├── image └── img_customer_churn.jpg ├── README.md ├── Churn_Prediction_in_Telecom_Industry_using_Logistic_Regression.ipynb └── data └── churn-bigml-20.csv /image/img_customer_churn.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yashuv/CodeClause_Churn_Prediction_in_Telecom_Industry_using_Logistic_Regression/main/image/img_customer_churn.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CodeClause_Churn_Prediction_in_Telecom_Industry_using_Logistic_Regression 2 | 3 | This is the first project I completed as a part of my Data Science internship at **CodeClause**. 4 | 5 | In this project, I accomplished the following:  6 | 7 | ## 1. Definition of Task 8 | Churn, in the context of churn prediction, refers to the loss of customers or clients. Churn prediction is the process of identifying which customers are likely to leave a company or stop using a product or service in the near future. 9 | 10 | Churn prediction is often used in industries such as telecommunications, banking, and subscription-based businesses, where retaining customers is important for long-term profitability. The goal of churn prediction is to identify customers who are at risk of churning and take steps to retain them, rather than losing them as customers and having to acquire new ones. 11 | 12 | ## 2. Importing necessary libraries 13 | Importing library in a Python script allows you to use the functions, classes, and other objects defined in those libraries in your code and makes it easier to accomplish tasks. 14 | 15 | For example, you might import the `numpy` library to use its array manipulation and numerical computing functions, or you might import the pandas library to use its data manipulation and analysis tools. 16 | 17 | ## 3. EDA 18 | It is a valuable tool for understanding and gaining insights from data. It can also be used to generate ideas for further research or to communicate findings to others.and is an important step in the data science process. 19 | 20 | for eg: summary statistics 21 | 22 | ## 4. Exploring data using visualizations 23 | It is an effective way to gain insights and understand patterns and relationships in the data. It can help you to gain a better understanding of the data and identify any potential issues or problems that may need to be addressed. 24 | 25 | For example, a scatter plot can be used to visualize the relationship between two continuous variables, while a bar chart is useful for comparing the values of a categorical variable. 26 | 27 | ## 5. Exploring feature distributions 28 | It is the process of understanding the distribution of values for a particular feature or variable in a dataset. This can be useful for understanding the characteristics of the data and identifying patterns and trends that may be relevant for our analysis. 29 | 30 | ## 6. Data preprocessing 31 | It is the process of preparing data for analysis by cleaning, transforming, and selecting the relevant data. It is an important step in the data science process, as it can help to improve the quality and accuracy of the data and make it easier to analyze. Some common data preprocessing steps are: 32 | 33 | * **Cleaning the data**: Removing errors, missing values, or duplicate records 34 | 35 | * **Handling missing values**: Imputing missing values or removing records with missing values 36 | 37 | * **Encoding categorical variables**: Converting categorical variables into numerical form. 38 | 39 | * **Normalizing or scaling the data**: Transforming the data to have a mean of zero and a standard deviation of one. 40 | 41 | * **Feature selection**: Selecting a subset of the features to include in the analysis. 42 | 43 | ## 7. Model building 44 | It is the process of creating a mathematical or statistical model to represent the relationships and patterns in a dataset. Model building is a common task in data science and machine learning, as it allows you to make predictions or inferences about the data based on the patterns identified in the model. 45 | 46 | There are many different types of models that can be built, including linear models, logistic regression models, decision trees, and neural networks. The choice of model will depend on the type of data and the specific goals of the analysis. 47 | 48 | ## 8. Performance evaluation 49 | It is an important step in the model building process, as it allows you to assess the effectiveness of the model and make any necessary adjustments to improve its performance. It is also important to evaluate the performance of a model on unseen data, as this can provide a more realistic assessment of its performance on real-world tasks. Some common performance evaluation metrics are: 50 | 51 | * **Accuracy**: The proportion of correct predictions made by the model 52 | * **Precision**: The proportion of correct positive predictions made by the model 53 | * **Recall**: The proportion of actual positive cases that were correctly predicted by the model 54 | * **F1 score**: The harmonic mean of precision and recall 55 | * **AUC-ROC**: The area under the receiver operating characteristic curve, which measures the ability of the model to distinguish between positive and negative cases 56 | 57 | ## 9. Making predictions 58 | It is the process of using a model to generate a prediction or estimate for a specific task or problem. It allows to make informed decisions or take specific actions. 59 | 60 | 61 | Thank you! 62 | 63 | -------------------------------------------------------------------------------- /Churn_Prediction_in_Telecom_Industry_using_Logistic_Regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "id": "2fe6b10e-6264-4c62-b85d-da27d6c87f26", 7 | "metadata": { 8 | "tags": [] 9 | }, 10 | "source": [ 11 | "# Project 01: Churn Prediction in Telecom Industry using Logistic Regression\n", 12 | "\n", 13 | "\n", 14 | "### Submitted By: Yashuv Baskota\n", 15 | "### Language- Python\n", 16 | "### Datasets :- https://www.kaggle.com/datasets/mnassrib/telecom-churn-datasets" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "id": "1c652282-0665-4fb4-a6fc-326b4f09daee", 22 | "metadata": {}, 23 | "source": [ 24 | "### Defining Customer Churn\n", 25 | "It is when an existing customer, user, player, subscriber or any kind of return client stops doing business or ends the relationship with a company." 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "id": "b41a9cba-e03e-407f-8fdc-9098027e771c", 31 | "metadata": { 32 | "tags": [] 33 | }, 34 | "source": [ 35 | "## 1. Importing necessary libraries" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "id": "5878a505-8080-49ef-85f5-5a7521de3d4b", 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "import pandas as pd\n", 46 | "import numpy as np\n", 47 | "import os\n", 48 | "\n", 49 | "import matplotlib.pyplot as plt\n", 50 | "import seaborn as sns\n", 51 | "\n", 52 | "from sklearn.linear_model import LogisticRegression\n", 53 | "from sklearn.model_selection import train_test_split\n", 54 | "from sklearn.preprocessing import StandardScaler\n", 55 | "\n", 56 | "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n", 57 | "from sklearn.metrics import roc_auc_score, roc_curve, f1_score, precision_score, recall_score\n", 58 | "\n", 59 | "import warnings\n", 60 | "warnings.filterwarnings(\"ignore\")\n", 61 | "\n", 62 | "%matplotlib inline" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "id": "b63963d5-57dc-4d07-bbff-f49fd29aa8bf", 68 | "metadata": { 69 | "tags": [] 70 | }, 71 | "source": [ 72 | "## 2. Exploratory Data Analysis" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": null, 78 | "id": "38796371-03b3-4757-a9d2-295d90983fcc", 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "# path to the dataset folder\n", 83 | "folder_path = '.\\data'\n", 84 | "\n", 85 | "# list all the filenames in the folder\n", 86 | "filenames = os.listdir(folder_path)\n", 87 | "\n", 88 | "# print the filenames\n", 89 | "for filenames in os.listdir(folder_path):\n", 90 | " print(os.path.join(folder_path,filenames))" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": null, 96 | "id": "e2ead7d6-b989-49dd-9c4a-f943d4431c20", 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "telcom1 = pd.read_csv(\"data/churn-bigml-80.csv\")\n", 101 | "telcom2 = pd.read_csv(\"data/churn-bigml-20.csv\")\n", 102 | "\n", 103 | "# load all dataset into a DataFrame\n", 104 | "telcom = pd.concat([telcom1, telcom2], ignore_index=True)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "id": "58ef0db3-cb92-4de0-a22a-59e95ae5b3b5", 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "telcom.head()" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": null, 120 | "id": "f9b2f3fa-0ec9-43d5-942b-22c8aedcaceb", 121 | "metadata": {}, 122 | "outputs": [], 123 | "source": [ 124 | "telcom.shape" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": null, 130 | "id": "def0d75a-ae0b-4ae1-b2e7-ebaca7b90ae2", 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [ 134 | "telcom.info()" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "id": "58edef20-3b41-4dbe-9fc0-3126af4a9ce7", 140 | "metadata": {}, 141 | "source": [ 142 | "Comment: Hence, we found that the dataset contains *3333* rows (customers) and *20* columns (features).
\n", 143 | "The `\"Churn\"` column is the target to predict." 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "id": "b959b1bc-0147-4ac1-8ad1-ac4c50fc3b32", 150 | "metadata": {}, 151 | "outputs": [], 152 | "source": [ 153 | "# accessing Churn feature\n", 154 | "telcom['Churn'].head(10)" 155 | ] 156 | }, 157 | { 158 | "cell_type": "markdown", 159 | "id": "cded30fc-d89f-4b37-9cfd-33ca834fb554", 160 | "metadata": {}, 161 | "source": [ 162 | "### Descriptive Analysis and Data Visualization" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "id": "ff3f2af2-10d0-434d-8a68-15b0103fb61c", 169 | "metadata": {}, 170 | "outputs": [], 171 | "source": [ 172 | "telcom.describe()" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": null, 178 | "id": "b594b130-eefd-4885-a896-bfd031c38e86", 179 | "metadata": {}, 180 | "outputs": [], 181 | "source": [ 182 | "# Count the number of data points in each category\n", 183 | "y = telcom['Churn'].value_counts()\n", 184 | "y" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "id": "783f8101-7d45-4f67-959d-fe7d2c37a7e7", 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [ 194 | "# Create the pie chart\n", 195 | "plt.pie(y, labels=y.index, autopct='%1.1f%%')\n", 196 | "\n", 197 | "# Customize the appearance of the pie chart\n", 198 | "plt.title('Distribution of Churn')\n", 199 | "plt.legend(title='Churn')\n", 200 | "plt.show()" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": null, 206 | "id": "b95d6c86-0447-4a73-85b5-0ee8edbaa329", 207 | "metadata": {}, 208 | "outputs": [], 209 | "source": [ 210 | "sns.barplot(x=y.index, y=y.values)" 211 | ] 212 | }, 213 | { 214 | "cell_type": "markdown", 215 | "id": "ee1e87c0-d7d8-480d-9317-e31929ef866e", 216 | "metadata": { 217 | "tags": [] 218 | }, 219 | "source": [ 220 | "### Summary statistics for both classes" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "id": "22b392bf-0314-4042-b88b-4209bf5e439c", 227 | "metadata": {}, 228 | "outputs": [], 229 | "source": [ 230 | "# Group telcom by 'Churn' and compute the mean\n", 231 | "telcom.groupby(['Churn']).mean()" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "id": "32bdc8df-16ab-44d7-ac6d-0bc166e76b51", 237 | "metadata": {}, 238 | "source": [ 239 | "Churners seem to make more customer service calls than non-churners." 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": null, 245 | "id": "fd644b70-1b80-4870-ad2f-d5b3e9133113", 246 | "metadata": {}, 247 | "outputs": [], 248 | "source": [ 249 | "telcom.groupby(['Churn']).std()" 250 | ] 251 | }, 252 | { 253 | "cell_type": "markdown", 254 | "id": "5bf86c33-3396-4140-b9ed-9c94d4eff338", 255 | "metadata": {}, 256 | "source": [ 257 | "### Churn by State" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": null, 263 | "id": "95fcace0-366b-4e61-841d-c46853431d64", 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "telcom.groupby('State')['Churn'].value_counts()" 268 | ] 269 | }, 270 | { 271 | "cell_type": "code", 272 | "execution_count": null, 273 | "id": "1703093e-edd5-46eb-a8ac-38a246b05002", 274 | "metadata": {}, 275 | "outputs": [], 276 | "source": [ 277 | "telcom.groupby(['State','Churn']).size().unstack().plot(kind='bar', stacked=True, figsize=(30,10))" 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "id": "0eb9d7f3-a76a-4a86-b0f2-5d0c02970903", 283 | "metadata": {}, 284 | "source": [ 285 | "Comment: This is useful information for a company!" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "id": "56c84d8a-6aca-499c-a2b9-f81d2d39a529", 291 | "metadata": {}, 292 | "source": [ 293 | "#### Exploring feature distributions" 294 | ] 295 | }, 296 | { 297 | "cell_type": "code", 298 | "execution_count": null, 299 | "id": "189568a2-6dc6-431e-a887-41628ead4470", 300 | "metadata": {}, 301 | "outputs": [], 302 | "source": [ 303 | "# visualize the distribution of 'Account length'\n", 304 | "sns.distplot(telcom['Account length'])\n", 305 | "\n", 306 | "# display the plot\n", 307 | "plt.show()" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": null, 313 | "id": "5a231de5-3195-42b6-a774-2944c95731ea", 314 | "metadata": {}, 315 | "outputs": [], 316 | "source": [ 317 | "sns.distplot(telcom['Total day minutes'])\n", 318 | "plt.show()" 319 | ] 320 | }, 321 | { 322 | "cell_type": "code", 323 | "execution_count": null, 324 | "id": "e5943e42-48e1-4579-93b1-0051c6c19585", 325 | "metadata": {}, 326 | "outputs": [], 327 | "source": [ 328 | "sns.distplot(telcom['Total eve minutes'])\n", 329 | "plt.show()" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "id": "d7271547-1957-4cf9-b554-aeecc1f4cbee", 336 | "metadata": {}, 337 | "outputs": [], 338 | "source": [ 339 | "sns.distplot(telcom['Total intl minutes'])\n", 340 | "plt.show()" 341 | ] 342 | }, 343 | { 344 | "cell_type": "markdown", 345 | "id": "77667ddc-6cec-4719-ae8b-79e7e85640fc", 346 | "metadata": {}, 347 | "source": [ 348 | "Comment: All of these features above appear to be well approximated by the normal distribution. If this were not the case, we would have to consider applying a feature transformation of some kind." 349 | ] 350 | }, 351 | { 352 | "cell_type": "markdown", 353 | "id": "9a740fc5-7e4f-46d4-a690-3f7b23606c62", 354 | "metadata": { 355 | "tags": [] 356 | }, 357 | "source": [ 358 | "## 3. Data preprocessing\n" 359 | ] 360 | }, 361 | { 362 | "cell_type": "markdown", 363 | "id": "0660b151-5831-44b5-a8cc-85cfd5016316", 364 | "metadata": {}, 365 | "source": [ 366 | "### Cleaning the data" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": null, 372 | "id": "afe013e7-a153-4036-a1a4-da3eeb54e009", 373 | "metadata": {}, 374 | "outputs": [], 375 | "source": [ 376 | "# Check for missing values\n", 377 | "has_missing = telcom.isnull().any()\n", 378 | "has_missing" 379 | ] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": null, 384 | "id": "26b35df2-66b2-4b12-8a49-283c68de9945", 385 | "metadata": {}, 386 | "outputs": [], 387 | "source": [ 388 | "# check for duplicate rows \n", 389 | "duplicate_rows = telcom[telcom.duplicated()]\n", 390 | "duplicate_rows" 391 | ] 392 | }, 393 | { 394 | "cell_type": "markdown", 395 | "id": "6e55ca9d-b03a-4b26-bb65-2f5cc45ac11e", 396 | "metadata": { 397 | "tags": [] 398 | }, 399 | "source": [ 400 | "### Identifying features to convert" 401 | ] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "execution_count": null, 406 | "id": "d4bb0823-5a1d-4f90-b218-a15b2a4b55db", 407 | "metadata": {}, 408 | "outputs": [], 409 | "source": [ 410 | "telcom.head()" 411 | ] 412 | }, 413 | { 414 | "cell_type": "code", 415 | "execution_count": null, 416 | "id": "aaf6807a-56c6-47fa-9000-77c379a856e7", 417 | "metadata": {}, 418 | "outputs": [], 419 | "source": [ 420 | "telcom.dtypes" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": null, 426 | "id": "13f808b4-3e01-436c-9dd3-5bc234394290", 427 | "metadata": {}, 428 | "outputs": [], 429 | "source": [ 430 | "# Find the columns that contain boolean values\n", 431 | "bool_columns = telcom.select_dtypes(include=['bool']).columns\n", 432 | "print(bool_columns)\n", 433 | "\n", 434 | "# Find the columns of object type\n", 435 | "object_columns = telcom.select_dtypes(include=['object']).columns\n", 436 | "print(object_columns)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "markdown", 441 | "id": "6504f4f4-1037-4c92-ae85-8b1402506a92", 442 | "metadata": {}, 443 | "source": [ 444 | "### Encoding binary features" 445 | ] 446 | }, 447 | { 448 | "cell_type": "code", 449 | "execution_count": null, 450 | "id": "90eaaa12-3008-4af4-94f0-afcd19ac04cc", 451 | "metadata": {}, 452 | "outputs": [], 453 | "source": [ 454 | "# Convert the boolean values to integers\n", 455 | "telcom[bool_columns] = telcom[bool_columns].astype(int)" 456 | ] 457 | }, 458 | { 459 | "cell_type": "code", 460 | "execution_count": null, 461 | "id": "62932873-f179-4a75-a64e-c657396a8643", 462 | "metadata": {}, 463 | "outputs": [], 464 | "source": [ 465 | "# Replace 'no' with 0 and 'yes' with 1 in 'International plan' and 'Voice mail plan'\n", 466 | "telcom[['International plan','Voice mail plan']] = telcom[['International plan','Voice mail plan']].apply(lambda x: x.map({'No': 0, 'Yes': 1}))" 467 | ] 468 | }, 469 | { 470 | "cell_type": "code", 471 | "execution_count": null, 472 | "id": "e590dd6b-61bd-4725-ab1b-a278bf797169", 473 | "metadata": {}, 474 | "outputs": [], 475 | "source": [ 476 | "# see the results\n", 477 | "telcom[['International plan','Voice mail plan','Churn']].head()" 478 | ] 479 | }, 480 | { 481 | "cell_type": "markdown", 482 | "id": "5b3974e9-68a2-4392-82fc-ec68ae9cad53", 483 | "metadata": {}, 484 | "source": [ 485 | "### Feature selection and engineering\n", 486 | "\n", 487 | "Dropping unnecessary and correlated features" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": null, 493 | "id": "505df437-c5dc-49b5-a509-83e84dc12514", 494 | "metadata": {}, 495 | "outputs": [], 496 | "source": [ 497 | "# drop 'State' feature\n", 498 | "telcom = telcom.drop(telcom[['State']], axis=1)\n", 499 | "\n", 500 | "# Calculate the correlation matrix\n", 501 | "corr_matrix = telcom.corr()\n", 502 | "\n", 503 | "# Select upper triangle of correlation matrix\n", 504 | "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n", 505 | "\n", 506 | "# Find index of feature columns with correlation greater than 0.95\n", 507 | "to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]\n", 508 | "print(to_drop)\n", 509 | "\n", 510 | "# Drop the correlated features from the dataset\n", 511 | "telcom = telcom.drop(telcom[to_drop], axis=1)\n", 512 | "\n", 513 | "telcom.head()" 514 | ] 515 | }, 516 | { 517 | "cell_type": "markdown", 518 | "id": "d4de722f-8dc0-4583-96af-46bc7e4c8642", 519 | "metadata": {}, 520 | "source": [ 521 | "### Feature scaling\n", 522 | "To ensure that all variables are on the same scale and have comparable influence on the model.
\n", 523 | "eg: Let's see the different scales of the `'Total intl calls'` and `'Total night minutes'` features:" 524 | ] 525 | }, 526 | { 527 | "cell_type": "code", 528 | "execution_count": null, 529 | "id": "4e496c7a-f3f6-423d-958f-cd7a4760875b", 530 | "metadata": {}, 531 | "outputs": [], 532 | "source": [ 533 | "telcom['Total intl calls'].describe()" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": null, 539 | "id": "5da3bb1f-f62e-41d6-bd59-f0ad466e3016", 540 | "metadata": { 541 | "tags": [] 542 | }, 543 | "outputs": [], 544 | "source": [ 545 | "telcom['Total night minutes'].describe()" 546 | ] 547 | }, 548 | { 549 | "cell_type": "code", 550 | "execution_count": null, 551 | "id": "efda37c9-0b58-4da2-ab66-6a01408f7e76", 552 | "metadata": {}, 553 | "outputs": [], 554 | "source": [ 555 | "# from sklearn.preprocessing import StandardScaler\n", 556 | "\n", 557 | "# Scale telcom using StandardScaler\n", 558 | "features_to_scale = [column for column in telcom.columns if column not in ['International plan','Voice mail plan','Churn']]\n", 559 | "# print(features_to_scale)\n", 560 | "telcom_scaled = StandardScaler().fit_transform(telcom[features_to_scale])\n", 561 | "\n", 562 | "# Add column names back for readability\n", 563 | "telcom_scaled_df = pd.DataFrame(telcom_scaled, columns=features_to_scale)\n", 564 | "\n", 565 | "# summary statistics\n", 566 | "print(telcom_scaled_df.describe())\n", 567 | "\n", 568 | "# final preprocessed dataframe\n", 569 | "telcom = pd.concat([telcom_scaled_df, telcom[['International plan', 'Voice mail plan','Churn']]], axis=1)" 570 | ] 571 | }, 572 | { 573 | "cell_type": "markdown", 574 | "id": "2baff5aa-f6af-41dc-8615-23f8e8be71d6", 575 | "metadata": { 576 | "tags": [] 577 | }, 578 | "source": [ 579 | "## 4. Model Building and Performance Evaluation" 580 | ] 581 | }, 582 | { 583 | "cell_type": "markdown", 584 | "id": "a1e76d8e-8914-47f5-92b5-2fe3c34567f1", 585 | "metadata": {}, 586 | "source": [ 587 | "### Model Selection:" 588 | ] 589 | }, 590 | { 591 | "cell_type": "markdown", 592 | "id": "a18b8965-565c-4d49-b1f1-10129f11b1de", 593 | "metadata": { 594 | "tags": [] 595 | }, 596 | "source": [ 597 | "* **Logistic Regression**\n", 598 | "\n", 599 | "We choose `Logistic Regression` as our estimator for this project." 600 | ] 601 | }, 602 | { 603 | "cell_type": "code", 604 | "execution_count": null, 605 | "id": "30c7dab5-3d89-47e5-898d-2e2de77b6b3b", 606 | "metadata": {}, 607 | "outputs": [], 608 | "source": [ 609 | "# from sklearn.linear_model import LogisticRegression\n", 610 | "\n", 611 | "# instantiate our classifier\n", 612 | "clf = LogisticRegression()" 613 | ] 614 | }, 615 | { 616 | "cell_type": "markdown", 617 | "id": "46836c2a-f14b-4d68-826d-c9c2523673b5", 618 | "metadata": {}, 619 | "source": [ 620 | "### Creating training and test sets" 621 | ] 622 | }, 623 | { 624 | "cell_type": "code", 625 | "execution_count": null, 626 | "id": "d4490d8b-3d65-4f25-9b82-b502b977930e", 627 | "metadata": {}, 628 | "outputs": [], 629 | "source": [ 630 | "# from sklearn.model_selection import train_test_split\n", 631 | "\n", 632 | "# create feature variable (which holds all of the features of telco by dropping the target variable 'Churn' from telco)\n", 633 | "X = telcom.drop(telcom[['Churn']], axis=1)\n", 634 | "\n", 635 | "# create target variable\n", 636 | "y = telcom['Churn']\n", 637 | "\n", 638 | "# Create training and testing sets (here 80% of the data is used for training.)\n", 639 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" 640 | ] 641 | }, 642 | { 643 | "cell_type": "code", 644 | "execution_count": null, 645 | "id": "f9f05b46-f35a-4ecd-910b-63e3fa2d875f", 646 | "metadata": {}, 647 | "outputs": [], 648 | "source": [ 649 | "# Fit to the training data\n", 650 | "clf.fit(X_train, y_train)\n", 651 | "\n", 652 | "# The predicted labels of classifier\n", 653 | "y_pred = clf.predict(X_test)" 654 | ] 655 | }, 656 | { 657 | "cell_type": "markdown", 658 | "id": "cc48b734-156e-4cb9-9c4c-f872e204da11", 659 | "metadata": {}, 660 | "source": [ 661 | "### Check each sets length" 662 | ] 663 | }, 664 | { 665 | "cell_type": "code", 666 | "execution_count": null, 667 | "id": "7a682202-81b1-4712-8593-ab0df6c08f51", 668 | "metadata": {}, 669 | "outputs": [], 670 | "source": [ 671 | "print(X_train.shape)\n", 672 | "print(X_test.shape)" 673 | ] 674 | }, 675 | { 676 | "cell_type": "markdown", 677 | "id": "6b85cedd-2ebc-4261-8202-d715082c226a", 678 | "metadata": {}, 679 | "source": [ 680 | "### Model Metrics:" 681 | ] 682 | }, 683 | { 684 | "cell_type": "code", 685 | "execution_count": null, 686 | "id": "af01dd80-963f-4690-8131-4e9f5bb1d09b", 687 | "metadata": {}, 688 | "outputs": [], 689 | "source": [ 690 | "# from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n", 691 | "# from sklearn.metrics import roc_auc_score, roc_curve, f1_score, precision_score, recall_score" 692 | ] 693 | }, 694 | { 695 | "cell_type": "markdown", 696 | "id": "79455307-8e1f-4e71-9308-633b35a954a1", 697 | "metadata": {}, 698 | "source": [ 699 | "#### Confusion matrix" 700 | ] 701 | }, 702 | { 703 | "cell_type": "code", 704 | "execution_count": null, 705 | "id": "d48b6c2e-d21b-4919-a635-40c4b80e41f3", 706 | "metadata": {}, 707 | "outputs": [], 708 | "source": [ 709 | "# Calculate the confusion matrix\n", 710 | "matrix = confusion_matrix(y_test, y_pred)\n", 711 | "# print(matrix)\n", 712 | "\n", 713 | "# Plot the confusion matrix using seaborn\n", 714 | "sns.heatmap(matrix, annot=True, fmt='d', cmap='magma')\n", 715 | "\n", 716 | "# Add labels to the plot\n", 717 | "plt.xlabel('Predicted labels')\n", 718 | "plt.ylabel('True labels')\n", 719 | "plt.title('Confusion Matrix')\n", 720 | "\n", 721 | "# Show the plot\n", 722 | "plt.show()" 723 | ] 724 | }, 725 | { 726 | "cell_type": "code", 727 | "execution_count": null, 728 | "id": "4f1f3424-60d0-48b0-ad57-ff64dc27f3b3", 729 | "metadata": {}, 730 | "outputs": [], 731 | "source": [ 732 | "print(classification_report(y_test, y_pred))" 733 | ] 734 | }, 735 | { 736 | "cell_type": "markdown", 737 | "id": "e3e1be81-2638-4684-bc2c-d8fa67f5c26f", 738 | "metadata": {}, 739 | "source": [ 740 | "#### Accuracy, Precision, Recall and F1 Score" 741 | ] 742 | }, 743 | { 744 | "cell_type": "markdown", 745 | "id": "8c034cbd-9758-47c9-93d8-e3c100cba551", 746 | "metadata": {}, 747 | "source": [ 748 | "Accuracy is a measure of how well a classifier performs in terms of correctly predicting the class of an input sample.\n", 749 | "\n", 750 | "Recall is a measure of the proportion of positive examples that were correctly classified by the model. It is calculated using the following formula:\n", 751 | "$$Recall = \\frac{True Positives}{True Positives + False Negatives}$$\n", 752 | "\n", 753 | "Precision is a measure of the proportion of predicted positive examples that are actually positive. It is calculated using the following formula:\n", 754 | "\n", 755 | "$$Precision = \\frac{True Positives}{True Positives + False Positives}$$\n", 756 | "\n", 757 | "The F1 score is a measure of the accuracy of a classifier, defined as the harmonic mean of precision and recall.\n", 758 | "\n", 759 | "$$F_1 = \\frac{2 \\cdot \\text{precision} \\cdot \\text{recall}}{\\text{precision} + \\text{recall}}$$" 760 | ] 761 | }, 762 | { 763 | "cell_type": "code", 764 | "execution_count": null, 765 | "id": "b04497e4-7a69-479e-b815-0fdd93af2528", 766 | "metadata": {}, 767 | "outputs": [], 768 | "source": [ 769 | "print(\"Accuracy: {:.2f}\".format(accuracy_score(y_test, y_pred)))\n", 770 | "print(\"Precision: {:.2f}\".format(precision_score(y_test, y_pred)))\n", 771 | "print(\"Recall: {:.2f}\".format(recall_score(y_test, y_pred)))\n", 772 | "print(\"F1 score: {:.2f}\".format(f1_score(y_test, y_pred)))" 773 | ] 774 | }, 775 | { 776 | "cell_type": "markdown", 777 | "id": "850d8bd5-6c51-4071-bba8-81b8de08e503", 778 | "metadata": {}, 779 | "source": [ 780 | "#### ROC Curve" 781 | ] 782 | }, 783 | { 784 | "cell_type": "code", 785 | "execution_count": null, 786 | "id": "df9d6a88-2867-44f4-8431-024c18622027", 787 | "metadata": {}, 788 | "outputs": [], 789 | "source": [ 790 | "# Generate the probabilities\n", 791 | "y_pred_prob = clf.predict_proba(X_test)[:,1]\n", 792 | "\n", 793 | "# Use roc_curve() to calculate the false positive rate, true positive rate, and thresholds.\n", 794 | "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n", 795 | "\n", 796 | "# Plot the ROC curve\n", 797 | "plt.plot(fpr, tpr)\n", 798 | "\n", 799 | "# Add labels and diagonal line\n", 800 | "plt.xlabel(\"False Positive Rate\")\n", 801 | "plt.ylabel(\"True Positive Rate\")\n", 802 | "plt.plot([0, 1], [0, 1], \"k--\")\n", 803 | "plt.show()" 804 | ] 805 | }, 806 | { 807 | "cell_type": "markdown", 808 | "id": "f52fbc3a-6eeb-45e6-a225-0568f6387c12", 809 | "metadata": {}, 810 | "source": [ 811 | "#### Area under the ROC curve" 812 | ] 813 | }, 814 | { 815 | "cell_type": "code", 816 | "execution_count": null, 817 | "id": "6b8ad22a-6a4a-42e6-8575-8f15f28be626", 818 | "metadata": {}, 819 | "outputs": [], 820 | "source": [ 821 | "# the area under the ROC curve\n", 822 | "roc_auc_score(y_test, y_pred_prob)" 823 | ] 824 | }, 825 | { 826 | "cell_type": "markdown", 827 | "id": "585aaf98-a007-4bf6-982e-bd44127c4eb5", 828 | "metadata": { 829 | "tags": [] 830 | }, 831 | "source": [ 832 | "## 5. Making Predictions (whether a new customer will churn)" 833 | ] 834 | }, 835 | { 836 | "cell_type": "code", 837 | "execution_count": null, 838 | "id": "52fbb023-55be-4429-819d-02237fc1db7e", 839 | "metadata": {}, 840 | "outputs": [], 841 | "source": [ 842 | "def make_prediction(customer):\n", 843 | " prediction = clf.predict(customer)\n", 844 | " if prediction[0] == 1:\n", 845 | " print(\"[1] The customer will Churn.\")\n", 846 | " else:\n", 847 | " print(\"[0] The customer will not Churn\")" 848 | ] 849 | }, 850 | { 851 | "cell_type": "code", 852 | "execution_count": null, 853 | "id": "24a9cb6c-cfa3-4252-b36c-ae20926c03a1", 854 | "metadata": {}, 855 | "outputs": [], 856 | "source": [ 857 | "# scaled input values\n", 858 | "new_customer1 = [[0.6262585675178604,\n", 859 | " 1.7188173197427594,\n", 860 | " -1.0535424482925813,\n", 861 | " -0.6197347815607696,\n", 862 | " -1.1276788128173842,\n", 863 | " 0.5464802852218092,\n", 864 | " -0.8676148392853111,\n", 865 | " 0.3011544282701762,\n", 866 | " 0.4523525497250106,\n", 867 | " -0.6011950896927287,\n", 868 | " -0.4279320210630441,\n", 869 | " 0.0,\n", 870 | " 0.0]]\n", 871 | "\n", 872 | "new_customer2 = [[0.5257967737031338,\n", 873 | " -0.5236032802413713,\n", 874 | " 0.9387740897371452,\n", 875 | " 1.5730210856813158,\n", 876 | " 0.8326323403400316,\n", 877 | " -0.0559403500169171,\n", 878 | " -0.3653036104833324,\n", 879 | " -2.20323162813801,\n", 880 | " 0.27323229022856793,\n", 881 | " -1.0075595662585095,\n", 882 | " -1.1882184955849664,\n", 883 | " 1.0,\n", 884 | " 0.0]]\n", 885 | "\n", 886 | "# make prediction on new customers\n", 887 | "make_prediction(new_customer1)\n", 888 | "make_prediction(new_customer2)" 889 | ] 890 | }, 891 | { 892 | "cell_type": "markdown", 893 | "id": "f5256a4e-37ab-42fa-9202-9484668822af", 894 | "metadata": { 895 | "tags": [] 896 | }, 897 | "source": [ 898 | "
\n", 899 | "\n", 900 | "## Thank You!" 901 | ] 902 | } 903 | ], 904 | "metadata": { 905 | "kernelspec": { 906 | "display_name": "Python 3 (ipykernel)", 907 | "language": "python", 908 | "name": "python3" 909 | }, 910 | "language_info": { 911 | "codemirror_mode": { 912 | "name": "ipython", 913 | "version": 3 914 | }, 915 | "file_extension": ".py", 916 | "mimetype": "text/x-python", 917 | "name": "python", 918 | "nbconvert_exporter": "python", 919 | "pygments_lexer": "ipython3", 920 | "version": "3.11.0" 921 | } 922 | }, 923 | "nbformat": 4, 924 | "nbformat_minor": 5 925 | } 926 | -------------------------------------------------------------------------------- /data/churn-bigml-20.csv: -------------------------------------------------------------------------------- 1 | State,Account length,Area code,International plan,Voice mail plan,Number vmail messages,Total day minutes,Total day calls,Total day charge,Total eve minutes,Total eve calls,Total eve charge,Total night minutes,Total night calls,Total night charge,Total intl minutes,Total intl calls,Total intl charge,Customer service calls,Churn 2 | LA,117,408,No,No,0,184.5,97,31.37,351.6,80,29.89,215.8,90,9.71,8.7,4,2.35,1,False 3 | IN,65,415,No,No,0,129.1,137,21.95,228.5,83,19.42,208.8,111,9.4,12.7,6,3.43,4,True 4 | NY,161,415,No,No,0,332.9,67,56.59,317.8,97,27.01,160.6,128,7.23,5.4,9,1.46,4,True 5 | SC,111,415,No,No,0,110.4,103,18.77,137.3,102,11.67,189.6,105,8.53,7.7,6,2.08,2,False 6 | HI,49,510,No,No,0,119.3,117,20.28,215.1,109,18.28,178.7,90,8.04,11.1,1,3.0,1,False 7 | AK,36,408,No,Yes,30,146.3,128,24.87,162.5,80,13.81,129.3,109,5.82,14.5,6,3.92,0,False 8 | MI,65,415,No,No,0,211.3,120,35.92,162.6,122,13.82,134.7,118,6.06,13.2,5,3.56,3,False 9 | ID,119,415,No,No,0,159.1,114,27.05,231.3,117,19.66,143.2,91,6.44,8.8,3,2.38,5,True 10 | VA,10,408,No,No,0,186.1,112,31.64,190.2,66,16.17,282.8,57,12.73,11.4,6,3.08,2,False 11 | WI,68,415,No,No,0,148.8,70,25.3,246.5,164,20.95,129.8,103,5.84,12.1,3,3.27,3,False 12 | MN,74,510,No,Yes,33,193.7,91,32.93,246.1,96,20.92,138.0,92,6.21,14.6,3,3.94,2,False 13 | HI,85,415,No,No,0,235.8,109,40.09,157.2,94,13.36,188.2,99,8.47,12.0,3,3.24,0,False 14 | MN,46,415,No,No,0,214.1,72,36.4,164.4,104,13.97,177.5,113,7.99,8.2,3,2.21,2,False 15 | VT,128,510,No,Yes,29,179.3,104,30.48,225.9,86,19.2,323.0,78,14.54,8.6,7,2.32,0,False 16 | LA,155,415,No,No,0,203.4,100,34.58,190.9,104,16.23,196.0,119,8.82,8.9,4,2.4,0,True 17 | MT,73,415,No,No,0,160.1,110,27.22,213.3,72,18.13,174.1,72,7.83,13.0,4,3.51,0,False 18 | ID,77,415,No,No,0,251.8,72,42.81,205.7,126,17.48,275.2,109,12.38,9.8,7,2.65,2,True 19 | MA,108,415,No,No,0,178.3,137,30.31,189.0,76,16.07,129.1,102,5.81,14.6,5,3.94,0,False 20 | KY,95,408,No,No,0,135.0,99,22.95,183.6,106,15.61,245.3,102,11.04,12.5,9,3.38,1,False 21 | MI,36,510,No,Yes,29,281.4,102,47.84,202.2,76,17.19,187.2,113,8.42,9.0,6,2.43,2,False 22 | CO,141,415,No,Yes,32,148.6,91,25.26,131.1,97,11.14,219.4,142,9.87,10.1,1,2.73,1,False 23 | AZ,63,415,No,No,0,58.9,125,10.01,169.6,59,14.42,211.4,88,9.51,9.4,3,2.54,1,False 24 | ID,97,408,No,No,0,239.8,125,40.77,214.8,111,18.26,143.3,81,6.45,8.7,5,2.35,2,False 25 | CA,75,408,No,No,0,166.3,125,28.27,158.2,86,13.45,256.7,80,11.55,6.1,5,1.65,1,False 26 | WA,127,408,No,No,0,146.7,91,24.94,203.5,78,17.3,203.4,110,9.15,13.7,3,3.7,1,False 27 | LA,121,408,No,No,0,181.5,121,30.86,218.4,98,18.56,161.6,103,7.27,8.5,5,2.3,1,False 28 | NE,117,415,No,No,0,102.8,119,17.48,206.7,91,17.57,299.0,105,13.46,10.1,7,2.73,1,False 29 | OH,65,408,No,No,0,187.9,116,31.94,157.6,117,13.4,227.3,86,10.23,7.5,6,2.03,1,False 30 | MO,6,510,No,No,0,183.6,117,31.21,256.7,72,21.82,178.6,79,8.04,10.2,2,2.75,1,False 31 | AL,32,510,No,No,0,230.9,87,39.25,187.4,90,15.93,154.0,53,6.93,6.3,2,1.7,0,False 32 | NH,64,408,No,Yes,27,182.1,91,30.96,169.7,98,14.42,164.7,86,7.41,10.6,5,2.86,2,False 33 | NM,25,415,No,No,0,119.3,87,20.28,211.5,101,17.98,268.9,86,12.1,10.5,4,2.84,3,False 34 | OR,65,415,No,No,0,116.8,87,19.86,178.9,93,15.21,182.4,150,8.21,14.1,2,3.81,1,False 35 | MI,127,415,No,No,0,202.1,103,34.36,229.4,86,19.5,195.2,113,8.78,11.5,3,3.11,2,False 36 | AZ,93,415,No,No,0,271.6,71,46.17,229.4,108,19.5,77.3,121,3.48,10.9,3,2.94,2,False 37 | TX,208,510,No,No,0,326.5,67,55.51,176.3,113,14.99,181.7,102,8.18,10.7,6,2.89,2,True 38 | IN,122,415,No,No,0,243.8,98,41.45,83.9,72,7.13,179.8,84,8.09,13.7,8,3.7,2,False 39 | LA,99,415,No,No,0,241.1,72,40.99,155.6,98,13.23,188.2,109,8.47,11.6,10,3.13,1,False 40 | MS,65,415,No,No,0,136.1,112,23.14,272.9,96,23.2,220.2,104,9.91,4.4,2,1.19,1,False 41 | IN,65,415,No,No,0,213.4,111,36.28,234.5,94,19.93,250.1,123,11.25,2.7,4,0.73,1,False 42 | KY,45,415,No,Yes,22,196.6,84,33.42,313.2,92,26.62,163.3,108,7.35,11.9,3,3.21,0,False 43 | MN,139,510,No,No,0,134.4,106,22.85,211.3,98,17.96,193.6,125,8.71,10.2,2,2.75,5,True 44 | WY,215,510,No,No,0,83.6,148,14.21,120.9,91,10.28,226.6,110,10.2,10.7,9,2.89,0,False 45 | AZ,94,408,No,No,0,181.8,85,30.91,202.4,98,17.2,245.9,97,11.07,9.2,2,2.48,4,False 46 | NM,119,510,No,Yes,23,154.0,114,26.18,278.0,137,23.63,228.4,112,10.28,11.8,4,3.19,2,False 47 | MI,86,510,No,Yes,41,119.0,101,20.23,230.0,134,19.55,236.9,58,10.66,9.5,3,2.57,0,False 48 | FL,106,408,No,Yes,32,165.9,126,28.2,216.5,93,18.4,173.1,86,7.79,14.1,8,3.81,4,False 49 | KS,92,408,Yes,No,0,62.6,111,10.64,180.6,126,15.35,221.7,80,9.98,10.4,2,2.81,1,True 50 | SC,78,510,No,No,0,168.3,110,28.61,221.2,73,18.8,241.0,136,10.85,12.5,1,3.38,1,False 51 | NC,155,408,No,No,0,262.4,55,44.61,194.6,113,16.54,146.5,85,6.59,8.3,6,2.24,2,False 52 | MO,64,510,No,Yes,48,94.4,104,16.05,136.2,101,11.58,147.4,89,6.63,4.5,4,1.22,0,False 53 | WA,83,415,No,No,0,221.4,103,37.64,231.8,103,19.7,122.5,100,5.51,9.8,5,2.65,3,False 54 | SD,144,408,No,Yes,48,189.8,96,32.27,123.4,67,10.49,214.2,106,9.64,6.5,2,1.76,2,True 55 | MT,143,415,No,No,0,172.3,97,29.29,174.0,108,14.79,188.2,119,8.47,13.0,4,3.51,2,False 56 | MN,81,415,No,No,0,198.4,93,33.73,210.9,108,17.93,193.3,71,8.7,10.4,6,2.81,2,False 57 | SD,145,408,No,Yes,24,147.5,90,25.08,175.7,108,14.93,252.1,102,11.34,15.6,3,4.21,2,False 58 | OK,89,510,No,No,0,303.9,95,51.66,260.9,114,22.18,312.1,89,14.04,5.3,3,1.43,1,True 59 | CT,199,415,No,Yes,34,230.6,121,39.2,219.4,99,18.65,299.3,94,13.47,8.0,2,2.16,0,False 60 | CT,96,415,No,Yes,37,172.7,93,29.36,120.1,116,10.21,216.1,86,9.72,10.3,5,2.78,5,True 61 | MN,94,415,No,No,0,181.5,98,30.86,199.9,88,16.99,287.7,114,12.95,6.6,5,1.78,1,False 62 | FL,127,415,No,No,0,266.6,106,45.32,264.8,168,22.51,207.2,119,9.32,5.9,2,1.59,1,True 63 | RI,121,408,No,No,0,170.4,108,28.97,350.5,68,29.79,297.0,87,13.37,11.2,3,3.02,0,True 64 | IN,122,408,No,No,0,296.4,99,50.39,214.8,89,18.26,133.9,107,6.03,11.4,3,3.08,4,True 65 | AL,121,408,No,Yes,35,68.7,95,11.68,209.2,69,17.78,197.4,42,8.88,11.4,4,3.08,1,False 66 | OR,77,510,No,No,0,233.8,104,39.75,266.5,94,22.65,212.7,104,9.57,7.6,3,2.05,2,False 67 | MS,64,408,Yes,No,0,236.2,77,40.15,218.6,85,18.58,194.1,97,8.73,13.2,2,3.56,2,True 68 | CA,124,408,Yes,No,0,244.6,89,41.58,188.8,80,16.05,206.0,114,9.27,11.3,4,3.05,1,False 69 | DE,148,415,No,No,0,124.4,83,21.15,179.7,81,15.27,253.0,99,11.39,11.3,6,3.05,0,False 70 | MS,77,408,No,No,0,230.0,87,39.1,103.2,138,8.77,309.6,136,13.93,11.3,3,3.05,2,False 71 | NC,135,415,No,No,0,201.8,81,34.31,225.0,114,19.13,204.4,82,9.2,10.3,6,2.78,1,False 72 | SD,87,415,No,Yes,21,214.0,113,36.38,180.0,114,15.3,134.5,82,6.05,10.6,5,2.86,0,False 73 | NH,54,510,No,No,0,210.5,102,35.79,204.5,83,17.38,127.8,53,5.75,8.5,5,2.3,1,False 74 | MA,35,415,No,No,0,105.6,129,17.95,258.2,129,21.95,213.1,77,9.59,8.7,3,2.35,0,False 75 | TX,84,408,No,No,0,138.6,102,23.56,199.0,93,16.92,204.1,137,9.18,7.8,4,2.11,0,False 76 | OR,94,415,No,No,0,234.4,103,39.85,279.3,109,23.74,234.2,121,10.54,2.0,2,0.54,1,True 77 | UT,96,408,No,Yes,26,145.8,108,24.79,192.2,89,16.34,165.1,96,7.43,9.9,2,2.67,1,False 78 | NV,64,415,No,No,0,97.2,80,16.52,186.2,90,15.83,189.0,92,8.5,10.4,6,2.81,2,False 79 | NE,85,415,No,No,0,259.8,85,44.17,242.3,117,20.6,168.8,72,7.6,5.4,1,1.46,0,False 80 | AZ,117,408,No,No,0,239.9,84,40.78,174.8,106,14.86,209.5,93,9.43,9.8,2,2.65,0,False 81 | DC,112,415,No,Yes,16,221.6,110,37.67,130.2,123,11.07,200.0,108,9.0,11.3,3,3.05,1,False 82 | MA,129,510,Yes,No,0,192.9,131,32.79,185.5,101,15.77,205.2,130,9.23,10.9,4,2.94,1,False 83 | AZ,140,408,No,No,0,173.2,91,29.44,196.8,106,16.73,209.3,128,9.42,11.2,5,3.02,3,False 84 | MI,81,415,No,No,0,153.5,99,26.1,197.6,102,16.8,198.5,86,8.93,6.3,2,1.7,2,False 85 | TX,70,510,No,No,0,59.5,103,10.12,257.2,106,21.86,208.3,86,9.37,11.1,6,3.0,0,False 86 | ID,79,510,No,Yes,21,264.3,79,44.93,202.8,118,17.24,173.4,92,7.8,6.3,3,1.7,4,False 87 | AL,85,408,No,No,0,127.9,107,21.74,271.2,124,23.05,202.2,76,9.1,12.5,5,3.38,0,False 88 | SD,91,510,No,No,0,149.0,115,25.33,245.3,105,20.85,260.0,94,11.7,8.3,3,2.24,0,False 89 | LA,149,415,No,Yes,20,198.9,77,33.81,274.0,88,23.29,190.7,76,8.58,14.3,9,3.86,1,False 90 | AZ,60,415,No,No,0,98.2,88,16.69,180.5,69,15.34,223.6,69,10.06,9.3,2,2.51,2,False 91 | RI,115,408,No,No,0,147.9,109,25.14,228.4,117,19.41,299.7,90,13.49,9.6,9,2.59,3,False 92 | OH,144,415,No,Yes,18,106.4,109,18.09,108.1,113,9.19,208.4,111,9.38,10.1,5,2.73,1,False 93 | AZ,86,415,No,Yes,32,70.9,163,12.05,166.7,121,14.17,244.9,105,11.02,11.1,5,3.0,3,False 94 | MI,139,415,No,Yes,20,214.6,101,36.48,235.1,132,19.98,162.8,132,7.33,14.8,12,4.0,0,False 95 | NV,124,408,No,No,0,151.1,123,25.69,187.4,104,15.93,255.4,93,11.49,5.3,3,1.43,1,False 96 | MA,102,510,Yes,No,0,233.8,103,39.75,221.6,131,18.84,146.9,106,6.61,12.8,3,3.46,0,False 97 | IN,78,415,No,No,0,208.9,119,35.51,252.4,132,21.45,280.2,120,12.61,12.8,7,3.46,0,False 98 | AL,55,415,Yes,No,0,191.9,91,32.62,256.1,110,21.77,203.7,101,9.17,14.3,6,3.86,1,True 99 | RI,129,415,No,Yes,33,119.6,104,20.33,278.7,88,23.69,263.4,175,11.85,5.9,2,1.59,2,False 100 | ME,75,408,Yes,No,0,211.3,61,35.92,105.6,119,8.98,175.9,63,7.92,9.7,4,2.62,4,True 101 | SD,126,415,No,Yes,23,114.3,102,19.43,190.3,103,16.18,240.4,111,10.82,12.6,7,3.4,3,False 102 | MO,92,415,No,No,0,154.0,122,26.18,329.8,88,28.03,288.0,117,12.96,5.6,2,1.51,3,True 103 | OK,52,408,No,No,0,214.7,68,36.5,158.6,138,13.48,123.4,114,5.55,9.4,4,2.54,2,False 104 | IL,87,510,No,No,0,231.3,105,39.32,171.7,108,14.59,67.7,136,3.05,13.0,6,3.51,1,False 105 | NJ,95,415,No,Yes,22,40.9,126,6.95,133.4,90,11.34,264.2,91,11.89,11.9,7,3.21,0,False 106 | LA,67,510,No,No,0,310.4,97,52.77,66.5,123,5.65,246.5,99,11.09,9.2,10,2.48,4,False 107 | OR,165,415,No,No,0,216.6,126,36.82,190.8,104,16.22,224.7,123,10.11,12.4,8,3.35,0,False 108 | RI,150,415,No,Yes,29,209.9,77,35.68,158.0,52,13.43,141.9,113,6.39,6.6,1,1.78,0,False 109 | NC,26,415,No,No,0,234.5,109,39.87,216.5,129,18.4,191.6,94,8.62,3.5,6,0.95,3,False 110 | MD,79,510,No,Yes,31,103.1,90,17.53,243.0,135,20.66,76.4,92,3.44,12.2,8,3.29,3,False 111 | WI,69,510,Yes,No,0,279.8,90,47.57,248.7,91,21.14,171.0,118,7.69,8.4,10,2.27,2,True 112 | VT,95,510,Yes,Yes,41,136.8,91,23.26,200.8,61,17.07,133.7,67,6.02,10.3,9,2.78,5,True 113 | NY,157,415,No,No,0,224.5,111,38.17,200.7,99,17.06,116.6,118,5.25,11.5,2,3.11,2,False 114 | VT,80,415,No,No,0,160.6,103,27.3,237.0,109,20.15,245.1,88,11.03,10.7,1,2.89,1,False 115 | AZ,75,510,No,Yes,37,121.5,97,20.66,271.4,110,23.07,248.7,97,11.19,11.3,5,3.05,2,False 116 | WV,44,510,No,No,0,228.1,121,38.78,276.5,79,23.5,279.8,77,12.59,9.9,5,2.67,2,True 117 | OK,101,408,No,No,0,89.7,118,15.25,260.1,79,22.11,170.1,93,7.65,13.5,11,3.65,5,True 118 | WI,117,408,No,Yes,14,80.2,81,13.63,219.0,103,18.62,122.6,102,5.52,8.6,2,2.32,1,False 119 | PA,82,408,No,No,0,125.7,96,21.37,207.6,137,17.65,183.1,103,8.24,12.9,2,3.48,1,False 120 | NY,39,408,No,No,0,160.4,68,27.27,102.6,103,8.72,235.3,106,10.59,9.1,5,2.46,2,False 121 | NM,30,415,No,No,0,169.9,144,28.88,225.2,118,19.14,169.7,93,7.64,11.4,7,3.08,1,False 122 | NC,63,415,No,Yes,29,142.3,107,24.19,118.7,56,10.09,240.1,91,10.8,6.6,8,1.78,1,False 123 | WY,111,415,No,No,0,146.2,55,24.85,261.5,83,22.23,163.2,116,7.34,8.7,3,2.35,3,False 124 | PA,91,510,No,No,0,231.8,120,39.41,150.6,106,12.8,269.2,129,12.11,11.6,7,3.13,3,False 125 | NV,105,415,Yes,Yes,29,220.7,82,37.52,217.7,110,18.5,190.5,100,8.57,13.2,6,3.56,1,True 126 | WA,166,408,Yes,Yes,35,128.2,138,21.79,274.5,113,23.33,298.9,130,13.45,8.8,7,2.38,2,False 127 | FL,79,510,No,No,0,130.2,119,22.13,290.9,121,24.73,194.8,140,8.77,14.0,6,3.78,3,False 128 | ME,105,408,No,Yes,33,209.6,68,35.63,146.9,140,12.49,121.0,131,5.44,10.6,3,2.86,2,False 129 | LA,172,415,No,No,0,215.7,140,36.67,146.3,84,12.44,264.6,83,11.91,7.1,1,1.92,3,False 130 | KS,121,408,No,No,0,150.7,105,25.62,197.3,133,16.77,169.0,116,7.61,9.2,15,2.48,1,False 131 | ND,88,415,No,No,0,161.5,92,27.46,173.5,108,14.75,206.2,95,9.28,7.9,4,2.13,2,False 132 | WV,153,408,No,Yes,28,235.6,74,40.05,227.9,37,19.37,170.3,103,7.66,15.4,9,4.16,0,False 133 | AR,54,415,No,Yes,39,206.9,143,35.17,127.8,72,10.86,199.2,120,8.96,9.2,1,2.48,3,False 134 | CA,79,510,No,No,0,157.6,85,26.79,194.1,92,16.5,231.5,86,10.42,9.4,10,2.54,5,True 135 | MN,55,415,No,No,0,175.6,147,29.85,161.8,118,13.75,289.5,55,13.03,9.3,4,2.51,0,False 136 | MT,109,408,No,No,0,264.7,69,45.0,305.0,120,25.93,197.4,86,8.88,9.5,9,2.57,1,True 137 | SD,65,408,No,Yes,31,282.3,70,47.99,152.0,89,12.92,225.5,93,10.15,12.0,4,3.24,1,False 138 | MT,27,510,No,No,0,193.8,102,32.95,118.9,104,10.11,135.9,124,6.12,9.2,3,2.48,0,False 139 | WV,32,408,No,Yes,26,266.7,109,45.34,232.3,107,19.75,212.8,98,9.58,16.3,4,4.4,1,False 140 | CT,3,415,No,Yes,36,118.1,117,20.08,221.5,125,18.83,103.9,89,4.68,11.9,6,3.21,2,False 141 | DE,119,415,No,No,0,176.8,90,30.06,224.7,81,19.1,204.6,77,9.21,7.5,15,2.03,1,False 142 | LA,43,415,No,No,0,241.9,101,41.12,129.4,121,11.0,264.8,104,11.92,5.9,3,1.59,1,False 143 | KS,116,510,No,No,0,189.5,90,32.22,189.8,118,16.13,205.8,83,9.26,13.1,2,3.54,1,False 144 | WV,107,415,No,No,0,123.1,100,20.93,158.4,82,13.46,256.1,82,11.52,9.3,5,2.51,0,False 145 | ND,123,408,No,No,0,159.1,94,27.05,241.6,119,20.54,202.4,120,9.11,6.5,1,1.76,1,False 146 | AK,110,408,No,No,0,100.1,90,17.02,233.3,93,19.83,204.4,57,9.2,11.1,8,3.0,3,False 147 | ME,176,415,No,No,0,223.2,76,37.94,214.4,131,18.22,154.4,80,6.95,10.1,2,2.73,3,False 148 | MN,13,510,No,Yes,21,315.6,105,53.65,208.9,71,17.76,260.1,123,11.7,12.1,3,3.27,3,False 149 | KS,88,415,No,No,0,189.8,111,32.27,197.3,101,16.77,234.5,111,10.55,14.9,3,4.02,2,False 150 | NJ,92,510,No,Yes,29,155.4,110,26.42,188.5,104,16.02,254.9,118,11.47,8.0,4,2.16,3,False 151 | WI,165,510,No,No,0,154.2,91,26.21,268.6,108,22.83,188.8,99,8.5,10.9,4,2.94,6,False 152 | AR,156,415,No,No,0,178.8,94,30.4,178.4,97,15.16,169.2,77,7.61,7.5,3,2.03,1,False 153 | WA,63,408,No,No,0,149.3,104,25.38,273.6,75,23.26,206.6,72,9.3,9.1,4,2.46,0,False 154 | ID,83,415,Yes,Yes,32,94.7,111,16.1,154.4,98,13.12,200.4,109,9.02,10.6,6,2.86,2,False 155 | VA,158,415,No,No,0,222.8,101,37.88,203.0,128,17.26,210.6,106,9.48,6.9,2,1.86,2,False 156 | WV,115,510,Yes,No,0,249.9,95,42.48,242.5,104,20.61,151.7,121,6.83,15.3,6,4.13,1,True 157 | UT,103,510,No,Yes,36,87.2,92,14.82,169.3,110,14.39,166.7,80,7.5,10.9,5,2.94,6,True 158 | NJ,64,415,No,No,0,224.8,111,38.22,190.0,101,16.15,221.4,110,9.96,9.2,2,2.48,1,False 159 | MS,86,510,No,Yes,39,261.2,122,44.4,214.2,101,18.21,154.9,101,6.97,12.7,5,3.43,2,False 160 | ME,151,415,No,Yes,26,196.5,98,33.41,175.8,111,14.94,221.8,124,9.98,13.4,5,3.62,0,False 161 | NM,87,510,Yes,No,0,167.3,119,28.44,198.5,119,16.87,133.1,88,5.99,11.0,6,2.97,1,False 162 | TX,90,415,No,No,0,109.6,88,18.63,137.6,108,11.7,159.7,121,7.19,11.0,5,2.97,2,False 163 | LA,108,510,No,Yes,30,276.6,99,47.02,220.1,113,18.71,177.9,95,8.01,9.8,6,2.65,2,False 164 | TN,33,415,No,Yes,35,186.8,124,31.76,261.0,69,22.19,317.8,103,14.3,15.0,5,4.05,0,False 165 | WI,149,415,Yes,Yes,28,126.9,97,21.57,166.9,102,14.19,145.2,77,6.53,8.8,3,2.38,5,True 166 | MT,106,510,No,No,0,169.4,107,28.8,197.2,71,16.76,202.2,79,9.1,10.7,4,2.89,1,False 167 | ND,167,408,Yes,No,0,219.1,100,37.25,242.9,90,20.65,168.9,101,7.6,10.1,4,2.73,2,False 168 | WI,35,510,No,Yes,27,241.7,87,41.09,142.0,101,12.07,288.9,68,13.0,9.4,4,2.54,1,False 169 | MO,107,415,No,Yes,22,281.1,83,47.79,143.7,130,12.21,239.4,128,10.77,11.2,9,3.02,1,False 170 | AZ,92,415,Yes,Yes,45,281.1,88,47.79,198.0,103,16.83,94.3,76,4.24,7.5,3,2.03,0,False 171 | VT,38,415,No,No,0,137.8,86,23.43,286.3,76,24.34,167.0,77,7.52,14.1,3,3.81,2,False 172 | VA,68,408,Yes,No,0,148.5,126,25.25,219.4,125,18.65,198.5,121,8.93,14.5,7,3.92,1,True 173 | NE,123,510,No,No,0,194.0,118,32.98,242.0,114,20.57,146.3,108,6.58,12.1,4,3.27,1,False 174 | WA,75,510,No,Yes,41,130.9,115,22.25,203.4,110,17.29,171.7,68,7.73,12.4,4,3.35,1,False 175 | NM,153,408,No,No,0,185.3,127,31.5,208.0,73,17.68,206.1,124,9.27,15.1,3,4.08,1,False 176 | FL,143,415,No,No,0,119.1,117,20.25,287.7,136,24.45,223.0,100,10.04,12.2,4,3.29,0,False 177 | SC,87,408,No,No,0,322.5,106,54.83,204.6,93,17.39,186.2,128,8.38,9.4,4,2.54,2,True 178 | IN,100,510,No,No,0,216.2,107,36.75,215.6,84,18.33,138.4,127,6.23,10.2,3,2.75,0,False 179 | FL,144,415,No,Yes,51,283.9,98,48.26,192.0,109,16.32,196.3,85,8.83,10.0,4,2.7,1,False 180 | ND,70,415,No,Yes,31,125.9,101,21.4,196.4,102,16.69,252.7,75,11.37,10.3,4,2.78,1,False 181 | MA,136,408,Yes,No,0,199.6,89,33.93,211.4,96,17.97,72.4,84,3.26,11.0,4,2.97,3,True 182 | MO,120,415,No,Yes,24,212.7,73,36.16,257.5,103,21.89,227.8,119,10.25,9.7,13,2.62,2,False 183 | WY,104,408,No,No,0,183.6,133,31.21,120.7,98,10.26,215.1,112,9.68,12.7,2,3.43,1,False 184 | CA,75,510,No,Yes,38,163.6,132,27.81,146.7,113,12.47,345.8,115,15.56,13.1,3,3.54,3,False 185 | NE,127,510,Yes,No,0,180.9,114,30.75,209.5,118,17.81,249.9,105,11.25,7.4,4,2.0,2,False 186 | ID,122,510,No,Yes,33,270.8,96,46.04,220.4,110,18.73,169.9,104,7.65,11.8,8,3.19,4,False 187 | VT,124,415,No,No,0,178.4,72,30.33,233.6,134,19.86,179.4,91,8.07,12.0,2,3.24,0,False 188 | MO,167,415,Yes,No,0,244.8,91,41.62,60.8,105,5.17,176.7,110,7.95,10.7,3,2.89,2,False 189 | VA,89,415,No,Yes,32,209.9,113,35.68,249.8,104,21.23,224.2,92,10.09,8.7,7,2.35,1,False 190 | VT,73,408,No,No,0,213.0,95,36.21,188.8,104,16.05,136.2,89,6.13,13.5,3,3.65,0,False 191 | MS,1,415,No,No,0,144.8,107,24.62,112.5,66,9.56,218.7,79,9.84,13.8,3,3.73,1,False 192 | KY,60,408,No,No,0,135.4,134,23.02,205.9,85,17.5,204.0,103,9.18,7.9,4,2.13,1,False 193 | WV,77,510,No,No,0,67.7,68,11.51,195.7,86,16.63,236.5,137,10.64,12.0,2,3.24,1,False 194 | DC,76,415,No,No,0,224.4,121,38.15,147.9,97,12.57,183.8,74,8.27,6.7,2,1.81,2,False 195 | IA,63,510,No,No,0,153.5,81,26.1,287.3,115,24.42,230.2,85,10.36,6.5,5,1.76,2,False 196 | MN,150,415,No,Yes,28,174.4,75,29.65,169.9,80,14.44,201.6,130,9.07,11.0,4,2.97,1,False 197 | VT,101,415,No,No,0,153.8,89,26.15,234.0,89,19.89,196.3,77,8.83,11.6,2,3.13,4,False 198 | AK,132,415,No,Yes,39,175.7,93,29.87,187.2,94,15.91,225.5,118,10.15,8.6,3,2.32,2,False 199 | CA,158,510,No,No,0,155.9,123,26.5,224.2,112,19.06,221.0,116,9.95,8.6,8,2.32,2,False 200 | MS,114,415,No,Yes,34,154.4,109,26.25,221.4,142,18.82,208.5,103,9.38,10.3,5,2.78,0,False 201 | AR,77,415,No,Yes,23,209.7,73,35.65,183.6,63,15.61,205.5,111,9.25,7.1,3,1.92,2,False 202 | NJ,107,415,No,No,0,212.1,95,36.06,150.1,88,12.76,219.8,111,9.89,7.7,2,2.08,3,False 203 | NJ,48,510,No,Yes,22,152.0,63,25.84,258.8,131,22.0,263.2,109,11.84,15.7,5,4.24,2,True 204 | TN,59,415,No,No,0,160.9,95,27.35,251.2,65,21.35,273.4,97,12.3,5.0,5,1.35,3,False 205 | DE,129,510,No,No,0,334.3,118,56.83,192.1,104,16.33,191.0,83,8.59,10.4,6,2.81,0,True 206 | TX,50,510,No,No,0,188.9,94,32.11,203.9,104,17.33,151.8,124,6.83,11.6,8,3.13,3,False 207 | MI,50,415,No,Yes,35,192.6,97,32.74,135.2,101,11.49,216.2,101,9.73,7.9,2,2.13,2,False 208 | MS,116,415,No,No,0,217.3,91,36.94,216.1,95,18.37,148.1,76,6.66,11.3,3,3.05,2,False 209 | OK,52,510,No,Yes,38,169.3,88,28.78,225.9,97,19.2,172.0,86,7.74,8.2,3,2.21,0,False 210 | ND,12,510,Yes,No,0,216.7,117,36.84,116.5,126,9.9,220.0,110,9.9,9.8,4,2.65,2,False 211 | ND,105,510,No,No,0,246.4,83,41.89,256.2,101,21.78,169.0,151,7.61,3.8,4,1.03,0,False 212 | NY,104,415,No,No,0,156.2,93,26.55,193.0,54,16.41,222.7,94,10.02,13.1,5,3.54,1,False 213 | WV,13,415,No,No,0,146.4,74,24.89,148.5,92,12.62,216.7,96,9.75,11.3,3,3.05,1,False 214 | WV,67,408,No,No,0,167.8,91,28.53,167.7,69,14.25,110.3,71,4.96,8.4,12,2.27,1,False 215 | DC,148,415,No,Yes,11,252.9,129,42.99,284.3,88,24.17,262.8,99,11.83,12.3,1,3.32,1,False 216 | MN,116,510,No,No,0,201.8,82,34.31,231.5,95,19.68,226.1,130,10.17,16.5,5,4.46,0,False 217 | DE,131,408,No,No,0,94.4,80,16.05,215.1,101,18.28,179.7,108,8.09,13.1,9,3.54,2,False 218 | VT,119,510,No,No,0,190.4,74,32.37,215.6,113,18.33,161.2,111,7.25,10.0,1,2.7,2,False 219 | NY,94,510,Yes,No,0,243.2,109,41.34,147.0,88,12.5,94.9,99,4.27,7.2,4,1.94,4,False 220 | TX,217,408,No,No,0,176.4,115,29.99,158.8,128,13.5,306.6,107,13.8,9.3,3,2.51,4,False 221 | OR,161,415,No,No,0,178.1,109,30.28,146.5,86,12.45,137.6,78,6.19,8.5,2,2.3,1,False 222 | KS,67,408,No,No,0,201.4,101,34.24,97.6,122,8.3,202.5,119,9.11,7.0,3,1.89,0,False 223 | ND,132,415,No,Yes,31,174.5,101,29.67,245.6,105,20.88,172.8,76,7.78,10.3,9,2.78,1,False 224 | PA,134,408,No,No,0,205.3,122,34.9,240.5,155,20.44,179.1,107,8.06,5.0,9,1.35,1,False 225 | NY,44,510,No,No,0,143.2,77,24.34,169.8,114,14.43,215.8,77,9.71,7.6,4,2.05,1,False 226 | WY,53,415,No,Yes,27,25.9,119,4.4,206.5,96,17.55,228.1,64,10.26,6.5,7,1.76,1,False 227 | NY,108,415,No,No,0,154.2,123,26.21,112.3,86,9.55,246.4,75,11.09,15.4,4,4.16,4,True 228 | ME,80,408,No,No,0,322.3,113,54.79,222.0,95,18.87,162.8,123,7.33,6.7,8,1.81,0,True 229 | SD,88,415,No,No,0,215.6,115,36.65,216.2,85,18.38,171.3,65,7.71,11.8,1,3.19,3,False 230 | UT,82,510,Yes,No,0,208.8,101,35.5,213.7,87,18.16,175.1,86,7.88,12.4,6,3.35,3,False 231 | AK,115,415,No,No,0,245.2,105,41.68,159.0,109,13.52,229.9,74,10.35,7.2,8,1.94,0,False 232 | RI,93,415,No,No,0,98.4,78,16.73,249.6,129,21.22,248.2,114,11.17,14.2,4,3.83,1,False 233 | AL,72,415,No,No,0,217.8,93,37.03,189.7,113,16.12,182.6,91,8.22,10.4,5,2.81,4,False 234 | DE,98,415,No,Yes,29,179.9,97,30.58,189.2,89,16.08,164.3,76,7.39,12.8,7,3.46,3,False 235 | ID,118,415,No,No,0,140.4,112,23.87,187.1,60,15.9,207.9,155,9.36,7.9,1,2.13,0,False 236 | MO,55,510,No,No,0,189.0,100,32.13,118.5,99,10.07,248.1,87,11.16,17.1,6,4.62,0,False 237 | IL,130,415,No,No,0,155.9,95,26.5,256.1,97,21.77,262.9,103,11.83,11.7,3,3.16,3,False 238 | FL,136,408,Yes,No,0,199.2,122,33.86,214.7,114,18.25,150.9,105,6.79,11.8,7,3.19,1,False 239 | KY,107,415,No,No,0,157.1,79,26.71,162.6,124,13.82,150.0,138,6.75,12.1,6,3.27,1,False 240 | MI,91,415,No,No,0,154.4,165,26.25,168.3,121,14.31,239.9,81,10.8,11.7,4,3.16,5,True 241 | NY,167,415,No,No,0,166.4,85,28.29,243.2,135,20.67,229.2,95,10.31,9.9,5,2.67,1,False 242 | KS,159,415,No,Yes,19,184.1,78,31.3,194.5,71,16.53,225.6,101,10.15,16.9,3,4.56,0,False 243 | PA,122,415,No,No,0,35.1,62,5.97,180.8,89,15.37,251.6,58,11.32,12.7,2,3.43,1,False 244 | MI,105,415,No,Yes,29,179.4,113,30.5,275.4,100,23.41,246.1,105,11.07,10.0,5,2.7,0,False 245 | NH,155,408,No,No,0,216.7,30,36.84,144.3,125,12.27,135.3,106,6.09,10.8,1,2.92,2,False 246 | WA,161,415,No,No,0,151.6,117,25.77,219.4,87,18.65,224.7,68,10.11,4.0,5,1.08,1,False 247 | NY,122,415,No,No,0,173.6,110,29.51,91.7,84,7.79,211.7,103,9.53,9.7,7,2.62,3,False 248 | MD,37,415,Yes,No,0,106.6,76,18.12,147.4,89,12.53,235.8,113,10.61,9.6,8,2.59,2,False 249 | KS,128,415,No,No,0,103.3,122,17.56,245.9,123,20.9,161.1,95,7.25,6.4,7,1.73,0,False 250 | DE,89,510,Yes,No,0,125.6,108,21.35,213.0,90,18.11,181.7,108,8.18,5.4,5,1.46,1,False 251 | IN,108,415,No,No,0,199.3,104,33.88,224.2,92,19.06,140.1,57,6.3,15.2,2,4.1,0,False 252 | VT,109,408,No,No,0,222.2,113,37.77,218.5,122,18.57,266.0,88,11.97,10.9,5,2.94,1,False 253 | WA,29,415,No,No,0,157.4,122,26.76,145.0,75,12.33,281.8,92,12.68,9.3,2,2.51,1,False 254 | NM,119,415,Yes,Yes,15,160.0,95,27.2,209.5,110,17.81,82.3,107,3.7,8.7,5,2.35,5,True 255 | MO,86,415,No,No,0,83.5,96,14.2,221.1,63,18.79,349.7,75,15.74,12.6,3,3.4,0,False 256 | IA,92,510,No,Yes,25,134.0,112,22.78,206.0,111,17.51,180.6,118,8.13,9.7,4,2.62,0,False 257 | WA,13,510,No,Yes,25,176.6,65,30.02,172.7,96,14.68,104.5,128,4.7,11.3,5,3.05,2,False 258 | CT,144,510,Yes,Yes,35,174.8,127,29.72,219.6,93,18.67,255.8,90,11.51,12.8,3,3.46,0,False 259 | NH,139,510,No,No,0,221.3,140,37.62,157.8,89,13.41,192.5,89,8.66,11.3,6,3.05,1,False 260 | LA,95,415,No,No,0,141.1,84,23.99,211.4,108,17.97,103.7,127,4.67,5.9,6,1.59,3,False 261 | ME,80,408,No,Yes,31,166.4,92,28.29,238.3,74,20.26,150.7,84,6.78,10.7,4,2.89,4,False 262 | MT,113,415,No,No,0,215.9,93,36.7,240.1,85,20.41,156.7,123,7.05,4.9,5,1.32,3,False 263 | ID,88,415,No,Yes,31,181.6,91,30.87,213.2,120,18.12,207.8,104,9.35,11.4,4,3.08,1,False 264 | UT,74,415,No,No,0,106.4,84,18.09,140.2,104,11.92,90.9,81,4.09,11.4,3,3.08,1,False 265 | IL,48,510,No,No,0,128.2,71,21.79,48.1,78,4.09,116.3,80,5.23,8.9,3,2.4,0,False 266 | AZ,163,510,No,No,0,178.7,56,30.38,215.7,79,18.33,152.7,84,6.87,10.6,2,2.86,4,False 267 | ID,56,510,No,No,0,150.9,79,25.65,161.8,87,13.75,167.7,115,7.55,11.7,5,3.16,3,False 268 | TX,64,415,No,No,0,168.0,116,28.56,192.4,94,16.35,166.5,98,7.49,10.1,3,2.73,2,False 269 | MT,116,415,No,Yes,35,182.8,122,31.08,212.7,119,18.08,193.8,103,8.72,11.0,2,2.97,1,False 270 | PA,101,415,Yes,No,0,193.7,108,32.93,186.6,98,15.86,223.0,100,10.04,11.6,8,3.13,0,False 271 | RI,85,415,Yes,No,0,197.2,97,33.52,211.7,115,17.99,210.1,133,9.45,8.3,4,2.24,4,False 272 | NH,90,415,No,No,0,76.1,121,12.94,290.3,73,24.68,236.9,89,10.66,10.8,3,2.92,0,False 273 | UT,73,415,No,No,0,182.3,115,30.99,199.2,97,16.93,120.2,113,5.41,18.0,5,4.86,1,False 274 | NH,55,408,No,Yes,20,189.3,95,32.18,118.6,113,10.08,250.2,102,11.26,12.5,4,3.38,2,False 275 | AK,76,415,No,Yes,22,160.1,107,27.22,168.7,136,14.34,23.2,102,1.04,9.5,4,2.57,3,False 276 | AZ,157,415,No,No,0,220.7,105,37.52,119.3,127,10.14,165.1,113,7.43,11.5,7,3.11,4,False 277 | NJ,131,415,No,No,0,211.8,115,36.01,260.5,102,22.14,144.2,96,6.49,10.8,7,2.92,0,False 278 | MI,51,415,No,No,0,229.7,129,39.05,336.0,104,28.56,192.8,128,8.68,9.6,1,2.59,1,True 279 | ID,166,415,No,No,0,220.7,106,37.52,177.8,118,15.11,206.1,102,9.27,12.4,9,3.35,1,False 280 | OR,66,408,No,No,0,87.6,76,14.89,262.0,111,22.27,184.6,125,8.31,9.2,5,2.48,1,False 281 | UT,49,415,No,No,0,266.3,90,45.27,207.8,117,17.66,205.0,98,9.23,14.0,2,3.78,2,True 282 | LA,89,415,No,No,0,141.1,92,23.99,249.1,126,21.17,136.0,73,6.12,10.8,2,2.92,2,False 283 | WI,90,415,No,No,0,200.9,92,34.15,164.3,91,13.97,249.0,98,11.21,8.9,7,2.4,1,False 284 | ID,193,415,No,No,0,96.8,92,16.46,142.6,103,12.12,210.1,115,9.45,10.9,5,2.94,2,True 285 | TN,114,510,No,No,0,172.0,145,29.24,276.4,101,23.49,193.7,100,8.72,10.1,9,2.73,1,False 286 | NV,7,408,No,Yes,30,221.4,114,37.64,165.8,116,14.09,247.0,105,11.12,10.8,12,2.92,1,False 287 | WY,71,415,No,No,0,243.7,124,41.43,60.0,90,5.1,189.0,129,8.5,11.3,2,3.05,0,False 288 | IL,31,415,No,Yes,28,210.5,101,35.79,250.5,86,21.29,241.6,125,10.87,11.5,2,3.11,1,False 289 | MA,111,415,No,No,0,284.4,89,48.35,157.0,113,13.35,242.8,91,10.93,8.4,8,2.27,0,True 290 | AZ,97,408,No,No,0,169.7,84,28.85,165.9,86,14.1,191.9,83,8.64,12.8,6,3.46,3,False 291 | ND,51,510,No,No,0,227.2,89,38.62,194.4,106,16.52,243.4,126,10.95,14.9,2,4.02,0,False 292 | SD,86,415,No,No,0,223.9,75,38.06,155.7,109,13.23,150.2,143,6.76,7.3,9,1.97,1,False 293 | ND,190,415,No,No,0,169.4,102,28.8,253.5,113,21.55,197.1,93,8.87,8.9,5,2.4,1,False 294 | CT,80,408,No,No,0,118.1,90,20.08,144.3,77,12.27,225.1,86,10.13,8.2,6,2.21,1,False 295 | MO,36,415,No,No,0,202.4,115,34.41,230.7,115,19.61,202.0,127,9.09,10.2,2,2.75,3,False 296 | NJ,45,510,No,No,0,155.7,110,26.47,260.3,103,22.13,192.2,98,8.65,11.0,1,2.97,1,False 297 | NC,170,415,No,No,0,246.4,107,41.89,228.1,124,19.39,166.4,95,7.49,9.1,8,2.46,0,False 298 | UT,103,415,No,No,0,189.8,110,32.27,115.5,83,9.82,191.3,103,8.61,12.2,4,3.29,0,False 299 | WI,112,408,No,No,0,167.8,88,28.53,247.9,81,21.07,155.1,108,6.98,11.9,4,3.21,0,False 300 | MT,125,510,No,No,0,143.2,80,24.34,88.1,94,7.49,233.2,135,10.49,8.8,7,2.38,4,True 301 | MS,73,415,No,Yes,31,82.3,105,13.99,256.1,91,21.77,229.6,98,10.33,11.8,2,3.19,6,True 302 | NM,232,408,No,No,0,165.6,104,28.15,195.9,115,16.65,118.3,77,5.32,11.8,3,3.19,1,False 303 | SD,95,408,No,Yes,20,165.7,78,28.17,215.6,94,18.33,243.3,91,10.95,9.8,6,2.65,0,False 304 | VA,182,415,No,No,0,176.1,90,29.94,174.9,106,14.87,234.7,134,10.56,9.7,4,2.62,1,False 305 | NM,55,510,No,No,0,119.7,148,20.35,231.8,96,19.7,222.3,113,10.0,4.6,2,1.24,2,False 306 | VA,86,415,No,Yes,30,99.9,84,16.98,263.5,125,22.4,254.7,90,11.46,9.8,7,2.65,2,False 307 | NJ,127,510,No,No,0,239.8,107,40.77,128.9,121,10.96,249.9,110,11.25,11.3,5,3.05,1,False 308 | TN,63,415,No,No,0,164.5,75,27.97,147.9,118,12.57,252.7,97,11.37,11.2,2,3.02,0,False 309 | MI,79,510,No,No,0,220.9,107,37.55,192.2,97,16.34,161.0,74,7.25,12.2,2,3.29,1,False 310 | LA,111,415,No,Yes,28,128.8,104,21.9,157.3,52,13.37,147.4,76,6.63,10.3,2,2.78,2,False 311 | NH,121,510,No,Yes,35,193.8,62,32.95,197.6,97,16.8,218.8,95,9.85,5.9,4,1.59,0,False 312 | RI,112,415,No,No,0,168.6,102,28.66,298.0,117,25.33,194.7,110,8.76,9.8,5,2.65,1,False 313 | LA,81,415,No,Yes,36,115.9,120,19.7,236.6,95,20.11,255.0,90,11.48,11.7,6,3.16,3,False 314 | AZ,72,510,No,No,0,272.4,88,46.31,107.9,125,9.17,185.5,81,8.35,12.7,2,3.43,0,False 315 | SC,64,510,No,Yes,40,210.0,116,35.7,232.7,89,19.78,168.8,94,7.6,5.9,4,1.59,8,False 316 | MD,163,408,No,No,0,223.0,120,37.91,227.0,98,19.3,188.3,125,8.47,8.8,5,2.38,1,False 317 | WA,104,415,No,No,0,139.7,78,23.75,202.6,119,17.22,203.6,102,9.16,11.3,5,3.05,2,False 318 | CA,93,510,No,Yes,19,136.8,113,23.26,179.5,105,15.26,71.1,95,3.2,12.5,3,3.38,2,False 319 | NJ,197,510,No,No,0,154.8,111,26.32,171.5,102,14.58,227.3,86,10.23,10.6,2,2.86,3,False 320 | AR,57,510,No,No,0,192.8,68,32.78,158.0,86,13.43,235.5,105,10.6,12.7,6,3.43,1,False 321 | CT,107,408,No,No,0,103.4,94,17.58,189.3,125,16.09,227.2,125,10.22,14.4,3,3.89,1,False 322 | SD,75,408,No,No,0,147.5,110,25.08,191.7,97,16.29,135.0,68,6.08,16.4,3,4.43,2,False 323 | FL,106,408,No,No,0,178.4,143,30.33,247.0,123,21.0,259.9,105,11.7,9.6,2,2.59,0,False 324 | MS,70,408,No,No,0,148.4,110,25.23,267.1,90,22.7,151.5,101,6.82,8.9,4,2.4,0,False 325 | MA,80,408,No,No,0,148.6,106,25.26,210.8,65,17.92,203.7,86,9.17,10.0,2,2.7,2,False 326 | VT,137,510,No,No,0,97.5,95,16.58,195.8,82,16.64,288.8,78,13.0,0.0,0,0.0,1,False 327 | IL,90,415,No,Yes,29,185.6,106,31.55,219.7,113,18.67,152.1,120,6.84,11.1,5,3.0,2,False 328 | CT,30,408,No,No,0,137.6,108,23.39,162.0,80,13.77,187.7,126,8.45,5.8,10,1.57,3,False 329 | KS,105,415,Yes,No,0,273.9,119,46.56,278.6,103,23.68,255.3,90,11.49,10.9,7,2.94,1,True 330 | MA,102,415,No,Yes,31,125.3,92,21.3,141.2,108,12.0,168.2,68,7.57,6.3,2,1.7,3,False 331 | NJ,83,415,No,No,0,178.8,102,30.4,167.9,84,14.27,178.9,65,8.05,8.6,4,2.32,3,False 332 | AR,63,510,No,Yes,49,214.9,86,36.53,198.2,89,16.85,170.8,139,7.69,8.2,5,2.21,0,False 333 | MS,155,408,No,No,0,163.0,93,27.71,203.9,102,17.33,159.0,109,7.15,15.1,4,4.08,2,False 334 | ME,105,408,No,No,0,115.5,73,19.64,267.3,83,22.72,114.2,90,5.14,13.3,5,3.59,3,False 335 | IA,73,415,No,No,0,137.1,102,23.31,210.8,114,17.92,191.4,120,8.61,11.1,4,3.0,1,False 336 | NM,21,415,No,Yes,19,132.7,94,22.56,204.6,101,17.39,154.7,78,6.96,12.9,7,3.48,3,False 337 | TX,71,415,No,Yes,39,183.2,103,31.14,209.4,111,17.8,172.4,109,7.76,11.9,6,3.21,1,False 338 | KY,112,415,No,No,0,170.5,113,28.99,193.2,129,16.42,188.0,91,8.46,11.2,6,3.02,0,False 339 | AZ,66,510,No,No,0,154.0,133,26.18,198.9,121,16.91,151.9,100,6.84,9.5,3,2.57,4,True 340 | TN,99,408,No,No,0,54.8,92,9.32,173.0,103,14.71,195.1,125,8.78,7.5,3,2.03,1,False 341 | WV,124,510,No,No,0,184.8,74,31.42,175.1,84,14.88,158.2,95,7.12,10.5,6,2.84,1,False 342 | CT,95,415,No,Yes,36,283.1,112,48.13,286.2,86,24.33,261.7,129,11.78,11.3,3,3.05,3,False 343 | NH,130,408,No,No,0,68.4,86,11.63,193.3,110,16.43,171.5,139,7.72,10.4,4,2.81,0,False 344 | MD,93,510,Yes,No,0,131.4,78,22.34,219.7,106,18.67,155.7,103,7.01,11.1,2,3.0,1,True 345 | WV,191,408,No,No,0,162.0,104,27.54,241.2,120,20.5,210.4,83,9.47,10.9,7,2.94,1,False 346 | KY,88,415,No,No,0,148.2,82,25.19,308.7,67,26.24,235.4,79,10.59,6.4,4,1.73,2,False 347 | WY,16,415,No,No,0,174.7,83,29.7,280.8,122,23.87,171.7,80,7.73,10.5,8,2.84,5,False 348 | GA,107,510,No,No,0,194.5,97,33.07,186.3,131,15.84,178.3,106,8.02,12.7,1,3.43,2,False 349 | AK,96,408,No,Yes,29,150.0,91,25.5,159.4,75,13.55,228.1,55,10.26,8.5,3,2.3,1,False 350 | CT,163,408,No,Yes,40,231.9,56,39.42,211.8,91,18.0,268.5,74,12.08,12.3,3,3.32,2,False 351 | OR,95,415,No,No,0,269.0,120,45.73,233.7,120,19.86,179.3,61,8.07,7.3,4,1.97,2,True 352 | OK,123,415,No,Yes,27,198.7,127,33.78,249.0,105,21.17,173.2,124,7.79,12.5,5,3.38,1,False 353 | MA,34,415,No,No,0,293.7,89,49.93,272.5,71,23.16,178.2,76,8.02,11.0,10,2.97,2,True 354 | DE,96,415,No,Yes,26,175.8,96,29.89,206.6,84,17.56,178.0,105,8.01,11.1,2,3.0,2,False 355 | OH,146,415,No,No,0,169.5,93,28.82,230.9,71,19.63,269.8,115,12.14,9.0,7,2.43,2,False 356 | ID,138,415,Yes,Yes,17,225.2,116,38.28,173.4,88,14.74,145.8,99,6.56,11.7,4,3.16,0,False 357 | MO,76,510,No,No,0,129.7,84,22.05,177.5,80,15.09,228.9,87,10.3,7.5,3,2.03,5,True 358 | NC,83,510,No,Yes,36,95.9,87,16.3,261.6,105,22.24,228.6,109,10.29,13.3,4,3.59,0,False 359 | HI,104,408,No,No,0,170.6,97,29.0,162.1,111,13.78,210.7,131,9.48,6.1,1,1.65,1,False 360 | NJ,106,415,No,No,0,191.4,124,32.54,200.7,116,17.06,230.1,76,10.35,8.2,3,2.21,1,False 361 | MS,84,510,No,No,0,75.3,96,12.8,179.9,113,15.29,193.8,134,8.72,12.3,1,3.32,1,False 362 | MN,99,408,No,No,0,128.8,86,21.9,203.9,105,17.33,282.6,131,12.72,14.1,4,3.81,2,False 363 | RI,134,415,No,No,0,141.7,95,24.09,205.6,101,17.48,218.5,60,9.83,8.8,6,2.38,0,False 364 | NJ,80,415,No,No,0,268.7,120,45.68,301.0,147,25.59,167.0,140,7.52,5.8,1,1.57,2,True 365 | MN,85,415,No,No,0,255.3,114,43.4,194.6,83,16.54,276.6,78,12.45,3.7,5,1.0,3,False 366 | WI,120,408,No,Yes,26,239.4,94,40.7,259.4,88,22.05,238.0,132,10.71,7.7,3,2.08,0,False 367 | KS,120,510,No,No,0,158.0,110,26.86,197.0,103,16.75,154.9,132,6.97,10.0,5,2.7,1,False 368 | NJ,125,415,No,No,0,182.3,64,30.99,139.8,121,11.88,171.6,96,7.72,11.6,7,3.13,2,False 369 | ND,118,415,No,Yes,39,91.5,125,15.56,219.9,113,18.69,229.0,99,10.31,12.7,8,3.43,2,False 370 | WY,62,415,No,No,0,172.4,132,29.31,230.5,100,19.59,228.2,109,10.27,11.0,5,2.97,0,False 371 | LA,112,510,No,No,0,174.5,127,29.67,259.3,71,22.04,170.5,120,7.67,11.3,7,3.05,1,False 372 | AL,68,510,No,No,0,157.3,83,26.74,220.9,85,18.78,218.9,129,9.85,12.0,7,3.24,1,False 373 | TX,201,415,No,Yes,21,192.0,97,32.64,239.1,81,20.32,116.1,125,5.22,15.1,3,4.08,1,False 374 | AR,116,510,No,No,0,160.7,69,27.32,146.8,106,12.48,287.8,144,12.95,8.2,5,2.21,0,False 375 | GA,133,510,No,No,0,227.4,90,38.66,73.2,135,6.22,114.3,99,5.14,4.7,7,1.27,0,False 376 | KY,125,408,No,No,0,191.6,115,32.57,205.6,108,17.48,210.2,123,9.46,9.2,3,2.48,2,False 377 | NE,21,510,No,No,0,225.0,110,38.25,244.2,111,20.76,221.2,93,9.95,10.7,4,2.89,0,False 378 | ND,110,408,No,No,0,196.1,103,33.34,199.7,123,16.97,135.9,71,6.12,12.9,1,3.48,3,False 379 | NH,83,415,No,No,0,231.3,100,39.32,210.4,84,17.88,217.4,106,9.78,12.4,2,3.35,3,False 380 | IN,101,415,No,Yes,42,209.2,82,35.56,159.7,74,13.57,181.6,100,8.17,9.5,3,2.57,0,False 381 | OR,117,415,No,Yes,17,221.3,82,37.62,167.6,100,14.25,262.7,87,11.82,4.4,4,1.19,0,False 382 | OH,127,408,No,No,0,139.6,94,23.73,240.9,112,20.48,127.1,88,5.72,8.8,4,2.38,2,False 383 | KS,138,510,No,Yes,26,183.9,83,31.26,240.7,93,20.46,185.7,125,8.36,15.0,3,4.05,1,False 384 | TX,120,415,No,No,0,203.3,108,34.56,259.9,66,22.09,115.9,103,5.22,7.8,2,2.11,3,False 385 | DC,141,415,No,Yes,37,185.4,87,31.52,178.5,128,15.17,218.3,107,9.82,8.0,3,2.16,4,False 386 | OR,87,510,No,Yes,22,240.8,102,40.94,75.9,106,6.45,224.6,115,10.11,7.1,3,1.92,2,False 387 | WA,97,408,No,No,0,276.1,82,46.94,201.1,106,17.09,231.3,73,10.41,8.9,4,2.4,0,True 388 | NC,162,408,No,Yes,26,179.7,144,30.55,218.1,129,18.54,212.3,105,9.55,9.3,8,2.51,1,True 389 | TX,119,510,No,No,0,81.9,75,13.92,253.8,114,21.57,213.1,125,9.59,8.9,1,2.4,2,True 390 | WI,107,510,No,Yes,25,248.6,91,42.26,119.3,115,10.14,194.3,83,8.74,12.0,1,3.24,1,False 391 | SC,140,510,No,Yes,28,157.1,77,26.71,172.4,97,14.65,184.5,94,8.3,11.1,9,3.0,1,False 392 | SD,91,415,No,No,0,153.0,123,26.01,141.1,127,11.99,171.5,76,7.72,10.3,15,2.78,1,True 393 | OH,135,415,No,No,0,218.8,123,37.2,242.8,64,20.64,85.8,80,3.86,10.3,3,2.78,4,False 394 | HI,86,408,No,Yes,21,197.9,99,33.64,165.6,100,14.08,208.0,120,9.36,10.1,9,2.73,0,False 395 | KY,131,415,No,No,0,166.5,129,28.31,210.2,107,17.87,257.2,93,11.57,9.9,5,2.67,1,False 396 | OH,86,415,No,Yes,29,225.4,79,38.32,187.1,112,15.9,281.1,112,12.65,12.9,3,3.48,1,False 397 | AL,85,415,No,No,0,210.3,66,35.75,195.8,76,16.64,221.6,82,9.97,11.2,7,3.02,1,False 398 | VT,195,415,No,Yes,36,231.7,110,39.39,225.1,88,19.13,201.7,89,9.08,12.1,2,3.27,0,False 399 | ND,177,408,No,No,0,189.5,99,32.22,176.3,117,14.99,225.9,112,10.17,14.2,2,3.83,1,False 400 | WA,100,408,No,No,0,70.8,94,12.04,215.6,102,18.33,230.8,125,10.39,9.5,1,2.57,6,True 401 | CT,124,415,No,No,0,131.8,82,22.41,284.3,119,24.17,305.5,101,13.75,11.3,2,3.05,1,False 402 | DE,81,510,Yes,No,0,250.6,85,42.6,187.9,50,15.97,120.3,131,5.41,7.8,5,2.11,1,False 403 | HI,105,415,No,No,0,281.3,124,47.82,301.5,96,25.63,202.8,109,9.13,8.7,3,2.35,0,True 404 | MI,126,415,Yes,Yes,26,129.3,123,21.98,176.5,114,15.0,154.5,102,6.95,9.6,7,2.59,1,False 405 | ND,71,510,No,No,0,238.0,82,40.46,278.5,94,23.67,193.1,134,8.69,11.8,10,3.19,0,True 406 | NE,139,415,No,No,0,211.1,103,35.89,206.9,108,17.59,193.9,70,8.73,5.6,4,1.51,0,False 407 | NE,77,415,No,No,0,169.4,102,28.8,184.9,144,15.72,234.3,89,10.54,2.0,7,0.54,1,False 408 | KY,72,408,No,No,0,177.1,97,30.11,184.7,105,15.7,174.1,94,7.83,8.0,6,2.16,1,False 409 | KS,74,415,No,Yes,32,174.6,107,29.68,310.6,115,26.4,234.7,92,10.56,9.0,4,2.43,1,False 410 | ND,124,415,No,No,0,150.3,101,25.55,255.9,112,21.75,136.7,62,6.15,12.5,4,3.38,2,False 411 | KY,113,408,No,Yes,20,157.8,83,26.83,161.5,56,13.73,271.5,100,12.22,8.7,2,2.35,5,True 412 | ME,66,510,No,No,0,118.0,133,20.06,248.1,99,21.09,214.4,122,9.65,5.3,5,1.43,1,False 413 | MT,124,415,No,Yes,30,144.5,35,24.57,262.3,101,22.3,226.5,82,10.19,12.0,7,3.24,2,False 414 | IL,134,408,No,No,0,183.8,111,31.25,123.5,92,10.5,160.7,105,7.23,6.1,2,1.65,1,False 415 | ME,41,415,No,Yes,30,191.7,109,32.59,193.0,86,16.41,149.4,93,6.72,11.1,4,3.0,1,False 416 | SC,159,415,No,Yes,23,153.6,93,26.11,216.9,88,18.44,161.3,91,7.26,12.6,3,3.4,2,False 417 | IL,87,408,Yes,Yes,36,171.2,138,29.1,185.8,102,15.79,227.6,97,10.24,10.8,3,2.92,1,False 418 | WA,132,415,No,No,0,240.1,115,40.82,180.4,91,15.33,133.4,122,6.0,8.0,6,2.16,3,False 419 | NE,86,408,No,No,0,83.8,121,14.25,240.2,96,20.42,158.6,108,7.14,6.7,8,1.81,1,False 420 | IL,55,415,Yes,No,0,269.6,121,45.83,171.7,91,14.59,219.0,98,9.86,8.2,6,2.21,1,False 421 | VT,101,415,No,No,0,136.2,92,23.15,220.9,110,18.78,196.9,116,8.86,13.3,7,3.59,3,False 422 | KS,94,408,No,No,0,269.2,104,45.76,193.8,144,16.47,257.6,61,11.59,8.9,2,2.4,3,True 423 | ND,68,408,No,No,0,219.6,97,37.33,141.1,144,11.99,205.7,101,9.26,10.8,4,2.92,2,False 424 | CO,98,408,No,No,0,236.2,122,40.15,189.4,110,16.1,153.6,104,6.91,13.3,4,3.59,0,False 425 | TX,123,408,No,No,0,260.9,85,44.35,168.5,103,14.32,178.3,91,8.02,13.3,5,3.59,3,False 426 | CA,111,408,No,No,0,249.8,109,42.47,242.4,106,20.6,231.8,78,10.43,11.6,4,3.13,0,True 427 | MS,69,510,No,Yes,27,268.8,78,45.7,246.6,89,20.96,271.9,102,12.24,16.4,3,4.43,0,False 428 | RI,127,415,No,Yes,27,140.1,59,23.82,223.4,111,18.99,257.9,73,11.61,3.8,10,1.03,1,False 429 | NM,105,415,No,No,0,193.7,108,32.93,183.2,124,15.57,293.7,72,13.22,10.8,5,2.92,1,False 430 | VA,75,415,No,No,0,224.7,116,38.2,192.0,79,16.32,212.2,98,9.55,11.3,11,3.05,3,False 431 | TX,126,415,No,No,0,190.9,143,32.45,149.7,72,12.72,191.4,87,8.61,13.0,3,3.51,1,False 432 | NM,41,415,No,No,0,232.1,74,39.46,327.1,88,27.8,226.5,119,10.19,10.9,2,2.94,3,True 433 | RI,113,415,No,No,0,90.6,130,15.4,170.6,100,14.5,137.4,74,6.18,5.4,9,1.46,1,False 434 | NY,78,510,No,No,0,152.9,81,25.99,256.6,82,21.81,173.6,112,7.81,5.3,6,1.43,2,False 435 | WA,110,415,No,No,0,241.2,105,41.0,174.3,85,14.82,245.3,59,11.04,8.5,4,2.3,2,False 436 | NE,112,415,Yes,Yes,16,200.3,72,34.05,197.8,91,16.81,151.1,92,6.8,10.4,3,2.81,1,False 437 | ID,104,510,No,No,0,97.2,88,16.52,155.6,85,13.23,261.6,105,11.77,12.4,5,3.35,0,False 438 | WV,127,510,No,No,0,224.3,112,38.13,185.7,103,15.78,159.4,83,7.17,10.0,1,2.7,2,False 439 | NE,61,408,No,No,0,267.1,104,45.41,180.4,131,15.33,230.6,106,10.38,17.3,4,4.67,1,True 440 | RI,65,415,No,Yes,29,215.5,129,36.64,161.9,77,13.76,128.3,91,5.77,8.8,5,2.38,2,False 441 | CT,152,408,No,Yes,20,239.1,105,40.65,209.1,111,17.77,268.2,130,12.07,13.3,3,3.59,5,False 442 | RI,63,415,Yes,No,0,62.9,112,10.69,202.9,111,17.25,259.0,58,11.66,8.9,8,2.4,1,False 443 | IA,100,408,No,No,0,210.9,85,35.85,329.3,69,27.99,127.1,78,5.72,9.4,5,2.54,4,False 444 | MS,29,510,No,No,0,313.2,103,53.24,216.3,151,18.39,218.4,106,9.83,12.8,4,3.46,2,True 445 | ME,175,415,No,No,0,132.0,95,22.44,231.2,74,19.65,313.4,108,14.1,8.7,10,2.35,1,False 446 | MS,98,415,Yes,Yes,23,245.5,54,41.74,292.7,83,24.88,184.0,90,8.28,10.8,7,2.92,1,False 447 | VA,121,415,No,No,0,134.1,112,22.8,195.1,104,16.58,159.6,139,7.18,10.5,2,2.84,2,False 448 | PA,104,415,No,No,0,144.5,107,24.57,180.5,85,15.34,226.0,94,10.17,17.0,6,4.59,2,False 449 | NE,123,415,No,No,0,150.0,98,25.5,89.8,95,7.63,326.0,91,14.67,11.1,3,3.0,3,False 450 | MI,61,415,No,Yes,33,270.7,53,46.02,200.7,116,17.06,201.7,102,9.08,10.9,3,2.94,3,False 451 | MD,160,415,No,No,0,256.0,111,43.52,187.4,61,15.93,119.1,81,5.36,11.5,4,3.11,3,False 452 | AR,153,408,No,No,0,154.6,56,26.28,263.0,84,22.36,367.7,89,16.55,15.5,2,4.19,1,False 453 | FL,31,510,No,No,0,165.4,84,28.12,203.7,107,17.31,201.7,65,9.08,8.2,1,2.21,1,False 454 | KY,122,415,No,No,0,168.3,96,28.61,87.6,91,7.45,247.2,87,11.12,8.4,6,2.27,1,False 455 | IN,46,415,No,Yes,34,191.4,102,32.54,361.8,96,30.75,147.5,132,6.64,7.2,2,1.94,1,False 456 | FL,103,415,No,No,0,158.7,90,26.98,198.4,117,16.86,181.1,76,8.15,10.5,4,2.84,1,False 457 | IL,68,415,Yes,Yes,29,195.5,113,33.24,171.6,96,14.59,204.0,85,9.18,13.5,9,3.65,1,True 458 | AL,149,415,No,Yes,20,264.4,102,44.95,219.6,123,18.67,200.4,89,9.02,11.3,3,3.05,2,False 459 | MT,102,408,No,No,0,273.2,85,46.44,211.1,82,17.94,203.7,129,9.17,13.1,7,3.54,2,True 460 | WI,153,510,No,No,0,159.5,103,27.12,275.5,90,23.42,176.7,126,7.95,10.1,2,2.73,1,True 461 | NY,122,510,No,No,0,145.6,102,24.75,284.7,111,24.2,228.2,91,10.27,12.2,5,3.29,0,False 462 | WY,127,510,Yes,No,0,247.5,99,42.08,108.5,118,9.22,232.0,72,10.44,10.6,3,2.86,2,False 463 | NY,126,415,Yes,No,0,239.7,87,40.75,281.7,92,23.94,183.5,113,8.26,11.4,1,3.08,1,True 464 | CO,90,408,No,No,0,109.9,102,18.68,220.8,114,18.77,104.0,133,4.68,10.9,6,2.94,0,False 465 | CA,37,510,No,No,0,239.9,120,40.78,261.6,88,22.24,207.1,88,9.32,8.9,4,2.4,2,True 466 | CO,132,415,No,No,0,197.8,66,33.63,133.9,119,11.38,177.3,94,7.98,10.9,3,2.94,4,True 467 | KY,38,408,No,Yes,36,115.4,98,19.62,166.2,83,14.13,184.7,79,8.31,15.2,6,4.1,2,False 468 | ID,92,415,No,No,0,197.2,113,33.52,242.3,116,20.6,192.0,76,8.64,11.0,5,2.97,2,False 469 | HI,111,408,No,Yes,13,193.1,104,32.83,111.6,98,9.49,227.4,94,10.23,12.1,4,3.27,1,False 470 | TX,121,415,No,Yes,31,263.1,70,44.73,279.3,118,23.74,127.1,143,5.72,9.7,4,2.62,5,False 471 | GA,131,408,No,No,0,197.0,79,33.49,201.0,114,17.09,151.2,111,6.8,11.6,5,3.13,1,False 472 | ME,19,415,No,No,0,201.5,123,34.26,129.2,110,10.98,220.6,98,9.93,12.9,4,3.48,1,False 473 | OH,135,415,No,No,0,173.4,107,29.48,222.0,84,18.87,64.2,94,2.89,13.7,6,3.7,1,False 474 | TN,129,408,No,No,0,101.4,145,17.24,249.1,116,21.17,157.6,107,7.09,7.1,6,1.92,1,False 475 | VT,142,415,No,No,0,232.5,74,39.53,181.8,142,15.45,203.1,86,9.14,10.4,6,2.81,4,False 476 | MD,130,415,No,Yes,45,174.5,120,29.67,217.5,95,18.49,220.3,67,9.91,12.2,2,3.29,1,False 477 | NE,92,415,No,No,0,181.4,98,30.84,164.5,98,13.98,171.0,110,7.69,10.9,4,2.94,2,False 478 | WY,146,415,No,Yes,11,180.7,82,30.72,173.7,90,14.76,231.5,89,10.42,10.1,4,2.73,2,False 479 | WV,152,510,Yes,Yes,41,146.8,128,24.96,285.6,96,24.28,213.6,80,9.61,4.3,2,1.16,1,True 480 | MS,189,415,No,No,0,227.8,124,38.73,169.5,112,14.41,201.1,91,9.05,5.6,4,1.51,3,False 481 | OH,95,415,No,Yes,23,160.3,87,27.25,202.4,101,17.2,191.1,122,8.6,7.4,3,2.0,0,False 482 | AK,1,408,No,No,0,175.2,74,29.78,151.7,79,12.89,230.5,109,10.37,5.3,3,1.43,1,False 483 | IN,141,415,No,Yes,39,116.9,127,19.87,276.5,88,23.5,289.9,125,13.05,12.3,2,3.32,0,False 484 | WA,111,510,No,No,0,152.2,114,25.87,137.2,102,11.66,185.9,97,8.37,9.8,3,2.65,0,False 485 | RI,143,408,No,No,0,167.8,72,28.53,211.0,99,17.94,153.5,109,6.91,10.5,6,2.84,4,True 486 | AL,148,415,No,Yes,21,262.9,135,44.69,149.5,96,12.71,140.5,109,6.32,8.1,4,2.19,1,False 487 | MT,143,408,No,Yes,22,141.8,116,24.11,167.3,99,14.22,178.1,130,8.01,7.8,3,2.11,1,False 488 | NM,90,415,No,No,0,167.5,96,28.48,139.1,104,11.82,138.4,87,6.23,13.0,1,3.51,1,False 489 | OH,169,408,No,No,0,147.2,115,25.02,161.9,123,13.76,142.1,103,6.39,7.2,6,1.94,3,False 490 | LA,53,415,No,No,0,145.1,116,24.67,233.7,82,19.86,208.7,95,9.39,7.9,5,2.13,2,False 491 | MO,15,415,No,No,0,135.2,101,22.98,152.5,79,12.96,224.8,83,10.12,8.4,5,2.27,2,False 492 | DC,123,408,No,Yes,28,124.7,105,21.2,250.4,78,21.28,216.4,128,9.74,7.8,8,2.11,1,False 493 | TN,95,510,No,No,0,174.0,57,29.58,281.1,118,23.89,197.2,94,8.87,9.7,2,2.62,0,False 494 | WA,82,510,No,No,0,265.2,122,45.08,178.7,102,15.19,174.7,90,7.86,10.7,9,2.89,2,False 495 | RI,180,415,No,No,0,143.3,134,24.36,180.5,113,15.34,184.2,87,8.29,10.1,4,2.73,1,False 496 | RI,125,408,No,No,0,113.0,108,19.21,169.2,107,14.38,156.6,61,7.05,9.2,5,2.48,2,True 497 | OR,138,415,Yes,Yes,28,211.2,117,35.9,312.5,98,26.56,178.0,118,8.01,10.7,2,2.89,3,True 498 | MA,90,408,No,No,0,157.9,72,26.84,234.0,93,19.89,210.0,86,9.45,12.2,5,3.29,2,False 499 | NC,190,408,No,No,0,182.2,101,30.97,212.3,95,18.05,233.0,123,10.49,9.3,4,2.51,2,False 500 | IA,143,510,No,Yes,33,141.4,130,24.04,186.4,114,15.84,210.0,111,9.45,7.7,6,2.08,1,False 501 | IL,183,510,No,No,0,108.3,87,18.41,183.6,116,15.61,176.6,109,7.95,13.5,2,3.65,0,False 502 | KY,58,415,No,No,0,247.2,116,42.02,303.7,103,25.81,105.4,94,4.74,9.3,2,2.51,2,True 503 | TN,123,415,No,No,0,166.9,98,28.37,221.8,77,18.85,243.9,114,10.98,12.8,4,3.46,3,False 504 | GA,155,510,No,No,0,71.2,90,12.1,304.4,119,25.87,183.3,103,8.25,8.6,4,2.32,0,False 505 | MN,53,415,No,No,0,164.1,106,27.9,206.0,56,17.51,194.7,124,8.76,11.4,2,3.08,1,False 506 | MT,101,415,No,No,0,154.4,130,26.25,217.2,101,18.46,185.4,52,8.34,13.9,4,3.75,1,False 507 | ND,122,408,No,No,0,231.2,141,39.3,267.8,136,22.76,240.3,100,10.81,8.8,5,2.38,1,True 508 | VA,163,415,No,No,0,202.9,100,34.49,178.6,46,15.18,203.8,116,9.17,12.8,3,3.46,5,False 509 | ND,120,415,No,No,0,177.2,88,30.12,270.4,99,22.98,231.5,90,10.42,14.0,2,3.78,2,False 510 | TN,196,415,No,No,0,133.1,80,22.63,206.5,120,17.55,221.6,96,9.97,10.3,8,2.78,1,False 511 | NE,147,415,No,Yes,38,243.4,126,41.38,273.8,109,23.27,282.9,91,12.73,14.1,8,3.81,2,False 512 | IN,81,408,No,Yes,46,168.3,124,28.61,270.9,103,23.03,222.5,98,10.01,6.7,2,1.81,4,False 513 | OR,165,415,No,Yes,33,111.6,140,18.97,213.3,111,18.13,267.6,115,12.04,16.0,3,4.32,0,False 514 | TN,115,415,No,No,0,206.2,113,35.05,176.4,102,14.99,297.1,119,13.37,11.0,7,2.97,1,False 515 | TX,90,408,No,Yes,27,156.7,51,26.64,236.5,118,20.1,123.2,111,5.54,12.6,6,3.4,2,False 516 | TN,37,415,No,No,0,221.0,126,37.57,204.5,110,17.38,118.0,98,5.31,6.8,3,1.84,4,False 517 | NC,76,510,No,No,0,165.7,94,28.17,257.4,80,21.88,170.8,114,7.69,10.0,4,2.7,1,False 518 | MT,109,510,No,No,0,154.8,82,26.32,287.7,109,24.45,208.4,80,9.38,5.9,9,1.59,3,False 519 | HI,105,510,No,No,0,125.4,116,21.32,261.5,95,22.23,241.6,104,10.87,11.4,9,3.08,2,False 520 | MI,74,415,No,No,0,124.8,114,21.22,133.0,121,11.31,160.3,85,7.21,10.6,7,2.86,3,False 521 | AL,76,415,No,No,0,179.2,85,30.46,222.9,66,18.95,188.2,113,8.47,12.4,2,3.35,0,False 522 | CT,116,415,No,No,0,245.9,73,41.8,240.1,87,20.41,158.7,89,7.14,8.9,5,2.4,3,False 523 | UT,138,510,No,No,0,205.9,96,35.0,257.1,94,21.85,209.0,63,9.4,12.1,8,3.27,0,False 524 | DC,102,415,No,No,0,186.8,92,31.76,173.7,123,14.76,250.9,131,11.29,9.7,4,2.62,2,False 525 | UT,164,510,No,No,0,192.1,95,32.66,249.8,94,21.23,132.6,100,5.97,7.3,3,1.97,3,False 526 | MN,125,415,No,No,0,240.7,82,40.92,269.4,85,22.9,187.1,74,8.42,10.1,3,2.73,0,True 527 | SC,209,510,No,No,0,255.1,124,43.37,230.6,110,19.6,218.0,69,9.81,8.5,5,2.3,3,True 528 | NH,116,408,No,Yes,24,183.6,138,31.21,203.8,90,17.32,166.9,89,7.51,6.0,3,1.62,2,False 529 | MD,189,415,No,Yes,30,155.2,116,26.38,195.5,50,16.62,170.1,108,7.65,15.4,6,4.16,1,False 530 | OK,84,510,No,No,0,203.4,125,34.58,182.9,88,15.55,213.7,121,9.62,13.8,2,3.73,1,False 531 | NJ,182,415,No,No,0,279.1,124,47.45,180.5,108,15.34,217.5,104,9.79,9.5,11,2.57,2,True 532 | HI,171,510,No,No,0,189.8,122,32.27,173.7,85,14.76,257.1,84,11.57,10.3,1,2.78,0,False 533 | DE,90,415,No,No,0,198.5,124,33.75,266.6,100,22.66,243.3,80,10.95,8.0,7,2.16,2,False 534 | AL,117,510,No,No,0,158.7,84,26.98,181.7,91,15.44,177.3,67,7.98,7.7,10,2.08,2,False 535 | KS,186,510,No,No,0,137.8,97,23.43,187.7,118,15.95,146.4,85,6.59,8.7,6,2.35,1,False 536 | AK,55,408,No,Yes,39,139.3,101,23.68,178.3,117,15.16,246.5,104,11.09,8.1,1,2.19,3,False 537 | WI,74,408,No,No,0,177.4,136,30.16,240.3,104,20.43,237.3,133,10.68,12.0,3,3.24,0,False 538 | IN,130,408,No,No,0,115.6,129,19.65,167.8,104,14.26,141.8,124,6.38,12.6,9,3.4,1,False 539 | DC,80,408,No,No,0,51.5,90,8.76,164.0,98,13.94,169.4,80,7.62,9.5,4,2.57,3,False 540 | MS,120,415,No,No,0,131.7,99,22.39,163.1,109,13.86,201.1,116,9.05,10.7,3,2.89,1,False 541 | NH,84,408,No,Yes,30,106.5,65,18.11,225.7,108,19.18,188.6,61,8.49,5.7,3,1.54,2,False 542 | NJ,134,510,No,Yes,34,247.2,105,42.02,225.5,133,19.17,186.3,76,8.38,6.1,5,1.65,2,True 543 | VT,68,415,No,No,0,158.8,119,27.0,211.8,105,18.0,198.1,101,8.91,10.3,3,2.78,1,False 544 | MD,119,408,No,No,0,239.1,88,40.65,243.5,79,20.7,230.9,92,10.39,10.9,3,2.94,3,True 545 | KS,166,415,Yes,Yes,28,175.8,126,29.89,253.6,76,21.56,128.5,72,5.78,11.4,5,3.08,1,False 546 | NH,90,415,No,Yes,42,193.3,66,32.86,263.3,85,22.38,214.4,97,9.65,11.1,4,3.0,0,False 547 | AZ,82,415,No,Yes,33,137.8,95,23.43,235.5,128,20.02,268.1,70,12.06,11.0,6,2.97,2,False 548 | ID,130,415,No,No,0,263.7,113,44.83,186.5,103,15.85,195.3,99,8.79,18.3,6,4.94,1,True 549 | TX,130,408,No,No,0,252.0,101,42.84,170.2,105,14.47,209.2,64,9.41,5.7,5,1.54,0,False 550 | RI,155,408,Yes,No,0,163.1,94,27.73,291.7,108,24.79,96.4,111,4.34,11.2,3,3.02,0,False 551 | OR,81,415,No,No,0,324.7,48,55.2,236.4,82,20.09,187.6,78,8.44,13.1,5,3.54,0,True 552 | NC,75,408,No,No,0,203.3,70,34.56,228.9,97,19.46,222.2,118,10.0,14.3,3,3.86,1,False 553 | NM,178,415,No,Yes,35,175.4,88,29.82,190.0,65,16.15,138.7,94,6.24,10.5,3,2.84,2,False 554 | SC,112,415,No,No,0,266.0,97,45.22,214.6,94,18.24,306.2,100,13.78,14.2,2,3.83,2,True 555 | AZ,100,510,No,No,0,78.7,98,13.38,225.6,102,19.18,150.4,106,6.77,14.0,8,3.78,0,False 556 | WV,104,415,No,No,0,225.9,123,38.4,162.8,106,13.84,272.1,85,12.24,10.1,4,2.73,1,False 557 | NY,160,408,No,No,0,216.8,77,36.86,207.3,117,17.62,228.6,117,10.29,5.6,2,1.51,1,False 558 | WV,87,415,No,No,0,58.0,125,9.86,67.5,116,5.74,185.9,136,8.37,11.5,3,3.11,0,False 559 | NM,81,415,No,No,0,173.2,80,29.44,236.2,94,20.08,240.2,84,10.81,11.8,6,3.19,2,False 560 | ME,28,415,No,No,0,236.8,102,40.26,167.1,87,14.2,280.2,115,12.61,9.7,3,2.62,3,False 561 | WI,95,408,No,No,0,237.3,83,40.34,154.0,65,13.09,237.0,105,10.67,11.2,6,3.02,1,False 562 | NJ,35,408,No,No,0,158.6,67,26.96,130.4,96,11.08,229.8,80,10.34,6.9,5,1.86,2,False 563 | WY,134,510,No,No,0,296.0,93,50.32,226.4,117,19.24,246.8,98,11.11,12.3,10,3.32,0,True 564 | AR,185,415,No,Yes,19,157.3,123,26.74,257.7,94,21.9,190.4,107,8.57,9.6,6,2.59,0,False 565 | PA,85,408,No,No,0,144.4,88,24.55,264.6,105,22.49,185.4,94,8.34,9.9,3,2.67,1,False 566 | TN,148,415,No,Yes,36,77.6,141,13.19,207.0,60,17.6,255.7,115,11.51,10.9,2,2.94,1,False 567 | CT,134,408,No,Yes,32,80.3,94,13.65,199.9,124,16.99,170.8,117,7.69,16.6,3,4.48,0,False 568 | MA,80,408,No,Yes,36,190.3,115,32.35,256.6,78,21.81,214.9,145,9.67,3.8,4,1.03,1,False 569 | OH,165,510,No,No,0,209.4,67,35.6,273.8,89,23.27,150.2,88,6.76,12.8,1,3.46,0,False 570 | KS,65,415,No,Yes,34,208.8,119,35.5,142.1,106,12.08,214.6,87,9.66,12.5,4,3.38,4,False 571 | OK,104,415,No,No,0,113.6,87,19.31,158.6,98,13.48,187.7,87,8.45,10.5,6,2.84,2,False 572 | UT,44,415,No,No,0,202.6,89,34.44,163.0,96,13.86,268.1,151,12.06,8.3,3,2.24,0,False 573 | OH,162,415,No,No,0,135.2,98,22.98,242.0,107,20.57,246.9,96,11.11,10.2,2,2.75,2,False 574 | WA,96,415,No,No,0,276.9,105,47.07,246.9,94,20.99,254.4,107,11.45,10.3,3,2.78,1,True 575 | IN,72,510,No,No,0,287.4,116,48.86,235.3,126,20.0,292.1,114,13.14,5.0,3,1.35,4,True 576 | NE,31,415,No,No,0,97.5,129,16.58,260.4,78,22.13,88.7,100,3.99,7.0,5,1.89,1,False 577 | AL,72,415,No,No,0,166.5,102,28.31,261.0,103,22.19,262.7,85,11.82,13.3,5,3.59,0,False 578 | PA,136,408,No,No,0,102.1,75,17.36,219.5,97,18.66,73.7,92,3.32,9.8,5,2.65,0,False 579 | OR,82,415,No,Yes,19,146.5,73,24.91,246.4,65,20.94,199.0,114,8.96,4.1,4,1.11,1,False 580 | MN,155,408,Yes,No,0,250.8,146,42.64,152.5,105,12.96,148.1,104,6.66,10.0,5,2.7,2,False 581 | GA,136,415,No,No,0,163.4,83,27.78,249.3,119,21.19,249.7,90,11.24,9.8,4,2.65,7,False 582 | TX,57,415,No,No,0,161.0,113,27.37,208.0,134,17.68,208.1,81,9.36,8.4,4,2.27,3,False 583 | NM,112,415,No,No,0,81.6,94,13.87,268.1,112,22.79,140.8,75,6.34,8.6,18,2.32,1,False 584 | NC,95,408,No,No,0,128.6,115,21.86,216.2,88,18.38,255.3,96,11.49,6.3,2,1.7,6,True 585 | ID,113,415,No,Yes,30,183.8,102,31.25,183.4,123,15.59,235.0,52,10.58,11.6,7,3.13,0,False 586 | WI,103,415,No,No,0,180.2,134,30.63,97.7,85,8.3,181.7,134,8.18,8.4,3,2.27,1,False 587 | SC,149,415,No,Yes,20,147.8,132,25.13,276.8,94,23.53,149.9,110,6.75,10.2,6,2.75,0,False 588 | NV,116,408,No,No,0,110.9,54,18.85,213.4,82,18.14,186.2,116,8.38,7.9,2,2.13,2,False 589 | NE,95,510,No,No,0,58.2,96,9.89,202.1,126,17.18,210.5,97,9.47,10.4,5,2.81,0,False 590 | UT,201,510,No,No,0,212.7,72,36.16,225.2,90,19.14,195.1,99,8.78,7.0,6,1.89,1,False 591 | HI,150,415,No,No,0,214.0,117,36.38,192.4,89,16.35,242.6,99,10.92,7.9,4,2.13,1,False 592 | MI,108,408,Yes,No,0,115.1,114,19.57,211.3,70,17.96,136.1,85,6.12,13.8,3,3.73,2,True 593 | MO,101,415,Yes,No,0,156.4,116,26.59,130.4,114,11.08,207.3,109,9.33,7.3,5,1.97,1,False 594 | AL,182,415,No,Yes,24,128.1,104,21.78,143.4,127,12.19,191.0,98,8.59,11.6,3,3.13,1,False 595 | VT,128,408,No,No,0,227.9,130,38.74,302.6,71,25.72,191.5,82,8.62,5.5,7,1.49,1,True 596 | VA,113,408,No,Yes,34,44.9,63,7.63,134.2,82,11.41,168.4,118,7.58,13.3,3,3.59,1,False 597 | RI,76,415,No,No,0,171.1,78,29.09,257.2,83,21.86,91.6,92,4.12,16.2,3,4.37,1,False 598 | MD,204,510,No,No,0,205.2,145,34.88,154.8,95,13.16,191.4,77,8.61,14.1,5,3.81,3,False 599 | OH,32,415,No,Yes,31,232.8,97,39.58,183.5,111,15.6,206.8,111,9.31,13.0,2,3.51,0,False 600 | CO,103,415,No,Yes,37,153.5,78,26.1,241.9,108,20.56,244.7,110,11.01,10.6,3,2.86,1,False 601 | UT,148,510,No,No,0,203.0,92,34.51,150.9,125,12.83,245.5,131,11.05,14.6,9,3.94,1,False 602 | CO,57,415,No,No,0,85.9,92,14.6,193.9,127,16.48,231.5,93,10.42,10.1,2,2.73,0,False 603 | KY,64,415,Yes,No,0,146.7,83,24.94,148.3,91,12.61,238.6,69,10.74,12.5,3,3.38,3,False 604 | DC,70,415,No,No,0,152.8,145,25.98,183.6,102,15.61,151.8,75,6.83,10.5,2,2.84,1,False 605 | MA,142,408,No,No,0,216.8,134,36.86,187.8,106,15.96,138.1,108,6.21,8.3,2,2.24,0,False 606 | AZ,88,415,No,No,0,172.8,81,29.38,193.4,90,16.44,89.6,107,4.03,12.8,5,3.46,2,False 607 | IL,131,510,No,No,0,263.4,123,44.78,151.9,74,12.91,218.5,101,9.83,10.7,2,2.89,2,False 608 | CO,130,408,No,No,0,271.8,129,46.21,237.2,128,20.16,210.1,91,9.45,8.7,2,2.35,4,True 609 | OR,166,510,No,No,0,199.6,93,33.93,214.3,99,18.22,196.8,110,8.86,7.2,5,1.94,3,False 610 | MS,130,510,No,No,0,203.9,63,34.66,191.8,93,16.3,132.5,125,5.96,12.1,4,3.27,3,False 611 | SC,111,510,No,No,0,78.3,119,13.31,198.2,94,16.85,248.5,94,11.18,12.1,4,3.27,1,False 612 | IN,100,415,No,Yes,32,125.2,123,21.28,230.9,101,19.63,192.0,106,8.64,12.6,9,3.4,3,False 613 | ME,57,415,No,No,0,221.1,101,37.59,236.7,65,20.12,252.3,137,11.35,9.5,1,2.57,0,False 614 | DE,139,408,No,No,0,139.0,110,23.63,132.9,93,11.3,272.0,120,12.24,12.1,1,3.27,0,False 615 | OR,113,415,No,No,0,159.8,143,27.17,210.1,93,17.86,175.1,86,7.88,13.1,7,3.54,2,False 616 | NJ,108,415,No,No,0,239.3,102,40.68,223.4,127,18.99,251.4,104,11.31,10.6,6,2.86,0,False 617 | MT,100,415,No,No,0,113.3,96,19.26,197.9,89,16.82,284.5,93,12.8,11.7,2,3.16,4,True 618 | WV,120,510,No,No,0,192.6,123,32.74,206.4,105,17.54,283.2,93,12.74,10.8,3,2.92,1,False 619 | NJ,107,408,No,Yes,36,96.3,83,16.37,179.6,91,15.27,166.3,121,7.48,10.3,2,2.78,1,False 620 | IN,104,408,No,No,0,280.4,127,47.67,179.4,79,15.25,150.6,77,6.78,15.2,6,4.1,5,False 621 | NM,62,415,No,No,0,245.3,91,41.7,122.9,130,10.45,228.4,102,10.28,8.5,4,2.3,4,False 622 | MT,34,415,No,Yes,14,151.5,100,25.76,248.7,126,21.14,199.8,120,8.99,10.7,5,2.89,2,False 623 | NM,183,415,No,No,0,190.0,100,32.3,246.6,78,20.96,304.2,107,13.69,9.5,4,2.57,1,False 624 | IL,123,408,No,No,0,114.8,94,19.52,150.0,104,12.75,268.6,119,12.09,9.6,4,2.59,2,False 625 | IN,64,408,No,No,0,113.8,97,19.35,192.3,97,16.35,214.9,89,9.67,10.4,1,2.81,3,False 626 | ND,27,415,No,No,0,227.4,67,38.66,248.0,115,21.08,61.4,109,2.76,7.8,6,2.11,1,False 627 | NH,123,408,No,No,0,224.0,99,38.08,210.7,80,17.91,231.9,75,10.44,2.1,5,0.57,0,False 628 | NM,84,408,No,Yes,41,153.9,102,26.16,140.7,117,11.96,217.7,101,9.8,12.8,5,3.46,1,False 629 | LA,91,415,No,No,0,190.5,128,32.39,205.5,103,17.47,130.7,63,5.88,13.8,5,3.73,0,False 630 | KY,95,510,No,No,0,157.3,116,26.74,197.5,77,16.79,128.2,111,5.77,8.4,4,2.27,2,False 631 | WV,73,415,No,No,0,240.3,130,40.85,162.5,83,13.81,231.9,136,10.44,11.9,3,3.21,0,False 632 | WV,58,408,No,Yes,39,211.9,40,36.02,274.4,76,23.32,210.5,139,9.47,5.4,4,1.46,1,False 633 | IA,88,415,No,No,0,113.7,67,19.33,165.1,127,14.03,141.5,142,6.37,10.8,3,2.92,1,False 634 | NE,67,415,No,Yes,41,174.7,86,29.7,160.6,93,13.65,155.3,108,6.99,13.4,1,3.62,0,False 635 | WA,152,510,No,No,0,161.4,84,27.44,163.6,88,13.91,153.2,121,6.89,11.8,5,3.19,1,False 636 | PA,142,510,No,Yes,40,230.7,101,39.22,256.8,88,21.83,263.9,92,11.88,6.4,3,1.73,1,False 637 | ID,105,408,No,No,0,232.6,96,39.54,253.4,117,21.54,154.0,101,6.93,10.5,9,2.84,1,False 638 | DC,93,408,No,Yes,22,306.2,123,52.05,189.7,83,16.12,240.3,107,10.81,11.7,2,3.16,0,False 639 | KY,79,415,No,No,0,236.8,135,40.26,186.4,87,15.84,126.9,112,5.71,10.4,5,2.81,2,False 640 | SC,88,408,No,No,0,153.5,94,26.1,251.7,118,21.39,182.2,99,8.2,8.5,6,2.3,1,False 641 | NC,110,408,No,No,0,159.5,145,27.12,202.3,101,17.2,256.0,96,11.52,16.7,2,4.51,2,False 642 | NY,209,415,No,No,0,153.7,105,26.13,188.6,87,16.03,200.8,95,9.04,10.7,2,2.89,0,False 643 | OR,27,510,No,No,0,232.1,81,39.46,210.8,101,17.92,165.4,87,7.44,15.0,6,4.05,5,False 644 | IL,117,415,No,No,0,201.9,86,34.32,212.3,96,18.05,176.9,98,7.96,7.8,10,2.11,1,False 645 | MA,87,408,No,No,0,186.9,79,31.77,182.6,105,15.52,143.1,90,6.44,4.2,14,1.13,1,False 646 | CT,129,510,No,Yes,27,196.6,89,33.42,180.6,95,15.35,245.0,83,11.03,6.6,5,1.78,1,False 647 | WI,142,510,No,No,0,232.1,102,39.46,168.2,110,14.3,197.3,120,8.88,9.9,3,2.67,1,False 648 | OK,112,415,No,No,0,166.0,79,28.22,74.6,100,6.34,247.9,74,11.16,6.3,7,1.7,0,False 649 | AZ,97,408,No,Yes,25,141.0,101,23.97,212.0,85,18.02,175.2,138,7.88,4.9,2,1.32,3,False 650 | KS,101,415,No,No,0,231.3,87,39.32,224.7,88,19.1,214.6,69,9.66,7.2,7,1.94,1,False 651 | KS,127,415,No,Yes,24,154.8,69,26.32,177.2,105,15.06,207.6,102,9.34,9.0,4,2.43,1,False 652 | WY,148,408,No,No,0,243.0,115,41.31,191.8,91,16.3,117.8,93,5.3,13.4,5,3.62,2,False 653 | OK,138,510,No,Yes,33,155.2,139,26.38,268.3,79,22.81,186.4,71,8.39,9.7,4,2.62,3,False 654 | WA,84,415,No,No,0,289.1,100,49.15,233.8,97,19.87,223.5,148,10.06,12.7,2,3.43,2,True 655 | MD,133,510,No,No,0,295.0,141,50.15,223.6,101,19.01,229.4,109,10.32,12.9,4,3.48,2,True 656 | NY,120,510,No,Yes,27,128.5,115,21.85,163.7,91,13.91,242.9,121,10.93,0.0,0,0.0,1,False 657 | WI,87,415,No,No,0,238.0,97,40.46,164.5,97,13.98,282.5,132,12.71,10.6,6,2.86,2,False 658 | AK,99,510,No,No,0,238.4,96,40.53,246.5,130,20.95,198.4,117,8.93,12.4,4,3.35,3,False 659 | AZ,48,415,No,Yes,27,141.1,109,23.99,224.7,94,19.1,174.3,122,7.84,13.2,2,3.56,1,False 660 | KS,57,415,No,No,0,158.1,117,26.88,115.2,149,9.79,182.4,92,8.21,11.8,7,3.19,0,False 661 | CA,127,510,No,No,0,107.9,128,18.34,187.0,77,15.9,218.5,95,9.83,0.0,0,0.0,0,False 662 | IN,114,408,No,No,0,203.8,85,34.65,87.8,110,7.46,166.2,122,7.48,11.7,4,3.16,1,False 663 | CA,84,415,No,No,0,280.0,113,47.6,202.2,90,17.19,156.8,103,7.06,10.4,4,2.81,0,True 664 | WI,114,415,No,Yes,26,137.1,88,23.31,155.7,125,13.23,247.6,94,11.14,11.5,7,3.11,2,False 665 | AL,106,408,No,Yes,29,83.6,131,14.21,203.9,131,17.33,229.5,73,10.33,8.1,3,2.19,1,False 666 | VT,60,415,No,No,0,193.9,118,32.96,85.0,110,7.23,210.1,134,9.45,13.2,8,3.56,3,False 667 | WV,159,415,No,No,0,169.8,114,28.87,197.7,105,16.8,193.7,82,8.72,11.6,4,3.13,1,False 668 | CT,184,510,Yes,No,0,213.8,105,36.35,159.6,84,13.57,139.2,137,6.26,5.0,10,1.35,2,False 669 | --------------------------------------------------------------------------------