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Tweets category Identification
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Tweets category Identification
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"text": [
"Collecting sweetviz\n",
" Downloading sweetviz-2.3.1-py3-none-any.whl (15.1 MB)\n",
"\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m15.1/15.1 MB\u001B[0m \u001B[31m49.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
"\u001B[?25hRequirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (1.5.3)\n",
"Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (1.23.5)\n",
"Requirement already satisfied: matplotlib>=3.1.3 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (3.7.1)\n",
"Requirement already satisfied: tqdm>=4.43.0 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (4.66.1)\n",
"Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (1.11.4)\n",
"Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (3.1.2)\n",
"Requirement already satisfied: importlib-resources>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from sweetviz) (6.1.1)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.11.1->sweetviz) (2.1.3)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.2.0)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (4.46.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.4.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (23.2)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (9.4.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (3.1.1)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.3->sweetviz) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2023.3.post1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.1.3->sweetviz) (1.16.0)\n",
"Installing collected packages: sweetviz\n",
"Successfully installed sweetviz-2.3.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import sweetviz as sv\n",
"import pandas as pd\n",
"\n",
"# Load your dataset (replace 'your_dataset.csv' with your file path)\n",
"data = pd.read_csv('/content/train.csv')\n",
"\n",
"# Analyze the dataset and generate a report\n",
"report = sv.analyze([data, 'Tweets data'])\n",
"\n",
"# Show the report in the default web browser\n",
"report.show_html()\n",
"\n",
"# Save the report as an HTML file\n",
"report.show_html('sweetviz_report.html')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104,
"referenced_widgets": [
"f8590f16a7b843e8885d1680133c108d",
"ddbb58a283e449c2bd40a9055d55bc41",
"6be09652ceb8407dbf5e6afc04fceb69",
"439de7cf31694a0e8617c67d27a47737",
"68cfd1c43a5b493fbae36709c4ca8066",
"ee9452fadb284edf86a14110412f5572",
"651288b0fa6c45fc8ecdedfb530aee77",
"c39c495118af4d989955e873a585e8e7",
"c26cf154bf4b4350ac9b1837aa3a8875",
"85e76b78d076416fb7d304dca6baefcc",
"d3c08b99e2184af5b36f546609a31a4c"
]
},
"id": "Vb4smQHJ_BzM",
"executionInfo": {
"status": "ok",
"timestamp": 1702750001160,
"user_tz": 360,
"elapsed": 2604,
"user": {
"displayName": "pavanmahaveer singara",
"userId": "17551489007904463854"
}
},
"outputId": "78661997-1a1b-41c4-c14c-6468620b1f6d"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" | | [ 0%] 00:00 -> (? left)"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f8590f16a7b843e8885d1680133c108d"
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n",
"Report sweetviz_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import re\n",
"\n",
"# Load your dataset (replace 'your_dataset.csv' with your file path)\n",
"data = pd.read_csv('/content/train.csv')\n",
"\n",
"# Function to clean text data\n",
"def clean_text(text):\n",
" # Remove special characters, links, emojis, etc.\n",
" text = re.sub(r'http\\S+', '', text) # Remove links\n",
" text = re.sub(r'[^a-zA-Z\\s]', '', text) # Remove special characters except whitespace\n",
" # Add more cleaning steps as needed\n",
"\n",
" return text\n",
"\n",
"# Apply cleaning function to the 'text' column\n",
"data['text'] = data['text'].apply(clean_text)\n",
"\n",
"# Display the cleaned text data\n",
"print(data['text'].head(10))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oOct1hHUCa9T",
"executionInfo": {
"status": "ok",
"timestamp": 1702760696752,
"user_tz": 360,
"elapsed": 151,
"user": {
"displayName": "pavanmahaveer singara",
"userId": "17551489007904463854"
}
},
"outputId": "510ea92f-eca4-40d9-d8b8-a69fa7e736e6"
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0 Our Deeds are the Reason of this earthquake Ma...\n",
"1 Forest fire near La Ronge Sask Canada\n",
"2 All residents asked to shelter in place are be...\n",
"3 people receive wildfires evacuation orders in...\n",
"4 Just got sent this photo from Ruby Alaska as s...\n",
"5 RockyFire Update California Hwy closed in bo...\n",
"6 flood disaster Heavy rain causes flash floodin...\n",
"7 Im on top of the hill and I can see a fire in ...\n",
"8 Theres an emergency evacuation happening now i...\n",
"9 Im afraid that the tornado is coming to our area\n",
"Name: text, dtype: object\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"data.head()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "Ulxbhu0f7TKK",
"executionInfo": {
"status": "ok",
"timestamp": 1702760699970,
"user_tz": 360,
"elapsed": 145,
"user": {
"displayName": "pavanmahaveer singara",
"userId": "17551489007904463854"
}
},
"outputId": "07607e8d-40ad-46b5-b7cf-9979e42be969"
},
"execution_count": 36,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" id keyword location text \\\n",
"0 1 NaN NaN Our Deeds are the Reason of this earthquake Ma... \n",
"1 4 NaN NaN Forest fire near La Ronge Sask Canada \n",
"2 5 NaN NaN All residents asked to shelter in place are be... \n",
"3 6 NaN NaN people receive wildfires evacuation orders in... \n",
"4 7 NaN NaN Just got sent this photo from Ruby Alaska as s... \n",
"\n",
" target \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
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" document.querySelector('#' + key + ' button');\n",
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" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
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" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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"</div>\n",
"\n",
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" </div>\n"
]
},
"metadata": {},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"\n",
"\n",
"# Check for missing/null values in each column\n",
"null_values = data.isnull().sum()\n",
"\n",
"# Display columns with missing/null values and their counts\n",
"print(\"Columns with Missing/Null Values:\")\n",
"print(null_values[null_values > 0])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UN183N9QCbJE",
"executionInfo": {
"status": "ok",
"timestamp": 1702760702621,
"user_tz": 360,
"elapsed": 156,
"user": {
"displayName": "pavanmahaveer singara",
"userId": "17551489007904463854"
}
},
"outputId": "3ef2e511-329a-4cc2-c27d-08297cbad104"
},
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Columns with Missing/Null Values:\n",
"keyword 61\n",
"location 2533\n",
"dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"# Assuming 'cleaned_text' contains the cleaned text data\n",
"cleaned_text = data['text']\n",
"\n",
"# Initialize the TF-IDF Vectorizer\n",
"tfidf_vectorizer = TfidfVectorizer(max_features=5000) # Adjust max_features as needed\n",
"\n",
"# Fit and transform the cleaned text data\n",
"tfidf_features = tfidf_vectorizer.fit_transform(cleaned_text)\n",
"\n",
"# Convert to DataFrame (optional)\n",
"tfidf_df = pd.DataFrame(tfidf_features.toarray(), columns=tfidf_vectorizer.get_feature_names_out())\n",
"\n",
"# Display the TF-IDF features DataFrame\n",
"print(tfidf_df.head())\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JmcH8eYlDAi3",
"executionInfo": {
"status": "ok",
"timestamp": 1702760705324,
"user_tz": 360,
"elapsed": 620,
"user": {
"displayName": "pavanmahaveer singara",
"userId": "17551489007904463854"
}
},
"outputId": "6b372a9a-3001-4c86-9931-6e584e61c1ef"
},
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" aa aba abandon abandoned abbswinston abc abcnews abe ability \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" ablaze ... yr yrs yugvani yyc zakbagans zayn zionist zombie \\\n",
"0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" zone zouma \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
"[5 rows x 5000 columns]\n"
]
}
]
},