Delete model.ipynb

model.ipynb

@@ -1,114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ace57031",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.023255813953488372\n",
-      "Prediction: [' is a member of the BC Partners for Mental Health and Addictions Information. The institute is dedicated to the study of substance use in support of community-wide efforts aimed at providing all people with access to healthier lives']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "# Step 1: Collect and preprocess data\n",
-    "# Get all the questions from Questions column and responses from Questions column in the dataset data.csv\n",
-    "# questions = data[\"Questions\"].tolist()\n",
-    "# responses = data[\"Responses\"].tolist()\n",
-    "questions = []\n",
-    "responses = []\n",
-    "q_id = []\n",
-    "with open(\"data.csv\", \"r\") as f:\n",
-    "    for line in f:\n",
-    "        \n",
-    "        array = line.split(\",\") \n",
-    "        # questions.append(question)\n",
-    "        # responses.append(response)\n",
-    "        # q_id.append(question_id)\n",
-    "        try:\n",
-    "            question = array[1]\n",
-    "            response = array[2]\n",
-    "            question_id = array[0]\n",
-    "            questions.append(question)\n",
-    "            responses.append(response)\n",
-    "            q_id.append(question_id)\n",
-    "        except:\n",
-    "            pass\n",
-    "\n",
-    "\n",
-    "        \n",
-    "\n",
-    "# print(questions)\n",
-    "# print(responses)\n",
-    "\n",
-    "\n",
-    "# questions = [\"What are some symptoms of depression?\",\n",
-    "#              \"How can I manage my anxiety?\",\n",
-    "#              \"What are the treatments for bipolar disorder?\"]\n",
-    "# responses = [\"Symptoms of depression include sadness, lack of energy, and loss of interest in activities.\",\n",
-    "#              \"You can manage your anxiety through techniques such as deep breathing, meditation, and therapy.\",\n",
-    "#              \"Treatments for bipolar disorder include medication, therapy, and lifestyle changes.\"]\n",
-    "\n",
-    "vectorizer = TfidfVectorizer()\n",
-    "X = vectorizer.fit_transform(questions)\n",
-    "y = responses\n",
-    "\n",
-    "# Step 2: Split data into training and testing sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
-    "\n",
-    "# Step 3: Choose a machine learning algorithm\n",
-    "model = LogisticRegression()\n",
-    "\n",
-    "# Step 4: Train the model\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Step 5: Evaluate the model\n",
-    "y_pred = model.predict(X_test)\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(\"Accuracy:\", accuracy)\n",
-    "\n",
-    "# Step 6: Use the model to make predictions\n",
-    "new_question = \"I feel sad\"\n",
-    "new_question_vector = vectorizer.transform([new_question])\n",
-    "prediction = model.predict(new_question_vector)\n",
-    "print(\"Prediction:\", prediction)\n"
-   ]
-  }
- ],
- "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": "3.10.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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- "nbformat": 4,
- "nbformat_minor": 5
-}