Upload BestTextClassifier.ipynb

BestTextClassifier.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "2YpCZ5QwOGCL"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.naive_bayes import MultinomialNB\n",
+        "from sklearn.utils import shuffle\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "import json\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Read the dataset\n",
+        "df = pd.read_json(\"DATA.json\", encoding=\"utf-8\")"
+      ],
+      "metadata": {
+        "id": "1BpsBFfLZbW8"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Shuffle the dataset\n",
+        "df = shuffle(df)"
+      ],
+      "metadata": {
+        "id": "lTijWIiEVjVc"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a training set and a test set\n",
+        "train_data = df[:int(len(df) * 0.8)]\n",
+        "test_data = df[int(len(df) * 0.8):]"
+      ],
+      "metadata": {
+        "id": "RDTKsDCsWGRk"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a vocabulary of all the words in the training set\n",
+        "vocabulary = set()\n",
+        "for d in df[\"content\"]:\n",
+        "    if isinstance(d, str):\n",
+        "        vocabulary.update(d.split())"
+      ],
+      "metadata": {
+        "id": "rta9jPyxVm-I"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert the list of text documents to a 2D array for the training set\n",
+        "bow_train = np.array([[str(doc).count(word) for word in vocabulary] for doc in train_data[\"content\"].fillna(\"\")])\n"
+      ],
+      "metadata": {
+        "id": "owPao6jdVprA"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert the list of text documents to a 2D array for the test set\n",
+        "bow_test = np.array([[str(doc).count(word) for word in vocabulary] for doc in test_data[\"content\"].fillna(\"\")])"
+      ],
+      "metadata": {
+        "id": "Blc4X_qNV72O"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert the labels to numeric values\n",
+        "label_encoder = LabelEncoder()\n",
+        "train_labels = label_encoder.fit_transform(train_data[\"label\"])"
+      ],
+      "metadata": {
+        "id": "pe1Y1uNEWRek"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a Naive Bayes model\n",
+        "model = MultinomialNB()"
+      ],
+      "metadata": {
+        "id": "pv1ps_IwWV8M"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train the model\n",
+        "model.fit(bow_train, train_labels)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "JYIiFXYdWY73",
+        "outputId": "a698f8a6-eeda-49ab-c434-f438c6967ad5"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "MultinomialNB()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test the model\n",
+        "test_labels = label_encoder.transform(test_data[\"label\"])\n",
+        "predictions = model.predict(bow_test)"
+      ],
+      "metadata": {
+        "id": "gDcK0uiWWeKc"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the accuracy of the model\n",
+        "accuracy = (predictions == test_labels).mean()\n",
+        "print(\"Accuracy:\", accuracy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eBMyVM5hWjiL",
+        "outputId": "4c26f478-76b9-4404-9d61-10da839d6465"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.9348500517063082\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def test_model(text):\n",
+        "    bow = {}\n",
+        "    for word in text.split():\n",
+        "        if word in vocabulary:\n",
+        "            bow[word] = text.count(word)\n",
+        "    bow = np.array([bow.get(word, 0) for word in vocabulary]).reshape(1, -1)\n",
+        "    prediction = model.predict(bow)[0]\n",
+        "    return label_encoder.inverse_transform([prediction])[0]\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TFH--bOmWpEU"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test the model with some text\n",
+        "text = \"Aura Azure Collagen Gummies Advantages, Official Website &amp; Reviews [2023]\"\n",
+        "prediction = test_model(text)\n",
+        "print(prediction)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GDL4OZ_5Wsc7",
+        "outputId": "e97b7b46-3a18-446e-e5f6-186bf285d96f"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "NSFW\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "CPxtDWlYWwYC"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}