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nlp.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "# Load the data\n",
+        "df = pd.read_csv('data.csv')\n",
+        "\n",
+        "# Preprocess the descriptions by lowercasing and removing punctuation\n",
+        "df['description'] = df['description'].str.lower()\n",
+        "df['description'] = df['description'].str.replace(r'[^\\w\\s]', '')\n",
+        "\n",
+        "# Split the dataset into training and test sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(df['description'], df['timeS'], test_size=0.2)\n",
+        "\n",
+        "# Extract features using a bag-of-words approach\n",
+        "vectorizer = CountVectorizer()\n",
+        "X_train_features = vectorizer.fit_transform(X_train)\n",
+        "X_test_features = vectorizer.transform(X_test)\n",
+        "\n",
+        "# Train a support vector machine classifier\n",
+        "classifier = SVC()\n",
+        "output = classifier.fit(X_train_features, y_train)\n",
+        "output.save('model.m5')\n",
+        "\n",
+        "# Make predictions on the test set\n",
+        "y_pred = classifier.predict(X_test_features)\n",
+        "\n",
+        "# Evaluate the model's performance\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "print(f'Model accuracy: {accuracy:.2f}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python (Pyodide)",
+      "language": "python",
+      "name": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}