ethereum_market_analysis.ipynb

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      "source": [
        "## Predicting Market Movement of Ethereum with Sentiments from Twitter\n",
        "\n",
        "####Abstract. \n",
        "As with any other industry, cryptocurrency and its market are also influenced by a variety of factors. These can range from general sentiments about blockchain technology and digital currencies, to more specific events like exchange hacks or partnerships between different companies. Twitter has developed into a huge social tool used all over the world for the ex-change of ideas and views. As a consequence of this, cryptocurren-cy investors and traders are looking to utilize the platform so that it can assist them in making sound financial choices. It has been demonstrated that the sentiments gleaned from the contents of Twitter, specifically tweets and comments, are a reliable indicator of the movements of markets. The objec-tive of this paper is to investigate and further explore how sentiments and volume of data from twitter can influence the movement of Ethereum Mar-ket. A number of different classification models were developed based on three features; for a given day, an average sentiment score of tweets, the volume of tweet and an interest score from google trends. Random Forest Classifier and Naïve Bayes model were trained to predict the change in Ethereum market given those features. \n"
      ],
      "metadata": {
        "id": "KtXstgGJ2wQs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "1rHUwYLHFMbd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Imports"
      ],
      "metadata": {
        "id": "Oe2oAWcQFPtD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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        "outputId": "d2580a08-5323-40cc-9c23-984ddad1eb1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          "metadata": {},
          "execution_count": 2
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      ],
      "source": [
        "# imports\n",
        "\n",
        "import requests\n",
        "import pandas as pd\n",
        "\n",
        "import nltk\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('vader_lexicon')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# twitter api credential\n",
        "bearer_token = \"AAAAAAAAAAAAAAAAAAAAALmDhAEAAAAAo00TTYY5BIu%2FcnSpPmpHGzlk7Ro%3Dw6fjC6W57HeVqQkthB5royMsaHfAkBBx9FoZNZmjUVvux8YSpK\""
      ],
      "metadata": {
        "id": "OBv6VBgbM3Wk"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting Data"
      ],
      "metadata": {
        "id": "7qnQiXH1M_cE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Twitter API Helper functions"
      ],
      "metadata": {
        "id": "zlzRCsJRlCZO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function for creating headers\n",
        "def create_headers(bearer_token):\n",
        "    headers = {\"Authorization\": \"Bearer {}\".format(bearer_token)}\n",
        "    return headers"
      ],
      "metadata": {
        "id": "zGNB9Hy9M6gb"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating an actual endpoint\n",
        "def connect_to_endpoint(url, headers, params, next_token = None):\n",
        "    params['next_token'] = next_token   #params object received from create_url function\n",
        "    response = requests.request(\"GET\", url, headers = headers, params = params)\n",
        "    print(\"Endpoint Response Code: \" + str(response.status_code))\n",
        "    if response.status_code != 200:\n",
        "        raise Exception(response.status_code, response.text)\n",
        "    return response.json()"
      ],
      "metadata": {
        "id": "cu4oybI3NvDH"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# function for creating a url for searching tweets containing a query\n",
        "def create_search_url(keyword, start_date, end_date, max_results = 10):\n",
        "    \n",
        "    search_url = \"https://api.twitter.com/2/tweets/search/all\" #Change to the endpoint you want to collect data from\n",
        "\n",
        "    #change params based on the endpoint you are using\n",
        "    query_params = {'query': keyword,\n",
        "                    'start_time': start_date,\n",
        "                    'end_time': end_date,\n",
        "                    'max_results': max_results,\n",
        "                    'tweet.fields': 'id,text,author_id,created_at,lang,public_metrics',\n",
        "                    'next_token': {}}\n",
        "    return (search_url, query_params)"
      ],
      "metadata": {
        "id": "5O0EzISLNKGQ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# function for creating a url for requesting volume of data in a specified range of time\n",
        "\n",
        "def create_volume_url(keyword, start_date, end_date):\n",
        "    \n",
        "    search_url = \"https://api.twitter.com/2/tweets/counts/all\" #Change to the endpoint you want to collect data from\n",
        "\n",
        "    #change params based on the endpoint you are using\n",
        "    query_params = {'query': keyword,\n",
        "                    'start_time': start_date,\n",
        "                    'end_time': end_date,\n",
        "                    'granularity': 'day',\n",
        "                    'next_token': '1jzu9lk96azp0b3x7888hrpw8fgwirw5wnhdu5ji15z1'}\n",
        "    return (search_url, query_params)"
      ],
      "metadata": {
        "id": "uUvXUKTKNi2f"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tweet Volume from March to July 2022"
      ],
      "metadata": {
        "id": "F0kYC_7ts9Ey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# meta details\n",
        "headers = create_headers(bearer_token)\n",
        "keyword = \"ethereum lang:en\"\n",
        "start_time = '2022-03-01T00:00:00.000Z' # March 2022\n",
        "end_time = '2022-08-01T00:00:00.000Z' # July 2022\n",
        "next_token = None\n",
        "\n",
        "# loop control\n",
        "has_next_page = True\n",
        "i = 1\n",
        "\n",
        "df1 = None\n",
        "df2 = None\n",
        "df3 = None\n",
        "df4 = None\n",
        "df5 = None\n",
        "df6 = None\n",
        "\n",
        "while(has_next_page):\n",
        "  # create url\n",
        "  url = create_volume_url(keyword, start_time,end_time)\n",
        "\n",
        "  # connect to endpoint\n",
        "  json_response = connect_to_endpoint(url[0], headers, url[1], next_token)\n",
        "\n",
        "  # create a dataframe\n",
        "  if i == 1:\n",
        "    df1 = pd.DataFrame(json_response['data'])\n",
        "  elif i == 2:\n",
        "    df2 = pd.DataFrame(json_response['data'])\n",
        "  elif i == 3:\n",
        "    df3 = pd.DataFrame(json_response['data'])\n",
        "  elif i == 4:\n",
        "    df4 = pd.DataFrame(json_response['data'])\n",
        "  elif i == 5:\n",
        "    df5 = pd.DataFrame(json_response['data'])\n",
        "  elif i == 6:\n",
        "      df6 = pd.DataFrame(json_response['data'])\n",
        "\n",
        "  i = i +  1\n",
        "  print(i)\n",
        "\n",
        "  try:\n",
        "    json_response['meta']['next_token']\n",
        "    next_token = json_response['meta']['next_token']\n",
        "  except KeyError:\n",
        "    has_next_page = False\n",
        "  "
      ],
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Endpoint Response Code: 200\n",
            "2\n",
            "Endpoint Response Code: 200\n",
            "3\n",
            "Endpoint Response Code: 200\n",
            "4\n",
            "Endpoint Response Code: 200\n",
            "5\n",
            "Endpoint Response Code: 200\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GxLLl1uWqmB1"
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      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DataFrame for tweet counts\n",
        "volume_df = pd.concat([df1, df2, df3, df4, df5], ignore_index=True, sort=False)"
      ],
      "metadata": {
        "id": "bdzedq62WR1i"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# save to a csv file\n",
        "volume_df.to_csv('tweets_volume.csv', encoding='utf-8', index=False)"
      ],
      "metadata": {
        "id": "7LCWQPRIsCsS"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volume_df = pd.read_csv('tweets_volume.csv')\n",
        "volume_df.head()"
      ],
      "metadata": {
        "id": "q7adxdrhSqf2",
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          "height": 206
        },
        "outputId": "299b2f13-86a5-46fc-d99c-22781ed09374"
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      "execution_count": 11,
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              "                        end                     start  tweet_count\n",
              "0  2022-07-02T00:00:00.000Z  2022-07-01T00:00:00.000Z        93609\n",
              "1  2022-07-03T00:00:00.000Z  2022-07-02T00:00:00.000Z        83643\n",
              "2  2022-07-04T00:00:00.000Z  2022-07-03T00:00:00.000Z        80860\n",
              "3  2022-07-05T00:00:00.000Z  2022-07-04T00:00:00.000Z       100957\n",
              "4  2022-07-06T00:00:00.000Z  2022-07-05T00:00:00.000Z       117632"
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    {
      "cell_type": "code",
      "source": [
        "plt = volume_df.plot()\n",
        "plt.set_xlabel(\"Day\")\n",
        "plt.set_ylabel(\"Tweet Count\")"
      ],
      "metadata": {
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      "execution_count": 12,
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            "text/plain": [
              "Text(0, 0.5, 'Tweet Count')"
            ]
          },
          "metadata": {},
          "execution_count": 12
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rSSP0IHphi1m"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "f5A2yMOAUplO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_list =    ['2022-03-01T00:00:00.000Z',\n",
        "                 '2022-04-01T00:00:00.000Z',\n",
        "                 '2022-05-01T00:00:00.000Z',\n",
        "                 '2022-06-01T00:00:00.000Z',\n",
        "                 '2022-07-01T00:00:00.000Z']\n",
        "\n",
        "end_list =      ['2022-03-31T00:00:00.000Z',\n",
        "                 '2022-04-30T00:00:00.000Z',\n",
        "                 '2022-05-31T00:00:00.000Z',\n",
        "                 '2022-06-30T00:00:00.000Z',\n",
        "                 '2022-07-31T00:00:00.000Z']\n",
        "\n",
        "                 "
      ],
      "metadata": {
        "id": "CaSedcEdUtPW"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Historical Ethereum Prices from March to July 2022\n",
        "Downloaded from Yahoo Finance."
      ],
      "metadata": {
        "id": "JMRVBGWcxV3d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ethereum_data = pd.read_csv(\"ETH-USD.csv\")"
      ],
      "metadata": {
        "id": "yOMfIcYXxbB4"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ethereum_data.info()"
      ],
      "metadata": {
        "id": "iQjTakHDxi3u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9e3b9fb-b5b3-4fdf-b890-1241b55eac70"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 153 entries, 0 to 152\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       153 non-null    object \n",
            " 1   Open       153 non-null    float64\n",
            " 2   High       153 non-null    float64\n",
            " 3   Low        153 non-null    float64\n",
            " 4   Close      153 non-null    float64\n",
            " 5   Adj Close  153 non-null    float64\n",
            " 6   Volume     153 non-null    int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 8.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume_df.info()"
      ],
      "metadata": {
        "id": "3hsk5ozazitk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "t = dateutil.parser.parse(tweet['created_at'])"
      ],
      "metadata": {
        "id": "r5SbDsTg35gY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import dateutil\n",
        "\n",
        "t = dateutil.parser.parse(\"2022-07-31T00:00:00.000Z\")"
      ],
      "metadata": {
        "id": "KIuL5jqq34sH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Retrieving Tweets on \"Ethereum\""
      ],
      "metadata": {
        "id": "SYOzOaYJFiFp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### VADER - NLTK's pretrained sentiment Analyzer"
      ],
      "metadata": {
        "id": "VEqOar7rEdHV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sentiment scores\n",
        "def sentiment_scores(str_tweets):\n",
        "  from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "  polarity_scores = []\n",
        "  sia = SentimentIntensityAnalyzer()\n",
        "  for s in str_tweets:\n",
        "    polarity_scores.append(sia.polarity_scores(s))\n",
        "\n",
        "  return polarity_scores"
      ],
      "metadata": {
        "id": "QNP7GJh7lwlK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Function to Clean Data"
      ],
      "metadata": {
        "id": "hSGOoiBFEHPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re, string\n",
        "from nltk.tag import pos_tag\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "\n",
        "def remove_noise(tweets):\n",
        "  # remove links\n",
        "  import re\n",
        "  tweets = [re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', tweet) for tweet in tweets]\n",
        "\n",
        "  # removing handles\n",
        "  tweets = [re.sub('(@[A-Za-z0-9_]+)', '', tweet) for tweet in tweets]\n",
        "  \n",
        "  return tweets\n",
        "\n",
        "\n",
        "# function to tokenize tweets\n",
        "def tokenize_tweets(tweets):\n",
        "  from nltk.tokenize import word_tokenize\n",
        "  tokenized_tweets = []\n",
        "\n",
        "  for tweet in tweets:\n",
        "    tokenized_tweets.append(word_tokenize(tweet))\n",
        "\n",
        "  return tokenized_tweets\n",
        "\n",
        "\n",
        "# main cleaning function\n",
        "def clean_data(tweet_tokens, stop_words=()):\n",
        "  \"\"\"\n",
        "    Cleans twitter text.\n",
        "      - filtering noise (hyperlinks, @)\n",
        "      - Part of speech Tagging\n",
        "      - Lemmatization\n",
        "      - filtering stop_words\n",
        "\n",
        "  \"\"\"\n",
        "  cleaned_tokens = []\n",
        "\n",
        "\n",
        "  # part of speech tagging\n",
        "  pos = pos_tag(tweet_tokens)\n",
        "\n",
        "  for token, tag in pos:\n",
        "\n",
        "    # removing links\n",
        "    token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\\(\\),]|'\\\n",
        "                       '(?:%[0-9a-fA-F][0-9a-fA-F]))+','', token)\n",
        "    \n",
        "    \n",
        "    # removing handles\n",
        "    token = re.sub(\"(@[A-Za-z0-9_]+)\",\"\", token)\n",
        "\n",
        "    # lemmatization\n",
        "\n",
        "    if tag.startswith('NN'):\n",
        "      pos = 'n'\n",
        "    elif tag.startswith('VB'):\n",
        "      pos = 'v'\n",
        "    else:\n",
        "      pos = 'a'\n",
        "\n",
        "    # lemmatizer = WordNetLemmatizer()\n",
        "    # token = lemmatizer.lemmatize(token, pos)\n",
        "\n",
        "    # filter stop words and punctuations\n",
        "    if len(token) > 0 and token not in string.punctuation:\n",
        "      cleaned_tokens.append(token.lower())\n",
        "\n",
        "  return cleaned_tokens"
      ],
      "metadata": {
        "id": "nNCrZd_3d4xT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Pipeline for processing tweets from the API"
      ],
      "metadata": {
        "id": "mcxeC31aEPBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compound_sentiment(date, tweets):\n",
        "\n",
        "  # print(tweets[4]) \n",
        "\n",
        "  # remove links\n",
        "  tweets_no_links = remove_noise(tweets)\n",
        "  # print(tweets_no_links[4]) \n",
        "\n",
        "  # word tokenize tweets\n",
        "  t_ = tokenize_tweets(tweets_no_links)\n",
        "  # print(t_[4]) \n",
        "\n",
        "  # clean tweets\n",
        "  cleaned_tweets = [clean_data(tweet) for tweet in t_]\n",
        "  # print(cleaned_tweets[4])\n",
        "\n",
        "  # join cleaned tokens \n",
        "  tweets_string = [\" \".join(c_t) for c_t in cleaned_tweets]\n",
        "  # print(tweets_string[4])\n",
        "\n",
        "  # check polarity\n",
        "  polarity_scores = sentiment_scores(tweets_string)\n",
        "  # print(polarity_scores[4])\n",
        "  # print(polarity_scores)\n",
        "\n",
        "  # average compound sentiment\n",
        "  num_tweets = len(tweets)\n",
        "  total_compound = 0\n",
        "  for score in polarity_scores:\n",
        "    total_compound += score['compound']\n",
        "  average_compound_sentiment =  total_compound / num_tweets\n",
        "\n",
        "  # return dictionary\n",
        "  return {\"Date\": date, \"Compound Sentiment\": average_compound_sentiment}\n",
        "\n",
        "  \n",
        "  "
      ],
      "metadata": {
        "id": "621gyArSonfH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Retrieving Twitter Data on Ethereum from March 2022 to July 2022"
      ],
      "metadata": {
        "id": "5kFl6uRDDnqQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_pipe(dates):\n",
        "  headers = create_headers(bearer_token)\n",
        "  keyword = \"ethereum lang:en -is:retweet -has:hashtags\"\n",
        "  max_results = 500\n",
        "\n",
        "  # track number of tweets\n",
        "  tweet_count = 0\n",
        "\n",
        "  # a list of dictionaries {Day, Sentiment}\n",
        "  sentiment_data = []\n",
        "\n",
        "  for i in range(len(dates)):\n",
        "    if i == len(dates) - 1:\n",
        "      break\n",
        "\n",
        "    # set start and end time\n",
        "    start_time = dates[i]\n",
        "    end_time = dates[i + 1]\n",
        "\n",
        "    # make request\n",
        "    url = create_search_url(keyword, start_time,end_time, max_results)\n",
        "    json_response = connect_to_endpoint(url[0], headers, url[1])\n",
        "    data = json_response['data']\n",
        "    for j in range(2):\n",
        "      try:\n",
        "        next_token = json_response['meta']['next_token']\n",
        "        json_response = connect_to_endpoint(url[0], headers, url[1], next_token)\n",
        "        data += json_response['data']\n",
        "        # wait a min\n",
        "        time.sleep(10)\n",
        "      except KeyError:\n",
        "        break\n",
        "      \n",
        "      \n",
        "\n",
        "    # put all tweets for that range in a list\n",
        "    tweets_in_range = [i['text'] for i in data]\n",
        "    \n",
        "    # update count\n",
        "    tweet_count += len(tweets_in_range)\n",
        "\n",
        "    # perform sentiment analysis\n",
        "    day_data = compound_sentiment(start_time, tweets_in_range)\n",
        "\n",
        "    # add to sentiment data\n",
        "    sentiment_data.append(day_data)\n",
        "\n",
        "  return {\"count\": tweet_count, 'data': sentiment_data}\n"
      ],
      "metadata": {
        "id": "SwlZywmYAMkj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import time\n",
        "# months = [march, april, may, june, july]\n",
        "\n",
        "# tweet_count = 0\n",
        "# all_data = []\n",
        "\n",
        "# for month in months:\n",
        "#   data = custom_pipe(month)\n",
        "#   all_data += data['data']\n",
        "\n",
        "#   tweet_count += data['count']\n",
        "\n",
        "#   # wait a min\n",
        "#   time.sleep(61)\n"
      ],
      "metadata": {
        "id": "IGINeS6NGq56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "45190 + 26510 = No of tweets"
      ],
      "metadata": {
        "id": "EsAXbsLgPwAb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert data to a DataFrame"
      ],
      "metadata": {
        "id": "HSy9D4-nTHiA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_df = pd.DataFrame(all_data)\n",
        "data_df.head()"
      ],
      "metadata": {
        "id": "e6lSh952N89D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# save to csv\n",
        "data_df.to_csv('daily_sentiment.csv', encoding='utf-8', index=False)"
      ],
      "metadata": {
        "id": "6vndSFjeQezu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Dates"
      ],
      "metadata": {
        "id": "NcB21oZcJ_W4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "march = [\n",
        "    \"2022-03-01T00:00:00.000Z\",\n",
        "    \"2022-03-02T00:00:00.000Z\",\n",
        "    \"2022-03-03T00:00:00.000Z\",\n",
        "    \"2022-03-04T00:00:00.000Z\",\n",
        "    \"2022-03-05T00:00:00.000Z\",\n",
        "    \"2022-03-06T00:00:00.000Z\",\n",
        "    \"2022-03-07T00:00:00.000Z\",\n",
        "    \"2022-03-08T00:00:00.000Z\",\n",
        "    \"2022-03-09T00:00:00.000Z\",\n",
        "    \"2022-03-10T00:00:00.000Z\",\n",
        "    \"2022-03-11T00:00:00.000Z\",\n",
        "    \"2022-03-12T00:00:00.000Z\",\n",
        "    \"2022-03-13T00:00:00.000Z\",\n",
        "    \"2022-03-14T00:00:00.000Z\",\n",
        "    \"2022-03-15T00:00:00.000Z\",\n",
        "    \"2022-03-16T00:00:00.000Z\",\n",
        "    \"2022-03-17T00:00:00.000Z\",\n",
        "    \"2022-03-18T00:00:00.000Z\",\n",
        "    \"2022-03-19T00:00:00.000Z\",\n",
        "    \"2022-03-20T00:00:00.000Z\",\n",
        "    \"2022-03-21T00:00:00.000Z\",\n",
        "    \"2022-03-22T00:00:00.000Z\",\n",
        "    \"2022-03-23T00:00:00.000Z\",\n",
        "    \"2022-03-24T00:00:00.000Z\",\n",
        "    \"2022-03-25T00:00:00.000Z\",\n",
        "    \"2022-03-26T00:00:00.000Z\",\n",
        "    \"2022-03-27T00:00:00.000Z\",\n",
        "    \"2022-03-28T00:00:00.000Z\",\n",
        "    \"2022-03-29T00:00:00.000Z\",\n",
        "    \"2022-03-30T00:00:00.000Z\",\n",
        "    \"2022-03-31T00:00:00.000Z\",\n",
        "    \"2022-04-01T00:00:00.000Z\",\n",
        "]\n",
        "\n",
        "\n",
        "april = [\n",
        "    \"2022-04-01T00:00:00.000Z\",\n",
        "    \"2022-04-02T00:00:00.000Z\",\n",
        "    \"2022-04-03T00:00:00.000Z\",\n",
        "    \"2022-04-04T00:00:00.000Z\",\n",
        "    \"2022-04-05T00:00:00.000Z\",\n",
        "    \"2022-04-06T00:00:00.000Z\",\n",
        "    \"2022-04-07T00:00:00.000Z\",\n",
        "    \"2022-04-08T00:00:00.000Z\",\n",
        "    \"2022-04-09T00:00:00.000Z\",\n",
        "    \"2022-04-10T00:00:00.000Z\",\n",
        "    \"2022-04-11T00:00:00.000Z\",\n",
        "    \"2022-04-12T00:00:00.000Z\",\n",
        "    \"2022-04-13T00:00:00.000Z\",\n",
        "    \"2022-04-14T00:00:00.000Z\",\n",
        "    \"2022-04-15T00:00:00.000Z\",\n",
        "    \"2022-04-16T00:00:00.000Z\",\n",
        "    \"2022-04-17T00:00:00.000Z\",\n",
        "    \"2022-04-18T00:00:00.000Z\",\n",
        "    \"2022-04-19T00:00:00.000Z\",\n",
        "    \"2022-04-20T00:00:00.000Z\",\n",
        "    \"2022-04-21T00:00:00.000Z\",\n",
        "    \"2022-04-22T00:00:00.000Z\",\n",
        "    \"2022-04-23T00:00:00.000Z\",\n",
        "    \"2022-04-24T00:00:00.000Z\",\n",
        "    \"2022-04-25T00:00:00.000Z\",\n",
        "    \"2022-04-26T00:00:00.000Z\",\n",
        "    \"2022-04-27T00:00:00.000Z\",\n",
        "    \"2022-04-28T00:00:00.000Z\",\n",
        "    \"2022-04-29T00:00:00.000Z\",\n",
        "    \"2022-04-30T00:00:00.000Z\",\n",
        "    \"2022-05-01T00:00:00.000Z\",\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "may = [\n",
        "    \"2022-05-01T00:00:00.000Z\",\n",
        "    \"2022-05-02T00:00:00.000Z\",\n",
        "    \"2022-05-03T00:00:00.000Z\",\n",
        "    \"2022-05-04T00:00:00.000Z\",\n",
        "    \"2022-05-05T00:00:00.000Z\",\n",
        "    \"2022-05-06T00:00:00.000Z\",\n",
        "    \"2022-05-07T00:00:00.000Z\",\n",
        "    \"2022-05-08T00:00:00.000Z\",\n",
        "    \"2022-05-09T00:00:00.000Z\",\n",
        "    \"2022-05-10T00:00:00.000Z\",\n",
        "    \"2022-05-11T00:00:00.000Z\",\n",
        "    \"2022-05-12T00:00:00.000Z\",\n",
        "    \"2022-05-13T00:00:00.000Z\",\n",
        "    \"2022-05-14T00:00:00.000Z\",\n",
        "    \"2022-05-15T00:00:00.000Z\",\n",
        "    \"2022-05-16T00:00:00.000Z\",\n",
        "    \"2022-05-17T00:00:00.000Z\",\n",
        "    \"2022-05-18T00:00:00.000Z\",\n",
        "    \"2022-05-19T00:00:00.000Z\",\n",
        "    \"2022-05-20T00:00:00.000Z\",\n",
        "    \"2022-05-21T00:00:00.000Z\",\n",
        "    \"2022-05-22T00:00:00.000Z\",\n",
        "    \"2022-05-23T00:00:00.000Z\",\n",
        "    \"2022-05-24T00:00:00.000Z\",\n",
        "    \"2022-05-25T00:00:00.000Z\",\n",
        "    \"2022-05-26T00:00:00.000Z\",\n",
        "    \"2022-05-27T00:00:00.000Z\",\n",
        "    \"2022-05-28T00:00:00.000Z\",\n",
        "    \"2022-05-29T00:00:00.000Z\",\n",
        "    \"2022-05-30T00:00:00.000Z\",\n",
        "    \"2022-05-31T00:00:00.000Z\",\n",
        "    \"2022-06-01T00:00:00.000Z\",\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "june = [\n",
        "    \"2022-06-01T00:00:00.000Z\",\n",
        "    \"2022-06-02T00:00:00.000Z\",\n",
        "    \"2022-06-03T00:00:00.000Z\",\n",
        "    \"2022-06-04T00:00:00.000Z\",\n",
        "    \"2022-06-05T00:00:00.000Z\",\n",
        "    \"2022-06-06T00:00:00.000Z\",\n",
        "    \"2022-06-07T00:00:00.000Z\",\n",
        "    \"2022-06-08T00:00:00.000Z\",\n",
        "    \"2022-06-09T00:00:00.000Z\",\n",
        "    \"2022-06-10T00:00:00.000Z\",\n",
        "    \"2022-06-11T00:00:00.000Z\",\n",
        "    \"2022-06-12T00:00:00.000Z\",\n",
        "    \"2022-06-13T00:00:00.000Z\",\n",
        "    \"2022-06-14T00:00:00.000Z\",\n",
        "    \"2022-06-15T00:00:00.000Z\",\n",
        "    \"2022-06-16T00:00:00.000Z\",\n",
        "    \"2022-06-17T00:00:00.000Z\",\n",
        "    \"2022-06-18T00:00:00.000Z\",\n",
        "    \"2022-06-19T00:00:00.000Z\",\n",
        "    \"2022-06-20T00:00:00.000Z\",\n",
        "    \"2022-06-21T00:00:00.000Z\",\n",
        "    \"2022-06-22T00:00:00.000Z\",\n",
        "    \"2022-06-23T00:00:00.000Z\",\n",
        "    \"2022-06-24T00:00:00.000Z\",\n",
        "    \"2022-06-25T00:00:00.000Z\",\n",
        "    \"2022-06-26T00:00:00.000Z\",\n",
        "    \"2022-06-27T00:00:00.000Z\",\n",
        "    \"2022-06-28T00:00:00.000Z\",\n",
        "    \"2022-06-29T00:00:00.000Z\",\n",
        "    \"2022-06-30T00:00:00.000Z\",\n",
        "    \"2022-07-01T00:00:00.000Z\",\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "july = [\n",
        "    \"2022-07-01T00:00:00.000Z\",\n",
        "    \"2022-07-02T00:00:00.000Z\",\n",
        "    \"2022-07-03T00:00:00.000Z\",\n",
        "    \"2022-07-04T00:00:00.000Z\",\n",
        "    \"2022-07-05T00:00:00.000Z\",\n",
        "    \"2022-07-06T00:00:00.000Z\",\n",
        "    \"2022-07-07T00:00:00.000Z\",\n",
        "    \"2022-07-08T00:00:00.000Z\",\n",
        "    \"2022-07-09T00:00:00.000Z\",\n",
        "    \"2022-07-10T00:00:00.000Z\",\n",
        "    \"2022-07-11T00:00:00.000Z\",\n",
        "    \"2022-07-12T00:00:00.000Z\",\n",
        "    \"2022-07-13T00:00:00.000Z\",\n",
        "    \"2022-07-14T00:00:00.000Z\",\n",
        "    \"2022-07-15T00:00:00.000Z\",\n",
        "    \"2022-07-16T00:00:00.000Z\",\n",
        "    \"2022-07-17T00:00:00.000Z\",\n",
        "    \"2022-07-18T00:00:00.000Z\",\n",
        "    \"2022-07-19T00:00:00.000Z\",\n",
        "    \"2022-07-20T00:00:00.000Z\",\n",
        "    \"2022-07-21T00:00:00.000Z\",\n",
        "    \"2022-07-22T00:00:00.000Z\",\n",
        "    \"2022-07-23T00:00:00.000Z\",\n",
        "    \"2022-07-24T00:00:00.000Z\",\n",
        "    \"2022-07-25T00:00:00.000Z\",\n",
        "    \"2022-07-26T00:00:00.000Z\",\n",
        "    \"2022-07-27T00:00:00.000Z\",\n",
        "    \"2022-07-28T00:00:00.000Z\",\n",
        "    \"2022-07-29T00:00:00.000Z\",\n",
        "    \"2022-07-30T00:00:00.000Z\",\n",
        "    \"2022-07-31T00:00:00.000Z\",\n",
        "    \"2022-08-01T00:00:00.000Z\",\n",
        "]\n",
        "\n",
        "total = len(march) + len(april) + len(may) + len(june) + len(july)"
      ],
      "metadata": {
        "id": "Xrxx_luEH4J0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eAr3Jrd7I-0v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Trends from Google"
      ],
      "metadata": {
        "id": "NobZTrSrrW5e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eth_trends = pd.read_csv(\"eth-trend.csv\")"
      ],
      "metadata": {
        "id": "k78h_J22rrHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eth_trends.head()"
      ],
      "metadata": {
        "id": "a7haBCMsxu9f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "65jfvfZ3b3gt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numerical EDA"
      ],
      "metadata": {
        "id": "TmBamM9UlmsB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eth_df = pd.read_csv(\"ETH-USD.csv\")\n",
        "volume_df = pd.read_csv('tweets_volume.csv')\n",
        "daily_sentiment = pd.read_csv(\"daily_sentiment.csv\")\n",
        "eth_trends = pd.read_csv(\"eth-trend.csv\")"
      ],
      "metadata": {
        "id": "ps7IOSo9b5zO"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(eth_df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dj7cvz9BcRUW",
        "outputId": "e58afc92-7eb9-4ad4-db37-85e7f62c327e"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 153 entries, 0 to 152\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       153 non-null    object \n",
            " 1   Open       153 non-null    float64\n",
            " 2   High       153 non-null    float64\n",
            " 3   Low        153 non-null    float64\n",
            " 4   Close      153 non-null    float64\n",
            " 5   Adj Close  153 non-null    float64\n",
            " 6   Volume     153 non-null    int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 8.5+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(volume_df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gY3dazIceiu",
        "outputId": "f21008d1-5668-45f0-aa23-9c73abaa81e1"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 153 entries, 0 to 152\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   end          153 non-null    object\n",
            " 1   start        153 non-null    object\n",
            " 2   tweet_count  153 non-null    int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 3.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(daily_sentiment.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tX8VmIevcjfF",
        "outputId": "2dc92bbd-ac71-4fb0-d520-e5f8ceefc314"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 153 entries, 0 to 152\n",
            "Data columns (total 2 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Date                153 non-null    object \n",
            " 1   Compound Sentiment  153 non-null    float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 2.5+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eth_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2nSE4_4szO7-",
        "outputId": "ba9c3880-400e-4802-8237-07b01e0d340b"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date         Open         High          Low        Close  \\\n",
              "0  2022-03-01  2919.775879  3029.651611  2868.938965  2972.485107   \n",
              "1  2022-03-02  2972.471924  3026.868164  2919.943115  2950.118408   \n",
              "2  2022-03-03  2950.156738  2964.673340  2797.319336  2834.468994   \n",
              "3  2022-03-04  2834.987305  2835.176270  2587.748291  2617.156006   \n",
              "4  2022-03-05  2618.473633  2679.102539  2596.989990  2664.831055   \n",
              "\n",
              "     Adj Close       Volume  Change  \n",
              "0  2972.485107  18757425786       1  \n",
              "1  2950.118408  16636517503       0  \n",
              "2  2834.468994  13091199728       0  \n",
              "3  2617.156006  14496939024       0  \n",
              "4  2664.831055   8072368396       1  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Date</th>\n",
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              "      <td>2868.938965</td>\n",
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              "      <td>2022-03-02</td>\n",
              "      <td>2972.471924</td>\n",
              "      <td>3026.868164</td>\n",
              "      <td>2919.943115</td>\n",
              "      <td>2950.118408</td>\n",
              "      <td>2950.118408</td>\n",
              "      <td>16636517503</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-03-03</td>\n",
              "      <td>2950.156738</td>\n",
              "      <td>2964.673340</td>\n",
              "      <td>2797.319336</td>\n",
              "      <td>2834.468994</td>\n",
              "      <td>2834.468994</td>\n",
              "      <td>13091199728</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-03-04</td>\n",
              "      <td>2834.987305</td>\n",
              "      <td>2835.176270</td>\n",
              "      <td>2587.748291</td>\n",
              "      <td>2617.156006</td>\n",
              "      <td>2617.156006</td>\n",
              "      <td>14496939024</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-03-05</td>\n",
              "      <td>2618.473633</td>\n",
              "      <td>2679.102539</td>\n",
              "      <td>2596.989990</td>\n",
              "      <td>2664.831055</td>\n",
              "      <td>2664.831055</td>\n",
              "      <td>8072368396</td>\n",
              "      <td>1</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Creating Our Labels"
      ],
      "metadata": {
        "id": "jsQcxKYLWCRT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def f(row):\n",
        "    change = ((row['Close'] - row[\"Open\"]) / row['Open']) * 100\n",
        "    if change > 0:\n",
        "      val = 1\n",
        "    else:\n",
        "      val = 0\n",
        "    return val\n",
        "\n",
        "eth_df[\"Change\"] = eth_df.apply(f, axis=1)"
      ],
      "metadata": {
        "id": "UqndRo0ERZeX"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sort dataframe by date\n",
        "volume_df = volume_df.sort_values(\"start\", ascending=True)\n",
        "daily_sentiment = daily_sentiment.sort_values(\"Date\", ascending=True)\n",
        "eth_df = eth_df.sort_values(\"Date\", ascending=True)\n",
        "eth_trends = eth_trends.sort_values(\"Day\", ascending=True)"
      ],
      "metadata": {
        "id": "Aw-ZaUw_e3NO"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "daily_sentiment['Count'] = volume_df['tweet_count'].to_numpy()\n",
        "daily_sentiment[\"Close\"] = eth_df['Close'].to_numpy()\n",
        "daily_sentiment[\"Change\"] = eth_df['Change'].to_numpy()\n",
        "daily_sentiment[\"Trend\"] = eth_trends['Ethereum: (Worldwide)'].to_numpy()\n",
        "daily_sentiment.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-vvdJlsEgN_j",
        "outputId": "b4b12b7e-8ec4-43df-a67e-743080f62459"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Date  Compound Sentiment   Count        Close  \\\n",
              "148  2022-07-27T00:00:00.000Z            0.135750  105810  1636.232666   \n",
              "149  2022-07-28T00:00:00.000Z            0.178897  122166  1725.468140   \n",
              "150  2022-07-29T00:00:00.000Z            0.111037   97136  1727.406982   \n",
              "151  2022-07-30T00:00:00.000Z            0.122775  104421  1695.969482   \n",
              "152  2022-07-31T00:00:00.000Z            0.138101  100275  1681.517334   \n",
              "\n",
              "     Change  Trend  \n",
              "148       1     40  \n",
              "149       1     48  \n",
              "150       1     46  \n",
              "151       0     41  \n",
              "152       0     39  "
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              "      <th></th>\n",
              "      <th>Date</th>\n",
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              "      <th>148</th>\n",
              "      <td>2022-07-27T00:00:00.000Z</td>\n",
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              "      <th>149</th>\n",
              "      <td>2022-07-28T00:00:00.000Z</td>\n",
              "      <td>0.178897</td>\n",
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              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2022-07-29T00:00:00.000Z</td>\n",
              "      <td>0.111037</td>\n",
              "      <td>97136</td>\n",
              "      <td>1727.406982</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>2022-07-30T00:00:00.000Z</td>\n",
              "      <td>0.122775</td>\n",
              "      <td>104421</td>\n",
              "      <td>1695.969482</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>2022-07-31T00:00:00.000Z</td>\n",
              "      <td>0.138101</td>\n",
              "      <td>100275</td>\n",
              "      <td>1681.517334</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1836a91c-9886-4841-aa82-baaf9bb62cc9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1836a91c-9886-4841-aa82-baaf9bb62cc9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1836a91c-9886-4841-aa82-baaf9bb62cc9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename column and drop all date columns\n",
        "\n",
        "df = daily_sentiment.rename(columns={'Compound Sentiment': 'Sentiment', \"Close\": \"Price\"})\n",
        "df.drop('Date', axis=1, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pe-zUbnTg76Z",
        "outputId": "ad7540b3-ab86-4ffb-a20b-061d1a875edb"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sentiment  Count        Price  Change  Trend\n",
              "0   0.243190  91952  2972.485107       1     58\n",
              "1   0.235499  86876  2950.118408       0     52\n",
              "2   0.148117  87218  2834.468994       0     47\n",
              "3   0.202503  83943  2617.156006       0     44\n",
              "4   0.317556  80204  2664.831055       1     41"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-81a23ff8-e705-4da6-8de5-059f140beb5c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\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>Sentiment</th>\n",
              "      <th>Count</th>\n",
              "      <th>Price</th>\n",
              "      <th>Change</th>\n",
              "      <th>Trend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.243190</td>\n",
              "      <td>91952</td>\n",
              "      <td>2972.485107</td>\n",
              "      <td>1</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.235499</td>\n",
              "      <td>86876</td>\n",
              "      <td>2950.118408</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.148117</td>\n",
              "      <td>87218</td>\n",
              "      <td>2834.468994</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.202503</td>\n",
              "      <td>83943</td>\n",
              "      <td>2617.156006</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.317556</td>\n",
              "      <td>80204</td>\n",
              "      <td>2664.831055</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81a23ff8-e705-4da6-8de5-059f140beb5c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81a23ff8-e705-4da6-8de5-059f140beb5c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81a23ff8-e705-4da6-8de5-059f140beb5c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.drop('Price', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "-v43stuas1Dc"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jgxa3vtnthpa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df.to_csv('data.csv', encoding='utf-8', index=False)"
      ],
      "metadata": {
        "id": "20XdBs5pr3q9"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sehjyg4fi13A",
        "outputId": "573439d5-c575-4d4a-d7b8-fdc132a95f3d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 153 entries, 0 to 152\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Sentiment  153 non-null    float64\n",
            " 1   Count      153 non-null    int64  \n",
            " 2   Price      153 non-null    float64\n",
            " 3   Change     153 non-null    int64  \n",
            " 4   Trend      153 non-null    int64  \n",
            "dtypes: float64(2), int64(3)\n",
            "memory usage: 7.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Correlations"
      ],
      "metadata": {
        "id": "smd7noPujoCb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = scaled_df.corr()\n",
        "corr_matrix[\"Change\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OC20lMSOjq14",
        "outputId": "7d466671-71d4-435a-a30e-db1e1afab5d9"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sentiment    0.103337\n",
              "Count       -0.029013\n",
              "Price        0.088131\n",
              "Change       1.000000\n",
              "Trend       -0.018307\n",
              "Name: Change, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Normalize Data"
      ],
      "metadata": {
        "id": "1NiS8FL4wYwv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# define min max scaler\n",
        "scaler = MinMaxScaler()\n",
        "# transform data\n",
        "scaled = scaler.fit_transform(df)\n",
        "scaled_df = pd.DataFrame(scaled)"
      ],
      "metadata": {
        "id": "kj3N9WhUwdtU"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df = scaled_df.rename({0: \"Sentiment\", 1: \"Count\",2: \"Price\", 3: \"Change\", 4: \"Trend\"}, axis=1)"
      ],
      "metadata": {
        "id": "rqbDhyl5w8EK"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visual EDA"
      ],
      "metadata": {
        "id": "om512YkEl9Am"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df.hist(bins=50, figsize=(16,12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "jioWVnVAmGKZ",
        "outputId": "f48f2b91-11e4-4aa1-95ec-6185758ee2e6"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_plot = scaled_df.plot(y=[\"Price\", \"Count\"], figsize=(10,5), grid=True)\n",
        "scaled_plot.set_xlabel(\"Day\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "MOSXbmECkVzZ",
        "outputId": "aec233ca-55a7-409b-a642-884ebabef358"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Day')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind=\"scatter\", x=\"Sentiment\", y=\"Change\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "H-a8JuvlkWfe",
        "outputId": "c3c024c4-2231-4fc9-9edc-7d216e837f25"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6b6145650>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind=\"scatter\", x=\"Trend\", y=\"Change\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "yh9QbMHYzfqW",
        "outputId": "f2ddbb62-2f07-4477-a049-bda3387eab46"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6b60b7210>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind=\"scatter\", x=\"Trend\", y=\"Change\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "CzEtie6tuYB9",
        "outputId": "8be1d8d9-5f6e-4123-8e49-3aed0986a45b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6b6098e50>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Preparing Data for ML"
      ],
      "metadata": {
        "id": "-YmRHunzHW5-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing Data for ML"
      ],
      "metadata": {
        "id": "sQ2Z9bTgHh98"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting Data"
      ],
      "metadata": {
        "id": "wWKAdIxoHldU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df[\"Change\"]\n",
        "X = df.drop([\"Price\", 'Change'], axis=1)\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Vebg0gCIMh4",
        "outputId": "e0537d7e-d82e-408a-e8de-31272cc26364"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(153, 3)\n",
            "(153,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)"
      ],
      "metadata": {
        "id": "SN2W-KVaHZjG"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNearestNeighbor"
      ],
      "metadata": {
        "id": "euLRlgFJbxq2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "model = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# fit regression model to training data\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_LX8aS-b1KE",
        "outputId": "d6fe4e85-0baf-4c31-bc45-bd0f91797888"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model Accuracy"
      ],
      "metadata": {
        "id": "9KoJ048eb7Gp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy of predict on training set\n",
        "model.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfPDKF44b9Yh",
        "outputId": "cbdd57b7-4d7a-49bc-e6ea-b01a1ee0bad5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6813186813186813"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy of predict on training set\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4miomMK_cAYl",
        "outputId": "65d00282-7c90-47b2-f2b9-ef37a87784c8"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.532258064516129"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation with Cross Validation"
      ],
      "metadata": {
        "id": "8WD0oSGlqmLE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy using cross-validation\n",
        "scores = cross_val_score(model, X_train, y_train, cv=5)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5Sc-cT-ql34",
        "outputId": "d738cf9f-3e50-4e41-d4a5-2d652ab047af"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.52631579, 0.44444444, 0.38888889, 0.66666667, 0.33333333])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Naive Bayes Classifier"
      ],
      "metadata": {
        "id": "YkjA9nRLcJlJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Gaussian Naive Bayes model\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "model = GaussianNB()\n",
        "\n",
        "# Train the model using the training sets\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckrWQ5ErcHrb",
        "outputId": "c68b5c48-ab1d-4e8b-cbca-ee913a6ddfe8"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Accuracy"
      ],
      "metadata": {
        "id": "C6uN0IeYc1li"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy of predict on train set\n",
        "model.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YCygGlkcun4",
        "outputId": "7bb3ff55-6a35-4fe9-d3f9-a1680749638d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5494505494505495"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy of predict on test set\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4z1mMODcwx_",
        "outputId": "5556e133-9a0a-4d63-d1fc-9d0134ff90a6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.43548387096774194"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluation with cross validation"
      ],
      "metadata": {
        "id": "IPQbTn2_q_0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy using cross-validation\n",
        "scores = cross_val_score(model, X_train, y_train, cv=5)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09plLJRsq2Qm",
        "outputId": "233c7a4c-2cac-4d7d-ecc1-56a1274fcf75"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.52631579, 0.44444444, 0.33333333, 0.5       , 0.33333333])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RandomForestClassifier"
      ],
      "metadata": {
        "id": "_E0NU-rjc8pR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Random Forest Model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "clf=RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "y_pred=clf.predict(X_test)"
      ],
      "metadata": {
        "id": "jT6Bc-rBdLIE"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHtpLYGRrbar",
        "outputId": "8f36eddc-8cbc-45f6-9d80-153eb8e7187a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.5161290322580645\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cross Validation"
      ],
      "metadata": {
        "id": "8W1ATCVKrg5R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy using cross-validation\n",
        "scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWXeVTairjST",
        "outputId": "eb0e38db-cd4e-48e9-b35a-3a2785ab8c75"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.47368421, 0.55555556, 0.5       , 0.66666667, 0.38888889])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    }
  ]
}