diff --git "a/training_classification.ipynb" "b/training_classification.ipynb"
new file mode 100644--- /dev/null
+++ "b/training_classification.ipynb"
@@ -0,0 +1,958 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "327bf18f-848b-431c-a26d-73f71293cc25",
+   "metadata": {},
+   "source": [
+    "## Training Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d1ac0cb-2f7e-4ead-99a6-942b73a69652",
+   "metadata": {},
+   "source": [
+    "### This data comes from PhiUSIIL. It was collected to predict phishing websites. There are 54 features that focus on certain characteristics within the URL and the websites. For example, the variable 'IsHTTPS' indicates if the webpage is running on unsecured HTTP or secured HTTPS. Unsecured HTTP can be a red flag for users. In the end, websites are categorized by the 'label' variable. Label can either be 0 or 1. If it is 1, then the website is legitimate. If it is 0, it is categorized as phishing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d73046d1-58a8-43d4-8f17-451d92742b5e",
+   "metadata": {},
+   "source": [
+    "### Classification is a machine learning task method that aims to find patterns in labeled data to predict what category it belongs to."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a2bceef-78db-4923-a953-a2d5ba15c96b",
+   "metadata": {},
+   "source": [
+    "### With this dataset, I will be using a decision tree to predict whether certain websites are legitimate or an act of phishing. This is because Naive Bayes assumes feature independence, which is not true for this case. Decision trees split data based on actual patterns, which is useful for phishing detection."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9816bc41-700f-4f8d-b5ee-dfb7f0984028",
+   "metadata": {},
+   "source": [
+    "### Overall, the data does not meet the criteria for Naive Bayes because the features are not independent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98544ee7-4ee7-43f6-8031-3f13461d92d0",
+   "metadata": {},
+   "source": [
+    "### Using decision trees for this case is crucial because it offers clear interpretability of the classification logic. It will be able to differentiate legitimate vs illegitimate websites and also give an answer to why."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71c4ac22-b704-45ef-b1fe-4f2a6a422b39",
+   "metadata": {},
+   "source": [
+    "#### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "6796782c-82a4-4fe3-9033-6bf3cc151eb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as pltz\n",
+    "from sklearn import tree\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from sklearn.metrics import classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "77144bec-ff99-4321-8fa2-a1ad81ffdccd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "from huggingface_hub import HfApi\n",
+    "import skops.io as sio\n",
+    "from skops import card\n",
+    "sns.set_theme(palette='colorblind')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92793306-0e43-40dd-9d17-6d2371ebc088",
+   "metadata": {},
+   "source": [
+    "#### Load dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "60ca3d73-52ba-4f7c-8894-6316ea788ae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('phishing.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef45018c-6033-49fe-a01d-62f9e107d594",
+   "metadata": {},
+   "source": [
+    "#### View all features and their data type. This is so that I can filter which features to use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "033654c8-2fd6-4843-82eb-ccbe3c67a866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FILENAME                       object\n",
+       "URL                            object\n",
+       "URLLength                       int64\n",
+       "Domain                         object\n",
+       "DomainLength                    int64\n",
+       "IsDomainIP                      int64\n",
+       "TLD                            object\n",
+       "URLSimilarityIndex            float64\n",
+       "CharContinuationRate          float64\n",
+       "TLDLegitimateProb             float64\n",
+       "URLCharProb                   float64\n",
+       "TLDLength                       int64\n",
+       "NoOfSubDomain                   int64\n",
+       "HasObfuscation                  int64\n",
+       "NoOfObfuscatedChar              int64\n",
+       "ObfuscationRatio              float64\n",
+       "NoOfLettersInURL                int64\n",
+       "LetterRatioInURL              float64\n",
+       "NoOfDegitsInURL                 int64\n",
+       "DegitRatioInURL               float64\n",
+       "NoOfEqualsInURL                 int64\n",
+       "NoOfQMarkInURL                  int64\n",
+       "NoOfAmpersandInURL              int64\n",
+       "NoOfOtherSpecialCharsInURL      int64\n",
+       "SpacialCharRatioInURL         float64\n",
+       "IsHTTPS                         int64\n",
+       "LineOfCode                      int64\n",
+       "LargestLineLength               int64\n",
+       "HasTitle                        int64\n",
+       "Title                          object\n",
+       "DomainTitleMatchScore         float64\n",
+       "URLTitleMatchScore            float64\n",
+       "HasFavicon                      int64\n",
+       "Robots                          int64\n",
+       "IsResponsive                    int64\n",
+       "NoOfURLRedirect                 int64\n",
+       "NoOfSelfRedirect                int64\n",
+       "HasDescription                  int64\n",
+       "NoOfPopup                       int64\n",
+       "NoOfiFrame                      int64\n",
+       "HasExternalFormSubmit           int64\n",
+       "HasSocialNet                    int64\n",
+       "HasSubmitButton                 int64\n",
+       "HasHiddenFields                 int64\n",
+       "HasPasswordField                int64\n",
+       "Bank                            int64\n",
+       "Pay                             int64\n",
+       "Crypto                          int64\n",
+       "HasCopyrightInfo                int64\n",
+       "NoOfImage                       int64\n",
+       "NoOfCSS                         int64\n",
+       "NoOfJS                          int64\n",
+       "NoOfSelfRef                     int64\n",
+       "NoOfEmptyRef                    int64\n",
+       "NoOfExternalRef                 int64\n",
+       "label                           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91208259-0468-495a-b16e-8ea0816e15a8",
+   "metadata": {},
+   "source": [
+    "#### Remove any irrelevant data for training, and set label to the test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "010d0ed1-5dbe-4119-b66b-a1a8498fcecb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "X = df.drop(columns=['FILENAME', 'URL', 'Domain', 'Title','TLD','label'])\n",
+    "y = df['label']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a42593d-39ea-43a6-9b55-151d6f041e1e",
+   "metadata": {},
+   "source": [
+    "#### Hold out the final test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "a4d979d2-8ad4-4391-b62e-f14b986bf60c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_full, X_test, y_train_full, y_test = train_test_split(X, y, test_size=0.2, random_state=67, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f88bd4bb-e1ef-4f70-8f76-459a77934cb7",
+   "metadata": {},
+   "source": [
+    "#### Save the test set for HuggingFace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "a0334326-8fd1-474e-af48-94ea9bc5133d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test_to_save = X_test.copy()\n",
+    "X_test_to_save['label'] = y_test.values\n",
+    "X_test_to_save.to_csv('phishing_test.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b36b001c-6053-49a9-b222-4ef0024cd562",
+   "metadata": {},
+   "source": [
+    "#### From the 80%, take 75% for training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "9417788a-f615-4d51-83e6-700ceacf535c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_hold, y_train, y_hold = train_test_split(\n",
+    "    X_train_full, y_train_full, train_size=0.75, random_state=67, stratify=y_train_full\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fc7b323-f338-4f6e-8557-3334a4c0c209",
+   "metadata": {},
+   "source": [
+    "#### Fit Decision Tree on X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0ee67466-b165-4505-94db-5d1f650cbc3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = tree.DecisionTreeClassifier(\n",
+    "    random_state=67, max_depth=2, splitter='random', min_samples_leaf=.2, min_samples_split=.2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "ae275623-b065-44f8-824f-7f7d89cf4bba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, min_samples_leaf=0.2, min_samples_split=0.2,\n",
+       "                       random_state=67, splitter=&#x27;random&#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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=2, min_samples_leaf=0.2, min_samples_split=0.2,\n",
+       "                       random_state=67, splitter=&#x27;random&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=2, min_samples_leaf=0.2, min_samples_split=0.2,\n",
+       "                       random_state=67, splitter='random')"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36e37081-8c1d-4ffc-ae8e-9c9b0d906580",
+   "metadata": {},
+   "source": [
+    "#### Get the train accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "0dc2e40c-8a7c-48d7-9a14-0b78264abad8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.890575853318914"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.score(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17dc00b7-688c-4899-a336-dc62760b3644",
+   "metadata": {},
+   "source": [
+    "#### Get the holdout accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a3e40efc-aa71-4a35-a6cf-84e0135bc22f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8888653279331623"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.score(X_hold,y_hold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3600d952-66e5-400f-b630-892d3cb82e83",
+   "metadata": {},
+   "source": [
+    "#### Get the test accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "0bfbf1ef-e5a3-4a95-90ae-88341b5b4c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8883564112894675"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.score(X_test,y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4872eea6-0034-4554-baeb-bda864db178e",
+   "metadata": {},
+   "source": [
+    "#### Visualize the decision tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "8640057d-d2e5-4ace-ae55-9e0f63b4d96b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,20))\n",
+    "tree.plot_tree(dt, rounded =True, class_names = ['0','1'],\n",
+    "      proportion=True, filled =True, impurity=False,fontsize=10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb888f3a-369d-46a5-8f64-1f5f1d5288e1",
+   "metadata": {},
+   "source": [
+    "### Overall, the model makes sense. It scored ~89% in the train, holdout, and test accuracy. This means that it performs reasonably well without overfitting. Since all scores are within the same range, it is safe to say that the model generalizes well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6a0cb29-973e-44f1-a007-0f939128eaba",
+   "metadata": {},
+   "source": [
+    "### Firstly, there are no leaves that are very small. min_samples_leaf is set to 0.2, or 20%. Every leaf grabs a good portion (1/5) of the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6893a8c-c2db-48cf-8be3-5ba1b510b431",
+   "metadata": {},
+   "source": [
+    "### Secondly, there is an interpretable number of levels. With max_depth set to 2, the tree has at most 2 levels of decision nodes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd62b406-83bb-438b-a977-cb8dd681173d",
+   "metadata": {},
+   "source": [
+    "#### Generate a classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "fe4f4610-b328-453d-87de-2fed4699cc6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.74      0.85     20189\n",
+      "           1       0.84      1.00      0.91     26970\n",
+      "\n",
+      "    accuracy                           0.89     47159\n",
+      "   macro avg       0.92      0.87      0.88     47159\n",
+      "weighted avg       0.91      0.89      0.88     47159\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = dt.predict(X_test)\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc65e462-ed17-4360-804a-89e392431e0e",
+   "metadata": {},
+   "source": [
+    "### The overall accuracy of the model was 89%, which is consistent with its previous results. For Class 0, the precision score was 100%, meaning it was able to predict every phishing website correctly. Its recall score was 74%, meaning it missed about 26% of actual phishing samples. For Class 1, its precision score was 84% and its recall score was 100%. This shows that the model leans toward predicting '1,' while favoring recall rather than precision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab05bb3e-ee0f-45d2-b0e1-0edd7ddb4a65",
+   "metadata": {},
+   "source": [
+    "### The model could possibly be used for real scenarios, but not for high risk use. It would be helpful in situations where you only need a general screening of phishing vs legitimate websites. So although it is accurate, it is not accurate enough. However, I would still trust this model. It performs well and behaves predictably. If more precision/recall is needed, then it would certainly make sense to use a more complex model. It all depends on what the model is being used for."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "244ce2a0-7385-4114-bd78-387a5b3f2b58",
+   "metadata": {},
+   "source": [
+    "### This task can definitely be done with machine learning. The 89% accuracy proves that it can be done."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}