\n",
"\n",
"These are a few of the notebooks related to Machine Learning, including Google's online Machine Learning course. See the [full course website](https://developers.google.com/machine-learning/crash-course/) for more.\n",
"- [Intro to Pandas DataFrame](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb)\n",
"- [Intro to RAPIDS cuDF to accelerate pandas](https://nvda.ws/rapids-cudf)\n",
"- [Getting Started with cuML's accelerator mode](https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cuml_sklearn_colab_demo.ipynb)\n",
"- [Linear regression with tf.keras using synthetic data](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/linear_regression_with_synthetic_data.ipynb)\n",
"\n",
"
\n",
"\n",
"
\n",
"### Using Accelerated Hardware\n",
"
\n",
"\n",
"- [TensorFlow with GPUs](/notebooks/gpu.ipynb)\n",
"- [TPUs in Colab](/notebooks/tpu.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P-H6Lw1vyNNd"
},
"source": [
"
\n",
"\n",
"
\n",
"\n",
"### Featured examples\n",
"\n",
"
\n",
"\n",
"- [Retraining an Image Classifier](https://tensorflow.org/hub/tutorials/tf2_image_retraining): Build a Keras model on top of a pre-trained image classifier to distinguish flowers.\n",
"- [Text Classification](https://tensorflow.org/hub/tutorials/tf2_text_classification): Classify IMDB movie reviews as either *positive* or *negative*.\n",
"- [Style Transfer](https://tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization): Use deep learning to transfer style between images.\n",
"- [Multilingual Universal Sentence Encoder Q&A](https://tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa): Use a machine learning model to answer questions from the SQuAD dataset.\n",
"- [Video Interpolation](https://tensorflow.org/hub/tutorials/tweening_conv3d): Predict what happened in a video between the first and the last frame.\n"
]
},
{
"cell_type": "code",
"source": [
"# NSL-KDD Intrusion Detection System using Ensemble Learning\n",
"# Multi-class Classification with Random Forest and Gradient Boosting\n",
"\n",
"\n",
"# SECTION 1: IMPORT LIBRARIESimport pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, RandomizedSearchCV\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"from sklearn.metrics import (classification_report, confusion_matrix, accuracy_score,\n",
" precision_score, recall_score, f1_score, ConfusionMatrixDisplay)\n",
"import warnings\n",
"import time\n",
"warnings.filterwarnings('ignore')"
],
"metadata": {
"id": "Y3la3BR2u8Bf"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Set style for better visualizationssns.set_style(\"whitegrid\")\n",
"plt.rcParams['figure.figsize'] = (12, 6)\n",
"\n",
"print(\"=\"*80)\n",
"print(\"NSL-KDD INTRUSION DETECTION SYSTEM - ENSEMBLE LEARNING\")\n",
"print(\"=\"*80)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K11U5tIovE5G",
"outputId": "58785ab7-91df-48b6-9947-c2bc63cd4821"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"================================================================================\n",
"NSL-KDD INTRUSION DETECTION SYSTEM - ENSEMBLE LEARNING\n",
"================================================================================\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 2: DATA LOADING\n",
"print(\"\\n[1] LOADING DATA...\")\n",
"\n",
"# ===== FILE PATHS - MODIFY THESE TO MATCH YOUR GOOGLE COLAB PATHS =====\n",
"# Example: '/content/KDDTrain+.txt' or '/content/drive/MyDrive/dataset/KDDTrain+.txt'\n",
"# IMPORTANT: Keep the quotes around the path!\n",
"TRAIN_FILE_PATH = '/content/kdd_train.csv' # <-- UPDATE THIS PATH (keep quotes!)\n",
"TEST_FILE_PATH = '/content/kdd_test.csv' # <-- UPDATE THIS PATH (keep quotes!)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F40L2ErwvK_L",
"outputId": "f17b033b-2ea1-47de-aaac-a0f2ecad573f"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[1] LOADING DATA...\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ID1VAGTnvUGz",
"outputId": "5865afbb-2347-47fc-ddfc-7b5c5cc5e649"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Column names for NSL-KDD dataset\n",
"column_names = [\n",
" 'duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land',\n",
" 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised',\n",
" 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
" 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count',\n",
" 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate',\n",
" 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
" 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate',\n",
" 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
" 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate',\n",
" 'attack_type', 'difficulty_level'\n",
"]\n"
],
"metadata": {
"id": "DJjkCddkvk4Y"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load training and testing data\n",
"print(f\"Loading training data from: {TRAIN_FILE_PATH}\")\n",
"print(f\"Loading testing data from: {TEST_FILE_PATH}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "D2YYEOz3vsGb",
"outputId": "0285cf05-4f27-43d7-db81-f86e99d7a5a2"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading training data from: /content/kdd_train.csv\n",
"Loading testing data from: /content/kdd_test.csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"uploaded = files.upload()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 108
},
"id": "dpYidF6XxthL",
"outputId": "6ec9d81f-0b91-4e78-987a-7e6c51021446"
},
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"
"
],
"text/html": [
"\n",
" \n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" \n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving kdd_train.csv to kdd_train (1).csv\n",
"Saving kdd_test.csv to kdd_test (1).csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Define TRAIN_FILE_PATH and TEST_FILE_PATH here to ensure they are available\n",
"TRAIN_FILE_PATH = '/content/kdd_train.csv'\n",
"TEST_FILE_PATH = '/content/kdd_test.csv'\n",
"\n",
"# Load data and inspect actual columns\n",
"train_df = pd.read_csv(TRAIN_FILE_PATH)\n",
"test_df = pd.read_csv(TEST_FILE_PATH)"
],
"metadata": {
"id": "o-USz4Qbwovx"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(f\"\\nβ Training data loaded: {train_df.shape[0]} rows, {train_df.shape[1]} columns\")\n",
"print(f\"β Testing data loaded: {test_df.shape[0]} rows, {test_df.shape[1]} columns\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d4yNBZbC1oDQ",
"outputId": "4fd92320-6b4f-4fba-d6ff-55704a6e9b56"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"β Training data loaded: 125973 rows, 42 columns\n",
"β Testing data loaded: 22544 rows, 42 columns\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Display actual column names\n",
"print(f\"\\n--- Actual Column Names in CSV ---\")\n",
"print(f\"Columns found: {list(train_df.columns)}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FyVTrm0u1r5G",
"outputId": "0d8b3e54-0e07-4b7b-bc62-6ab0370247fe"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"--- Actual Column Names in CSV ---\n",
"Columns found: ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'labels']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Try to identify the target column (attack type)\n",
"possible_target_cols = ['attack_type', 'label', 'class', 'attack', 'target', 'attack_cat']\n",
"target_col = None\n",
"\n",
"for col in train_df.columns:\n",
" if any(keyword in col.lower() for keyword in ['attack', 'label', 'class', 'target']):\n",
" target_col = col\n",
" print(f\"\\nβ Target column identified: '{target_col}'\")\n",
" break\n",
"\n",
"if target_col is None:\n",
" # Assume last column is the target\n",
" target_col = train_df.columns[-1]\n",
" print(f\"\\nβ Target column not clearly identified, using last column: '{target_col}'\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ss0yappY1uDa",
"outputId": "52592357-a2d3-4f93-f608-c7ddb7f9a5ec"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"β Target column identified: 'labels'\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Rename for consistency with rest of code\n",
"if target_col != 'attack_type':\n",
" train_df = train_df.rename(columns={target_col: 'attack_type'})\n",
" test_df = test_df.rename(columns={target_col: 'attack_type'})\n",
" print(f\"β Renamed '{target_col}' to 'attack_type'\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GnDuvmFX1zyF",
"outputId": "de8d4963-8c54-45ba-bcb1-e96133d1df2d"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Renamed 'labels' to 'attack_type'\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Remove 'difficulty_level' if it exists\n",
"if 'difficulty_level' in train_df.columns:\n",
" train_df = train_df.drop('difficulty_level', axis=1)\n",
" test_df = test_df.drop('difficulty_level', axis=1)\n",
" print(f\"β Removed 'difficulty_level' column\")"
],
"metadata": {
"id": "CoqmBZI513mP"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# SECTION 3: DATA EXPLORATION\n",
"\n",
"print(\"\\n[2] DATA EXPLORATION...\")\n",
"\n",
"print(\"\\n--- Training Data Info ---\")\n",
"print(train_df.info())\n",
"\n",
"print(\"\\n--- Statistical Summary (Numerical Features) ---\")\n",
"print(train_df.describe())\n",
"\n",
"print(\"\\n--- Missing Values ---\")\n",
"print(f\"Training set missing values:\\n{train_df.isnull().sum().sum()}\")\n",
"print(f\"Testing set missing values:\\n{test_df.isnull().sum().sum()}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5_LZpCN117Nc",
"outputId": "814199a8-e30c-4537-b74e-e5456ad46b1b"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[2] DATA EXPLORATION...\n",
"\n",
"--- Training Data Info ---\n",
"\n",
"RangeIndex: 125973 entries, 0 to 125972\n",
"Data columns (total 42 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 duration 125973 non-null int64 \n",
" 1 protocol_type 125973 non-null object \n",
" 2 service 125973 non-null object \n",
" 3 flag 125973 non-null object \n",
" 4 src_bytes 125973 non-null int64 \n",
" 5 dst_bytes 125973 non-null int64 \n",
" 6 land 125973 non-null int64 \n",
" 7 wrong_fragment 125973 non-null int64 \n",
" 8 urgent 125973 non-null int64 \n",
" 9 hot 125973 non-null int64 \n",
" 10 num_failed_logins 125973 non-null int64 \n",
" 11 logged_in 125973 non-null int64 \n",
" 12 num_compromised 125973 non-null int64 \n",
" 13 root_shell 125973 non-null int64 \n",
" 14 su_attempted 125973 non-null int64 \n",
" 15 num_root 125973 non-null int64 \n",
" 16 num_file_creations 125973 non-null int64 \n",
" 17 num_shells 125973 non-null int64 \n",
" 18 num_access_files 125973 non-null int64 \n",
" 19 num_outbound_cmds 125973 non-null int64 \n",
" 20 is_host_login 125973 non-null int64 \n",
" 21 is_guest_login 125973 non-null int64 \n",
" 22 count 125973 non-null int64 \n",
" 23 srv_count 125973 non-null int64 \n",
" 24 serror_rate 125973 non-null float64\n",
" 25 srv_serror_rate 125973 non-null float64\n",
" 26 rerror_rate 125973 non-null float64\n",
" 27 srv_rerror_rate 125973 non-null float64\n",
" 28 same_srv_rate 125973 non-null float64\n",
" 29 diff_srv_rate 125973 non-null float64\n",
" 30 srv_diff_host_rate 125973 non-null float64\n",
" 31 dst_host_count 125973 non-null int64 \n",
" 32 dst_host_srv_count 125973 non-null int64 \n",
" 33 dst_host_same_srv_rate 125973 non-null float64\n",
" 34 dst_host_diff_srv_rate 125973 non-null float64\n",
" 35 dst_host_same_src_port_rate 125973 non-null float64\n",
" 36 dst_host_srv_diff_host_rate 125973 non-null float64\n",
" 37 dst_host_serror_rate 125973 non-null float64\n",
" 38 dst_host_srv_serror_rate 125973 non-null float64\n",
" 39 dst_host_rerror_rate 125973 non-null float64\n",
" 40 dst_host_srv_rerror_rate 125973 non-null float64\n",
" 41 attack_type 125973 non-null object \n",
"dtypes: float64(15), int64(23), object(4)\n",
"memory usage: 40.4+ MB\n",
"None\n",
"\n",
"--- Statistical Summary (Numerical Features) ---\n",
" duration src_bytes dst_bytes land \\\n",
"count 125973.00000 1.259730e+05 1.259730e+05 125973.000000 \n",
"mean 287.14465 4.556674e+04 1.977911e+04 0.000198 \n",
"std 2604.51531 5.870331e+06 4.021269e+06 0.014086 \n",
"min 0.00000 0.000000e+00 0.000000e+00 0.000000 \n",
"25% 0.00000 0.000000e+00 0.000000e+00 0.000000 \n",
"50% 0.00000 4.400000e+01 0.000000e+00 0.000000 \n",
"75% 0.00000 2.760000e+02 5.160000e+02 0.000000 \n",
"max 42908.00000 1.379964e+09 1.309937e+09 1.000000 \n",
"\n",
" wrong_fragment urgent hot num_failed_logins \\\n",
"count 125973.000000 125973.000000 125973.000000 125973.000000 \n",
"mean 0.022687 0.000111 0.204409 0.001222 \n",
"std 0.253530 0.014366 2.149968 0.045239 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 0.000000 \n",
"max 3.000000 3.000000 77.000000 5.000000 \n",
"\n",
" logged_in num_compromised ... dst_host_count \\\n",
"count 125973.000000 125973.000000 ... 125973.000000 \n",
"mean 0.395736 0.279250 ... 182.148945 \n",
"std 0.489010 23.942042 ... 99.206213 \n",
"min 0.000000 0.000000 ... 0.000000 \n",
"25% 0.000000 0.000000 ... 82.000000 \n",
"50% 0.000000 0.000000 ... 255.000000 \n",
"75% 1.000000 0.000000 ... 255.000000 \n",
"max 1.000000 7479.000000 ... 255.000000 \n",
"\n",
" dst_host_srv_count dst_host_same_srv_rate dst_host_diff_srv_rate \\\n",
"count 125973.000000 125973.000000 125973.000000 \n",
"mean 115.653005 0.521242 0.082951 \n",
"std 110.702741 0.448949 0.188922 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 10.000000 0.050000 0.000000 \n",
"50% 63.000000 0.510000 0.020000 \n",
"75% 255.000000 1.000000 0.070000 \n",
"max 255.000000 1.000000 1.000000 \n",
"\n",
" dst_host_same_src_port_rate dst_host_srv_diff_host_rate \\\n",
"count 125973.000000 125973.000000 \n",
"mean 0.148379 0.032542 \n",
"std 0.308997 0.112564 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.060000 0.020000 \n",
"max 1.000000 1.000000 \n",
"\n",
" dst_host_serror_rate dst_host_srv_serror_rate dst_host_rerror_rate \\\n",
"count 125973.000000 125973.000000 125973.000000 \n",
"mean 0.284452 0.278485 0.118832 \n",
"std 0.444784 0.445669 0.306557 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 1.000000 1.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" dst_host_srv_rerror_rate \n",
"count 125973.000000 \n",
"mean 0.120240 \n",
"std 0.319459 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 \n",
"\n",
"[8 rows x 38 columns]\n",
"\n",
"--- Missing Values ---\n",
"Training set missing values:\n",
"0\n",
"Testing set missing values:\n",
"0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Attack type distribution\n",
"print(\"\\n--- Attack Type Distribution (Training) ---\")\n",
"print(train_df['attack_type'].value_counts())\n",
"\n",
"# Combine datasets for consistent preprocessing\n",
"train_df['dataset'] = 'train'\n",
"test_df['dataset'] = 'test'\n",
"combined_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
"\n",
"print(f\"\\nβ Combined dataset: {combined_df.shape[0]} rows\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "goYbMHUh2EP3",
"outputId": "14470c88-0556-40b3-fc32-d43cc7944f3a"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"--- Attack Type Distribution (Training) ---\n",
"attack_type\n",
"normal 67343\n",
"neptune 41214\n",
"satan 3633\n",
"ipsweep 3599\n",
"portsweep 2931\n",
"smurf 2646\n",
"nmap 1493\n",
"back 956\n",
"teardrop 892\n",
"warezclient 890\n",
"pod 201\n",
"guess_passwd 53\n",
"buffer_overflow 30\n",
"warezmaster 20\n",
"land 18\n",
"imap 11\n",
"rootkit 10\n",
"loadmodule 9\n",
"ftp_write 8\n",
"multihop 7\n",
"phf 4\n",
"perl 3\n",
"spy 2\n",
"Name: count, dtype: int64\n",
"\n",
"β Combined dataset: 148517 rows\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"# SECTION 4: DATA PREPROCESSING\n",
"\n",
"print(\"\\n[3] DATA PREPROCESSING...\")\n",
"\n",
"# Drop difficulty level column if it exists (not needed for classification)\n",
"if 'difficulty_level' in combined_df.columns:\n",
" combined_df = combined_df.drop('difficulty_level', axis=1)\n",
" print(\"β Dropped 'difficulty_level' column\")\n",
"\n",
"# Map attack types to broader categories for multi-class classification\n",
"attack_mapping = {\n",
" 'normal': 'normal',\n",
" 'neptune': 'DoS', 'back': 'DoS', 'land': 'DoS', 'pod': 'DoS', 'smurf': 'DoS',\n",
" 'teardrop': 'DoS', 'mailbomb': 'DoS', 'apache2': 'DoS', 'processtable': 'DoS',\n",
" 'udpstorm': 'DoS',\n",
" 'ipsweep': 'Probe', 'nmap': 'Probe', 'portsweep': 'Probe', 'satan': 'Probe',\n",
" 'mscan': 'Probe', 'saint': 'Probe',\n",
" 'ftp_write': 'R2L', 'guess_passwd': 'R2L', 'imap': 'R2L', 'multihop': 'R2L',\n",
" 'phf': 'R2L', 'spy': 'R2L', 'warezclient': 'R2L', 'warezmaster': 'R2L',\n",
" 'sendmail': 'R2L', 'named': 'R2L', 'snmpgetattack': 'R2L', 'snmpguess': 'R2L',\n",
" 'xlock': 'R2L', 'xsnoop': 'R2L', 'worm': 'R2L',\n",
" 'buffer_overflow': 'U2R', 'loadmodule': 'U2R', 'perl': 'U2R', 'rootkit': 'U2R',\n",
" 'httptunnel': 'U2R', 'ps': 'U2R', 'sqlattack': 'U2R', 'xterm': 'U2R'\n",
"}\n",
"\n",
"combined_df['attack_category'] = combined_df['attack_type'].map(attack_mapping)\n",
"\n",
"# Handle any unmapped attacks\n",
"if combined_df['attack_category'].isnull().any():\n",
" print(\"β Warning: Some attack types were not mapped. Filling with 'other'\")\n",
" combined_df['attack_category'].fillna('other', inplace=True)\n",
"\n",
"print(f\"\\n--- Attack Category Distribution ---\")\n",
"print(combined_df['attack_category'].value_counts())\n",
"\n",
"# Identify categorical and numerical columns\n",
"categorical_cols = ['protocol_type', 'service', 'flag']\n",
"numerical_cols = [col for col in combined_df.columns\n",
" if col not in categorical_cols + ['attack_type', 'attack_category', 'dataset']]\n",
"\n",
"print(f\"\\nβ Categorical features: {len(categorical_cols)}\")\n",
"print(f\"β Numerical features: {len(numerical_cols)}\")\n",
"\n",
"# Encode categorical features\n",
"label_encoders = {}\n",
"for col in categorical_cols:\n",
" le = LabelEncoder()\n",
" combined_df[col] = le.fit_transform(combined_df[col].astype(str))\n",
" label_encoders[col] = le\n",
"\n",
"print(f\"β Categorical features encoded\")\n",
"\n",
"# Encode target variable\n",
"target_encoder = LabelEncoder()\n",
"combined_df['attack_category_encoded'] = target_encoder.fit_transform(combined_df['attack_category'])\n",
"\n",
"print(f\"β Target variable encoded: {list(target_encoder.classes_)}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KZGtBhJN2JrV",
"outputId": "2c2081e6-74db-4977-cd99-b14abc1054d4"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[3] DATA PREPROCESSING...\n",
"\n",
"--- Attack Category Distribution ---\n",
"attack_category\n",
"normal 78588\n",
"DoS 54022\n",
"Probe 13813\n",
"R2L 1963\n",
"U2R 131\n",
"Name: count, dtype: int64\n",
"\n",
"β Categorical features: 3\n",
"β Numerical features: 38\n",
"β Categorical features encoded\n",
"β Target variable encoded: ['DoS', 'Probe', 'R2L', 'U2R', 'normal']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns # Added this line\n",
"\n",
"# SECTION 5: EXPLORATORY DATA ANALYSIS (EDA)\n",
"\n",
"print(\"\\n[4] EXPLORATORY DATA ANALYSIS...\")\n",
"\n",
"# Separate train and test again\n",
"train_processed = combined_df[combined_df['dataset'] == 'train'].copy()\n",
"test_processed = combined_df[combined_df['dataset'] == 'test'].copy()\n",
"\n",
"# Figure 1: Attack Category Distribution\n",
"plt.figure(figsize=(10, 6))\n",
"train_processed['attack_category'].value_counts().plot(kind='bar', color='steelblue')\n",
"plt.title('Attack Category Distribution in Training Data', fontsize=14, fontweight='bold')\n",
"plt.xlabel('Attack Category', fontsize=12)\n",
"plt.ylabel('Count', fontsize=12)\n",
"plt.xticks(rotation=45)\n",
"plt.tight_layout()\n",
"plt.savefig('attack_distribution.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: attack_distribution.png\")\n",
"\n",
"# Figure 2: Correlation Matrix\n",
"plt.figure(figsize=(16, 12))\n",
"correlation_cols = numerical_cols[:20] # Top 20 features for readability\n",
"corr_matrix = train_processed[correlation_cols].corr()\n",
"sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', center=0,\n",
" square=True, linewidths=0.5)\n",
"plt.title('Correlation Matrix of Numerical Features', fontsize=14, fontweight='bold')\n",
"plt.tight_layout()\n",
"plt.savefig('correlation_matrix.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: correlation_matrix.png\")\n",
"\n",
"# Figure 3: Feature Distribution\n",
"fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
"features_to_plot = ['duration', 'src_bytes', 'dst_bytes', 'count', 'srv_count', 'dst_host_count']\n",
"for idx, feature in enumerate(features_to_plot):\n",
" ax = axes[idx // 3, idx % 3]\n",
" train_processed[feature].hist(bins=50, ax=ax, color='teal', alpha=0.7, edgecolor='black')\n",
" ax.set_title(f'Distribution of {feature}', fontweight='bold')\n",
" ax.set_xlabel(feature)\n",
" ax.set_ylabel('Frequency')\n",
"plt.tight_layout()\n",
"plt.savefig('feature_distributions.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: feature_distributions.png\")\n",
"\n",
"\n",
"# SECTION 6: FEATURE SCALING AND DATA PREPARATION\n",
"\n",
"print(\"\\n[5] FEATURE SCALING AND DATA PREPARATION...\")\n",
"\n",
"# Prepare features and target\n",
"X_train = train_processed[categorical_cols + numerical_cols]\n",
"y_train = train_processed['attack_category_encoded']\n",
"X_test = test_processed[categorical_cols + numerical_cols]\n",
"y_test = test_processed['attack_category_encoded']\n",
"\n",
"print(f\"β Training features shape: {X_train.shape}\")\n",
"print(f\"β Testing features shape: {X_test.shape}\")\n",
"\n",
"# Scale numerical features\n",
"scaler = StandardScaler()\n",
"X_train[numerical_cols] = scaler.fit_transform(X_train[numerical_cols])\n",
"X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
"\n",
"print(f\"β Features scaled using StandardScaler\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "K7AUhG-Q2hal",
"outputId": "77d564f8-7083-418c-c07a-345cefc806e6"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[4] EXPLORATORY DATA ANALYSIS...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: attack_distribution.png\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: correlation_matrix.png\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: feature_distributions.png\n",
"\n",
"[5] FEATURE SCALING AND DATA PREPARATION...\n",
"β Training features shape: (125973, 41)\n",
"β Testing features shape: (22544, 41)\n",
"β Features scaled using StandardScaler\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 7: MODEL DEVELOPMENT - RANDOM FOREST\n",
"\n",
"print(\"\\n[6] RANDOM FOREST CLASSIFIER...\")\n",
"\n",
"# Initial Random Forest model\n",
"print(\"\\n--- Training Initial Random Forest Model ---\")\n",
"start_time = time.time()\n",
"rf_initial = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1, max_depth=20)\n",
"rf_initial.fit(X_train, y_train)\n",
"rf_initial_time = time.time() - start_time\n",
"\n",
"y_pred_rf_initial = rf_initial.predict(X_test)\n",
"print(f\"β Training time: {rf_initial_time:.2f} seconds\")\n",
"print(f\"β Initial accuracy: {accuracy_score(y_test, y_pred_rf_initial):.4f}\")\n",
"\n",
"# Hyperparameter tuning - Use subset for faster tuning\n",
"print(\"\\n--- Hyperparameter Tuning for Random Forest ---\")\n",
"print(\"Using subset of training data for faster hyperparameter tuning...\")\n",
"\n",
"# Sample 20% of training data for hyperparameter tuning\n",
"sample_size = int(0.2 * len(X_train))\n",
"X_train_sample = X_train.sample(n=sample_size, random_state=42)\n",
"y_train_sample = y_train.loc[X_train_sample.index]\n",
"\n",
"print(f\"Tuning on {sample_size:,} samples (20% of training data)\")\n",
"\n",
"rf_param_distributions = {\n",
" 'n_estimators': [100, 150, 200],\n",
" 'max_depth': [20, 30],\n",
" 'min_samples_split': [2, 5],\n",
" 'max_features': ['sqrt']\n",
"}\n",
"\n",
"print(f\"Testing 8 random combinations with RandomizedSearchCV...\")\n",
"print(\"Estimated time: 1-2 minutes...\")\n",
"start_time = time.time()\n",
"\n",
"rf_grid = RandomizedSearchCV(\n",
" RandomForestClassifier(random_state=42, n_jobs=-1),\n",
" rf_param_distributions,\n",
" n_iter=8, # Test only 8 random combinations\n",
" cv=2, # Reduced from 3 to 2 folds for speed\n",
" scoring='accuracy',\n",
" n_jobs=-1,\n",
" verbose=2,\n",
" random_state=42\n",
")\n",
"rf_grid.fit(X_train_sample, y_train_sample)\n",
"rf_tuning_time = time.time() - start_time\n",
"\n",
"print(f\"\\nβ Best parameters: {rf_grid.best_params_}\")\n",
"print(f\"β Best cross-validation score: {rf_grid.best_score_:.4f}\")\n",
"print(f\"β Tuning time: {rf_tuning_time:.2f} seconds\")\n",
"\n",
"# Train best model on FULL training data\n",
"print(\"\\n--- Training Best Random Forest on Full Data ---\")\n",
"start_time = time.time()\n",
"rf_best = RandomForestClassifier(**rf_grid.best_params_, random_state=42, n_jobs=-1)\n",
"rf_best.fit(X_train, y_train)\n",
"rf_full_train_time = time.time() - start_time\n",
"print(f\"β Full training time: {rf_full_train_time:.2f} seconds\")\n",
"\n",
"y_pred_rf = rf_best.predict(X_test)\n",
"\n",
"print(\"\\n--- Random Forest Performance ---\")\n",
"rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
"rf_precision = precision_score(y_test, y_pred_rf, average='weighted', zero_division=0)\n",
"rf_recall = recall_score(y_test, y_pred_rf, average='weighted', zero_division=0)\n",
"rf_f1 = f1_score(y_test, y_pred_rf, average='weighted', zero_division=0)\n",
"\n",
"print(f\"Accuracy: {rf_accuracy:.4f}\")\n",
"print(f\"Precision: {rf_precision:.4f}\")\n",
"print(f\"Recall: {rf_recall:.4f}\")\n",
"print(f\"F1-Score: {rf_f1:.4f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BmipJbPg4grT",
"outputId": "5342432c-3e90-4d99-a343-e028e9f9061c"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[6] RANDOM FOREST CLASSIFIER...\n",
"\n",
"--- Training Initial Random Forest Model ---\n",
"β Training time: 12.01 seconds\n",
"β Initial accuracy: 0.9199\n",
"\n",
"--- Hyperparameter Tuning for Random Forest ---\n",
"Using subset of training data for faster hyperparameter tuning...\n",
"Tuning on 25,194 samples (20% of training data)\n",
"Testing 8 random combinations with RandomizedSearchCV...\n",
"Estimated time: 1-2 minutes...\n",
"Fitting 2 folds for each of 8 candidates, totalling 16 fits\n",
"\n",
"β Best parameters: {'n_estimators': 150, 'min_samples_split': 2, 'max_features': 'sqrt', 'max_depth': 20}\n",
"β Best cross-validation score: 0.9967\n",
"β Tuning time: 23.75 seconds\n",
"\n",
"--- Training Best Random Forest on Full Data ---\n",
"β Full training time: 11.74 seconds\n",
"\n",
"--- Random Forest Performance ---\n",
"Accuracy: 0.9209\n",
"Precision: 0.9287\n",
"Recall: 0.9209\n",
"F1-Score: 0.9070\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 8: MODEL DEVELOPMENT - GRADIENT BOOSTING\n",
"\n",
"print(\"\\n[7] GRADIENT BOOSTING CLASSIFIER...\")\n",
"\n",
"# Initial Gradient Boosting model (using smaller n_estimators for speed)\n",
"print(\"\\n--- Training Initial Gradient Boosting Model ---\")\n",
"start_time = time.time()\n",
"gb_initial = GradientBoostingClassifier(n_estimators=50, random_state=42, max_depth=5, verbose=1)\n",
"gb_initial.fit(X_train, y_train)\n",
"gb_initial_time = time.time() - start_time\n",
"\n",
"y_pred_gb_initial = gb_initial.predict(X_test)\n",
"print(f\"β Training time: {gb_initial_time:.2f} seconds\")\n",
"print(f\"β Initial accuracy: {accuracy_score(y_test, y_pred_gb_initial):.4f}\")\n",
"\n",
"# Hyperparameter tuning - Use subset for faster tuning\n",
"print(\"\\n--- Hyperparameter Tuning for Gradient Boosting ---\")\n",
"print(f\"Using subset of training data for faster hyperparameter tuning...\")\n",
"print(f\"Tuning on {sample_size:,} samples (20% of training data)\")\n",
"\n",
"gb_param_distributions = {\n",
" 'n_estimators': [50, 100, 150],\n",
" 'learning_rate': [0.1, 0.2],\n",
" 'max_depth': [3, 5],\n",
" 'subsample': [0.8, 1.0]\n",
"}\n",
"\n",
"print(f\"Testing 8 random combinations with RandomizedSearchCV...\")\n",
"print(\"Estimated time: 1-2 minutes...\")\n",
"start_time = time.time()\n",
"\n",
"gb_grid = RandomizedSearchCV(\n",
" GradientBoostingClassifier(random_state=42),\n",
" gb_param_distributions,\n",
" n_iter=8, # Test only 8 random combinations\n",
" cv=2, # Reduced from 3 to 2 folds for speed\n",
" scoring='accuracy',\n",
" n_jobs=-1,\n",
" verbose=2,\n",
" random_state=42\n",
")\n",
"gb_grid.fit(X_train_sample, y_train_sample)\n",
"gb_tuning_time = time.time() - start_time\n",
"\n",
"print(f\"\\nβ Best parameters: {gb_grid.best_params_}\")\n",
"print(f\"β Best cross-validation score: {gb_grid.best_score_:.4f}\")\n",
"print(f\"β Tuning time: {gb_tuning_time:.2f} seconds\")\n",
"\n",
"# Train best model on FULL training data (with optimization)\n",
"print(\"\\n--- Training Best Gradient Boosting on Full Data ---\")\n",
"start_time = time.time()\n",
"\n",
"# Optimize: Cap n_estimators at 100 for full training to save time\n",
"optimized_params = gb_grid.best_params_.copy()\n",
"if optimized_params['n_estimators'] > 100:\n",
" print(f\"β Reducing n_estimators from {optimized_params['n_estimators']} to 100 for faster training\")\n",
" optimized_params['n_estimators'] = 100\n",
"\n",
"gb_best = GradientBoostingClassifier(**optimized_params, random_state=42)\n",
"gb_best.fit(X_train, y_train)\n",
"gb_full_train_time = time.time() - start_time\n",
"print(f\"β Full training time: {gb_full_train_time:.2f} seconds\")\n",
"\n",
"y_pred_gb = gb_best.predict(X_test)\n",
"\n",
"print(\"\\n--- Gradient Boosting Performance ---\")\n",
"gb_accuracy = accuracy_score(y_test, y_pred_gb)\n",
"gb_precision = precision_score(y_test, y_pred_gb, average='weighted', zero_division=0)\n",
"gb_recall = recall_score(y_test, y_pred_gb, average='weighted', zero_division=0)\n",
"gb_f1 = f1_score(y_test, y_pred_gb, average='weighted', zero_division=0)\n",
"\n",
"print(f\"Accuracy: {gb_accuracy:.4f}\")\n",
"print(f\"Precision: {gb_precision:.4f}\")\n",
"print(f\"Recall: {gb_recall:.4f}\")\n",
"print(f\"F1-Score: {gb_f1:.4f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KYv8_0uj466P",
"outputId": "46556f72-bb3b-4314-b779-8edbae30d4c6"
},
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[7] GRADIENT BOOSTING CLASSIFIER...\n",
"\n",
"--- Training Initial Gradient Boosting Model ---\n",
" Iter Train Loss Remaining Time \n",
" 1 0.7238 5.51m\n",
" 2 0.5849 4.84m\n",
" 3 0.4829 4.18m\n",
" 4 0.4038 4.05m\n",
" 5 0.3410 3.42m\n",
" 6 0.2886 3.00m\n",
" 7 0.2466 2.68m\n",
" 8 0.2117 2.44m\n",
" 9 0.1824 2.25m\n",
" 10 0.1557 2.14m\n",
" 20 0.0374 1.26m\n",
" 30 0.0127 45.71s\n",
" 40 0.0058 22.16s\n",
" 50 0.0035 0.00s\n",
"β Training time: 107.51 seconds\n",
"β Initial accuracy: 0.9248\n",
"\n",
"--- Hyperparameter Tuning for Gradient Boosting ---\n",
"Using subset of training data for faster hyperparameter tuning...\n",
"Tuning on 25,194 samples (20% of training data)\n",
"Testing 8 random combinations with RandomizedSearchCV...\n",
"Estimated time: 1-2 minutes...\n",
"Fitting 2 folds for each of 8 candidates, totalling 16 fits\n",
"\n",
"β Best parameters: {'subsample': 1.0, 'n_estimators': 150, 'max_depth': 5, 'learning_rate': 0.1}\n",
"β Best cross-validation score: 0.9966\n",
"β Tuning time: 216.14 seconds\n",
"\n",
"--- Training Best Gradient Boosting on Full Data ---\n",
"β Reducing n_estimators from 150 to 100 for faster training\n",
"β Full training time: 188.36 seconds\n",
"\n",
"--- Gradient Boosting Performance ---\n",
"Accuracy: 0.9293\n",
"Precision: 0.9320\n",
"Recall: 0.9293\n",
"F1-Score: 0.9185\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 9: MODEL COMPARISON\n",
"\n",
"print(\"\\n[8] MODEL PERFORMANCE COMPARISON...\")\n",
"\n",
"# Create comparison dataframe\n",
"comparison_df = pd.DataFrame({\n",
" 'Model': ['Random Forest', 'Gradient Boosting'],\n",
" 'Accuracy': [rf_accuracy, gb_accuracy],\n",
" 'Precision': [rf_precision, gb_precision],\n",
" 'Recall': [rf_recall, gb_recall],\n",
" 'F1-Score': [rf_f1, gb_f1],\n",
" 'Training Time (s)': [rf_initial_time, gb_initial_time]\n",
"})\n",
"\n",
"print(\"\\n--- Performance Metrics Comparison ---\")\n",
"print(comparison_df.to_string(index=False))\n",
"\n",
"# Figure 4: Performance Comparison\n",
"fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
"\n",
"# Metrics comparison\n",
"metrics_df = comparison_df[['Model', 'Accuracy', 'Precision', 'Recall', 'F1-Score']].set_index('Model')\n",
"metrics_df.T.plot(kind='bar', ax=axes[0], color=['steelblue', 'coral'])\n",
"axes[0].set_title('Performance Metrics Comparison', fontsize=14, fontweight='bold')\n",
"axes[0].set_ylabel('Score', fontsize=12)\n",
"axes[0].set_xlabel('Metrics', fontsize=12)\n",
"axes[0].set_ylim([0, 1])\n",
"axes[0].legend(title='Model')\n",
"axes[0].grid(axis='y', alpha=0.3)\n",
"\n",
"# Training time comparison\n",
"comparison_df.plot(x='Model', y='Training Time (s)', kind='bar', ax=axes[1],\n",
" color=['steelblue', 'coral'], legend=False)\n",
"axes[1].set_title('Training Time Comparison', fontsize=14, fontweight='bold')\n",
"axes[1].set_ylabel('Time (seconds)', fontsize=12)\n",
"axes[1].set_xlabel('Model', fontsize=12)\n",
"axes[1].grid(axis='y', alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('model_comparison.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: model_comparison.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 585
},
"id": "ET6ElDMi7mjT",
"outputId": "a316b915-72e8-4ccc-d4a1-e68f665ce296"
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[8] MODEL PERFORMANCE COMPARISON...\n",
"\n",
"--- Performance Metrics Comparison ---\n",
" Model Accuracy Precision Recall F1-Score Training Time (s)\n",
" Random Forest 0.920866 0.928737 0.920866 0.907022 12.013508\n",
"Gradient Boosting 0.929294 0.931966 0.929294 0.918512 107.506581\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: model_comparison.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 10: CONFUSION MATRICES\n",
"\n",
"print(\"\\n[9] CONFUSION MATRICES...\")\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
"\n",
"# Random Forest Confusion Matrix\n",
"cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
"disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf,\n",
" display_labels=target_encoder.classes_)\n",
"disp_rf.plot(ax=axes[0], cmap='Blues', values_format='d')\n",
"axes[0].set_title('Random Forest - Confusion Matrix', fontsize=14, fontweight='bold')\n",
"\n",
"# Gradient Boosting Confusion Matrix\n",
"cm_gb = confusion_matrix(y_test, y_pred_gb)\n",
"disp_gb = ConfusionMatrixDisplay(confusion_matrix=cm_gb,\n",
" display_labels=target_encoder.classes_)\n",
"disp_gb.plot(ax=axes[1], cmap='Oranges', values_format='d')\n",
"axes[1].set_title('Gradient Boosting - Confusion Matrix', fontsize=14, fontweight='bold')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('confusion_matrices.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: confusion_matrices.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 499
},
"id": "mcxzacNp784d",
"outputId": "96d97f31-7372-44e0-df65-d5c83f2ad373"
},
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[9] CONFUSION MATRICES...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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58iXOnz+f47x27typNCWWu7s7OnTogMDAQGzfvh3Jycl48+YNOnbsiPv370NTM+PP3D///AN7e3v4+PggICAAu3fvBgDEx8djxYoVWLduXa7r2LhxYzx9+hT37t3D5s2bMWPGDJw6dQr+/v4AFNdadg9vMjU1RbNmzeDq6gozMzNoa2sjODgYBw8exKtXrxAZGYnx48fj2LFjnz1XALK8XffixYuoUaMGmjZtiujoaJQqVSrbelWvXh2zZs3CpEmTAABDhw6Fl5cX+vbtK6YZPXo0mjZt+tn3iIiIKL0zZ86I/zczM8swx3JuXbx4Ee7u7mjbti0EQRCn8tPR0UHNmjXx1VdfwcLCAoaGhoiIiMCZM2dw/fp1CIKAH3/8EV27doWenh4AYMqUKUrtt/r166N+/fq4fPky/vjjj1yXLTIyEu3bt0dISAgAwNHRUTze/v37cf/+fbFN9+TJk0zbXs+fP4euri4GDRoEPT09rF27FrGxsQA+9QG+pJ2QHScnp0zXlyhRQqldkJSUhA4dOohzoWtoaKBHjx6wt7fHoUOHcO/ePQCKPlblypXRs2dPAHlr4/322294//49AMX106dPH1hYWIht77R9uNTn84wdO1ZclzoNK5C3tu6lS5fQoUMHuLm5YdeuXeIzp3bt2oX58+eL7f3011KdOnXQsGFDXLx4UWmKzIKQ9jMHIFf9Rh8fH7HvBgDffPMN3NzccOrUKVy9ehUA8Ntvv2Hu3LmYNm1apnnkpA+e+tyHuXPnig8wbtq0KZo1awbgy54jkJv+Y3byep1n9n7k5BrKjREjRuDSpUtiv2/ChAlYuXIlAMDIyAh9+vTJchwjt+MsnztXWbl69Sr09fXRvXt32NnZ4datW5+denXz5s24fv06AgMDcffuXcyaNQumpqa4fPkyAEV/6ueff4ZcLs/tW0ZU+KjyLwWk3tL/1TSzRV9fX1i0aFGWebx8+VLYv3+/sHr1amHx4sXCokWLBDs7O3H/WbNmZXm8Dh06iLdwhYaGKv1ld+XKleJ+5cuXF9eXLVtW6Za0tNNAIM1fpZOTk5XuDKhVq5aQlJQk7jdu3Dil/W7duiVuS7vezc1NnPrlxIkTSts8PT3FPNNPsRIZGZmjc/C5aHPgU5RtftXJ2Ng401t8vby8xDQ9e/ZU2rZ3715xm6ampngL7PDhw8X18+bNy5BnWFiY0i2V2UV+S+mff/5ROu748eNzvG/aCAIjIyMhODhY3Hbs2DGlfNNOn5M2ekgmkwk3b94Ut40cOVLcZm5urnS89NEe6eUlsj4sLEzp/MfHxyvtl5KSIjx//lxpXdp80kbKeHp6iutLly4txMTEiNvWrFmjtN/BgwfFbWmvdQMDA6U7G9JGxVeuXDlDnTOTvq6rVq1SijJbv3690LBhQwGAoK2tLbx9+1bpnGR2J0dCQoJw4cIFYfPmzcKyZcuERYsWCX369BH30dHRUZoK6nPnKrM0HTt2zDTaJKvIekFQfPYbNGggbjc0NBT/7+XlleF8EhER5VTaCNfq1asrbXv48GG2bVNByNi+r1mzphAbG5vl8e7cuSP8/PPPwooVK4RFixYJP/30k9L+qVHtiYmJSr93aaM1U1JShGbNmintl5PI+hUrVojrzczMlKZ0iYqKUooQX7FiRab5AVCaxnD58uVZ9gFy0k7ITvrI+swWPT098S7NVAcPHlRKs2bNGnFbTEyMUpvM09NT3JaXNt7SpUvFdZlNxRMVFSUEBQUprcuqjZmTNOnf05EjR4rbbt++rbTt8OHDgiBkvJa8vb3FPlRycrLYXszsWpJCq1atlI6X3eclrVu3bintN27cOHFbUlKS0h2k5ubm4uclr31wQcg4bUp62Z3LrD6Hue0/ZtX3yet1npdr6HPSR9YnJSUJpUqVEgAIDg4OwtmzZ8Xtw4YNy3BOMusT52acRRA+f67Sp9HQ0FCayibV56aFOn36tCCTyQRAMS6go6Mjpt27d2+O3i+iooCR9VSodejQAQMHDsywPjQ0FL169fpsVEtmT7dPNWjQIMhkMgCKp4yXKFECwcHBACD+VTgqKkqMZgcUEcE6Ojri6+7du2Pjxo0Z8n78+LHSnQHdu3dX+ktxr169sHDhQvH11atX8dVXX2XIp0uXLtDS0gIAlC5dWmlbx44dxTxTo8NTffjwAUZGRlnWPS/yq049e/bMEM0bExOD27dvi6937NiR5UM5k5KS8M8//6BFixaoW7euGCUwZcoUHD58GBUqVED58uVRo0YN1K1bN18fjprZnQeAtA+hSo1SARQPLLO0tBRft2zZEiVLlhQjiq5evZrpw71q1aoFLy8v8XX58uXF/6de61IyMzODu7s77t+/j8jISDg5OaFatWpwcXFBpUqV0Lhx4yyjtdKKiYmBn5+f+Lpz585i9BuguLYGDx4svr569Srat2+fIZ927dopRank1/vRvXt3TJgwAWFhYZg5cyYCAwPFclpbW2e7765duzBy5Egx2i4z8fHxCAkJgY2NTZ7LOGnSpFxHm8jlcuzcuROenp4ICwtDVFQUAEBfXx979uyBtrZ2nstDRESUKrVd/iXGjBkDXV3dDOtv3ryJnj17KkUFZya17/Do0SPx9w4Avv32W/H3UyaTwcfHBydPnsxV2VKjPwFFe8PCwiLLtFeuXMHw4cMzrLe1tUW7du3E12nbMKn55ncfIFXaB8yeO3cOZ86cQWxsLFq3bo2jR4+iSZMmAJTbrgCUIor19PTQpUsXMcrfz89PjHLOSxuvdu3akMlkEAQB69evx/Xr1+Hm5oby5cujatWqaNiwIaysrPL/zfhP2jJldi6AjNeSj4+P2D+Ry+Xo1asX/vrrr1wd98qVK7hy5UqG9VI+DDX9ee3Vq5f4fw0NDXTv3l1MExYWhsePH8PV1TVDPjnpg0spv/qPebnO9fX1M+STk2sotzQ0NDBkyBCMHz8eAQEB6NGjBwDFd9ewYcPw5s2bLPfNj3GWnGjZsiUqV66c6/0aN26MMWPGYNGiRUhKShIfmPv999+jc+fOX1QmosKEg/VUaHTt2hWenp64cuUKjh49CkAxgPX27VucPn1aqQHft2/fHN1+mvYJ9+mlH/xOOwifkpICAAgPD1dKk3agFECWjb+0g9qZpUv/Oqsf4rQDiukHxNJuSz/VR2r5c6N+/frZTtORX3WqUKFChnUfPnwQp3nJidTB6W+++QZjxozBqlWrEB8fj6tXryo1nBwdHfHHH3/A3d09x3lnJ+1ts2l9brDezs5O6XXa22A/J+37ntn1ZmVlJb4fWb3n2V3ruXnf00u/b3aft927d+Pbb7/FgwcPEBgYiN9//13cJpfLMWLECKVbnzOT/jpJ/34YGBjA0NBQ7BDl5f3Iy2cnlZ6eHvr3748FCxaIA/WA4lbU7KQOIOTk2Nm9xzmR2ecvJ+zt7dG+fXts2bJFXNekSZMMnQoiIqLcsLOzw5MnTwAAT548gSAIYpvf0tJSHOyaPn26OKibncx+52JjY9GmTRu8ffv2s/un/s7mtQ+QnfRt6eyktu3Sy64NA3xZO+Zz+vfvLx5/ypQpaNiwIc6dO4f4+HgMHz4cDx48AKBcT0NDQxgYGCjlk/a9EwQB4eHhEBTP0ss0DZB1G6969epYunQppk6diqioKNy8eRM3b94U9ytRogT27duHBg0afPkbkIm05yOrc5H+WkofwPG5gI7MnDx5EjNnzsywfvr06Z8drM+sX5JZgFV6+dUXlKodntN+SX71H/NynWc2WJ+Taygv+vXrh5kzZyImJkYcnG/ZsiVcXFyyHazPj3GWnMhrnwQAhgwZgiVLlii9P0OHDv2i8hAVNpzMiQqNFi1aYOLEiThy5Ah++OEHcf3Zs2eV5ruOjo4WB/MBxV9Xnz17hqSkJAiCkOM57FIj1lNlFs2Tfu7Cd+/eKb1OjQJIz9zcPNt06V9nNs94ZmVMK7O5uKWUX3VK35ABFPPLpfX1119j0aJFWS5p/wq/aNEiBAcH49ixY1i6dCkGDhwo/iHj5cuXStEKqmJra6vUIDlx4kSOOoyA8vue2fWWdl1Or6MviVxLG5WdOkdqqtTOdmY8PDxw//59+Pn5Ydu2bZg8eTJatmwJQNEQXbZs2WejiszMzJTKnv79iI6OVopcKoj3I70hQ4YofTZr1qz52e+kffv2iY1NmUyGPXv2ICoqCoIg5GlO3Oxk9vnLifPnz2Pbtm1K6w4fPoxDhw59eaGIiEhtNW7cWPx/WFgYDh8+LL42NzfHmDFjMGbMGKUo6+xk9jt34cIFpXbXjz/+iPfv30MQBERHR2eaT/q2aU77ANlJ26azsbHJtq07YMCATPOQsg2TW2nbNw8fPhQHpdPWMyoqKsN7nPa9k8lkMDU1/aI23siRIxEcHIwzZ85g5cqVGDZsGFxcXAAAISEhShHg+S3t+cjqXHzuWkp97lhBSfuZA5ChfZcVqfq3X3INp903N/2S/Og/5uU6z0xOrqG8MDc3R/fu3ZXWZXa3Tlr5Nc6SE3ntkwiCgH79+mX4Q8aAAQOQmJiYH0UjKhQ4WE+F0vz585UGymfNmoXk5GQAQEREhPh/AGjdujXKlCkDDQ0NPH78WOkWyi9lZGSkFDn622+/Kf0VOe0fEdIqX7680g/4zz//rFTm7du3K6WX6nbF/CRlnQwMDJQiOkJDQzFixAixg5a69O/fH/b29mKkg7+/P8LDw2FiYoKWLVti1KhRWLt2LVavXi3mlTa6Jn3jMCcRWmmlRv2kX3IibXR1XFwcOnfunGmE1YcPH7B8+XLxddr38fjx40oN/D///FMp8qogrqO0Dc3379/j2bNnABTRFVlNEwRAnOaoUqVK6NWrF3766SccO3YMHh4eYpq05yoz+vr68PT0FF/v27dPqWGefuokVXyuHBwc0KFDB/H15xrFgOJ6T2ViYoIuXbqIDdi0D3RO70uv55z68OEDevToITaK097S3K9fP6W7CIiIiHJj6NChSlNODBw4UGlqxPyQ9ncWUExDUqJECQBZ/85WqFABhoaG4us9e/aIv4OCIGDXrl25Lkfadsn79+/RrFmzDG3dH3/8EV999RWqV6+e6/zTk7qdcP36daXXqf2C9O2vtO2z2NhYpffc09MT+vr6eW7jBQYGIjg4GPr6+mjUqBGGDRuGlStXKj1Y99WrV0rXQNqgCqnaTmmlv5Z+/fVXsf8gCEKGPlROzJgxI9M+yYwZMz67b/v27eHo6Ci+Xr16NXbv3p1p2jNnzogP6U1/XtOWOzk5WalfbG5uXiB3X6btl6R9MOyJEyfg6+ub6T657T9mJS/XeUFL2w+pUKHCZx/6+iXjLGm/b6T8XC1ZsgSnT58GoDj/qXem3LhxI8uHGhMVRZwGhwolU1NTDBkyBHPnzgUAPH36FL/++iu+++47WFpawtTUVIze+Omnn/Du3TskJSVhy5YtX3xLVnp9+/bFuHHjxHLUqlULbdu2xb1793DgwIFM95HL5Rg1ahSmTp0KQDGnXZ06ddCsWTM8evRI6ce7YcOGSo3TwkrqOo0dOxY+Pj4AFHN6enh4oG3btjAzM0NoaChu3bqFS5cuwcbGBt26dQOgaOxOnz4dDRo0gIuLC2xsbBAdHY09e/aI+aZtxJUsWRJaWlriX90nT56MO3fuQEtLCw0aNEDVqlXz9N7kRP/+/XH48GH8+eefYh2dnZ3RsWNHODs7Iy4uDn5+fjh58iQsLS3FuedHjRqF33//HYIg4OPHj6hWrRq+++47REVFKU1JYm5uLmnkUKr0ERW1a9dG/fr1cfPmTTx9+jTL/WrWrAlbW1vUrVsXtra2MDY2xp07d5QafVlFnKT1448/ivMuvnjxAtWqVUOHDh0QGBio1GkoV64cWrduncva5Y/Fixfju+++A4AclSFtZyY8PBytW7eGt7c3Ll26lO18uOlvY/7uu+/g7e0NuVyOHj165NscrQMGDEBAQAAAwN3dHdeuXUPjxo1x7do1hIaGomfPnjh16pRKo/uIiKhocnd3x+zZszFp0iQAiijjqlWromXLlqhSpQq0tLTg7++PyMjIPB8j/aBh9+7d0bVrV7x48QI7d+7MdB9NTU307NkTa9asAaCIzm/UqBHq16+Py5cv48yZM7kuR+/evfHTTz8hJCQESUlJqF27Njp37oyyZcsiPj4ejx8/xrlz5xAcHIy//vorR8/zyU5+txM2btwozll/4cIFpekzy5cvL87B37p1a5QvX1587tewYcNw/fp12NnZ4dChQ3j58qW436hRo8T/56WNd+HCBfj4+KBOnTpwdXWFra0tkpOTlfpo2traSgOldnZ2YhmWLFmC0NBQ6OnpwcvLK0PUeX7Q1NRE7969xcHgc+fOoVGjRqhXr16G97Eg6OjoYNu2bWjevDkSEhKQnJwMHx8frF69Gg0bNoShoSHevHmDs2fP4uHDh9i6dSvq1q0LT09PNG7cWLz2Fy5ciOfPn8Pd3R0nT55UmkpmxIgRuX5GUl5Uq1ZNbCvv3LkTb968gZ6eXrbt59z2H7OS1+u8ILm7u+PEiROIiYmBs7PzZ9vqXzLOYmdnJ/YFt23bBj09PRgZGcHZ2VkpkOlL3Lp1C5MnTxZfr169GiYmJmjbti0AxTXZvHlzyaa9IipQBfAQW6JMpX/Sd/onuL97907Q19cXt7u7u4tPjp8/f77SvqlLxYoVhSpVqoive/XqleXx0j9ZPKsnmCckJAje3t6ZHq9BgwZZPkk9KSlJ6Ny5c6b7pS6urq7CmzdvlMqR1Xvi7++f5bbP1S0raetcv379z6bP7zqlN3HixGzzBiA4OjqK6efNm/fZ9CtXrlQ6RocOHTJNt2jRohy9Z18iOjpa6Nu3b67qKAiCsGzZMkEul2eZ3sTEROnaEwRBqF+/fqafA0EQhK1btyrtn9NtqerWrZtpOVq1apXl50FHRyfbOjs5OQnh4eFi+uyumdGjR2ebl62trXDv3j2lfbL6fAuCIEyfPj3L9z4r6T+Pq1at+uw+ac9J2s9baGioYGtrm2ldevXqleVnOy4uTrCxscl0v+vXrwuCkLPzmb5saa+XzZs3i+u1tLQEX19fQRAE4fHjx0rfzwsXLszR+0ZERJSZFStWfLatkLqMHj1a3C+nbeAWLVrk6Hc2bZsjLCxMKFeuXKb7pe8DpD1u2jzTt68vX74slChR4rN1TNuGyi6/7Oqfk3ZCdtK2j7JbdHR0hJMnTyrt++DBA8He3j7b/YYPH57hmLlt4+3ZsydX14sgCMKoUaMyTTdkyBAxTVbXxOfaVVnt9+HDB6FChQqZHrdly5ZKr1++fPnZc5Mfzp49m2X7M6t6vH37VnBzc8s2fadOnYTExERxn7z2wT+3TRAE4dSpU4JMJstQBgsLC6F69eqZfm5y23/Mrp+Ql+s8r9dQdtJ+R1hYWHw2ffpzkvb7Ji/jLIKg+A7PbL/WrVuLaT53PjMrW+r1Eh0drfQZ6tSpk7hP2v61vb29EBYWlqP3jagw4zQ4VGiVLFkS/fr1E1/fv38fBw8eBACMHz8e//vf/1CuXDloaWnB2toa/fv3x/nz55VuM8wPWlpaOHnyJMaOHQs7Oztoa2ujfPnyWLJkCTZt2pTlfhoaGti7dy/27duHVq1awdLSEpqamjAxMUGNGjWwaNEiXL9+XelBsYWd1HWaO3cuLl++jO7du8PJyQk6OjrQ0tKCnZ0dmjVrhrlz5ypFMrVv3x7Tpk1DkyZNULp0aejr60NTUxM2NjZo3bo1Dh8+jGHDhikdY+PGjejVqxesrKwKJOIjLX19fWzatAm3bt3C0KFD4enpCVNTU2hoaMDExATVqlXD9OnTcfz4caX9Ro4ciWvXrqFHjx5wdHSEtrY29PT04OrqilGjRuHu3bsFGkFw+PBh9OvXDyVLloSOjg48PDywadMmpdtH01u7di369OkDDw8PlCxZEpqamjA0NISHhwfGjRuHa9euZXhGRFaWLFmCU6dOoVOnTrC1tYWWlhYMDQ3x1VdfYerUqfDz88u3hwoXBHNzc1y6dAkdO3aEsbEx9PT0UK1aNRw4cAC9e/fOcj8dHR0cO3YMzZo1g7Gxcb6X6+nTp0q3z06ePFl8XkS5cuWwYMECcduUKVNydMswERFRZoYPHw5/f3/MmDEDderUEdsKenp6KFWqFJo2bYoZM2bg5s2bWLJkSa7z/+233zBy5EjY2NhAW1sbZcuWxdy5c7F58+Ys9zEzM8OlS5fQv39/sc3j6emJrVu3Yvr06Xmqp7e3N+7fv4+pU6eiSpUqMDY2hoaGBkxNTVGlShUMHToUp06dQr169fKUf1pStRNkMhkMDAzg6uqKH374Abdu3ULTpk2V0ri6uuLOnTuYMWMGKleuDENDQ7GN3qFDB5w4cQIrVqzIkHdu23h16tTBnDlz0Lp1azg7O8PIyAiampooWbIkGjdujG3btmW4XubMmYMRI0bA3t5eaQomKZmamuLixYv44YcfYGlpKV5LO3bsQM+ePTOkLQgNGzbEkydPsG7dOrRu3Rp2dnbQ1dWFtrY2HB0d0blzZ+zbtw9du3YV97G2tsb169exZMkS1KpVCyYmJuL73aJFC/zyyy/Yv39/gT1brUmTJjh48CAqV64MbW1tWFhYwMfHB76+vkrTNqaVl/5jVvJ6nRdmeR1nGTJkCGbMmIEyZcpIcv5HjRqFR48eAVDcAbB27Vpx27Jly8Q7kV6/fo3+/fvn+/GJCppMEHI44TIREREREREREeVKbGxspg9K/uabb/Dbb78BAFxcXPDvv/8WdNGIiKiQ4Zz1REREREREREQSKV++PJo3b47q1avD1tYW7969w/79+3Hs2DExTdo7GomISH0xsp6IiIiIiIiISCKmpqaIiIjIcnv//v2xfv36zz4ElIiIij8O1hMRERERERERSWTBggU4fvw4Hj16hLCwMMjlctjY2KBmzZro27cvGjdurOoiEhFRIcHBeiIiIiIiIiIiIiIiFZOrugBEREREREREREREROqOg/VERERERERERERERCqmqeoCFGcpKSkIDAyEkZERHxRDRESkhgRBwMePH2Frawu5XPUxEnFxcUhISJD0GNra2tDV1ZX0GETqhH0KIiIi9aWO/QlAvfsUHKyXUGBgIBwcHFRdDCIiIlKxgIAA2Nvbq7QMcXFx0DOyAJJiJD2OtbU1/P391bZxTZTf2KcgIiKiwtKfKGmoh6hk6Y+lzn0KDtZLyMjICABQacyv0NDRV3FpKL0To+upughERPkmNqEAWkyUax8/RqJSudJim0CVEhISgKQY6Lj1AjS0pTlIcgKCHmxHQkKCWjasiaSQ+v1x42tdGGoxsr6wsVl9X9VFICLKN0JirKqLQOlEfoxCKfcahaY/EZUMjCqjAR0Jg/zjU4Blz4PUtk/BwXoJpd6mqqGjDw1dAxWXhtIzNjZWdRGIiPKNFgfrC7VCNXWFpi5kEg3WCzLV35pLVNykfn8YaslgxMH6QsfYWPWDJ0RE+UVI5DBhYVWY+hM6ckBXQ8ryCBLmXfixR0VEREREREREREREpGL8kxkRERGROpEBkCoyp/AE/BARERERkQRkMum6E6n5qzNG1hMRERERERERERERqRgj64mIiIjUiUyuWKTKm4iIiIiIii05pI3+VvcehbrXn4iIiIiIiIiIiIhI5RhZT0RERKROpJxkUt0nmCQiIiIiKuY4Z720GFlPRERERERERERERKRijKwnIiIiUiecs56IiIiIiPJI9t8iZf7qjD0qIiIiIiIiIiIiIiIVY2Q9ERERkTrhnPVERERERJRHnLNeWoysJyIiIiIiIiIiIiJSMUbWExEREakVCeesZxwIEREREVGxJoe0rX5171Goe/2JiIiIiIiIiIiIiFSOkfVERERE6oRz1hMRERERUR5xznppMbKeiIiIiIiIiIiIiEjFGFlPREREpE5kEs5ZL9lc+EREREREVBjI/lukzF+dsUdFRERERERERERERKRijKwnIiIiUiecs56IiIiIiPKIc9ZLi5H1REREREREREREREQqxsh6IiIiInXCOeuJiIiIiCiPOGe9tNijIiIiIiIiIiIiIiJSMUbWExEREakTzllPRERERER5JJcpFinzV2eMrCciIiIiIiIiIiIiUjFG1hMRERGpE85ZT0REREREecQ566XFHhURERERERERERERkYoxsp6IiIhInchkEkbWq3scDBERERFR8SblI7BS81dnjKwnIiIiIiIiIiIiIlIxRtYTERERqRO5TLFIlTcRERERERVbnLNeWoysJyIiIiIiIiIiIiJSMUbWExEREakTmVzCOesZB0JEREREVJzJZIKkN9TKZIJ0mRcB7FEREREREREREREREakYI+uJiIiI1IlMplikypuIiIiIiIotzlkvLUbWExERERERERERERGpGCPriYiIiNQJ56wnIiIiIqI8YmS9tNijIiIiIiIiIiIiIiJSMUbWExEREakTzllPRERERER5JGV3IjV/dcbIeiIiIiIiIiIiIiIiFWNkPREREZE64Zz1RERERESUR5yzXlrsURERERERERERERERqRgj64mIiIjUCeesJyIiIiKiPJLLFIuU+aszRtYTEREREREREREREakYI+uJiIiI1AnnrCciIiIiojzinPXSYo+KiIiIiIiIiIiIiEjFGFmvRvYNqgkbE70M6w/4vsbSU0+w6ruv4FXKTGnboVtvsPjEv+LrCtZGGNjAGeWtDQEADwI/Yu25p3j6LhoA4FXKFF2q2cPVxhgG2pp4/SEGu68F4NSDYAlrph4u33yKVTtP486jVwgKicTPi/qjdQNPcfvgGTux549rSvs0rumK/auGFHRR6T8b957Hqp/P4F1oJCq62GHB2M6o4l5a1cUi8Nyo0qqdpzBv3VH061wfs0Z2BAC8eB2CWf87hH/8niMhIQkNa7rip1GdUNLcWNzv2at3mP2/33H9rj8SE5PgWtYW4/q1Ru0qLqqqStHFOeuJ6AtYLrgEzRIOGdZHn92BiF1TAQBazpVh3GEstMp8BaQkIzHgAUKX9gAS4wEA5sM2QdPBDRrGFkiJjkT8w0uI3D8PKeHvAADa5WvCoGlfaDt9BZmeIZKD/RF1fANirx0qqGqqhYtbV+HMqrmo8W0/tBw7GwCQGB+Hk0tn4t7J35GUEI+ytRqg9cT5MLQoqeLSFn8vfK/iyo61CHzoh6iQYHRdsgWuDVuK2w9OH4E7R/Yq7eNcqwF6/G9PQReVAPzz61Zc3rEGUaHvYV3ODS3HzYF9RS9VF0ttXNq+FmfWLEKNrr3RYvQ0AEBSfDxOrJiD+6eOIikxAWVr1EWrcbOUvr/ePLiDM/9biMBH9yCTyWDn5okmQyfAupyrqqpSJEnZnUjNX50Vu8j63r17QyaTQSaTQUtLC1ZWVmjatCm2bNmClJSUHOeTnJyM+fPno0KFCtDT04O5uTlq1KiBTZs2SVh6afXf5ouvV10Wl5F7bgMA/nr8Xkxz+HagUpo1fz0Tt+lpaWBJV08ER8ZhwA5fDP75JmISkrCkiyc0/nv6Q0U7Ezx7F40pB++h15Z/cOxuEKa0cYW3s0WB1rU4iomNR8Vydlg0rmuWaRrXcsOjP+eKy6Y5fQqwhJTWgZO+mLL8IMb3a4lzO8ejoosdOg37H96HfVR10dQez43q3H74Ej//fgVuZW3FdTGx8fh21BrIIMO+lUPx+7qRSEhMRq9xG5V+t3uN24Dk5BTsWzkEx7eMgVtZO/QctwHvQiNVURUiKsbYn8heyOyvETSqqriELP4OABB74w8AioF6i5HbEX//AkJ++hrvZ3+N6DPbAUEQ84h/dBUf1g3Bu8mNELZmIDRLOsJ80Dpxu7ZzFSS9foSwNQPxfnpzxFzeB9N+S6Hj0ahgK1uMvbl/G76/7YSVi5vS+hNLpuPxxZPovGAD+mw8gI/vg/HrmL4qKqV6SYyLgVU5N7SeMDfLNGW9G+LHk3fE5Zt5awuwhJTq3onfcWLpDDQY8CN+2H0CVi5u+HnIt4gKC1F10dTCmwd34HtwD6zKVlBaf3z5bPx76Qw6z1uN3mv34GPIO+ydMFjcnhATjV0j+sDEyhb9thxEnw17oa1vgJ9H9EJyUmJBV4MoS8VusB4AWrRogbdv3+LFixf4888/0bBhQ4wYMQJt2rRBUlJSjvKYOXMmli1bhtmzZ+PBgwf466+/MGDAAISHh0tbeAmFxyYiLDpBXLzLWuD1hxjcehUupolLTFZKE5OQLG4rZaEPEz0tbL7oj4CwWPiHxGDr5RewMNSBtbEuAGDn1ZfYdNEf995EIjA8DvtuvMa152GoX56RGF+qaW13TBnUFm0aemaZRkdbE1YljMXF1Fi/AEtIaa3ZfRY923vD5+taqFDGBksndoO+rjZ+PnxV1UVTezw3qhEdE4+hM3di0fhuMDH69N30j58/AoLCsHyKD1ydbeHqbIsVU3xw51EALvk+AQCEhkfhecB7DO3eBG5l7VDGwRKTB7ZFbFwCHj1/q6oqFWHyT/PW5/dSPJuWpIbYn8haSlQYUiLfi4uuZ2MkBb9AwuO/AQAmXaci+sw2RP25FkmBT5Ac/BxxN/4AkhLEPKJPbUbi81tIDn2DxGe++HhsDbTKeAEaihu/o479Dx8PLUHiM18kv3+F6NNbEX/vPPSqtMy0TJQ78THR+G3yELSduhi6xibi+riPkbh5aA+aj56JMtXrwNbNE+1mLEPAnesI8PNVYYnVg0vtxmg8ZAJcG7XKMo2GtjaMSliKi56xacEVkERXd61H5Q4+8GrXDZZlyqPN5IXQ0tXDrd95l4PUEmKicWDaKLSdNFf5+ysqErcO70PzEZPhVNUbtq6V0G7qQgT4+eL13VsAgJCXzxAbGY4GP4xCCccysCxTDvX7DUd0WAgi3r5RVZWKJFkBLOqsWPaodHR0YG1tDTs7O1SuXBmTJk3C77//jj///BPbtm0DALx69Qrt2rWDoaEhjI2N0aVLFwQHf5qq5fDhwxg8eDA6d+4MJycneHp6om/fvhgzZoyKapW/NOUyNHO3wh9+QUrrm7pb4ejw2tjRtxp+qF8GOpqfLpFXYTEIj0lAG08baMpl0NaUo42HDfxDohEUEZflsQx1NBAZy79SFoRLvk/g0mwCqnWahdHzf0FYeJSqi6SWEhKTcPtRABpULy+uk8vlqF+9PK7f9VdhyYjnRnUmLdmHxrXcUK9aeaX1CYlJkMlk0Nb6NDOfjrYW5HIZ/vF7DgAwNzGAcylL7Dt+HTGx8UhKSsbO36+ghJkhPMpnnIqBipYLFy6gbdu2sLW1hUwmw6FDh5S2C4KAadOmwcbGBnp6emjSpAmePHmilCYsLAw+Pj4wNjaGqakp+vbti6go5d9APz8/1K1bF7q6unBwcMDChQszlGXfvn2oUKECdHV1UalSJRw7dizf60tFA/sTOaShBb2aHRBzSTE1h9zIAtrOlZESGYoSEw/AaukNWIz7Fdplq2aZhczABPo12yPhmS+QnPUfQmR6RkiJDs/vGqilY/MnolydxnCuUU9pfeBDP6QkJaJMjbriupJOLjCxtsNrvxsFXUzKxIsbV7GwcUWs6lAHR+eOR0x4mKqLpHaSEhMQ+NBP6XMil8tRpkZdvOYftSR3bNF0uNRuiDLV6yitf/vonuL7K836EqWdYWJti4B7NwEAFqXKQM/EDLcO70VyYgIS4+Jw6/BelChdFqY29gVaD8pfxa0/USwH6zPTqFEjeHp64sCBA0hJSUG7du0QFhaG8+fP49SpU3j+/Dm6dv00vYi1tTXOnj2L9+/fZ5Nr0VWvXAkY6mri2N1PEYmn7gdj9pGHGL7nNnZefYXm7laY1vbTbZGxCckYtvs2mrlb4cyY+jg1uh5qlDHHmL13kJzmtta0GlUoiQo2xkrHIWk09nbF2hk9cGjNMMwY1g5Xbj5F5xFrkZyc89u1KX+EhkchOTkFJc2NlNaXNDfmlB0qxnOjGodO38Tdf19j4sC2GbZVcS8NfV1tzFlzGDFxCYiJjces1YeQnJwinhOZTIZfVwzBvX9fw6XpeDg1GoMNv/yFXUsH8Q6ivEidZFKqJZeio6Ph6emJ//3vf5luX7hwIVauXIl169bh2rVrMDAwQPPmzREX9ylQwMfHB/fv38epU6dw9OhRXLhwAQMGDBC3R0ZGolmzZnB0dISvry8WLVqEGTNmYMOGDWKaK1eu4Ntvv0Xfvn1x69YttG/fHu3bt8e9e/dyXScqntifyEjXqxnk+saIubIPAKBRshQAwKjdSERf2IPQ5b2Q+PIeLMbshoZlaaV9jb6ZAOs1D2Gz0g8aFnYIW9Uv6+NUbQ3t0h7iHwUo7+6eOIS3j+6i8bBJGbZFhb6DhpY29IxMlNYbWJREVGjxvY6LirLeDdFh9kr0WrcPTYZPxgvfq/h5mA9SkpM/vzPlm5jwMAjJyTA0V549wMC8JKJC36moVOrh3skjePv4HpoMHpdhW1Toe2hoaUPXyFhpvYF5CfH7S8fAEL3X7obf8d8xp54b5jWsiKd/X4DP8q2Qa/KRnrkhl0m/5EZx60+o1dVYoUIF+Pn54cyZM7h79y78/f3h4KCIyNuxYwfc3d1x/fp1VKtWDUuXLsU333wDa2truLu7w9vbG+3atUPLllnfehkfH4/4+HjxdWRk4R34ae1hi2vPwxAa9el21MN3Pg2oP38fjdDoeKz81gu2proIDI+DtqYcE1tVwN3XEZjx+wNoyGXoVt0Bizp7oN92XyQkKQ8Ke5UyxcRWrlh4/BH8Q2IKrG7qqlOzTxFL7mXt4F7WDl4dZuCS7xPUr14+mz2JiKTzJvgDpi3/Db8sHwxdHa0M2y3MDLF+dh9MXLwXm/dfgFwuQ/smlVGpvD3k/w38CoKASUv2oYSZIQ6uGQ5dHS3sOXIVvcdtwLFNP8KqhEmGfKnoaNmyZZbtK0EQsHz5ckyZMgXt2rUDoGizWVlZ4dChQ+jWrRsePnyI48eP4/r166haVfFbuGrVKrRq1QqLFy+Gra0tdu3ahYSEBGzZsgXa2tpwd3fH7du3sXTpUrERvmLFCrRo0QJjx44FAMyePRunTp3C6tWrsW7dukzLR+pH6v4EULT6FPp1uyL+7jnxwbCK6bCA6PO7EHtZMYAf+eo+dFxrQ79OF3w88CkCLfr4esRc/BWaFnYw/HokzPotQ9iKjM9b0i5fC6bfL0b49glICnySYTvlXETQGxxfNBU91vwKLR1dVReHcqlS8/bi/61cXGHl4oaVX9fEixtXlKK8iYqjiOBAHF86Cz1W7YCmjk6e8kiMi8PhORNQyqMKOs1eDiE5BVd2bcTu0X3Rf+shaOnye7GoKm79CbWJrAcUJ0gmk+Hhw4dwcHAQG9YA4ObmBlNTUzx8+FB8fe/ePfz999/4/vvv8e7dO7Rt2xb9+mUd8TFv3jyYmJiIS9r8CxMrYx1ULW2GI3eyj3Z/EKjoGNibKaIWm7pZwdpEF3P/eIRHQR9xPzASMw8/gI2JHuq6lFDa9ysHUyz4phJWnXmC4/eCM+RN0ittXwIWpoZ4/ppRMAXNwtQQGhryDA8sfR8WCUsL4yz2ooLAc1Pw/B4HIORDFJp/vxgO9UbBod4oXL31FJv3X4BDvVFITk5BgxoVcHXfNPgd/Qn3/piDVdN6IOh9BErZKh5Ofsn3X5y+ch9rZ/VGdY8y8CjvgHljukBXRwt7//xHxTUsgmQy6easz0NkfXb8/f0RFBSEJk2aiOtMTExQo0YNXL2qeM7E1atXYWpqKjasAaBJkyaQy+W4du2amKZevXrQ1tYW0zRv3hyPHz/Ghw8fxDRpj5OaJvU4RID0/Qmg6PQpNCzsoONWBzEXfxHXpUQoBu2TAp8qpU16+xQaFnZK61KiPiA52B/xDy7hw/qh0PVoBC3nykpptMvVgPnwzYj8ZRZirx6QqCbqI/ChH6LDQrDepxlmVrPHzGr2eOl7Fdd+2YyZ1exhaF4SyYkJiP0YobRfdOh7GFrwGWSFjbm9I/RNzREWwKkcC5K+qTlkGhqIClPuZ0eHvYehhaWKSlX8vX10D9EfQrG+19eY5e2CWd4ueHnzGq7t3Y5Z3i4wMC+B5MQExH1U/gN3dFiI+P119+TvCA98jXZTF8LOzRP2lbzQafZyhAcG4PGFU6qoVpFVUHPWR0ZGKi1pgxlyqij2J9RqsP7hw4dwcnLKcXq5XI5q1aph5MiROHDgALZt24bNmzfD3z/zH8OJEyciIiJCXAICAvKr6PmqtYcNPsQk4OrT0GzTuVgqpokIjVJ8GHS15EgRBKSd8EYQAAGC0i0qXqVMsbBzJaw791wpWp8K1pvgDwiLiIYVByALnLaWJr6q4IDz1x+L61JSUnDh+r+oVinn30GU/3huCl7dKuVwdud4nNo2Vlw8KzigY7MqOLVtLDQ0PjVFLEwNYWKkj0u+/yLkQxSa1akIAIiNUzz3RJ5uIFgmk0NIyXwaNlKt/GhYA0BQkOLZOlZWVkrrraysxG1BQUGwtFTuHGtqasLc3FwpTWZ5pD1GVmlStxMB0vcngKLTp9Cv3RkpkaGI8zsrrksOCUDyhyBoWpdRSqtpXQbJoa+zzEv2X0S+TPNTB1i7fE2Yj9iKyP3zEXOBD23MD2Wq18WgvX9h4J7T4mLr5gmPlh3F/8s1teD/z0Vxn5AXTxER9Ab2Hlk/d4BUIyI4EDERH2BY0urziSnfaGppw9bVA/7/XBLXpaSk4Pk/l2DvUUWFJSvenKp6Y9DuPzFw51FxsXWtBI/m7cT/yzW18Pz6ZXGfkJfPEREUCIeKij8EJ8bFQSZXDi6R/RdsIgicPrgwcnBwUApgmDdvXq7zKIr9CbWZBufs2bO4e/cuRo0aBXt7ewQEBCAgIECMVHnw4AHCw8Ph5uaWZR6p26KjozPdrqOjA5083o5TUGQAWlWywfG7QUrzzNua6qKpmxX+fhaKiLgkOJc0wPDGLrj1KhzP3ivqe93/AwY3dMaPzcphv+9ryGWAT01HJKcIuPkyHMB/A/XfeGDfjdc49/g9zA0UDe7E5BR8jMv6gVH0eVEx8fAP+PTX+5eBobj7+DVMTfRhZmyABRuP4etGX8HKwhj+r0MwfdUhlHEogca1XFVYavU1+LtGGDxzJ7xcS6Gye2ms3fMXomPj4dO2pqqLpvZ4bgqWoYEuKpSxVVqnr6cDM2MDcf0vf/wNF0drWJgawve+P6YtP4ABXeujrKOioVOlYmmYGOljxE8/Y1SfFtDV0cKuw1cR8DYUjb3dC7xO9HnpI4GnT5+OGTNmqKYwRPmkIPoTQNHoU0Amg16dzoi5sh9IUZ4vO+r4ehi1G4XEgIdIDLgPfe9voGntjJg1AwEAWk5fQcvJEwlPrkOIiYBGSUcYt/8RScEvkPBM8RBA7fK1YD5iC6JPb0Gc75+QGyuiIoXkBAjRylHflHM6BoawKltBaZ2Wnj70TMzE9ZXbf4sTS2ZAz9gMOgaGOLZwCuw9qsKBg5CSi4+JVoqSD3/zCm8f34OesSn0TMxwfv0SuDZuDcMSlvgQ8AKnVsyGuYMTytZqoLpCq6laPj/g4PQRsHXzhJ37V/h790YkxsbA6+tuqi5asaVjYAhLZ+XpfRXfX6bieq+vO+PkijnQMzaFjoEh/lwyE/aVKsO+khcAwLl6HZxaNQ/HFk1D9c69IAgpuLx9HeQaGihdpVaB14k+LyAgAMbGnwJQC337KJ8Uy8H6+Ph4BAUFITk5GcHBwTh+/DjmzZuHNm3aoGfPnpDL5ahUqRJ8fHywfPlyJCUlYfDgwahfv754y8M333yD2rVrw9vbG9bW1vD398fEiRNRrlw5VKhQ4TMlKLyqljaDtYku/vBTjnhPShZQtbQ5ulRzgK6WHO8i43Hu8Xtsv/JCTPMqLAbj99/F97VLY12PyhAE4N/gKIzZ64fQaMXc9y0rWUNPWwM9vR3R09tR3PfWqw8Ytvt2QVSx2Lr98CXaDlwpvp68THEr8Leta2DJhK548PQNfvnjGiI+xsK6pAka1aiASQPbQEc74xzRJL2OzaogJDwKc9f/gXehH1GpnB32rxzCqVYKAZ6bwufZq3eYt+4owiNj4GBjjuG9mmFA1wbidgtTQ+xeMhDzN/yBLsNXIzEpGeWdbLB1fj+4u9hlnTFlLnXKGqnyRv41rK2trQEAwcHBsLGxEdcHBwfjq6++EtO8e6f8QLekpCSEhYWJ+1tbWyM4WHlavtTXn0uTup3UC/sT2dNxqwNNC/tMH/gafXoLZFo6MOk2FTIDUyQFPEToEh8kv38FABASYqFXuQWM242CTEcPyeHvEX/vHD4eXQUkKfoU+rU7Qa6jD6PWQ2HUeqiYd/yjqwhdxMEwKTX/cSZkMjl+HdsPyQnxcK7VAK0nzld1sdRC4IM72D6gk/j6xNIZAADPtl3QZuJ8BD95gNtH9yLuYySMSlrBuWZ9NBo8Hpra6jF4VZhUbN4O0R9C8dfahYgKfQ/r8u7ovno3p4tSsRYjp+KETI69EwcjOSEBzjXrovW42eL2EqWd8e3ijTi/aSU29+sEmVwOm3Ju6L58G4xKcAqj3JDJ8n32ywz5A4CxsbFSnyIvimJ/QiYIQrG6f7x3797Yvn07AMUtC2ZmZvD09MR3332HXr16QS5XdCJfvXqFYcOG4cyZM5DL5WjRogVWrVol3q6wceNG7NmzB/fu3UNERASsra3RqFEjzJgxA46OjlkeP63IyEiYmJjgq8lHoKFrIE2FKc8uTWio6iIQEeWb2ITkzyeiAhcZGYnSNuaIiIj44oZmfpTFxMQEOs0XQ6alJ8kxhMRYxJ8Yk+f6ymQyHDx4EO3bt1fkJwiwtbXFmDFj8OOPPwJQ1MPS0hLbtm0THwjl5uaGGzduoEoVReTnyZMn0aJFC7x+/Rq2trZYu3YtJk+ejODgYGhpKf6IPWnSJBw4cACPHj0CAHTt2hUxMTE4cuSIWB5vb294eHjwAbNqpjD1J4BPn91HnfRgpCVhz5jyxHbzS1UXgYgo3wiJsaouAqUTGfkRpqXcC1V/Yq2XHHoa0rVJYpMFDLqVkqc6F4f+RLEbrC9MOFhfuHGwnoiKEw7WF06FcrC+xRJpB+uP/5ir+kZFReHpU8XDKL28vLB06VI0bNgQ5ubmKFWqFBYsWID58+dj+/btcHJywtSpU+Hn54cHDx5AV1cXANCyZUsEBwdj3bp1SExMRJ8+fVC1alXs3r0bABAREYHy5cujWbNmGD9+PO7du4fvv/8ey5Ytw4ABAwAAV65cQf369TF//ny0bt0av/zyC+bOnYubN2+iYsWKErxbRDnDwfrCjYP1RFSccLC+8CmMg/XrCmCwfmAuBuuLW3+iWE6DQ0RERERFw40bN9Cw4ac/oI8ePRoA0KtXL2zbtg3jxo1DdHQ0BgwYgPDwcNSpUwfHjx8XG9YAsGvXLgwdOhSNGzeGXC5Hp06dsHLlp6njTExMcPLkSQwZMgRVqlRBiRIlMG3aNLFhDSiiXnbv3o0pU6Zg0qRJcHFxwaFDhzhQT0RERERUiBW3/gQj6yXEyPrCjZH1RFScMLK+cCqUkfUtl0kbWf/nqEJRX6LigpH1hRsj64moOGFkfeFTGCPr11eWPrL+h5t5mwanOJDo6WJERERERERERERERJRTnAaHiIiISJ3IZIpFqryJiIiIiKjYkkHa6G9171Ewsp6IiIiIiIiIiIiISMUYWU9ERESkTmRyxSJV3kREREREVGxJeaNuav7qjD0qIiIiIiIiIiIiIiIVY2Q9ERERkTrhnPVERERERJRHMkg7r7y69ygYWU9EREREREREREREpGKMrCciIiJSIzKZDDJG1hMRERERUR7IAcglbPare2S5utefiIiIiIiIiIiIiEjlGFlPREREpEYYWU9ERERERHnFOeulxch6IiIiIiIiIiIiIiIVY2Q9ERERkTqRMhRG3cNgiIiIiIiKOblM4jnr1bxPwch6IiIiIiIiIiIiIiIVY2Q9ERERkRrhnPVERERERJRXckgb/a3ukeXqXn8iIiIiIiIiIiIiIpVjZD0RERGRGmFkPRERERER5ZVMJm2zX927FIysJyIiIiIiIiIiIiJSMUbWExEREakRRtYTEREREVFecc56aal7/YmIiIiIiIiIiIiIVI6R9URERERqhJH1RERERESUV5yzXlqMrCciIiIiIiIiIiIiUjFG1hMRERGpE9l/i1R5ExERERFRsSWXCZBL2O6XywTpMi8CGFlPRERERERERERERKRijKwnIiIiUiOcs56IiIiIiPJKDmmjv9U9slzd609EREREREREREREpHKMrCciIiJSIzIZJIyslyZbIiIiIiIqHBT9CWnzV2eMrCciIiIiIiIiIiIiUjFG1hMRERGpERkknLOeofVERERERMWaDNJGf6t7j4KR9UREREREREREREREKsbIeiIiIiI1IpNJGFmv7hNMEhEREREVc5yzXlqMrCciIiIiIiIiIiIiUjFG1hMRERGpExmkmwhSzaNgiIiIiIiKOzmkjf5W98hyda8/EREREREREREREZHKMbKeiIiISJ1IOGe9oO4TTBIRERERFXNymWKRMn91xsh6IiIiIiIiIiIiIiIVY2Q9ERERkRqRSRhZL1W+RERERERUOMhkikXK/NUZI+uJiIiIiIiIiIiIiFSMkfVEREREaoSR9URERERElFdySBv9re6R5epefyIiIiIiIiIiIiIilWNkPREREZE6kf23SJU3EREREREVW5yzXlqMrCciIiIiIiIiIiIiUjFG1hMRERGpEc5ZT0REREREecU566Wl7vUnIiIiIiIiIiIiIlI5RtYXgOOj6sLY2FjVxaB0nr+LVnURKAtlLA1UXQSiIkdLgxHNhVFhPC+MrCcqmqxX3YOxsZGqi0HppAT5qboIlAW5tYeqi0BU9Ghoq7oElF4hPCdymWKRMn91xsh6IiIiIiIiIiIiIiIVY2Q9ERERkRphZD0REREREeWV7L9FyvzVGSPriYiIiIiIiIiIiIhUjJH1RERERGqEkfVERERERJRXnLNeWoysJyIiIiIiIiIiIiJSMUbWExEREakTKSeZVPMoGCIiIiIidcBmv3QYWU9EREREREREREREpGKMrCciIiJSI5yznoiIiIiI8opz1kuLkfVERERERERERERERCrGyHoiIiIiNcLIeiIiIiIiyiu5TJA4sl6QLvMigJH1REREREREREREREQqxsh6IiIiIjXCyHoiIiIiIsor2X+LlPmrM0bWExERERERERERERGpGCPriYiIiNSJlKEw6h4GQ0RERERUzMllkHjOeunyLgoYWU9EREREREREREREpGKMrCciIiJSI5yznoiIiIiI8opz1kuLkfVERERERERERERERCrGyHoiIiIiNcLIeiIiIiIiyivOWS8tRtYTEREREREREREREakYI+uJiIiI1IgMEkbWq/0Mk0RERERExZsc0kZ/q3tkubrXn4iIiIiIiIiIiIhI5RhZT0RERKRGOGc9ERERERHllUymWKTMX50xsp6IiIiIiIiIiIiISMUYWU9ERESkTmT/LVLlTURERERExZZcplikzF+dMbKeiIiIiIiIiIiIiEjFGFlPREREpEY4Zz0REREREeWVlDfqpuavzhhZT0RERERERERERESkYoysJyIiIlIjjKwnIiIiIqK8kkG6/oQifwAQJMu/sGNkPRERERERERERERGRijGynoiIiEiNyGSKRaq8iYiIiIioGJOwP5GavzpjZD0RERERERERERERkYoxsp6IiIhIjSgi66Was16SbImIiIiIqLCQ8lZd4L/Ies5ZT0REREREREREREREKsLIeiIiIiJ1ImUgDCPriYiIiIiKNckD69W8T8HIeiIiIiIiIiIiIiIiFWNkPREREZEakclkEs5Zr+ZhMERERERExZyU/QlF/pJlXSQwsp6IiIiIiIiIiIiISMU4WE9ERESkRlLnmJRqISIiIiKi4is1sl7KJTeSk5MxdepUODk5QU9PD87Ozpg9ezYEQRDTCIKAadOmwcbGBnp6emjSpAmePHmilE9YWBh8fHxgbGwMU1NT9O3bF1FRUUpp/Pz8ULduXejq6sLBwQELFy7M+xuZBQ7WExEREZFKFLeGNRERERERFawFCxZg7dq1WL16NR4+fIgFCxZg4cKFWLVqlZhm4cKFWLlyJdatW4dr167BwMAAzZs3R1xcnJjGx8cH9+/fx6lTp3D06FFcuHABAwYMELdHRkaiWbNmcHR0hK+vLxYtWoQZM2Zgw4YN+VofzllPREREpEbkchnkcmlC4IVc5pvasN6+fTvc3d1x48YN9OnTByYmJhg+fDiATw3r7du3w8nJCVOnTkXz5s3x4MED6OrqAlA0rN++fYtTp04hMTERffr0wYABA7B7924AnxrWTZo0wbp163D37l18//33MDU1VWqAExERERHRZ8ghbfh3LvO+cuUK2rVrh9atWwMASpcujT179uCff/4BoAj+Wb58OaZMmYJ27doBAHbs2AErKyscOnQI3bp1w8OHD3H8+HFcv34dVatWBQCsWrUKrVq1wuLFi2Fra4tdu3YhISEBW7Zsgba2Ntzd3XH79m0sXbo0X/sUjKwnIiIiIpVI27AuXbo0vvnmGzRr1izLhrWHhwd27NiBwMBAHDp0CADEhvWmTZtQo0YN1KlTB6tWrcIvv/yCwMBAAFBqWLu7u6Nbt24YPnw4li5dqqqqExERERFRPvD29saZM2fw77//AgDu3LmDS5cuoWXLlgAAf39/BAUFoUmTJuI+JiYmqFGjBq5evQoAuHr1KkxNTcWBegBo0qQJ5HI5rl27JqapV68etLW1xTTNmzfH48eP8eHDh3yrDwfriYiIiNRIQcxZHxkZqbTEx8dnWpbi1rAmIiIiIiruCmrO+pz2KSZMmIBu3bqhQoUK0NLSgpeXF0aOHAkfHx8AQFBQEADAyspKaT8rKytxW1BQECwtLZW2a2pqwtzcXClNZnmkPUZ+4GA9EREREeUrBwcHmJiYiMu8efMyTVfcGtZERERERJQ/ctqn2Lt3L3bt2oXdu3fj5s2b2L59OxYvXozt27cXcInzB+esJyIiIlIjaaNVpMgbAAICAmBsbCyu19HRyTR92oZ16pyPI0eOhK2tLXr16iVJGYmIiIiIKO/S3lErVf5AzvsUY8eOFYOAAKBSpUp4+fIl5s2bh169esHa2hoAEBwcDBsbG3G/4OBgfPXVVwAAa2trvHv3TinfpKQkhIWFiftbW1sjODhYKU3q69Q0+YGR9URERESUr4yNjZWWnDSsK1WqhB49emDUqFFi1EzahnVawcHBSo3mwtKwJiIiIiKi/JHTPkVMTAzkcuUhbg0NDaSkpAAAnJycYG1tjTNnzojbIyMjce3aNdSqVQsAUKtWLYSHh8PX11dMc/bsWaSkpKBGjRpimgsXLiAxMVFMc+rUKZQvXx5mZmb5U2moaWR97969ER4eLj6YjD6Zv+EYFm76U2mdi6Mlru2big8R0Zi/4Rj+uvYIr4M/wMLUEK3re2DSwNYwNtRTUYmLh5v3nmPHbxfw8OlrhIR9xOIpPdGwlru4PSY2Hqu2/YlzV+8j4mMMbK3M0e3r2vimVU0xTUjYR6zY8geu3XqC6Nh4ONqXRN+ujdC4diUAQGBwGDbtOYPrfs8Q+uEjSpgbo1VDL/Tt2ghaWmr5VSC5jXvPY9XPZ/AuNBIVXeywYGxnVHEvrepiEXhuCtry7Sfxxzk/PHkZDD0dLVSr5IRpQ75GWUfFNCQfIqKxYOOfOPfPI7z57/elZb1KmPjDp9+XsIhoDJy+HQ+eBuJDRDRKmBmhRb1KmDKoDYwM+BuUG1JGwuQ239w0rFOjXlIb1oMGDQKg3LCuUqUKgMwb1pMnT0ZiYiK0tLQASNOwJvXCPkXmzq1fjPMblB/ebOHojKEHLgIAfA/8jLvHD+Lto7tIiI7C+HMPoWtkooqiFjsv797Fld/24e3TJ4gKC0OXKdNRwdtb3B714QPObN2MZzd9ERcdDceKFdFi4BBY2NmJabaPH4uXd/2U8q3SshVaDxuhtO72qZP4++ABhL55DR19fbjVqYdWQ4ZKW0E19M+vW3F5xxpEhb6HdTk3tBw3B/YVvVRdLALPjSq8vPk3ruxch8CHdxEVEoyuizehQoMW4vZDM0bhztF9Svs416qP7qt2ia9jIz7gz0VT8fjiachkcrg2aomWY2ZBW9+gwOpRHEh5p64i/9ylb9u2LebMmYNSpUrB3d0dt27dwtKlS/H999//l58MI0eOxE8//QQXFxc4OTlh6tSpsLW1Rfv27QEArq6uaNGiBfr3749169YhMTERQ4cORbdu3WBrawsA+O677zBz5kz07dsX48ePx71797BixQosW7YsP6tf+Afre/fuLc4xpKWlhVKlSqFnz56YNGkSNDULffGLpAplbHBw9aeGlqamohP9NiQCb0MiMGtEe5R3skbA2zD8OP9XvA2JwPb5fVVV3GIhNi4B5Zxs8HXTqhg7Z2eG7Us3HsV1v2eYPaYbbK3M8PfNJ5i/5hBKmhujfk03AMC0pb8iKjoWS6f1hqmxPo6fv40J83dh5/JhqOBshxcB75EiCJg0tCMcbCzw7GUwflr1G2LjEjCqX5uCrnKxd+CkL6YsP4ilE7qiSsXSWLfnL3Qa9j9c3z8NJc2NVF08tcZzU/Cu3HqK7zvVhZdbKSQlp2DO2iPoPGINLu2ZBAM9HQSFRCAoJAIzh7VDOSdrvA76gDELfkVQSAS2zlP8vshlMrSsWwmTfmgDC1ND+L9+j/GL92FMZAzWz+J0KUVVcWtYU+HFPkXBKulcHj3X/Cq+lmtoiP9PjItF2VoNULZWA5xZnfncs5Q3CXFxsHIqA69mzbH3p1lK2wRBwK+zZ0JDQwNdp82Ajr4+/j54AD9PmoBB6zdCW1dXTFu5RUs06N5TfK2lqxzJePXAb/j74G9o8n0/2FWogMS4OISnu3uJvty9E7/jxNIZaDNpAewqeeHvXRvx85BvMfTgJRial1B18dQaz41qJMTGwMrFDV993RV7x/bPNE1Z7wZoN+3TH4w1tLWVth+YOgwfQ96hx/92IyUpCb/PHI0jc8ah05z/SVp2ktaqVaswdepUDB48GO/evYOtrS1++OEHTJs2TUwzbtw4REdHY8CAAQgPD0edOnVw/Phx6Kb5/du1axeGDh2Kxo0bQy6Xo1OnTli5cqW43cTEBCdPnsSQIUNQpUoVlChRAtOmTcOAAQPytT5FomXaokULbN26FfHx8Th27BiGDBkCLS0tTJw4USldQkICtNN9ECn3NDXksCphnGG9m7MtdizoJ752si+JyYPaYuD0HUhKSoampkaGfShnaletgNpVK2S53e/RS7RpXBlVPZwBAB1b1sBvf17D/X8DxMF6v4cvMXFIB1Qs7wAA6NetMXYfuoSHT9+ggrMdvKuWh3fV8mKe9jYWePnmPfb/8TcH6yWwZvdZ9GzvDZ+vFbdULZ3YDScv38fPh69iVO9mKi6deuO5KXh7lw9Wer1qqg9cW07GnUcB8PYqC1dnW2xL80dfJ/uSmDSwDQbP+PT7Ymqsjz6d6oppHGzM0adjXfxv1xlQ7hTEnPU5Vdwa1lS4sU9RcOQaGjAsYZnptprfKQZYXty4UpBFUgsu1arBpVq1TLeFvXmDN48eYuDa9bB0LA0AaD1kGJb4dMO9c3+hcouWYlotHR0Ymptnmk/sx4/4a+d2dJs+E2W++hRFbOVUJv8qQgCAq7vWo3IHH3i1U8zB3GbyQjy5dAa3ft+Dun2Gqbh06o3nRjVcajeCS+1G2abR0NLJ8vfnvf8TPL1yDv13/AFbN08AQMuxs7FrRE80GzkVRiU5NWKOyf5bpMw/F4yMjLB8+XIsX7486yxlMsyaNQuzZs3KMo25uTl2796d7bE8PDxw8eLF3BUwl4rEnPU6OjqwtraGo6MjBg0ahCZNmuDw4cPo3bs32rdvjzlz5sDW1hblyysGIu/evYtGjRpBT08PFhYWGDBgAKKiojLkO3PmTJQsWRLGxsYYOHAgEhISxG0pKSmYN28enJycoKenB09PT+zfv7/A6qxKzwPew63VZHi1n4EBU7fjdVBYlmkjo2JhZKDLgXqJeVRwxIVrD/EuJAKCIOD6nWd4FfgeNSu7fErj6oiTF+4g4mMMUlJScOL8bcQnJKJqpawbzlHRcTA24vQR+S0hMQm3HwWgQfVPfxyRy+WoX708rt/1V2HJiOemcIiMigMAmBnrZ5Mm+9+XoPcR+OPcHXh7lZWkjFQwUhvWL1++RGxsLJ49e4affvpJaaA0tWEdFBSEuLg4nD59GuXKlVPKJ7Vh/fHjR0RERGDLli0wNDRUSpPasI6Li8Pr168xfvz4AqkjFR7sUxScsFf+WNLcCyu+rokDk4cg4u1rVRdJ7SX9N7+uZtrvV7kcmlpaCHhwXynt3b/+wqJunbF20ACc2boFiXFx4rbnt25CSEnBx9AQrPmhH5b18MH+uT8h4r3ys0PoyyQlJiDwoR/K1PgUqCCXy1GmRl289vPNZk+SGs9N4fbC9yoWNfXE6o71cHTeRMSEfxC3vfbzha6RiThQDwBlqteFTC7H63u3VFFcokwVicj69PT09BAaGgoAOHPmDIyNjXHq1CkAQHR0NJo3b45atWrh+vXrePfuHfr164ehQ4di27ZtYh5nzpyBrq4uzp07hxcvXqBPnz6wsLDAnDlzAADz5s3Dzz//jHXr1sHFxQUXLlxA9+7dUbJkSdSvX7/A61xQqlR0xOpp3eHiaImgkEgs3PQnWg1Yjst7JsHIQFcpbWh4FBZvOY5e7b2zyI3yy7hB7fDTqt/QstdcaGjIIZfJMGV4J1Su+GkgfsEEH0xYsAuNus2EhoYcujpaWDylJxxsM78NLyAwBL8cuYyRfVsXVDXURmh4FJKTUzJMqVLS3BhPXvAWYVXiuVG9lJQUTFl+ANU9ysDV2TbTNKHhUVi69QR6tKudYduAqdtw/MJdxMYnonmdilg26Vupi1zsFKbIeiJVYp9CGnYVK6PdjOUoUdoZH9+/w/mNS7C1XwcM2vsXdAwMP58BSaKEgwNMSlri7NYtaD1sBLR1dfH3oQOIDAnBx7BPwVkVGzSEiaUljMwt8O6FP05v2YzQN6/RZYrijqcPQUEQBAGXfv0FzX8YBF0DA/y1Yxt+njwRA/+3Dhr/PReEvkxMeBiE5GQYmpdUWm9gXhIhL56qqFQE8NwUZmVrNYBrw5YwtXPAh9cvceZ/C7BreHf03XoYcg0NRIW+h4GZhdI+ck1N6BmbIir0vYpKXTQVtjnri5siNVgvCALOnDmDEydOYNiwYXj//j0MDAywadMmMQJr48aNiIuLw44dO2BgoHhAxOrVq9G2bVssWLAAVlaKh9lpa2tjy5Yt0NfXh7u7O2bNmoWxY8di9uzZSExMxNy5c3H69GnxqcBlypTBpUuXsH79+iwb1vHx8YiPjxdfR0ZGSvl2SKKp96eHmrq72KFqRUd4fD0dh07fQo92tcRtkVGx6DpqHco7WWP8gFaqKKpa+eXwZdx79ArLpvWCjaUZbt7zx4K1ijnra3gpouvX7jyJj1FxWDunP0yN9XHu7/uYMH8XNi0cCJfSNkr5vQuJwNBpW9Ckjgc6tqihiioRkZoav2gfHj17i6MbRmS6/WN0LL4bvR7lSltjXP+WGbbPHtkRY/u2xLNX7/DT2iOYtuIgFo7rInWxiagYYZ9CWmmnKLBycYN9JS8sb10d908dRuX236mwZOpNQ1MTnadMw5EVS7Go6zeQyeUo4+WFslWrQRAEMV2Vlp/6dlZOTjA0M8fOSeMR9jYQ5ja2EIQUpCQlocXAwXCurHiod8fxE7HU51v4+91B2SpVC7xuREQAULF5O/H/VmVdYVXWFSvb18YL36soU72OCktGlDtFYrD+6NGjMDQ0RGJiIlJSUvDdd99hxowZGDJkCCpVqqR0q/TDhw/h6ekpNqoBoHbt2khJScHjx4/FhrWnpyf09T/dfl+rVi1ERUUhICAAUVFRiImJQdOmTZXKkZCQAC+vrJ/uPW/ePMycOTO/ql0omBjpo2wpS/i//vRXxo/Rceg8Yi2M9HWwc2F/aHEKHEnFxSfifztOYPHkHqhb3RUA4OJkg8fPA7HzwAXU8HJBwNtQ/Hr0CvauGQVnR8U8a+XK2OLWvRfYd/QqJg3tKOb3PjQSP0zcAE9XR0wZ1jHTY9KXsTA1hIaGHO/DPiqtfx8WCUuLjM+DoILDc6Na4xfvw8nL93F43QjYWppl2B4VHYeuI9fCUF8H2xf0y/T3xcrCGFYWxnApbQVTY320HbgCo79vDusSJgVRhWJBJpMuWkXdo2CocGOfQjV0jUxg4VgGYQEvVF0UtWfr4oIfVq9FXHQ0kpMSYWBiik0jh8PWpVyW+9hVUDxXKyxQMVhvZKaYy75kqVJiGgMTU+gbGyOSU+HkG31Tc8g0NBAVphztGx32HoYWmc/HTQWD56boMLN3hL6pOcICXqBM9TowtCiJ6A+hSmlSkpIQGxkOQ4uSWeRCmZGyP5GavzorEnPWN2zYELdv38aTJ08QGxuL7du3iw3ntA3o/JI6F+Uff/yB27dvi8uDBw+ynWNy4sSJiIiIEJeAgIB8L1tBi4qJh/+bEPGBs5FRseg07H/Q1tLAriU/QFeHtzlKLSk5GUlJyZDLlb+tNOQypPwXBRMXr5gbVZ7uG02uIUNKyqdImXchERgwYT1cy9ph+sjOkMuLxFdAkaOtpYmvKjjg/PXH4rqUlBRcuP4vqlVyUmHJiOdGNQRBwPjF+3DsvB8OrB4KR1uLDGk+Rsei84g10NLUxM7FA3L0+5L6HZiQkJTvZSai4od9CtVIiIlG2OuXMMrigX9U8HQNDGBgYorQN2/w9ukTlK9VK8u0Qc+eAQCM/nvgrIOb4k7skNefnkMQ+zESMZGRMLG0krDU6kVTSxu2rh7w/+eSuC4lJQXP/7kEe48qKiwZ8dwUHZHBgYiJ+CD+/th7VEHcxwgEPvQT0/jfuAwhJQX2FbP+IzpRQSsSkfUGBgYoWzZnD5BzdXXFtm3bEB0dLTa6L1++DLlcLj4sCgDu3LmD2NhY6OkpHq75999/w9DQEA4ODjA3N4eOjg5evXqVq7kkdXR0oKOjk4uaFT5TVxxEi7oV4WBtjrchEZi/4Rg05HJ0alZFMVA/fA1i4xKwflZPfIyKw8f/HhJYwkwRrUp5ExMbj4DAT3/hDQwKw+NngTA20oONpRmqVCqDFVuOQUdbCzaWZvC9+xx/nL2JUf3aAABK21vCwdYCc1YfxMi+rWFirI9zV+/j2q2nWD69N4D/BuonrodNSTOM7NsaHyKixeOVSDd/N325wd81wuCZO+HlWgqV3Utj7Z6/EB0bD5+2NVVdNLXHc1Pwxi/ah99O+mLHwn4wNNBFcKhiSgdjA13o6WorBuqHr0FsXCLWzOiBj9Fx+Bj93+/Lf3dDnLpyH+/DPsLLtRQM9HTwyD8IM1cdQnWPMiiVyeA/ZU0GCeesh5qHwVChxj5FwTi5bCbK1WsGUxt7fHwfhHPrF0Mul6Niiw4AgKiQd4gKfYewAMWD3YOfPoKOvgFMrO2gZ5LxrivKuYTYWIQFBoqvw4ODEPTsGfSMjGBiaYkHFy9A38QEJiUt8e6FP46vX4fyNWuJ09mEvQ3Evb/+Qtlq1aFvbIRgf3+c3LAepSpWgpWT4llZFvb2KF+zFk6sX4s2w0ZAR98AZ7ZtQQl7e5T28My0XJQ3tXx+wMHpI2Dr5gk796/w9+6NSIyNgdfX3VRdNLXHc6MaCTHRSndpfXgTgKDH96FnYgo9Y1Oc27gUbo1awdDCEmGvX+L0yjkwdygN51qK3+CSTi4o690AR34ahzYT5yE5KQnHFk5BxWZfw6iktYpqVTRxznppFYnB+tzw8fHB9OnT0atXL8yYMQPv37/HsGHD0KNHD/F2VUBx+2nfvn0xZcoUvHjxAtOnT8fQoUMhl8thZGSEMWPGYNSoUUhJSUGdOnUQERGBy5cvw9jYGL169VJhDaUV+C4c/adsQ1hEDCzMDFHTswxObhmNEmZGuOT7BL73XgAAqnScpbTf7UMzOFjyBR48eY0fJm4QXy/ddBQA0KZxFcwc3QVzx32H1dv/xJTFvyDyYwysLc0wuGdzfNNKMbiopamBlTO+x6ptf2LUrG2IiY2Hg20JzBzdBXWqKW5d/fvWEwQEhiIgMBQte81VOr7vHwsKqKbqo2OzKggJj8Lc9X/gXehHVCpnh/0rh3CqlUKA56bgbT2giDxqP3iV0vqVU3zwbZsa8Hv0Gr73XwIAqn8zWymN74HpKGVrAT0dLfz8+xVMXX4QCYlJsLU0ResGnhjRs0nBVIKI1Ar7FHkX+e4tfps0GLERH6BvZoFSX1VD321HxYf63fhtB85vWCqm39ZPMYjfbvoyfPV1V5WUubgIfPIvdkwYJ74+uXE9AMCzSVO0Gz0GH8PCcHLjekSFh8PIzBwejZug3refniOgoamJ57dv4drvB5EQFweTkiVRoXYd1PtW+WHu7ceMxYkN67FnxjTIZDI4VvLAd7PnQEOz2A0vqFTF5u0Q/SEUf61diKjQ97Au747uq3dzuo5CgOdGNQIf3MH2gZ+eVXVymWLKOM82ndF6wly8e/IId47uR9zHSBiVtIJzzXpoOHAsNLU//QG84+xVOLZwCnYM7gaZTA7XRq3QcuysDMciUiWZkPZpMoVQ7969ER4ejkOHDuV42927dzFixAhcvXoV+vr66NSpE5YuXQpDQ0Ol/Tw9PfG///0P8fHx+Pbbb7Fq1SoxikUQBKxcuRJr167F8+fPYWpqisqVK2PSpEmoV69ejsoeGRkJExMTBIWEw9iYg0CFjf/7GFUXgbJQxjL/b0UnKu6SklNUXQTKRGRkJOwszRAREaHytkBqu8Rj4mFo6ErzPZscFw2/eV8XivoSpVUc+hThgc9gbMy7IQsbIfiuqotAWZBbe6i6CERFjpCSrOoiUDqRkR9haudSKNrXqW2Sh530YKQlXfj7x0QBrr/FFoo6q0KhH6wvyjhYX7hxsL7w4mA9Ue5xsL5w4mA9EX0pDtYXbhysL7w4WE+UexysL3w4WK9+fQrep0ZERESkRqScY1LKuSuJiIiIiEj1ZDJp55VX9y4FnwhKRERERERERERERKRijKwnIiIiUiNSRsKoexQMEREREVFxJ+Wduor8Jcu6SGBkPRERERERERERERGRijGynoiIiEiNcM56IiIiIiLKK85ZLy1G1hMRERERERERERERqRgj64mIiIjUCOesJyIiIiKivJM4tF7NMbKeiIiIiIiIiIiIiEjFGFlPREREpEY4Zz0REREREeUV56yXFiPriYiIiIiIiIiIiIhUjJH1REREROpEykgYNY+CISIiIiIq7qS8U1eRv2RZFwmMrCciIiIiIiIiIiIiUjFG1hMRERGpEc5ZT0REREREecXIemkxsp6IiIiIiIiIiIiISMUYWU9ERESkRmQSzlmv7lEwRERERETFnZT9idT81Rkj64mIiIiIiIiIiIiIVIyR9URERERqhHPWExERERFRnkkeWi9d1kUBI+uJiIiIiIiIiIiIiFSMkfVEREREaoRz1hMRERERUV5xznppMbKeiIiIiIiIiIiIiEjFGFlPREREpEY4Zz0REREREeWZhP0JRf7SZV0UMLKeiIiIiIiIiIiIiEjFGFlPREREpEYYWU9ERERERHnFOeulxch6IiIiIiIiIiIiIiIVY2Q9ERERkRqRMhJG3aNgiIiIiIiKPclD66XLuihgZD0RERERERERERERkYoxsp6IiIhIjXDOeiIiIiIiyisp+xOK/CXLukhgZD0RERERERERERERkYoxsp6IiIhIjXDOeiIiIiIiyivJp6xX8z4FI+uJiIiIiIiIiIiIiFSMkfVEREREaoRz1hMRERERUV4pIuulnLNekCzvooCR9UREREREREREREREKsbIeiIiIiI1IoOEc9ZLky0RERERERUWMkjb8FfzTgUj64mIiIiIiIiIiIiIVIyR9URERERqRC6TQS5RaL1U+RIRERERUeEgk8shk0sX/y1T89ByNa8+EREREREREREREZHqMbKeiIiISI3IZBLOWc/AeiIiIiKi4k3KDkVq/mqMkfVERERERERERERERCrGyHoiIiIiNSKTySCTKFpFqnyJiIiIiKiQYGS9pBhZT0RERERERERERESkYoysJyIiIlIjcplikSpvIiIiIiIqvmSQQyaTLv5b3bsUjKwnIiIiIiIiIiIiIlIxRtYTERERqROZhHPLq3sYDBERERFRccc56yXFyHoiIiIiIiIiIiIiIhVjZD0RERGRGpEyEEbNg2CIiIiIiIo/RtZLioP1BUAmk0l3uznlWRlLA1UXgbKQkiKougiUBTmfHlloRccnq7oIlIkYnhciyifsUxROMmsPVReBsiCkJKm6CJQFmZxDUYVWwkdVl4DS4zlRO/yGJCIiIlIjsv/+SZU3EREREREVX1IHEKh7cALnrCciIiIiIiIiIiIiUjFG1hMRERGpEblMsUiVNxERERERFWMyuWKRLH/psi4KGFlPRERERERERERERKRijKwnIiIiUiNSzjGp7vNLEhEREREVdzK5DDIJb6mVMu+igJH1REREREREREREREQqxsh6IiIiIjUikykWqfImIiIiIqJiTMoORWr+aoyR9UREREREREREREREKsbIeiIiIiI1IpfJIJcoWkWqfImIiIiIqJCQyRWLZPlLl3VRwMh6IiIiIiIiIiIiIiIVY2Q9ERERkRrhnPVERERERJRXMpkMMgkb/lLmXRQwsp6IiIiIiIiIiIiISMUYWU9ERESkRqSMhFH3KBgiIiIiomJPylt1U/NXY4ysJyIiIiIiIiIiIiJSMUbWExEREakRzllPRERERER5JoPEkfXSZV0UMLKeiIiIiIiIiIiIiEjFGFlPREREpEbkMhnkEkXCSJUvEREREREVDjKZHDKZdPHfMpkgWd5FASPriYiIiIiIiIiIiIhULEeR9YcPH85xhl9//XWeC0NERERE0pJBumkgGVdP2WGfgoiIiKgYkPIhWKn5q7EcDda3b98+R5nJZDIkJyd/SXmIiIiIiKgYYp+CiIiIiCh7ORqsT0lJkbocRERERFQAZDIZZBJFq0iVLxUP7FMQERERFX0yuQwyuXTtfinzLgq+aM76uLi4/CoHERERERGpIfYpiIiIiIgUcj1Yn5ycjNmzZ8POzg6GhoZ4/vw5AGDq1KnYvHlzvheQiIiIiPKPXCbtQpQT7FMQERERFVEyufSLGst17efMmYNt27Zh4cKF0NbWFtdXrFgRmzZtytfCERERERFR8cM+BRERERFRRrkerN+xYwc2bNgAHx8faGhoiOs9PT3x6NGjfC0cEREREeWv1DnrpVqIcoJ9CiIiIqIiSiaTflFjuR6sf/PmDcqWLZthfUpKChITE/OlUEREREREVHyxT0FERERElFGuB+vd3Nxw8eLFDOv3798PLy+vfCkUEREREUmHATCkauxTEBERERVNMkh7p64Mue9YvHnzBt27d4eFhQX09PRQqVIl3LhxQ9wuCAKmTZsGGxsb6OnpoUmTJnjy5IlSHmFhYfDx8YGxsTFMTU3Rt29fREVFKaXx8/ND3bp1oaurCwcHByxcuDBvb2I2cj1YP23aNAwdOhQLFixASkoKDhw4gP79+2POnDmYNm1avheQiIiIiIqv4tSwppxjn4KIiIiI8sOHDx9Qu3ZtaGlp4c8//8SDBw+wZMkSmJmZiWkWLlyIlStXYt26dbh27RoMDAzQvHlzxMXFiWl8fHxw//59nDp1CkePHsWFCxcwYMAAcXtkZCSaNWsGR0dH+Pr6YtGiRZgxYwY2bNiQr/XJ9WB9u3btcOTIEZw+fRoGBgaYNm0aHj58iCNHjqBp06b5WjgiIiIiyl+Fac764tawppxjn4KIiIioiCpkc9YvWLAADg4O2Lp1K6pXrw4nJyc0a9YMzs7OABTBP8uXL8eUKVPQrl07eHh4YMeOHQgMDMShQ4cAAA8fPsTx48exadMm1KhRA3Xq1MGqVavwyy+/IDAwEACwa9cuJCQkYMuWLXB3d0e3bt0wfPhwLF26NF/fXs287FS3bl2cOnUqXwtCREREROolbcM6lZOTk/j/9A1rQPFgUisrKxw6dAjdunUTG9bXr19H1apVAQCrVq1Cq1atsHjxYtja2io1rLW1teHu7o7bt29j6dKlSoP6VLDYpyAiIiKiL3X48GE0b94cnTt3xvnz52FnZ4fBgwejf//+AAB/f38EBQWhSZMm4j4mJiaoUaMGrl69im7duuHq1aswNTUV+xMA0KRJE8jlcly7dg0dOnTA1atXUa9ePWhra4tpmjdvjgULFuDDhw9KAUdfIteR9alu3LiBnTt3YufOnfD19c2XwhARERGRtOQyaRdAEcmedomPj8+0LIcPH0bVqlXRuXNnWFpawsvLCxs3bhS3f65hDeCzDevUNJk1rB8/fowPHz7k23tLucc+BREREVERI5NLvyDnfYrnz59j7dq1cHFxwYkTJzBo0CAMHz4c27dvBwAEBQUBAKysrJT2s7KyErcFBQXB0tJSabumpibMzc2V0mSWR9pj5IdcR9a/fv0a3377LS5fvgxTU1MAQHh4OLy9vfHLL7/A3t4+3wpHREREREWPg4OD0uvp06djxowZGdKlNqxHjx6NSZMm4fr16xg+fDi0tbXRq1evfG1Yp43YT5tnUFBQvkXBUM6xT0FERERE2clpnyIlJQVVq1bF3LlzAQBeXl64d+8e1q1bh169ehVEUfNVriPr+/Xrh8TERDx8+BBhYWEICwvDw4cPkZKSgn79+klRRiIiIiLKJwUxZ31AQAAiIiLEZeLEiZmWJSUlBZUrV8bcuXPh5eWFAQMGoH///li3bl1BviWkAuxTEBERERVNUvcnctunsLGxgZubm9I6V1dXvHr1CgBgbW0NAAgODlZKExwcLG6ztrbGu3fvlLYnJSUhLCxMKU1meaQ9Rn7I9WD9+fPnsXbtWpQvX15cV758eaxatQoXLlzIt4IRERERUdFkbGystOjo6GSarrg1rCnn2KcgIiIiouzktE9Ru3ZtPH78WGndv//+C0dHRwCKZ2JZW1vjzJkz4vbIyEhcu3YNtWrVAgDUqlUL4eHhStMynj17FikpKahRo4aY5sKFC0hMTBTTnDp1CuXLl8/XO3VzPVjv4OCgVKhUycnJsLW1zZdCEREREZE0ZBIvuVHcGtaUc+xTEBERERVRUj8ES567XsWoUaPw999/Y+7cuXj69Cl2796NDRs2YMiQIQAUdwKMHDkSP/30Ew4fPoy7d++iZ8+esLW1Rfv27QEoAoZatGiB/v37459//sHly5cxdOhQdOvWTWybfvfdd9DW1kbfvn1x//59/Prrr1ixYgVGjx6dv29vbndYtGgRhg0bhhs3bojrbty4gREjRmDx4sX5WjgiIiIiKr6KW8Oaco59CiIiIiLKD9WqVcPBgwexZ88eVKxYEbNnz8by5cvh4+Mjphk3bhyGDRuGAQMGoFq1aoiKisLx48ehq6srptm1axcqVKiAxo0bo1WrVqhTpw42bNggbjcxMcHJkyfh7++PKlWq4Mcff8S0adMwYMCAfK2PTBAE4XOJzMzMxPmCACA6OhpJSUnQ1FQ8nzb1/wYGBggLC8vXAhZlkZGRMDExQXBoBIyNjVVdHKIiIyXls19LpCLyXP6FmwpOREzGCFVSvY+RkXBxKIGICNW3BVLbJT22XIW2vqEkx0iIicLO72vlqr5Hjx7FxIkT8eTJEzg5OWH06NHo37+/uF0QBEyfPh0bNmxAeHg46tSpgzVr1qBcuXJimrCwMAwdOhRHjhyBXC5Hp06dsHLlShgafqqnn58fhgwZguvXr6NEiRIYNmwYxo8fn3+Vp89inyJvUj+7EW+fw9jYSNXFISoyhJQkVReBsiCTa6q6CJQFIS5c1UWgdCIjP8LUqXKh6k+ETvOCsa6GdMeJS4bFrFuFos6qkKNvyOXLl0tcDCIiIiJSR23atEGbNm2y3C6TyTBr1izMmjUryzTm5ubYvXt3tsfx8PDAxYsX81xO+nLsUxARERERZS9Hg/W9evWSuhxEREREVABkMsUiVd5EWWGfgoiIiKgYkLJDkZq/Gvuie4/i4uKQkJCgtE4db08gIiIiIqK8YZ+CiIiIiEgh1w+YjY6OxtChQ2FpaQkDAwOYmZkpLURERERUeMlkMkkXopxgn4KIiIioiEqNrJdyUWO5HqwfN24czp49i7Vr10JHRwebNm3CzJkzYWtrix07dkhRRiIiIiIiKkbYpyAiIiIiyijX0+AcOXIEO3bsQIMGDdCnTx/UrVsXZcuWhaOjI3bt2gUfHx8pyklERERE+YBz1lNhwD4FERERUdGk6E9I1/BX9z5FriPrw8LCUKZMGQCKuSTDwsIAAHXq1MGFCxfyt3RERERERFTssE9BRERERJRRrgfry5QpA39/fwBAhQoVsHfvXgCK6BhTU9N8LRwRERER5S+5TCbpQpQT7FMQERERFVEyufSLGsv1NDh9+vTBnTt3UL9+fUyYMAFt27bF6tWrkZiYiKVLl0pRRioENu49j1U/n8G70EhUdLHDgrGdUcW9tKqLpdYu33yKVTtP486jVwgKicTPi/qjdQNPVRdLLVy59RSrfz6D249eITgkEjsW9kPr+or3PjEpGXPWHcXpK/fx8k0ojAx1Ub9aeUwb0g42JU0y5BWfkIhm3y/BvSdvcG7neFQqZ1/Q1VFL/E4rWMnJKVi+7TgOnvTF+7CPsCphjG9aVMewnk0zvX1y0pK92H34KqYObY++neuL6/tN3IQHT98gJDwKJoZ6qFOlHCYMbAurEhk/W0RUuLFPoZ7++XUrLu9Yg6jQ97Au54aW4+bAvqKXqoul1l74XsWVHWsR+NAPUSHB6LpkC1wbtlR1sYq9lzf/xpWd6/9739+h6+KNqNCghbg9KvQ9Tq+ai2d/X0Dcx0g4Vq6BlmNnw6KUk5jG98Au3D1+CG8f30NCdBTG/3UPukZsExUEfm5UY3nHxogICsywvmrHb1Hbpy9WdGqS6X7f/LQM7o0Un6+Z3q4ZtneauRgVm7bO38ISfYFc/6li1KhRGD58OACgSZMmePToEXbv3o1bt25hxIgR+V7AvOrduzdkMhlkMhm0tLTg5OSEcePGIS4uDgDw4sUL9O3bF05OTtDT04OzszOmT5+OhIQEMY9z585BJpMhPDxcRbUoHA6c9MWU5Qcxvl9LnNs5HhVd7NBp2P/wPuyjqoum1mJi41GxnB0Wjeuq6qKonZjYeLi72GHh2C4ZtsXGJcDvcQDGfN8CZ3eMw/b5/fD01Tv4jFmfaV4zVv0Oaw40Fih+pxW8dbvP4Offr2DWyI44vWMCJvzQBuv3nMW23y5mSHv8gh9uPXiZ6QB8Ta+yWD2jF87unIh1s/vgZWAoBk3bVgA1KF5S56yXaiHKiaLQp2B/In/dO/E7TiydgQYDfsQPu0/AysUNPw/5FlFhIaoumlpLjIuBVTk3tJ4wV9VFUSsJsbGwcnFFq/E/ZdgmCAJ+HdMPH968Qrclm/HDruMwsbbHzsHfIiE2RkyXGBeLst4NULfP0IIsOoGfG1Xpv3kffjxyQVx6rNgMAHBv1ALGltZK2348cgEN+g2Ftr4+XGrWVcqn3eS5Sukq1Mt8kJ+yIXWHQs07FbmOrE/P0dERjo6O+VGWfNeiRQts3boViYmJ8PX1Ra9evSCTybBgwQI8evQIKSkpWL9+PcqWLYt79+6hf//+iI6OxuLFi1Vd9EJlze6z6NneGz5f1wIALJ3YDScv38fPh69iVO9mKi6d+mpa2x1Na7uruhhqqYm3O5p4Z/7eGxvq4cAq5QbzgjGd0bTPYrwOCoO9tbm4/vSV+/jrn0fYNq8vTl99IGmZ6RN+pxU83/sv0LR2RTSqpfjcONiY4/CZW7jz6JVSuqD34Zix8gB2LPoBfSZszJBPvy4NxP/bW5tjkE9jDJi8BYlJydDS1JC0DkQkrcLap2B/Iv9c3bUelTv4wKtdNwBAm8kL8eTSGdz6fQ/q9hmm4tKpL5fajeFSu7Gqi6F2XGo3hEvthpluC3vlj9d3b2LQr6dh6VweANBm4lwsbl4Z9078jsrtvwUA1PyuHwDgxY2rBVNoEvFzoxoGZuZKry/t3Agzu1Jw9KoGmUwGQ4uSStsfnT8Dt0YtoK1voLRe19AoQ1qiwiRHg/UrV67McYapETKFgY6ODqytrQEADg4OaNKkCU6dOoUFCxagRYsWaNHi021mZcqUwePHj7F27Vo2rtNISEzC7UcBSgNYcrkc9auXx/W7/iosGVHRERkVC5lMBmNDPXHdu9BIjJz7C3Yu6g99XW0Vlk698DtNNaq4l8buo1fxPOAdyjhY4sHTN7hx9zmmDGkvpklJScGoObswoFtDlHOy+Wye4ZHROHTKF1UqluZAfS6lRgpLlTdRVopin4L9ifyRlJiAwId+qJNmUF4ul6NMjbp47eerwpIRFT5JifEAAE0dHXGdTC6HprY2Xt3+RxysJ1JnyYkJ8DtxBLW69c60/Rn46D6CnjxEqzFTM2w7tmQ2Ds+fCjNbB1Tt0BVfte7INmwuSdmfSM1fneVosH7ZsmU5ykwmkxWahnV69+7dw5UrV7KN2ImIiIC5uXmW2z8nPj4e8fHx4uvIyMg851VYhIZHITk5BSXNjZTWlzQ3xpMXwSoqFVHRERefiFmrD6NTsyriYL0gCBg6+2f07lgbXq6l8CowVMWlVB/8TlONQT6N8TEmDo17zIeGXIbkFAFj+rVC+6ZVxDRrd5+FpoYcfTrVyzaveeuOYMfBS4iNS4CXmyO2zO8vdfGJKJ8U9T5FQfQngOLZp4gJD4OQnAxDc+VIRgPzkgh58VRFpSIqnEqULgsTazucWb0AbSbNg7aePq7u2oTI4LeICnmn6uIRFQqPLpxBXNRHfNWqQ6bbbx3ZjxKlneFQSfm5KA36D4NTlZrQ0tHFs38u44/Fs5AQE4MaXXoURLGJciRHg/X+/kUz2vDo0aMwNDREUlIS4uPjIZfLsXr16kzTPn36FKtWrfqiKJh58+Zh5syZed6fiIqXxKRk9J28BQIELBr3aX77DXvPIyo6HqN6ccoVUg9H/7qN30/dxIqp3VGutDUePH2DWasPiQ+avfs4AFt/u4A/Nv742SiKH7o1RNfWNfAm6ANWbD+B0XN3Ycv8/moffZEbcuThoUW5yJsoK0WxT1HQ/QmAfQoidaehqYUuizbg8OyxWNioEmQaGihTvQ7KejcEIKi6eESFwq0jv8GlZl0YlbTMsC0xPg53T/2Ber0HZdhWv89g8f825d2QGBeLK7u3cLA+t+RyxSJl/mrsi+esL8waNmyItWvXIjo6GsuWLYOmpiY6deqUId2bN2/QokULdO7cGf375z1Cb+LEiRg9erT4OjIyEg4ODnnOrzCwMDWEhoY8w4MX34dFwtLCWEWlIir8EpOS8f2kLQh4G4ZDa4YrTYFz8ca/uH7PHzZ1Rynt07j3InzTvCrWTGdDQSr8TlONeWuPYJBPY3zduDIAoIKzLd4Ef8CaXWfwTYvq+MfvOUI/RMG7yyxxn+TkFMxZ8zu27D+Py79OE9ebmxrC3NQQZRwsUdbRCrU6z8TN+y9RpWLpgq4WEamBgu5PAMWzT6Fvag6Zhgaiwt4rrY8Oew9Di4wDLUTqztbVAwN3n0BcVCSSExNhYGaBTb3awsbNQ9VFI1K58Ldv8PzGVXSZm/n0eg/OnkBiXBw8W7b7bF52bh64sHUtkhISoKnN6WmpcCjWg/UGBgYoW7YsAGDLli3w9PTE5s2b0bdvXzFNYGAgGjZsCG9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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: confusion_matrices.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 11: CLASSIFICATION REPORTS\n",
"\n",
"print(\"\\n[10] DETAILED CLASSIFICATION REPORTS...\")\n",
"\n",
"print(\"\\n--- Random Forest Classification Report ---\")\n",
"print(classification_report(y_test, y_pred_rf,\n",
" target_names=target_encoder.classes_,\n",
" zero_division=0))\n",
"\n",
"print(\"\\n--- Gradient Boosting Classification Report ---\")\n",
"print(classification_report(y_test, y_pred_gb,\n",
" target_names=target_encoder.classes_,\n",
" zero_division=0))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KzWUSJ9c8Ebv",
"outputId": "ccff3e94-5536-4d3c-8a63-fbcb24b9cc18"
},
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[10] DETAILED CLASSIFICATION REPORTS...\n",
"\n",
"--- Random Forest Classification Report ---\n",
" precision recall f1-score support\n",
"\n",
" DoS 0.99 0.94 0.96 8095\n",
" Probe 0.96 0.87 0.91 2157\n",
" R2L 0.99 0.13 0.23 968\n",
" U2R 1.00 0.15 0.26 79\n",
" normal 0.87 0.99 0.93 11245\n",
"\n",
" accuracy 0.92 22544\n",
" macro avg 0.96 0.62 0.66 22544\n",
"weighted avg 0.93 0.92 0.91 22544\n",
"\n",
"\n",
"--- Gradient Boosting Classification Report ---\n",
" precision recall f1-score support\n",
"\n",
" DoS 0.99 0.94 0.97 8095\n",
" Probe 0.95 0.91 0.93 2157\n",
" R2L 0.91 0.20 0.32 968\n",
" U2R 0.91 0.25 0.40 79\n",
" normal 0.89 0.99 0.94 11245\n",
"\n",
" accuracy 0.93 22544\n",
" macro avg 0.93 0.66 0.71 22544\n",
"weighted avg 0.93 0.93 0.92 22544\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 12: FEATURE IMPORTANCE ANALYSIS\n",
"\n",
"print(\"\\n[11] FEATURE IMPORTANCE ANALYSIS...\")\n",
"\n",
"# Get feature names\n",
"feature_names = categorical_cols + numerical_cols\n",
"\n",
"# Random Forest feature importance\n",
"rf_importance = pd.DataFrame({\n",
" 'Feature': feature_names,\n",
" 'Importance': rf_best.feature_importances_\n",
"}).sort_values('Importance', ascending=False)\n",
"\n",
"# Gradient Boosting feature importance\n",
"gb_importance = pd.DataFrame({\n",
" 'Feature': feature_names,\n",
" 'Importance': gb_best.feature_importances_\n",
"}).sort_values('Importance', ascending=False)\n",
"\n",
"print(\"\\n--- Top 15 Important Features (Random Forest) ---\")\n",
"print(rf_importance.head(15).to_string(index=False))\n",
"\n",
"print(\"\\n--- Top 15 Important Features (Gradient Boosting) ---\")\n",
"print(gb_importance.head(15).to_string(index=False))\n",
"\n",
"# Figure 5: Feature Importance Comparison\n",
"fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n",
"\n",
"# Random Forest top features\n",
"rf_top = rf_importance.head(15)\n",
"axes[0].barh(rf_top['Feature'], rf_top['Importance'], color='steelblue')\n",
"axes[0].set_xlabel('Importance Score', fontsize=12)\n",
"axes[0].set_title('Top 15 Features - Random Forest', fontsize=14, fontweight='bold')\n",
"axes[0].invert_yaxis()\n",
"axes[0].grid(axis='x', alpha=0.3)\n",
"\n",
"# Gradient Boosting top features\n",
"gb_top = gb_importance.head(15)\n",
"axes[1].barh(gb_top['Feature'], gb_top['Importance'], color='coral')\n",
"axes[1].set_xlabel('Importance Score', fontsize=12)\n",
"axes[1].set_title('Top 15 Features - Gradient Boosting', fontsize=14, fontweight='bold')\n",
"axes[1].invert_yaxis()\n",
"axes[1].grid(axis='x', alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('feature_importance.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: feature_importance.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "qSsIWUac8H_8",
"outputId": "40161a41-3d65-42bc-9cb0-b697f4fe33e3"
},
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[11] FEATURE IMPORTANCE ANALYSIS...\n",
"\n",
"--- Top 15 Important Features (Random Forest) ---\n",
" Feature Importance\n",
" src_bytes 0.141438\n",
" same_srv_rate 0.085659\n",
" flag 0.082872\n",
" diff_srv_rate 0.071837\n",
" dst_bytes 0.070692\n",
" dst_host_serror_rate 0.061853\n",
" count 0.052399\n",
" dst_host_srv_serror_rate 0.043149\n",
" dst_host_same_srv_rate 0.042592\n",
" dst_host_diff_srv_rate 0.039342\n",
"dst_host_same_src_port_rate 0.039129\n",
" protocol_type 0.030393\n",
" serror_rate 0.027465\n",
" srv_count 0.026800\n",
" service 0.022476\n",
"\n",
"--- Top 15 Important Features (Gradient Boosting) ---\n",
" Feature Importance\n",
" src_bytes 0.402258\n",
" same_srv_rate 0.269365\n",
" dst_host_diff_srv_rate 0.050418\n",
"dst_host_srv_diff_host_rate 0.048621\n",
" diff_srv_rate 0.047859\n",
" protocol_type 0.045274\n",
" dst_host_srv_count 0.021100\n",
" count 0.019875\n",
" dst_host_serror_rate 0.018126\n",
" wrong_fragment 0.014860\n",
" dst_bytes 0.012025\n",
" service 0.009097\n",
" num_compromised 0.006645\n",
"dst_host_same_src_port_rate 0.005217\n",
" logged_in 0.005074\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: feature_importance.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 13: CROSS-VALIDATION ANALYSIS\n",
"\n",
"print(\"\\n[12] CROSS-VALIDATION ANALYSIS...\")\n",
"\n",
"# Use a smaller sample for cross-validation (computational efficiency)\n",
"sample_size = 10000\n",
"X_sample = X_train.sample(n=sample_size, random_state=42)\n",
"y_sample = y_train.loc[X_sample.index]\n",
"\n",
"print(f\"\\nPerforming 5-fold cross-validation on {sample_size} samples...\")\n",
"\n",
"# Random Forest CV\n",
"print(\"\\n--- Random Forest Cross-Validation ---\")\n",
"rf_cv_scores = cross_val_score(rf_best, X_sample, y_sample, cv=5, scoring='accuracy', n_jobs=-1)\n",
"print(f\"CV Scores: {rf_cv_scores}\")\n",
"print(f\"Mean CV Score: {rf_cv_scores.mean():.4f} (+/- {rf_cv_scores.std() * 2:.4f})\")\n",
"\n",
"# Gradient Boosting CV\n",
"print(\"\\n--- Gradient Boosting Cross-Validation ---\")\n",
"gb_cv_scores = cross_val_score(gb_best, X_sample, y_sample, cv=5, scoring='accuracy', n_jobs=-1)\n",
"print(f\"CV Scores: {gb_cv_scores}\")\n",
"print(f\"Mean CV Score: {gb_cv_scores.mean():.4f} (+/- {gb_cv_scores.std() * 2:.4f})\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YT17BCn_8O9b",
"outputId": "b9e7b5d3-e82b-4d76-aa2c-db42de8c88a5"
},
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[12] CROSS-VALIDATION ANALYSIS...\n",
"\n",
"Performing 5-fold cross-validation on 10000 samples...\n",
"\n",
"--- Random Forest Cross-Validation ---\n",
"CV Scores: [0.9935 0.994 0.998 0.9955 0.9945]\n",
"Mean CV Score: 0.9951 (+/- 0.0032)\n",
"\n",
"--- Gradient Boosting Cross-Validation ---\n",
"CV Scores: [0.994 0.9935 0.9985 0.9965 0.9935]\n",
"Mean CV Score: 0.9952 (+/- 0.0040)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 14: OVERFITTING/UNDERFITTING ANALYSIS\n",
"\n",
"print(\"\\n[13] OVERFITTING/UNDERFITTING ANALYSIS...\")\n",
"\n",
"# Calculate training accuracy\n",
"y_train_pred_rf = rf_best.predict(X_train)\n",
"y_train_pred_gb = gb_best.predict(X_train)\n",
"\n",
"rf_train_accuracy = accuracy_score(y_train, y_train_pred_rf)\n",
"gb_train_accuracy = accuracy_score(y_train, y_train_pred_gb)\n",
"\n",
"print(\"\\n--- Training vs Testing Accuracy ---\")\n",
"print(f\"Random Forest:\")\n",
"print(f\" Training Accuracy: {rf_train_accuracy:.4f}\")\n",
"print(f\" Testing Accuracy: {rf_accuracy:.4f}\")\n",
"print(f\" Difference: {abs(rf_train_accuracy - rf_accuracy):.4f}\")\n",
"\n",
"print(f\"\\nGradient Boosting:\")\n",
"print(f\" Training Accuracy: {gb_train_accuracy:.4f}\")\n",
"print(f\" Testing Accuracy: {gb_accuracy:.4f}\")\n",
"print(f\" Difference: {abs(gb_train_accuracy - gb_accuracy):.4f}\")\n",
"\n",
"# Figure 6: Overfitting Analysis\n",
"fig, ax = plt.subplots(figsize=(10, 6))\n",
"models = ['Random Forest', 'Gradient Boosting']\n",
"train_scores = [rf_train_accuracy, gb_train_accuracy]\n",
"test_scores = [rf_accuracy, gb_accuracy]\n",
"\n",
"x = np.arange(len(models))\n",
"width = 0.35\n",
"\n",
"bars1 = ax.bar(x - width/2, train_scores, width, label='Training', color='steelblue')\n",
"bars2 = ax.bar(x + width/2, test_scores, width, label='Testing', color='coral')\n",
"\n",
"ax.set_xlabel('Model', fontsize=12)\n",
"ax.set_ylabel('Accuracy', fontsize=12)\n",
"ax.set_title('Training vs Testing Accuracy', fontsize=14, fontweight='bold')\n",
"ax.set_xticks(x)\n",
"ax.set_xticklabels(models)\n",
"ax.legend()\n",
"ax.set_ylim([0, 1])\n",
"ax.grid(axis='y', alpha=0.3)\n",
"\n",
"# Add value labels on bars\n",
"for bar in bars1 + bars2:\n",
" height = bar.get_height()\n",
" ax.text(bar.get_x() + bar.get_width()/2., height,\n",
" f'{height:.4f}', ha='center', va='bottom', fontsize=10)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('overfitting_analysis.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"print(\"β Figure saved: overfitting_analysis.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 850
},
"id": "FiWDAT0y9FS9",
"outputId": "ec860ccd-6c39-4b9f-ca5b-6c548cd6474d"
},
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[13] OVERFITTING/UNDERFITTING ANALYSIS...\n",
"\n",
"--- Training vs Testing Accuracy ---\n",
"Random Forest:\n",
" Training Accuracy: 0.9999\n",
" Testing Accuracy: 0.9209\n",
" Difference: 0.0790\n",
"\n",
"Gradient Boosting:\n",
" Training Accuracy: 0.9999\n",
" Testing Accuracy: 0.9293\n",
" Difference: 0.0706\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"β Figure saved: overfitting_analysis.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 15: SUMMARY AND INSIGHTS\n",
"\n",
"print(\"\\n\" + \"=\"*80)\n",
"print(\"SUMMARY AND KEY INSIGHTS\")\n",
"print(\"=\"*80)\n",
"\n",
"print(\"\\n[1] MODEL PERFORMANCE:\")\n",
"print(f\" β’ Random Forest achieved {rf_accuracy:.2%} accuracy\")\n",
"print(f\" β’ Gradient Boosting achieved {gb_accuracy:.2%} accuracy\")\n",
"print(f\" β’ Best performing model: {'Random Forest' if rf_accuracy > gb_accuracy else 'Gradient Boosting'}\")\n",
"\n",
"print(\"\\n[2] COMPUTATIONAL EFFICIENCY:\")\n",
"print(f\" β’ Random Forest training time: {rf_initial_time:.2f}s\")\n",
"print(f\" β’ Gradient Boosting training time: {gb_initial_time:.2f}s\")\n",
"print(f\" β’ Faster model: {'Random Forest' if rf_initial_time < gb_initial_time else 'Gradient Boosting'}\")\n",
"\n",
"print(\"\\n[3] TOP INFLUENTIAL FEATURES:\")\n",
"print(f\" Random Forest top 5:\")\n",
"for idx, row in rf_importance.head(5).iterrows():\n",
" print(f\" - {row['Feature']}: {row['Importance']:.4f}\")\n",
"print(f\" Gradient Boosting top 5:\")\n",
"for idx, row in gb_importance.head(5).iterrows():\n",
" print(f\" - {row['Feature']}: {row['Importance']:.4f}\")\n",
"\n",
"print(\"\\n[4] OVERFITTING ASSESSMENT:\")\n",
"rf_overfit = rf_train_accuracy - rf_accuracy\n",
"gb_overfit = gb_train_accuracy - gb_accuracy\n",
"print(f\" β’ Random Forest gap: {rf_overfit:.4f}\")\n",
"print(f\" β’ Gradient Boosting gap: {gb_overfit:.4f}\")\n",
"if rf_overfit < 0.05 and gb_overfit < 0.05:\n",
" print(f\" β’ Both models show good generalization (gap < 5%)\")\n",
"elif rf_overfit > 0.1 or gb_overfit > 0.1:\n",
" print(f\" β’ Warning: Potential overfitting detected (gap > 10%)\")\n",
"else:\n",
" print(f\" β’ Models show acceptable generalization\")\n",
"\n",
"print(\"\\n[5] RECOMMENDATIONS:\")\n",
"print(f\" β’ Use {'Random Forest' if rf_accuracy > gb_accuracy else 'Gradient Boosting'} for better accuracy\")\n",
"print(f\" β’ Focus on top features: {', '.join(rf_importance.head(5)['Feature'].tolist())}\")\n",
"print(f\" β’ Consider ensemble voting between both models for robustness\")\n",
"print(f\" β’ Implement real-time monitoring on key features\")\n",
"print(f\" β’ Regular model retraining with new attack patterns\")\n",
"\n",
"print(\"\\n\" + \"=\"*80)\n",
"print(\"ANALYSIS COMPLETE - All results saved!\")\n",
"print(\"=\"*80)\n",
"\n",
"print(\"\\nπ Generated Files:\")\n",
"print(\" 1. attack_distribution.png\")\n",
"print(\" 2. correlation_matrix.png\")\n",
"print(\" 3. feature_distributions.png\")\n",
"print(\" 4. model_comparison.png\")\n",
"print(\" 5. confusion_matrices.png\")\n",
"print(\" 6. feature_importance.png\")\n",
"print(\" 7. overfitting_analysis.png\")\n",
"\n",
"print(\"\\nβ All tasks completed successfully!\")\n",
"print(\"β Ready for report generation and demonstration\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1erGQ45N9IT2",
"outputId": "7d07c43b-78a0-4a4b-f3ed-6ca1ae5c09b2"
},
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"================================================================================\n",
"SUMMARY AND KEY INSIGHTS\n",
"================================================================================\n",
"\n",
"[1] MODEL PERFORMANCE:\n",
" β’ Random Forest achieved 92.09% accuracy\n",
" β’ Gradient Boosting achieved 92.93% accuracy\n",
" β’ Best performing model: Gradient Boosting\n",
"\n",
"[2] COMPUTATIONAL EFFICIENCY:\n",
" β’ Random Forest training time: 12.01s\n",
" β’ Gradient Boosting training time: 107.51s\n",
" β’ Faster model: Random Forest\n",
"\n",
"[3] TOP INFLUENTIAL FEATURES:\n",
" Random Forest top 5:\n",
" - src_bytes: 0.1414\n",
" - same_srv_rate: 0.0857\n",
" - flag: 0.0829\n",
" - diff_srv_rate: 0.0718\n",
" - dst_bytes: 0.0707\n",
" Gradient Boosting top 5:\n",
" - src_bytes: 0.4023\n",
" - same_srv_rate: 0.2694\n",
" - dst_host_diff_srv_rate: 0.0504\n",
" - dst_host_srv_diff_host_rate: 0.0486\n",
" - diff_srv_rate: 0.0479\n",
"\n",
"[4] OVERFITTING ASSESSMENT:\n",
" β’ Random Forest gap: 0.0790\n",
" β’ Gradient Boosting gap: 0.0706\n",
" β’ Models show acceptable generalization\n",
"\n",
"[5] RECOMMENDATIONS:\n",
" β’ Use Gradient Boosting for better accuracy\n",
" β’ Focus on top features: src_bytes, same_srv_rate, flag, diff_srv_rate, dst_bytes\n",
" β’ Consider ensemble voting between both models for robustness\n",
" β’ Implement real-time monitoring on key features\n",
" β’ Regular model retraining with new attack patterns\n",
"\n",
"================================================================================\n",
"ANALYSIS COMPLETE - All results saved!\n",
"================================================================================\n",
"\n",
"π Generated Files:\n",
" 1. attack_distribution.png\n",
" 2. correlation_matrix.png\n",
" 3. feature_distributions.png\n",
" 4. model_comparison.png\n",
" 5. confusion_matrices.png\n",
" 6. feature_importance.png\n",
" 7. overfitting_analysis.png\n",
"\n",
"β All tasks completed successfully!\n",
"β Ready for report generation and demonstration\n"
]
}
]
}
],
"metadata": {
"colab": {
"name": "Welcome To Colab",
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"accelerator": "GPU"
},
"nbformat": 4,
"nbformat_minor": 0
}