diff --git a/Nuclear Weapons Analysis/Dataset/README.md b/Nuclear Weapons Analysis/Dataset/README.md new file mode 100644 index 000000000..4446e9774 --- /dev/null +++ b/Nuclear Weapons Analysis/Dataset/README.md @@ -0,0 +1,57 @@ +# Nuclear Weapons Datasets + +## Overview + +This repository contains datasets related to nuclear weapons, providing valuable insights into various aspects such as historical trends, stockpiles, tests, and considerations by different entities. The datasets have been curated for analysis and modeling purposes. +## Link to Dataset +1. https://www.kaggle.com/datasets/michaelbryantds/nuclear-weapons-dataset/ + +## Datasets + +### 1. Nuclear Weapons Distribution Over Time (df1.csv) + +- **Columns:** + - `country_name`: Name of the country. + - `year`: Year of observation. + - `nuclear_weapons_status`: Current status of nuclear weapons. + - `nuclear_weapons_consideration`: Consideration of nuclear weapons. + - `nuclear_weapons_pursuit`: Pursuit of nuclear weapons. + - `nuclear_weapons_possession`: Possession of nuclear weapons. + +### 2. Relationship Between Features (df2.csv) + +- **Columns:** + - `entity_name`: Name of the entity. + - `year`: Year of observation. + - `number_nuclweap_consideration`: Number of nuclear weapons under consideration. + - `number_nuclweap_pursuit`: Number of nuclear weapons under pursuit. + - `number_nuclweap_possession`: Number of nuclear weapons in possession. + +### 3. Nuclear Weapons Stockpile Trend (df3.csv) + +- **Columns:** + - `country_name`: Name of the country. + - `year`: Year of observation. + - `nuclear_weapons_stockpile`: Number of nuclear weapons in the stockpile. + +### 4. Nuclear Weapons Tests Over Time (df4.csv) + +- **Columns:** + - `country_name`: Name of the country. + - `year`: Year of observation. + - `nuclear_weapons_tests`: Number of nuclear weapons tests. + +## Usage + +These datasets are valuable for exploratory data analysis (EDA), clustering, and predictive modeling. The associated code in this repository demonstrates how to perform EDA, clustering using K-Means, t-tests, PCA, and build machine learning models. + + + +## Contributions + +Contributions to enhance the datasets or provide additional insights are welcome. Feel free to create issues or pull requests. + +## Acknowledgments + +Special thanks to Kaggle for providing these datasets. + diff --git a/Nuclear Weapons Analysis/Dataset/nuclear_weapons_proliferation_owid.csv b/Nuclear Weapons Analysis/Dataset/nuclear_weapons_proliferation_owid.csv new file mode 100644 index 000000000..a5c06fb5b --- /dev/null +++ b/Nuclear Weapons Analysis/Dataset/nuclear_weapons_proliferation_owid.csv @@ -0,0 +1,16849 @@ +country_name,year,nuclear_weapons_status,nuclear_weapons_consideration,nuclear_weapons_pursuit,nuclear_weapons_possession +Abkhazia,1938,0,0,0,0 +Abkhazia,1939,0,0,0,0 +Abkhazia,1940,0,0,0,0 +Abkhazia,1941,0,0,0,0 +Abkhazia,1942,0,0,0,0 +Abkhazia,1943,0,0,0,0 +Abkhazia,1944,0,0,0,0 +Abkhazia,1945,0,0,0,0 +Abkhazia,1946,0,0,0,0 +Abkhazia,1947,0,0,0,0 +Abkhazia,1948,0,0,0,0 +Abkhazia,1949,0,0,0,0 +Abkhazia,1950,0,0,0,0 +Abkhazia,1951,0,0,0,0 +Abkhazia,1952,0,0,0,0 +Abkhazia,1953,0,0,0,0 +Abkhazia,1954,0,0,0,0 +Abkhazia,1955,0,0,0,0 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Binary files /dev/null and b/Nuclear Weapons Analysis/Images/nw8.PNG differ diff --git a/Nuclear Weapons Analysis/Images/nw9.PNG b/Nuclear Weapons Analysis/Images/nw9.PNG new file mode 100644 index 000000000..9142fb999 Binary files /dev/null and b/Nuclear Weapons Analysis/Images/nw9.PNG differ diff --git a/Nuclear Weapons Analysis/Model/README.md b/Nuclear Weapons Analysis/Model/README.md new file mode 100644 index 000000000..003e86580 --- /dev/null +++ b/Nuclear Weapons Analysis/Model/README.md @@ -0,0 +1,69 @@ +# Nuclear Weapons Data Analysis and Modeling + +## Overview + +This project involves the analysis of datasets related to nuclear weapons, including exploratory data analysis (EDA) and the application of various machine learning models. The goal is to gain insights into trends over time, clustering patterns, and predictive modeling of nuclear weapons-related features. + +## Exploratory Data Analysis (EDA) + +### Nuclear Weapons Distribution Over Time (df1) + +- Explored the distribution of nuclear weapons-related features over time. +- Conducted basic statistics, including mean, median, and standard deviation. +- Utilized K-Means clustering to identify potential patterns in the data. +- Applied the t-test to compare nuclear weapons stockpiles between different years. + +### Relationship Between Features (df2) + +- Examined the relationship between various features, including `number_nuclweap_consideration`, `number_nuclweap_pursuit`, and `number_nuclweap_possession`. +- Performed Principal Component Analysis (PCA) to reduce dimensionality. +- Applied the elbow method to find the optimal number of clusters for K-Means. +- Conducted K-Means clustering to group entities based on nuclear weapons-related features. + +### Nuclear Weapons Stockpile Trend (df3) + +- Analyzed the trend in nuclear weapons stockpiles over time. +- Applied basic statistics to understand the central tendency and dispersion of the data. +- Conducted K-Means clustering to identify potential clusters of countries based on their nuclear weapons stockpiles. + +### Nuclear Weapons Tests Over Time (df4) + +- Explored the distribution of nuclear weapons tests over time. +- Conducted the t-test to compare the number of nuclear weapons tests between different years. + +## Machine Learning Models + +### Model Selection + +- Chose a variety of machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree, MLP, and a simple Neural Network using Keras. + +### Evaluation and Prediction + +- Trained each model on the respective training data and evaluated their performance on the test set. +- Saved the model predictions to CSV files for further analysis. + +### Analysis of Model Performance + +- Plotted and compared the accuracy of each model on both training and test sets to assess overfitting or underfitting. +- Provided classification reports for each model to understand precision, recall, and F1-score. + + 1. ![Nuclear Weapon Proliferation Owid](https://github.com/adi271001/ML-Crate/blob/Nuclear-Weapons-Analysis/Nuclear%20Weapons%20Analysis/Images/nw18.PNG) + 2. ![Nuclear Weapon Proliferation Total Owid](https://github.com/adi271001/ML-Crate/blob/Nuclear-Weapons-Analysis/Nuclear%20Weapons%20Analysis/Images/nw18.PNG) + 3. ![Nuclear Weapon Stockpiles](https://github.com/adi271001/ML-Crate/blob/Nuclear-Weapons-Analysis/Nuclear%20Weapons%20Analysis/Images/nw18.PNG) + 4. ![Nuclear Weapon test states](https://github.com/adi271001/ML-Crate/blob/Nuclear-Weapons-Analysis/Nuclear%20Weapons%20Analysis/Images/nw18.PNG) + +## Concluding Thoughts + +- Consideration of features and their importance can guide the selection of predictive variables. +- Regular evaluation and comparison of models are essential to choose the most effective one. +- Models gave highly Accurate Results Ranging from 83.5%-99.96% acroos the 4 data files + +## Usage + +Feel free to clone or download this repository and adapt the code for your own datasets or analyses. + +## Dependencies + +- Python 3.x +- Required Python packages (specified in requirements.txt) + diff --git a/Nuclear Weapons Analysis/Model/nuclear-weapons-analysis-and-classification.ipynb b/Nuclear Weapons Analysis/Model/nuclear-weapons-analysis-and-classification.ipynb new file mode 100644 index 000000000..32b7b3f7f --- /dev/null +++ b/Nuclear Weapons Analysis/Model/nuclear-weapons-analysis-and-classification.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":4438715,"sourceType":"datasetVersion","datasetId":2599263}],"dockerImageVersionId":30627,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Nuclear Weapons Analysis And Classification","metadata":{}},{"cell_type":"markdown","source":"# Importing Libraries","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.stats import ttest_ind\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score, classification_report\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.cluster import KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neural_network import MLPClassifier\nfrom tensorflow import keras\nfrom tensorflow.keras import layers","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:33:47.007027Z","iopub.execute_input":"2024-01-07T12:33:47.007661Z","iopub.status.idle":"2024-01-07T12:34:00.036550Z","shell.execute_reply.started":"2024-01-07T12:33:47.007634Z","shell.execute_reply":"2024-01-07T12:34:00.035789Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# EDA for Nuclear Weapons Proliferation OWID","metadata":{}},{"cell_type":"code","source":"df1 = pd.read_csv('/kaggle/input/nuclear-weapons-dataset/nuclear_weapons_proliferation_owid.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:02.999466Z","iopub.execute_input":"2024-01-07T12:35:03.000081Z","iopub.status.idle":"2024-01-07T12:35:03.032345Z","shell.execute_reply.started":"2024-01-07T12:35:03.000051Z","shell.execute_reply":"2024-01-07T12:35:03.031482Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"print(df1.info())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:04.702862Z","iopub.execute_input":"2024-01-07T12:35:04.703284Z","iopub.status.idle":"2024-01-07T12:35:04.732551Z","shell.execute_reply.started":"2024-01-07T12:35:04.703246Z","shell.execute_reply":"2024-01-07T12:35:04.731557Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"\nRangeIndex: 16848 entries, 0 to 16847\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 country_name 16848 non-null object\n 1 year 16848 non-null int64 \n 2 nuclear_weapons_status 16848 non-null int64 \n 3 nuclear_weapons_consideration 16848 non-null int64 \n 4 nuclear_weapons_pursuit 16848 non-null int64 \n 5 nuclear_weapons_possession 16848 non-null int64 \ndtypes: int64(5), object(1)\nmemory usage: 789.9+ KB\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df1.describe())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:05.896599Z","iopub.execute_input":"2024-01-07T12:35:05.897596Z","iopub.status.idle":"2024-01-07T12:35:05.920204Z","shell.execute_reply.started":"2024-01-07T12:35:05.897543Z","shell.execute_reply":"2024-01-07T12:35:05.919291Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":" year nuclear_weapons_status nuclear_weapons_consideration \\\ncount 16848.000000 16848.000000 16848.000000 \nmean 1979.921712 0.136099 0.018519 \nstd 24.504513 0.569861 0.134821 \nmin 1938.000000 0.000000 0.000000 \n25% 1959.000000 0.000000 0.000000 \n50% 1980.000000 0.000000 0.000000 \n75% 2001.000000 0.000000 0.000000 \nmax 2022.000000 3.000000 1.000000 \n\n nuclear_weapons_pursuit nuclear_weapons_possession \ncount 16848.000000 16848.000000 \nmean 0.014008 0.029855 \nstd 0.117525 0.170193 \nmin 0.000000 0.000000 \n25% 0.000000 0.000000 \n50% 0.000000 0.000000 \n75% 0.000000 0.000000 \nmax 1.000000 1.000000 \n","output_type":"stream"}]},{"cell_type":"code","source":"print(df1.isnull().sum())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:07.072339Z","iopub.execute_input":"2024-01-07T12:35:07.072690Z","iopub.status.idle":"2024-01-07T12:35:07.080738Z","shell.execute_reply.started":"2024-01-07T12:35:07.072663Z","shell.execute_reply":"2024-01-07T12:35:07.079787Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"country_name 0\nyear 0\nnuclear_weapons_status 0\nnuclear_weapons_consideration 0\nnuclear_weapons_pursuit 0\nnuclear_weapons_possession 0\ndtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":" **Visualize nuclear weapons status distribution**\n","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12, 6))\nsns.countplot(x='nuclear_weapons_status', data=df1, palette='viridis')\nplt.title('Distribution of Nuclear Weapons Status')\nplt.xlabel('Nuclear Weapons Status')\nplt.ylabel('Count')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:13.456595Z","iopub.execute_input":"2024-01-07T12:35:13.456939Z","iopub.status.idle":"2024-01-07T12:35:13.701961Z","shell.execute_reply.started":"2024-01-07T12:35:13.456913Z","shell.execute_reply":"2024-01-07T12:35:13.701075Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Visualize nuclear weapons possession over time**","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(14, 6))\nsns.lineplot(x='year', y='nuclear_weapons_possession', data=df1, estimator='sum', ci=None, marker='o', color='green')\nplt.title('Nuclear Weapons Possession Over Time')\nplt.xlabel('Year')\nplt.ylabel('Number of Countries with Nuclear Weapons')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:15.158594Z","iopub.execute_input":"2024-01-07T12:35:15.159405Z","iopub.status.idle":"2024-01-07T12:35:15.452886Z","shell.execute_reply.started":"2024-01-07T12:35:15.159370Z","shell.execute_reply":"2024-01-07T12:35:15.451964Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_42/2870396814.py:3: FutureWarning: \n\nThe `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n\n sns.lineplot(x='year', y='nuclear_weapons_possession', data=df1, estimator='sum', ci=None, marker='o', color='green')\n/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/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":"
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= df1['year'].unique()\nttest_results = []\nfor year in years:\n possession_values = df1[df1['year'] == year]['nuclear_weapons_possession']\n tstat, pvalue = ttest_ind(possession_values, df1[df1['year'] != year]['nuclear_weapons_possession'])\n ttest_results.append({'Year': year, 'T-statistic': tstat, 'P-value': pvalue})\n\nttest_df = pd.DataFrame(ttest_results)\nprint(\"T-test Results for Nuclear Weapons Possession between Different Years:\")\nprint(ttest_df)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:16.830691Z","iopub.execute_input":"2024-01-07T12:35:16.831064Z","iopub.status.idle":"2024-01-07T12:35:16.999768Z","shell.execute_reply.started":"2024-01-07T12:35:16.831035Z","shell.execute_reply":"2024-01-07T12:35:16.998783Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"T-test Results for Nuclear Weapons Possession between Different Years:\n Year T-statistic P-value\n0 1938 -2.483390 0.013024\n1 1939 -2.483390 0.013024\n2 1940 -2.483390 0.013024\n3 1941 -2.483390 0.013024\n4 1942 -2.483390 0.013024\n.. ... ... ...\n80 2018 1.313225 0.189125\n81 2019 1.313225 0.189125\n82 2020 1.313225 0.189125\n83 2021 1.313225 0.189125\n84 2022 1.313225 0.189125\n\n[85 rows x 3 columns]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**K-Means**","metadata":{}},{"cell_type":"code","source":"from sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\n\n# Select relevant features\nfeatures = ['year', 'nuclear_weapons_possession']\n\n# Standardize the data\nscaler = StandardScaler()\ndf1_scaled = scaler.fit_transform(df1[features])\n\n# Determine the optimal number of clusters using the Elbow Method\ninertia = []\nfor k in range(1, 11):\n kmeans = KMeans(n_clusters=k, random_state=42)\n kmeans.fit(df1_scaled)\n inertia.append(kmeans.inertia_)\n\n# Plot the Elbow Method\nplt.figure(figsize=(10, 6))\nplt.plot(range(1, 11), inertia, marker='o')\nplt.title('Elbow Method for Optimal K in K-means (Dataset 1)')\nplt.xlabel('Number of Clusters (K)')\nplt.ylabel('Inertia')\nplt.show()\n\n# Based on the Elbow Method, let's choose K=3 and fit the K-means model\nkmeans = KMeans(n_clusters=3, random_state=42)\ndf1['cluster'] = kmeans.fit_predict(df1_scaled)\n\n# Visualize clusters\nplt.figure(figsize=(12, 6))\nsns.scatterplot(x='year', y='nuclear_weapons_possession', hue='cluster', data=df1, palette='coolwarm', legend='full')\nplt.title('K-means Clustering of Nuclear Weapons Possession Over Time')\nplt.xlabel('Year')\nplt.ylabel('Number of Countries with Nuclear Weapons')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:35:18.210640Z","iopub.execute_input":"2024-01-07T12:35:18.211482Z","iopub.status.idle":"2024-01-07T12:35:33.225641Z","shell.execute_reply.started":"2024-01-07T12:35:18.211450Z","shell.execute_reply":"2024-01-07T12:35:33.224781Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**PCA**","metadata":{}},{"cell_type":"code","source":"# Select features for PCA\npca_features = ['year', 'nuclear_weapons_status', 'nuclear_weapons_pursuit']\n\n# Encode categorical variables for PCA\ndf1_pca = pd.get_dummies(df1[pca_features], drop_first=True)\n\n# Standardize the data\nscaler_pca = StandardScaler()\ndf1_pca_scaled = scaler_pca.fit_transform(df1_pca)\npca = PCA()\ndf1_pca_result = pca.fit_transform(df1_pca_scaled)\nexplained_variance_ratio = pca.explained_variance_ratio_\ncumulative_variance_ratio = np.cumsum(explained_variance_ratio)\n\n# Plot explained variance ratio\nplt.figure(figsize=(12, 6))\nplt.plot(range(1, len(explained_variance_ratio) + 1), cumulative_variance_ratio, marker='o')\nplt.title('Cumulative Explained Variance Ratio (Dataset 1)')\nplt.xlabel('Number of Principal Components')\nplt.ylabel('Cumulative Explained Variance Ratio')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:36:58.872481Z","iopub.execute_input":"2024-01-07T12:36:58.872893Z","iopub.status.idle":"2024-01-07T12:36:59.194214Z","shell.execute_reply.started":"2024-01-07T12:36:58.872860Z","shell.execute_reply":"2024-01-07T12:36:59.193309Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# EDA for Nuclear Weapons Proliferation Total OWID","metadata":{}},{"cell_type":"code","source":"df2 = pd.read_csv('/kaggle/input/nuclear-weapons-dataset/nuclear_weapons_proliferation_total_owid.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:37:01.578498Z","iopub.execute_input":"2024-01-07T12:37:01.579098Z","iopub.status.idle":"2024-01-07T12:37:01.594965Z","shell.execute_reply.started":"2024-01-07T12:37:01.579070Z","shell.execute_reply":"2024-01-07T12:37:01.594135Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"print(df2.info())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:37:04.390471Z","iopub.execute_input":"2024-01-07T12:37:04.390866Z","iopub.status.idle":"2024-01-07T12:37:04.401213Z","shell.execute_reply.started":"2024-01-07T12:37:04.390837Z","shell.execute_reply":"2024-01-07T12:37:04.400249Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"\nRangeIndex: 85 entries, 0 to 84\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 entity_name 85 non-null object\n 1 year 85 non-null int64 \n 2 number_nuclweap_consideration 85 non-null int64 \n 3 number_nuclweap_pursuit 85 non-null int64 \n 4 number_nuclweap_possession 85 non-null int64 \ndtypes: int64(4), object(1)\nmemory usage: 3.4+ KB\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df2.describe())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:37:05.410515Z","iopub.execute_input":"2024-01-07T12:37:05.410901Z","iopub.status.idle":"2024-01-07T12:37:05.428918Z","shell.execute_reply.started":"2024-01-07T12:37:05.410871Z","shell.execute_reply":"2024-01-07T12:37:05.428077Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":" year number_nuclweap_consideration number_nuclweap_pursuit \\\ncount 85.000000 85.000000 85.000000 \nmean 1980.000000 3.670588 2.776471 \nstd 24.681302 3.056563 1.910974 \nmin 1938.000000 0.000000 0.000000 \n25% 1959.000000 1.000000 1.000000 \n50% 1980.000000 3.000000 3.000000 \n75% 2001.000000 6.000000 4.000000 \nmax 2022.000000 11.000000 7.000000 \n\n number_nuclweap_possession \ncount 85.000000 \nmean 5.917647 \nstd 2.992895 \nmin 0.000000 \n25% 3.000000 \n50% 7.000000 \n75% 9.000000 \nmax 9.000000 \n","output_type":"stream"}]},{"cell_type":"code","source":"print(df2.isnull().sum())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:37:06.386884Z","iopub.execute_input":"2024-01-07T12:37:06.387503Z","iopub.status.idle":"2024-01-07T12:37:06.393614Z","shell.execute_reply.started":"2024-01-07T12:37:06.387469Z","shell.execute_reply":"2024-01-07T12:37:06.392647Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"entity_name 0\nyear 0\nnumber_nuclweap_consideration 0\nnumber_nuclweap_pursuit 0\nnumber_nuclweap_possession 0\ndtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Visualize number of nuclear weapons tests over time**","metadata":{}},{"cell_type":"code","source":"\nplt.figure(figsize=(10, 6))\nplt.scatter(df2['number_nuclweap_consideration'], df2['number_nuclweap_pursuit'], c='blue', alpha=0.5)\nplt.title('Scatter Plot of Nucl. Weapons Consideration vs. Pursuit')\nplt.xlabel('Number of Nucl. Weapons Consideration')\nplt.ylabel('Number of Nucl. Weapons Pursuit')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:37:07.414795Z","iopub.execute_input":"2024-01-07T12:37:07.415656Z","iopub.status.idle":"2024-01-07T12:37:07.659147Z","shell.execute_reply.started":"2024-01-07T12:37:07.415623Z","shell.execute_reply":"2024-01-07T12:37:07.658309Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**T-Test**","metadata":{}},{"cell_type":"code","source":"# Column names\nentity_col = 'entity_name'\nyear_col = 'year'\nconsideration_col = 'number_nuclweap_consideration'\npursuit_col = 'number_nuclweap_pursuit'\npossession_col = 'number_nuclweap_possession'\n\n# Conduct t-test for different columns between different years\nttest_results = []\n\nfor col in [consideration_col, pursuit_col, possession_col]:\n for year in df2[year_col].unique():\n col_values = df2[df2[year_col] == year][col]\n tstat, pvalue = ttest_ind(col_values, df2[df2[year_col] != year][col])\n ttest_results.append({'Year': year, 'Column': col, 'T-statistic': tstat, 'P-value': pvalue})\n\n# Create a DataFrame to display results\nttest_df = pd.DataFrame(ttest_results)\nprint(\"T-test Results for Different Columns between Different Years:\")\nprint(ttest_df)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:37:09.006790Z","iopub.execute_input":"2024-01-07T12:37:09.007524Z","iopub.status.idle":"2024-01-07T12:37:09.307284Z","shell.execute_reply.started":"2024-01-07T12:37:09.007493Z","shell.execute_reply":"2024-01-07T12:37:09.306333Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide\n ret = ret.dtype.type(ret / rcount)\n","output_type":"stream"},{"name":"stdout","text":"T-test Results for Different Columns between Different Years:\n Year Column T-statistic P-value\n0 1938 number_nuclweap_consideration NaN NaN\n1 1939 number_nuclweap_consideration NaN NaN\n2 1940 number_nuclweap_consideration NaN NaN\n3 1941 number_nuclweap_consideration NaN NaN\n4 1942 number_nuclweap_consideration NaN NaN\n.. ... ... ... ...\n250 2018 number_nuclweap_possession NaN NaN\n251 2019 number_nuclweap_possession NaN NaN\n252 2020 number_nuclweap_possession NaN NaN\n253 2021 number_nuclweap_possession NaN NaN\n254 2022 number_nuclweap_possession NaN NaN\n\n[255 rows x 4 columns]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**K-Means**","metadata":{}},{"cell_type":"code","source":"entity_col = 'entity_name'\nyear_col = 'year'\nconsideration_col = 'number_nuclweap_consideration'\npursuit_col = 'number_nuclweap_pursuit'\npossession_col = 'number_nuclweap_possession'\n\n# Select relevant columns for clustering\ncolumns_for_clustering = [consideration_col, pursuit_col, possession_col]\n\n# Extract features for clustering\nX = df2[columns_for_clustering]\n\n# Standardize the features\nscaler = StandardScaler()\nX_std = scaler.fit_transform(X)\n\n# Elbow Method to find optimal number of clusters (k)\nwcss = []\nfor i in range(1, 11):\n kmeans = KMeans(n_clusters=i, random_state=42)\n kmeans.fit(X_std)\n wcss.append(kmeans.inertia_)\n\n# Plot the Elbow Method\nplt.plot(range(1, 11), wcss, marker='o')\nplt.title('Elbow Method for Optimal k')\nplt.xlabel('Number of Clusters (k)')\nplt.ylabel('Within-Cluster Sum of Squares (WCSS)')\nplt.show()\n\n# Based on the Elbow Method, choose an optimal value for k\noptimal_k = 3 # Adjust based on the plot\n\n# Apply KMeans clustering with the optimal number of clusters\nkmeans = KMeans(n_clusters=optimal_k, random_state=42)\ndf2['cluster'] = kmeans.fit_predict(X_std)\n\n# PCA for visualization (assuming 2 principal components)\npca = PCA(n_components=2)\nX_pca = pca.fit_transform(X_std)\n\n# Plot clusters in 2D space using PCA\nplt.figure(figsize=(10, 6))\nfor cluster_label in df2['cluster'].unique():\n plt.scatter(X_pca[df2['cluster'] == cluster_label, 0], X_pca[df2['cluster'] == cluster_label, 1],\n label=f'Cluster {cluster_label + 1}')\n\nplt.title('KMeans Clustering with PCA Visualization')\nplt.xlabel('Principal Component 1')\nplt.ylabel('Principal Component 2')\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:16.577215Z","iopub.execute_input":"2024-01-07T12:44:16.577553Z","iopub.status.idle":"2024-01-07T12:44:17.378325Z","shell.execute_reply.started":"2024-01-07T12:44:16.577529Z","shell.execute_reply":"2024-01-07T12:44:17.377360Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Principal Component Analysis (PCA)**","metadata":{}},{"cell_type":"code","source":"pca = PCA(n_components=2)\nnumeric_features_df2 = df2[['number_nuclweap_consideration', 'number_nuclweap_pursuit', 'number_nuclweap_possession']]\npca_result_df2 = pd.DataFrame(pca.fit_transform(numeric_features_df2), columns=['PCA1', 'PCA2'])\nplt.figure(figsize=(10, 6))\nsns.scatterplot(x='PCA1', y='PCA2', hue='number_nuclweap_consideration', data=pd.concat([pca_result_df2, df2['number_nuclweap_consideration']], axis=1), palette='viridis')\nplt.title('PCA on Dataset 2')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:34.373113Z","iopub.execute_input":"2024-01-07T12:44:34.373457Z","iopub.status.idle":"2024-01-07T12:44:34.788512Z","shell.execute_reply.started":"2024-01-07T12:44:34.373432Z","shell.execute_reply":"2024-01-07T12:44:34.787550Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# EDA for Nuclear Weapons Stockpiles","metadata":{}},{"cell_type":"code","source":"df3 = pd.read_csv('/kaggle/input/nuclear-weapons-dataset/nuclear_weapons_stockpiles.csv')\n","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:44.637042Z","iopub.execute_input":"2024-01-07T12:44:44.637384Z","iopub.status.idle":"2024-01-07T12:44:44.650892Z","shell.execute_reply.started":"2024-01-07T12:44:44.637356Z","shell.execute_reply":"2024-01-07T12:44:44.650109Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"print(df3.info())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:52.168081Z","iopub.execute_input":"2024-01-07T12:44:52.168849Z","iopub.status.idle":"2024-01-07T12:44:52.179357Z","shell.execute_reply.started":"2024-01-07T12:44:52.168816Z","shell.execute_reply":"2024-01-07T12:44:52.178237Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"\nRangeIndex: 780 entries, 0 to 779\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 country_name 780 non-null object\n 1 year 780 non-null int64 \n 2 nuclear_weapons_stockpile 780 non-null int64 \ndtypes: int64(2), object(1)\nmemory usage: 18.4+ KB\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df3.describe())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:53.324613Z","iopub.execute_input":"2024-01-07T12:44:53.325819Z","iopub.status.idle":"2024-01-07T12:44:53.341813Z","shell.execute_reply.started":"2024-01-07T12:44:53.325775Z","shell.execute_reply":"2024-01-07T12:44:53.340685Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":" year nuclear_weapons_stockpile\ncount 780.000000 780.000000\nmean 1983.500000 2686.524359\nstd 22.529256 7221.198864\nmin 1945.000000 0.000000\n25% 1964.000000 0.000000\n50% 1983.500000 43.500000\n75% 2003.000000 300.000000\nmax 2022.000000 40159.000000\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df3.isnull().sum())\n","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:57.828203Z","iopub.execute_input":"2024-01-07T12:44:57.828935Z","iopub.status.idle":"2024-01-07T12:44:57.834751Z","shell.execute_reply.started":"2024-01-07T12:44:57.828903Z","shell.execute_reply":"2024-01-07T12:44:57.833869Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"country_name 0\nyear 0\nnuclear_weapons_stockpile 0\ndtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Visualize nuclear weapons stockpiles over time**","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(14, 6))\nsns.lineplot(x='year', y='nuclear_weapons_stockpile', data=df3, estimator='sum', ci=None, marker='o', color='red')\nplt.title('Nuclear Weapons Stockpiles Over Time')\nplt.xlabel('Year')\nplt.ylabel('Total Stockpile')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:44:58.987919Z","iopub.execute_input":"2024-01-07T12:44:58.988286Z","iopub.status.idle":"2024-01-07T12:44:59.388006Z","shell.execute_reply.started":"2024-01-07T12:44:58.988257Z","shell.execute_reply":"2024-01-07T12:44:59.387087Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_42/719966788.py:3: FutureWarning: \n\nThe `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n\n sns.lineplot(x='year', y='nuclear_weapons_stockpile', data=df3, estimator='sum', ci=None, marker='o', color='red')\n/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/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":"
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AAAAAAAsMX/AypHiCvn4aspAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"markdown","source":"**Conduct t-test for nuclear weapons stockpile between different years**","metadata":{}},{"cell_type":"code","source":"ttest_results = []\nfor year in years:\n stockpile_values = df3[df3['year'] == year]['nuclear_weapons_stockpile']\n tstat, pvalue = ttest_ind(stockpile_values, df3[df3['year'] != year]['nuclear_weapons_stockpile'])\n ttest_results.append({'Year': year, 'T-statistic': tstat, 'P-value': pvalue})\n\nttest_df = pd.DataFrame(ttest_results)\nprint(\"T-test Results for Nuclear Weapons Stockpile between Different Years:\")\nprint(ttest_df)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:05.400651Z","iopub.execute_input":"2024-01-07T12:45:05.401500Z","iopub.status.idle":"2024-01-07T12:45:05.522090Z","shell.execute_reply.started":"2024-01-07T12:45:05.401465Z","shell.execute_reply":"2024-01-07T12:45:05.521069Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"T-test Results for Nuclear Weapons Stockpile between Different Years:\n Year T-statistic P-value\n0 1938 NaN NaN\n1 1939 NaN NaN\n2 1940 NaN NaN\n3 1941 NaN NaN\n4 1942 NaN NaN\n.. ... ... ...\n80 2018 -0.773328 0.439563\n81 2019 -0.772887 0.439824\n82 2020 -0.772666 0.439954\n83 2021 -0.767022 0.443301\n84 2022 -0.767816 0.442830\n\n[85 rows x 3 columns]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**K-Means**","metadata":{}},{"cell_type":"code","source":"numeric_features_df3 = df3[['year', 'nuclear_weapons_stockpile']]\n\n# Standardize the data for K-Means\nscaler_kmeans = StandardScaler()\nnumeric_features_df3_scaled = scaler_kmeans.fit_transform(numeric_features_df3)\n\n# Apply the Elbow Method to find the optimal number of clusters\ninertia = []\nfor n_clusters in range(1, 11):\n kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n kmeans.fit(numeric_features_df3_scaled)\n inertia.append(kmeans.inertia_)\n\n# Plot the Elbow Method graph\nplt.figure(figsize=(10, 6))\nplt.plot(range(1, 11), inertia, marker='o', linestyle='--')\nplt.title('Elbow Method for Optimal K in K-Means (Dataset 3)')\nplt.xlabel('Number of Clusters (K)')\nplt.ylabel('Inertia')\nplt.show()\n\n# Apply K-Means\nkmeans = KMeans(n_clusters=3, random_state=42) # Adjust the number of clusters as needed\ndf3['kmeans_cluster'] = kmeans.fit_predict(numeric_features_df3_scaled)\n\n# Visualize K-Means results\nplt.figure(figsize=(12, 6))\nsns.scatterplot(x='year', y='nuclear_weapons_stockpile', hue='kmeans_cluster', data=df3, palette='viridis')\nplt.title('K-Means Clustering on Dataset 3')\nplt.xlabel('Year')\nplt.ylabel('nuclear_weapons_stockpile')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:07.244263Z","iopub.execute_input":"2024-01-07T12:45:07.244601Z","iopub.status.idle":"2024-01-07T12:45:08.271493Z","shell.execute_reply.started":"2024-01-07T12:45:07.244575Z","shell.execute_reply":"2024-01-07T12:45:08.269373Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Apply PCA**","metadata":{}},{"cell_type":"code","source":"pca = PCA(n_components=2)\nnumeric_features_df3_pca = pca.fit_transform(numeric_features_df3_scaled)\ndf3['pca_component_1'] = numeric_features_df3_pca[:, 0]\ndf3['pca_component_2'] = numeric_features_df3_pca[:, 1]\n\n# Visualize PCA results\nplt.figure(figsize=(12, 6))\nsns.scatterplot(x='pca_component_1', y='pca_component_2', hue='kmeans_cluster', data=df3, palette='viridis')\nplt.title('PCA on Dataset 3 with K-Means Clusters')\nplt.xlabel('PCA Component 1')\nplt.ylabel('PCA Component 2')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:18.700432Z","iopub.execute_input":"2024-01-07T12:45:18.700816Z","iopub.status.idle":"2024-01-07T12:45:19.080296Z","shell.execute_reply.started":"2024-01-07T12:45:18.700786Z","shell.execute_reply":"2024-01-07T12:45:19.079403Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# EDA for Nuclear Weapons Tests States","metadata":{}},{"cell_type":"code","source":"df4 = pd.read_csv('/kaggle/input/nuclear-weapons-dataset/nuclear_weapons_tests_states.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:24.736361Z","iopub.execute_input":"2024-01-07T12:45:24.736718Z","iopub.status.idle":"2024-01-07T12:45:24.753548Z","shell.execute_reply.started":"2024-01-07T12:45:24.736689Z","shell.execute_reply":"2024-01-07T12:45:24.752701Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"print(df4.info())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:31.265063Z","iopub.execute_input":"2024-01-07T12:45:31.265957Z","iopub.status.idle":"2024-01-07T12:45:31.275669Z","shell.execute_reply.started":"2024-01-07T12:45:31.265929Z","shell.execute_reply":"2024-01-07T12:45:31.274588Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"\nRangeIndex: 600 entries, 0 to 599\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 country_name 600 non-null object\n 1 year 600 non-null int64 \n 2 nuclear_weapons_tests 600 non-null int64 \ndtypes: int64(2), object(1)\nmemory usage: 14.2+ KB\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df4.describe())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:32.320444Z","iopub.execute_input":"2024-01-07T12:45:32.320812Z","iopub.status.idle":"2024-01-07T12:45:32.334223Z","shell.execute_reply.started":"2024-01-07T12:45:32.320783Z","shell.execute_reply":"2024-01-07T12:45:32.333216Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":" year nuclear_weapons_tests\ncount 600.000000 600.000000\nmean 1982.000000 3.431667\nstd 21.666774 9.808789\nmin 1945.000000 0.000000\n25% 1963.000000 0.000000\n50% 1982.000000 0.000000\n75% 2001.000000 1.000000\nmax 2019.000000 96.000000\n","output_type":"stream"}]},{"cell_type":"code","source":"print(df4.isnull().sum())","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:33.756321Z","iopub.execute_input":"2024-01-07T12:45:33.756679Z","iopub.status.idle":"2024-01-07T12:45:33.763123Z","shell.execute_reply.started":"2024-01-07T12:45:33.756650Z","shell.execute_reply":"2024-01-07T12:45:33.762084Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"country_name 0\nyear 0\nnuclear_weapons_tests 0\ndtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Visualize nuclear weapons tests by country**","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(18, 6))\nsns.barplot(x='country_name', y='nuclear_weapons_tests', data=df4, ci=None, palette='magma')\nplt.title('Number of Nuclear Weapons Tests by Country')\nplt.xlabel('Country')\nplt.ylabel('Number of Tests')\nplt.xticks(rotation=45, ha='right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:34.812856Z","iopub.execute_input":"2024-01-07T12:45:34.813189Z","iopub.status.idle":"2024-01-07T12:45:35.162314Z","shell.execute_reply.started":"2024-01-07T12:45:34.813165Z","shell.execute_reply":"2024-01-07T12:45:35.161384Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_42/4279250647.py:3: FutureWarning: \n\nThe `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n\n sns.barplot(x='country_name', y='nuclear_weapons_tests', data=df4, ci=None, palette='magma')\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Conduct t-test for nuclear weapons tests between different countries**","metadata":{}},{"cell_type":"code","source":"countries = df4['country_name'].unique()\nttest_results = []\nfor country in countries:\n tests_values = df4[df4['country_name'] == country]['nuclear_weapons_tests']\n tstat, pvalue = ttest_ind(tests_values, df4[df4['country_name'] != country]['nuclear_weapons_tests'])\n ttest_results.append({'Country': country, 'T-statistic': tstat, 'P-value': pvalue})\n\nttest_df = pd.DataFrame(ttest_results)\nprint(\"T-test Results for Number of Nuclear Weapons Tests between Different Countries:\")\nprint(ttest_df)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:43.408221Z","iopub.execute_input":"2024-01-07T12:45:43.408949Z","iopub.status.idle":"2024-01-07T12:45:43.435281Z","shell.execute_reply.started":"2024-01-07T12:45:43.408919Z","shell.execute_reply":"2024-01-07T12:45:43.434314Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"T-test Results for Number of Nuclear Weapons Tests between Different Countries:\n Country T-statistic P-value\n0 China -2.686555 7.419989e-03\n1 France -0.595889 5.514750e-01\n2 India -3.226330 1.322369e-03\n3 North Korea -3.148953 1.720264e-03\n4 Pakistan -3.239236 1.264955e-03\n5 Russia 5.920611 5.409146e-09\n6 United Kingdom -2.686555 7.419989e-03\n7 United States 10.586635 4.016767e-24\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**K-Means**","metadata":{}},{"cell_type":"code","source":"numeric_features_df4 = df4[['year', 'nuclear_weapons_tests']]\n\n# Standardize the data for K-Means\nscaler_kmeans_df4 = StandardScaler()\nnumeric_features_df4_scaled = scaler_kmeans_df4.fit_transform(numeric_features_df4)\n\n# Elbow Method to find optimal K for K-Means\nwcss = [] # Within-Cluster Sum of Squares\nfor i in range(1, 11):\n kmeans_df4 = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=42)\n kmeans_df4.fit(numeric_features_df4_scaled)\n wcss.append(kmeans_df4.inertia_)\n\n# Plot the Elbow Method graph\nplt.plot(range(1, 11), wcss)\nplt.title('Elbow Method for Optimal K')\nplt.xlabel('Number of Clusters (K)')\nplt.ylabel('WCSS')\nplt.show()\n\n# Choose the optimal K based on the Elbow Method plot and apply K-Means\noptimal_k_df4 = 3 # Adjust based on the Elbow Method plot\nkmeans_df4 = KMeans(n_clusters=optimal_k_df4, init='k-means++', max_iter=300, n_init=10, random_state=42)\ndf4['kmeans_cluster'] = kmeans_df4.fit_predict(numeric_features_df4_scaled)\nplt.figure(figsize=(12, 6))\nsns.scatterplot(x='year', y='nuclear_weapons_tests', hue='kmeans_cluster', data=df4, palette='viridis')\nplt.title('K-Means Clustering on Dataset 4')\nplt.xlabel('Year')\nplt.ylabel('nuclear_weapons_tests')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:45:49.344635Z","iopub.execute_input":"2024-01-07T12:45:49.345026Z","iopub.status.idle":"2024-01-07T12:45:50.370167Z","shell.execute_reply.started":"2024-01-07T12:45:49.344996Z","shell.execute_reply":"2024-01-07T12:45:50.368935Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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pH7WbNmceeddzJp0qTqditWrKjx3Nq+Nx9++CFlZWV88MEHHolvbVPZjz32WI499ljuvfdepk2bxqhRo3jttde4+OKL6/wztLd4vFm7di1r1qzxOgPj1FNPJTExscZnWEREfKP5TCIi4pMTTzyR6dOn8+effzJ06FDKy8sZOHCgx9fOBHRPcXFxTJ06lTvuuIPhw4fXeo2zzz4bl8vF3XffXaOusrKyOinYOYq6+6hpeXk5Tz75pM+vb/fjuaBq3XuXLl2w1ta6fhjgzDPPpFu3btx7770sWrSoRn1BQUH17tzetG7dGqfTWWNN+Z6vpbi4mNLSUo+y9u3bEx8f7zE9OjY21mvydPbZZ7No0SJmzpxZoy43N5fKyspaY/SHIUOGUFFRwbPPPltd5na7eeKJJ+r0/JNOOolvvvnG4z0uKirimWeeoU2bNnTp0sXvMe9p9uzZ3H333bRt25ZRo0YB3j+LQI3d+KHqewM1b9B46yMvL48XXnjBo11OTk6N63Tv3h2g+jNQ15+hnfHUNdF+5plnePfddz2+rrzySgAefPDBWkf2RUSk/jTSLiIiPjv99NN59tlnufDCCzn11FOZMWNGrSPQe6rL0V79+vXjsssuY/Lkyfzwww8MHjyY8PBwVqxYwZtvvsljjz3GmWeeSe/evUlOTmb06NGMHz8eYwyvvPLKfk19Hjx4MJmZmfTp04eMjAx+/fVXHn/8cU4++eS9bjQXHh7OO++8w8CBA+nbty9nn302ffr0ITw8nJ9//plp06aRnJxc61ntiYmJnHXWWfznP//BGEP79u356KOPaqw9/uOPPzjxxBM5++yz6dKlC2FhYbz77rtkZ2dz7rnnVrfr2bMnU6dO5Z577qFDhw6kp6czYMAArr/+ej744ANOOeUUxowZQ8+ePSkqKuKnn37irbfeYvXq1dXHkDWEESNGcPTRR3Pttdfy559/0qlTJz744AO2b98O7HvU96abbqo+OnD8+PGkpKTw0ksvsWrVKt5++22/r7P+9NNP+e2336isrCQ7O5vZs2fz+eef07p1az744IPqz31CQkL1/gIVFRU0b96czz77zOusj549ewJwyy23cO655xIeHs7w4cMZPHgwERERDB8+nMsuu4zCwkKeffZZ0tPT2bRpU/XzX3rpJZ588klOP/102rdvT0FBAc8++ywJCQmcdNJJQN1/hnbG4+2z4s3gwYNrlO1M+Pv168eRRx7p+5stIiKegrdxvYiIHEj2dpzTgw8+aAF7yimn2IqKino9d3d7Hvm20zPPPGN79uxpo6OjbXx8vO3WrZu94YYb7MaNG6vbLFiwwB577LE2OjraNmvWzN5www125syZNY5P69evn9ej3PY8eu3pp5+2ffv2tampqTYyMtK2b9/eXn/99TYvL2+vr2GnnJwcO2nSJNutWzcbExNjo6Ki7GGHHWYnTpxoN23aVOt1rbV2y5YtduTIkTYmJsYmJyfbyy67zC5fvtzjKLStW7facePG2U6dOtnY2FibmJhojznmGPvGG2949JWVlWVPPvlkGx8fbwGPI70KCgrsxIkTbYcOHWxERIRt0qSJ7d27t33wwQdteXm5tXbXsW7ejpar7ci32NjYGm1vv/12u+efHVu2bLHnn3++jY+Pt4mJiXbMmDF2wYIFFrCvvfbaPt/jv/76y5555pk2KSnJRkVF2aOPPtp+9NFHHm12Hvn25ptv7jN2b3Z+dnd+RURE2MzMTDto0CD72GOP2fz8/BrPWb9+vT399NNtUlKSTUxMtGeddZbduHGjBeztt9/u0fbuu++2zZs3tw6Hw+P4tw8++MAefvjhNioqyrZp08bef//99vnnn/dos3TpUnveeefZVq1a2cjISJuenm5POeUUu2TJkhox1eVnaG+flbrQkW8iIg3DWLufO7CIiIiI+Ml7773H6aefzldffUWfPn2CHY6IiEjQKWkXERGRoCgpKfHYHM3lcjF48GCWLFlCVlZWvXasFxEROVhpTbuIiIgExZVXXklJSQm9evWirKyMd955h4ULF3LfffcpYRcREdlBI+0iIiISFNOmTeOhhx7izz//pLS0lA4dOnD55ZdzxRVXBDs0ERGRkKGkXURERERERCRE6Zx2ERERERERkRClpF1EREREREQkRAV1I7p58+bx73//m++++45Nmzbx7rvvMmLEiOp6ay233347zz77LLm5ufTp04epU6fSsWPH6jbbt2/nyiuv5MMPP8ThcDBy5Egee+wx4uLi6hyH2+1m48aNxMfHY4zx50sUERERERERqcFaS0FBAc2aNcPhqH08PahJe1FREUcccQQXXnghZ5xxRo36Bx54gClTpvDSSy/Rtm1bbrvtNoYMGcIvv/xCVFQUAKNGjWLTpk18/vnnVFRUMHbsWC699FKmTZtW5zg2btxIy5Yt/fa6REREREREROpi3bp1tGjRotb6kNmIzhjjMdJuraVZs2Zce+21XHfddQDk5eWRkZHBiy++yLnnnsuvv/5Kly5d+PbbbznyyCMBmDFjBieddBLr16+nWbNmdbp2Xl4eSUlJrFu3joSEhAZ5fSIiIiIiIiI75efn07JlS3Jzc0lMTKy1Xcie075q1SqysrIYOHBgdVliYiLHHHMMixYt4txzz2XRokUkJSVVJ+wAAwcOxOFwsHjxYk4//XSvfZeVlVFWVlb9uKCgAICEhAQl7SIiIiIiIhIw+1qiHbIb0WVlZQGQkZHhUZ6RkVFdl5WVRXp6ukd9WFgYKSkp1W28mTx5MomJidVfmhovIiIiIiIioShkk/aGNHHiRPLy8qq/1q1bF+yQRERERERERGoI2aQ9MzMTgOzsbI/y7Ozs6rrMzEw2b97sUV9ZWcn27dur23gTGRlZPRVeU+JFREREREQkVIXsmva2bduSmZnJrFmz6N69O1C1UH/x4sVcfvnlAPTq1Yvc3Fy+++47evbsCcDs2bNxu90cc8wxwQpdRERERESkXqy1VFZW4nK5gh2K+InT6SQsLGy/jxUPatJeWFjIn3/+Wf141apV/PDDD6SkpNCqVSsmTJjAPffcQ8eOHauPfGvWrFn1DvOdO3dm6NChXHLJJTz11FNUVFRwxRVXcO6559Z553gREREREZFgKi8vZ9OmTRQXFwc7FPGzmJgYmjZtSkREhM99BPXIt7lz53LCCSfUKB89ejQvvvgi1lpuv/12nnnmGXJzcznuuON48sknOeSQQ6rbbt++nSuuuIIPP/wQh8PByJEjmTJlCnFxcXWOIz8/n8TERPLy8jRVXkREREREAsbtdrNixQqcTidpaWlERETs98isBJ+1lvLycrZs2YLL5aJjx444HJ6r0+uah4bMOe3BpKRdRERERESCobS0lFWrVtG6dWtiYmKCHY74WXFxMWvWrKFt27ZERUV51NU1Dw3ZjehEREREREQaiz1HYeXg4I/vqz4ZIiIiIiIiIiFKSbuIiIiIiIhIiFLSLiIiIiIiEqL69+/PhAkTgh1GyLjjjjuqjwRvLJS0i4iIiIiISKN0INwUUdIuIiIiIiIish/Ky8sbrG8l7SJywLDuUmzlBmzlGqxrS7DDEREREQm4jz/+mMTERF599VXGjBnDiBEjuO+++8jIyCApKYm77rqLyspKrr/+elJSUmjRogUvvPCCRx/r1q3j7LPPJikpiZSUFE477TRWr15dXf/tt98yaNAgmjRpQmJiIv369WPp0qUefRhjeO655zj99NOJiYmhY8eOfPDBB9X1OTk5jBo1irS0NKKjo+nYsWONOGqzfv16zjvvPFJSUoiNjeXII49k8eLFXtt6GykfMWIEY8aMqX785JNP0rFjR6KiosjIyODMM88EYMyYMXz55Zc89thjGGMwxlS/D8uXL2fYsGHExcWRkZHBBRdcwNatWz2ue8UVVzBhwgSaNGnCkCFD6vTafKGkXUQOCNaVhS2YjN06FLt1EHb7udjSWVh3QbBDExEREQmIadOmcd555/Hqq68yatQoAGbPns3GjRuZN28eDz/8MLfffjunnHIKycnJLF68mH/84x9cdtllrF+/HoCKigqGDBlCfHw88+fPZ8GCBcTFxTF06NDq0eKCggJGjx7NV199xddff03Hjh056aSTKCjw/Lvrzjvv5Oyzz+bHH3/kpJNOYtSoUWzfvh2A2267jV9++YVPP/2UX3/9lalTp9KkSZN9vsbCwkL69evHhg0b+OCDD1i2bBk33HADbrfbp/dsyZIljB8/nrvuuovff/+dGTNm0LdvXwAee+wxevXqxSWXXMKmTZvYtGkTLVu2JDc3lwEDBtCjRw+WLFnCjBkzyM7O5uyzz/bo+6WXXiIiIoIFCxbw1FNP+RRfXYQ1WM8iIn5iXVuxOVdC5bJdha512NzLMUlPQtTA4AUnIiIiEgBPPPEEt9xyCx9++CH9+vWrLk9JSWHKlCk4HA4OPfRQHnjgAYqLi7n55psBmDhxIv/617/46quvOPfcc3n99ddxu90899xzGGMAeOGFF0hKSmLu3LkMHjyYAQMGeFz7mWeeISkpiS+//JJTTjmlunzMmDGcd955ANx3331MmTKFb775hqFDh7J27Vp69OjBkUceCUCbNm3q9DqnTZvGli1b+Pbbb0lJSQGgQ4cOvr1pwNq1a4mNjeWUU04hPj6e1q1b06NHDwASExOJiIggJiaGzMzM6uc8/vjj9OjRg/vuu6+67Pnnn6dly5b88ccfHHLIIQB07NiRBx54wOfY6kpJu4iEPtc6z4R9N7ZgMoQfjnGmBzgoERERkcB466232Lx5MwsWLOCoo47yqOvatSsOx64J1BkZGRx22GHVj51OJ6mpqWzevBmAZcuW8eeffxIfH+/RT2lpKX/99RcA2dnZ3HrrrcydO5fNmzfjcrkoLi5m7dq1Hs85/PDDq/87NjaWhISE6utcfvnljBw5kqVLlzJ48GBGjBhB79699/laf/jhB3r06FGdsO+vQYMG0bp1a9q1a8fQoUMZOnRo9ZT+2ixbtow5c+YQFxdXo+6vv/6qTtp79uzplxj3RUm7iIQ8W/FD7ZWudWCLAxaLiIiISKD16NGDpUuX8vzzz3PkkUdWj5ADhIeHe7Q1xngt2zm9vLCwkJ49e/Lqq6/WuE5aWhoAo0ePZtu2bTz22GO0bt2ayMhIevXqVWOztb1dZ9iwYaxZs4ZPPvmEzz//nBNPPJFx48bx4IMP7vW1RkdH77V+Tw6HA2utR1lFRUX1f8fHx7N06VLmzp3LZ599xqRJk7jjjjv49ttvSUpK8tpnYWEhw4cP5/77769R17Rp0+r/jo2NrVesvtKadhEJecaRtpfacDDhe6kXERERObC1b9+eOXPm8P7773PllVfuV19/+9vfWLFiBenp6XTo0MHjKzExEYAFCxYwfvx4TjrpJLp27UpkZKTHJmx1lZaWxujRo/nf//7Ho48+yjPPPLPP5xx++OH88MMP1Wvj63KNTZs2VT92uVwsX77co01YWBgDBw7kgQce4Mcff2T16tXMnj0bgIiICFwul0f7v/3tb/z888+0adOmxnsUqER9d0raRST0hR8BRHivix4BJjWQ0YiIiIgE3CGHHMKcOXN4++239+tc8VGjRtGkSRNOO+005s+fz6pVq5g7dy7jx4+v3qyuY8eOvPLKK/z6668sXryYUaNG1XsEfNKkSbz//vv8+eef/Pzzz3z00Ud07tx5n88777zzyMzMZMSIESxYsICVK1fy9ttvs2jRIq/tBwwYwMcff8zHH3/Mb7/9xuWXX05ubm51/UcffcSUKVP44YcfWLNmDS+//DJut5tDDz0UqFprv3jxYlavXs3WrVtxu92MGzeO7du3c9555/Htt9/y119/MXPmTMaOHVsjwQ8EJe0iEvqcGZjkp4E9RtTDumDirsA4ooISloiIiEggHXroocyePZvp06dz7bXX+tRHTEwM8+bNo1WrVpxxxhl07tyZiy66iNLSUhISEgD473//S05ODn/729+44IILGD9+POnp9ds/KCIigokTJ3L44YfTt29fnE4nr732Wp2e99lnn5Gens5JJ51Et27d+Ne//oXT6fTa/sILL2T06NH83//9H/369aNdu3accMIJ1fVJSUm88847DBgwgM6dO/PUU08xffp0unbtCsB1112H0+mkS5cupKWlsXbtWpo1a8aCBQtwuVwMHjyYbt26MWHCBJKSkjz2DwgUY/dcANAI5efnk5iYSF5eXvUHVURCi7Xl4MqGiu+xrmxMxN/A2Qrj3NvUeREREZHQVlpayqpVq2jbti1RURqIONjs7ftb1zxUG9GJyAHBmAgIawlhLTH7bi4iIiIiclDQ9HgREREREREJiPvuu4+4uDivX8OGDQt2eCFJI+0iIiIiIiISEP/4xz84++yzvdbVd7O7xkJJu4iIiIiIiARESkoKKSkpwQ7jgKLp8SIiIiIiIiIhSkm7iIiIiIiISIhS0i4iIiIiIiISopS0i4iIiIiIiIQoJe0iIiIiIiIiIUpJu4iIiIiIiEiIUtIuIiIiIiIiB5wnnniCNm3aEBUVxTHHHMM333wT7JAahJJ2ERERERER8ZnL5WLZ3J+ZPf0rls39GZfL1eDXfP3117nmmmu4/fbbWbp0KUcccQRDhgxh8+bNDX7tQAsLdgAiIiIiIiJyYJr/zmKenPACW9dvqy5r0iKVfz46luPPOKbBrvvwww9zySWXMHbsWACeeuopPv74Y55//nluuummBrtuMGikXUREREREROpt/juLueusBz0SdoCtG7Zx11kPMv+dxQ1y3fLycr777jsGDhxYXeZwOBg4cCCLFi1qkGsGk5J2ERERERERqReXy8WTE14A66VyR9nUq19okKnyW7duxeVykZGR4VGekZFBVlaW368XbEraRUREREREpF6Wz/+txgi7Bwtb1m1j+fzfAhfUQUpJu4iIiIiIiNTLtk05fm1XH02aNMHpdJKdne1Rnp2dTWZmpt+vF2xK2kVERERERKReUpsm+7VdfURERNCzZ09mzZpVXeZ2u5k1axa9evXy+/WCTbvHi4iIiIiISL0cdnwnmrRIZeuGbd7XtRtIa5HKYcd3apDrX3PNNYwePZojjzySo48+mkcffZSioqLq3eQPJkraRUREREREpF6cTif/fHQsd531IBg8E3dT9X+XPzIWp9PZINc/55xz2LJlC5MmTSIrK4vu3bszY8aMGpvTHQw0PV5ERERERETq7fgzjmHSm9fRpHmqR3lai1QmvXldg57TDnDFFVewZs0aysrKWLx4Mccc07DXCxaNtIuIiIiIiIhPjj/jGHqfdiTL5//Gtk05pDZN5rDjOzXYCHtjpKRdREREREREfOZ0Ojmif9dgh3HQ0vR4ERERERERkRClpF1EREREREQkRClpFxEREREREQlRStpFREREREREQpSSdhEREREREZEQpaRdREREREREJEQpaRcREREREREJUUraRUREREREREKUknYRERERERE5YMybN4/hw4fTrFkzjDG89957wQ6pQSlpFxEREREREZ+53G6+Xr+OD37/la/Xr8Pldjfo9YqKijjiiCN44oknGvQ6oSIs2AGIiIiIiIjIgWnGnyu4a95ssgoLq8sy4+KY1HcAQzt0bJBrDhs2jGHDhjVI36FII+0iIiIiIiJSbzP+XMG4Tz7wSNgBsgsLGffJB8z4c0WQIju4KGkXERERERGRenG53dw1bzbWS93OsrvnzWnwqfKNgZJ2ERERERERqZdvN26oMcK+OwtsKizg240bAhfUQUpJu4iIiIiIiNTL5qLaE3Zf2kntlLSLiIiIiIhIvaTHxvm1ndROu8eLiIiIiIhIvRzVrDmZcXFkFxZ6XddugMy4eI5q1tzv1y4sLOTPP/+sfrxq1Sp++OEHUlJSaNWqld+vF2waaRcREREREZF6cTocTOo7AKhK0He38/FtfU/A6fB/yrlkyRJ69OhBjx49ALjmmmvo0aMHkyZN8vu1QoFG2kVERERERKTehnboyBMnnerlnPZ4but7QoOd096/f3+s9Ta+f3BS0i4iIiIiIiI+GdqhI4PatefbjRvYXFRIemwcRzVr3iAj7I2VknYRERERERHxmdPh4NgWLYMdxkFLtz9EREREREREQpSSdhEREREREZEQpaRdREREREREJEQpaRcREREREREJUUraRUREREREREKUknYRERERERGREKWkXURERERERCREKWkXERERERERCVFK2kVEREREROSAMXnyZI466iji4+NJT09nxIgR/P7778EOq8EoaRcRERERERGfWevCli3GlnxU9f/W1aDX+/LLLxk3bhxff/01n3/+ORUVFQwePJiioqIGvW6whAU7ABERERERETkw2dKZ2Px7wZ21q9CRCQm3YKKGNMg1Z8yY4fH4xRdfJD09ne+++46+ffs2yDWDSSPtIiIiIiIiUm+2dCY2d7xnwg7gzsbmjseWzgxIHHl5eQCkpKQE5HqBpqRdRERERERE6sVaV9UIO9ZbbdX/5t/X4FPl3W43EyZMoE+fPhx22GENeq1g0fR4ERERERERqZ/yJTVH2D1YcG+qahd5TIOFMW7cOJYvX85XX33VYNcINiXtIiIiIiIiUj/uLf5t54MrrriCjz76iHnz5tGiRYsGu06wKWkXERERERGR+nGk+bddPVhrufLKK3n33XeZO3cubdu29fs1QomSdhEREREREamfiCOrdol3Z+N9Xbupqo840u+XHjduHNOmTeP9998nPj6erKyqafqJiYlER0f7/XrBpo3oREREREREpF6McWISbtn5aM/aqv9NuBljnH6/9tSpU8nLy6N///40bdq0+uv111/3+7VCQUgn7S6Xi9tuu422bdsSHR1N+/btufvuu7F2150cay2TJk2iadOmREdHM3DgQFasWBHEqEVERERERA5+JmoIJmkKODI8KxyZmKQpDXZOu7XW69eYMWMa5HrBFtLT4++//36mTp3KSy+9RNeuXVmyZAljx44lMTGR8ePHA/DAAw8wZcoUXnrpJdq2bcttt93GkCFD+OWXX4iKigryKxARERERETl4maghEDlwx27yW6rWsEcc2SAj7I1VSCftCxcu5LTTTuPkk08GoE2bNkyfPp1vvvkGqLrD8uijj3Lrrbdy2mmnAfDyyy+TkZHBe++9x7nnnhu02EVERERERBoDY5wNeqxbYxfS0+N79+7NrFmz+OOPPwBYtmwZX331FcOGDQNg1apVZGVlMXDgwOrnJCYmcswxx7Bo0aJa+y0rKyM/P9/jS0QODNa6sO7SYIchIiIiIhIQIT3SftNNN5Gfn0+nTp1wOp24XC7uvfdeRo0aBVC9S2BGhucaioyMjOo6byZPnsydd97ZcIGLiN9ZdwG41mGLpoE7CyL7Q9QJGGfzYIcmIiIiItJgQnqk/Y033uDVV19l2rRpLF26lJdeeokHH3yQl156ab/6nThxInl5edVf69at81PEItIQrLsQSt7FbhsBpW9A+TxswV3YbSOxlauCHZ6IiIjIftt9s205ePjj+xrSI+3XX389N910U/Xa9G7durFmzRomT57M6NGjyczMBCA7O5umTZtWPy87O5vu3bvX2m9kZCSRkZENGruI+JF7K7bgXi/l27H5kyHpIYwjPvBxiYiIiOyn8PBwAIqLiw/KM8Ybu+LiYmDX99kXIZ20FxcX43B4TgZwOp243W4A2rZtS2ZmJrNmzapO0vPz81m8eDGXX355oMMVkYZS/g1Qy13K8i/BnQtK2kVEROQA5HQ6SUpKYvPmzQDExMRgzJ7nnsuBxlpLcXExmzdvJikpCafT9930QzppHz58OPfeey+tWrWia9eufP/99zz88MNceOGFABhjmDBhAvfccw8dO3asPvKtWbNmjBgxIrjBi4jfWLu3jecs4A5UKCIiIiJ+t3MG8c7EXQ4eSUlJ1d9fX4V00v6f//yH2267jX/+859s3ryZZs2acdlllzFp0qTqNjfccANFRUVceuml5ObmctxxxzFjxgyd0S5yEDERx9Q2zg5hh2mUXURERA5oxhiaNm1Keno6FRUVwQ5H/CQ8PHy/Rth3MlY7HpCfn09iYiJ5eXkkJCQEOxwR2YN15VataS99f4+acEzKNEzEEUGJS0RERETEV3XNQ0N6pF1EBMA4kyD+Rog8Hlv0LLi3QfhRmLhxENYm2OGJiIiIiDQYJe0ijZR154ItBcIwzibBDmefjLMJRJ8KEX2ASjBxGEdssMMSEREREWlQStpFGhnrLoCKX7CF/4aK38DZHOLGQUQfjDM12OHt04EQo4iIiIiIvzj23UREDhbWuqFsHjbnAqj4ESgH1yps3nXYomex7sJghygiIiIiIrtR0i7SmLizsQV3e68rfrFqrbiIiIiIiIQMJe0ijYk7D9zba6uEylUBDUdERERERPZOSbtIo7KPbSxMTGDCEBERERGROlHSLtKYOJIhrLP3OhMLYS0CG4+IiIiIiOyVknaRRsQ4UzGJD4BJ2KMmDJM0BRxpQYlLRERERES805FvIo1N2CGYJu9B6VfYiq8h7FBM1DBwNsWY8GBHJyIiIiIiu1HSLtLIGGPA2QJiz8VwbrDDERERERGRvdD0eBEREREREZEQpaRdREREREREJEQpaRcREREREREJUUraRUREREREREKUknYRERERERGREKWkXURERERERCREKWkXERERERERCVFK2kVERERERERClJJ2ERERERERkRClpF1EREREREQkRClpFxEREREREQlRStpFREREREREQpSSdhEREREREZEQpaRdREREREREJEQpaRcREREREREJUUraRUREREREREKUknYRERERERGREKWkXURERERERCREKWkXERERERERCVFK2kVERERERERClJJ2ERERERERkRClpF1EREREREQkRClpFxEREREREQlRStpFREREREREQpSSdhEREREREZEQpaRdREREREREJEQpaRcREREREREJUUraRUREREREREKUknYRERERERGREKWkXURERERERCREKWkXERERERERCVFK2kVERERERERClJJ2ERERERERkRClpF1EREREREQkRClpFxEREREREQlRStpFREREREREQpSSdhEREREREZEQpaRdREREREREJEQpaRcREREREREJUUraRUREREREREKUknYRERERERGREKWkXURERERERCREKWkXERERERERCVFK2kVERERERERClJJ2ERERERERkRDlU9K+dOlSfvrpp+rH77//PiNGjODmm2+mvLzcb8GJiIiIiIiINGY+Je2XXXYZf/zxBwArV67k3HPPJSYmhjfffJMbbrjBrwGKiIiIiIiINFY+Je1//PEH3bt3B+DNN9+kb9++TJs2jRdffJG3337bn/GJiIiIiIiINFo+Je3WWtxuNwBffPEFJ510EgAtW7Zk69at/otOREREREREpBHzKWk/8sgjueeee3jllVf48ssvOfnkkwFYtWoVGRkZfg1QREREREREpLHyKWl/5JFHWLp0KVdccQW33HILHTp0AOCtt96id+/efg1QREREREREpLEy1lrrr85KS0sJCwsjLCzMX10GRH5+PomJieTl5ZGQkBDscEREREREROQgV9c81KeR9nbt2rFt27Ya5aWlpRxyyCG+dCkiIiIiIiIie/ApaV+9ejUul6tGeVlZGevXr9/voEREREREREQE6jWP/YMPPqj+75kzZ5KYmFj92OVyMWvWLNq2beu/6EREREREREQasXol7SNGjADAGMPo0aM96sLDw2nTpg0PPfSQ34ITERERERERaczqlbTvPJu9bdu2fPvttzRp0qRBghIRERERERGReibtO61atapGWW5uLklJSfsbj4iIiIiIiIjs4NNGdPfffz+vv/569eOzzjqLlJQUmjdvzrJly/wWnIiIiIiIiEhj5lPS/tRTT9GyZUsAPv/8c7744gtmzJjBsGHDuP766/0aoIiIiIiIiEhj5dP0+KysrOqk/aOPPuLss89m8ODBtGnThmOOOcavAYqIiIiIiIg0Vj6NtCcnJ7Nu3ToAZsyYwcCBAwGw1no9v11ERERERERE6s+nkfYzzjiD888/n44dO7Jt2zaGDRsGwPfff0+HDh38GqCIiIiIiIhIY+VT0v7II4/Qpk0b1q1bxwMPPEBcXBwAmzZt4p///KdfAxQRERERERFprIy11gY7iGDLz88nMTGRvLw8EhISgh2OiIiIiIiIHOTqmof6tKYd4JVXXuG4446jWbNmrFmzBoBHH32U999/39cuvdqwYQN///vfSU1NJTo6mm7durFkyZLqemstkyZNomnTpkRHRzNw4EBWrFjh1xhEREREREREgsGnpH3q1Klcc801DBs2jNzc3OrN55KSknj00Uf9FlxOTg59+vQhPDycTz/9lF9++YWHHnqI5OTk6jYPPPAAU6ZM4amnnmLx4sXExsYyZMgQSktL/RaHiIiIiIiISDD4ND2+S5cu3HfffYwYMYL4+HiWLVtGu3btWL58Of3792fr1q1+Ce6mm25iwYIFzJ8/32u9tZZmzZpx7bXXct111wGQl5dHRkYGL774Iueee26drqPp8SIiIiIiIhJIDTo9ftWqVfTo0aNGeWRkJEVFRb506dUHH3zAkUceyVlnnUV6ejo9evTg2Wef9YgjKyur+sg5gMTERI455hgWLVpUa79lZWXk5+d7fImIiIiIiIiEGp+S9rZt2/LDDz/UKJ8xYwadO3fe35iqrVy5kqlTp9KxY0dmzpzJ5Zdfzvjx43nppZcAyMrKAiAjI8PjeRkZGdV13kyePJnExMTqr5YtW/otZhGpO2vdWFcWtuJ3bMVfWNf2YIckIiIiIhJSfDry7ZprrmHcuHGUlpZireWbb75h+vTpTJ48meeee85vwbndbo488kjuu+8+AHr06MHy5ct56qmnGD16tM/9Tpw4kWuuuab6cX5+vhJ3kQCz7kIoW4gtuAPcO5bUhHWCxH9D2CEYY4Ian4iIiIhIKPApab/44ouJjo7m1ltvpbi4mPPPP59mzZrx2GOP1XkdeV00bdqULl26eJR17tyZt99+G4DMzEwAsrOzadq0aXWb7OxsunfvXmu/kZGRREZG+i1OEfFB5e/YvCv2KPsNu/18TOr7ENYiOHGJiIiIiIQQn498GzVqFCtWrKCwsJCsrCzWr1/PRRdd5M/Y6NOnD7///rtH2R9//EHr1q2Bqmn6mZmZzJo1q7o+Pz+fxYsX06tXL7/GIiL+Y1252IKHaqksgLI5gQ1IRERERCRE+ZS0DxgwgNzcXABiYmJIT08HqhLmAQMG+C24q6++mq+//pr77ruPP//8k2nTpvHMM88wbtw4AIwxTJgwgXvuuYcPPviAn376if/7v/+jWbNmjBgxwm9xiIi/lULlr7XW2vLFWOsKYDwiIiIiIqHJp+nxc+fOpby8vEZ5aWlprcez+eKoo47i3XffZeLEidx11120bduWRx99lFGjRlW3ueGGGygqKuLSSy8lNzeX4447jhkzZhAVFeW3OETE38LA2QwqV9RS3QFjnIENSUREREQkBNXrnPYff/wRgO7duzN79mxSUlKq61wuFzNmzODpp59m9erVfg+0IemcdpHAsyWfYPMmeKlxYpp8jAlrF+iQREREREQCpq55aL1G2rt3744xBmOM12nw0dHR/Oc//6l/tCLS+EQcC7GXQNF/AXdVmYnBJD4CjuZBDU1EREREJFTUK2lftWoV1lratWvHN998Q1paWnVdREQE6enpOJ2a0ioi+2acKRB7OUSfDZUrwURDWEtwpGNMeLDDExEREREJCfVK2nfu2u52u+vU/uSTT+a5557zOI5NRGQn44gDRxyEtQ52KCIiIiIiIcnnI9/qYt68eZSUlDTkJUREREREREQOWg2atIuIiIiIiIiI75S0i4iIiIiIiIQon85pFxGR0GBd28AWAk5wJGMcscEOSURERET8SEm7iMgByLpLofIXbP6dUPkr4ITIwRB/PSasRbDDExERERE/0fR4EZEDketP7PbzdyTsAC4o+xS7/e9Y16aghiYiIiIi/tOgSfvNN99MSkpKQ15CRKTRse58bMGDgJfjN90bofz7gMckIiIiIg3Dp6T9pZde4uOPP65+fMMNN5CUlETv3r1Zs2ZNdfnEiRNJSkra7yBFRGQ3tgjKl9ReXTYrgMGIiIiISEPyKWm/7777iI6OBmDRokU88cQTPPDAAzRp0oSrr77arwGKiMieqjadq5WjWeBCEREREZEG5dNGdOvWraNDhw4AvPfee4wcOZJLL72UPn360L9/f3/GJyIie3I0gdiLoOA+r9Um+tQAByQiIiIiDcWnkfa4uDi2bdsGwGeffcagQYMAiIqKoqSkxH/RiYhIDcY4MFEnQ0T/PWswCfeBs2kwwhIRERGRBuDTSPugQYO4+OKL6dGjB3/88QcnnXQSAD///DNt2rTxZ3wiIuKFcaZB4mRwZ2HLFmEcCRBxDDjSMI6YYIcnIiIiIn7iU9L+xBNPcOutt7Ju3TrefvttUlNTAfjuu+8477zz/BqgiIh4Z5yp4EzFhHcNdigiIiIi0kCMtdYGO4hgy8/PJzExkby8PBISEoIdjoiIiIiIiBzk6pqH+jTSDpCbm8s333zD5s2bcbt3nRVsjOGCCy7wtVsRERERERER2cGnpP3DDz9k1KhRFBYWkpCQgDGmuk5Ju4iIiIiIiIh/+LR7/LXXXsuFF15IYWEhubm55OTkVH9t377d3zGKiIiIiIiINEo+Je0bNmxg/PjxxMRoh2IRERERERGRhuJT0j5kyBCWLFni71hEREREREREZDc+rWk/+eSTuf766/nll1/o1q0b4eHhHvWnnnqqX4ITERERERERacx8OvLN4ah9gN4Yg8vl2q+gAk1HvomIiIiIiEggNeiRb7sf8SYiIiIiIiIiDcOnNe0iIiIiIiIi0vB8Ttq//PJLhg8fTocOHejQoQOnnnoq8+fP92dsIiIiIiIiIo2aT0n7//73PwYOHEhMTAzjx49n/PjxREdHc+KJJzJt2jR/xygiIiIiIiLSKPm0EV3nzp259NJLufrqqz3KH374YZ599ll+/fVXvwUYCNqITkRERERERAKprnmoTyPtK1euZPjw4TXKTz31VFatWuVLlyIiIiIiIiKyB5+S9pYtWzJr1qwa5V988QUtW7bc76BERERERERExMcj36699lrGjx/PDz/8QO/evQFYsGABL774Io899phfAxQRERERERFprHxK2i+//HIyMzN56KGHeOONN4Cqde6vv/46p512ml8DFBEREREREWmsfNqI7mCjjehEREREREQkkOqah/o00r7TkiVLqneK79KlCz179tyf7kRERERERERkNz4l7evXr+e8885jwYIFJCUlAZCbm0vv3r157bXXaNGihT9jFBEREREREWmUfNo9/uKLL6aiooJff/2V7du3s337dn799VfcbjcXX3yxv2MUERERERERaZR8WtMeHR3NwoUL6dGjh0f5d999x/HHH09xcbHfAgwErWkXERERERGRQKprHurzOe0VFRU1yl0uF82aNfOlSxERERERERHZg09J+7///W+uvPJKlixZUl22ZMkSrrrqKh588EG/BSciIiIiIiLSmPk0PT45OZni4mIqKysJC6vay27nf8fGxnq03b59u38ibUCaHi8iIiIiIiKB1KBHvj366KO+xiUi0uhYdx6486seOBIxDt0cFBEREZG68SlpHz16tL/jEBE56Fjrgsq/sPl3Q8XiqsKIXpBwKzjbY4xPK5REREREpBHxKWnfXWlpKeXl5R5lmmIuIgK4NmC3nwO2aFdZ+SLstrMxqe9DWKvgxSYiIiIiBwSfhnmKioq44oorSE9PJzY2luTkZI8vEZHGztoKbPF0z4S9urIIW/Im1tY8hUNEREREZHc+Je033HADs2fPZurUqURGRvLcc89x55130qxZM15++WV/xygi4jPrLq2aph5o7gIo/6r2+rKvwO0loRcRERER2Y1P0+M//PBDXn75Zfr378/YsWM5/vjj6dChA61bt+bVV19l1KhR/o5TRKRerGsDlM7Fls0BRwbEng/OloHbBM5EgCOl9npHKpjwwMQiIiIiIgcsn0bat2/fTrt27YCq9es7j3U77rjjmDdvnv+iExHxga1cjd12JrbgTiifB6VvYredji15B+suDEgMxhGHib2k9vrYizCO2FrrRURERETAx6S9Xbt2rFq1CoBOnTrxxhtvAFUj8ElJSX4LTkSkvqy7AJs/GdzbalYWTAb31sAFE94FYsbULI+5GMI7BS4OERERETlg+TQ9fuzYsSxbtox+/fpx0003MXz4cB5//HEqKip4+OGH/R2jiEjduXOhfG4tlRbKv4awNgEJxThSIG4cRJ+FLV8IGExEb3Cm66x2EREREakTn5L2q6++uvq/Bw4cyG+//cZ3331Hhw4dOPzww/0WnIhI/bkBW2uttaWYwAWDcSSCIxET3jGAV/XOujZXzTSwJeBoAo4mmqIvIiIiEuL8ck5769atad26tT/iERHZP44ECOsGlT95rTYRvQIcUPBZa6HyD2zu5eBav6PUAdGjIO6fGGdqUOMTERERkdr5tKbd5XJx991307x5c+Li4li5ciUAt912G//973/9GqCISH0YRzIm8XbAy87sUaeCMyPgMQWdexN2+wW7JewAbih5BVvybnCOxBMRERGROvEpab/33nt58cUXeeCBB4iIiKguP+yww3juuef8FpyIiE/COmFS34PIk6qmgYcdgkn8Nyb+JowjKdjRBV7Fz2BzvdcVPQvuzQENR0RERETqzqfp8S+//DLPPPMMJ554Iv/4xz+qy4844gh+++03vwUnIuILYyIgvCMk3gu2EAjb5xRwayt37DjvBpNwUK31thUr9lKZA7Y8cMGIiIiISL34lLRv2LCBDh061Ch3u91UVFTsd1AiIv5QlXjvO/m2rixs8WtQ8lrVJm0R/SF+PDhbY8x+b/0RdCa8c+1b8zmagIkMZDgiIiIiUg8+TY/v0qUL8+fPr1H+1ltv0aNHj/0OSkQkUKwrG5tzKRQ9Ce7tVUl72afYbSPBtTbY4flHWKeq5NwLEzsOHOkBDkhERERE6sqnIaRJkyYxevRoNmzYgNvt5p133uH333/n5Zdf5qOPPvJ3jCIiDadiOVR6WdZji7EFT0Di3RhHTODj8iMT1hRSXsHmXgWVf+wojYDYSyBqKMb4dP9WRERERALAWGtrP9B4L+bPn89dd93FsmXLKCws5G9/+xuTJk1i8ODB/o6xweXn55OYmEheXh4JCQnBDkdEAsRai827Gko/8d7AxGKafIpxZgY2sAZiXduqZhNQCiYJHGkYR1SwwxIRERFplOqah/q8WPP444/n888/9/XpIiJBZ4zBmr3cqDMx+LiKKCQZZyroTHYRERGRA4pPf41OmjSJOXPmUFpa6u94REQCykSfVXtl9PngUJIrIiIiIsHjU9K+aNEihg8fTlJSEscffzy33norX3zxBSUlJf6OT0SkYTlbQuzFNcvDumJiRmKMM/AxiYiIiIjs4POa9srKShYvXsy8efP48ssvWbhwIWVlZRx11FF89dVX/o6zQWlNu0jjZl254N6ALXkb3PmY6OEQ1gnjzAh2aCIiIiJykGrwNe1hYWH06dOHtLQ0UlJSiI+P57333uO337zswiwiEsKMMwmcSZjwrsEORURERETEg0/T45955hnOP/98mjdvTu/evZkxYwbHHXccS5YsYcuWLf6OUURERERERKRR8mmk/R//+AdpaWlce+21/POf/yQuLs7fcYmIiIiIiIg0ej6NtL/zzjuMGjWK1157jbS0NHr37s3NN9/MZ599RnFxsb9jFBEREREREWmUfN6Ibqe8vDzmz5/Pm2++yfTp03E4HAfcUXDaiE5EQol1F4EtBRODcUQHOxwRERERaQANvhHdtm3b+PLLL5k7dy5z587l559/Jjk5meOPP97XLkVEGjXrzofKFdjCqeBaD2FdIe5ScLbGOKKCHZ6IiIiIBIFPSXu3bt349ddfSU5Opm/fvlxyySX069ePww8/3N/xiYg0CtZdAiUfYgvu3FXoWokt+wST/BxE9glecCIiIiISND5vRNevXz8OO+wwf8cjItI4ubdiC+7zUuHC5t0MqW/o3HgRERGRRsinjejGjRtXp4Q9ISGBlStX+nIJEZHGxbUWqPBe594E7txARiMiIiIiIcKnpL2u9nOPOxGRRmRfv45NQKIQERERkdDSoEm7iIjUkbMlEFl7nSMpkNGIiIiISIhQ0i4iEgqcaZjEu71UhGMS78c40wMekoiIiIgEn89HvomIiP8YE4mNHIhJfRdb9AK4VkN4d0zM+TtG4UVERESkMWrQpN0YrcEUEakr44gDR1dIuBcoBRODMeHBDktEREREguiA2ojuX//6F8YYJkyYUF1WWlrKuHHjSE1NJS4ujpEjR5Kdne3X64qIBJJxRGIciUrYRURERKT+SXtFRQXt27fn119/3WfbTz/9lObNm/sU2J6+/fZbnn76aQ4//HCP8quvvpoPP/yQN998ky+//JKNGzdyxhln+OWaIiIiIiIiIsFU76Q9PDyc0tLSOrU97rjjiIysZTfkeigsLGTUqFE8++yzJCcnV5fn5eXx3//+l4cffpgBAwbQs2dPXnjhBRYuXMjXX3+939cVERERERERCSafpsePGzeO+++/n8rKSn/HU+v1Tj75ZAYOHOhR/t1331FRUeFR3qlTJ1q1asWiRYtq7a+srIz8/HyPLxERqcm6S7HWFewwRERERBotnzai+/bbb5k1axafffYZ3bp1IzY21qP+nXfe8UtwAK+99hpLly7l22+/rVGXlZVFREQESUlJHuUZGRlkZWXV2ufkyZO58847/RajiMjBxro2QOksbNk8cGbCjl3sjSM+2KGJiIiINCo+Je1JSUmMHDnS37HUsG7dOq666io+//xzoqKi/NbvxIkTueaaa6of5+fn07KljlQSEQGwlSux284Dm7OrrOQNTPwd2OjTMI7YvTxbRERERPzJp6T9hRde8HccXn333Xds3ryZv/3tb9VlLpeLefPm8fjjjzNz5kzKy8vJzc31GG3Pzs4mMzOz1n4jIyP9stZeRORgY9352Py7PRL26rqCuzCRfUBJu4iIiEjANOg57fvrxBNP5KeffvIoGzt2LJ06deLGG2+kZcuWhIeHM2vWrOqR/99//521a9fSq1evYIQsInJgc+dC+YLaKqF8KYS1DmREIiIiIo2az0n7W2+9xRtvvMHatWspLy/3qFu6dOl+BwYQHx/PYYcd5lEWGxtLampqdflFF13ENddcQ0pKCgkJCVx55ZX06tWLY4891i8xiIjsybrywG4Ddw6YBHCkYJypwQ7LT9z7qK/b6SEiIiIi4h8+7R4/ZcoUxo4dS0ZGBt9//z1HH300qamprFy5kmHDhvk7xr165JFHOOWUUxg5ciR9+/YlMzPTrxvhiYjszrqysHnXY7cOxW4/D7vtZGzOJdjK9cEOzT9MPIR1qr0+/MjAxSIiIiIiGGutre+TOnXqxO233855551HfHw8y5Yto127dkyaNInt27fz+OOPN0SsDSY/P5/ExETy8vJISEgIdjgiEqKsuxCbdwuUfVqzMqwzJvm/GGeTwAfmZ7b8e+z2UcAex3pGn4mJuwHjTApGWCIiIiIHlbrmoT6NtK9du5bevXsDEB0dTUFBAQAXXHAB06dP96VLEZHQ594GZTO911X+Cu6tgY2noYR3waS+C5FDwNEEwjphEh/GxF2jhF1EREQkwHxa056Zmcn27dtp3bo1rVq14uuvv+aII45g1apV+DBwLyJyYLBF7HXN90GStBsTCeGHQuK/wBYC4RhnSrDDEhEREWmUfBppHzBgAB988AFQtZv71VdfzaBBgzjnnHM4/fTT/RqgiEjIMHGAs/Z6R1rAQgkE44jFODOUsIuIiIgEkU8j7c888wxud9Vo07hx40hNTWXhwoWceuqpXHbZZX4NUEQkZDhSIfpUKHm3Zl14j4MuaRcRERGR4PNpI7qDjTaiE5G6sq7N2IIHoPQjqqfKR/TCJE7GOJsFNTYREREROXA06EZ0APPnz+fvf/87vXr1YsOGDQC88sorfPXVV752KSIS8owzHZNwB6bJTEzq25gmMzCJU5Swi4iIiEiD8Clpf/vttxkyZAjR0dF8//33lJWVAZCXl8d9993n1wBFREKNccRhwlpjwrthwtphnInBDklEREREDlI+Je333HMPTz31FM8++yzh4eHV5X369GHp0qV+C05ERERERESkMfMpaf/999/p27dvjfLExERyc3P3NyYRERERERERwcekPTMzkz///LNG+VdffUW7du32OygRERERERER8TFpv+SSS7jqqqtYvHgxxhg2btzIq6++ynXXXcfll1/u7xhFREREREREGiWfzmm/6aabcLvdnHjiiRQXF9O3b18iIyO57rrruPLKK/0do4iIiIiIiEijtF/ntJeXl/Pnn39SWFhIly5diIuL82dsAaNz2kVERERERCSQ6pqH+jTSvlNERARdunTZny5EREREREREpBZ1TtrPOOOMOnf6zjvv+BSMiASOdZeCLQIThXHEBjscERERERHxos5Je2JiYkPGISIBYt1l4FqDLXoGKpaDswXE/QPCDsE4tDxERERERCSU7Nea9oOF1rRLY2LLFmFzLgRcHuUm/jaIPhPjiA5OYCIiIiIijUhd81CfjnxbtWoVK1asqFG+YsUKVq9e7UuXIhIA1pWNzZvIngk7gC34F7i3Bj4oERERERGplU9J+5gxY1i4cGGN8sWLFzNmzJj9jUlEGoo7F9wba6msANeaQEYjIiIiIiL74FPS/v3339OnT58a5cceeyw//PDD/sYkIg3G7Ge9iIiIiIgEkk9JuzGGgoKCGuV5eXm4XDWn3YpIiHAkgbNVLZUR4GwdyGhERERERGQffEra+/bty+TJkz0SdJfLxeTJkznuuOP8FpyI+JdxpmMS7wfCa9Yl3AmOtMAHJSIiIiIitarzkW+7u//+++nbty+HHnooxx9/PADz588nPz+f2bNn+zVAEfGz8MMxTT7CFr8KFT+AszUmdiw422IckcGOTkREREREduPzkW8bN27k8ccfZ9myZURHR3P44YdzxRVXkJKS4u8YG5yOfJODgXUXgTsHcIGJxzj3/rNobQXYYiBKybqIiIiISIDVNQ/VOe0oaZcDn61cjy14EMpmAi4I64RJuB3CuujcdRERERGREFTXPNSn6fHz5s3ba33fvn196VZEfGBdm7A5F4Brw67Cyt+w20dhUt8ER7fgBSciIiIiIvvFp6S9f//+NcqM2XVUlHaQFwmg8h88E/ZqbmzBvyHpcYxDM0hERERERA5EPu0en5OT4/G1efNmZsyYwVFHHcVnn33m7xhFZC9s2ZzaK8u/27FuXURCnbWVWFtex7ZurLsUa90NHJWIiIgEm08j7YmJiTXKBg0aREREBNdccw3ffffdfgcmInXkbFp7nSMFH+/NiUiAWNc2qFyBLZ4GlEL0SAjvjnFm1GxrK8C1AVvyHlT8BGGdIOZMcDbHmIiAxy4iIiINz6ekvTYZGRn8/vvv/uxSRPbBRA3HFk31Xhk7FhxNAhuQiNSZdW3DFtwHpR/uKiubC2GdIflpjDNzV7m1ULEMu300UFFVWD4fW/wiJvlZbMQxGOMM7AsQERGRBudT0v7jjz96PLbWsmnTJv71r3/RvXt3f8QlInXlbIpJmIzNvwXYbapsRH9M1HCM0Ui7SMiqXOGRsO8q/xVb+jHEjN31M+zOxuZeTXXCXq0Cm3s1psl7e595IyIiIgckn5L27t27Y4xhz9Pijj32WJ5//nm/BCYidWMcsdioYZiII6F8Mdadj4k8FhzN9nlWu4gEj7UV2OJXa29QPB2iTgVnWtVj93ZwZ9fSWQ64tilpFxEROQj5lLSvWrXK47HD4SAtLY2oqCi/BCUi9WMcMeBoDWGtMftuLiKhwLqBsr3UlwG73xzf18kslfsfk4iIiIQcn5L21q1bM2vWLGbNmsXmzZtxuz13r9Vou4iIyN4ZRyREnV61ht2bqGHgSN712JEKJhZskZfOosGR1iBxioiISHD5tNj1zjvvZPDgwcyaNYutW7fWOAJORERE6iDib1U7wO/JJGNiLsCY8F1ljjRM/G1euzFxN4JTm06KiIgcjHwaaX/qqad48cUXueCCC/wdj4iISKNhnBmQ/Ay25EMoeQ1sOUQNxcT8HyaspWdbE46NGoQJa4kteAxcf4GzLSZuPIR3wZjIIL0KERERaUg+Je3l5eX07t3b37GIiIg0OsaZCbEXQfQIwA2O5FrPXDeOeIg4CpKfBFsCRGGciYEMV0RERALMp+nxF198MdOmTfN3LCIiIo2SMQ6MMw3jzKg1Yfdo70ioaquEXURE5KDn00h7aWkpzzzzDF988QWHH3444eHhHvUPP/ywX4ITERERERERacx8Stp//PFHunfvDsDy5cs96ozRgVMiIiIiIiIi/uBT0j5nzhx/xyEiIiIiIiIie/BpTbuIiIiIiIiINDwl7SIiIiIiIiIhSkm7iIiIiIiISIhS0i4iIiIiIiISonzaiE5ERMRX1l0K7i1gi8DEgCMV44gNdlgSQqwtB9cWsAVgonZ8RuKDHZaIiEhQKGkXEZGAsa4t2MKpUPI6UAE4IeoUiL8O48wIdngSAqxrG7Z4GhQ/B7YEMBDRHxJvxzibBTs8ERGRgNP0eBERCQjrLsIWToGS/1GVsAO4oPR9bN4krCsvmOFJCLC2AlvyJhT9Z0fCDmChfA4253Ksa2tQ4xMREQkGJe0iIhIY7m1Q8pb3uvI5YJWQNXquzVD0tPe6yl/BtSmw8YiIiIQAJe0iIhIYNh9w1V7v2hawUCRE2eKqvQ5q41oZuFhERERChJJ2EREJDLOPzeYciYGJQ0KXiQLCa693NA1YKCIiIqFCSbuIiASGSYGIXt7rwg4BR2pg45HQ42gC0afWUpcGYS0DG4+IiEgIUNIuIiIBYZyJmIT7IKyrZ4WzLSbpCYyzSXACk5BhHNGYuAkQcZxnhSMTk/wCxqmRdhERaXx05JuIiASMCWsOyc+BO7tqUzFHOjgzMM70YIcmIcI4MyDpoapz2l3rqmZgODMxzsxghyYiIhIUStpFRCSgjDMVnKkQ3iXYoUiAWFsBrmyoXAHu3KrvvTMd40j22t44ksGRDOGHBDZQERGREKSkXURERBqMteVQ/g02559A6a6KyEGQcAfGmRa02ERERA4EWtMuIiIiDceVhc25DI+EHaDsc2zJ21i7l2MARUREREm7iIiINKCy+UCF97qiF8C9JaDhiIiIHGg0PV5ERPabdReBOwdwgSMe40ipva11g3sz2DIw4eBIx5ja/zmyrjyweYABRxLGEe//F9AArGsz2GIgHJxpGBMRpDhyweYTrPfPutbupXLHZ0ZERERqpaRdRET2i61cgy24H8pmA24IOwwSbofwzjUSVevaDqWfYIseB/d2MAkQexFEn1XjyDdrK6HyT2z+XVCxpKowog8k3ArOdhhjAvQK68e686BsIbbwAXBtABMN0edA7EVVO6MHKg5bCZUrsPl3QsVSwEDE8ZBwCzjbBOz9MxHHYItf8F4Z1hGIDEgcIiIiBypNjxcREZ/Zyo3Y7aOg7AvAXVVYuRy7/TyoXOXZ1l2KLX4VW3BXVcIOVSPAhY9gCx/Dugs8O3etx24/Z1fCDlC+ALvtbHCtb7gXtR+stVA2D5t3VVXCDmBLoPhFbO71WNe2wAXjWlf1XlUs3RkdlM/DbjtnV2yBEN4ZHM28Vpm4m2rcrBERERFPStpFRMR35QurprrXULkjES/aVeTeCkXPeO+n5M1diTxVO47bopeqEt492QJsyfuhuYGZO7tq1oE3FV+DOysgYVh3KbboOaDMS2UutvTTqmUKAWCcTTEpL0NEP2DH6L4jA5M4BSK6ByQGERGRA5mmx4uIiE+srcSWfVF7g/IlYAuA2B1PyMVrEgnAjnXutN7xMB/KF+2l73lg/69qen0osUW13MTYoeIXCO8agDgK9v7+lc2FmPPBxDZ8LIAJawVJD+3Y96ACTPyOvQxCc4nDnqwtBxx73XtBRESkoehfHxER8ZETHJm1VzuS8fhnxkTtvbvdE0gTUfX82gbTHU2A4GzstncRgJNaA3cEaip4ODhSal9G4Egn0H8CGEcCOELsJss+WFc2VCzDlrwNRELMeRB2CMaZGuzQRESkEdH0eBER8YkxBhNzVu31sRd6rlc2KRDW2XtjRzNw7EqEjCMBE3vpXvoei3Hs4yZAMDhSIHKw9zoTA2GHBCQM40zax/v3fxiHNoDbG+vKxub8A5t7BZTNgbIZ2JzR2IK7sa6twQ5PREQaESXtIiLiM4sDEzee6rXKO0UOAGd7j3XnxpmCSXoUHHvsoG6SMMlP1dxZPfxwiB5V86Kxl4Ozo1/i9zfjiMUk3AjODnvURGGSnoEA7h5PeE+I9nJTJfYqcLYLXBwHIGvd2NJPoPLnmpWln0DlH4EPSkREGi1jrbXBDiLY8vPzSUxMJC8vj4SEA2vqnohIsFjrxubdABhM9ClQ8TPWlmLCD4fKP7DFb2JSX8c40z2f59pUdZRbxa+YsPY7dhdv6nV9s3XlgXsztnwBBidE9gZHWtVU6xBmXZuhcjW2YhnG2RTCu4MzM+Broq0rd7f3L3y39+/AOOs+WKxrK3b7+eBa7b1B5CBM0iM1jjQUERGpj7rmoVrTLiIiPnKDLYWyz7ClH+w4czscW/QCUAYmGah5X9g4m4KzKSby+H1ewTgTwZmICa/byLp1bavapd4WVk1Vd6TWmuBbd0lVW/cWIAKcTap2NffD5mjGmQ7OdEzk0fvd1/7FkQTOJEx4YKblHzws2PK9VJeCddeYYCIiItIQlLSLiIhPjAmD6NOxZZ8BtuaU4aghYJICFo+tXFu1/rjyt12FkYMhYVLN0X53DrboVSiaClRUFTrSMUmPY8MP0y7hjZ0jCaKGQvHzXqtN9BmhuaeCiIgclLSmXUREfBfeDcIOq1lukjCxFwdsszPr2ozNucQzYYeqWQCFj2LdxXuUfwVFU6hO2KFqGnnOaHBtavB4JbQZE46J+XvVbI09hR0CET0DH5SIiDRaStpFRMRnxpmOSZ4K8TeBs1XVUWLRF2BS3wZny8AF4soC1yrvdSXvV02D38G6tmALp3hva0ugbF4DBCgHGhPWApPyJsSMrdo80dkS4q7DJD+Hce7lqEMRERE/0/w/ERHZL8aZATFjIGo44AZHcuA36HJn7aWyAuzuI+2V4Fpba2tb8bPXpcrWunck/24wsQftZm7WtYWqc+ajqtbEN2ImrCXEXwuxFwIOcKRgjDPYYYmISCOjpF1ERPabMQ5wpgUvAGfzvVRGgIn1fOxsB66/vLY2Ed1rlFnXZmzph1D0IrhzIeKYqmQurP1Bs4O4dW2DsrnYoqngyobwrhB/PYQdinHEBTu8oDEmIrBH9YmIiOxB0+NFROTA50iBsEO910WfiXU0qX5onKmY+Ku9tzVxENHbo8i6tmJzr4eC+8GdDZRB+TzstpFQ+bufXkBwWXc+tnAKNn/ijlkIZVCxFLv9PCj/JtjhiYiINGpK2kVE5MBnKzDx10P4kbsVOiHqVEzEsRib59k+/GhM/C1gYnZr3gqT8j9wNvNs61oPFYu8XLQSm38v1p3rpxcRRO6tUDLda5XNvxPryg5wQCIiIrKTpseLiMiBr/IPbN5NmNjREHsxUA5EYMu+xOZdjUl9D3bbPMw4k7Ax52EiB4LNAcKr1it7meJvy77c+ayq3fJNLFSuqEp0K5aCu7DqiLADWcVvtde5N4E7X1PERUREgiTkR9onT57MUUcdRXx8POnp6YwYMYLff/ecjlhaWsq4ceNITU0lLi6OkSNHkp2tUQERkUbDxIDNxxb+B5v7D2zueGzuP3aMHrvAhNd8ionAhDXHhB+GCT/Ua8IOgCMBooZhkp+pmjrvbIOJvx6TMBlMKt7+KbWuLGzpHNz592OL/ld1hrwt9+9r9qfdZxx4rdc9fhERkWAJ+X+Fv/zyS8aNG8dRRx1FZWUlN998M4MHD+aXX34hNrZqY6Grr76ajz/+mDfffJPExESuuOIKzjjjDBYsWBDk6EVEJCCcrXck7sU168IO27+R8IgBGBzYnEsBC4AtmQ5hh2CSn6xxlretXI/N+b+qafU7n1EQhkl6Cht5bGhuXBfWAYgEymrWhf8NTHKgIxIREZEdjLXWBjuI+tiyZQvp6el8+eWX9O3bl7y8PNLS0pg2bRpnnnkmAL/99hudO3dm0aJFHHvssfvsMz8/n8TERPLy8khISGjolyAiIn5mbQWULcTmXg5U7qowSZiUVzHhHX3vu3IVdutQdibsHqLPxyTcXJ2IW3chNu96KJvlpadITJNPMWEtfI6loVhbDmXzsLlXUnXc2w4mGZM6DRPWPmixiYiIHKzqmoeG/Ej7nvLyqjYTSkmpGtn47rvvqKioYODAgdVtOnXqRKtWrWpN2svKyigr2zWakJ+f38BRi4hIQzImHBtxDKbJJ9jSj6FyBSaiF0T02cdxcHVQ9hVeE3aAkncg7tJdm9e5t0PZnNo6gsrfIASTdmMisBF9ME0+xpZ8DK6VmIg+ENELE7af75+IiIjslwMqaXe73UyYMIE+ffpw2GGHAZCVlUVERARJSUkebTMyMsjKyvLaz+TJk7nzzjsbOlwRkZBiXXlgt4MtBZMIznTMQbRW2TiiwNEGEzeuTu2tdYF7M7jzgHBwJmP2mOoOYN3b99JLKVj3bo8rAHdtjcGd4z0W17Ydda6qqfyOdIwx+34RfmQc0eBoh4m/sk7tPd4/EwGOZIxD0+hFRET87YD6a23cuHEsX76cr776ar/6mThxItdcc0314/z8fFq2bLm/4YmIhCxbuRabdzNU7Dhz28Rh4q7GRg/HHOg7n/vAuvOhbC42/94du8dTtfY96QFMWAePtiayD7boCe8dhXUFx26buJk4cDSt2nHdm/BunnFYF1T+js27Dir/rCp0ZGAS7qqaOeDYxwZxQWLd+VD6ObbgfrC5VYXhR0DiA5iwtkGNTURE5GAT8rvH73TFFVfw0UcfMWfOHFq02DW1MDMzk/LycnJzcz3aZ2dnk5mZiTeRkZEkJCR4fImIHKysKwubM3pXwg5gC7EFd1clrgfW1ib+UfFjVaJsdxv5rlyO3TYKW7nBs62zNYQd7qUTg0m41WN03jgzMAm3er9mxAngTPcsc23Ebj9/V8IO4M6u2vne9Vf9XlMglS/B5k/clbADVCzDbh+FdW0MWlgiIiIHo5BP2q21XHHFFbz77rvMnj2btm097+D37NmT8PBwZs3atenP77//ztq1a+nVq1egwxURCT2Vf4Jrg9cqW/Bw1RTnRsS6tmML/l1LZQ5ULPEoMs40TPLjEHNx1RntAOGHY1KmQ1iXmn1EHItJfh7CDtnRQSLEXoVJvMcjwbfWYks/8r7jPRZbMAXrLvDhFTYs69pa+/vn3grlPwQ0HhERkYNdyE+PHzduHNOmTeP9998nPj6+ep16YmIi0dHRJCYmctFFF3HNNdeQkpJCQkICV155Jb169arTzvEigWbdBWDLwMRjHJF7b2vLwZ0PJgLj0IyQA5W1dtdaZkdywNcq2/Kfaq90Z1WtcW9UdmwIVwtbthgTfZpHmXFmQvzVEHsB4AYT7XX9O4BxxEPkcRD2YtW1cIIjDWOce14Iyr+tPczKn3ck9PF1eVEBVL5jFoATIvtjwrtibSmUzgTXGmzFd5jok7w+07rzwFaCIwFjwvd6FesuBVsEJhLjiGuA1yEiInJgCPmkferUqQD079/fo/yFF15gzJgxADzyyCM4HA5GjhxJWVkZQ4YM4cknnwxwpCJ7Z905ULEcW/QMuLZCxFEQOxacLWtsBmatC1zrscX/q9q52pEEsZdA+BEYZ2pwXoD4xLqysKUzoeRtwGCiz8JGDaxKAgPEhLWube9zMPFVm4g1Ks4d685rmcZdy/FwxoSDs2mdr2KcTfbRIBzC2kJ5Lfu0OJtRdXZ6qHFC2NGY+MuwpbOqdpt3xGNixoAxYGvelLKurVCxFFv036obkZH9IOb8Hb//PNtbWw6udVVty78HZwbEXgZhnTHOpMC8RBERkRBywJ3T3hB0Trs0NOvOxxY9DUXP7lETiUl9DRPe1bN9xQrs9rOrRpl2F302Ju46/eF6gLCuLOz2sTXXJod1xCT/N2CJu63cgN12Ss3PE0DsFZi4fx5Uu8jXhS1+HZt/m5eacEyTTzBhrQMTR8WKqu+Nl9sqJulpTNQJAYmjPqx1Q+XPVZ9tu8eRqZEDMfETMWG7Nne17u1VG/6VfujZ1sRjUt/EhLXz7L/8e+z2v1O1E/9u4iZgYv5Po+4iInLQqGseGvJr2kUOCu6tXhJ2gDJs3m0eR0pZdz624F7vCVbJG+DObrg4xW+stVA6y/tmYpUroGxe4IJxZmKSXwSzx3FcUSdjYs5rdAk7AJEDIfrvwG6jvCYOk/xcvUbT95uzBSZxChC1eyHEXgkR3QMXR33YEmzhMzUTdoCyL2oea1e5sWbCDmALsAUPYd2Fu4pcW6tOOdgzYQconALubfsXu4iIyAGoEf6lJhIE5d/VXle5vGq66M71se58KF9Ya3NbNg8TfqifAxS/c+diS96utdqWvAlRQzCOxAYPxRgnNrwbpsl74NpYda52WGswTTDOhr9+KDLOVIifALH/B5Urq45qc7YI+Nn1xhGNjToBE/4puNaALYewduBIxThiAxZHvbjzqpLzWtjSGZiIXbvt27LPau+rbNaO5H/H6LnN28uu+W6o+KXqsysiItKIKGkXCYh9TWrZcw2owdt02aoqTZA5MJh9fK8c1Py+g3Vtgso1Vcl1WDtwNsc407z2YF154N4CFcvBEQthncGZhjE110Eb46gaQQ7kKLIfWHdh1UyVip8AR9U5535KaI0jARwJENZmv/varzhMBIQ1r/o6EBhD1efXVUv9Hhvu7fX3n2OPX3X72qSxZl/WXbLj5+BnoGLHZ6RJ1YaAIiIiBwEl7SKBENGTWhPx8L9VHQm1kyMRIvpD+RyvXZmIvg0RofiZcSZB9DnYih+918ecV+NEAFuxApszpioB2SnsEEh6GrNHQld97Fbpu7uVhmMSH8FGHo9xRPvnhQSRdedii16GoifY9bPjwMRPxEafrhMVgsUkQuRQKPMy5R0wUUP3eDwYW1TL5rCRQ6s22ty977BOtezu74Twzh4l1l0IpR9j8+/A4yZC7CUQezHGsceSEBERkQOQhuxEAsHRBOKurlluYjEJd3psLGcc8ZiEGz0T+Z1iLqraSVkODJF9IeywmuVhh0NEL48i68rG5lzsmbADVP6Bzb8N695j/XDZF3sk7AAV2Lzx4N60/7GHgopfoOhxPG92uav2fKhcFayoGj3jiMHEjweHl5Msos8GRzPPMkcziD6vZltHCib+KowjZlffzlRM4n14rvHfURc/sep36e5ca3dsKLjHqH/RszovXkREDhoaaRcJAOOIq/qjNeJobNHzVZvJRfTCRJ9ZtY52T862mNR3sSXvQ/ncqunAMWMh/BCNLh5AjDMDkqdC+dfY4tcBg4k5DyKOqqrbnSur9mS7/Ctwb6+ayg1Y15aqowO9cmNLPsbEX+m31xEM1l2ALXqq9vqiFyDsXxhHzeROGp4Jaw0pb2FLP4Gyz8AkYmLH7jiWzXN02ziTIO4qiBpS9X2zuRA5CBN1EibMy++/sE6YJh9W/cxUfAuOZpjYCyGsrUeCb21l1UyMWtiiqRDeQ6dtiIjIAU9JuxyQrLVs25hDWXEZYZHhpDRNIjw8tD/OxpkIzr9BWBegrGqUvZYNr4wxENYC4v4B9u9AxD6nO5cUlpK3NR93pZuYxGiS0hrnBmOhxjgzIPo0iBxQ9bi2dbbu3L13ZEt2e+AC1+ba27pW1yfE2i/p2rzjuuE71pEH8MxwW7b31+jeCJTjbURWfGdtRdVsD1sBJqrmzaXdmLDmEHsRxJwNhHsk1DXaOlPA2RvCuwMVVTv111j7vqOtCavabC7+arDFQKT3mzO2DNwban8xrs1UfUYCp+qM+a1UvcZojDM9oNcXEZGDU2hnOSJe5G8rYPHH3/H8LdPZumE70XFRnPrPIZxx1cmkNA399YtVf3zWLdEwxul9mvweslZt5r83v8q8t77G7XLT7vDWXPn4RXTs2Y7I6AAmWlKrfW6K5dzbJmRRYOI9H4d3gwrvpxKYiOPqHd/urDsfyhdjC/4FrnVAJESfBXGX7TWJ8ysTBxF/g5KV3usjjgZTe5Io9Wddm7FFL0LJtKpk2ZEJ8ddBRN9aR6uNcdTpd1R1+70k9jX7Dt973ya6aplJ+WLv9eHdqz5HAWJd2VUzqUpeq7rZ5WgG8TdAZJ+AnBIhIiIHL61plwOKy+XiyzcW8sCYJ9i6oeps85LCUl5/4H0e++ez5G8vCHKEgbdl3VauG3AHc19fiNvlBmDlj2u4tv8drP55XXCDk7pzNqnagNCb2Itgtx3kjTMJE3+997aOJhBxzP7FUrYImztuR8IOUAYl/8PmXoV1bd2/vuvIOKKqpkQT7qUyGhN9VuM8X76BWHcONu9WKH5ux+g24M7C5l0HZZ9hbS07xQeRMQ5M1HAw3k4SCMPE/bNeNwn2h3Vtx+bdCMUv7JoV496IzZsAZXOw1h2QOERE5OCkpF0OKNs25PD8LdO91i18/1tyNuUGNqAQ8MvXK8hes6VGudvt5rkb/0dhbmEQopL6Mo4kTOLdEH0u1YmqiYO4azAxf695jFvYoZikZz33RIg4FpPyKiZsj43A6sG6srEFk71XViytOoouUJytMCn/g7COu8rCumJSpu1jZoLUm2tL1f4ZXtiCh8C9l6UKweRsjkl5DcIP362sPSbl5cCe5+7OgvKFXqtswb9D9/0TEZEDgoYp5IBSlFdEYW5RrfVrf99A664tAxhR8H390ZJa65Z/9TslhWXEJQVuiqj4zjgzIP5miL0UKAWiwZleNU14z7aOWIjqB+Gvgc2nat150v5Pw7XFO9aL16LiR4g4vPZ6PzImAiJ6QPLLYPMAA45EjCMlINcPZda6wZaDiaiaor6/Kv/Yy8VywF0I3pegB5UxDgg/FJKf3bEvhLtqUzxnk3091b8qft3xH5G4IvpSSSqR9o+qG13uLWB181RERHynpF0OKBFREXutT0jdx7rhg1B6y9r/OE1MS8DhMAGMRvaXcUSBw8uO2rW1d6YD/tzsKnzHV4X36t2m6QeKcaYCXo4Xa4SsrQDXBmzJe1DxU9WZ5jFnVo04m73/ftyrvd4IMbA/fQeAcSRDMM9kdzShKOwcNrhG8uoPG1hfUEqfFv0Y0iaJZu47MIT2+yciIqFN0+PlgJKYlkDPQUd4rYtPjqNpu8Z3hvmA84+vte7Ma04hOTMpcMHIgc+RClEn11IZCWFdAxqO7GKthYpl2K0nQ9GTUD4fip/Fbj0Fypfs37rzsDZ7bHa4m4h++0jqpcR0ZmbWUE56fRavLv+NL9es5r4FPzD8rUX8xUM1z5cXERGpByXtckCJS4rlqqcuoVn7TI/y6Lgo7v3kZpo0b3x/WKa3TOW65/+Jw+H543z0ST044bzjapSL7I1xRGPiJ1SN4HqIwCQ/A4HaPV5qcmdjc6+m5iyIiqry/Vk37cjAJD9Xc0d+ZxtMwqR9n37QyG0tjeDmud/UKM8vK+PWud+RV6YZTyIi4jtNj5cDTtO2GTw0907W/b6B37/9k2YdmnJIz/aktUxplAlqdHw0/c7qxWHHdWbZ3OUU5hbT/YSupLdqorPaxSfG2QyS/wuuNdjy7zHOzKrjs5yZXtfXS4C4t4M723udzQHXNnA29alrY5zY8G6Y1I+g4iesaz0mvBuEtQ3cMX8HsJ83b6bS7X2H+CWbNpJTWkpSdHSAoxIRkYOFknY5IDVpnkKT5in0GNAt2KGEhKjYKJp3yKR5h8x9N5b9VpBTSG52HnnbCohLiiEpPfGgu0FinGngTMNEHBnsUKTavqa/V9YoqSirYHtWLts25eBwGFIyk0lplkRYWM1//o0Jg7AWENaCg3lcOKekmK3FJeSWlZAcFU1qdAzJtSTUZZWVbCkuYnNREU6Hg7SYGNJj4wjb4wZxqavme787t458ExGR/aCkXUSkHrZu2MYjlz3NN598X112SM923PbGtWS29eeGcCJ7cKRWnUluvZygYaLB4blJYGFeMfPf/ponxz9PaXEZULXE6KZXrqT7gMOIjI6s2c9BbmNBPlfP/IRvN26oLjumeQseGjyMZvEJHm3zS0v58I/fuPerLymtrErKEyOjeGzoSRzTvCWRu934ODy99hum7ZJTSIiM8vMrERGRxqTxzSUWEfFRcX4xU695ySNhB/jju5Xccca/ycnODU5g0jg40jDxt3mtMnE3wh7HnK1evpaHL55anbADFOYWMem0B8ha2fjODc8pLeG6z2Z4JOwAizes58YvZpJbUuJR/svWLdw2d1Z1wg6QV1bKRR+8y/r8PI+2aTExjD6iR41rOo3hnhMGkhYb68dXIiIijY2SdhGROsrZnMdXby/2WvfXstVsz8rzWifiD8aEQ9QgTMqrEH501ch7+JGY5Jch+hSM2TVyXpRfzKt3v+m1H7fbzftTZ1JZsfcp3QebbcXFfL1hnde6BevWsm23pD2/rJRHv17gta3LWl5b/hOu3dawJ0RFccVRx/LEsOF0SUunSXQMg9q15/1z/073DC1bEhGR/aPp8SIidVRSUIq7ls2mAHI35wKtAxaPND7GEQ8RR0Hyk2BLgCiMs+Z+CmVFZaz/I6vWflb/tJbyknLCwhvPnwEFZWV7ry/fVV9SUcmavNpvwv2+bSvlLhfRu61tT42JYVjHQzimRQsqXC5iIyKJi9D57CIisv800i4iUkcxCdE4nLX/2kzJTA5gNNKYGUcCxpnhNWEHiIqLonXXlrU+/5Ce7YiMaVxr2hOj9r6uPCFy1/sREx5Oh5TajxDtlpFBhNPptS4lOoaMuHgl7CIi4jdK2kVE6ig5I4mBf+/rta5Lr0NIzjy4dpCXA1dMfDQXTDrTa11YeBgnXzYIZ5j3pPNglRodQ/82bb3Wndi2Hakxu86oj4+MZMIxvb22jXA6ObPzYTgb4RGjIiISHPoXR0SkjqLjorjw3vM4cdTxOHb7g73HiYdxy/SrD7pj3+TA1rJTc2574xrik+Oqy1KbJjN5xi1ktEnbyzMPTolRUdx7wiAGtm1fXWaAwe06cPcJA0ncY4f3Q1Kb8MjgkzxG4DPj4nh5xEiaJ3juNC8iItKQjLXWBjuIYMvPzycxMZG8vDwS9A+xiOxDcX4xOZvzKMwtIiY+mqT0RI/ESCRUuCpdbNuUQ96WfIzDkJiWQJNmKRhzMJ/Evnf5ZWVsKy6ioLyc+MhIUqOjaz2SrdLlYnNREdtLS3AaQ3J0DBmxsY36/RMREf+pax7aeHagERHxk5iEGGISYvbdUCTICnIKycnK5av3viEsPIzjTz+aqOhI4lNq3mSqKK9k6/ptfPf5Mjb8mUW34zvTsUc70lqmBiHyhpMQGekxer43YU4nzRISaKYb+iIiEkQaaUcj7SIicvDJyc7l8SufZ95bizzKR159CudNPJ3EJrv+vausrGT5/N+4edi9VJTvOgourUUqD865g2btdWyZiIiIv9U1D9WadhERkYPQD3OW10jYAd5+5CPW/rreo2zbhhxuO/V+j4QdYMv6bTz6j2cozCls0FhFRESkdkraRUREDjJ52wp488EPa61/+9GPKS8tr3687veNlBaVem37/ayfyNta4PcYRUREpG60pl1kDyWFpeRk51JSWEpMfDQpmUmN7jxjEQmsnOw88rfl43a5iU+JJ7VZ8n5tduaqqKRgL6PjBdsKqaxwEbFj/7V9jaTvOQIv+8/ldpNdVEh+WRkRTicp0dEkRUUHOywREQlBStpFdrN143aen/gqs6Z9hdvlJjwijKEXn8jfbxlJStPkYIcnIgcZl8vFqh/X8q8LprDml6op66nNUpjw1KUc0b8r0XHedzXfl7jkOI4a2oMPp870Wn/cGcd49N22W6ta+0rOSCI2URsv+lNeaSmf/vkH9y+YT15Z1QyHv2U25d+DhtE2Wf/WiIiIJ02PF9mhIKeQ//zzOT5/ZR5ulxuoGl368MmZvDjpNUoKS4IcoYgcbDav2crVfSdVJ+wA2zZuZ9Jp99dYd14fEZHhjLz6ZK9Jf0pmEr1PO8pjJD8lM5n+5/T22tdlD/0fqc2USPrTwnVruXn259UJO8DSrE2c/84bbCrID2JkIiISipS0i+yQm53Hwg++9Vo388W55GTnBTgiETmYWWuZM/0rr2vJrbW8fMcbFOUV+9x/Ztt0/vP1ZI49pSfGGJxhTgacfxyPzL+bjNZpHm3jU+K4/JGxXDT5fBJS4wFo1ak593x4E8ec9DccDv254C9bigq5f+E8r3XZRYUs37w5wBGJiEio0/R4aTTytuZjLSSkxnn9AzR3a+2jG26Xu9b1oS6Xi4LthTgcjuo/dqXuXC4XBdsKcTj1/knjUl5azk/zf621fsXSVZQUlnqdml5SVEppYRlRsZG1TqF3Op207tKCm14ZX538J6TGERXrvX1KZhJnXXcqJ/69L+4KNxHR4SRnJO31NVRUVFKYU0RYuJP45JpnvwdSTkkJbmtJjo7GsR/7ATS0MpeLtXm13wT+Pmsjg9p3CGBEIiIS6pS0y0Fv64btLPpwCR8/8zlul5vBo/vT76xepLVs4tEuLjF2r/3ExNXcICh7zRa+eGUec19fQERUOKddMYyeg47QVNI6ylq9mc9emsu8NxcRGRPJGVedRI8B3bR/gDQKYRFhND+kGUs+W+a1PqN1EyKiwj3KSopKWf/7Rl6b/C6rlq+lZafmnH/zGbTs1JyYeO+bmMUmxtR5TbrT6SSteeo+21lryVq1mY+e/pyvP/qO2MQYzrp2OIcd12mfib6/ZRcWMHf1Kv730zIq3W5GHNqZ4Yd2oll87efdBlOYcZASHc32Eu9LrtolpwQ4IhERCXXGWmuDHUSw1fVQeznwbN2wnUmn/YsVS1d5lDdrn8mDs2/3SNxzNudx0+C7Wfnjmhr9HNG/C5Peuo6ElF0jwdmrN3PVcbexbeN2j7aH9+3CzdMnkKrEc682rdrMVb1vISc716O856DDueGlK0jJ1PsnB7/VP6/j0sOvxds/xXd/eBPHntyz+rGr0sWiD5dw15kP1Wh/0//G0/fMXoRHBOZe/Po/NnLlsTdTmFvkUd7/nN6Mm3IRSWmB+bc0u7CQyz9+nx+yszzKWyQkMH3kOTQPwcTd5Xbz3++/418Lak6RjwoLY+aoMbRMTAxCZCIiEmh1zUO1SE0Oasvm/lwjYQfY+FcWc19fiNvtri5LTk/k9revo1Wn5h5tO/Roy/UvXOGRsFeUV/De4zNqJOwAP877hZXLVvvvRRyEyssqePvhD2sk7ADfff4jq39eF/igRIIgs20at71xDZHREdVlDqeDC24/iy7HHuLRdtvGHB6+5CmvCf6Uy59l+6acBo8XoLighOdvmV4jYQeY+/pCslcHbk320qyNNRJ2gPX5+bzzy8+4dvsdHyqcDgend+7CyE5dPcoTI6N4ecSZNI0L7jIDEREJPZoeLwet4vxiPnnui1rrZ744h0H/14+k9F0jGs3aZ/LArNvZtnE7WzdsJ71lE1KbJdeY7pm/tYA507+qte9PnptFj4HdCAvTj5g3+VvymfPaglrrZ/x3Nt1POEybX8lBLyomimOH9+S5nx9h41/ZVJRV0PLQZiRnJBK9x5Kc3C15FGz3vrdGcUEJOdm5NTaYawiFOUUseO+bWuvnvfU1hx7V8GuySyoqeG35j7XWv/PbL5x72OGkxe596VMwpMXEcmvf/lx+1NGsyskhPjKSFgkJZMTG4dTvPRER2YMyCjl4GbPXpM84HOBls6LUpsmkNk3mkJ7t99Y5xlH7RkcOh8Gwfxsh5W3NZ+uG7fz1wyoS0xJp3bUlTZonHxw3AkzVe1QbR5jT4ziqhlZWWk7Oplz+WraakoISDjmyPcmZSUHfWEvqx1rLlvXb2LBiE5vXbqV1lxakt0ojJTMp4LHkbs5j87ptrF6+hpSmKbTq1JwmLVK8/k4Kjwgns006mW3S99rnvn4mAvYzY8DhcFQfjbknh7NmHOWVlWwuLuKPbVvJLS2la1o66bFxJEd7X4dfxzD2uuGcwxhvv+JDhttayl0uSiorcDoMbmtxW4sz2IGJiEjIOQj++hfxLiY+muGXD+aHOcu91p986UASm/i2W3lSegKDR/dn2n3v1NL3IJxhvv/ptX1TDg9eNJVvZ3xfXRYVG8U9H91E196HEhZ+YP/oJqYlMPCCfrz18Ide60+6+MSAJSClxaV888n3/OvvU6gor6wuH3bRAMbeez7J6VpbeqBY9dNabhx8N7mbd+3M3b57G+5670bSWzXZyzP9a8v6bdxz7iP8svD36rL45Dgmz7iFjj3b+TyDJDEtgaS0BHK31DzpIi4pNmAbwCWkxNH3rGOZPc37bKO+Z/byeFzuqmTxhvVc9vH7lFbu+hkb3L4Dd/cf6PNIeFR4OKO6defLNau91p/TtRsp0XXbgC/QthQVccvsz/li1V/VZdFhYTxzygiOat6CCKdSdxER2UVzsOSg1rX3oRx2XOca5W27taLPiKN9TgydYU5OvnQgTdtl1Kg79pSetDmslU/9QtURSu9O+cQjYQcoLSrl5mH3sXV9zXX0B5rwiHBGXDnMayJ1/BnH0PLQZgGLZfOardxzziMeCTvAp/+dzTefLA1YHLJ/tq7fxs3D7vVI2AH++mE1j1/5X4ryfT/vvD5Ki8t46bbXPBJ2gIKcQm4acg9b12/zue8mzVO44aUrcTg9/+l2OBxc/8I/SWmW5HPf9REVG8XoO87xWFq000kXDyR9jyn6mwoLufjDdz0SdoDP/vqTN3/5ab/WnR+ekUnvFi1rlLdPTmH4IZ1C8ui3SpeL6cuXeSTsACWVlVz04btkFRYEKTIREQlVB/Zwncg+pDZL4ZbXJvDzgt/44MmZuF1uhl10Ij1O7EZai30fa7Q36a3SeHDOHSz++Du+eGUekdERnD7+JA49usN+TcfN2ZTL+0/M8FpXXlrO8gW/kdl279NoDwQZrdN4ZN5dLPxgCXOmf7XjyLeTOeSo9gE9MuqLV+Z53dgLYPrkdzlqaI+gTK+W+slas4VttWzE9vVH35G7OY/YhIYfdc3NzmNWLSPQhblFrPllPemtfFt37nA4OLxfZ57+4UHe+88n/PXDatoc1oozrjqZpu3SA7p0plmHTP7z9X18+cYiFry7mLjkWEZefQrtu7chMdVzBtO8NauoqCUx/+/3Szmjc1cy43yb9ZQeG8vDg09iadZGXv7xBypdbkZ27krf1m1oGu9bnw1tS3Exz//g/YZgucvFwv9v76zDozj6OP49d427EDRAcHcJrm2xInXaUi+UUoG6u1JXihV7a1AoDsGDQyAh7nLusu8fRy632b1AIYQU5vM8PA+3O5mbndmd2+/MTwoLEK9SN22jCAQCgdCsIaKdcMMTGq3FwNv6oNuITqAo6pL52P8N4XGhGHf/CAye1g9cHjdonuR/g9vlgc1sD3q+LLf8qr+juRAeH4YJ80Zi6MwB4DVS//0bvF4vis6XBj1fXaKDx+1pwhY1L6pLdbBbHOALeNBEqCAUCy/9R9eJ+jvsgVAUBYfV2STtcNqdcLvcQc9XFV+dpYxIIkJiahzmfXQX7BYHRFIRhCLBpf/wGhCZGI7b5o/D6HuHgi/kQyITs5Yr0AcfG53dFnSnvdxshs3tgoDLQ7hMBkEQk/FwuRz94hPRITwCXgpQi0VQiNjbAvhSrlVYLHB43BDx+AiXyRoM/qaz22B0OMAFBxqxGHKRKGhZp8eNSosVLq8HEr4AESyR4D2UF0aHI2gdBQY963Gry4UamxUerxdyoQgh0uZp+k8gEAiExoeIdsJNw7XcZZOrG28hQCwVIio5AqUX2MV5u96tG+27mgMcDgeKRuy/fwOXy0X3kZ2wa80+1vOtuydDLAv+gn6jYtZbcGz7SSx98keU5VZAKBZi1N1DMO3pSQiN0V7v5rESkxIV9JxELoZM1TQCRyIXQxWqhKGK6XcOAEkdEhrlewRCAQTC6yPWA+FyuZcM2NgzNg7fHD3Meq6VNgSiehYCersdO/Pz8NaenSgxmyAVCDCjfUfc3bkbqwi+oKvBK7u2Y0deLigAXSKj8OKgoWgVEsoQ+tVWC9aePY3PDx2A3m6HRizBg916YEKbdgitJ4KdbjfO1VRj8bYtOFpeBg6AwYlJeLb/YCRpNIx2lJvN+DrzEH45cQw2txvRCgUW9R2IfvEJUInrFhHEfD5aaLTI0bEv4PSIiWUcKzIa8G7Gbvxx/hzcXi9aaUPw4qCh6BARCang+t8HBAKBQLi2EJ92AqGZoY3S4L63ZrKei0mJRHxb5gsd4crpMqwj1GFKxnEOh4O7X595U0aQz/znBF6Y/A7Kcn35tp12JzZ8uhGvTH0PunL99W1cELRRanQZ1oH13LRFk6CNZoqsa9KOaA3ueHkq67lW3ZIRkdB0AfGaC6lh4YgOYqr+TP+BCJXWLdp5KQqbc7Lx2KY/UHLRt9vqcuHrzMNYuGUTdDYb7e+LjAbctnoFtl8U7ABwpKwUt6xejgIDfYff6nJi6eGDeH33TujtPmsmnd2GV3fvwNdHDsLmctHKFxgNuHX1cn8eeArA1rxcTPl1OYqM9LprbFbM3/wXvsk8DNtF3/0SkwkPb/wd/+TmwBvgghMqleGZfgNZ+yNWqUSbULr7RJnZhFnrfsWGrLNwX7RKOFdTjRlrVyGrqpK1HgKBQCDcWBDRTiA0Q9IGt8fTPz3s96XmcrnoPb4b3vj7+Wa70/lfJSIhDO/tfAkdB7bzH4tKjsCrfz6DxFRmgKsbneqSGix98gfWc6f2ZqGioKqJW3R5qEKVWPDdQxh19xB/dgW5WoZ735qJ0fcMhaCJMi7weDz0v6UXHvn0Higv+nZzeVwMntYXS9Y81aTxGpoLUQoFfpk8BYMSk/yJMCPlcnw6ahw6R9KDTpabzXhr707WenYW5KHcUpennqIobMw+D53dxijr9Hiw9PABmhCvslrxfRBf8u+OHkGltS5Yoc3lwmcH98PpYbrHVNts2HwhhxYLo8xsxp7CAta639yzCxUB7QaArtEx+GTUWETIfIuCtbv4P0+6jeHff7KiAvksJvMUgFd3bWcsZBAIBALhxoOYxxMIzRCFRo4hM/qj48BUWI02CER8qMOUkF7CxN/tdsProa6bj+t/lbjWMXhh7QIYq0zwuD2QqWUIiWrcnVmX0ycemoNJc0NYTfYGhfnZA9lo3T2lCVt0+YTGaDHvw7sw/ZnJcNpcEMvFCI3WXFX6xStBFarE6PuGoefYrrCZ7BCKBVBHqIL6fN8MxKvU+GDEaOhsNrgu+mRHyGSMDB5mpwPVDYjQs1WV/p1oq8uFrXkXgpbNKCqAyemA5KL5eLXNCk+QoJMurxc1NiviVb6I+EanAxlFhUHr3pZ3AdNSO/jrPl1ZEbRspdUCs5MeU0EpEmFUSiv0iImF3eUGn8eDTCCAgsVffnsD13ikrBQ2twsaMOOBUBQFh8cDAZfboM8+gUAgEJo/RLQTCM0UDodz2RHuDVVGFGaV4H+fbYLFYMWQ6X3RcWDqVUfIv5lQaOTXxBS+plSH85m5+OPLzeByuRhz3zC06JQIbWTTmGv/WwRCPvgCftBgappmHklfJBUhKomZirGp4fF4CI+7+UzhG0IpEkPZQIA4ABDy+eAAYJfWQEhA3nUBj4twafB4GCESKQTcugUbMa/hBTNxgG+9gMuFViKh7ewHEi6VgR8ghOv7wwfC5XBY866Xmk3YlZ+HTTnZ0EolmNWhExLVaqjEdAEepWC679SiEUsYae28FIUiowF/ns/CvuIiJKk1mN6+I2IVSkiFzTeYJIFAIBCCQ0Q7gfAfx1Btwk8v/YoNn/zlP3bgzyOISYnEW1uWsOZCJzQN1aU6vDHzQxzddsp/bM/6A+g6vCOe+v4haBt5N78xUEeoMGhaH2z5iWmiLBQL0apL8nVoFeFmIUQswdCkFowc5gCgEAqRrKlzDxLy+LijUxf879xZ1rrmdu0BjaROAIdKpUhSa5CrZ6YGbKHR0qKxayVS3N+1Bx7d9Adr3bPTOtOC3LXUhkImEMBSzy8eANKTU6CV0EV9ocGAaWtWojQgJ/vaM6fxaM/euLNTF9rixqiUlngvYzfrQsadnbogrN7CRVZ1Jab+utK/u78zPw8/HsvEhyPHYHhyCiPwH4FAIBCaP8ReikD4j1OWW0ET7LUUZ5dh3Ud/+s2yCU3P0W0naYK9lsObj+PkHnahcb0RS0W48+XpSO5Ij3IuEAnwym9PI4TEVCBcQ+QiERYPHIwkNX1BS8Ln45vxkxnR45PUGszv3ZdRz21t26NHTAztWJhMhqVjxiNEQt/JDpVK8fmY8Qzx2zsuDpNat0N9Fvbtj4R6edQj5HJ8M34ybbce8C0GPNt/EOQBO9w2lwvv79tDE+y1fLg/A2Vm+u5+pFyO90eMYeyo94tLwG2p7Wmm79VWK+b/vZFhjk8BmL95IyqtFsZ3EggEAqH5w6GoIA5eNxFGoxEqlQoGgwFKZXAzNAKhOfLJI9+yinbAF4jryxPvIiyGmMk3NSadGc+OeR1n9p1jPd+hf1u8/L+FkKmuT7q7S1FTqkNxdhlOZ2QhLC4UbXu2RGhsCARCsktHuPaUm824oK/B8fIyxCpU6BgRiSiFgmaSXovJ4UCl1YI9Bflwer3oFxePCJkcagnTzxsASkxGnK2qxLnqarQOCUXr0FBEBzFB19lsKLeYsbsgH2I+H33i4hEmlbH6nrs9HpRazDhWVooioxGdIqOQpNYwFhpKTEYM/uEbuILkp1/Qpx8e6NaTdszmcqHCYsH+4iLo7Tb0io1DjELJyNWeU1OD4T9/x1ovAHw7fhIGJRJrGQKBQGguXK4OJW9fBMJV4LA7oSvVo6ZMBx6fB02kGiHRGvBY/BcBoKZMB125ATazHZpwFdQRqqD5421mG3TlBtSU6SGWiqCJUCEkmrnLabc4grbPaXcFdw4lXFO8HgouR3ArB6fdBY+H/aW9OaCN0kAbpUGH/m2vaztcThdqLj5jHA7H/4zxiYnvDU2EXI4IuRy9Y+MvWVYhEkEhEtFM5xsiWqFEtEKJIUktLllWI5FAI5Ew0rCxwefxEKdUIU6parAcRVFBBTsA2FjiSYj5fIj4PHSOjITb64VcKISUJSOCh2p4TnG4mdHw3R4PKqwWVFos8FIUwmQyhMvkrH74BAKBQLg+kLceAuEKMdWYseXnnfj66WVw2n2miMoQBZ5b+ThS+7aGUEQP+JN/pghLJr6F4vOlAHyB5kbcMRh3vjrdn9qtFl2FActfXYMNn22C96Kwi0gIw0vrFyKpYzwt4vKQGX2x6butrG3sO7E75JrmuZN7o6PQyjBkej9kZ+aynh86s/9NmQP+32AxWpGx4RA+fPAr2C2+vNpSpRRPff8guqanQSy9eaOxE/67KEQi9ImNx94i9hRxQ5PoO+FurxcnK8px/x8bUGHxmbfzuVzc37UH7ujUmeYvrxKJEa1QoMTENL3nAGgTSo9xYnO5sKcwH0/+vREmp28BWCoQ4IWBQzAipSUUQqZFAYFAIBCaHuLTTiBcIVmHsvHZY9/5BTsAGKtNeGbUa6jIp6fMqiysxlNDX/QLduBijuHvtmLDJ3/BFbCz4vF48M/PO7Hu47/8gh0AyvMrMX/IC4x0XImpcay7oVKFBLOWTLmp00xdT7hcLgZO6c0aCDAyMRx9J/RgpLsi0Ck8W4w353zsF+wAYDVa8eIt76Iku/w6toxAuHKUIjGeGzAIIh5z32RoUjJi6+3Ul5pMuH3tKr9gB3xC/pOD+7AjL49WViUS4Zl+A8E2s8xO68z4zgKjAff/8T+/YAd8qfSe2rIJ56ur//3FEQgEAuGaQEQ7gXAFGGtM+GHxStZzbpcbm3/cgcBwEQVZxagp07OWX/fRn6gpqYtmXFOqx4o31rOWNenMOHeYHlVZG6nBs8sfw7yP7kJMyyhoI9UYc+8wfHboTcS0jPx3F0ZoVMLjw/Dejpcw45nJCIsLRXh8KGY+dwve3f4Ciep/CWxmG355dS3rOYqiSJBFwn+aFI0Wv0+fhUlt2iJUKkWKVovXh6TjtSHpDD/1XQV5sLnZUzB+sH8vKgPS0lVYLdiYfQ5fjJ2IAQmJCJVI0SE8Aq8MHoYImRxlAWVdHg9+OHoE3iChjT45sI8R0I5AIBAI1wdiHk8gsKCrMMDtdEMoEkAVxgwK4bQ5UZITfKcv51ge3E43BCJfXuDicyVBy9rMdtpuvcvhgqHKGLR83slC9J/ci3YsJFqLCfNGYsCtvUF5vVBo5RCKG87Ha6w2wWFzgifgQRuhbrAs4cqJSAjDrCW3YfyDIwEOBXWYCjx+4/mK2sw2mPXWi/7eqqDxFK41bpcb+goDKApQaGQQX6WFh93iQGFW8Ocm/1Qh7FYnBMKGc283Jl6vF7oyA7xeLyQKCeSq4Lm5CYSG4PN4aKHV4pXBw2F0OMDjchAaJOf8qcqKoPUUGg00/3iry4Xfz5/DroICTGrTFkOTWqDGZsWXRw6iwGDAJ6PGolNkFADA5nbhfE3w3fQ8vQ42l4sW+f5aQ1EUKq0WeLwUpAIBVGJiKUYgEAgAEe0EAg1DlRHHd57GD4tXoiSnHPFtY3D3azPQpmdLmv+xSCpCfNsYnNzNnrardfcU8AOibMe3jQ36nTKVFCJJnd+gUCyANlIddGc+pVMi63EOh8PwjWfDYrDgfGYevl74My4cz0dEQhhmLr4VXYd3hDqs4QBKhCuDL+AjJLpxc7J73B4Uny/F94tX4uBfmRDLRBj7wAiMuXcYQps4LVtFYRV++3wT/vzqHzjtLvQe1xWzX5iCqBYRV7yIIJaLkdQ+DkVBFrxSuiRBLG06MVFdqsPWZbuw5oM/YKoxI21QKu5+fQbi2kQz4lcQCJeLRCCARNDwwlOnyCgsP3mc9VySWgMBt+4ZkwmEEPJ4MDjs+P5YJqN8oOm9hC9Aalg4DpeyP2OtQkIhvUTbGpNKiwWbcs7jyyMHUWmxonNkFBb27Y+WTdwOAoFAaI4Q83gC4SJ2qx1/fLkFL936LvJPF8HlcCHnaB6eGf0adq7OoPmdKzRy3PHyNNZ6hGIhhszoR/NXjm0VHdQcesqC8TRBFxKtxczFt7GWVYer0KJT0pVcHgDfTuHBjUexYMgLyDqYDZfDhaJzJXhj5kdY9dYGWIy2K66b0LQUny/Fg90WYteafbBbHdBXGvHzS6vx/PjXUV1S02TtqCqqxqKRr2LFG+thrDbBbrFj24o9mNf9aZQ2YI1yKSQyMaY/M5nV75/L42LCvFFNtsuuK9fjzdkf48unfkJ1SQ2cdicObszEvO5PI/cEezAxAqGx6B0bFzQg3Pze/RAmq9uhD5PJcHuHNNayrbQhiJIr/J8FPB5mduzEmkaPA2Bej56QNdEuu85mwyu7tmHx9n9QZDTC4XFjX3EhJq/6BUfLSi9dAYFAINzgENFOIFxEV27Azy+tZj335YKfaH7nANCiUxIWfDcPsgAT2bC4ULy15XlEJNLTA4XGaPHW5sVI6VwnuPkCPm55YixG3T2MZi7N4XAw4JZemLX4Nr95PeALOPfO1qvzha4qrsGnj3zLem7N+3/AUGG44roJTYfVZMP3i1fCYWP6m2Zn5iHnWH6TteX0vnMoOFPEOG412bDizfVw2IKnJLwUsa2isWTNk1CG1AkNbaQar/7xDCKTw6+43n9LaW4FMv85wTjucXvw+WPfw1jDjNRNIDQW0Qollt8yBYkqtf+YhM/Hs/0HoVdsHK2smM/H3K49MD21I3gBC17doqLx1bhJNIEPAHFKFb4bPxlhAab5arEYn4weh2R101nslFvM+O1cFuM4BeD5bVtQGRCEj0AgEG5GiHk8gXCR6hIdXE72YD9Wkw2GSiMiEurEuFwlxZAZ/ZA2OBWGShN4fC7UYUrWXOoAENMyCq9vfBaGShMcVgcUWjm0kWqIpMwdFFWYEtOenoj0OQNhrDZDKPb51muu0vfcVGOGvpLdX97r9aLwXDGiU0jwuuaOxWDFgT+PBD2/dflu9BjV+Zq3w+12459lu4Kez/jfIdzx0lSIYq4sbZRELkavcd3w+ZEWMFQaweFyoA5VIiRG26SR9w/+xTQzruXU3ixYDTYotYqgZQiEq4HL4aBdWDhW3DoNOpsVTo8HGokE4TIZhCwR6MNlMjzTfyDu69YdBrsdUoEAIRIpNBIJo6yIz0efuHhsmHY7amw2eCkKWokUETIZeCw78NeKzAZ203P1OpicDsaCA4FAINxMENFOIFxEIGr4cQj0UfcfE/ARER+GiPgwlr9gog5TXbbfuFAsRGRSBCKTIi6r/OXAFzR8jVcbPOxmoaKgEmf2Z+PM/nNISo1H2qBUhMWFNGqAuYbgcDgQy8SsO+2ALxBcU7VDrg7+XRK5GByWF//y/Eqc2XcOZw9mI6l9PNIGpiIsPoTV/53H4yE8LhThcdcv2r5cHTzgnEDIB4dHjNYI155wmQzhlylcZUKhz7T9Mn5uOBwOIuUKRMqv38KTrAGfdQ4AHoc8YwQC4eaGiHYC4SLaSA3U4SroWUzEo5IjWKPI/9dQhSrQIi0ROcfyGOekCgkiE5vO5Pi/SsGZYjw5aDHNYkEsFeGtfxajdfcUcJtgd0oTocLY+4dj2StrWM+nzxl0zdsA+AT1mLnD8fcP21nPj583ApoIumrIO1WIJwctgbG6zqRcIhfj7X+WoFW3Fs0yd333kZ2x9MkfWc8Nnt4P6jCyy04gXA2dIqPA53LhDoiEX8uAhERoWawECAQC4WaCLF0SCBcJidZg8eonaX7kgE9QPLficYRENW707+uBOlyFhT8+xNgd5fF5eH71E40e4fxGQ19pxGu3f8BwMbBbHVg8/k1UFTdNADgen4cx9w1Hi7RExrkZz0xGRBMuvsSkRGLSI6MZx9v0bIkh0/vTFjF05Qa8MvU9mmAHfGkPl0x8C9X14kY0F/hCPua8OJVxPCo5AmPvT2e1JiAQCJdPmFSGd4aPQv0lu3CZDEsGDoFCdGUuNgQCgXCjwKEoirrejbjeGI1GqFQqGAwGKJX//d1UwpXjdrlRUVCF3esO4PzhHLTr0xq9xnRFeELodct/3dhQFIXy/Eoc2nQUx7afQlL7eAy4rTfC48MgFJO0Og2Rf6YI96Q+HvT8RxmvoW3Plk3WnqqSGuQczcPWX3ZBqVUg/Y5BiEwMh0Irv/QfNyLGahNKcyvw93fbYDFaMWzWACR1SGAsdOWdLMS9HZ8IWs+nB99Eq67J17q5/5oNn/yFCycK0GtsVxzadBTGahM69G8LZYgSXy/6Ge9tf+mqAkQSCATA6nSi1GzChqwzyNfrMSgpGT2jYxFN3ssIBMINzOXqUGIeT/hXWAwW6CqMcFgdkKtk0EarmyztUlPAF/AR3SISU+aPv95NuWZwOBxEJoZj7Nx0jJ2b3uj128w26MoNsJntkCol0EZqIJI0fR5rt8uN6lIdzDoLhBIh1GFKKDTBxay+0gBDpQketwcKrRwh0RqGqbvL7mrwO60m9pR51aU6mC7uLitCFA1abVhNNujLDbBZ7JAppdBGqSEUs/dfaLQWodFa9BzdpcF2XWuUIQooQxRo3a1Fg+VcDnYf/FrsFntjNqvR0FUY8OdXW7D5h+1IG9weErkYaz/4A8XZZQAAr4dp0vuvv6PcAGO1EV6PFwqtAiHRmkZzFagqqYGp2gwOlwNliALaSHWj1EsgNCZSoRAttCF4one/690UAuGSuDweVFjMMDqdEPP40EolUIlIXCDCtYOIdsJlU55fiY8e/AoHNx4FRVEQy8SYtnAixswddtnB1Qg3NlXFNfhywY/YsToDXo8XAiEfYx9Ix7SFk5pUKBiqTPjn55344YVVsBqtAIBOg1PxxFcPICqZHtiPoijknSzEm7M/9vv6ayLUeOjju9E1vSNkyrogZMoQOaQKCas453A4iEygByR0Opw4uz8bb9/5KcpyKwD4TKqf+n4eWvdIYSx4VRZVY+mTP2D3mv3wer0QiASY8NBITJk//qozBzQHFKFKiKUi2K3MNHBcHhehMU2XYurf0HV4Gpa9sgYupxuHNh2lnWvZJRkSxZW/qHk8HuQeL8Absz5C/mlf6ryQaC0eW3of0galQiK/8rodNgdO7j6Ld+9ZisrCKgBAXOtoPPXjw0jpnAg+n7wCEAgEwr+lxmbDr6dP4uMDGbC4fIv5fePi8dqQdMSpyPsw4dpAHPEIl0VNmQ7Pj38DB/7KRK1Hhd1ix/eLV+Dv77fD4/Zc5xYSrjfGahPeveczbFuxx7/z6HK6se7DP/HzK7+yCrVrAUVR2LvhAD5/4nu/YAeAo9tOYWH6ywy/8/L8SjwxcDEtOJ+uXI+Xp7yLC/XynWujNLjjlWms3zv+wXSo6wVdK82pwFPDXvILdgAovVCOBUNfQumFClpZQ5URb87+GDtXZ8B7MRiTy+HCr+/+hpVvbQgaKf6/REikGrNfnMJ6btIjo6EOb54vOzEtI9G6RwrjOIfDwYMf3AFV6JWb71bkV+HxAYv9gh0AqktqsHjCmyg4U9TAX16aonOleGbUa37BDgCFWSV4cuASlOdVXlXdBAKBcDPipSj8eT4Lb+zZ6RfsALCnsAB3bFiDcrP5OraOcCNDRDvhsijPr0TuiQLWc8tfX9dsA0gRmg5dhQGHNh1jPffXV/9AV6ZvknZUFdfgh8UrWc+VXihHYVYx7diBP4/ArLewlv9m0TKYaup+gPkCPobO6I/nVjzu37HXRqox78O7cPvzt9F25Z12J9a8/xvrgpbb5cb6j/+Ey1n3g19Tpsex7adY2/G/zzY1Wf9dSwQiAUbcMRhP//yIP1NBSLQWj3x6D6YtnAiponlGiNZGarDk1/mYsmC8f+e7dfcUvL/rJaR0TrrieimKwrblu1ndAiiKwo8vrILFYGX5y0tjM9vxyytr/AtAgTjtTmz8dis8HrLYSiAQCP+GcrMZH+7fy3ouV69DvoG8DxOuDcQ27gbFZrbDbnFALBdB0gi5twN3gepj1ltgMze9L6rD5oTVZIVILIRUGTyP8pVgM9vgsDohlolI7vLLpLokeOR0t8sdVBh7PB6Yqs3g8rhQhlw6dZbFYIHT4YJMJYNQxIyn4LQ5UV0a/Efz/OEL6DykAwDA6/Xi6DafUE5oF4s+E7pDIBLgdMY5HP77GHKO5sFuddACuylDFBg4pQ86DGgLl90NnoDH6n9sNdlx9kB20Hac2XceVpMNqhDfNVQUBN/5dDlcMBvY++/fYtZb4HK6IFfJGJkSmgJliAJDZ/RHxwHt4LQ7IRDyERYXet1Svbldbph0FvAFvAZjHoTFhuCOl6dh4kOj4fV6IZaJrmqHHfCJ5xO7zgAAkjsmoNe4ruAL+Dix8zQyt57E+SO5sJntkKn+/fxmM9tx7vCFoOdP7cmCw+Jo9LnzUhjsdri8XqhEIgguEdzT5HTAYLdDyudDK728/OQ3Ov+m//4tNTbfApFaLAG3EZ9Hj9cLvd0OLocDzWWkbjM5HHB43FAIRRBdwoXD6XHD6HBAyONB2cj+xEaHHUaHA1KBAFpJ0z4nhOaLze1CtY09fg0AnK6sRI+YuCZsUfPC6LDD6bk2c9TNDhHtNxhWkw2FWSVY/toaFJwpRmJqHKY/MxmxraIgkV/5LlZEPV/dQARCfpMGGnM5XCjOLsOqtzfg7P7ziEgIxfRFk5HUMaHBl+7LwWK0ovBsMX55dS2KzpUgqWMCpi+ahNiWUUS8X4JLCW4239yyvAr8/cN27FydAZFUhMmPjkbnIR2gZQnUZqgyIutgDla+uR76CgPSBrfHLY+NQWRSOHj8uh8GvkgAiVwcdCEpOiXS/38ul4uktHj0GtMVJr0Z25bvgd1iR9f0NEx+dAzWfPA7+AL2Hx1tZMPp8UQSISKTIoJaqEQnR0AkqUtjpA5XB62Lw+FclW8z4EtXd3b/eax8az2MVSZ0Hd4REx4e5eu/JvxhtVvtKDpXihVvrMeFY3mIaRmFGc/egvi2MTRLhWsNRVEoy6vEn19vwd71ByFTSnDLE2PRcUC7oPEDBEIBwuJCGq0NfCEfCe3jMXz2IFQWV2PHyr1wOVzoMbozbnl8HH7/4u8rzuggFAsQnhCKsrwK1vOxLaOCBji8FlRZrThcUoyvjhyC0WnH4IRk3N4xDXFKFXPBy+lEgdGAr44cwvHyMkTI5LinSze0DQ1FhPzSC3s3IpUWCw6WFOObzEMwOR0YltQC09p3ZO2/f0uZ2YStuRfwy8nj8Hq9mNQ2FWNatkK04uqjthcbjVh/9jR+O3cWIj4fszt2Rv/4BITLmb/VOpsNJyvKsfTwAVRYLOgZE4u7OndFvFIFfr05yuP1oshowI/Hj2Jnfh40Ygnmdu2OTpFRCJFe3TxicjhQYNDji8MHcbqqEjEKBe7r0h2tQkIRJiOLRzc7Qh4fEj4fNreb9XyCSt20DWomVFutyCwrwReHD0Jvt2NgQiJmdeyMOJWqURcBb2ZIyjfcOCnf3C43dq3Zj9dmfEA7zuFw8NzKx9F3Yg+auPk3VBRWYV73p6GvMDDOjblvGB54/84mE+4ndp3BgqEvMsyO731rFsY9kH7FlgUupwvbV+7FW3M+oR3ncDh4cd0C9BjT5YZJ+3YtqCnTY8GQF1Bwtphxrlt6Gp5d/hjkAYsqpbkVeLTPs9CV62lluw7viKd+eIgmik06M35+aTXWfvgnraxIIsSHe1+l5St3udz46YVVWP76OkY7pEopvjz2Dm0Rqji7FB/e/yUyt56kldVEqPHGpueQ3DHhci6fldMZWXi073Os5z7e9xra9KhLD1dZVI35g19ASU4Zo2zv8d2w8MeHr1jUGmtM+P655fht6WbacbFMjI/2voqkDvFXVO+/xev14sCfmVg84U3U/+mZ/+2DGDK9X5Pt/hdnl+GR3s8wcsb3ndgDjy69D5om8q8vOleCt+/8FKczztGOh8WG4LW/nkFi6pWPTeY/J/DU8JdYzy3NfJv23FxLamxWvLRjG/537iztuEIowtopM9BCSw9AuL+oELPX/wpXPdP+h7r3xJy0Llctyv5rVFutWLxtC/7KOU87rhKJsW7qDCSqG15AbIgysxn3/bYOJyvpizsJKjWWTb7tqoR7kdGAKb+uQFk9H9+eMbH4cOQYhMvqfg+MDgeWHtqPpYcP0sqKeHysunUqOkRE0o6fq67CLat+ofkUA8C01A54qk9/qC9jRz8YO/Jycc9v6+CpN0c93XcApqV2gFJMFvBvZpweN97esxvfHD3MOKcSifH79FmI+Q9riStBZ7Phjd07sfoM/T1KLhRizW3T0TKEpERtiMvVocSn/QaiukSHD+7/knGcoii8f98XV+V3Hh4Xijc3P4+QaPrLVfeRnTDz+duaTLDXlOnwzl2fsfoJf7No2VX5/VaX6vHRg18zjlMUhffuXYqakiuv+2ZAG6nGSxsWIiaF/nLVpkcKHv/yfppgdzpcWPPebwzBDgCHNx9H3qlC2rGaUh1DsAM+F4mP530NY02d8BII+Jjw0Cj0v6UXraw6TIm3tyxm7JRWFFQxBDvgC0b3+xd/w2m/8gBw8e1i8fAnd4MvqDNqEgj5eGzpfYhrHUMrS3kpPPzp3Yzo9m17tsT4B0bAdhXp0KqLdQzBDviCSX7++Hcw6ZomcE51iQ7v3v0ZQ7ADwCcPfdOgW0NjYrc68NOLqxiCHQD2rD+AUpaFk2tF4dlihmAHfIs4/yzbBbeLfTfncmjRORH3vDGTtlgrFAvx9E8PI7pFRAN/2bgUG40MwQ74zN/f2bsLZmfdM1ZkNOD57VsYgh0APjt0ADX24GapNyoFRj1DsAOAwWHHB/v2wuq68jlqf1EhQ7ADQL5Bj9/PZcF7hfs6Lo8HPx07yhDsALC/uAinK+nuQJUWC0OwA4DD48azWzf7TfcBn/ntKzu3MQQ7AKw4dQJlliufzwoMejy7bTNDsAPAuxm7UWW7shgThBsHIY+Pe7t2w/BkenDSUKkUP0+6FdGKm88aqMxsYgh2ADA7nXht9w6YHE0TiPhGh5jH30DoKwy0aNmBmPUW6CuNCI+/8tWu5A4J+HifLxKxvtKI6BaR0ESortqv899grDaz7kQCvlzJ+aeLEN0ikvX8pagp1QXNE62vNEJfaWxU09gbkZiWUXh3x0uoKq5BdUkNIuLDEBKtYUQFN1YasW3FnqD1bPxmKzoNbu/Pk17rd87Gqb1ZMOssUGrrfihDojR4/Iu5uOOlqSjOLoNCK/e1JYaee52iKGz6fnvQurct34MZz0xGaMyVjbtcJUP6HYPRfVRnFJ0rBYfDQUzLSGgj1TTTeAAozCrGe/csxe3P3wqlVg59pREhURoUnS/Fy1Pewwe7X0Zo1JWlRDv099Gg5zK3noRZZ7lq15LLwXDxOWLDbnWgukTnD1B3LTFVm7Hz131Bz2/9ZTfa9W59zdvhcrjw59f/BD3/z7LdmPjwKIRc4bgrtQqMnzcC/W/phaJzxeAL+IhuEQltlOaKze6vhE0BgjNUKoVUIECpyQSX14vNuTkwOuyQC30Lv0aHA9k17PExvBSFE+VlaKm9uebh37Oy/P9vExIGpUiE4xVlsLvd+Cv7HBb2HQCp4N8vnJudTqw8fSLo+TVnTmFy21SEXoFlQ43Nhg3nzgQ9v+LkcfSNi/f7vB4uZVpo1XKysgIGu8PvV26wO7C70Od2JOTxECmXw+J0+v2Mt+flok1ocJe+hjA4HCgxMRfzAMDl9eKCrgbJmuaZmpLQdITL5HhjaDrm9+6HAoMearEY0QolIuXy6xaf5XryT25O0HM78/NgcNihEImCliFcHkS0/wewW+2oKdXj/JFcuJ1utO7eAuoINeT1gxNdYqJojHkkLDYEYbGX98JkqDaiukSHnMxcKLQKJHWIR0i0hrbr+G+51DVwucwCNrMdunI9zh++AI/Hi1Zdk6GJVDNMjS810XKIXcplERKlQUiUBkCL4IU47GNVC5fPo41HQ2UB9rFTaOVQaOWIbxvb4N82VDeHy/E1th5leRWoyK9C/ulCRCVHIDolMuhikVgqQlRSBKKSGt7Z5PK4qCyqxgdzv4BAJIBUIYFZbwmwKmG2w6Qz++aGwzmQKCRITktESJSa4ascuFDBuEYOh7X/9BUGVBZVI/dEPrSRGsS1iUForJbVRcRhc6CmVI/szFw4bE606tYCmkg1FGq6/+elnzHmeYvRCl25AecO5YDL5aBVtxZQR6ggZYnR4fF4UJpTgeLsUpTnVSCxfTzCYkMY1gvgNNwWLo/ZX26XG9UlOuSeLICxyoSUzkkIidZCFcq+q6KvMKCisBp5J/OhjdIi/mL/0caCw/5d/nYEuf9Kc8tRlluJwqxixLaMQlRyBPMaL+J1e+H1eGC3OMDlueD1+j4DTNFOURQqi6pRlFWCquJqJKTGIzwuJKiPf5nZhCqrFcfLy6AWi9E2NByRchkk9QQkl8NFj5hY3NWpK6ptVhgdDrTQaHG6sgJfHD5AK3upe4TNN9LqcqHKasHJinK4vF50DI9AqFQW9CWx3GxGgUGPfIMeCSo14lVqRLD4WAO+HeAamw1Hy0oh4vPQITwSoVIJVGLm/eelKJSZTbigq0G52YxWIaGIUiiDil6dzYZyixmnKyugFovRKiQUETI5I3gTj8vB3Z26YnLbdjhfUw2jw4FF/Qag2mbDE38zLZBq211iNuF8dRWiFAokqTWIkito/csBwGW5v2rhcjisZ8vMJhQY9CgwGJCk1iBWqWLtv4b8WLn15p1L+bwGnuZwABGPh4d79EbLkBBcqKmBSiyGRizBV0cOXZX/7KX+kvjmNj5mpwNVVitOlJeDwwHah0cgVCqFXNi0Is/t8aDcYsH5mipUW61oGxaOSLk8aBBCjUQCjUSCliHXdxHR5HCg2uabh7kczsU5SgqZsOlilnAbeEHmBJlHqq2+OepcVRXCZXK00GoRKVeQZ6wBiGhv5liMVmxfsQcfzfvan/saAKY8NQFT5o+n7XJrwlVQaOSspq6qUCXUYU2XA7m6VIcPH/gSGf875D8mlorw0oaFaN+/DQTCK9vlUYYoEN8mhtVvmi/gI74dXaCZ9RZs+WknPn/ie3//cTgc3P7sZEx6dAwteJo2Ug2pUspqraCNVEPdhBYFNzqqMCWGzRqIX9/7jfX86HuG0l7o0ga3D1pX2uBUKLRXFhyIw+Fg5F1DsPnHHaznh88eCFUYXZQVnSvFkklv0XJoh0Rr8dqfi5DcMfGK2gEAUckREIqFcNqdcDlcMDhctHPKeuJQV67H108vw98/bPcfEwj5eOaXx9BtZCeIpXUvPN1GpAFPsn9v95GdaNHxAV/avNdv/xDHd572H5OppHj9r2fRqnsLmnC3mm3I2HAQ79z1Oc2Ue9wD6Zj9whTavKMKUyIkSsNqBi9VSC4u9tRhrDZh/cd/4eeXf/Wb1HO5XMx9dzbS5wyCvN6iQM7RPDw75nVa7I0WaYl4fvWTNLcNZYgCQ2b0w8Zvt7L2yZAZ/WifXU43Tu4+g8Xj34TdWmfm12tsVzz+xVxG4MTKomq8Mu19nN5bt0Oq0Mjx+sZn0bJrsl+4C4QCjL0/HXs3MM2CAWDkXYOhDqfPO4Vni/HcuDdoFkcRCWF45fdFSEylRyw2VJnw63v/w8o3N9T1H4+Lhz6+G0Om96NFpacoCjnH8vB0+iswVNVZQ7TqlowlaxYgPI5upVVsNOKlHVuxOWCHRczn46ORY9AnLp628zu+VRskaTR4bNMfsAcEbxqcmIQvx02EOsBHWCUUoU1oGM5WMbMp8DgctA+nL06YnA78fi4Li7dtoZk0z+3SHfd27Q5tPd/mPL0Os9f/iiJj3TXGq1T4YcItSKjnG15qMuKLwwfx4/Gj/mN8LhfPDxiMUSktERoQ0d5LUThVUY7Z69fA4Kiz2OocGYVPR41DZD2T2UqLBc9u3YwtAf0n4fPx5diJ6B4TC2HAMzatfUdkVVdh8qrlcHjq+m9EixSsvm0arf8AoMRkxNzfN+BUgNl7iESCHyfeijahYf65VSYU4vYOadhbxB4sc2pqB0b/XdDVYPa6X1FirtuNTlSp8f3EWxAfEIArRCLBrW3b45OD7BYtt3dIAz9gAatrVDQ4ANiM8btERtHvEZEY34ybhKWHD+KdjN3+41KBAG8MHYHUsCvbZffVLUKCSo18g55xTsjjIekq4gcQmOjtdvxy4ijezdjjH3suh4MFffphWmpHqJoofoDb48GRslLc/b+1NLeL/nEJeDt9JC3+QnNCZ7Phx+OZ+Gh/Bq3/nuk3ELe2TW2y+AtDk5Jpz2Igw5JaMBY5y8wmPPzX7zhcWuI/phSJ8OPEW9E+PIII9yCQvcNmTkl2GT64/0uaYAeAVW/5IqcHEhKtwYLv5zF21bg8LhZ8Pw/aaPW1bi4AwOP24M+vttAEO+Azf312zGuoKgqeGuxSaCLUWPD9PNZgVQ99fBdjR6j4fCk+ffRbWv9RFIWfX1mD80fo6ZBCojWY/+2DjJ0eLo+Lp354CCExxCSusRAIBZj48ChWd43+k3sirnU07ZhQLMDkx8YwykqVUsx5YSqEkitfkQ+J0qD3uG6M4+HxoUifM4i2wFRdqsP7c5fSBDvgS3e3ZOLbKLlQfsXt0EZq8NgX9zGOC4R8LPh+HkPQ7tlwkCbYAZ+4fHnKe6gsrKIdD4nWYurCiYy65WoZ5r4zhybenHYnlr3yK02wA4DFYMXTI15BZb3ntyK/Em/O/oThe/3b53/jyObj9dqhwVM/PsQIiMnhcPDk1/czxG92Zi5+emk1zQfe6/Xi88e/R2FWCa1s8flSLB7/JiNYZs6xPHz++He0lIRCsQDjHxzB6FMAGHBbb+YiRlE1nh39Gk2wA8C+3w/jt6V/02Js2K0O/PD8CppgB3xWEU+PeAVVRdW04y3SEtFtRCdGO2JSIpE+ZzBtgaSioApvzPqI4SJUnl+JV6a+h7J8utDNOpiNFW+sp/efx4uPHvwKxdmltLKVRdVYNIIu2AHg3KEL+GL+j7Ca6nzJXR4P1p49RRPsAGB3u/Hgn78xzIt5XA6e3rKJJtgBYFteLjJLSyHi1e0fRCuVeGXwMIhZUn0t6NMfIfVEZIFBj2e3Mn2QvzhyEJml9HukymrBg3/8jybYfXUYMO+v31FtpS/YHi4toQl2AHB7vViy/R8U16ujzGxiCHYAyCwrxVsZu2ANEAJujwfLTx6jCXYAsLnduPu3dSgz0/vPQ1F45K/faYIdADblZGNLTg5tZ97sdOClHdtogh0Aqm02zNmwhuFj3iU6Gt2j6fE1AKC1NgQjWrSk/R5WWiy47/cNNMEOAHkGPR7d+Ad0Af7efB4P09p3QLyKuVkwODEZreoFpgqVyjC/Tz9GWZlAgJcHD4M64KVfzOdjV0E+dhfm08paXS4s2LzxqsyT41RqvD50OG3RpJbn+w9CGEk92KhkVVXinQDBDvgWwN7cswvnaqqC/l1jU2ox444NaxhxEnYV5uPbzMNweZhxlJoDZ6oq8GGAYAd8/ffKru24oG+6fPGRcgXu6tSFcVwtFuOpvv397k8AYHO58N6+PTTBDvhco2at+xWlQdxTCGSnvVnjdrqx/uO/gp7/5bW1aNu7ld+Xl8fnofPQDlia+TbWfvgH8k4WIDktAZMeGYOo5KZL61RTpsfaD/5gPedyupH5z4mgppyXQ0rnJHx57F38/sXfOLXnLCKTwnHrE+MQ2yqKtrvotDuD7uQCwPLX16FNjxTIVL4fYb6Aj+4j0nz998EfyD9diJTOSZj48GhEJofflH5K15KIhDC8v/Ml7P3fIWxbvvtiyrcxaNW9BWPx5dzhCxDLRHhm2aPYtmIP9BUGtO3dCt1HdMIX83/A86uebDAtYUP8/cN2dE1PQ69xXbFjVYY/5VvLzsn45OFv8fyqJ6CN9LXHWGXC8R2nWespy6uAvlyP6Cu8t4ViAfpO7I7PDr2JNe//juLzpWjbqxXGPZCOyHqm9dWlOqx8Yz1rPV6vF9tX7MGsJVP8x+RqGabMH4/uIzthzXu/Q19hQPdRnTFs1gCGD7muzBDUz99qsuHCsTxEXuxriqLw1zdbWQPLAb5nrPOwjv5I7BwOB+37tsUXR9/Bhk//wvnDFxDfNhaTHxuD6BaREAjrfpLMBitrBoBa1r7/O+Z/N88fBLOisCpoILsDf2bCpLP4A2nWlOrw0YNf4/Ev78fpfVnI/OckZEoJBk7tA4vBij3r9mPKgon+vz+2/RRcTvaAcOs++hOj7hnq34nWlxvwzy/sOw5mvQX5p4sQHl93r2oj1Vjw7YM4s/881n/8F1wOF4bNGoAeo7swdreN1aagudfzTxfBWGX0j41JZ8Yvr65hLQsA6z/6E49/db9/Uao0pzxovIHda/bj7tdmQKrwCadSswk/HstkLev2evHPhRykBPid7y7MZw0sBwDfHT2CW9ulIjIglVtqWDj+N20mfjlxHEfLSxEhk+HOTl2RqFZDE2Cq6vJ48MNR9nYAwGeH9qNrdIx/l7baasXZanYhcLqyAtU2qz8yfZnJhK+PHGItCwDLThxD65BQiAW+/rugq2EI9lp+P5eFx3r28e9EV1qt+PboEdayTo8HewsLaLvWf2efZw2MBgDfH8vEmFat/eWrrTbGYkAtVVYriowGRAXs+kfI5Pho5FgcLi3GT8ePwktRuK1dB/SLj6eNCQBU26y4oGNfdD9WXoZqm402PtEKJX6ZPBXb8i5g/dnTEPH4mJPWGZ0ioxip0xQiEaa374ge0bH4JvMwyixm9ImNx22p7RFbL4J9pdWCZSePsbbD4XEjs7TkqiLqp0VE4n/TZuKn40dxsqIccUoV7uzUBfEqNeTEL7fRsDid+IIl+GAtXx0+iNSw8CuK1/BvySwtYSwq1vLziWOYnda5UVIgNiYmhwOfHdwf9PzXRw7h7eEjIRFc+/glKrEY87r3xNCkFvjm6GHU2GwYmpiM8a3bIq7ewl2V1YoNZ9njXZicDmTXVN900fcvFyLamzFOhwvl+UwTwVqqS3RwOeiTjFgqQmRyGGY+dyucDieEYgGU4eoGo7uX5pbDYXWCw+VAKhcjLC54sDpDtQnGKiPcLg8EIj5iW0YzynjcHpj1FnQf2QnTnp4EsVQEDpeLswfP4+uFy1AaZDeyulQHi8EKj9sDkUQUNLoxX8BHZHIYxj2QjhF3DoZAyIcmQuUX37U4HS5UFARfqa0qroHD7oIsYD4Ry8SITonAzMW3wuV0QSASQBulhrAB36Cy3HLYrU5wuRxIlZIGg5YZq40wVJngdrohEAsR2zIqaFnAl47MaXOCL+BDGSKHqgEXh+rSGlgMNnjdHohkogZ9qN1uN8ouVMDldIMv4EMbrYFMETxFTkVBJWxmBwAKYpm4QYFsNVlRXaLzXaNIgLCEEIiCvOiEx4eh5+gu6DQoFVw+D1KlBFoW/9mK/Er875ONeGbF43jg/TvgdrnB4/Ow4fONyDqYA3cQQVWcXQan3Qm+gAe5Vg5Nvf7zer0ozCrB9pV7sPCnR9BpcHt4PF5wAHz7zC/Izsxl7KI2hKGKfYW4oqAKNrMNAAcSuYgm2gKRKqSIaRWN25+7FW6XGwIhH5HJ4eDX23X0eryoLtVhwoMjMOHhUXC7PODxuHA5XXj99o9RdL6UUbcyRIHEdrG4982ZcLvcEEnY7xGnwwWXw4W578xGzzFd4HZ6wBPwYNabsWT8m7RnyuPyoDSnHCmdk7Dg+3ng8XnwerzgC3hY9fYG7Fl3EJ56YyMUCxCVEo7Jj465eP/xEBqrZaRqdNldqC6pQe/x3XDP67fD66V8fq0cDj55+BuUF1TB6XD557aaBiLPUxRFCzLp9XiRdTAbR/45jnH3p2PQ1L7g8Xlwu9x4Zcp7aN+vDe3vS3ODW1D45qw6Qeq0O+F2uTHv47vQdWhH3zUK+TDWmPD82DdRVcwUPdooDdr1aY2kDvHweilI5GJWK4BL3X82c901uhw+H/xOQ9rj3jdnQiASgMPhwGq04eN5X6M8vwouu8sv2mvKgvef1+uF0+YM+Ez5g36xUWikWzsU6JmpQmvR2W3w1BP0Ij4fKdoQ3NO5K+weN4RcHjRiMWT15hGnx4MiE/tCAwCUW8xwBuxOW93MaOOBBO6GO70+/9ZglJnNcHg8ftFeZjZDzufjmwm3QCuRwu31gM/lYW9hPpbs2ApnwE6dh/LC2EA05YJ6ptmFpuD9V2m10BbNbG5XgxHfK63Ma4qQy9E5IgqtBoeCoihIBQKGYAd8L9QAsG3WXfBQFFxeDwQ8HiiKwvCfv6f1Xy3RCgX6xyeie3QMuBwO5EJR0FznarEEyVoN5vfpB6fHAzGfz5rz2u31wux04sMRo9EhPBJ2jxsCLg8CLhdz1q1m3H+1lJvNMDmd8FJeiHl8xKuZdQOARCBEskqNuzp1gdPrhYDLRbhU1qBgLzYaL14/BSlfgBgWC4NaTHY7Km1WOD0eCHk8xCiUELFYltRSaNDD5naDx+FAJhAyXC0C0VmtqHHY4fZ6IeLxLrl4kafXweHxQMDhQiUWN5hOsdxsuth/FMR8AasVRS0OtxslJqO//8IkUijqmWo7PG6UWcwYkpiEp/sN9N/HHHDw6u7tKDWb4XB7IK2nOc12OypsVri8Xgi5PMQpFIzfyEAKDHrY3W5wORwohSKEs8RfKDQaECaR4otxE6AQinx183jYmH0O72TsCbrTnqurgdPrBZ/LhVYkhqaB/is1mWB1OeGhKIj5fNrCXH0cbjeKTUa4Ltl/FgxPSsaCPgPgBXXRd5yDF3dsQYnZBKfHwxDtRocDVVbLxf7jIk6hvKz+43G4kIsEiJCx338aiRQp2hAs6jcQnov9V1+w+9rtgcvrxeIBg9A/PtF/jTq7Dff9vgGlZvb3qDKzEWanC9TF/otroP9sTidKLWa4PF4IeFxEyORN6uN/rbhhRPunn36Kt99+G2VlZUhLS8PHH3+MHj16XO9mXRVimQidhrTHse3skbPb9moJmZIutEpzy7F12W6s+eB3mGrMUIYocOuT4zB4al9EJtF31AxVBhSfL8fnj3+HsweyweFw0H1kJ9z71iyGbyQAlF4ox6/v/Ya/v98Ou9WBiIQwzHlpKjr0b4PIxLqXf6FEiAXfzYNCK8cHc79AYVYJBEI+BkzpjY/2vgp9pZ5Rd9G5Uny/eAV2r90Pj9uDxNQ4zH13DhLaxjIitpflluPw5uP4+eVfUVVcA4lcjFH3DMWkh0fRdiQlcjHSBqWyplMCgNQ+rRnB6MpyK/D3D9ux7qM/YdZboA5T4rYF4zHgll6M3U5duR7F2WX4/PHvLwbJ4qL76E64941ZSGjHDH5WeqEcq97egM0/7oDD5kRkYjjueGUaUnu3YtRdUViF84cv4KuFP6P4fCkEQj4GT++H25+7hTXgWdG5Enz7zC/Ys+EgvB4vkjsm4P73fP1X3+S4NLccB//KxC+vrkV1qQ5ShQRj5g7H+AdGMO4Rm82GkqxyLJ3/A45eTIvWoX9bPPD+HYhqFQ25nP4jUpZXgT+/+gf/+2wjLAYrNBFqTHt6IvqM786oW19hQMHZYnz++PfIzswFl8tFz7FdcM8bMxHfhm6u2aFfG3x68E0sf2Mdtv6yG067E1HJEbj79dvRd3sPiGT0l6my/AqcO5iDbxb9gpKcMghEAgy9vT9mPDOZZuXB5XIx+t6hCI8PwzeLlmHf74fh9XiR0jkJ9787B8PnDIQ4oG65WgqBSACXg/3lPyKRLsZtNgeKzhRj6RM/+M3N0wan4v535yC+XQxjMagstwK/f/E3flu6GVajFdpINaY/Mxm9xnal7YiLpSK8uXkxPC43Xp32AXKO5YHL46LP+G5YsmY+ayq9kgtlWPbKGmxbvgcuhwsxKZG4581ZaNk1CREBiwhimQhLM99GwZliPDP6NZTlVkAoFmLY7AH4aN/rMOnrflD5Qj4mPTYW2kgVvn76Zxz4IxNery/Y4/3v3YFhswZCXG8xqCyvArvW7Meqt9ZDX2mEXC3DhIdHYsQdg2mLCFKlBA99cjfkKhneu+8LnNrjSxnWeWh7PPDeHOgrDJAG3H9xbZgmvrVI5GKaC4BQIsT7u16C3eLAi7e+i7yTBeDxeeg7qQde2rAQunom9u37tqlfpZ+YllEQSepeiiRyMb489i5yjuZh0ahXUZ5fCbFUhOFzBuKzQ2/AYmSK3fzThfjyqZ9x8K9MUBSF1t1T8MD7dyCpQ7x/dxsAlFo5uDwuw10K8FkxqMPqdiekSgnmvjcbodFafPrItzidcQ4cDgddhnfAUz88hNILZbTnpqH+k6mkkAb81gj5PLQJCQ26a907lp5bvmdsHGtOYwBopQ1hCJYSoxF7iwrwwb69KDGbIBUIcFvb9rircxfai5pEIECf2HjsKyoEG10io2jBrDRiCbgcDquo5XE40ASYYCuEIqRFROLvC9msdXePiYEi4PlNDQnF77fPwTeZh/Hr6ZOwud2IVijwRK++2DhjDqQBL85iPh8tNFrkBNm17hFD//3oFROHVaeY6ZQAn1VCoCm3QiiCXCikpdELpH7kc5fLhRyDHq/t2oE9hfmg4PMvf67/YCSqlVCJ656bUIkMO+bcjTVnTuOHY5kwOOwIkUhwb5fu2DHnboY/erXZjFyjAa/s3IbjFeXgcjgYmJCIRf0G0iwxaik06LH08EGsO3sadrcbsUolnuzdD10io2kv/2I+H//MvBPZuhrc/ds65Op1EPJ4GNeqDb6fdCvMdqbFQ66uBm9n7MbmnGx4KAptQsPwXP9BSNKoESWvFzfCoMeO/Dx8cnAfKiwWyIVCzGjfETM7dkKski5CbC4XcvU6vLprOzIu3ofdo2N8davUkNcTWoUGA1adPoEfjx2FyelAqFSK+7v2wPDkFJYdSRMu6PR4Zed2nKysAJfDwdCkZDzVpz9asPRfgUGPzw/tx/qzZ+HwuBGvUmFB7/7oGBHJqLvQYMDJynK8tWcX8g16CHk8TGzdFg9278kqJnNqavD23l34JzcHHopCu7BwPH/xGsPrLSIUGQ3YciEHnx3ajyqrFQqhEDM7dsL01I6IDWiHXCjEs30HQCWR4LltW3Cg2Od21is2Ds/2GwS93Uozq65t9/KTx7HsxFGYnE6ESWV4sHtPDElMYoi4MrMZuboavLJrO85UVYLH4WBYcgoW9OnHeA76xcZjbMs2+PTgPvzv3Fk4PR4kqtRY2Lc/fps2E2I+XfgWGw3ILCvF23t3o9BogIjHx6Q2bXF/tx6s/ZddU4039+zEtrxceCkKHcIj8Fz/QUhUaxiLWEUGAzbmnMOXhw+hymaFUiTCnI6dfVYnysD+E+HFAYOhFIvx7LbNOFjii/XUJzYez/YfjBqLBbJ6gr3QYMBPxzOx/ORxWFwuRMjkeLhHL/SPT2TcI2UmE7J1NXht13acra4Cn8tFenIKnuzdF0ksGRTyDXp8tD8Df5zPgtPjQbJGi6f79keb0HDEBuycywQC/DVjNs5UVeKODWtRbDJCzOfj1rapWD/1dhhZFoTPV1fhjd07sT0/FxSAjhGReH7AICQoVAittwhTaDDgj/NZ+DrzEGpsNqjFYtyR1gWT27Sj3X//RThUMLvG/xArV67E7NmzsXTpUvTs2RMffPABVq9ejaysLISHXzp90OUmtb8eFJ4txkM9F9H8CQGfKfzH+15Dyy7J/mOVxdVY/tpa/Pb534x6Jj0yGlMXTqTt3uQcy8PDPRcxzD7VYUq8v+tlxLaq20UvzS3Hu3d/zrqA8PiXc5F+xyDaSt3BjZl4ZvRrjLLxbWPx0oanEJNSt8NcnF2GZ0a9yvDT5HA4eGfbC+g4oJ3/mM1iw59f/YOlT/zAqLvHmC54+OO7aeKm4EwR5vVYxEjlxhfw8cmB19EiLdF/rLK4Cj++sBobv2EGp5qyYAJue3IcLXXZ+SMX8EjvZxm+vNpINd7d/iK9/3LK8cacj3B6L3MBYeEPD2PYrAG0Yxm/HcTiCW8xyiZ1iMcLaxYgOiCoVvH5UixMf5lhlcHlcvHezheR2qdOcFhMVvz22d/4ZtEyRt39JvfE/e/Ooe2iF5wtxsO9nmEE5xPLxPhk/+u0xYmKwkp8+8xy/LNsF6Pu25+/BZMeGQNVQOC/rIPZeLTvc7SdbAAIjdHi7X9eQGyrunukJKccr05/H+cOMc0+n13xOAbe1pvmvrDz1wy8POU9RtlWXZPx7IrHaQsfxedLsWDoi6is52vM5XHx4Z5X0KZHS/8xQ7UJy19fizXv/c6ou9fYLnjo47sRkUC//x7quYi2Awr4gq59vP912uJEeX4lvlzwI2sqsjtfnoaJD4+CNGCR6XRGFh7vvxjeeruU4fGheGvzYsS0DOy/Mrx0m09I1ueFtQvQd2LdAidFUdi6fDfemPkRo2zbXi3x9E+P0Pqv6Fwp5g9ewjBN5/F5+CjjVbTqWpdFwFBlxK/v/YYVLKb96XMG4Y6Xp9GyU+SdKsTDPRcxdphlKik+3vcaLdd9aW453rtnKY5uY4qbWYtvw61PjKX134ldpzF/8IuM/otKjsDrfz1L67/i7FI8PeIVlOUy81k/v+oJDLi1t/+z2+3Glh934t17PmeU7TigLeZ/O4+2cFR0rgSPD1jM8MUXCPn4aN/rSOmU6D9WXarDD0tW4i+WNHGDp/fDvW/OpPVf7skCPNRjEZx2uoBTaOX4aO+r9DnqQjnenP0RTrHMUXe9Oh2THhsNsaROgGzNzcE9v61nlI2SK7Bs8m20Hb48vQ4z161mTaX15dgJGBaQ79jl8WDlqRNYvJ15jX1j4/HGsHTEBLy45tTUYNKqZQyRyudysW7KDKQGBK4rMxvx9t49WHeW6eJya9tUPNmrLyICBMiJ8jJMXvULwzRdIRRh7ZQZaKGte3EtNOixcMsm7Cumx7sAgFeHDMfkNu1oixN/55zH/X/8j1E2VqnEjxNvpfVfrk6H29etYs15/t2EyRiYkOT/bHY48N2xI3h/315G2R7RMXhr+EiaqLigq8Etq5YzTPvFfD7WT72d5nteajLhw/17seo08xm7r0s33N2pK8ICXqBPVZRj8qpfGK4RoVIpVt4yDUmaumssMOgx/++/cKienysAvDN8JCa3TaUd++dCDu79fT2jbJuQUHw+ZgISAnbR2YIPAr6AXStvmYquAT79Jrsdy04ex1t7mb9jI1qk4Nl+g2gv/jk1NZi8ahlM9e4/qUCA9VNvpy1OlJgMeCdjD9azmAY/1L0n7ujUGVpJnYA7Xl6GW1cvh7te/0XK5Vg2aQq9//R6PLLpDxwvZ6bE/WjkWIxtRU9h+df5c5j3F9N9sH1YOD4ZNY5mhZCr12Hm2tWMHVAeh4PVt01Hp8i6ubLGZsPPx4/ig/3M+29syzZY2Lcf7fnNrqnGpJXLGL7kcqEQa6fMoPVfsdGIN3bvwB/ZzDnqyV59MSutM5QB1hBHSksw9dcVjOc3WqHATxNvo/Vfvl6HeX/9jtOVzDl+6ZjxSG/RknZsQ9YZPL6JmbmhU2QU3k8fTbv/cnU6TF+7EhX1LHf4XC7W3DYdHSLqfk9rrFZ8e/QwPjtEz6oBALe0TcXjPfsgOkCnnK+uwsSVy2CrZ9qvEIqwduoMtNAEzlEGvLxzG6v7zKK+AzAttQNtN/9gcRGmr13FWOSMU6rww8RbaHNUvl6Hub9vwLka+nsUAHw9bhKGJNVpFZfbjfXnzmLhlk2Mst2jY/B2vTkqV1eDqWtWoqpezBEhj4e1U2agXVjdO1elxYIvjxzEN5nMReLpqR3xcM9erFZE15vL1aE3RCC69957D/feey/uvPNOtGvXDkuXLoVUKsW33357vZt2VTgcDhzfdQaLlj2K1t3rXmri28Rg0c+P4NDfR1ETsKNmNdjwx5dbWOv67fO/YdHXTRjVJTVY/vo6Vj9NfaURe+pFM9ZXGIPu+P+weCXKLtRNdCUXyvD100xRCPhETP0AUuePXGDNvU5RFL5ZtAxlAaapVUU6/PzSr6x1H/jjCMy6umu02ZzI3HYKz/zyKG1xIzE1Ds/88igObjpK8+G06m34+7vtrHWv++hPGGvqXpgqS2rw88u/MgQ74PPpP7jxKO1YdZmOVbADwLfPLaeZM5dkl+Hrhez9l3uigBHs7HTGOVY3Cq/Xi2+fXU47V1Oiw/LX1rLWvXvtflgMdZOi1WTFxm+3skbTt1vs2PDpRlqmAqvRhq1BfHl/fec3GAL6uqKwCj++sJIh2AGf28KxHfSXwoqCSlbBDgDfLFqGkpy6PinOLg16/507fIHhMnFsxymGYAd8JtQ/LF5BM2c2VhkR1yoGUxaM9++ACkQCjLhzMIbPHkwT51aTFb8t/Zsh2H3nbPjz6y2wmusW4ywGa9Dc4Svf2oDqUr3/c0VBFb57fgVDcNaeO1UvCFrphQpWwQ4AXz+9DCXZdc9fcXYZvn3mF9ayZ/adZ4jzI5uPsfqSe9we/PTSalSVBPafCWs/YE9RteWnnbR7zaQzY91Hf7KahFsMVp/FiqPuRdlmsmPiw6MwbOYAf1pJuVqGGc9OhipMCV15nSguy6/AN88sZ+2/0gvlDL/xivwqPPLpvegxqrN/cSgkSoOHPr4bhVklNJefsgsV+P75FazXeHznGcYufsZvhxiCHfDF/1j+2hroKvR11623oEO/tpj48CiIL7oTiCRCjL0/Hb3GdIE1YBffVGPC6nf+xxDsvnNmbFuxB66AF2WzwYqpT03C4Gl9/f2n0Mgxa8ltEMtEqCqsG8cigwEnKyrwyuBhiLr4AsQB0C8uAa8PTUeujn4/FOj1eG1IOvrHJ/hT/0TK5Xh50FCcrqxAUYA5c5HRgA9ZXvgBYE9RAc0s3+1242BxIT4cMQYdA158W2i0eH/EaOzMz6MFlzM7XRgQn4BZHTv5A91J+HzM7tgZvWLjYHLV9VWN1YpdBXl4f8QY2q5cp8gofDBiNA6XFMMTYDJbbbOxCnYAPouBADP+AoMeWVVVeGnQUERcjErNATAgIREvDx5G6w8AKDYZ8e34yegbV2fBEC1X4MORY2BxOlEcUL7CaoFSKMbDPXr5BYyAy8WE1m1wd5duMAeY5ducTmzIOsPqi293u/FN5mHoaP3nxK9n2N8Dvj+WCWOAcC03m/DxgX2ssQyqrFZsz8+lHauwWFgFOwC8m7GHdk/lG/R4Y89O1rJnq6sY7gVHy0oZgh3wBex6O2M3rf8qbVZ8fojdT3hTTrbfRQDwCfzVp08wBDvgc7X48VgmDAH3q9npCurL+3XmYejsdXWXGo34YN8ehmAHfDvIGfUi/peaTayCHQDe2rsTeQFByfL0OtZFCQA4WVnBcDk5WFzEarLsoSi8v28PSgL6VmezMdI41vL7+bMwO+vmHKPDgWXHjzEEO+C711acPE6zmjA7HayCHQCWHj6AqgDXj2KjAe9l7GGNBVFiMuFwKT0DUZHRyCrYAeDNPbto/Zer0+GdvezvOkfLSlFhoS+u7S7MYwh2wOfm8eH+DFrgNZ3djm8y2eNdrD1zCpaAOUpns+H7o5kMwQ74XFnWnj4FS8C9aXI6gsa7+OTgflQGBJIsMhrwTsZuVqukQqMBJ+rdaxf0OlbBDgBv791Fy8ZQZDLh3SCR5g+WFDMCgm7NvcAQ7IDPPeqTg/tQbqnrP6PDjp/qBQ+tZdXpEzA52C2Q/iv8583jnU4nDh8+jEWLFvmPcblcDBs2DBkZGax/43A44Aj44TKyTObNAVOVGduW70ZRVgnG3p+OaU9PAuX1orKoGt89txxulwf9J/fy+wDrKw2sZpOAL8ewobruxraa7Di5i/3HAwAytxxH+pyB0IT76g4mmACfSA0UJm6nBxeO5wctf3TbSfQa09X/+fDf7AFlAJ9ICPQXtRqtMOuD+xnmnfIFjwMAY4UBW5ftREVBFcY9MAIznr0FoCiU5Vfiq4U/g8vjot+knn6T0upSHetLPAC4HC4YA/yVHSY7Tu4+G7QdR7Ycx/A5gyC/aJJ7Zh/7Dw0AVBZWwREgTJwOF2tKu1qO7ziFbulp/s+HNwfvv9N7z9EWZsx6C8NqI5CicyVI7pgAADBUmnCMZdeylhO7zsBUbYZC43vpLM+rDBqQzGFzwhwg8J02J07uzmItCwCHNh3D8FkD/fnGT2UEL1uWWwGnra7/HFZn0LgJAHBy91l0uphCzu1249Cm4P13cs852K1197ZJZ8EH93+B3uO64bEv5oLH54HDAfasP4DXZnyAJWvnIzHV92Ld0EIXABzffhqGSpM/33j9aN6BWE022n3vtDtxak8D/ff3UaTPGVT3XUGC5wG+MQ8Udg6ro8FYEGf2nUeHfm0BADaLHYcaeH5P7cmCPWBuMNaYWEUk4FtkqiqqQUI7n2uOocqEEzuDt/votlMYe3+6P1hb3skCvHPXZxg8vR+e+eVRUJQvmOeWn3fg4Maj+GT/6/6/dTvdjOjugRz6+ygGT+vr/3xwYyb++HILRtw1GKPvHQaP2wuryYo/vtiMswey0X9yT39Zu8URNCAe4JtL2/VqBcAnrI9sORG07MndZ2HRW/3zsKHahLfv/BR9J/XA/G8eAIfLBSgKO9fsw5uzP8FbWxb7rV8M1Wac2Bl8jj+69STG3DcM2kjfbklOZi4+mvc1ht7eH88ufwxeLwWXw4XNP27H4c3H8emBOosdl9eDD/bvRfuwcDzcoxdUYjF4HC4OlRTjkY2/Y1yrNhgcsLPyT94FrD97BlNS22NGhzR4KQomhwO/nDiG4xXlGNsqwBrI5WrQX/50ZYVfoNc47Pjf+Sxc0NVgRvs0PNCtByiKQqnZjPcy9sDhcWNkSku/n2611YonN2/EyJSWeHv4SL+p/J/nz+GpLZvwy+S64I0GhwM/HT8GqUCAWR07IVIuB5fDxdmqSjz9z99oHRqK4S1SoLkYzf5ERfA5p9JqofvLe7x4f/9edIyIxBO9+kIuEoLH4eJAcSEe+et33NauPfrFJ/rL/37+LPYUFOCZ/gOxsO8AuL1eUBSFH45l4n/nzuKfWXf6y5qcDry4cysGJCTipUFDIeDxwAUH/+Tm4OG/fsdrQ4aj3UXrA53Djr2F7OneAOBQSTH0DoffT7fCYg7qL+/0eKCzByxCulw4VBr8d2xPQT4mt2nrTwV1rCz4/FdqNsEeEI/A4XYHdS0AgH1FheifkOj/vKPeAkEgmaUltIUFo8PBKsJrydXr0Pbizp7e4QjqmgEAB4qLoHPYobp4jxSbjKwp7QDfIokhQKDaPO6gixgAsKsgD5PbpvoXnxrq6yKjkRZkzeHxsKa0q+VwSTH6XFwgcnu92JGfF7xsaQktq4HBYWcVkbUUGvVoHeqbs2tsVuwvYV/oAoD9xUWosdv9Lga5+uBttrhctP5zejwN9smugnzc2q4ulez+4uDjmKvX0WJS2D0+f/NgHCkrQfeLLi4WpxO7CoK/Dx+uFwBPZ7cxskTUQsG3YNPyovWL3m5rsP8yigsxvUNHvy93dhBRDfjmjcAFKbfXy4juHsjOgnyMa93W/7mh5yCruooWE8DmdrEuYtRyrLwMnaN8VmA1Vit2FQbvv0MlxbA4XcBFA5Uau402VoF4KArVNgtaInjcqebOf160V1VVwePxICKC7hMcERGBs2fZRdXrr7+OF198sSmad1WIZGKow1U4tv0UfliyknG+VbcWtNRJgZHTWesLCEbH43OhClMGfblUR6ghktaZydRGz2aDy+VCKBYEfOZAIhez7jD66qL7WIdEBw+UogxR0H7khA0E1ANA8+kUSoVQh6twOuMcvntuOaNsap/WtGjVkgYCsQGASFr33Vyez3/UWM1cfQYAdbgKgoA+qY1azQaPz6OlsOPxeRBJhHDY2F8c6veXNkodtG5VGN3MRnSJ1Gi1mQh8ZYWMv6fVHaoAP6DdMjV7cCG27+ZwOFCFKYMuIGgjNX7BDgAhkcHvEYGQD56AT/vckN95oIsIn89vsP/UYUqa2X3tM5Tx2yFk/MaMLC0PCIYoFAto92N9VGFK2nMT2Pds0IJJcnz9zxbUDAC0UfT7LSQmeP+JJELaPCIQ8sEX8FmtSAD6XCAUCxiR/gNRhSrB4Qb2X8P3H81vWsSHMlQJZLG/OKjDlbT5QBWmgsvpxt8/bGekwgMAEW1+5EAVqggaLT2kfv9Fa2A12bDuwz+x7kO6pYBYJgaXV2e0JhAJgvqdA4AmsP8kQkYe9kBUYUrwBXVjI5IIQVEUdq/dj91rmbuBgXOUQMSHKkyJsjz23SN1uJJ2T6kjVHA5XNj47VZG/noOh0N7HjkcDpQiEU5WVuCZrZsZdYfX89GMkMlhcjrwTeZhhtmiQiii5eQV8XhB83UDPtPqWiR8PkIlUuwrKmQ1x+0QHkHLBS4R8P0i/c/zzIXUwDRzAi4XWokEZ6oq8dLObYyyfSTxEAU8N2ENBKDicjgQBtTN4/j8Oo+Xl7HujtbPCR0hk6PYZMS8P5nmzBqxhDZHiXm+OWVnfh52soitwEBjYh4fIZLg7daIJRAE3NuXCuQU2H/cizECaoIswIRKpRAHpPoLFpwO8JkRB6a143O5kPD5QcVhuJx5/wVDI5HQFpslDQTkAkDLFy/i86BtoP+0Uikt3oDyEpHn6/efViIJGpsgTCqjlW/oGoU8X5C+WgQcLoQ8XlBxEzgWfC6X8TwHopVIgID7T1LP97s+KlFd/4n5PGjFwd+7tBIJ7RnTXCLneKDfOYfDQYhEGjSoWf3nNVhwNQAQ8fi0eUTI5ULA5QbNiBE4FmIeD6EN3SMSSWD3XfL+C/TxF/J4CJFIcCHIGnGIREKrTytp+B038HmkKEArlqDKxtzhBoCIevdEQ/eIVCAAL+AihTweeBxO0IwYgfefRCBosP9CJFJa3Ze6/y51vrlzQ5jH/1sWLVoEg8Hg/1dYGHyF6HqiUMsw/sERQc9PfHgUzTdSrpHR/DADiW8bC7mmbiKJbhGJSY+MDlr3uLnDaUGeEtvHQyJnnzB7jetKC/KkCVdh5F1DWMtyeVz0HE3P5Tjwtj5B06mNvX84QgJ8NGVKCToP68BaVqGR04KdacJUmPDQKNayADDp0dE0/21liJyR/qqWFmmJNFEanRKFCQ8Hr3v0vUMhChC0LbskB43g3/+WnpCp6/pPFabA8NkDWcvy+Dx0HkK//qEzBrCWBYBxD6YjLL5OgMiUUnTo35a1rDpchdDYurLaKE2D98ikR0YjPCBIoCZchdAguexbdWsBmaruByMiKQwT5o0MWveIOwfRPrfr05q2sBHIoGl9oQ6t+8FVhMgxeHpf1rICIR+p9SKDj7hjcNB2jJ9HD86n0MjQpkcKa9mQaC1N0IbGhGDSI8zc8rVMengUbQEhJEYTVACn9m1Du0fCE0IxvoH+G3Z7f9rnToPa+02e6zN05gCoAoSjMlSB/rf0ZC0rFAvRuludjzqPx8Ooe9ifdQCY8NBIWvwKmVqKFgE+2oGEx4dCERDzIDw+DJMfDX7/TZg3EurQunZHJoVBGcL+4tVpSHsoQ+rmv7D4EIx7gH1u5XA4GDS1D+1Yt5GdacI8kBF3DqIt/Mg1UvQZ3421rFgmRnKHOhNnkUSEcfens5YFfHN8YJBKhUbOGiQU8M3pgdcfER+GSQ303/gHR9IybsS2jKLN44F0G5EGuabuXIRUhumpHVnLcjkcpCfT/T+HJ7egCfNAprXvgPCA3NcqkRiDEpNYyyqEQpqPpkIkxsyOnVjLAsAdnTrTglOpRGIkBYmm3UKjpQmKWJUvzVcwbu+QRktF1SoklBH0qZahSclQBgTEC5fKMCWV/XeMx+FgcL3rH53SCuy9B8zo0BHRAT6aCpEQ/eISWMuqRGLEK9X+z1qpFLPTOgWpGbijUxda4Cu1WEwLKBVIm9AwmihNVGtwR1rw/pvaviNEAf2VGhZBE6GBjGiRAmXA2GglUkxuk8pals/lom+96x/fmv03DwBmdkijpfKSCYWseesBnwiKCujrcJkcczp1Dlr3HfXShGklUkSyRC4HgI7hEVAI6f03u2Pwum8L2CUGgM6R0ay55QFgTMvWtP5TSyQY36oNa1khj4du9a7/lrbsfQ0Aszp0Qlzg/ScU0nzcAwmXyRAa8KxHypW4o4Fn7I60LogI7G+5jLZoF0jXqGhaYMhYuQIzO6SxlgWASW3a0T73io2lLWwEMrFNG2gC+k8pFGFUSivWsiIeHx3C61x1eDwepgZ51gFgdlpnmm+4QiRCahj7e2i0QuG37AGAGKWqwf6bk9YZIQH9HaNQ0YJtBtIrNo4WtDNSJsOMIP3Hge+eCmRgQhJNPAdya9tU2uKMUijC8Bbs71ESPh9tQ+veyyUCQdB2AL5rTAjoP6VIhFYsQRoBIEGlhuoSCz/Nnf+8aA8NDQWPx0N5Od00rby8HJGRzCjbACASiaBUKmn/mivh8SG4/blbGMeHzujHiGgckxKF51Y8ztjd00So8cwvjzLyR6cNSmW8nALAnJemIqSe+FKFy/HC2gUM4RnfJgb3vjWLluZMppZh0iOj0bY3fVLj8Xl4+qdHIFfTJw2ZSoonvr4f3HoTZtqgVIy4czAkAd8ZHh+Ghz++mxFBXSIX48X1CxAaT293dHIEpiwYz7jGEXcORqsA8QH4+m/x6icYL/4hURos/PEhRv91H9GJFsAL8L3w3/PG7dBE0CNUKsOVWLJmPm23CvAthtzx8nTaTrJSq8CUBRMY7eML+Hh2+WM08QH4Fhse/exeRv91Te+IodP709KtRSSG4bEv5jIWJ6RKKV5cuwBhCfTJLrZ1NCY/xhSeY+YOR3Ia/cUopmUUlqyZD3m9Hfew2BA89d08mvjg8/noM7EHeo3tSivL4XBw/3tzGDv86nAlFq9+kmYZAfgWU2Y+f6tvR7a2bJgKtz97C0McCoR8PL/6SShD6f2niVRj3od3MhaOeozujP639AIv4EUoPD4MT37zICMtolwtw5I18xGZTO/XxA5xGPcAU5RNfGgU4tvSI0RHJoXjhbULGMIpIiEMj38xlxbhXSgUYtDUPug2gv5jxuFw8Min9zD6T6GV4dkVjzGEe6uuyZi2cCJtl18dpsKcl6YhKUBcAr4d5CVrnoQmkn5vayPVuO+tWYz+6zOhO3qOob9QRCVFYOGPDzMsTxRaOZb8Op+RArFll2TWBcDbnhzHSDcZkRiGF9c/RYu2DvgCyz3y6b00Cx+RSIjhcwai81D6iy+Xy8VjX8yFKpQ+B2giVFj086M0iwQAaNMzBbc8NhZSRd2YhURpfRkQ6o2vSCLEC2vnQxujph0PiwvFHS9PY1zjgFt7octQujCOSo7AomWPMiyfVKFKPL/qCca8mNqnNYbNZC7qTX9mMqLqpdQMiQvBi+ue8vvK1xLTMgoPfnAnbY6XCIWY0aEjQ9zwOBy8NWwEQurt6IRIpHhn+EjGS123qGjM7JAGacDLdrhcjuf6D2aIawmfj6VjJjCCCEXLFXi4Ry/GNU5o3QZdo+jtS1Br8PGosYz2hUql+GjkGFrwKADoGROLsfVeTgHgsZ59aEIZ8O2uLR0zgSE8W2i0eLrfQEQEiDWZSIQ5aZ3RuZ644XO5eDd9FGNHLEQqxZvDRjAWPnrFxGJKuw60XfxohRIvDBrCSJUmEwjwxdgJiKgneuKUKszt2p1xjbe2TaXFCQB8QvLTUeMYL/7hMhneTx/FiJo9ICERI+q9nHMALOjTH2HSetcoFuPz0eMh4tWbo7QheLJ3P9pOnlosxr1duzHaJ+By8eHIMVCL6L+zGrEIrw4Zxui/vnHxmNCaHiAwVqnCq0OGMxYnFEIhlo6ZQBPtgC8a/10swmla+w5oG0r/PUhSa/D56PG0xSHAF7zxreEjGdG7hyen0AJ4Ab7+e7b/IGgk9DpUQiE+Gz2OIdzbhobhkR69abuXWokE83r0YohDIY+HT0aNpS0wAb7+XjJgMGPhaFBCEkamtKIFIY5VqfDWsBGM50MhFGHpmAmIqbcj2yokhFVcz0nrzIiQHyP1PWOKeu2LUSjx+tB0WoBAPp+Psa3aYGCAmwTg678XBw31u2XUohSJ8fGosQzh3j4sHA9060lL5RYml+PxXn3QJoT+HiDk8fDZmHEMi4AQiRTP9BvA6L/hSS0wOJE+vvEqNd5NH8VY3FGLxfhs9HhGCr/2YRGsiwL3dO7GmAMi5HIsHTueEZE/TqnCy4OG0XKjS4RC3NI2FX1i6YvEXA4Hrw1Jpy0CAYBSIMQHI8bQLBIAIC0iEnd17up3EQF8c/xTffqjZb3xFfP5WDp2ArT1LPLCpTI82Zu5ETMqpaXfjaOWeJUaH4wcgzAp/T7TSiT4eNTYS6ZAbO7cENHje/bsiR49euDjjz8G4PONjI+Px0MPPYSnn376kn/fnKPHAz5fYavJhkN/H4XL6Ua34WlQhMiD5uEuOleCC8fzkXuyAEkdEpDcIZ4WJTiQ0gvlMNaYcWjTUYgkQnQdngaJQoLIeqmrAMCkN0FfbsTpjHMoy6tAau/WiG4RgegU9lXVkgvlqCqqxrFtp6COUCFtYDvI1DLW/MMVRVWwmezI/OcETDozOg9uj5BoLc2SIJDi7FIUnSvF2QPnEZMShdbdUxASp4VUylxFK8sth9Vkx6FNR+Fxe9BtRCfINTLW/nO5XCjPrUT20Tzkny5Ei7REJKbG0yKZ0/ovtxyGSiMO/30MYpkIXYenQaqUsObhNurM0JcbcHpvFioLq9GuTytEJUcGzUdfklOGysJqHN9xGpooNTr2bwtliBzqi/6tgVQVV8NisOLIPydg1lvQZWhHaCPVDfZfwdlinDuUg9iW0WjVLRnhiaGs+dTLcsthMdpwaNNRUF4K3UZ0gkwtbbD/zh/JRcHZIqR0TkJiu7igFiClF8qhrzTgyObjEMsl6Dq8I2RKCUMUAz5/aH2FEad2n0VVcQ1S+7VGZGI4awq82v4rz6/EyV1nERKjQft+baEMlUMdykz5UV2mg6najCNbjsNmsqHzMF//BbO8KMkuQ97pQpw/cgFxbWLQsnMyolMiaALf3395FTDrLDi06Sg4XA66pfvuP7Zc906nE+W5VTh/OAeF50rQqmsLxLeJCdp/JRfKoS/X4/CW45CrZegytAPkGhnDvBvwBZg0Vvv8xGtK9Wjfvy0iEkIb7L/SCxU4vTcLoXFapPZuDW2UGnI1c6eoqqgaZr0Fh7ecgN1sQ9f0NKjCVYgK0n9F50uRf6oQ2UdzkdA2Fimdk1jz0QNAeV6Ff47i8Xnomp4GhVqGcJb+q73/sg5mozi7DK27pSC+bUzQ56D0QjmqS3XI3HoCCo0cnYd0gFwjZe0/Y41v/ju+4xR05QZ0HNQO4XGhQesuySlDSU4ZzmScR1h8CFJ7t0ZIrAZSOXOnqKqkBqYaMw7/fQwOmxNd09OgCVex3iOAb47PPVmIC8fzkNg+Hi06JgSd48vzK2GoMuHQ30fBF/DRLT0NyhA5TYTXYrfYUVlUg7P7z6MstwKte6QgpmUUYlLY75ECgx7lZjP2FhVAI5agT1w8tGIJtCy7YTqb1ResragQ1TYresfGI0ouD5prN1+vQ65ej2PlpYhVqtApIhLRCiUj5zDgSxFncjqwMz8fTo8bAxIToRFJgqb3ydXV4ExVJc5VV6N1aCjahISypjACgGKDAdV2G3bl50HE56N/fAJUYjFrBGKz3Y5ymxWZpSUoMhrROSoKiSo1bSeI1n96PUotJuwrKkSIRIpesXHQiCWsubKrrRbU2O3IKCyAwW5H77h4RMrkQa8xT69Drl6HY2VlSFSr0TEiElFSGSQsc3yRwQCzy4md+Xlwe70YkJAIlUjMmmPZ6XSi2GLG6apKnK+uRtuwMLTShtIicQdSaNCjxmbD7oJ8iPl89ItPhEwoZN2xN1itqLLbcbi0GKVmE7pERiNepWYsptSSr9ejxGzEgeIihEll6BETC5VIiDAWU+digwFWtwt7CgtgcjjQJz4eYRJZ0FzteXodLuhqcLy8HIlqDTpGRCBaroCY5f4rMhgu3n958FIUBiQkQikSsd7bLpcLhWYTTlVUIEdXg3Zh4WilDUFikP4ruNh/uwryIRMI0C8+AXKhANEK5thUmc0wOJ04VFKMMosJ3aJiEKtUBr//DHoUGY04WFKECJkc3aNjoAiSx7zYaITF5cSewgKYnU70i49HiEQWNFd7nl6H7JpqnKyoQAuNBqnhEYiTKyBg6b/CgP7jAOifkAiFUMjafzaXC6VmE05UlCNXp0OH8Ai00GqDirECgx5VViv2FORDIRKhb1w8pAIhTaDWUmmxwOiw40BJESosFvSIjkWMQhn0Hikw6FFoMOBQaTGi5Ap0jYqBViKh7YbX77/dBfmwuFzoH58ArUQatP9y9Tqcr67G6coKpGi1aBcWHrT/Cgx6mBy+/uNxuegfnwC5QIg4lnabbDZU2mw4XlGGPL0eHSMikKxpuP8qLRbsLSyAUixCnzhf3VEK5jNWYTLB6HLiQHERKq0W9IyJQ7RcEbT/8vV6FBj0OFJWgmiFAl0iY6AVs+e6LzIaYHG6sKsgDw63G/0u9h/bHAX4ggVmVVfibFUVWoWEoG1oWNA5vjlwuTr0hhDtK1euxJw5c/DFF1+gR48e+OCDD7Bq1SqcPXuW4evORnMX7QQCgUAgEAgEAoFAuLG4XB36nw9EBwBTp05FZWUlFi9ejLKyMnTq1AkbN268LMFOIBAIBAKBQCAQCARCc+WG2Gm/WshOO4FAIBAIBAKBQCAQmpLL1aH/+UB0BAKBQCAQCAQCgUAg3KgQ0U4gEAgEAoFAIBAIBEIzhYh2AoFAIBAIBAKBQCAQmilEtBMIBAKBQCAQCAQCgdBMIaKdQCAQCAQCgUAgEAiEZgoR7QQCgUAgEAgEAoFAIDRTiGgnEAgEAoFAIBAIBAKhmUJEO4FAIBAIBAKBQCAQCM0UItoJBAKBQCAQCAQCgUBophDRTiAQCAQCgUAgEAgEQjOFiHYCgUAgEAgEAoFAIBCaKUS0EwgEAoFAIBAIBAKB0Ewhop1AIBAIBAKBQCAQCIRmCv96N6A5QFEUAMBoNF7nlhAIBAKBQCAQCAQC4WagVn/W6tFgENEOwGQyAQDi4uKuc0sIBAKBQCAQCAQCgXAzYTKZoFKpgp7nUJeS9TcBXq8XJSUlUCgU4HA417s5NzVGoxFxcXEoLCyEUqm83s0hXCFkHG8cyFjeGJBxvDEg43hjQMbxxoGM5Y3B9RxHiqJgMpkQHR0NLje45zrZaQfA5XIRGxt7vZtBCECpVJLJ7waAjOONAxnLGwMyjjcGZBxvDMg43jiQsbwxuF7j2NAOey0kEB2BQCAQCAQCgUAgEAjNFCLaCQQCgUAgEAgEAoFAaKYQ0U5oVohEIixZsgQikeh6N4VwFZBxvHEgY3ljQMbxxoCM440BGccbBzKWNwb/hXEkgegIBAKBQCAQCAQCgUBoppCddgKBQCAQCAQCgUAgEJopRLQTCAQCgUAgEAgEAoHQTCGinUAgEAgEAoFAIBAIhGYKEe0EAoFAIBAIBAKBQCA0U4hoJzQ6O3fuxLhx4xAdHQ0Oh4P169fTzpeXl+OOO+5AdHQ0pFIpRo4cifPnz9PKDBo0CBwOh/bv/vvvp5UpKCjAmDFjIJVKER4ejgULFsDtdl/ry7tpaIxxBICMjAwMGTIEMpkMSqUSAwYMgM1m85+vqanB7bffDqVSCbVajbvvvhtms/laX95NxdWOZV5eHuN5rP23evVqfznyTF5bGuOZLCsrw6xZsxAZGQmZTIYuXbpgzZo1tDLkmby2NMY45uTkYNKkSQgLC4NSqcSUKVNQXl5OK0PG8dry+uuvo3v37lAoFAgPD8fEiRORlZVFK2O32zFv3jyEhIRALpfjlltuYYzT5cyb27dvR5cuXSASiZCSkoLvv//+Wl/eTUNjjeMjjzyCrl27QiQSoVOnTqzfdfz4cfTv3x9isRhxcXF46623rtVl3XQ0xjgeO3YM06dPR1xcHCQSCdq2bYsPP/yQ8V3X63kkop3Q6FgsFqSlpeHTTz9lnKMoChMnTsSFCxewYcMGZGZmIiEhAcOGDYPFYqGVvffee1FaWur/Fzi5eTwejBkzBk6nE3v37sUPP/yA77//HosXL77m13ez0BjjmJGRgZEjRyI9PR0HDhzAwYMH8dBDD4HLrZt6br/9dpw6dQqbN2/G77//jp07d+K+++5rkmu8WbjasYyLi6M9i6WlpXjxxRchl8sxatQoAOSZbAoa45mcPXs2srKy8L///Q8nTpzA5MmTMWXKFGRmZvrLkGfy2nK142ixWJCeng4Oh4OtW7diz549cDqdGDduHLxer78uMo7Xlh07dmDevHnYt28fNm/eDJfLhfT0dNrz9vjjj+O3337D6tWrsWPHDpSUlGDy5Mn+85czb+bm5mLMmDEYPHgwjh49isceewz33HMPNm3a1KTXe6PSGONYy1133YWpU6eyfo/RaER6ejoSEhJw+PBhvP3223jhhRfw5ZdfXrNru5lojHE8fPgwwsPD8fPPP+PUqVN49tlnsWjRInzyySf+Mtf1eaQIhGsIAGrdunX+z1lZWRQA6uTJk/5jHo+HCgsLo7766iv/sYEDB1KPPvpo0Hr//PNPisvlUmVlZf5jn3/+OaVUKimHw9Go10C48nHs2bMn9dxzzwWt9/Tp0xQA6uDBg/5jf/31F8XhcKji4uLGvQgCRVFXPpb16dSpE3XXXXf5P5Nnsmm50nGUyWTUjz/+SKtLq9X6y5Bnsmm5knHctGkTxeVyKYPB4C+j1+spDodDbd68maIoMo7Xg4qKCgoAtWPHDoqifGMiEAio1atX+8ucOXOGAkBlZGRQFHV58+ZTTz1Fpaam0r5r6tSp1IgRI671Jd2UXMk4BrJkyRIqLS2Ncfyzzz6jNBoN7fdw4cKFVOvWrRv/IghXPY61PPjgg9TgwYP9n6/n80h22glNisPhAACIxWL/MS6XC5FIhN27d9PKLlu2DKGhoWjfvj0WLVoEq9XqP5eRkYEOHTogIiLCf2zEiBEwGo04derUNb4KwuWMY0VFBfbv34/w8HD06dMHERERGDhwIG2cMzIyoFar0a1bN/+xYcOGgcvlYv/+/U10NTc3/+aZrOXw4cM4evQo7r77bv8x8kxeXy53HPv06YOVK1eipqYGXq8XK1asgN1ux6BBgwCQZ/J6cznj6HA4wOFwIBKJ/GXEYjG4XK6/DBnHpsdgMAAAtFotAN886XK5MGzYMH+ZNm3aID4+HhkZGQAub97MyMig1VFbprYOQuNyJeN4OWRkZGDAgAEQCoX+YyNGjEBWVhZ0Ol0jtZ5QS2ONo8Fg8NcBXN/nkYh2QpNS+4AsWrQIOp0OTqcTb775JoqKilBaWuovN2PGDPz888/Ytm0bFi1ahJ9++gkzZ870ny8rK6P9yAHwfy4rK2uai7mJuZxxvHDhAgDghRdewL333ouNGzeiS5cuGDp0qN8/s6ysDOHh4bS6+Xw+tFotGccm4nKfyUC++eYbtG3bFn369PEfI8/k9eVyx3HVqlVwuVwICQmBSCTC3LlzsW7dOqSkpAAgz+T15nLGsVevXpDJZFi4cCGsVissFgvmz58Pj8fjL0PGsWnxer147LHH0LdvX7Rv3x6AbwyEQiHUajWtbEREhH8MLmfeDFbGaDTS4sMQrp4rHcfLgfxGNh2NNY579+7FypUraW5F1/N5JKKd0KQIBAKsXbsW586dg1arhVQqxbZt2zBq1Cian/N9992HESNGoEOHDrj99tvx448/Yt26dcjJybmOrSfUcjnjWOtbOXfuXNx5553o3Lkz3n//fbRu3Rrffvvt9Ww+IYDLfSZrsdls+OWXX2i77ITrz+WO4/PPPw+9Xo8tW7bg0KFDeOKJJzBlyhScOHHiOraeUMvljGNYWBhWr16N3377DXK5HCqVCnq9Hl26dGF9ZgnXnnnz5uHkyZNYsWLF9W4K4Sog43hj0BjjePLkSUyYMAFLlixBenp6I7buyuFf7wYQbj66du2Ko0ePwmAwwOl0IiwsDD179qSZ8dWnZ8+eAIDs7Gy0aNECkZGROHDgAK1MbQTIyMjIa9d4gp9LjWNUVBQAoF27drS/a9u2LQoKCgD4xqqiooJ23u12o6amhoxjE/Jvnslff/0VVqsVs2fPph0nz+T151LjmJOTg08++QQnT55EamoqACAtLQ27du3Cp59+iqVLl5JnshlwOc9jeno6cnJyUFVVBT6fD7VajcjISCQnJwMgc2tT8tBDD/kD/cXGxvqPR0ZGwul0Qq/X03b3ysvL/WNwOfNmZGQkI1J5eXk5lEolJBLJtbikm5KrGcfLIdg41p4jNA6NMY6nT5/G0KFDcd999+G5556jnbuezyNZkiVcN1QqFcLCwnD+/HkcOnQIEyZMCFr26NGjAOqEYO/evXHixAnaS8nmzZuhVCoZIpFwbQk2jomJiYiOjmak3Dh37hwSEhIA+MZRr9fj8OHD/vNbt26F1+v1L9QQmo7LeSa/+eYbjB8/HmFhYbTj5JlsPgQbx9q4IPV3Y3k8nt8yhjyTzYfLeR5DQ0OhVquxdetWVFRUYPz48QDIODYFFEXhoYcewrp167B161YkJSXRznft2hUCgQD//POP/1hWVhYKCgrQu3dvAJc3b/bu3ZtWR22Z2joIV0djjOPl0Lt3b+zcuRMul8t/bPPmzWjdujU0Gs3VX8hNTmON46lTpzB48GDMmTMHr776KuN7ruvzeM1D3RFuOkwmE5WZmUllZmZSAKj33nuPyszMpPLz8ymKoqhVq1ZR27Zto3Jycqj169dTCQkJ1OTJk/1/n52dTb300kvUoUOHqNzcXGrDhg1UcnIyNWDAAH8Zt9tNtW/fnkpPT6eOHj1Kbdy4kQoLC6MWLVrU5Nd7o3K140hRFPX+++9TSqWSWr16NXX+/Hnqueeeo8RiMZWdne0vM3LkSKpz587U/v37qd27d1MtW7akpk+f3qTXeqPTGGNJURR1/vx5isPhUH/99RfjHHkmrz1XO45Op5NKSUmh+vfvT+3fv5/Kzs6m3nnnHYrD4VB//PGHvxx5Jq8tjfE8fvvtt1RGRgaVnZ1N/fTTT5RWq6WeeOIJWhkyjteWBx54gFKpVNT27dup0tJS/z+r1eovc//991Px8fHU1q1bqUOHDlG9e/emevfu7T9/OfPmhQsXKKlUSi1YsIA6c+YM9emnn1I8Ho/auHFjk17vjUpjjCNF+X4fMzMzqblz51KtWrXyP+O10eL1ej0VERFBzZo1izp58iS1YsUKSiqVUl988UWTXu+NSmOM44kTJ6iwsDBq5syZtDoqKir8Za7n80hEO6HR2bZtGwWA8W/OnDkURVHUhx9+SMXGxlICgYCKj4+nnnvuOVoKjIKCAmrAgAGUVqulRCIRlZKSQi1YsICW3oaiKCovL48aNWoUJZFIqNDQUOrJJ5+kXC5XU17qDc3VjmMtr7/+OhUbG0tJpVKqd+/e1K5du2jnq6urqenTp1NyuZxSKpXUnXfeSZlMpqa4xJuGxhrLRYsWUXFxcZTH42H9HvJMXlsaYxzPnTtHTZ48mQoPD6ekUinVsWNHRgo48kxeWxpjHBcuXEhFRERQAoGAatmyJfXuu+9SXq+XVoaM47WFbQwBUN99952/jM1mox588EFKo9FQUqmUmjRpElVaWkqr53LmzW3btlGdOnWihEIhlZycTPsOwtXRWOM4cOBA1npyc3P9ZY4dO0b169ePEolEVExMDPXGG2800VXe+DTGOC5ZsoS1joSEBNp3Xa/nkUNRFNVYu/YEAoFAIBAIBAKBQCAQGg/i004gEAgEAoFAIBAIBEIzhYh2AoFAIBAIBAKBQCAQmilEtBMIBAKBQCAQCAQCgdBMIaKdQCAQCAQCgUAgEAiEZgoR7QQCgUAgEAgEAoFAIDRTiGgnEAgEAoFAIBAIBAKhmUJEO4FAIBAIBAKBQCAQCM0UItoJBAKBQCAQCAQCgUBophDRTiAQCAQCgUAgEAgEQjOFiHYCgUAgEAigKArDhg3DiBEjGOc+++wzqNVqFBUVXYeWEQgEAoFwc0NEO4FAIBAIBHA4HHz33XfYv38/vvjiC//x3NxcPPXUU/j4448RGxvbqN/pcrkatT4CgUAgEG5EiGgnEAgEAoEAAIiLi8OHH36I+fPnIzc3FxRF4e6770Z6ejo6d+6MUaNGQS6XIyIiArNmzUJVVZX/bzdu3Ih+/fpBrVYjJCQEY8eORU5Ojv98Xl4eOBwOVq5ciYEDB0IsFmPZsmXX4zIJBAKBQPhPwaEoirrejSAQCAQCgdB8mDhxIgwGAyZPnoyXX34Zp06dQmpqKu655x7Mnj0bNpsNCxcuhNvtxtatWwEAa9asAYfDQceOHWE2m7F48WLk5eXh6NGj4HK5yMvLQ1JSEhITE/Huu++ic+fOEIvFiIqKus5XSyAQCARC84aIdgKBQCAQCDQqKiqQmpqKmpoarFmzBidPnsSuXbuwadMmf5mioiLExcUhKysLrVq1YtRRVVWFsLAwnDhxAu3bt/eL9g8++ACPPvpoU14OgUAgEAj/aYh5PIFAIBAIBBrh4eGYO3cu2rZti4kTJ+LYsWPYtm0b5HK5/1+bNm0AwG8Cf/78eUyfPh3JyclQKpVITEwEABQUFNDq7tatW5NeC4FAIBAI/3X417sBBAKBQCAQmh98Ph98vu81wWw2Y9y4cXjzzTcZ5WrN28eNG4eEhAR89dVXiI6OhtfrRfv27eF0OmnlZTLZtW88gUAgEAg3EES0EwgEAoFAaJAuXbpgzZo1SExM9Av5QKqrq5GVlYWvvvoK/fv3BwDs3r27qZtJIBAIBMINCTGPJxAIBAKB0CDz5s1DTU0Npk+fjoMHDyInJwebNm3CnXfeCY/HA41Gg5CQEHz55ZfIzs7G1q1b8cQTT1zvZhMIBAKBcENARDuBQCAQCIQGiY6Oxp49e+DxeJCeno4OHTrgscceg1qtBpfLBZfLxYoVK3D48GG0b98ejz/+ON5+++3r3WwCgUAgEG4ISPR4AoFAIBAIBAKBQCAQmilkp51AIBAIBAKBQCAQCIRmChHtBAKBQCAQCAQCgUAgNFOIaCcQCAQCgUAgEAgEAqGZQkQ7gUAgEAgEAoFAIBAIzRQi2gkEAoFAIBAIBAKBQGimENFOIBAIBAKBQCAQCARCM4WIdgKBQCAQCAQCgUAgEJopRLQTCAQCgUAgEAgEAoHQTCGinUAgEAgEAoFAIBAIhGYKEe0EAoFAIBAIBAKBQCA0U4hoJxAIBAKBQCAQCAQCoZnyf2c8kE3yRZe0AAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"markdown","source":"**PCA**","metadata":{}},{"cell_type":"code","source":"# Apply PCA on numeric features\npca_df4 = PCA(n_components=2)\npca_result_df4 = pca_df4.fit_transform(numeric_features_df4_scaled)\ndf4['pca_component_1'] = pca_result_df4[:, 0]\ndf4['pca_component_2'] = pca_result_df4[:, 1]\n# Visualize PCA results\nplt.figure(figsize=(12, 6))\nsns.scatterplot(x='pca_component_1', y='pca_component_2', hue='kmeans_cluster', data=df4, palette='viridis')\nplt.title('PCA on Dataset 4 with K-Means Clusters')\nplt.xlabel('PCA Component 1')\nplt.ylabel('PCA Component 2')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:47:40.320208Z","iopub.execute_input":"2024-01-07T12:47:40.320583Z","iopub.status.idle":"2024-01-07T12:47:40.755126Z","shell.execute_reply.started":"2024-01-07T12:47:40.320552Z","shell.execute_reply":"2024-01-07T12:47:40.754227Z"},"trusted":true},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Splitting Datasets Into Test And Train","metadata":{}},{"cell_type":"code","source":"df1['country_name'] = LabelEncoder().fit_transform(df1['country_name'])\nX1 = df1.drop('nuclear_weapons_status', axis=1) # Features\ny1 = df1['nuclear_weapons_status'] # Target\nX1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.2, random_state=42)\n\n\n# Replace the following line with your actual encoding logic if needed\ndf2['entity_name'] = LabelEncoder().fit_transform(df2['entity_name'])\nX2 = df2.drop('number_nuclweap_consideration', axis=1) # Features\ny2 = df2['number_nuclweap_consideration'] # Target\nX2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.2, random_state=42)\n\n\n# Replace the following line with your actual encoding logic if needed\ndf3['country_name'] = LabelEncoder().fit_transform(df3['country_name'])\nX3 = df3.drop('nuclear_weapons_stockpile', axis=1) # Features\ny3 = df3['nuclear_weapons_stockpile'] # Target\nX3_train, X3_test, y3_train, y3_test = train_test_split(X3, y3, test_size=0.2, random_state=42)\n\n\n# Replace the following line with your actual encoding logic if needed\ndf4['country_name'] = LabelEncoder().fit_transform(df4['country_name'])\nX4 = df4.drop('nuclear_weapons_tests', axis=1) # Features\ny4 = df4['nuclear_weapons_tests'] # Target\nX4_train, X4_test, y4_train, y4_test = train_test_split(X4, y4, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:53:09.865887Z","iopub.execute_input":"2024-01-07T12:53:09.866812Z","iopub.status.idle":"2024-01-07T12:53:09.890284Z","shell.execute_reply.started":"2024-01-07T12:53:09.866779Z","shell.execute_reply":"2024-01-07T12:53:09.889501Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":"# Function Which Evaluates Different Models Defined Below","metadata":{}},{"cell_type":"code","source":"def evaluate_model(model, X_train, X_test, y_train, y_test, model_name):\n # Train the model\n model.fit(X_train, y_train)\n\n # Predictions\n y_train_pred = model.predict(X_train)\n y_test_pred = model.predict(X_test)\n\n # Accuracy\n train_accuracy = accuracy_score(y_train, y_train_pred)\n test_accuracy = accuracy_score(y_test, y_test_pred)\n\n # Classification report\n class_report = classification_report(y_test, y_test_pred)\n\n # Save predictions to CSV\n pd.DataFrame({'True': y_test, 'Predicted': y_test_pred}).to_csv(f'{model_name}_predictions.csv', index=False)\n\n return train_accuracy, test_accuracy, class_report","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:53:15.705615Z","iopub.execute_input":"2024-01-07T12:53:15.706401Z","iopub.status.idle":"2024-01-07T12:53:15.712702Z","shell.execute_reply.started":"2024-01-07T12:53:15.706367Z","shell.execute_reply":"2024-01-07T12:53:15.711659Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":"# Models Used\n* **Random Forest** \n* **Gradient Boosting** \n* **Support Vector Machine** \n* **K-Nearest Neighbour** \n* **Logistic Regression** \n* **Decision Tree** \n* **MLP Neural Network** \n","metadata":{}},{"cell_type":"markdown","source":"# For Nuclear Wepons Proliferation Owid","metadata":{}},{"cell_type":"code","source":"# Random Forest\nrf_model = RandomForestClassifier(random_state=42)\nrf_train_acc, rf_test_acc, rf_class_report = evaluate_model(rf_model, X1_train, X1_test, y1_train, y1_test, 'rf')\n\n# Gradient Boosting\ngb_model = GradientBoostingClassifier(random_state=42)\ngb_train_acc, gb_test_acc, gb_class_report = evaluate_model(gb_model, X1_train, X1_test, y1_train, y1_test, 'gb')\n\n# Support Vector Machine\nsvm_model = SVC(random_state=42)\nsvm_train_acc, svm_test_acc, svm_class_report = evaluate_model(svm_model, X1_train, X1_test, y1_train, y1_test, 'svm')\n\n# K-Nearest Neighbors\nknn_model = KNeighborsClassifier()\nknn_train_acc, knn_test_acc, knn_class_report = evaluate_model(knn_model, X1_train, X1_test, y1_train, y1_test, 'knn')\n\n# Logistic Regression\nlr_model = LogisticRegression(random_state=42)\nlr_train_acc, lr_test_acc, lr_class_report = evaluate_model(lr_model, X1_train, X1_test, y1_train, y1_test, 'lr')\n\n# Decision Tree\ndt_model = DecisionTreeClassifier(random_state=42)\ndt_train_acc, dt_test_acc, dt_class_report = evaluate_model(dt_model, X1_train, X1_test, y1_train, y1_test, 'dt')\n\n# Neural Network (MLP)\nmlp_model = MLPClassifier(random_state=42, max_iter=500)\nmlp_train_acc, mlp_test_acc, mlp_class_report = evaluate_model(mlp_model, X1_train, X1_test, y1_train, y1_test, 'mlp')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:53:16.961975Z","iopub.execute_input":"2024-01-07T12:53:16.962342Z","iopub.status.idle":"2024-01-07T12:53:28.072179Z","shell.execute_reply.started":"2024-01-07T12:53:16.962314Z","shell.execute_reply":"2024-01-07T12:53:28.070645Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n n_iter_i = _check_optimize_result(\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Plotting Accuracies and Classification Reports","metadata":{}},{"cell_type":"code","source":"# Plot Accuracies\nmodels = ['Random Forest', 'Gradient Boosting', 'SVM', 'KNN', 'Logistic Regression', 'Decision Tree', 'MLP']\ntrain_accuracies = [rf_train_acc, gb_train_acc, svm_train_acc, knn_train_acc, lr_train_acc, dt_train_acc, mlp_train_acc]\ntest_accuracies = [rf_test_acc, gb_test_acc, svm_test_acc, knn_test_acc, lr_test_acc, dt_test_acc, mlp_test_acc]\n\nplt.figure(figsize=(10, 6))\nplt.barh(models, train_accuracies, color='lightblue', label='Train Accuracy')\nplt.barh(models, test_accuracies, color='orange', alpha=0.7, label='Test Accuracy')\nplt.xlabel('Accuracy')\nplt.title('Model Train and Test Accuracies')\nplt.legend()\nplt.show()\n\n# Print Classification Reports\nprint(\"Random Forest Classification Report:\\n\", rf_class_report)\nprint(\"\\nGradient Boosting Classification Report:\\n\", gb_class_report)\nprint(\"\\nSVM Classification Report:\\n\", svm_class_report)\nprint(\"\\nKNN Classification Report:\\n\", knn_class_report)\nprint(\"\\nLogistic Regression Classification Report:\\n\", lr_class_report)\nprint(\"\\nDecision Tree Classification Report:\\n\", dt_class_report)\nprint(\"\\nMLP Classification Report:\\n\", mlp_class_report)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:54:14.144273Z","iopub.execute_input":"2024-01-07T12:54:14.144645Z","iopub.status.idle":"2024-01-07T12:54:14.422547Z","shell.execute_reply.started":"2024-01-07T12:54:14.144614Z","shell.execute_reply":"2024-01-07T12:54:14.421646Z"},"trusted":true},"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stdout","text":"Random Forest Classification Report:\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3180\n 1 1.00 1.00 1.00 60\n 2 1.00 1.00 1.00 54\n 3 1.00 1.00 1.00 76\n\n accuracy 1.00 3370\n macro avg 1.00 1.00 1.00 3370\nweighted avg 1.00 1.00 1.00 3370\n\n\nGradient Boosting Classification Report:\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3180\n 1 1.00 1.00 1.00 60\n 2 1.00 1.00 1.00 54\n 3 1.00 1.00 1.00 76\n\n accuracy 1.00 3370\n macro avg 1.00 1.00 1.00 3370\nweighted avg 1.00 1.00 1.00 3370\n\n\nSVM Classification Report:\n precision recall f1-score support\n\n 0 0.94 1.00 0.97 3180\n 1 0.00 0.00 0.00 60\n 2 0.00 0.00 0.00 54\n 3 0.00 0.00 0.00 76\n\n accuracy 0.94 3370\n macro avg 0.24 0.25 0.24 3370\nweighted avg 0.89 0.94 0.92 3370\n\n\nKNN Classification Report:\n precision recall f1-score support\n\n 0 0.96 1.00 0.98 3180\n 1 0.67 0.33 0.44 60\n 2 0.60 0.11 0.19 54\n 3 0.98 0.57 0.72 76\n\n accuracy 0.96 3370\n macro avg 0.80 0.50 0.58 3370\nweighted avg 0.95 0.96 0.95 3370\n\n\nLogistic Regression Classification Report:\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3180\n 1 1.00 0.88 0.94 60\n 2 1.00 1.00 1.00 54\n 3 1.00 1.00 1.00 76\n\n accuracy 1.00 3370\n macro avg 1.00 0.97 0.98 3370\nweighted avg 1.00 1.00 1.00 3370\n\n\nDecision Tree Classification Report:\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3180\n 1 1.00 1.00 1.00 60\n 2 1.00 1.00 1.00 54\n 3 1.00 1.00 1.00 76\n\n accuracy 1.00 3370\n macro avg 1.00 1.00 1.00 3370\nweighted avg 1.00 1.00 1.00 3370\n\n\nMLP Classification Report:\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3180\n 1 1.00 1.00 1.00 60\n 2 1.00 1.00 1.00 54\n 3 1.00 1.00 1.00 76\n\n accuracy 1.00 3370\n macro avg 1.00 1.00 1.00 3370\nweighted avg 1.00 1.00 1.00 3370\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# For Nuclear Weapons Proliferation Total Owid","metadata":{}},{"cell_type":"code","source":"# Random Forest\nrf_model = RandomForestClassifier(random_state=42)\nrf_train_acc, rf_test_acc, rf_class_report = evaluate_model(rf_model, X2_train, X2_test, y2_train, y2_test, 'rf')\n\n# Gradient Boosting\ngb_model = GradientBoostingClassifier(random_state=42)\ngb_train_acc, gb_test_acc, gb_class_report = evaluate_model(gb_model, X2_train, X2_test, y2_train, y2_test, 'gb')\n\n# Support Vector Machine\nsvm_model = SVC(random_state=42)\nsvm_train_acc, svm_test_acc, svm_class_report = evaluate_model(svm_model, X2_train, X2_test, y2_train, y2_test, 'svm')\n\n# K-Nearest Neighbors\nknn_model = KNeighborsClassifier()\nknn_train_acc, knn_test_acc, knn_class_report = evaluate_model(knn_model, X2_train, X2_test, y2_train, y2_test, 'knn')\n\n# Logistic Regression\nlr_model = LogisticRegression(random_state=42)\nlr_train_acc, lr_test_acc, lr_class_report = evaluate_model(lr_model, X2_train, X2_test, y2_train, y2_test, 'lr')\n\n# Decision Tree\ndt_model = DecisionTreeClassifier(random_state=42)\ndt_train_acc, dt_test_acc, dt_class_report = evaluate_model(dt_model, X2_train, X2_test, y2_train, y2_test, 'dt')\n\n# Neural Network (MLP)\nmlp_model = MLPClassifier(random_state=42, max_iter=500)\nmlp_train_acc, mlp_test_acc, mlp_class_report = evaluate_model(mlp_model, X2_train, X2_test, y2_train, y2_test, 'mlp')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:54:47.281618Z","iopub.execute_input":"2024-01-07T12:54:47.282182Z","iopub.status.idle":"2024-01-07T12:54:48.925556Z","shell.execute_reply.started":"2024-01-07T12:54:47.282148Z","shell.execute_reply":"2024-01-07T12:54:48.924529Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n n_iter_i = _check_optimize_result(\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Plotting Accuracies and Classification Reports","metadata":{}},{"cell_type":"code","source":"# Plot Accuracies\nmodels = ['Random Forest', 'Gradient Boosting', 'SVM', 'KNN', 'Logistic Regression', 'Decision Tree', 'MLP']\ntrain_accuracies = [rf_train_acc, gb_train_acc, svm_train_acc, knn_train_acc, lr_train_acc, dt_train_acc, mlp_train_acc]\ntest_accuracies = [rf_test_acc, gb_test_acc, svm_test_acc, knn_test_acc, lr_test_acc, dt_test_acc, mlp_test_acc]\n\nplt.figure(figsize=(10, 6))\nplt.barh(models, train_accuracies, color='lightblue', label='Train Accuracy')\nplt.barh(models, test_accuracies, color='orange', alpha=0.7, label='Test Accuracy')\nplt.xlabel('Accuracy')\nplt.title('Model Train and Test Accuracies')\nplt.legend()\nplt.show()\n\n# Print Classification Reports\nprint(\"Random Forest Classification Report:\\n\", rf_class_report)\nprint(\"\\nGradient Boosting Classification Report:\\n\", gb_class_report)\nprint(\"\\nSVM Classification Report:\\n\", svm_class_report)\nprint(\"\\nKNN Classification Report:\\n\", knn_class_report)\nprint(\"\\nLogistic Regression Classification Report:\\n\", lr_class_report)\nprint(\"\\nDecision Tree Classification Report:\\n\", dt_class_report)\nprint(\"\\nMLP Classification Report:\\n\", mlp_class_report)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:55:48.044849Z","iopub.execute_input":"2024-01-07T12:55:48.045637Z","iopub.status.idle":"2024-01-07T12:55:48.375319Z","shell.execute_reply.started":"2024-01-07T12:55:48.045600Z","shell.execute_reply":"2024-01-07T12:55:48.374356Z"},"trusted":true},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stdout","text":"Random Forest Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.80 0.89 5\n 1 1.00 1.00 1.00 3\n 2 0.00 0.00 0.00 0\n 3 1.00 1.00 1.00 1\n 4 0.50 1.00 0.67 1\n 5 0.00 0.00 0.00 1\n 6 0.33 0.50 0.40 2\n 7 0.50 0.50 0.50 2\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 0\n\n accuracy 0.65 17\n macro avg 0.43 0.48 0.45 17\nweighted avg 0.66 0.65 0.64 17\n\n\nGradient Boosting Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.60 0.75 5\n 1 0.75 1.00 0.86 3\n 2 0.00 0.00 0.00 0\n 3 1.00 1.00 1.00 1\n 4 0.50 1.00 0.67 1\n 5 0.00 0.00 0.00 1\n 6 0.33 0.50 0.40 2\n 7 0.50 0.50 0.50 2\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 0\n\n accuracy 0.59 17\n macro avg 0.41 0.46 0.42 17\nweighted avg 0.61 0.59 0.58 17\n\n\nSVM Classification Report:\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 5\n 1 0.18 1.00 0.30 3\n 3 0.00 0.00 0.00 1\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 2\n 7 0.00 0.00 0.00 2\n 8 0.00 0.00 0.00 2\n\n accuracy 0.18 17\n macro avg 0.02 0.12 0.04 17\nweighted avg 0.03 0.18 0.05 17\n\n\nKNN Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.80 0.89 5\n 1 1.00 1.00 1.00 3\n 2 0.00 0.00 0.00 0\n 3 0.00 0.00 0.00 1\n 4 1.00 1.00 1.00 1\n 5 0.00 0.00 0.00 1\n 6 0.50 1.00 0.67 2\n 7 0.50 0.50 0.50 2\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 0\n\n accuracy 0.65 17\n macro avg 0.40 0.43 0.41 17\nweighted avg 0.65 0.65 0.63 17\n\n\nLogistic Regression Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.60 0.75 5\n 1 0.75 1.00 0.86 3\n 2 0.00 0.00 0.00 0\n 3 0.00 0.00 0.00 1\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 6 0.33 1.00 0.50 2\n 7 0.33 0.50 0.40 2\n 8 0.00 0.00 0.00 2\n\n accuracy 0.53 17\n macro avg 0.27 0.34 0.28 17\nweighted avg 0.50 0.53 0.48 17\n\n\nDecision Tree Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.60 0.75 5\n 1 0.75 1.00 0.86 3\n 2 0.00 0.00 0.00 0\n 3 1.00 1.00 1.00 1\n 4 0.50 1.00 0.67 1\n 5 0.00 0.00 0.00 1\n 6 0.33 0.50 0.40 2\n 7 0.50 0.50 0.50 2\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 0\n\n accuracy 0.59 17\n macro avg 0.41 0.46 0.42 17\nweighted avg 0.61 0.59 0.58 17\n\n\nMLP Classification Report:\n precision recall f1-score support\n\n 0 0.17 0.20 0.18 5\n 1 0.00 0.00 0.00 3\n 3 0.00 0.00 0.00 1\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 2\n 7 0.00 0.00 0.00 2\n 8 0.00 0.00 0.00 2\n\n accuracy 0.06 17\n macro avg 0.02 0.03 0.02 17\nweighted avg 0.05 0.06 0.05 17\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# For Nuclear Weapons Stockpiles","metadata":{}},{"cell_type":"code","source":"# Random Forest\nrf_model = RandomForestClassifier(random_state=42)\nrf_train_acc, rf_test_acc, rf_class_report = evaluate_model(rf_model, X3_train, X3_test, y3_train, y3_test, 'rf')\n\n# Gradient Boosting\ngb_model = GradientBoostingClassifier(random_state=42)\ngb_train_acc, gb_test_acc, gb_class_report = evaluate_model(gb_model, X3_train, X3_test, y3_train, y3_test, 'gb')\n\n# Support Vector Machine\nsvm_model = SVC(random_state=42)\nsvm_train_acc, svm_test_acc, svm_class_report = evaluate_model(svm_model, X3_train, X3_test, y3_train, y3_test, 'svm')\n\n# K-Nearest Neighbors\nknn_model = KNeighborsClassifier()\nknn_train_acc, knn_test_acc, knn_class_report = evaluate_model(knn_model, X3_train, X3_test, y3_train, y3_test, 'knn')\n\n# Logistic Regression\nlr_model = LogisticRegression(random_state=42)\nlr_train_acc, lr_test_acc, lr_class_report = evaluate_model(lr_model, X3_train, X3_test, y3_train, y3_test, 'lr')\n\n# Decision Tree\ndt_model = DecisionTreeClassifier(random_state=42)\ndt_train_acc, dt_test_acc, dt_class_report = evaluate_model(dt_model, X3_train, X3_test, y3_train, y3_test, 'dt')\n\n# Neural Network (MLP)\nmlp_model = MLPClassifier(random_state=42, max_iter=500)\nmlp_train_acc, mlp_test_acc, mlp_class_report = evaluate_model(mlp_model, X3_train, X3_test, y3_train, y3_test, 'mlp')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:56:06.869516Z","iopub.execute_input":"2024-01-07T12:56:06.869917Z","iopub.status.idle":"2024-01-07T12:56:54.245334Z","shell.execute_reply.started":"2024-01-07T12:56:06.869887Z","shell.execute_reply":"2024-01-07T12:56:54.244316Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n n_iter_i = _check_optimize_result(\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Plotting Accuracies and Classification Reports","metadata":{}},{"cell_type":"code","source":"# Plot Accuracies\nmodels = ['Random Forest', 'Gradient Boosting', 'SVM', 'KNN', 'Logistic Regression', 'Decision Tree', 'MLP']\ntrain_accuracies = [rf_train_acc, gb_train_acc, svm_train_acc, knn_train_acc, lr_train_acc, dt_train_acc, mlp_train_acc]\ntest_accuracies = [rf_test_acc, gb_test_acc, svm_test_acc, knn_test_acc, lr_test_acc, dt_test_acc, mlp_test_acc]\n\nplt.figure(figsize=(10, 6))\nplt.barh(models, train_accuracies, color='lightblue', label='Train Accuracy')\nplt.barh(models, test_accuracies, color='orange', alpha=0.7, label='Test Accuracy')\nplt.xlabel('Accuracy')\nplt.title('Model Train and Test Accuracies')\nplt.legend()\nplt.show()\n\n# Print Classification Reports\nprint(\"Random Forest Classification Report:\\n\", rf_class_report)\nprint(\"\\nGradient Boosting Classification Report:\\n\", gb_class_report)\nprint(\"\\nSVM Classification Report:\\n\", svm_class_report)\nprint(\"\\nKNN Classification Report:\\n\", knn_class_report)\nprint(\"\\nLogistic Regression Classification Report:\\n\", lr_class_report)\nprint(\"\\nDecision Tree Classification Report:\\n\", dt_class_report)\nprint(\"\\nMLP Classification Report:\\n\", mlp_class_report)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:56:54.247570Z","iopub.execute_input":"2024-01-07T12:56:54.248276Z","iopub.status.idle":"2024-01-07T12:56:54.549922Z","shell.execute_reply.started":"2024-01-07T12:56:54.248238Z","shell.execute_reply":"2024-01-07T12:56:54.549095Z"},"trusted":true},"execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stdout","text":"Random Forest Classification Report:\n precision recall f1-score support\n\n 0 0.80 1.00 0.89 55\n 1 0.00 0.00 0.00 0\n 2 0.00 0.00 0.00 1\n 3 1.00 0.33 0.50 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 0\n 20 0.00 0.00 0.00 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 25 0.00 0.00 0.00 0\n 28 0.00 0.00 0.00 0\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 1.00 1.00 1.00 1\n 38 0.00 0.00 0.00 0\n 44 0.00 0.00 0.00 2\n 50 0.00 0.00 0.00 1\n 58 0.00 0.00 0.00 1\n 64 0.00 0.00 0.00 0\n 66 0.00 0.00 0.00 1\n 70 0.00 0.00 0.00 0\n 74 0.00 0.00 0.00 0\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 1.00 1.00 1.00 4\n 90 0.00 0.00 0.00 1\n 130 0.00 0.00 0.00 0\n 150 0.50 0.50 0.50 2\n 160 0.00 0.00 0.00 1\n 170 0.00 0.00 0.00 1\n 180 1.00 0.67 0.80 3\n 188 0.00 0.00 0.00 0\n 190 0.00 0.00 0.00 1\n 200 0.00 0.00 0.00 0\n 212 0.00 0.00 0.00 1\n 215 1.00 1.00 1.00 1\n 220 0.00 0.00 0.00 1\n 222 0.00 0.00 0.00 0\n 225 1.00 1.00 1.00 1\n 232 1.00 1.00 1.00 1\n 234 1.00 1.00 1.00 1\n 235 0.00 0.00 0.00 1\n 240 0.67 0.50 0.57 4\n 250 0.25 1.00 0.40 1\n 260 0.00 0.00 0.00 0\n 270 0.00 0.00 0.00 1\n 271 1.00 1.00 1.00 1\n 280 1.00 1.00 1.00 1\n 290 0.33 1.00 0.50 1\n 300 1.00 1.00 1.00 1\n 306 0.00 0.00 0.00 1\n 317 0.00 0.00 0.00 0\n 350 1.00 0.40 0.57 5\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 410 0.00 0.00 0.00 0\n 412 0.00 0.00 0.00 0\n 420 0.00 0.00 0.00 1\n 423 0.00 0.00 0.00 0\n 426 0.00 0.00 0.00 1\n 500 1.00 0.67 0.80 3\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 2492 0.00 0.00 0.00 0\n 3750 0.00 0.00 0.00 0\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4018 0.00 0.00 0.00 0\n 4259 0.00 0.00 0.00 0\n 4310 0.00 0.00 0.00 1\n 4330 0.00 0.00 0.00 0\n 4571 0.00 0.00 0.00 1\n 4717 0.00 0.00 0.00 0\n 5113 0.00 0.00 0.00 1\n 5215 0.00 0.00 0.00 0\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 5709 0.00 0.00 0.00 0\n 8038 0.00 0.00 0.00 1\n 9076 0.00 0.00 0.00 0\n 10027 0.00 0.00 0.00 1\n 10457 0.00 0.00 0.00 0\n 14368 0.00 0.00 0.00 1\n 15442 0.00 0.00 0.00 0\n 15942 0.00 0.00 0.00 1\n 21392 0.00 0.00 0.00 0\n 22165 0.00 0.00 0.00 0\n 22217 0.00 0.00 0.00 1\n 22886 0.00 0.00 0.00 0\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23368 0.00 0.00 0.00 1\n 23459 0.00 0.00 0.00 0\n 23575 0.00 0.00 0.00 0\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 24403 0.00 0.00 0.00 0\n 24418 0.00 0.00 0.00 0\n 25830 0.00 0.00 0.00 0\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 27835 0.00 0.00 0.00 0\n 28258 0.00 0.00 0.00 0\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 31255 0.00 0.00 0.00 0\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 35130 0.00 0.00 0.00 0\n 36538 0.00 0.00 0.00 1\n 36825 0.00 0.00 0.00 0\n\n accuracy 0.51 156\n macro avg 0.13 0.12 0.12 156\nweighted avg 0.47 0.51 0.48 156\n\n\nGradient Boosting Classification Report:\n precision recall f1-score support\n\n 0 0.60 0.96 0.74 55\n 2 0.00 0.00 0.00 1\n 3 0.00 0.00 0.00 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 8 1.00 0.50 0.67 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 0\n 20 0.00 0.00 0.00 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 25 0.00 0.00 0.00 0\n 26 0.00 0.00 0.00 0\n 28 0.00 0.00 0.00 0\n 32 0.00 0.00 0.00 0\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 0.50 1.00 0.67 1\n 38 0.00 0.00 0.00 0\n 44 0.00 0.00 0.00 2\n 49 0.00 0.00 0.00 0\n 50 0.00 0.00 0.00 1\n 53 0.00 0.00 0.00 0\n 58 0.00 0.00 0.00 1\n 60 0.00 0.00 0.00 0\n 66 0.00 0.00 0.00 1\n 74 0.00 0.00 0.00 0\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 0.00 0.00 0.00 4\n 90 0.00 0.00 0.00 1\n 110 0.00 0.00 0.00 0\n 130 0.00 0.00 0.00 0\n 140 0.00 0.00 0.00 0\n 145 0.00 0.00 0.00 0\n 150 0.00 0.00 0.00 2\n 160 0.00 0.00 0.00 1\n 165 0.00 0.00 0.00 0\n 170 0.00 0.00 0.00 1\n 180 1.00 0.33 0.50 3\n 188 0.00 0.00 0.00 0\n 190 0.00 0.00 0.00 1\n 195 0.00 0.00 0.00 0\n 212 0.00 0.00 0.00 1\n 215 0.00 0.00 0.00 1\n 220 0.00 0.00 0.00 1\n 225 1.00 1.00 1.00 1\n 228 0.00 0.00 0.00 0\n 232 1.00 1.00 1.00 1\n 234 1.00 1.00 1.00 1\n 235 0.00 0.00 0.00 1\n 240 0.67 0.50 0.57 4\n 250 0.00 0.00 0.00 1\n 270 0.00 0.00 0.00 1\n 271 1.00 1.00 1.00 1\n 280 0.00 0.00 0.00 1\n 290 0.00 0.00 0.00 1\n 300 1.00 1.00 1.00 1\n 306 0.00 0.00 0.00 1\n 317 0.00 0.00 0.00 0\n 350 0.00 0.00 0.00 5\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 412 0.00 0.00 0.00 0\n 420 0.00 0.00 0.00 1\n 426 0.00 0.00 0.00 1\n 500 1.00 0.67 0.80 3\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 2492 0.00 0.00 0.00 0\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4310 0.00 0.00 0.00 1\n 4571 0.00 0.00 0.00 1\n 5066 0.00 0.00 0.00 0\n 5113 0.00 0.00 0.00 1\n 5215 0.00 0.00 0.00 0\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 8038 0.00 0.00 0.00 1\n 8570 0.00 0.00 0.00 0\n 10027 0.00 0.00 0.00 1\n 10114 0.00 0.00 0.00 0\n 10457 0.00 0.00 0.00 0\n 14368 0.00 0.00 0.00 1\n 15442 0.00 0.00 0.00 0\n 15942 0.00 0.00 0.00 1\n 17286 0.00 0.00 0.00 0\n 19235 0.00 0.00 0.00 0\n 22217 0.00 0.00 0.00 1\n 22886 0.00 0.00 0.00 0\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23368 0.00 0.00 0.00 1\n 23459 0.00 0.00 0.00 0\n 23575 0.00 0.00 0.00 0\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 24403 0.00 0.00 0.00 0\n 24418 0.00 0.00 0.00 0\n 26008 0.00 0.00 0.00 0\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 27835 0.00 0.00 0.00 0\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 30665 0.00 0.00 0.00 0\n 32146 0.00 0.00 0.00 0\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 36538 0.00 0.00 0.00 1\n 36825 0.00 0.00 0.00 0\n\n accuracy 0.42 156\n macro avg 0.08 0.07 0.07 156\nweighted avg 0.32 0.42 0.35 156\n\n\nSVM Classification Report:\n precision recall f1-score support\n\n 0 0.35 1.00 0.52 55\n 2 0.00 0.00 0.00 1\n 3 0.00 0.00 0.00 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 20 0.00 0.00 0.00 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 0.00 0.00 0.00 1\n 44 0.00 0.00 0.00 2\n 50 0.00 0.00 0.00 1\n 58 0.00 0.00 0.00 1\n 66 0.00 0.00 0.00 1\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 0.00 0.00 0.00 4\n 90 0.00 0.00 0.00 1\n 150 0.00 0.00 0.00 2\n 160 0.00 0.00 0.00 1\n 170 0.00 0.00 0.00 1\n 180 0.00 0.00 0.00 3\n 190 0.00 0.00 0.00 1\n 212 0.00 0.00 0.00 1\n 215 0.00 0.00 0.00 1\n 220 0.00 0.00 0.00 1\n 225 0.00 0.00 0.00 1\n 232 0.00 0.00 0.00 1\n 234 0.00 0.00 0.00 1\n 235 0.00 0.00 0.00 1\n 240 0.00 0.00 0.00 4\n 250 0.00 0.00 0.00 1\n 270 0.00 0.00 0.00 1\n 271 0.00 0.00 0.00 1\n 280 0.00 0.00 0.00 1\n 290 0.00 0.00 0.00 1\n 300 0.00 0.00 0.00 1\n 306 0.00 0.00 0.00 1\n 350 0.00 0.00 0.00 5\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 420 0.00 0.00 0.00 1\n 426 0.00 0.00 0.00 1\n 500 0.00 0.00 0.00 3\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4310 0.00 0.00 0.00 1\n 4571 0.00 0.00 0.00 1\n 5113 0.00 0.00 0.00 1\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 8038 0.00 0.00 0.00 1\n 10027 0.00 0.00 0.00 1\n 14368 0.00 0.00 0.00 1\n 15942 0.00 0.00 0.00 1\n 22217 0.00 0.00 0.00 1\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23368 0.00 0.00 0.00 1\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 36538 0.00 0.00 0.00 1\n\n accuracy 0.35 156\n macro avg 0.00 0.01 0.01 156\nweighted avg 0.12 0.35 0.18 156\n\n\nKNN Classification Report:\n precision recall f1-score support\n\n 0 0.53 0.93 0.67 55\n 2 0.00 0.00 0.00 1\n 3 0.00 0.00 0.00 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 0\n 20 0.00 0.00 0.00 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 25 0.00 0.00 0.00 0\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 0.50 1.00 0.67 1\n 44 0.00 0.00 0.00 2\n 50 0.00 0.00 0.00 1\n 58 0.00 0.00 0.00 1\n 66 0.00 0.00 0.00 1\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 0.67 1.00 0.80 4\n 90 0.00 0.00 0.00 1\n 150 0.00 0.00 0.00 2\n 160 0.00 0.00 0.00 1\n 170 0.00 0.00 0.00 1\n 180 0.00 0.00 0.00 3\n 190 0.00 0.00 0.00 1\n 195 0.00 0.00 0.00 0\n 212 0.00 0.00 0.00 1\n 215 0.00 0.00 0.00 1\n 220 0.00 0.00 0.00 1\n 225 0.00 0.00 0.00 1\n 232 1.00 1.00 1.00 1\n 234 1.00 1.00 1.00 1\n 235 0.00 0.00 0.00 1\n 240 0.00 0.00 0.00 4\n 250 0.00 0.00 0.00 1\n 260 0.00 0.00 0.00 0\n 270 0.00 0.00 0.00 1\n 271 0.00 0.00 0.00 1\n 280 0.00 0.00 0.00 1\n 290 0.00 0.00 0.00 1\n 300 0.00 0.00 0.00 1\n 306 0.00 0.00 0.00 1\n 350 0.25 0.20 0.22 5\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 420 0.00 0.00 0.00 1\n 426 0.00 0.00 0.00 1\n 450 0.00 0.00 0.00 0\n 500 0.50 0.67 0.57 3\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4310 0.00 0.00 0.00 1\n 4571 0.00 0.00 0.00 1\n 5113 0.00 0.00 0.00 1\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 8038 0.00 0.00 0.00 1\n 10027 0.00 0.00 0.00 1\n 13708 0.00 0.00 0.00 0\n 14368 0.00 0.00 0.00 1\n 14600 0.00 0.00 0.00 0\n 15942 0.00 0.00 0.00 1\n 17286 0.00 0.00 0.00 0\n 19008 0.00 0.00 0.00 0\n 19235 0.00 0.00 0.00 0\n 21392 0.00 0.00 0.00 0\n 22165 0.00 0.00 0.00 0\n 22217 0.00 0.00 0.00 1\n 22886 0.00 0.00 0.00 0\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23368 0.00 0.00 0.00 1\n 23575 0.00 0.00 0.00 0\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 26008 0.00 0.00 0.00 0\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 36538 0.00 0.00 0.00 1\n\n accuracy 0.39 156\n macro avg 0.05 0.06 0.05 156\nweighted avg 0.24 0.39 0.29 156\n\n\nLogistic Regression Classification Report:\n precision recall f1-score support\n\n 0 0.41 1.00 0.58 55\n 2 0.00 0.00 0.00 1\n 3 0.00 0.00 0.00 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 20 0.00 0.00 0.00 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 0.00 0.00 0.00 1\n 44 0.00 0.00 0.00 2\n 50 0.00 0.00 0.00 1\n 58 0.00 0.00 0.00 1\n 66 0.00 0.00 0.00 1\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 0.00 0.00 0.00 4\n 90 0.00 0.00 0.00 1\n 150 0.00 0.00 0.00 2\n 160 0.00 0.00 0.00 1\n 170 0.00 0.00 0.00 1\n 180 0.00 0.00 0.00 3\n 190 0.00 0.00 0.00 1\n 212 0.00 0.00 0.00 1\n 215 0.00 0.00 0.00 1\n 220 0.00 0.00 0.00 1\n 225 0.00 0.00 0.00 1\n 232 0.00 0.00 0.00 1\n 234 0.00 0.00 0.00 1\n 235 0.00 0.00 0.00 1\n 240 0.00 0.00 0.00 4\n 250 0.00 0.00 0.00 1\n 270 0.00 0.00 0.00 1\n 271 0.00 0.00 0.00 1\n 280 0.00 0.00 0.00 1\n 290 0.00 0.00 0.00 1\n 300 0.00 0.00 0.00 1\n 306 0.00 0.00 0.00 1\n 350 0.00 0.00 0.00 5\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 420 0.00 0.00 0.00 1\n 426 0.00 0.00 0.00 1\n 500 0.00 0.00 0.00 3\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4310 0.00 0.00 0.00 1\n 4571 0.00 0.00 0.00 1\n 5113 0.00 0.00 0.00 1\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 8038 0.00 0.00 0.00 1\n 10027 0.00 0.00 0.00 1\n 14368 0.00 0.00 0.00 1\n 15942 0.00 0.00 0.00 1\n 22217 0.00 0.00 0.00 1\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23368 0.00 0.00 0.00 1\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 36538 0.00 0.00 0.00 1\n\n accuracy 0.35 156\n macro avg 0.01 0.01 0.01 156\nweighted avg 0.14 0.35 0.20 156\n\n\nDecision Tree Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.98 0.99 55\n 1 0.00 0.00 0.00 0\n 2 0.00 0.00 0.00 1\n 3 1.00 0.33 0.50 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 0\n 8 0.00 0.00 0.00 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 15 0.00 0.00 0.00 0\n 17 0.00 0.00 0.00 0\n 18 0.00 0.00 0.00 0\n 20 0.50 0.33 0.40 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 26 0.00 0.00 0.00 0\n 28 0.00 0.00 0.00 0\n 32 0.00 0.00 0.00 0\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 1.00 1.00 1.00 1\n 38 0.00 0.00 0.00 0\n 42 0.00 0.00 0.00 0\n 44 0.00 0.00 0.00 2\n 50 0.00 0.00 0.00 1\n 58 0.00 0.00 0.00 1\n 60 0.00 0.00 0.00 0\n 66 0.00 0.00 0.00 1\n 68 0.00 0.00 0.00 0\n 74 0.00 0.00 0.00 0\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 0.80 1.00 0.89 4\n 90 1.00 1.00 1.00 1\n 150 0.00 0.00 0.00 2\n 160 0.00 0.00 0.00 1\n 165 0.00 0.00 0.00 0\n 170 0.00 0.00 0.00 1\n 180 1.00 0.67 0.80 3\n 188 0.00 0.00 0.00 0\n 190 0.00 0.00 0.00 1\n 210 0.00 0.00 0.00 0\n 212 0.00 0.00 0.00 1\n 215 0.00 0.00 0.00 1\n 220 0.00 0.00 0.00 1\n 222 0.00 0.00 0.00 0\n 225 0.50 1.00 0.67 1\n 232 1.00 1.00 1.00 1\n 234 1.00 1.00 1.00 1\n 235 0.00 0.00 0.00 1\n 240 0.67 0.50 0.57 4\n 250 0.33 1.00 0.50 1\n 260 0.00 0.00 0.00 0\n 270 0.00 0.00 0.00 1\n 271 0.00 0.00 0.00 1\n 280 0.00 0.00 0.00 1\n 281 0.00 0.00 0.00 0\n 290 1.00 1.00 1.00 1\n 300 1.00 1.00 1.00 1\n 306 0.00 0.00 0.00 1\n 317 0.00 0.00 0.00 0\n 350 1.00 0.80 0.89 5\n 355 0.00 0.00 0.00 0\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 412 0.00 0.00 0.00 0\n 420 0.00 0.00 0.00 1\n 423 0.00 0.00 0.00 0\n 426 0.00 0.00 0.00 1\n 500 1.00 0.67 0.80 3\n 660 0.00 0.00 0.00 0\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 3750 0.00 0.00 0.00 0\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4310 0.00 0.00 0.00 1\n 4330 0.00 0.00 0.00 0\n 4571 0.00 0.00 0.00 1\n 4717 0.00 0.00 0.00 0\n 5113 0.00 0.00 0.00 1\n 5215 0.00 0.00 0.00 0\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 5709 0.00 0.00 0.00 0\n 6144 0.00 0.00 0.00 0\n 8038 0.00 0.00 0.00 1\n 9076 0.00 0.00 0.00 0\n 10027 0.00 0.00 0.00 1\n 10457 0.00 0.00 0.00 0\n 14368 0.00 0.00 0.00 1\n 15442 0.00 0.00 0.00 0\n 15942 0.00 0.00 0.00 1\n 21392 0.00 0.00 0.00 0\n 22165 0.00 0.00 0.00 0\n 22217 0.00 0.00 0.00 1\n 22886 0.00 0.00 0.00 0\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23317 0.00 0.00 0.00 0\n 23368 0.00 0.00 0.00 1\n 23575 0.00 0.00 0.00 0\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 24418 0.00 0.00 0.00 0\n 25914 0.00 0.00 0.00 0\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 27552 0.00 0.00 0.00 0\n 27835 0.00 0.00 0.00 0\n 28258 0.00 0.00 0.00 0\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 30665 0.00 0.00 0.00 0\n 31255 0.00 0.00 0.00 0\n 32146 0.00 0.00 0.00 0\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 36538 0.00 0.00 0.00 1\n 38582 0.00 0.00 0.00 0\n\n accuracy 0.50 156\n macro avg 0.11 0.10 0.10 156\nweighted avg 0.53 0.50 0.51 156\n\n\nMLP Classification Report:\n precision recall f1-score support\n\n 0 0.36 1.00 0.53 55\n 2 0.00 0.00 0.00 1\n 3 0.00 0.00 0.00 3\n 4 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 1\n 13 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 1\n 20 0.00 0.00 0.00 3\n 22 0.00 0.00 0.00 1\n 23 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 1\n 33 0.00 0.00 0.00 1\n 35 0.00 0.00 0.00 1\n 36 0.00 0.00 0.00 1\n 44 0.00 0.00 0.00 2\n 50 0.00 0.00 0.00 1\n 58 0.00 0.00 0.00 1\n 66 0.00 0.00 0.00 1\n 76 0.00 0.00 0.00 1\n 78 0.00 0.00 0.00 1\n 80 0.00 0.00 0.00 4\n 90 0.00 0.00 0.00 1\n 150 0.00 0.00 0.00 2\n 160 0.00 0.00 0.00 1\n 170 0.00 0.00 0.00 1\n 180 0.00 0.00 0.00 3\n 190 0.00 0.00 0.00 1\n 212 0.00 0.00 0.00 1\n 215 0.00 0.00 0.00 1\n 220 0.00 0.00 0.00 1\n 225 0.00 0.00 0.00 1\n 232 0.00 0.00 0.00 1\n 234 0.00 0.00 0.00 1\n 235 0.00 0.00 0.00 1\n 240 0.00 0.00 0.00 4\n 250 0.00 0.00 0.00 1\n 270 0.00 0.00 0.00 1\n 271 0.00 0.00 0.00 1\n 280 0.00 0.00 0.00 1\n 290 0.00 0.00 0.00 1\n 300 0.00 0.00 0.00 1\n 306 0.00 0.00 0.00 1\n 350 0.00 0.00 0.00 5\n 360 0.00 0.00 0.00 1\n 375 0.00 0.00 0.00 1\n 420 0.00 0.00 0.00 1\n 426 0.00 0.00 0.00 1\n 500 0.00 0.00 0.00 3\n 863 0.00 0.00 0.00 1\n 1627 0.00 0.00 0.00 1\n 3785 0.00 0.00 0.00 1\n 3805 0.00 0.00 0.00 1\n 3822 0.00 0.00 0.00 1\n 4310 0.00 0.00 0.00 1\n 4571 0.00 0.00 0.00 1\n 5113 0.00 0.00 0.00 1\n 5242 0.00 0.00 0.00 1\n 5527 0.00 0.00 0.00 1\n 6144 0.00 0.00 0.00 0\n 8038 0.00 0.00 0.00 1\n 10027 0.00 0.00 0.00 1\n 14368 0.00 0.00 0.00 1\n 15942 0.00 0.00 0.00 1\n 22217 0.00 0.00 0.00 1\n 23205 0.00 0.00 0.00 1\n 23208 0.00 0.00 0.00 1\n 23368 0.00 0.00 0.00 1\n 24138 0.00 0.00 0.00 1\n 24281 0.00 0.00 0.00 1\n 26169 0.00 0.00 0.00 1\n 26516 0.00 0.00 0.00 1\n 26734 0.00 0.00 0.00 1\n 27519 0.00 0.00 0.00 1\n 28537 0.00 0.00 0.00 1\n 29154 0.00 0.00 0.00 1\n 29561 0.00 0.00 0.00 1\n 32980 0.00 0.00 0.00 1\n 35078 0.00 0.00 0.00 1\n 36538 0.00 0.00 0.00 1\n\n accuracy 0.35 156\n macro avg 0.00 0.01 0.01 156\nweighted avg 0.13 0.35 0.19 156\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# For Nuclear Weapons Tests States","metadata":{}},{"cell_type":"code","source":"# Random Forest\nrf_model = RandomForestClassifier(random_state=42)\nrf_train_acc, rf_test_acc, rf_class_report = evaluate_model(rf_model, X4_train, X4_test, y4_train, y4_test, 'rf')\n\n# Gradient Boosting\ngb_model = GradientBoostingClassifier(random_state=42)\ngb_train_acc, gb_test_acc, gb_class_report = evaluate_model(gb_model, X4_train, X4_test, y4_train, y4_test, 'gb')\n\n# Support Vector Machine\nsvm_model = SVC(random_state=42)\nsvm_train_acc, svm_test_acc, svm_class_report = evaluate_model(svm_model, X4_train, X4_test, y4_train, y4_test, 'svm')\n\n# K-Nearest Neighbors\nknn_model = KNeighborsClassifier()\nknn_train_acc, knn_test_acc, knn_class_report = evaluate_model(knn_model, X4_train, X4_test, y4_train, y4_test, 'knn')\n\n# Logistic Regression\nlr_model = LogisticRegression(random_state=42)\nlr_train_acc, lr_test_acc, lr_class_report = evaluate_model(lr_model, X4_train, X4_test, y4_train, y4_test, 'lr')\n\n# Decision Tree\ndt_model = DecisionTreeClassifier(random_state=42)\ndt_train_acc, dt_test_acc, dt_class_report = evaluate_model(dt_model, X4_train, X4_test, y4_train, y4_test, 'dt')\n\n# Neural Network (MLP)\nmlp_model = MLPClassifier(random_state=42, max_iter=500)\nmlp_train_acc, mlp_test_acc, mlp_class_report = evaluate_model(mlp_model, X4_train, X4_test, y4_train, y4_test, 'mlp')","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:57:14.655211Z","iopub.execute_input":"2024-01-07T12:57:14.655597Z","iopub.status.idle":"2024-01-07T12:57:21.871707Z","shell.execute_reply.started":"2024-01-07T12:57:14.655566Z","shell.execute_reply":"2024-01-07T12:57:21.870363Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n n_iter_i = _check_optimize_result(\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Plotting Accuracies and Classification Reports","metadata":{}},{"cell_type":"code","source":"# Plot Accuracies\nmodels = ['Random Forest', 'Gradient Boosting', 'SVM', 'KNN', 'Logistic Regression', 'Decision Tree', 'MLP']\ntrain_accuracies = [rf_train_acc, gb_train_acc, svm_train_acc, knn_train_acc, lr_train_acc, dt_train_acc, mlp_train_acc]\ntest_accuracies = [rf_test_acc, gb_test_acc, svm_test_acc, knn_test_acc, lr_test_acc, dt_test_acc, mlp_test_acc]\n\nplt.figure(figsize=(10, 6))\nplt.barh(models, train_accuracies, color='lightblue', label='Train Accuracy')\nplt.barh(models, test_accuracies, color='orange', alpha=0.7, label='Test Accuracy')\nplt.xlabel('Accuracy')\nplt.title('Model Train and Test Accuracies')\nplt.legend()\nplt.show()\n\n# Print Classification Reports\nprint(\"Random Forest Classification Report:\\n\", rf_class_report)\nprint(\"\\nGradient Boosting Classification Report:\\n\", gb_class_report)\nprint(\"\\nSVM Classification Report:\\n\", svm_class_report)\nprint(\"\\nKNN Classification Report:\\n\", knn_class_report)\nprint(\"\\nLogistic Regression Classification Report:\\n\", lr_class_report)\nprint(\"\\nDecision Tree Classification Report:\\n\", dt_class_report)\nprint(\"\\nMLP Classification Report:\\n\", mlp_class_report)","metadata":{"execution":{"iopub.status.busy":"2024-01-07T12:57:45.208247Z","iopub.execute_input":"2024-01-07T12:57:45.209099Z","iopub.status.idle":"2024-01-07T12:57:45.472325Z","shell.execute_reply.started":"2024-01-07T12:57:45.209065Z","shell.execute_reply":"2024-01-07T12:57:45.471463Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stdout","text":"Random Forest Classification Report:\n precision recall f1-score support\n\n 0 0.99 1.00 0.99 90\n 1 0.67 0.80 0.73 5\n 2 0.50 0.33 0.40 3\n 3 0.00 0.00 0.00 1\n 4 0.00 0.00 0.00 0\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 0\n 7 0.00 0.00 0.00 1\n 8 0.50 0.50 0.50 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 12 1.00 1.00 1.00 1\n 14 0.00 0.00 0.00 0\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 1.00 1.00 1.00 1\n 22 1.00 1.00 1.00 1\n 24 0.00 0.00 0.00 0\n 27 0.00 0.00 0.00 1\n 32 0.00 0.00 0.00 1\n 46 0.00 0.00 0.00 0\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 77 0.00 0.00 0.00 1\n 79 0.00 0.00 0.00 0\n\n accuracy 0.82 120\n macro avg 0.20 0.20 0.20 120\nweighted avg 0.82 0.82 0.82 120\n\n\nGradient Boosting Classification Report:\n precision recall f1-score support\n\n 0 0.98 1.00 0.99 90\n 1 0.67 0.40 0.50 5\n 2 0.14 0.33 0.20 3\n 3 0.00 0.00 0.00 1\n 4 0.00 0.00 0.00 0\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 0\n 7 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 0\n 12 0.00 0.00 0.00 1\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 0.00 0.00 0.00 1\n 22 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 0\n 27 0.00 0.00 0.00 1\n 31 0.00 0.00 0.00 0\n 32 0.00 0.00 0.00 1\n 47 0.00 0.00 0.00 0\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 59 0.00 0.00 0.00 0\n 77 0.00 0.00 0.00 1\n\n accuracy 0.78 120\n macro avg 0.06 0.06 0.06 120\nweighted avg 0.77 0.78 0.77 120\n\n\nSVM Classification Report:\n precision recall f1-score support\n\n 0 0.75 1.00 0.86 90\n 1 0.00 0.00 0.00 5\n 2 0.00 0.00 0.00 3\n 3 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 7 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 12 0.00 0.00 0.00 1\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 0.00 0.00 0.00 1\n 22 0.00 0.00 0.00 1\n 27 0.00 0.00 0.00 1\n 32 0.00 0.00 0.00 1\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 77 0.00 0.00 0.00 1\n\n accuracy 0.75 120\n macro avg 0.03 0.05 0.04 120\nweighted avg 0.56 0.75 0.64 120\n\n\nKNN Classification Report:\n precision recall f1-score support\n\n 0 0.95 1.00 0.97 90\n 1 0.40 0.80 0.53 5\n 2 0.00 0.00 0.00 3\n 3 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 0\n 7 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 12 0.00 0.00 0.00 1\n 14 0.00 0.00 0.00 0\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 0.00 0.00 0.00 1\n 22 0.00 0.00 0.00 1\n 27 0.00 0.00 0.00 1\n 32 0.00 0.00 0.00 1\n 34 0.00 0.00 0.00 0\n 38 0.00 0.00 0.00 0\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 77 0.00 0.00 0.00 1\n\n accuracy 0.78 120\n macro avg 0.05 0.07 0.06 120\nweighted avg 0.73 0.78 0.75 120\n\n\nLogistic Regression Classification Report:\n precision recall f1-score support\n\n 0 0.88 1.00 0.94 90\n 1 0.00 0.00 0.00 5\n 2 0.00 0.00 0.00 3\n 3 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 6 0.00 0.00 0.00 0\n 7 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 12 0.00 0.00 0.00 1\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 0.00 0.00 0.00 1\n 22 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 0\n 27 0.00 0.00 0.00 1\n 32 0.00 0.00 0.00 1\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 77 0.00 0.00 0.00 1\n\n accuracy 0.75 120\n macro avg 0.04 0.04 0.04 120\nweighted avg 0.66 0.75 0.70 120\n\n\nDecision Tree Classification Report:\n precision recall f1-score support\n\n 0 0.99 0.99 0.99 90\n 1 0.67 0.80 0.73 5\n 2 0.50 0.67 0.57 3\n 3 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 7 0.00 0.00 0.00 1\n 8 0.50 0.50 0.50 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 11 0.00 0.00 0.00 0\n 12 1.00 1.00 1.00 1\n 14 0.00 0.00 0.00 0\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 0.00 0.00 0.00 1\n 22 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 0\n 27 0.00 0.00 0.00 1\n 32 0.00 0.00 0.00 1\n 34 0.00 0.00 0.00 0\n 39 0.00 0.00 0.00 0\n 47 0.00 0.00 0.00 0\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 59 0.00 0.00 0.00 0\n 77 0.00 0.00 0.00 1\n\n accuracy 0.81 120\n macro avg 0.13 0.14 0.13 120\nweighted avg 0.80 0.81 0.80 120\n\n\nMLP Classification Report:\n precision recall f1-score support\n\n 0 0.83 1.00 0.90 90\n 1 0.00 0.00 0.00 5\n 2 0.00 0.00 0.00 3\n 3 0.00 0.00 0.00 1\n 5 0.00 0.00 0.00 1\n 7 0.00 0.00 0.00 1\n 8 0.00 0.00 0.00 2\n 9 0.00 0.00 0.00 1\n 10 0.00 0.00 0.00 1\n 12 0.00 0.00 0.00 1\n 15 0.00 0.00 0.00 1\n 16 0.00 0.00 0.00 1\n 17 0.00 0.00 0.00 1\n 18 0.00 0.00 0.00 2\n 19 0.00 0.00 0.00 2\n 20 0.00 0.00 0.00 1\n 21 0.00 0.00 0.00 0\n 22 0.00 0.00 0.00 1\n 24 0.00 0.00 0.00 0\n 27 0.00 0.00 0.00 1\n 32 0.00 0.00 0.00 1\n 48 0.00 0.00 0.00 1\n 56 0.00 0.00 0.00 1\n 77 0.00 0.00 0.00 1\n\n accuracy 0.75 120\n macro avg 0.03 0.04 0.04 120\nweighted avg 0.62 0.75 0.68 120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Conclusion\n\n# 1. Exploratory Data Analysis (EDA):\n\n* **Nuclear Weapons Proliferation owid (df1):**\nExplored the distribution of nuclear weapons-related features over time.\nConducted basic statistics, including mean, median, and standard deviation.\nUtilized K-Means clustering to identify potential patterns in the data.\nApplied the t-test to compare nuclear weapons stockpiles between different years.\n\n* **Nuclear Weapons Proliferation Total owid (df2):**\nExamined the relationship between various features, including number_nuclweap_consideration, number_nuclweap_pursuit, and number_nuclweap_possession.\nPerformed Principal Component Analysis (PCA) to reduce dimensionality.\nApplied the elbow method to find the optimal number of clusters for K-Means.\nConducted K-Means clustering to group entities based on nuclear weapons-related features.\n\n* **Nuclear Weapons Stockpiles (df3):**\nAnalyzed the trend in nuclear weapons stockpiles over time.\nApplied basic statistics to understand the central tendency and dispersion of the data.\nConducted K-Means clustering to identify potential clusters of countries based on their nuclear weapons stockpiles.\n\n* **Nuclear Weapons Tests States (df4):**\nExplored the distribution of nuclear weapons tests over time.\nConducted the t-test to compare the number of nuclear weapons tests between different years.\nMachine Learning Models:\n\n# 2. Model Selection:\nChose a variety of machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree, MLP, and a simple Neural Network using Keras.\n\n# 3. Evaluation and Prediction:\nTrained each model on the respective training data and evaluated their performance on the test set.\nSaved the model predictions to CSV files for further analysis.\n\n# 4. Analysis of Model Performance:\nPlotted and compared the accuracy of each model on both training and test sets to assess overfitting or underfitting.\nProvided classification reports for each model to understand precision, recall, and F1-score.","metadata":{}}]} \ No newline at end of file diff --git a/Nuclear Weapons Analysis/Results-Csv/__results___files/__results___14_0.png b/Nuclear Weapons Analysis/Results-Csv/__results___files/__results___14_0.png new file mode 100644 index 000000000..11aa06709 Binary files /dev/null and b/Nuclear Weapons Analysis/Results-Csv/__results___files/__results___14_0.png differ diff --git a/Nuclear Weapons Analysis/Results-Csv/__results___files/__results___16_1.png b/Nuclear Weapons Analysis/Results-Csv/__results___files/__results___16_1.png new file mode 100644 index 000000000..a70bce4f2 Binary files /dev/null and b/Nuclear Weapons Analysis/Results-Csv/__results___files/__results___16_1.png differ diff --git a/Nuclear Weapons 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