eda-twitter-sentiment-analysis-using-nn (3).ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-block alert-danger\">  \n","<h2><center><strong>Importing Python Libraries 📕 📗 📘 📙</strong></center></h2>\n","        \n","</div>"]},{"cell_type":"markdown","metadata":{},"source":["- Libraries are important and we call them to perform the different actions on our data and for training the models.\n","- Its a first step to load the library to perform the specific task"]},{"cell_type":"code","execution_count":57,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (1.25.0)\n","Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (2.0.3)\n","Requirement already satisfied: tensorflow in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (2.15.0)\n","Requirement already 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wordcloud"]},{"cell_type":"code","execution_count":58,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:06:57.322357Z","iopub.status.busy":"2023-04-03T08:06:57.321911Z","iopub.status.idle":"2023-04-03T08:06:59.980710Z","shell.execute_reply":"2023-04-03T08:06:59.979609Z","shell.execute_reply.started":"2023-04-03T08:06:57.322267Z"},"id":"-E4TvIVLMa73","scrolled":true,"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package stopwords to\n","[nltk_data]     /Users/jagadeesh/nltk_data...\n","[nltk_data]   Package stopwords is already up-to-date!\n","[nltk_data] Downloading package wordnet to\n","[nltk_data]     /Users/jagadeesh/nltk_data...\n","[nltk_data]   Package wordnet is already up-to-date!\n"]}],"source":["import numpy as np \n","import pandas as pd \n","import seaborn as sns \n","import matplotlib.pyplot as plt\n","plt.style.use('ggplot')\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.metrics import classification_report, confusion_matrix\n","\n","import nltk\n","nltk.download('stopwords')\n","nltk.download('wordnet')\n","from nltk.corpus import stopwords\n","from nltk.stem import PorterStemmer, WordNetLemmatizer\n","from nltk.tokenize import RegexpTokenizer\n","\n","\n","from sklearn.model_selection import train_test_split\n","from mlxtend.plotting import plot_confusion_matrix\n","import matplotlib.cm as cm\n","from matplotlib import rcParams\n","from collections import Counter\n","\n","import re\n","import string\n","\n","from tensorflow.keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.optimizers import RMSprop\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","from tensorflow.keras.preprocessing import sequence\n","%matplotlib inline\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","from scipy.stats import skew, kurtosis\n","\n","from collections import Counter\n","from wordcloud import STOPWORDS"]},{"cell_type":"code","execution_count":59,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:06:59.983639Z","iopub.status.busy":"2023-04-03T08:06:59.983151Z","iopub.status.idle":"2023-04-03T08:07:13.738350Z","shell.execute_reply":"2023-04-03T08:07:13.737330Z","shell.execute_reply.started":"2023-04-03T08:06:59.983591Z"},"trusted":true},"outputs":[],"source":["data = pd.read_csv(\"training.1600000.processed.noemoticon.csv\", encoding = \"ISO-8859-1\", engine=\"python\")\n","data.columns = [\"label\", \"id\", \"date\", \"query\", \"username\", \"text\"]"]},{"cell_type":"markdown","metadata":{},"source":["#### Five top records of data"]},{"cell_type":"code","execution_count":60,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:13.740591Z","iopub.status.busy":"2023-04-03T08:07:13.740119Z","iopub.status.idle":"2023-04-03T08:07:13.762702Z","shell.execute_reply":"2023-04-03T08:07:13.761647Z","shell.execute_reply.started":"2023-04-03T08:07:13.740542Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      <th>id</th>\n","      <th>date</th>\n","      <th>query</th>\n","      <th>username</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1467810672</td>\n","      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>scotthamilton</td>\n","      <td>is upset that he can't update his Facebook by ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>1467810917</td>\n","      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>mattycus</td>\n","      <td>@Kenichan I dived many times for the ball. Man...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>1467811184</td>\n","      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>ElleCTF</td>\n","      <td>my whole body feels itchy and like its on fire</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1467811193</td>\n","      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>Karoli</td>\n","      <td>@nationwideclass no, it's not behaving at all....</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>1467811372</td>\n","      <td>Mon Apr 06 22:20:00 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>joy_wolf</td>\n","      <td>@Kwesidei not the whole crew</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   label          id                          date     query       username  \\\n","0      0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY  scotthamilton   \n","1      0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY       mattycus   \n","2      0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY        ElleCTF   \n","3      0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY         Karoli   \n","4      0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY       joy_wolf   \n","\n","                                                text  \n","0  is upset that he can't update his Facebook by ...  \n","1  @Kenichan I dived many times for the ball. Man...  \n","2    my whole body feels itchy and like its on fire   \n","3  @nationwideclass no, it's not behaving at all....  \n","4                      @Kwesidei not the whole crew   "]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["data.head()"]},{"cell_type":"markdown","metadata":{},"source":["#### Five last records of data"]},{"cell_type":"code","execution_count":61,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:13.764971Z","iopub.status.busy":"2023-04-03T08:07:13.764636Z","iopub.status.idle":"2023-04-03T08:07:13.780465Z","shell.execute_reply":"2023-04-03T08:07:13.779237Z","shell.execute_reply.started":"2023-04-03T08:07:13.764937Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      <th>id</th>\n","      <th>date</th>\n","      <th>query</th>\n","      <th>username</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1599994</th>\n","      <td>4</td>\n","      <td>2193601966</td>\n","      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>AmandaMarie1028</td>\n","      <td>Just woke up. Having no school is the best fee...</td>\n","    </tr>\n","    <tr>\n","      <th>1599995</th>\n","      <td>4</td>\n","      <td>2193601969</td>\n","      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>TheWDBoards</td>\n","      <td>TheWDB.com - Very cool to hear old Walt interv...</td>\n","    </tr>\n","    <tr>\n","      <th>1599996</th>\n","      <td>4</td>\n","      <td>2193601991</td>\n","      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>bpbabe</td>\n","      <td>Are you ready for your MoJo Makeover? Ask me f...</td>\n","    </tr>\n","    <tr>\n","      <th>1599997</th>\n","      <td>4</td>\n","      <td>2193602064</td>\n","      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>tinydiamondz</td>\n","      <td>Happy 38th Birthday to my boo of alll time!!! ...</td>\n","    </tr>\n","    <tr>\n","      <th>1599998</th>\n","      <td>4</td>\n","      <td>2193602129</td>\n","      <td>Tue Jun 16 08:40:50 PDT 2009</td>\n","      <td>NO_QUERY</td>\n","      <td>RyanTrevMorris</td>\n","      <td>happy #charitytuesday @theNSPCC @SparksCharity...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         label          id                          date     query  \\\n","1599994      4  2193601966  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n","1599995      4  2193601969  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n","1599996      4  2193601991  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n","1599997      4  2193602064  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n","1599998      4  2193602129  Tue Jun 16 08:40:50 PDT 2009  NO_QUERY   \n","\n","                username                                               text  \n","1599994  AmandaMarie1028  Just woke up. Having no school is the best fee...  \n","1599995      TheWDBoards  TheWDB.com - Very cool to hear old Walt interv...  \n","1599996           bpbabe  Are you ready for your MoJo Makeover? Ask me f...  \n","1599997     tinydiamondz  Happy 38th Birthday to my boo of alll time!!! ...  \n","1599998   RyanTrevMorris  happy #charitytuesday @theNSPCC @SparksCharity...  "]},"execution_count":61,"metadata":{},"output_type":"execute_result"}],"source":["data.tail()"]},{"cell_type":"markdown","metadata":{},"source":["#### Coloumns/features in data"]},{"cell_type":"code","execution_count":62,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:13.784702Z","iopub.status.busy":"2023-04-03T08:07:13.784219Z","iopub.status.idle":"2023-04-03T08:07:13.792982Z","shell.execute_reply":"2023-04-03T08:07:13.791936Z","shell.execute_reply.started":"2023-04-03T08:07:13.784653Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Index(['label', 'id', 'date', 'query', 'username', 'text'], dtype='object')"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["data.columns"]},{"cell_type":"markdown","metadata":{},"source":["#### Length of data"]},{"cell_type":"code","execution_count":63,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:13.797052Z","iopub.status.busy":"2023-04-03T08:07:13.796678Z","iopub.status.idle":"2023-04-03T08:07:13.803593Z","shell.execute_reply":"2023-04-03T08:07:13.802494Z","shell.execute_reply.started":"2023-04-03T08:07:13.797017Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["lenght of data is 1599999\n"]}],"source":["print('lenght of data is', len(data))"]},{"cell_type":"markdown","metadata":{},"source":["#### Shape of data"]},{"cell_type":"code","execution_count":64,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:13.805803Z","iopub.status.busy":"2023-04-03T08:07:13.805374Z","iopub.status.idle":"2023-04-03T08:07:13.814158Z","shell.execute_reply":"2023-04-03T08:07:13.813064Z","shell.execute_reply.started":"2023-04-03T08:07:13.805768Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(1599999, 6)"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"markdown","metadata":{},"source":["#### Data information"]},{"cell_type":"code","execution_count":65,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:13.815585Z","iopub.status.busy":"2023-04-03T08:07:13.815243Z","iopub.status.idle":"2023-04-03T08:07:14.392341Z","shell.execute_reply":"2023-04-03T08:07:14.391313Z","shell.execute_reply.started":"2023-04-03T08:07:13.815553Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1599999 entries, 0 to 1599998\n","Data columns (total 6 columns):\n"," #   Column    Non-Null Count    Dtype \n","---  ------    --------------    ----- \n"," 0   label     1599999 non-null  int64 \n"," 1   id        1599999 non-null  int64 \n"," 2   date      1599999 non-null  object\n"," 3   query     1599999 non-null  object\n"," 4   username  1599999 non-null  object\n"," 5   text      1599999 non-null  object\n","dtypes: int64(2), object(4)\n","memory usage: 73.2+ MB\n"]}],"source":["data.info()"]},{"cell_type":"markdown","metadata":{},"source":["#### Data types of all coloumns"]},{"cell_type":"code","execution_count":66,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:14.393912Z","iopub.status.busy":"2023-04-03T08:07:14.393621Z","iopub.status.idle":"2023-04-03T08:07:14.402041Z","shell.execute_reply":"2023-04-03T08:07:14.400877Z","shell.execute_reply.started":"2023-04-03T08:07:14.393884Z"},"trusted":true},"outputs":[{"data":{"text/plain":["label        int64\n","id           int64\n","date        object\n","query       object\n","username    object\n","text        object\n","dtype: object"]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["data.dtypes"]},{"cell_type":"markdown","metadata":{},"source":["#### Checking Null values"]},{"cell_type":"code","execution_count":67,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:14.404144Z","iopub.status.busy":"2023-04-03T08:07:14.403805Z","iopub.status.idle":"2023-04-03T08:07:14.972744Z","shell.execute_reply":"2023-04-03T08:07:14.971602Z","shell.execute_reply.started":"2023-04-03T08:07:14.404110Z"},"scrolled":true,"trusted":true},"outputs":[{"data":{"text/plain":["0"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["np.sum(data.isnull().any(axis=1))"]},{"cell_type":"markdown","metadata":{},"source":["#### Rows and columns in the dataset"]},{"cell_type":"code","execution_count":68,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:14.974909Z","iopub.status.busy":"2023-04-03T08:07:14.974572Z","iopub.status.idle":"2023-04-03T08:07:14.981879Z","shell.execute_reply":"2023-04-03T08:07:14.980766Z","shell.execute_reply.started":"2023-04-03T08:07:14.974875Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Count of columns in the data is:   6\n"]}],"source":["print('Count of columns in the data is:  ', len(data.columns))"]},{"cell_type":"code","execution_count":69,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:14.983888Z","iopub.status.busy":"2023-04-03T08:07:14.983366Z","iopub.status.idle":"2023-04-03T08:07:14.993506Z","shell.execute_reply":"2023-04-03T08:07:14.992527Z","shell.execute_reply.started":"2023-04-03T08:07:14.983832Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Count of rows in the data is:   1599999\n"]}],"source":["print('Count of rows in the data is:  ', len(data))"]},{"cell_type":"markdown","metadata":{},"source":["# Univariate Analysis"]},{"cell_type":"code","execution_count":70,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean tweet length: 74.09008568130355\n","Median tweet length: 69.0\n","Mode tweet length: 138\n"]}],"source":["# Mean, Meadian, Mode of Tweet Length\n","\n","data['tweet_length'] = data['text'].apply(len)\n","\n","# Calculate mean, median, and mode for tweet length\n","mean_tweet_length = data['tweet_length'].mean()\n","median_tweet_length = data['tweet_length'].median()\n","mode_tweet_length = data['tweet_length'].mode().values[0]\n","\n","print(\"Mean tweet length:\", mean_tweet_length)\n","print(\"Median tweet length:\", median_tweet_length)\n","print(\"Mode tweet length:\", mode_tweet_length)"]},{"cell_type":"code","execution_count":71,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Standard Deviation of tweet length: 36.44113578973038\n","Variance of tweet length: 1327.9563776455686\n"]}],"source":["# Calculate standard deviation and variance for tweet length\n","std_dev_tweet_length = data['tweet_length'].std()\n","var_tweet_length = data['tweet_length'].var()\n","\n","print(\"Standard Deviation of tweet length:\", std_dev_tweet_length)\n","print(\"Variance of tweet length:\", var_tweet_length)"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Skewness of tweet length: 0.2710193073199834\n","Kurtosis of tweet length: -1.0200599286609988\n"]}],"source":["# Calculate skewness and kurtosis for tweet length\n","skewness_tweet_length = skew(data['tweet_length'])\n","kurtosis_tweet_length = kurtosis(data['tweet_length'])\n","\n","print(\"Skewness of tweet length:\", skewness_tweet_length)\n","print(\"Kurtosis of tweet length:\", kurtosis_tweet_length)"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Calculate kurtosis for tweet length\n","kurtosis_tweet_length = kurtosis(data['tweet_length'])\n","\n","# Plot the histogram of kurtosis values\n","plt.figure(figsize=(10, 6))\n","plt.hist(kurtosis_tweet_length, bins=20, color='skyblue', edgecolor='black')\n","plt.xlabel('Kurtosis')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Kurtosis for Tweet Length')\n","plt.grid(True)\n","plt.show()"]},{"cell_type":"code","execution_count":74,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a seaborn displot with a histogram of tweet lengths and kurtosis value\n","sns.displot(data['tweet_length'], kind='hist', kde=True)\n","plt.title(f'Distribution of Tweet Lengths\\nKurtosis: {kurtosis_tweet_length:.2f}')\n","plt.show()"]},{"cell_type":"code","execution_count":75,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sentiment_counts = data['label'].value_counts()\n","\n","sentiment_counts.plot(kind='bar')\n","plt.title('Distribution of Emotions')\n","plt.xlabel('Emotion/Sentiment')\n","plt.ylabel('Frequency')\n","plt.xticks([0, 1], ['Negative', 'Positive'])\n","plt.show()"]},{"cell_type":"code","execution_count":76,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of unique IDs:  1598314\n"]}],"source":["unique_ids = data['id'].unique()\n","print(\"Number of unique IDs: \", len(unique_ids))"]},{"cell_type":"code","execution_count":77,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Calculate the length of each tweet (number of characters)\n","data['tweet_length'] = data['text'].apply(len)\n","\n","# Create a histogram for tweet lengths\n","plt.hist(data['tweet_length'], bins=30, color='blue', alpha=0.7)\n","plt.xlabel('Tweet Length')\n","plt.ylabel('Frequency')\n","plt.title('Histogram of Tweet Lengths')\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["# Bivaraite Analysis"]},{"cell_type":"code","execution_count":78,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['date'] = pd.to_datetime(data['date'])\n","data['year'] = data['date'].dt.year\n","data['month'] = data['date'].dt.month\n","plt.figure(figsize=(10, 6))\n","data.groupby('month')['id'].count().plot(kind='bar')\n","plt.title('Number of Tweets Over Years')\n","plt.xlabel('Month')\n","plt.ylabel('Number of Tweets')\n","plt.xticks([0, 1, 2], ['April', 'May', 'June'])\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Scatterplot of tweet length"]},{"cell_type":"code","execution_count":79,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:14.996112Z","iopub.status.busy":"2023-04-03T08:07:14.995782Z","iopub.status.idle":"2023-04-03T08:07:15.040576Z","shell.execute_reply":"2023-04-03T08:07:15.039479Z","shell.execute_reply.started":"2023-04-03T08:07:14.996080Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["columns = ['label', 'id', 'date', 'query', 'username', 'text']\n","data['tweet_length'] = data['text'].apply(len)\n","plt.figure(figsize=(10, 6))\n","plt.scatter(data['tweet_length'], data['label'], alpha=1)\n","plt.title('Scatterplot of Tweet Length vs Sentiment')\n","plt.xlabel('Tweet Length')\n","plt.ylabel('Sentiment (0 = negative, 4 = positive)')\n","plt.grid(True)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["#### Assigning Kedeplot"]},{"cell_type":"code","execution_count":80,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:15.042427Z","iopub.status.busy":"2023-04-03T08:07:15.041931Z","iopub.status.idle":"2023-04-03T08:07:15.066103Z","shell.execute_reply":"2023-04-03T08:07:15.065109Z","shell.execute_reply.started":"2023-04-03T08:07:15.042390Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['tweet_length'] = data['text'].apply(len)\n","plt.figure(figsize=(10, 6))\n","sns.kdeplot(data['tweet_length'], shade=True)\n","plt.title('Kernel Density Estimate (KDE) Plot for Tweet Length')\n","plt.xlabel('Tweet Length')\n","plt.ylabel('Density')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Multivariate"]},{"cell_type":"markdown","metadata":{},"source":["#### Pairplot"]},{"cell_type":"code","execution_count":81,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:15.176637Z","iopub.status.busy":"2023-04-03T08:07:15.176355Z","iopub.status.idle":"2023-04-03T08:07:15.206969Z","shell.execute_reply":"2023-04-03T08:07:15.205999Z","shell.execute_reply.started":"2023-04-03T08:07:15.176609Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 6 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data['sentiment'] = data['label'].replace({0: 'negative', 4: 'positive'})\n","numerical_vars = ['tweet_length', 'label']\n","sns.pairplot(data[numerical_vars])\n","plt.suptitle('Pairplot of Numerical Variables', y=1.02)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["#### Heatmap for Most commonly used words"]},{"cell_type":"code","execution_count":82,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","text_data = ' '.join(data['text'].values)\n","\n","# Define custom stopwords\n","custom_stopwords = set(STOPWORDS)\n","custom_stopwords.update(['http', 'https', 'co', 'com', 'www'])\n","\n","# Split the text into words and remove stopwords\n","words = [word for word in text_data.lower().split() if word not in custom_stopwords]\n","\n","# Count the occurrences of each word\n","word_counts = Counter(words)\n","\n","# Create a DataFrame from the word counts\n","word_counts_df = pd.DataFrame.from_dict(word_counts, orient='index', columns=['count'])\n","\n","# Sort the DataFrame by count in descending order\n","word_counts_df = word_counts_df.sort_values(by='count', ascending=False).head(10)\n","\n","# Create a heatmap\n","plt.figure(figsize=(12, 8))\n","sns.heatmap(word_counts_df.transpose(), cmap='Blues', annot=True, fmt='d')\n","plt.title('Most Commonly Used Words in Tweet Text (Heatmap)')\n","plt.xlabel('Word')\n","plt.ylabel('Count')\n","plt.xticks(rotation=45)\n","plt.yticks(rotation=0)\n","plt.show()"]},{"cell_type":"code","execution_count":83,"metadata":{},"outputs":[{"data":{"text/plain":["\"\\ndef process_text(text):\\n    words = [word for word in text.lower().split() if word not in custom_stopwords]\\n    return words\\n\\n# Apply text processing to tweet text\\ndata['processed_text'] = data['text'].apply(process_text)\\n\\n# Initialize counters for each sentiment category\\nnegative_counter = Counter()\\nneutral_counter = Counter()\\npositive_counter = Counter()\\n\\n# Update counters based on sentiment category\\nfor index, row in data.iterrows():\\n    for word in row['processed_text']:\\n        if row['label'] == 0:\\n            negative_counter[word] += 1\\n        elif row['label'] == 2:\\n            neutral_counter[word] += 1\\n        elif row['label'] == 4:\\n            positive_counter[word] += 1\\n\\n# Select the top N most common words for each sentiment category\\ntop_words_negative = negative_counter.most_common(10)\\ntop_words_neutral = neutral_counter.most_common(10)\\ntop_words_positive = positive_counter.most_common(10)\\n\\n# Create a DataFrame for visualization\\nword_counts_df = pd.DataFrame({\\n    'negative': dict(top_words_negative),\\n    'neutral': dict(top_words_neutral),\\n    'positive': dict(top_words_positive)\\n})\\n\\n# Plot the word frequencies for each sentiment category\\nplt.figure(figsize=(12, 8))\\nsns.barplot(data=word_counts_df, orient='h')\\nplt.title('Top 10 Most Used Words in Tweet Text by Sentiment')\\nplt.xlabel('Frequency')\\nplt.ylabel('Word')\\nplt.legend(title='Sentiment', labels=['Negative', 'Neutral', 'Positive'])\\nplt.show()\\n\""]},"execution_count":83,"metadata":{},"output_type":"execute_result"}],"source":["'''\n","def process_text(text):\n","    words = [word for word in text.lower().split() if word not in custom_stopwords]\n","    return words\n","\n","# Apply text processing to tweet text\n","data['processed_text'] = data['text'].apply(process_text)\n","\n","# Initialize counters for each sentiment category\n","negative_counter = Counter()\n","neutral_counter = Counter()\n","positive_counter = Counter()\n","\n","# Update counters based on sentiment category\n","for index, row in data.iterrows():\n","    for word in row['processed_text']:\n","        if row['label'] == 0:\n","            negative_counter[word] += 1\n","        elif row['label'] == 2:\n","            neutral_counter[word] += 1\n","        elif row['label'] == 4:\n","            positive_counter[word] += 1\n","\n","# Select the top N most common words for each sentiment category\n","top_words_negative = negative_counter.most_common(10)\n","top_words_neutral = neutral_counter.most_common(10)\n","top_words_positive = positive_counter.most_common(10)\n","\n","# Create a DataFrame for visualization\n","word_counts_df = pd.DataFrame({\n","    'negative': dict(top_words_negative),\n","    'neutral': dict(top_words_neutral),\n","    'positive': dict(top_words_positive)\n","})\n","\n","# Plot the word frequencies for each sentiment category\n","plt.figure(figsize=(12, 8))\n","sns.barplot(data=word_counts_df, orient='h')\n","plt.title('Top 10 Most Used Words in Tweet Text by Sentiment')\n","plt.xlabel('Frequency')\n","plt.ylabel('Word')\n","plt.legend(title='Sentiment', labels=['Negative', 'Neutral', 'Positive'])\n","plt.show()\n","'''"]},{"cell_type":"code","execution_count":85,"metadata":{},"outputs":[],"source":["def process_text(text):\n","    words = [word for word in text.lower().split() if word not in custom_stopwords]\n","    return words\n","\n","# Apply text processing to tweet text\n","data['processed_text'] = data['text'].apply(process_text)\n","\n","negative_counter = Counter()\n","neutral_counter = Counter()\n","positive_counter = Counter()\n","\n","# Update counters based on sentiment category\n","for index, row in data.iterrows():\n","    for word in row['processed_text']:\n","        if row['label'] == 0:\n","            negative_counter[word] += 1\n","        elif row['label'] == 2:\n","            neutral_counter[word] += 1\n","        elif row['label'] == 4:\n","            positive_counter[word] += 1\n","\n","# Select the top N most common words for each sentiment category\n","top_words_negative = dict(negative_counter.most_common(10))\n","top_words_neutral = dict(neutral_counter.most_common(10))\n","top_words_positive = dict(positive_counter.most_common(10))\n"]},{"cell_type":"code","execution_count":86,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1800x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot the word frequencies for each sentiment category\n","plt.figure(figsize=(18, 6))\n","\n","# Plot for negative sentiment\n","plt.subplot(1, 2, 1)\n","sns.barplot(x=list(top_words_negative.keys()), y=list(top_words_negative.values()), palette='rocket')\n","plt.title('Top 10 Words - Negative Sentiment')\n","plt.xlabel('Word')\n","plt.ylabel('Frequency')\n","\n","# Plot for positive sentiment\n","plt.subplot(1, 2, 2)\n","sns.barplot(x=list(top_words_positive.keys()), y=list(top_words_positive.values()), palette='mako')\n","plt.title('Top 10 Words - Positive Sentiment')\n","plt.xlabel('Word')\n","plt.ylabel('Frequency')\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-block alert-danger\">  \n","<h1><center><strong>Data Preparation 📝</strong></center></h1>\n","</div>"]},{"cell_type":"code","execution_count":87,"metadata":{},"outputs":[{"data":{"text/plain":["19995                            one more day of holidays \n","19996    feeling so down right now .. i hate you damn h...\n","19997    geez,i hv to read the whole book of personalit...\n","19998    i threw my sign at donnie and he bent over to ...\n","19999    @heather2711 good thing i didn't find any then...\n","Name: text, dtype: object"]},"execution_count":87,"metadata":{},"output_type":"execute_result"}],"source":["# Selecting the text and label coloumn\n","word_data = data[['text','label']]\n","\n","# Assigning 1 to Positive sentment 4\n","word_data['label'][word_data['label']==4]=1\n","\n","# Separating positive and negative tweets\n","data_pos = word_data[word_data['label'] == 1]\n","data_neg = word_data[word_data['label'] == 0]\n","\n","# taking one fourth data so we can run on our machine easily \n","data_pos = data_pos.iloc[:int(20000)]\n","data_neg = data_neg.iloc[:int(20000)]\n","\n","# Combining positive and negative tweets\n","word_data = pd.concat([data_pos, data_neg])\n","\n","# Making statement text in lower case\n","word_data['text'] = word_data['text'].str.lower()\n","\n","word_data['text'].tail()"]},{"cell_type":"code","execution_count":88,"metadata":{},"outputs":[{"data":{"text/plain":["799999                love @health4uandpets u guys r best!!\n","800000    im meeting one besties tonight! cant wait!! - ...\n","800001    @darealsunisakim thanks twitter add, sunisa! g...\n","800002    sick really cheap hurts much eat real food plu...\n","800003                      @lovesbrooklyn2 effect everyone\n","Name: text, dtype: object"]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["# Cleaning and removing Stop words of english\n","stopwords_list = stopwords.words('english')\n","\n","\", \".join(stopwords.words('english'))\n","\n","# Cleaning and removing the above stop words list from the tweet text\n","STOPWORDS = set(stopwords.words('english'))\n","def cleaning_stopwords(text):\n","    return \" \".join([word for word in str(text).split() if word not in STOPWORDS])\n","word_data['text'] = word_data['text'].apply(lambda text: cleaning_stopwords(text))\n","word_data['text'].head()"]},{"cell_type":"code","execution_count":89,"metadata":{},"outputs":[{"data":{"text/plain":["19995                                     one day holidays\n","19996                     feeling right  hate damn humprey\n","19997    geezi hv read whole book personality types emb...\n","19998     threw sign donnie bent get thingee made sad face\n","19999    heather2711 good thing find none ones like com...\n","Name: text, dtype: object"]},"execution_count":89,"metadata":{},"output_type":"execute_result"}],"source":["# Cleaning and removing punctuations \n","english_punctuations = string.punctuation\n","punctuations_list = english_punctuations\n","def cleaning_punctuations(text):\n","    translator = str.maketrans('', '', punctuations_list)\n","    return text.translate(translator)\n","\n","word_data['text'] = word_data['text'].apply(lambda x: cleaning_punctuations(x))\n","word_data['text'].tail()"]},{"cell_type":"code","execution_count":90,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:15.208755Z","iopub.status.busy":"2023-04-03T08:07:15.208452Z","iopub.status.idle":"2023-04-03T08:07:15.243452Z","shell.execute_reply":"2023-04-03T08:07:15.242469Z","shell.execute_reply.started":"2023-04-03T08:07:15.208726Z"},"trusted":true},"outputs":[],"source":["# Cleaning and removing repeating characters\n","def cleaning_repeating_char(text):\n","    return re.sub(r'(.)\\1+', r'\\1', text)"]},{"cell_type":"code","execution_count":91,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:15.245653Z","iopub.status.busy":"2023-04-03T08:07:15.245207Z","iopub.status.idle":"2023-04-03T08:07:15.256487Z","shell.execute_reply":"2023-04-03T08:07:15.255624Z","shell.execute_reply.started":"2023-04-03T08:07:15.245604Z"},"trusted":true},"outputs":[{"data":{"text/plain":["19995                                     one day holidays\n","19996                       feling right hate damn humprey\n","19997    gezi hv read whole bok personality types embar...\n","19998       threw sign donie bent get thinge made sad face\n","19999    heather271 god thing find none ones like come ...\n","Name: text, dtype: object"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text'] = word_data['text'].apply(lambda x: cleaning_repeating_char(x))\n","word_data['text'].tail()"]},{"cell_type":"markdown","metadata":{},"source":["#### Cleaning and removing email"]},{"cell_type":"code","execution_count":92,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.046420Z","iopub.status.busy":"2023-04-03T08:07:16.045876Z","iopub.status.idle":"2023-04-03T08:07:16.051278Z","shell.execute_reply":"2023-04-03T08:07:16.050171Z","shell.execute_reply.started":"2023-04-03T08:07:16.046362Z"},"trusted":true},"outputs":[],"source":["def cleaning_email(data):\n","    return re.sub('@[^\\s]+', ' ', data)"]},{"cell_type":"code","execution_count":93,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.053347Z","iopub.status.busy":"2023-04-03T08:07:16.052987Z","iopub.status.idle":"2023-04-03T08:07:16.128762Z","shell.execute_reply":"2023-04-03T08:07:16.127849Z","shell.execute_reply.started":"2023-04-03T08:07:16.053290Z"},"trusted":true},"outputs":[{"data":{"text/plain":["19995                                     one day holidays\n","19996                       feling right hate damn humprey\n","19997    gezi hv read whole bok personality types embar...\n","19998       threw sign donie bent get thinge made sad face\n","19999    heather271 god thing find none ones like come ...\n","Name: text, dtype: object"]},"execution_count":93,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text']= word_data['text'].apply(lambda x: cleaning_email(x))\n","word_data['text'].tail()"]},{"cell_type":"markdown","metadata":{},"source":["#### Cleaning and removing URL's"]},{"cell_type":"code","execution_count":94,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.130626Z","iopub.status.busy":"2023-04-03T08:07:16.130077Z","iopub.status.idle":"2023-04-03T08:07:16.135607Z","shell.execute_reply":"2023-04-03T08:07:16.134287Z","shell.execute_reply.started":"2023-04-03T08:07:16.130570Z"},"trusted":true},"outputs":[],"source":["def cleaning_URLs(data):\n","    return re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))',' ',data)"]},{"cell_type":"code","execution_count":95,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.137889Z","iopub.status.busy":"2023-04-03T08:07:16.137554Z","iopub.status.idle":"2023-04-03T08:07:16.317718Z","shell.execute_reply":"2023-04-03T08:07:16.316858Z","shell.execute_reply.started":"2023-04-03T08:07:16.137856Z"},"trusted":true},"outputs":[{"data":{"text/plain":["19995                                     one day holidays\n","19996                       feling right hate damn humprey\n","19997    gezi hv read whole bok personality types embar...\n","19998       threw sign donie bent get thinge made sad face\n","19999    heather271 god thing find none ones like come ...\n","Name: text, dtype: object"]},"execution_count":95,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text'] = word_data['text'].apply(lambda x: cleaning_URLs(x))\n","word_data['text'].tail()"]},{"cell_type":"markdown","metadata":{},"source":["#### Cleaning and removing Numeric numbers"]},{"cell_type":"code","execution_count":96,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.319441Z","iopub.status.busy":"2023-04-03T08:07:16.318950Z","iopub.status.idle":"2023-04-03T08:07:16.323331Z","shell.execute_reply":"2023-04-03T08:07:16.322487Z","shell.execute_reply.started":"2023-04-03T08:07:16.319386Z"},"trusted":true},"outputs":[],"source":["def cleaning_numbers(data):\n","    return re.sub('[0-9]+', '', data)"]},{"cell_type":"code","execution_count":97,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.329940Z","iopub.status.busy":"2023-04-03T08:07:16.329364Z","iopub.status.idle":"2023-04-03T08:07:16.452657Z","shell.execute_reply":"2023-04-03T08:07:16.451414Z","shell.execute_reply.started":"2023-04-03T08:07:16.329742Z"},"trusted":true},"outputs":[{"data":{"text/plain":["19995                                     one day holidays\n","19996                       feling right hate damn humprey\n","19997    gezi hv read whole bok personality types embar...\n","19998       threw sign donie bent get thinge made sad face\n","19999    heather god thing find none ones like come siz...\n","Name: text, dtype: object"]},"execution_count":97,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text'] = word_data['text'].apply(lambda x: cleaning_numbers(x))\n","word_data['text'].tail()"]},{"cell_type":"markdown","metadata":{},"source":["#### Getting tokenization of tweet text"]},{"cell_type":"code","execution_count":98,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.454479Z","iopub.status.busy":"2023-04-03T08:07:16.454097Z","iopub.status.idle":"2023-04-03T08:07:16.597642Z","shell.execute_reply":"2023-04-03T08:07:16.596640Z","shell.execute_reply.started":"2023-04-03T08:07:16.454445Z"},"trusted":true},"outputs":[],"source":["tokenizer = RegexpTokenizer(r'\\w+')\n","word_data['text'] = word_data['text'].apply(tokenizer.tokenize)"]},{"cell_type":"code","execution_count":99,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.600837Z","iopub.status.busy":"2023-04-03T08:07:16.600376Z","iopub.status.idle":"2023-04-03T08:07:16.614218Z","shell.execute_reply":"2023-04-03T08:07:16.612684Z","shell.execute_reply.started":"2023-04-03T08:07:16.600789Z"},"trusted":true},"outputs":[{"data":{"text/plain":["799999             [love, healthuandpets, u, guys, r, best]\n","800000    [im, meting, one, besties, tonight, cant, wait...\n","800001    [darealsunisakim, thanks, twiter, ad, sunisa, ...\n","800002    [sick, realy, cheap, hurts, much, eat, real, f...\n","800003                      [lovesbroklyn, efect, everyone]\n","Name: text, dtype: object"]},"execution_count":99,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text'].head()"]},{"cell_type":"markdown","metadata":{},"source":["#### Applying Stemming"]},{"cell_type":"code","execution_count":100,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:16.615994Z","iopub.status.busy":"2023-04-03T08:07:16.615591Z","iopub.status.idle":"2023-04-03T08:07:24.781361Z","shell.execute_reply":"2023-04-03T08:07:24.780017Z","shell.execute_reply.started":"2023-04-03T08:07:16.615961Z"},"trusted":true},"outputs":[],"source":["st = nltk.PorterStemmer()\n","def stemming_on_text(data):\n","    text = [st.stem(word) for word in data]\n","    return data\n","\n","word_data['text']= word_data['text'].apply(lambda x: stemming_on_text(x))"]},{"cell_type":"code","execution_count":101,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:24.783363Z","iopub.status.busy":"2023-04-03T08:07:24.783012Z","iopub.status.idle":"2023-04-03T08:07:24.792087Z","shell.execute_reply":"2023-04-03T08:07:24.790904Z","shell.execute_reply.started":"2023-04-03T08:07:24.783331Z"},"trusted":true},"outputs":[{"data":{"text/plain":["799999             [love, healthuandpets, u, guys, r, best]\n","800000    [im, meting, one, besties, tonight, cant, wait...\n","800001    [darealsunisakim, thanks, twiter, ad, sunisa, ...\n","800002    [sick, realy, cheap, hurts, much, eat, real, f...\n","800003                      [lovesbroklyn, efect, everyone]\n","Name: text, dtype: object"]},"execution_count":101,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text'].head()"]},{"cell_type":"markdown","metadata":{},"source":["#### Applying Lemmatizer"]},{"cell_type":"code","execution_count":102,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:24.793598Z","iopub.status.busy":"2023-04-03T08:07:24.793309Z","iopub.status.idle":"2023-04-03T08:07:27.893919Z","shell.execute_reply":"2023-04-03T08:07:27.892678Z","shell.execute_reply.started":"2023-04-03T08:07:24.793570Z"},"trusted":true},"outputs":[],"source":["lm = nltk.WordNetLemmatizer()\n","def lemmatizer_on_text(data):\n","    text = [lm.lemmatize(word) for word in data]\n","    return data\n","\n","word_data['text'] = word_data['text'].apply(lambda x: lemmatizer_on_text(x))"]},{"cell_type":"code","execution_count":103,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:27.895832Z","iopub.status.busy":"2023-04-03T08:07:27.895414Z","iopub.status.idle":"2023-04-03T08:07:27.904120Z","shell.execute_reply":"2023-04-03T08:07:27.903292Z","shell.execute_reply.started":"2023-04-03T08:07:27.895799Z"},"trusted":true},"outputs":[{"data":{"text/plain":["799999             [love, healthuandpets, u, guys, r, best]\n","800000    [im, meting, one, besties, tonight, cant, wait...\n","800001    [darealsunisakim, thanks, twiter, ad, sunisa, ...\n","800002    [sick, realy, cheap, hurts, much, eat, real, f...\n","800003                      [lovesbroklyn, efect, everyone]\n","Name: text, dtype: object"]},"execution_count":103,"metadata":{},"output_type":"execute_result"}],"source":["word_data['text'].head()"]},{"cell_type":"code","execution_count":104,"metadata":{},"outputs":[],"source":["data = word_data"]},{"cell_type":"markdown","metadata":{},"source":["#### Separating input feature and label"]},{"cell_type":"code","execution_count":105,"metadata":{},"outputs":[],"source":["X=data.text\n","y=data.label"]},{"cell_type":"markdown","metadata":{},"source":["#### Preparing the input features for training \n","- We converting the text words into arrays form. \n","- Maximum 500 features/words selected for training. These 500 words will be selected on the importance that will distinguish between the positive tweets and negative tweets. "]},{"cell_type":"code","execution_count":106,"metadata":{},"outputs":[],"source":["max_len = 500\n","tok = Tokenizer(num_words=2000)\n","tok.fit_on_texts(X)\n","sequences = tok.texts_to_sequences(X)\n","sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)"]},{"cell_type":"markdown","metadata":{},"source":["As we can see that there total 40000 tweets and the number words/features are 500."]},{"cell_type":"code","execution_count":107,"metadata":{},"outputs":[{"data":{"text/plain":["(40000, 500)"]},"execution_count":107,"metadata":{},"output_type":"execute_result"}],"source":["sequences_matrix.shape"]},{"cell_type":"markdown","metadata":{},"source":["#### Separating the 70% data for training data and 30% for testing data"]},{"cell_type":"markdown","metadata":{},"source":["As we prepared all the tweets, now we are separating/splitting the tweets into training data and testing data.\n","- 70% tweets will be used in the training \n","- 30% tweets will be used to test the performance of the model."]},{"cell_type":"code","execution_count":108,"metadata":{},"outputs":[],"source":["X_train, X_test, Y_train, Y_test = train_test_split(sequences_matrix, y, test_size=0.3, random_state=2)"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-block alert-danger\">  \n","<h2><center><strong>Implementing Models for training 🧪</strong></center></h2>   \n","</div>"]},{"cell_type":"markdown","metadata":{},"source":["# Naive Bayes Model:\n","Naive Bayes is a simple yet effective algorithm for classification tasks. We can use the Multinomial Naive Bayes implementation from scikit-learn for text classification."]},{"cell_type":"code","execution_count":109,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Naive Bayes Model Accuracy: 0.5176666666666667\n"]}],"source":["from sklearn.naive_bayes import MultinomialNB\n","\n","# Initialize the Naive Bayes classifier\n","nb_classifier = MultinomialNB()\n","\n","# Train the classifier\n","nb_classifier.fit(X_train, Y_train)\n","\n","# Evaluate the classifier\n","accuracy_nb = nb_classifier.score(X_test, Y_test)\n","print(\"Naive Bayes Model Accuracy:\", accuracy_nb)\n"]},{"cell_type":"markdown","metadata":{},"source":["# SVM Model:\n","Support Vector Machines (SVM) are powerful classifiers that can handle both linear and non-linear data. We'll use the scikit-learn implementation for SVM."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.svm import SVC\n","\n","# Initialize the SVM classifier\n","svm_classifier = SVC(kernel='linear')\n","\n","# Train the classifier\n","svm_classifier.fit(X_train, Y_train)\n","\n","# Evaluate the classifier\n","accuracy_svm = svm_classifier.score(X_test, Y_test)\n","print(\"SVM Model Accuracy:\", accuracy_svm)\n"]},{"cell_type":"markdown","metadata":{},"source":["# Linear Regression Model:"]},{"cell_type":"code","execution_count":110,"metadata":{},"outputs":[{"ename":"AttributeError","evalue":"'list' object has no attribute 'lower'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","Cell \u001b[0;32mIn[110], line 19\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# Vectorizing the text data\u001b[39;00m\n\u001b[1;32m     18\u001b[0m vectorizer \u001b[38;5;241m=\u001b[39m TfidfVectorizer(ngram_range\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m), max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500000\u001b[39m)\n\u001b[0;32m---> 19\u001b[0m X_train_vectorized \u001b[38;5;241m=\u001b[39m \u001b[43mvectorizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m X_test_vectorized \u001b[38;5;241m=\u001b[39m vectorizer\u001b[38;5;241m.\u001b[39mtransform(X_test)\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# Training the linear regression model\u001b[39;00m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:2138\u001b[0m, in \u001b[0;36mTfidfVectorizer.fit_transform\u001b[0;34m(self, raw_documents, y)\u001b[0m\n\u001b[1;32m   2131\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_params()\n\u001b[1;32m   2132\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf \u001b[38;5;241m=\u001b[39m TfidfTransformer(\n\u001b[1;32m   2133\u001b[0m     norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm,\n\u001b[1;32m   2134\u001b[0m     use_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_idf,\n\u001b[1;32m   2135\u001b[0m     smooth_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msmooth_idf,\n\u001b[1;32m   2136\u001b[0m     sublinear_tf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msublinear_tf,\n\u001b[1;32m   2137\u001b[0m )\n\u001b[0;32m-> 2138\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2139\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[1;32m   2140\u001b[0m \u001b[38;5;66;03m# X is already a transformed view of raw_documents so\u001b[39;00m\n\u001b[1;32m   2141\u001b[0m \u001b[38;5;66;03m# we set copy to False\u001b[39;00m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1347\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1348\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1349\u001b[0m     )\n\u001b[1;32m   1350\u001b[0m ):\n\u001b[0;32m-> 1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:1389\u001b[0m, in \u001b[0;36mCountVectorizer.fit_transform\u001b[0;34m(self, raw_documents, y)\u001b[0m\n\u001b[1;32m   1381\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m   1382\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUpper case characters found in\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1383\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m vocabulary while \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlowercase\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1384\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is True. These entries will not\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1385\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m be matched with any documents\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1386\u001b[0m             )\n\u001b[1;32m   1387\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m-> 1389\u001b[0m vocabulary, X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_count_vocab\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfixed_vocabulary_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbinary:\n\u001b[1;32m   1392\u001b[0m     X\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfill(\u001b[38;5;241m1\u001b[39m)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:1276\u001b[0m, in \u001b[0;36mCountVectorizer._count_vocab\u001b[0;34m(self, raw_documents, fixed_vocab)\u001b[0m\n\u001b[1;32m   1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m raw_documents:\n\u001b[1;32m   1275\u001b[0m     feature_counter \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m-> 1276\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m feature \u001b[38;5;129;01min\u001b[39;00m \u001b[43manalyze\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1277\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1278\u001b[0m             feature_idx \u001b[38;5;241m=\u001b[39m vocabulary[feature]\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:110\u001b[0m, in \u001b[0;36m_analyze\u001b[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m preprocessor \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 110\u001b[0m         doc \u001b[38;5;241m=\u001b[39m \u001b[43mpreprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    111\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tokenizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         doc \u001b[38;5;241m=\u001b[39m tokenizer(doc)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:68\u001b[0m, in \u001b[0;36m_preprocess\u001b[0;34m(doc, accent_function, lower)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Chain together an optional series of text preprocessing steps to\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;124;03mapply to a document.\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m    preprocessed string\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lower:\n\u001b[0;32m---> 68\u001b[0m     doc \u001b[38;5;241m=\u001b[39m \u001b[43mdoc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m()\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m accent_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m     doc \u001b[38;5;241m=\u001b[39m accent_function(doc)\n","\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'lower'"]}],"source":["import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import mean_squared_error\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","# Assuming 'data' contains the loaded dataset\n","\n","# Preprocessing the data\n","# For simplicity, let's assume 'label' is the target variable and 'text' is the feature\n","X = data['text']\n","y = data['label']\n","\n","# Splitting the data into train and test sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Vectorizing the text data\n","vectorizer = TfidfVectorizer(ngram_range=(1, 2), max_features=500000)\n","X_train_vectorized = vectorizer.fit_transform(X_train)\n","X_test_vectorized = vectorizer.transform(X_test)\n","\n","# Training the linear regression model\n","lr_model = LinearRegression()\n","lr_model.fit(X_train_vectorized, y_train)\n","\n","# Predicting on the test set\n","y_pred = lr_model.predict(X_test_vectorized)\n","\n","# Evaluating the model\n","mse = mean_squared_error(y_test, y_pred)\n","print(\"Mean Squared Error:\", mse)"]},{"cell_type":"markdown","metadata":{},"source":["# Decision Tree Model:\n","Decision Trees are non-parametric supervised learning models used for classification tasks."]},{"cell_type":"code","execution_count":111,"metadata":{},"outputs":[{"ename":"ValueError","evalue":"setting an array element with a sequence.","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a real number, not 'list'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[111], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m dt_classifier \u001b[38;5;241m=\u001b[39m DecisionTreeClassifier()\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Train the classifier\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[43mdt_classifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Evaluate the classifier\u001b[39;00m\n\u001b[1;32m     10\u001b[0m accuracy_dt \u001b[38;5;241m=\u001b[39m dt_classifier\u001b[38;5;241m.\u001b[39mscore(X_test, Y_test)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1347\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1348\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1349\u001b[0m     )\n\u001b[1;32m   1350\u001b[0m ):\n\u001b[0;32m-> 1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/tree/_classes.py:1009\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    979\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    980\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[1;32m    981\u001b[0m \n\u001b[1;32m    982\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[38;5;124;03m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m   1007\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1009\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1010\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1011\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1012\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1013\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1014\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1015\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/tree/_classes.py:252\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[0;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[1;32m    248\u001b[0m check_X_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    249\u001b[0m     dtype\u001b[38;5;241m=\u001b[39mDTYPE, accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    250\u001b[0m )\n\u001b[1;32m    251\u001b[0m check_y_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 252\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_separately\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_y_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m missing_values_in_feature_mask \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    257\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_missing_values_in_feature_mask(X)\n\u001b[1;32m    258\u001b[0m )\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:645\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_X_params:\n\u001b[1;32m    644\u001b[0m     check_X_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_X_params}\n\u001b[0;32m--> 645\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_y_params:\n\u001b[1;32m    647\u001b[0m     check_y_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params}\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/utils/validation.py:951\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    949\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    950\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 951\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    952\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[1;32m    953\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    954\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    955\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/utils/_array_api.py:521\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    519\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39marray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    520\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 521\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39masarray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    523\u001b[0m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[1;32m    524\u001b[0m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/core/series.py:917\u001b[0m, in \u001b[0;36mSeries.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    870\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    871\u001b[0m \u001b[38;5;124;03mReturn the values as a NumPy array.\u001b[39;00m\n\u001b[1;32m    872\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;124;03m      dtype='datetime64[ns]')\u001b[39;00m\n\u001b[1;32m    915\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    916\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[0;32m--> 917\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(values, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_copy_on_write() \u001b[38;5;129;01mand\u001b[39;00m astype_is_view(values\u001b[38;5;241m.\u001b[39mdtype, arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m    919\u001b[0m     arr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mview()\n","\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."]}],"source":["from sklearn.tree import DecisionTreeClassifier\n","\n","# Initialize the Decision Tree classifier\n","dt_classifier = DecisionTreeClassifier()\n","\n","# Train the classifier\n","dt_classifier.fit(X_train, Y_train)\n","\n","# Evaluate the classifier\n","accuracy_dt = dt_classifier.score(X_test, Y_test)\n","print(\"Decision Tree Model Accuracy:\", accuracy_dt)"]},{"cell_type":"markdown","metadata":{},"source":["## Logistic Regression"]},{"cell_type":"code","execution_count":112,"metadata":{},"outputs":[{"ename":"ValueError","evalue":"setting an array element with a sequence.","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a real number, not 'list'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[112], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m logreg_classifier \u001b[38;5;241m=\u001b[39m LogisticRegression(max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Train the classifier\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[43mlogreg_classifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Evaluate the classifier\u001b[39;00m\n\u001b[1;32m     10\u001b[0m accuracy_lr \u001b[38;5;241m=\u001b[39m logreg_classifier\u001b[38;5;241m.\u001b[39mscore(X_test, Y_test)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1347\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1348\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1349\u001b[0m     )\n\u001b[1;32m   1350\u001b[0m ):\n\u001b[0;32m-> 1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1201\u001b[0m, in \u001b[0;36mLogisticRegression.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1198\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1199\u001b[0m     _dtype \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mfloat64, np\u001b[38;5;241m.\u001b[39mfloat32]\n\u001b[0;32m-> 1201\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1207\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msolver\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mliblinear\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msag\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msaga\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1208\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1209\u001b[0m check_classification_targets(y)\n\u001b[1;32m   1210\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(y)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:650\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    648\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m    649\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 650\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    651\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    653\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/utils/validation.py:1192\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1187\u001b[0m         estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m   1188\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1189\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1190\u001b[0m     )\n\u001b[0;32m-> 1192\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1193\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1194\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1195\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1196\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1197\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1198\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1199\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1200\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1201\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1208\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[1;32m   1210\u001b[0m check_consistent_length(X, y)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/utils/validation.py:951\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    949\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    950\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 951\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    952\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[1;32m    953\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    954\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    955\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/utils/_array_api.py:521\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    519\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39marray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    520\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 521\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39masarray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    523\u001b[0m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[1;32m    524\u001b[0m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/core/series.py:917\u001b[0m, in \u001b[0;36mSeries.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    870\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    871\u001b[0m \u001b[38;5;124;03mReturn the values as a NumPy array.\u001b[39;00m\n\u001b[1;32m    872\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;124;03m      dtype='datetime64[ns]')\u001b[39;00m\n\u001b[1;32m    915\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    916\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[0;32m--> 917\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(values, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_copy_on_write() \u001b[38;5;129;01mand\u001b[39;00m astype_is_view(values\u001b[38;5;241m.\u001b[39mdtype, arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m    919\u001b[0m     arr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mview()\n","\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."]}],"source":["from sklearn.linear_model import LogisticRegression\n","\n","# Initialize the Logistic Regression classifier\n","logreg_classifier = LogisticRegression(max_iter=1000)\n","\n","# Train the classifier\n","logreg_classifier.fit(X_train, Y_train)\n","\n","# Evaluate the classifier\n","accuracy_lr = logreg_classifier.score(X_test, Y_test)\n","print(\"Logistic Regression Model Accuracy:\", accuracy_lr)\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-block alert-danger\">  \n","<h2><center><strong>Model Evaluation</strong></center></h2>   \n","</div>"]},{"cell_type":"markdown","metadata":{},"source":["### Evaluate Model Function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def model_Evaluate(model):\n","    \n","    # Predict values for Test dataset\n","    y_pred = model.predict(X_test)\n","\n","    # Print the evaluation metrics for the dataset.\n","    print(classification_report(y_test, y_pred))\n","    \n","    # Compute and plot the Confusion matrix\n","    cf_matrix = confusion_matrix(y_test, y_pred)\n","\n","    categories  = ['Negative','Positive']\n","    group_names = ['True Neg','False Pos', 'False Neg','True Pos']\n","    group_percentages = ['{0:.2%}'.format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)]\n","\n","    labels = [f'{v1}\\n{v2}' for v1, v2 in zip(group_names,group_percentages)]\n","    labels = np.asarray(labels).reshape(2,2)\n","\n","    sns.heatmap(cf_matrix, annot = labels, cmap = 'Blues',fmt = '',\n","                xticklabels = categories, yticklabels = categories)\n","\n","    plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 10)\n","    plt.ylabel(\"Actual values\"   , fontdict = {'size':14}, labelpad = 10)\n","    plt.title (\"Confusion Matrix\", fontdict = {'size':18}, pad = 20)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Evaluating Naive Bayes model\n","print(\"Naive Bayes Model Evaluation:\")\n","model_Evaluate(nb_classifier)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Evaluating SVM model\n","print(\"SVM Model Evaluation:\")\n","model_Evaluate(svm_classifier)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Evaluating Logistic Regression model\n","print(\"Logistic Regression Model Evaluation:\")\n","model_Evaluate(logreg_classifier)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Evaluating Decision Tree model\n","print(\"Decision Tree Model Evaluation:\")\n","model_Evaluate(dt_classifier)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Evaluating Linear Regression model\n","print(\"Decision Tree Model Evaluation:\")\n","model_Evaluate(lr_model)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["## Saving the models"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pickle\n","import os\n","\n","# Create a directory to store the models if it doesn't exist\n","models_folder = \"Models\"\n","if not os.path.exists(models_folder):\n","    os.makedirs(models_folder)\n","\n","# Save Naive Bayes model\n","with open(os.path.join(models_folder, \"naive_bayes_model.pickle\"), \"wb\") as f:\n","    pickle.dump(nb_classifier, f)\n","\n","# Save SVM model\n","with open(os.path.join(models_folder, \"svm_model.pickle\"), \"wb\") as f:\n","    pickle.dump(svm_classifier, f)\n","\n","# Save Logistic Regression model\n","with open(os.path.join(models_folder, \"logistic_regression_model.pickle\"), \"wb\") as f:\n","    pickle.dump(logreg_classifier, f)\n","\n","# Save Decision Tree model\n","with open(os.path.join(models_folder, \"decision_tree_model.pickle\"), \"wb\") as f:\n","    pickle.dump(dt_classifier, f)\n","\n","# Save Linear Regression model\n","with open(os.path.join(models_folder, \"linear_regression_model.pickle\"), \"wb\") as f:\n","    pickle.dump(lr_model, f)\n","\n","print(\"Models saved successfully.\")\n"]},{"cell_type":"markdown","metadata":{},"source":["## Using the model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def preprocess_text(user_texts):\n","    preprocessed_texts = []\n","    \n","    # Iterate over each string in the list\n","    for user_text in user_texts:\n","        # Convert text to lowercase\n","        user_text = user_text.lower()\n","\n","        # Remove stopwords\n","        STOPWORDS = set(stopwords.words('english'))\n","        user_text = ' '.join([word for word in user_text.split() if word not in STOPWORDS])\n","\n","        # Remove punctuations\n","        punctuations_list = string.punctuation\n","        translator = str.maketrans('', '', punctuations_list)\n","        user_text = user_text.translate(translator)\n","\n","        # Remove repeating characters\n","        user_text = re.sub(r'(.)\\1+', r'\\1', user_text)\n","\n","        # Remove email addresses\n","        user_text = re.sub('@[^\\s]+', ' ', user_text)\n","\n","        # Remove URLs\n","        user_text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))',' ',user_text)\n","\n","        # Remove numbers\n","        user_text = re.sub('[0-9]+', '', user_text)\n","\n","        # Tokenize text\n","        tokenizer = RegexpTokenizer(r'\\w+')\n","        tokens = tokenizer.tokenize(user_text)\n","\n","        # Stemming\n","        st = PorterStemmer()\n","        stemmed_tokens = [st.stem(word) for word in tokens]\n","\n","        # Lemmatization\n","        lm = WordNetLemmatizer()\n","        lemmatized_tokens = [lm.lemmatize(word) for word in stemmed_tokens]\n","        \n","        # Append preprocessed text to the list\n","        preprocessed_texts.append(lemmatized_tokens)\n","\n","    return preprocessed_texts"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package stopwords to\n","[nltk_data]     /Users/jagadeesh/nltk_data...\n","[nltk_data]   Package stopwords is already up-to-date!\n","[nltk_data] Downloading package wordnet to\n","[nltk_data]     /Users/jagadeesh/nltk_data...\n","[nltk_data]   Package wordnet is already up-to-date!\n"]},{"ename":"AttributeError","evalue":"'list' object has no attribute 'lower'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","Cell \u001b[0;32mIn[147], line 47\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;66;03m# Loading the models.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;66;03m#vectoriser, LRmodel = load_models()\u001b[39;00m\n\u001b[1;32m     41\u001b[0m     \n\u001b[1;32m     42\u001b[0m     \u001b[38;5;66;03m# Text to classify should be in a list.\u001b[39;00m\n\u001b[1;32m     43\u001b[0m     text \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI hate twitter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     44\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMay the Force be with you.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     45\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMr. Stark, I don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt feel so good\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m---> 47\u001b[0m     df \u001b[38;5;241m=\u001b[39m \u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvectoriser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mLRmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mhead())\n","Cell \u001b[0;32mIn[147], line 25\u001b[0m, in \u001b[0;36mpredict\u001b[0;34m(vectoriser, model, text)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(vectoriser, model, text):\n\u001b[1;32m     24\u001b[0m     \u001b[38;5;66;03m# Predict the sentiment\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     textdata \u001b[38;5;241m=\u001b[39m \u001b[43mvectoriser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreprocess_text\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m     sentiment \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(textdata)\n\u001b[1;32m     28\u001b[0m     \u001b[38;5;66;03m# Make a list of text with sentiment.\u001b[39;00m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:2162\u001b[0m, in \u001b[0;36mTfidfVectorizer.transform\u001b[0;34m(self, raw_documents)\u001b[0m\n\u001b[1;32m   2145\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Transform documents to document-term matrix.\u001b[39;00m\n\u001b[1;32m   2146\u001b[0m \n\u001b[1;32m   2147\u001b[0m \u001b[38;5;124;03mUses the vocabulary and document frequencies (df) learned by fit (or\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2158\u001b[0m \u001b[38;5;124;03m    Tf-idf-weighted document-term matrix.\u001b[39;00m\n\u001b[1;32m   2159\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2160\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe TF-IDF vectorizer is not fitted\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2162\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf\u001b[38;5;241m.\u001b[39mtransform(X, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:1434\u001b[0m, in \u001b[0;36mCountVectorizer.transform\u001b[0;34m(self, raw_documents)\u001b[0m\n\u001b[1;32m   1431\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_vocabulary()\n\u001b[1;32m   1433\u001b[0m \u001b[38;5;66;03m# use the same matrix-building strategy as fit_transform\u001b[39;00m\n\u001b[0;32m-> 1434\u001b[0m _, X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_count_vocab\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfixed_vocab\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbinary:\n\u001b[1;32m   1436\u001b[0m     X\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfill(\u001b[38;5;241m1\u001b[39m)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:1276\u001b[0m, in \u001b[0;36mCountVectorizer._count_vocab\u001b[0;34m(self, raw_documents, fixed_vocab)\u001b[0m\n\u001b[1;32m   1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m raw_documents:\n\u001b[1;32m   1275\u001b[0m     feature_counter \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m-> 1276\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m feature \u001b[38;5;129;01min\u001b[39;00m \u001b[43manalyze\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1277\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1278\u001b[0m             feature_idx \u001b[38;5;241m=\u001b[39m vocabulary[feature]\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:110\u001b[0m, in \u001b[0;36m_analyze\u001b[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m preprocessor \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 110\u001b[0m         doc \u001b[38;5;241m=\u001b[39m \u001b[43mpreprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    111\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tokenizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         doc \u001b[38;5;241m=\u001b[39m tokenizer(doc)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:68\u001b[0m, in \u001b[0;36m_preprocess\u001b[0;34m(doc, accent_function, lower)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Chain together an optional series of text preprocessing steps to\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;124;03mapply to a document.\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m    preprocessed string\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lower:\n\u001b[0;32m---> 68\u001b[0m     doc \u001b[38;5;241m=\u001b[39m \u001b[43mdoc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m()\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m accent_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m     doc \u001b[38;5;241m=\u001b[39m accent_function(doc)\n","\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'lower'"]}],"source":["def load_models():\n","    '''\n","    Replace '..path/' by the path of the saved models.\n","    '''\n","\n","    # Load the LR Model.\n","    file = open('./Sentiment-LR.pickle', 'rb')\n","    LRmodel = pickle.load(file)\n","    file.close()\n","    \n","    return vectoriser, LRmodel\n","\n","def predict(vectoriser, model, text):\n","    # Predict the sentiment\n","    textdata = vectoriser.transform(preprocess_text(text))\n","    sentiment = model.predict(textdata)\n","    \n","    # Make a list of text with sentiment.\n","    data = []\n","    for text, pred in zip(text, sentiment):\n","        data.append((text,pred))\n","        \n","    # Convert the list into a Pandas DataFrame.\n","    df = pd.DataFrame(data, columns = ['text','sentiment'])\n","    df = df.replace([0,1], [\"Negative\",\"Positive\"])\n","    return df\n","\n","if __name__==\"__main__\":\n","    # Loading the models.\n","    #vectoriser, LRmodel = load_models()\n","    \n","    # Text to classify should be in a list.\n","    text = [\"I hate twitter\",\n","            \"May the Force be with you.\",\n","            \"Mr. Stark, I don't feel so good\"]\n","    \n","    df = predict(vectoriser, LRmodel, text)\n","    print(df.head())"]},{"cell_type":"markdown","metadata":{},"source":["<b> <h3> Labels : </h3></b>  labels are the targets like in this project senitments of the tweets are labels. "]},{"cell_type":"markdown","metadata":{},"source":["<b> <h3> Inputs : </h3></b>  Inputs are the data that we feed into machine learning like in this project tweets texts are the inputs. "]},{"cell_type":"markdown","metadata":{},"source":["<b> <h3> Training Data </h3></b>  We use training data when we train the models. We feed train data to machine learning and deep learning models so that model can learn from the data."]},{"cell_type":"markdown","metadata":{},"source":["<b> <h3> Validation Data </h3></b>  We use validation data while training the model. We use this data to evalaute the performance that how the model perform on training time."]},{"cell_type":"markdown","metadata":{},"source":["<b> <h3> Testing Data </h3></b>  We use testing data after training the model. We use this data to evalaute the performance that how the model perform after training. So in this way first we get predictions from the trained model without giving the labels and then we compare the true labels with predictions and get the performance of th model.."]},{"cell_type":"markdown","metadata":{},"source":["####  Separating input feature and label"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:27.906140Z","iopub.status.busy":"2023-04-03T08:07:27.905517Z","iopub.status.idle":"2023-04-03T08:07:27.913936Z","shell.execute_reply":"2023-04-03T08:07:27.912979Z","shell.execute_reply.started":"2023-04-03T08:07:27.906108Z"},"trusted":true},"outputs":[],"source":["X=data.text\n","y=data.label"]},{"cell_type":"markdown","metadata":{},"source":["#### Preparing the input features for training \n","- We converting the text words into arrays form. \n","- Maximum 500 features/words selected for training. These 500 words will be selected on the importance that will distinguish between the positive tweets and negative tweets. "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:27.915589Z","iopub.status.busy":"2023-04-03T08:07:27.915064Z","iopub.status.idle":"2023-04-03T08:07:28.795463Z","shell.execute_reply":"2023-04-03T08:07:28.794447Z","shell.execute_reply.started":"2023-04-03T08:07:27.915552Z"},"id":"plIFObh4jYJV","outputId":"cc21833e-e053-454b-d410-b8e9d48b4392","trusted":true},"outputs":[],"source":["max_len = 500\n","tok = Tokenizer(num_words=2000)\n","tok.fit_on_texts(X)\n","sequences = tok.texts_to_sequences(X)\n","sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)"]},{"cell_type":"markdown","metadata":{},"source":["As we can see that there total 40000 tweets and the number words/features are 500."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:28.797125Z","iopub.status.busy":"2023-04-03T08:07:28.796659Z","iopub.status.idle":"2023-04-03T08:07:28.802844Z","shell.execute_reply":"2023-04-03T08:07:28.801436Z","shell.execute_reply.started":"2023-04-03T08:07:28.797093Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(1599999, 500)"]},"execution_count":120,"metadata":{},"output_type":"execute_result"}],"source":["sequences_matrix.shape"]},{"cell_type":"markdown","metadata":{},"source":["#### Separating the 70% data for training data and 30% for testing data"]},{"cell_type":"markdown","metadata":{},"source":["As we prepared all the tweets, now we are separating/splitting the tweets into training data and testing data.\n","- 70% tweets will be used in the training \n","- 30% tweets will be used to test the performance of the model."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:28.804947Z","iopub.status.busy":"2023-04-03T08:07:28.804538Z","iopub.status.idle":"2023-04-03T08:07:28.851833Z","shell.execute_reply":"2023-04-03T08:07:28.850793Z","shell.execute_reply.started":"2023-04-03T08:07:28.804900Z"},"id":"oOlkTTg4oRqR","trusted":true},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### TF-IDF indicates"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"ename":"AttributeError","evalue":"'numpy.ndarray' object has no attribute 'lower'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","Cell \u001b[0;32mIn[122], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TfidfVectorizer\n\u001b[1;32m      2\u001b[0m vectoriser \u001b[38;5;241m=\u001b[39m TfidfVectorizer(ngram_range\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m), max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500000\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mvectoriser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVectoriser fitted.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo. of feature_words: \u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mlen\u001b[39m(vectoriser\u001b[38;5;241m.\u001b[39mget_feature_names()))\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1347\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1348\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1349\u001b[0m     )\n\u001b[1;32m   1350\u001b[0m ):\n\u001b[0;32m-> 1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:2108\u001b[0m, in \u001b[0;36mTfidfVectorizer.fit\u001b[0;34m(self, raw_documents, y)\u001b[0m\n\u001b[1;32m   2101\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_warn_for_unused_params()\n\u001b[1;32m   2102\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf \u001b[38;5;241m=\u001b[39m TfidfTransformer(\n\u001b[1;32m   2103\u001b[0m     norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm,\n\u001b[1;32m   2104\u001b[0m     use_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_idf,\n\u001b[1;32m   2105\u001b[0m     smooth_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msmooth_idf,\n\u001b[1;32m   2106\u001b[0m     sublinear_tf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msublinear_tf,\n\u001b[1;32m   2107\u001b[0m )\n\u001b[0;32m-> 2108\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2109\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[1;32m   2110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1347\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1348\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1349\u001b[0m     )\n\u001b[1;32m   1350\u001b[0m ):\n\u001b[0;32m-> 1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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These entries will not\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1385\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m be matched with any documents\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1386\u001b[0m             )\n\u001b[1;32m   1387\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m-> 1389\u001b[0m vocabulary, X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_count_vocab\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfixed_vocabulary_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbinary:\n\u001b[1;32m   1392\u001b[0m     X\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfill(\u001b[38;5;241m1\u001b[39m)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:1276\u001b[0m, in \u001b[0;36mCountVectorizer._count_vocab\u001b[0;34m(self, raw_documents, fixed_vocab)\u001b[0m\n\u001b[1;32m   1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m raw_documents:\n\u001b[1;32m   1275\u001b[0m     feature_counter \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m-> 1276\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m feature \u001b[38;5;129;01min\u001b[39;00m \u001b[43manalyze\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1277\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1278\u001b[0m             feature_idx \u001b[38;5;241m=\u001b[39m vocabulary[feature]\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:110\u001b[0m, in \u001b[0;36m_analyze\u001b[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m preprocessor \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 110\u001b[0m         doc \u001b[38;5;241m=\u001b[39m \u001b[43mpreprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    111\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tokenizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         doc \u001b[38;5;241m=\u001b[39m tokenizer(doc)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/feature_extraction/text.py:68\u001b[0m, in \u001b[0;36m_preprocess\u001b[0;34m(doc, accent_function, lower)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Chain together an optional series of text preprocessing steps to\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;124;03mapply to a document.\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m    preprocessed string\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lower:\n\u001b[0;32m---> 68\u001b[0m     doc \u001b[38;5;241m=\u001b[39m \u001b[43mdoc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m()\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m accent_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m     doc \u001b[38;5;241m=\u001b[39m accent_function(doc)\n","\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'lower'"]}],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:28.853569Z","iopub.status.busy":"2023-04-03T08:07:28.853275Z","iopub.status.idle":"2023-04-03T08:07:28.860928Z","shell.execute_reply":"2023-04-03T08:07:28.859834Z","shell.execute_reply.started":"2023-04-03T08:07:28.853539Z"},"trusted":true},"outputs":[],"source":["def tensorflow_based_model(): #Defined tensorflow_based_model function for training tenforflow based model\n","    inputs = Input(name='inputs',shape=[max_len])#step1\n","    layer = Embedding(2000,50,input_length=max_len)(inputs) #step2\n","    layer = LSTM(64)(layer) #step3\n","    layer = Dense(256,name='FC1')(layer) #step4\n","    layer = Activation('relu')(layer) # step5\n","    layer = Dropout(0.5)(layer) # step6\n","    layer = Dense(1,name='out_layer')(layer) #step4 again but this time its giving only one output as because we need to classify the tweet as positive or negative\n","    layer = Activation('sigmoid')(layer) #step5 but this time activation function is sigmoid for only one output.\n","    model = Model(inputs=inputs,outputs=layer) #here we are getting the final output value in the model for classification\n","    return model #function returning the value when we call it"]},{"cell_type":"markdown","metadata":{},"source":["# Model compilation"]},{"cell_type":"markdown","metadata":{},"source":["- First we are calling the model\n","- We are using 2 classes so we set \"binary_crossentropy\" and if we use more than two classes then we use \"categorical_crossentropy\" \n","- Optimizer is a function that used to change the features of neural network such as learning rate (how the model learn with features) in order to reduce the losses. So the learning rate of neural network to reduce the losses is defined by optimizer.\n","- We are setting metrics=accuracy because we are going to caluclate the percentage of correct predictions over all predictions on the validation set"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:28.862755Z","iopub.status.busy":"2023-04-03T08:07:28.862457Z","iopub.status.idle":"2023-04-03T08:07:29.197013Z","shell.execute_reply":"2023-04-03T08:07:29.196099Z","shell.execute_reply.started":"2023-04-03T08:07:28.862726Z"},"trusted":true},"outputs":[],"source":["model = tensorflow_based_model() # here we are calling the function of created model\n","model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])  "]},{"cell_type":"markdown","metadata":{},"source":["#### Training and validating with parameter tuning\n","- We are feeding the training data and getting 10% data for validation from training data\n","* We set the following parameters:\n","- Batch size =80 so the model take 80 tweets in each iteration and train them. Batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration. \n","- Epochs =6 so the model will train on the data 6 times.Epoch is a term used in machine learning and indicates the number of passes of the entire training dataset the machine learning algorithm has completed. \n","- We can choose batch_size, and epochs as we want so the good practice is to set some values and train the model if the model will not give the good results we can change it and then try again for the training of the model. We can repeat this process many time untill we will not get the good results and this process called as parameter tuning.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:07:29.198483Z","iopub.status.busy":"2023-04-03T08:07:29.198154Z","iopub.status.idle":"2023-04-03T08:21:48.133043Z","shell.execute_reply":"2023-04-03T08:21:48.131766Z","shell.execute_reply.started":"2023-04-03T08:07:29.198452Z"},"trusted":true},"outputs":[],"source":["history=model.fit(X_train,Y_train,batch_size=80,epochs=6, validation_split=0.1)# here we are starting the training of model by feeding the training data\n","print('Training finished !!')"]},{"cell_type":"markdown","metadata":{},"source":["<h1> We need to do all the above configurations to train the model. If we will not set all settings correctly then we could not get the desired results.</h1>"]},{"cell_type":"markdown","metadata":{"id":"aQ72A5F4U0wr"},"source":["#### Testing the Trained model on test data\n","- Getting predictions/classifying the sentiments (positve/negative) on the test data using trained model."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:21:48.136111Z","iopub.status.busy":"2023-04-03T08:21:48.135145Z","iopub.status.idle":"2023-04-03T08:22:16.748346Z","shell.execute_reply":"2023-04-03T08:22:16.747021Z","shell.execute_reply.started":"2023-04-03T08:21:48.136060Z"},"executionInfo":{"elapsed":2136,"status":"ok","timestamp":1590595289545,"user":{"displayName":"Muhammad Imran Zaman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjYO6GnhoI_aryKI-bhtIReU4wH4wLPGZdwWVtS=s64","userId":"06817026978292405035"},"user_tz":-300},"id":"LlxD3pU9U0ws","outputId":"a1adf83f-f07c-4f10-fc1c-a895f6e39d56","trusted":true},"outputs":[],"source":["accr1 = model.evaluate(X_test,Y_test) #we are starting to test the model here"]},{"cell_type":"markdown","metadata":{"id":"h5ZOTOh14WKO"},"source":["#### Accuracy\n","- Accuracy is the number of correctly classify tweets from all the tweets of positive and negative. \n","- For example, if the trained model classify the 70 tweets correct and 30 tweets wrong from total of 100 tweets then the accuracy score will be 70%. \n","- Accuracy= Total number of correct predictions/Total number of predictions"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:22:16.750791Z","iopub.status.busy":"2023-04-03T08:22:16.750445Z","iopub.status.idle":"2023-04-03T08:22:16.758308Z","shell.execute_reply":"2023-04-03T08:22:16.756728Z","shell.execute_reply.started":"2023-04-03T08:22:16.750760Z"},"executionInfo":{"elapsed":3304,"status":"ok","timestamp":1590596501745,"user":{"displayName":"Muhammad Imran Zaman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjYO6GnhoI_aryKI-bhtIReU4wH4wLPGZdwWVtS=s64","userId":"06817026978292405035"},"user_tz":-300},"id":"E2bQq4jaU0wt","outputId":"fe61d1b5-954a-4770-d423-ab897f64b495","trusted":true},"outputs":[],"source":["print('Test set\\n  Accuracy: {:0.2f}'.format(accr1[1])) #the accuracy of the model on test data is given below"]},{"cell_type":"markdown","metadata":{},"source":["#### Getting prediction of the test data and then we will compare the true labels/classes of the data with predictions\n","- As the model give probabilties so we are setting a threshold 0.5. More than 0.5 will be the positive tweets and lower will be negative tweets"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:22:16.762381Z","iopub.status.busy":"2023-04-03T08:22:16.761132Z","iopub.status.idle":"2023-04-03T08:22:42.699811Z","shell.execute_reply":"2023-04-03T08:22:42.698716Z","shell.execute_reply.started":"2023-04-03T08:22:16.762330Z"},"trusted":true},"outputs":[],"source":["y_pred = model.predict(X_test) #getting predictions on the trained model\n","y_pred = (y_pred > 0.5) "]},{"cell_type":"markdown","metadata":{"id":"40MjbuBA7Og9"},"source":["#### Confusion matrix\n","- These are the evaluation measures to evaluate the performance of the model.\n","- Dark blue boxes are the correct predictions with the trained model and sky blue boxes shows the wrong predictions.\n","- 4610 tweets correctly predicted as negative sentiments. 1399 tweets predicted positive sentiments but that were actually negative sentiments.\n","- 4247 tweets correctly predicted as postive sentiments. 1744 tweets predicted negative sentiments but that were actually positive sentiments."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:22:42.701958Z","iopub.status.busy":"2023-04-03T08:22:42.701316Z","iopub.status.idle":"2023-04-03T08:22:43.004857Z","shell.execute_reply":"2023-04-03T08:22:43.003346Z","shell.execute_reply.started":"2023-04-03T08:22:42.701924Z"},"executionInfo":{"elapsed":17851,"status":"ok","timestamp":1590596595973,"user":{"displayName":"Muhammad Imran Zaman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjYO6GnhoI_aryKI-bhtIReU4wH4wLPGZdwWVtS=s64","userId":"06817026978292405035"},"user_tz":-300},"id":"WERD7KXs8YmQ","outputId":"db07021b-ec87-4be8-ce17-14cffe8749a6","trusted":true},"outputs":[],"source":["print('\\n')\n","print(\"confusion matrix\")\n","print('\\n')\n","CR=confusion_matrix(Y_test, y_pred)\n","print(CR)\n","print('\\n')\n","\n","fig, ax = plot_confusion_matrix(conf_mat=CR,figsize=(10, 10),\n","                                show_absolute=True,\n","                                show_normed=True,\n","                                colorbar=True)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"wlWpCx_OXEIq"},"source":["# ROC CURVE\n","- ROC curve show the performance of the model as well. \n","- We can see that the model started from the 0 percent predictions and then moved to true positive predictions that are correct\n","- ROC curve (receiver operating characteristic curve) show the performance of a classification model at all the classification thresholds. ROC plots two parameters, True Positive Rate (correct predictions/classifications) False Positive Rate (wrong predictions/classifications)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-04-03T08:22:43.007230Z","iopub.status.busy":"2023-04-03T08:22:43.006745Z","iopub.status.idle":"2023-04-03T08:22:43.179625Z","shell.execute_reply":"2023-04-03T08:22:43.178430Z","shell.execute_reply.started":"2023-04-03T08:22:43.007147Z"},"executionInfo":{"elapsed":1346,"status":"ok","timestamp":1589235307230,"user":{"displayName":"Uğur Mertoğlu","photoUrl":"","userId":"13630859382453662878"},"user_tz":-180},"id":"uypTDMUZXEIq","outputId":"f18a045e-3bbc-4b9b-afbf-29db50e9cbfd","scrolled":true,"trusted":true},"outputs":[],"source":["fpr, tpr, thresholds = roc_curve(Y_test, y_pred)\n","roc_auc = auc(fpr, tpr)\n","plt.figure()\n","plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc)\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('ROC CURVE')\n","plt.legend(loc=\"lower right\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-block alert-success\">  \n","<h1><center><strong>Conclusion 📝</strong></center></h1>\n","    <p>\n","<li>We used the twitter sentiment analysis dataset and explored the data with different ways.</li>\n","        <li>We prepared the text data of tweets by removing the unnecessary things.</li>\n","          <li>We trained model based on tensorflow with all settings. </li>\n","        <li>We evaluated thye model with different evaluation measures.</li>\n","         <li>If you are interested to work on any text based project, you can simply apply the same methodolgy but might be you will need to change little settings like name of coloumns etc.</li>\n","        <li>We worked on the classification problem and sepcifically we call it binary classification which is two class classification.</li>\n","        </p>\n","</div>"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.4"}},"nbformat":4,"nbformat_minor":4}