{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "884cc4c7",
   "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>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": {
      "text/plain": [
       "np.int64(2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80fb05a1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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