diff --git a/src/base.ipynb b/src/base.ipynb
index f2a28ef..de1d686 100644
--- a/src/base.ipynb
+++ b/src/base.ipynb
@@ -1 +1 @@
-{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9300356,"sourceType":"datasetVersion","datasetId":5631196}],"dockerImageVersionId":30761,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"![\"Kaggle\"](\"https://kaggle.com/static/images/open-in-kaggle.svg\" "\\"Open")
","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import (\n accuracy_score,\n precision_score,\n recall_score,\n roc_auc_score,\n classification_report\n)\n\n# Load the dataset\ndf = pd.read_csv('/kaggle/input/ai-human/AI_Human.csv')\n\n# Total entries\nprint(df.count().sum())\n\n# A brief overview\ndf.describe()\n\n# Check for missing values\nmissing_values = df.isnull().sum()\nprint(\"Missing Values:\\n\", missing_values)\n\n# Class distribution in 'generated' column\nclass_distribution = df['generated'].value_counts()\nprint(\"\\nClass Distribution:\\n\", class_distribution)\n\n# Text length analysis\ndf['text_length'] = df['text'].apply(len)\n# Summary statistics for text length\ntext_length_stats = df['text_length'].describe()\nprint(\"\\nText Length Statistics:\\n\", text_length_stats)\n\n# Function to clean text without NLTK\ndef clean_text_no_nltk(text):\n # Convert to lowercase\n text = text.lower()\n # Remove special characters, numbers, and punctuation\n text = re.sub(r'[^a-z\\s]', '', text)\n # Tokenize and remove simple stopwords manually\n stop_words = {'the', 'and', 'is', 'in', 'to', 'of', 'for', 'it', 'on', 'this', 'that', 'with', 'a', 'as'}\n tokens = text.split()\n tokens = [word for word in tokens if word not in stop_words]\n # Join tokens back into a cleaned string\n cleaned_text = ' '.join(tokens)\n return cleaned_text\n\n# Apply the cleaning function to the 'text' column\ndf['cleaned_text'] = df['text'].apply(clean_text_no_nltk)\n\n# Display the first few rows to verify cleaning\nprint(df[['text', 'cleaned_text']].head())\n\n# Text length features\ndf['text_length'] = df['cleaned_text'].apply(len)\ndf['word_count'] = df['cleaned_text'].apply(lambda x: len(x.split()))\ndf['avg_word_length'] = df['cleaned_text'].apply(lambda x: np.mean([len(word) for word in x.split()]) if x.split() else 0)\n\n# Display the new features\nprint(df[['text_length', 'word_count', 'avg_word_length']].head())\n\n# Plot histograms for each feature\nplt.figure(figsize=(15, 5))\n\nplt.subplot(1, 3, 1)\ndf['text_length'].hist(bins=30, color='skyblue')\nplt.title('Text Length Distribution')\nplt.xlabel('Text Length')\nplt.ylabel('Frequency')\n\nplt.subplot(1, 3, 2)\ndf['word_count'].hist(bins=30, color='lightgreen')\nplt.title('Word Count Distribution')\nplt.xlabel('Word Count')\nplt.ylabel('Frequency')\n\nplt.subplot(1, 3, 3)\ndf['avg_word_length'].hist(bins=30, color='lightcoral')\nplt.title('Average Word Length Distribution')\nplt.xlabel('Avg Word Length')\nplt.ylabel('Frequency')\n\nplt.tight_layout()\nplt.show()\n\n# Extracting unigrams and bigrams using TF-IDF\ntfidf_vectorizer = TfidfVectorizer(ngram_range=(1, 2), max_features=500)\ntfidf_matrix = tfidf_vectorizer.fit_transform(df['cleaned_text'])\n\n# Convert the TF-IDF matrix to a DataFrame for easier interpretation\ntfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=tfidf_vectorizer.get_feature_names_out())\n\n# Display the top features (unigrams and bigrams)\nprint(\"\\nTF-IDF Features (Unigrams and Bigrams):\\n\", tfidf_df.head())\n\n# Compute the average TF-IDF score for each feature\ntfidf_mean = tfidf_df.mean().sort_values(ascending=False)\n\n# Select the top 20 features\ntop_features = tfidf_mean.head(20)\n\n# Plot the top features\nplt.figure(figsize=(10, 6))\ntop_features.plot(kind='bar', color='skyblue')\nplt.title('Top 20 TF-IDF Features (Unigrams and Bigrams)')\nplt.ylabel('Average TF-IDF Score')\nplt.xlabel('Features')\nplt.xticks(rotation=45, ha='right')\nplt.show()\n\n# Vectorize text data\nvectorizer = TfidfVectorizer(max_features=5000) # Reduce features for efficiency\nX = vectorizer.fit_transform(df['text'])\ny = df['generated']\n\n# Split data\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Logistic Regression\nlog_model = LogisticRegression(max_iter=1000, solver='liblinear') # Ensure solver compatibility\nlog_model.fit(X_train, y_train)\n\n# Predictions and evaluation\ny_pred = log_model.predict(X_test)\ny_score = log_model.decision_function(X_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"Logistic Regression Accuracy: {accuracy}\")\nprint(\"Classification Report:\")\nprint(classification_report(y_test, y_pred, zero_division=0))\n\n# **Visualization 1: Confusion Matrix**\ncm = confusion_matrix(y_test, y_pred)\nplt.figure(figsize=(6, 4))\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Human', 'AI'], yticklabels=['Human', 'AI'])\nplt.title('Confusion Matrix')\nplt.xlabel('Predicted Label')\nplt.ylabel('True Label')\nplt.show()\n\n# **Visualization 2: ROC Curve**\nfpr, tpr, _ = roc_curve(y_test, y_score)\nroc_auc = auc(fpr, tpr)\nplt.figure(figsize=(6, 4))\nplt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC Curve (AUC = {roc_auc:.2f})')\nplt.plot([0, 1], [0, 1], color='navy', linestyle='--')\nplt.title('ROC Curve')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.legend(loc=\"lower right\")\nplt.show()\n\n# **Visualization 3: Precision-Recall Curve**\nprecision, recall, _ = precision_recall_curve(y_test, y_score)\nplt.figure(figsize=(6, 4))\nplt.plot(recall, precision, marker='.', label='Precision-Recall')\nplt.fill_between(recall, precision, alpha=0.3, color='blue')\nplt.title('Precision-Recall Curve')\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.legend()\nplt.show()\n\n# **Visualization 4: Top Positive and Negative Features**\n# Extract feature coefficients\nfeature_names = vectorizer.get_feature_names_out()\ncoefficients = log_model.coef_[0]\n\n# Combine features and coefficients into a DataFrame\ncoef_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})\ntop_positive = coef_df.nlargest(10, 'Coefficient')\ntop_negative = coef_df.nsmallest(10, 'Coefficient')\n\n# Plot top positive coefficients\nplt.figure(figsize=(8, 5))\nsns.barplot(x='Coefficient', y='Feature', data=top_positive, palette='Greens', hue=None)\nplt.legend([], [], frameon=False) # Explicitly disable the legend\nplt.title('Top 10 Positive Features (AI Indicating Words)')\nplt.xlabel('Coefficient Value')\nplt.ylabel('Feature')\nplt.show()\n\n# Plot top negative coefficients\nplt.figure(figsize=(8, 5))\nsns.barplot(x='Coefficient', y='Feature', data=top_negative, palette='Reds', hue=None)\nplt.legend([], [], frameon=False) # Explicitly disable the legend\nplt.title('Top 10 Negative Features (Human Indicating Words)')\nplt.xlabel('Coefficient Value')\nplt.ylabel('Feature')\nplt.show()\n\n# **Visualization 5: Histogram of Predictions**\nplt.figure(figsize=(6, 4))\nsns.histplot(y_score, kde=True, bins=30, color='purple')\nplt.axvline(0, color='red', linestyle='--', label='Decision Boundary')\nplt.title('Histogram of Decision Function Scores')\nplt.xlabel('Decision Score')\nplt.ylabel('Frequency')\nplt.legend()\nplt.show()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T03:52:41.771457Z","iopub.execute_input":"2024-12-09T03:52:41.77201Z","iopub.status.idle":"2024-12-09T04:04:35.819697Z","shell.execute_reply.started":"2024-12-09T03:52:41.77196Z","shell.execute_reply":"2024-12-09T04:04:35.818485Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Initialize K-Fold Cross-Validation\nkf = KFold(n_splits=5, shuffle=True, random_state=42)\n\n# Initialize Logistic Regression model\nmodel = LogisticRegression(max_iter=1000, solver='liblinear')\n\n# Lists to store performance metrics\naccuracies = []\nprecisions = []\nrecalls = []\nroc_aucs = []\n\nprint(\"Performing K-Fold Cross-Validation...\")\n\n# Perform K-Fold Cross-Validation\nfor fold, (train_index, test_index) in enumerate(kf.split(X)):\n print(f\"Fold {fold + 1}\")\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n \n # Train the model\n model.fit(X_train, y_train)\n \n # Predict on the test set\n y_pred = model.predict(X_test)\n y_proba = model.predict_proba(X_test)[:, 1] # Probabilities for ROC AUC\n \n # Evaluate metrics\n acc = accuracy_score(y_test, y_pred)\n prec = precision_score(y_test, y_pred)\n rec = recall_score(y_test, y_pred)\n roc_auc = roc_auc_score(y_test, y_proba)\n \n # Store metrics\n accuracies.append(acc)\n precisions.append(prec)\n recalls.append(rec)\n roc_aucs.append(roc_auc)\n \n print(f\"Accuracy: {acc:.4f}, Precision: {prec:.4f}, Recall: {rec:.4f}, ROC AUC: {roc_auc:.4f}\")\n print(\"-\" * 50)\n\n# Print average metrics\nprint(\"\\nK-Fold Cross-Validation Results:\")\nprint(f\"Accuracy: {np.mean(accuracies):.4f} ± {np.std(accuracies):.4f}\")\nprint(f\"Precision: {np.mean(precisions):.4f} ± {np.std(precisions):.4f}\")\nprint(f\"Recall: {np.mean(recalls):.4f} ± {np.std(recalls):.4f}\")\nprint(f\"ROC AUC: {np.mean(roc_aucs):.4f} ± {np.std(roc_aucs):.4f}\")\n\n# Train the final model on the entire dataset (if needed)\nmodel.fit(X, y)\nprint(\"\\nFinal Model Trained on Full Dataset\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T04:04:35.822037Z","iopub.execute_input":"2024-12-09T04:04:35.82252Z","iopub.status.idle":"2024-12-09T04:07:32.622421Z","shell.execute_reply.started":"2024-12-09T04:04:35.82247Z","shell.execute_reply":"2024-12-09T04:07:32.621007Z"}},"outputs":[],"execution_count":null}]}
\ No newline at end of file
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9300356,"sourceType":"datasetVersion","datasetId":5631196}],"dockerImageVersionId":30761,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"import pandas as pd # For data manipulation\nimport numpy as np # For numerical operations\nimport re # For regular expressions in text cleaning\nimport matplotlib.pyplot as plt # For plotting\nimport seaborn as sns # For advanced visualizations\nfrom sklearn.feature_extraction.text import TfidfVectorizer # For TF-IDF vectorization\nfrom sklearn.model_selection import train_test_split # For splitting the dataset\nfrom sklearn.linear_model import LogisticRegression # For Logistic Regression model\nfrom sklearn.metrics import (\n accuracy_score,\n precision_score,\n recall_score,\n roc_auc_score,\n classification_report,\n confusion_matrix,\n roc_curve,\n auc,\n precision_recall_curve,\n) # For model evaluation metrics","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T13:10:56.031003Z","iopub.execute_input":"2024-12-09T13:10:56.031449Z","iopub.status.idle":"2024-12-09T13:10:56.041294Z","shell.execute_reply.started":"2024-12-09T13:10:56.031409Z","shell.execute_reply":"2024-12-09T13:10:56.040079Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"# Load the dataset\ndf = pd.read_csv('/kaggle/input/ai-human/AI_Human.csv')\n\n# Total entries\nprint(df.count().sum())\n\n# A brief overview\ndf.describe()\n\n# Check for missing values\nmissing_values = df.isnull().sum()\nprint(\"Missing Values:\\n\", missing_values)\n\n# Class distribution in 'generated' column\nclass_distribution = df['generated'].value_counts()\nprint(\"\\nClass Distribution:\\n\", class_distribution)\n\n# Text length analysis\ndf['text_length'] = df['text'].apply(len)\n# Summary statistics for text length\ntext_length_stats = df['text_length'].describe()\nprint(\"\\nText Length Statistics:\\n\", text_length_stats)\n\n# Function to clean text without NLTK\ndef clean_text_no_nltk(text):\n # Convert to lowercase\n text = text.lower()\n # Remove special characters, numbers, and punctuation\n text = re.sub(r'[^a-z\\s]', '', text)\n # Tokenize and remove simple stopwords manually\n stop_words = {'the', 'and', 'is', 'in', 'to', 'of', 'for', 'it', 'on', 'this', 'that', 'with', 'a', 'as'}\n tokens = text.split()\n tokens = [word for word in tokens if word not in stop_words]\n # Join tokens back into a cleaned string\n cleaned_text = ' '.join(tokens)\n return cleaned_text\n\n# Apply the cleaning function to the 'text' column\ndf['cleaned_text'] = df['text'].apply(clean_text_no_nltk)\n\n# Display the first few rows to verify cleaning\nprint(df[['text', 'cleaned_text']].head())\n\n# Text length features\ndf['text_length'] = df['cleaned_text'].apply(len)\ndf['word_count'] = df['cleaned_text'].apply(lambda x: len(x.split()))\ndf['avg_word_length'] = df['cleaned_text'].apply(lambda x: np.mean([len(word) for word in x.split()]) if x.split() else 0)\n\n# Display the new features\nprint(df[['text_length', 'word_count', 'avg_word_length']].head())\n\n# Plot histograms for each feature\nplt.figure(figsize=(15, 5))\n\nplt.subplot(1, 3, 1)\ndf['text_length'].hist(bins=30, color='skyblue')\nplt.title('Text Length Distribution')\nplt.xlabel('Text Length')\nplt.ylabel('Frequency')\n\nplt.subplot(1, 3, 2)\ndf['word_count'].hist(bins=30, color='lightgreen')\nplt.title('Word Count Distribution')\nplt.xlabel('Word Count')\nplt.ylabel('Frequency')\n\nplt.subplot(1, 3, 3)\ndf['avg_word_length'].hist(bins=30, color='lightcoral')\nplt.title('Average Word Length Distribution')\nplt.xlabel('Avg Word Length')\nplt.ylabel('Frequency')\n\nplt.tight_layout()\nplt.show()\n\n# Extracting unigrams and bigrams using TF-IDF\ntfidf_vectorizer = TfidfVectorizer(ngram_range=(1, 2), max_features=500)\ntfidf_matrix = tfidf_vectorizer.fit_transform(df['cleaned_text'])\n\n# Convert the TF-IDF matrix to a DataFrame for easier interpretation\ntfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=tfidf_vectorizer.get_feature_names_out())\n\n# Display the top features (unigrams and bigrams)\nprint(\"\\nTF-IDF Features (Unigrams and Bigrams):\\n\", tfidf_df.head())\n\n# Compute the average TF-IDF score for each feature\ntfidf_mean = tfidf_df.mean().sort_values(ascending=False)\n\n# Select the top 20 features\ntop_features = tfidf_mean.head(20)\n\n# Plot the top features\nplt.figure(figsize=(10, 6))\ntop_features.plot(kind='bar', color='skyblue')\nplt.title('Top 20 TF-IDF Features (Unigrams and Bigrams)')\nplt.ylabel('Average TF-IDF Score')\nplt.xlabel('Features')\nplt.xticks(rotation=45, ha='right')\nplt.show()\n\n# Vectorize text data\nvectorizer = TfidfVectorizer(max_features=5000) # Reduce features for efficiency\nX = vectorizer.fit_transform(df['text'])\ny = df['generated']\n\n# Split data\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Logistic Regression\nlog_model = LogisticRegression(max_iter=1000, solver='liblinear') # Ensure solver compatibility\nlog_model.fit(X_train, y_train)\n\n# Predictions and evaluation\ny_pred = log_model.predict(X_test)\ny_score = log_model.decision_function(X_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"Logistic Regression Accuracy: {accuracy}\")\nprint(\"Classification Report:\")\nprint(classification_report(y_test, y_pred, zero_division=0))\n\n# **Visualization 1: Confusion Matrix**\ncm = confusion_matrix(y_test, y_pred)\nplt.figure(figsize=(6, 4))\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Human', 'AI'], yticklabels=['Human', 'AI'])\nplt.title('Confusion Matrix')\nplt.xlabel('Predicted Label')\nplt.ylabel('True Label')\nplt.show()\n\n# **Visualization 2: ROC Curve**\nfpr, tpr, _ = roc_curve(y_test, y_score)\nroc_auc = auc(fpr, tpr)\nplt.figure(figsize=(6, 4))\nplt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC Curve (AUC = {roc_auc:.2f})')\nplt.plot([0, 1], [0, 1], color='navy', linestyle='--')\nplt.title('ROC Curve')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.legend(loc=\"lower right\")\nplt.show()\n\n# **Visualization 3: Precision-Recall Curve**\nprecision, recall, _ = precision_recall_curve(y_test, y_score)\nplt.figure(figsize=(6, 4))\nplt.plot(recall, precision, marker='.', label='Precision-Recall')\nplt.fill_between(recall, precision, alpha=0.3, color='blue')\nplt.title('Precision-Recall Curve')\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.legend()\nplt.show()\n\n# **Visualization 4: Top Positive and Negative Features**\n# Extract feature coefficients\nfeature_names = vectorizer.get_feature_names_out()\ncoefficients = log_model.coef_[0]\n\n# Combine features and coefficients into a DataFrame\ncoef_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})\ntop_positive = coef_df.nlargest(10, 'Coefficient')\ntop_negative = coef_df.nsmallest(10, 'Coefficient')\n\n# Plot top positive coefficients\nplt.figure(figsize=(8, 5))\nsns.barplot(x='Coefficient', y='Feature', data=top_positive, palette='Greens', hue=None)\nplt.legend([], [], frameon=False) # Explicitly disable the legend\nplt.title('Top 10 Positive Features (AI Indicating Words)')\nplt.xlabel('Coefficient Value')\nplt.ylabel('Feature')\nplt.show()\n\n# Plot top negative coefficients\nplt.figure(figsize=(8, 5))\nsns.barplot(x='Coefficient', y='Feature', data=top_negative, palette='Reds', hue=None)\nplt.legend([], [], frameon=False) # Explicitly disable the legend\nplt.title('Top 10 Negative Features (Human Indicating Words)')\nplt.xlabel('Coefficient Value')\nplt.ylabel('Feature')\nplt.show()\n\n# **Visualization 5: Histogram of Predictions**\nplt.figure(figsize=(6, 4))\nsns.histplot(y_score, kde=True, bins=30, color='purple')\nplt.axvline(0, color='red', linestyle='--', label='Decision Boundary')\nplt.title('Histogram of Decision Function Scores')\nplt.xlabel('Decision Score')\nplt.ylabel('Frequency')\nplt.legend()\nplt.show()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T13:10:56.043914Z","iopub.execute_input":"2024-12-09T13:10:56.044413Z","iopub.status.idle":"2024-12-09T13:22:45.317034Z","shell.execute_reply.started":"2024-12-09T13:10:56.044361Z","shell.execute_reply":"2024-12-09T13:22:45.315467Z"}},"outputs":[{"name":"stdout","text":"974470\nMissing Values:\n text 0\ngenerated 0\ndtype: int64\n\nClass Distribution:\n generated\n0.0 305797\n1.0 181438\nName: count, dtype: int64\n\nText Length Statistics:\n count 487235.000000\nmean 2269.586592\nstd 988.814028\nmin 1.000000\n25% 1583.000000\n50% 2102.000000\n75% 2724.000000\nmax 18322.000000\nName: text_length, dtype: float64\n text \\\n0 Cars. Cars have been around since they became ... \n1 Transportation is a large necessity in most co... \n2 \"America's love affair with it's vehicles seem... \n3 How often do you ride in a car? Do you drive a... \n4 Cars are a wonderful thing. They are perhaps o... \n\n cleaned_text \n0 cars cars have been around since they became f... \n1 transportation large necessity most countries ... \n2 americas love affair its vehicles seems be coo... \n3 how often do you ride car do you drive one or ... \n4 cars are wonderful thing they are perhaps one ... \n text_length word_count avg_word_length\n0 2693 433 5.221709\n1 2243 335 5.698507\n2 3622 535 5.771963\n3 3344 525 5.371429\n4 3848 644 4.976708\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"name":"stdout","text":"\nTF-IDF Features (Unigrams and Bigrams):\n able about accidents according action activities activity \\\n0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.000000 \n1 0.0 0.057603 0.0 0.000000 0.000000 0.0 0.000000 \n2 0.0 0.015019 0.0 0.095195 0.033244 0.0 0.000000 \n3 0.0 0.041003 0.0 0.043315 0.000000 0.0 0.044676 \n4 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.000000 \n\n actually additionally advantages ... years you you are you can \\\n0 0.00000 0.000000 0.000000 ... 0.0 0.035298 0.0 0.0 \n1 0.00000 0.000000 0.000000 ... 0.0 0.027184 0.0 0.0 \n2 0.00000 0.029097 0.059461 ... 0.0 0.042525 0.0 0.0 \n3 0.03736 0.000000 0.081167 ... 0.0 0.174145 0.0 0.0 \n4 0.00000 0.000000 0.052994 ... 0.0 0.050534 0.0 0.0 \n\n you have you should you will young your youre \n0 0.000000 0.0 0.0 0.0 0.000000 0.000000 \n1 0.000000 0.0 0.0 0.0 0.034769 0.000000 \n2 0.000000 0.0 0.0 0.0 0.054392 0.000000 \n3 0.037645 0.0 0.0 0.0 0.222743 0.085121 \n4 0.000000 0.0 0.0 0.0 0.096953 0.000000 \n\n[5 rows x 500 columns]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"name":"stdout","text":"Logistic Regression Accuracy: 0.9943148583332478\nClassification Report:\n precision recall f1-score support\n\n 0.0 0.99 1.00 1.00 61112\n 1.0 1.00 0.99 0.99 36335\n\n accuracy 0.99 97447\n macro avg 0.99 0.99 0.99 97447\nweighted avg 0.99 0.99 0.99 97447\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"iVBORw0KGgoAAAANSUhEUgAAAhgAAAGJCAYAAADIVkprAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABiwElEQVR4nO3dd1hT1/8H8HcSSNjDIkux4B4oTvzirIqiddaF1SqCq67aujdu2jpbxVkBV+uqq84qap2tExeKFaUuQKkKMgPJ+f3hz7SUIcFAGO/X8+SRnJxz7ztXIB/uOFcihBAgIiIi0iGpvgMQERFRycMCg4iIiHSOBQYRERHpHAsMIiIi0jkWGERERKRzLDCIiIhI51hgEBERkc6xwCAiIiKdY4FBREREOscCg4iIiHSOBQZRKRASEgKJRKJ5GBgYoFy5chg4cCCePHmS7RghBDZt2oQWLVrAysoKJiYmqF27NubMmYOkpKQc17V792506NABNjY2kMvlcHR0RO/evXH8+PE8ZU1NTcXSpUvRuHFjWFpawsjICFWrVsWoUaNw9+7dfL1/Iip8Et6LhKjkCwkJga+vL+bMmQMXFxekpqbi999/R0hICJydnXHz5k0YGRlp+qtUKvTt2xfbt29H8+bN0b17d5iYmOD06dP48ccfUbNmTRw7dgx2dnaaMUII+Pn5ISQkBPXq1UPPnj1hb2+P6Oho7N69G5cvX8bZs2fRpEmTHHPGxcWhffv2uHz5Mjp16gRPT0+YmZkhIiICW7duRUxMDJRKZYFuKyLSEUFEJV5wcLAAIC5evJipfdKkSQKA2LZtW6b2BQsWCABi/PjxWZa1b98+IZVKRfv27TO1L1y4UAAQX375pVCr1VnGbdy4Ufzxxx+55uzYsaOQSqVi586dWV5LTU0V48aNy3V8XqWnp4u0tDSdLIuIsscCg6gUyKnA2L9/vwAgFixYoGlLTk4W1tbWomrVqiI9PT3b5fn6+goA4vz585oxZcqUEdWrVxcZGRn5yvj7778LAGLIkCF56t+yZUvRsmXLLO0+Pj7iww8/1Dx/8OCBACAWLlwoli5dKipWrCikUqn4/fffhUwmE7NmzcqyjDt37ggAYvny5Zq2ly9fijFjxojy5csLuVwuKlWqJL7++muhUqm0fq9EpQHPwSAqxaKiogAA1tbWmrYzZ87g5cuX6Nu3LwwMDLIdN2DAAADA/v37NWNevHiBvn37QiaT5SvLvn37AAD9+/fP1/h3CQ4OxvLlyzF06FAsXrwYDg4OaNmyJbZv356l77Zt2yCTydCrVy8AQHJyMlq2bInNmzdjwIAB+P7779G0aVNMmTIFY8eOLZC8RMVd9r89iKhEio+PR1xcHFJTU/HHH39g9uzZUCgU6NSpk6ZPeHg4AMDNzS3H5bx97fbt25n+rV27dr6z6WIZuXn8+DHu3buHsmXLatq8vb0xbNgw3Lx5E66urpr2bdu2oWXLlppzTJYsWYLIyEhcvXoVVapUAQAMGzYMjo6OWLhwIcaNGwcnJ6cCyU1UXHEPBlEp4unpibJly8LJyQk9e/aEqakp9u3bh/Lly2v6vH79GgBgbm6e43LevpaQkJDp39zGvIsulpGbHj16ZCouAKB79+4wMDDAtm3bNG03b95EeHg4vL29NW07duxA8+bNYW1tjbi4OM3D09MTKpUKp06dKpDMRMUZ92AQlSKBgYGoWrUq4uPjERQUhFOnTkGhUGTq8/YD/m2hkZ3/FiEWFhbvHPMu/16GlZVVvpeTExcXlyxtNjY2aNOmDbZv3465c+cCeLP3wsDAAN27d9f0+/PPP3H9+vUsBcpbz54903leouKOBQZRKeLu7o6GDRsCALp164ZmzZqhb9++iIiIgJmZGQCgRo0aAIDr16+jW7du2S7n+vXrAICaNWsCAKpXrw4AuHHjRo5j3uXfy2jevPk7+0skEohsrrJXqVTZ9jc2Ns62vU+fPvD19UVYWBjq1q2L7du3o02bNrCxsdH0UavVaNu2LSZOnJjtMqpWrfrOvESlDQ+REJVSMpkMAQEBePr0KVasWKFpb9asGaysrPDjjz/m+GG9ceNGANCcu9GsWTNYW1vjp59+ynHMu3Tu3BkAsHnz5jz1t7a2xqtXr7K0//XXX1qtt1u3bpDL5di2bRvCwsJw9+5d9OnTJ1OfSpUqITExEZ6entk+KlSooNU6iUoDFhhEpdhHH30Ed3d3LFu2DKmpqQAAExMTjB8/HhEREZg2bVqWMQcOHEBISAi8vLzwv//9TzNm0qRJuH37NiZNmpTtnoXNmzfjwoULOWbx8PBA+/bt8cMPP2DPnj1ZXlcqlRg/frzmeaVKlXDnzh08f/5c03bt2jWcPXs2z+8fAKysrODl5YXt27dj69atkMvlWfbC9O7dG+fPn8eRI0eyjH/16hUyMjK0WidRacCZPIlKgbczeV68eFFziOStnTt3olevXli1ahU+//xzAG8OM3h7e+Pnn39GixYt0KNHDxgbG+PMmTPYvHkzatSogdDQ0EwzearVagwcOBCbNm1C/fr1NTN5xsTEYM+ePbhw4QLOnTsHDw+PHHM+f/4c7dq1w7Vr19C5c2e0adMGpqam+PPPP7F161ZER0cjLS0NwJurTlxdXeHm5oZBgwbh2bNnWL16Nezs7JCQkKC5BDcqKgouLi5YuHBhpgLl37Zs2YLPPvsM5ubm+OijjzSXzL6VnJyM5s2b4/r16xg4cCAaNGiApKQk3LhxAzt37kRUVFSmQypEBM7kSVQa5DTRlhBCqFQqUalSJVGpUqVMk2SpVCoRHBwsmjZtKiwsLISRkZGoVauWmD17tkhMTMxxXTt37hTt2rUTZcqUEQYGBsLBwUF4e3uLkydP5ilrcnKyWLRokWjUqJEwMzMTcrlcVKlSRYwePVrcu3cvU9/NmzeLihUrCrlcLurWrSuOHDmS60RbOUlISBDGxsYCgNi8eXO2fV6/fi2mTJkiKleuLORyubCxsRFNmjQRixYtEkqlMk/vjag04R4MIiIi0jmeg0FEREQ6xwKDiIiIdI4FBhEREekcCwwiIiLSORYYREREpHMsMIiIiEjnSt29SNRqNZ4+fQpzc3NIJBJ9xyEiIio2hBB4/fo1HB0dIZXmvo+i1BUYT58+hZOTk75jEBERFVuPHj1C+fLlc+1T6gqMt7eXfvTokeb20ERERPRuCQkJcHJy0nyW5qbUFRhvD4tYWFiwwCAiIsqHvJxiwJM8iYiISOdYYBAREZHOscAgIiIinWOBQURERDrHAoOIiIh0jgUGERER6RwLDCIiItI5vRYYp06dQufOneHo6AiJRII9e/a8c8zJkydRv359KBQKVK5cGSEhIQWek4iIiLSj1wIjKSkJbm5uCAwMzFP/Bw8eoGPHjmjVqhXCwsLw5ZdfYvDgwThy5EgBJyUiIiJt6HUmzw4dOqBDhw557r969Wq4uLhg8eLFAIAaNWrgzJkzWLp0Kby8vAoqZuFSpQOqNEClBNRKICMFUGdkfgjVm38zUt70kRgAQv2mXaj/8/hPW+ITwKQsIAQA8Wadb7/+d9t/n2fqk83rufbPYRmpL968B1O7f96/Zvn/9p+2LH20fL2gllkYy9BHrrz8n/z7eWI0oEoFzCtkMy432a0nt+5a9s/POvK1nhKUqzC2cUn6fyyU95KPdVTuBtTop/2491Sspgo/f/48PD09M7V5eXnhyy+/zHFMWloa0tLSNM8TEhIKKt4/hBqIuwW8+hN4fAqABEhPBNJevflATU9684i5CBhZvxmTkfbml7JQF3w+IiIqPSwrATUKf7XFqsCIiYmBnZ1dpjY7OzskJCQgJSUFxsbGWcYEBARg9uzZhRPw8SngnD/w/Pqbv87zIvVlwWYiIqJSI+LZB9h42Q3z2h9HHm4XUqCKVYGRH1OmTMHYsWM1z9/eCU7nrnwPnBij3Ri5OaB8/eZrG1dApnjzMDD+/6/lgFQOPLsC2LsDhiaA1ACQyP7/XwPg9aM3hzyMygAS6ZvXJNL/PP7T9voRYOkCQIJ/vgMl/3r+/20SSdY+2b2en/5v+6Qn/f/7lf9rw2TzU5HlJ0Wi3esFscxsf3pLSK535n7HGJUSMDDKYVwutP6NmI/foPn6rVtCchXKe8/Pekr59tJBLpVKjSUrbmLGd1eQlqZCDe8Z+KxP5Tcvyt9959OCUKwKDHt7e8TGxmZqi42NhYWFRbZ7LwBAoVBAoVAUbDDla+D0lMxt5ZoBZesC1lUBiwqAhfObwyEGxm8ehiZvPuyJiIjeQ3j4c/j57cUffzwBAHh5VUILr3qAuaVecxWrAsPDwwMHDx7M1Hb06FF4eHjoKdH/u38AyEh+87XLx0DX3f/5i5yIiEi3MjLUWLToHPz9T0KpVMHSUoElS7zg61s3T7dTL2h6LTASExNx7949zfMHDx4gLCwMZcqUQYUKFTBlyhQ8efIEGzduBAB8/vnnWLFiBSZOnAg/Pz8cP34c27dvx4EDB/T1Ft648cM/X9cbxeKCiIgK3Gef7cK2bbcAAB07VsGaNZ1QrpyFnlP9Q6/76C9duoR69eqhXr16AICxY8eiXr16mDlzJgAgOjoaDx8+1PR3cXHBgQMHcPToUbi5uWHx4sX44Ycf9H+JavyDf762qqK/HEREVGoMH94QZcoYY8OGbvjll0+LVHEBABIh8nXhbrGVkJAAS0tLxMfHw8JCR/8ZW1sAT06/+XpMyv+f3EZERKQ716/H4s6dOPTuXUvT9vp1GszNC/g8w3/R5jO0WJ2DUWRlpPzztazw/qOJiKjkUypVCAg4jXnzTkMul6FBAwdUqlQGAAq1uNAWCwxdiL305l9D03xe1kRERJTV1avR8PXdi2vX3lxB2alTVZiaFo/z/Fhg6IKFM5AQ9WZOByIiovekVKowb94pBAScQUaGGh98YIwVKz6Gt3etInGFSF6wwNCFYvKfTURERV96ugoeHutx5Uo0AKBnz5pYsaID7OzM9JxMOywwdMnEVt8JiIiomDM0lKFTpyp49CgegYEfo1evWu8eVASxwCAiItKzCxeewMTEEK6ub/5QnTatBUaNckfZsqZ6TpZ/nKuaiIhIT1JTMzBp0lF4eKzHgAG7kZ6uAgDI5bJiXVwA3INBRESkF+fPP4Kf3z7cuRMHAKhRoyxSUzNgaCjTczLdYIFBRERUiFJS0jFjxgksWXIeQgD29mZYs6YTunSppu9oOsUCg4iIqJA8fpyANm024u7dvwEAAwa4YelSL5Qpk/0dwYszFhhERESFxMHBDDY2JkhMVGLt2k7o2LGqviMVGBYYulC6budCRERaOHv2IerVc4CJiSFkMil++qkHLCwUsLIq2fet4lUkOsUJt4iI6I3ERCVGjz6IZs2CMWPGcU17hQqWJb64ALgHg4iISOdOnHiAQYP24cGDVwDeFBtCiGIzzbcusMAgIiLSkdev0zBp0jGsWvXmJpgVKlhi3brOaNeukp6TFT4WGERERDpw6dJT9Oy5HX/9FQ8A+PzzBvjmm7awsCi6t1QvSCwwiIiIdMDe3gwvX6bC2dkK69d3QevWLvqOpFcsMIiIiPLp5s1nmvuHlC9vgUOH+qFOHTuYmcn1nEz/eBUJERGRluLjUzF48D7Urr0Khw79qWlv0sSJxcX/Y4FBRESkhYMH/0StWiuxfv1VSCTAlSvR+o5UJPEQiU5woi0iopLu5csUfPXVEWzYcA0AUKVKGQQFdUWzZhX0nKxoYoGhS6Xo+mYiotLkyJF78PXdi+joREgkwFdf/Q9z57aGiYmhvqMVWSwwiIiI3iE5OR3R0YmoVu0DBAV1RZMmTvqOVOSxwCAiIspGdPRrODiYAwA++aQGNm/+BN2714CxMfda5AVP8iQiIvqXuLhk9O37M1xdVyE2NlHT3q9fHRYXWmCBQURE9P927gxHrVor8dNPNxEfn4rjxx/oO1KxxUMkRERU6j17loRRow5ix45wAICrqy2Cg7uiYUNHPScrvlhgEBFRqbZ9+y2MHHkQcXHJkMkkmDKlGaZPbwGFgh+R74Nbj4iISrWTJ6MQF5eMOnXsEBzcFfXrO+g7UonAAoOIiEoVIQSSktI1U3p/840nXFysMGbM/yCXy/ScruTgSZ66IDiTJxFRcRAd/RqffLINXbtuhfj/393m5gpMmNCUxYWOcQ+GTnEmTyKiokgIgc2br2PMmMN4+TIVhoZSXL0aw8MhBYgFBhERlWhPniRg2LD9OHDgzV1P69d3QEhIV9SubafnZCUbCwwiIiqRhBAICQnDV18dQXx8GuRyGfz9W2LChCYwNOThkILGAoOIiEqk9HQ1Fi8+j/j4NDRq5Ijg4K6oVctW37FKDRYYRERUYgghoFYLyGRSyOUyBAd3xfHjDzBuXBMYGPC6hsLErU1ERCXCw4fxaN9+C7799qymrVGjcpg0qRmLCz3gFiciomJNCIE1ay6hVq2V+PXXSHz77TkkJKTpO1apx0MkRERUbD148BKDB/+iuSlZ06ZOCArqCgsLhZ6TEQsMneBEW0REhUmtFli16iImTTqGpKR0GBsbICCgDUaNcodMxp3zRQELDF2ScKItIqLCEBX1CuPG/Yq0NBWaN6+AoKCuqFy5jL5j0b+wwCAiomJBCAHJ//8hV7GiNb75xhMymRQjRjSCVMo/8Ioa7kciIqIi788//0br1hvxxx+PNW1jxvwPo0a5s7goolhgEBFRkaVSqbF06Xm4ua3GyZNRGD36kOYmZVS08RAJEREVSRERcfDz24dz5x4BANq0ccEPP3TRHCahok3vezACAwPh7OwMIyMjNG7cGBcuXMi1/7Jly1CtWjUYGxvDyckJX331FVJTUwspLRERFTSVSo2FC8+ibt01OHfuEczN5VizphOOHu0PZ2crfcejPNLrHoxt27Zh7NixWL16NRo3boxly5bBy8sLERERsLXNOl/8jz/+iMmTJyMoKAhNmjTB3bt3MXDgQEgkEixZskQP74CIiHRt9+47mDjxGACgXbtKWLeuMypUsNRzKtKWXvdgLFmyBEOGDIGvry9q1qyJ1atXw8TEBEFBQdn2P3fuHJo2bYq+ffvC2dkZ7dq1w6effvrOvR5ERFR89OhRAz161MD69V1w+HA/FhfFlN4KDKVSicuXL8PT0/OfMFIpPD09cf78+WzHNGnSBJcvX9YUFPfv38fBgwfx8ccf57ietLQ0JCQkZHoQEVHRcfPmM3zyyTbN9N4SiQQ7d/aGn189nm9RjOmtwIiLi4NKpYKdnV2mdjs7O8TExGQ7pm/fvpgzZw6aNWsGQ0NDVKpUCR999BGmTp2a43oCAgJgaWmpeTg5Oen0fQAAeEYzEZHW0tNVmD//FOrXX4M9e+5g5swT+o5EOqT3kzy1cfLkSSxYsAArV67ElStXsGvXLhw4cABz587NccyUKVMQHx+veTx69KgAE7LSJiLKi+vXY9G48Q+YPv0E0tPV6NSpKiZObKrvWKRDejvJ08bGBjKZDLGxsZnaY2NjYW9vn+2YGTNmoH///hg8eDAAoHbt2khKSsLQoUMxbdo0SKVZ6yWFQgGFgje9ISIqCpRKFQICTmPevNPIyFDD2toI33/fAf361ebhkBJGb3sw5HI5GjRogNDQUE2bWq1GaGgoPDw8sh2TnJycpYiQyWQAwIlXiIiKgRkzjmPWrN+QkaFGt27VER4+Ep99VofFRQmk18tUx44dCx8fHzRs2BDu7u5YtmwZkpKS4OvrCwAYMGAAypUrh4CAAABA586dsWTJEtSrVw+NGzfGvXv3MGPGDHTu3FlTaBARUdE1blwT7N0bgVmzPoK3dy0WFiWYXgsMb29vPH/+HDNnzkRMTAzq1q2Lw4cPa078fPjwYaY9FtOnT4dEIsH06dPx5MkTlC1bFp07d8b8+fP19RaIiCgXly8/xc8/38aCBW0AALa2prh1awRvqV4KSEQpO7aQkJAAS0tLxMfHw8LCQjcLXeMEJD4GzMoBwx6/uz8RUQmXlpaBOXN+wzffnIVKJfDzz73RvXsNfcei96TNZyjvRUJERDp18eITDBy4F+HhzwEA3t610Lx5BT2nosLGAoOIiHQiNTUDs2adxMKF56BWC9jammLVqo7cc1FKscDQiVJ1lImIKFvdum3FkSORAIC+fWvj++/b44MPTPScivSFBYZO8WxoIiq9vvrqf7h+PRarVnVE167V9R2H9IwFBhER5cvZsw8RHZ2Inj1rAgC8vCrj3r0vYGJiqOdkVBSwwCAiIq0kJ6dj2rRQfPfdHzAzk8PdvZzmjqcsLugtFhhERJRnp079BT+/vYiMfAkA6NGjJszN5XpORUURCwwiInqnpCQlpkwJxfLlFwAA5cqZY926zujQoYqek1FRxQKDiIhylZKSjrp11+DevRcAgMGD62HRonawtDTSczIqyjhXKxER5crY2BDdulWDk5MFDh/uh3XrurC4oHdigUFERFmEht7HnTtxmudz5rTCzZsj4OVVWY+pqDhhgaETnGiLiEqGhIQ0DBv2Czw9N8HXdy9UKjWAN3sxLCwUek5HxQnPwdAl3naYiIqxX3+NxODB+/DoUQIAoH59eyiVKhgb829R0h4LDCKiUi4+PhXjxv2K9euvAgBcXKwQFNQVH33krN9gVKyxwCAiKsXu3v0brVtvwJMnrwEAX3zhjgUL2sDUlHNb0PthgUFEVIq5uFjBzs4MxsaGCArqgubNP9R3JCoh3qvASE1NhZERL1UiIipOjh6NRIsWH0KhMIChoQy7dvVG2bKmnOabdErrM3fUajXmzp2LcuXKwczMDPfv3wcAzJgxA+vXr9d5QCIi0o0XL1LQv/9utGu3GXPnntK0f/ihFYsL0jmtC4x58+YhJCQE3377LeTyf47Rubq64ocfftBpOCIi0o09e+6gZs1AbN58HVKpRHP5KVFB0brA2LhxI9auXYt+/fpBJpNp2t3c3HDnzh2dhiMiovcTF5eMvn1/xiefbENsbBKqV7fB2bN+CAjw1Hc0KuG0PgfjyZMnqFw560xuarUa6enpOglFRETv77ffotC79048e5YEqVSCiRObwN//IxgZ8fx+Knhaf5fVrFkTp0+fxocfZj7TeOfOnahXr57OghUrgjN5ElHR4+RkicREJWrVKovg4K5o1KicviNRKaJ1gTFz5kz4+PjgyZMnUKvV2LVrFyIiIrBx40bs37+/IDIWI5zJk4j0RwiBq1djUL++AwCgYkVrHDvWH/XrO0Ch4F4LKlxan4PRtWtX/PLLLzh27BhMTU0xc+ZM3L59G7/88gvatm1bEBmJiOgdYmMT0bPnDjRosBYnT0Zp2j08nFhckF7k67uuefPmOHr0qK6zEBGRloQQ2Lr1JkaNOoQXL1JgYCDFrVvPOM036Z3WezAqVqyIv//+O0v7q1evULFiRZ2EIiKid4uOfo1PPtmGvn134cWLFNSta4+LF4dg5Eh3fUcj0n4PRlRUFFQqVZb2tLQ0PHnyRCehiIgodzt23MKwYfvx8mUqDA2lmDGjBSZPbgZDQ9m7BxMVgjwXGPv27dN8feTIEVhaWmqeq1QqhIaGwtnZWafhiIgoe0qlCi9fpqJ+fQeEhHRF7dp2+o5ElEmeC4xu3boBACQSCXx8fDK9ZmhoCGdnZyxevFin4YiI6A0hBB4/ToCT05s/7vr2rQ0DAym6d6/BvRZUJOW5wFCr30wr6+LigosXL8LGxqbAQhER0T8eP07A0KG/4MqVaISHj0SZMsaQSCTw9nbVdzSiHGl9kueDBw9YXGTBibaISPeEEFi//gpq1VqJQ4fu4dWrVJw//0jfsYjyJF+XqSYlJeG3337Dw4cPoVQqM732xRdf6CRYsSThRFtEpBsPH8ZjyJBf8OuvkQCA//2vPIKCuqBGjbJ6TkaUN1oXGFevXsXHH3+M5ORkJCUloUyZMoiLi4OJiQlsbW1Ld4FBRKQDa9dexvjxv+L1ayWMjAwwb14rfPnl/yCTab3TmUhvtP5u/eqrr9C5c2e8fPkSxsbG+P333/HXX3+hQYMGWLRoUUFkJCIqVc6de4TXr5Vo2tQJ1659jnHjmrC4oGJH6z0YYWFhWLNmDaRSKWQyGdLS0lCxYkV8++238PHxQffu3QsiJxFRiaVWCyQmKmFhoQAALF3qBXf3chg2rAELCyq2tP7ONTQ0hFT6ZpitrS0ePnwIALC0tMSjRzz5iIhIG/fvv0SbNhvRt+/PEP9/Z2Zra2OMGNGIxQUVa1rvwahXrx4uXryIKlWqoGXLlpg5cybi4uKwadMmuLrykikiorxQqwUCAy9g8uRQJCenw8TEEHfv/o1q1XiVHpUMWpfHCxYsgIPDm1sBz58/H9bW1hg+fDieP3+ONWvW6DwgEVFJc+/eC3z0UQi++OIwkpPT8dFHzrh+/XMWF1SiaL0Ho2HDhpqvbW1tcfjwYZ0GIiIqqVQqNZYvv4CpU0ORkpIBU1NDfPttW3z+eUNIpbzMnUoWnR3gu3LlCjp16qSrxRUvghNtEdG7paWpEBh4ESkpGWjd2gU3b47AiBGNWFxQiaTVHowjR47g6NGjkMvlGDx4MCpWrIg7d+5g8uTJ+OWXX+Dl5VVQOYsJ/pIgosxUKjUkEgmkUglMTAwRHNwV4eHPMWRIfUg4OR+VYHneg7F+/Xp06NABISEh+Oabb/C///0PmzdvhoeHB+zt7XHz5k0cPHiwILMSERUrt28/R7NmwVi+/A9NW7NmFTB0aAMWF1Ti5bnA+O677/DNN98gLi4O27dvR1xcHFauXIkbN25g9erVqFGjRkHmJCIqNjIy1PjmmzOoV28Nfv/9Mb755ixSUzP0HYuoUOX5EElkZCR69eoFAOjevTsMDAywcOFClC9fvsDCEREVN7duPYOv715cvPgUANChQ2WsXdsZRkb5uvUTUbGV5+/4lJQUmJiYAAAkEgkUCoXmclUiotIuPV2Fb789izlzTkGpVMHSUoFly9rDx8eNh0OoVNKqpP7hhx9gZmYGAMjIyEBISEiWW7dre7OzwMBALFy4EDExMXBzc8Py5cvh7u6eY/9Xr15h2rRp2LVrF168eIEPP/wQy5Ytw8cff6zVeomIdOnu3b8xa9ZvyMhQo1OnqlizphMcHc31HYtIbyRC5O0aS2dn53dW4RKJBPfv38/zyrdt24YBAwZg9erVaNy4MZYtW4YdO3YgIiICtra2WforlUo0bdoUtra2mDp1KsqVK4e//voLVlZWcHNzy9M6ExISYGlpifj4eFhYWOQ5a65W2QPJsYCFMzDkgW6WSURFnhAi0+/FxYvPwc7ODP361eZeCyqRtPkMzXOBURAaN26MRo0aYcWKFQAAtVoNJycnjB49GpMnT87Sf/Xq1Vi4cCHu3LkDQ0PDfK2TBQYR6UJYWAyGDv0Fa9d2Rt269vqOQ1QotPkM1duddJRKJS5fvgxPT89/wkil8PT0xPnz57Mds2/fPnh4eGDkyJGws7ODq6srFixYAJVKleN60tLSkJCQkOlBRJRfSqUK/v4n0KjROly8+BTjx/+q70hERZLeCoy4uDioVCrY2dllarezs0NMTEy2Y+7fv4+dO3dCpVLh4MGDmDFjBhYvXox58+bluJ6AgABYWlpqHk5OTjp9H29wJk+i0uDKlWg0arQOc+acQkaGGj161MCWLd31HYuoSCpW9wJWq9WwtbXF2rVr0aBBA3h7e2PatGlYvXp1jmOmTJmC+Ph4zaNAbynPY65EJVJaWgamTz8Od/d1uH49FjY2Jti+vSd27uwNOzszfccjKpL0dmG2jY0NZDIZYmNjM7XHxsbC3j7745kODg4wNDSETCbTtNWoUQMxMTFQKpWQy+VZxigUCigUCt2GJ6JS5ccfb2D+/NMAgN69a2HFig4oW9ZUz6mIija97cGQy+Vo0KABQkNDNW1qtRqhoaHw8PDIdkzTpk1x7949qNVqTdvdu3fh4OCQbXFBRKQLPj510b17Dezc2QvbtvVkcUGUB/kqMCIjIzF9+nR8+umnePbsGQDg0KFDuHXrllbLGTt2LNatW4cNGzbg9u3bGD58OJKSkuDr6wsAGDBgAKZMmaLpP3z4cLx48QJjxozB3bt3ceDAASxYsAAjR47Mz9sgIsrW778/RufOPyE5OR0AIJVK8PPPvdGjR009JyMqPrQuMH777TfUrl0bf/zxB3bt2oXExEQAwLVr1+Dv76/Vsry9vbFo0SLMnDkTdevWRVhYGA4fPqw58fPhw4eIjo7W9HdycsKRI0dw8eJF1KlTB1988QXGjBmT7SWtRETaSklJx4QJv6Jp0yDs338XCxac1nckomJL63kwPDw80KtXL4wdOxbm5ua4du0aKlasiAsXLqB79+54/PhxQWXViYKZB8MOSH4GWLoAg/M+0RgRFR1nzz6En98+3L37NwCgf/86WLasPcqUMdZzMqKiQ5vPUK1P8rxx4wZ+/PHHLO22traIi4vTdnFERHqVnJyOadNC8d13f0AIwNHRHGvWdEKnTlX1HY2oWNP6EImVlVWmwxZvXb16FeXKldNJKCKiwjJ27BEsW/amuBg4sC5u3hzO4oJIB7QuMPr06YNJkyYhJiYGEokEarUaZ8+exfjx4zFgwICCyFj06W+2dSJ6TzNmtICrqy0OHuyL4OCusLbmIREiXdC6wFiwYAGqV68OJycnJCYmombNmmjRogWaNGmC6dOnF0TGYoQTbREVdSdPRmH69OOa5+XKWeD69c/RoUMVPaYiKnm0PgdDLpdj3bp1mDFjBm7evInExETUq1cPVarwh5OIiq7ERCUmTTqKlSsvAQCaN68AL6/KAMA7nxIVAK0LjDNnzqBZs2aoUKECKlSoUBCZiIh0KjT0PgYP/gVRUa8AAJ9/3gAeHgVxXyIiekvrQyStW7eGi4sLpk6divDw8ILIRESkEwkJafj88/3w9NyEqKhX+PBDSxw71h+rVnWChQVvIUBUkLQuMJ4+fYpx48bht99+g6urK+rWrYuFCxcW+fkviKh0EUKgXbtNWLPmMgBgxIiGuHFjONq0qajnZESlg9YFho2NDUaNGoWzZ88iMjISvXr1woYNG+Ds7IzWrVsXREYiIq1JJBJMntwMLi5WOHHCB4GBHWFuzr0WRIVF65k8/0ulUuHQoUOYMWMGrl+/DpVKpatsBaJAZvJcaQukPAcsKwKDI3WzTCLS2uHD95CamoFu3apr2tLSMqBQ6O3G0UQlijafofm+m+rZs2cxYsQIODg4oG/fvnB1dcWBAwfyuzgionx7+TIFvr570aHDFgwatA8xMYma11hcEOmH1j95U6ZMwdatW/H06VO0bdsW3333Hbp27QoTE5OCyEdElKv9++9i2LD9ePr0NSQSwMfHjSdwEhUBWhcYp06dwoQJE9C7d2/Y2NgURKZiiDN5EhW2Fy9S8OWXh7Fp03UAQNWqHyAoqAuaNuXl80RFgdYFxtmzZwsiR8nAyXqICkV8fCpcXVciOjoRUqkEY8f+D3PmtIKxsaG+oxHR/8tTgbFv3z506NABhoaG2LdvX659u3TpopNgREQ5sbQ0wiefVMfx41EIDu6K//2vvL4jEdF/5OkqEqlUipiYGNja2kIqzfm8UIlEUkqvIikLpMQBVpWAQfd0s0wiymT37ttwc7NHxYrWAICkJCVkMimMjHgSJ1Fh0eYzNE8/mWq1OtuviYgK2vPnSRg9+hC2bbuFjz5yRmjoAEilEpiayvUdjYhyofVlqhs3bkRaWlqWdqVSiY0bN+okFBERAOzYcQu1aq3Etm23IJNJ0LSpE1Qq/pFDVBxoXWD4+voiPj4+S/vr16/h6+urk1BEVLrFxiaiZ8/t6N17J54/T0bt2rb444/BmDevNQwNZfqOR0R5oPXBSyFEtrc2fvz4MSwtLXUSiohKr7CwGHh6bsTff6fAwECKqVObYdq0FpDLWVgQFSd5LjDq1asHiUQCiUSCNm3awMDgn6EqlQoPHjxA+/btCyQkEZUeNWrYwN7eDOXLWyAkpBvq1rXXdyQiyoc8FxjdunUDAISFhcHLywtmZmaa1+RyOZydndGjRw+dBywW3u92LkSlmhACe/dGoGPHKjA0lEGhMMDBg/3g4GDGwyFExVieCwx/f38AgLOzM7y9vWFkZFRgoYovTrRFpI2nT19j2LD92L//LubPb42pU5sDACpU4OFWouJO63MwfHx8CiIHEZUiQghs3HgNX355BK9epcLQUApDw3zfe5GIiqA8FRhlypTB3bt3YWNjA2tr62xP8nzrxYsXOgtHRCXP48cJGDr0Fxw69GZSuoYNHREc3BWurrZ6TkZEupSnAmPp0qUwNzfXfJ1bgUFElJP9+++iX79dSEhIg1wuw5w5H2HcuCYwMODeC6KSJk8Fxr8PiwwcOLCgshBRCVe5chmkpWWgceNyCA7uiho1yuo7EhEVEK3/bLhy5Qpu3Liheb53715069YNU6dOhVKp1Gk4IirehBD4/ffHmufVq9vg9GlfnD3rx+KCqITTusAYNmwY7t69CwC4f/8+vL29YWJigh07dmDixIk6D0hExVNU1Cu0bbsJTZsGZSoyGjUqB5mMh0SISjqtf8rv3r2LunXrAgB27NiBli1b4scff0RISAh+/vlnXecjomJGrRZYufIiXF1XIjT0ARQKGe7d48nfRKVNvqYKf3tH1WPHjqFTp04AACcnJ8TFxek2XbHBibaIAOD+/ZcYNGgfTp6MAgA0b14B69d3QZUqH+g3GBEVOq0LjIYNG2LevHnw9PTEb7/9hlWrVgEAHjx4ADs7O50HLFZ4dQ2VYj/8cAVjxhxGcnI6TEwM8fXXbTBypDukUv5cEJVGWhcYy5YtQ79+/bBnzx5MmzYNlStXBgDs3LkTTZo00XlAIio+kpPT0bLlh1i/vgsqVSqj7zhEpEdaFxh16tTJdBXJWwsXLoRMxvsGEJUWKpUaDx/Gw8XFGgAwaFA9fPCBMbp2rc69FkSkfYHx1uXLl3H79m0AQM2aNVG/fn2dhSKioi0iIg5+fvvw8GE8bt0aAQsLBSQSCT75pIa+oxFREaF1gfHs2TN4e3vjt99+g5WVFQDg1atXaNWqFbZu3YqyZXltO1FJpVKpsWzZ75g+/QRSUzNgZibH1avRaNnSWd/RiKiI0foy1dGjRyMxMRG3bt3Cixcv8OLFC9y8eRMJCQn44osvCiIjERUBd+7EoVmzYIwffxSpqRlo27Yibt4czuKCiLKl9R6Mw4cP49ixY6hR459doTVr1kRgYCDatWun03BEpH9CCCxceA4zZ55AWpoKFhYKLFnSDn5+9XhfIiLKkdYFhlqthqGhYZZ2Q0NDzfwYRFRySCQSXLr0FGlpKrRvXxlr13aCk5OlvmMRURGn9SGS1q1bY8yYMXj69Kmm7cmTJ/jqq6/Qpk0bnYYjIv1IT1chPj5V83zFio+xYUM3HDzYl8UFEeWJ1gXGihUrkJCQAGdnZ1SqVAmVKlWCi4sLEhISsHz58oLIWAxwJk8qOa5fj8X//rcegwf/ommztTXFgAFuPCRCRHmm9SESJycnXLlyBaGhoZrLVGvUqAFPT0+dhyt++MuXiq/0dBW+/voM5s49hfR0Ne7ff4lHj+K5x4KI8kWrAmPbtm3Yt28flEol2rRpg9GjRxdULiIqRGFhMfD13YuwsBgAQJcu1bB6dUc4OJjrORkRFVd5LjBWrVqFkSNHokqVKjA2NsauXbsQGRmJhQsXFmQ+IipASqUK8+efwoIFZ5CRoUaZMsZYvrwDPv3UlYdDiOi95PkcjBUrVsDf3x8REREICwvDhg0bsHLlyoLMRkQFLDU1AyEh15CRoUb37jVw69YI9O1bm8UFEb23PBcY9+/fh4+Pj+Z53759kZGRgejo6PcOERgYCGdnZxgZGaFx48a4cOFCnsZt3boVEokE3bp1e+8MRKWFUqmCEG9OTLawUCA4uCu2bu2BnTt7wd7eTM/piKikyHOBkZaWBlNT038GSqWQy+VISUl5rwDbtm3D2LFj4e/vjytXrsDNzQ1eXl549uxZruOioqIwfvx4NG/e/L3WT1SaXLr0FPXrr8G6dVc0ba1bu8Dbm4dEiEi3JOLtnzLvIJVKMXToUJiYmGjaAgMD8dlnn8HS8p+zzJcsWaJVgMaNG6NRo0ZYsWIFgDcTeTk5OWH06NGYPHlytmNUKhVatGgBPz8/nD59Gq9evcKePXvytL6EhARYWloiPj4eFhYWWmXNUWAZIPUlYF0V8IvQzTKJdCg1NQOzZ5/EwoXnoFIJVK5cBrdvj4SBgdZXqhNRKabNZ2ieT/Js0aIFIiIyf3g2adIE9+/f1zzX9i8gpVKJy5cvY8qUKZo2qVQKT09PnD9/Psdxc+bMga2tLQYNGoTTp0/nuo60tDSkpaVpnickJGiVkai4+/33x/Dz24vbt+MAAJ9+6orvv+/A4oKIClSeC4yTJ0/qfOVxcXFQqVSws7PL1G5nZ4c7d+5kO+bMmTNYv349wsLC8rSOgIAAzJ49+32j5i5vO4GIClVKSjpmzjyBJUt+h1otYGdnitWrO6Fbt+r6jkZEpUCx+hPm9evX6N+/P9atWwcbG5s8jZkyZQri4+M1j0ePHhVgQh7DpqLj1q3nmuKif/86CA8fyeKCiAqN1jN56pKNjQ1kMhliY2MztcfGxsLe3j5L/8jISERFRaFz586atrc3WDMwMEBERAQqVaqUaYxCoYBCoSiA9ERFjxBCc6iyYUNHLFjQGrVq2aJTp6p6TkZEpY1e92DI5XI0aNAAoaGhmja1Wo3Q0FB4eHhk6V+9enXcuHEDYWFhmkeXLl3QqlUrhIWFwcnJqTDjExUpp0//hTp1VuP27eeatkmTmrG4ICK90OseDAAYO3YsfHx80LBhQ7i7u2PZsmVISkqCr68vAGDAgAEoV64cAgICYGRkBFdX10zjraysACBLO1FpkZSkxNSpoVi+/AKEAKZNO45du7z1HYuISjm9Fxje3t54/vw5Zs6ciZiYGNStWxeHDx/WnPj58OFDSKXF6lQRokLz229R8PPbh/v3XwIA/PzqYvFiLz2nIiLSYh6Mfzt9+jTWrFmDyMhI7Ny5E+XKlcOmTZvg4uKCZs2aFUROnSmQeTBWWANprwDraoBf9le/EOlSYqISkycfQ2DgRQCAk5MF1q3rDC+vynpORkQlmTafoVrvGvj555/h5eUFY2NjXL16VTPHRHx8PBYsWJC/xESklaCgq5riYujQ+rh5cwSLCyIqUrQuMObNm4fVq1dj3bp1MDQ01LQ3bdoUV65cyWUkEenKiBGN0KNHDRw71h9r1nSGhQWvlCKiokXrAiMiIgItWrTI0m5paYlXr17pIlMxxIm2qGD9+mskPv54C9LSMgAABgZS7NzZG23aVNRzMiKi7GldYNjb2+PevXtZ2s+cOYOKFUv5LzveLIp0LD4+FUOG7IOX12YcOnQPS5f+ru9IRER5ovVVJEOGDMGYMWMQFBQEiUSCp0+f4vz58xg/fjxmzJhREBmJSqXDh+9hyJBf8Pjxm/vnjB7tjlGj3PWciogob7QuMCZPngy1Wo02bdogOTkZLVq0gEKhwPjx4zF69OiCyEhUqrx6lYqxY48gODgMAFCpkjWCgrqiRYsP9RuMiEgL+bpMFXhzJ9R79+4hMTERNWvWhJmZma6zFYiCuUzVCkiLB8pUB3xv62aZVGr17fszfvrpJiQSYMyYxpg/vw1MTAzfPZCIqIAVyO3a/0sul6NmzZr5HU5EOZg/vzUiIv7G99+3R9OmFfQdh4goX7QuMFq1aqW5mVJ2jh8//l6BiEqbffsicOnSU8yZ0woA4OJijUuXhuT6c0ZEVNRpXWDUrVs30/P09HSEhYXh5s2b8PHx0VUuohLv77+TMWbMYWzZcgMA0LZtRTRv/uY8CxYXRFTcaV1gLF26NNv2WbNmITEx8b0DEZUGu3bdxogRBxAbmwSpVIIJE5qgUaNy+o5FRKQzOruL2GeffYagoCBdLY6oRHr+PAl9+uxEjx7bERubhJo1y+L8+UH4+mtPGBnp/d6DREQ6o7PfaOfPn4eRkZGuFle85O9CHCpl1GqBli1DcPt2HGQyCSZNaoqZM1tCoWBhQUQlj9a/2bp3757puRAC0dHRuHTpEifaAo+bU86kUglmzGiBgIAzCA7uigYNHPUdiYiowGhdYFhaWmZ6LpVKUa1aNcyZMwft2rXTWTCi4k4IgW3bbsHMTI5OnaoCAPr0cUXPnjVhaCjTczoiooKlVYGhUqng6+uL2rVrw9rauqAyERV7MTGJGD78APbsuQM7O1PcujUCH3xgAolEwuKCiEoFrU7ylMlkaNeuXSm+aypR7oQQ2LLlOmrWDMSePXdgYCDF8OENYW7O26kTUemi9SESV1dX3L9/Hy4uLgWRh6jYevr0NT7/fD9++eUuAKBePXsEB3eFm5u9npMRERU+rQuMefPmYfz48Zg7dy4aNGgAU1PTTK/r7P4eRMVIbGwiXF1X4uXLVBgaSjFzZktMmtSUh0OIqNTKc4ExZ84cjBs3Dh9//DEAoEuXLplmGxRCQCKRQKVS6T4lURFnZ2eGbt2q48aNZwgO7gpXV1t9RyIi0qs8301VJpMhOjoat2/nfrfQli1b6iRYQSmQu6kutwSUCUCZGoBvuG6WSUWaEAIhIWFo27YSypd/832UlKSEQmEAAwOdzV9HRFSkFMjdVN/WIUW9gNAPTrRVmjx8GI+hQ3/BkSOR6NChMg4c6AuJRAJTU7m+oxERFRlanYPBGzC9A7dPiSaEwA8/XMG4cb/i9WslFAoZWrd2gRD8ryci+i+tCoyqVau+s8h48eLFewUiKoqiol5hyJBfcOzYfQBAkyZOCArqgmrVbPScjIioaNKqwJg9e3aWmTyJSrpz5x7By2szEhOVMDY2wIIFbTB6tDtkMp5rQUSUE60KjD59+sDWlmfHU+lSr549HBzMYGdnhqCgLqhS5QN9RyIiKvLyXGDw/AsqLdRqge3bb6FXr5qQyaQwNjbE8eM+cHQ0h1TKnwMiorzI8z7ePF7NSlSs3bv3Aq1abcCnn/6M77//Q9NevrwFiwsiIi3keQ+GWq0uyBxEeqVWCyxf/gemTAlFSkoGTE0NYWbGy06JiPJL66nCiUqau3f/hp/fXpw9+wgA0Lq1C374oTNcXHjHYCKi/GKBQaXaTz/dgJ/fPqSmZsDMTI5Fi9pi6NAGPOeIiOg9scDQBZ6fUmzVrFkWGRlqtG1bEevWdcaHH1rpOxIRUYnAAkOn+FdvUZeRocbvvz9Gs2YVAABubva4cGEw6ta1514LIiId4kxBVGrcuvUMTZqsR6tWG3D1arSmvV49BxYXREQ6xgKDSryMDDUWLDiN+vXX4uLFpzA1NcSjRwn6jkVEVKLxEAmVaDduxMLXdy8uX36zx6JjxypYs6YTypXL/TbDRET0flhgUIm1aNE5TJ0aivR0NaysjPD99+3x2Wd1eDiEiKgQsMCgEksulyE9XY0uXaph9eqOcHAw13ckIqJSgwUGlRhKpQqPHsWjUqUyAIBRo9xRpUoZtG9fmXstiIgKGU/ypBLh6tVoNGq0Dl5em5GUpAQASKUSdOhQhcUFEZEesMDQCU60pS9paRmYMeM4GjVah+vXYxEfn4Y7d+L0HYuIqNTjIRJd4l/KherSpacYOHAPbt16DgDo3bsWVqzogLJlTfWcjIiIWGBQsZORocbMmSfw7bdnoVIJlC1rgpUrO6Jnz5r6jkZERP+PBQYVOzKZBDduPINKJfDpp674/vsOsLEx0XcsIiL6FxYYVCykpKQjPV0NCwsFJBIJ1qzphAsXnqBbt+r6jkZERNkoEid5BgYGwtnZGUZGRmjcuDEuXLiQY99169ahefPmsLa2hrW1NTw9PXPtT8Xf+fOPUK/eGowefUjT5uhozuKCiKgI03uBsW3bNowdOxb+/v64cuUK3Nzc4OXlhWfPnmXb/+TJk/j0009x4sQJnD9/Hk5OTmjXrh2ePHlSyMmpoCUnp2PcuCNo2jQIERF/4+jRSMTFJes7FhER5YFECKHXaywbN26MRo0aYcWKFQAAtVoNJycnjB49GpMnT37neJVKBWtra6xYsQIDBgx4Z/+EhARYWloiPj4eFhY6uh/F92ZAehJg4wr43NDNMku506f/gp/fPty79wIA4OPjhqVLvWBtbaznZEREpZc2n6F6PQdDqVTi8uXLmDJliqZNKpXC09MT58+fz9MykpOTkZ6ejjJlymT7elpaGtLS0jTPExJ4F82iLClJialTQ7F8+QUIAZQrZ461azvj44+r6DsaERFpQa+HSOLi4qBSqWBnZ5ep3c7ODjExMXlaxqRJk+Do6AhPT89sXw8ICIClpaXm4eTk9N65s9DvTqASJS1Nhe3bwyEE4OdXFzdvjmBxQURUDBXrq0i+/vprbN26FSdPnoSRkVG2faZMmYKxY8dqnickJBRMkQEA4ERb+ZGcnA5jYwNIJBKUKWOMkJCuEAJo376yvqMREVE+6XUPho2NDWQyGWJjYzO1x8bGwt7ePtexixYtwtdff41ff/0VderUybGfQqGAhYVFpgcVHcePP0CtWiuxefN1TZuXV2UWF0RExZxeCwy5XI4GDRogNDRU06ZWqxEaGgoPD48cx3377beYO3cuDh8+jIYNGxZGVNKx16/TMHz4frRpsxFRUa+wdOnv0PP5xkREpEN6P0QyduxY+Pj4oGHDhnB3d8eyZcuQlJQEX19fAMCAAQNQrlw5BAQEAAC++eYbzJw5Ez/++COcnZ0152qYmZnBzMxMb++D8u7YsfsYNGgfHj6MBwCMGNEQX3/tybueEhGVIHovMLy9vfH8+XPMnDkTMTExqFu3Lg4fPqw58fPhw4eQSv/Z0bJq1SoolUr07Nkz03L8/f0xa9aswoxOWoqPT8WECUexbt0VAICLixXWr++CVq1c9JyMiIh0Te/zYBS2ApkH4ztTICMZsKkN+Fx/d/9S6vTpv9CiRQgAYNSoRggI8ISZmVy/oYiIKM+KzTwYVPKpVGrIZG/2QDVv/iEWLGiNJk2c0LKls36DERFRgdL7VOFUch04cBc1agQiMvKFpm3KlOYsLoiISgEWGKRzL1+mwMdnDzp1+gl//vkCc+ee0nckIiIqZDxEohOl6jSWXO3bF4Fhw/YjJiYREgkwdqwH5sxppe9YRERUyFhg6FIpvszy77+TMWbMYWzZ8uZmb9WqfYDg4K7w8CioWVOJiKgoY4FBOrFmzWVs2XIDUqkE48d7YNasj2BsbKjvWEREpCcsMEgnxo9vgrCwGIwf3wTu7uX0HYeIiPSMJ3lSvuzYcQvt229GeroKACCXy7B9ey8WF0REBIAFBmnp2bMk9Oq1A71778SRI5FYu/ayviMREVERxEMklCdCCGzbdgujRh3E33+nQCaTYOrU5hg8uL6+oxERURHEAoPeKSYmESNGHMDu3XcAAG5udggO7op69Rz0nIyIiIoqFhj0ToMH78OBA3/CwECK6dObY8qU5pDLZfqORURERRgLDJ0o2RNtLV7cDi9epGDVqo5wc7PXdxwiIioGWGDoVPGfaEsIgY0bryEq6hX8/T8CAFSrZoOzZ/0gKcUTiRERkXZYYJDGkycJGDp0Pw4e/BMSCdCpU1U0aOAIACwuiIhIKywwCEIIhISE4auvjiA+Pg1yuQyzZ3/EwyFERJRvLDBKuUeP4jFkyC84ciQSAODuXg7BwV1Rs2ZZPScjIqLijAVGKZaerkLTpkF49CgBCoUMc+e2wldfecDAgPOvERHR+2GBUYoZGsrg798S69dfRVBQV1SvbqPvSEREVELwT9VSRK0WWLXqIn79NVLT5udXD6dP+7K4ICIineIejFLiwYOXGDRoH06ciEL58ha4dWsELCwUkEgkkMl4hQgREekWCwxdEEV3oi21WmDlyouYPPkYkpLSYWxsgAkTmsDMTK7vaEREVIKxwNCporUnIDLyBfz89uHUqb8AAC1afIj167ugcuUyek5GREQlHQuMEioq6hXq1FmN5OR0mJoa4ptvPDF8eCNIpUWrCCIiopKJBUYJ5exshS5dqiE2NhHr13eBi4u1viMREVEpwgKjhFCp1AgMvAhv71qwszMDAKxf3wVGRgbca0FERIWOl6mWABERcWjePBhjxhzGyJEHNe0mJoYsLoiISC+4B6MYU6nUWLLkPGbMOIG0NBXMzeXw8qoEIQRvTkZERHrFAqOYCg9/Dl/fvbhw4QkAoH37yli7thOcnCz1nIyIiIgFRrH066+R6Nz5JyiVKlhaKrB0qRcGDqzLvRZERFRksMAohjw8ysPBwQyurrZYs6YTypWz0HckogInhEBGRgZUKpW+oxCVaIaGhpDJZO+9HBYYOlGwM3mmp6vw4483MGCAGyQSCczNFTh/fhDs7c2414JKBaVSiejoaCQnJ+s7ClGJJ5FIUL58eZiZmb3Xclhg6FIBfNhfuxYDX9+9uHo1BqmpGRg2rCEAwMHBXOfrIiqK1Go1Hjx4AJlMBkdHR8jlchbWRAVECIHnz5/j8ePHqFKlynvtyWCBUUQplSoEBJzGvHmnkZGhhrW1EaysjPQdi6jQKZVKqNVqODk5wcTERN9xiEq8smXLIioqCunp6SwwSpqrV6Ph67sX167FAgA++aQ6Vq7sCHv799tdRVScSaWctoeoMOhqDyELjCJm9epLGD36EDIy1LCxMcGKFR3Qu3ct7hImIqJihQVGEVO/vgPUaoFevWpixYqPYWtrqu9IREREWuM+Rz1LS8vAb79FaZ67u5fDtWufY/v2XiwuiKhU+/vvv2Fra4uoqCh9Rykx+vTpg8WLFxfKulhg6NGFC09Qv/5atGu3GeHhzzXtrq62ekxFRLowcOBASCQSSCQSGBoawsXFBRMnTkRqamqWvvv370fLli1hbm4OExMTNGrUCCEhIdku9+eff8ZHH30ES0tLmJmZoU6dOpgzZw5evHiRa54TJ07g448/xgcffAATExPUrFkT48aNw5MnT3TxdgvE/Pnz0bVrVzg7O2d5zcvLCzKZDBcvXszy2kcffYQvv/wyS3tISAisrKwytSUkJGDatGmoXr06jIyMYG9vD09PT+zatQtCFMwUBNHR0ejbty+qVq0KqVSabdbsPHz4EB07doSJiQlsbW0xYcIEZGRkZOpz8uRJ1K9fHwqFApUrV87yfTR9+nTMnz8f8fHxOno3OWOBoQepqRmYNOkoPDzWIzz8OaysjBATk6jvWESkY+3bt0d0dDTu37+PpUuXYs2aNfD398/UZ/ny5ejatSuaNm2KP/74A9evX0efPn3w+eefY/z48Zn6Tps2Dd7e3mjUqBEOHTqEmzdvYvHixbh27Ro2bdqUY441a9bA09MT9vb2+PnnnxEeHo7Vq1cjPj7+vf6aVSqV+R77LsnJyVi/fj0GDRqU5bWHDx/i3LlzGDVqFIKCgvK9jlevXqFJkybYuHEjpkyZgitXruDUqVPw9vbGxIkTC+xDOC0tDWXLlsX06dPh5uaWpzEqlQodO3aEUqnEuXPnsGHDBoSEhGDmzJmaPg8ePEDHjh3RqlUrhIWF4csvv8TgwYNx5MgRTR9XV1dUqlQJmzdv1vn7ykKUMvHx8QKAiI+P191ClxgKsQhCbKz3zq7nzj0U1aotF8AsAcwS/fr9LOLiknSXhaiESUlJEeHh4SIlJUXfUbTi4+Mjunbtmqmte/fuol69f35PPHz4UBgaGoqxY8dmGf/9998LAOL3338XQgjxxx9/CABi2bJl2a7v5cuX2bY/evRIyOVy8eWXX+Y6zt/fX7i5uWV6benSpeLDDz/M8p7mzZsnHBwchLOzs5gyZYpwd3fPstw6deqI2bNna56vW7dOVK9eXSgUClGtWjURGBiYbZ63duzYIcqWLZvta7NmzRJ9+vQRt2/fFpaWliI5OTnT6y1bthRjxozJMi44OFhYWlpqng8fPlyYmpqKJ0+eZOn7+vVrkZ6enmtGXcgp638dPHhQSKVSERMTo2lbtWqVsLCwEGlpaUIIISZOnChq1aqVaZy3t7fw8vLK1DZ79mzRrFmzHNeV28+cNp+hPMlTp3K/0mPatFAEBJyBEIC9vRnWrOmELl2qFVI2ohJmc0MgKaZw12lqD3x2KV9Db968iXPnzuHDDz/UtO3cuRPp6elZ9lQAwLBhwzB16lT89NNPaNy4MbZs2QIzMzOMGDEi2+X/d9f/Wzt27IBSqcTEiRO1GpeT0NBQWFhY4OjRo5q2gIAAREZGolKlSgCAW7du4fr16/j5558BAFu2bMHMmTOxYsUK1KtXD1evXsWQIUNgamoKHx+fbNdz+vRpNGjQIEu7EALBwcEIDAxE9erVUblyZezcuRP9+/fX6n2o1Wps3boV/fr1g6OjY5bXc5vF8vTp0+jQoUOuy1+zZg369eunVabcnD9/HrVr14adnZ2mzcvLC8OHD8etW7dQr149nD9/Hp6enpnGeXl5ZTkE4+7ujvnz5yMtLQ0KhUJnGf+LBUYhsrBQQAhgwAA3LF3qhTJljPUdiaj4SooBEovu+QPAm3MrzMzMkJGRgbS0NEilUqxYsULz+t27d2FpaQkHB4csY+VyOSpWrIi7d+8CAP78809UrFgRhoaGWmX4888/YWFhke068sPU1BQ//PAD5HK5ps3NzQ0//vgjZsyYAeBNQdG4cWNUrlwZAODv74/Fixeje/fuAAAXFxeEh4djzZo1ORYYf/31V7Yf/MeOHUNycjK8vLwAAJ999hnWr1+vdYERFxeHly9fonr16lqNA4CGDRsiLCws1z7/LgR0ISYmJssy3z6PiYnJtU9CQgJSUlJgbPzmM8fR0RFKpRIxMTGZCl5dY4FRgJKSlIiOTkTlymUAAOPGNYG7ezm0auWi52REJYCpfZFfZ6tWrbBq1SokJSVh6dKlMDAwQI8ePfK1apHPEw6FEDqdR6d27dqZigsA6NevH4KCgjBjxgwIIfDTTz9h7NixAICkpCRERkZi0KBBGDJkiGZMRkYGLC0tc1xPSkoKjIyyzl4cFBQEb29vGBi8+fj69NNPMWHChEx7UPIiv9sTAIyNjTXFU3H0ttAo6Hv7sMAoIKdO/QU/v72Qy2W4cmUYjIwMYGAgZXFBpCv5PFRRmExNTTUfREFBQXBzc8t04mLVqlURHx+Pp0+fZvlrXalUIjIyEq1atdL0PXPmDNLT07Xai/F2HdHR0bnuxZBKpVk+dNPT07N9T//16aefYtKkSbhy5QpSUlLw6NEjeHt7AwASE9+cwL5u3To0btw407jcpqG2sbHBy5cvM7W9ePECu3fvRnp6OlatWqVpV6lUCAoKwvz58wEAFhYW2Z6g+erVK01RU7ZsWVhZWeHOnTs5ZsiJPg6R2Nvb48KFC5naYmNjNa+9/fdt27/7WFhYaIoKAJorjsqWLauzfNkpEleRBAYGwtnZGUZGRmjcuHGWjfhfO3bs0FxSVLt2bRw8eLCQkr5bYqISo0cfRMuWIYiMfInXr5V48ODluwcSUYkmlUoxdepUTJ8+HSkpKQCAHj16wNDQMNsrOVavXo2kpCR8+umnAIC+ffsiMTERK1euzHb5r169yra9Z8+ekMvl+Pbbb3MdV7ZsWcTExGQqMt51GOCt8uXLo2XLltiyZQu2bNmCtm3bwtb2zeX2dnZ2cHR0xP3791G5cuVMDxeXnP/gqlevHsLDwzO1bdmyBeXLl8e1a9cQFhameSxevBghISFQqVQAgGrVquHKlStZlnnlyhVUrVoVwJv/jz59+mDLli14+vRplr6JiYlZLgF96+0hktweXbp0ydO2yysPDw/cuHEDz54907QdPXoUFhYWqFmzpqZPaGhopnFHjx6Fh4dHprabN2+ifPnysLGx0WnGLN55GmgB27p1q5DL5SIoKEjcunVLDBkyRFhZWYnY2Nhs+589e1bIZDLx7bffivDwcDF9+nRhaGgobty4kaf1FeRVJMcntxUuLss0V4gMGbJPvHpVvM58JypqStJVJOnp6aJcuXJi4cKFmralS5cKqVQqpk6dKm7fvi3u3bsnFi9eLBQKhRg3blym8RMnThQymUxMmDBBnDt3TkRFRYljx46Jnj175nh1iRBCBAYGColEIvz8/MTJkydFVFSUOHPmjBg6dKjmCpbw8HAhkUjE119/Le7duydWrFghrK2ts72KJDvr1q0Tjo6OwsbGRmzatCnLa8bGxuK7774TERER4vr16yIoKEgsXrw4x8zXr18XBgYG4sWLF5o2Nzc3MWnSpCx9X716JeRyudi/f78QQojIyEhhZGQkRo8eLa5duybu3LkjFi9eLAwMDMShQ4c04/7++29RvXp1Ub58ebFhwwZx69YtcffuXbF+/XpRuXLlHK/M0YWrV6+Kq1evigYNGoi+ffuKq1evilu3bmle37Vrl6hWrZrmeUZGhnB1dRXt2rUTYWFh4vDhw6Js2bJiypQpmj73798XJiYmYsKECeL27dsiMDBQyGQycfjw4Uzr9vHxEX5+fjlm09VVJHovMNzd3cXIkSM1z1UqlXB0dBQBAQHZ9u/du7fo2LFjprbGjRuLYcOG5Wl9BVFgpH5rJIZ7dNQUFhUqLBW//npPZ8snKs1KUoEhhBABAQGibNmyIjExUdO2d+9e0bx5c2FqaiqMjIxEgwYNRFBQULbL3bZtm2jRooUwNzcXpqamok6dOmLOnDnv/DA8evSo8PLyEtbW1sLIyEhUr15djB8/Xjx9+lTTZ9WqVcLJyUmYmpqKAQMGiPnz5+e5wHj58qVQKBTCxMREvH79OsvrW7ZsEXXr1hVyuVxYW1uLFi1aiF27duWa2d3dXaxevVoIIcSlS5cEAHHhwoVs+3bo0EF88sknmucXLlwQbdu2FWXLlhWWlpaicePGYvfu3VnGvXr1SkyePFlUqVJFyOVyYWdnJzw9PcXu3buFWq3ONd/7AJDl8e9tHRwcLP67DyAqKkp06NBBGBsbCxsbGzFu3Lgsl9KeOHFCs50rVqwogoODM72ekpIiLC0txfnz53PMViIKjLS0NCGTybL8pw8YMEB06dIl2zFOTk5i6dKlmdpmzpwp6tSpk23/1NRUER8fr3k8evRI5wWGerGh8KzSXwCzxPDh+0VCQqrOlk1U2hXXAoPe3/79+0WNGjWESqXSd5QSY+XKlaJt27a59ikR82DExcVBpVJle1lNTife5HQZztvLdP4rICAAs2fP1k3gHEgkwLpev+C+2h2tA/zfPYCIiN6pY8eO+PPPP/HkyRM4OTnpO06JYGhoiOXLlxfKukr8VSRTpkzRXC4FvJl3XuffqIPvwxmAs1S769OJiCh3eb1PB+XN4MGDC21dei0wbGxsIJPJsr2s5u1lN/+V02U4OfVXKBQFOlMZAMC8fMEun4iIqJjR62WqcrkcDRo0yHRZjVqtRmhoaJbLat7K62U4REREpD96P0QyduxY+Pj4oGHDhnB3d8eyZcuQlJQEX19fAMCAAQNQrlw5BAQEAADGjBmDli1bYvHixejYsSO2bt2KS5cuYe3atfp8G0RUwEQB3TqbiDLT1c+a3gsMb29vPH/+HDNnzkRMTAzq1q2Lw4cPa07kfPjwIaTSf3a0NGnSBD/++COmT5+OqVOnokqVKtizZw9cXV319RaIqAC9nbUyOTk502yERFQwlEolgNxnWs0LiShlfxYkJCTA0tIS8fHxsLCw0HccIsqD6OhovHr1Cra2tjAxMdHpvTWI6B9qtRpPnz6FoaEhKlSokOVnTZvPUL3vwSAiepe3J3H/e5pkIioYUqk02+JCWywwiKjIk0gkcHBwgK2tbbY34CIi3ZHL5ZlOTcgvFhhEVGzIZLL3Pi5MRIWjSNxNlYiIiEoWFhhERESkcywwiIiISOdK3TkYb6/KTUhI0HMSIiKi4uXtZ2deZrgodQXG69evAYB35iMiIsqn169fw9LSMtc+pW6irbeTiJibm+tssp63d2h99OgRJ+/SEW5T3eM21S1uT93jNtWtgtieQgi8fv0ajo6O77yUtdTtwZBKpShfvmDufmphYcEfCh3jNtU9blPd4vbUPW5T3dL19nzXnou3eJInERER6RwLDCIiItI5Fhg6oFAo4O/vD4VCoe8oJQa3qe5xm+oWt6fucZvqlr63Z6k7yZOIiIgKHvdgEBERkc6xwCAiIiKdY4FBREREOscCg4iIiHSOBUYeBQYGwtnZGUZGRmjcuDEuXLiQa/8dO3agevXqMDIyQu3atXHw4MFCSlp8aLNN161bh+bNm8Pa2hrW1tbw9PR85/9BaaPt9+hbW7duhUQiQbdu3Qo2YDGk7TZ99eoVRo4cCQcHBygUClStWpU/+/+i7fZctmwZqlWrBmNjYzg5OeGrr75CampqIaUt+k6dOoXOnTvD0dEREokEe/bseeeYkydPon79+lAoFKhcuTJCQkIKLqCgd9q6dauQy+UiKChI3Lp1SwwZMkRYWVmJ2NjYbPufPXtWyGQy8e2334rw8HAxffp0YWhoKG7cuFHIyYsubbdp3759RWBgoLh69aq4ffu2GDhwoLC0tBSPHz8u5ORFk7bb860HDx6IcuXKiebNm4uuXbsWTthiQtttmpaWJho2bCg+/vhjcebMGfHgwQNx8uRJERYWVsjJiyZtt+eWLVuEQqEQW7ZsEQ8ePBBHjhwRDg4O4quvvirk5EXXwYMHxbRp08SuXbsEALF79+5c+9+/f1+YmJiIsWPHivDwcLF8+XIhk8nE4cOHCyQfC4w8cHd3FyNHjtQ8V6lUwtHRUQQEBGTbv3fv3qJjx46Z2ho3biyGDRtWoDmLE2236X9lZGQIc3NzsWHDhoKKWKzkZ3tmZGSIJk2aiB9++EH4+PiwwPgPbbfpqlWrRMWKFYVSqSysiMWKtttz5MiRonXr1pnaxo4dK5o2bVqgOYurvBQYEydOFLVq1crU5u3tLby8vAokEw+RvINSqcTly5fh6empaZNKpfD09MT58+ezHXP+/PlM/QHAy8srx/6lTX626X8lJycjPT0dZcqUKaiYxUZ+t+ecOXNga2uLQYMGFUbMYiU/23Tfvn3w8PDAyJEjYWdnB1dXVyxYsAAqlaqwYhdZ+dmeTZo0weXLlzWHUe7fv4+DBw/i448/LpTMJVFhfzaVupudaSsuLg4qlQp2dnaZ2u3s7HDnzp1sx8TExGTbPyYmpsByFif52ab/NWnSJDg6Omb5YSmN8rM9z5w5g/Xr1yMsLKwQEhY/+dmm9+/fx/Hjx9GvXz8cPHgQ9+7dw4gRI5Ceng5/f//CiF1k5Wd79u3bF3FxcWjWrBmEEMjIyMDnn3+OqVOnFkbkEimnz6aEhASkpKTA2NhYp+vjHgwqdr7++mts3boVu3fvhpGRkb7jFDuvX79G//79sW7dOtjY2Og7TomhVqtha2uLtWvXokGDBvD29sa0adOwevVqfUcrlk6ePIkFCxZg5cqVuHLlCnbt2oUDBw5g7ty5+o5GecQ9GO9gY2MDmUyG2NjYTO2xsbGwt7fPdoy9vb1W/Uub/GzTtxYtWoSvv/4ax44dQ506dQoyZrGh7faMjIxEVFQUOnfurGlTq9UAAAMDA0RERKBSpUoFG7qIy8/3qIODAwwNDSGTyTRtNWrUQExMDJRKJeRyeYFmLsrysz1nzJiB/v37Y/DgwQCA2rVrIykpCUOHDsW0adMglfLvY23l9NlkYWGh870XAPdgvJNcLkeDBg0QGhqqaVOr1QgNDYWHh0e2Yzw8PDL1B4CjR4/m2L+0yc82BYBvv/0Wc+fOxeHDh9GwYcPCiFosaLs9q1evjhs3biAsLEzz6NKlC1q1aoWwsDA4OTkVZvwiKT/fo02bNsW9e/c0xRoA3L17Fw4ODqW6uADytz2Tk5OzFBFvizfBW2jlS6F/NhXIqaMlzNatW4VCoRAhISEiPDxcDB06VFhZWYmYmBghhBD9+/cXkydP1vQ/e/asMDAwEIsWLRK3b98W/v7+vEz1P7Tdpl9//bWQy+Vi586dIjo6WvN4/fq1vt5CkaLt9vwvXkWSlbbb9OHDh8Lc3FyMGjVKREREiP379wtbW1sxb948fb2FIkXb7env7y/Mzc3FTz/9JO7fvy9+/fVXUalSJdG7d299vYUi5/Xr1+Lq1avi6tWrAoBYsmSJuHr1qvjrr7+EEEJMnjxZ9O/fX9P/7WWqEyZMELdv3xaBgYG8TLUoWL58uahQoYKQy+XC3d1d/P7775rXWrZsKXx8fDL13759u6hataqQy+WiVq1a4sCBA4WcuOjTZpt++OGHAkCWh7+/f+EHL6K0/R79NxYY2dN2m547d040btxYKBQKUbFiRTF//nyRkZFRyKmLLm22Z3p6upg1a5aoVKmSMDIyEk5OTmLEiBHi5cuXhR+8iDpx4kS2vxffbkcfHx/RsmXLLGPq1q0r5HK5qFixoggODi6wfLxdOxEREekcz8EgIiIinWOBQURERDrHAoOIiIh0jgUGERER6RwLDCIiItI5FhhERESkcywwiIiISOdYYBAREZHOscAgKmFCQkJgZWWl7xj5JpFIsGfPnlz7DBw4EN26dSuUPESUPywwiIqggQMHQiKRZHncu3dP39EQEhKiySOVSlG+fHn4+vri2bNnOll+dHQ0OnToAACIioqCRCJBWFhYpj7fffcdQkJCdLK+nMyaNUvzPmUyGZycnDB06FC8ePFCq+WwGKLSirdrJyqi2rdvj+Dg4ExtZcuW1VOazCwsLBAREQG1Wo1r167B19cXT58+xZEjR9572TndvvvfLC0t33s9eVGrVi0cO3YMKpUKt2/fhp+fH+Lj47Ft27ZCWT9RccY9GERFlEKhgL29faaHTCbDkiVLULt2bZiamsLJyQkjRoxAYmJijsu5du0aWrVqBXNzc1hYWKBBgwa4dOmS5vUzZ86gefPmMDY2hpOTE7744gskJSXlmk0ikcDe3h6Ojo7o0KEDvvjiCxw7dgwpKSlQq9WYM2cOypcvD4VCgbp16+Lw4cOasUqlEqNGjYKDgwOMjIzw4YcfIiAgINOy3x4icXFxAQDUq1cPEokEH330EYDMewXWrl0LR0fHTLdJB4CuXbvCz89P83zv3r2oX78+jIyMULFiRcyePRsZGRm5vk8DAwPY29ujXLly8PT0RK9evXD06FHN6yqVCoMGDYKLiwuMjY1RrVo1fPfdd5rXZ82ahQ0bNmDv3r2avSEnT54EADx69Ai9e/eGlZUVypQpg65duyIqKirXPETFCQsMomJGKpXi+++/x61bt7BhwwYcP34cEydOzLF/v379UL58eVy8eBGXL1/G5MmTYWhoCACIjIxE+/bt0aNHD1y/fh3btm3DmTNnMGrUKK0yGRsbQ61WIyMjA9999x0WL16MRYsW4fr16/Dy8kKXLl3w559/AgC+//577Nu3D9u3b0dERAS2bNkCZ2fnbJd74cIFAMCxY8cQHR2NXbt2ZenTq1cv/P333zhx4oSm7cWLFzh8+DD69esHADh9+jQGDBiAMWPGIDw8HGvWrEFISAjmz5+f5/cYFRWFI0eOQC6Xa9rUajXKly+PHTt2IDw8HDNnzsTUqVOxfft2AMD48ePRu3dvtG/fHtHR0YiOjkaTJk2Qnp4OLy8vmJub4/Tp0zh79izMzMzQvn17KJXKPGciKtIK7D6tRJRvPj4+QiaTCVNTU82jZ8+e2fbdsWOH+OCDDzTPg4ODhaWlpea5ubm5CAkJyXbsoEGDxNChQzO1nT59WkilUpGSkpLtmP8u/+7du6Jq1aqiYcOGQgghHB0dxfz58zONadSokRgxYoQQQojRo0eL1q1bC7Vane3yAYjdu3cLIYR48OCBACCuXr2aqc9/by/ftWtX4efnp3m+Zs0a4ejoKFQqlRBCiDZt2ogFCxZkWsamTZuEg4NDthmEEMLf319IpVJhamoqjIyMNLfCXrJkSY5jhBBi5MiRokePHjlmfbvuatWqZdoGaWlpwtjYWBw5ciTX5RMVFzwHg6iIatWqFVatWqV5bmpqCuDNX/MBAQG4c+cOEhISkJGRgdTUVCQnJ8PExCTLcsaOHYvBgwdj06ZNmt38lSpVAvDm8Mn169exZcsWTX8hBNRqNR48eIAaNWpkmy0+Ph5mZmZQq9VITU1Fs2bN8MMPPyAhIQFPnz5F06ZNM/Vv2rQprl27BuDN4Y22bduiWrVqaN++PTp16oR27dq917bq168fhgwZgpUrV0KhUGDLli3o06cPpFKp5n2ePXs20x4LlUqV63YDgGrVqmHfvn1ITU3F5s2bERYWhtGjR2fqExgYiKCgIDx8+BApKSlQKpWoW7durnmvXbuGe/fuwdzcPFN7amoqIiMj87EFiIoeFhhERZSpqSkqV66cqS0qKgqdOnXC8OHDMX/+fJQpUwZnzpzBoEGDoFQqs/2gnDVrFvr27YsDBw7g0KFD8Pf3x9atW/HJJ58gMTERw4YNwxdffJFlXIUKFXLMZm5ujitXrkAqlcLBwQHGxsYAgISEhHe+r/r16+PBgwc4dOgQjh07ht69e8PT0xM7d+5859icdO7cGUIIHDhwAI0aNcLp06exdOlSzeuJiYmYPXs2unfvnmWskZFRjsuVy+Wa/4Ovv/4aHTt2xOzZszF37lwAwNatWzF+/HgsXrwYHh4eMDc3x8KFC/HHH3/kmjcxMRENGjTIVNi9VVRO5CV6XywwiIqRy5cvQ61WY/HixZq/zt8e789N1apVUbVqVXz11Vf49NNPERwcjE8++QT169dHeHh4lkLmXaRSabZjLCws4OjoiLNnz6Jly5aa9rNnz8Ld3T1TP29vb3h7e6Nnz55o3749Xrx4gTJlymRa3tvzHVQqVa55jIyM0L17d2zZsgX37t1DtWrVUL9+fc3r9evXR0REhNbv87+mT5+O1q1bY/jw4Zr32aRJE4wYMULT5797IORyeZb89evXx7Zt22BrawsLC4v3ykRUVPEkT6JipHLlykhPT8fy5ctx//59bNq0CatXr86xf0pKCkaNGoWTJ0/ir7/+wtmzZ3Hx4kXNoY9Jkybh3LlzGDVqFMLCwvDnn39i7969Wp/k+W8TJkzAN998g23btiEiIgKTJ09GWFgYxowZAwBYsmQJfvrpJ9y5cwd3797Fjh07YG9vn+3kYLa2tjA2Nsbhw4cRGxuL+Pj4HNfbr18/HDhwAEFBQZqTO9+aOXMmNm7ciNmzZ+PWrVu4ffs2tm7diunTp2v13jw8PFCnTh0sWLAAAFClShVcunQJR44cwd27dzFjxgxcvHgx0xhnZ2dcv34dERERiIuLQ3p6Ovr16wcbGxt07doVp0+fxoMHD3Dy5El88cUXePz4sVaZiIosfZ8EQkRZZXdi4FtLliwRDg4OwtjYWHh5eYmNGzcKAOLly5dCiMwnYaalpYk+ffoIJycnIZfLhaOjoxg1alSmEzgvXLgg2rZtK8zMzISpqamoU6dOlpM0/+2/J3n+l0qlErNmzRLlypUThoaGws3NTRw6dEjz+tq1a0XdunWFqampsLCwEG3atBFXrlzRvI5/neQphBDr1q0TTk5OQiqVipYtW+a4fVQqlXBwcBAARGRkZJZchw8fFk2aNBHGxsbCwsJCuLu7i7Vr1+b4Pvz9/YWbm1uW9p9++kkoFArx8OFDkZqaKgYOHCgsLS2FlZWVGD58uJg8eXKmcc+ePdNsXwDixIkTQgghoqOjxYABA4SNjY1QKBSiYsWKYsiQISI+Pj7HTETFiUQIIfRb4hAREVFJw0MkREREpHMsMIiIiEjnWGAQERGRzrHAICIiIp1jgUFEREQ6xwKDiIiIdI4FBhEREekcCwwiIiLSORYYREREpHMsMIiIiEjnWGAQERGRzv0fC9Ci/cI3ngAAAAAASUVORK5CYII="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"# Initialize K-Fold Cross-Validation\nkf = KFold(n_splits=5, shuffle=True, random_state=42)\n\n# Initialize Logistic Regression model\nmodel = LogisticRegression(max_iter=1000, solver='liblinear')\n\n# Lists to store performance metrics\naccuracies = []\nprecisions = []\nrecalls = []\nroc_aucs = []\n\nprint(\"Performing K-Fold Cross-Validation...\")\n\n# Perform K-Fold Cross-Validation\nfor fold, (train_index, test_index) in enumerate(kf.split(X)):\n print(f\"Fold {fold + 1}\")\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n \n # Train the model\n model.fit(X_train, y_train)\n \n # Predict on the test set\n y_pred = model.predict(X_test)\n y_proba = model.predict_proba(X_test)[:, 1] # Probabilities for ROC AUC\n \n # Evaluate metrics\n acc = accuracy_score(y_test, y_pred)\n prec = precision_score(y_test, y_pred)\n rec = recall_score(y_test, y_pred)\n roc_auc = roc_auc_score(y_test, y_proba)\n \n # Store metrics\n accuracies.append(acc)\n precisions.append(prec)\n recalls.append(rec)\n roc_aucs.append(roc_auc)\n \n print(f\"Accuracy: {acc:.4f}, Precision: {prec:.4f}, Recall: {rec:.4f}, ROC AUC: {roc_auc:.4f}\")\n print(\"-\" * 50)\n\n# Print average metrics\nprint(\"\\nK-Fold Cross-Validation Results:\")\nprint(f\"Accuracy: {np.mean(accuracies):.4f} ± {np.std(accuracies):.4f}\")\nprint(f\"Precision: {np.mean(precisions):.4f} ± {np.std(precisions):.4f}\")\nprint(f\"Recall: {np.mean(recalls):.4f} ± {np.std(recalls):.4f}\")\nprint(f\"ROC AUC: {np.mean(roc_aucs):.4f} ± {np.std(roc_aucs):.4f}\")\n\n# Train the final model on the entire dataset (if needed)\nmodel.fit(X, y)\nprint(\"\\nFinal Model Trained on Full Dataset\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T13:22:45.31909Z","iopub.execute_input":"2024-12-09T13:22:45.319708Z","iopub.status.idle":"2024-12-09T13:25:46.9079Z","shell.execute_reply.started":"2024-12-09T13:22:45.319651Z","shell.execute_reply":"2024-12-09T13:25:46.90572Z"}},"outputs":[{"name":"stdout","text":"Performing K-Fold Cross-Validation...\nFold 1\nAccuracy: 0.9943, Precision: 0.9966, Recall: 0.9881, ROC AUC: 0.9995\n--------------------------------------------------\nFold 2\nAccuracy: 0.9941, Precision: 0.9953, Recall: 0.9889, ROC AUC: 0.9995\n--------------------------------------------------\nFold 3\nAccuracy: 0.9944, Precision: 0.9959, Recall: 0.9890, ROC AUC: 0.9995\n--------------------------------------------------\nFold 4\nAccuracy: 0.9942, Precision: 0.9952, Recall: 0.9891, ROC AUC: 0.9995\n--------------------------------------------------\nFold 5\nAccuracy: 0.9946, Precision: 0.9963, Recall: 0.9891, ROC AUC: 0.9994\n--------------------------------------------------\n\nK-Fold Cross-Validation Results:\nAccuracy: 0.9943 ± 0.0002\nPrecision: 0.9959 ± 0.0005\nRecall: 0.9889 ± 0.0004\nROC AUC: 0.9995 ± 0.0000\n\nFinal Model Trained on Full Dataset\n","output_type":"stream"}],"execution_count":14}]}
\ No newline at end of file