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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "884cc4c7",
"metadata": {},
"outputs": [
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"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Input</th>\n",
" <th>Tags</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Title: What is the effective differencial effe...</td>\n",
" <td>['electronics']</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Title: Heat sensor with fan cooling Body: Can ...</td>\n",
" <td>['electronics']</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Title: Outlet Installation--more wires than my...</td>\n",
" <td>['electronics']</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Title: Buck Converter Operation Question Body:...</td>\n",
" <td>['electronics']</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Title: Urgent help in area of ASIC design, ver...</td>\n",
" <td>['electronics']</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Input Tags\n",
"0 Title: What is the effective differencial effe... ['electronics']\n",
"1 Title: Heat sensor with fan cooling Body: Can ... ['electronics']\n",
"2 Title: Outlet Installation--more wires than my... ['electronics']\n",
"3 Title: Buck Converter Operation Question Body:... ['electronics']\n",
"4 Title: Urgent help in area of ASIC design, ver... ['electronics']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"dataset = pd.read_csv(\"/home/darth/#/SEQuestionClassifier/data/hackerank/combined_data.csv\")\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d9597ebd",
"metadata": {},
"outputs": [],
"source": [
"df = dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6626fbc7",
"metadata": {},
"outputs": [],
"source": [
"import ast\n",
"\n",
"def clean_tags(tag_string):\n",
" # Convert the string to a list\n",
" tag_list = ast.literal_eval(tag_string)\n",
" # Join the list into a comma-separated string\n",
" return ', '.join(tag_list)\n",
"\n",
"df['Tags'] = df['Tags'].apply(clean_tags)"
]
},
{
"cell_type": "markdown",
"id": "5c32d48e",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "52801f74",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"def vectorirse_text(text):\n",
" \"\"\" Recieves text as input and returns TF-IDF vectors\"\"\"\n",
" tfidf = TfidfVectorizer(max_features=500000)\n",
" X = tfidf.fit_transform(text)\n",
" return X\n",
"\n",
"def label_encoding(input):\n",
" label_encoder = LabelEncoder()\n",
" return label_encoder.fit_transform(input)\n",
"\n",
"\n",
"X = vectorirse_text(df['Input'])\n",
"y = label_encoding(df['Tags'])\n",
"\n",
"# Import necessary libraries\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1380ee74",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
"\n",
"# Assuming df is already loaded\n",
"# And the following functions are available from your preprocessing notebook:\n",
"# - vectorise_text\n",
"# - label_encoding\n",
"\n",
"# Step 1: Preproces\n",
"\n",
"# Step 2: Train Logistic Regression\n",
"model = LogisticRegression(max_iter=1000)\n",
"model.fit(X, y)\n",
"\n",
"# Step 3: Predict on the entire dataset\n",
"y_preds = model.predict(X)\n",
"\n",
"# Step 4: Evaluate\n",
"acc = accuracy_score(y, y_preds)\n",
"print(f\"✅ Accuracy: {acc:.4f}\\n\")\n",
"\n",
"print(\"✅ Classification Report:\\n\")\n",
"print(classification_report(y, y_preds))\n",
"\n",
"print(\"✅ Confusion Matrix:\\n\")\n",
"print(confusion_matrix(y, y_preds))\n",
"\n",
"# Step 5: Save results to a CSV\n",
"output_df = pd.DataFrame({\n",
" 'y_true': y,\n",
" 'y_pred': y_preds\n",
"})\n",
"\n",
"output_df.to_csv('part-1.csv', index=False)\n",
"\n",
"print(\"\\n✅ part-1.csv saved successfully!\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79783f66",
"metadata": {},
"outputs": [
{
"data": {
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{
"cell_type": "code",
"execution_count": null,
"id": "80fb05a1",
"metadata": {},
"outputs": [],
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}
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"kernelspec": {
"display_name": "major02",
"language": "python",
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