├── Movie_Genre_Classification.ipynb
├── README.md
├── SPAM_SMS_DETECTION.ipynb
└── credit_card_fraud_Detection.ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # CodeWay-ML-Internship 
 2 | 
 3 | Welcome to the repository for my machine learning internship  at Codeway!
 4 | 
 5 | Task-1: MOVIE GENRE CLASSIFICATION
 6 | -Creating a machine learning model that can predict the genre of a
 7 | movie based on its plot summary or other textual information. We
 8 | can use techniques like TF-IDF or word embeddings with classifiers
 9 | such as Naive Bayes, Logistic Regression, or Support Vector
10 | Machines.
11 | 
12 | DATA SET LINK: www.kaggle.com/datasets/hijest/genre-classification-dataset-imdb
13 | 
14 | Task-2: CREDIT CARD FRAUD DETECTION
15 | -Building a model to detect fraudulent credit card transactions. Use a
16 | dataset containing information about credit card transactions, and
17 | experiment with algorithms like Logistic Regression, Decision Trees,
18 | or Random Forests to classify transactions as fraudulent or legitimate.
19 | 
20 | DATA SET LINK: www.kaggle.com/datasets/kartik2112/fraud-detection
21 | 
22 | Task-3: CUSTOMER CHURN PREDICTION
23 | -Developing a model to predict customer churn for a subscription- based
24 | service or business. Use historical customer data, including features like
25 | usage behavior and customer demographics, and try algorithms like
26 | Logistic Regression, Random Forests, or Gradient Boosting to predict
27 | churn.
28 | 
29 | DATA SET LINK: www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction
30 | 
31 | Task-4: SPAM SMS DETECTION
32 | -Building an AI model that can classify SMS messages as spam or legitimate.
33 | Use techniques like TF-IDF or word embeddings with classifiers like 
34 | Naive Bayes, Logistic Regression, or Support Vector Machines to identify spam messages.
35 | 
36 | DATA SET LINK: www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
37 | 
38 | Task-5: HANDWRITTEN TEXT GENERATION
39 | -Implementing a character-level recurrent neural network (RNN) to generate
40 | handwritten-like text. Train the model on a dataset of handwritten text
41 | examples, and let it generate new text based on the learned patterns.
42 | 
43 | DATA SET LINK: www.paperswithcode.com/dataset/deepwriting
44 | 
45 | DATA SET LINK: www.paperswithcode.com/dataset/iam
46 | 


--------------------------------------------------------------------------------
/credit_card_fraud_Detection.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "provenance": [],
   7 |       "authorship_tag": "ABX9TyMbEY1vks5Dwg9BIzeZIV30",
   8 |       "include_colab_link": true
   9 |     },
  10 |     "kernelspec": {
  11 |       "name": "python3",
  12 |       "display_name": "Python 3"
  13 |     },
  14 |     "language_info": {
  15 |       "name": "python"
  16 |     }
  17 |   },
  18 |   "cells": [
  19 |     {
  20 |       "cell_type": "markdown",
  21 |       "metadata": {
  22 |         "id": "view-in-github",
  23 |         "colab_type": "text"
  24 |       },
  25 |       "source": [
  26 |         "<a href=\"https://colab.research.google.com/github/naidu199/CodeWay-ML-/blob/main/credit_card_fraud_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  27 |       ]
  28 |     },
  29 |     {
  30 |       "cell_type": "code",
  31 |       "execution_count": 36,
  32 |       "metadata": {
  33 |         "id": "nkdTwCvQYLU0"
  34 |       },
  35 |       "outputs": [],
  36 |       "source": [
  37 |         "import numpy as np\n",
  38 |         "import pandas as pd\n",
  39 |         "import matplotlib.pyplot as plt\n",
  40 |         "import seaborn as sns\n",
  41 |         "import plotly.express as px"
  42 |       ]
  43 |     },
  44 |     {
  45 |       "cell_type": "code",
  46 |       "source": [
  47 |         "from google.colab import drive\n",
  48 |         "drive.mount('/content/drive')"
  49 |       ],
  50 |       "metadata": {
  51 |         "id": "4BtsOfP4L2Gd",
  52 |         "colab": {
  53 |           "base_uri": "https://localhost:8080/"
  54 |         },
  55 |         "outputId": "1775b664-0d0c-4acc-c572-c6c8246808e8"
  56 |       },
  57 |       "execution_count": 37,
  58 |       "outputs": [
  59 |         {
  60 |           "output_type": "stream",
  61 |           "name": "stdout",
  62 |           "text": [
  63 |             "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
  64 |           ]
  65 |         }
  66 |       ]
  67 |     },
  68 |     {
  69 |       "cell_type": "code",
  70 |       "source": [
  71 |         "raw_data=pd.read_csv(\"/content/drive/MyDrive/FML_DATA_SETS/credit card fraud Detection /PS_20174392719_1491204439457_log.csv\")\n",
  72 |         "raw_data.head()"
  73 |       ],
  74 |       "metadata": {
  75 |         "id": "tFf6NpkCL3Uv",
  76 |         "colab": {
  77 |           "base_uri": "https://localhost:8080/",
  78 |           "height": 206
  79 |         },
  80 |         "outputId": "d64fcb4a-926b-46f3-ce47-a7fc3e9091fc"
  81 |       },
  82 |       "execution_count": 38,
  83 |       "outputs": [
  84 |         {
  85 |           "output_type": "execute_result",
  86 |           "data": {
  87 |             "text/plain": [
  88 |               "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
  89 |               "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
  90 |               "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
  91 |               "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
  92 |               "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
  93 |               "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
  94 |               "\n",
  95 |               "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
  96 |               "0  M1979787155             0.0             0.0        0               0  \n",
  97 |               "1  M2044282225             0.0             0.0        0               0  \n",
  98 |               "2   C553264065             0.0             0.0        1               0  \n",
  99 |               "3    C38997010         21182.0             0.0        1               0  \n",
 100 |               "4  M1230701703             0.0             0.0        0               0  "
 101 |             ],
 102 |             "text/html": [
 103 |               "\n",
 104 |               "  <div id=\"df-b2ed7ed1-7259-4ef7-9f79-b0b49c3b1628\" class=\"colab-df-container\">\n",
 105 |               "    <div>\n",
 106 |               "<style scoped>\n",
 107 |               "    .dataframe tbody tr th:only-of-type {\n",
 108 |               "        vertical-align: middle;\n",
 109 |               "    }\n",
 110 |               "\n",
 111 |               "    .dataframe tbody tr th {\n",
 112 |               "        vertical-align: top;\n",
 113 |               "    }\n",
 114 |               "\n",
 115 |               "    .dataframe thead th {\n",
 116 |               "        text-align: right;\n",
 117 |               "    }\n",
 118 |               "</style>\n",
 119 |               "<table border=\"1\" class=\"dataframe\">\n",
 120 |               "  <thead>\n",
 121 |               "    <tr style=\"text-align: right;\">\n",
 122 |               "      <th></th>\n",
 123 |               "      <th>step</th>\n",
 124 |               "      <th>type</th>\n",
 125 |               "      <th>amount</th>\n",
 126 |               "      <th>nameOrig</th>\n",
 127 |               "      <th>oldbalanceOrg</th>\n",
 128 |               "      <th>newbalanceOrig</th>\n",
 129 |               "      <th>nameDest</th>\n",
 130 |               "      <th>oldbalanceDest</th>\n",
 131 |               "      <th>newbalanceDest</th>\n",
 132 |               "      <th>isFraud</th>\n",
 133 |               "      <th>isFlaggedFraud</th>\n",
 134 |               "    </tr>\n",
 135 |               "  </thead>\n",
 136 |               "  <tbody>\n",
 137 |               "    <tr>\n",
 138 |               "      <th>0</th>\n",
 139 |               "      <td>1</td>\n",
 140 |               "      <td>PAYMENT</td>\n",
 141 |               "      <td>9839.64</td>\n",
 142 |               "      <td>C1231006815</td>\n",
 143 |               "      <td>170136.0</td>\n",
 144 |               "      <td>160296.36</td>\n",
 145 |               "      <td>M1979787155</td>\n",
 146 |               "      <td>0.0</td>\n",
 147 |               "      <td>0.0</td>\n",
 148 |               "      <td>0</td>\n",
 149 |               "      <td>0</td>\n",
 150 |               "    </tr>\n",
 151 |               "    <tr>\n",
 152 |               "      <th>1</th>\n",
 153 |               "      <td>1</td>\n",
 154 |               "      <td>PAYMENT</td>\n",
 155 |               "      <td>1864.28</td>\n",
 156 |               "      <td>C1666544295</td>\n",
 157 |               "      <td>21249.0</td>\n",
 158 |               "      <td>19384.72</td>\n",
 159 |               "      <td>M2044282225</td>\n",
 160 |               "      <td>0.0</td>\n",
 161 |               "      <td>0.0</td>\n",
 162 |               "      <td>0</td>\n",
 163 |               "      <td>0</td>\n",
 164 |               "    </tr>\n",
 165 |               "    <tr>\n",
 166 |               "      <th>2</th>\n",
 167 |               "      <td>1</td>\n",
 168 |               "      <td>TRANSFER</td>\n",
 169 |               "      <td>181.00</td>\n",
 170 |               "      <td>C1305486145</td>\n",
 171 |               "      <td>181.0</td>\n",
 172 |               "      <td>0.00</td>\n",
 173 |               "      <td>C553264065</td>\n",
 174 |               "      <td>0.0</td>\n",
 175 |               "      <td>0.0</td>\n",
 176 |               "      <td>1</td>\n",
 177 |               "      <td>0</td>\n",
 178 |               "    </tr>\n",
 179 |               "    <tr>\n",
 180 |               "      <th>3</th>\n",
 181 |               "      <td>1</td>\n",
 182 |               "      <td>CASH_OUT</td>\n",
 183 |               "      <td>181.00</td>\n",
 184 |               "      <td>C840083671</td>\n",
 185 |               "      <td>181.0</td>\n",
 186 |               "      <td>0.00</td>\n",
 187 |               "      <td>C38997010</td>\n",
 188 |               "      <td>21182.0</td>\n",
 189 |               "      <td>0.0</td>\n",
 190 |               "      <td>1</td>\n",
 191 |               "      <td>0</td>\n",
 192 |               "    </tr>\n",
 193 |               "    <tr>\n",
 194 |               "      <th>4</th>\n",
 195 |               "      <td>1</td>\n",
 196 |               "      <td>PAYMENT</td>\n",
 197 |               "      <td>11668.14</td>\n",
 198 |               "      <td>C2048537720</td>\n",
 199 |               "      <td>41554.0</td>\n",
 200 |               "      <td>29885.86</td>\n",
 201 |               "      <td>M1230701703</td>\n",
 202 |               "      <td>0.0</td>\n",
 203 |               "      <td>0.0</td>\n",
 204 |               "      <td>0</td>\n",
 205 |               "      <td>0</td>\n",
 206 |               "    </tr>\n",
 207 |               "  </tbody>\n",
 208 |               "</table>\n",
 209 |               "</div>\n",
 210 |               "    <div class=\"colab-df-buttons\">\n",
 211 |               "\n",
 212 |               "  <div class=\"colab-df-container\">\n",
 213 |               "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2ed7ed1-7259-4ef7-9f79-b0b49c3b1628')\"\n",
 214 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
 215 |               "            style=\"display:none;\">\n",
 216 |               "\n",
 217 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
 218 |               "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
 219 |               "  </svg>\n",
 220 |               "    </button>\n",
 221 |               "\n",
 222 |               "  <style>\n",
 223 |               "    .colab-df-container {\n",
 224 |               "      display:flex;\n",
 225 |               "      gap: 12px;\n",
 226 |               "    }\n",
 227 |               "\n",
 228 |               "    .colab-df-convert {\n",
 229 |               "      background-color: #E8F0FE;\n",
 230 |               "      border: none;\n",
 231 |               "      border-radius: 50%;\n",
 232 |               "      cursor: pointer;\n",
 233 |               "      display: none;\n",
 234 |               "      fill: #1967D2;\n",
 235 |               "      height: 32px;\n",
 236 |               "      padding: 0 0 0 0;\n",
 237 |               "      width: 32px;\n",
 238 |               "    }\n",
 239 |               "\n",
 240 |               "    .colab-df-convert:hover {\n",
 241 |               "      background-color: #E2EBFA;\n",
 242 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 243 |               "      fill: #174EA6;\n",
 244 |               "    }\n",
 245 |               "\n",
 246 |               "    .colab-df-buttons div {\n",
 247 |               "      margin-bottom: 4px;\n",
 248 |               "    }\n",
 249 |               "\n",
 250 |               "    [theme=dark] .colab-df-convert {\n",
 251 |               "      background-color: #3B4455;\n",
 252 |               "      fill: #D2E3FC;\n",
 253 |               "    }\n",
 254 |               "\n",
 255 |               "    [theme=dark] .colab-df-convert:hover {\n",
 256 |               "      background-color: #434B5C;\n",
 257 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 258 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 259 |               "      fill: #FFFFFF;\n",
 260 |               "    }\n",
 261 |               "  </style>\n",
 262 |               "\n",
 263 |               "    <script>\n",
 264 |               "      const buttonEl =\n",
 265 |               "        document.querySelector('#df-b2ed7ed1-7259-4ef7-9f79-b0b49c3b1628 button.colab-df-convert');\n",
 266 |               "      buttonEl.style.display =\n",
 267 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 268 |               "\n",
 269 |               "      async function convertToInteractive(key) {\n",
 270 |               "        const element = document.querySelector('#df-b2ed7ed1-7259-4ef7-9f79-b0b49c3b1628');\n",
 271 |               "        const dataTable =\n",
 272 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 273 |               "                                                    [key], {});\n",
 274 |               "        if (!dataTable) return;\n",
 275 |               "\n",
 276 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 277 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 278 |               "          + ' to learn more about interactive tables.';\n",
 279 |               "        element.innerHTML = '';\n",
 280 |               "        dataTable['output_type'] = 'display_data';\n",
 281 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
 282 |               "        const docLink = document.createElement('div');\n",
 283 |               "        docLink.innerHTML = docLinkHtml;\n",
 284 |               "        element.appendChild(docLink);\n",
 285 |               "      }\n",
 286 |               "    </script>\n",
 287 |               "  </div>\n",
 288 |               "\n",
 289 |               "\n",
 290 |               "<div id=\"df-a7da6032-a31c-499e-980c-55ac9fba040e\">\n",
 291 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7da6032-a31c-499e-980c-55ac9fba040e')\"\n",
 292 |               "            title=\"Suggest charts\"\n",
 293 |               "            style=\"display:none;\">\n",
 294 |               "\n",
 295 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 296 |               "     width=\"24px\">\n",
 297 |               "    <g>\n",
 298 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
 299 |               "    </g>\n",
 300 |               "</svg>\n",
 301 |               "  </button>\n",
 302 |               "\n",
 303 |               "<style>\n",
 304 |               "  .colab-df-quickchart {\n",
 305 |               "      --bg-color: #E8F0FE;\n",
 306 |               "      --fill-color: #1967D2;\n",
 307 |               "      --hover-bg-color: #E2EBFA;\n",
 308 |               "      --hover-fill-color: #174EA6;\n",
 309 |               "      --disabled-fill-color: #AAA;\n",
 310 |               "      --disabled-bg-color: #DDD;\n",
 311 |               "  }\n",
 312 |               "\n",
 313 |               "  [theme=dark] .colab-df-quickchart {\n",
 314 |               "      --bg-color: #3B4455;\n",
 315 |               "      --fill-color: #D2E3FC;\n",
 316 |               "      --hover-bg-color: #434B5C;\n",
 317 |               "      --hover-fill-color: #FFFFFF;\n",
 318 |               "      --disabled-bg-color: #3B4455;\n",
 319 |               "      --disabled-fill-color: #666;\n",
 320 |               "  }\n",
 321 |               "\n",
 322 |               "  .colab-df-quickchart {\n",
 323 |               "    background-color: var(--bg-color);\n",
 324 |               "    border: none;\n",
 325 |               "    border-radius: 50%;\n",
 326 |               "    cursor: pointer;\n",
 327 |               "    display: none;\n",
 328 |               "    fill: var(--fill-color);\n",
 329 |               "    height: 32px;\n",
 330 |               "    padding: 0;\n",
 331 |               "    width: 32px;\n",
 332 |               "  }\n",
 333 |               "\n",
 334 |               "  .colab-df-quickchart:hover {\n",
 335 |               "    background-color: var(--hover-bg-color);\n",
 336 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 337 |               "    fill: var(--button-hover-fill-color);\n",
 338 |               "  }\n",
 339 |               "\n",
 340 |               "  .colab-df-quickchart-complete:disabled,\n",
 341 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
 342 |               "    background-color: var(--disabled-bg-color);\n",
 343 |               "    fill: var(--disabled-fill-color);\n",
 344 |               "    box-shadow: none;\n",
 345 |               "  }\n",
 346 |               "\n",
 347 |               "  .colab-df-spinner {\n",
 348 |               "    border: 2px solid var(--fill-color);\n",
 349 |               "    border-color: transparent;\n",
 350 |               "    border-bottom-color: var(--fill-color);\n",
 351 |               "    animation:\n",
 352 |               "      spin 1s steps(1) infinite;\n",
 353 |               "  }\n",
 354 |               "\n",
 355 |               "  @keyframes spin {\n",
 356 |               "    0% {\n",
 357 |               "      border-color: transparent;\n",
 358 |               "      border-bottom-color: var(--fill-color);\n",
 359 |               "      border-left-color: var(--fill-color);\n",
 360 |               "    }\n",
 361 |               "    20% {\n",
 362 |               "      border-color: transparent;\n",
 363 |               "      border-left-color: var(--fill-color);\n",
 364 |               "      border-top-color: var(--fill-color);\n",
 365 |               "    }\n",
 366 |               "    30% {\n",
 367 |               "      border-color: transparent;\n",
 368 |               "      border-left-color: var(--fill-color);\n",
 369 |               "      border-top-color: var(--fill-color);\n",
 370 |               "      border-right-color: var(--fill-color);\n",
 371 |               "    }\n",
 372 |               "    40% {\n",
 373 |               "      border-color: transparent;\n",
 374 |               "      border-right-color: var(--fill-color);\n",
 375 |               "      border-top-color: var(--fill-color);\n",
 376 |               "    }\n",
 377 |               "    60% {\n",
 378 |               "      border-color: transparent;\n",
 379 |               "      border-right-color: var(--fill-color);\n",
 380 |               "    }\n",
 381 |               "    80% {\n",
 382 |               "      border-color: transparent;\n",
 383 |               "      border-right-color: var(--fill-color);\n",
 384 |               "      border-bottom-color: var(--fill-color);\n",
 385 |               "    }\n",
 386 |               "    90% {\n",
 387 |               "      border-color: transparent;\n",
 388 |               "      border-bottom-color: var(--fill-color);\n",
 389 |               "    }\n",
 390 |               "  }\n",
 391 |               "</style>\n",
 392 |               "\n",
 393 |               "  <script>\n",
 394 |               "    async function quickchart(key) {\n",
 395 |               "      const quickchartButtonEl =\n",
 396 |               "        document.querySelector('#' + key + ' button');\n",
 397 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
 398 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
 399 |               "      try {\n",
 400 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
 401 |               "            'suggestCharts', [key], {});\n",
 402 |               "      } catch (error) {\n",
 403 |               "        console.error('Error during call to suggestCharts:', error);\n",
 404 |               "      }\n",
 405 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
 406 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
 407 |               "    }\n",
 408 |               "    (() => {\n",
 409 |               "      let quickchartButtonEl =\n",
 410 |               "        document.querySelector('#df-a7da6032-a31c-499e-980c-55ac9fba040e button');\n",
 411 |               "      quickchartButtonEl.style.display =\n",
 412 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 413 |               "    })();\n",
 414 |               "  </script>\n",
 415 |               "</div>\n",
 416 |               "    </div>\n",
 417 |               "  </div>\n"
 418 |             ]
 419 |           },
 420 |           "metadata": {},
 421 |           "execution_count": 38
 422 |         }
 423 |       ]
 424 |     },
 425 |     {
 426 |       "cell_type": "code",
 427 |       "source": [
 428 |         "data=raw_data.iloc[:2620]"
 429 |       ],
 430 |       "metadata": {
 431 |         "id": "-8jzuXzEMf3n"
 432 |       },
 433 |       "execution_count": 39,
 434 |       "outputs": []
 435 |     },
 436 |     {
 437 |       "cell_type": "code",
 438 |       "source": [
 439 |         "data.shape"
 440 |       ],
 441 |       "metadata": {
 442 |         "colab": {
 443 |           "base_uri": "https://localhost:8080/"
 444 |         },
 445 |         "id": "8S3vS7h8MuFR",
 446 |         "outputId": "f8231664-bc1b-4a4c-85df-2fbbc3d8542d"
 447 |       },
 448 |       "execution_count": 40,
 449 |       "outputs": [
 450 |         {
 451 |           "output_type": "execute_result",
 452 |           "data": {
 453 |             "text/plain": [
 454 |               "(2620, 11)"
 455 |             ]
 456 |           },
 457 |           "metadata": {},
 458 |           "execution_count": 40
 459 |         }
 460 |       ]
 461 |     },
 462 |     {
 463 |       "cell_type": "code",
 464 |       "source": [
 465 |         "data.describe()"
 466 |       ],
 467 |       "metadata": {
 468 |         "colab": {
 469 |           "base_uri": "https://localhost:8080/",
 470 |           "height": 300
 471 |         },
 472 |         "id": "ByZFhuUbM00B",
 473 |         "outputId": "13ba0028-452f-4581-ef59-698e185af00b"
 474 |       },
 475 |       "execution_count": 41,
 476 |       "outputs": [
 477 |         {
 478 |           "output_type": "execute_result",
 479 |           "data": {
 480 |             "text/plain": [
 481 |               "         step        amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
 482 |               "count  2620.0  2.620000e+03   2.620000e+03    2.620000e+03    2.620000e+03   \n",
 483 |               "mean      1.0  1.055404e+05   8.207836e+05    8.398497e+05    7.024779e+05   \n",
 484 |               "std       0.0  2.468862e+05   1.936319e+06    1.980422e+06    2.143229e+06   \n",
 485 |               "min       1.0  6.420000e+00   0.000000e+00    0.000000e+00    0.000000e+00   \n",
 486 |               "25%       1.0  3.915938e+03   9.150250e+01    0.000000e+00    0.000000e+00   \n",
 487 |               "50%       1.0  9.910020e+03   2.211219e+04    1.386267e+04    0.000000e+00   \n",
 488 |               "75%       1.0  1.146133e+05   2.011165e+05    2.015433e+05    3.560640e+05   \n",
 489 |               "max       1.0  3.776389e+06   1.010284e+07    1.024625e+07    1.951612e+07   \n",
 490 |               "\n",
 491 |               "       newbalanceDest      isFraud  isFlaggedFraud  \n",
 492 |               "count    2.620000e+03  2620.000000          2620.0  \n",
 493 |               "mean     9.559343e+05     0.006107             0.0  \n",
 494 |               "std      2.860721e+06     0.077922             0.0  \n",
 495 |               "min      0.000000e+00     0.000000             0.0  \n",
 496 |               "25%      0.000000e+00     0.000000             0.0  \n",
 497 |               "50%      0.000000e+00     0.000000             0.0  \n",
 498 |               "75%      2.650924e+05     0.000000             0.0  \n",
 499 |               "max      1.916920e+07     1.000000             0.0  "
 500 |             ],
 501 |             "text/html": [
 502 |               "\n",
 503 |               "  <div id=\"df-0db04721-3019-42f9-8a61-b3163e4517a5\" class=\"colab-df-container\">\n",
 504 |               "    <div>\n",
 505 |               "<style scoped>\n",
 506 |               "    .dataframe tbody tr th:only-of-type {\n",
 507 |               "        vertical-align: middle;\n",
 508 |               "    }\n",
 509 |               "\n",
 510 |               "    .dataframe tbody tr th {\n",
 511 |               "        vertical-align: top;\n",
 512 |               "    }\n",
 513 |               "\n",
 514 |               "    .dataframe thead th {\n",
 515 |               "        text-align: right;\n",
 516 |               "    }\n",
 517 |               "</style>\n",
 518 |               "<table border=\"1\" class=\"dataframe\">\n",
 519 |               "  <thead>\n",
 520 |               "    <tr style=\"text-align: right;\">\n",
 521 |               "      <th></th>\n",
 522 |               "      <th>step</th>\n",
 523 |               "      <th>amount</th>\n",
 524 |               "      <th>oldbalanceOrg</th>\n",
 525 |               "      <th>newbalanceOrig</th>\n",
 526 |               "      <th>oldbalanceDest</th>\n",
 527 |               "      <th>newbalanceDest</th>\n",
 528 |               "      <th>isFraud</th>\n",
 529 |               "      <th>isFlaggedFraud</th>\n",
 530 |               "    </tr>\n",
 531 |               "  </thead>\n",
 532 |               "  <tbody>\n",
 533 |               "    <tr>\n",
 534 |               "      <th>count</th>\n",
 535 |               "      <td>2620.0</td>\n",
 536 |               "      <td>2.620000e+03</td>\n",
 537 |               "      <td>2.620000e+03</td>\n",
 538 |               "      <td>2.620000e+03</td>\n",
 539 |               "      <td>2.620000e+03</td>\n",
 540 |               "      <td>2.620000e+03</td>\n",
 541 |               "      <td>2620.000000</td>\n",
 542 |               "      <td>2620.0</td>\n",
 543 |               "    </tr>\n",
 544 |               "    <tr>\n",
 545 |               "      <th>mean</th>\n",
 546 |               "      <td>1.0</td>\n",
 547 |               "      <td>1.055404e+05</td>\n",
 548 |               "      <td>8.207836e+05</td>\n",
 549 |               "      <td>8.398497e+05</td>\n",
 550 |               "      <td>7.024779e+05</td>\n",
 551 |               "      <td>9.559343e+05</td>\n",
 552 |               "      <td>0.006107</td>\n",
 553 |               "      <td>0.0</td>\n",
 554 |               "    </tr>\n",
 555 |               "    <tr>\n",
 556 |               "      <th>std</th>\n",
 557 |               "      <td>0.0</td>\n",
 558 |               "      <td>2.468862e+05</td>\n",
 559 |               "      <td>1.936319e+06</td>\n",
 560 |               "      <td>1.980422e+06</td>\n",
 561 |               "      <td>2.143229e+06</td>\n",
 562 |               "      <td>2.860721e+06</td>\n",
 563 |               "      <td>0.077922</td>\n",
 564 |               "      <td>0.0</td>\n",
 565 |               "    </tr>\n",
 566 |               "    <tr>\n",
 567 |               "      <th>min</th>\n",
 568 |               "      <td>1.0</td>\n",
 569 |               "      <td>6.420000e+00</td>\n",
 570 |               "      <td>0.000000e+00</td>\n",
 571 |               "      <td>0.000000e+00</td>\n",
 572 |               "      <td>0.000000e+00</td>\n",
 573 |               "      <td>0.000000e+00</td>\n",
 574 |               "      <td>0.000000</td>\n",
 575 |               "      <td>0.0</td>\n",
 576 |               "    </tr>\n",
 577 |               "    <tr>\n",
 578 |               "      <th>25%</th>\n",
 579 |               "      <td>1.0</td>\n",
 580 |               "      <td>3.915938e+03</td>\n",
 581 |               "      <td>9.150250e+01</td>\n",
 582 |               "      <td>0.000000e+00</td>\n",
 583 |               "      <td>0.000000e+00</td>\n",
 584 |               "      <td>0.000000e+00</td>\n",
 585 |               "      <td>0.000000</td>\n",
 586 |               "      <td>0.0</td>\n",
 587 |               "    </tr>\n",
 588 |               "    <tr>\n",
 589 |               "      <th>50%</th>\n",
 590 |               "      <td>1.0</td>\n",
 591 |               "      <td>9.910020e+03</td>\n",
 592 |               "      <td>2.211219e+04</td>\n",
 593 |               "      <td>1.386267e+04</td>\n",
 594 |               "      <td>0.000000e+00</td>\n",
 595 |               "      <td>0.000000e+00</td>\n",
 596 |               "      <td>0.000000</td>\n",
 597 |               "      <td>0.0</td>\n",
 598 |               "    </tr>\n",
 599 |               "    <tr>\n",
 600 |               "      <th>75%</th>\n",
 601 |               "      <td>1.0</td>\n",
 602 |               "      <td>1.146133e+05</td>\n",
 603 |               "      <td>2.011165e+05</td>\n",
 604 |               "      <td>2.015433e+05</td>\n",
 605 |               "      <td>3.560640e+05</td>\n",
 606 |               "      <td>2.650924e+05</td>\n",
 607 |               "      <td>0.000000</td>\n",
 608 |               "      <td>0.0</td>\n",
 609 |               "    </tr>\n",
 610 |               "    <tr>\n",
 611 |               "      <th>max</th>\n",
 612 |               "      <td>1.0</td>\n",
 613 |               "      <td>3.776389e+06</td>\n",
 614 |               "      <td>1.010284e+07</td>\n",
 615 |               "      <td>1.024625e+07</td>\n",
 616 |               "      <td>1.951612e+07</td>\n",
 617 |               "      <td>1.916920e+07</td>\n",
 618 |               "      <td>1.000000</td>\n",
 619 |               "      <td>0.0</td>\n",
 620 |               "    </tr>\n",
 621 |               "  </tbody>\n",
 622 |               "</table>\n",
 623 |               "</div>\n",
 624 |               "    <div class=\"colab-df-buttons\">\n",
 625 |               "\n",
 626 |               "  <div class=\"colab-df-container\">\n",
 627 |               "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0db04721-3019-42f9-8a61-b3163e4517a5')\"\n",
 628 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
 629 |               "            style=\"display:none;\">\n",
 630 |               "\n",
 631 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
 632 |               "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
 633 |               "  </svg>\n",
 634 |               "    </button>\n",
 635 |               "\n",
 636 |               "  <style>\n",
 637 |               "    .colab-df-container {\n",
 638 |               "      display:flex;\n",
 639 |               "      gap: 12px;\n",
 640 |               "    }\n",
 641 |               "\n",
 642 |               "    .colab-df-convert {\n",
 643 |               "      background-color: #E8F0FE;\n",
 644 |               "      border: none;\n",
 645 |               "      border-radius: 50%;\n",
 646 |               "      cursor: pointer;\n",
 647 |               "      display: none;\n",
 648 |               "      fill: #1967D2;\n",
 649 |               "      height: 32px;\n",
 650 |               "      padding: 0 0 0 0;\n",
 651 |               "      width: 32px;\n",
 652 |               "    }\n",
 653 |               "\n",
 654 |               "    .colab-df-convert:hover {\n",
 655 |               "      background-color: #E2EBFA;\n",
 656 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 657 |               "      fill: #174EA6;\n",
 658 |               "    }\n",
 659 |               "\n",
 660 |               "    .colab-df-buttons div {\n",
 661 |               "      margin-bottom: 4px;\n",
 662 |               "    }\n",
 663 |               "\n",
 664 |               "    [theme=dark] .colab-df-convert {\n",
 665 |               "      background-color: #3B4455;\n",
 666 |               "      fill: #D2E3FC;\n",
 667 |               "    }\n",
 668 |               "\n",
 669 |               "    [theme=dark] .colab-df-convert:hover {\n",
 670 |               "      background-color: #434B5C;\n",
 671 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 672 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 673 |               "      fill: #FFFFFF;\n",
 674 |               "    }\n",
 675 |               "  </style>\n",
 676 |               "\n",
 677 |               "    <script>\n",
 678 |               "      const buttonEl =\n",
 679 |               "        document.querySelector('#df-0db04721-3019-42f9-8a61-b3163e4517a5 button.colab-df-convert');\n",
 680 |               "      buttonEl.style.display =\n",
 681 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 682 |               "\n",
 683 |               "      async function convertToInteractive(key) {\n",
 684 |               "        const element = document.querySelector('#df-0db04721-3019-42f9-8a61-b3163e4517a5');\n",
 685 |               "        const dataTable =\n",
 686 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 687 |               "                                                    [key], {});\n",
 688 |               "        if (!dataTable) return;\n",
 689 |               "\n",
 690 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 691 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 692 |               "          + ' to learn more about interactive tables.';\n",
 693 |               "        element.innerHTML = '';\n",
 694 |               "        dataTable['output_type'] = 'display_data';\n",
 695 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
 696 |               "        const docLink = document.createElement('div');\n",
 697 |               "        docLink.innerHTML = docLinkHtml;\n",
 698 |               "        element.appendChild(docLink);\n",
 699 |               "      }\n",
 700 |               "    </script>\n",
 701 |               "  </div>\n",
 702 |               "\n",
 703 |               "\n",
 704 |               "<div id=\"df-71906155-e6e2-4589-9257-54afbdf42eb6\">\n",
 705 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-71906155-e6e2-4589-9257-54afbdf42eb6')\"\n",
 706 |               "            title=\"Suggest charts\"\n",
 707 |               "            style=\"display:none;\">\n",
 708 |               "\n",
 709 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 710 |               "     width=\"24px\">\n",
 711 |               "    <g>\n",
 712 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
 713 |               "    </g>\n",
 714 |               "</svg>\n",
 715 |               "  </button>\n",
 716 |               "\n",
 717 |               "<style>\n",
 718 |               "  .colab-df-quickchart {\n",
 719 |               "      --bg-color: #E8F0FE;\n",
 720 |               "      --fill-color: #1967D2;\n",
 721 |               "      --hover-bg-color: #E2EBFA;\n",
 722 |               "      --hover-fill-color: #174EA6;\n",
 723 |               "      --disabled-fill-color: #AAA;\n",
 724 |               "      --disabled-bg-color: #DDD;\n",
 725 |               "  }\n",
 726 |               "\n",
 727 |               "  [theme=dark] .colab-df-quickchart {\n",
 728 |               "      --bg-color: #3B4455;\n",
 729 |               "      --fill-color: #D2E3FC;\n",
 730 |               "      --hover-bg-color: #434B5C;\n",
 731 |               "      --hover-fill-color: #FFFFFF;\n",
 732 |               "      --disabled-bg-color: #3B4455;\n",
 733 |               "      --disabled-fill-color: #666;\n",
 734 |               "  }\n",
 735 |               "\n",
 736 |               "  .colab-df-quickchart {\n",
 737 |               "    background-color: var(--bg-color);\n",
 738 |               "    border: none;\n",
 739 |               "    border-radius: 50%;\n",
 740 |               "    cursor: pointer;\n",
 741 |               "    display: none;\n",
 742 |               "    fill: var(--fill-color);\n",
 743 |               "    height: 32px;\n",
 744 |               "    padding: 0;\n",
 745 |               "    width: 32px;\n",
 746 |               "  }\n",
 747 |               "\n",
 748 |               "  .colab-df-quickchart:hover {\n",
 749 |               "    background-color: var(--hover-bg-color);\n",
 750 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 751 |               "    fill: var(--button-hover-fill-color);\n",
 752 |               "  }\n",
 753 |               "\n",
 754 |               "  .colab-df-quickchart-complete:disabled,\n",
 755 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
 756 |               "    background-color: var(--disabled-bg-color);\n",
 757 |               "    fill: var(--disabled-fill-color);\n",
 758 |               "    box-shadow: none;\n",
 759 |               "  }\n",
 760 |               "\n",
 761 |               "  .colab-df-spinner {\n",
 762 |               "    border: 2px solid var(--fill-color);\n",
 763 |               "    border-color: transparent;\n",
 764 |               "    border-bottom-color: var(--fill-color);\n",
 765 |               "    animation:\n",
 766 |               "      spin 1s steps(1) infinite;\n",
 767 |               "  }\n",
 768 |               "\n",
 769 |               "  @keyframes spin {\n",
 770 |               "    0% {\n",
 771 |               "      border-color: transparent;\n",
 772 |               "      border-bottom-color: var(--fill-color);\n",
 773 |               "      border-left-color: var(--fill-color);\n",
 774 |               "    }\n",
 775 |               "    20% {\n",
 776 |               "      border-color: transparent;\n",
 777 |               "      border-left-color: var(--fill-color);\n",
 778 |               "      border-top-color: var(--fill-color);\n",
 779 |               "    }\n",
 780 |               "    30% {\n",
 781 |               "      border-color: transparent;\n",
 782 |               "      border-left-color: var(--fill-color);\n",
 783 |               "      border-top-color: var(--fill-color);\n",
 784 |               "      border-right-color: var(--fill-color);\n",
 785 |               "    }\n",
 786 |               "    40% {\n",
 787 |               "      border-color: transparent;\n",
 788 |               "      border-right-color: var(--fill-color);\n",
 789 |               "      border-top-color: var(--fill-color);\n",
 790 |               "    }\n",
 791 |               "    60% {\n",
 792 |               "      border-color: transparent;\n",
 793 |               "      border-right-color: var(--fill-color);\n",
 794 |               "    }\n",
 795 |               "    80% {\n",
 796 |               "      border-color: transparent;\n",
 797 |               "      border-right-color: var(--fill-color);\n",
 798 |               "      border-bottom-color: var(--fill-color);\n",
 799 |               "    }\n",
 800 |               "    90% {\n",
 801 |               "      border-color: transparent;\n",
 802 |               "      border-bottom-color: var(--fill-color);\n",
 803 |               "    }\n",
 804 |               "  }\n",
 805 |               "</style>\n",
 806 |               "\n",
 807 |               "  <script>\n",
 808 |               "    async function quickchart(key) {\n",
 809 |               "      const quickchartButtonEl =\n",
 810 |               "        document.querySelector('#' + key + ' button');\n",
 811 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
 812 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
 813 |               "      try {\n",
 814 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
 815 |               "            'suggestCharts', [key], {});\n",
 816 |               "      } catch (error) {\n",
 817 |               "        console.error('Error during call to suggestCharts:', error);\n",
 818 |               "      }\n",
 819 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
 820 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
 821 |               "    }\n",
 822 |               "    (() => {\n",
 823 |               "      let quickchartButtonEl =\n",
 824 |               "        document.querySelector('#df-71906155-e6e2-4589-9257-54afbdf42eb6 button');\n",
 825 |               "      quickchartButtonEl.style.display =\n",
 826 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 827 |               "    })();\n",
 828 |               "  </script>\n",
 829 |               "</div>\n",
 830 |               "    </div>\n",
 831 |               "  </div>\n"
 832 |             ]
 833 |           },
 834 |           "metadata": {},
 835 |           "execution_count": 41
 836 |         }
 837 |       ]
 838 |     },
 839 |     {
 840 |       "cell_type": "code",
 841 |       "source": [
 842 |         "data['isFraud'].value_counts()"
 843 |       ],
 844 |       "metadata": {
 845 |         "id": "Nb8OmICVNCE7",
 846 |         "colab": {
 847 |           "base_uri": "https://localhost:8080/"
 848 |         },
 849 |         "outputId": "df1a6a72-35dd-437a-f684-5908435d3abf"
 850 |       },
 851 |       "execution_count": 42,
 852 |       "outputs": [
 853 |         {
 854 |           "output_type": "execute_result",
 855 |           "data": {
 856 |             "text/plain": [
 857 |               "0    2604\n",
 858 |               "1      16\n",
 859 |               "Name: isFraud, dtype: int64"
 860 |             ]
 861 |           },
 862 |           "metadata": {},
 863 |           "execution_count": 42
 864 |         }
 865 |       ]
 866 |     },
 867 |     {
 868 |       "cell_type": "code",
 869 |       "source": [
 870 |         "data.drop(['nameOrig', 'nameDest'], axis=1, inplace=True)\n"
 871 |       ],
 872 |       "metadata": {
 873 |         "colab": {
 874 |           "base_uri": "https://localhost:8080/"
 875 |         },
 876 |         "id": "8Qb9m-A6ORB_",
 877 |         "outputId": "251e7470-8135-4c35-c186-dd100e303d9f"
 878 |       },
 879 |       "execution_count": 43,
 880 |       "outputs": [
 881 |         {
 882 |           "output_type": "stream",
 883 |           "name": "stderr",
 884 |           "text": [
 885 |             "<ipython-input-43-2e4bed1068a4>:1: SettingWithCopyWarning:\n",
 886 |             "\n",
 887 |             "\n",
 888 |             "A value is trying to be set on a copy of a slice from a DataFrame\n",
 889 |             "\n",
 890 |             "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
 891 |             "\n"
 892 |           ]
 893 |         }
 894 |       ]
 895 |     },
 896 |     {
 897 |       "cell_type": "code",
 898 |       "source": [
 899 |         "data.head()"
 900 |       ],
 901 |       "metadata": {
 902 |         "colab": {
 903 |           "base_uri": "https://localhost:8080/",
 904 |           "height": 206
 905 |         },
 906 |         "id": "2CE77zbVO5LS",
 907 |         "outputId": "ae3b3629-dc0b-4138-8f25-94dcae5b08ca"
 908 |       },
 909 |       "execution_count": 44,
 910 |       "outputs": [
 911 |         {
 912 |           "output_type": "execute_result",
 913 |           "data": {
 914 |             "text/plain": [
 915 |               "   step      type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
 916 |               "0     1   PAYMENT   9839.64       170136.0       160296.36             0.0   \n",
 917 |               "1     1   PAYMENT   1864.28        21249.0        19384.72             0.0   \n",
 918 |               "2     1  TRANSFER    181.00          181.0            0.00             0.0   \n",
 919 |               "3     1  CASH_OUT    181.00          181.0            0.00         21182.0   \n",
 920 |               "4     1   PAYMENT  11668.14        41554.0        29885.86             0.0   \n",
 921 |               "\n",
 922 |               "   newbalanceDest  isFraud  isFlaggedFraud  \n",
 923 |               "0             0.0        0               0  \n",
 924 |               "1             0.0        0               0  \n",
 925 |               "2             0.0        1               0  \n",
 926 |               "3             0.0        1               0  \n",
 927 |               "4             0.0        0               0  "
 928 |             ],
 929 |             "text/html": [
 930 |               "\n",
 931 |               "  <div id=\"df-36696843-dfc2-4ca9-bdf0-faea7ff607f6\" class=\"colab-df-container\">\n",
 932 |               "    <div>\n",
 933 |               "<style scoped>\n",
 934 |               "    .dataframe tbody tr th:only-of-type {\n",
 935 |               "        vertical-align: middle;\n",
 936 |               "    }\n",
 937 |               "\n",
 938 |               "    .dataframe tbody tr th {\n",
 939 |               "        vertical-align: top;\n",
 940 |               "    }\n",
 941 |               "\n",
 942 |               "    .dataframe thead th {\n",
 943 |               "        text-align: right;\n",
 944 |               "    }\n",
 945 |               "</style>\n",
 946 |               "<table border=\"1\" class=\"dataframe\">\n",
 947 |               "  <thead>\n",
 948 |               "    <tr style=\"text-align: right;\">\n",
 949 |               "      <th></th>\n",
 950 |               "      <th>step</th>\n",
 951 |               "      <th>type</th>\n",
 952 |               "      <th>amount</th>\n",
 953 |               "      <th>oldbalanceOrg</th>\n",
 954 |               "      <th>newbalanceOrig</th>\n",
 955 |               "      <th>oldbalanceDest</th>\n",
 956 |               "      <th>newbalanceDest</th>\n",
 957 |               "      <th>isFraud</th>\n",
 958 |               "      <th>isFlaggedFraud</th>\n",
 959 |               "    </tr>\n",
 960 |               "  </thead>\n",
 961 |               "  <tbody>\n",
 962 |               "    <tr>\n",
 963 |               "      <th>0</th>\n",
 964 |               "      <td>1</td>\n",
 965 |               "      <td>PAYMENT</td>\n",
 966 |               "      <td>9839.64</td>\n",
 967 |               "      <td>170136.0</td>\n",
 968 |               "      <td>160296.36</td>\n",
 969 |               "      <td>0.0</td>\n",
 970 |               "      <td>0.0</td>\n",
 971 |               "      <td>0</td>\n",
 972 |               "      <td>0</td>\n",
 973 |               "    </tr>\n",
 974 |               "    <tr>\n",
 975 |               "      <th>1</th>\n",
 976 |               "      <td>1</td>\n",
 977 |               "      <td>PAYMENT</td>\n",
 978 |               "      <td>1864.28</td>\n",
 979 |               "      <td>21249.0</td>\n",
 980 |               "      <td>19384.72</td>\n",
 981 |               "      <td>0.0</td>\n",
 982 |               "      <td>0.0</td>\n",
 983 |               "      <td>0</td>\n",
 984 |               "      <td>0</td>\n",
 985 |               "    </tr>\n",
 986 |               "    <tr>\n",
 987 |               "      <th>2</th>\n",
 988 |               "      <td>1</td>\n",
 989 |               "      <td>TRANSFER</td>\n",
 990 |               "      <td>181.00</td>\n",
 991 |               "      <td>181.0</td>\n",
 992 |               "      <td>0.00</td>\n",
 993 |               "      <td>0.0</td>\n",
 994 |               "      <td>0.0</td>\n",
 995 |               "      <td>1</td>\n",
 996 |               "      <td>0</td>\n",
 997 |               "    </tr>\n",
 998 |               "    <tr>\n",
 999 |               "      <th>3</th>\n",
1000 |               "      <td>1</td>\n",
1001 |               "      <td>CASH_OUT</td>\n",
1002 |               "      <td>181.00</td>\n",
1003 |               "      <td>181.0</td>\n",
1004 |               "      <td>0.00</td>\n",
1005 |               "      <td>21182.0</td>\n",
1006 |               "      <td>0.0</td>\n",
1007 |               "      <td>1</td>\n",
1008 |               "      <td>0</td>\n",
1009 |               "    </tr>\n",
1010 |               "    <tr>\n",
1011 |               "      <th>4</th>\n",
1012 |               "      <td>1</td>\n",
1013 |               "      <td>PAYMENT</td>\n",
1014 |               "      <td>11668.14</td>\n",
1015 |               "      <td>41554.0</td>\n",
1016 |               "      <td>29885.86</td>\n",
1017 |               "      <td>0.0</td>\n",
1018 |               "      <td>0.0</td>\n",
1019 |               "      <td>0</td>\n",
1020 |               "      <td>0</td>\n",
1021 |               "    </tr>\n",
1022 |               "  </tbody>\n",
1023 |               "</table>\n",
1024 |               "</div>\n",
1025 |               "    <div class=\"colab-df-buttons\">\n",
1026 |               "\n",
1027 |               "  <div class=\"colab-df-container\">\n",
1028 |               "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36696843-dfc2-4ca9-bdf0-faea7ff607f6')\"\n",
1029 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
1030 |               "            style=\"display:none;\">\n",
1031 |               "\n",
1032 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
1033 |               "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1034 |               "  </svg>\n",
1035 |               "    </button>\n",
1036 |               "\n",
1037 |               "  <style>\n",
1038 |               "    .colab-df-container {\n",
1039 |               "      display:flex;\n",
1040 |               "      gap: 12px;\n",
1041 |               "    }\n",
1042 |               "\n",
1043 |               "    .colab-df-convert {\n",
1044 |               "      background-color: #E8F0FE;\n",
1045 |               "      border: none;\n",
1046 |               "      border-radius: 50%;\n",
1047 |               "      cursor: pointer;\n",
1048 |               "      display: none;\n",
1049 |               "      fill: #1967D2;\n",
1050 |               "      height: 32px;\n",
1051 |               "      padding: 0 0 0 0;\n",
1052 |               "      width: 32px;\n",
1053 |               "    }\n",
1054 |               "\n",
1055 |               "    .colab-df-convert:hover {\n",
1056 |               "      background-color: #E2EBFA;\n",
1057 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1058 |               "      fill: #174EA6;\n",
1059 |               "    }\n",
1060 |               "\n",
1061 |               "    .colab-df-buttons div {\n",
1062 |               "      margin-bottom: 4px;\n",
1063 |               "    }\n",
1064 |               "\n",
1065 |               "    [theme=dark] .colab-df-convert {\n",
1066 |               "      background-color: #3B4455;\n",
1067 |               "      fill: #D2E3FC;\n",
1068 |               "    }\n",
1069 |               "\n",
1070 |               "    [theme=dark] .colab-df-convert:hover {\n",
1071 |               "      background-color: #434B5C;\n",
1072 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1073 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1074 |               "      fill: #FFFFFF;\n",
1075 |               "    }\n",
1076 |               "  </style>\n",
1077 |               "\n",
1078 |               "    <script>\n",
1079 |               "      const buttonEl =\n",
1080 |               "        document.querySelector('#df-36696843-dfc2-4ca9-bdf0-faea7ff607f6 button.colab-df-convert');\n",
1081 |               "      buttonEl.style.display =\n",
1082 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1083 |               "\n",
1084 |               "      async function convertToInteractive(key) {\n",
1085 |               "        const element = document.querySelector('#df-36696843-dfc2-4ca9-bdf0-faea7ff607f6');\n",
1086 |               "        const dataTable =\n",
1087 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1088 |               "                                                    [key], {});\n",
1089 |               "        if (!dataTable) return;\n",
1090 |               "\n",
1091 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
1092 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1093 |               "          + ' to learn more about interactive tables.';\n",
1094 |               "        element.innerHTML = '';\n",
1095 |               "        dataTable['output_type'] = 'display_data';\n",
1096 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
1097 |               "        const docLink = document.createElement('div');\n",
1098 |               "        docLink.innerHTML = docLinkHtml;\n",
1099 |               "        element.appendChild(docLink);\n",
1100 |               "      }\n",
1101 |               "    </script>\n",
1102 |               "  </div>\n",
1103 |               "\n",
1104 |               "\n",
1105 |               "<div id=\"df-6cc3df6d-a2d2-4fcf-951d-68744ffd03c3\">\n",
1106 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6cc3df6d-a2d2-4fcf-951d-68744ffd03c3')\"\n",
1107 |               "            title=\"Suggest charts\"\n",
1108 |               "            style=\"display:none;\">\n",
1109 |               "\n",
1110 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1111 |               "     width=\"24px\">\n",
1112 |               "    <g>\n",
1113 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
1114 |               "    </g>\n",
1115 |               "</svg>\n",
1116 |               "  </button>\n",
1117 |               "\n",
1118 |               "<style>\n",
1119 |               "  .colab-df-quickchart {\n",
1120 |               "      --bg-color: #E8F0FE;\n",
1121 |               "      --fill-color: #1967D2;\n",
1122 |               "      --hover-bg-color: #E2EBFA;\n",
1123 |               "      --hover-fill-color: #174EA6;\n",
1124 |               "      --disabled-fill-color: #AAA;\n",
1125 |               "      --disabled-bg-color: #DDD;\n",
1126 |               "  }\n",
1127 |               "\n",
1128 |               "  [theme=dark] .colab-df-quickchart {\n",
1129 |               "      --bg-color: #3B4455;\n",
1130 |               "      --fill-color: #D2E3FC;\n",
1131 |               "      --hover-bg-color: #434B5C;\n",
1132 |               "      --hover-fill-color: #FFFFFF;\n",
1133 |               "      --disabled-bg-color: #3B4455;\n",
1134 |               "      --disabled-fill-color: #666;\n",
1135 |               "  }\n",
1136 |               "\n",
1137 |               "  .colab-df-quickchart {\n",
1138 |               "    background-color: var(--bg-color);\n",
1139 |               "    border: none;\n",
1140 |               "    border-radius: 50%;\n",
1141 |               "    cursor: pointer;\n",
1142 |               "    display: none;\n",
1143 |               "    fill: var(--fill-color);\n",
1144 |               "    height: 32px;\n",
1145 |               "    padding: 0;\n",
1146 |               "    width: 32px;\n",
1147 |               "  }\n",
1148 |               "\n",
1149 |               "  .colab-df-quickchart:hover {\n",
1150 |               "    background-color: var(--hover-bg-color);\n",
1151 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1152 |               "    fill: var(--button-hover-fill-color);\n",
1153 |               "  }\n",
1154 |               "\n",
1155 |               "  .colab-df-quickchart-complete:disabled,\n",
1156 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
1157 |               "    background-color: var(--disabled-bg-color);\n",
1158 |               "    fill: var(--disabled-fill-color);\n",
1159 |               "    box-shadow: none;\n",
1160 |               "  }\n",
1161 |               "\n",
1162 |               "  .colab-df-spinner {\n",
1163 |               "    border: 2px solid var(--fill-color);\n",
1164 |               "    border-color: transparent;\n",
1165 |               "    border-bottom-color: var(--fill-color);\n",
1166 |               "    animation:\n",
1167 |               "      spin 1s steps(1) infinite;\n",
1168 |               "  }\n",
1169 |               "\n",
1170 |               "  @keyframes spin {\n",
1171 |               "    0% {\n",
1172 |               "      border-color: transparent;\n",
1173 |               "      border-bottom-color: var(--fill-color);\n",
1174 |               "      border-left-color: var(--fill-color);\n",
1175 |               "    }\n",
1176 |               "    20% {\n",
1177 |               "      border-color: transparent;\n",
1178 |               "      border-left-color: var(--fill-color);\n",
1179 |               "      border-top-color: var(--fill-color);\n",
1180 |               "    }\n",
1181 |               "    30% {\n",
1182 |               "      border-color: transparent;\n",
1183 |               "      border-left-color: var(--fill-color);\n",
1184 |               "      border-top-color: var(--fill-color);\n",
1185 |               "      border-right-color: var(--fill-color);\n",
1186 |               "    }\n",
1187 |               "    40% {\n",
1188 |               "      border-color: transparent;\n",
1189 |               "      border-right-color: var(--fill-color);\n",
1190 |               "      border-top-color: var(--fill-color);\n",
1191 |               "    }\n",
1192 |               "    60% {\n",
1193 |               "      border-color: transparent;\n",
1194 |               "      border-right-color: var(--fill-color);\n",
1195 |               "    }\n",
1196 |               "    80% {\n",
1197 |               "      border-color: transparent;\n",
1198 |               "      border-right-color: var(--fill-color);\n",
1199 |               "      border-bottom-color: var(--fill-color);\n",
1200 |               "    }\n",
1201 |               "    90% {\n",
1202 |               "      border-color: transparent;\n",
1203 |               "      border-bottom-color: var(--fill-color);\n",
1204 |               "    }\n",
1205 |               "  }\n",
1206 |               "</style>\n",
1207 |               "\n",
1208 |               "  <script>\n",
1209 |               "    async function quickchart(key) {\n",
1210 |               "      const quickchartButtonEl =\n",
1211 |               "        document.querySelector('#' + key + ' button');\n",
1212 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
1213 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
1214 |               "      try {\n",
1215 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
1216 |               "            'suggestCharts', [key], {});\n",
1217 |               "      } catch (error) {\n",
1218 |               "        console.error('Error during call to suggestCharts:', error);\n",
1219 |               "      }\n",
1220 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1221 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1222 |               "    }\n",
1223 |               "    (() => {\n",
1224 |               "      let quickchartButtonEl =\n",
1225 |               "        document.querySelector('#df-6cc3df6d-a2d2-4fcf-951d-68744ffd03c3 button');\n",
1226 |               "      quickchartButtonEl.style.display =\n",
1227 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1228 |               "    })();\n",
1229 |               "  </script>\n",
1230 |               "</div>\n",
1231 |               "    </div>\n",
1232 |               "  </div>\n"
1233 |             ]
1234 |           },
1235 |           "metadata": {},
1236 |           "execution_count": 44
1237 |         }
1238 |       ]
1239 |     },
1240 |     {
1241 |       "cell_type": "code",
1242 |       "source": [
1243 |         "from sklearn.preprocessing import LabelEncoder\n",
1244 |         "encoder=LabelEncoder()\n",
1245 |         "data['type']=encoder.fit_transform(data['type'])"
1246 |       ],
1247 |       "metadata": {
1248 |         "id": "rLdvROOSPFW0",
1249 |         "colab": {
1250 |           "base_uri": "https://localhost:8080/"
1251 |         },
1252 |         "outputId": "51593faf-7cba-44f5-c4d2-0a53a1ef3e1a"
1253 |       },
1254 |       "execution_count": 45,
1255 |       "outputs": [
1256 |         {
1257 |           "output_type": "stream",
1258 |           "name": "stderr",
1259 |           "text": [
1260 |             "<ipython-input-45-2e9cdcdb6579>:3: SettingWithCopyWarning:\n",
1261 |             "\n",
1262 |             "\n",
1263 |             "A value is trying to be set on a copy of a slice from a DataFrame.\n",
1264 |             "Try using .loc[row_indexer,col_indexer] = value instead\n",
1265 |             "\n",
1266 |             "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1267 |             "\n"
1268 |           ]
1269 |         }
1270 |       ]
1271 |     },
1272 |     {
1273 |       "cell_type": "code",
1274 |       "source": [
1275 |         "data.head()"
1276 |       ],
1277 |       "metadata": {
1278 |         "colab": {
1279 |           "base_uri": "https://localhost:8080/",
1280 |           "height": 206
1281 |         },
1282 |         "id": "Bjzd0w1My4rY",
1283 |         "outputId": "1fc53da8-2076-440c-e092-b4cc0dbaab58"
1284 |       },
1285 |       "execution_count": 46,
1286 |       "outputs": [
1287 |         {
1288 |           "output_type": "execute_result",
1289 |           "data": {
1290 |             "text/plain": [
1291 |               "   step  type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
1292 |               "0     1     3   9839.64       170136.0       160296.36             0.0   \n",
1293 |               "1     1     3   1864.28        21249.0        19384.72             0.0   \n",
1294 |               "2     1     4    181.00          181.0            0.00             0.0   \n",
1295 |               "3     1     1    181.00          181.0            0.00         21182.0   \n",
1296 |               "4     1     3  11668.14        41554.0        29885.86             0.0   \n",
1297 |               "\n",
1298 |               "   newbalanceDest  isFraud  isFlaggedFraud  \n",
1299 |               "0             0.0        0               0  \n",
1300 |               "1             0.0        0               0  \n",
1301 |               "2             0.0        1               0  \n",
1302 |               "3             0.0        1               0  \n",
1303 |               "4             0.0        0               0  "
1304 |             ],
1305 |             "text/html": [
1306 |               "\n",
1307 |               "  <div id=\"df-d28ca866-6805-4036-9116-5cede08d557c\" class=\"colab-df-container\">\n",
1308 |               "    <div>\n",
1309 |               "<style scoped>\n",
1310 |               "    .dataframe tbody tr th:only-of-type {\n",
1311 |               "        vertical-align: middle;\n",
1312 |               "    }\n",
1313 |               "\n",
1314 |               "    .dataframe tbody tr th {\n",
1315 |               "        vertical-align: top;\n",
1316 |               "    }\n",
1317 |               "\n",
1318 |               "    .dataframe thead th {\n",
1319 |               "        text-align: right;\n",
1320 |               "    }\n",
1321 |               "</style>\n",
1322 |               "<table border=\"1\" class=\"dataframe\">\n",
1323 |               "  <thead>\n",
1324 |               "    <tr style=\"text-align: right;\">\n",
1325 |               "      <th></th>\n",
1326 |               "      <th>step</th>\n",
1327 |               "      <th>type</th>\n",
1328 |               "      <th>amount</th>\n",
1329 |               "      <th>oldbalanceOrg</th>\n",
1330 |               "      <th>newbalanceOrig</th>\n",
1331 |               "      <th>oldbalanceDest</th>\n",
1332 |               "      <th>newbalanceDest</th>\n",
1333 |               "      <th>isFraud</th>\n",
1334 |               "      <th>isFlaggedFraud</th>\n",
1335 |               "    </tr>\n",
1336 |               "  </thead>\n",
1337 |               "  <tbody>\n",
1338 |               "    <tr>\n",
1339 |               "      <th>0</th>\n",
1340 |               "      <td>1</td>\n",
1341 |               "      <td>3</td>\n",
1342 |               "      <td>9839.64</td>\n",
1343 |               "      <td>170136.0</td>\n",
1344 |               "      <td>160296.36</td>\n",
1345 |               "      <td>0.0</td>\n",
1346 |               "      <td>0.0</td>\n",
1347 |               "      <td>0</td>\n",
1348 |               "      <td>0</td>\n",
1349 |               "    </tr>\n",
1350 |               "    <tr>\n",
1351 |               "      <th>1</th>\n",
1352 |               "      <td>1</td>\n",
1353 |               "      <td>3</td>\n",
1354 |               "      <td>1864.28</td>\n",
1355 |               "      <td>21249.0</td>\n",
1356 |               "      <td>19384.72</td>\n",
1357 |               "      <td>0.0</td>\n",
1358 |               "      <td>0.0</td>\n",
1359 |               "      <td>0</td>\n",
1360 |               "      <td>0</td>\n",
1361 |               "    </tr>\n",
1362 |               "    <tr>\n",
1363 |               "      <th>2</th>\n",
1364 |               "      <td>1</td>\n",
1365 |               "      <td>4</td>\n",
1366 |               "      <td>181.00</td>\n",
1367 |               "      <td>181.0</td>\n",
1368 |               "      <td>0.00</td>\n",
1369 |               "      <td>0.0</td>\n",
1370 |               "      <td>0.0</td>\n",
1371 |               "      <td>1</td>\n",
1372 |               "      <td>0</td>\n",
1373 |               "    </tr>\n",
1374 |               "    <tr>\n",
1375 |               "      <th>3</th>\n",
1376 |               "      <td>1</td>\n",
1377 |               "      <td>1</td>\n",
1378 |               "      <td>181.00</td>\n",
1379 |               "      <td>181.0</td>\n",
1380 |               "      <td>0.00</td>\n",
1381 |               "      <td>21182.0</td>\n",
1382 |               "      <td>0.0</td>\n",
1383 |               "      <td>1</td>\n",
1384 |               "      <td>0</td>\n",
1385 |               "    </tr>\n",
1386 |               "    <tr>\n",
1387 |               "      <th>4</th>\n",
1388 |               "      <td>1</td>\n",
1389 |               "      <td>3</td>\n",
1390 |               "      <td>11668.14</td>\n",
1391 |               "      <td>41554.0</td>\n",
1392 |               "      <td>29885.86</td>\n",
1393 |               "      <td>0.0</td>\n",
1394 |               "      <td>0.0</td>\n",
1395 |               "      <td>0</td>\n",
1396 |               "      <td>0</td>\n",
1397 |               "    </tr>\n",
1398 |               "  </tbody>\n",
1399 |               "</table>\n",
1400 |               "</div>\n",
1401 |               "    <div class=\"colab-df-buttons\">\n",
1402 |               "\n",
1403 |               "  <div class=\"colab-df-container\">\n",
1404 |               "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d28ca866-6805-4036-9116-5cede08d557c')\"\n",
1405 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
1406 |               "            style=\"display:none;\">\n",
1407 |               "\n",
1408 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
1409 |               "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1410 |               "  </svg>\n",
1411 |               "    </button>\n",
1412 |               "\n",
1413 |               "  <style>\n",
1414 |               "    .colab-df-container {\n",
1415 |               "      display:flex;\n",
1416 |               "      gap: 12px;\n",
1417 |               "    }\n",
1418 |               "\n",
1419 |               "    .colab-df-convert {\n",
1420 |               "      background-color: #E8F0FE;\n",
1421 |               "      border: none;\n",
1422 |               "      border-radius: 50%;\n",
1423 |               "      cursor: pointer;\n",
1424 |               "      display: none;\n",
1425 |               "      fill: #1967D2;\n",
1426 |               "      height: 32px;\n",
1427 |               "      padding: 0 0 0 0;\n",
1428 |               "      width: 32px;\n",
1429 |               "    }\n",
1430 |               "\n",
1431 |               "    .colab-df-convert:hover {\n",
1432 |               "      background-color: #E2EBFA;\n",
1433 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1434 |               "      fill: #174EA6;\n",
1435 |               "    }\n",
1436 |               "\n",
1437 |               "    .colab-df-buttons div {\n",
1438 |               "      margin-bottom: 4px;\n",
1439 |               "    }\n",
1440 |               "\n",
1441 |               "    [theme=dark] .colab-df-convert {\n",
1442 |               "      background-color: #3B4455;\n",
1443 |               "      fill: #D2E3FC;\n",
1444 |               "    }\n",
1445 |               "\n",
1446 |               "    [theme=dark] .colab-df-convert:hover {\n",
1447 |               "      background-color: #434B5C;\n",
1448 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1449 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1450 |               "      fill: #FFFFFF;\n",
1451 |               "    }\n",
1452 |               "  </style>\n",
1453 |               "\n",
1454 |               "    <script>\n",
1455 |               "      const buttonEl =\n",
1456 |               "        document.querySelector('#df-d28ca866-6805-4036-9116-5cede08d557c button.colab-df-convert');\n",
1457 |               "      buttonEl.style.display =\n",
1458 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1459 |               "\n",
1460 |               "      async function convertToInteractive(key) {\n",
1461 |               "        const element = document.querySelector('#df-d28ca866-6805-4036-9116-5cede08d557c');\n",
1462 |               "        const dataTable =\n",
1463 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1464 |               "                                                    [key], {});\n",
1465 |               "        if (!dataTable) return;\n",
1466 |               "\n",
1467 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
1468 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1469 |               "          + ' to learn more about interactive tables.';\n",
1470 |               "        element.innerHTML = '';\n",
1471 |               "        dataTable['output_type'] = 'display_data';\n",
1472 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
1473 |               "        const docLink = document.createElement('div');\n",
1474 |               "        docLink.innerHTML = docLinkHtml;\n",
1475 |               "        element.appendChild(docLink);\n",
1476 |               "      }\n",
1477 |               "    </script>\n",
1478 |               "  </div>\n",
1479 |               "\n",
1480 |               "\n",
1481 |               "<div id=\"df-fcea3c72-3508-4f9c-8da1-2c1f899f3f37\">\n",
1482 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fcea3c72-3508-4f9c-8da1-2c1f899f3f37')\"\n",
1483 |               "            title=\"Suggest charts\"\n",
1484 |               "            style=\"display:none;\">\n",
1485 |               "\n",
1486 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1487 |               "     width=\"24px\">\n",
1488 |               "    <g>\n",
1489 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
1490 |               "    </g>\n",
1491 |               "</svg>\n",
1492 |               "  </button>\n",
1493 |               "\n",
1494 |               "<style>\n",
1495 |               "  .colab-df-quickchart {\n",
1496 |               "      --bg-color: #E8F0FE;\n",
1497 |               "      --fill-color: #1967D2;\n",
1498 |               "      --hover-bg-color: #E2EBFA;\n",
1499 |               "      --hover-fill-color: #174EA6;\n",
1500 |               "      --disabled-fill-color: #AAA;\n",
1501 |               "      --disabled-bg-color: #DDD;\n",
1502 |               "  }\n",
1503 |               "\n",
1504 |               "  [theme=dark] .colab-df-quickchart {\n",
1505 |               "      --bg-color: #3B4455;\n",
1506 |               "      --fill-color: #D2E3FC;\n",
1507 |               "      --hover-bg-color: #434B5C;\n",
1508 |               "      --hover-fill-color: #FFFFFF;\n",
1509 |               "      --disabled-bg-color: #3B4455;\n",
1510 |               "      --disabled-fill-color: #666;\n",
1511 |               "  }\n",
1512 |               "\n",
1513 |               "  .colab-df-quickchart {\n",
1514 |               "    background-color: var(--bg-color);\n",
1515 |               "    border: none;\n",
1516 |               "    border-radius: 50%;\n",
1517 |               "    cursor: pointer;\n",
1518 |               "    display: none;\n",
1519 |               "    fill: var(--fill-color);\n",
1520 |               "    height: 32px;\n",
1521 |               "    padding: 0;\n",
1522 |               "    width: 32px;\n",
1523 |               "  }\n",
1524 |               "\n",
1525 |               "  .colab-df-quickchart:hover {\n",
1526 |               "    background-color: var(--hover-bg-color);\n",
1527 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1528 |               "    fill: var(--button-hover-fill-color);\n",
1529 |               "  }\n",
1530 |               "\n",
1531 |               "  .colab-df-quickchart-complete:disabled,\n",
1532 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
1533 |               "    background-color: var(--disabled-bg-color);\n",
1534 |               "    fill: var(--disabled-fill-color);\n",
1535 |               "    box-shadow: none;\n",
1536 |               "  }\n",
1537 |               "\n",
1538 |               "  .colab-df-spinner {\n",
1539 |               "    border: 2px solid var(--fill-color);\n",
1540 |               "    border-color: transparent;\n",
1541 |               "    border-bottom-color: var(--fill-color);\n",
1542 |               "    animation:\n",
1543 |               "      spin 1s steps(1) infinite;\n",
1544 |               "  }\n",
1545 |               "\n",
1546 |               "  @keyframes spin {\n",
1547 |               "    0% {\n",
1548 |               "      border-color: transparent;\n",
1549 |               "      border-bottom-color: var(--fill-color);\n",
1550 |               "      border-left-color: var(--fill-color);\n",
1551 |               "    }\n",
1552 |               "    20% {\n",
1553 |               "      border-color: transparent;\n",
1554 |               "      border-left-color: var(--fill-color);\n",
1555 |               "      border-top-color: var(--fill-color);\n",
1556 |               "    }\n",
1557 |               "    30% {\n",
1558 |               "      border-color: transparent;\n",
1559 |               "      border-left-color: var(--fill-color);\n",
1560 |               "      border-top-color: var(--fill-color);\n",
1561 |               "      border-right-color: var(--fill-color);\n",
1562 |               "    }\n",
1563 |               "    40% {\n",
1564 |               "      border-color: transparent;\n",
1565 |               "      border-right-color: var(--fill-color);\n",
1566 |               "      border-top-color: var(--fill-color);\n",
1567 |               "    }\n",
1568 |               "    60% {\n",
1569 |               "      border-color: transparent;\n",
1570 |               "      border-right-color: var(--fill-color);\n",
1571 |               "    }\n",
1572 |               "    80% {\n",
1573 |               "      border-color: transparent;\n",
1574 |               "      border-right-color: var(--fill-color);\n",
1575 |               "      border-bottom-color: var(--fill-color);\n",
1576 |               "    }\n",
1577 |               "    90% {\n",
1578 |               "      border-color: transparent;\n",
1579 |               "      border-bottom-color: var(--fill-color);\n",
1580 |               "    }\n",
1581 |               "  }\n",
1582 |               "</style>\n",
1583 |               "\n",
1584 |               "  <script>\n",
1585 |               "    async function quickchart(key) {\n",
1586 |               "      const quickchartButtonEl =\n",
1587 |               "        document.querySelector('#' + key + ' button');\n",
1588 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
1589 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
1590 |               "      try {\n",
1591 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
1592 |               "            'suggestCharts', [key], {});\n",
1593 |               "      } catch (error) {\n",
1594 |               "        console.error('Error during call to suggestCharts:', error);\n",
1595 |               "      }\n",
1596 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1597 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1598 |               "    }\n",
1599 |               "    (() => {\n",
1600 |               "      let quickchartButtonEl =\n",
1601 |               "        document.querySelector('#df-fcea3c72-3508-4f9c-8da1-2c1f899f3f37 button');\n",
1602 |               "      quickchartButtonEl.style.display =\n",
1603 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1604 |               "    })();\n",
1605 |               "  </script>\n",
1606 |               "</div>\n",
1607 |               "    </div>\n",
1608 |               "  </div>\n"
1609 |             ]
1610 |           },
1611 |           "metadata": {},
1612 |           "execution_count": 46
1613 |         }
1614 |       ]
1615 |     },
1616 |     {
1617 |       "cell_type": "code",
1618 |       "source": [
1619 |         "data.isna().sum()"
1620 |       ],
1621 |       "metadata": {
1622 |         "colab": {
1623 |           "base_uri": "https://localhost:8080/"
1624 |         },
1625 |         "id": "ntfOh8Ew7BkS",
1626 |         "outputId": "15962cea-5a74-41b7-a86d-82db2ea87083"
1627 |       },
1628 |       "execution_count": 47,
1629 |       "outputs": [
1630 |         {
1631 |           "output_type": "execute_result",
1632 |           "data": {
1633 |             "text/plain": [
1634 |               "step              0\n",
1635 |               "type              0\n",
1636 |               "amount            0\n",
1637 |               "oldbalanceOrg     0\n",
1638 |               "newbalanceOrig    0\n",
1639 |               "oldbalanceDest    0\n",
1640 |               "newbalanceDest    0\n",
1641 |               "isFraud           0\n",
1642 |               "isFlaggedFraud    0\n",
1643 |               "dtype: int64"
1644 |             ]
1645 |           },
1646 |           "metadata": {},
1647 |           "execution_count": 47
1648 |         }
1649 |       ]
1650 |     },
1651 |     {
1652 |       "cell_type": "code",
1653 |       "source": [
1654 |         "fig = px.scatter(data, x = 'amount', y =data.index, color = data.isFraud,\n",
1655 |         "                title = 'Distribution of Amount Values')\n",
1656 |         "fig.update_layout(xaxis_title='Transaction Amount (in €)',\n",
1657 |         "                    yaxis_title='Transactions')\n",
1658 |         "fig.show()"
1659 |       ],
1660 |       "metadata": {
1661 |         "colab": {
1662 |           "base_uri": "https://localhost:8080/",
1663 |           "height": 542
1664 |         },
1665 |         "id": "OP05kcp4y7Ux",
1666 |         "outputId": "afe85dd4-4bc1-4bc2-8edf-9545c7e19627"
1667 |       },
1668 |       "execution_count": 48,
1669 |       "outputs": [
1670 |         {
1671 |           "output_type": "display_data",
1672 |           "data": {
1673 |             "text/html": [
1674 |               "<html>\n",
1675 |               "<head><meta charset=\"utf-8\" /></head>\n",
1676 |               "<body>\n",
1677 |               "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
1678 |               "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"935ea706-dde8-42d2-b5b5-cc14a2223900\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"935ea706-dde8-42d2-b5b5-cc14a2223900\")) {                    Plotly.newPlot(                        \"935ea706-dde8-42d2-b5b5-cc14a2223900\",                        [{\"hovertemplate\":\"amount=%{x}\\u003cbr\\u003eindex=%{y}\\u003cbr\\u003eisFraud=%{marker.color}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":[0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\"coloraxis\":\"coloraxis\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"showlegend\":false,\"x\":[9839.64,1864.28,181.0,181.0,11668.14,7817.71,7107.77,7861.64,4024.36,5337.77,9644.94,3099.97,2560.74,11633.76,4098.78,229133.94,1563.82,1157.86,671.64,215310.3,1373.43,9302.79,1065.41,3876.41,311685.89,6061.13,9478.39,8009.09,8901.99,9920.52,3448.92,4206.84,5885.56,5307.88,5031.22,24213.67,8603.42,2791.42,7413.54,3295.19,1684.81,5758.59,110414.71,7823.46,5086.48,5281.48,13875.98,56953.9,5346.89,2204.04,2641.47,23261.3,2330.64,1614.64,9164.71,2970.97,38.66,2252.44,62610.8,5529.13,82940.31,4510.22,8727.74,2599.46,4874.49,6440.78,4910.14,6444.64,5149.66,7292.16,47458.86,136872.92,94253.33,2998.04,3454.08,4316.2,2131.84,12986.61,42712.39,77957.68,17231.46,78766.03,224606.64,125872.53,379856.23,1505626.01,554026.99,147543.1,761507.39,1429051.47,358831.92,367768.4,209711.11,583848.46,1724887.05,710544.77,581294.26,11996.58,2875.1,8586.98,871.75,1035.36,1063.53,1019.9,4059.38,1876.44,28404.6,75405.1,50101.88,14121.82,78292.91,176149.9,212228.35,85423.63,11648.5,6165.58,3705.83,419801.4,335416.51,3372.29,661.43,864.68,1203.44,330757.04,1915.43,8.73,3058.8,5154.97,5213.25,5392.75,25.12,17150.89,679502.24,177652.91,5443.26,244.87,8281.57,1175.59,1097.74,184986.8,1718.29,3671.32,3580.26,8550.9,9753.55,3026.98,467177.03,3875.99,2148.89,466.97,154139.72,82685.93,83857.23,99109.77,187726.67,281339.92,186447.51,81029.86,139054.95,154716.2,53631.83,289645.52,267148.82,47913.58,193605.38,344464.4,169409.87,25793.74,114712.48,89038.75,1341.58,44443.08,34729.64,105979.34,57794.61,15918.79,19162.08,220691.42,9536.54,29784.06,190445.51,57279.11,112086.81,80311.89,151829.91,26004.52,3146.16,296752.75,191702.06,365510.05,202332.08,55105.9,171508.59,178439.26,88834.86,210370.09,36437.06,82691.56,338767.1,187728.59,271002.7,323105.08,10782.94,89962.11,101940.14,163100.34,136030.66,68912.23,73464.06,106117.28,503333.79,71991.42,91379.93,26759.05,128487.35,252908.87,64489.64,171369.73,4493.29,12121.5,2278.19,7707.44,4013.02,5775.15,4843.93,2397.82,4391.44,4497.87,7452.89,3930.81,71532.24,61159.51,12401.9,32702.61,6926.67,893.57,480222.51,3608.41,5946.79,6099.96,372.34,4635.18,1267.97,6911.99,1795.67,3199.06,10265.17,9029.12,7600.57,2057.71,3823.08,2806.0,2806.0,6737.2,9161.98,1239.06,365.0,5237.54,2618.76,339.82,1393.46,2731.37,8364.73,6598.71,4304.58,2867.14,2946.38,507.12,3020.86,8948.03,280877.92,1084.9,9456.69,5812.36,157883.07,2734.77,102153.86,584.52,6113.14,369989.2,215338.28,65521.42,116977.58,262392.36,51121.05,202624.59,118235.16,118056.71,498960.91,691738.36,58820.08,4419.04,4270.02,3007.83,1824.59,993.46,302693.14,1445.06,4059.25,3417.18,3428.95,5580.98,3455.85,7265.63,3852.64,7870.29,12872.92,6675.34,410201.89,13310.78,664.35,2130.3,7771.4,36349.35,749.34,2340.34,9686.85,15785.85,9006.78,9014.95,14420.66,4802.31,28527.44,4182.93,4088.21,8491.9,6666.78,6536.86,40684.97,1553.21,7344.92,106267.21,5491.5,3666.42,5507.18,4565.14,11348.71,123179.97,7073.6,2803.83,106.81,6297.71,2610.01,5320.1,1460.63,522264.81,3584.21,3704.97,2773.61,3896.42,18781.84,6660.33,26923.42,53783.57,80333.28,211075.91,100677.86,473499.51,1538200.39,2421578.09,1349670.68,314403.14,1457213.54,180368.84,445038.71,1123206.66,996.0,176334.26,161217.61,108443.61,75442.68,55536.86,438437.09,928722.69,1478772.04,2545478.01,2061082.82,983479.66,635507.97,848231.57,739112.93,324397.94,982862.81,955855.0,179253.24,5446.88,648.64,8494.71,373068.26,143236.26,228451.89,35902.49,232953.64,65912.95,193492.68,60836.64,62325.15,349640.51,135324.19,380015.14,418688.27,311023.52,151862.38,228710.05,220431.09,182861.45,231881.5,38071.17,87117.29,114271.72,73199.1,764772.98,106847.36,6287.28,24936.34,55837.16,63255.29,104605.78,154160.82,257348.03,201073.81,53560.68,334234.39,227335.16,134426.08,2643.45,134977.11,25090.03,147388.67,217615.09,355294.5,12336.48,349505.89,285185.34,132953.45,214851.41,54988.56,62871.06,61150.85,131409.27,10844.33,2054.84,313373.56,13289.87,27070.11,127954.7,197031.04,144366.64,7965.49,123103.74,106634.77,74933.22,120234.99,5763.99,156922.36,162665.98,110226.34,142577.44,6915.38,157108.01,223555.14,146331.12,141131.23,222711.47,84980.31,628719.07,79285.8,57809.81,75043.17,248196.92,18851.44,403418.39,235564.81,32325.24,345347.7,355500.25,103005.78,108803.98,259753.27,234094.24,273305.73,186838.95,193161.04,152823.85,91457.82,277807.48,8679.13,2099.59,221693.08,206097.65,22384.21,73210.99,236747.6,6284.18,100198.77,160347.32,289272.75,63189.26,196725.32,5882.32,625.92,5937.72,9796.29,13449.95,4749.6,6460.86,3605.26,624.61,3854.92,420532.33,4402.86,147804.02,151.2,58.21,5936.82,145944.67,7206.33,5581.55,5853.21,2528.1,5655.17,1456.09,4252.82,276460.62,8811.67,12666.68,95636.49,195314.34,6049.09,14306.8,1117.34,3179.7,2880.68,3060.54,879.42,3486.78,21530.96,2426.31,5110.1,207.75,18437.04,8690.69,4946.03,5432.09,7649.41,3943.17,3101.33,51719.13,13465.91,19561.05,9762.28,9778.81,562903.81,5055.94,2858.89,8531.42,84.22,227478.01,5033.02,100588.8,9374.72,598.24,4531.55,2998.2,9269.99,1209.64,1298.7,7797.0,5831.03,7067.9,14895.51,3316.21,22276.0,16505.51,636.95,2056.59,704.74,3222.32,11889.61,9491.89,6131.21,11938.61,4233.45,32643.79,622.15,4573.12,6057.8,5770.32,9152.97,12154.7,8378.01,9616.45,2231.28,4932.54,65413.89,98560.08,15054.61,161632.16,47529.19,9577.45,414238.59,89327.65,194900.16,24470.43,217668.38,9067.51,336827.91,45317.95,230021.01,154578.28,314134.01,206405.92,158995.29,188238.54,21255.8,205955.77,178370.36,42547.96,15152.15,39249.67,101669.17,81694.11,10266.7,35502.25,183250.0,219220.21,254895.5,143148.97,132457.52,148834.28,106727.81,223123.71,194311.34,338964.91,247575.4,29467.02,90340.16,198847.32,155075.41,76932.89,92971.79,7322.98,222126.95,62983.91,1271.77,246816.65,21898.97,30811.56,100275.09,46630.53,158775.58,90556.08,173948.89,66216.71,216133.99,371883.82,770537.37,71275.47,336298.77,317393.38,181002.01,311449.38,270824.42,1449.03,3551.48,227768.63,172986.7,4940.2,2755.96,8807.01,3111.86,10040.52,5363.0,11990.19,266.88,1928.55,10648.06,962.43,8348.1,6969.67,20128.0,20128.0,154.87,4040.84,6490.17,1101.09,4347.78,213922.13,83859.59,33269.25,51749.94,86410.3,10856.63,161613.93,244361.81,596617.87,108600.78,377520.94,60758.42,86204.31,1234.0,18288.91,248979.22,126774.5,38428.19,109732.52,17851.54,5223.97,63807.17,406297.68,4779.93,16631.14,2771.16,3154.06,7813.63,360.13,431381.17,43670.84,7342.63,7417.09,8865.26,213490.87,7444.4,287265.38,416001.33,6432.26,8690.86,3791.28,4111.34,7163.9,6557.51,17348.25,1586.28,6811.97,36651.07,4804.68,19375.5,13833.75,21008.52,22043.92,32635.14,11438.39,13527.16,11881.78,9805.46,9470.85,20560.86,13679.84,15448.0,1987.4,4145.04,3511.45,1118.1,15.06,352568.35,3043.27,9758.09,5924.89,9550.43,4355.66,6973.12,2358.63,374.36,8946.63,20573.4,8358.12,1006.76,10498.05,463722.76,138857.19,5184.9,3626.96,282.42,8703.54,3673.1,9800.58,88987.11,4227.19,2881.51,4376.46,11656.12,345698.98,3292.21,7367.06,2937.34,1150.14,3563.36,2380.46,2266.02,32132.45,51627.39,62073.54,13614.91,2290.73,637161.0,639378.37,34390.85,1867849.02,1193410.46,348186.2,1440296.14,799483.57,969631.31,483544.3,326349.91,7546.88,2469.75,1741.24,553.25,6606.31,2075.94,84212.94,312108.51,1647.93,3533.32,7659.86,7203.07,116206.75,30331.38,2063.77,3035.35,123797.1,8233.25,2271.34,3801.74,1530.83,4833.96,5842.61,2871.89,5297.36,621561.65,829.07,3418.07,41924.75,173597.57,77642.84,144964.23,94038.9,26051.33,134143.27,497424.19,607616.73,681093.57,114057.61,205279.62,173512.93,166817.98,113283.13,179030.05,94332.0,141111.15,45756.23,6039.61,46261.02,312471.16,195490.04,107308.84,48252.82,25674.03,63830.49,96178.8,155122.86,171757.76,8809.04,27320.2,517915.91,34629.16,94296.25,285360.06,161445.91,386683.04,169854.57,126167.7,152757.58,49661.18,73513.5,166335.32,234268.82,306280.12,194620.68,128278.74,256773.54,105759.12,59390.46,131991.26,65025.3,82054.36,69708.79,9104.26,92553.64,66312.08,142282.63,114499.14,76635.24,132574.0,39485.21,236825.42,327066.19,156819.6,13136.44,4306.89,8309.52,5363.35,132.42,19644.08,895.24,7641.08,31188.22,13954.75,10497.33,5843.75,7260.2,10285.86,6249.78,848.74,6993.7,795.29,9191.46,132038.41,6495.27,1679.68,47477.97,1260.62,15034.23,3238.39,251241.14,3644.12,283123.48,2851.83,94726.64,17020.69,6523.19,1383.56,1580.66,2466.95,1017.85,2270.41,102265.88,13758.63,5221.77,12256.66,8227.3,527.15,950.25,12728.46,1002.96,6917.1,1977.17,9314.84,1157.05,3766.99,3464.41,68662.06,8679.29,15266.05,8348.75,8419.73,6688.74,1979.94,9574.31,5287.68,10627.18,5809.78,411363.54,4086.5,19741.22,5270.72,55827.09,174068.82,1277212.77,1277212.77,24105.5,6854.06,111899.09,1231.79,3380.1,2869.65,1658.87,823.36,7541.47,3635.93,1328.59,56145.85,973.18,619.24,300.41,700967.19,8354.27,5423.28,708.59,8358.81,2245.62,4593.59,399.66,1617.9,2167.26,9738.95,3396.25,6780.78,2284.54,502.61,8716.47,209370.56,117023.79,10891.96,4149.86,13252.81,1213.64,5800.85,8094.53,3825.39,12162.01,472385.25,13427.34,1873.13,6443.58,4008.01,197186.52,1220.37,1683.95,2713.67,4867.56,242940.23,4433.98,2168.8,10059.7,8092.1,11770.54,1576.78,320.53,3390.74,2736.35,6029.92,7506.65,12621.09,1582.67,881.26,4651.67,8074.16,4337.08,429.42,7645.96,221654.28,10932.41,4518.42,1616.52,3545.37,159334.62,334262.17,5403.87,13288.84,7079.24,4451.95,25554.79,6757.51,2756.3,4163.82,5416.8,8356.44,7257.7,5570.5,9181.99,663.34,1532.35,1180.55,7950.29,5334.81,760.21,1697.56,112.56,13373.96,6606.47,208501.33,1045.63,363482.02,2935.86,88626.65,540789.56,10430.39,4279.07,3537.25,66225.76,964.77,12178.51,310157.55,2272.54,6078.56,4108.8,2671.31,12426.38,17373.04,7272.63,6548.43,2644.63,6776.93,1810.41,2586.39,47564.74,5485.04,2644.54,9361.47,1193.24,9549.7,593.4,1122.14,264361.19,8148.08,8264.22,3393.22,2392.53,5324.43,160700.51,8901.32,9864.23,8791.02,35063.63,35063.63,1066.04,5430.64,161796.96,107292.25,6488.6,1617.97,427175.03,6765.12,3269.72,9499.4,865.77,151096.79,4244.4,885.9,4322.17,7664.12,3644.85,12462.9,3014.54,5880.33,14025.72,12187.88,10777.89,708.82,5633.3,2223.41,2195.63,1668.11,281425.06,18561.23,39069.42,63186.93,419757.69,127920.4,11136.72,33782.98,3776389.09,1127058.69,112401.53,668237.36,789419.02,569068.84,682894.68,443116.03,3942.44,539430.95,598782.65,600581.61,221297.05,249592.99,5170.23,8553.73,9980.32,11982.62,2763.95,265763.02,2364.01,1432.47,233.76,13259.12,2364.34,3903.48,301298.69,6623.21,1009.43,1639.33,2033.14,45864.43,11411.34,23.31,613.83,7154.46,1091.54,3193.99,1849.56,4170.82,4099.91,5923.01,208376.61,131033.44,223848.92,68332.36,205368.78,424141.36,373185.07,2258388.15,250968.18,19772.53,43023.82,1628887.72,755499.13,289203.45,902485.36,351854.11,605790.2,353312.93,855931.4,699928.86,5886.25,200774.62,1088.94,3500.0,109552.96,59531.87,133637.07,75752.73,100362.16,191177.92,435589.96,1950.77,2223005.62,902505.61,1201716.66,494972.01,1417001.19,3445.96,2650.82,2073.2,319825.94,129431.13,105351.94,38167.0,4682.74,1280.14,3039.84,5998.0,4273.0,3637.13,892.53,10707.6,6380.75,271161.74,274108.4,2676.97,7512.61,8612.28,413.4,6794.39,9247.18,2821.51,378703.81,9818.95,13268.46,378896.52,65671.39,35492.2,18510.43,83587.86,62455.76,44708.03,343189.13,60215.46,235288.68,206955.99,383446.61,145095.94,300632.87,119081.76,203148.34,104335.07,251482.63,92784.06,21670.38,175443.85,226031.29,234052.7,229848.96,111655.85,51284.87,270.78,154442.99,34918.59,57262.72,189703.56,288838.13,61505.91,404167.6,57323.56,68988.21,74230.21,377287.8,185897.79,173842.23,31646.31,48134.99,229982.47,216806.48,117257.18,174413.77,72897.92,82564.24,63764.9,183074.81,223744.8,46179.38,91611.03,128022.19,157949.72,26516.56,105647.45,61858.19,245041.35,42922.81,137465.55,170207.14,18513.77,199318.34,26295.3,111549.83,56912.01,414285.51,298109.36,269736.99,18622.16,183816.31,143405.8,449.97,2743.55,1548.73,4828.43,1737.12,1089.31,5237.7,3364.48,1889.41,2685.44,1238.08,17994.24,6396.49,2380.21,139754.72,8787.63,2261.24,2807.73,4247.14,4504.48,3051.76,5675.84,194.4,8176.58,4074.17,13695.83,8092.22,286803.5,3620.25,153952.87,1335.03,96.32,412906.88,288638.46,2918.83,4852.65,13343.47,1672.55,1020.89,4440.79,12271.39,379953.15,9198.49,660.52,3485.64,413.84,3865.44,124832.65,302151.25,1854.51,2850.6,130905.88,8782.48,254286.27,10239.16,7276.69,4993.81,8531.44,4297.27,7287.73,5375.2,3830.45,70543.76,3558.79,6891.56,2875.66,4632.68,13693.11,81222.77,2801.1,273951.34,490341.11,226.29,204691.97,3399.54,261.95,1815.83,10287.6,2633.08,8818.07,271.77,7017.21,16709.74,2020.04,3601.28,6027.22,7886.77,421520.68,2444.49,2786.6,2186.58,265803.38,12363.37,3014.72,9533.48,139555.44,13525.15,5134.7,4881.15,130470.14,722161.86,5085.47,4617.63,3941.6,1471.39,5480.96,1834.74,365914.64,16472.92,31922.09,6450.56,315325.25,2869.11,2738.86,60503.37,450.55,8161.64,40149.38,5829.39,9119.89,3854.39,1053.13,7296.95,9114.34,18625.28,2134.11,3743.03,17457.7,1098.78,1393.49,3466.83,12960.72,3383.61,3285.68,4898.07,9642.19,1919.77,21820.68,18187.81,23069.76,6820.53,2635.06,5371.1,358124.44,2645.88,5454.05,2691.57,3269.57,3623.4,102.37,5601.3,151768.14,2725.19,1534.54,402.54,12617.81,3631.87,333946.96,7648.78,11679.19,272.8,1380.95,29587.32,1056.64,2515.46,6077.94,8648.45,5264.43,3821.75,4920.42,5635.6,11508.76,2147.21,8380.77,3850.6,5238.73,6173.13,1196.9,150.07,624.1,1012.86,3688.28,26112.94,286590.92,5430.53,13840.98,11479.22,119570.96,818.99,2736.66,6166.35,4052.14,6730.59,259096.62,21502.38,1810.88,700.86,7273.9,4845.31,1153.98,1624.03,8076.53,1313.6,6128.41,11301.82,4508.96,2588.24,909.33,7123.18,4449.8,663.34,4043.76,15350.84,11917.23,14145.51,28301.88,6976.97,19572.25,17389.94,12123.91,20007.91,18389.77,5947.8,804.16,14711.9,2407.47,2074.51,5711.01,21897.3,5462.01,2185.54,5655.43,727.94,7868.9,4029.56,2637.07,3129.06,6899.83,38366.56,6778.32,96015.94,2304.2,5575.09,1667.88,24659.46,221557.88,1117.17,7731.23,69490.69,5437.85,3984.3,5630.13,3399.78,7883.75,635.55,2248.26,9261.94,9614.73,30308.45,1012.84,6534.05,9460.66,2552.79,5912.4,8009.83,8625.97,244964.08,5663.87,1892.34,561.01,3218.06,9731.47,4018.12,6376.99,5983.88,280.59,5263.67,176370.68,62783.13,90956.1,120556.62,475857.13,239510.08,50938.65,288438.0,10349.31,7672.12,1203.13,1635.86,5072.52,3176.52,2891.7,1557.98,19316.5,8212.99,5284.56,16265.77,14054.64,11334.96,12445.65,42.0,10685.2,5778.99,7366.3,10810.73,5935.34,8539.76,1703.38,7566.04,186.33,10425.95,4764.33,9632.45,1573.42,9125.93,119352.09,221452.04,354832.51,3677.29,16749.98,10142.56,182385.22,4448.53,58408.09,141.87,793.64,98247.27,7315.54,3599.77,6831.38,2419.45,94740.26,6577.3,12429.59,6522.37,12762.36,11287.39,10884.19,1592.5,15751.26,351.26,3734.21,8403.66,9945.01,12491.01,7889.84,5169.09,13496.51,3548.72,9273.52,4522.66,997.34,3041.43,698.41,7269.74,8329.3,2312.71,7550.29,17396.31,3876.42,3835.42,18792.11,27.55,218753.79,42963.87,92184.04,669936.4,43198.52,171683.57,34784.78,521419.58,209859.04,61650.32,235362.23,327686.42,348463.93,137209.38,279846.24,159270.52,19872.97,203283.36,37475.31,151764.65,72311.62,198443.49,532111.14,839300.36,11436.9,37554.62,33499.16,283308.0,147844.59,65525.9,10844.67,455440.65,135327.21,211094.41,189595.83,66413.56,4552.47,1285.04,7627.89,846.91,206336.47,689.85,9837.13,9463.48,6456.26,96833.52,1146.72,13812.23,332573.12,13812.46,6159.72,11711.1,5881.78,1461.06,4242.85,2724.24,2344.79,8525.08,15640.06,1533.55,3256.43,4591.98,5567.55,4213.36,3970.45,4497.03,301706.01,3065.41,229316.19,1898.84,652.67,1795.14,5088.99,9305.93,498178.68,1897.66,1033.44,1252.27,4911.89,9755.38,300932.58,47940.14,118365.47,16626.81,96757.4,297448.33,287509.05,1212719.61,210650.2,1636507.63,651387.84,548288.31,2107293.71,1918461.7,291753.7,556542.19,588.33,3559.49,2488.09,353.9,8440.86,9160.94,11063.08,18392.23,2357.63,5157.05,5746.44,5607.36,6360.79,67200.07,2453.34,79.26,3758.16,24495.45,1284.49,3283.87,42136.28,27008.84,331545.25,42529.28,252129.73,473469.94,2317408.88,864389.49,276094.92,132355.33,169106.97,2604219.11,249740.75,7977.96,7848.22,7625.73,3499.43,642.26,41295.56,13576.03,29572.74,53569.21,139247.62,60360.45,161795.9,607537.17,254399.35,427823.47,16662.21,173570.67,54684.17,87963.9,565349.9,94527.39,315552.62,48169.69,118753.07,106705.8,328967.35,55855.58,370751.08,49177.38,63557.19,33663.22,20986.8,301680.88,9320.15,241488.05,60850.8,476934.35,598674.03,135357.16,422125.99,57643.24,11261.67,416106.78,4447.55,25071.46,25071.46,3239.95,10286.48,2011.81,9520.02,4273.56,26844.84,634.41,58516.68,5077.6,4471.34,6525.58,12749.19,7652.07,450465.15,17540.66,2673.64,4215.08,3468.74,244.09,137.88,7155.31,2432.88,9021.44,4476.2,14546.65,272709.33,7669.42,14750.26,107421.26,36448.64,12939.66,198137.4,112193.65,40030.78,207283.84,141648.13,361980.67,614317.64,233255.73,1420.96,132842.64,4881.52,14403.17,4843.76,7143.2,2279.65,2571.49,2873.37,7394.33,21902.51,1730.16,11491.76,14204.94,14432.6,2501.21,27845.63,900.37,28147.47,3782.98,4828.64,6540.9,21840.26,5805.93,5018.24,1066.91,1902.02,1982.04,7020.05,11488.23,1253.6,10183.1,146534.96,5616.1,5272.81,236343.99,86401.93,3923.32,289326.88,4444.35,5323.28,2141.82,328.07,1439.72,4416.74,84051.0,1246.74,1527.08,38829.9,749.26,2560.3,383736.31,917.38,6905.9,3611.72,8949.54,2193.94,9479.66,10757.7,6259.18,7003.44,6872.19,911.27,7694.63,11353.99,6683.73,4261.17,6146.81,4479.63,144659.77,53094.17,43721.1,119537.32,33349.84,335979.26,183842.53,286737.98,472609.82,271982.1,154611.64,125275.0,443200.31,256481.43,236277.01,7071.77,6511.48,8592.89,2356.99,287126.39,7363.65,4613.1,3787.43,10543.05,10719.85,3857.29,7028.85,2402.16,3375.94,8999.94,2562.66,8480.48,9471.15,2569.57,4470.38,1068.22,1128.69,3488.78,6086.87,688.53,2778.17,5078.54,5610.36,5202.26,5785.77,7299.46,47440.86,7538.0,4372.05,748.42,1296.58,2545.15,2527.41,8361.63,216007.53,2092.06,3920.09,2224.06,5284.22,1632.21,5965.13,56565.27,5130.16,10364.62,577.01,9420.9,3735.16,982.67,9469.26,32282.57,2673.65,12911.82,9334.02,3783.07,7102.49,4804.74,291713.22,2358.9,461.65,4457.72,633.75,7891.16,1522.55,3660.66,44670.27,64945.57,4461.08,3223.87,3224.21,79386.85,7415.77,6.42,1559.66,2548.85,271.32,1454.92,2666.96,3180.02,2550.35,842.33,16085.74,4223.24,1993.24,457552.45,4894.96,7331.22,7286.11,6258.29,16859.52,4416.9,4139.95,25642.6,6394.36,53.35,1902.1,5568.56,406.11,15028.75,32101.86,6473.96,7577.43,20135.64,18832.61,11468.9,138647.54,3870.28,4627.42,2517.79,100939.43,3455.98,1068.45,454.51,302665.03,885.95,4137.89,5504.59,3780.15,3504.75,2716.67,4219.94,5831.05,9855.36,10726.49,1833.22,34115.82,2210.66,22190.99,168451.53,123545.77,28463.91,42012.45,170686.08,283561.25,215742.75,56869.53,13553.79,192461.6,357220.77,32726.88,28212.4,360997.83,141632.37,332359.2,78423.86,131011.3,215798.0,65236.81,65934.11,336237.38,293530.31,551607.7,80448.13,58447.8,36860.96,99295.78,49031.62,134469.08,265098.67,65591.87,266803.57,616461.19,80487.01,46442.24,50137.65,474447.95,70139.43,201342.13,28625.92,125492.59,82550.39,114275.55,43906.79,131250.53,4865.48,283711.39,8921.26,91721.95,94096.34,157964.89,51311.41,46757.44,15069.79,783.31,25927.54,42672.03,49236.53,16236.25,276772.02,95956.43,632840.5,863.08,4058.66,12040.95,4685.16,218499.73,107440.87,5055.39,7846.95,4412.45,14736.92,17488.56,264.93,222321.36,8972.41,4079.37,6226.79,6490.69,7837.56,4348.75,46961.7,373610.77,90667.11,114580.22,4448.09,549884.88,3697.53,6350.75,3154.47,23430.56,5875.05,2261.94,219630.79,10021.49,2349.71,512866.85,4280.41,471.01,1939.39,2399.75,156232.74,930.99,331.4,11607.88,3603.99,159657.1,68051.17,2607.6,345858.07,6990.18,11020.46,194415.49,7434.58,6111.1,7991.07,299976.78,78772.04,85584.66,187256.62,629989.76,29025.9,27948.65,91223.06,62684.91,355252.74,374419.58,71151.31,66358.89,346761.87,291839.31,285578.26,398354.37,112637.63,162591.2,193448.55,334092.55,90380.34,160053.19,286279.93,3879.16,158483.1,240680.1,42198.41,280403.82,421555.71,150550.59,83153.23,269603.45,188071.25,263590.95,373152.4,121854.66,31657.28,428248.35,323406.83,258938.11,226945.73,171505.17,184547.7,155608.53,69865.39,44655.46,411935.8,552812.46,84022.18,57357.33,286.87,11068.42,3524.08,11430.86,6769.91,8271.42,452.57,235238.66,235238.66,2707.76,200612.49,4253.87,3619.35,22940.17,5376.67,6822.41,9855.76,186.22,4189.31,26784.93,4623.81,2765.61,1637.53,2893.12,8246.79,24174.02,5340.28,2369.48,3009.63,32535.91,5230.88,163.72,2388.85,6980.56,153596.45,436.15,1928.87,884.42,2655.04,4528.17,36010.9,183465.11,57716.15,4074.2,601898.44,4601.56,2286.28,10855.04,7590.6,4887.09,637.26,149617.83,108019.99,5509.5,83845.22,7059.05,462688.96,75915.44,383533.35,2270.53,952.08,7855.62,347391.16,10255.93,10729.47,2288.61,5674.62,4546.83,23235.78,3697.26,1215.86,4377.04,5899.63,12128.78,10972.97,14157.15,22941.92,14223.81,62820.46,6001.21,12273.24,594.09,145515.24,6598.82,15326.3,6705.75,11608.18,6848.97,1130.44,2279.92,6913.46,3837.3,2589.19,2388.2,2232.83,93413.63,2221.22,214202.66,2387.09,10493.4,6433.58,136564.27,9800.44,6983.76,1994.48,2064.34,10808.31,10019.06,6650.0,9591.5,9769.9,727.25,6221.43,1361.29,4594.28,12122.55,93459.27,1937.5,2421.15,2840.25,279422.78,3215.74,13890.24,8371.5,5954.9,9370.51,9496.55,3004.57,4351.58,4674.0,2518.13,6365.0,26035.82,12000.58,5717.58,15646.06,4905.54,7029.12,2374.49,6579.9,2481.59,6720.15,6426.8,5274.17,2475.77,4149.27,11380.13,119542.86,25424.36,289715.28,3149.19,18952.73,2983.5,13.54,6159.35,4209.57,6.93,7055.84,1058.63,95983.62,5973.9,6397.18,3076.45,197761.38,100740.53,14166.38,134543.6,48206.82,282944.06,19270.14,5493.19,5983.51,2388.24,6381.32,9899.52,2368.4,5764.6,3236.86,4209.87,11388.1,124041.53,12061.28,1072.36,189476.67,8054.62,457194.09,219583.95,6015.17,6748.38,271566.78,190398.63,149651.68,84527.66,1030100.15,55091.05,318209.62,294676.6,294304.91,168276.37,38583.71,2338.05,276273.67,10928.31,11335.23,924.07,7677.95,132540.87,170914.25,17487.62,230892.79,734657.1,452932.69,70299.66,245679.75,300768.79,90697.75,130280.74,72984.81,336230.03,75162.99,147987.48,76305.39,18194.34,68931.2,231460.09,156316.71,232720.95,18536.86,89380.43,568912.68,3351.14,4564.51,3050.39,942.95,121.27,3798.36,2246.11,1217.83,1285.77,25767.46,6054.6,1816.52,5171.22,1566.11,7014.89,3858.3,27018.5,2879.51,18975.26,3056.61,798.26,2807.18,2746.12,5256.18,2153.73,4378.75,16910.42,4195.41,247199.96,2505.99,5986.18,6794.37,5029.67,4489.66,5193.39,4188.03,4975.75,91424.21,1780.48,2211.92,9245.27,9467.87,519973.96,7071.42,166808.69,7852.4,4453.49,1880.05,8607.73,5111.72,232.74,8326.13,10169.55,4655.03,2732.73,6948.21,92236.11,104267.94,176127.45,94087.34,172867.65,277611.59,1755646.72,2441078.3,1147953.82,395709.23,168001.93,2032275.62,44944.32,957339.52,459709.79,637758.38,525.22,3847.27,14126.82,5478.51,194351.55,1039.62,230943.69,6904.63,3065.84,5641.56,3422.04,1380.16,681.05,2064.02,12605.06,8923.42,7761.72,2274.37,10273.19,2673.52,6022.64,891.93,5055.22,5726.49],\"xaxis\":\"x\",\"y\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1332,1333,1334,1335,1336,1337,1338,1339,1340,1341,1342,1343,1344,1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1355,1356,1357,1358,1359,1360,1361,1362,1363,1364,1365,1366,1367,1368,1369,1370,1371,1372,1373,1374,1375,1376,1377,1378,1379,1380,1381,1382,1383,1384,1385,1386,1387,1388,1389,1390,1391,1392,1393,1394,1395,1396,1397,1398,1399,1400,1401,1402,1403,1404,1405,1406,1407,1408,1409,1410,1411,1412,1413,1414,1415,1416,1417,1418,1419,1420,1421,1422,1423,1424,1425,1426,1427,1428,1429,1430,1431,1432,1433,1434,1435,1436,1437,1438,1439,1440,1441,1442,1443,1444,1445,1446,1447,1448,1449,1450,1451,1452,1453,1454,1455,1456,1457,1458,1459,1460,1461,1462,1463,1464,1465,1466,1467,1468,1469,1470,1471,1472,1473,1474,1475,1476,1477,1478,1479,1480,1481,1482,1483,1484,1485,1486,1487,1488,1489,1490,1491,1492,1493,1494,1495,1496,1497,1498,1499,1500,1501,1502,1503,1504,1505,1506,1507,1508,1509,1510,1511,1512,1513,1514,1515,1516,1517,1518,1519,1520,1521,1522,1523,1524,1525,1526,1527,1528,1529,1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541,1542,1543,1544,1545,1546,1547,1548,1549,1550,1551,1552,1553,1554,1555,1556,1557,1558,1559,1560,1561,1562,1563,1564,1565,1566,1567,1568,1569,1570,1571,1572,1573,1574,1575,1576,1577,1578,1579,1580,1581,1582,1583,1584,1585,1586,1587,1588,1589,1590,1591,1592,1593,1594,1595,1596,1597,1598,1599,1600,1601,1602,1603,1604,1605,1606,1607,1608,1609,1610,1611,1612,1613,1614,1615,1616,1617,1618,1619,1620,1621,1622,1623,1624,1625,1626,1627,1628,1629,1630,1631,1632,1633,1634,1635,1636,1637,1638,1639,1640,1641,1642,1643,1644,1645,1646,1647,1648,1649,1650,1651,1652,1653,1654,1655,1656,1657,1658,1659,1660,1661,1662,1663,1664,1665,1666,1667,1668,1669,1670,1671,1672,1673,1674,1675,1676,1677,1678,1679,1680,1681,1682,1683,1684,1685,1686,1687,1688,1689,1690,1691,1692,1693,1694,1695,1696,1697,1698,1699,1700,1701,1702,1703,1704,1705,1706,1707,1708,1709,1710,1711,1712,1713,1714,1715,1716,1717,1718,1719,1720,1721,1722,1723,1724,1725,1726,1727,1728,1729,1730,1731,1732,1733,1734,1735,1736,1737,1738,1739,1740,1741,1742,1743,1744,1745,1746,1747,1748,1749,1750,1751,1752,1753,1754,1755,1756,1757,1758,1759,1760,1761,1762,1763,1764,1765,1766,1767,1768,1769,1770,1771,1772,1773,1774,1775,1776,1777,1778,1779,1780,1781,1782,1783,1784,1785,1786,1787,1788,1789,1790,1791,1792,1793,1794,1795,1796,1797,1798,1799,1800,1801,1802,1803,1804,1805,1806,1807,1808,1809,1810,1811,1812,1813,1814,1815,1816,1817,1818,1819,1820,1821,1822,1823,1824,1825,1826,1827,1828,1829,1830,1831,1832,1833,1834,1835,1836,1837,1838,1839,1840,1841,1842,1843,1844,1845,1846,1847,1848,1849,1850,1851,1852,1853,1854,1855,1856,1857,1858,1859,1860,1861,1862,1863,1864,1865,1866,1867,1868,1869,1870,1871,1872,1873,1874,1875,1876,1877,1878,1879,1880,1881,1882,1883,1884,1885,1886,1887,1888,1889,1890,1891,1892,1893,1894,1895,1896,1897,1898,1899,1900,1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060,2061,2062,2063,2064,2065,2066,2067,2068,2069,2070,2071,2072,2073,2074,2075,2076,2077,2078,2079,2080,2081,2082,2083,2084,2085,2086,2087,2088,2089,2090,2091,2092,2093,2094,2095,2096,2097,2098,2099,2100,2101,2102,2103,2104,2105,2106,2107,2108,2109,2110,2111,2112,2113,2114,2115,2116,2117,2118,2119,2120,2121,2122,2123,2124,2125,2126,2127,2128,2129,2130,2131,2132,2133,2134,2135,2136,2137,2138,2139,2140,2141,2142,2143,2144,2145,2146,2147,2148,2149,2150,2151,2152,2153,2154,2155,2156,2157,2158,2159,2160,2161,2162,2163,2164,2165,2166,2167,2168,2169,2170,2171,2172,2173,2174,2175,2176,2177,2178,2179,2180,2181,2182,2183,2184,2185,2186,2187,2188,2189,2190,2191,2192,2193,2194,2195,2196,2197,2198,2199,2200,2201,2202,2203,2204,2205,2206,2207,2208,2209,2210,2211,2212,2213,2214,2215,2216,2217,2218,2219,2220,2221,2222,2223,2224,2225,2226,2227,2228,2229,2230,2231,2232,2233,2234,2235,2236,2237,2238,2239,2240,2241,2242,2243,2244,2245,2246,2247,2248,2249,2250,2251,2252,2253,2254,2255,2256,2257,2258,2259,2260,2261,2262,2263,2264,2265,2266,2267,2268,2269,2270,2271,2272,2273,2274,2275,2276,2277,2278,2279,2280,2281,2282,2283,2284,2285,2286,2287,2288,2289,2290,2291,2292,2293,2294,2295,2296,2297,2298,2299,2300,2301,2302,2303,2304,2305,2306,2307,2308,2309,2310,2311,2312,2313,2314,2315,2316,2317,2318,2319,2320,2321,2322,2323,2324,2325,2326,2327,2328,2329,2330,2331,2332,2333,2334,2335,2336,2337,2338,2339,2340,2341,2342,2343,2344,2345,2346,2347,2348,2349,2350,2351,2352,2353,2354,2355,2356,2357,2358,2359,2360,2361,2362,2363,2364,2365,2366,2367,2368,2369,2370,2371,2372,2373,2374,2375,2376,2377,2378,2379,2380,2381,2382,2383,2384,2385,2386,2387,2388,2389,2390,2391,2392,2393,2394,2395,2396,2397,2398,2399,2400,2401,2402,2403,2404,2405,2406,2407,2408,2409,2410,2411,2412,2413,2414,2415,2416,2417,2418,2419,2420,2421,2422,2423,2424,2425,2426,2427,2428,2429,2430,2431,2432,2433,2434,2435,2436,2437,2438,2439,2440,2441,2442,2443,2444,2445,2446,2447,2448,2449,2450,2451,2452,2453,2454,2455,2456,2457,2458,2459,2460,2461,2462,2463,2464,2465,2466,2467,2468,2469,2470,2471,2472,2473,2474,2475,2476,2477,2478,2479,2480,2481,2482,2483,2484,2485,2486,2487,2488,2489,2490,2491,2492,2493,2494,2495,2496,2497,2498,2499,2500,2501,2502,2503,2504,2505,2506,2507,2508,2509,2510,2511,2512,2513,2514,2515,2516,2517,2518,2519,2520,2521,2522,2523,2524,2525,2526,2527,2528,2529,2530,2531,2532,2533,2534,2535,2536,2537,2538,2539,2540,2541,2542,2543,2544,2545,2546,2547,2548,2549,2550,2551,2552,2553,2554,2555,2556,2557,2558,2559,2560,2561,2562,2563,2564,2565,2566,2567,2568,2569,2570,2571,2572,2573,2574,2575,2576,2577,2578,2579,2580,2581,2582,2583,2584,2585,2586,2587,2588,2589,2590,2591,2592,2593,2594,2595,2596,2597,2598,2599,2600,2601,2602,2603,2604,2605,2606,2607,2608,2609,2610,2611,2612,2613,2614,2615,2616,2617,2618,2619],\"yaxis\":\"y\",\"type\":\"scattergl\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Transaction Amount (in \\u20ac)\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Transactions\"}},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"isFraud\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Distribution of Amount Values\"}},                        {\"responsive\": true}                    ).then(function(){\n",
1679 |               "                            \n",
1680 |               "var gd = document.getElementById('935ea706-dde8-42d2-b5b5-cc14a2223900');\n",
1681 |               "var x = new MutationObserver(function (mutations, observer) {{\n",
1682 |               "        var display = window.getComputedStyle(gd).display;\n",
1683 |               "        if (!display || display === 'none') {{\n",
1684 |               "            console.log([gd, 'removed!']);\n",
1685 |               "            Plotly.purge(gd);\n",
1686 |               "            observer.disconnect();\n",
1687 |               "        }}\n",
1688 |               "}});\n",
1689 |               "\n",
1690 |               "// Listen for the removal of the full notebook cells\n",
1691 |               "var notebookContainer = gd.closest('#notebook-container');\n",
1692 |               "if (notebookContainer) {{\n",
1693 |               "    x.observe(notebookContainer, {childList: true});\n",
1694 |               "}}\n",
1695 |               "\n",
1696 |               "// Listen for the clearing of the current output cell\n",
1697 |               "var outputEl = gd.closest('.output');\n",
1698 |               "if (outputEl) {{\n",
1699 |               "    x.observe(outputEl, {childList: true});\n",
1700 |               "}}\n",
1701 |               "\n",
1702 |               "                        })                };                            </script>        </div>\n",
1703 |               "</body>\n",
1704 |               "</html>"
1705 |             ]
1706 |           },
1707 |           "metadata": {}
1708 |         }
1709 |       ]
1710 |     },
1711 |     {
1712 |       "cell_type": "code",
1713 |       "source": [
1714 |         "X = data.drop(columns=['isFraud'], axis=1)\n",
1715 |         "Y = data.isFraud"
1716 |       ],
1717 |       "metadata": {
1718 |         "id": "tvcLfN924yHi"
1719 |       },
1720 |       "execution_count": 49,
1721 |       "outputs": []
1722 |     },
1723 |     {
1724 |       "cell_type": "code",
1725 |       "source": [
1726 |         "from sklearn.model_selection import train_test_split\n",
1727 |         "xtrain,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.27,random_state=41)"
1728 |       ],
1729 |       "metadata": {
1730 |         "id": "ngE3UCEz0R-g"
1731 |       },
1732 |       "execution_count": 50,
1733 |       "outputs": []
1734 |     },
1735 |     {
1736 |       "cell_type": "code",
1737 |       "source": [
1738 |         "#handling data imbalance using smote\n",
1739 |         "from imblearn.over_sampling import SMOTE\n",
1740 |         "xtrain,ytrain = SMOTE().fit_resample(xtrain,ytrain)"
1741 |       ],
1742 |       "metadata": {
1743 |         "id": "-Xj5aj1p6Wub"
1744 |       },
1745 |       "execution_count": 51,
1746 |       "outputs": []
1747 |     },
1748 |     {
1749 |       "cell_type": "code",
1750 |       "source": [
1751 |         "ytrain.value_counts()"
1752 |       ],
1753 |       "metadata": {
1754 |         "colab": {
1755 |           "base_uri": "https://localhost:8080/"
1756 |         },
1757 |         "id": "c48pQ-yB6nMF",
1758 |         "outputId": "dc50a523-e903-498f-8562-01028fea03d5"
1759 |       },
1760 |       "execution_count": 52,
1761 |       "outputs": [
1762 |         {
1763 |           "output_type": "execute_result",
1764 |           "data": {
1765 |             "text/plain": [
1766 |               "0    1900\n",
1767 |               "1    1900\n",
1768 |               "Name: isFraud, dtype: int64"
1769 |             ]
1770 |           },
1771 |           "metadata": {},
1772 |           "execution_count": 52
1773 |         }
1774 |       ]
1775 |     },
1776 |     {
1777 |       "cell_type": "code",
1778 |       "source": [
1779 |         "xtrain.info()"
1780 |       ],
1781 |       "metadata": {
1782 |         "colab": {
1783 |           "base_uri": "https://localhost:8080/"
1784 |         },
1785 |         "id": "d1Zio1jo6w0N",
1786 |         "outputId": "97449573-636e-4f87-a4f6-f92d7ab8ae4a"
1787 |       },
1788 |       "execution_count": 53,
1789 |       "outputs": [
1790 |         {
1791 |           "output_type": "stream",
1792 |           "name": "stdout",
1793 |           "text": [
1794 |             "<class 'pandas.core.frame.DataFrame'>\n",
1795 |             "RangeIndex: 3800 entries, 0 to 3799\n",
1796 |             "Data columns (total 8 columns):\n",
1797 |             " #   Column          Non-Null Count  Dtype  \n",
1798 |             "---  ------          --------------  -----  \n",
1799 |             " 0   step            3800 non-null   int64  \n",
1800 |             " 1   type            3800 non-null   int64  \n",
1801 |             " 2   amount          3800 non-null   float64\n",
1802 |             " 3   oldbalanceOrg   3800 non-null   float64\n",
1803 |             " 4   newbalanceOrig  3800 non-null   float64\n",
1804 |             " 5   oldbalanceDest  3800 non-null   float64\n",
1805 |             " 6   newbalanceDest  3800 non-null   float64\n",
1806 |             " 7   isFlaggedFraud  3800 non-null   int64  \n",
1807 |             "dtypes: float64(5), int64(3)\n",
1808 |             "memory usage: 237.6 KB\n"
1809 |           ]
1810 |         }
1811 |       ]
1812 |     },
1813 |     {
1814 |       "cell_type": "code",
1815 |       "source": [
1816 |         "from sklearn.linear_model import LogisticRegression\n",
1817 |         "lr=LogisticRegression()\n",
1818 |         "lr.fit(xtrain,ytrain)"
1819 |       ],
1820 |       "metadata": {
1821 |         "colab": {
1822 |           "base_uri": "https://localhost:8080/",
1823 |           "height": 74
1824 |         },
1825 |         "id": "2rai8l9466iQ",
1826 |         "outputId": "6f625d27-07a4-414e-fadb-eb394177c654"
1827 |       },
1828 |       "execution_count": 54,
1829 |       "outputs": [
1830 |         {
1831 |           "output_type": "execute_result",
1832 |           "data": {
1833 |             "text/plain": [
1834 |               "LogisticRegression()"
1835 |             ],
1836 |             "text/html": [
1837 |               "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
1838 |             ]
1839 |           },
1840 |           "metadata": {},
1841 |           "execution_count": 54
1842 |         }
1843 |       ]
1844 |     },
1845 |     {
1846 |       "cell_type": "code",
1847 |       "source": [
1848 |         "ypred=lr.predict(xtest)\n"
1849 |       ],
1850 |       "metadata": {
1851 |         "id": "CJRIfdtS8ynz"
1852 |       },
1853 |       "execution_count": 61,
1854 |       "outputs": []
1855 |     },
1856 |     {
1857 |       "cell_type": "code",
1858 |       "source": [
1859 |         "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
1860 |         "print(\"accuracy_score :\", accuracy_score(ytest, ypred))\n",
1861 |         "print(\"recall_score :\", recall_score(ytest, ypred))\n",
1862 |         "print(\"precision_score :\", precision_score(ytest, ypred))\n"
1863 |       ],
1864 |       "metadata": {
1865 |         "colab": {
1866 |           "base_uri": "https://localhost:8080/"
1867 |         },
1868 |         "id": "Sqmyo3_t84E7",
1869 |         "outputId": "60d35975-5a83-4059-8573-821fb37236a4"
1870 |       },
1871 |       "execution_count": 57,
1872 |       "outputs": [
1873 |         {
1874 |           "output_type": "stream",
1875 |           "name": "stdout",
1876 |           "text": [
1877 |             "accuracy_score : 0.9081920903954802\n",
1878 |             "recall_score : 1.0\n",
1879 |             "precision_score : 0.057971014492753624\n"
1880 |           ]
1881 |         }
1882 |       ]
1883 |     },
1884 |     {
1885 |       "cell_type": "code",
1886 |       "source": [
1887 |         "from sklearn.metrics import confusion_matrix\n",
1888 |         "cm=confusion_matrix(ytest, ypred)\n",
1889 |         "print(cm)\n",
1890 |         "sns.heatmap(cm,annot=True)"
1891 |       ],
1892 |       "metadata": {
1893 |         "id": "x4KvjUaI9I_k",
1894 |         "colab": {
1895 |           "base_uri": "https://localhost:8080/",
1896 |           "height": 482
1897 |         },
1898 |         "outputId": "1fe5fa23-4cd0-4976-fb19-2447482beea8"
1899 |       },
1900 |       "execution_count": 59,
1901 |       "outputs": [
1902 |         {
1903 |           "output_type": "stream",
1904 |           "name": "stdout",
1905 |           "text": [
1906 |             "[[639  65]\n",
1907 |             " [  0   4]]\n"
1908 |           ]
1909 |         },
1910 |         {
1911 |           "output_type": "execute_result",
1912 |           "data": {
1913 |             "text/plain": [
1914 |               "<Axes: >"
1915 |             ]
1916 |           },
1917 |           "metadata": {},
1918 |           "execution_count": 59
1919 |         },
1920 |         {
1921 |           "output_type": "display_data",
1922 |           "data": {
1923 |             "text/plain": [
1924 |               "<Figure size 640x480 with 2 Axes>"
1925 |             ],
1926 |             "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAGdCAYAAACGtNCDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAApTklEQVR4nO3de1yUdd7/8TcIjIoCojIjGmp3llKeQsPpeKesVNSdK5X6M8NydXORUlKL1tSsDdcOlqVSVuJuuZXtbQdLzbC0Eg/RWoaHrNxIaQbRlKR1QGbuP/o5cl2igg3O2LyePa7HQ77XYT6z+zA+fT7f7/cK8Xg8HgEAAPx/of4OAAAABBaSAwAAYEByAAAADEgOAACAAckBAAAwIDkAAAAGJAcAAMCA5AAAABiQHAAAAIMwfwdwVHX5t/4OAQg43ROH+DsEICBtL9vUqM/35e+k8Dbn+uxZZ0rAJAcAAAQMd42/I/Ar2goAAMCAygEAAGYet78j8CuSAwAAzNwkBwAAoBZPkFcOmHMAAAAMqBwAAGBGWwEAABjQVgAAADiGygEAAGZBvgkSyQEAAGa0FQAAAI6hcgAAgBmrFQAAQG1sggQAAFALlQMAAMxoKwAAAAPaCgAAwMBd47ujgfbs2aNbb71VrVu3VrNmzdS9e3d9+umn3vMej0dTp05Vu3bt1KxZM6WkpGjnzp2GZ+zfv1/Dhw9XVFSUYmJiNGrUKB06dKjeMZAcAAAQIH788UdddtllCg8P1/Lly7V161Y9/vjjatWqlfeaWbNmac6cOcrLy9OGDRsUGRmp1NRUHT582HvN8OHDVVxcrFWrVmnZsmVau3atxowZU+84Qjwej8en3+w0VZd/6+8QgIDTPXGIv0MAAtL2sk2N+nzXtg989ixLt6vrfe19992nTz75RB999FGd5z0ej+Lj43XPPfdo4sSJkqSDBw/KarUqPz9fQ4cO1bZt25SYmKhNmzapT58+kqQVK1bouuuu0+7duxUfH3/KOKgcAABg5nb77HC5XKqoqDAcLperzo9966231KdPH918882Ki4tT7969tWDBAu/5Xbt2yeFwKCUlxTsWHR2t5ORkFRYWSpIKCwsVExPjTQwkKSUlRaGhodqwYUO9vj7JAQAAjSg3N1fR0dGGIzc3t85rv/32W82fP19dunTRypUrNXbsWN11111atGiRJMnhcEiSrFar4T6r1eo953A4FBcXZzgfFham2NhY7zWnwmoFAADMfLhaIScnR9nZ2YYxi8VS57Vut1t9+vTRI488Iknq3bu3vvzyS+Xl5SkjI8NnMZ0KlQMAAMx82FawWCyKiooyHCdKDtq1a6fExETDWLdu3VRSUiJJstlskiSn02m4xul0es/ZbDaVlZUZzh85ckT79+/3XnMqJAcAAASIyy67TDt27DCMffXVV+rYsaMkqXPnzrLZbCooKPCer6io0IYNG2S32yVJdrtdBw4cUFFRkfea1atXy+12Kzk5uV5x0FYAAMDE42n4/gS+MGHCBF166aV65JFHdMstt2jjxo167rnn9Nxzz0mSQkJCNH78eD388MPq0qWLOnfurAceeEDx8fEaNGiQpF8qDddcc41Gjx6tvLw8VVdXa9y4cRo6dGi9VipIJAcAABzPTzsk9u3bV0uXLlVOTo5mzJihzp0768knn9Tw4cO910yePFmVlZUaM2aMDhw4oMsvv1wrVqxQ06ZNvde8/PLLGjdunAYMGKDQ0FClp6drzpw59Y6DfQ6AAMY+B0DdGnufg8Obl/nsWU17Xe+zZ50pVA4AADDjxUsAAMAgyF+8RHIAAIDZabww6beEpYwAAMCAygEAAGa0FQAAgEGQT0ikrQAAAAyoHAAAYEZbAQAAGNBWAAAAOIbKAQAAZkFeOSA5AADAxF9vZQwUtBUAAIABlQMAAMxoKwAAAAOWMgIAAIMgrxww5wAAABhQOQAAwIy2AgAAMKCtAAAAcAyVAwAAzGgrAAAAA9oKAAAAx1A5AADALMgrByQHAACYBfmcA9oKAADAgMoBAABmtBUAAIBBkLcVSA4AADAL8soBcw4AAIABlQMAAMxoKwAAAAPaCgAAAMdQOQAAwCzIKwckBwAAmHk8/o7Ar2grAAAAAyoHAACY0VYAAAAGQZ4c0FYAAAAGVA4AADBjEyQAAGAQ5G0FkgMAAMxYyggAAHAMlQMAAMxoKwAAAIMgTw5oKwAAAAMqBwAAmAX5UkYqBwAAmHjcHp8dDTF9+nSFhIQYjq5du3rPHz58WJmZmWrdurVatGih9PR0OZ1OwzNKSkqUlpam5s2bKy4uTpMmTdKRI0caFAeVAwAAAsiFF16o999/3/tzWNixX9UTJkzQO++8oyVLlig6Olrjxo3T4MGD9cknn0iSampqlJaWJpvNpnXr1umHH37QbbfdpvDwcD3yyCP1joHkAAAAMz9OSAwLC5PNZjtu/ODBg3rhhRe0ePFi9e/fX5K0cOFCdevWTevXr1e/fv303nvvaevWrXr//fdltVrVq1cvPfTQQ7r33ns1ffp0RURE1CsG2goAAJh53D47XC6XKioqDIfL5TrhR+/cuVPx8fE699xzNXz4cJWUlEiSioqKVF1drZSUFO+1Xbt2VUJCggoLCyVJhYWF6t69u6xWq/ea1NRUVVRUqLi4uN5fn+QAAIBGlJubq+joaMORm5tb57XJycnKz8/XihUrNH/+fO3atUtXXHGFfvrpJzkcDkVERCgmJsZwj9VqlcPhkCQ5HA5DYnD0/NFz9UVbAQAAswZOJDyZnJwcZWdnG8YsFkud11577bXeP/fo0UPJycnq2LGjXnvtNTVr1sxnMZ0KlQMAAMzcbp8dFotFUVFRhuNEyYFZTEyMzj//fH399dey2WyqqqrSgQMHDNc4nU7vHAWbzXbc6oWjP9c1j+FESA4AADDzYXLwaxw6dEjffPON2rVrp6SkJIWHh6ugoMB7fseOHSopKZHdbpck2e12bdmyRWVlZd5rVq1apaioKCUmJtb7c2krAAAQICZOnKgbbrhBHTt2VGlpqaZNm6YmTZpo2LBhio6O1qhRo5Sdna3Y2FhFRUUpKytLdrtd/fr1kyQNHDhQiYmJGjFihGbNmiWHw6EpU6YoMzOz3tUKieQAAIDj+emVzbt379awYcO0b98+tW3bVpdffrnWr1+vtm3bSpJmz56t0NBQpaeny+VyKTU1VfPmzfPe36RJEy1btkxjx46V3W5XZGSkMjIyNGPGjAbFEeLxBMZLq6vLv/V3CAHLubdcT8x7UR+v/1SHD7uU0CFeD90/QRd1O/+U9372RbFuHzdZ53XupH8umtuoca5c/ZGeWfA37XE41bFDe00Ye7uuvPQSSVL1kSN6+rlF+qjwU+0u/UEtIiPVr29vTbjzdsW1bd2ocZ3NuicO8XcIQSfO1lYTp2bpyv52NW3WVCW7duv+u2foy8+3SZJy50zT74deb7jno9WFGj30Ln+EG7S2l21q1Of//MRonz2refYCnz3rTKFyEOAOVvykEXfeo0su7qm8xx9Sq5hofff9HkW1bHHKeyt+OqT7H3pMyUm9tG//gV8Vx8bPvtCUvzyu9/65qM7z/9qyVZOnz9Tdf7xdV112id5970PdlfOQlix8Wl3O7aTDh13auuMb/XHkMF1w3rmq+OknzXzqWY2790G99uKcXxUb4CtR0S31j2XPa8MnRRo97G7t33dAnc49RwcPVhiuW1uwTvfffey/xKpcVWc6VKBRkRwEuBdfXiJbXFs9/Odjy2A6xNdvxumMR59W2u+uVmiTUK1eW2g453a79cJLS/T6W8tVvu9HdUxorztHDtPAq684rThfeu1NXZbcR3cMv0mSlDXmNhVu+kyLX39b0yZnqWWLSD3/lHHrzvuzx2rYH8brB0eZ2tniTutzAV/6Q1aGfih1Gn7x7ykpPe66qqoqlZftO5Oh4Uzz4VLGsxGrFQLcBx+v14Vduyh7yl90ZdpQ3TQyU6+/tfyU9y195z3tLnVo7B3D6zy/4O+v6q0VBZo6KUtvvJSn2275ve6b8ag2/euL04rz8+JtsvfpZRi7NDlJnxdvO+E9hw79rJCQELVsGXlanwn4Wv/UK/Tl5m168vlcfVK8Uv9b8JJuvnXQcdddcmmSPileqeXrXte0WfcqplX0mQ8WjcuHOySejRpcOSgvL9eLL76owsJC725LNptNl156qUaOHOmdNAHf2F3q0KtvvKPbhgzW6NuG6MttXyl3dp7Cw8J043W/q/Oe777fo9nzF+pv8x5VWFiT485XVVXp+b+9qgVP5arXRd0kSee0b6fPvijWkjeXq2/vHg2Os3zfj2od28ow1ia2lcr3/Vjn9S5XlWbPf1HXpVylFpEkBwgM53Rsr2Ej05Wft1jPPrlQ3XtfqD//5R5VV1frjVffkSR9tHqd3nvnA+0p2aNzOnXQhPv/pOf+8ZSGXneH3H7cjx/wpQYlB5s2bVJqaqqaN2+ulJQUnX/+LxPinE6n5syZo5kzZ2rlypXq06fPSZ/jcrmO21c61OVq0DKLYOF2e3Rh1y4af+dISVK388/Tzm+/02tvvFtnclBTU6PJ0/+qzFG3qlNChzqfWbL7B/3nsEujx99vGK+uPqJu5/+X9+e+Kb8/FkeNW1XV1Yax6wf217TJWQ3+TtVHjuieBx6Rx+PRA5PGNfh+oLGEhIaq+PNtmv3IL7O/t335lbp0PVdDMwZ7k4N331jlvf6rbd9ox9av9f6mN3TJZUla/1HjTpLDGRTkbYUGJQdZWVm6+eablZeXp5CQEMM5j8ejO++8U1lZWd4XQJxIbm6uHnzwQcPYlEl3aerkuxsSTlBo2zpW/9UpwTB2bqdz9P6Hn9R5feXP/1Hx9p3avvMbPTL7l3/Bud0eeTwe9bwyTc/N/ouaNW0qSZr36IOytm1juD88PNz753/mH1vd8EXxds2e/6IWPjPLOxYZ2dz75zatW2nffmOVoHz/j2rT2lhNOJoYlDrL9OKcmVQNEFD2Osv19Q7jyqlvdv5bA6/vf8J7dn+3R/vLf1THzh1IDn5DPEFeBWpQcvD5558rPz//uMRAkkJCQjRhwgT17t37lM+pa5/p0J/2NCSUoNG7R6L+XbLbMPZdyZ4TTuBrEdlcS/8+3zD2yv8u08aiz/XEX/6s9u1s8rjdiogI1w/OvSdtISR0iPf+2VFWriZNmhjGaut5YTetL9qsEUOOVRYKN/1LPS/s5v35aGJQ8n2pXnx6pmKio078xQE/+NfGz9X5vI6GsU7nJqh094lfWGNtF6eY2GiVOZmgiN+OBk1ItNls2rhx4wnPb9y48bi3QdXl1+wzHWxGDBmkL4q367lFr6hkd6neee8Dvf7Wcg0bfGyd9ez5C5Xz0GOSpNDQUHU5t5PhiG0Vo4iICHU5t5OaN2uqyMjmGjksXbPmPKc3312lkt2l2rrja7285E29+e6qE4VyUrfecqM+WV+k/H/8U99+973mvvCSirfv1P+76QZJvyQG2X/+i4q379TMaZPldrtVvm+/yvftV3V19a//Hwrwgfxn/6GeSd31x7tHKqFzB10/OFW3jPi9Xn5xiSSpeWQzTZp2l3omXaT257RTvyv6at7fHlPJru/18Qcnr5jiLOP2+O44CzWocjBx4kSNGTNGRUVFGjBggDcRcDqdKigo0IIFC/TYY481SqDBqnu3C/Rk7gN6Ki9fefmL1b6dTffe/Uddn3qszFm+b79+cJad5CnHyxp9m1rFROv5v7+m70sdimoRqW4XnKfRt53epju9uyfqr9Pv1dPPLdJTz+arY4f2mpP7gLqc20mSVLZ3nz74eL0k6aaRmYZ7X3z6r7rk4oZPggR87cvNW5U1cpKy/5ypP93zB+0uKVXuA09o2T9XSJJqaty6IPE8DbolTS2jW2qvY68++XCDnvprnqqrSHJ/U87SVQa+0uAdEl999VXNnj1bRUVFqqmpkfTLdo1JSUnKzs7WLbfcclqBsEMicDx2SATq1tg7JFbOqHsZ+OmInPqyz551pjR4KeOQIUM0ZMgQVVdXq7y8XJLUpk0bw0Q2AABw9jrtHRLDw8PVrl07X8YCAEBgYLUCAAAwOEsnEvoK2ycDAAADKgcAAJgF+WoFkgMAAMxoKwAAABxD5QAAABPerQAAAIxoKwAAABxD5QAAALMgrxyQHAAAYMZSRgAAYBDklQPmHAAAAAMqBwAAmHiCvHJAcgAAgFmQJwe0FQAAgAGVAwAAzNghEQAAGNBWAAAAOIbKAQAAZkFeOSA5AADAxOMJ7uSAtgIAADCgcgAAgBltBQAAYEByAAAAagv27ZOZcwAAAAyoHAAAYBbklQOSAwAAzIJ792TaCgAAwIjKAQAAJsE+IZHkAAAAsyBPDmgrAAAAAyoHAACYBfmERJIDAABMgn3OAW0FAABgQOUAAACzIG8rUDkAAMDE4/b47DhdM2fOVEhIiMaPH+8dO3z4sDIzM9W6dWu1aNFC6enpcjqdhvtKSkqUlpam5s2bKy4uTpMmTdKRI0ca9NkkBwAAmLl9eJyGTZs26dlnn1WPHj0M4xMmTNDbb7+tJUuWaM2aNSotLdXgwYO952tqapSWlqaqqiqtW7dOixYtUn5+vqZOndqgzyc5AAAggBw6dEjDhw/XggUL1KpVK+/4wYMH9cILL+iJJ55Q//79lZSUpIULF2rdunVav369JOm9997T1q1b9dJLL6lXr1669tpr9dBDD2nu3LmqqqqqdwwkBwAAmHjcvjtcLpcqKioMh8vlOuFnZ2ZmKi0tTSkpKYbxoqIiVVdXG8a7du2qhIQEFRYWSpIKCwvVvXt3Wa1W7zWpqamqqKhQcXFxvb8/yQEAAGY+bCvk5uYqOjracOTm5tb5sa+88oo+++yzOs87HA5FREQoJibGMG61WuVwOLzX1E4Mjp4/eq6+WK0AAEAjysnJUXZ2tmHMYrEcd93333+vu+++W6tWrVLTpk3PVHh1onIAAICJL9sKFotFUVFRhqOu5KCoqEhlZWW6+OKLFRYWprCwMK1Zs0Zz5sxRWFiYrFarqqqqdODAAcN9TqdTNptNkmSz2Y5bvXD056PX1AfJAQAAZn5YrTBgwABt2bJFmzdv9h59+vTR8OHDvX8ODw9XQUGB954dO3aopKREdrtdkmS327VlyxaVlZV5r1m1apWioqKUmJhY71hoKwAAEABatmypiy66yDAWGRmp1q1be8dHjRql7OxsxcbGKioqSllZWbLb7erXr58kaeDAgUpMTNSIESM0a9YsORwOTZkyRZmZmXVWK06E5AAAABNPgO6QOHv2bIWGhio9PV0ul0upqamaN2+e93yTJk20bNkyjR07Vna7XZGRkcrIyNCMGTMa9DkhHo8nIN4uUV3+rb9DAAJO98Qh/g4BCEjbyzY16vPLBlzls2fFFazx2bPOFCoHAACYBGrl4ExhQiIAADCgcgAAgJknxN8R+BXJAQAAJrQVAAAAaqFyAACAicdNWwEAANRCWwEAAKAWKgcAAJh4WK0AAABqo60AAABQC5UDAABMWK0AAAAMAuOVhP5DcgAAgEmwVw6YcwAAAAyoHAAAYBLslQOSAwAATIJ9zgFtBQAAYEDlAAAAE9oKAADAINi3T6atAAAADKgcAABgEuzvViA5AADAxE1bAQAA4BgqBwAAmAT7hESSAwAATFjKCAAADNghEQAAoBYqBwAAmNBWAAAABixlBAAAqIXKAQAAJixlBAAABqxWAAAAqIXKAQAAJsE+IZHkAAAAk2Cfc0BbAQAAGFA5AADAJNgnJJIcAABgwpyDANEs/gp/hwAEnOD+1xPgP8w5AAAAqCVgKgcAAAQK2goAAMAgyOcj0lYAAABGVA4AADChrQAAAAxYrQAAAFALyQEAACZuHx4NMX/+fPXo0UNRUVGKioqS3W7X8uXLvecPHz6szMxMtW7dWi1atFB6erqcTqfhGSUlJUpLS1Pz5s0VFxenSZMm6ciRIw2Kg+QAAAATj0J8djREhw4dNHPmTBUVFenTTz9V//79deONN6q4uFiSNGHCBL399ttasmSJ1qxZo9LSUg0ePNh7f01NjdLS0lRVVaV169Zp0aJFys/P19SpUxsUR4jHExg7SIdFtPd3CEDACe6uJ3Bi1VV7GvX5a203++xZVzqW/Kr7Y2Nj9eijj+qmm25S27ZttXjxYt10002SpO3bt6tbt24qLCxUv379tHz5cl1//fUqLS2V1WqVJOXl5enee+/V3r17FRERUa/PpHIAAICJ2+O7w+VyqaKiwnC4XK5TxlBTU6NXXnlFlZWVstvtKioqUnV1tVJSUrzXdO3aVQkJCSosLJQkFRYWqnv37t7EQJJSU1NVUVHhrT7UB8kBAAAmboX47MjNzVV0dLThyM3NPeFnb9myRS1atJDFYtGdd96ppUuXKjExUQ6HQxEREYqJiTFcb7Va5XA4JEkOh8OQGBw9f/RcfbGUEQAAk4bOFTiZnJwcZWdnG8YsFssJr7/gggu0efNmHTx4UK+//royMjK0Zs0an8VTHyQHAAA0IovFctJkwCwiIkLnnXeeJCkpKUmbNm3SU089pSFDhqiqqkoHDhwwVA+cTqdsNpskyWazaePGjYbnHV3NcPSa+qCtAACAib+WMtYZi9stl8ulpKQkhYeHq6CgwHtux44dKikpkd1ulyTZ7XZt2bJFZWVl3mtWrVqlqKgoJSYm1vszqRwAAGDiy7ZCQ+Tk5Ojaa69VQkKCfvrpJy1evFgffvihVq5cqejoaI0aNUrZ2dmKjY1VVFSUsrKyZLfb1a9fP0nSwIEDlZiYqBEjRmjWrFlyOByaMmWKMjMzG1S9IDkAACBAlJWV6bbbbtMPP/yg6Oho9ejRQytXrtTvfvc7SdLs2bMVGhqq9PR0uVwupaamat68ed77mzRpomXLlmns2LGy2+2KjIxURkaGZsyY0aA42OcACGDscwDUrbH3OVhhHeqzZ13jfMVnzzpTqBwAAGDii7kCZzMmJAIAAAMqBwAAmPhrQmKgIDkAAMDEHdy5AW0FAABgROUAAAATN20FAABQW0Cs8fcjkgMAAExYyggAAFALlQMAAEzcIcw5AAAAtQT7nAPaCgAAwIDKAQAAJsE+IZHkAAAAE3ZIBAAAqIXKAQAAJuyQCAAADFitAAAAUAuVAwAATIJ9QiLJAQAAJixlBAAABsw5AAAAqIXKAQAAJsw5AAAABsE+54C2AgAAMKByAACASbBXDkgOAAAw8QT5nAPaCgAAwIDKAQAAJrQVAACAQbAnB7QVAACAAZUDAABMgn37ZJIDAABM2CERAAAYMOcAAACgFioHAACYBHvlgOQAAACTYJ+QSFsBAAAYUDkAAMCE1QoAAMAg2Occ0FYAAAAGVA4AADAJ9gmJJAcAAJi4gzw9oK0AAAAMqBwAAGAS7BMSSQ4AADAJ7qYCbQUAAI7j9uHRELm5uerbt69atmypuLg4DRo0SDt27DBcc/jwYWVmZqp169Zq0aKF0tPT5XQ6DdeUlJQoLS1NzZs3V1xcnCZNmqQjR47UOw6SAwAAAsSaNWuUmZmp9evXa9WqVaqurtbAgQNVWVnpvWbChAl6++23tWTJEq1Zs0alpaUaPHiw93xNTY3S0tJUVVWldevWadGiRcrPz9fUqVPrHUeIx+MJiOpJWER7f4cABJwg36QNOKHqqj2N+vypnYb77Fkz/v3yad+7d+9excXFac2aNbryyit18OBBtW3bVosXL9ZNN90kSdq+fbu6deumwsJC9evXT8uXL9f111+v0tJSWa1WSVJeXp7uvfde7d27VxEREaf8XCoHAACYuOXx2fFrHDx4UJIUGxsrSSoqKlJ1dbVSUlK813Tt2lUJCQkqLCyUJBUWFqp79+7exECSUlNTVVFRoeLi4np9LhMSAQBoRC6XSy6XyzBmsVhksVhOep/b7db48eN12WWX6aKLLpIkORwORUREKCYmxnCt1WqVw+HwXlM7MTh6/ui5+qByAACAiceHR25urqKjow1Hbm7uKWPIzMzUl19+qVdeecXXX++UqBwAAGDiy30OcnJylJ2dbRg7VdVg3LhxWrZsmdauXasOHTp4x202m6qqqnTgwAFD9cDpdMpms3mv2bhxo+F5R1czHL3mVKgcAADQiCwWi6KiogzHiZIDj8ejcePGaenSpVq9erU6d+5sOJ+UlKTw8HAVFBR4x3bs2KGSkhLZ7XZJkt1u15YtW1RWVua9ZtWqVYqKilJiYmK9YqZyAACAib/erZCZmanFixfrzTffVMuWLb1zBKKjo9WsWTNFR0dr1KhRys7OVmxsrKKiopSVlSW73a5+/fpJkgYOHKjExESNGDFCs2bNksPh0JQpU5SZmXnKisVRJAcAAJj4a43//PnzJUn//d//bRhfuHChRo4cKUmaPXu2QkNDlZ6eLpfLpdTUVM2bN897bZMmTbRs2TKNHTtWdrtdkZGRysjI0IwZM+odB/scAAGMfQ6AujX2PgeTOw3z2bNm/fsfPnvWmULlAAAAE168BAAADPw15yBQkBwAAGAS3KkBSxkBAIAJlQMAAEyYcwAAAAw8Qd5YoK0AAAAMqBwAAGBCWwEAABgE+1JG2goAAMCAygEAACbBXTegcoBaxt6Zoa+/Wq9DFd9o3cdvq2+fXv4OCQgokyZlqrpqjx5/7EF/h4JG5pbHZ8fZiOQAkqSbb/4fPfboND308BPqm3yNPv9iq95952W1bdva36EBAaFPUk+N/sOt+uKLrf4OBWh0JAeQJE24e7Sef2GxFv3tNW3btlN/yrxPP//8H90+cqi/QwP8LjKyuRb97RndOXayfvzxgL/DwRng9uFxNiI5gMLDw3XxxT1UsPoj75jH41HB6o/Vr1+SHyMDAsPTcx7R8ncLtLrW3xH8tnl8+M/ZiAmJUJs2sQoLC1OZs9wwXla2V10v+C8/RQUEhltu+R/17n2R+tnT/B0KzqCz9b/4fcXnlYPvv/9ed9xxx0mvcblcqqioMBwez9mZXQH47erQIV5PPD5Dt2VkyeVy+Tsc4IzxeXKwf/9+LVq06KTX5ObmKjo62nB43D/5OhTUU3n5fh05ckRx1jaG8bi4tnI49/opKsD/Lr64u6zWttq4YYX+8/N3+s/P3+mqqy7VuHF36D8/f6fQUDqzv1W0FRrorbfeOun5b7/99pTPyMnJUXZ2tmGsVeuuDQ0FPlJdXa3PPvtC/a++XG+9tVKSFBISov5XX6558xf6OTrAf1av/li9evc3jD2/4Ant2PGNHn1srtzuYC8+/3YF+/+zDU4OBg0apJCQkJO2AUJCQk76DIvFIovF0qB70LhmP7VAC1+YraLPvtCmTf/SXVmjFRnZTPmLXvV3aIDfHDpUqeLiHYaxysqftW/fj8eNA78lDU4O2rVrp3nz5unGG2+s8/zmzZuVlMQM97PNkiVvqW2bWE2fOlE2W1t9/nmx0q6/VWVl5ae+GQB+Y9xBPg+uwclBUlKSioqKTpgcnKqqgMA1b36+5s3P93cYQEBL+d3N/g4BZ0Cw/xZrcHIwadIkVVZWnvD8eeedpw8++OBXBQUAAPynwcnBFVdccdLzkZGRuuqqq047IAAA/O1sfSeCr7AJEgAAJmfrEkRfYZEuAAAwoHIAAIAJ+xwAAAAD5hwAAAAD5hwAAADUQuUAAAAT5hwAAACDYN/pl7YCAAAwoHIAAIAJqxUAAIBBsM85oK0AAAAMqBwAAGAS7PsckBwAAGAS7HMOaCsAAAADKgcAAJgE+z4HJAcAAJgE+2oFkgMAAEyCfUIicw4AAIABlQMAAEyCfbUCyQEAACbBPiGRtgIAADCgcgAAgEmwtxWoHAAAYOLx4T8NsXbtWt1www2Kj49XSEiI3njjDWNcHo+mTp2qdu3aqVmzZkpJSdHOnTsN1+zfv1/Dhw9XVFSUYmJiNGrUKB06dKhBcZAcAAAQICorK9WzZ0/NnTu3zvOzZs3SnDlzlJeXpw0bNigyMlKpqak6fPiw95rhw4eruLhYq1at0rJly7R27VqNGTOmQXGEeAJk1kVYRHt/hwAEnBB/BwAEqOqqPY36/CvbD/DZs9buKTit+0JCQrR06VINGjRI0i9Vg/j4eN1zzz2aOHGiJOngwYOyWq3Kz8/X0KFDtW3bNiUmJmrTpk3q06ePJGnFihW67rrrtHv3bsXHx9frs6kcAABg4vHh4XK5VFFRYThcLleDY9q1a5ccDodSUlK8Y9HR0UpOTlZhYaEkqbCwUDExMd7EQJJSUlIUGhqqDRs21PuzSA4AAGhEubm5io6ONhy5ubkNfo7D4ZAkWa1Ww7jVavWeczgciouLM5wPCwtTbGys95r6YLUCAAAmvlytkJOTo+zsbMOYxWLx2fMbA8kBAAAmvkwOLBaLT5IBm80mSXI6nWrXrp133Ol0qlevXt5rysrKDPcdOXJE+/fv995fH7QVAAAw8Xg8Pjt8pXPnzrLZbCooODbBsaKiQhs2bJDdbpck2e12HThwQEVFRd5rVq9eLbfbreTk5Hp/FpUDAAACxKFDh/T11197f961a5c2b96s2NhYJSQkaPz48Xr44YfVpUsXde7cWQ888IDi4+O9Kxq6deuma665RqNHj1ZeXp6qq6s1btw4DR06tN4rFSSSAwAAjuOvHRI//fRTXX311d6fj85VyMjIUH5+viZPnqzKykqNGTNGBw4c0OWXX64VK1aoadOm3ntefvlljRs3TgMGDFBoaKjS09M1Z86cBsXBPgdAAGOfA6Bujb3PQd/4K332rE2la332rDOFOQcAAMCAtgIAACYBUlT3G5IDAABMeCsjAABALVQOAAAwoa0AAAAMaCsAAADUQuUAAAATT5BXDkgOAAAwcTPnAAAA1BbslQPmHAAAAAMqBwAAmNBWAAAABrQVAAAAaqFyAACACW0FAABgQFsBAACgFioHAACY0FYAAAAGtBUAAABqoXIAAICJx+P2dwh+RXIAAICJO8jbCiQHAACYeIJ8QiJzDgAAgAGVAwAATGgrAAAAA9oKAAAAtVA5AADAhB0SAQCAATskAgAA1ELlAAAAk2CfkEhyAACASbAvZaStAAAADKgcAABgQlsBAAAYsJQRAAAYBHvlgDkHAADAgMoBAAAmwb5ageQAAAAT2goAAAC1UDkAAMCE1QoAAMCAFy8BAADUQuUAAAAT2goAAMCA1QoAAAC1UDkAAMCECYkAAMDA4/H47GiouXPnqlOnTmratKmSk5O1cePGRviGJ0dyAACAib+Sg1dffVXZ2dmaNm2aPvvsM/Xs2VOpqakqKytrpG9atxBPgMy6CIto7+8QgIAT4u8AgABVXbWnUZ8f7sPfSQ2JNTk5WX379tUzzzwjSXK73TrnnHOUlZWl++67z2cxnQqVAwAATDw+PFwulyoqKgyHy+U67jOrqqpUVFSklJQU71hoaKhSUlJUWFjYaN+1LgEzIfFII2eBqB+Xy6Xc3Fzl5OTIYrH4OxwgIPD3Ivj48nfS9OnT9eCDDxrGpk2bpunTpxvGysvLVVNTI6vVahi3Wq3avn27z+Kpj4BpKyAwVFRUKDo6WgcPHlRUVJS/wwECAn8v8Gu4XK7jKgUWi+W4RLO0tFTt27fXunXrZLfbveOTJ0/WmjVrtGHDhjMSrxRAlQMAAH6L6koE6tKmTRs1adJETqfTMO50OmWz2RorvDox5wAAgAAQERGhpKQkFRQUeMfcbrcKCgoMlYQzgcoBAAABIjs7WxkZGerTp48uueQSPfnkk6qsrNTtt99+RuMgOYCBxWLRtGnTmHQF1MLfC5wpQ4YM0d69ezV16lQ5HA716tVLK1asOG6SYmNjQiIAADBgzgEAADAgOQAAAAYkBwAAwIDkAAAAGJAcwCsQXhMKBJK1a9fqhhtuUHx8vEJCQvTGG2/4OyTgjCA5gKTAeU0oEEgqKyvVs2dPzZ0719+hAGcUSxkhKXBeEwoEqpCQEC1dulSDBg3ydyhAo6NygIB6TSgAwP9IDnDS14Q6HA4/RQUA8BeSAwAAYEBygIB6TSgAwP9IDhBQrwkFAPgfb2WEpMB5TSgQSA4dOqSvv/7a+/OuXbu0efNmxcbGKiEhwY+RAY2LpYzweuaZZ/Too496XxM6Z84cJScn+zsswG8+/PBDXX311ceNZ2RkKD8//8wHBJwhJAcAAMCAOQcAAMCA5AAAABiQHAAAAAOSAwAAYEByAAAADEgOAACAAckBAAAwIDkAAAAGJAcAAMCA5AAAABiQHAAAAAOSAwAAYPB/IJsYp2UEIz8AAAAASUVORK5CYII=\n"
1927 |           },
1928 |           "metadata": {}
1929 |         }
1930 |       ]
1931 |     },
1932 |     {
1933 |       "cell_type": "code",
1934 |       "source": [
1935 |         "from sklearn.ensemble import RandomForestClassifier\n",
1936 |         "rfc = RandomForestClassifier(n_estimators=10).fit(xtrain, ytrain)\n"
1937 |       ],
1938 |       "metadata": {
1939 |         "id": "vN7HfaB4Cssk"
1940 |       },
1941 |       "execution_count": 64,
1942 |       "outputs": []
1943 |     },
1944 |     {
1945 |       "cell_type": "code",
1946 |       "source": [
1947 |         "ypred1=rfc.predict(xtest)"
1948 |       ],
1949 |       "metadata": {
1950 |         "id": "ORoQetdiFs0F"
1951 |       },
1952 |       "execution_count": 65,
1953 |       "outputs": []
1954 |     },
1955 |     {
1956 |       "cell_type": "code",
1957 |       "source": [
1958 |         "print(\"accuracy_score :\", accuracy_score(ytest, ypred1))\n",
1959 |         "print(\"recall_score :\", recall_score(ytest, ypred1))\n",
1960 |         "print(\"confusion_matrix :\", precision_score(ytest, ypred1))\n",
1961 |         "cm=confusion_matrix(ytest, ypred1)\n",
1962 |         "print(\"precision_score :\", cm)\n",
1963 |         "sns.heatmap(cm,annot=True)"
1964 |       ],
1965 |       "metadata": {
1966 |         "colab": {
1967 |           "base_uri": "https://localhost:8080/",
1968 |           "height": 569
1969 |         },
1970 |         "id": "C49N6K68F2vA",
1971 |         "outputId": "89d809be-7ca2-4a34-f33d-7d00a3b7f06f"
1972 |       },
1973 |       "execution_count": 66,
1974 |       "outputs": [
1975 |         {
1976 |           "output_type": "stream",
1977 |           "name": "stdout",
1978 |           "text": [
1979 |             "accuracy_score : 0.9915254237288136\n",
1980 |             "recall_score : 0.75\n",
1981 |             "precision_score : 0.375\n",
1982 |             "precision_score : [[699   5]\n",
1983 |             " [  1   3]]\n",
1984 |             "[[699   5]\n",
1985 |             " [  1   3]]\n"
1986 |           ]
1987 |         },
1988 |         {
1989 |           "output_type": "execute_result",
1990 |           "data": {
1991 |             "text/plain": [
1992 |               "<Axes: >"
1993 |             ]
1994 |           },
1995 |           "metadata": {},
1996 |           "execution_count": 66
1997 |         },
1998 |         {
1999 |           "output_type": "display_data",
2000 |           "data": {
2001 |             "text/plain": [
2002 |               "<Figure size 640x480 with 2 Axes>"
2003 |             ],
2004 |             "image/png": "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\n"
2005 |           },
2006 |           "metadata": {}
2007 |         }
2008 |       ]
2009 |     },
2010 |     {
2011 |       "cell_type": "code",
2012 |       "source": [],
2013 |       "metadata": {
2014 |         "id": "cIR_TkyiH9BG"
2015 |       },
2016 |       "execution_count": null,
2017 |       "outputs": []
2018 |     }
2019 |   ]
2020 | }


--------------------------------------------------------------------------------