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+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Explore here"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 29,
+            "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>url</th>\n",
+                            "      <th>is_spam</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>https://briefingday.us8.list-manage.com/unsubs...</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>https://www.hvper.com/</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>https://briefingday.com/m/v4n3i4f3</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>https://briefingday.com/n/20200618/m#commentform</td>\n",
+                            "      <td>False</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>https://briefingday.com/fan</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                                 url  is_spam\n",
+                            "0  https://briefingday.us8.list-manage.com/unsubs...     True\n",
+                            "1                             https://www.hvper.com/     True\n",
+                            "2                 https://briefingday.com/m/v4n3i4f3     True\n",
+                            "3   https://briefingday.com/n/20200618/m#commentform    False\n",
+                            "4                        https://briefingday.com/fan     True"
+                        ]
+                    },
+                    "execution_count": 29,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "import pandas as pd\n",
+                "import regex as re\n",
+                "from nltk import download\n",
+                "from nltk.corpus import stopwords\n",
+                "from nltk.stem import WordNetLemmatizer\n",
+                "import matplotlib.pyplot as plt\n",
+                "from wordcloud import WordCloud\n",
+                "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+                "from sklearn.model_selection import train_test_split\n",
+                "from sklearn.svm import SVC\n",
+                "from sklearn.metrics import accuracy_score\n",
+                "import numpy as np\n",
+                "from sklearn.model_selection import GridSearchCV\n",
+                "# load the dataset\n",
+                "\n",
+                "\n",
+                "total_data = pd.read_csv(\"https://raw.githubusercontent.com/4GeeksAcademy/NLP-project-tutorial/main/url_spam.csv\")\n",
+                "total_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 15,
+            "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>url</th>\n",
+                            "      <th>is_spam</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>https://briefingday.us8.list-manage.com/unsubs...</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>https://www.hvper.com/</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>https://briefingday.com/m/v4n3i4f3</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>https://briefingday.com/n/20200618/m#commentform</td>\n",
+                            "      <td>0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>https://briefingday.com/fan</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                                 url  is_spam\n",
+                            "0  https://briefingday.us8.list-manage.com/unsubs...        1\n",
+                            "1                             https://www.hvper.com/        1\n",
+                            "2                 https://briefingday.com/m/v4n3i4f3        1\n",
+                            "3   https://briefingday.com/n/20200618/m#commentform        0\n",
+                            "4                        https://briefingday.com/fan        1"
+                        ]
+                    },
+                    "execution_count": 15,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "#Categorical to numerical\n",
+                "total_data[\"is_spam\"] = total_data[\"is_spam\"].apply(lambda x: 1 if x else 0).astype(int)\n",
+                "total_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 16,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "(2999, 2)\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "(2369, 2)"
+                        ]
+                    },
+                    "execution_count": 16,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "#remove repeated values\n",
+                "print(total_data.shape)\n",
+                "total_data = total_data.drop_duplicates()\n",
+                "total_data = total_data.reset_index(inplace = False, drop = True)\n",
+                "total_data.shape"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 17,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Spam: 244\n",
+                        "No spam: 2125\n"
+                    ]
+                }
+            ],
+            "source": [
+                "print(f\"Spam: {len(total_data.loc[total_data.is_spam == 1])}\")\n",
+                "print(f\"No spam: {len(total_data.loc[total_data.is_spam == 0])}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 18,
+            "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>url</th>\n",
+                            "      <th>is_spam</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>[https, briefingday, us, list, manage, com, un...</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>[https, www, hvper, com]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>[https, briefingday, com, v, i]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>[https, briefingday, com, m, commentform]</td>\n",
+                            "      <td>0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>[https, briefingday, com, fan]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                                 url  is_spam\n",
+                            "0  [https, briefingday, us, list, manage, com, un...        1\n",
+                            "1                           [https, www, hvper, com]        1\n",
+                            "2                    [https, briefingday, com, v, i]        1\n",
+                            "3          [https, briefingday, com, m, commentform]        0\n",
+                            "4                     [https, briefingday, com, fan]        1"
+                        ]
+                    },
+                    "execution_count": 18,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "#Text processing\n",
+                "def preprocess_text(text):\n",
+                "    # Remove any character that is not a letter (a-z) or white space ( )\n",
+                "    text = re.sub(r'[^a-z ]', \" \", text)\n",
+                "    \n",
+                "    # Remove white spaces\n",
+                "    text = re.sub(r'\\s+[a-zA-Z]\\s+', \" \", text)\n",
+                "    text = re.sub(r'\\^[a-zA-Z]\\s+', \" \", text)\n",
+                "\n",
+                "    # Multiple white spaces into one\n",
+                "    text = re.sub(r'\\s+', \" \", text.lower())\n",
+                "\n",
+                "    # Remove tags\n",
+                "    text = re.sub(\"&lt;/?.*?&gt;\",\" &lt;&gt; \", text)\n",
+                "\n",
+                "    return text.split()\n",
+                "\n",
+                "total_data[\"url\"] = total_data[\"url\"].apply(preprocess_text)\n",
+                "total_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 19,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "[nltk_data] Downloading package wordnet to /home/vscode/nltk_data...\n",
+                        "[nltk_data]   Package wordnet is already up-to-date!\n",
+                        "[nltk_data] Downloading package stopwords to /home/vscode/nltk_data...\n",
+                        "[nltk_data]   Package stopwords is already up-to-date!\n"
+                    ]
+                },
+                {
+                    "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>url</th>\n",
+                            "      <th>is_spam</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>[http, briefingday, list, manage, unsubscribe]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>[http, hvper]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>[http, briefingday]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>[http, briefingday, commentform]</td>\n",
+                            "      <td>0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>[http, briefingday]</td>\n",
+                            "      <td>1</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                              url  is_spam\n",
+                            "0  [http, briefingday, list, manage, unsubscribe]        1\n",
+                            "1                                   [http, hvper]        1\n",
+                            "2                             [http, briefingday]        1\n",
+                            "3                [http, briefingday, commentform]        0\n",
+                            "4                             [http, briefingday]        1"
+                        ]
+                    },
+                    "execution_count": 19,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "download(\"wordnet\")\n",
+                "lemmatizer = WordNetLemmatizer()\n",
+                "\n",
+                "download(\"stopwords\")\n",
+                "stop_words = stopwords.words(\"english\")\n",
+                "\n",
+                "def lemmatize_text(words, lemmatizer = lemmatizer):\n",
+                "    tokens = [lemmatizer.lemmatize(word) for word in words]\n",
+                "    tokens = [word for word in tokens if word not in stop_words]\n",
+                "    tokens = [word for word in tokens if len(word) > 3]\n",
+                "    return tokens\n",
+                "\n",
+                "total_data[\"url\"] = total_data[\"url\"].apply(lemmatize_text)\n",
+                "total_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 20,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x800 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "wordcloud = WordCloud(width = 800, height = 800, background_color = \"black\", max_words = 1000, min_font_size = 20, random_state = 42)\\\n",
+                "    .generate(str(total_data[\"url\"]))\n",
+                "\n",
+                "fig = plt.figure(figsize = (8, 8), facecolor = None)\n",
+                "plt.imshow(wordcloud)\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 21,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "array([[0., 0., 0., ..., 0., 0., 0.],\n",
+                            "       [0., 0., 0., ..., 0., 0., 0.],\n",
+                            "       [0., 0., 0., ..., 0., 0., 0.],\n",
+                            "       [0., 0., 0., ..., 0., 0., 0.],\n",
+                            "       [0., 0., 0., ..., 0., 0., 0.]])"
+                        ]
+                    },
+                    "execution_count": 21,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "tokens_list = total_data[\"url\"]\n",
+                "tokens_list = [\" \".join(tokens) for tokens in tokens_list]\n",
+                "\n",
+                "vectorizer = TfidfVectorizer(max_features = 5000, max_df = 0.8, min_df = 5)\n",
+                "X = vectorizer.fit_transform(tokens_list).toarray()\n",
+                "y = total_data[\"is_spam\"]\n",
+                "\n",
+                "X[:5]"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 23,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 25,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<style>#sk-container-id-1 {color: black;}#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>SVC(kernel=&#x27;linear&#x27;, random_state=42)</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</pre></div></div></div></div></div>"
+                        ],
+                        "text/plain": [
+                            "SVC(kernel='linear', random_state=42)"
+                        ]
+                    },
+                    "execution_count": 25,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "model = SVC(kernel = \"linear\", random_state = 42)\n",
+                "model.fit(X_train, y_train)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 26,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+                            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0])"
+                        ]
+                    },
+                    "execution_count": 26,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "y_pred = model.predict(X_test)\n",
+                "y_pred"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 28,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "0.9514767932489452"
+                        ]
+                    },
+                    "execution_count": 28,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "accuracy_score(y_test, y_pred)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 30,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(kernel=&#x27;linear&#x27;, random_state=42),\n",
+                            "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
+                            "                         &#x27;degree&#x27;: [1, 2, 3, 4, 5], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
+                            "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;,\n",
+                            "                                    &#x27;precomputed’&#x27;]},\n",
+                            "             scoring=&#x27;accuracy&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(kernel=&#x27;linear&#x27;, random_state=42),\n",
+                            "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
+                            "                         &#x27;degree&#x27;: [1, 2, 3, 4, 5], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
+                            "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;,\n",
+                            "                                    &#x27;precomputed’&#x27;]},\n",
+                            "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
+                        ],
+                        "text/plain": [
+                            "GridSearchCV(cv=5, estimator=SVC(kernel='linear', random_state=42),\n",
+                            "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
+                            "                         'degree': [1, 2, 3, 4, 5], 'gamma': ['scale', 'auto'],\n",
+                            "                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid',\n",
+                            "                                    'precomputed’']},\n",
+                            "             scoring='accuracy')"
+                        ]
+                    },
+                    "execution_count": 30,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "hyperparams = {\n",
+                "    \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
+                "    \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\", \"precomputed’\"],\n",
+                "    \"degree\": [1, 2, 3, 4, 5],\n",
+                "    \"gamma\": [\"scale\", \"auto\"]\n",
+                "}\n",
+                "\n",
+                "# random search\n",
+                "grid = GridSearchCV(model, hyperparams, scoring = \"accuracy\", cv = 5)\n",
+                "grid"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 31,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Best hyperparameters: {'C': 1000, 'degree': 1, 'gamma': 'auto', 'kernel': 'poly'}\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n",
+                        "350 fits failed out of a total of 1750.\n",
+                        "The score on these train-test partitions for these parameters will be set to nan.\n",
+                        "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+                        "\n",
+                        "Below are more details about the failures:\n",
+                        "--------------------------------------------------------------------------------\n",
+                        "350 fits failed with the following error:\n",
+                        "Traceback (most recent call last):\n",
+                        "  File \"/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n",
+                        "    estimator.fit(X_train, y_train, **fit_params)\n",
+                        "  File \"/home/vscode/.local/lib/python3.11/site-packages/sklearn/base.py\", line 1145, in wrapper\n",
+                        "    estimator._validate_params()\n",
+                        "  File \"/home/vscode/.local/lib/python3.11/site-packages/sklearn/base.py\", line 638, in _validate_params\n",
+                        "    validate_parameter_constraints(\n",
+                        "  File \"/home/vscode/.local/lib/python3.11/site-packages/sklearn/utils/_param_validation.py\", line 96, in validate_parameter_constraints\n",
+                        "    raise InvalidParameterError(\n",
+                        "sklearn.utils._param_validation.InvalidParameterError: The 'kernel' parameter of SVC must be a str among {'sigmoid', 'precomputed', 'poly', 'linear', 'rbf'} or a callable. Got 'precomputed’' instead.\n",
+                        "\n",
+                        "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+                        "/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.89182058 0.89182058 0.89182058 0.89182058        nan 0.89182058\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.89182058 0.89182058\n",
+                        " 0.89182058 0.89182058        nan 0.89182058 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.89182058 0.89182058 0.89182058 0.89182058\n",
+                        "        nan 0.89182058 0.89182058 0.89182058 0.89182058        nan\n",
+                        " 0.89182058 0.89182058 0.89182058 0.89182058        nan 0.89182058\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.89182058 0.89182058\n",
+                        " 0.89182058 0.89182058        nan 0.89182058 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.89182058 0.89182058 0.89182058 0.89182058\n",
+                        "        nan 0.89182058 0.89182058 0.89182058 0.89182058        nan\n",
+                        " 0.89182058 0.89182058 0.89182058 0.89182058        nan 0.89182058\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.89182058 0.89182058\n",
+                        " 0.89182058 0.89182058        nan 0.89182058 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.89182058 0.89182058 0.89182058 0.89182058\n",
+                        "        nan 0.89182058 0.89182058 0.89182058 0.89182058        nan\n",
+                        " 0.89182058 0.89182058 0.89182058 0.89182058        nan 0.89182058\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.91134565 0.91398417\n",
+                        " 0.91081794 0.91029024        nan 0.91134565 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.91134565 0.91187335 0.91081794 0.91029024\n",
+                        "        nan 0.91134565 0.89182058 0.89182058 0.89182058        nan\n",
+                        " 0.91134565 0.91081794 0.91081794 0.91029024        nan 0.91134565\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.91134565 0.90976253\n",
+                        " 0.91081794 0.91029024        nan 0.91134565 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.91134565 0.90765172 0.91081794 0.91029024\n",
+                        "        nan 0.91134565 0.89182058 0.89182058 0.89182058        nan\n",
+                        " 0.92506596 0.92401055 0.92295515 0.92453826        nan 0.92506596\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.92506596 0.92453826\n",
+                        " 0.92295515 0.92453826        nan 0.92506596 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.92506596 0.92295515 0.92295515 0.92453826\n",
+                        "        nan 0.92506596 0.89182058 0.89182058 0.89182058        nan\n",
+                        " 0.92506596 0.92348285 0.92295515 0.92453826        nan 0.92506596\n",
+                        " 0.89182058 0.89182058 0.89182058        nan 0.92506596 0.92137203\n",
+                        " 0.92295515 0.92453826        nan 0.92506596 0.89182058 0.89182058\n",
+                        " 0.89182058        nan 0.91662269 0.91451187 0.92612137 0.90712401\n",
+                        "        nan 0.91662269 0.89182058 0.89604222 0.89182058        nan\n",
+                        " 0.91662269 0.92664908 0.92612137 0.90712401        nan 0.91662269\n",
+                        " 0.89182058 0.89604222 0.89182058        nan 0.91662269 0.92453826\n",
+                        " 0.92612137 0.90712401        nan 0.91662269 0.89182058 0.89604222\n",
+                        " 0.89182058        nan 0.91662269 0.92401055 0.92612137 0.90712401\n",
+                        "        nan 0.91662269 0.89182058 0.89604222 0.89182058        nan\n",
+                        " 0.91662269 0.92084433 0.92612137 0.90712401        nan 0.91662269\n",
+                        " 0.89182058 0.89604222 0.89182058        nan 0.9055409  0.9055409\n",
+                        " 0.92612137 0.88548813        nan 0.9055409  0.91926121 0.92084433\n",
+                        " 0.91926121        nan 0.9055409  0.92664908 0.92612137 0.88548813\n",
+                        "        nan 0.9055409  0.89182058 0.92084433 0.91926121        nan\n",
+                        " 0.9055409  0.92453826 0.92612137 0.88548813        nan 0.9055409\n",
+                        " 0.89182058 0.92084433 0.91926121        nan 0.9055409  0.92401055\n",
+                        " 0.92612137 0.88548813        nan 0.9055409  0.89182058 0.92084433\n",
+                        " 0.91926121        nan 0.9055409  0.92084433 0.92612137 0.88548813\n",
+                        "        nan 0.9055409  0.89182058 0.92084433 0.91926121        nan\n",
+                        " 0.90448549 0.90448549 0.92612137 0.87387863        nan 0.90448549\n",
+                        " 0.9292876  0.92453826 0.9292876         nan 0.90448549 0.92664908\n",
+                        " 0.92612137 0.87387863        nan 0.90448549 0.89182058 0.92453826\n",
+                        " 0.9292876         nan 0.90448549 0.92453826 0.92612137 0.87387863\n",
+                        "        nan 0.90448549 0.89182058 0.92453826 0.9292876         nan\n",
+                        " 0.90448549 0.92401055 0.92612137 0.87387863        nan 0.90448549\n",
+                        " 0.89182058 0.92453826 0.9292876         nan 0.90448549 0.92084433\n",
+                        " 0.92612137 0.87387863        nan 0.90448549 0.89182058 0.92453826\n",
+                        " 0.9292876         nan]\n",
+                        "  warnings.warn(\n"
+                    ]
+                }
+            ],
+            "source": [
+                "grid.fit(X_train, y_train)\n",
+                "\n",
+                "print(f\"Best hyperparameters: {grid.best_params_}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 32,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "0.9620253164556962"
+                        ]
+                    },
+                    "execution_count": 32,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "opt_model = SVC(C = 1000, degree = 1, gamma = \"auto\", kernel = \"poly\", random_state = 42)\n",
+                "opt_model.fit(X_train, y_train)\n",
+                "y_pred = opt_model.predict(X_test)\n",
+                "accuracy_score(y_test, y_pred)\n",
+                "#96% accuracy score"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 34,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from pickle import dump\n",
+                "\n",
+                "dump(model, open(\"../models/svm_classifier_C-1000_deg-1_gam-auto_ker-poly_42.sav\", \"wb\"))"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3.8.13 64-bit ('3.8.13')",
+            "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": "3.11.4"
+        },
+        "orig_nbformat": 4,
+        "vscode": {
+            "interpreter": {
+                "hash": "110cc1dee26208153f2972f08a2ad52b6a56238dc66d48e87fb757ef2996db56"
+            }
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
diff --git a/models/svm_classifier_C-1000_deg-1_gam-auto_ker-poly_42.sav b/models/svm_classifier_C-1000_deg-1_gam-auto_ker-poly_42.sav
new file mode 100644
index 0000000000..cf0ba69580
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