02-d-ModelTraining-RandomForest.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"7564f0ecea9c4b3891a8a74d6065ca72","deepnote_cell_type":"text-cell-h1"},"source":"# Random Forest Classifier Model Training and Evaluation on PhiUSIIL Phishing URL Dataset","block_group":"32c5b4489761487b96e2f8f4bc4ca13b"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"1c167b0c64cd4e70bfbc16f3e799f864","deepnote_cell_type":"text-cell-h2"},"source":"## 1. Import Required Libraries","block_group":"d9e8e0dfbef24e85b5f56a8e76d1c251"},{"cell_type":"code","metadata":{"source_hash":"6860c416","execution_start":1732283993402,"execution_millis":1079,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"a9d0c853d11b47ccabe00fc51f202e70","deepnote_cell_type":"code"},"source":"import pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\nimport joblib\nimport shap\nfrom sklearn.preprocessing import LabelEncoder\nimport numpy as np","block_group":"a9d0c853d11b47ccabe00fc51f202e70","execution_count":1,"outputs":[{"name":"stderr","text":"/shared-libs/python3.11/py/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n  from .autonotebook import tqdm as notebook_tqdm\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/798ea7b8-d5ba-4361-af7b-ad0f8b684242","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"c775789e8fc342e985ec4f28cd8c53e1","deepnote_cell_type":"text-cell-h2"},"source":"## 2. Load the dataset","block_group":"6008c770f423419691435f734642c474"},{"cell_type":"code","metadata":{"source_hash":"789ce9f8","execution_start":1732284012734,"execution_millis":1884,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"f8d97d7c3b7340c4b370f7901e3ea8ff","deepnote_cell_type":"code"},"source":"data = pd.read_csv('/work/data/phishing_urls.csv')","block_group":"6e03de7d79ca44b79136ce6bb7d0e17a","execution_count":2,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"c7fa294321d74ba897afc5b442898aa2","deepnote_cell_type":"text-cell-h2"},"source":"## 3. Pre-processing the dataset","block_group":"7309b26a772241b08c536a42d7ce8cc0"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"b67303f2feee4ddfb0d0c09049da20ef","deepnote_cell_type":"text-cell-p"},"source":"The columns FILENAME, URL, and Title are textual or identifiers that aren't directly useful for modelling.","block_group":"73bec65d983240fbbc847873951dc876"},{"cell_type":"code","metadata":{"source_hash":"e33142aa","execution_start":1732284017501,"execution_millis":29,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"5ded1ad6b3af45a5a0afeedde23e62e5","deepnote_cell_type":"code"},"source":"# Drop irrelevant columns\ndata.drop(columns=['FILENAME', 'URL', 'Title'], inplace=True)\n","block_group":"f6fd9021c90842cd993c0e9eece906d1","execution_count":3,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[{"type":"marks","marks":{"bold":true},"toCodePoint":37,"fromCodePoint":0}],"cell_id":"5cd6bf613c254bac992de3d04c0e2b00","deepnote_cell_type":"text-cell-p"},"source":"Separate Features (X) and Target (y):","block_group":"1d3cfc4cd5bf43d6b052d54b5e5c4d7f"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"ad3fbf04c117477c8cff37ae2494ad1b","deepnote_cell_type":"text-cell-p"},"source":"X: Contains all the feature columns used for prediction.\ny: Contains the target variable (1 for legitimate URLs, 0 for phishing URLs).","block_group":"c430382de92245babc4f84d21577a29d"},{"cell_type":"code","metadata":{"source_hash":"74bc44ad","execution_start":1732284024640,"execution_millis":16,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"d0cac1ecc35941b2bedaec9e31da2f70","deepnote_cell_type":"code"},"source":"# Define features and target\nX = data.drop(columns=['label'])\ny = data['label']","block_group":"0b5b6fd3944344a5ae9b4cf809022ada","execution_count":4,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"3f57fadd4feb4305bfbd7ee0357c86bd","deepnote_cell_type":"text-cell-p"},"source":"Ensures no missing data disrupts the model training process by replacing missing values with 0.","block_group":"2eb41497509846dc9e634016e23d7106"},{"cell_type":"code","metadata":{"source_hash":"e4be2867","execution_start":1732284026676,"execution_millis":2,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"46d470fcca27495895d85941cfaffcc0","deepnote_cell_type":"code"},"source":"# Handle missing values (if any)\nX.fillna(0, inplace=True)","block_group":"648bc7d0362d4f85859390dbfa869a6e","execution_count":5,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"code","metadata":{"source_hash":"b28dc4a7","execution_start":1732284032098,"execution_millis":1,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"14bb5eae7c1149d3b307bfe4e117d381","deepnote_cell_type":"code"},"source":"# Check data types and identify non-numeric columns\nnon_numeric_columns = X.select_dtypes(include=['object']).columns\nprint(\"Non-numeric columns:\", non_numeric_columns)","block_group":"f26386c2522045b3895c724bcefc3f9d","execution_count":7,"outputs":[{"name":"stdout","text":"Non-numeric columns: Index(['Domain', 'TLD'], dtype='object')\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/9fece870-afaf-4048-b509-d292dcb152ba","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[{"type":"marks","marks":{"bold":true},"toCodePoint":29,"fromCodePoint":0}],"cell_id":"dd14f03ac85d42b3b62a52e30502f3c0","deepnote_cell_type":"text-cell-p"},"source":"Encoding Non-Numeric Features","block_group":"f3cc4fa5342f4aff89bd7fb387c9be3b"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"18b04ff94ac84d24a2737ce2cb561b71","deepnote_cell_type":"text-cell-bullet"},"source":"- Converts non-numeric columns (Domain and TLD) to strings to handle mixed data types.","block_group":"b2f6c51e574a40afa4c2ebd421791a8d"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"62f920ef9db84f1baf1f603fe0564210","deepnote_cell_type":"text-cell-bullet"},"source":"- Uses `LabelEncoder` to assign unique numeric codes to each unique string value, enabling model compatibility.","block_group":"a99ba3f9cf7c48b283135db7605dd831"},{"cell_type":"code","metadata":{"source_hash":"d4524c27","execution_start":1732284035923,"execution_millis":383,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"22ee91f43e924474b8135ca7f9afd420","deepnote_cell_type":"code"},"source":"# Encode 'Domain' and 'TLD' columns\nlabel_encoder = LabelEncoder()\nfor col in ['Domain', 'TLD']:\n    X[col] = label_encoder.fit_transform(X[col])","block_group":"9068adfd850d48c68b62d05e6e2c6156","execution_count":8,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"10d90254e495452caa20ef0bd8b8377b","deepnote_cell_type":"text-cell-h2"},"source":"## 4. Splitting the Dataset","block_group":"ffac0a6917aa478da6ca203b5005091d"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"89f8b438a389490d81813267d82eafad","deepnote_cell_type":"text-cell-p"},"source":"Splits the dataset into training and testing sets:\nTraining Set (70%): Used for model training.\nTesting Set (30%): Used for model evaluation.\nstratify=y: Ensures the class distribution in the target variable is preserved in the splits.","block_group":"1d3fecbedfa0421d84168888035431b6"},{"cell_type":"code","metadata":{"source_hash":"3e231d3","execution_start":1732284038490,"execution_millis":118,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"99816bb82cb0447caaf1a4d67a2676f7","deepnote_cell_type":"code"},"source":"# Split data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n","block_group":"aa9afb6820b24f3a969d8989fc2fc8ff","execution_count":9,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"9a23f6e48c3e4a308634c61b1e680939","deepnote_cell_type":"text-cell-h2"},"source":"## 5. Hyperparameter Tuning with GridSearchCV","block_group":"db56c78ce47041f9b56760354df6698a"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"917d809b28cb4d82a2693231cddbcea5","deepnote_cell_type":"text-cell-p"},"source":"Performs a grid search over the specified hyperparameters:\nn_estimators: Number of decision trees.\nmax_depth: Maximum depth of each tree.\nmin_samples_split: Minimum samples required to split an internal node.\nmin_samples_leaf: Minimum samples at a leaf node.\nGridSearchCV:\nEvaluates all combinations of hyperparameters using 5-fold cross-validation.\nSelects the best model based on accuracy.","block_group":"797e0bef056142bc80f5072bd47646df"},{"cell_type":"code","metadata":{"source_hash":"f84b73e4","execution_start":1732284098243,"execution_millis":1039875,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"a2683f4ebc9243fa86a2a7ab514be694","deepnote_cell_type":"code"},"source":"# Hyperparameter tuning using GridSearchCV\nparam_grid = {\n    'n_estimators': [100, 200],\n    'max_depth': [10, 20],\n    'min_samples_split': [2, 5],\n    'min_samples_leaf': [1, 2]\n}\n\ngrid_search = GridSearchCV(\n    estimator=RandomForestClassifier(random_state=42, class_weight='balanced'),\n    param_grid=param_grid,\n    scoring='accuracy',\n    cv=4,\n    verbose=2,\n    n_jobs=-1\n)\n\nprint(\"Performing grid search for hyperparameter tuning...\")\ngrid_search.fit(X_train, y_train)\nprint(\"Best parameters found using grid search:\", grid_search.best_params_)","block_group":"5562510a9cd846e28f8e535f44d9b432","execution_count":11,"outputs":[{"name":"stdout","text":"Performing grid search for hyperparameter tuning...\nFitting 4 folds for each of 16 candidates, totalling 64 fits\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.2s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.4s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  11.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  13.7s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  21.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  22.2s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  22.1s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  22.0s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.8s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  21.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  20.9s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  20.1s\n[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  20.2s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  10.4s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  10.2s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   9.9s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  10.1s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  20.6s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  20.3s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  20.1s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  20.6s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.2s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   9.8s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.1s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.2s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.5s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.2s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.3s\n[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.2s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.9s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  11.0s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.5s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.7s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  21.5s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  21.3s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  20.2s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  21.3s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.8s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.2s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.4s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.5s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  21.5s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  20.7s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  21.2s\n[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  21.1s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  11.0s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  10.6s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  10.1s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  10.8s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  21.5s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  21.6s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  20.4s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  21.4s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.5s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.7s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.5s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.3s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  21.3s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  21.0s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.7s\n[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.4s\nBest parameters found using grid search: {'max_depth': 20, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n","output_type":"stream"}],"outputs_reference":"s3:deepnote-cell-outputs-production/28959b31-899a-490d-817d-2e1c74aea3ac","content_dependencies":null},{"cell_type":"code","metadata":{"source_hash":"6fdb1fe9","execution_start":1732285549976,"execution_millis":2,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"9f56cae1719846b68d66fd289183c48d","deepnote_cell_type":"code"},"source":"\n\n# Best model after tuning\nbest_rf_model_grid_search = grid_search.best_estimator_","block_group":"7942ec27baf4456d82b80469f7f4c018","execution_count":12,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"9e6cf486ccf34c929e96edf9125788eb","deepnote_cell_type":"text-cell-h2"},"source":"## 6. Cross-Validation","block_group":"07907d25457348b283d5343c066265cc"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"bee0a26292c34527aea21bd5e7742774","deepnote_cell_type":"text-cell-p"},"source":"Evaluate the model’s performance using cross-validation, providing a robust estimate of its accuracy and standard deviation across folds.","block_group":"a1314f6900084d379c528c0bb174119d"},{"cell_type":"code","metadata":{"source_hash":"da0c1f8f","execution_start":1732285562518,"execution_millis":165672,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"365f688284c24daa876a1ecbc1d47d87","deepnote_cell_type":"code"},"source":"# Cross-validation for performance validation\ncv_scores = cross_val_score(best_rf_model_grid_search, X, y, cv=5, scoring='accuracy')\nprint(f\"Cross-validation Accuracy: {cv_scores.mean():.4f} ± {cv_scores.std():.4f}\")\n","block_group":"f1fd4c2b827f470ca444defc516ea2e1","execution_count":13,"outputs":[{"name":"stdout","text":"Cross-validation Accuracy: 1.0000 ± 0.0000\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/00d7c163-adf5-4a1b-9e9b-135d28c8cb9e","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"e300cdf29b104be796968aee3d293904","deepnote_cell_type":"text-cell-h2"},"source":"## 7. Training the Final Model","block_group":"a52446172f924870b6c40a2b47a2e849"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"61e6f350e607431184875751b6293822","deepnote_cell_type":"text-cell-p"},"source":"Train the best Random Forest model identified from GridSearchCV on the training dataset.","block_group":"204b2ab44418478a8de7dc4ee2cdee7a"},{"cell_type":"code","metadata":{"source_hash":"b0b3ed81","execution_start":1732285728234,"execution_millis":28578,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"3595b683bae1486da4faa92d2a55f9dd","deepnote_cell_type":"code"},"source":"# Train the best model on training data\nbest_rf_model_grid_search.fit(X_train, y_train)","block_group":"858750244adb411b97ec304839cf8ad0","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"RandomForestClassifier(class_weight='balanced', max_depth=20,\n                       min_samples_split=5, n_estimators=200, random_state=42)","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>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=20,\n                       min_samples_split=5, n_estimators=200, 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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=20,\n                       min_samples_split=5, n_estimators=200, random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}],"outputs_reference":"s3:deepnote-cell-outputs-production/59842c35-bd2d-4595-a1df-d9e2557d5a8e","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"aa1abb7ab4a94e7dbe8bd66c87b7557b","deepnote_cell_type":"text-cell-h2"},"source":"## 8. Saving and Loading the Model","block_group":"b7b0d13e7ccf43be994a722adc04d377"},{"cell_type":"code","metadata":{"source_hash":"a4f4dcf7","execution_start":1732285756854,"execution_millis":343,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"a53e04d3a24c49308ffa60f2fdc66fe2","deepnote_cell_type":"code"},"source":"# Save the trained model\nmodel_path = 'rf_model_phiusiil_grid_search.pkl'\njoblib.dump(best_rf_model_grid_search, model_path)\nprint(f\"Model saved to {model_path}\")\n","block_group":"a30d58a9cf084f2dbeb114108b774c43","execution_count":15,"outputs":[{"name":"stdout","text":"Model saved to rf_model_phiusiil_grid_search.pkl\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/369ddc05-a643-4113-9597-b1af4107cde8","content_dependencies":null},{"cell_type":"code","metadata":{"source_hash":"1e9b6369","execution_start":1732286478010,"execution_millis":111,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"6b9ed482daf4449fa7e8ff7ed9571c80","deepnote_cell_type":"code"},"source":"# Load the model\nmodel_path = 'rf_model_phiusiil_grid_search.pkl'\nbest_rf_model = joblib.load(model_path)\nprint(\"Model loaded successfully!\")","block_group":"cac4e05cab5c41bca0bbba9d70ba7928","execution_count":16,"outputs":[{"name":"stdout","text":"Model loaded successfully!\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/6f426f73-8cd1-4356-9f43-44d4a64ffdc5","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"ba4fcd6f9f5546829c5e306614febe95","deepnote_cell_type":"text-cell-h2"},"source":"## 9. Model Evaluation","block_group":"47c8f7e2da3b4c858a6e2b8ea4ee72e3"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"eea6467cd0474df19c196165e81cb5ac","deepnote_cell_type":"text-cell-p"},"source":"accuracy_score: Provides overall accuracy.\nclassification_report: Offers precision, recall, and F1-score for each class.\nconfusion_matrix: Shows true positives, true negatives, false positives, and false negatives.","block_group":"252cc63b971844b9876fe21f02415d1c"},{"cell_type":"code","metadata":{"source_hash":"26569daf","execution_start":1732286480686,"execution_millis":827,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"4cc468fda62c46348f4da3a2d6df4ac7","deepnote_cell_type":"code"},"source":"# Make predictions and evaluate the model\ny_pred = best_rf_model.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nTest Accuracy: {accuracy:.4f}\")\nprint(\"\\nClassification Report:\")\nprint(classification_report(y_test, y_pred))\nprint(\"\\nConfusion Matrix:\")\nprint(confusion_matrix(y_test, y_pred))\n","block_group":"2b1ce0edfcb64345a876849ae71013ff","execution_count":17,"outputs":[{"name":"stdout","text":"\nTest Accuracy: 1.0000\n\nClassification Report:\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00     30284\n           1       1.00      1.00      1.00     40455\n\n    accuracy                           1.00     70739\n   macro avg       1.00      1.00      1.00     70739\nweighted avg       1.00      1.00      1.00     70739\n\n\nConfusion Matrix:\n[[30284     0]\n [    0 40455]]\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/88add615-e66f-45ed-858b-cd299fec61c6","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"462316b520354b3ca17bcc351f763eab","deepnote_cell_type":"text-cell-h2"},"source":"## 10. Feature Importance","block_group":"1f08060f9ca54a3b9beb6117fdb33694"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"b84149ab5cd44b9e9057877ee14f397a","deepnote_cell_type":"text-cell-p"},"source":"Lists the most influential features in the Random Forest model, aiding interpretability and feature selection.","block_group":"3adda152ce654106addd222324498603"},{"cell_type":"code","metadata":{"source_hash":"16efba64","execution_start":1732286525158,"execution_millis":0,"execution_context_id":"e9dbedbd-b329-48fd-86d3-2800278b28c5","cell_id":"8f88306385a844aa828e40ed4fdc1549","deepnote_cell_type":"code"},"source":"# Feature importance analysis\nfeature_importances = pd.DataFrame({\n    'Feature': X.columns,\n    'Importance': best_rf_model.feature_importances_\n}).sort_values(by='Importance', ascending=False)\n\nprint(\"\\nTop 10 Features by Importance:\")\nprint(feature_importances.head(10))\n","block_group":"818f03c51fd746a7bccd3f668364c4ce","execution_count":18,"outputs":[{"name":"stdout","text":"\nTop 10 Features by Importance:\n               Feature  Importance\n5   URLSimilarityIndex    0.213593\n24          LineOfCode    0.115887\n51     NoOfExternalRef    0.111005\n46           NoOfImage    0.092498\n49         NoOfSelfRef    0.076345\n48              NoOfJS    0.062096\n45    HasCopyrightInfo    0.053735\n38        HasSocialNet    0.049124\n1               Domain    0.048771\n47             NoOfCSS    0.032345\n","output_type":"stream"}],"outputs_reference":"dbtable:cell_outputs/cb5d28d3-d44f-4a63-9d49-5a14a9d9602f","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"33467b77be72450785fecb6b961e8517","deepnote_cell_type":"text-cell-h3"},"source":"### FUTURE WORK (To be completed...)","block_group":"7bb834e0350f4edb83b2dffe196c455d"},{"cell_type":"code","metadata":{"cell_id":"db8c252ffabf4f4da6ff3ee4e8657769","deepnote_cell_type":"code"},"source":"# Use default values for features that cannot be extracted.\ndef prepare_features(input_features, required_features, default_value=0):\n    \"\"\"\n    Aligns and fills missing features for prediction.\n    \n    Args:\n    - input_features (dict): Subset of features provided for prediction.\n    - required_features (list): List of all features the model was trained on.\n    - default_value: Value to use for missing features.\n    \n    Returns:\n    - pd.DataFrame: DataFrame with all required features.\n    \"\"\"\n    # Fill missing features with default value\n    full_features = {feature: input_features.get(feature, default_value) for feature in required_features}\n    return pd.DataFrame([full_features])\n\nrequired_features = ['Domain', 'TLD', 'URLLength', 'IsHTTPS', 'HasTitle', 'CharContinuationRate']","block_group":"7e89c17fa86541ce9367f09844d4f78c","execution_count":null,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"74dfd6d2580540bfbbbc8101941f50a6","deepnote_cell_type":"text-cell-p"},"source":"Features for URL to be analysed","block_group":"539e3037f0e748c385ed501b2980f858"},{"cell_type":"code","metadata":{"cell_id":"851a3984feb94110b61bdf0f6b3c1795","deepnote_cell_type":"code"},"source":"input_features = {'Domain': 'example', 'TLD': 'com', 'URLLength': 20}\n\n# Align features for prediction\naligned_features = prepare_features(input_features, required_features)\nprint(aligned_features)","block_group":"1412ad2ba048489285be16d629a9666f","execution_count":null,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"code","metadata":{"cell_id":"a8c6ad0dba21459e981c98ee20c281cd","deepnote_cell_type":"code"},"source":"# Predict using the aligned feature set\nprediction = best_rf_model.predict(aligned_features)\nprint(\"Phishing\" if prediction[0] == 0 else \"Legitimate\")","block_group":"64a65b9dadd9436caa8ef0c795165c86","execution_count":null,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"code","metadata":{"source_hash":"437ea354","execution_start":1732114585792,"execution_millis":679,"execution_context_id":"5cbb8280-4956-488f-8214-38b2eeb2cf39","deepnote_to_be_reexecuted":true,"cell_id":"a7474d3fff0c43978f67ec71d33b9941","deepnote_cell_type":"code"},"source":"from sklearn.model_selection import RandomizedSearchCV\n\n# Hyperparameter tuning using RandomizedSearchCV\nparam_dist = {\n    'n_estimators': [100, 200, 300],\n    'max_depth': [10, 20, 30],\n    'min_samples_split': [2, 5, 10],\n    'min_samples_leaf': [1, 2, 4]\n}\n\nrandomized_search = RandomizedSearchCV(\n    estimator=RandomForestClassifier(random_state=42, class_weight='balanced'),\n    param_distributions=param_dist,\n    n_iter=150,  # Limit to n random combinations\n    scoring='accuracy',\n    cv=4,\n    verbose=2,\n    n_jobs=-1\n)\n\nprint(\"Performing random search for hyperparameter tuning...\")\nrandomized_search.fit(X_train, y_train)\nprint(\"Best parameters found using random search:\", randomized_search.best_params_)","block_group":"b7f58bf4bea34a38972d19e57e021184","execution_count":1,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'RandomForestClassifier' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 12\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Hyperparameter tuning using RandomizedSearchCV\u001b[39;00m\n\u001b[1;32m      4\u001b[0m param_dist \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_estimators\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m200\u001b[39m, \u001b[38;5;241m300\u001b[39m],\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_depth\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m30\u001b[39m],\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_samples_split\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m10\u001b[39m],\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_samples_leaf\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m4\u001b[39m]\n\u001b[1;32m      9\u001b[0m }\n\u001b[1;32m     11\u001b[0m randomized_search \u001b[38;5;241m=\u001b[39m RandomizedSearchCV(\n\u001b[0;32m---> 12\u001b[0m     estimator\u001b[38;5;241m=\u001b[39m\u001b[43mRandomForestClassifier\u001b[49m(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m, class_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbalanced\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m     13\u001b[0m     param_distributions\u001b[38;5;241m=\u001b[39mparam_dist,\n\u001b[1;32m     14\u001b[0m     n_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m,  \u001b[38;5;66;03m# Limit to 200 random combinations\u001b[39;00m\n\u001b[1;32m     15\u001b[0m     scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     16\u001b[0m     cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m     17\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m     18\u001b[0m     n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     19\u001b[0m )\n\u001b[1;32m     20\u001b[0m randomized_search\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBest parameters:\u001b[39m\u001b[38;5;124m\"\u001b[39m, randomized_search\u001b[38;5;241m.\u001b[39mbest_params_)\n","\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"]}],"outputs_reference":"s3:deepnote-cell-outputs-production/7c33ecce-8d65-4bb0-bc30-678495f8f431","content_dependencies":null},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=ac08460d-c6ae-4140-88f4-81d0f3a60c24' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote_notebook_id":"08b0c68271f04289a4721a4fc5d8bf37"}}