'negative': ((negative/100)*len(df_datawise.index)), 'total': len(df_datawise.index)}, index=[0])\n",
        "    # print(df_temp_df_noOfTweets)\n",
        "    # polarity /= df_temp_df_noOfTweets['total']\n",
        "    df_noOfTweets = df_noOfTweets.append(df_temp_df_noOfTweets,sort=True, ignore_index=True)\n",
        "    \n",
        "    # below df format is for polarity_line with sentiments\n",
        "    df_temp_polarity_line = pd.DataFrame({'date': df_datawise.iloc[0]['date'], 'polarity': polarity}, index=[0])\n",
        "    df_polarity_line = df_polarity_line.append(df_temp_polarity_line, sort=True, ignore_index=True)\n",
        "\n",
        "df_noOfTweets = df_noOfTweets.sort_values(by = 'date')\n",
        "df_noOfTweets.reset_index(inplace=True)\n",
        "required_tweets = ['date','positive','neutral','negative','total']\n",
        "df_noOfTweets = df_noOfTweets[required_tweets]\n",
        "\n",
        "df_polarity_line = df_polarity_line.sort_values(by = 'date')\n",
        "df_polarity_line.reset_index(inplace=True)\n",
        "required_polarity = ['date','polarity']\n",
        "df_polarity_line = df_polarity_line[required_polarity]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIK9a8AcqlcW",
        "outputId": "12d54878-1edd-467e-b514-8310d0570509",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(df_polarity_line.shape)    \n",
        "print(len(df_polarity_line)) "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(91, 2)\n",
            "91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4y3CauAbrYnY",
        "outputId": "b8f04bbb-4cfe-4596-cf83-c70ff68edfd4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "prediction_days = 15\n",
        "req = ['polarity']\n",
        "df_polarity_line = df_polarity_line[req]\n",
        "df_train= df_polarity_line[:len(df_polarity_line) - prediction_days] \n",
        "df_test= df_polarity_line[len(df_polarity_line)-prediction_days:]\n",
        "\n",
        "training_set = df_train.values\n",
        "print(training_set)\n",
        "training_set = np.reshape(training_set, (len(training_set), 1))\n",
        "test_set = df_test.values\n",
        "inputs = np.reshape(test_set, (len(test_set), 1))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 44.84127886]\n",
            " [ 34.72393849]\n",
            " [ 30.12381043]\n",
            " [ 43.90672063]\n",
            " [ 51.60989305]\n",
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            " [ 38.1382328 ]\n",
            " [ 56.79393598]\n",
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            " [ 40.71855783]\n",
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            " [ 46.36906472]\n",
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            " [ 33.32747926]\n",
            " [ 47.538048  ]\n",
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            " [ 44.61395399]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVGZ-it30DE5"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range=(0, 1)) # changed here\n",
        "training_set = sc.fit_transform(training_set)\n",
        "inputs = sc.fit_transform(inputs)\n",
        "\n",
        "X_train = training_set[0:len(training_set)-1]\n",
        "y_train = training_set[1:len(training_set)]\n",
        "\n",
        "X_train = np.reshape(X_train, (len(X_train), 1, 1))\n",
        "inputs = np.reshape(inputs, (len(inputs), 1, 1))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ax30MMVk0NJK",
        "outputId": "a9818282-2379-49f3-a8a9-66416f4c491f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Importing the Keras libraries and packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "\n",
        "# Initialising the RNN\n",
        "regressor = Sequential()\n",
        "\n",
        "# Adding the input layer and the LSTM layer (4 LSTM blocks or neurons).\n",
        "# Default sigmoid activation function is used for the LSTM blocks.\n",
        "regressor.add(LSTM(units = 4, activation = 'sigmoid', input_shape=(1, 1)))\n",
        "\n",
        "# Adding the output layer\n",
        "regressor.add(Dense(units = 1))\n",
        "\n",
        "# Compiling the RNN\n",
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "\n",
        "# Fitting the RNN to the Training set\n",
        "regressor.fit(X_train, y_train, batch_size = 5, epochs = 4)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "15/15 [==============================] - 0s 1ms/step - loss: 0.8911\n",
            "Epoch 2/4\n",
            "15/15 [==============================] - 0s 1ms/step - loss: 0.8247\n",
            "Epoch 3/4\n",
            "15/15 [==============================] - 0s 1ms/step - loss: 0.7630\n",
            "Epoch 4/4\n",
            "15/15 [==============================] - 0s 1ms/step - loss: 0.7060\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff00ed8a828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9N6SCtHc0TuE",
        "outputId": "7c829aef-5f89-46d2-bd9c-8328f78a553d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predicted_polarity = regressor.predict(inputs)\n",
        "predicted_polarity = sc.inverse_transform(predicted_polarity)\n",
        "dataframe = pd.DataFrame.from_records(predicted_polarity)\n",
        "print(dataframe)\n",
        "dataframe.columns = ['Predicted_Polarity']"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            0\n",
            "0   28.135138\n",
            "1   28.331886\n",
            "2   26.263968\n",
            "3   26.497793\n",
            "4   24.950741\n",
            "5   27.413074\n",
            "6   29.591003\n",
            "7   27.260651\n",
            "8   27.520458\n",
            "9   26.123434\n",
            "10  28.759674\n",
            "11  32.157257\n",
            "12  31.257305\n",
            "13  35.698135\n",
            "14  30.505526\n",
            "            0\n",
            "0   28.135138\n",
            "1   28.331886\n",
            "2   26.263968\n",
            "3   26.497793\n",
            "4   24.950741\n",
            "5   27.413074\n",
            "6   29.591003\n",
            "7   27.260651\n",
            "8   27.520458\n",
            "9   26.123434\n",
            "10  28.759674\n",
            "11  32.157257\n",
            "12  31.257305\n",
            "13  35.698135\n",
            "14  30.505526\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTIATVgG1DNT",
        "outputId": "28041e70-541c-4f3a-d19b-fc2fd136fa79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data = pd.read_csv(bitcoin_file)\n",
        "req_features = ['Timestamp','Close']\n",
        "df_bitcoin = pd.DataFrame(columns=['date','close'])\n",
        "frequency=0\n",
        "sum=0\n",
        "cur_date=\"\"\n",
        "for index in data.index:\n",
        "  date = data['Timestamp'][index]\n",
        "  date = date.split(' ')[0]\n",
        "  value = float(data['Close'][index])\n",
        "  if date != cur_date:\n",
        "    if cur_date != \"\":\n",
        "      sum = sum/frequency\n",
        "      temp = pd.DataFrame({\"date\":[cur_date], \"close\":[sum]}) \n",
        "      df_bitcoin = df_bitcoin.append(temp, ignore_index = True)\n",
        "    cur_date = date\n",
        "    frequency = 1\n",
        "    sum = value\n",
        "  else:\n",
        "    sum += value\n",
        "    frequency += 1  \n",
        "sum = sum/frequency\n",
        "temp = pd.DataFrame({\"date\":[cur_date], \"close\":[sum]}) \n",
        "df_bitcoin = df_bitcoin.append(temp, ignore_index = True)\n",
        "print(df_bitcoin)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "           date         close\n",
            "0    2017-01-01    972.979660\n",
            "1    2017-01-02   1009.515215\n",
            "2    2017-01-03   1016.678812\n",
            "3    2017-01-04   1061.083632\n",
            "4    2017-01-05   1087.840451\n",
            "..          ...           ...\n",
            "360  2017-12-27  15823.691576\n",
            "361  2017-12-28  14551.233222\n",
            "362  2017-12-29  14428.807889\n",
            "363  2017-12-30  13898.596042\n",
            "364  2017-12-31  13197.474549\n",
            "\n",
            "[365 rows x 2 columns]\n",
            "           date         close\n",
            "0    2017-01-01    972.979660\n",
            "1    2017-01-02   1009.515215\n",
            "2    2017-01-03   1016.678812\n",
            "3    2017-01-04   1061.083632\n",
            "4    2017-01-05   1087.840451\n",
            "..          ...           ...\n",
            "360  2017-12-27  15823.691576\n",
            "361  2017-12-28  14551.233222\n",
            "362  2017-12-29  14428.807889\n",
            "363  2017-12-30  13898.596042\n",
            "364  2017-12-31  13197.474549\n",
            "\n",
            "[365 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGO_Lsbv6tte",
        "outputId": "3226d0f2-d1bd-47f4-c13d-dd3ff93bfb58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df_bitcoin1 = df_bitcoin[len(df_bitcoin)-46:]\n",
        "df_bitcoin2 = df_bitcoin1[:15]\n",
        "\n",
        "df_bitcoin2.reset_index(inplace=True)\n",
        "# print(df_bitcoin2)\n",
        "req = ['date','close']\n",
        "df_bitcoin3 = df_bitcoin2[req]\n",
        "# print(df_bitcoin3)\n",
        "# print(dataframe)\n",
        "dataframe1 = pd.concat([df_bitcoin3, dataframe.reindex(df_bitcoin3.index)], axis=1)\n",
        "print(dataframe1)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          date         close  Predicted_Polarity\n",
            "0   2017-11-16   7334.280826           28.135138\n",
            "1   2017-11-17   7844.846618           28.331886\n",
            "2   2017-11-18   7708.000882           26.263968\n",
            "3   2017-11-19   7783.521396           26.497793\n",
            "4   2017-11-20   8077.297201           24.950741\n",
            "5   2017-11-21   8202.975861           27.413074\n",
            "6   2017-11-22   8201.752792           29.591003\n",
            "7   2017-11-23   8202.604819           27.260651\n",
            "8   2017-11-24   8164.645583           27.520458\n",
            "9   2017-11-25   8390.028278           26.123434\n",
            "10  2017-11-26   8988.064681           28.759674\n",
            "11  2017-11-27   9619.731590           32.157257\n",
            "12  2017-11-28   9822.075715           31.257305\n",
            "13  2017-11-29  10484.614472           35.698135\n",
            "14  2017-11-30  10047.583646           30.505526\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zajqY8Jz-X_C",
        "outputId": "2cab5092-59a5-4ada-b3d0-d878d565861b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import plotly.graph_objs as go\n",
        "from plotly.offline import *\n",
        "import plotly\n",
        "\n",
        "trace1 = go.Scatter(\n",
        "    x=dataframe1['date'],\n",
        "    y=dataframe1['Predicted_Polarity'],\n",
        "    mode = 'lines',\n",
        "    name='Predicted polarity'\n",
        ")\n",
        "\n",
        "#total number of tweets in dataset\n",
        "# print(df_noOfTweets['total'].sum())\n",
        "\n",
        "trace2 = go.Scatter(\n",
        "    x=dataframe1['date'],\n",
        "    y=dataframe1['close'],\n",
        "    mode = 'lines',\n",
        "    name='Bitcoin price',\n",
        "    yaxis='y2'\n",
        ")\n",
        "data = [trace1, trace2]\n",
        "layout = go.Layout(\n",
        "    title='Sentiment Polarity Vs. Closing Price (in USD)',\n",
        "    yaxis=dict(\n",
        "        title='Predicted polarity'\n",
        "    ),\n",
        "    yaxis2=dict(\n",
        "        title='Closing Price',\n",
        "        titlefont=dict(\n",
        "            color='rgb(148, 103, 189)'\n",
        "        ),\n",
        "        tickfont=dict(\n",
        "            color='rgb(148, 103, 189)'\n",
        "        ),\n",
        "        overlaying='y',\n",
        "        side='right'\n",
        "    )\n",
        ")\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "plotly.offline.iplot(fig, filename='No_Of_Tweets.html') # Plot No. of Tweets\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Sentiment Polarity Vs. Closing Price (in USD)\"}, \"yaxis\": {\"title\": {\"text\": \"Closing Price.\"}}, \"yaxis2\": {\"overlaying\": \"y\", \"side\": \"right\", \"tickfont\": {\"color\": \"rgb(148, 103, 189)\"}, \"title\": {\"font\": {\"color\": \"rgb(148, 103, 189)\"}, \"text\": \"Polarity\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('dc43ba17-ba9e-4eaa-95cc-312de00633a5');\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPIDY5OpBV0-",
        "outputId": "b3c61b32-1da7-40ab-93c0-c8f9790ec9c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# Regression in polarity vs closing price\n",
        "import seaborn as sns\n",
        "import matplotlib as plt\n",
        "sns.regplot(x=dataframe1['Predicted_Polarity'], y=dataframe1['close'], color='red', marker='*')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff00a017be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFoilWBXBsR6",
        "outputId": "77762206-7b04-4920-fbe6-5bcf2dc21e33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Calculating the Pearson coefficient\n",
        "from scipy import stats\n",
        "pearson_coef, p_value = stats.pearsonr(dataframe1['Predicted_Polarity'], dataframe1['close'])\n",
        "print(pearson_coef)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8317188691680245\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}