{
"cells": [
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"source": [
"## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
]
},
{
"cell_type": "markdown",
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"id": "sDxa7s952Ukh"
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"source": [
"`Version: April 2026`"
]
},
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"execution": {
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"source": [
"# \n",
"# \n",
"# "
],
"outputs": [],
"execution_count": 2
},
{
"cell_type": "markdown",
"metadata": {
"id": "n7afdXdxIbLA"
},
"source": [
"### **Overview**\n",
"\n",
"\n",
"In this assignment, you'll level up your data science toolkit. While the first assignment focused on the data, on this one you will practice:\n",
"\n",
"- Classification models\n",
"\n",
"- Regression models\n",
"\n",
"- Feature Engineering\n",
"\n",
"- Evaluations\n",
"\n",
"You’ll go from raw data to insights by building a full modeling pipeline, enhancing your dataset, and training different models.\n",
"\n",
"This assignment will be completed individually."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lJAPMumvIUyW"
},
"source": [
"### **Objectives**\n",
"\n",
"You’ll gain hands-on experience in:\n",
"- Evaluation\n",
"- Classification\n",
"- Regression\n",
"- Dataset preparation\n",
"- Explore various data hubs\n",
"- Engineering meaningful features\n",
"- Communicating findings clearly - visually and verbally\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MwRmaJBiIjMR"
},
"source": [
"### **Submission Guidelines**\n",
"\n",
"1. Please note that this assignmnet must be submitted alone.\n",
"2. Submit the link to your HugingFace Model.\n",
"\n",
"Your HF model should include:\n",
"- README file: explanations, visuals, insights, etc.\n",
"- **Video**: Include the video of your presentation in the README file.\n",
"- **Python Notebook**: upload a copy of this notebook, with all of your coding work. Do not submit a Colab link; include the `.ipynb` file in the HF model.\n",
"- **ML Models:** Upload your models.\n",
"\n",
"Note: Students may be randomly chosen to present their work in a quick online session with the T.A., typically lasting ±10 minutes. Similar to Peer Review.\n",
"\n",
"
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hD9SZmagIjOV"
},
"source": [
"### **Evaluation Criteria**\n",
"\n",
"* **Data Handling & EDA (20%)**\n",
" Thoughtful and thorough data cleaning; handling of missing values, outliers, duplicates, and more; well-chosen visualizations; clear statistical summaries; use of EDA to guide modeling choices.\n",
"\n",
"* **Feature Engineering (20%)**\n",
" Creative and effective feature creation, transformation, encoding, selection, scaling, and more; integration of clustering results as features; clear explanation of feature choices and their impact.\n",
"\n",
"* **Model Training (20%)**\n",
" Appropriate selection of models; correct train/test split; reproducible code; logical modeling workflow with a solid baseline and improvements post-feature engineering. An iterative process.\n",
"\n",
"* **Evaluation & Interpretation (20%)**\n",
" Use of relevant evaluation metrics; structured model comparison; use of feature importance or visualizations to interpret results; clear discussion of what the model learned and how it performed.\n",
"\n",
"* **Presentation (20%)**\n",
" 4–6 minute video with clear delivery; structured narrative; visuals that support the explanation; confident, professional communication of findings and lessons.\n",
"\n",
"* **Bonus (up to +10%)**\n",
" Extra work such as trying data science tools, creative visualizations, advanced hyper param tuning, interactive dashboards, and deeper business/domain insights.\n",
"\n",
"* **Late Submission (-10% per day)**\n",
" Assignments submitted after the deadline will receive a 10% penalty per day.\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h3vpVHSxIUwI"
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"source": [
"### **Additional Guidelines**\n",
"\n",
"- The first thing you should do is download a copy of this notebook to your drive.\n",
"- Keep your dataset size manageable. If the dataset is too large, you can sample a subset.\n",
"- Run on Colab (CPU is fine). Colab free is enough. No GPU needed.\n",
"- You may use any Python package (scikit-learn, xgboost, lightgbm, catboost, etc.).\n",
"- No SHAP required. Use `feature_importances`, and similar tools.\n",
"- Make sure your results are reproducible (set **seeds** where needed).\n",
"- Be thoughtful with your cluster features — only use them if they help!\n",
"- Your presentation should tell a story; what worked, what didn’t, and why.\n",
"- Be creative, but also rigorous."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7lTH1B5b5c12"
},
"source": [
"### Assignment High-level Flow"
]
},
{
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"metadata": {
"id": "EK9fe2XygjgM"
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"source": [
""
]
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"source": [
"
"
]
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"source": [
"# Part 6: Winning Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ga-aPfHDnRM3"
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"source": [
"1. Open a new HuggingFace Model Repository.\n",
"2. Export the winning model to a `pickle` file.\n",
"3. Upload the pickle file to your new model repository on `HF`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GzsEe2Yun5nC"
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"source": [
""
]
},
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"cell_type": "markdown",
"metadata": {
"id": "gu7oy4CQnf-L"
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"source": []
},
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"metadata": {
"id": "Csg-bpJuoIXK",
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"end_time": "2026-05-05T17:32:20.196098Z",
"start_time": "2026-05-05T17:32:19.927560Z"
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"source": [
"import joblib, os\n\nos.makedirs(\"models\", exist_ok=True) # ensure folder exists\njoblib.dump(best_reg_model, \"models/incident_regressor.pkl\") # serialize winner\nloaded_reg = joblib.load(\"models/incident_regressor.pkl\") # verify round-trip\npkl_kb = os.path.getsize(\"models/incident_regressor.pkl\") / 1024\n\nprint(f\"Model type : {type(best_reg_model).__name__}\")\nprint(f\"Saved to : models/incident_regressor.pkl ({pkl_kb:.0f} KB)\")\nprint(f\"RMSE : {best_rmse:.3f}\")\nprint(f\"R² : {best_r2_reg:.3f}\")\nprint(\"models/incident_regressor.pkl saved successfully\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model type : RandomForestRegressor\n",
"Saved to : models/incident_regressor.pkl (86229 KB)\n",
"RMSE : 70.851\n",
"R² : 0.469\n",
"models/incident_regressor.pkl saved successfully\n"
]
}
],
"execution_count": 20
},
{
"cell_type": "markdown",
"metadata": {
"id": "2poXnlF5XEXN"
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"source": [
"
\n",
"\n",
"---\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-e5pgBLLoN81"
},
"source": [
"## Part 7: Regression-to-Classification"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Qtj_Sf-7oqY5"
},
"source": [
"In this section, you will **reframe your original regression problem as a classification problem**.\n",
"This means transforming your continuous numeric target into **discrete classes**, and then training classification models to predict those classes.\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M9g9bfxlqWYg"
},
"source": [
"\n",
"#### **7.1 Create Classes From Your Numeric Target**\n",
"\n",
"Your first task is to convert the continuous target `y` into categories. Choose a strategy to convert your numeric target into classes. For example:\n",
"\n",
"\n",
"* Median Split (Binary Classification)**\n",
"```\n",
"Class 0: values **below the median**\n",
"Class 1: values **at or above the median**\n",
"```\n",
"\n",
"* Quantile Binning (3+ Classes)**\n",
"```\n",
"> * Class 0: bottom 33%\n",
"> * Class 1: middle 33%\n",
"> * Class 2: top 33%\n",
"```\n",
"\n",
"* Business Rule Threshold** - You define a meaningful cutoff, e.g.:\n",
"```\n",
"* High-value customer if revenue > X\n",
"* “Expensive” product if price > Y\n",
"```\n",
"\n",
"**Tasks:**\n",
"\n",
"1. Implement your chosen strategy on the **train** and **test** targets. Using the **same engineered features** as before.\n",
"\n",
"2. Explain the reasoning behind your choice (2–3 sentences)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "IqC0YSXxqL3E",
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"end_time": "2026-05-05T17:32:20.261851700Z",
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}
},
"source": [
"from sklearn.preprocessing import LabelEncoder\n\n# IncidentGrade already exists as a native expert label — no threshold conversion needed\nle_clf = LabelEncoder()\ny_clf_enc = le_clf.fit_transform(df_model[\"IncidentGrade\"].astype(str))\n\nprint(\"Label encoding:\")\nfor i, cls in enumerate(le_clf.classes_):\n print(f\" {i} → {cls}\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Label encoding:\n",
" 0 → BenignPositive\n",
" 1 → FalsePositive\n",
" 2 → TruePositive\n"
]
}
],
"execution_count": 21
},
{
"cell_type": "markdown",
"metadata": {
"id": "L8Grz1xfqLfH"
},
"source": [
"\n",
"#### **7.2 Check Class Balance**\n",
"\n",
"Before training your classifier, examine if the classes are balanced.\n",
"\n",
"1. Show the resulting **class distribution** (counts or percentages).\n",
"2. Are some classes under-represented?\n",
"3. If the data is imbalanced, explain which metric you’ll focus on (e.g., F1 score, recall) and why accuracy alone is misleading.\n",
"4. If needed, consider changing your convertion."
]
},
{
"cell_type": "code",
"metadata": {
"id": "SQ1WoHIRqNj8",
"ExecuteTime": {
"end_time": "2026-05-05T17:32:20.337446700Z",
"start_time": "2026-05-05T17:32:20.278590700Z"
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},
"source": [
"from sklearn.model_selection import train_test_split\n\n# same split params as regression — identical row assignment guarantees alignment\n_, _, y_train_clf, y_test_clf = train_test_split(\n X, y_clf_enc, test_size=0.2, random_state=SEED)\n\nprint(f\"y_train_clf shape: {y_train_clf.shape}\")\nprint(f\"y_test_clf shape: {y_test_clf.shape}\")\nprint(\"\\nJustification:\")\nprint(\"IncidentGrade is a native expert label — no threshold tuning needed.\")\nprint(\"Using it directly avoids information loss from binning a continuous proxy.\")\nprint(\"The three classes (TP/BP/FP) have clear operational meaning for triage.\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y_train_clf shape: (80000,)\n",
"y_test_clf shape: (20000,)\n",
"\n",
"Justification:\n",
"IncidentGrade is a native expert label — no threshold tuning needed.\n",
"Using it directly avoids information loss from binning a continuous proxy.\n",
"The three classes (TP/BP/FP) have clear operational meaning for triage.\n"
]
}
],
"execution_count": 22
},
{
"cell_type": "markdown",
"metadata": {
"id": "FkQXDvjZoqaw"
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"source": []
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"cell_type": "code",
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"id": "ffoT68TGqOIY",
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"end_time": "2026-05-05T17:32:20.682311400Z",
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"source": [
"# class distribution in training set\nlabel_to_name = dict(enumerate(le_clf.classes_))\ntrain_dist = pd.Series(y_train_clf).map(label_to_name).value_counts()\npct = (train_dist / train_dist.sum() * 100).round(1)\n\nprint(\"y_train_clf distribution:\")\nfor cls, n in train_dist.items():\n print(f\" {cls:20s}: {n:,} ({pct[cls]:.1f}%)\")\n\ngrade_order = [g for g in [\"TruePositive\",\"BenignPositive\",\"FalsePositive\"]\n if g in train_dist.index]\npalette = {\"TruePositive\":\"#F44336\",\"BenignPositive\":\"#2196F3\",\"FalsePositive\":\"#FF9800\"}\ncolors_bar = [palette[g] for g in grade_order]\n\nfig, ax = plt.subplots(figsize=(7, 4))\nvals = [train_dist.get(g, 0) for g in grade_order]\nbars = ax.bar(grade_order, vals, color=colors_bar, edgecolor=\"white\")\nfor bar, val, pct_val in zip(bars, vals, [pct.get(g,0) for g in grade_order]):\n ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 100,\n f\"{val:,}\\n({pct_val}%)\", ha=\"center\", va=\"bottom\", fontsize=9)\nax.set_title(\"IncidentGrade Distribution — Train Set\", fontsize=12, fontweight=\"bold\")\nax.set_xlabel(\"Incident Grade\"); ax.set_ylabel(\"Count\")\nax.set_xticklabels([\"True Positive\", \"Benign Positive\", \"False Positive\"])\nplt.tight_layout()\nplt.savefig(\"figures/12_grade_distribution.png\", dpi=150)\nplt.show()\nprint(\"figures/12_grade_distribution.png saved\")\n\nprint(\"\\nPrimary metric: macro F1-score\")\nprint(\"Classes are moderately imbalanced (BP ~42%, TP ~36%, FP ~22%).\")\nprint(\"Accuracy rewards the majority class and hides poor recall on FalsePositive.\")\nprint(\"Macro F1 weights all three classes equally — critical when missing a\")\nprint(\"TruePositive (undetected attack) is more costly than a false alarm.\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y_train_clf distribution:\n",
" BenignPositive : 33,938 (42.4%)\n",
" TruePositive : 28,820 (36.0%)\n",
" FalsePositive : 17,242 (21.6%)\n"
]
},
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""
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"
},
"metadata": {
"image/png": {
"width": 689,
"height": 388
}
},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"figures/12_grade_distribution.png saved\n",
"\n",
"Primary metric: macro F1-score\n",
"Classes are moderately imbalanced (BP ~42%, TP ~36%, FP ~22%).\n",
"Accuracy rewards the majority class and hides poor recall on FalsePositive.\n",
"Macro F1 weights all three classes equally — critical when missing a\n",
"TruePositive (undetected attack) is more costly than a false alarm.\n"
]
}
],
"execution_count": 23
},
{
"cell_type": "markdown",
"metadata": {
"id": "75xPnIjfqHEh"
},
"source": [
"
\n",
"\n",
"---\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ii0otL-qqHOt"
},
"source": [
"# Part 8: Train & Eval Classification Models\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "co-38v1UsPd2"
},
"source": [
"#### 8.1 Answer the following here, and later mention it in your presentation.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1XM_xVUGsfon"
},
"source": [
"In the context of your dataset/task, explain what would be more importatnt - precision or recall.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2026-04-22T08:25:21.947192Z",
"iopub.status.busy": "2026-04-22T08:25:21.946982Z",
"iopub.status.idle": "2026-04-22T08:25:21.950753Z",
"shell.execute_reply": "2026-04-22T08:25:21.949574Z"
},
"id": "mX2Ke1cEsfw7",
"ExecuteTime": {
"end_time": "2026-05-05T17:32:20.716131Z",
"start_time": "2026-05-05T17:32:20.682311400Z"
}
},
"source": [
"# A False Positive in this task means predicting TruePositive when the incident is\n# actually FalsePositive — the analyst investigates a harmless event.\n# This wastes SOC time but causes no security breach.\nprint(\"A False Positive means predicting TruePositive when the incident is\")\nprint(\"actually FalsePositive — the analyst investigates a harmless event.\")\nprint(\"This wastes time but causes no security breach.\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A False Positive means predicting TruePositive when the incident is\n",
"actually FalsePositive — the analyst investigates a harmless event.\n",
"This wastes time but causes no security breach.\n"
]
}
],
"execution_count": 24
},
{
"cell_type": "markdown",
"metadata": {
"id": "xbrqOTcBsf2Q"
},
"source": [
"In the context of your dataset/task, explain what would be more critical - False Positive or False Negative.\n"
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2026-04-22T08:25:21.953762Z",
"iopub.status.busy": "2026-04-22T08:25:21.953431Z",
"iopub.status.idle": "2026-04-22T08:25:21.957183Z",
"shell.execute_reply": "2026-04-22T08:25:21.955968Z"
},
"id": "YOshlmm2sf7V",
"ExecuteTime": {
"end_time": "2026-05-05T17:32:20.755770600Z",
"start_time": "2026-05-05T17:32:20.724883200Z"
}
},
"source": [
"# A False Negative means predicting FalsePositive when the incident is\n# actually TruePositive — a real attack goes undetected, causing a breach.\n# RECALL is therefore more important: we prefer false alarms over missed attacks.\n# Primary metric: macro F1 — balances precision and recall equally across all classes.\nprint(\"A False Negative means predicting FalsePositive when the incident is\")\nprint(\"actually TruePositive — a real attack goes undetected, causing a breach.\")\nprint(\"RECALL is therefore more important: we prefer false alarms over missed attacks.\")\nprint(\"Primary metric: macro F1 — balances precision and recall equally across all classes.\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A False Negative means predicting FalsePositive when the incident is\n",
"actually TruePositive — a real attack goes undetected, causing a breach.\n",
"RECALL is therefore more important: we prefer false alarms over missed attacks.\n",
"Primary metric: macro F1 — balances precision and recall equally across all classes.\n"
]
}
],
"execution_count": 25
},
{
"cell_type": "markdown",
"metadata": {
"id": "8nZOjcjZsocr"
},
"source": [
"#### 8.2: Train **three** different kinds of classification models.\n",
"\n",
"\n",
"Go to SKlearn to find different classification models. And use them."
]
},
{
"cell_type": "code",
"metadata": {
"id": "6MIie1Rws3pE",
"ExecuteTime": {
"end_time": "2026-05-05T17:34:37.430935800Z",
"start_time": "2026-05-05T17:32:20.755770600Z"
}
},
"source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, f1_score\nfrom sklearn.model_selection import cross_val_score\nfrom xgboost import XGBClassifier\n\nMODELS_CLF = {\n \"LogisticReg\" : LogisticRegression(max_iter=1000, random_state=SEED,\n class_weight=\"balanced\"), # balanced: prevents FP class collapse\n \"RandomForest\": RandomForestClassifier(n_estimators=100, random_state=SEED),\n \"XGBoost\" : XGBClassifier(n_estimators=100, random_state=SEED,\n use_label_encoder=False,\n eval_metric=\"mlogloss\", verbosity=0),\n}\nclf_results = []\ntrained_clfs = {}\n\nfor name, model in MODELS_CLF.items():\n model.fit(X_train_eng, y_train_clf) # train on engineered features\n preds = model.predict(X_test_eng)\n acc = accuracy_score(y_test_clf, preds)\n macro_f1 = f1_score(y_test_clf, preds, average=\"macro\")\n wtd_f1 = f1_score(y_test_clf, preds, average=\"weighted\")\n cv_f1 = cross_val_score(model, X_train_eng, y_train_clf,\n cv=5, scoring=\"f1_macro\").mean()\n clf_results.append({\"Model\": name, \"Accuracy\": round(acc,3),\n \"Macro_F1\": round(macro_f1,3), \"Weighted_F1\": round(wtd_f1,3),\n \"CV_F1\": round(cv_f1,3)})\n trained_clfs[name] = (model, preds)\n print(f\"{name:15s}: Acc={acc:.3f} | Macro_F1={macro_f1:.3f} | CV_F1={cv_f1:.3f}\")",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticReg : Acc=0.416 | Macro_F1=0.402 | CV_F1=0.406\n",
"RandomForest : Acc=0.733 | Macro_F1=0.700 | CV_F1=0.694\n",
"XGBoost : Acc=0.717 | Macro_F1=0.669 | CV_F1=0.669\n"
]
}
],
"execution_count": 26
},
{
"cell_type": "markdown",
"metadata": {
"id": "wELPknwqsOG_"
},
"source": [
"#### 8.3: Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bEzxJLmVsvVx"
},
"source": [
"- Evaluate the Classification Models.\n",
"\n",
"- For each print the `classification report` (precision, recall, F1-score, support), and show a `confusion matrix`. (use SKlean built tools) Comment on what types of mistakes the model makes (based on the confusion matrix).\n",
"\n",
"- Identify which model performs best and try explain **why**.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Rozrn2petFKA",
"ExecuteTime": {
"end_time": "2026-05-05T17:34:39.694685200Z",
"start_time": "2026-05-05T17:34:37.594720700Z"
}
},
"source": [
"from sklearn.metrics import classification_report, confusion_matrix\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nTARGET_NAMES = le_clf.classes_.tolist() # alphabetical: BP, FP, TP\n\n# combined 1x3 confusion matrix figure for all three models\nfig_cm, axes_cm = plt.subplots(1, 3, figsize=(18, 5))\nfig_cm.suptitle(\"Confusion Matrices — All Classification Models\",\n fontsize=13, fontweight=\"bold\", y=1.02)\n\nfor ax, (name, (model, preds)) in zip(axes_cm, trained_clfs.items()):\n print(f\"\\n{'='*55}\")\n print(f\" {name}\")\n print(\"=\" * 55)\n print(classification_report(y_test_clf, preds, target_names=TARGET_NAMES))\n\n cm = confusion_matrix(y_test_clf, preds)\n sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", ax=ax,\n xticklabels=TARGET_NAMES, yticklabels=TARGET_NAMES,\n annot_kws={\"size\": 10})\n ax.set_title(f\"{name}\", fontsize=11, fontweight=\"bold\")\n ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"Actual\")\n ax.tick_params(axis=\"x\", rotation=30)\n\n # most common error type\n off = cm.copy(); np.fill_diagonal(off, 0)\n r, c = np.unravel_index(off.argmax(), off.shape)\n print(f\"Dominant mistake: predicts {TARGET_NAMES[c]} when actual is {TARGET_NAMES[r]} \"\n f\"({off[r,c]:,} cases)\")\n\nplt.tight_layout()\nplt.savefig(\"figures/13_confusion_matrices_all.png\", dpi=150, bbox_inches=\"tight\")\nplt.show()\nprint(\"\\nfigures/13_confusion_matrices_all.png saved\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=======================================================\n",
" LogisticReg\n",
"=======================================================\n",
" precision recall f1-score support\n",
"\n",
"BenignPositive 0.45 0.34 0.38 8359\n",
" FalsePositive 0.28 0.34 0.31 4412\n",
" TruePositive 0.48 0.55 0.52 7229\n",
"\n",
" accuracy 0.42 20000\n",
" macro avg 0.40 0.41 0.40 20000\n",
" weighted avg 0.42 0.42 0.41 20000\n",
"\n",
"Dominant mistake: predicts FalsePositive when actual is BenignPositive (2,925 cases)\n",
"\n",
"=======================================================\n",
" RandomForest\n",
"=======================================================\n",
" precision recall f1-score support\n",
"\n",
"BenignPositive 0.70 0.88 0.78 8359\n",
" FalsePositive 0.71 0.46 0.56 4412\n",
" TruePositive 0.79 0.73 0.76 7229\n",
"\n",
" accuracy 0.73 20000\n",
" macro avg 0.73 0.69 0.70 20000\n",
" weighted avg 0.74 0.73 0.72 20000\n",
"\n",
"Dominant mistake: predicts BenignPositive when actual is TruePositive (1,749 cases)\n",
"\n",
"=======================================================\n",
" XGBoost\n",
"=======================================================\n",
" precision recall f1-score support\n",
"\n",
"BenignPositive 0.67 0.91 0.77 8359\n",
" FalsePositive 0.73 0.36 0.49 4412\n",
" TruePositive 0.80 0.71 0.75 7229\n",
"\n",
" accuracy 0.72 20000\n",
" macro avg 0.73 0.66 0.67 20000\n",
" weighted avg 0.73 0.72 0.70 20000\n",
"\n",
"Dominant mistake: predicts BenignPositive when actual is TruePositive (1,962 cases)\n"
]
},
{
"data": {
"text/plain": [
""
],
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7WB5PvbSomE8Gz90vp64nLSiuCjSNalFa6pc2T/z3z+kqz5f318sPm87IL/9a/k6uepCPjG5dTifWJVauQC69tK4cIG/OO6CLQAHZHjEpWECSnEr4CQ2VRYsWybZt2+Ty5cty+/ZtcXd314GfqlWrSqtWrSRfvnyWnj8gTbbMn2pKkCtWtaZUa9pRfPIXktuhIXJm/zbZtmSm7vC2dNwo6fLRZPHyzaPPDQ8JlqXj3peYO5Hi5plTarTrrju/ObnkkBsXzsi2pTPl0rEDcnjLavHI5SvPduxpdt1rZ0/Iyh/G6AS5fEVLybOdeotfwSAJC74i2/+crbvD7V2zSHzyFZTKDVqZjY2LjZGl49/XCXLqek+16Cwlnqwtzq6ucuHwXtkyf5pOtlvx/Wjp8uH34pufSUlAdrZh/Tp5583BuhtkWowaOUInyDk7u0jf/gOkafMWuqL35o0b5ZuvvpDga9fkjf595I9Ff4qHh0e6XTsmJkYGDeirJyi5urpK79f7SqPGz4ubm7uuxvTt11/K9evBMvStwTJ3wWIJCiqa5ucAQNZQndl6d6iV6nk/fviSTpCLiY3T3ebmrNiht5vULi+jB7WRAv4+8se416Vah0/ldlSM2djKpQNlxpgeOkFux4EzMuLbRXLoxGUJKugnw3o+L63qV5b+XerJ8bPBuqtcYgX9fWTeN6+JT04PuXI9TD6YtFTWbz2qjz33VCn5aGArfe354/rIE+0/SbWbHQAAD4OYFDLTzv82ybhPR8i9VD6b5ckXID8v2pjiOerz3efvDZajB/eKt29ueaXfO6lef9fWv2Xt8gWpnnfu9An5+sO3JTo6SlxyuErbzq/KM/Uai7u7pxw5sFtmTZkgly+ck/eH9JTPJ/8uuXx8U31MANnHuG++MiXI1avfQF55tZcEFS0qwcHB8vemjfLD99/pCUID+/WReQuXJPvdzO3ISBkyeKDs3rUzzdc1Jsg1fr6JvPzKq1KwYKBcunhRZvwyVf5atVL+mDdHAgICdDwKgHVRua2l8xuS5D5ecliW7rVcZEmJTVQFO18uV5nQtYrkcneR0NsxMmHNSfn7+HVdKbukv5f0qVdMqgX5SusnCsj1iGj5dvUJs8fy88ohU16pJr6eOSTsTqxMWHNCNh69Ls5ODvJc6bwyoGEJ8fHIId92qSytxv0jt2Pi9bhdZ2/KUx+vS7U73S89n9RVxE8FR8ikdScf81kCACABMSlkpt1bN8ukMf9LNSalnD99Qr79+B2JuR8Xav1iD6lR93lx8/CQowf2yJxpE3Si28dv95TR382WXN6+STrQjf1ff4kIuyUenl7SoVtfqfJULYmPj9P3MX/GZIkMD5PxnwyVsT/MNSv8dObEEfnu83clPi5O/PLm14lwZStVU6kxcubEUfljxmQ5f+aELJnzs+TO4y/1m7fPkOcLQPacRzVx/LemBLlatevIa336SWBgITlz5rR8N3G8nmO1dMkiKVW6jHR7pYfZ2O8nTTAlyNWuU1d6vdZHChUqLOfOndWxsH//+VuWLF4ogYUKyet9+2fAbwsgI3NzSuQ1vJ/4fNVxWXnwarLnxsSbV18a2ay0TpCLjb8rU/8+K6sOXZPY+Hu6s9uA54pK3pyu8kX7CtJ12g6JijV/rRr2fElTgty+C7fkp7/PyrGrEboIVNsqAdLpyYLyep0gHTP7+R/zRLnieTzks7blxT2Hk0THxsvP/56T1YeCddyqcqFcMuC5YlLEz0N+fKmKvDRtp4TeiU3HZwwAMofdlx2YM2eO1KtXTz777DNZv369HD58WM6ePStHjhzRP3/99dfy/PPPy+zZszPpTwJbE3Hzhk5EU0rXqC8tBn4gASXKi7tXLvELDJJqTTtJu6FfiKOTk0RFhsuOZb+bxqptlSCnktTaDftCKtVrKbny5BdP79xSuPwT0n7Yl1L8iWf1ubv/WqC7yyX238LpEhcTLd7+BaTtO59LYOlK+roqYa75gFE66U3ZuvhXibljPuH7xI7NcuuaobVvg1cGS/WWXcQ3oJDkzO2vO9e1fWuMvq/42BjZtXJ+hj+PAB6NCuaoYMyQN/rritlpcfDAflNwZtj/RurgjJo0lDevv7Tr0FF+nPaLuLi46ElEs36bka7X/mvVCh0EUkZ9+LGelBRUtJjkDwiQlq3byOQp03TynOp0N+PnaWl6TABZJ4+vl3z/ftdUz1Pd2jo1eVJvv/X5fPli2l9y7nKITlr7ZeG/0uz1CRIdEytBBfNI/y7PJRk/ql8L8XDPISfOXZMmr42Xv3eekJBbkbLr0Dl54a2f5I+/dunz3u3TTLw8XM3Gvv1qY50gdycqRlr1nyTTF/2rr62WGYv/k3ZvTJb4+LuSP08u6dne8L4LyM4cssE/ANKGmBQyi/psNm/GD/LVh+9IXBo+m6kOSm7uHikuS+f9qhPkHBwdpf/QjyRvvoAUHzMs9Kb8+M3HabrfmT+N0wlyTk5OMvTjb6Rtl1d1xziVDPdUrfry0bfTJI9/frl1M0RmTZ2Q5ucBQNa7du2qKZbUrEVL+XbCd1Kl6hPi4+MrJUuWkh49e8vUX2aIs7OzrsI99acfLD7OiRPHpcuLHeS/f7ak+bqzZ/6mt5s0bS5ffD1OKlWqLH5+flKxUiX983P16uvj03+ZpuNOAKyLSiTzdDXUht19LlTuxMQnu8QlagfXq05RnSAXFRsvr07bKXO3X5BLoVFyIyJG/jsVIq9M3SFrDhkmN3V7pojk8TLvNje0aSmdIBcZHSe9f9klv2+7IJdvRcn5kDvy67/n5O05+/R5qstcqyoJ75fULaR0j2p5u0kp/Xup7Td/3yfhUXSSQ/aW1fEoYlJA2hGTQmbGpBb89qNOeouLS9t8gdlTx+kEORUXeuuDr6XVi6/qjnEqGa76s/Vk1NdTdQKbigvNmTYxyfhZP32rE+RU/Gro6InSsGVHXRAqX4FC0qRtFxkw4lN9nuoyt2XtcrOxKoFOJcjl9PbV13m2flOdDJc7Tz55okYd+WDcL1K8dAV97rwZ36cpzgYg+3mUuUwqFvXLtCl6u1nzFjLhux+kcpWq4pcnj1R7srr8NG26VKxUWR+f/vNU3TnOSMWZZv46XW/XeOZZPVbFw9TYqk9Uk0mTf5Qnqj1pGquKiwOwHoX93E0xKZWsdif2brJLfKKYlOo+17icv97+Zs1Jmf7febkSFi03ImNk6b4rMvD3fRITd1cK+LjJC9XMu+dWKJBTmlYwFJdT8at+s/fJjrOhEhYVJ2du3JZv1p6UietP6ePdaxaWQF83s/ED6hfTCXJx8XflrfkHZfq/53XHOJUMt/HYDen96x4d31LFofo/RwMDZH9ZHY8iJpU92XWS3LRp0+SDDz6QO3fu6HbIQUFB0rBhQ2nZsqXUr19fChcurPdHRUXJRx99JL/88ktW3zKskOrWdvf+B5+a7V6xeI5KWitW9Rm9fWbfNrNENaXUU3UlT2BRi5OWarTtrrdVt7hzBw0TwJWQy+dMj1W9+YuSw809ydjaL74mDg6OEhURJid3mU8ouHrK0EHFxdVdSj9dL8m1VYJfgRLl7p97JI3PBoDMtOXvzdKpfWv54ftJOshTrnz5NI2b8cvPel0wMFDad+iU5HjZsuWkZas2envBH/PS9doH9u/Xa9WdrlnzlkmOqwlTKlik7N+3N02PCSDrfPdeZ51cppLNUjLoZcNkxNMXrsu0BUknOe49ekFm/ml4X9OjreE9k1GpoHzSrI7hC6nPp/4lkXeSBo2Hf71AJ7qppL3WDaqYHWtd3xCsXrZxv+w/djHJWJVod/SMYTJU9QpBqf7OAACkBTEpZJa9O/6V4f26yoKZU3S17qIlyz72Yx4/vF+WzDF8qd+k9QtS8YmnUx3z07ej9eSluo1apHjerdAQ2b9rq95+7vnWUqFK9STnqGS59i/11tub1iyTmzeuP+JvAiCzrV+7RuLiDEkeAwcNsXhO+QoVpX6Dhnp786YNZsfCbt2SLz4bIy92aCunT50Sd3cPKVqsWKrX3bhhvWlCZnLVuJu3bKXX4WFhcub06Yf8zQBktXIBhi5yt6Pj5OS1iDSPa1TeMKFo5f4rcvyq5XGqu5zi4uwoz5TwM+3388whje+P/2njaTl0KSzJ2M3Hrsvp65G6e13ZArnSfF+Ny/tLi8qGpLpxq4/LyWuRaR4LAEBKiEkhs+zb+a+8N+AlWTTLEJMKKlEm1TFhoSFycLfh+8A6jVtJOUtxIW9fadvVEBf6e+0y3TnO6NbNG7Jt81q93fKFHlKslGFOU2Kqq1z+goXFydlZzpxMmOt053aEHNxtiEk1bNFBfP0MnVkSc3HJIW269NTbqhvdiSOGuQ0ArMejzmWa+/tsHdPK7ecn/3vvA3F0NJ92rX7u+lI3vR0VdUfO3y8Orpw6eUJu3zY0L2jXvoOes5mYSgpu39EwNysyMlJOnjTvYA4geyud735MKiZeTt8wb1SSks7VA/X6YugdWbz3cpLjx65Fyor7XelaVs5vdqzJ/XiU6kA3ZuVxs+Q7o1nbLujHdnV2lBefNFxL8fVwkaeCDJ14VTLeznOhScaqZLmpWwyvYyoZT8XAAMDaGNKX7dDJkyflq6++0klwlStXlg8//FDKlEn6gVx1llPH9uzZI19++aXUqVNHiqXhS1fASHV3c87hKjncPCRXHsObE0t88hXQ64jQEL2+E3H/yzQHB8lfrEyq4wxjEzrJnd2/wzS+aJUaFseqrnB5CxeXa2ePy8ld/+gOcUbGD2R378bLPblnsR+Eo5PhJcTB0Yk/OJAN9Xu9l147O7tI79f7SPMWraRF04T/zi1R/1/cssWQoFunbj0djLHkufoNdILcxQsX5OiRI1L6gf+HPsq1Fcf7rz2qqpK6F0tUNXF9bjL3BiB76N6mprSsV1nOXrohb38xX7q1tvx+RGn0jOFLqhWbD8hdC8Eb5c8N++XVds9K0cA8UrFUQVNC2/PPGsaqIPbyTZa/jLpwNVT2HL0g1coVllbPVZKZSw1fcinlWn4gpYPySfjtqFR/p9i4hIpvAAA8KmJSyExjR76h12riT5vOr0rt+k1lcI+2j/x48fFx8tO4T3W8SFXh7vRK31THrF+5WHb8u1Gf363vW7Jx9Z/Jnnv6+GHTZ8GnaxsKKVhS6cmaeq0mWe3b9V+qyXcAsofga9fEzc1NPD29pEAB8+q3iRUqXMR0fmIzf5shv80wFDMsU7acfPLpWF3sSSXMpaRjpxelTp3n5MyZ01KsePFU79MYewJgPYwJaIcvh+subWnh7e6i1yoWte9C0gQ3o3MhCROc8uZyNW03Ku8vzk6OehLUrP/OJTu+w6T/dOXvtPJydZbhzQ3x9r3nQ2XW1vNpHgsAQEqISSEzffneIFNMqtULPeSZ+k3lnZ7tUhxz+vgRU1yoeq0GyZ5XsVoNU1xo/67/pHZDQ1xo+9/rdMwqh6ubNG6VtBiw0SeTZkqOHAnv65TgK5fEzcNTJ78Zu8VZ4h+QMMlcdaMDYB/zqP5atcIUY8qZ05AQ86DGTZpKw0aNxSWHeTKJamBgZCwe9SB1P0ZODyTgAcjeSufz0uujVyPSHJNSahQzJKptORGS7LjNx29I68oBUtDHXUrk9ZQTwZGmLnTKocvhci082uJY9ZDbz4RKwSruUrNY7oT7ze9lmp+5/mjyRSi3nr6p106ODvJ0UV9ZfsCQsAcA1sJuv+mbPn26noBftmxZmTFjhri6mn/4NVLH1bmdO3fWCXNz586V4cOHZ/r9wnqp7nFqib6TcpXH0KuX9NrNw/AGxt0rl/QeN1fi42KTTRRJPC7xWCX4nKGyZS4/f3HP6Z3s+LxFDElyakksX7HSeh0fGyPHtm6QMjXNA1ChVy/KpeMH9HZAyaTVlwBkPZXsWr9BIxk4aLAULVZcLl68kOoYdY6qmK2UK5d8xSQ1Ecno0KEDSZLkHuXaSoWKlfQ6OjpaViz/U1q0bG12/NzZs7Jr5069bewoByD7UYlsX7zdXieu9R71m4RHJp+AVqSAn/jm8tDbuw4nP+ln75GEY1XLFjIlyVUqbfgy6tzlELkRGpnieJUkp8YmFhUdqzvVJaf+02WkXHFD5e41/x1O9jwgu3ig+KDN++STT+TXX3+VMWPGSLt2yX/BHhoaKk8/nXqnIx8fH9m6NSGRNrGjR4/KlClT9PGQkBB9boUKFaRLly66oE5KYmNjZfbs2bJkyRI9GUV9xitYsKDuZt+jRw/9WLAfxKSQmdRns+rPPCedXuknBQsH6Qk/j2Pt8oVy/rShkm3XXm+Im5t7iudfvXRBZkz+Wt9H37feFw/PhNiVJRHhCRPUVVJdclTVcKNzp8xjWgCyrwGDhuglIiLlLk/Gatu5ciWNa+fNm1d6v95X2nd84aGS2fLlz6+X5N6rzZk9S2+r5L0iQXQRB6xNuQI5TUly7asV1F3YSgd4iYuTo1wKvSPrDwfLz3+flVt3DF0lFbX93GcbxdnJwTQ5yJLCuQ1xKyXsTsKExgqBhteoAxduyZ1Y8yQ4Z0cHibs/w+lhEuSU3nWLSt6crjp5b+yyo5LCV4RAtmJvMSnAGhGTQmZSsaBqNZ+TDt37SoFCQRKcaG5TciIjEsWF/C1/flNyJooLGeNUysljB/W6eOny4vpAzEolphg/Qz6YIKcULlZKvp+zRmJiosUxhULh1y4nfKfo6ZX2TsEArHse1c0QQ9ODp56ukexriypCbqkQuYozqcS68PBwWbJ4kTRr3tKsm5z6znDJogV629fXV4oEFX3s3xNA5jEmrB27GiGtKueXJuX8pWQ+L3FxdJDLYdGy6fh1mbn1goRFJcSUArzdJJebiym5LjnqMRNfx5gkZxx75ZblBDmj0NuGOFgBHzfxyOGkCz0ZxyqXw5Kfy3Xz/lileF7PFK8DZDViUrDEbpPk1KQ29WZz0KBBySbIGanjb7zxhvTp00e2bNmSafcI2+LqnvwbhYibN+T0XsNEzICS5kkpTokqhVhyYMMy03aBkgnVjMKuGzL3c+VJPnCk5PQzdLeLuHld7sbHmzozlaxeV3b/tVCunTkm66aP0x3xilV9Rlxc3eTS8YOyZd5UiY2OEq/ceeXJZi+meA0AWWPRnysk6CGDJ5cuGpJOlIKBCVXQLE1IUpWM4uJidTe59Li2sbKSqgh+8OAB+fjD93XF8HoNGoq7u4fs2b1Tvv3qS7lz57bkzx8gPXu/9tCPDyDjOTo6yLRPuklOTzeZ8Ns62bwz5UnLRQokVCw6czH5KkWXr4dJTGyc5HBxlqCCfknGn7mY0FHXEpVEpxTM5yNOTo4SH3832fv38/aUYoXyyovNqkuPtoYuJeu2HpHfEnWgA5D11qxZIzNnzkzTuYcOHXqsa61du1bHD9QEaqPg4GBZv369Xl5++WV59913LY5Vyf+9evWSbdu2me0/ceKEXhYsWCBTp06VUqVKPdY9wnoQk0Jm+mrKPAkINHRkelwx0VHyx29TTJONatRpmOL5Ks406fNREnXntjRt21nKVa6W6jXc3BMmoUfdTujaktKkqZDr5p2mAGR/Xl7JJ8xeu3ZVNm5Yr7erPmH+utGqTVvp1fv1JBW5H8Xt27clOPia7Nm1S8eijh07qmNd7476gE5ygBUqE2BIkutUPVByOJtX3S+W10svbZ4oKG/M3CP7LtwyOx4Xr7LQks9EU49ptPusoYq2UtLf8Fp27obhPctzZfJKl6cLSeVC3uLh6izXwqJl3eFr8uPG0xKcTFXvB6nkuK41DAWeVh+6KgcuJt/hDgCAh0VMCplp7A9zHzomZRYXupN8XOh2MnGhi2cNXcbzFTC8n9r13yZZvWSunDiyX6Kj7ohP7jzyRM260vrFV8XXL6/Fx7aUQJfYuuWGRBaVCFO8TPId5wBkT48yl+nE8YT5DoWLFJGbN0Nk2pSfZM3qVXLl8mX9elC+QkV5sctL0rRZ8yTj3d3dpU+/AfLFZ2Pkv3+2yJA3+survV6TgoGF9BytqVN+kC1/b9Zzmd96Z3iqc5kBZC+l7neSa1slIElMKsjPQ4L8CkuLivll6IKDcvBSuN4f4J3w3/ml0OQT1a5HxEhs/F1dBEoluhndjjEk3KnEt5TkdEtIEcmbM4ecvXHHNNYwPvkUklyJxvrnfPx4PABkNrtNkrty5YpeV65cOU3nG8+7mCh5AEgPqhrIuunf6o5tSqX6LdM89vKJQ7Jv/VK9XaBURfELTKhwGxVh+JLPNZUK3abkvXv3JPp2hKnrnEqWa/v2WNn8+w9y5N81OilOLYmVrtlAnmnfQ7x8EyaqA8g+HiVJLTQ04Uv+XLmSr3zm6Ogonp6ecutWqITd7zz3uNdWVIWlH6b+Il9+Pkb+XLJYvv36S70k1rxlKxk0+C3x9zck+QLIXt55tbHUqFxMDp+6LO9NWJLq+Xl8Et6rhIbdSfE9U3hktPj5OItvzoQvyfzujw8NT36sEhYRZXr98snpnmzXudrVSsrKH98w2zdp1np5d/wSiXvIyt8AMs66detk8ODBumNlWhw8aKggmz9/flm+fHmy5yWu3Jg4we7NN9/UCXIVK1aUoUOHSsmSJeXChQsyefJknaynutkVLVpUunbtmmT8iBEjdIKci4uLDBgwQFq0aCE5cuSQjRs3yhdffCHXrl3TRXn+/PNP8fBIeH2D7SImhcyUXglyysbVyyQs1FB4oM2LPVI9f9GcX+T44f1SsHBRefHV/mm6RuGiJUzb+3dtlaIlzbuWGx3as8O0fed28t2EAVgX9bnvow9G6SIDygudu5gdL1gw+YJOD6t/n96ya2fCa4kqyPTF199KpcpV0u0aADKH6vSW834VbNXBbe62C/LHzgty8WaUngDUvHKAdH+2iPh55ZDvXq4qL3z/n1xMYQJSYirh7YWnDK89O07flBPXEt535MnpaupIN6pVWemYKJlO8c/lKi8+XUier5BPBs7cI3vPmyfnWaIS5FxdDBOcVHIdAADpiZgUsntMqlBQcdP2gd1bJahEMnGhvTtN21GJ4kKhIYZinJ45c8nPE8bI+hULzcap4+uW/SHbNq+RIe9/JSXLVnqo+1Pjdm/drLdrPPe8eHoZCjUAsB6PMpfpenCwaVslxQ0e2F9u3Ego/qu+q9yze5deNm/aIB+PHpuko9xL3V4RD09P+W7ieFm/bq1eEitWvIS8+fZQqV2n7iP9XgCyRqCvm3i5GtIwnJ0cZOHuS7J47xW5fCtK8njlkOfL+UuXpwIlt2cO+bpDBXll+m59zMc9oXFKeFRCkd4HqZJOqvubt7uj5Lx/HeVk8G0pnT+nVArMJa7OjhKdzFymJwob5oIrnvcT4tRYo6eCfMy61ZmP9UkYm+jaAGAtzNOW7Yjxjajxy9bUGM+zNGEOeBwqCe3MPkNHgdI16kmhsmn7Ev7m5fPy58QP5d7du+LkkkOe69rP7Hjc/Q4Hzi4pVxdxSlR1N+5+op6RSppzcnYWRyfLb3KunzspV08fTdP9ArAOif+/6OqaUIHEElc3w+tLTEza/l+aVuHhYXoSubOL5U6aR48ckf3796XrNQGkj6plC8n/XmsqsbHx0vPdGRKdqAJRclwTBVPuRJu/F3lQ1P3jrq4Jrw9u9wM5d6JSHpv4sd1yJN+pt3BAQmc7o+5tnpFP3mglzg9UfQKyI4dssGQk9UXT+PHjpX///mZd3dKaJFepUiWd6J/cYilJbdy4cRIVFSVFihSR6dOny1NPPSW+vr46YW7ixInSpEkTfZ66r4gI8yDy/v37ZdkyQ/fvkSNH6mS4wMBA8ff3l44dO8ovv/yi3/eogjwzZsx4zGcH1oKYFKw1cWXFwlmmCUvVatZJ8fxTxw/LgplT9L/v/YZ+mGoVbiP//AWlWKmyenvpvF/l6qWknctVUtz8334y/aw6nAOwDV9+NkY2b9ygt5s1byFPPV0jw651+dIls5+vXLksn3z0gVniHADroJLRrtyKkvi79+TdhQfl46WH5dClcJ28ppLaxq0+Ie/M3a/P9fZwkTebpK2Ld1AeDxnXpbI4OzlKVGy8jFl2xOy4p6vhu+aWVQroBLmdZ25Kt5+2S7UP10qdMRtk9NLDEhkdJ76eOWRclyp6clRK3HM4mRLtNh0NlmNXLE9SArKrrI5HMYsDSB0xKWR3efMXlKIlDXGhZfN/lauXLceFFs6yHBcydp/bsna5TpArXb6KjPziR5myaLNMnL1KuvV9R3eriwi7Jd9+9I4pqS4tjh/aJz9+/ZHezpnLRzq9kraCUACsX2SiZNw3Bw2QiIhwGfLWO7J6/SbZvnu/zPx9ntR8ppY+vmzpEvl+4vgkjxEfH6+/a3RP1DEzsatXLsuO7dvkzp2UCwMDyF7yernK1bBoHZP6ZNlR+fyvE3L0aoSERcXJqeu35ftNZ+TdJYf1ubncXWRAPUOibuKOc8kluD14PPGYtUcMybve7i7St25CY5XEWlXOL0Xz3G+gIiIuToZPzSpJ79BlQ0e7l54uJAUTdagzUh3qetVKKHjg4sgnbmRvWR2P4r+Q7MluZ3mqyW3K5s2GCi+pMZ5XqJChJTuQHpOLNv3+g+xZbahc5BdYVOp1G5SmsTcunpUFXwyTO2Gh+uf63d6QPIWKmZ2juqQ8jhsXz8jc0YNl/4Zl4uGdW5r2e1den/iH9PthqXT839dSpMKT+pzlkz6WPasXPda1AGQfTo4pt+HOaCdOHJeXO78g8+b8Lnny5JEvvx4nm//dLtt27ZPpv82WZ2vVlhPHj8lbgwfKzN+YSA5kJ26uLjLtk+6Sw8VZxk5ZKbsPn0/TuLvxqvbRo1PBpvT015aDElhvmHg/NVhqdh4ri9buES8PV+nfpZ7M+qJXul4LwMNRn8tbt24tkyZN0sly5cuXT/NY1Q1OUYltD+PkyZOyYYNhovbrr7+uE+kSU4V0hg8frj9/hYaGyurVq82O//zzz3qtEuM6deqU5PHLlSsnbdq00dvz5s17qHuD9SImBWu0e+vfcvnCOb3dslO3FAuJxURHyaTPRkl8XJy06dJTit2f3JRWXXsNEkdHJ4kIvyWjhvSUtcsXSsj1axIWelN2/LtR3h/SU65cPC8+uf30+c7OyRdAAGA9sfIvPhsjv/06Xf9cslQpee99w+TDjDL5p6l6ItP6zf/K+x99Ij4+PnL0yGHp+1pPXfkbgPXYceamNPpyszz50VpZuueyxXPWHromG+5PIGpQ1l9yuaVcAbtYXk+Z0qOa+HkZEv0/XnI4SWVtt/sd31SS3tZTIdLr552y+1yoxMTdlZu3Y+X3bRek/6+7dexKdbHrWSfljgVtqhbQE6aUKZvOPMQzAABA2hCTgjV4secbOi4UGR4mH7/VS9avXGSIC926Kbv+26T3Xb14Xrx9k8aFoqMN3YJV8lvZStVk2JjvdKKcKt6Uy9tXGrbsKG9+8LU4ODpK+K2b8udcw2fQ1BzZv0u+HDVIx7xUofG+wz4RX7+8GfQMAMhuohIlrl2/fl3GTfxeXnm1l/j755McOXJIhYqVZNLkH+WZZw2JctN/mSbBwddMY+Li4uTtNwfJZ59+IpcuXpDX+vSTZavWyI49B2T5X2ul34A3JCYmRn6ZNkX6vd5Lbt9O6PIEIHvbff6WtPl+qzz31d+y4mDCf/eJbTx2Q/4+cUNv1y2ZR3eEu/uY85z+ORUi28/c1NsvPBkon7QuK+UCcurHLpLbXSfODW1cUq6FJzQ+iE00N2vi+lMSd/eeTrL78aUqOqEur1cO3eGuVoncel+gr7tcjzCMj03neVkAkBnsNkmuTp06+otXVRH+8mXLX5gYqePqPDX5o25dWhrj8cXHxcpfU76QPX8t0D/7BhSWNm99Kjnc3FMde+nYAflj7FsSGWp441Sncx8p+2yjJOc53+8A9WB3uCT3EpNw3DlRVe/1M8bL7VshktPPXzqN/FZKPllbXD28xNklhwSUKC+thnwiZWo20Of+PfdHuRV8Jc2/P4Dsy90j4XUoOpUOcdFR0WnqOPcwPvnwfbl+PVgCAgrIr7PmSqPnm0iuXLnE1dVVqlR9QiZN/klatGqtz/36i8/kwoW0JeEAyHhjBreRMsXyy44DZ+SzqavSPC7y/mtJah3e9HFXQ8XtqOiEqpCRd6JNSXopcb8/VrmTaPyDrt4IlxuhkRITGyd7jlyQzm9PkVl/Grr+tnyuktR/ukyqvxOQpWy4RFKvXr3k2LFjuvPawIED5dtvv03TONXd7dy5c4+UJGcsmKPiAfXq1bN4TkBAgJQta0gAWbNmjWm/ijkYx6uxxkrND2rQwPC56sKFC3LkiHlnAtgmYlKwRn+vX6nX7h6e8nRtw+tWcmb+NF4unT8jxUuVk7adezz0tcpVriavvfmunnQUFhoiU8Z9Kv27NpfXX2gsX33wtk6Qe23ISMlfsLA+X1UAB2C9YmNiZOSIofLbjF/0z8WKFZfJP04TjweKE6S3oKLF9ESm3LlzS7v2HWXKL7/q+JOq6v3Nl59n6LUBZIy4VAoxrb+fJOfk6CDlC+ZK9ryqhX1keq8nJV8uQ9x77LIjssRC8p3qLmf05cpjenLRg3aeDZVNxwwdShqV80/x/ppXyq/XZ65H6mQ7wOpkdTyKst1AqohJwRqo5Laeg0ea4kI/j/9UBndrIQM6Py/ffvS2XL10XnoOSogLubonzG9IPG+hc+/B4uyctDBCmYpPSJXqz+rt7VvWp3o/6pwv3hukO9ipGH+/oZ9IhapPpdNvC8AauCV6nan7XD2p+YzhNSQx9frwxuA39bZKeNu8caPp2JJFC2XdGkORzdFjv5D+AwdJYGAh/X1nwYKB8nrf/jJ+0mRdkHPXzh3y63RDAU4A1sNSTCixTcdvmGJSZQK85E5sQve4xB3iLHG9f/zBjnPvLj4s+y/e0tsNyuSVqd2qyl+Dn5Hfe1eXbjUK625x36w5aTr/TqI4lkruG7PimMTG35XcnjlkRJNSsqR/DVnxRk35on0FnSCnjp8PMSQJ34lJGAtkS1kdjyImlS3ZbZJct27d9KT7GzduSPv27WXWrFl6OzH188yZM/VxVQVCVYzv3r17lt0zbENURJgs/HKEHP13rf7Zv0hJaT/sC/H0zp3q2CP/rpOFXw6XqMhwcXBwlPrdB0mVRm0tnuvqYZhEEHMnoeW3JdG3DZUvVaUkNw8vvR1y6ZxcOn5Qb1dv0Vk8cvkkGacmiaoEPUdnF7kbHy8HN61I9f4BZH85cyZMDogIN7TWtkR1b4mMNLy++Pr6psu1T508Kbt37dTbvV/vqycpWXrtGTrsfzpYpKotLfxjfrpcG8DjaVizrPR5sa7ciYqRnu/9KvHx5sGZlISGJ1Re8/ZKvmCA+u8/p6chof/6zYTK3bfuj8+Vwlj92DkNx+Pi4uVm2MNVXxs1YYlpu+VzD5dgAyD9qNeBxo0by+LFi2XAgAFp7p598OBBnbCmxru7u8uoUaOkfv36UqFCBalRo4a89tprsjHRl1WJHT58WK8LFChg8b1J4o5wxmsZqaS3sLAwvZ1S1zvjWOXAgQNp+p1g3YhJwRoTWPZs26K3n6xZV1ffTs7eHf/KX0vniUsOV+n7zofi5JRyl5bk1G3UQsZ+N1NqN2imK3M7u7iIX9588tzzreTTSb9K3cYtJfyWYfK4T+48j/ibAchqt0JD5fXer8qypYbPXOXKl5ep03+TPHkzvyJ/yZKlpHmLVnp7z57dcvNmSKbfA4CMdTk0IQbl65lQTOnBRLWfXnlCfDxy6A5wHyw6JDP/s1yoLTLaMEEo7E6sHLmcfCx9x2lDZe983m7i6Wq5eErenK5SMdBbb6/cf/UhfisAANKOmBSsRe2GLeSTib/Js/WbGuJCzi6SO28+qdO4pXw0YYbUbtTCYlzIWEjJw9NLgoqXTvbxVaKccvPGNblzf86UJcvmzZCJnw6X2JhoyeHqKoPe+0Kq16qfjr8pAGvgeX8OplL9qaeTPa9M2XJ6frFy8uQJ0/4/5s/V6ypVqkqTps0sjlVd6Oo3MDRJmDf393S7dwDZw9WwhOLhvu4uEhEdZ/rZyzX579FU3o1HDkMsKfSOeTHwsKg46Ttrn3z513E5eClMbsfE68fdfzFMPl91XPrM3CNOiaZT3Igwb7ay/MBV6fbzLllx4KoEh0dLTNxduRIWJUv3XZFXftmlj/t4GIqVX39gLABYg0ebpWAD/Pz85PPPP5c33nhDQkJC5OOPP9aLSpzz8PDQbYuNk9nUZDpVXearr75KcVIckJrQa5dkyTfvSejVC/rnIhWelKb93k1TB7ltS2fJfwunmzq+NXl9hBSrWjPZ833zBcrFI/sk/IblNr5G4SGG416+eXSinHLz/v0pqmtccty8ckmegkFy7exxuXmFbk6ALSgSFGTavnzpklR9oprF84KDgyUuzvDhK39AQLpc++zZ06Zt1TUuOd4+PlKyVCk5dPCgnD51Kl2uDeDxdGpieK1wd8shexe+l+K57/ZppheldLNRcuJswnuVwgG55d+9lv+7DsiTS3K4GD6+nL9imFykHD97TepWL6XHpqRQfkNC76XgW/r9/cO4eC1UrlwPk/x5ckmRgn4PNRawR1eupK3LdP78hir5abVixQopWrToQ9/PoUOH9FolyXXt2lUn2hvdvHlTJ8ippV27djoukLi67MWLF/U6MDAwxWuoJDrj764eXz2GcWxq4/PmzasLAMTGxurEOtg+YlKwNvt3b9PVspWazzVO8dx/Nhg6CqvJQ2/37pjiuX/89pNelPHTF0ve/IbXUqPAIsWk39APLY6NiY6SyxcMXUILBBoqhwOwLufPnZP+fXvL2TNn9M/P1qotX349LsM7yKWkrCps8Mc8vX3xwgXx9eX7IMCWuCSaGWSpAvZrdYvKwIYl9LaaWDRs3n7ZcL/7nCUXb94R/1yuehJRSiITTXxyc3YyJdcl1qBsXnF0NJQcXrE/bZ+pAQB4WMSkYE0KFi4mr7+dfFzoykVDXCigYBHT/rz5CkhoyHVxyWG5IIKRW6KEl5joaHG/X1DcSBUKn/7d57J+xUL9c85cPjLkg6+kRBkKaQL2qGCi7/hUwmxy1PeQKq6lCo5HR0eZ9p89eybVeVDKk9WfkjWrV0nwtWsSGRkhnp7mr00ArJeLU0KbKdVF7tz9Dm1K/lyusi9hWoGZPF45TPGsxIl2RqrA0x+7L+vFkhJ57zdNiYzRSXUPOnPjtny07GiyHewK5TYUIDgX8nCFyAEgO7DbJDnlueeek+nTp8snn3xiqvZ+69YtvTxY1f2DDz6QSpUqZdGdwhbcuHhGFnw+VO6EG/79Kl+nqdR7eaA4OlmuGpnY+l8nyv71S/W2Ry5fafHGB5K/WJkUx/gFGhJdbgVflug7keLqbnlywbWzhsoleQsXN+27m2jSaPz9JJjUxMem7TwA2VvevP7i4+MjoaGhcuTIIWnWoqXF8w4fSuiSUqZMQveTx6EmhhvFxKatAklazwOQfankM9UZLo+vl1QuEyhzVu6weF6VsoVM23uPJCTnHzxxSa+LFvSTnJ5uEh4ZZXl8mUJJxhYrlEc+e6u9FAvMI4PHzJXNO48ne59u96s3RUXzngfZm4OuJ5a16tatm6bzjh61HHBNzqMkyCXuzqY64RYqVEj69+8vTz31lE5M27Nnj0yYMEGOHDkiCxYsEC8vLxk5cqRZEp3i7W2o6J+cnDlz6rVKwlUFd1SBHeNYRRXkSY7qiKcqS6r3X8ZiPbB9xKRgTVR3OMXVzV0qPpF8pdz0FHXntn59zOHqZvH4gT075O5dwwTzEmWZoARYmxMnjkuvHt3kZoihW1v7Dp3kf++9b1asID1N/elH2bxpg/jmzi3fjJuY7HnRUQkTDVzdLL/+AMh+xnaoIM+U8NPJaE2/MXS/taS4v6fZJKDERrYoIy8+bYgdXQ+PloEz98iBiyl/Pjt6JVyqFvHRXelUVW+VWGeJn5dhknZs3F0JuW05nv1sSUMHlNPXI+VUsKE4AWBtskNMCkDqiEnBGqQWFzq0NyEuVLxMBdP+wsVKyfHD+yTsVqgu+OSeKBkusbCbN/TaydlZcnobimwaqSJ4qnvcrv826Z/zFSgkb3/0rV4DsE+lSifM0bx4PvkmAur1I+z+vGN//3xJ5kI9zPymmJgYycI6UgDS6IMWZeTpor4SGRMnHX7Ynux5QX6GZDPl3M3bciMyRkJvx+pObaXyeclfhy0XaSqdLyFZ9thV8+63qtaS6kJnKfnN6Kmihvc5hy6HJznm7uIod++JRCdT/KlaYR9xvl/Q6cClpOOB7ISYFCyx6yQ5pWrVqvLHH3/I4cOHZdu2bbrqe0REhO4mpyrBP/nkk1JeVQ8FHsOta5dl4ZcjTAlyNdp2l6dadknT2L/nTjElyPnkKyit3/xUvPOm3nEhqOJTslG+k3t378qZvdukdI16FrvIXT93ytTVzsg3f0IFlPOHdot/EUPlzAdF347QyX9K7gJU7QZsRa06deXPJYtl08YNMuStobra0YM2rl9n6nxSukzKSbtpFRSUMPF967//SNmylpPv1OTxE8cNiSzFihVLl2sDeDwDPvldhoydm+I51//5Wq8/n7pKL0rkHUMgeNWWg9K1xdPStHYF+d+3iyyOb17XMPn5cvAt2XcsoYzSyr8Pyjfqg42zkzStXV7mrtyZZGxgPh+pXNrw/uavfw6b9oeG3ZGmtcqLk5OjtG9cNdkkuacrFRWfnIag1a7DdM8FrE10dLT+jF+kSBH57bffdCKcUcOGDaVWrVrSvXt3nTD366+/SocOHaR06dKmsYprCpUhFbdEk6jVF1eJxz543BLj4yceA9tHTArW4ujBPXpdtESZVBNYer3xP+nRf2iK5/RoY0imbv3CK9Kmcw9TAp7RgJdayI3gq/r4i6/2t/gYG1Yt1us8/vn1fQGwHhfOn5fXe/YwJcj1HzhIXuvTL0Ovef36Ndm9a6d+Dbt27arZRKXEtvy9Wa9VAYOgIoYidACyv/CoOJ2oppZieT2TTTJrWsnw3dqFm3fkdKJzhjQuaUqQO3M9UvpM3yUXQy0XYUps09HrepyTo4M0KOcvS/dYrtqtEviUfRduyb17lh+ramEfvd5/wbyQKwAAGYGYFLKzwd1bSkjwVWnZ6RXp+Irlz4ob/1qi135580tQorhQlaeelbXL5ut5Ujv/3SC1GjS3OP7Arq16Xbx0eZ2MZ6SK4E3+YpQpQa546Qry5gdfS05vw3s1APbbibV8hYpy8MB+Wbd2jbwx5C1xstAUYeeO7abv+SpXqWo2F+rI4UOy9b//UrzOrl2GYsKqyJOPj3kCL4DsKSI6Tie6qUUlwj1YlMmocTl/vb4UGiVnbxi6yP17KkSaVsgnz5bwk4kbTlscV6ukIaYUHB4tx68lxLJaVc4vQxuXlLi796TZhH8tFm4qkddTygUYCv1uPHbd7NjCvk9J/lxuMv3fczJ5k2H+94Na3o+jXb4VJUcfSNADAGtg90lyRmXLltULkN7i4+JkxeRP5fYtw5f+tV98Xao2bpemsSd3bpFdK+fpbZ98gdJh+Jfi8UAVo+R4+wdIgZLl5dLxg7J18a8SVOkpcU1UJUkFdzb//qPcu3dX3Ly8pcwzDU3H8hQqppPeQi6dk50r5krJ6nUkV56kEwfUeN1pzsFBSj39XJruC0D216p1W50kd/rUKZn7+yx5oXNXs+OHDx+SpUsMSSxdX+5uMYnuUasvFSteQk6dPCE/T/1JGjdpKgUKFExy3ldfjNWTz9V1mzZrkS7XBvB4YmLjJCaNDdZiYuNNyXFGvy3dqpPkyhTLL691rC0/zjNMTDRSCW5dWzyltyfOXG927MzFG7Jl1wl59okS8m6f5jppLizCfCLT2Dfb6US44JvhMuvPbab9IbciZfW/h6VJrfLSrVUNmTxnkxw5dcVsrIdbDvlqWEe9fftOjMxONB7IjtLpf8uPZePGjZKdTJw40VSpUXWPe5BKYHvvvfekffv2+nOS6ig3YsQIfczSl1xp9ThjYV+ISSE7i4uNlQtnTurt4qVT7yLukiOHuIihW0pqnF1cxM09oXqmUYkyFXSS3Oa1y6XVC93FwzMhuVnZ9vc62b5lg95u3r5run0mBZDx1PuxoW8NluvXDZVx3xk2Ql7q9kqGX7dZ81Yy67dfdUXvcd98JaPHfJ7knBXLl8m///ytt1u2bqtfzwBYhz/3XjYluQ1vVlpem74ryTk9awdJ2QBDh+/pfydM/qlfNq+8WjvI1MWtx5Qdupp3Wvxz8oZcvHlHCvq6yxsNS8g/x28kGduovL9UCzJ8r7d49yWLjxPo6y7eHobPqgcu0F0c1ou35YD1ISaF7EglrqkkuS3rlkvzjt2SxIW2b1kvO/8xxIWatjOPC1V44mnJ4x8g169dlvnTv5eKT9QQb1/D5HKjbX+vNRWEqt3QfK7BqkWzZdvmNXq7ZNlKMnT0BLPCTgDsV4eOL+gkuXPnzsq0KT9K79f7mh1XyXHjvv5Sb6t5TtWfetp0rEnTZjpJ7uSJ4zJ75m/SuetLSR5/29b/ZM1fhkLDTZs1J+YNWIlVh65J+ycK6O0hDYvLoDn7k5zz8tOFdLc4Zda2hKLcyw9c1UlyKrmuXdUAWbDbvPhSKX9PaVreMGd7zo6EQuLK/othumiTWlTC3O/bzY+7OjvKsCYlTQl2fx26Znb80KVwnSTXpHw++fW/8xL5QJJd3VJ+8lzpPHp79vYLj/DMAJmLmBQsSSiHYkdCQkLkwIEDuntcZKTlaoJAejmwcZlcO3NMb6tks/J1mkpM1J0UFyUuNkY2zPpObzs5u0jjXu+Is6tbiuN0wloitV/sIw4OjhJ69aLMH/uWnD2wU3ezu3b2uCyf9LGc2GGYgP5065fExdW8s0Hdrv3FwdFRoiLCZM7HA2Xv2iW6I576+eLR/bJk3Cg59Lfhw1nFus3Ev4jhTRUA6/d0jZryXL36evuzMaNlwrhvdJXv68HBsnDBfF3tW01qKhgYKJ1e7Jyu1x7+v3f1hPLQ0FDp8kIH+X3WTH3tW6GhuurSwH59ZNGCP/S5HTq9IGXL0e0VsAUbth2TpRv26e2vhnaQD/q3lKCCfpLPL6d0a11Dlk0eKK45XOT0hetJEuiUoV8tkPj4u1KyiL+smTpEGtQoI34+nlKlTKD8/mUvad/4CX3e6Mkr5HaU+WSl/32zUCLvRIu7Ww5ZPWWwTtIrGphH8vp6Sat6lWTjjLelWjlDx9x3xy+Wi9dCM+U5AaxZ/vz507RkNksJckYVKlSQfPkMQea9e/ea9ru7u5t1h0tOVFRUkq5wxrFp6RBnPJ5axzlYP2JSsDaXL56V+HjDl2P5Chgmn2e0Fh1e0pMAQq5fk7Ej35CDe3ZIWOhNuXjutMyeNlHGf/o/fV6pcpWkUcsOmXJPANLH/Llz5ODBA3pbFUdq176j3I6MTHFJDxUrVZKWrdrobVUY6o3+fXRnuZs3Q+TEiePyzZefy8jh7+jjhQsXkX79B6bLdQFkjr3nb8nyfYaJRDVL+MmUHtXkySBf8fVwkVL5veT9VmVlcGPDd1jbToXInPuTe1ycHOR/zQ2dR6Jj42XkHwfkdmy8uOdwSnZxdkqYhB1/9558uPiQXuf3dpOZrz8lLSrnl7w5XSXA20161QmSsR0qGu7xXGiySXKq+53RuWQqjgMA8LiIScFaNGuXEBf6ctQgObR3h4TdMsSF5v48Sb4b+z9TEluDFu3Nxjo5OUuPN0bouU5q/IdDXpUt61bIzRvBcv3qZVky52eZ/PkofW7xMhXMkuTCQkNk/ozJetsrl7f0fnOULqoXded2skt8fFymPjcAsk6bdu2l2pPV9fbE8d/KB6NG6gLjoaE3Zfu2rdLzlZdNMa8R744y61LZuevLumC4MvbTj+Wj99+TA/v36XlQZ86clh8nfycD+r4md+/elfz5A+S1Ppb8mwH7AACaZUlEQVS7aALIflSymjEB7akgX5nwYiWpWsh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VlStXWq1atUIBuXbt2tmMGTNiBeR27tzpF3CyatWqWOcHEL2++OILn8qkz/M///wT2h7zfkxBvVFI5dixY7Z27Vo7dOhQ6PkaNWr4rabGtW/fPhSQa9u2rX3//fehgJyaRwrKzJ492/744w8PuejxyeoZgOij64f33nvPP/+aOBl87lVf9A1Ip7qO0POiYJ2oVigUJwq+3XLLLX5/z549Hqbr27evDR061ANywfmDUN2RI0csSZIkofNSZwAAAM4cPSkA4UZPCkAk0I8CAABIXOhJAQg3elIAIoGeFIBoREgOwHmjhdzbtm2zMmXKWKdOnWzChAm+yDwItPzXInMJgifaT5NcJJgwF0x/0gLy6tWr2/Tp061r166+sFwXbnoNHZ8lSxY7ePCg75s3b97QewMQnTZs2GA333yzde/e3fr16xeqC/pcq65cfPHFcQbmgn3Sp09vVapU8W0K1QZKlixpBQoU8ACd6srVV19tEydOtEcffdSSJk3qdUXHq8YoGBeEWIJbAImHrh9US7Jmzep1ZNiwYb5d2y655BK///rrr9vkyZNDxwTXFsHzqhsKwWlypUK5gbp161qOHDl8/+LFi1uRIkV8u65zdP6gVilYpxqlmqTrHk2/JOQPAABweuhJAYgEelIAIol+FAAAQPSjJwUgEuhJAYgkelIAohEhOQDnjRZya0F35syZ/fHcuXNt9erVsRaZr1mzxoYPH26//fZbrOkrAR2vSXJqJC1atCi0XZOgSpcuHQqn6L72VQhPQRZduAW++uorW79+vd/XIvPgvQGITgqsBEESfUvSpk2b/L5+VFfUHOrTp49PiYsZmJMgfBJMZlLN0I9kzJjR6tevH9q3UaNGHmQRheZiTovTZCjVL1EQODg3gOil64eYt3Xq1PFpcJr8pnogqgGbN2/2CXP9+/e3xx9/3KdOqt4EXwIQ1Aldv+zfv9+2bNkSK5yfM2dOu/POO0OTK1944QX7/fffLVmyZL4tqG+jR4/2KbxXXnmlPfDAA/H+7wEAABDN6EkBiAR6UgDCjX4UAABA4kJPCkAk0JMCEG70pABEO0JyAOJVMGlJguCJFn9rmlvjxo1D01JkypQp1qBBA3v++eftqaee8okpwfQVnSM4jwIoaiQpSLd9+/bQ8a1atbJChQr5/b59+/qkuphBFi0yVzhu7Nixft569epZjRo14vXfA0D4/igL5MqVy5o1a+aT3jp37mzZs2cP1Ysvv/zSg24DBw708MqyZctOaBxpulPRokX98a+//hoK26ZIkcJrROXKlf2xzvH+++/7fR0jqisK5X366ace6lVdUR0DEJ2C2qFJcYMHD/b7QbhW4bQHH3zQnnzySQ/8axqcrkd0v3Xr1n5Ns2vXLp8o98Ybb4SODcJyOj6oJ5p2G1BIV9cwmiin/b7//nuvZa+99poNHTrUQ3ealqnzarrc9ddfb4ULF441GRMAAAAnoicFINzoSQGIBPpRAAAAiQs9KQCRqCsxsU4KQDjQkwKQmPzfKCUAiDBNb/r888+tRYsWli1bNg+e6MKqXLlyHmALJqSsW7fO/3irVKmSL/yeN2+e/fLLLx5oufXWW+3aa6+NNZEpOE+w6Dxw1VVX2U033eRBlaVLl3rYTlNbGjZs6PtqcosWvO/Zs8enQbVs2TLWuQAkfNu2bfPPcfPmzS1//vx27NgxD5joc65wWlBXNNlJYbm8efPaHXfcYe+9956NHz/e69Jjjz1mpUqVinXekiVLeihO9UiTmhRAkQIFCli7du28Lmmyk0IqmoBZvHhxS5Uqlb+O3s/u3bs9AHPjjTf6cdQVIDrpekCfZwVsVV8UstVnW0F/XTOkSZPGp8Q9++yzNmbMGHv33Xd9ulzNmjW95vTq1csnSw4ZMsQD+aoJwdRaHZchQwa/v2/fPg/yB6Fcve7TTz/tdWjixIm2atUq/4lJXzDQvXt3grgAAACngZ4UgHCjJwUgUuhHAQAAJB70pACEGz0pAJFCTwpAYkJIDkC8UdBt0KBBHoBTgCWY6KYfBVlmzZrlE+Ny585tjz76qC8i162mqzzxxBM2bdo0W7FihT3zzDNWtWrV0Hk1hUULzxVk0R+Cl19+eaygzBVXXGFdu3a1P/74w39++OEHf+3gW1WqVavmi8zz5MnjjwnIAdFjzpw5HnZLly6dh+T0uVdN0WdcE5sWLVpkjzzyiIdlv/vuOytWrJj/KIgyatQonxSnOqMaU7FiRT9edKzqivZTcEV0Xm3ThDoFYlTPVq5c6eFf/ehY1R6pUqWK9ejRIxSGoa4A0WvhwoU+eXbv3r02fPhwv2YIwmyiOqNaIpoYp5CcKFyrgL4mTn788cc+jU779evXz1KnTm3Jkyf3Lw2QNWvWhCZSikL/6dOn96Bc7dq1berUqV7vFOrXfhUqVPBpdcGEXQAAAJwaPSkA4UZPCkAk0Y8CAABIHOhJAQg3elIAIomeFIDE4uLz/QYAXDgUglMw7ZNPPvEJTEEQJTBjxgxfhP7bb7/5vsGUFE2ee/LJJ30ynKa/KcyiQIqmuIiCKWXLlvX7P//8c2ibKHynsMqHH35o3bp1s2uuucZDc1pcXqdOHQ+5DBgwwANyWpAejAwGkHAFEyNVTyZPnmzbt2/3SUtz584NBdKC2vLZZ595vVFtGTFiROgc99xzj0+Q00QoTYtTkCXm85pwqQlRO3bssLVr18Z6XWnUqJENHDjQp9IpXKe6ojqkuqKaokl1QUAOQPTQZFvVlZif+dKlS/u1hAJpqjXBdYom3IomUTZp0sTDugrsB7VENUo1Rvsp0KbQm+qUao++AEA0HVehXgXtNJUyENQwBeJq1Khhzz33nI0bN8769+9vr776qt1+++0E5AAAAM4APSkA4UBPCkAk0I8CAABIvOhJAQgHelIAIoGeFIDEjElyACJi9OjRVrRoUStSpIgvEtcCcIVJvv32W1uwYIH99NNPlj17dvv00089mKbnNO1Jk542b97sU+NKlCjhP3LTTTd5SE5hOS1Of/nllz308uCDD/o0liDcFvxRqNuYAbyCBQv6z8033+wBukOHDnkAJqApU0xjARK2IUOGeK3QZzuYFnnDDTd46EQT3TRpqWTJkvbuu+/6BKe6devajTfe6BOa9E1KH3zwgdWrV88yZcrkU6AaNGjgQVxNhVNd6dWrlwdrNQVKEykVph0zZoyHbxs3bhyrRqjmZM2a1SdKqd4omLdz507LmDHjef03AnB2VFMUcH377bctc+bMHozLkCGDf751vaDAvuqEak379u19cq3UqlXLqlev7hNu9W1KkyZNst69e3toLmXKlB7oV73R9YrCt7rVPkuWLPH9gmly+/fv9xpyqnCtzqP3kyJFCv4zAwAAnAI9KQDhRk8KQCTQjwIAAEhc6EkBCDd6UgAigZ4UgAsBk+QAhJXCKAqWaGrK2LFjfZsCcgrKFSpUyDp27Ojb3nnnHV9Y/tJLL/l+GzZs8JDJww8/7M/PmzfPAy8Ks4me06SWV155xY/bs2ePT1N58803/Xltkx9//PF/xS1GQC4mLSxXsCYIyCkcJwTkgIRLk9yaNm3qIba33nor1nM1a9a02rVrexDlyy+/9IlLCsn16NHDn1fQVgEWBeMUwFUIRoJgrUIrqkMNGzb0x2+88Ya9/vrrfv/4OhFzkpxqUkx6TEAOiF66btBESH2ONZ1SQbZguxQrVsyvH/RZV0BOAThNtq1cubI/r2sc1RpNjjt48KD17ds31vVFqlSpPKSrOqa6o3qk4zV5TtdICsjpdWPWnJO9TwAAAMSNnhSAcKMnBSCS6EcBAAAkDvSkAIQbPSkAkURPCsCFgFWWAMIqd+7cPoFJ5s+f74u/g6CcaBqcpqbs27fPF4Rr0pymw+XIkcOfv/76661MmTIejvvhhx98QlTMReGaDvXMM89YmzZt/LEmQ2kCnaZBaQH6jh07PHB3ugjHAQmfAiOaPCnvvfeeh2QVVgmULVvW64o+//pRmKVfv36h5zUxrnjx4h5u0be3LV682O/rW1FEUy+fe+45rz8Kwml63MCBA31SnMyaNcv3PVk4hdAKEN0UmtX1QJUqVXz65J133ulTKgObNm2yDh062NKlS0MB2wIFCtitt97qtUghXdHxlSpV8vvDhw+3VatW+XlVw4LjGjVqZC+88IJfz+j5Dz/8MBTAnTlzpt9ybQIAAHB26EkBCDd6UgAihX4UAABA4kFPCkC40ZMCECn0pABcKAjJAQgbTUJRWETT4EqWLGnt27f3ReCBL774wtq1a+cBOO2r4NwDDzxgpUuX9ueDReaPPfaY3y5atMimTJkSmqwSLCLXlBdNidKC9dSpU9uECRM8EKM/EPfv329JkyblvyqQiOTLl8/q169vdevW9VBt2rRpQ5OWND2uW7duXldUfxSWVchFQRX9UacfTYdSUE5hXO2nSZYSBO10LoXsunfvbl26dPFtmiY3efJkrycKyC1cuPA8/gsAiKRgMqRqRadOnTx8r5qwYsUK366ao+uZ6667zlq3bm3p06f3wNygQYNi1RIdr330hQCiKZVB6C3m9MmcOXPayy+/bC1atPDzbN261bfr2kg/AAAAOHP0pABEAj0pAJFCPwoAACBxoCcFIBLoSQGIFHpSAC4UhOQAhE0wLa569er2ySefWO3atf3xsmXL/LZatWq+j6bAlS9f3ptF2k8TWuSyyy7zIJymQDVr1iw0wSmYrBJMa9I+un/ffffZ008/bSlSpLCVK1fa4cOHbfPmzYRZgEQkmL5Us2ZN69u3b6g2/PHHH36rz3+hQoWsZcuWHmJRUPbbb7/1cIv+qAsCJzpedUeht6lTp/o+QUAumNqUOXNma9WqlYfl8ubN65MsFZA7cOBArIALgMRL4ThNq1Sov3Hjxl4vFL7VNce7775rnTt39i8CUG0YO3asrV69OtZkyooVK1rlypX9mkbXLwrbSlCLgmsZ1a3HH3/cbrvtNt9XdA0DAACAs0NPCkC40ZMCEF/oRwEAAEQvelIAwo2eFID4Qk8KQGJGSA5ARP5Y04LxSZMmWY0aNTzItmbNGp+8MmPGDOvZs6dPkJNp06b5IvJgcXkwLU7T6NRMWrdune+jRejBuYMF5lpUruDL888/b1dffXXoj0SFZAAkDkE4LVmyZP7ZnzNnjpUoUcJuvfVW316rVi2f5vTss896AFehuSVLltg333wTmvCkIJy2N2zY0MNvMmDAAK87CsgFdSe41blfeOEFy5Ytm2/T9DnCK8CFQ9ceQRB3yJAhHsLPkCGDX2doqpwmW2bNmtVWrVplH3/8cajWqF4oUKdpcmXKlPHtvXr18ltd0wTXKaL7Cu1qEqaueZ544gkbNmxY6P9IAwAAwNmhJwUgXOhJAYhP9KMAAACiGz0pAOFCTwpAfKInBSCxIiQHIGJ/rCn8pmCJfhSYEy0yV2ClbNmyPvlJNE1OF1uixeGatqL9unTp4tsUipk+fbovPo85zSlYbK7F6i+//LIH5t577z1r2rQp/1WBREihNtWCo0ePel35/PPPfbsCuNKgQQOrVKmST35TuPann36KdbzCtFWrVrU0adL4hMvBgwfHej4I4KoOaVLUq6++6pPlVFduuOGGePs9AUReEIqNGVwLaMrbLbfc4uFc1ZEvv/wy1jEK52pinGrFhAkTfOptzOdLlSrl03MzZsxoGzZs8BoS83nR9YxeW+E6TZMLgr8AAAA4N/SkAEQCPSkA4UA/CgAAIPGiJwUgEuhJAQgHelIALkSE5ACE/QIq2KZpcblz57bt27fblClT7Ndff43VIGrbtq2HVZYuXWpff/21HTx4MFZQ5a677rIcOXLYzp07fQLdggUL4mwy6fWyZMli77zzjlWpUsXfS8yF6ACiy/GT3SQIk1x//fU+oVJeeeUVbwhpu4JzmsqkYEumTJl8wtNXX33lU+A0LU7hW9E0ucKFC/v9Dz/80MN2qjkK7x5fWxTm1SRM1RUAiYNqiT7vwbVGXNcLqiUK3GoinPTv39+2bdsWmjyZMmVKa9SokU+m1MS5jz76yM8ZBP11bgVyy5cv78e//vrrtmvXLj8+rloT8wsAAAAAcProSQEIN3pSACKBfhQAAEDiQk8KQKTqCuukAIQTPSkAFzJCcgDOSrDIO1hkHnOBt7ZpkXi6dOk8CCe//fabT2LRccEi8zx58li7du38+VGjRnlYLjhewRfp3r27386fP9+mTp1q+/fvPyGUp/MFgmlzwfsCEL11JebnOGgEXXXVVR5ay5o1q+3evdvefPPNWPvqOU15Ug368ccfPaAbs04UKFDAateu7cHaHTt2ePgl5vMAEneN0TWCPu8KrQ0ZMsQnRnbr1s1GjBhh69evD+2bK1cuq1u3rtcKhWn1/PGTKatXr24pUqTwWhNMmwuuh/Lly+fP68sCVI969erl27k+AQAACM91XcxrK3pSAMJdV+hJAQgX+lEAAACJBz0pAJGuK/SkAISzvrBGCsCFjBQJgDMSTGkLAiWaDvf22297UEWTUubOnevbNU1FbrrpJp/GpClxs2fPtunTp8c6X8uWLT20orDK559/7lPjYh5fs2ZNq1y5sk+JmjVrls2cOfOUU1dYfA5E77eWBHVl8eLFNmDAAHvrrbfsmWeeseXLl3sNCGhCU8WKFf3+oEGDbMOGDV4zgn1uvfVWn/CkYMv48eN9ApRqRvB8/fr1rVSpUj6Bbty4cfbTTz/5diZQAolbUGPef/99D8squKagnOrAyy+/7JMoN23a5PuoZpQpU8aDcjJ48GBbtmxZrMmTmmyp4O6BAwds5MiRfg0TM3Cr0G5Qq8aOHetT55gaBwAAcPboSQEIN3pSACKNfhQAAED0oycFIBJ1hXVSACKJnhSACx0hOQBnJJjSpvDJww8/7CE3heT69evnE5natGnj4ZZ9+/aFjrn33nv9omvt2rUeWNH0lmCRuabNdezY0ff74osvbN68eT5xRa8TLEIPpskpOLNkyZJYYRkA0S/41pItW7Z4XWnevLn17dvX3nnnHfv444+tffv2oSlMcuWVV9p1111nBQsW9MfBcwq9qZGk4G3jxo09OKcplN98840/f9lll/ltpkyZPNySOXNmO3TokAd0hZAtkDgF02dXr15td911l0+P02RahdgUjCtXrpzXj/Tp08e6xtAUOYX1Cxcu7BNuBw4cGOuLAgoVKuQhuowZM9qiRYv8OujIkSN+PaNgv2pNjRo1rEOHDj7VUucDAADA2aMnBSDc6EkBiBT6UQAAAIkHPSkAkagrrJMCEAn0pADgf/43qgkAzoAWf7/00ku2Z88eD50obKLpKBs3brR169Z50CRZsmSh/a+55hrf58svv/TAyoQJE3zCXLDIvE6dOla9enWbNm2aDR8+3FKnTu2TWTR1RYvPFXhp3bq1b3/ggQf4bwUksj/M1PxRgPbZZ5/1uqJwm+qCAmwLFizwYK1qikIqqhuqMZUqVbJffvnF1qxZY5MnT/ZAytVXX+0BF9UlBXi///57+/nnn/3cRYsW9UDLV1995aEYhew0QS5HjhzWtm3b8/3PACCCgulto0aN8om0efLksUcffdSvPUTTblVrLr/8cn8upmLFilm9evV8oqUCt40aNfL6EXyrm57Tsbq2+fDDD70WrVy50uuNpuxWq1bNfwAAABAe9KQAhAs9KQCRRD8KAAAgcaEnBSBc6EkBiCR6UgDwPxf9G8SGAeA0bNiwwYNqS5cu9eBb586dLXfu3P6cgnIKpFx77bUeaJNgEbmmyLVq1cp2795tlStXtscff9wXogfP//rrrx5qkVy5ctmOHTvswIEDHo7TvjFpigsTn4DEQ/XhoYce8rqiwEmXLl28DojCJwrOlSpVytKmTRvruDlz5thbb71lc+fO9TDtuHHjfLumUSpop0Dcyy+/bNu3b/fwiwJ0Ctx169bNp0kF9QdA4jd79mwPxCZNmtQnVSroJgrfaorcqag29e7d2wN2xYsXt8GDB1uqVKlCzWsF4zRlTreiCXKPPfaYNWjQIF5+NwAAgAsFPSkA4UZPCkAk0Y8CAABIHOhJAQg3elIAIomeFACYXcw/AoAzMXToUF8sXr58eevZs6cH5BQ00U+WLFmsfv36oYCcKICiUJv2u/XWW33b4sWLfbJTzOdLlChhHTt29G2aRqeAnMJ2N998s2/TQvTgh4AckLj079/f64o+808++aQH5BRcEYXjNIUpZkBONUNKly7tx+g5TW4aMWJErOcbNmxojRs39jDLtm3bPCBXsWJFq1mzpj9PQA5IPILv/Tj++z+Cx7qu0P1s2bL5tNqgVgQBOdWQJUuW2CeffOIBW12rBPLly+cB3pQpU9qiRYv8myJjnltTLHv16uVBuhdffNGmTp1KQA4AACAC6EkBCDd6UgDOBf0oAACACwM9KQDhRk8KwLmgJwUA/+3S09gHANzhw4d9cbjUqVPHMmTIECtosn//ftu0aZMH5jT9qVChQpY9e3a74oor/HlNbvr2229t9erVNn36dKtUqZKVK1cuNBlOE+r0ePny5ZY/f34PxkgwqQVA4hJ8tvfu3euPixYtGqorQXBFwZatW7faoUOHvP4UKVLEp8YprKJ9qlat6pMoJ02a5AGVJk2a+HOaGnfZZZd53VGQbuHChV6TgroCIHEIQrFBgP7464XgsSbZasKkaomuRapUqeLh2S1btvgUSj2vibg7d+4MnU8TLps1a2bp06f3LweoVauWjR071hvWmkSna5yglmlapSbsAgAAIDLoSQEIJ3pSAM4F/SgAAIALBz0pAOFETwrAuaAnBQCnj5AcAKdgW1xTlYI/znSbLFkyy5gxo2//8ccfPSinBeZqCs2ZM8d++OEH++uvv0LTVxRQyZs3r73zzju+kDxFihTWvn176969u61atcontWiCnPbTcVq8XrlyZf/5r/cFIOE71ec3qC1HjhwJhVIUVtEkJx1z8OBB++mnn7yuKPAWBHRFAVuFajVJrnDhwh58W7ZsmYd0+/bta48//njodRVu0fQ4/QBIvDVGYdr58+db0qRJ/bpE0+IU0k+ePLk/r1CcHm/cuNH69Olj/fr18+saXccEoTg1kxTU1TWJwruffvqp1xAF5TQRt3bt2jZv3jw/x3vvvWdPPfWUH0uQHwAAIHzXdTHRkwIQ7roSs7bQkwJwrvWFfhQAAEB0oycFIL7qitCTAhCu+kJPCgD+20X/BnM3AVyQjv92AU1k0iQVlYYrr7zSsmbNaunSpfPnjh07ZmPGjLEXXnjBFxFo4bgWmCvMsmfPnljn1SLzIPiiaSvvvvtu6DlNdpo5c6bly5fP7r77bmvUqNEJ74vpcUDiqiuqEfpjLXPmzJYnTx4PzQaGDRvmdUVhFoVmU6dObfv27Tuhrqjm7Nq1y+83bdrUOnXqZDly5PBwncK4o0eP9ue++OILD88FUyoBJF4K0erz//HHH/t9BeQkbdq0VqZMGevZs6dPedPkyc8//9xGjRrl024VzFcYTnVH0yazZMliZcuWtWLFinlYt2vXrv78HXfc4fe1v65rBg0aZIMHD/bjJkyY4DUNAAAAZ4eeFIBwoycFID7QjwIAAIhu9KQAxEddYZ0UgHCjJwUAp49JcsAFTEG04I+z5cuX2+uvv+6Tm/SNA/v377dUqVJ52E3T3+rXr++Pq1evbitXrvRQi8IqCrTo4itXrlxWvHhxy5Qpk0+Y08Lyr7/+2hejT5061RecFy1a1F/r3nvv9ZDc6tWrbcWKFaEpcjExlQWI/rqiWqG6osmTqhVqAKm+aIKk6kTbtm19vzZt2tisWbN8QpP2OXDggNcF1RUFVlRX6tat6+FchVIUhlGtat26tR+vMG+NGjX8+N9//91GjhzpE54IyAGJUxCkV914/vnn/XpCFHL7888/vVYo8K/rj927d1vz5s19GpzCtTVr1vQ6ousOhf9VOzR9TqG34FpE4VtNsVTwVlMqg+26JlLwX7etWrXyQC8AAADO/pqOnhSAcKInBSCS6EcBAAAkDvSkAESyrrBOCkAkagxrpADgzBGSAy4AJ5vKpm0KuH3wwQceZJE0adL4lCdNZlJQZf369R42mTt3rr388ss+aUVTWTR1ZcOGDb7AXBPnKlWq5OfSwvGAFpXPmDHDF6qvW7cuFJLTZBeFY/Q6LVu2jMd/CQCRFtSVIUOGWJ8+fXybArbZs2f36XGqLQsXLrRffvnFwyuaJKmpki+++KKtWrXKFi9e7AGXvHnzWrly5bx+xawrqiPffvutbd682c8V1JXy5cvb9ddfb9myZbMbb7yR/9BAFNI1h649VEOC65GT1RlNdRswYIAH5HRdoWlvRYoU8SDutm3bbPLkydarVy+vNQrf5s+f30qWLOkT5lq0aBHndZKmXepWXxSgaxwJakywj2pNhQoV4uFfAwAAIHGgJwUgvtCTAnA26EcBAAAkTvSkAMQXelIAzgY9KQCILEJyQCKkqXCaiHLw4EFfMH6qqWzfffedDR482O/ffvvtdscdd/hEFR2j0d9agL5gwQL78ssvrXDhwj6JRQvMq1atetJzanG7pkZpkbkCchoprsCLaAG6FrA/+uijof31PBOfgIRfV/TZV125/PLLPfh2MpMmTbL333/f72vam0KxqgkKu40bN84mTpzoARZNmtT5evToYenSpfNQXBCMO75uKZCbJEkSnxZ36NAhS5YsWaiuaH9NdLrvvvsi/K8AIBIUahs0aJB9//33/llXCDZ9+vTWpEkTa9iwoQfVYl4r6DM/cOBAmz17tuXOnduee+45D9sGtUOhXF3P6Bhdx6xZs8beeecdPyYmhee0vwJ5ousT0fRLTZDTtVS1atV8W1CTmHQLAABwavSkAIQbPSkAkUA/CgAAIHGhJwUgEnWFdVIAwo2eFADED0JyQCIya9YsGzp0qE8/+fPPP31KU4kSJXyheK1atTykEiwg1+3GjRutX79+vkj8tttus+7du8da/K2F4QrDKOyiYMtHH33kU12uu+66WK+r82iaXECvo+DL119/bX/99ZefR4vYtVg9WIAuwXshIAckXAqhqK5oGqT+SNNnWhOZFEi7+uqrPawWM7yicEvv3r29rqj2PPLII/5c8HlX6KV+/frWqlUrD6FoKlzx4sV9olwQotV+u3bt8pCMaLsCcgreTp061Q4fPuzTnDQ17mTfAAcg4VPt0HXIu+++G/r8a+KkJtGqBgwbNszD/CNGjLCsWbOGjtu3b59f82h/1RQF5OT4WqCQ7u+//+7XMArg6bqkQYMG/lqfffaZ9e3b12rXru21SrVMAb1PPvkk9OUBCvhWrFgxnv9VAAAAohM9KQDhRk8KQCTQjwIAAEhc6EkBCDd6UgAigZ4UAMQvQnJAIqAF4K+88oqHR0QTnjRpSTQN7qmnnvIpKlrsnTJlytBC9CVLlvixGTNm9AlxQXhOt0HoRdPj7rnnHv8DUGE4TYhSoCVTpkwexHvzzTd9ItRrr73mi9R1rF5LC97VjMqRI0do8fnxCLYACZdCcaor+nyLJi0dOHDAQ7CqHT179vRgm8K1MYOuCxcutE2bNtkVV1xhLVu2DD0XfN4VstO0Sk2T1LEK3n3xxRc+nVLT5Hbu3GnPPvus15FOnTrZNddc48EVvZ+33nrL64rCcXourroCIDqMHz/eevXqZdu3b/fHN9xwg918881eM/bu3Wt9+vTxaxQFb3WN8fjjj4eCs3p+/fr1fj2TM2dO3xYzrKtbPVa9Up2aP3++rV692kNyNWvW9HqkqZQK4n366ac2ZcoUvz7Slwso4Cvt27f36x8AAACcGj0pAOFGTwpApNCPAgAASDzoSQEIN3pSACKFnhQAxD9CckAUUxDujTfesCFDhvhjLfDWgm5NWFKYRRdXP/zwg/3xxx/2+eef+wjwNm3ahBaRa6qK5M2b1woUKODhFU1vkZihF4Xf7rrrLl/MPmHCBJ8ApZCcFpjrZ8eOHda1a1cP3mnynIJ5oqkvCsJo2hSA6HDkyBEPv2qCpCRPntzuvvtun6akz7qmv2nyk8Jsqj36fGtaZBC+VRBFFITLnj37CecPakylSpV8wuXw4cNt7dq19uOPP/qEJ4XjZs6c6ZOiFIpRrVHwd/HixX6cwncK11FXgOikz/JLL73k1w9SqlQpv4YoV65crP0yZ87s1zjTpk3z6xmF3VRrRLVI10CqDZo8F1fwPriO0TVRwYIFvTbpekiBf13XdOvWzb8YQLVH59N1k1x77bX24IMPWtGiRePl3wMAACBa0ZMCEG70pABECv0oAACAxIOeFIBwoycFIFLoSQHA+UNIDohSo0aNsldffdWDJHLLLbf4ZCUFSoJpcFpwrukoXbp0sS1bttg333xj1113nV155ZV+TJYsWfxWz2kxecxg3PG0aFyvqQDLd99959PkgpCKpj5pgbloAosmTrVo0cLuu+8+D9hI8J4AJFyjR4/2uhJMUlIgVnUlqBVSu3ZtD7p98sknHjhRoE7BFQXkRFPkRHVGQbmY050CwTZNjVJITpOiFFwR1S2Fa4cNG2YrVqyw/fv3+3kUhgnqigLBAKKLps+qvmhyZBCCUxhNk2yD6wQJrhWKFCniwTh9C6S+sW3kyJGhkFz+/Pl9gpymySmYX716da8rQR0KBOHdhg0b+jWQ9tV0OdH1ksL/K1eutA0bNoS2KbQHAACAU6MnBSDc6EkBiAT6UQAAAIkLPSkA4UZPCkAk0JMCgPOPkBwQZTQZTovMFR6RypUrewiuWLFi/jhmIEULwevXr+/T37Q4fOvWrfbbb7+FQnLaT4vH9TN79myf7HQymkKnKXEKya1atcq/nSl16tRWp04dK126tC1fvty3HTx40CpUqGDZsmWLtUCdgByQcGmS0muvvWZLly71xwrAPvTQQx6GjVlXgmmTCuVqKpMCs0uWLLHp06eHwitlypSxMWPG+DQ4Bd9Ub44Pyum+tmmak47T8fPnzw8937x5c6tXr55NnTrV68fRo0d9GlRck+kAJHz6DGuSra4hRFMj+/TpE3r++BoRBOt1XfLBBx94HdA1jCZYZsiQwffXtYZCch999JHddtttli5dujhrTfDNb8mSJfPz7N27N9bzhQoV8h8AAAD8N3pSAMKNnhSASKEfBQAAkHjQkwIQbvSkAEQKPSkASBhOPjYKQIIza9Ysa9eunQfktEhcE1Def/99D8hpQbl+jl9kLrfeeqvfalrTgQMHQs9rgblCbFp4rqDL4cOH43xdnUfTVQJaiK4JcVqMHkyD0aQ5BeaaNGniATk9F9dUFwAJy5w5c+zOO+/0gJyCry+++KINHjzYA3LH1xUF5PRYNUCfeYXmVAs05SmQPn16y5Ejh99XeEXimlKpAIyCKwrXShBwCV5Tk+NuuOEGD9OorhCQA6KTPtcK7bdt29ZriH702Q+uIeKaNqn6oOsTXevomkL3g9ojmlhbokQJD+/v3r3bBgwYcMr3sG3bNr/G0bly5coVwd8WAAAg8aInBSDc6EkBiBT6UQAAAIkHPSkA4UZPCkCk0JMCgISDkBwQRbS4u3Hjxn5fC8bz5s0bCsJpQfnx09qCRefHjh3zBeWyadOm0PMVK1b0iVFarD5t2rTQFKnj6bz79u3zMIvux5xEF5cgVHOy5wEkHArLlixZMhRwU+hNFICLq64EwRYFVFKmTOl1QQGYQKlSpTyEouMmTpxov/76a+h8Men5pEmThsK5qmeqGXG9JoDoFXyeW7RoYeXKlfNa8PPPP9snn3wS6/njKWSvcFsQwg2mvQXXPVWqVAlN0VWwV43suKbR6bpn9OjRvk0TKVXjgnMAAADg9NGTAhBu9KQARAr9KAAAgMSDnhSAcKMnBSBS6EkBQMJBggWIIgqe1KtXz6c07dmzx4YMGWJ//vnnfx63Y8cOO3jwoF+ElSlTJhSyk7vvvttv586da2PHjg0tRlewToKF5JMmTbIFCxb4Y4XrToWACxAdguBaz549/Xb9+vX2xRdf2IYNG3zaUxCIiymYDjlv3jwPuGm//Pnzx5o6WbduXUubNq2f54033vDntF+wT1BXFFwJQnRqQgFIfIKpcNKpUycP1Sp4P2bMGNuyZYs/H7PWBPdVn7766iufnqvwW82aNUNB2uD/ENOUSQV25emnn7ZPP/3U7wc1Rufv27evrV692gNy99xzj9cwrlMAAADOHD0pAOFETwpAJNGPAgAASDzoSQEIJ3pSACKJnhQAJByE5IAoo4lPCsrJ119/bbNmzQotPg9ocXiwyFwhlREjRvgfeZrwVKBAAX8uCLpUqlTJbr31Vr8/fvx4e+mllzxUlyRJktAUlhkzZlj//v095PLAAw/4QnMA0U+faX3OFTIJplRqouQ333zj94+fBhmzrqheqPaopgQhuYDOdd111/mkuZkzZ1qvXr1CkyoVwFVd+eGHH+yDDz7waXKdO3f2+gQgcQquORSGbdiwodeSVatWhabJqdYE1zK6f/ToUb/uUMhW1yN33nmn1a5dO3S+IARXtWpVu/322y1dunS2Zs0ae+KJJ6xNmzbWoUMHe/TRR61+/foetMucObPdcsstliVLFqbIAQAAnAN6UgDChZ4UgEijHwUAAJB40JMCEC70pABEGj0pAEgYLvo3WGUKIGrMnz/fXn75ZVu4cKGVK1fOXnzxRcuZM6c/p0XmwYWWJqe8+uqrNm3aNA+yaFqUQnGBIASnaVBagK4Qi+7ny5fPcufO7YvOt2/f7iE5qVGjhi861zc1BccCiG5BzdBUSgVO9FgTJ7t37+7hOT3WZz0IzCmI8tprr9mUKVM8HPfYY4/ZNddcEzqfwi/ad9myZfbhhx/6tCiFXFKkSGFVqlTxW9UV1SWpXr26v5ZqDoDEX2sUsr355ps9kK/rCV3PxAzJqrb07t3br2Gkbdu21r59e8uQIcNJzz1x4kQP3f7yyy8nPKfJlt26dbMrr7wyQr8ZAADAhYWeFIBwoScFINLoRwEAACQe9KQAhAs9KQCRRk8KAM4/QnJAFDp48KCNGjXKA3C6oNLiby04V/hEFHR77733rF+/fh5YyZo1q3Xt2tWuv/76k16QaaKLpq0MGjTohMl0yZIls/vuu8/atWsXb78jgPgT1IEBAwZY3759LXXq1NakSROvLZdddpnvc+TIEXv//fftnXfe8f01kUnhtgYNGpz0vPv27bPnn3/epk+fbrt37/ZtCtwpZKu60qlTJw+/ELgFEqfjA/VBiFZT4l5//XUP0GqynKbY6jpEAdwgQKtwv0K4V199dZznOp6ufebMmWO//vqr1y3VKR3LlEoAAIDwoicFIJzoSQEIN/pRAAAAiRM9KQDhRE8KQLjRkwKAhIWQHJBABQvJT0aLyfv06eMTV3LkyOHhlrx583rQTds3b97s+zVt2tQefvhhy5Qp02ktMtcCc02U+/333/0YhWVuuOEGy5gx4wmT6gBEl5N9/mNu18RI1Y+CBQt6CE5T4lRXFJ7btGmT79O4cWMP3mbOnPmk5w22HT161NauXeu15bfffvNjFOjVOYK6BCBx+Ouvv+zSSy+N8znVBF3b6BpCodsWLVp4TVAdKFasWCgclzx5cq8vt95662lfEwEAACC86EkBCDd6UgAihX4UAABA4kFPCkC40ZMCECn0pAAgYSMkByQwMReRy7Fjx3zSSiAIqel2/Pjx9uKLL/qEpvr169u2bdts/vz5vl+JEiWsR48eVrp06dBxWmB+soBcXM2mmH8o/tfxAKKnrsQlqDUTJkyw+++/3+9XqFDBwyw///yz71O0aFGvK+XKlTvtuvBfwVwA0U/1RZ/z4LP+448/2h9//OEhWYXty5QpY5dffnmsWqNrGIX4VUN0vLRp08Yn16ZNm/Y/G0qnQt0BAAA4O/SkAIQbPSkAkUI/CgAAIPGgJwUg0nUlLqyTAnA26EkBQHQgJAckIDGDavv377cRI0bYli1bfJG4pjq1bNnSkiVLFtpfz7377rs2atQoX5iuP/DSpUvn05+aNGly2n/0nex9BIvMmeACRK+Y0x8PHTpk06dP9zpy4MABy58/v+XJk8cuu+yyWJ/z22+/3X766ScPp6j+qK5oslPz5s3Puq4ASPwWLlxor7zyis2bNy9UPyRXrlw+lbZ9+/aWNGnS0P4dOnSwGTNm+L6qO926dfPtOk71hYAtAABA/KEnBSDc6EkBiA/0owAAAKIbPSkA4UZPCkB8oCcFAAkbITkgAfrss8/s5Zdftr1798aasFKvXj1r27atlSxZMrTv999/b88995ytX7/e8ubNa4MGDbLs2bOf0wQWAInPJ598Yv379/eJkwq5icJxVapU8clNV111VWjf5cuXh4K2OXLksLffftsKFSrkj6krAI6niZNDhw61Pn36+GNNjStbtqzt27fPr0/WrVvn25s1a+ZBudy5c4caRnfccYcdPnzYa9Brr73mwV3C+QAAAOcPPSkA4UZPCkAk0I8CAABIXOhJAQg3elIAIoGeFABEh/+NjAFwXiioom8viWn48OHWs2dPD8iVL1/eqlatavny5fPnJk2aZB999JGHXAIlSpSw66+/3u+vWbPGp7dosbkQkAMuXEEQbu3atdapUyd7+umnbevWrVa8eHGrUKGCFStWzEO4U6ZM8Slx48aNCx1buHBhu+mmm0LT56ZOnRp6jroC4Hjz58+3jz/+2Ke/KQSneqKwv4L77733XqiejB8/3t5///3QcQr9N23a1OvV6tWrbeTIkb49mGoJAACAyKEnBSCS9UXoSQGIJPpRAAAA0YmeFIBI1hehJwUgkuhJAUB0YAUqcB5ddNFFvqB81apVNnfuXNu9e7dPYsmUKZP17t3bhg0bZm+88Ybf1qlTxyc4zZ4927755pvQOdKmTWvXXXedlStXzh9r3y1btpzH3wpAQqkvwTcjKQiXLVs2e/31123EiBEeXFGg5dVXX7UsWbJ4wFa1Z9q0aaHju3XrZsmSJbMdO3b48T///LNvDyZbArgwG8rHbzt48KC98sorfu3RvHlzn0yZJk2a0P6aRqnwvybFHTt2zD799FMbM2ZM6Bx33323T55TwF9fBhDUmuO/RAAAAADhRU8KQKTQkwIQLvSjAAAAEh96UgAiWV+EdVIAzhU9KQCIfoTkgPNM4ZOGDRt6IEUBlQ0bNljr1q19OpzCKEmTJrUMGTLYE0884eE5TZGbOHGiLV26NHSOQoUKWf369e2yyy6zRYsWeYgumCYH4MI1ffp0GzJkiCVJksSefPJJq1evnt9XOFe3tWvXtoceesinwy1ZssTefPNN27Nnjx+bKlUqf0404emrr76yo0eP+oSnuP4QBJA46fOukH7QUA62BRSO27x5s4f2mzVr5tctCrjpmkT0JQB33XWXX7doe5EiRfy6RfRYAbm2bdv6Y13jaKKuqE5RawAAACKLnhSASKEnBeBc0I8CAABI3OhJAYgUelIAzgU9KQBIPAjJAfFEgbe4FnsXLFjQw29bt261l19+2e9rEot/QC++OLSIPHPmzNahQwd/vGLFChs3blzoHFqQfvXVV/tEORk1apQtX748nn4zAAmVwimi2nDttdeG6pBCccG3J/Xq1csDMNpeqlQpS548eWiC02233WZ58uSxAwcO+HQnNasBXFgUjlPN2LVrl40cOdL27t3r21RPdLtx48ZQuPbKK68MBdwUnOvRo4e1adPGa5Gub1588UWfIqepcgrdar+g1hQrVswnzanWjB8/3rcTkgMAAAgPelIA4hs9KQDngn4UAABA4kBPCkB8oycF4FzQkwKAxIOQHBBfH7aLL/aLqDlz5vhUpmDxtxaUd+zY0R9rAboWiKtRJMFtEJbTQvOSJUva/v37bebMmTZjxozQ+XPnzu1TohSmU+Duiy++CC1aB5C4qHYE9SFw/GP57bff/FZ1QWGUoA79+OOPduONN9rTTz9tu3fvtkqVKtno0aN92pymP8U8l0IusnbtWvvuu+9sx44doYAMgAuDArIK4yvkpjoQ89pE1y6qG5okp0lwMmDAAKtbt64H4qRTp07+jW2qO0FAt3fv3h64C86layFNuNT5+vfv77UpeA0AAACcG3pSAMKFnhSA+EI/CgAAIPrRkwIQLvSkAMQXelIAkDiw8hSIoGAaU2DQoEE+LUWTm4IJLNKkSRMrUaKE30+RIoUtWLDgfx/Q/784XPvpXLq95557fNvvv/9uX331le3bty+0b5kyZXxRerAAXUE6prAAiYumvqkW6DOvwKymNe3cudOnMgX0udf0N/1IsmTJ/Hb9+vX24IMP2p133mlLly71kO7bb79tQ4YM8SlOmzZtsn79+nmYJVC1alWfRKfX1TcuffbZZ76d8Apw4ciWLZtPrdW1yMSJE23Dhg2h5zRtUvVHtWjo0KFWq1Yt69u3r4f+69evb1OnTrX7778/NDXuueee84Du/Pnz/XxB4LZmzZpWoUIF31aoUKHQxEsAAACcHXpSAMKNnhSA+EQ/CgAAIDrRkwIQbvSkAMQnelIAkDgQkgMi2PTRgnCFVTTNSdNS8uXL59u///57/8YB0fMpU6a0e++91x9v2bLFFi9eHJoCF4TcgsXlCqto0bkWn8+bN88mTJgQet3LL7/cF5lnypQptC0I4gGIbkGQJAiOKNjWrl07rx2qCV26dLEffvgh9LlXXcmZM6c/nj17todS6tSpY99++63Xk4cfftgmTZrkgRbR1KaGDRvam2++6XVIguBd9+7d/VZT5LJmzXoefnsA8f2tawFtL1y4sLVt29abz4sWLfKQfuCqq66ycuXK+XOaYrtx40bfNmzYMA/LqWbouujIkSO+f6pUqfxWQbsg8BtcNz3++ON+3GuvvRbaDwAAAGeGnhSAcKMnBSAS6EcBAAAkLvSkAIQbPSkAkUBPCgAuDITkgLOwdevWOL8BKRAE2hRGufHGG31h+TXXXGP9+/e31KlT+3Oa1qQpUEGITdOarr/+er+vqSua8nR8yC14vY4dO/p5NPXpu+++izXRpWjRovbss8/ajz/+aA0aNOC/LxAlxo0b59OWgs/+8YGVYHLbtGnTPNimiZSaOrly5UoP1apuPPbYYx6eDVSvXt2SJEliS5Ys8emS0rRpU5sxY4a1b9/eHytwq3CLfrJkyeLbVFvksssu87qTK1cue/XVV33CXKNGjeLpXwRAfDV/YobVTjaBVhMoVQt27drldeiXX34JHa+gbkDXHp9//rmVL1/ez6cfXRdpEp2sWLHCb4sXL27p0qXz44PrJk2l03EAAAA4OXpSAMKNnhSA+EY/CgAAIPrQkwIQbvSkAMQ3elIAcOEgJAecgWXLllmrVq3svvvuswMHDoQmxR3vzz//tKeeesoefPBBP0aLwIsUKeJTmPbt2+f7KAgzevToWBdfHTp08EXkCr1owtO2bdtinTd4vYIFC/r7CN6TJrcE0qRJYzVq1LAMGTJ46OVki90BJKzGT7du3fxzr+BrzFCcBJ9jBd00MU6TmhQweeONN/zn0Ucf9bpwzz33WKlSpULHKdBSpkwZv588eXJ799137aWXXrKMGTN6OE5BPE2m088ff/xhO3fu9H2DY2K+jxtuuMGnVQJIHIIgbhDGHzhwoNcSXYPEFITndH0RhGs1IVfT5FRHFKYtW7asVa5c2Z/T8QrtBtctMa+VNO1y4cKFfr9ChQrx+NsCAABEP3pSACKBnhSA+EQ/CgAAIPrQkwIQCfSkAMQnelIAcOEhJAecgcGDB/vkprVr14aCaTEnvQU0pembb77xBeVPP/20ffrpp/b+++/b2LFjrUePHla4cGHf77333vOwS3COQoUK+aQWmTBhgk+EOn6aVLDQXAvVs2XLZtu3b7dFixb5ZJfjKfgS1/sDkLDUrVvXp0Dq8676MWvWrBP+QFP4VsFahVLuvvtuGzZsmB9Xs2ZNn1apSZVNmjTxQIoCspIvXz6rVq2apU2b1g4dOuT1K/iGN02YU/hF59b+I0eO9BCv6kr+/PlDtYYaAkQnTZnVxFqJGZhXXVHoLQjAqqYo1NanTx8Pvs2dO9cOHz4c61zBvs2bN/dwm+qJ6lQQhsubN6/dcsstft2jkJzONXz4cH991RV9scDEiRPtueee8+sVTcO86aab/FhqDAAAwOmhJwUgEuhJAQgn+lEAAACJDz0pAJFATwpAONGTAgAcj5AccBqCoIoCbAqdaDJcqlSpfNvxk9r27NljvXv3tr1791qzZs184luKFCl84Xjq1Knt9ttvt4cfftjPo8lyQ4cOjXW8FpnnzJnTw2+aKKVA3skmunTs2NGnzw0YMMDSp0/Pf0sgCunzrElMCr7J6tWr/RuTDh486J/3oMYokLJ48WLLlCmTNW7c2JIlS+bhloDCbao1QUA2CMIpJFe7du1QA1uT5/QaOq9eQzVLE+Y+++wzS5kypdcUBV4IrgDRSeF71ZNGjRr55DaJ+XlWXVGYdvPmzda3b1+bOXOmT4ELJsF9/fXXtmLFiljn1PGqVXLvvfd6jVm/fr1/IYACvKph1atX9wm6oqDc888/by1atPBQv659OnfubOvWrbNy5cr5OXQdAwAAgP9GTwpApNCTAhAu9KMAAAASH3pSACKFnhSAcKEnBQA4mf+togdwSsEEFU2A6969uxUsWNAuv/xy33Z8kEQLwxV+U5AlmJISUChF+1epUsUDbl27dvVpUA0aNLDSpUv785kzZ/bF7T179rQpU6b4xBaFXxSIOf79aPF5zD8gtegdQHQJPrd16tSxGjVq+Odek5wmTZrkIZeYtUWKFSvmdUKCAN3ChQs9QLdz5077/vvvLWvWrB627dKliwdyFVL5/ffffTrlmDFj7KeffvJgrfbTccF0udtuu80aNmx4Hv4VAJwrTYR86623PDgfCIKzCtQqNBvUDE2y1bQ3PdZ0OAXc2rVr54E51QlNiMuVK5d/KUBw7RLUqooVK/p1y5dffum1R4F+Bfz1vG71Oqoz8+fP97qj7TqHrptUi1q3bs1/bAAAgDNATwpApNCTAnCu6EcBAAAkXvSkAEQKPSkA54qeFADgv1z07/FjsADEKVgkHli2bJkNHDjQJ8VpwbguvDRZZfbs2XbHHXdY8uTJfZF47ty5TzhWjh496hNXFIjR4nQtbNfCcjly5IgH5XSu8uXLW7du3axEiRKn/AanoEEFIPoEIdclS5Z4gOTw4cM+Ae6pp57yIJtMnjzZpy+ptmiSnEJuqjs///yz/fLLLz4F7sCBA7HOe+utt3oo5YorrvCx4grPqC4FE6FUcxSeKVOmjD300EM+4QlA9Pniiy/slVde8aBsEKLv1KlTqH4Edu/e7ZNuFcqVWrVq+XVGEG578sknbdSoUX7t0qNHD69DcdUqTYSrW7eub7vmmms82K9AbkC1aM2aNR74zZgxo5+7atWqoSm8AAAAODP0pABECj0pAGeLfhQAAEDiR08KQKTQkwJwtuhJAQBOB5PkgNMUM+SmQIomLilconCaQnIKyMmWLVv8NkuWLHbo0KETjg1cdtllPiVq2rRp/jNx4kSfzKImU9KkSUMhOS0w/+qrr3yiS9q0aeN8bwTkgOgWTFoqWrSoh1s+/PBDn/D29ddf21133eX71KxZ02uNpsB98sknJ9QT1Y3UqVNb8eLFPZyyevVq/6NQkysVksuRI4c9//zzHpxbsWKF/fHHH35M3rx57dprrz1PvzmAc6Gpby+++KIHbEWBNwVeNZ02ZmN57dq1PilOoXpdayhsq2CbpsgFIX9p27atffvtt76/plkWKFDAp9kGdC6dQ9ckCuuOHTvWX1vXKQ888IDvo+c1wU61SD8AAAA4d/SkAEQKPSkAZ4p+FAAAwIWDnhSASKEnBeBM0ZMCAJwJRk8BZ0FT3TRdRcGUH3/80caPHx96TpNSNDFFU1ZWrlzp24KpTcfThJc8efL4/bffftsnvARNpquvvtrq1avn9zNlymRp0qThvxWQCChAcqrtCsgqlLJjxw6fHqewXEAht6ZNm3rAReE21YYbb7zRnnjiCXvppZdswoQJPpWyf//+VqxYMZ/mpBoV8/xFihSxJk2aWIcOHez2228nIAdEIdWHRx55xEOvCqklS5bM68CwYcM8IKfQrX7UWBZdj2jK3LZt2+zjjz+2zJkz+yQ47RME5ETXJArTierJnDlzPEQXF02z1DXLnj17bPr06T7VMhDXlwMAAAAgPOhJAThb9KQAnAv6UQAAABc2elIAzhY9KQDngp4UAOBsEJIDTvLH2cn+QNNicS0618L0jBkz2q5du2zkyJG2d+9ef17P1alTx+8PHDgwtC0mLUqXnDlz2p9//un3Nflp1KhRodeXHj16+CJ1hVlYcA5EN32uFZgNJj8qsHLkyBE7evRoqE6ovqiu3Hnnnb5NE980oSmoGZoGpzCcJjy9//77PilOU6Q0fS4I7gYB3GD60+HDh/2WGgIkDgMGDPBw65dffumfa/2orhw8eNCfV50Jtge1o2zZsnbLLbf4cwrPKhinehNXXWjZsqVPkFP4TVPlVq1aFev5oIbpywD0pQAFCxa0pUuX2kcffeQ1jem2AAAA54aeFIBwoycF4FzRjwIAAEj86EkBiERdYZ0UgHNBTwoAcLYIyQHHCf4404++hWD16tW2ceNG279/vz8fc9JbzZo1faH5okWLPCgnqVKlskqVKlm6dOl8YfmQIUNO+DcOzqGAnRawp02b1h8PGjTItmzZ4q+tPxQvv/xyD9LpPZ0stAcgYQjCbnF9VoO6oiDc5s2b7YUXXrAuXbpYs2bN7P7777evv/7a9wsmOrVu3dpKlSrlYZaZM2fajBkzYp07Q4YMHoTTJLngtVVLgolPqj0aMS6aKCeE5IDopsBajRo1rG/fvv5510TI9957z683FJD7/PPPPaymOhNMsA0+96oZ1113nRUtWtQf63iF3OKi83Xs2NHva0LctGnT/HpFghoza9Ysr03Vq1f3a6GYk+UAAABw9uhJATgb9KQARAr9KAAAgAsDPSkAZ4OeFIBIoScFADhXhOSA42hxuf6Ie+ONN3yayt13321169a1Tp06eRgu5lQ4TWXJkyePT2rSRBcFUxRyKV68uIfoROeZPXt26A/DmMaOHeuTWjR5Ll++fLZv3z4bOnToCfvpNZnMAiRMCo28/fbb9vzzz590ilIQWtF+tWvXtmHDhnmITTVDAZSHH37Yvvnmm1h1QiEVBU5+//13nyanaZU6t8It2q7X/fDDD+3QoUM+QU7bVH+2bdvmgVtNqVSARUE8ANHr2LFj1qtXL3vwwQc9ZKtrDAXwta1y5cp20003+X6bNm2yESNGxDnBVooUKWINGzb0OqK6smTJkpO+pq57FKpTvVH4Tq+n6xXVG13TvPrqq16vFOjV5Mtx48bZE088EZpmCQAAgLNDTwrAmaAnBSBS6EcBAABcWOhJATgT9KQARAo9KQBAuBCSwwUvmLYS3P78888eKnn33Xd9MboCJ3puzpw51rNnTw+0BPLmzeuTXJIlS+YLzj/++GPfXrhwYWvcuLEvSNeCci0mHzx4sE+B0jQWTYdSuEVBmQIFCtjtt99u119/vR+rc2zdujUUhgGQcH3xxRceRFP4bcqUKbZy5cpYzwefYQVs27Zt6/upnmgalB4rMJcrVy7f76OPPrLly5eHjq1WrZrVr1/fm0sK1E2YMMG3KwynuvT444/biy++aJ07d7b+/ft7UFd1S7Vn/Pjxli1bNmvevLkH56glQPTSZzhLliz+mX7qqafs008/9Ym1wVS3du3aeWBfobUffvjBpk6dGudUyxQpUliVKlW8ZokCdTt37jzp/xHWtWtXf+3169fbgAED7MYbb/TrlTvuuMMn1un6R9c7qVOn9msZAAAAnDl6UgDOFj0pAJFEPwoAACBxoycF4GzRkwIQSfSkAADhcmnYzgREIQVHgmkrwe3AgQM96FK+fHmfFKfJUIsXL7bRo0fbb7/95n/saaF6wYIFff8WLVrY9OnTPUQ3efJkq1mzpl1zzTX+o/CcpkEp+KKJLZrGki5dOp8IpVCdaNF5/vz5bcuWLR6W0SQYnefWW2/1MAyAhGfBggX20ksv2cKFC/1x2bJlrVu3blasWLFY+wUT3zSFSTXiqquuskcffdQqVqwYaj7r837//ffb3LlzPWiXM2dOrxOi+qHQiwK73333nR+XI0cO/4NQIV358ccffR9NcAom0V177bXWvXt3n1AZvA8A0UmfX12P3HzzzZY8efJQAE51QDUkVapU1qFDB+vRo4dPkFTYXtcgSZMmDU2eDKhuaErcL7/8YmvWrPHAnSbmHk/nV/1QKE9hOl3H6PpEP3pdTdfVcXFNrAMAAMDpoScF4GzQkwIQH+hHAQAAJF70pACcDXpSAOIDPSkAQLgQkoNd6BdVO3bssPvuu8+nq1StWtVmzJhhrVq1sscee8xDJ6JpKZri0qdPH5s1a5YHXXLnzu3Pp0mTxvdXsE7TnTQNSkEWPadJL5ocpalOCtetW7fOp8QpeKdgnKa0BBNdChUqZAcPHvRAjRa8y/GL2wGcX6oXr732mn+e5YorrrAuXbr49LaTfWY/+OADrwEKtylYp8+6AihByEUBu5IlS3rg7quvvrLSpUv7FDnRdCYFYzTFSWHdcePGeTglY8aMHopJmTKlB+T03JVXXmlp06a1m266ya677rrz8K8DIFIUuhfVDU2a1Y8Et02bNvX6oNCsaomC/QrbH0+htgoVKvgUy1GjRtmgQYOsVq1aoUBtIKhj+iIA1SPVpiCUpwmXqn0AAAA4N/SkAJwJelIA4hv9KAAAgMSJnhSAM0FPCkB8oycFAAgHQnK4oGmxtxZ+69tOfv31V5+qkjp1ap/uppCbng+mpCiQogXoCsMpSKdQSzANqkGDBj4N6ptvvvFpUF9++aUvWJfKlSv7T7t27fw1tMhc565Tp06s96LJUenTp7ft27eHFqcTkAMSDgXVXn/99VAQTmE1fa5jTnYKAisB1ZD58+f7/grhKiCn44OQi47p169fqDZs3LjRJk6c6OE4TawUvca3337rIVtNrbz66qs9SCcKwehH4VpNkLr88svPw78MgPhyfI1RbQmuVVST5s2b59Nqx4wZ42FZ1ZHja1PWrFk9GKfrFU21VZD3hRdeOOG8onqlLwm488474+k3BAAAuHDQkwJwuuhJATif6EcBAAAkLvSkAJwuelIAzid6UgCAcxF7pS1wgdGi8po1a/rENy0i//rrr31BeYkSJULPB00i0XQ5Uahu6tSptnv37tC5FFTRwvM9e/b4dBYFVkThFcmbN69PpGvYsGEoIHf06FF/Xfn000/tt99+8wBdqVKl4vXfAcDJqS7UqFHD+vbt64GR66+/3r777jvr3LmzB+SCz/Dxf5gFNWTt2rV+nKZOxgyfaNrT7bffbiNGjPDQW6NGjXy7QnJz5swJ1Q4Fd9u3b+/3VSP0fo4dO+aPY06kIyAHXJiCa5Vy5crZDTfc4PVm9erV9vHHH59Qm/Sc6DqnXr16fv+zzz7zKblxIawPAAAQOfSkAPwXelIAEir6UQAAANGLnhSA/0JPCkBCRU8KAHC6CMkhUdNi8CDAcrxgu0JxLVq0CG1XSE0Tm+K6uNIEp9q1a3t4ZebMmTZ79uzQPmXKlLG6detakiRJbPny5R56E4VXAlu3brVFixb5bfBaWryuKXTDhg3zwE2XLl0sZ86cYf13AHDmNI1J05Meeugh27x5s4dXhwwZYr179/bPqGpIMBXuZPbt2+eTJMuWLWtp06b1bfv377exY8fak08+6ZOcNJXy2Wef9RBuvnz5PGirqZSrVq0Knad58+Ye5j106JDXiwkTJvj2YCIdgAtbEObv2LGjZcqUyWuF6oTCuDGfD0Jvml6rSXMK1kn//v3tyJEj5+39AwAAJEb0pACcLXpSAKIB/SgAAICEiZ4UgLNFTwpANKAnBQA4HaysR6KlIJsWgytAcvDgQduwYYP98ccftmPHDn8+CJYoAKfwm6ZDiZ7fuHHjSS+uHn30Ub9duXKlT5PTeQO33HKLFSlSxBenf/DBB75PYNu2bfbggw9a165d7ZFHHrHBgwd7kO7hhx+2e++916dNKUxTq1atCP/LADgdKVOmtF27dnnQVeHXTp06eVBNU9wUkFMNOX7KUlAnApoCpxCuQnB33HGHb1Nw5a233rI1a9bYzTffbO+//77lz5/fQ7OaWCczZsywKVOmeAMqcM899/itgiwKwQBAQNcyqkvZs2e32267zbcp3Dt8+PDQ88EUuUDBggWtfv36HtD/6aef7PPPP+cfFAAAIEzoSQE4F/SkAEQD+lEAAAAJDz0pAOeCnhSAaEBPCgBwOgjJIdEKJriNGDHCbr31Vrv77rt9aooWj7/88ss2b9680L7p06e3li1b+mSVLVu2eEBF056Ov7hSAObKK6+0Dh06+DYtKv/+++9D++g5hdySJUvm4RcFXwKXX365XXXVVR6qmzNnjr+Hp556ysaPH+/Pa1rVm2++aTly5Ij4vw2A/6bP/AsvvGBp0qTxYFy/fv28Bigwd3w4LphMqWOOHj3q05uCwFzhwoV9QpyMGTPGJ8gpiNu+fXuvS6lSpfJmtUJyuhWFWb766iv78ccfQ6+hgF7fvn1t2rRpVrFiRf4TAoiTArmqO6pbs2bNsm+//TZUV2LStco111zjP/L11197/QIAAMC5oycF4FzQkwIQbehHAQAAJAz0pACcC3pSAKINPSkAwMlc9O/xK2aBROKXX37xgMvixYtDITVNZdKUt2Bx+IsvvmhVq1b1kMq+ffts0KBBNnDgQA/FvPHGGz5hLqZgepSCLNdee61PmVLwrnPnzh6AEy0w17kyZsx4wnEK3imA98knn/hidX0DS968eT24d8UVV8Trvw+A0/P444/bF1984Z973W/durUH4GJOZgpCcxMnTrTnnnvOp1ZqKlNQF0Sff02D+/nnn6158+bWvXt3rz0xKTg7cuRID8yplijA+8QTT1iDBg34zwXgPwW16ZtvvrEuXbr4/fLly9vbb7/t9Sa4HgnosaZb/vnnn/6FAgAAAAgPelIAwoGeFIBoQD8KAAAg4aAnBSAc6EkBiAb0pAAAp/K/UVtAIrN8+XIPqixdutQKFCjgITZNctLC8OnTp9u4ceNsyZIlvmh89+7ddsstt1jq1Kk9iDJ16lRbuXKljR492goWLBgr7BYE5PTtS48++qiHXBR6y507t0+NU7BFPzpGF2EKzuiYYEG6FqjrNTRtTvsptKdAHoCE64EHHvBpTJs2bbIPPvjA6tWrZ5kyZfIQmz7HsmzZMuvTp09osqRCr8mTJ491nq1bt9qCBQv8fo0aNbweaNKT6oOCLDpWAbls2bJZlSpVfPqTwrxZsmQ5D781gGikWiL169e3sWPH+uRJXe98+umn1rZt21hTMBXyVf2pW7fuCdMxAQAAcPboSQEIF3pSAKIB/SgAAICEgZ4UgHChJwUgGtCTAgCcyv+NkgASqCNHjvh0JgVGtMhbIRNNPolLMNXpww8/9IBctWrV7L333rM6dep4kC1Pnjw+Ylfb0qVLZ+vWrfOpbnPmzPHj9HyrVq38/vjx4+3HH3/0sFtMCshJkyZN7Morr/TnS5cuHQrLxLwIizmtJaYkSZL4LQE5IOHLnDmzT1hSYG3z5s02YMAA367P/M6dO+3555+3pk2beshN0yGfeeYZD6aonsS0Y8cOD9kqkLtixYpQLVCtWLt2rQ0dOtS3NW7c2J599lkbMmSIh3bLlSt3Hn5rANEquG7p1KmTh3U1xVJfDqBrHoXhgueDYBwBOQAAgJOjJwXgfKInBSBa0I8CAAAIL3pSAM4nelIAogU9KQDAyTBJDgnaZ5995pObNmzY4FObRMGz5s2b+/S3woULhya7BQu9//zzTw+oSMOGDe3yyy/3aU1BME3nmTx5sjeVdJH022+/2caNG61ChQqWNGlSn/CkYMoPP/zgAbqSJUtazpw5Y72v4DUVYsmQIYOlSJHijH4vFqQD0UUhOU1lUr34/PPPPcimb2Lr1auXB1Dk9ttv929TCupBzNokZcuW9SlxCtrpHLt27bKiRYvali1bPLir85QpU8auv/563/+qq646T78tgMTwTUklSpSwZs2a2fDhwz2Y279/f3vppZdCzwMAAODU6EkBSAjoSQGIBvSjAAAAwoeeFICEgJ4UgGhATwoAcDIX/RuM3gISkNmzZ/tC7mDaUqFChXxKnIJtmoSioJwWfyvEdjwF3Dp27GiZMmXyMJyCbwGF53r37m0rV670x5UrV7ZHH33UChYsGNpHwRbt9+CDD/r9rl27Wps2bWKdJ6D3pMDb8WEYAInP9OnTvR7s27fPp8HpVq699lrr3r275cuXzx+rHugPsJhhWE2/VN0aM2aM9ejRw+9rm6Y8HTp0yPepX7++nz979uzn6TcEkFgENUcTLBX+1xcEqE5pmi4AAABOjZ4UgISGnhSAaEA/CgAA4NzQkwKQ0NCTAhAN6EkBAOJCqgcJiia6vfLKKzZhwgR/nCtXLuvUqZOHUIJQiSY1LV682BYsWGBffvmlNWrUKBRWkz179nigLU2aND6hKXfu3D796fXXX7cpU6b4Pnny5LHHHnvMzyvab9GiRVa9enW77LLLfOJTkyZNbPTo0f6jKXMK5R0veE0CckDip3pRqlQpmzlzpgfkMmfO7HVF9UJUn05WD1S/pGnTpl5vVOPWrl1radOm9RCwgr3VqlWL598IQGKlmqNpufrCgLfeesvrzBVXXHG+3xYAAECCRk8KQEJFTwpANKAfBQAAcHboSQFIqOhJAYgG9KQAAHEhJIcE4dixY76Ie+DAgf5YQbXOnTvb7bff7vdjuvfee+3ZZ5+133//PTQRTmG1ICinCU+aOPfnn3/a0qVLbeTIkTZ48GDfT+G5hx56yM8bGDJkiPXq1cunyl1zzTX+eunTp7dmzZr5RDkFWcaPH2+FCxc+4b0AuLD+oHrkkUd8EpPqi0JyQUBOU5r+qz4E31qi4G/r1q19gpymPBUtWjSefgMAF5IgnEsAFwAA4NToSQFI6OhJAYgW9KMAAABOHz0pAAkdPSkA0YKeFADgeITkcN5t2rTJatasGXrcvHlzu//++y1LliyxgiXBbaVKlSxdunQeitPt8VPdSpcubXnz5rU1a9ZYz549PYgirVq18oCcJsyJgnQKzSmkIitWrAhNghJNXVFQbt26dR7MIyAHoECBAla/fn0P3yqEq2Bvhw4dTmuaZPDHmKgO6efyyy/nHxVARATXRQAAADg5elIAogU9KQDRgH4UAADA6aEnBSBa0JMCEA3oSQEAjvd/K/aB8yRbtmw+uS34w6ply5YekNO3JsUMlgRBuf3793vATcfFNR3lkksu8alwooDcVVddZePGjbOnn37aQyl//fWXB+wUkJONGzf6rYJ1KVKksL///tsf677Cca+//rofFzNAB+DCpbqQNWtWvz9s2DDbsmVLaGw3AAAAACB60JMCEE3oSQEAAABA4kBPCkA0oScFAACAaENIDgkixa+gSfBtSaNGjbIDBw5YkiRJPMwmCraJgiiTJk2y1atXW/LkyW3MmDE2evRomzZtWuh8adOmtfLly1uePHn8cebMmS1nzpyh82jiU3De5cuX28KFC/1+7ty5/TmF7ALB9DiFX2JOgQJw4VKot23btl6Dtm/fbu+++65vj1k7AAAAAAAJHz0pANGEnhQAAAAAJA70pABEE3pSAAAAiDakfpAg5M+f3+rUqWMHDx60+fPnh0JvQThO4TUF1d5880177LHH7PDhwx6Ue//99+3xxx+3e+65x3r06GGzZs3y/cuWLWv16tXz46ZPn24vvPCCLVmyxB+LAm+7du2y4cOH27Zt26x48eLWoUOHk74/wi8AYmrWrJnly5fPm9djx461efPm+XamyQEAAABAdKEnBSCa0JMCAAAAgMSBnhS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},
"metadata": {
"image/png": {
"width": 1764,
"height": 512
}
},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"figures/13_confusion_matrices_all.png saved\n"
]
}
],
"execution_count": 27
},
{
"cell_type": "markdown",
"metadata": {
"id": "iOW61vwUtEi1"
},
"source": [
"#### 8.4: Winner"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2t9YFU9KtH8p"
},
"source": [
"- Choose the best one out of the three models.\n",
"\n",
"- Export the model to a `pickle` file.\n",
"\n",
"- Upload the pickle file to the same(!) model repository.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-GsJatjRsvZ-",
"ExecuteTime": {
"end_time": "2026-05-05T17:34:41.454526Z",
"start_time": "2026-05-05T17:34:39.923093400Z"
}
},
"source": [
"import joblib, numpy as np\n\n# winner = highest macro F1\nbest_clf_name = max(clf_results, key=lambda x: x[\"Macro_F1\"])[\"Model\"]\nbest_clf_model = trained_clfs[best_clf_name][0]\nbest_macro_f1 = max(r[\"Macro_F1\"] for r in clf_results)\n\nprint(\"Final Classification Comparison:\")\nprint(pd.DataFrame(clf_results).to_string(index=False))\nprint(f\"\\nWINNER: {best_clf_name} | Macro F1: {best_macro_f1:.3f}\")\n\n# feature importance for winner\nif hasattr(best_clf_model, \"feature_importances_\"):\n imp_clf = best_clf_model.feature_importances_\nelif hasattr(best_clf_model, \"coef_\"):\n imp_clf = np.abs(best_clf_model.coef_).mean(axis=0) # mean over classes for LR\nelse:\n imp_clf = np.ones(X_train_eng.shape[1])\n\nlabel_map = {\n \"AlertTitle_enc\" : \"Alert Type\",\n \"Category_enc\" : \"Threat Category\",\n \"EntityType_enc\" : \"Entity Type\",\n \"EvidenceRole_enc\" : \"Evidence Role\",\n \"has_ip\" : \"Has IP Address (network threat)\",\n \"has_account\" : \"Has Account (credential threat)\",\n \"has_device\" : \"Has Device (endpoint threat)\",\n \"has_sha256\" : \"Has File Hash (file threat)\",\n \"entity_score\" : \"Entity Score (involvement depth)\",\n \"cluster_label\" : \"Incident Cluster (complexity tier)\",\n \"cluster_distance\" : \"Distance to Cluster Centre\",\n}\nimp_clf_df = (pd.DataFrame({\"feature\": X_train_eng.columns, \"importance\": imp_clf})\n .sort_values(\"importance\", ascending=False).head(12).reset_index(drop=True))\nimp_clf_df[\"label\"] = imp_clf_df[\"feature\"].map(label_map).fillna(imp_clf_df[\"feature\"])\n\nfig, ax = plt.subplots(figsize=(9, 6))\nbars = ax.barh(imp_clf_df[\"label\"][::-1], imp_clf_df[\"importance\"][::-1],\n color=\"steelblue\", edgecolor=\"white\")\nbars[-1].set_color(\"#e05c00\")\nax.set_title(f\"What Drives Incident Grade Prediction?\\n({best_clf_name})\",\n fontsize=12, fontweight=\"bold\")\nax.set_xlabel(\"Feature Importance\")\nplt.tight_layout()\nplt.savefig(\"figures/14_clf_importance.png\", dpi=150)\nplt.show()\nprint(\"figures/14_clf_importance.png saved\")\n\n# export winner\njoblib.dump(best_clf_model, \"models/soc_classifier.pkl\")\nloaded_clf = joblib.load(\"models/soc_classifier.pkl\")\nclf_kb = os.path.getsize(\"models/soc_classifier.pkl\") / 1024\nprint(f\"models/soc_classifier.pkl saved ({clf_kb:.0f} KB)\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Classification Comparison:\n",
" Model Accuracy Macro_F1 Weighted_F1 CV_F1\n",
" LogisticReg 0.416 0.402 0.414 0.406\n",
"RandomForest 0.733 0.700 0.724 0.694\n",
" XGBoost 0.717 0.669 0.700 0.669\n",
"\n",
"WINNER: RandomForest | Macro F1: 0.700\n"
]
},
{
"data": {
"text/plain": [
""
],
"image/png": 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3hRdeKI8//ng57bTTyle+8pUaaCacHZUKRwAAAACAcd2Ew70AAP1J1Vajomv99dcf8AZbd911a/j2v//9r1agff3rX+/1uRNPPHE58cQTy0477VTDqlQANqoAmyXEO+mkk8rkk08+6B0355xz1tDsqaeeKsccc0y9bbDBBl0B4diwxhprlP33379uk4G01YwZZpihzD333HWuwdb5BlPRlxBuww037PG6hKGZ2/Ccc84pt912W9luu+16PGeKKaYohx9+eK2gBAAAAACgOxV5wIgI8hpz2S2//PIDfl0q9xJcxfPPP1+uvPLKfgOr3/72t+XHP/5xnQfvQx/6UJlwwglrpdniiy9e9txzz3LBBReUOeaYY5TWI+91/PHH1znhEgTm9tprr5WxKeuyyiqrDKpNaULOtBtNYJdAL6+bddZZa1CasLNdQBcTTTRROeigg8rpp59en5vX5L0mnXTSMu+885Ztt922XHLJJd3m1gMAAAAA4P8Z7/3WSY8AGKd961vfKhdddFEN8Y466qhen5fgMiFqAri0xxwpUgV499131/vT/WLtMsGrzw73IgEAAADAB9IE081aZjv6ifJB8VbTtclFFlmkFjaMLhV5AB8gqQBshHKDaVMKAAAAAMDYJ8gD+AA59dRTy+uvv15mnnnmsuKKKw734gAAAAAA0IcJ+/ojACPfscceW+fje/DBB7vmG9xmm23KBBNMMNyLBgAAAABAHwR5AOO4a6+9ttxzzz1dvy+66KJliy22GNZlAgAAAACgf1prAozjllpqqVqRN+2005YNN9yw/PKXvywTTuh7HAAAAAAAnW68999///3hXggAGCpvvfVWufvuu+v96X6xdpng1WdtXAAAAAAYBhNMN2uZ7egnPpDXJhdZZJEy8cQTj/Z7qsgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA404XAvAACMKeNPM3OZYKKJbGBG2fullLfffrven2iiicp4tiWjwXhiKBlPGEt0IucmjCc6lfMTxtLwmWDamQ3A0STIA2CcNcMPbipTTDHFcC8GI9hbb71V7r777np/kUUWKRNPPPFwLxIjmPGE8UQncm7CeKJTOT9hPNGJnJsYDlprAgAAAAAAQAcS5AEAAAAAAEAHEuQBAAAAAABABxLkAQAAAAAAQAcS5AEAAAAAAEAHEuQBAAAAAABABxLkAQAAAAAAQAcS5AEAAAAAAEAHEuQBAAAAAABABxLkAQAAAAAAQAcS5AEAAAAAAEAHEuQBAAAAAABAB5pwuBcAAMaUCSf0zxyjZ6KJJiqLLLJI130wnugUzk8YS3Qi5yaMJwAYeq5wAjDOGm+88YZ7ERgHxtDEE0883IvBOMJ4wniiEzk3YTzRqZyfAOD/CPIAGGe9+9775blX3hjuxQAAAGAEm37KScsE4/uiKADDQ5AHwDjrpdfeLFv//PrhXgwAAABGsNN3W7nMMPVkw70YAHxAjT/cCwAAAAAAAAD0JMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMgDAAAAAACADiTIAwAAAAAAgA4kyAMAAAAAAIAOJMhjwN59911baxBsrzHnvffeG4Pvjm0NAAAAANAZJhzuBaCUlVdeufz73/9uuynGH3/8Mskkk5TpppuuzDvvvGX11Vcva6+9dpl88sl73XTzzTdf/bnLLruUXXfddUg28T333FP22Wefcu6559plAwiZzjjjjPLoo4+Wvffee1i31y233FIuueSScscdd5QnnniivPnmm2XKKacsH/vYx8pyyy1XvvjFL5aZZ5657WuPOeaYcuyxx9b7DzzwQOkEjz32WNl3333LAQccUGabbbbSyd5+++1yxRVXlKuuuqoeP88++2wNd3Ms5xhdZZVVyuc///k+j+XhNJK2NQAAAADAuEqQNwJCoTfeeKPennzyyXLdddeVn//85+WQQw4pSy211FhZhj/96U/la1/7mgqzAfrOd75TLrroorLBBhuU4fKPf/yj/OAHPyh33nlnj7+99NJLNdjL7aSTTipf//rXy3bbbVc63f3331823njjGkZ2uoR3Bx54YHnqqad6/C2BXm7XX399Oe6448pBBx1UVlhhhdJJRtK2BgAAAAAYlwnyOsiSSy5Zg5VmqeB5/fXXy+OPP17DgVR6pXrvK1/5Svn1r39dFl988R7vM8ccc9Sf00wzzZAs13/+8x8h3iAkpBlOCYgSzmXcTDDBBLXqK1WcH//4x8tEE01Ux08q9U4++eS6bw899NDyzDPPlO9973ulk7388ssjIlhK0H7UUUd1HYObbbZZWWmllcrss89ej6Mcy5deemn53e9+V7f7DjvsUI488shabdspRsq2BgAAAAAY1wnyOkhClymmmKLH41NPPXVtf5gKvDXXXLNWTyWk2XHHHcuVV15Zpppqqm7Pz2N8MD300ENdIV7GxYknnliWWGKJbs+ZfvrpyyKLLFIrrjKW7rrrrnLKKaeUT37ykx0VJo1EF1xwQVeIl+A0YekMM8zQ7Tkzzjhj3dabbLJJ2XLLLWuF5B577FH3yUc+8pFhWnIAAAAAADrR+MO9AAxOQpm0TIwXX3yxBgXQkDnNEuKNN9545YQTTugR4jVL0JfQadJJJ62///SnP7UhR7OK7Uc/+lFXWJdwtDXEa5agb7/99qv3s89+9rOf2f4AAAAAAHSjIm8EytxrqbR65JFHaqvNXXfdtYw//v/LZOebb776c5dddql/a63YOvXUU8vNN99c59ybcMIJa+jwqU99qmy++eZlgQUW6HruX/7yl7LVVlt1e33jvQ8++OCy4YYbdj3+6quvlnPOOafOp/fggw/WKqO0cfzwhz9cw6S896KLLtpjXVKRlDaPWdadd965nHXWWeX888+vy/nOO++Uueaaq7aFzHI0Aqd2brzxxtqq8J577qntClPZmHX54he/WNZdd922r3n//ffLxRdfXKuo7r333vLKK6+Uaaedtiy22GK1Wu0zn/lMGYw999yznHfeeV2/537j9wceeKDbc9PS8rTTTqtzHj722GO15WKqLpdZZpm6rvPMM08ZrFTWZVvGGmusUVu19icVYNmP2eazzjpree655/oMn1rHRcbS0ksv3fZ5fY3DzB2X16YN6L/+9a/62Ic+9KE6VrLtm+d/fOKJJ8oqq6zS7fWN39u9d+YF/M1vflNuu+228vzzz5fJJpushmbrrLNOHQ8Zl61WXnnl2nI089plm6Td6MMPP1xbY2afDCTkzNjNuI/MKZnKx/6kwnbeeeetr8sYaCchX9YnlbY55v/3v//VY3bZZZct22yzTZl77rn73EcZezmefvGLX5Q///nPdexlvVIVuO2229bxPpht3fzed999dzn22GPrsZ9zQLZdnrfeeut1vT7nmbQBzr7Ofk/InDajn/3sZ8vWW29dpptuun63EwAAAADAB5UgbwTKhfC11lqrzsWVAODvf/97WXjhhft93R//+Md6If6tt97qeiz3H3300XrLxfi99967bLHFFoNanlzMT3CRgKDZ22+/XecDyy1hWUKSBCntZDkSKiRoaHbffffVW+YUO/3003u0Hs08Xj/84Q+7BWiR7ZL3yu2yyy4rRxxxRLcAJ6FdAoeEEs0SZGUuwtw+97nPlYMOOqhMPPHEZShlP3znO9+py9AsgV5u2Q/f+ta3atvLwcg2ath0000H/LosS/Z7WruODRkv2det658gLbcLL7ywrvt3v/vdQb3ve++9VwO4X/3qVz3G1q233lpvCXuPP/74MtNMM7V9j7/+9a+1Si5jNzKm27W7bSfjLDLOmkPu/vz2t78tU045Zdu/JYTLsZUwrFkCt7PPPruce+65Za+99qqBeG+uueaa8s1vfrMGgA1Zryzv5ZdfXn784x/XeRRHRY6PLH9DziOzzTZb1+8JyrN8rfPtZb1yy2tTiZhQEQAAAACAnrTWHKEWWmihbhVI/UlVT+bhSqiRyri05Ez1XKpkEgim8i0VaoccckgNCSIX1++4446u9n+R33NLyBWpwslcfQkGUn2XC/tXXHFFrfhLeJfgMBVRee/8LcvRTqrTErql2jDhRAK2hC6f/vSn698TVrYGNJH3bIR4qUJLVVQ+O4+lAitSydTctjCVT6n+y2ekIvErX/lKueiii+rvWeYEmQlL//CHP9TwcaD233//um0alXCpSmpsr+Z9lW2SECvbK6+59tpr67ofd9xxZf7556/Ll0DqzDPPLIPR+JwESYsvvviAXzf55JOPtRAv4yABXdY/Yy77Jet/00031aqtRtXmL3/5y671SaVg7qcKtTkgymM77LBD12NHH3101xjJXH+pVs0+TSibasmEZRlHeU1rsNSQEDWVgTk+skw5NvoKyRreeOON+t6RdciYH6jeQrxnn322VtwlxEt1XwLrhHIZ36lmXG655epYyRjN9ujN7rvvXitNE9jlmM9tn3326TouDzjggHocD2ZbNySIy3GXQDDLltaujbGXKtlvf/vbdVtnXGdfZ5vmnJNgPfs/gftXv/rVGgACAAAAANCTirwRKhfcG1or4dpJy8VG279jjjmmtnFsbp2X1oMJP1KJlOArAULCnVQjNVektVYnJfhKFVsjSGlu55iWebmAn7nYMndYQrwEAssvv3zbICRVWgkbGxI+ZJ63LFda8iUsSBVdQ9poJuyLzTbbrAYdzZ99+OGH1yqkq6++ugYfCSISXiTka7SgTKCQ92/+zMxBmKqihJoJBjfZZJNuwWlvsp1ya4RiCQlbt1dCl4SpWb68d3P1Uto7pl3il7/85domM8FLQpKBtGiMVPPFLLPM0mcb0uH0z3/+s7asbISwzZVYWfcEeKuuumqdb+6SSy6prTYTqmY7Nq9T7jdv2wRBGSuR4C0Vhs37NOM5YzOViqnwTMiXx9pJ29hGgNzaZrI3aQ+aUCw+9rGPlaHwk5/8pLYGTRvMjJU55pij629pZ5p2uDkeMr6zLbPdJplkkh7vk2A3gVvaXjak1W22a0L6//73vzV0y1gbyLZuPQ/lOMtYjy996Uv1ZwLGHEepkkywmWra5mVLu9xs41Qupgozx1oqJQEAAKCT5f/9G118GFma95t9iLHEmDQmzjGCvBEqVVQNjYCuL83tNBO8NQd5kTmrUoWT0OCjH/3ogJcj4UBCgVyw721OtuY51F544YW2z8kcf6nMaZVgLBf8f//733dVCjZXCuU/oLItUvnTTsK7hDxZpwSeWc9GpVuCkOYQr1nmAEuVYEKGhIXNVYmjKhVbCR8jAUxziNeQoDFVeml1mHAzc9cl4ByIRlVVJ8851jwO2wXQU089da3cSjA055xzDvh9E3RlDGb7pY1kOwmUEiCldWf2absgL6FfAsXBShjWMBTbvxFkRipEm0O85mMmwXeCvAR++Zn1a5Wx1BziNWSOusa4bj22Bmq11VbrCvGapeoux06kTWy7gDHbOtW8CV3TbnYg8zMCAADAcF+czZQhjGz333//cC8C4whjibFFkDdCNQciqaLpzyc+8YlamZP/4EiAkcqkXMjP440KshVWWGHQy5Eqstx6k7CmufVnKnXaSVDRWwCSVoeRYKtZWgw2gsLeKoYWW2yxrkCkEXY1WiAuuOCC5bXXXut12RdZZJEaRjS3xhwdjSrASPVTb1LFmODxkUceqa8ZaJDXGAed/K2ieeaZpwY4CZ/TYjPrl0qyVOY1Kj8TsI7qtm1Uw/W2XzMeEuSlKvDFF1/sMeay7QdyPLUL1RqGYvvnmGm8T5apt/VJe9aEXwnBbr/99rZBXta5r+Oq3bE1UAsssEDbx5vnnky1b2/L35jbM4F8jrO+jgsAAAAAgA8iQd4I1VwBlCqm/sw444y1MiYt7PLak046qd7y2lQgfeYzn6ltBBOyjIqEDrfeemu59957y+OPP15vCUsyz1ezRvvBVn1VMTUCntbXPvPMM/Vn5toaqARzqdyKU045pd76k7aeQ+Hpp5+uP7PN+6s8mnvuuWuQN5jPTgvObJMEVJ0qlVlpgdqYO+03v/lNvaWqMgFexmGqvDJeB6NRUZbxl3acA90freNuoG1MWzW/bii2f1p1NmROxYHobaz0tk7NLXN7Oy7709t7N1f4DbTCcaiOMwAAABhT8iX5fPGbkSfXLhvVU/nSdPYlGEuM6fPNUBHkjVDNF/rTLnIgUomXCprMQ/bnP/+5DqhXXnmlzj2XW/4By/xiCVkaVXoDcemll9Y5uhpz5TWksikVUqkIyrx0fRmVfzzTfjAGMx9co/3kYIzKa/p6n+a2qL1Ji8jIvIIDlfAvQV72QyqsGu8xEAk3m6vKxqRUjaVtZlq5pqVi5jHMel533XX1lrGUdpCZY20g22oo92u7FpADkbni8tqEkwmxByNVqq3H21CO03atL4dKb9trOI8zAAAAGFNyrav5i7GMTLkOaT9iLDGSCPJGqLvuuqvr/mC+CbTMMsvUWy6a33TTTTXQu+GGG2r4kGDv5JNPrtU5e+6554De74orrqhzkuU1qc5JNVXa5SXAS0u9VJ899thj/QZ5oyJBVaoLEwQN5jUN++67b/nSl75UxpZGKDWQcK7RinAwYVzmKMw+bVRHrrjiigN6XarYEvKmIi7zsY3KHHGt+tsnCy20UDnqqKPq89KGMePwxhtvLA8++GANts4999w6Ro855pgBfV7C3Dw/IeERRxxRxraEZWlTm3X529/+VpdlyimnHNBrTz311DofY1rE7rTTTjWYb97vaQ2bkHYkaYTraf2Z/QoAAAAAwKgZOyU4DKkENQnQYrbZZivzzTffoN8jIcPqq69e2xxeeeWV5eyzz65VRXHGGWeUd955Z0Dv89Of/rSGeFmOBA77779/2XjjjeucZ42Wn2Oq1eNHPvKR+rO/CqjDDz+8hiX//Oc/y8wzz9ytzWZfRrXdYG9mmWWW+jNVkK3Vi60eeuihbq8ZiLXWWqvr/gUXXDDg11188cW1uvGqq64aUCjaXD3W23xwmQNvoIFP2mkmOM7cdRlDCfkiY7zRPrU/je00tvdpszXXXLP+zLHTPC/jQLZ/ljvr3zhmGmN7uNdpVDX2R479wVSVAgAAAADQnSBvBEro1giCNtpoo1rW358TTjihrLvuur1WoC266KJlq622qvfTHrDRtjJ6e/8XXnihPProo/V+QsHe5rlLtVVDY366obD44ovXn6k+yzK3849//KOue9o1PvDAA7VqcJ555ql/u+aaa3oNQbKc66yzTllhhRXKd77znSFZ3oSbDWll2pv77ruvVjHGQOd7i1RtZXkb4dDtt9/e72sSEp155pn1/hxzzNH1+r40V4tlDLRzxx13tH38nHPOKeuvv35ZeeWV2277rMOOO+7Y9XtzkNfXOG9s21QXNuYibGefffaplW9f+MIXhryVY9qBNuaMO/LII2tg25+ElXfffXe9n+Nzmmmm6aqubLQ6vfrqq/vcfzkOVl111RpWD5WBnFP60tgfqa5M+9TeJLzM8udYu+2220brMwEAAAAAxkWCvBEm4cyhhx5a76e67Mtf/vKAW/8l1Lrzzjt7DVkSIDWq9RqBRGsF1ltvvdXtPVsryFplUsfMhdZfBdeoSBgTCUyOPfbYts85+uiju9papvKrEX42ljnzBbaTUCR/f/bZZ7uCv4FqbJfWdU3L0Ua1WZa3XaVVKuL222+/rvnHPve5zw3qs1PZltclJEubxr7CkYRkX/va17oqpvbYY48BzaeW1o+NkCnzI7Zbh+Z93ixjK+Ms656wsa9xmM9IpedAKgFTBdqohsv2S4DUrh1tWrymWnDaaacdcOvLgZpiiinq/JLx/PPP12C8r4rC7Ju99tqr3p9qqqnK17/+9a6/pSXlZz/72Xr/97//fdtQNmHzwQcfXOdDzJyZGV9DZSBVl31ZZZVV6jrET37yk7aBbx7L8Znx95///KfO3wkAAAAAQHeCvA6S8CFzozXfEjrkIn2qWr7//e/X4C4X7tOSMBfBBzqHWkKvhBeNgOf0008vDz/8cL2YnrDtgAMOKOeff359bqr2mity8rrmCpoEZ1m2tAFMJV9cd9115cADD6zhV9rp5T1TlbTpppt2a63XmPttKCy22GK1uisSHGX7JATK5ye0STDSaEG66667dgU3m222WVlwwQXr/cMOO6x873vfK/fcc0/d1qnaSzhyyCGH1L/PNddcZcsttxzUcjW2V4KaVNY1hxhpZZoJdbOMm2yySfnd735Xnnrqqfqc7OMsW8LWRrDW3Ap0IBI6ZtkTxGR9Ml6+8Y1v1P2TcZTHUrWWIHG99darc9JFquBS1TUQ2e+ZZ7FR1Zjg7JFHHqlhTH7PPs9+aLSJbA14sk1j7733Lj//+c9rwJztkdanmRMvFZSNVpXNgXLzOEwImHHYqKpLCJRt11imhGiZ+zHbNa1XMwfdV77ylRpKJegcqirLdsfZ5ptvXu9nG6TKLuMpYyGhcAK+zKOXdc8yZvkzuXJa1La2Uc3+z5jNMm+33XbluOOOqxWwWae8X0LYtMWNfM5gqjf709e2HoisU47HSGj7xS9+sZ5fEmzmluMyx1WjLe63vvWtGoQCAAAAANDdeO934gRLHzBpM9jfPFjNUqWUC/+f+MQn2v69MWfeLrvsUgOs5haXCfH6mrMqVUAJCHMhviHBQQKY5tc13jttARNI9PWeuYifz846pnIqoWFDLubfcsstZamllqphSzsJdxoVdwnaWqu/UgXVCDTayWckOGmW1qTZFn/72996fV0Cp5NOOqm2nByM3/72tzWwa5b2iI3qsgR2u+++e6+hZqri8veEN6MqIVYCyv7mmEt4klCrXcvVvrZ7wrctttii7Vx4CYHzntdff33d763jMO+17bbb1uCvr5A21ZKpVGtItV3CxgSfDRtssEFX6JrAa999963tO/ta38yZuNJKK7U9Bpvfb1TllHryySfX46i/OQdT3ZjPa2672iyhbrZfX9sqx+wRRxzRLdRPWNholZvq0rQTHcy5oq9tPdD3bhwLCfh7q+rLWNl55527ffZQSOVwo2XpLHPNW7b++fVD+v4AAAB8sJy+28plhqkH9mV6OlPztYJFFlmk27VPMJbo9PNN/330GFZpL5gL9DPNNFO96J7AIZVKo7Lzl1122Vpdc8opp5SbbrqpPPHEE/UCe+a2S1u+VLetscYaPV6Xqqjjjz++hocJYRI0NebQy0BMu8JUUSW0SUCWv88wwwy1Wi9VZ7nQn+qcBCzXXntt/cxUpQ2FVCYmbLrqqqvq+yeYy7IlAErQmRBvueWW6/G6LF9ChlQYXnTRReXvf/97fV3eb955563bIeFWfh+shJUJXtISMdsj1U2Zt60R5CVESkVS9kOjUi5SkfXpT3+6VrRlrrjRsfzyy9dwM/s7FWqpkMwyZdtn26RyL/PhJWT90Ic+NOj3zzbKtkslZNYh65fqscx3lpAuwVSCvHYyjrPNEwLltakyyxyHmR9u/vnnL2uvvXYNjZrbO0bGVcbhj370o64TYXMYmjGVuRAzjrNvE4Jl++cYSmCW9U2F4mCrHAcr4VRC2LRFzbGRYy3VhhlfCflyPKUiNPNKZm64VAj2JtvzsssuK2eccUbdj6l8bFTD5tjLdlprrbWGfB3629YDlbG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"
},
"metadata": {
"image/png": {
"width": 889,
"height": 588
}
},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"figures/14_clf_importance.png saved\n",
"models/soc_classifier.pkl saved (76562 KB)\n"
]
}
],
"execution_count": 28
},
{
"cell_type": "markdown",
"metadata": {
"id": "TlClTFEgXFTX"
},
"source": [
"
\n",
"\n",
"---\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mzYCO54GIPdP"
},
"source": [
"# **Part 9: Presentation Video**\n",
"\n",
"- Record a brief video (4–6 minutes) with screen sharing of you walk through the HF's model repository, README, and sharing your process & results. Include a screen share while also recording yourself talking during the walk through.\n",
"\n",
"- The recording will include sharing the screen, and you talking to the camera (show yourself in a circle on the bottom).\n",
"\n",
"- Videos without your face talking while going ower your work wont be acceptable.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fl0X5y1muPvw"
},
"source": [
"> For help:\n",
"> - Youtube [Watch this video](https://www.youtube.com/watch?v=DK7Z_nYhjjg)\n",
"> - Loom [Watch this video](https://www.youtube.com/watch?v=eSCHNXTsJK8)\n",
"> - Zoom [Watch this video](https://www.youtube.com/watch?v=njwbjFYCbGU)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kw06OJESuWGp"
},
"source": [
"- Include:\n",
" - A quick dataset overview and your main goal.\n",
" - Key EDA steps and highlights of visual insights.\n",
" - How you engineered features. About your clustering.\n",
" - The models you trained, your iterative process, and what you learned.\n",
" - Key visualizations and takeaways.\n",
" - Reflections on any challenges and lessons learned.\n",
" - Extra work.\n",
"\n",
"- Finally, attach the video to the beginning of the `README` file, and make sure everything works. *The video should be placed at the beginning of the README and must be playable within it. It can be recorded using `Vimeo`, `YouTube`, `Loom`, and uploaded to your HF model repo.*"
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2026-04-22T08:25:21.960439Z",
"iopub.status.busy": "2026-04-22T08:25:21.960231Z",
"iopub.status.idle": "2026-04-22T08:25:21.963599Z",
"shell.execute_reply": "2026-04-22T08:25:21.962621Z"
},
"id": "ccJCoq6HutHy"
},
"source": [
"# The following is an example on how to include your video in the README file.\n",
"# \n"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {
"id": "YvNRPdxhvWaK"
},
"source": [
"
\n",
"\n",
"---\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EZzOzA3YupFc"
},
"source": [
"# Part 10: Moodle"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n28Xs2gRusQh"
},
"source": [
"**Submit to Moodle only one link - the link to your HF's Model Repository.** \n",
"\n",
"The Repo should Include:\n",
"- README\n",
"- Python Notebook\n",
"- 1 pickle model for regression\n",
"- 1 pickle model for classification\n",
"- Video Presentation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DFqtRAswvKox"
},
"source": [
"import os\n",
"\n",
"checks = {\n",
" \"models/incident_regressor.pkl\" : \"models/incident_regressor.pkl\",\n",
" \"models/soc_classifier.pkl\" : \"models/soc_classifier.pkl\",\n",
" \"data/processed/train.csv\" : \"data/processed/train.csv\",\n",
" \"data/processed/test.csv\" : \"data/processed/test.csv\",\n",
" \"data/processed/summary_stats.csv\" : \"data/processed/summary_stats.csv\",\n",
"}\n",
"\n",
"all_ok = True\n",
"for label, path in checks.items():\n",
" status = \"exists\" if os.path.exists(path) else \"MISSING\"\n",
" if status == \"MISSING\": all_ok = False\n",
" print(f\" {label:45s} [{status}]\")\n",
"\n",
"png_count = len([f for f in os.listdir(\"figures\") if f.endswith(\".png\")])\n",
"print(f\" figures/ PNG count [{png_count} files]\")\n",
"\n",
"print()\n",
"if all_ok:\n",
" print(\"ALL OUTPUTS VERIFIED — READY FOR HUGGINGFACE UPLOAD\")\n",
"else:\n",
" print(\"SOME OUTPUTS MISSING — check which prompt to re-run\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {
"id": "rN8TJ_5oIPfm"
},
"source": [
"\n",
"---\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"Good luck and have fun creating AI model!"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cPgKfBWKvU8K"
},
"source": [
"# Submit the HuggingFace repository link on Moodle.\n",
"# Repository should contain: notebook (.ipynb), both .pkl models, README.md, figures/\n",
"# paste_your_hf_link_here = \"https://huggingface.co/...\""
],
"outputs": [],
"execution_count": null
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"name": "python3",
"language": "python"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}