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DS_04
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DS_04
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "4177ffeb",
"metadata": {},
"outputs": [],
"source": [
"#importing the necessary libraries\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import pandas as pd \n",
"import numpy as np \n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns \n",
"from tqdm.auto import tqdm "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "32e90b9c",
"metadata": {},
"outputs": [],
"source": [
"import nltk \n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import WordNetLemmatizer \n",
"import re \n",
"from collections import Counter\n",
"from string import punctuation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "aceeb583",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder \n",
"from sklearn.metrics import precision_score, recall_score , f1_score, accuracy_score,confusion_matrix\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e6da01db",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "67e19e88",
"metadata": {},
"outputs": [],
"source": [
"from gensim.models import Word2Vec\n",
"import torch \n",
"import torch.nn as nn \n",
"from torch.optim import Adam\n",
"from torch.utils.data import DataLoader , TensorDataset\n",
"from torchmetrics import ConfusionMatrix \n",
"from mlxtend.plotting import plot_confusion_matrix\n",
"\n",
"lemma = WordNetLemmatizer()\n",
"lb = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bef9b0f7",
"metadata": {},
"outputs": [],
"source": [
"#Loading the train data \n",
"df = pd.read_csv('twitter_training.csv')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "00447e85",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2401</th>\n",
" <th>Borderlands</th>\n",
" <th>Positive</th>\n",
" <th>im getting on borderlands and i will murder you all ,</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2401</td>\n",
" <td>Borderlands</td>\n",
" <td>Positive</td>\n",
" <td>I am coming to the borders and I will kill you...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2401</td>\n",
" <td>Borderlands</td>\n",
" <td>Positive</td>\n",
" <td>im getting on borderlands and i will kill you ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2401</td>\n",
" <td>Borderlands</td>\n",
" <td>Positive</td>\n",
" <td>im coming on borderlands and i will murder you...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2401</td>\n",
" <td>Borderlands</td>\n",
" <td>Positive</td>\n",
" <td>im getting on borderlands 2 and i will murder ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2401</td>\n",
" <td>Borderlands</td>\n",
" <td>Positive</td>\n",
" <td>im getting into borderlands and i can murder y...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2401 Borderlands Positive \\\n",
"0 2401 Borderlands Positive \n",
"1 2401 Borderlands Positive \n",
"2 2401 Borderlands Positive \n",
"3 2401 Borderlands Positive \n",
"4 2401 Borderlands Positive \n",
"\n",
" im getting on borderlands and i will murder you all , \n",
"0 I am coming to the borders and I will kill you... \n",
"1 im getting on borderlands and i will kill you ... \n",
"2 im coming on borderlands and i will murder you... \n",
"3 im getting on borderlands 2 and i will murder ... \n",
"4 im getting into borderlands and i can murder y... "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#viewing first few rows\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "eec92825",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Amazon', 'ApexLegends', 'AssassinsCreed', 'Battlefield',\n",
" 'Borderlands', 'CS-GO', 'CallOfDuty', 'CallOfDutyBlackopsColdWar',\n",
" 'Cyberpunk2077', 'Dota2', 'FIFA', 'Facebook', 'Fortnite', 'Google',\n",
" 'GrandTheftAuto(GTA)', 'Hearthstone', 'HomeDepot',\n",
" 'LeagueOfLegends', 'MaddenNFL', 'Microsoft', 'NBA2K', 'Nvidia',\n",
" 'Overwatch', 'PlayStation5(PS5)',\n",
" 'PlayerUnknownsBattlegrounds(PUBG)', 'RedDeadRedemption(RDR)',\n",
" 'TomClancysGhostRecon', 'TomClancysRainbowSix', 'Verizon',\n",
" 'WorldOfCraft', 'Xbox(Xseries)', 'johnson&johnson'], dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(df['Borderlands'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "59212e78",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Irrelevant', 'Negative', 'Neutral', 'Positive'], dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(df['Positive'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0b6cc305",
"metadata": {},
"outputs": [],
"source": [
"df = df.drop('2401',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f15020f9",
"metadata": {},
"outputs": [],
"source": [
"df= df.rename(columns={\"Borderlands\":\"Feature2\",\"im getting on borderlands and i will murder you all ,\":\"Feature1\",\"Positive\": \"labels\"})"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "498e2fac",
"metadata": {},
"outputs": [],
"source": [
"df[\"tweets\"]= df[\"Feature1\"].astype(str) +\" \"+ df[\"Feature2\"].astype(str)\n",
"df= df.drop([\"Feature1\",\"Feature2\"],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b1372d0d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Irrelevant': 0, 'Negative': 1, 'Neutral': 2, 'Positive': 3}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_labels = {key : value for value , key in enumerate(np.unique(df['labels']))}\n",
"df_labels"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c2b28c72",
"metadata": {},
"outputs": [],
"source": [
"def getlabel(n) : \n",
" for x , y in df_labels.items() : \n",
" if y==n : \n",
" return x"
]
},
{
"cell_type": "markdown",
"id": "e21850e6",
"metadata": {},
"source": [
"### Performing Exploratory Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "05082411",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"object\n",
"['Positive' 'Neutral' 'Negative' 'Irrelevant']\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Check data types and unique values\n",
"print(df['labels'].dtype)\n",
"print(df['labels'].unique())\n",
"\n",
"# Create a count plot\n",
"plt.figure(figsize=(8, 6))\n",
"sns.countplot(data=df, x='labels')\n",
"plt.title('Count Label')\n",
"plt.xlabel('Label')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0204a8c9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"label_count = df['labels'].value_counts()\n",
"fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))\n",
"\n",
"sns.set_theme(style='darkgrid', palette='pastel')\n",
"color = sns.color_palette(palette='pastel')\n",
"explode = [0.02] * len(label_count)\n",
"\n",
"axes.pie(label_count.values, labels=label_count.index, autopct='%1.1f%%', colors=color, explode=explode)\n",
"axes.set_title('Percentage Label')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b17909b1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] C:\\Users\\Angela\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('stopwords')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c88e92eb",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] C:\\Users\\Angela\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1600x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import re\n",
"import nltk\n",
"import matplotlib.pyplot as plt\n",
"from nltk.corpus import stopwords\n",
"from string import punctuation\n",
"from collections import Counter\n",
"\n",
"# Download the 'stopwords' resource\n",
"nltk.download('stopwords')\n",
"\n",
"def Most_Words_used(tweets, num_of_words):\n",
" all_text = ''.join(df[tweets].values)\n",
" \n",
" all_text = re.sub('<.*?>', '', all_text) # HTML tags\n",
" all_text = re.sub(r'\\d+', '', all_text) # numbers\n",
" all_text = re.sub(r'[^\\w\\s]', '', all_text) # special characters\n",
" all_text = re.sub(r'http\\S+', '', all_text) # URLs or web links\n",
" all_text = re.sub(r'@\\S+', '', all_text) # mentions\n",
" all_text = re.sub(r'#\\S+', '', all_text) # hashtags\n",
" \n",
" words = all_text.split() \n",
" \n",
" # remove puncs \n",
" punc = list(punctuation) \n",
" words = [word for word in words if word not in punc]\n",
" \n",
" # remove stopwords (now that 'stopwords' is downloaded)\n",
" stop_words = set(stopwords.words('english'))\n",
" words = [word for word in words if not word in stop_words]\n",
" \n",
" word_counts = Counter(words)\n",
" top_words = word_counts.most_common(num_of_words)\n",
" \n",
" return top_words\n",
"\n",
"top_words = Most_Words_used('tweets', 50)\n",
"\n",
"xaxis = [word[0] for word in top_words]\n",
"yaxis = [word[1] for word in top_words]\n",
"\n",
"plt.figure(figsize=(16, 5))\n",
"plt.bar(xaxis, yaxis)\n",
"plt.xlabel('Word')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Most Commonly Used Words', fontsize=25)\n",
"plt.xticks(rotation=65)\n",
"plt.subplots_adjust(bottom=0.15)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "d992f081",
"metadata": {},
"source": [
"### Data Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f4c2a63c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to\n",
"[nltk_data] C:\\Users\\Angela\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fab8fbc4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['C:\\\\Users\\\\Angela/nltk_data', 'C:\\\\Users\\\\Angela\\\\anaconda3\\\\nltk_data', 'C:\\\\Users\\\\Angela\\\\anaconda3\\\\share\\\\nltk_data', 'C:\\\\Users\\\\Angela\\\\anaconda3\\\\lib\\\\nltk_data', 'C:\\\\Users\\\\Angela\\\\AppData\\\\Roaming\\\\nltk_data', 'C:\\\\nltk_data', 'D:\\\\nltk_data', 'E:\\\\nltk_data']\n"
]
}
],
"source": [
"import nltk.data\n",
"\n",
"print(nltk.data.path)\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "dccabe7c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to\n",
"[nltk_data] C:\\Users\\Angela\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to\n",
"[nltk_data] C:\\Users\\Angela\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
}
],
"source": [
"import nltk\n",
"import pandas as pd\n",
"\n",
"# Specify the data directory explicitly\n",
"nltk.data.path.append(\"C:\\\\Users\\\\Angela/nltk_data\")\n",
"\n",
"# Download the 'punkt' and 'wordnet' resources if not already downloaded\n",
"nltk.download('punkt')\n",
"nltk.download('wordnet')\n",
"\n",
"# Define your DataPrep function\n",
"def DataPrep(text):\n",
" # Your existing code for text preprocessing here\n",
" \n",
" # Tokenization\n",
" tokens = nltk.word_tokenize(text)\n",
" \n",
" # Remove punctuation, stopwords, and perform lemmatization (assuming 'lemma' is defined)\n",
" stop_words = set(nltk.corpus.stopwords.words('english'))\n",
" words = [word.lower() for word in tokens if word.isalpha() and word.lower() not in stop_words]\n",
" words = [lemma.lemmatize(word) for word in words]\n",
"\n",
" # Join the cleaned words back into a text\n",
" cleaned_text = ' '.join(words)\n",
" \n",
" return cleaned_text\n",
"\n",
"# Assuming you have a DataFrame 'df' with a 'tweets' column\n",
"df['cleaned_tweets'] = df['tweets'].apply(DataPrep)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ff9dfc67",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are around 13946 duplicated tweets, we will remove them.\n"
]
}
],
"source": [
"print(f'There are around {int(df[\"cleaned_tweets\"].duplicated().sum())} duplicated tweets, we will remove them.')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "92e84921",
"metadata": {},
"outputs": [],
"source": [
"df.drop_duplicates(\"cleaned_tweets\", inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "de7a8cc7",
"metadata": {},
"outputs": [],
"source": [
"df['tweet_len'] = [len(text.split()) for text in df.cleaned_tweets]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "67171b74",
"metadata": {},
"outputs": [],
"source": [
"df = df[df['tweet_len'] < df['tweet_len'].quantile(0.995)]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9951f9f5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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uV+k1snq+XKHXyCqXK/Qa2f18mbnXyCqXK/QaWeVyhV7jxqWxkyZN0s8//6yVK1e6RK+RVa6pU6c6uarbl10uM/cb2eUqLP0GZwC6gNOnT6tr167atWuXFi1a5BJnIezYsUN79uzJsHbjuvrTp087o6Tbsnv3bh08eFDz5s1TcHCwgoODtWDBAp06dUrBwcHasGGDs0u8ZRaLJdNlKjdOt09ISHBGSfkiICBA1apVczTk0v+/Bs16uv2fffHFF2rQoIEqVark7FLyxc6dO1W/fv0MazcahiNHjjijpHzTp08f7dy5U19//bW2bNmievXqyTAMValSxdml5WjlypUaNGiQHnzwQS1atEhFixaVdP3n66/v5Te+v+eeewq8zry6WS4zyy6TmfuMm+Uye5+RVS5X6DVu9nyZvdfI7r3QzL1GTu+FZu01bpbL7L1Gds+XGXuNc+fO6eOPP1Z6erpjzWq1qkaNGjp9+rRpe42ccplVbnKZsd/IKVdh6zcYAJrchQsX1L17dyUmJmr16tUKCwtzdkn5YunSpZn+dWP37t1yd3dX1apVnVPUbWjQoIH+/e9/68MPP1RcXJzi4uL097//XWXLllVcXJwp3txuZvjw4erZs2eGtRtvcjf+FcSMQkJCtG/fPl27ds2xduDAAUkq1M1Qbu3cudNl3i+k679E7d+/P8PajefLjO8ZN6xatUqvv/66rFar7rnnHrm5uelf//qXKlasqGrVqjm7vGytXr1akyZNUteuXRUZGZnhMpwmTZpo586dGZqlbdu2qVq1aoX+M3myy2VW2WUyc5+RXS4z9xk3y2X2XiO758vMvUZ2uczca+TmvdCMvUZ2uczca2SXy6y9xunTpzV8+HD98MMPjrW0tDT997//VY0aNUzba+SUy6xyymXWfiOnXIWt32AAaHJTp07V8ePHNX36dJUqVUpnzpxxfP35zc5sevTooZ9//llz5szRb7/9pk8//VTTp0/Xiy++KD8/P2eXl2dFixZVlSpVMnz5+vrK3d1dVapUKdR3esvJ448/rm+//Vbz58/XsWPHtHnzZo0ZM0aPP/64qf+S+vvf/y43NzcNHz5cBw4c0M6dO/Xaa68pNDRUdevWdXZ5tyU9PV3x8fGZPhjZzF566SVt3bpVkZGROnbsmLZt26ZRo0apZcuWqlOnjrPLu2U1a9bUunXrtG7dOp08eVJr1qxRTEyMhg8f7uzSsnXkyBFNmTJFbdu2Vd++fXXu3DnH302XLl3S008/rcuXL2vs2LGKj4/XBx98oNjYWPXt29fZpWcrp1xmlFMms/YZOeUya5+RXa60tDTT9ho5PV9m7TVyymXWXiM374Vm7DVyymXWXiOnXGbtNQIDA9WiRQtNmDBBO3bs0IEDBxQREaGLFy8qPDzctL1GTrnMKqdcZu03cspV2PoNPgPQxOx2uz799FOlpaWpe/fumbZ/+eWXqlixohMqu30hISFasGCBIiMjtWTJEpUqVUo9evRQ7969nV0a/uKhhx7S7NmzFRMTo5iYGJUoUUIdOnRw3O7crEqVKqVVq1Zp6tSpeu655+Tp6ak2bdpo9OjRzi7ttiUlJSktLU0lS5Z0din5pkWLFlqwYIGioqIUGxsrPz8/tW3bVq+88oqzS7stoaGhmjx5smJiYvSPf/xDVapU0fTp0/Xoo486u7Rsff7550pLS9PGjRu1cePGDNs6deqkN998U4sXL9bkyZPVqVMnlSlTRiNHjlSnTp2cVHHu5CaX2WSXqWPHjvrXv/5lyj4jN8+VGfsMV3wNSrnLZcZeIze5zNhr5CaXGXuN3L5vmK3XyE0uM/YaFotFkZGRmjFjhoYMGaJLly4pJCREq1atUvny5SXJlL1GbnKZUXa5AgICTDvXyOn5Kl++fKHqNyyGmW4xBQAAAAAAACBPuAQYAAAAAAAAcGEMAAEAAAAAAAAXxgAQAAAAAAAAcGEMAAEAAAAAAAAXxgAQAAAAAAAAcGEMAAEAAAAAAAAXxgAQAAAAAAAAcGEMAAEAAAAAAAAXxgAQAAAAAAAAcGEMAAEAAAAAAAAXxgAQAAAAAAAAcGH/B6g5AHB6df4TAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1600x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,5))\n",
"ax = sns.countplot(x='tweet_len', data=df[(df['tweet_len']<=1000) & (df['tweet_len']>10)], palette='Blues_r')\n",
"plt.title('Count of tweets with high number of words', fontsize=25)\n",
"plt.yticks([])\n",
"ax.bar_label(ax.containers[0])\n",
"plt.ylabel('count')\n",
"plt.xlabel('')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6f4badca",
"metadata": {},
"source": [
"### Split the data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "59b1e089",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Split your data into training and validation sets\n",
"x_train, x_val, y_train, y_val = train_test_split(df['cleaned_tweets'], df['labels'], train_size=0.85, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "3336b470",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(51290, 9052)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(x_train) ,len(x_val)"
]
},
{
"cell_type": "markdown",
"id": "bbf379b3",
"metadata": {},
"source": [
"### Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "20f19ff9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No. of feature words: 22361\n"
]
}
],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"vec = TfidfVectorizer()\n",
"vec.fit(x_train)\n",
"\n",
"# Get the feature names\n",
"feature_names = vec.get_feature_names_out()\n",
"\n",
"print(\"No. of feature words: \", len(feature_names))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6656e427",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import numpy as np # Import NumPy if not already imported\n",
"\n",
"# Convert your text data to lowercase before vectorization\n",
"x_train = [text.lower() for text in x_train]\n",
"\n",
"# Create a TfidfVectorizer\n",
"vec = TfidfVectorizer()\n",
"\n",
"# Transform the lowercase text data\n",
"x_train = vec.fit_transform(x_train) # Keep it as a sparse matrix\n",
"\n",
"# Transform the validation data in the same way\n",
"x_val = [text.lower() for text in x_val]\n",
"x_val = vec.transform(x_val)\n"
]
},
{
"cell_type": "markdown",
"id": "60888bda",
"metadata": {},
"source": [
"### Encoding"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "91e6c9b9",
"metadata": {},
"outputs": [],
"source": [
"y_train = lb.fit_transform(y_train)\n",
"y_val = lb.fit_transform(y_val)"
]
},
{
"cell_type": "markdown",
"id": "820c34a4",
"metadata": {},
"source": [
"### Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "ec5770f5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div>"
],
"text/plain": [
"LogisticRegression(random_state=42)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Performing logistic Regression\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"# Create an instance of the LogisticRegression model\n",
"lr = LogisticRegression(random_state=42)\n",
"\n",
"# Fit the model to your training data\n",
"lr.fit(x_train, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "22895bfb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div>"
],
"text/plain": [
"LogisticRegression(random_state=42)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LogisticRegression(random_state=42)\n",
"lr.fit(x_train , y_train)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "9907bb79",
"metadata": {},
"outputs": [],
"source": [
"train_acc1 = lr.score(x_train , y_train)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "e7a20846",
"metadata": {},
"outputs": [],
"source": [
"lr_pred = lr.predict(x_val)\n",
"\n",
"val_acc1 = accuracy_score(y_val , lr_pred) \n",
"\n",
"val_precision1 = precision_score(y_val , lr_pred , average='weighted')\n",
"val_recall1 = recall_score(y_val , lr_pred , average='weighted')\n",
"val_f1score1 = f1_score(y_val , lr_pred , average='weighted')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "8a9069d7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The training accuracy for logistic regression : 82.57%\n",
"\n",
"The validation accuracy for logistic regression : 74.93%\n",
"\n",
"The precision for logistic regression : 0.75\n",
"\n",
"The recall for logistic regression : 0.75\n",
"\n",
"The f1 score for logistic regression : 0.75\n",
"\n"
]
}
],
"source": [
"print(f\"The training accuracy for logistic regression : {(train_acc1*100):0.2f}%\\n\")\n",
"print(f\"The validation accuracy for logistic regression : {(val_acc1*100):0.2f}%\\n\")\n",
"print(f\"The precision for logistic regression : {val_precision1:0.2f}\\n\")\n",
"print(f\"The recall for logistic regression : {val_recall1:0.2f}\\n\")\n",
"print(f\"The f1 score for logistic regression : {val_f1score1:0.2f}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "2df9a96d",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lr_cm = confusion_matrix(y_val , lr_pred)\n",
"sns.heatmap(lr_cm, annot=True,fmt='3g')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "1e54c57f",
"metadata": {},
"source": [
"### Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "6991f61e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"RandomForestClassifier()"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Performing random forest classifier\n",
"rf = RandomForestClassifier()\n",
"rf.fit(x_train , y_train)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "2cfc13bd",
"metadata": {},
"outputs": [],
"source": [
"train_acc2 = rf.score(x_train , y_train)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "359de96f",
"metadata": {},
"outputs": [],
"source": [
"rf_pred = rf.predict(x_val)\n",
"\n",