├── .github
    └── workflows
    │   └── r.yml
├── Bank_Data
    ├── Bank Data.ipynb
    └── README.md
├── Boston Housing Data
    ├── Housing_Data.ipynb
    └── README.md
├── Forecasting Risk Analytics
    ├── Forecasting Risk ES_VAR.R
    ├── Project_Forecasting Risk.pdf
    └── README.md
├── Option_Pricing
    ├── Binomial-Tree-Call.ipynb
    ├── Finite Difference, Binomial Trees.ipynb
    ├── Monte Carlo.ipynb
    └── README.md
├── R
    └── Copula Modeling
    │   ├── ACF.pdf
    │   ├── Actual Plot.png
    │   ├── Copula Project.R
    │   ├── README.md
    │   └── Simulated Plot,Frank Copula.png
├── README.md
├── Risk Management
    ├── Default_Estimation.ipynb
    ├── EuroDollar Futures.ipynb
    ├── Forecasting Sales using ARIMA.ipynb
    ├── README.md
    └── Var Exercise-Monte Carlo.ipynb
└── SECURITY.md


/.github/workflows/r.yml:
--------------------------------------------------------------------------------
 1 | # This workflow uses actions that are not certified by GitHub.
 2 | # They are provided by a third-party and are governed by
 3 | # separate terms of service, privacy policy, and support
 4 | # documentation.
 5 | #
 6 | # See https://github.com/r-lib/actions/tree/master/examples#readme for
 7 | # additional example workflows available for the R community.
 8 | 
 9 | name: R
10 | 
11 | on:
12 |   push:
13 |     branches: [ "main" ]
14 |   pull_request:
15 |     branches: [ "main" ]
16 | 
17 | permissions:
18 |   contents: read
19 | 
20 | jobs:
21 |   build:
22 |     runs-on: macos-latest
23 |     strategy:
24 |       matrix:
25 |         r-version: ['3.6.3', '4.1.1']
26 | 
27 |     steps:
28 |       - uses: actions/checkout@v3
29 |       - name: Set up R ${{ matrix.r-version }}
30 |         uses: r-lib/actions/setup-r@f57f1301a053485946083d7a45022b278929a78a
31 |         with:
32 |           r-version: ${{ matrix.r-version }}
33 |       - name: Install dependencies
34 |         run: |
35 |           install.packages(c("remotes", "rcmdcheck"))
36 |           remotes::install_deps(dependencies = TRUE)
37 |         shell: Rscript {0}
38 |       - name: Check
39 |         run: rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
40 |         shell: Rscript {0}
41 | 


--------------------------------------------------------------------------------
/Bank_Data/Bank Data.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {
   7 |     "id": "e6sxfiNJljkT"
   8 |    },
   9 |    "outputs": [],
  10 |    "source": [
  11 |     "url=\"C://Users//adib-//OneDrive//Desktop//Assignment_1//bank-additional-full.csv\""
  12 |    ]
  13 |   },
  14 |   {
  15 |    "cell_type": "code",
  16 |    "execution_count": 2,
  17 |    "metadata": {
  18 |     "id": "bg4GB1NFeN6w"
  19 |    },
  20 |    "outputs": [],
  21 |    "source": [
  22 |     "import pandas as pd\n",
  23 |     "from math import log2\n",
  24 |     "from sklearn.model_selection import train_test_split"
  25 |    ]
  26 |   },
  27 |   {
  28 |    "cell_type": "code",
  29 |    "execution_count": 3,
  30 |    "metadata": {
  31 |     "colab": {
  32 |      "base_uri": "https://localhost:8080/",
  33 |      "height": 487
  34 |     },
  35 |     "id": "FIelj9yAeN6x",
  36 |     "outputId": "452157b0-699e-4e00-cff8-ef6902b2d0e8"
  37 |    },
  38 |    "outputs": [
  39 |     {
  40 |      "data": {
  41 |       "text/html": [
  42 |        "<div>\n",
  43 |        "<style scoped>\n",
  44 |        "    .dataframe tbody tr th:only-of-type {\n",
  45 |        "        vertical-align: middle;\n",
  46 |        "    }\n",
  47 |        "\n",
  48 |        "    .dataframe tbody tr th {\n",
  49 |        "        vertical-align: top;\n",
  50 |        "    }\n",
  51 |        "\n",
  52 |        "    .dataframe thead th {\n",
  53 |        "        text-align: right;\n",
  54 |        "    }\n",
  55 |        "</style>\n",
  56 |        "<table border=\"1\" class=\"dataframe\">\n",
  57 |        "  <thead>\n",
  58 |        "    <tr style=\"text-align: right;\">\n",
  59 |        "      <th></th>\n",
  60 |        "      <th>age</th>\n",
  61 |        "      <th>job</th>\n",
  62 |        "      <th>marital</th>\n",
  63 |        "      <th>education</th>\n",
  64 |        "      <th>default</th>\n",
  65 |        "      <th>housing</th>\n",
  66 |        "      <th>loan</th>\n",
  67 |        "      <th>contact</th>\n",
  68 |        "      <th>month</th>\n",
  69 |        "      <th>day_of_week</th>\n",
  70 |        "      <th>...</th>\n",
  71 |        "      <th>campaign</th>\n",
  72 |        "      <th>pdays</th>\n",
  73 |        "      <th>previous</th>\n",
  74 |        "      <th>poutcome</th>\n",
  75 |        "      <th>emp.var.rate</th>\n",
  76 |        "      <th>cons.price.idx</th>\n",
  77 |        "      <th>cons.conf.idx</th>\n",
  78 |        "      <th>euribor3m</th>\n",
  79 |        "      <th>nr.employed</th>\n",
  80 |        "      <th>y</th>\n",
  81 |        "    </tr>\n",
  82 |        "  </thead>\n",
  83 |        "  <tbody>\n",
  84 |        "    <tr>\n",
  85 |        "      <th>0</th>\n",
  86 |        "      <td>56</td>\n",
  87 |        "      <td>housemaid</td>\n",
  88 |        "      <td>married</td>\n",
  89 |        "      <td>basic.4y</td>\n",
  90 |        "      <td>no</td>\n",
  91 |        "      <td>no</td>\n",
  92 |        "      <td>no</td>\n",
  93 |        "      <td>telephone</td>\n",
  94 |        "      <td>may</td>\n",
  95 |        "      <td>mon</td>\n",
  96 |        "      <td>...</td>\n",
  97 |        "      <td>1</td>\n",
  98 |        "      <td>999</td>\n",
  99 |        "      <td>0</td>\n",
 100 |        "      <td>nonexistent</td>\n",
 101 |        "      <td>1.1</td>\n",
 102 |        "      <td>93.994</td>\n",
 103 |        "      <td>-36.4</td>\n",
 104 |        "      <td>4.857</td>\n",
 105 |        "      <td>5191.0</td>\n",
 106 |        "      <td>no</td>\n",
 107 |        "    </tr>\n",
 108 |        "    <tr>\n",
 109 |        "      <th>1</th>\n",
 110 |        "      <td>57</td>\n",
 111 |        "      <td>services</td>\n",
 112 |        "      <td>married</td>\n",
 113 |        "      <td>high.school</td>\n",
 114 |        "      <td>unknown</td>\n",
 115 |        "      <td>no</td>\n",
 116 |        "      <td>no</td>\n",
 117 |        "      <td>telephone</td>\n",
 118 |        "      <td>may</td>\n",
 119 |        "      <td>mon</td>\n",
 120 |        "      <td>...</td>\n",
 121 |        "      <td>1</td>\n",
 122 |        "      <td>999</td>\n",
 123 |        "      <td>0</td>\n",
 124 |        "      <td>nonexistent</td>\n",
 125 |        "      <td>1.1</td>\n",
 126 |        "      <td>93.994</td>\n",
 127 |        "      <td>-36.4</td>\n",
 128 |        "      <td>4.857</td>\n",
 129 |        "      <td>5191.0</td>\n",
 130 |        "      <td>no</td>\n",
 131 |        "    </tr>\n",
 132 |        "    <tr>\n",
 133 |        "      <th>2</th>\n",
 134 |        "      <td>37</td>\n",
 135 |        "      <td>services</td>\n",
 136 |        "      <td>married</td>\n",
 137 |        "      <td>high.school</td>\n",
 138 |        "      <td>no</td>\n",
 139 |        "      <td>yes</td>\n",
 140 |        "      <td>no</td>\n",
 141 |        "      <td>telephone</td>\n",
 142 |        "      <td>may</td>\n",
 143 |        "      <td>mon</td>\n",
 144 |        "      <td>...</td>\n",
 145 |        "      <td>1</td>\n",
 146 |        "      <td>999</td>\n",
 147 |        "      <td>0</td>\n",
 148 |        "      <td>nonexistent</td>\n",
 149 |        "      <td>1.1</td>\n",
 150 |        "      <td>93.994</td>\n",
 151 |        "      <td>-36.4</td>\n",
 152 |        "      <td>4.857</td>\n",
 153 |        "      <td>5191.0</td>\n",
 154 |        "      <td>no</td>\n",
 155 |        "    </tr>\n",
 156 |        "    <tr>\n",
 157 |        "      <th>3</th>\n",
 158 |        "      <td>40</td>\n",
 159 |        "      <td>admin.</td>\n",
 160 |        "      <td>married</td>\n",
 161 |        "      <td>basic.6y</td>\n",
 162 |        "      <td>no</td>\n",
 163 |        "      <td>no</td>\n",
 164 |        "      <td>no</td>\n",
 165 |        "      <td>telephone</td>\n",
 166 |        "      <td>may</td>\n",
 167 |        "      <td>mon</td>\n",
 168 |        "      <td>...</td>\n",
 169 |        "      <td>1</td>\n",
 170 |        "      <td>999</td>\n",
 171 |        "      <td>0</td>\n",
 172 |        "      <td>nonexistent</td>\n",
 173 |        "      <td>1.1</td>\n",
 174 |        "      <td>93.994</td>\n",
 175 |        "      <td>-36.4</td>\n",
 176 |        "      <td>4.857</td>\n",
 177 |        "      <td>5191.0</td>\n",
 178 |        "      <td>no</td>\n",
 179 |        "    </tr>\n",
 180 |        "    <tr>\n",
 181 |        "      <th>4</th>\n",
 182 |        "      <td>56</td>\n",
 183 |        "      <td>services</td>\n",
 184 |        "      <td>married</td>\n",
 185 |        "      <td>high.school</td>\n",
 186 |        "      <td>no</td>\n",
 187 |        "      <td>no</td>\n",
 188 |        "      <td>yes</td>\n",
 189 |        "      <td>telephone</td>\n",
 190 |        "      <td>may</td>\n",
 191 |        "      <td>mon</td>\n",
 192 |        "      <td>...</td>\n",
 193 |        "      <td>1</td>\n",
 194 |        "      <td>999</td>\n",
 195 |        "      <td>0</td>\n",
 196 |        "      <td>nonexistent</td>\n",
 197 |        "      <td>1.1</td>\n",
 198 |        "      <td>93.994</td>\n",
 199 |        "      <td>-36.4</td>\n",
 200 |        "      <td>4.857</td>\n",
 201 |        "      <td>5191.0</td>\n",
 202 |        "      <td>no</td>\n",
 203 |        "    </tr>\n",
 204 |        "    <tr>\n",
 205 |        "      <th>...</th>\n",
 206 |        "      <td>...</td>\n",
 207 |        "      <td>...</td>\n",
 208 |        "      <td>...</td>\n",
 209 |        "      <td>...</td>\n",
 210 |        "      <td>...</td>\n",
 211 |        "      <td>...</td>\n",
 212 |        "      <td>...</td>\n",
 213 |        "      <td>...</td>\n",
 214 |        "      <td>...</td>\n",
 215 |        "      <td>...</td>\n",
 216 |        "      <td>...</td>\n",
 217 |        "      <td>...</td>\n",
 218 |        "      <td>...</td>\n",
 219 |        "      <td>...</td>\n",
 220 |        "      <td>...</td>\n",
 221 |        "      <td>...</td>\n",
 222 |        "      <td>...</td>\n",
 223 |        "      <td>...</td>\n",
 224 |        "      <td>...</td>\n",
 225 |        "      <td>...</td>\n",
 226 |        "      <td>...</td>\n",
 227 |        "    </tr>\n",
 228 |        "    <tr>\n",
 229 |        "      <th>41183</th>\n",
 230 |        "      <td>73</td>\n",
 231 |        "      <td>retired</td>\n",
 232 |        "      <td>married</td>\n",
 233 |        "      <td>professional.course</td>\n",
 234 |        "      <td>no</td>\n",
 235 |        "      <td>yes</td>\n",
 236 |        "      <td>no</td>\n",
 237 |        "      <td>cellular</td>\n",
 238 |        "      <td>nov</td>\n",
 239 |        "      <td>fri</td>\n",
 240 |        "      <td>...</td>\n",
 241 |        "      <td>1</td>\n",
 242 |        "      <td>999</td>\n",
 243 |        "      <td>0</td>\n",
 244 |        "      <td>nonexistent</td>\n",
 245 |        "      <td>-1.1</td>\n",
 246 |        "      <td>94.767</td>\n",
 247 |        "      <td>-50.8</td>\n",
 248 |        "      <td>1.028</td>\n",
 249 |        "      <td>4963.6</td>\n",
 250 |        "      <td>yes</td>\n",
 251 |        "    </tr>\n",
 252 |        "    <tr>\n",
 253 |        "      <th>41184</th>\n",
 254 |        "      <td>46</td>\n",
 255 |        "      <td>blue-collar</td>\n",
 256 |        "      <td>married</td>\n",
 257 |        "      <td>professional.course</td>\n",
 258 |        "      <td>no</td>\n",
 259 |        "      <td>no</td>\n",
 260 |        "      <td>no</td>\n",
 261 |        "      <td>cellular</td>\n",
 262 |        "      <td>nov</td>\n",
 263 |        "      <td>fri</td>\n",
 264 |        "      <td>...</td>\n",
 265 |        "      <td>1</td>\n",
 266 |        "      <td>999</td>\n",
 267 |        "      <td>0</td>\n",
 268 |        "      <td>nonexistent</td>\n",
 269 |        "      <td>-1.1</td>\n",
 270 |        "      <td>94.767</td>\n",
 271 |        "      <td>-50.8</td>\n",
 272 |        "      <td>1.028</td>\n",
 273 |        "      <td>4963.6</td>\n",
 274 |        "      <td>no</td>\n",
 275 |        "    </tr>\n",
 276 |        "    <tr>\n",
 277 |        "      <th>41185</th>\n",
 278 |        "      <td>56</td>\n",
 279 |        "      <td>retired</td>\n",
 280 |        "      <td>married</td>\n",
 281 |        "      <td>university.degree</td>\n",
 282 |        "      <td>no</td>\n",
 283 |        "      <td>yes</td>\n",
 284 |        "      <td>no</td>\n",
 285 |        "      <td>cellular</td>\n",
 286 |        "      <td>nov</td>\n",
 287 |        "      <td>fri</td>\n",
 288 |        "      <td>...</td>\n",
 289 |        "      <td>2</td>\n",
 290 |        "      <td>999</td>\n",
 291 |        "      <td>0</td>\n",
 292 |        "      <td>nonexistent</td>\n",
 293 |        "      <td>-1.1</td>\n",
 294 |        "      <td>94.767</td>\n",
 295 |        "      <td>-50.8</td>\n",
 296 |        "      <td>1.028</td>\n",
 297 |        "      <td>4963.6</td>\n",
 298 |        "      <td>no</td>\n",
 299 |        "    </tr>\n",
 300 |        "    <tr>\n",
 301 |        "      <th>41186</th>\n",
 302 |        "      <td>44</td>\n",
 303 |        "      <td>technician</td>\n",
 304 |        "      <td>married</td>\n",
 305 |        "      <td>professional.course</td>\n",
 306 |        "      <td>no</td>\n",
 307 |        "      <td>no</td>\n",
 308 |        "      <td>no</td>\n",
 309 |        "      <td>cellular</td>\n",
 310 |        "      <td>nov</td>\n",
 311 |        "      <td>fri</td>\n",
 312 |        "      <td>...</td>\n",
 313 |        "      <td>1</td>\n",
 314 |        "      <td>999</td>\n",
 315 |        "      <td>0</td>\n",
 316 |        "      <td>nonexistent</td>\n",
 317 |        "      <td>-1.1</td>\n",
 318 |        "      <td>94.767</td>\n",
 319 |        "      <td>-50.8</td>\n",
 320 |        "      <td>1.028</td>\n",
 321 |        "      <td>4963.6</td>\n",
 322 |        "      <td>yes</td>\n",
 323 |        "    </tr>\n",
 324 |        "    <tr>\n",
 325 |        "      <th>41187</th>\n",
 326 |        "      <td>74</td>\n",
 327 |        "      <td>retired</td>\n",
 328 |        "      <td>married</td>\n",
 329 |        "      <td>professional.course</td>\n",
 330 |        "      <td>no</td>\n",
 331 |        "      <td>yes</td>\n",
 332 |        "      <td>no</td>\n",
 333 |        "      <td>cellular</td>\n",
 334 |        "      <td>nov</td>\n",
 335 |        "      <td>fri</td>\n",
 336 |        "      <td>...</td>\n",
 337 |        "      <td>3</td>\n",
 338 |        "      <td>999</td>\n",
 339 |        "      <td>1</td>\n",
 340 |        "      <td>failure</td>\n",
 341 |        "      <td>-1.1</td>\n",
 342 |        "      <td>94.767</td>\n",
 343 |        "      <td>-50.8</td>\n",
 344 |        "      <td>1.028</td>\n",
 345 |        "      <td>4963.6</td>\n",
 346 |        "      <td>no</td>\n",
 347 |        "    </tr>\n",
 348 |        "  </tbody>\n",
 349 |        "</table>\n",
 350 |        "<p>41188 rows × 21 columns</p>\n",
 351 |        "</div>"
 352 |       ],
 353 |       "text/plain": [
 354 |        "       age          job  marital            education  default housing loan  \\\n",
 355 |        "0       56    housemaid  married             basic.4y       no      no   no   \n",
 356 |        "1       57     services  married          high.school  unknown      no   no   \n",
 357 |        "2       37     services  married          high.school       no     yes   no   \n",
 358 |        "3       40       admin.  married             basic.6y       no      no   no   \n",
 359 |        "4       56     services  married          high.school       no      no  yes   \n",
 360 |        "...    ...          ...      ...                  ...      ...     ...  ...   \n",
 361 |        "41183   73      retired  married  professional.course       no     yes   no   \n",
 362 |        "41184   46  blue-collar  married  professional.course       no      no   no   \n",
 363 |        "41185   56      retired  married    university.degree       no     yes   no   \n",
 364 |        "41186   44   technician  married  professional.course       no      no   no   \n",
 365 |        "41187   74      retired  married  professional.course       no     yes   no   \n",
 366 |        "\n",
 367 |        "         contact month day_of_week  ...  campaign  pdays  previous  \\\n",
 368 |        "0      telephone   may         mon  ...         1    999         0   \n",
 369 |        "1      telephone   may         mon  ...         1    999         0   \n",
 370 |        "2      telephone   may         mon  ...         1    999         0   \n",
 371 |        "3      telephone   may         mon  ...         1    999         0   \n",
 372 |        "4      telephone   may         mon  ...         1    999         0   \n",
 373 |        "...          ...   ...         ...  ...       ...    ...       ...   \n",
 374 |        "41183   cellular   nov         fri  ...         1    999         0   \n",
 375 |        "41184   cellular   nov         fri  ...         1    999         0   \n",
 376 |        "41185   cellular   nov         fri  ...         2    999         0   \n",
 377 |        "41186   cellular   nov         fri  ...         1    999         0   \n",
 378 |        "41187   cellular   nov         fri  ...         3    999         1   \n",
 379 |        "\n",
 380 |        "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
 381 |        "0      nonexistent          1.1          93.994          -36.4      4.857   \n",
 382 |        "1      nonexistent          1.1          93.994          -36.4      4.857   \n",
 383 |        "2      nonexistent          1.1          93.994          -36.4      4.857   \n",
 384 |        "3      nonexistent          1.1          93.994          -36.4      4.857   \n",
 385 |        "4      nonexistent          1.1          93.994          -36.4      4.857   \n",
 386 |        "...            ...          ...             ...            ...        ...   \n",
 387 |        "41183  nonexistent         -1.1          94.767          -50.8      1.028   \n",
 388 |        "41184  nonexistent         -1.1          94.767          -50.8      1.028   \n",
 389 |        "41185  nonexistent         -1.1          94.767          -50.8      1.028   \n",
 390 |        "41186  nonexistent         -1.1          94.767          -50.8      1.028   \n",
 391 |        "41187      failure         -1.1          94.767          -50.8      1.028   \n",
 392 |        "\n",
 393 |        "       nr.employed    y  \n",
 394 |        "0           5191.0   no  \n",
 395 |        "1           5191.0   no  \n",
 396 |        "2           5191.0   no  \n",
 397 |        "3           5191.0   no  \n",
 398 |        "4           5191.0   no  \n",
 399 |        "...            ...  ...  \n",
 400 |        "41183       4963.6  yes  \n",
 401 |        "41184       4963.6   no  \n",
 402 |        "41185       4963.6   no  \n",
 403 |        "41186       4963.6  yes  \n",
 404 |        "41187       4963.6   no  \n",
 405 |        "\n",
 406 |        "[41188 rows x 21 columns]"
 407 |       ]
 408 |      },
 409 |      "execution_count": 3,
 410 |      "metadata": {},
 411 |      "output_type": "execute_result"
 412 |     }
 413 |    ],
 414 |    "source": [
 415 |     "data=pd.read_csv(url)\n",
 416 |     "data"
 417 |    ]
 418 |   },
 419 |   {
 420 |    "cell_type": "code",
 421 |    "execution_count": 4,
 422 |    "metadata": {
 423 |     "colab": {
 424 |      "base_uri": "https://localhost:8080/"
 425 |     },
 426 |     "id": "rR7Jn8U2dTdt",
 427 |     "outputId": "0f525032-a7b7-44d6-9a71-d2f14b125fa1"
 428 |    },
 429 |    "outputs": [
 430 |     {
 431 |      "data": {
 432 |       "text/plain": [
 433 |        "999    39673\n",
 434 |        "3        439\n",
 435 |        "6        412\n",
 436 |        "4        118\n",
 437 |        "9         64\n",
 438 |        "2         61\n",
 439 |        "7         60\n",
 440 |        "12        58\n",
 441 |        "10        52\n",
 442 |        "5         46\n",
 443 |        "13        36\n",
 444 |        "11        28\n",
 445 |        "1         26\n",
 446 |        "15        24\n",
 447 |        "14        20\n",
 448 |        "8         18\n",
 449 |        "0         15\n",
 450 |        "16        11\n",
 451 |        "17         8\n",
 452 |        "18         7\n",
 453 |        "19         3\n",
 454 |        "22         3\n",
 455 |        "21         2\n",
 456 |        "26         1\n",
 457 |        "20         1\n",
 458 |        "25         1\n",
 459 |        "27         1\n",
 460 |        "Name: pdays, dtype: int64"
 461 |       ]
 462 |      },
 463 |      "execution_count": 4,
 464 |      "metadata": {},
 465 |      "output_type": "execute_result"
 466 |     }
 467 |    ],
 468 |    "source": [
 469 |     "# For most of the data points, pdays feature remains constant as 999. So we remove this feature\n",
 470 |     "data['pdays'].value_counts()"
 471 |    ]
 472 |   },
 473 |   {
 474 |    "cell_type": "code",
 475 |    "execution_count": 5,
 476 |    "metadata": {
 477 |     "colab": {
 478 |      "base_uri": "https://localhost:8080/"
 479 |     },
 480 |     "id": "KcoAOPSFdfs1",
 481 |     "outputId": "dae2c872-9e8a-4cf2-9776-d13afbed1a8b"
 482 |    },
 483 |    "outputs": [
 484 |     {
 485 |      "data": {
 486 |       "text/plain": [
 487 |        "5228.1    16234\n",
 488 |        "5099.1     8534\n",
 489 |        "5191.0     7763\n",
 490 |        "5195.8     3683\n",
 491 |        "5076.2     1663\n",
 492 |        "5017.5     1071\n",
 493 |        "4991.6      773\n",
 494 |        "5008.7      650\n",
 495 |        "4963.6      635\n",
 496 |        "5023.5      172\n",
 497 |        "5176.3       10\n",
 498 |        "Name: nr.employed, dtype: int64"
 499 |       ]
 500 |      },
 501 |      "execution_count": 5,
 502 |      "metadata": {},
 503 |      "output_type": "execute_result"
 504 |     }
 505 |    ],
 506 |    "source": [
 507 |     "data['nr.employed'].value_counts() #Index Feature"
 508 |    ]
 509 |   },
 510 |   {
 511 |    "cell_type": "code",
 512 |    "execution_count": 6,
 513 |    "metadata": {
 514 |     "colab": {
 515 |      "base_uri": "https://localhost:8080/"
 516 |     },
 517 |     "id": "xNCZxGSJeN6y",
 518 |     "outputId": "cfdfee3f-e459-438f-fcd8-e8e093c004cf"
 519 |    },
 520 |    "outputs": [
 521 |     {
 522 |      "data": {
 523 |       "text/plain": [
 524 |        "False"
 525 |       ]
 526 |      },
 527 |      "execution_count": 6,
 528 |      "metadata": {},
 529 |      "output_type": "execute_result"
 530 |     }
 531 |    ],
 532 |    "source": [
 533 |     "# There are no missing numeric values in the whole dataframe\n",
 534 |     "data.isnull().values.any()"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": 7,
 540 |    "metadata": {},
 541 |    "outputs": [
 542 |     {
 543 |      "name": "stdout",
 544 |      "output_type": "stream",
 545 |      "text": [
 546 |       "yes        21576\n",
 547 |       "no         18622\n",
 548 |       "unknown      990\n",
 549 |       "Name: housing, dtype: int64\n",
 550 |       "no         33950\n",
 551 |       "yes         6248\n",
 552 |       "unknown      990\n",
 553 |       "Name: loan, dtype: int64\n",
 554 |       "no         32588\n",
 555 |       "unknown     8597\n",
 556 |       "yes            3\n",
 557 |       "Name: default, dtype: int64\n",
 558 |       "university.degree      12168\n",
 559 |       "high.school             9515\n",
 560 |       "basic.9y                6045\n",
 561 |       "professional.course     5243\n",
 562 |       "basic.4y                4176\n",
 563 |       "basic.6y                2292\n",
 564 |       "unknown                 1731\n",
 565 |       "illiterate                18\n",
 566 |       "Name: education, dtype: int64\n",
 567 |       "admin.           10422\n",
 568 |       "blue-collar       9254\n",
 569 |       "technician        6743\n",
 570 |       "services          3969\n",
 571 |       "management        2924\n",
 572 |       "retired           1720\n",
 573 |       "entrepreneur      1456\n",
 574 |       "self-employed     1421\n",
 575 |       "housemaid         1060\n",
 576 |       "unemployed        1014\n",
 577 |       "student            875\n",
 578 |       "unknown            330\n",
 579 |       "Name: job, dtype: int64\n",
 580 |       "nonexistent    35563\n",
 581 |       "failure         4252\n",
 582 |       "success         1373\n",
 583 |       "Name: poutcome, dtype: int64\n",
 584 |       "married     24928\n",
 585 |       "single      11568\n",
 586 |       "divorced     4612\n",
 587 |       "unknown        80\n",
 588 |       "Name: marital, dtype: int64\n"
 589 |      ]
 590 |     }
 591 |    ],
 592 |    "source": [
 593 |     "print(data['housing'].value_counts())\n",
 594 |     "print(data['loan'].value_counts())\n",
 595 |     "print(data['default'].value_counts())\n",
 596 |     "print(data['education'].value_counts())\n",
 597 |     "print(data['job'].value_counts())\n",
 598 |     "print(data['poutcome'].value_counts())\n",
 599 |     "print(data['marital'].value_counts())\n",
 600 |     "\n",
 601 |     "\n",
 602 |     "\n"
 603 |    ]
 604 |   },
 605 |   {
 606 |    "cell_type": "code",
 607 |    "execution_count": 8,
 608 |    "metadata": {
 609 |     "colab": {
 610 |      "base_uri": "https://localhost:8080/",
 611 |      "height": 423
 612 |     },
 613 |     "id": "tni4X_EfupUV",
 614 |     "outputId": "f89f7cda-9be7-4388-bdf0-56895698d225"
 615 |    },
 616 |    "outputs": [
 617 |     {
 618 |      "data": {
 619 |       "text/html": [
 620 |        "<div>\n",
 621 |        "<style scoped>\n",
 622 |        "    .dataframe tbody tr th:only-of-type {\n",
 623 |        "        vertical-align: middle;\n",
 624 |        "    }\n",
 625 |        "\n",
 626 |        "    .dataframe tbody tr th {\n",
 627 |        "        vertical-align: top;\n",
 628 |        "    }\n",
 629 |        "\n",
 630 |        "    .dataframe thead th {\n",
 631 |        "        text-align: right;\n",
 632 |        "    }\n",
 633 |        "</style>\n",
 634 |        "<table border=\"1\" class=\"dataframe\">\n",
 635 |        "  <thead>\n",
 636 |        "    <tr style=\"text-align: right;\">\n",
 637 |        "      <th></th>\n",
 638 |        "      <th>age</th>\n",
 639 |        "      <th>job</th>\n",
 640 |        "      <th>marital</th>\n",
 641 |        "      <th>education</th>\n",
 642 |        "      <th>default</th>\n",
 643 |        "      <th>housing</th>\n",
 644 |        "      <th>loan</th>\n",
 645 |        "      <th>contact</th>\n",
 646 |        "      <th>month</th>\n",
 647 |        "      <th>day_of_week</th>\n",
 648 |        "      <th>duration</th>\n",
 649 |        "      <th>campaign</th>\n",
 650 |        "      <th>previous</th>\n",
 651 |        "      <th>poutcome</th>\n",
 652 |        "      <th>y</th>\n",
 653 |        "    </tr>\n",
 654 |        "  </thead>\n",
 655 |        "  <tbody>\n",
 656 |        "    <tr>\n",
 657 |        "      <th>0</th>\n",
 658 |        "      <td>56</td>\n",
 659 |        "      <td>housemaid</td>\n",
 660 |        "      <td>married</td>\n",
 661 |        "      <td>basic.4y</td>\n",
 662 |        "      <td>no</td>\n",
 663 |        "      <td>no</td>\n",
 664 |        "      <td>no</td>\n",
 665 |        "      <td>telephone</td>\n",
 666 |        "      <td>may</td>\n",
 667 |        "      <td>mon</td>\n",
 668 |        "      <td>261</td>\n",
 669 |        "      <td>1</td>\n",
 670 |        "      <td>0</td>\n",
 671 |        "      <td>nonexistent</td>\n",
 672 |        "      <td>no</td>\n",
 673 |        "    </tr>\n",
 674 |        "    <tr>\n",
 675 |        "      <th>1</th>\n",
 676 |        "      <td>57</td>\n",
 677 |        "      <td>services</td>\n",
 678 |        "      <td>married</td>\n",
 679 |        "      <td>high.school</td>\n",
 680 |        "      <td>unknown</td>\n",
 681 |        "      <td>no</td>\n",
 682 |        "      <td>no</td>\n",
 683 |        "      <td>telephone</td>\n",
 684 |        "      <td>may</td>\n",
 685 |        "      <td>mon</td>\n",
 686 |        "      <td>149</td>\n",
 687 |        "      <td>1</td>\n",
 688 |        "      <td>0</td>\n",
 689 |        "      <td>nonexistent</td>\n",
 690 |        "      <td>no</td>\n",
 691 |        "    </tr>\n",
 692 |        "    <tr>\n",
 693 |        "      <th>2</th>\n",
 694 |        "      <td>37</td>\n",
 695 |        "      <td>services</td>\n",
 696 |        "      <td>married</td>\n",
 697 |        "      <td>high.school</td>\n",
 698 |        "      <td>no</td>\n",
 699 |        "      <td>yes</td>\n",
 700 |        "      <td>no</td>\n",
 701 |        "      <td>telephone</td>\n",
 702 |        "      <td>may</td>\n",
 703 |        "      <td>mon</td>\n",
 704 |        "      <td>226</td>\n",
 705 |        "      <td>1</td>\n",
 706 |        "      <td>0</td>\n",
 707 |        "      <td>nonexistent</td>\n",
 708 |        "      <td>no</td>\n",
 709 |        "    </tr>\n",
 710 |        "    <tr>\n",
 711 |        "      <th>3</th>\n",
 712 |        "      <td>40</td>\n",
 713 |        "      <td>admin.</td>\n",
 714 |        "      <td>married</td>\n",
 715 |        "      <td>basic.6y</td>\n",
 716 |        "      <td>no</td>\n",
 717 |        "      <td>no</td>\n",
 718 |        "      <td>no</td>\n",
 719 |        "      <td>telephone</td>\n",
 720 |        "      <td>may</td>\n",
 721 |        "      <td>mon</td>\n",
 722 |        "      <td>151</td>\n",
 723 |        "      <td>1</td>\n",
 724 |        "      <td>0</td>\n",
 725 |        "      <td>nonexistent</td>\n",
 726 |        "      <td>no</td>\n",
 727 |        "    </tr>\n",
 728 |        "    <tr>\n",
 729 |        "      <th>4</th>\n",
 730 |        "      <td>56</td>\n",
 731 |        "      <td>services</td>\n",
 732 |        "      <td>married</td>\n",
 733 |        "      <td>high.school</td>\n",
 734 |        "      <td>no</td>\n",
 735 |        "      <td>no</td>\n",
 736 |        "      <td>yes</td>\n",
 737 |        "      <td>telephone</td>\n",
 738 |        "      <td>may</td>\n",
 739 |        "      <td>mon</td>\n",
 740 |        "      <td>307</td>\n",
 741 |        "      <td>1</td>\n",
 742 |        "      <td>0</td>\n",
 743 |        "      <td>nonexistent</td>\n",
 744 |        "      <td>no</td>\n",
 745 |        "    </tr>\n",
 746 |        "    <tr>\n",
 747 |        "      <th>...</th>\n",
 748 |        "      <td>...</td>\n",
 749 |        "      <td>...</td>\n",
 750 |        "      <td>...</td>\n",
 751 |        "      <td>...</td>\n",
 752 |        "      <td>...</td>\n",
 753 |        "      <td>...</td>\n",
 754 |        "      <td>...</td>\n",
 755 |        "      <td>...</td>\n",
 756 |        "      <td>...</td>\n",
 757 |        "      <td>...</td>\n",
 758 |        "      <td>...</td>\n",
 759 |        "      <td>...</td>\n",
 760 |        "      <td>...</td>\n",
 761 |        "      <td>...</td>\n",
 762 |        "      <td>...</td>\n",
 763 |        "    </tr>\n",
 764 |        "    <tr>\n",
 765 |        "      <th>41183</th>\n",
 766 |        "      <td>73</td>\n",
 767 |        "      <td>retired</td>\n",
 768 |        "      <td>married</td>\n",
 769 |        "      <td>professional.course</td>\n",
 770 |        "      <td>no</td>\n",
 771 |        "      <td>yes</td>\n",
 772 |        "      <td>no</td>\n",
 773 |        "      <td>cellular</td>\n",
 774 |        "      <td>nov</td>\n",
 775 |        "      <td>fri</td>\n",
 776 |        "      <td>334</td>\n",
 777 |        "      <td>1</td>\n",
 778 |        "      <td>0</td>\n",
 779 |        "      <td>nonexistent</td>\n",
 780 |        "      <td>yes</td>\n",
 781 |        "    </tr>\n",
 782 |        "    <tr>\n",
 783 |        "      <th>41184</th>\n",
 784 |        "      <td>46</td>\n",
 785 |        "      <td>blue-collar</td>\n",
 786 |        "      <td>married</td>\n",
 787 |        "      <td>professional.course</td>\n",
 788 |        "      <td>no</td>\n",
 789 |        "      <td>no</td>\n",
 790 |        "      <td>no</td>\n",
 791 |        "      <td>cellular</td>\n",
 792 |        "      <td>nov</td>\n",
 793 |        "      <td>fri</td>\n",
 794 |        "      <td>383</td>\n",
 795 |        "      <td>1</td>\n",
 796 |        "      <td>0</td>\n",
 797 |        "      <td>nonexistent</td>\n",
 798 |        "      <td>no</td>\n",
 799 |        "    </tr>\n",
 800 |        "    <tr>\n",
 801 |        "      <th>41185</th>\n",
 802 |        "      <td>56</td>\n",
 803 |        "      <td>retired</td>\n",
 804 |        "      <td>married</td>\n",
 805 |        "      <td>university.degree</td>\n",
 806 |        "      <td>no</td>\n",
 807 |        "      <td>yes</td>\n",
 808 |        "      <td>no</td>\n",
 809 |        "      <td>cellular</td>\n",
 810 |        "      <td>nov</td>\n",
 811 |        "      <td>fri</td>\n",
 812 |        "      <td>189</td>\n",
 813 |        "      <td>2</td>\n",
 814 |        "      <td>0</td>\n",
 815 |        "      <td>nonexistent</td>\n",
 816 |        "      <td>no</td>\n",
 817 |        "    </tr>\n",
 818 |        "    <tr>\n",
 819 |        "      <th>41186</th>\n",
 820 |        "      <td>44</td>\n",
 821 |        "      <td>technician</td>\n",
 822 |        "      <td>married</td>\n",
 823 |        "      <td>professional.course</td>\n",
 824 |        "      <td>no</td>\n",
 825 |        "      <td>no</td>\n",
 826 |        "      <td>no</td>\n",
 827 |        "      <td>cellular</td>\n",
 828 |        "      <td>nov</td>\n",
 829 |        "      <td>fri</td>\n",
 830 |        "      <td>442</td>\n",
 831 |        "      <td>1</td>\n",
 832 |        "      <td>0</td>\n",
 833 |        "      <td>nonexistent</td>\n",
 834 |        "      <td>yes</td>\n",
 835 |        "    </tr>\n",
 836 |        "    <tr>\n",
 837 |        "      <th>41187</th>\n",
 838 |        "      <td>74</td>\n",
 839 |        "      <td>retired</td>\n",
 840 |        "      <td>married</td>\n",
 841 |        "      <td>professional.course</td>\n",
 842 |        "      <td>no</td>\n",
 843 |        "      <td>yes</td>\n",
 844 |        "      <td>no</td>\n",
 845 |        "      <td>cellular</td>\n",
 846 |        "      <td>nov</td>\n",
 847 |        "      <td>fri</td>\n",
 848 |        "      <td>239</td>\n",
 849 |        "      <td>3</td>\n",
 850 |        "      <td>1</td>\n",
 851 |        "      <td>failure</td>\n",
 852 |        "      <td>no</td>\n",
 853 |        "    </tr>\n",
 854 |        "  </tbody>\n",
 855 |        "</table>\n",
 856 |        "<p>41188 rows × 15 columns</p>\n",
 857 |        "</div>"
 858 |       ],
 859 |       "text/plain": [
 860 |        "       age          job  marital            education  default housing loan  \\\n",
 861 |        "0       56    housemaid  married             basic.4y       no      no   no   \n",
 862 |        "1       57     services  married          high.school  unknown      no   no   \n",
 863 |        "2       37     services  married          high.school       no     yes   no   \n",
 864 |        "3       40       admin.  married             basic.6y       no      no   no   \n",
 865 |        "4       56     services  married          high.school       no      no  yes   \n",
 866 |        "...    ...          ...      ...                  ...      ...     ...  ...   \n",
 867 |        "41183   73      retired  married  professional.course       no     yes   no   \n",
 868 |        "41184   46  blue-collar  married  professional.course       no      no   no   \n",
 869 |        "41185   56      retired  married    university.degree       no     yes   no   \n",
 870 |        "41186   44   technician  married  professional.course       no      no   no   \n",
 871 |        "41187   74      retired  married  professional.course       no     yes   no   \n",
 872 |        "\n",
 873 |        "         contact month day_of_week  duration  campaign  previous     poutcome  \\\n",
 874 |        "0      telephone   may         mon       261         1         0  nonexistent   \n",
 875 |        "1      telephone   may         mon       149         1         0  nonexistent   \n",
 876 |        "2      telephone   may         mon       226         1         0  nonexistent   \n",
 877 |        "3      telephone   may         mon       151         1         0  nonexistent   \n",
 878 |        "4      telephone   may         mon       307         1         0  nonexistent   \n",
 879 |        "...          ...   ...         ...       ...       ...       ...          ...   \n",
 880 |        "41183   cellular   nov         fri       334         1         0  nonexistent   \n",
 881 |        "41184   cellular   nov         fri       383         1         0  nonexistent   \n",
 882 |        "41185   cellular   nov         fri       189         2         0  nonexistent   \n",
 883 |        "41186   cellular   nov         fri       442         1         0  nonexistent   \n",
 884 |        "41187   cellular   nov         fri       239         3         1      failure   \n",
 885 |        "\n",
 886 |        "         y  \n",
 887 |        "0       no  \n",
 888 |        "1       no  \n",
 889 |        "2       no  \n",
 890 |        "3       no  \n",
 891 |        "4       no  \n",
 892 |        "...    ...  \n",
 893 |        "41183  yes  \n",
 894 |        "41184   no  \n",
 895 |        "41185   no  \n",
 896 |        "41186  yes  \n",
 897 |        "41187   no  \n",
 898 |        "\n",
 899 |        "[41188 rows x 15 columns]"
 900 |       ]
 901 |      },
 902 |      "execution_count": 8,
 903 |      "metadata": {},
 904 |      "output_type": "execute_result"
 905 |     }
 906 |    ],
 907 |    "source": [
 908 |     "# Dropping constant features(mostly) or index features as they provide no valuable information to the model\n",
 909 |     "data.drop(['cons.price.idx','cons.conf.idx','pdays','euribor3m','emp.var.rate','nr.employed'],axis=1,inplace=True)\n",
 910 |     "data"
 911 |    ]
 912 |   },
 913 |   {
 914 |    "cell_type": "code",
 915 |    "execution_count": 9,
 916 |    "metadata": {
 917 |     "id": "D5TCIyqIVFPY"
 918 |    },
 919 |    "outputs": [],
 920 |    "source": [
 921 |     "# Replacing the categorical features'unknown values with their most common values\n",
 922 |     "data['housing'].replace('unknown','yes',inplace=True)\n",
 923 |     "data['default'].replace('unknown','yes',inplace=True)\n",
 924 |     "data['loan'].replace('unknown','yes',inplace=True)\n",
 925 |     "data['education'].replace('unknown','university.degree',inplace=True)\n",
 926 |     "data['job'].replace('unknown','admin.',inplace=True)\n",
 927 |     "data['poutcome'].replace('nonexistent','failure',inplace=True)\n",
 928 |     "data['marital'].replace('unknown','married',inplace=True)\n",
 929 |     "\n"
 930 |    ]
 931 |   },
 932 |   {
 933 |    "cell_type": "code",
 934 |    "execution_count": 10,
 935 |    "metadata": {
 936 |     "id": "cl3xlbG4eN63"
 937 |    },
 938 |    "outputs": [],
 939 |    "source": [
 940 |     "import numpy as np"
 941 |    ]
 942 |   },
 943 |   {
 944 |    "cell_type": "code",
 945 |    "execution_count": 11,
 946 |    "metadata": {
 947 |     "colab": {
 948 |      "base_uri": "https://localhost:8080/",
 949 |      "height": 423
 950 |     },
 951 |     "id": "HiVmj-z5eN64",
 952 |     "outputId": "f5f34e29-f648-42d7-aec2-507992c3ebfb"
 953 |    },
 954 |    "outputs": [
 955 |     {
 956 |      "data": {
 957 |       "text/html": [
 958 |        "<div>\n",
 959 |        "<style scoped>\n",
 960 |        "    .dataframe tbody tr th:only-of-type {\n",
 961 |        "        vertical-align: middle;\n",
 962 |        "    }\n",
 963 |        "\n",
 964 |        "    .dataframe tbody tr th {\n",
 965 |        "        vertical-align: top;\n",
 966 |        "    }\n",
 967 |        "\n",
 968 |        "    .dataframe thead th {\n",
 969 |        "        text-align: right;\n",
 970 |        "    }\n",
 971 |        "</style>\n",
 972 |        "<table border=\"1\" class=\"dataframe\">\n",
 973 |        "  <thead>\n",
 974 |        "    <tr style=\"text-align: right;\">\n",
 975 |        "      <th></th>\n",
 976 |        "      <th>age</th>\n",
 977 |        "      <th>job</th>\n",
 978 |        "      <th>marital</th>\n",
 979 |        "      <th>education</th>\n",
 980 |        "      <th>default</th>\n",
 981 |        "      <th>housing</th>\n",
 982 |        "      <th>loan</th>\n",
 983 |        "      <th>contact</th>\n",
 984 |        "      <th>month</th>\n",
 985 |        "      <th>day_of_week</th>\n",
 986 |        "      <th>duration</th>\n",
 987 |        "      <th>campaign</th>\n",
 988 |        "      <th>previous</th>\n",
 989 |        "      <th>poutcome</th>\n",
 990 |        "      <th>y</th>\n",
 991 |        "    </tr>\n",
 992 |        "  </thead>\n",
 993 |        "  <tbody>\n",
 994 |        "    <tr>\n",
 995 |        "      <th>0</th>\n",
 996 |        "      <td>56</td>\n",
 997 |        "      <td>housemaid</td>\n",
 998 |        "      <td>married</td>\n",
 999 |        "      <td>basic.4y</td>\n",
1000 |        "      <td>no</td>\n",
1001 |        "      <td>no</td>\n",
1002 |        "      <td>no</td>\n",
1003 |        "      <td>telephone</td>\n",
1004 |        "      <td>may</td>\n",
1005 |        "      <td>mon</td>\n",
1006 |        "      <td>261</td>\n",
1007 |        "      <td>1</td>\n",
1008 |        "      <td>0</td>\n",
1009 |        "      <td>failure</td>\n",
1010 |        "      <td>no</td>\n",
1011 |        "    </tr>\n",
1012 |        "    <tr>\n",
1013 |        "      <th>1</th>\n",
1014 |        "      <td>57</td>\n",
1015 |        "      <td>services</td>\n",
1016 |        "      <td>married</td>\n",
1017 |        "      <td>high.school</td>\n",
1018 |        "      <td>yes</td>\n",
1019 |        "      <td>no</td>\n",
1020 |        "      <td>no</td>\n",
1021 |        "      <td>telephone</td>\n",
1022 |        "      <td>may</td>\n",
1023 |        "      <td>mon</td>\n",
1024 |        "      <td>149</td>\n",
1025 |        "      <td>1</td>\n",
1026 |        "      <td>0</td>\n",
1027 |        "      <td>failure</td>\n",
1028 |        "      <td>no</td>\n",
1029 |        "    </tr>\n",
1030 |        "    <tr>\n",
1031 |        "      <th>2</th>\n",
1032 |        "      <td>37</td>\n",
1033 |        "      <td>services</td>\n",
1034 |        "      <td>married</td>\n",
1035 |        "      <td>high.school</td>\n",
1036 |        "      <td>no</td>\n",
1037 |        "      <td>yes</td>\n",
1038 |        "      <td>no</td>\n",
1039 |        "      <td>telephone</td>\n",
1040 |        "      <td>may</td>\n",
1041 |        "      <td>mon</td>\n",
1042 |        "      <td>226</td>\n",
1043 |        "      <td>1</td>\n",
1044 |        "      <td>0</td>\n",
1045 |        "      <td>failure</td>\n",
1046 |        "      <td>no</td>\n",
1047 |        "    </tr>\n",
1048 |        "    <tr>\n",
1049 |        "      <th>3</th>\n",
1050 |        "      <td>40</td>\n",
1051 |        "      <td>admin.</td>\n",
1052 |        "      <td>married</td>\n",
1053 |        "      <td>basic.6y</td>\n",
1054 |        "      <td>no</td>\n",
1055 |        "      <td>no</td>\n",
1056 |        "      <td>no</td>\n",
1057 |        "      <td>telephone</td>\n",
1058 |        "      <td>may</td>\n",
1059 |        "      <td>mon</td>\n",
1060 |        "      <td>151</td>\n",
1061 |        "      <td>1</td>\n",
1062 |        "      <td>0</td>\n",
1063 |        "      <td>failure</td>\n",
1064 |        "      <td>no</td>\n",
1065 |        "    </tr>\n",
1066 |        "    <tr>\n",
1067 |        "      <th>4</th>\n",
1068 |        "      <td>56</td>\n",
1069 |        "      <td>services</td>\n",
1070 |        "      <td>married</td>\n",
1071 |        "      <td>high.school</td>\n",
1072 |        "      <td>no</td>\n",
1073 |        "      <td>no</td>\n",
1074 |        "      <td>yes</td>\n",
1075 |        "      <td>telephone</td>\n",
1076 |        "      <td>may</td>\n",
1077 |        "      <td>mon</td>\n",
1078 |        "      <td>307</td>\n",
1079 |        "      <td>1</td>\n",
1080 |        "      <td>0</td>\n",
1081 |        "      <td>failure</td>\n",
1082 |        "      <td>no</td>\n",
1083 |        "    </tr>\n",
1084 |        "    <tr>\n",
1085 |        "      <th>...</th>\n",
1086 |        "      <td>...</td>\n",
1087 |        "      <td>...</td>\n",
1088 |        "      <td>...</td>\n",
1089 |        "      <td>...</td>\n",
1090 |        "      <td>...</td>\n",
1091 |        "      <td>...</td>\n",
1092 |        "      <td>...</td>\n",
1093 |        "      <td>...</td>\n",
1094 |        "      <td>...</td>\n",
1095 |        "      <td>...</td>\n",
1096 |        "      <td>...</td>\n",
1097 |        "      <td>...</td>\n",
1098 |        "      <td>...</td>\n",
1099 |        "      <td>...</td>\n",
1100 |        "      <td>...</td>\n",
1101 |        "    </tr>\n",
1102 |        "    <tr>\n",
1103 |        "      <th>41183</th>\n",
1104 |        "      <td>73</td>\n",
1105 |        "      <td>retired</td>\n",
1106 |        "      <td>married</td>\n",
1107 |        "      <td>professional.course</td>\n",
1108 |        "      <td>no</td>\n",
1109 |        "      <td>yes</td>\n",
1110 |        "      <td>no</td>\n",
1111 |        "      <td>cellular</td>\n",
1112 |        "      <td>nov</td>\n",
1113 |        "      <td>fri</td>\n",
1114 |        "      <td>334</td>\n",
1115 |        "      <td>1</td>\n",
1116 |        "      <td>0</td>\n",
1117 |        "      <td>failure</td>\n",
1118 |        "      <td>yes</td>\n",
1119 |        "    </tr>\n",
1120 |        "    <tr>\n",
1121 |        "      <th>41184</th>\n",
1122 |        "      <td>46</td>\n",
1123 |        "      <td>blue-collar</td>\n",
1124 |        "      <td>married</td>\n",
1125 |        "      <td>professional.course</td>\n",
1126 |        "      <td>no</td>\n",
1127 |        "      <td>no</td>\n",
1128 |        "      <td>no</td>\n",
1129 |        "      <td>cellular</td>\n",
1130 |        "      <td>nov</td>\n",
1131 |        "      <td>fri</td>\n",
1132 |        "      <td>383</td>\n",
1133 |        "      <td>1</td>\n",
1134 |        "      <td>0</td>\n",
1135 |        "      <td>failure</td>\n",
1136 |        "      <td>no</td>\n",
1137 |        "    </tr>\n",
1138 |        "    <tr>\n",
1139 |        "      <th>41185</th>\n",
1140 |        "      <td>56</td>\n",
1141 |        "      <td>retired</td>\n",
1142 |        "      <td>married</td>\n",
1143 |        "      <td>university.degree</td>\n",
1144 |        "      <td>no</td>\n",
1145 |        "      <td>yes</td>\n",
1146 |        "      <td>no</td>\n",
1147 |        "      <td>cellular</td>\n",
1148 |        "      <td>nov</td>\n",
1149 |        "      <td>fri</td>\n",
1150 |        "      <td>189</td>\n",
1151 |        "      <td>2</td>\n",
1152 |        "      <td>0</td>\n",
1153 |        "      <td>failure</td>\n",
1154 |        "      <td>no</td>\n",
1155 |        "    </tr>\n",
1156 |        "    <tr>\n",
1157 |        "      <th>41186</th>\n",
1158 |        "      <td>44</td>\n",
1159 |        "      <td>technician</td>\n",
1160 |        "      <td>married</td>\n",
1161 |        "      <td>professional.course</td>\n",
1162 |        "      <td>no</td>\n",
1163 |        "      <td>no</td>\n",
1164 |        "      <td>no</td>\n",
1165 |        "      <td>cellular</td>\n",
1166 |        "      <td>nov</td>\n",
1167 |        "      <td>fri</td>\n",
1168 |        "      <td>442</td>\n",
1169 |        "      <td>1</td>\n",
1170 |        "      <td>0</td>\n",
1171 |        "      <td>failure</td>\n",
1172 |        "      <td>yes</td>\n",
1173 |        "    </tr>\n",
1174 |        "    <tr>\n",
1175 |        "      <th>41187</th>\n",
1176 |        "      <td>74</td>\n",
1177 |        "      <td>retired</td>\n",
1178 |        "      <td>married</td>\n",
1179 |        "      <td>professional.course</td>\n",
1180 |        "      <td>no</td>\n",
1181 |        "      <td>yes</td>\n",
1182 |        "      <td>no</td>\n",
1183 |        "      <td>cellular</td>\n",
1184 |        "      <td>nov</td>\n",
1185 |        "      <td>fri</td>\n",
1186 |        "      <td>239</td>\n",
1187 |        "      <td>3</td>\n",
1188 |        "      <td>1</td>\n",
1189 |        "      <td>failure</td>\n",
1190 |        "      <td>no</td>\n",
1191 |        "    </tr>\n",
1192 |        "  </tbody>\n",
1193 |        "</table>\n",
1194 |        "<p>41188 rows × 15 columns</p>\n",
1195 |        "</div>"
1196 |       ],
1197 |       "text/plain": [
1198 |        "       age          job  marital            education default housing loan  \\\n",
1199 |        "0       56    housemaid  married             basic.4y      no      no   no   \n",
1200 |        "1       57     services  married          high.school     yes      no   no   \n",
1201 |        "2       37     services  married          high.school      no     yes   no   \n",
1202 |        "3       40       admin.  married             basic.6y      no      no   no   \n",
1203 |        "4       56     services  married          high.school      no      no  yes   \n",
1204 |        "...    ...          ...      ...                  ...     ...     ...  ...   \n",
1205 |        "41183   73      retired  married  professional.course      no     yes   no   \n",
1206 |        "41184   46  blue-collar  married  professional.course      no      no   no   \n",
1207 |        "41185   56      retired  married    university.degree      no     yes   no   \n",
1208 |        "41186   44   technician  married  professional.course      no      no   no   \n",
1209 |        "41187   74      retired  married  professional.course      no     yes   no   \n",
1210 |        "\n",
1211 |        "         contact month day_of_week  duration  campaign  previous poutcome    y  \n",
1212 |        "0      telephone   may         mon       261         1         0  failure   no  \n",
1213 |        "1      telephone   may         mon       149         1         0  failure   no  \n",
1214 |        "2      telephone   may         mon       226         1         0  failure   no  \n",
1215 |        "3      telephone   may         mon       151         1         0  failure   no  \n",
1216 |        "4      telephone   may         mon       307         1         0  failure   no  \n",
1217 |        "...          ...   ...         ...       ...       ...       ...      ...  ...  \n",
1218 |        "41183   cellular   nov         fri       334         1         0  failure  yes  \n",
1219 |        "41184   cellular   nov         fri       383         1         0  failure   no  \n",
1220 |        "41185   cellular   nov         fri       189         2         0  failure   no  \n",
1221 |        "41186   cellular   nov         fri       442         1         0  failure  yes  \n",
1222 |        "41187   cellular   nov         fri       239         3         1  failure   no  \n",
1223 |        "\n",
1224 |        "[41188 rows x 15 columns]"
1225 |       ]
1226 |      },
1227 |      "execution_count": 11,
1228 |      "metadata": {},
1229 |      "output_type": "execute_result"
1230 |     }
1231 |    ],
1232 |    "source": [
1233 |     "data"
1234 |    ]
1235 |   },
1236 |   {
1237 |    "cell_type": "code",
1238 |    "execution_count": 12,
1239 |    "metadata": {
1240 |     "id": "CfATYB2lf5CH"
1241 |    },
1242 |    "outputs": [],
1243 |    "source": [
1244 |     "\n",
1245 |     "\n",
1246 |     "from math import log2"
1247 |    ]
1248 |   },
1249 |   {
1250 |    "cell_type": "code",
1251 |    "execution_count": 13,
1252 |    "metadata": {
1253 |     "id": "LLguWJmTeN65"
1254 |    },
1255 |    "outputs": [],
1256 |    "source": [
1257 |     " def entropy(S,target_attribute):\n",
1258 |     "\n",
1259 |     "  d=dict(S[target_attribute].value_counts())\n",
1260 |     "  result=0\n",
1261 |     "  for i in d.values():\n",
1262 |     "    result-= ((i)/(len(S[target_attribute])))*log2((i)/(len(S[target_attribute])))\n",
1263 |     "  return result\n",
1264 |     "\n"
1265 |    ]
1266 |   },
1267 |   {
1268 |    "cell_type": "code",
1269 |    "execution_count": 14,
1270 |    "metadata": {
1271 |     "colab": {
1272 |      "base_uri": "https://localhost:8080/"
1273 |     },
1274 |     "id": "5mAhaDfeeN65",
1275 |     "outputId": "3706e768-cefd-432e-bddf-409aa455c916"
1276 |    },
1277 |    "outputs": [
1278 |     {
1279 |      "data": {
1280 |       "text/plain": [
1281 |        "0.507870194516452"
1282 |       ]
1283 |      },
1284 |      "execution_count": 14,
1285 |      "metadata": {},
1286 |      "output_type": "execute_result"
1287 |     }
1288 |    ],
1289 |    "source": [
1290 |     "entropy(data,'y')"
1291 |    ]
1292 |   },
1293 |   {
1294 |    "cell_type": "code",
1295 |    "execution_count": 15,
1296 |    "metadata": {
1297 |     "id": "3HSJS4WNpFxP"
1298 |    },
1299 |    "outputs": [],
1300 |    "source": [
1301 |     "def Information_Gain(S,attribute):\n",
1302 |     "\n",
1303 |     "  total_entropy = entropy(S,'y')  \n",
1304 |     "     \n",
1305 |     "  values,counts= np.unique(data[attribute],return_counts=True)  \n",
1306 |     "       \n",
1307 |     "  Weighted_Entropy = np.sum([(counts[i]/np.sum(counts))*entropy(data.where(data[attribute]==values[i]).dropna(),'y') for i in range(len(values))])  \n",
1308 |     "      \n",
1309 |     "  Information_Gain = total_entropy - Weighted_Entropy  \n",
1310 |     "  \n",
1311 |     "  return Information_Gain\n",
1312 |     "  "
1313 |    ]
1314 |   },
1315 |   {
1316 |    "cell_type": "code",
1317 |    "execution_count": 16,
1318 |    "metadata": {},
1319 |    "outputs": [
1320 |     {
1321 |      "name": "stdout",
1322 |      "output_type": "stream",
1323 |      "text": [
1324 |       "40.02406040594348\n"
1325 |      ]
1326 |     }
1327 |    ],
1328 |    "source": [
1329 |     "# Converting the numeric feature into string, but first we sort dataset according to the ascending order of age\n",
1330 |     "data=data.sort_values(by=['age'])\n",
1331 |     "print(np.mean(data.age))\n"
1332 |    ]
1333 |   },
1334 |   {
1335 |    "cell_type": "code",
1336 |    "execution_count": 17,
1337 |    "metadata": {},
1338 |    "outputs": [],
1339 |    "source": [
1340 |     "# Although we can start by choosing by 40 as the threshold value, we start by choosing a bit higher threshold such as 56 \n",
1341 |     "# and then +-5 intervals and see what happens to the Information Gain"
1342 |    ]
1343 |   },
1344 |   {
1345 |    "cell_type": "code",
1346 |    "execution_count": 18,
1347 |    "metadata": {
1348 |     "id": "PSa-dNZecyNU"
1349 |    },
1350 |    "outputs": [],
1351 |    "source": [
1352 |     "# This output list represent the trial & error process for 56-62 threshold values for age and their information gain.Age 60 gives the highest information gain and afterwards it decreases\n",
1353 |     "# 56,50,51,52,53,54,55,57,58,59,60,61,62=[0.006041613935893975,0.0020598988482228853,0.0024383397975635823,0.0028021424452681076,0.003408171214054123,0.004124783828806988,0.0051480816768750826,\n",
1354 |     "#                          0.007731777401046247,0.009965340775132947,0.011579407576361389,0.012205424195230852,0.011276350586099493,0.010641970374872078]"
1355 |    ]
1356 |   },
1357 |   {
1358 |    "cell_type": "code",
1359 |    "execution_count": 19,
1360 |    "metadata": {
1361 |     "id": "eFSCPnv8cyKb"
1362 |    },
1363 |    "outputs": [],
1364 |    "source": [
1365 |     "# Choosing 60 as the threshold value that gives the highest information by trial & error\n",
1366 |     "threshold=60\n",
1367 |     "\n",
1368 |     "idx=data[data['age']<=threshold].index\n",
1369 |     "data['l_age']= '>'+str(threshold)\n",
1370 |     "data.loc[idx,'l_age']= '<='+str(threshold)"
1371 |    ]
1372 |   },
1373 |   {
1374 |    "cell_type": "code",
1375 |    "execution_count": 20,
1376 |    "metadata": {
1377 |     "colab": {
1378 |      "base_uri": "https://localhost:8080/"
1379 |     },
1380 |     "id": "6MfdW4tsbY4f",
1381 |     "outputId": "2cb965c0-abbb-4ea8-875e-19e4f7b1196b"
1382 |    },
1383 |    "outputs": [
1384 |     {
1385 |      "data": {
1386 |       "text/plain": [
1387 |        "0.012205424195230852"
1388 |       ]
1389 |      },
1390 |      "execution_count": 20,
1391 |      "metadata": {},
1392 |      "output_type": "execute_result"
1393 |     }
1394 |    ],
1395 |    "source": [
1396 |     "Information_Gain(data,'l_age')"
1397 |    ]
1398 |   },
1399 |   {
1400 |    "cell_type": "code",
1401 |    "execution_count": 21,
1402 |    "metadata": {
1403 |     "colab": {
1404 |      "base_uri": "https://localhost:8080/",
1405 |      "height": 423
1406 |     },
1407 |     "id": "Yzc_BDKh6MZO",
1408 |     "outputId": "8e4afde9-54bd-4a8c-a3b0-c13b679d4b17"
1409 |    },
1410 |    "outputs": [
1411 |     {
1412 |      "data": {
1413 |       "text/html": [
1414 |        "<div>\n",
1415 |        "<style scoped>\n",
1416 |        "    .dataframe tbody tr th:only-of-type {\n",
1417 |        "        vertical-align: middle;\n",
1418 |        "    }\n",
1419 |        "\n",
1420 |        "    .dataframe tbody tr th {\n",
1421 |        "        vertical-align: top;\n",
1422 |        "    }\n",
1423 |        "\n",
1424 |        "    .dataframe thead th {\n",
1425 |        "        text-align: right;\n",
1426 |        "    }\n",
1427 |        "</style>\n",
1428 |        "<table border=\"1\" class=\"dataframe\">\n",
1429 |        "  <thead>\n",
1430 |        "    <tr style=\"text-align: right;\">\n",
1431 |        "      <th></th>\n",
1432 |        "      <th>age</th>\n",
1433 |        "      <th>job</th>\n",
1434 |        "      <th>marital</th>\n",
1435 |        "      <th>education</th>\n",
1436 |        "      <th>default</th>\n",
1437 |        "      <th>housing</th>\n",
1438 |        "      <th>loan</th>\n",
1439 |        "      <th>contact</th>\n",
1440 |        "      <th>month</th>\n",
1441 |        "      <th>day_of_week</th>\n",
1442 |        "      <th>duration</th>\n",
1443 |        "      <th>campaign</th>\n",
1444 |        "      <th>previous</th>\n",
1445 |        "      <th>poutcome</th>\n",
1446 |        "      <th>y</th>\n",
1447 |        "      <th>l_age</th>\n",
1448 |        "    </tr>\n",
1449 |        "  </thead>\n",
1450 |        "  <tbody>\n",
1451 |        "    <tr>\n",
1452 |        "      <th>38274</th>\n",
1453 |        "      <td>17</td>\n",
1454 |        "      <td>student</td>\n",
1455 |        "      <td>single</td>\n",
1456 |        "      <td>university.degree</td>\n",
1457 |        "      <td>no</td>\n",
1458 |        "      <td>no</td>\n",
1459 |        "      <td>yes</td>\n",
1460 |        "      <td>cellular</td>\n",
1461 |        "      <td>oct</td>\n",
1462 |        "      <td>tue</td>\n",
1463 |        "      <td>896</td>\n",
1464 |        "      <td>1</td>\n",
1465 |        "      <td>2</td>\n",
1466 |        "      <td>success</td>\n",
1467 |        "      <td>yes</td>\n",
1468 |        "      <td>&lt;=60</td>\n",
1469 |        "    </tr>\n",
1470 |        "    <tr>\n",
1471 |        "      <th>37579</th>\n",
1472 |        "      <td>17</td>\n",
1473 |        "      <td>student</td>\n",
1474 |        "      <td>single</td>\n",
1475 |        "      <td>basic.9y</td>\n",
1476 |        "      <td>no</td>\n",
1477 |        "      <td>yes</td>\n",
1478 |        "      <td>yes</td>\n",
1479 |        "      <td>cellular</td>\n",
1480 |        "      <td>aug</td>\n",
1481 |        "      <td>fri</td>\n",
1482 |        "      <td>498</td>\n",
1483 |        "      <td>2</td>\n",
1484 |        "      <td>1</td>\n",
1485 |        "      <td>failure</td>\n",
1486 |        "      <td>yes</td>\n",
1487 |        "      <td>&lt;=60</td>\n",
1488 |        "    </tr>\n",
1489 |        "    <tr>\n",
1490 |        "      <th>37539</th>\n",
1491 |        "      <td>17</td>\n",
1492 |        "      <td>student</td>\n",
1493 |        "      <td>single</td>\n",
1494 |        "      <td>basic.9y</td>\n",
1495 |        "      <td>no</td>\n",
1496 |        "      <td>yes</td>\n",
1497 |        "      <td>no</td>\n",
1498 |        "      <td>cellular</td>\n",
1499 |        "      <td>aug</td>\n",
1500 |        "      <td>fri</td>\n",
1501 |        "      <td>182</td>\n",
1502 |        "      <td>2</td>\n",
1503 |        "      <td>2</td>\n",
1504 |        "      <td>failure</td>\n",
1505 |        "      <td>no</td>\n",
1506 |        "      <td>&lt;=60</td>\n",
1507 |        "    </tr>\n",
1508 |        "    <tr>\n",
1509 |        "      <th>37140</th>\n",
1510 |        "      <td>17</td>\n",
1511 |        "      <td>student</td>\n",
1512 |        "      <td>single</td>\n",
1513 |        "      <td>university.degree</td>\n",
1514 |        "      <td>no</td>\n",
1515 |        "      <td>yes</td>\n",
1516 |        "      <td>no</td>\n",
1517 |        "      <td>cellular</td>\n",
1518 |        "      <td>aug</td>\n",
1519 |        "      <td>wed</td>\n",
1520 |        "      <td>432</td>\n",
1521 |        "      <td>3</td>\n",
1522 |        "      <td>2</td>\n",
1523 |        "      <td>success</td>\n",
1524 |        "      <td>no</td>\n",
1525 |        "      <td>&lt;=60</td>\n",
1526 |        "    </tr>\n",
1527 |        "    <tr>\n",
1528 |        "      <th>37558</th>\n",
1529 |        "      <td>17</td>\n",
1530 |        "      <td>student</td>\n",
1531 |        "      <td>single</td>\n",
1532 |        "      <td>basic.9y</td>\n",
1533 |        "      <td>no</td>\n",
1534 |        "      <td>yes</td>\n",
1535 |        "      <td>no</td>\n",
1536 |        "      <td>cellular</td>\n",
1537 |        "      <td>aug</td>\n",
1538 |        "      <td>fri</td>\n",
1539 |        "      <td>92</td>\n",
1540 |        "      <td>3</td>\n",
1541 |        "      <td>2</td>\n",
1542 |        "      <td>success</td>\n",
1543 |        "      <td>no</td>\n",
1544 |        "      <td>&lt;=60</td>\n",
1545 |        "    </tr>\n",
1546 |        "    <tr>\n",
1547 |        "      <th>...</th>\n",
1548 |        "      <td>...</td>\n",
1549 |        "      <td>...</td>\n",
1550 |        "      <td>...</td>\n",
1551 |        "      <td>...</td>\n",
1552 |        "      <td>...</td>\n",
1553 |        "      <td>...</td>\n",
1554 |        "      <td>...</td>\n",
1555 |        "      <td>...</td>\n",
1556 |        "      <td>...</td>\n",
1557 |        "      <td>...</td>\n",
1558 |        "      <td>...</td>\n",
1559 |        "      <td>...</td>\n",
1560 |        "      <td>...</td>\n",
1561 |        "      <td>...</td>\n",
1562 |        "      <td>...</td>\n",
1563 |        "      <td>...</td>\n",
1564 |        "    </tr>\n",
1565 |        "    <tr>\n",
1566 |        "      <th>40450</th>\n",
1567 |        "      <td>92</td>\n",
1568 |        "      <td>retired</td>\n",
1569 |        "      <td>married</td>\n",
1570 |        "      <td>university.degree</td>\n",
1571 |        "      <td>no</td>\n",
1572 |        "      <td>no</td>\n",
1573 |        "      <td>yes</td>\n",
1574 |        "      <td>cellular</td>\n",
1575 |        "      <td>aug</td>\n",
1576 |        "      <td>tue</td>\n",
1577 |        "      <td>1064</td>\n",
1578 |        "      <td>1</td>\n",
1579 |        "      <td>1</td>\n",
1580 |        "      <td>success</td>\n",
1581 |        "      <td>yes</td>\n",
1582 |        "      <td>&gt;60</td>\n",
1583 |        "    </tr>\n",
1584 |        "    <tr>\n",
1585 |        "      <th>38921</th>\n",
1586 |        "      <td>94</td>\n",
1587 |        "      <td>retired</td>\n",
1588 |        "      <td>married</td>\n",
1589 |        "      <td>basic.9y</td>\n",
1590 |        "      <td>no</td>\n",
1591 |        "      <td>no</td>\n",
1592 |        "      <td>no</td>\n",
1593 |        "      <td>cellular</td>\n",
1594 |        "      <td>nov</td>\n",
1595 |        "      <td>wed</td>\n",
1596 |        "      <td>134</td>\n",
1597 |        "      <td>1</td>\n",
1598 |        "      <td>1</td>\n",
1599 |        "      <td>failure</td>\n",
1600 |        "      <td>no</td>\n",
1601 |        "      <td>&gt;60</td>\n",
1602 |        "    </tr>\n",
1603 |        "    <tr>\n",
1604 |        "      <th>27826</th>\n",
1605 |        "      <td>95</td>\n",
1606 |        "      <td>retired</td>\n",
1607 |        "      <td>divorced</td>\n",
1608 |        "      <td>basic.6y</td>\n",
1609 |        "      <td>no</td>\n",
1610 |        "      <td>no</td>\n",
1611 |        "      <td>no</td>\n",
1612 |        "      <td>cellular</td>\n",
1613 |        "      <td>mar</td>\n",
1614 |        "      <td>thu</td>\n",
1615 |        "      <td>85</td>\n",
1616 |        "      <td>1</td>\n",
1617 |        "      <td>0</td>\n",
1618 |        "      <td>failure</td>\n",
1619 |        "      <td>no</td>\n",
1620 |        "      <td>&gt;60</td>\n",
1621 |        "    </tr>\n",
1622 |        "    <tr>\n",
1623 |        "      <th>38455</th>\n",
1624 |        "      <td>98</td>\n",
1625 |        "      <td>retired</td>\n",
1626 |        "      <td>married</td>\n",
1627 |        "      <td>basic.4y</td>\n",
1628 |        "      <td>yes</td>\n",
1629 |        "      <td>yes</td>\n",
1630 |        "      <td>no</td>\n",
1631 |        "      <td>cellular</td>\n",
1632 |        "      <td>oct</td>\n",
1633 |        "      <td>fri</td>\n",
1634 |        "      <td>272</td>\n",
1635 |        "      <td>2</td>\n",
1636 |        "      <td>0</td>\n",
1637 |        "      <td>failure</td>\n",
1638 |        "      <td>yes</td>\n",
1639 |        "      <td>&gt;60</td>\n",
1640 |        "    </tr>\n",
1641 |        "    <tr>\n",
1642 |        "      <th>38452</th>\n",
1643 |        "      <td>98</td>\n",
1644 |        "      <td>retired</td>\n",
1645 |        "      <td>married</td>\n",
1646 |        "      <td>basic.4y</td>\n",
1647 |        "      <td>yes</td>\n",
1648 |        "      <td>yes</td>\n",
1649 |        "      <td>no</td>\n",
1650 |        "      <td>cellular</td>\n",
1651 |        "      <td>oct</td>\n",
1652 |        "      <td>fri</td>\n",
1653 |        "      <td>476</td>\n",
1654 |        "      <td>1</td>\n",
1655 |        "      <td>2</td>\n",
1656 |        "      <td>success</td>\n",
1657 |        "      <td>yes</td>\n",
1658 |        "      <td>&gt;60</td>\n",
1659 |        "    </tr>\n",
1660 |        "  </tbody>\n",
1661 |        "</table>\n",
1662 |        "<p>41188 rows × 16 columns</p>\n",
1663 |        "</div>"
1664 |       ],
1665 |       "text/plain": [
1666 |        "       age      job   marital          education default housing loan  \\\n",
1667 |        "38274   17  student    single  university.degree      no      no  yes   \n",
1668 |        "37579   17  student    single           basic.9y      no     yes  yes   \n",
1669 |        "37539   17  student    single           basic.9y      no     yes   no   \n",
1670 |        "37140   17  student    single  university.degree      no     yes   no   \n",
1671 |        "37558   17  student    single           basic.9y      no     yes   no   \n",
1672 |        "...    ...      ...       ...                ...     ...     ...  ...   \n",
1673 |        "40450   92  retired   married  university.degree      no      no  yes   \n",
1674 |        "38921   94  retired   married           basic.9y      no      no   no   \n",
1675 |        "27826   95  retired  divorced           basic.6y      no      no   no   \n",
1676 |        "38455   98  retired   married           basic.4y     yes     yes   no   \n",
1677 |        "38452   98  retired   married           basic.4y     yes     yes   no   \n",
1678 |        "\n",
1679 |        "        contact month day_of_week  duration  campaign  previous poutcome    y  \\\n",
1680 |        "38274  cellular   oct         tue       896         1         2  success  yes   \n",
1681 |        "37579  cellular   aug         fri       498         2         1  failure  yes   \n",
1682 |        "37539  cellular   aug         fri       182         2         2  failure   no   \n",
1683 |        "37140  cellular   aug         wed       432         3         2  success   no   \n",
1684 |        "37558  cellular   aug         fri        92         3         2  success   no   \n",
1685 |        "...         ...   ...         ...       ...       ...       ...      ...  ...   \n",
1686 |        "40450  cellular   aug         tue      1064         1         1  success  yes   \n",
1687 |        "38921  cellular   nov         wed       134         1         1  failure   no   \n",
1688 |        "27826  cellular   mar         thu        85         1         0  failure   no   \n",
1689 |        "38455  cellular   oct         fri       272         2         0  failure  yes   \n",
1690 |        "38452  cellular   oct         fri       476         1         2  success  yes   \n",
1691 |        "\n",
1692 |        "      l_age  \n",
1693 |        "38274  <=60  \n",
1694 |        "37579  <=60  \n",
1695 |        "37539  <=60  \n",
1696 |        "37140  <=60  \n",
1697 |        "37558  <=60  \n",
1698 |        "...     ...  \n",
1699 |        "40450   >60  \n",
1700 |        "38921   >60  \n",
1701 |        "27826   >60  \n",
1702 |        "38455   >60  \n",
1703 |        "38452   >60  \n",
1704 |        "\n",
1705 |        "[41188 rows x 16 columns]"
1706 |       ]
1707 |      },
1708 |      "execution_count": 21,
1709 |      "metadata": {},
1710 |      "output_type": "execute_result"
1711 |     }
1712 |    ],
1713 |    "source": [
1714 |     "data"
1715 |    ]
1716 |   },
1717 |   {
1718 |    "cell_type": "code",
1719 |    "execution_count": 22,
1720 |    "metadata": {
1721 |     "id": "YIyw1pBL6M5p"
1722 |    },
1723 |    "outputs": [],
1724 |    "source": [
1725 |     "# We do the similar tactic with the duration feature as it's continuous\n",
1726 |     "data=data.sort_values(by=['duration'])"
1727 |    ]
1728 |   },
1729 |   {
1730 |    "cell_type": "code",
1731 |    "execution_count": 23,
1732 |    "metadata": {
1733 |     "colab": {
1734 |      "base_uri": "https://localhost:8080/"
1735 |     },
1736 |     "id": "hc1BM0G16U3c",
1737 |     "outputId": "cff38374-f078-4171-a47a-4936b22c084a"
1738 |    },
1739 |    "outputs": [
1740 |     {
1741 |      "data": {
1742 |       "text/plain": [
1743 |        "258.2850101971448"
1744 |       ]
1745 |      },
1746 |      "execution_count": 23,
1747 |      "metadata": {},
1748 |      "output_type": "execute_result"
1749 |     }
1750 |    ],
1751 |    "source": [
1752 |     "np.mean(data['duration'])"
1753 |    ]
1754 |   },
1755 |   {
1756 |    "cell_type": "code",
1757 |    "execution_count": 24,
1758 |    "metadata": {
1759 |     "id": "juU4Vvz06are"
1760 |    },
1761 |    "outputs": [],
1762 |    "source": [
1763 |     "# # Starting with 258 as threshold value for duration. Here we take some jumps in threshold values. We have the following Information Gain by trial & error. Hence, choosing 450 as as threshold value\n",
1764 |     "# 258,289.350,375,450,500,600=[0.0650935234793425,0.06529231501228083,0.06719093723384761,0.06834116976822469,0.06875318837819422,0.06891361639584348,0.06764866059681529,0.060781066259440075]"
1765 |    ]
1766 |   },
1767 |   {
1768 |    "cell_type": "code",
1769 |    "execution_count": 25,
1770 |    "metadata": {
1771 |     "id": "NhpTyqWj6dq6"
1772 |    },
1773 |    "outputs": [],
1774 |    "source": [
1775 |     "# Choosing 450 as the threshold value that gives the highest information for the duration feature\n",
1776 |     "th=450\n",
1777 |     "\n",
1778 |     "idx=data[data['duration']<=th].index\n",
1779 |     "data['l_duration']= '>'+str(th)\n",
1780 |     "data.loc[idx,'l_duration']= '<='+str(th)"
1781 |    ]
1782 |   },
1783 |   {
1784 |    "cell_type": "code",
1785 |    "execution_count": 26,
1786 |    "metadata": {
1787 |     "colab": {
1788 |      "base_uri": "https://localhost:8080/",
1789 |      "height": 467
1790 |     },
1791 |     "id": "TDzt2N_e7ACu",
1792 |     "outputId": "81a6632d-7dcb-4196-ab42-02070a66dd31"
1793 |    },
1794 |    "outputs": [
1795 |     {
1796 |      "data": {
1797 |       "text/html": [
1798 |        "<div>\n",
1799 |        "<style scoped>\n",
1800 |        "    .dataframe tbody tr th:only-of-type {\n",
1801 |        "        vertical-align: middle;\n",
1802 |        "    }\n",
1803 |        "\n",
1804 |        "    .dataframe tbody tr th {\n",
1805 |        "        vertical-align: top;\n",
1806 |        "    }\n",
1807 |        "\n",
1808 |        "    .dataframe thead th {\n",
1809 |        "        text-align: right;\n",
1810 |        "    }\n",
1811 |        "</style>\n",
1812 |        "<table border=\"1\" class=\"dataframe\">\n",
1813 |        "  <thead>\n",
1814 |        "    <tr style=\"text-align: right;\">\n",
1815 |        "      <th></th>\n",
1816 |        "      <th>age</th>\n",
1817 |        "      <th>job</th>\n",
1818 |        "      <th>marital</th>\n",
1819 |        "      <th>education</th>\n",
1820 |        "      <th>default</th>\n",
1821 |        "      <th>housing</th>\n",
1822 |        "      <th>loan</th>\n",
1823 |        "      <th>contact</th>\n",
1824 |        "      <th>month</th>\n",
1825 |        "      <th>day_of_week</th>\n",
1826 |        "      <th>duration</th>\n",
1827 |        "      <th>campaign</th>\n",
1828 |        "      <th>previous</th>\n",
1829 |        "      <th>poutcome</th>\n",
1830 |        "      <th>y</th>\n",
1831 |        "      <th>l_age</th>\n",
1832 |        "      <th>l_duration</th>\n",
1833 |        "    </tr>\n",
1834 |        "  </thead>\n",
1835 |        "  <tbody>\n",
1836 |        "    <tr>\n",
1837 |        "      <th>6251</th>\n",
1838 |        "      <td>39</td>\n",
1839 |        "      <td>admin.</td>\n",
1840 |        "      <td>married</td>\n",
1841 |        "      <td>high.school</td>\n",
1842 |        "      <td>no</td>\n",
1843 |        "      <td>yes</td>\n",
1844 |        "      <td>no</td>\n",
1845 |        "      <td>telephone</td>\n",
1846 |        "      <td>may</td>\n",
1847 |        "      <td>tue</td>\n",
1848 |        "      <td>0</td>\n",
1849 |        "      <td>4</td>\n",
1850 |        "      <td>0</td>\n",
1851 |        "      <td>failure</td>\n",
1852 |        "      <td>no</td>\n",
1853 |        "      <td>&lt;=60</td>\n",
1854 |        "      <td>&lt;=450</td>\n",
1855 |        "    </tr>\n",
1856 |        "    <tr>\n",
1857 |        "      <th>33015</th>\n",
1858 |        "      <td>31</td>\n",
1859 |        "      <td>blue-collar</td>\n",
1860 |        "      <td>married</td>\n",
1861 |        "      <td>basic.9y</td>\n",
1862 |        "      <td>no</td>\n",
1863 |        "      <td>no</td>\n",
1864 |        "      <td>no</td>\n",
1865 |        "      <td>cellular</td>\n",
1866 |        "      <td>may</td>\n",
1867 |        "      <td>mon</td>\n",
1868 |        "      <td>0</td>\n",
1869 |        "      <td>2</td>\n",
1870 |        "      <td>0</td>\n",
1871 |        "      <td>failure</td>\n",
1872 |        "      <td>no</td>\n",
1873 |        "      <td>&lt;=60</td>\n",
1874 |        "      <td>&lt;=450</td>\n",
1875 |        "    </tr>\n",
1876 |        "    <tr>\n",
1877 |        "      <th>28063</th>\n",
1878 |        "      <td>53</td>\n",
1879 |        "      <td>blue-collar</td>\n",
1880 |        "      <td>divorced</td>\n",
1881 |        "      <td>high.school</td>\n",
1882 |        "      <td>no</td>\n",
1883 |        "      <td>yes</td>\n",
1884 |        "      <td>no</td>\n",
1885 |        "      <td>cellular</td>\n",
1886 |        "      <td>apr</td>\n",
1887 |        "      <td>fri</td>\n",
1888 |        "      <td>0</td>\n",
1889 |        "      <td>3</td>\n",
1890 |        "      <td>0</td>\n",
1891 |        "      <td>failure</td>\n",
1892 |        "      <td>no</td>\n",
1893 |        "      <td>&lt;=60</td>\n",
1894 |        "      <td>&lt;=450</td>\n",
1895 |        "    </tr>\n",
1896 |        "    <tr>\n",
1897 |        "      <th>23031</th>\n",
1898 |        "      <td>59</td>\n",
1899 |        "      <td>management</td>\n",
1900 |        "      <td>married</td>\n",
1901 |        "      <td>university.degree</td>\n",
1902 |        "      <td>no</td>\n",
1903 |        "      <td>yes</td>\n",
1904 |        "      <td>no</td>\n",
1905 |        "      <td>cellular</td>\n",
1906 |        "      <td>aug</td>\n",
1907 |        "      <td>tue</td>\n",
1908 |        "      <td>0</td>\n",
1909 |        "      <td>10</td>\n",
1910 |        "      <td>0</td>\n",
1911 |        "      <td>failure</td>\n",
1912 |        "      <td>no</td>\n",
1913 |        "      <td>&lt;=60</td>\n",
1914 |        "      <td>&lt;=450</td>\n",
1915 |        "    </tr>\n",
1916 |        "    <tr>\n",
1917 |        "      <th>28239</th>\n",
1918 |        "      <td>44</td>\n",
1919 |        "      <td>blue-collar</td>\n",
1920 |        "      <td>married</td>\n",
1921 |        "      <td>basic.9y</td>\n",
1922 |        "      <td>no</td>\n",
1923 |        "      <td>no</td>\n",
1924 |        "      <td>no</td>\n",
1925 |        "      <td>cellular</td>\n",
1926 |        "      <td>apr</td>\n",
1927 |        "      <td>tue</td>\n",
1928 |        "      <td>1</td>\n",
1929 |        "      <td>1</td>\n",
1930 |        "      <td>0</td>\n",
1931 |        "      <td>failure</td>\n",
1932 |        "      <td>no</td>\n",
1933 |        "      <td>&lt;=60</td>\n",
1934 |        "      <td>&lt;=450</td>\n",
1935 |        "    </tr>\n",
1936 |        "    <tr>\n",
1937 |        "      <th>...</th>\n",
1938 |        "      <td>...</td>\n",
1939 |        "      <td>...</td>\n",
1940 |        "      <td>...</td>\n",
1941 |        "      <td>...</td>\n",
1942 |        "      <td>...</td>\n",
1943 |        "      <td>...</td>\n",
1944 |        "      <td>...</td>\n",
1945 |        "      <td>...</td>\n",
1946 |        "      <td>...</td>\n",
1947 |        "      <td>...</td>\n",
1948 |        "      <td>...</td>\n",
1949 |        "      <td>...</td>\n",
1950 |        "      <td>...</td>\n",
1951 |        "      <td>...</td>\n",
1952 |        "      <td>...</td>\n",
1953 |        "      <td>...</td>\n",
1954 |        "      <td>...</td>\n",
1955 |        "    </tr>\n",
1956 |        "    <tr>\n",
1957 |        "      <th>7727</th>\n",
1958 |        "      <td>37</td>\n",
1959 |        "      <td>unemployed</td>\n",
1960 |        "      <td>married</td>\n",
1961 |        "      <td>professional.course</td>\n",
1962 |        "      <td>no</td>\n",
1963 |        "      <td>yes</td>\n",
1964 |        "      <td>no</td>\n",
1965 |        "      <td>telephone</td>\n",
1966 |        "      <td>may</td>\n",
1967 |        "      <td>fri</td>\n",
1968 |        "      <td>3631</td>\n",
1969 |        "      <td>2</td>\n",
1970 |        "      <td>0</td>\n",
1971 |        "      <td>failure</td>\n",
1972 |        "      <td>yes</td>\n",
1973 |        "      <td>&lt;=60</td>\n",
1974 |        "      <td>&gt;450</td>\n",
1975 |        "    </tr>\n",
1976 |        "    <tr>\n",
1977 |        "      <th>13820</th>\n",
1978 |        "      <td>31</td>\n",
1979 |        "      <td>technician</td>\n",
1980 |        "      <td>married</td>\n",
1981 |        "      <td>professional.course</td>\n",
1982 |        "      <td>no</td>\n",
1983 |        "      <td>no</td>\n",
1984 |        "      <td>no</td>\n",
1985 |        "      <td>cellular</td>\n",
1986 |        "      <td>jul</td>\n",
1987 |        "      <td>thu</td>\n",
1988 |        "      <td>3643</td>\n",
1989 |        "      <td>1</td>\n",
1990 |        "      <td>0</td>\n",
1991 |        "      <td>failure</td>\n",
1992 |        "      <td>yes</td>\n",
1993 |        "      <td>&lt;=60</td>\n",
1994 |        "      <td>&gt;450</td>\n",
1995 |        "    </tr>\n",
1996 |        "    <tr>\n",
1997 |        "      <th>40537</th>\n",
1998 |        "      <td>27</td>\n",
1999 |        "      <td>admin.</td>\n",
2000 |        "      <td>single</td>\n",
2001 |        "      <td>high.school</td>\n",
2002 |        "      <td>no</td>\n",
2003 |        "      <td>no</td>\n",
2004 |        "      <td>no</td>\n",
2005 |        "      <td>telephone</td>\n",
2006 |        "      <td>aug</td>\n",
2007 |        "      <td>fri</td>\n",
2008 |        "      <td>3785</td>\n",
2009 |        "      <td>1</td>\n",
2010 |        "      <td>0</td>\n",
2011 |        "      <td>failure</td>\n",
2012 |        "      <td>no</td>\n",
2013 |        "      <td>&lt;=60</td>\n",
2014 |        "      <td>&gt;450</td>\n",
2015 |        "    </tr>\n",
2016 |        "    <tr>\n",
2017 |        "      <th>22192</th>\n",
2018 |        "      <td>52</td>\n",
2019 |        "      <td>blue-collar</td>\n",
2020 |        "      <td>married</td>\n",
2021 |        "      <td>basic.4y</td>\n",
2022 |        "      <td>no</td>\n",
2023 |        "      <td>no</td>\n",
2024 |        "      <td>no</td>\n",
2025 |        "      <td>telephone</td>\n",
2026 |        "      <td>aug</td>\n",
2027 |        "      <td>thu</td>\n",
2028 |        "      <td>4199</td>\n",
2029 |        "      <td>3</td>\n",
2030 |        "      <td>0</td>\n",
2031 |        "      <td>failure</td>\n",
2032 |        "      <td>yes</td>\n",
2033 |        "      <td>&lt;=60</td>\n",
2034 |        "      <td>&gt;450</td>\n",
2035 |        "    </tr>\n",
2036 |        "    <tr>\n",
2037 |        "      <th>24091</th>\n",
2038 |        "      <td>33</td>\n",
2039 |        "      <td>technician</td>\n",
2040 |        "      <td>single</td>\n",
2041 |        "      <td>professional.course</td>\n",
2042 |        "      <td>no</td>\n",
2043 |        "      <td>yes</td>\n",
2044 |        "      <td>no</td>\n",
2045 |        "      <td>telephone</td>\n",
2046 |        "      <td>nov</td>\n",
2047 |        "      <td>mon</td>\n",
2048 |        "      <td>4918</td>\n",
2049 |        "      <td>1</td>\n",
2050 |        "      <td>0</td>\n",
2051 |        "      <td>failure</td>\n",
2052 |        "      <td>no</td>\n",
2053 |        "      <td>&lt;=60</td>\n",
2054 |        "      <td>&gt;450</td>\n",
2055 |        "    </tr>\n",
2056 |        "  </tbody>\n",
2057 |        "</table>\n",
2058 |        "<p>41188 rows × 17 columns</p>\n",
2059 |        "</div>"
2060 |       ],
2061 |       "text/plain": [
2062 |        "       age          job   marital            education default housing loan  \\\n",
2063 |        "6251    39       admin.   married          high.school      no     yes   no   \n",
2064 |        "33015   31  blue-collar   married             basic.9y      no      no   no   \n",
2065 |        "28063   53  blue-collar  divorced          high.school      no     yes   no   \n",
2066 |        "23031   59   management   married    university.degree      no     yes   no   \n",
2067 |        "28239   44  blue-collar   married             basic.9y      no      no   no   \n",
2068 |        "...    ...          ...       ...                  ...     ...     ...  ...   \n",
2069 |        "7727    37   unemployed   married  professional.course      no     yes   no   \n",
2070 |        "13820   31   technician   married  professional.course      no      no   no   \n",
2071 |        "40537   27       admin.    single          high.school      no      no   no   \n",
2072 |        "22192   52  blue-collar   married             basic.4y      no      no   no   \n",
2073 |        "24091   33   technician    single  professional.course      no     yes   no   \n",
2074 |        "\n",
2075 |        "         contact month day_of_week  duration  campaign  previous poutcome  \\\n",
2076 |        "6251   telephone   may         tue         0         4         0  failure   \n",
2077 |        "33015   cellular   may         mon         0         2         0  failure   \n",
2078 |        "28063   cellular   apr         fri         0         3         0  failure   \n",
2079 |        "23031   cellular   aug         tue         0        10         0  failure   \n",
2080 |        "28239   cellular   apr         tue         1         1         0  failure   \n",
2081 |        "...          ...   ...         ...       ...       ...       ...      ...   \n",
2082 |        "7727   telephone   may         fri      3631         2         0  failure   \n",
2083 |        "13820   cellular   jul         thu      3643         1         0  failure   \n",
2084 |        "40537  telephone   aug         fri      3785         1         0  failure   \n",
2085 |        "22192  telephone   aug         thu      4199         3         0  failure   \n",
2086 |        "24091  telephone   nov         mon      4918         1         0  failure   \n",
2087 |        "\n",
2088 |        "         y l_age l_duration  \n",
2089 |        "6251    no  <=60      <=450  \n",
2090 |        "33015   no  <=60      <=450  \n",
2091 |        "28063   no  <=60      <=450  \n",
2092 |        "23031   no  <=60      <=450  \n",
2093 |        "28239   no  <=60      <=450  \n",
2094 |        "...    ...   ...        ...  \n",
2095 |        "7727   yes  <=60       >450  \n",
2096 |        "13820  yes  <=60       >450  \n",
2097 |        "40537   no  <=60       >450  \n",
2098 |        "22192  yes  <=60       >450  \n",
2099 |        "24091   no  <=60       >450  \n",
2100 |        "\n",
2101 |        "[41188 rows x 17 columns]"
2102 |       ]
2103 |      },
2104 |      "execution_count": 26,
2105 |      "metadata": {},
2106 |      "output_type": "execute_result"
2107 |     }
2108 |    ],
2109 |    "source": [
2110 |     "data"
2111 |    ]
2112 |   },
2113 |   {
2114 |    "cell_type": "code",
2115 |    "execution_count": 27,
2116 |    "metadata": {
2117 |     "colab": {
2118 |      "base_uri": "https://localhost:8080/"
2119 |     },
2120 |     "id": "xMqbHXz77BAc",
2121 |     "outputId": "2f7bc22c-496a-48c2-fc6d-828170f040ae"
2122 |    },
2123 |    "outputs": [
2124 |     {
2125 |      "data": {
2126 |       "text/plain": [
2127 |        "0.06891361639584348"
2128 |       ]
2129 |      },
2130 |      "execution_count": 27,
2131 |      "metadata": {},
2132 |      "output_type": "execute_result"
2133 |     }
2134 |    ],
2135 |    "source": [
2136 |     "Information_Gain(data,'l_duration')"
2137 |    ]
2138 |   },
2139 |   {
2140 |    "cell_type": "code",
2141 |    "execution_count": 28,
2142 |    "metadata": {
2143 |     "id": "ESVSnEwF7FS3"
2144 |    },
2145 |    "outputs": [],
2146 |    "source": [
2147 |     "# Now we drop the initial age and duration columns\n",
2148 |     "data.drop(['age','duration'],axis=1,inplace=True)"
2149 |    ]
2150 |   },
2151 |   {
2152 |    "cell_type": "code",
2153 |    "execution_count": 29,
2154 |    "metadata": {},
2155 |    "outputs": [
2156 |     {
2157 |      "data": {
2158 |       "text/html": [
2159 |        "<div>\n",
2160 |        "<style scoped>\n",
2161 |        "    .dataframe tbody tr th:only-of-type {\n",
2162 |        "        vertical-align: middle;\n",
2163 |        "    }\n",
2164 |        "\n",
2165 |        "    .dataframe tbody tr th {\n",
2166 |        "        vertical-align: top;\n",
2167 |        "    }\n",
2168 |        "\n",
2169 |        "    .dataframe thead th {\n",
2170 |        "        text-align: right;\n",
2171 |        "    }\n",
2172 |        "</style>\n",
2173 |        "<table border=\"1\" class=\"dataframe\">\n",
2174 |        "  <thead>\n",
2175 |        "    <tr style=\"text-align: right;\">\n",
2176 |        "      <th></th>\n",
2177 |        "      <th>job</th>\n",
2178 |        "      <th>marital</th>\n",
2179 |        "      <th>education</th>\n",
2180 |        "      <th>default</th>\n",
2181 |        "      <th>housing</th>\n",
2182 |        "      <th>loan</th>\n",
2183 |        "      <th>contact</th>\n",
2184 |        "      <th>month</th>\n",
2185 |        "      <th>day_of_week</th>\n",
2186 |        "      <th>campaign</th>\n",
2187 |        "      <th>previous</th>\n",
2188 |        "      <th>poutcome</th>\n",
2189 |        "      <th>y</th>\n",
2190 |        "      <th>l_age</th>\n",
2191 |        "      <th>l_duration</th>\n",
2192 |        "    </tr>\n",
2193 |        "  </thead>\n",
2194 |        "  <tbody>\n",
2195 |        "    <tr>\n",
2196 |        "      <th>6251</th>\n",
2197 |        "      <td>admin.</td>\n",
2198 |        "      <td>married</td>\n",
2199 |        "      <td>high.school</td>\n",
2200 |        "      <td>no</td>\n",
2201 |        "      <td>yes</td>\n",
2202 |        "      <td>no</td>\n",
2203 |        "      <td>telephone</td>\n",
2204 |        "      <td>may</td>\n",
2205 |        "      <td>tue</td>\n",
2206 |        "      <td>4</td>\n",
2207 |        "      <td>0</td>\n",
2208 |        "      <td>failure</td>\n",
2209 |        "      <td>no</td>\n",
2210 |        "      <td>&lt;=60</td>\n",
2211 |        "      <td>&lt;=450</td>\n",
2212 |        "    </tr>\n",
2213 |        "    <tr>\n",
2214 |        "      <th>33015</th>\n",
2215 |        "      <td>blue-collar</td>\n",
2216 |        "      <td>married</td>\n",
2217 |        "      <td>basic.9y</td>\n",
2218 |        "      <td>no</td>\n",
2219 |        "      <td>no</td>\n",
2220 |        "      <td>no</td>\n",
2221 |        "      <td>cellular</td>\n",
2222 |        "      <td>may</td>\n",
2223 |        "      <td>mon</td>\n",
2224 |        "      <td>2</td>\n",
2225 |        "      <td>0</td>\n",
2226 |        "      <td>failure</td>\n",
2227 |        "      <td>no</td>\n",
2228 |        "      <td>&lt;=60</td>\n",
2229 |        "      <td>&lt;=450</td>\n",
2230 |        "    </tr>\n",
2231 |        "    <tr>\n",
2232 |        "      <th>28063</th>\n",
2233 |        "      <td>blue-collar</td>\n",
2234 |        "      <td>divorced</td>\n",
2235 |        "      <td>high.school</td>\n",
2236 |        "      <td>no</td>\n",
2237 |        "      <td>yes</td>\n",
2238 |        "      <td>no</td>\n",
2239 |        "      <td>cellular</td>\n",
2240 |        "      <td>apr</td>\n",
2241 |        "      <td>fri</td>\n",
2242 |        "      <td>3</td>\n",
2243 |        "      <td>0</td>\n",
2244 |        "      <td>failure</td>\n",
2245 |        "      <td>no</td>\n",
2246 |        "      <td>&lt;=60</td>\n",
2247 |        "      <td>&lt;=450</td>\n",
2248 |        "    </tr>\n",
2249 |        "    <tr>\n",
2250 |        "      <th>23031</th>\n",
2251 |        "      <td>management</td>\n",
2252 |        "      <td>married</td>\n",
2253 |        "      <td>university.degree</td>\n",
2254 |        "      <td>no</td>\n",
2255 |        "      <td>yes</td>\n",
2256 |        "      <td>no</td>\n",
2257 |        "      <td>cellular</td>\n",
2258 |        "      <td>aug</td>\n",
2259 |        "      <td>tue</td>\n",
2260 |        "      <td>10</td>\n",
2261 |        "      <td>0</td>\n",
2262 |        "      <td>failure</td>\n",
2263 |        "      <td>no</td>\n",
2264 |        "      <td>&lt;=60</td>\n",
2265 |        "      <td>&lt;=450</td>\n",
2266 |        "    </tr>\n",
2267 |        "    <tr>\n",
2268 |        "      <th>28239</th>\n",
2269 |        "      <td>blue-collar</td>\n",
2270 |        "      <td>married</td>\n",
2271 |        "      <td>basic.9y</td>\n",
2272 |        "      <td>no</td>\n",
2273 |        "      <td>no</td>\n",
2274 |        "      <td>no</td>\n",
2275 |        "      <td>cellular</td>\n",
2276 |        "      <td>apr</td>\n",
2277 |        "      <td>tue</td>\n",
2278 |        "      <td>1</td>\n",
2279 |        "      <td>0</td>\n",
2280 |        "      <td>failure</td>\n",
2281 |        "      <td>no</td>\n",
2282 |        "      <td>&lt;=60</td>\n",
2283 |        "      <td>&lt;=450</td>\n",
2284 |        "    </tr>\n",
2285 |        "    <tr>\n",
2286 |        "      <th>...</th>\n",
2287 |        "      <td>...</td>\n",
2288 |        "      <td>...</td>\n",
2289 |        "      <td>...</td>\n",
2290 |        "      <td>...</td>\n",
2291 |        "      <td>...</td>\n",
2292 |        "      <td>...</td>\n",
2293 |        "      <td>...</td>\n",
2294 |        "      <td>...</td>\n",
2295 |        "      <td>...</td>\n",
2296 |        "      <td>...</td>\n",
2297 |        "      <td>...</td>\n",
2298 |        "      <td>...</td>\n",
2299 |        "      <td>...</td>\n",
2300 |        "      <td>...</td>\n",
2301 |        "      <td>...</td>\n",
2302 |        "    </tr>\n",
2303 |        "    <tr>\n",
2304 |        "      <th>7727</th>\n",
2305 |        "      <td>unemployed</td>\n",
2306 |        "      <td>married</td>\n",
2307 |        "      <td>professional.course</td>\n",
2308 |        "      <td>no</td>\n",
2309 |        "      <td>yes</td>\n",
2310 |        "      <td>no</td>\n",
2311 |        "      <td>telephone</td>\n",
2312 |        "      <td>may</td>\n",
2313 |        "      <td>fri</td>\n",
2314 |        "      <td>2</td>\n",
2315 |        "      <td>0</td>\n",
2316 |        "      <td>failure</td>\n",
2317 |        "      <td>yes</td>\n",
2318 |        "      <td>&lt;=60</td>\n",
2319 |        "      <td>&gt;450</td>\n",
2320 |        "    </tr>\n",
2321 |        "    <tr>\n",
2322 |        "      <th>13820</th>\n",
2323 |        "      <td>technician</td>\n",
2324 |        "      <td>married</td>\n",
2325 |        "      <td>professional.course</td>\n",
2326 |        "      <td>no</td>\n",
2327 |        "      <td>no</td>\n",
2328 |        "      <td>no</td>\n",
2329 |        "      <td>cellular</td>\n",
2330 |        "      <td>jul</td>\n",
2331 |        "      <td>thu</td>\n",
2332 |        "      <td>1</td>\n",
2333 |        "      <td>0</td>\n",
2334 |        "      <td>failure</td>\n",
2335 |        "      <td>yes</td>\n",
2336 |        "      <td>&lt;=60</td>\n",
2337 |        "      <td>&gt;450</td>\n",
2338 |        "    </tr>\n",
2339 |        "    <tr>\n",
2340 |        "      <th>40537</th>\n",
2341 |        "      <td>admin.</td>\n",
2342 |        "      <td>single</td>\n",
2343 |        "      <td>high.school</td>\n",
2344 |        "      <td>no</td>\n",
2345 |        "      <td>no</td>\n",
2346 |        "      <td>no</td>\n",
2347 |        "      <td>telephone</td>\n",
2348 |        "      <td>aug</td>\n",
2349 |        "      <td>fri</td>\n",
2350 |        "      <td>1</td>\n",
2351 |        "      <td>0</td>\n",
2352 |        "      <td>failure</td>\n",
2353 |        "      <td>no</td>\n",
2354 |        "      <td>&lt;=60</td>\n",
2355 |        "      <td>&gt;450</td>\n",
2356 |        "    </tr>\n",
2357 |        "    <tr>\n",
2358 |        "      <th>22192</th>\n",
2359 |        "      <td>blue-collar</td>\n",
2360 |        "      <td>married</td>\n",
2361 |        "      <td>basic.4y</td>\n",
2362 |        "      <td>no</td>\n",
2363 |        "      <td>no</td>\n",
2364 |        "      <td>no</td>\n",
2365 |        "      <td>telephone</td>\n",
2366 |        "      <td>aug</td>\n",
2367 |        "      <td>thu</td>\n",
2368 |        "      <td>3</td>\n",
2369 |        "      <td>0</td>\n",
2370 |        "      <td>failure</td>\n",
2371 |        "      <td>yes</td>\n",
2372 |        "      <td>&lt;=60</td>\n",
2373 |        "      <td>&gt;450</td>\n",
2374 |        "    </tr>\n",
2375 |        "    <tr>\n",
2376 |        "      <th>24091</th>\n",
2377 |        "      <td>technician</td>\n",
2378 |        "      <td>single</td>\n",
2379 |        "      <td>professional.course</td>\n",
2380 |        "      <td>no</td>\n",
2381 |        "      <td>yes</td>\n",
2382 |        "      <td>no</td>\n",
2383 |        "      <td>telephone</td>\n",
2384 |        "      <td>nov</td>\n",
2385 |        "      <td>mon</td>\n",
2386 |        "      <td>1</td>\n",
2387 |        "      <td>0</td>\n",
2388 |        "      <td>failure</td>\n",
2389 |        "      <td>no</td>\n",
2390 |        "      <td>&lt;=60</td>\n",
2391 |        "      <td>&gt;450</td>\n",
2392 |        "    </tr>\n",
2393 |        "  </tbody>\n",
2394 |        "</table>\n",
2395 |        "<p>41188 rows × 15 columns</p>\n",
2396 |        "</div>"
2397 |       ],
2398 |       "text/plain": [
2399 |        "               job   marital            education default housing loan  \\\n",
2400 |        "6251        admin.   married          high.school      no     yes   no   \n",
2401 |        "33015  blue-collar   married             basic.9y      no      no   no   \n",
2402 |        "28063  blue-collar  divorced          high.school      no     yes   no   \n",
2403 |        "23031   management   married    university.degree      no     yes   no   \n",
2404 |        "28239  blue-collar   married             basic.9y      no      no   no   \n",
2405 |        "...            ...       ...                  ...     ...     ...  ...   \n",
2406 |        "7727    unemployed   married  professional.course      no     yes   no   \n",
2407 |        "13820   technician   married  professional.course      no      no   no   \n",
2408 |        "40537       admin.    single          high.school      no      no   no   \n",
2409 |        "22192  blue-collar   married             basic.4y      no      no   no   \n",
2410 |        "24091   technician    single  professional.course      no     yes   no   \n",
2411 |        "\n",
2412 |        "         contact month day_of_week  campaign  previous poutcome    y l_age  \\\n",
2413 |        "6251   telephone   may         tue         4         0  failure   no  <=60   \n",
2414 |        "33015   cellular   may         mon         2         0  failure   no  <=60   \n",
2415 |        "28063   cellular   apr         fri         3         0  failure   no  <=60   \n",
2416 |        "23031   cellular   aug         tue        10         0  failure   no  <=60   \n",
2417 |        "28239   cellular   apr         tue         1         0  failure   no  <=60   \n",
2418 |        "...          ...   ...         ...       ...       ...      ...  ...   ...   \n",
2419 |        "7727   telephone   may         fri         2         0  failure  yes  <=60   \n",
2420 |        "13820   cellular   jul         thu         1         0  failure  yes  <=60   \n",
2421 |        "40537  telephone   aug         fri         1         0  failure   no  <=60   \n",
2422 |        "22192  telephone   aug         thu         3         0  failure  yes  <=60   \n",
2423 |        "24091  telephone   nov         mon         1         0  failure   no  <=60   \n",
2424 |        "\n",
2425 |        "      l_duration  \n",
2426 |        "6251       <=450  \n",
2427 |        "33015      <=450  \n",
2428 |        "28063      <=450  \n",
2429 |        "23031      <=450  \n",
2430 |        "28239      <=450  \n",
2431 |        "...          ...  \n",
2432 |        "7727        >450  \n",
2433 |        "13820       >450  \n",
2434 |        "40537       >450  \n",
2435 |        "22192       >450  \n",
2436 |        "24091       >450  \n",
2437 |        "\n",
2438 |        "[41188 rows x 15 columns]"
2439 |       ]
2440 |      },
2441 |      "execution_count": 29,
2442 |      "metadata": {},
2443 |      "output_type": "execute_result"
2444 |     }
2445 |    ],
2446 |    "source": [
2447 |     "data"
2448 |    ]
2449 |   },
2450 |   {
2451 |    "cell_type": "code",
2452 |    "execution_count": 30,
2453 |    "metadata": {},
2454 |    "outputs": [
2455 |     {
2456 |      "data": {
2457 |       "text/plain": [
2458 |        "6251      no\n",
2459 |        "33015     no\n",
2460 |        "28063     no\n",
2461 |        "23031     no\n",
2462 |        "28239     no\n",
2463 |        "        ... \n",
2464 |        "7727     yes\n",
2465 |        "13820    yes\n",
2466 |        "40537     no\n",
2467 |        "22192    yes\n",
2468 |        "24091     no\n",
2469 |        "Name: y, Length: 41188, dtype: object"
2470 |       ]
2471 |      },
2472 |      "execution_count": 30,
2473 |      "metadata": {},
2474 |      "output_type": "execute_result"
2475 |     }
2476 |    ],
2477 |    "source": [
2478 |     "o=data['y']\n",
2479 |     "o"
2480 |    ]
2481 |   },
2482 |   {
2483 |    "cell_type": "code",
2484 |    "execution_count": 31,
2485 |    "metadata": {},
2486 |    "outputs": [],
2487 |    "source": [
2488 |     "data.drop(columns='y',axis=1,inplace=True)"
2489 |    ]
2490 |   },
2491 |   {
2492 |    "cell_type": "code",
2493 |    "execution_count": 32,
2494 |    "metadata": {},
2495 |    "outputs": [],
2496 |    "source": [
2497 |     "data=pd.concat([data,o],axis=1)"
2498 |    ]
2499 |   },
2500 |   {
2501 |    "cell_type": "code",
2502 |    "execution_count": 33,
2503 |    "metadata": {},
2504 |    "outputs": [
2505 |     {
2506 |      "data": {
2507 |       "text/html": [
2508 |        "<div>\n",
2509 |        "<style scoped>\n",
2510 |        "    .dataframe tbody tr th:only-of-type {\n",
2511 |        "        vertical-align: middle;\n",
2512 |        "    }\n",
2513 |        "\n",
2514 |        "    .dataframe tbody tr th {\n",
2515 |        "        vertical-align: top;\n",
2516 |        "    }\n",
2517 |        "\n",
2518 |        "    .dataframe thead th {\n",
2519 |        "        text-align: right;\n",
2520 |        "    }\n",
2521 |        "</style>\n",
2522 |        "<table border=\"1\" class=\"dataframe\">\n",
2523 |        "  <thead>\n",
2524 |        "    <tr style=\"text-align: right;\">\n",
2525 |        "      <th></th>\n",
2526 |        "      <th>job</th>\n",
2527 |        "      <th>marital</th>\n",
2528 |        "      <th>education</th>\n",
2529 |        "      <th>default</th>\n",
2530 |        "      <th>housing</th>\n",
2531 |        "      <th>loan</th>\n",
2532 |        "      <th>contact</th>\n",
2533 |        "      <th>month</th>\n",
2534 |        "      <th>day_of_week</th>\n",
2535 |        "      <th>campaign</th>\n",
2536 |        "      <th>previous</th>\n",
2537 |        "      <th>poutcome</th>\n",
2538 |        "      <th>l_age</th>\n",
2539 |        "      <th>l_duration</th>\n",
2540 |        "      <th>y</th>\n",
2541 |        "    </tr>\n",
2542 |        "  </thead>\n",
2543 |        "  <tbody>\n",
2544 |        "    <tr>\n",
2545 |        "      <th>6251</th>\n",
2546 |        "      <td>admin.</td>\n",
2547 |        "      <td>married</td>\n",
2548 |        "      <td>high.school</td>\n",
2549 |        "      <td>no</td>\n",
2550 |        "      <td>yes</td>\n",
2551 |        "      <td>no</td>\n",
2552 |        "      <td>telephone</td>\n",
2553 |        "      <td>may</td>\n",
2554 |        "      <td>tue</td>\n",
2555 |        "      <td>4</td>\n",
2556 |        "      <td>0</td>\n",
2557 |        "      <td>failure</td>\n",
2558 |        "      <td>&lt;=60</td>\n",
2559 |        "      <td>&lt;=450</td>\n",
2560 |        "      <td>no</td>\n",
2561 |        "    </tr>\n",
2562 |        "    <tr>\n",
2563 |        "      <th>33015</th>\n",
2564 |        "      <td>blue-collar</td>\n",
2565 |        "      <td>married</td>\n",
2566 |        "      <td>basic.9y</td>\n",
2567 |        "      <td>no</td>\n",
2568 |        "      <td>no</td>\n",
2569 |        "      <td>no</td>\n",
2570 |        "      <td>cellular</td>\n",
2571 |        "      <td>may</td>\n",
2572 |        "      <td>mon</td>\n",
2573 |        "      <td>2</td>\n",
2574 |        "      <td>0</td>\n",
2575 |        "      <td>failure</td>\n",
2576 |        "      <td>&lt;=60</td>\n",
2577 |        "      <td>&lt;=450</td>\n",
2578 |        "      <td>no</td>\n",
2579 |        "    </tr>\n",
2580 |        "    <tr>\n",
2581 |        "      <th>28063</th>\n",
2582 |        "      <td>blue-collar</td>\n",
2583 |        "      <td>divorced</td>\n",
2584 |        "      <td>high.school</td>\n",
2585 |        "      <td>no</td>\n",
2586 |        "      <td>yes</td>\n",
2587 |        "      <td>no</td>\n",
2588 |        "      <td>cellular</td>\n",
2589 |        "      <td>apr</td>\n",
2590 |        "      <td>fri</td>\n",
2591 |        "      <td>3</td>\n",
2592 |        "      <td>0</td>\n",
2593 |        "      <td>failure</td>\n",
2594 |        "      <td>&lt;=60</td>\n",
2595 |        "      <td>&lt;=450</td>\n",
2596 |        "      <td>no</td>\n",
2597 |        "    </tr>\n",
2598 |        "    <tr>\n",
2599 |        "      <th>23031</th>\n",
2600 |        "      <td>management</td>\n",
2601 |        "      <td>married</td>\n",
2602 |        "      <td>university.degree</td>\n",
2603 |        "      <td>no</td>\n",
2604 |        "      <td>yes</td>\n",
2605 |        "      <td>no</td>\n",
2606 |        "      <td>cellular</td>\n",
2607 |        "      <td>aug</td>\n",
2608 |        "      <td>tue</td>\n",
2609 |        "      <td>10</td>\n",
2610 |        "      <td>0</td>\n",
2611 |        "      <td>failure</td>\n",
2612 |        "      <td>&lt;=60</td>\n",
2613 |        "      <td>&lt;=450</td>\n",
2614 |        "      <td>no</td>\n",
2615 |        "    </tr>\n",
2616 |        "    <tr>\n",
2617 |        "      <th>28239</th>\n",
2618 |        "      <td>blue-collar</td>\n",
2619 |        "      <td>married</td>\n",
2620 |        "      <td>basic.9y</td>\n",
2621 |        "      <td>no</td>\n",
2622 |        "      <td>no</td>\n",
2623 |        "      <td>no</td>\n",
2624 |        "      <td>cellular</td>\n",
2625 |        "      <td>apr</td>\n",
2626 |        "      <td>tue</td>\n",
2627 |        "      <td>1</td>\n",
2628 |        "      <td>0</td>\n",
2629 |        "      <td>failure</td>\n",
2630 |        "      <td>&lt;=60</td>\n",
2631 |        "      <td>&lt;=450</td>\n",
2632 |        "      <td>no</td>\n",
2633 |        "    </tr>\n",
2634 |        "    <tr>\n",
2635 |        "      <th>...</th>\n",
2636 |        "      <td>...</td>\n",
2637 |        "      <td>...</td>\n",
2638 |        "      <td>...</td>\n",
2639 |        "      <td>...</td>\n",
2640 |        "      <td>...</td>\n",
2641 |        "      <td>...</td>\n",
2642 |        "      <td>...</td>\n",
2643 |        "      <td>...</td>\n",
2644 |        "      <td>...</td>\n",
2645 |        "      <td>...</td>\n",
2646 |        "      <td>...</td>\n",
2647 |        "      <td>...</td>\n",
2648 |        "      <td>...</td>\n",
2649 |        "      <td>...</td>\n",
2650 |        "      <td>...</td>\n",
2651 |        "    </tr>\n",
2652 |        "    <tr>\n",
2653 |        "      <th>7727</th>\n",
2654 |        "      <td>unemployed</td>\n",
2655 |        "      <td>married</td>\n",
2656 |        "      <td>professional.course</td>\n",
2657 |        "      <td>no</td>\n",
2658 |        "      <td>yes</td>\n",
2659 |        "      <td>no</td>\n",
2660 |        "      <td>telephone</td>\n",
2661 |        "      <td>may</td>\n",
2662 |        "      <td>fri</td>\n",
2663 |        "      <td>2</td>\n",
2664 |        "      <td>0</td>\n",
2665 |        "      <td>failure</td>\n",
2666 |        "      <td>&lt;=60</td>\n",
2667 |        "      <td>&gt;450</td>\n",
2668 |        "      <td>yes</td>\n",
2669 |        "    </tr>\n",
2670 |        "    <tr>\n",
2671 |        "      <th>13820</th>\n",
2672 |        "      <td>technician</td>\n",
2673 |        "      <td>married</td>\n",
2674 |        "      <td>professional.course</td>\n",
2675 |        "      <td>no</td>\n",
2676 |        "      <td>no</td>\n",
2677 |        "      <td>no</td>\n",
2678 |        "      <td>cellular</td>\n",
2679 |        "      <td>jul</td>\n",
2680 |        "      <td>thu</td>\n",
2681 |        "      <td>1</td>\n",
2682 |        "      <td>0</td>\n",
2683 |        "      <td>failure</td>\n",
2684 |        "      <td>&lt;=60</td>\n",
2685 |        "      <td>&gt;450</td>\n",
2686 |        "      <td>yes</td>\n",
2687 |        "    </tr>\n",
2688 |        "    <tr>\n",
2689 |        "      <th>40537</th>\n",
2690 |        "      <td>admin.</td>\n",
2691 |        "      <td>single</td>\n",
2692 |        "      <td>high.school</td>\n",
2693 |        "      <td>no</td>\n",
2694 |        "      <td>no</td>\n",
2695 |        "      <td>no</td>\n",
2696 |        "      <td>telephone</td>\n",
2697 |        "      <td>aug</td>\n",
2698 |        "      <td>fri</td>\n",
2699 |        "      <td>1</td>\n",
2700 |        "      <td>0</td>\n",
2701 |        "      <td>failure</td>\n",
2702 |        "      <td>&lt;=60</td>\n",
2703 |        "      <td>&gt;450</td>\n",
2704 |        "      <td>no</td>\n",
2705 |        "    </tr>\n",
2706 |        "    <tr>\n",
2707 |        "      <th>22192</th>\n",
2708 |        "      <td>blue-collar</td>\n",
2709 |        "      <td>married</td>\n",
2710 |        "      <td>basic.4y</td>\n",
2711 |        "      <td>no</td>\n",
2712 |        "      <td>no</td>\n",
2713 |        "      <td>no</td>\n",
2714 |        "      <td>telephone</td>\n",
2715 |        "      <td>aug</td>\n",
2716 |        "      <td>thu</td>\n",
2717 |        "      <td>3</td>\n",
2718 |        "      <td>0</td>\n",
2719 |        "      <td>failure</td>\n",
2720 |        "      <td>&lt;=60</td>\n",
2721 |        "      <td>&gt;450</td>\n",
2722 |        "      <td>yes</td>\n",
2723 |        "    </tr>\n",
2724 |        "    <tr>\n",
2725 |        "      <th>24091</th>\n",
2726 |        "      <td>technician</td>\n",
2727 |        "      <td>single</td>\n",
2728 |        "      <td>professional.course</td>\n",
2729 |        "      <td>no</td>\n",
2730 |        "      <td>yes</td>\n",
2731 |        "      <td>no</td>\n",
2732 |        "      <td>telephone</td>\n",
2733 |        "      <td>nov</td>\n",
2734 |        "      <td>mon</td>\n",
2735 |        "      <td>1</td>\n",
2736 |        "      <td>0</td>\n",
2737 |        "      <td>failure</td>\n",
2738 |        "      <td>&lt;=60</td>\n",
2739 |        "      <td>&gt;450</td>\n",
2740 |        "      <td>no</td>\n",
2741 |        "    </tr>\n",
2742 |        "  </tbody>\n",
2743 |        "</table>\n",
2744 |        "<p>41188 rows × 15 columns</p>\n",
2745 |        "</div>"
2746 |       ],
2747 |       "text/plain": [
2748 |        "               job   marital            education default housing loan  \\\n",
2749 |        "6251        admin.   married          high.school      no     yes   no   \n",
2750 |        "33015  blue-collar   married             basic.9y      no      no   no   \n",
2751 |        "28063  blue-collar  divorced          high.school      no     yes   no   \n",
2752 |        "23031   management   married    university.degree      no     yes   no   \n",
2753 |        "28239  blue-collar   married             basic.9y      no      no   no   \n",
2754 |        "...            ...       ...                  ...     ...     ...  ...   \n",
2755 |        "7727    unemployed   married  professional.course      no     yes   no   \n",
2756 |        "13820   technician   married  professional.course      no      no   no   \n",
2757 |        "40537       admin.    single          high.school      no      no   no   \n",
2758 |        "22192  blue-collar   married             basic.4y      no      no   no   \n",
2759 |        "24091   technician    single  professional.course      no     yes   no   \n",
2760 |        "\n",
2761 |        "         contact month day_of_week  campaign  previous poutcome l_age  \\\n",
2762 |        "6251   telephone   may         tue         4         0  failure  <=60   \n",
2763 |        "33015   cellular   may         mon         2         0  failure  <=60   \n",
2764 |        "28063   cellular   apr         fri         3         0  failure  <=60   \n",
2765 |        "23031   cellular   aug         tue        10         0  failure  <=60   \n",
2766 |        "28239   cellular   apr         tue         1         0  failure  <=60   \n",
2767 |        "...          ...   ...         ...       ...       ...      ...   ...   \n",
2768 |        "7727   telephone   may         fri         2         0  failure  <=60   \n",
2769 |        "13820   cellular   jul         thu         1         0  failure  <=60   \n",
2770 |        "40537  telephone   aug         fri         1         0  failure  <=60   \n",
2771 |        "22192  telephone   aug         thu         3         0  failure  <=60   \n",
2772 |        "24091  telephone   nov         mon         1         0  failure  <=60   \n",
2773 |        "\n",
2774 |        "      l_duration    y  \n",
2775 |        "6251       <=450   no  \n",
2776 |        "33015      <=450   no  \n",
2777 |        "28063      <=450   no  \n",
2778 |        "23031      <=450   no  \n",
2779 |        "28239      <=450   no  \n",
2780 |        "...          ...  ...  \n",
2781 |        "7727        >450  yes  \n",
2782 |        "13820       >450  yes  \n",
2783 |        "40537       >450   no  \n",
2784 |        "22192       >450  yes  \n",
2785 |        "24091       >450   no  \n",
2786 |        "\n",
2787 |        "[41188 rows x 15 columns]"
2788 |       ]
2789 |      },
2790 |      "execution_count": 33,
2791 |      "metadata": {},
2792 |      "output_type": "execute_result"
2793 |     }
2794 |    ],
2795 |    "source": [
2796 |     "data"
2797 |    ]
2798 |   },
2799 |   {
2800 |    "cell_type": "code",
2801 |    "execution_count": 34,
2802 |    "metadata": {
2803 |     "id": "J5Lh8_FU_fFT"
2804 |    },
2805 |    "outputs": [],
2806 |    "source": [
2807 |     "def ID3(data,origin_data,features,target_attribute_name='y',parent_node_class = None):\n",
2808 |     "  if len(np.unique(data[target_attribute_name]))<=1:\n",
2809 |     "    return np.unique(data[target_attribute_name])[0]\n",
2810 |     "\n",
2811 |     "  elif len(data)==0:\n",
2812 |     "    return np.unique(origin_data[target_attribute_name])[np.argmax(np.unique(origin_data[target_attribute_name],return_counts=True)[1])]\n",
2813 |     "\n",
2814 |     "  elif len(features)==0:  #Here we can adjust the depth of the tree by tampering with the len(feature)==X.\n",
2815 |     "    return parent_node_class  \n",
2816 |     "      \n",
2817 |     "    #If none of the above holds true, grow the tree!  \n",
2818 |     "      \n",
2819 |     "  else:\n",
2820 |     "    parent_node_class = np.unique(data[target_attribute_name])[np.argmax(np.unique(data[target_attribute_name],return_counts=True)[1])]  \n",
2821 |     "          \n",
2822 |     "    #Select the feature which best splits the dataset via List Comprehension\n",
2823 |     "    info_values = [Information_Gain(data,feature) for feature in features] \n",
2824 |     "    best_feature_index = np.argmax(info_values)  \n",
2825 |     "    best_feature = features[best_feature_index]  \n",
2826 |     "          \n",
2827 |     "    #Create the tree structure.\n",
2828 |     "    tree = {best_feature:{}}  \n",
2829 |     "          \n",
2830 |     "          \n",
2831 |     "    #Remove the feature with the best inforamtion gain from the feature space  \n",
2832 |     "    features = [i for i in features if i != best_feature]  \n",
2833 |     "          \n",
2834 |     "  \n",
2835 |     "          \n",
2836 |     "    for value in np.unique(data[best_feature]):  \n",
2837 |     "      #Split the dataset along the value of the feature with the largest information gain \n",
2838 |     "      sub_data = data.where(data[best_feature] == value).dropna()  \n",
2839 |     "              \n",
2840 |     "      #Call the ID3 algorithm for each of those sub_datasets with recursion.\n",
2841 |     "      subtree = ID3(sub_data,origin_data,features,target_attribute_name,parent_node_class)  \n",
2842 |     "               \n",
2843 |     "      tree[best_feature][value] = subtree\n",
2844 |     "    return(tree)      "
2845 |    ]
2846 |   }
2847 |  ],
2848 |  "metadata": {
2849 |   "colab": {
2850 |    "collapsed_sections": [],
2851 |    "name": "Assignment_BUET.ipynb",
2852 |    "provenance": []
2853 |   },
2854 |   "interpreter": {
2855 |    "hash": "12d56e101fb626aa06cc0610ba999bd152779df661ca54f10a56d5b4a60f6a64"
2856 |   },
2857 |   "kernelspec": {
2858 |    "display_name": "Python 3",
2859 |    "language": "python",
2860 |    "name": "python3"
2861 |   },
2862 |   "language_info": {
2863 |    "codemirror_mode": {
2864 |     "name": "ipython",
2865 |     "version": 3
2866 |    },
2867 |    "file_extension": ".py",
2868 |    "mimetype": "text/x-python",
2869 |    "name": "python",
2870 |    "nbconvert_exporter": "python",
2871 |    "pygments_lexer": "ipython3",
2872 |    "version": "3.8.5"
2873 |   }
2874 |  },
2875 |  "nbformat": 4,
2876 |  "nbformat_minor": 1
2877 | }
2878 | 


--------------------------------------------------------------------------------
/Bank_Data/README.md:
--------------------------------------------------------------------------------
1 | ## Decision Tree-Mortgage Data Classification Problem
2 | This is a small project where I use a bank data with various features determining mortgage approval. This is a classificiation problem where I use ID3 algorithm to build a decision tree. And for that different types of feature engineering are tested. 
3 | 


--------------------------------------------------------------------------------
/Boston Housing Data/README.md:
--------------------------------------------------------------------------------
1 | ## Testing three different ML models via cross validation
2 | Boston Dataset Contains information about different houses in Boston. There are 506 samples and 13 feature variables in this dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/. 
3 | Three types of model are used in this notebook: Linear Regression, KNN & Decision Tree. Elbow test is also done for KNN to find the optimum K-value. And lastly through cross validation, accuracy scores are represented
4 | 


--------------------------------------------------------------------------------
/Forecasting Risk Analytics/Forecasting Risk ES_VAR.R:
--------------------------------------------------------------------------------
  1 | rm(list=ls())
  2 | library(copula)
  3 | library(tseries)
  4 | library(sn)
  5 | library("Ecdat")
  6 | library("fGarch")
  7 | library("evir")
  8 | library("forecast")
  9 | library(ks)
 10 | library(quantmod)
 11 | library(MASS)
 12 | library(fitdistrplus)
 13 | getSymbols("SPXL",src='yahoo',auto.assign = TRUE,from='2013-09-29',to='2023-09-29')
 14 | getSymbols("AMD",src='yahoo',auto.assign = TRUE,from='2013-09-29',to='2023-09-29')
 15 | spxl_r=dailyReturn(SPXL$SPXL.Adjusted,type='log')
 16 | amd_r=dailyReturn(AMD$AMD.Adjusted,type='log')
 17 | spxl_r=coredata(spxl_r)
 18 | amd_r=coredata(amd_r)
 19 | plot(amd_r)
 20 | points(spxl_r)
 21 | ## Preliminary Stats
 22 | plot(spxl_r)
 23 | plot(amd_r)
 24 | par(mfrow=c(1,1))
 25 | acf(spxl_r,main='SPXL Daily Returns')
 26 | acf(amd_r, main='AMD Daily Returns')
 27 | summary(amd_r)
 28 | summary(spxl_r)
 29 | ##Let's Try seeing the residuals with AR(1) and validate the justification of time dependence & volatility clustering
 30 | fitAR1_s=arima(spxl_r,order=c(0,0,2),include.mean=TRUE)#fit ARIMA model
 31 | fitAR1_s
 32 | res_s=residuals(fitAR1_s)
 33 | acf(res_s,main='Residual MA(2)') ## Not white noise. Therefore, it is more important to capture the time dependencies
 34 | acf(res_s^2,main='Residual Sq MA(2)') ## Numerous spikes at variety of lags
 35 | ##AR(1)+GARCH(1,1)
 36 | fit_s=garchFit(formula=~arma(1,0)+garch(1,1),data=spxl_r,cond.dist="norm",trace=FALSE)
 37 | fit_a=garchFit(formula=~arma(1,0)+garch(1,1),data=amd_r,cond.dist="norm",trace=FALSE)
 38 | fit_a
 39 | res_a=residuals(fit_a,standardize=TRUE) ##White noise
 40 | res_s=residuals(fit_s,standardize=TRUE)
 41 | fit_ta=fitdistr(res_a,'t',upper=10)
 42 | fit_ts=fitdistr(res_s,'t',upper=10)
 43 | fit_ts
 44 | par(mfrow=c(1,1))
 45 | acf(res_s,main='SPXL Residual AR(1)-GARCH (1,1)')
 46 | acf(res_s^2, main='SPXL Residual Sq AR(1)-GARCH(1,1)')
 47 | acf(res_a,main='AMD Residual AR(1)-GARCH(1,1)')
 48 | acf(res_a^2,main='AMD Residual Sq AR(1)-GARCH(1,1)')
 49 | ## Copula modeling
 50 | 
 51 | est1=fit_ta$estimate
 52 | est2=fit_ts$estimate
 53 | 
 54 | u1=pt(res_a,df=est1['df'])
 55 | u2=pt(res_s,df=est2['df'])
 56 | 
 57 | ##Combining uniform data
 58 | uhat=cbind(u1,u2)
 59 | 
 60 | ##Copula
 61 | Ct=fitCopula(copula=tCopula(dim=2),data=uhat,method="ml")## T Copula
 62 | Ct@estimate
 63 | Ct_logL=loglikCopula(param=Ct@estimate,u=uhat,copula=tCopula(dim=2));#compute loglikelihood function
 64 | Ct_logL
 65 | -2*Ct_logL+2*length(Ct@estimate)#AIC
 66 | Cg=fitCopula(copula=normalCopula(dim=2),data=uhat,method="ml")#fit Gaussian copula
 67 | Cg@estimate;
 68 | Cg_logL=loglikCopula(param=Cg@estimate,u=uhat,copula=normalCopula(dim=2));
 69 | Cg_logL
 70 | -2*Cg_logL+2*length(Cg@estimate);
 71 | Ccl=fitCopula(copula=claytonCopula(dim=2),data=uhat,method="ml");#fit clayton copula
 72 | Ccl@estimate
 73 | Ccl_logL=loglikCopula(param=Ccl@estimate,u=uhat,copula=claytonCopula(dim=2));
 74 | Ccl_logL
 75 | -2*Ccl_logL+2*length(Ccl@estimate);#compute AIC
 76 | 
 77 | Cgmbl=fitCopula(copula=gumbelCopula(dim=2),data=uhat,method="ml");#fit gumbel copula
 78 | Cgmbl@estimate
 79 | Cgmbl
 80 | Cgmbl_logL=loglikCopula(param=Cgmbl@estimate,u=uhat,copula=gumbelCopula(dim=2))
 81 | Cgmbl_logL
 82 | -2*Cgmbl_logL+2*length(Cgmbl@estimate);
 83 | 
 84 | ##So T Copula is the best for joint distribution
 85 | Ct@estimate
 86 | rho=Ct@estimate[1]
 87 | correlation_matrix=matrix(c(1,rho,rho,1),nrow=2)
 88 | correlation_matrix
 89 | confidence_level <- 0.99
 90 | n=length(amd_r)
 91 | Simu_U=rCopula(n,tCopula(dim=2,Ct@estimate[1],df=Ct@estimate[2]))
 92 | Simu_X1=qt(Simu_U[,1],df=est1['df'])
 93 | Simu_X2=qt(Simu_U[,2],df=est2['df'])
 94 | plot(Simu_X1,Simu_X2,xlab='AMD',ylab='SPXL',main='T-Copula 
 95 |      Scatter Plot-Simulated AMD & SPXL Residuals ',col='red')
 96 | points(res_a,res_s,col='blue',pch=4)
 97 | summary(Simu_U)
 98 | 
 99 | forecasted_s=predict(fit_s,n.ahead=1)
100 | forecasted_a=predict(fit_a,n.ahead=1)
101 | 
102 | forecasted_volatility_a=forecasted_a$standardDeviation
103 | forecasted_volatility_s=forecasted_s$standardDeviation
104 | 
105 | forecasted_s$meanForecast
106 | forecasted_a$meanForecast## Scaling 
107 | scaled_residuals_x1=Simu_X1*forecasted_volatility_a
108 | scaled_residuals_x2=Simu_X2*forecasted_volatility_s
109 | 
110 | plot(scaled_residuals_x1,scaled_residuals_x2)
111 | 
112 | plot(spxl_r)
113 | plot(amd_r)
114 | weight_values=seq(0.1,0.9,by=0.1)
115 | n_simulations=10000
116 | VaR_estimates=numeric(length(weight_values))
117 | VaR_estimates
118 | 
119 | for (i in seq_along(weight_values)){
120 |   weight=weight_values[i]
121 |   portfolio_returns=weight*(forecasted_a$meanForecast+scaled_residuals_x1)+(1-weight)*(forecasted_s$meanForecast+scaled_residuals_x2)
122 |   VaR_estimates[i]=quantile(portfolio_returns,1-confidence_level)
123 | }
124 | VaR_estimates
125 | ##Expected Shortfall
126 | confidence_level=0.975
127 | ES_estimates=numeric(length(weight_values))
128 | VaR_estimates=numeric(length(weight_values))
129 | for (i in seq_along(weight_values)){
130 |   weight=weight_values[i]
131 |   portfolio_returns=weight*(forecasted_a$meanForecast+scaled_residuals_x1)+(1-weight)*(forecasted_s$meanForecast+scaled_residuals_x2)
132 |   VaR_estimates[i]=quantile(portfolio_returns,1-confidence_level)
133 |   ES_estimates[i]=mean(portfolio_returns[portfolio_returns<VaR_estimates[i]])}
134 | 
135 | ES_estimates
136 | 


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/Forecasting Risk Analytics/Project_Forecasting Risk.pdf:
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https://raw.githubusercontent.com/adibyaser/Side_Projects/0aea18fbe6f40cdfef1544274253b166bbbc02c3/Forecasting Risk Analytics/Project_Forecasting Risk.pdf


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/Forecasting Risk Analytics/README.md:
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1 | ## The target of this project is to introduce volatility clustering and the importance of AR-GARCH models. Just ARIMA models are not enough to forecast time series data. In this small case study, we justify the need for different models to account for time dependency and volalitility clustering. Finally, taking the random component of two stocks, to account for their dependency structure from their marginals so that we can appropriately estimate their Value-at-Risk and Expected Shortfall. Different weights for portfolio weight are tried to see how risky the portfolio becomes. Further improvements will be made later by adding uncertainty. In that case, a nested loop will be necessary.
2 | 


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/Option_Pricing/Binomial-Tree-Call.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "from scipy.sparse import csc_matrix\n",
 12 |     "\n",
 13 |     "\n",
 14 |     "N=5\n",
 15 |     "t=1\n",
 16 |     "t=t/(N-1)\n",
 17 |     "S0=100\n",
 18 |     "K=98\n",
 19 |     "r=0.01\n",
 20 |     "sigma=0.4\n",
 21 |     "u=np.exp(sigma*np.sqrt(t))\n",
 22 |     "d=1/u\n",
 23 |     "\n",
 24 |     "p=(np.exp(-r*t)-d)/(u-d)\n",
 25 |     "stock_prices=csc_matrix((N,N))\n",
 26 |     "option_prices=csc_matrix((N,N))"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 4,
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "name": "stderr",
 36 |      "output_type": "stream",
 37 |      "text": [
 38 |       "c:\\Users\\adib-\\anaconda3\\lib\\site-packages\\scipy\\sparse\\_index.py:103: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
 39 |       "  self._set_intXint(row, col, x.flat[0])\n",
 40 |       "c:\\Users\\adib-\\anaconda3\\lib\\site-packages\\scipy\\sparse\\_index.py:146: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
 41 |       "  self._set_arrayXarray(i, j, x)\n"
 42 |      ]
 43 |     },
 44 |     {
 45 |      "name": "stdout",
 46 |      "output_type": "stream",
 47 |      "text": [
 48 |       "  (0, 0)\t15.651893735074287\n",
 49 |       "  (1, 0)\t5.169554201231045\n",
 50 |       "  (2, 0)\t0.39224411893132055\n",
 51 |       "  (3, 0)\t0.0\n",
 52 |       "  (4, 0)\t0.0\n",
 53 |       "  (1, 1)\t28.868523201533833\n",
 54 |       "  (2, 1)\t11.18194072866753\n",
 55 |       "  (3, 1)\t0.8857134061662616\n",
 56 |       "  (4, 1)\t0.0\n",
 57 |       "  (2, 2)\t51.182469764127035\n",
 58 |       "  (3, 2)\t24.14027581601698\n",
 59 |       "  (4, 2)\t2.0\n",
 60 |       "  (3, 3)\t84.2118800390509\n",
 61 |       "  (4, 3)\t51.182469764127035\n",
 62 |       "  (4, 4)\t124.55409284924676\n"
 63 |      ]
 64 |     },
 65 |     {
 66 |      "data": {
 67 |       "image/png": 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",
 68 |       "text/plain": [
 69 |        "<Figure size 640x480 with 1 Axes>"
 70 |       ]
 71 |      },
 72 |      "metadata": {},
 73 |      "output_type": "display_data"
 74 |     }
 75 |    ],
 76 |    "source": [
 77 |     "stock_prices[0,0]=S0\n",
 78 |     "\n",
 79 |     "for i in range (1,N):\n",
 80 |     "    M=i+1\n",
 81 |     "    stock_prices[i,0]=d*stock_prices[i-1,0]\n",
 82 |     "    for j in range (1,M):\n",
 83 |     "        stock_prices[i,j]=u*stock_prices[i-1,j-1]\n",
 84 |     "\n",
 85 |     "expiration=stock_prices[-1,:].toarray()-K\n",
 86 |     "expiration=np.where(expiration>=0,expiration,0)\n",
 87 |     "\n",
 88 |     "\n",
 89 |     "option_prices[-1,:]=expiration\n",
 90 |     "\n",
 91 |     "\n",
 92 |     "for i in range(N - 2,-1,-1):\n",
 93 |     "    for j in range(i+1):\n",
 94 |     "        option_prices[i,j]=np.exp(-r * t) * ((1-p) * option_prices[i+1,j] + p * option_prices[i+1,j+1])\n",
 95 |     "        if stock_prices[i,j]-K>option_prices[i,j]:\n",
 96 |     "            option_prices[i,j]=stock_prices[i,j]-K\n",
 97 |     "        \n",
 98 |     "\n",
 99 |     "\n",
100 |     "\n",
101 |     "\n",
102 |     "plt.spy(option_prices)\n",
103 |     "print(option_prices)"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": null,
109 |    "metadata": {},
110 |    "outputs": [],
111 |    "source": []
112 |   }
113 |  ],
114 |  "metadata": {
115 |   "kernelspec": {
116 |    "display_name": "base",
117 |    "language": "python",
118 |    "name": "python3"
119 |   },
120 |   "language_info": {
121 |    "codemirror_mode": {
122 |     "name": "ipython",
123 |     "version": 3
124 |    },
125 |    "file_extension": ".py",
126 |    "mimetype": "text/x-python",
127 |    "name": "python",
128 |    "nbconvert_exporter": "python",
129 |    "pygments_lexer": "ipython3",
130 |    "version": "3.9.13"
131 |   },
132 |   "orig_nbformat": 4,
133 |   "vscode": {
134 |    "interpreter": {
135 |     "hash": "4f77cdfaf4507c9d7563b1dafbbefd1211a5ec1bc40dd9ff43d298279ac97956"
136 |    }
137 |   }
138 |  },
139 |  "nbformat": 4,
140 |  "nbformat_minor": 2
141 | }
142 | 


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/Option_Pricing/Finite Difference, Binomial Trees.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "6.918695403001518\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "##Binomial Tree- Call\n",
 18 |     "\n",
 19 |     "import numpy as np\n",
 20 |     "N=12\n",
 21 |     "T=0.25\n",
 22 |     "dt=T/N\n",
 23 |     "S0=100\n",
 24 |     "K=95\n",
 25 |     "r=0.03\n",
 26 |     "div=0.02\n",
 27 |     "sigma=0.2\n",
 28 |     "u = np.exp(sigma * np.sqrt(dt))\n",
 29 |     "d = 1/u\n",
 30 |     "p = (np.exp((r-div)*dt)-d)/ (u - d)\n",
 31 |     "\n",
 32 |     "\n",
 33 |     "stock_prices = np.zeros((N, N))\n",
 34 |     "call_prices = np.zeros((N, N))\n",
 35 |     "\n",
 36 |     "stock_prices[0,0] = S0\n",
 37 |     "\n",
 38 |     "\n",
 39 |     "for i in range(1, N):\n",
 40 |     "    M = i + 1\n",
 41 |     "    stock_prices[i, 0] = d * stock_prices[i-1, 0]\n",
 42 |     "    for j in range(1, M ):\n",
 43 |     "        stock_prices[i, j] = u * stock_prices[i - 1, j - 1]\n",
 44 |     "\n",
 45 |     "expiration = stock_prices[-1,:] - K\n",
 46 |     "expiration = np.where(expiration >= 0, expiration, 0)\n",
 47 |     "\n",
 48 |     "\n",
 49 |     "call_prices[-1,:] =  expiration\n",
 50 |     "\n",
 51 |     "\n",
 52 |     "for i in range(N - 2,-1,-1):\n",
 53 |     "    for j in range(i + 1):\n",
 54 |     "        call_prices[i,j] = np.exp(-r*dt) * ((1-p) * call_prices[i+1,j] + p * call_prices[i+1,j+1])\n",
 55 |     "\n",
 56 |     "\n",
 57 |     "print(call_prices[0,0])"
 58 |    ]
 59 |   },
 60 |   {
 61 |    "cell_type": "code",
 62 |    "execution_count": 2,
 63 |    "metadata": {},
 64 |    "outputs": [],
 65 |    "source": [
 66 |     "from scipy.stats import norm"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 3,
 72 |    "metadata": {},
 73 |    "outputs": [],
 74 |    "source": [
 75 |     "##Black Scholes -Gaussian\n",
 76 |     "\n",
 77 |     "def BlackScholes(r,S0,K,T,sigma,div):\n",
 78 |     "    d1=(np.log(S0/K) + (r+sigma**2/2)*T)/(sigma*np.sqrt(T))\n",
 79 |     "    d2=d1-sigma*np.sqrt(T)\n",
 80 |     "\n",
 81 |     "    price=(S0*np.exp(-div*T))*norm.cdf(d1,0,1) - K*np.exp(-r*T)*norm.cdf(d2,0,1)\n",
 82 |     "\n",
 83 |     "    return price\n",
 84 |     "    "
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": 4,
 90 |    "metadata": {},
 91 |    "outputs": [
 92 |     {
 93 |      "data": {
 94 |       "text/plain": [
 95 |        "7.0107551333798455"
 96 |       ]
 97 |      },
 98 |      "execution_count": 4,
 99 |      "metadata": {},
100 |      "output_type": "execute_result"
101 |     }
102 |    ],
103 |    "source": [
104 |     "BlackScholes(0.03,100,95,0.25,0.2,0.02)"
105 |    ]
106 |   },
107 |   {
108 |    "cell_type": "code",
109 |    "execution_count": 5,
110 |    "metadata": {},
111 |    "outputs": [
112 |     {
113 |      "name": "stdout",
114 |      "output_type": "stream",
115 |      "text": [
116 |       "6.918695403001518\n"
117 |      ]
118 |     }
119 |    ],
120 |    "source": [
121 |     "##American Call Binomial\n",
122 |     "\n",
123 |     "import numpy as np\n",
124 |     "import matplotlib.pyplot as plt\n",
125 |     "\n",
126 |     "\n",
127 |     "N=12\n",
128 |     "t=0.25\n",
129 |     "dt=t/N\n",
130 |     "S0=100\n",
131 |     "K=95\n",
132 |     "r=0.03\n",
133 |     "div=0.02\n",
134 |     "sigma=0.2\n",
135 |     "u=np.exp(sigma*np.sqrt(dt))\n",
136 |     "d=1/u\n",
137 |     "\n",
138 |     "p=(np.exp((r-div)*dt)-d)/(u-d)\n",
139 |     "stock_prices=np.zeros((N,N))\n",
140 |     "option_prices=np.zeros((N,N))\n",
141 |     "\n",
142 |     "stock_prices[0,0]=S0\n",
143 |     "\n",
144 |     "for i in range (1,N):\n",
145 |     "    M=i+1\n",
146 |     "    stock_prices[i,0]=d*stock_prices[i-1,0]\n",
147 |     "    for j in range (1,M):\n",
148 |     "        stock_prices[i,j]=u*stock_prices[i-1,j-1]\n",
149 |     "\n",
150 |     "expiration=stock_prices[-1,:]-K\n",
151 |     "expiration=np.where(expiration>=0,expiration,0)\n",
152 |     "\n",
153 |     "\n",
154 |     "option_prices[-1,:]=expiration\n",
155 |     "\n",
156 |     "\n",
157 |     "for i in range(N - 2,-1,-1):\n",
158 |     "    for j in range(i+1):\n",
159 |     "        option_prices[i,j]=np.exp(-r*dt) * ((1-p) * option_prices[i+1,j] + p * option_prices[i+1,j+1])\n",
160 |     "        if stock_prices[i,j]-K>option_prices[i,j]:\n",
161 |     "            option_prices[i,j]=stock_prices[i,j]-K\n",
162 |     "        \n",
163 |     "\n",
164 |     "print(option_prices[0,0])"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 6,
170 |    "metadata": {},
171 |    "outputs": [],
172 |    "source": [
173 |     "##Finite Difference\n",
174 |     "\n",
175 |     "def fin_diff(S,K,T,sigma,r,q,N,k):\n",
176 |     "    dt = T/N;   \n",
177 |     "    dx = sigma*np.sqrt(3*dt)\n",
178 |     "    nu = r - q - 0.5*sigma**2\n",
179 |     "    pu = 0.5*dt*((sigma/dx)**2 + nu/dx)\n",
180 |     "    pm = 1.0 - dt*(sigma/dx)**2 - r*dt  \n",
181 |     "    pd = 0.5*dt*((sigma/dx)**2 - nu/dx)\n",
182 |     "    grid = np.zeros((N+1,2*k+1))\n",
183 |     "    \n",
184 |     "\n",
185 |     "    St = [S*np.exp(-k*dx)]\n",
186 |     "    for j in range(1, 2*k+1):\n",
187 |     "        St.append(St[j-1]*np.exp(dx))\n",
188 |     "    \n",
189 |     "    \n",
190 |     "    for j in range(2*k+1):\n",
191 |     "            grid[N,j] = max(0, St[j] - K)\n",
192 |     "        \n",
193 |     "    \n",
194 |     "    \n",
195 |     "    for i in range(N-1, -1, -1):\n",
196 |     "        for j in range(1, 2*k):\n",
197 |     "            grid[i,j] =pu*grid[i+1,j+1] + pm*grid[i+1,j] + pd*grid[i+1,j-1]\n",
198 |     "    \n",
199 |     "     \n",
200 |     "        grid[i,0] = grid[i,1]\n",
201 |     "        grid[i,2*k] = grid[i,2*k-1] + (St[2*k]-St[2*k-1])\n",
202 |     "    \n",
203 |     "    return grid[0,k]"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 7,
209 |    "metadata": {},
210 |    "outputs": [
211 |     {
212 |      "data": {
213 |       "text/plain": [
214 |        "7.018645027227497"
215 |       ]
216 |      },
217 |      "execution_count": 7,
218 |      "metadata": {},
219 |      "output_type": "execute_result"
220 |     }
221 |    ],
222 |    "source": [
223 |     "fin_diff(100,95,0.25,0.2,0.03,0.02,8,3)"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": null,
229 |    "metadata": {},
230 |    "outputs": [],
231 |    "source": []
232 |   }
233 |  ],
234 |  "metadata": {
235 |   "kernelspec": {
236 |    "display_name": "base",
237 |    "language": "python",
238 |    "name": "python3"
239 |   },
240 |   "language_info": {
241 |    "codemirror_mode": {
242 |     "name": "ipython",
243 |     "version": 3
244 |    },
245 |    "file_extension": ".py",
246 |    "mimetype": "text/x-python",
247 |    "name": "python",
248 |    "nbconvert_exporter": "python",
249 |    "pygments_lexer": "ipython3",
250 |    "version": "3.9.13"
251 |   },
252 |   "orig_nbformat": 4,
253 |   "vscode": {
254 |    "interpreter": {
255 |     "hash": "4f77cdfaf4507c9d7563b1dafbbefd1211a5ec1bc40dd9ff43d298279ac97956"
256 |    }
257 |   }
258 |  },
259 |  "nbformat": 4,
260 |  "nbformat_minor": 2
261 | }
262 | 


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/Option_Pricing/Monte Carlo.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "7.01766531898455\n"
13 |      ]
14 |     }
15 |    ],
16 |    "source": [
17 |     "##Monte Carlo\n",
18 |     "T=0.25\n",
19 |     "S0=100\n",
20 |     "K=95\n",
21 |     "r=0.03\n",
22 |     "div=0.02\n",
23 |     "sigma=0.2\n",
24 |     "\n",
25 |     "\n",
26 |     "import numpy as np\n",
27 |     "np.random.seed(1)\n",
28 |     "\n",
29 |     "iterations=1000000 # 1 million iterations\n",
30 |     "\n",
31 |     "z=np.random.standard_normal(iterations)\n",
32 |     "ST=S0*np.exp((r-0.5 * sigma**2-div)*T + sigma * np.sqrt(T)*z) #Euler Equation modified with dividend yield\n",
33 |     "payoff=np.maximum(ST-K,0) ##Payoff at expiration\n",
34 |     "Call_price=np.exp (-r*T) * np.sum(payoff)/iterations\n",
35 |     "\n",
36 |     "print(Call_price)"
37 |    ]
38 |   },
39 |   {
40 |    "cell_type": "code",
41 |    "execution_count": null,
42 |    "metadata": {},
43 |    "outputs": [],
44 |    "source": []
45 |   }
46 |  ],
47 |  "metadata": {
48 |   "kernelspec": {
49 |    "display_name": "base",
50 |    "language": "python",
51 |    "name": "python3"
52 |   },
53 |   "language_info": {
54 |    "codemirror_mode": {
55 |     "name": "ipython",
56 |     "version": 3
57 |    },
58 |    "file_extension": ".py",
59 |    "mimetype": "text/x-python",
60 |    "name": "python",
61 |    "nbconvert_exporter": "python",
62 |    "pygments_lexer": "ipython3",
63 |    "version": "3.9.13"
64 |   },
65 |   "orig_nbformat": 4,
66 |   "vscode": {
67 |    "interpreter": {
68 |     "hash": "4f77cdfaf4507c9d7563b1dafbbefd1211a5ec1bc40dd9ff43d298279ac97956"
69 |    }
70 |   }
71 |  },
72 |  "nbformat": 4,
73 |  "nbformat_minor": 2
74 | }
75 | 


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/Option_Pricing/README.md:
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1 | ## Binomial Tree Option Pricing. This is a four period model with an american call setting as well as european call setting. Finite Difference and Monte Carlo methods are used to verify the option prices approximately
2 | 


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/R/Copula Modeling/ACF.pdf:
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https://raw.githubusercontent.com/adibyaser/Side_Projects/0aea18fbe6f40cdfef1544274253b166bbbc02c3/R/Copula Modeling/ACF.pdf


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/R/Copula Modeling/Actual Plot.png:
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https://raw.githubusercontent.com/adibyaser/Side_Projects/0aea18fbe6f40cdfef1544274253b166bbbc02c3/R/Copula Modeling/Actual Plot.png


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/R/Copula Modeling/Copula Project.R:
--------------------------------------------------------------------------------
 1 | library(copula)
 2 | library(sn)
 3 | library(ks)
 4 | library(quantmod)
 5 | library(MASS)
 6 | library(fitdistrplus)
 7 | rm(list=ls())
 8 | getSymbols("NVDA",src='yahoo',auto.assign = TRUE,from='2013-09-29',to='2023-09-29')
 9 | getSymbols("AMD",src='yahoo',auto.assign = TRUE,from='2013-09-29',to='2023-09-29')
10 | nvd_r=monthlyReturn(NVDA$NVDA.Adjusted,type='log')
11 | amd_r=monthlyReturn(AMD$AMD.Adjusted,type='log')
12 | nvd_r=coredata(nvd_r)
13 | amd_r=coredata(amd_r)
14 | n=length(nvd_r)
15 | par(mfrow=c(2,1))
16 | acf(amd_r,main='AMD Monthly Log Returns')
17 | acf(nvd_r,main='NVIDIA Monthly Log Returns ')
18 | ##Fit The following Distributions: Normal, Skewed normal,T,skewed T
19 | 
20 | fit_normaln=fitdistr(nvd_r,densfun='normal')
21 | fit_normala=fitdistr(amd_r,densfun='normal')
22 | 
23 | 
24 | fit_tn=fitdistr(nvd_r,densfun='t',upper=30)
25 | fit_ta=fitdistr(amd_r,densfun='t',upper=50)
26 | 
27 | fit_skt_n=st.mple(y=nvd_r)
28 | fit_skt_a=st.mple(y=amd_r)
29 | 
30 | 
31 | fit_skn_n=sn.mple(y=nvd_r)
32 | fit_skn_a=sn.mple(y=amd_r)
33 | fit_ta
34 | 
35 | ##Check AIC For Each of them 
36 | 
37 | -2*fit_normaln$loglik+2*(2)
38 | -2*fit_normala$loglik+2*(2)
39 | 
40 | -2*fit_tn$loglik+2*(3)
41 | -2*fit_ta$loglik+2*(3)
42 | 
43 | -2*fit_skn_n$logL+2*(3)
44 | -2*fit_skn_a$logL+2*(3)
45 | 
46 | -2*fit_skt_n$logL+2*(4)
47 | -2*fit_skt_a$logL+2*(4)
48 | 
49 | ## Skewed Normal & Normal
50 | est1=fit_normala$estimate
51 | est2=fit_skn_n$cp
52 | 
53 | u1=pnorm(amd_r,est1)
54 | u2=psn(nvd_r,xi=est2['mean'],omega=est2['s.d.'],alpha=est2['gamma1'])
55 | ##U hat Combinations
56 | uhat=cbind(u1,u2)
57 | 
58 | ###Copula
59 | Ct=fitCopula(copula=tCopula(dim=2),data=uhat,method="ml")## T Copula
60 | Ct@estimate
61 | Ct_logL=loglikCopula(param=Ct@estimate,u=uhat,copula=tCopula(dim=2));#compute loglikelihood function
62 | -2*Ct_logL+2*length(Ct@estimate)#AIC
63 | Cfr=fitCopula(copula=frankCopula(dim=2),data=uhat,method="ml")#fit frank copula
64 | Cfr@estimate;
65 | Cfr_logL=loglikCopula(param=Cfr@estimate,u=uhat,copula=frankCopula(dim=2));
66 | -2*Cfr_logL+2*length(Cfr@estimate);
67 | Ccl=fitCopula(copula=claytonCopula(dim=2),data=uhat,method="ml");#fit clayton copula
68 | Ccl@estimate
69 | Ccl_logL=loglikCopula(param=Ccl@estimate,u=uhat,copula=claytonCopula(dim=2));
70 | -2*Ccl_logL+2*length(Ccl@estimate);#compute AIC
71 | Cg=fitCopula(copula=normalCopula(dim=2),data=uhat,method="ml");#fit Gaussian copula
72 | Cg_logL=loglikCopula(param=Cg@estimate,u=uhat,copula=normalCopula(dim=2))
73 | -2*Cg_logL+2*length(Cg@estimate);#compute AIC
74 | ##Simulation For Frank Copula
75 | n=length(amd_r)
76 | Simu_U=rCopula(n,frankCopula(dim=2,Cfr@estimate))
77 | summary(Simu_U)
78 | plot(Simu_U[,1],Simu_U[,2],main='Simulated Data-Uniform Distribution',xlab='Simulated AMD',ylab='Simulated NVIDIA')
79 | Simu_X1=qnorm(Simu_U[,1],est1)
80 | Simu_X2=qsn(Simu_U[,2],dp=est2)
81 | par(mfrow=c(1,1))
82 | plot(Simu_X1,Simu_X2,main='Simulated Plot of AMD & NVIDIA',xlab='Simulated_AMD',ylab='Simulated_NVIDIA')
83 | plot(coredata(amd_r),coredata(nvd_r),main='Actual Plot of AMD & NVIDIA',xlab="AMD",ylab='NVIDIA')
84 | 
85 | 


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/R/Copula Modeling/README.md:
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1 | ## This project relates to separating dependency structures from univariate marginal distributions for two stocks namely: NVIDIA and AMD. Secondly, we plot a simulated transformed data to see if our model resembles the actual plot of the data. Sometimes correlated stocks are held in a portfolio. Investors and analysts want to figure out the correlations between the assets. However, correlations only measure linear relationships, excluding non linear ones.That is why it's important to consider the non linear relationships, tail dependencies and estimated distributions to fit around the actual distributions. Copula models thus come into play.Copula models estimate their joint behavior, considering non-linearities as well as provide us with insightful estimates, which is more sophisticated than standard regressions. In this R script, we test out 4 individual distribution pair analysis: Normal, T, Skewed Normal and Skewed T. Then we try several Copula models for fitting and best estimates.
2 | 


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/R/Copula Modeling/Simulated Plot,Frank Copula.png:
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https://raw.githubusercontent.com/adibyaser/Side_Projects/0aea18fbe6f40cdfef1544274253b166bbbc02c3/R/Copula Modeling/Simulated Plot,Frank Copula.png


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/README.md:
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1 | # Hi,this is my own codespace to work around and showcase my projects. Feel free to look around.
2 | 


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/Risk Management/EuroDollar Futures.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "## Here we are given a 90 days EuroDollar Futures contract. Assume the investor goes long, he/she will accrue daily profit/loss as the future contract is Marked to Market. \n",
   8 |     "## Assume first Initial Margin is $ 1,485 (USD)\n",
   9 |     "## Maintenance Margin is $ 1,110 (USD)\n",
  10 |     "## We know that investor buys 100 contracts\n",
  11 |     "## 1) Calculate the Daily Profit & Loss, assuming the investor does not take any profit for himself. Calculate daily cash flow to the contract.\n",
  12 |     "## 2) Calculate the Daily Profit & Loss, assuming the investor does take profit for himself. Calculate daily cash flow to the contract."
  13 |    ]
  14 |   },
  15 |   {
  16 |    "cell_type": "code",
  17 |    "execution_count": 35,
  18 |    "metadata": {},
  19 |    "outputs": [
  20 |     {
  21 |      "data": {
  22 |       "text/html": [
  23 |        "<div>\n",
  24 |        "<style scoped>\n",
  25 |        "    .dataframe tbody tr th:only-of-type {\n",
  26 |        "        vertical-align: middle;\n",
  27 |        "    }\n",
  28 |        "\n",
  29 |        "    .dataframe tbody tr th {\n",
  30 |        "        vertical-align: top;\n",
  31 |        "    }\n",
  32 |        "\n",
  33 |        "    .dataframe thead th {\n",
  34 |        "        text-align: right;\n",
  35 |        "    }\n",
  36 |        "</style>\n",
  37 |        "<table border=\"1\" class=\"dataframe\">\n",
  38 |        "  <thead>\n",
  39 |        "    <tr style=\"text-align: right;\">\n",
  40 |        "      <th></th>\n",
  41 |        "      <th>Date_1</th>\n",
  42 |        "      <th>Prices_1</th>\n",
  43 |        "    </tr>\n",
  44 |        "  </thead>\n",
  45 |        "  <tbody>\n",
  46 |        "    <tr>\n",
  47 |        "      <th>0</th>\n",
  48 |        "      <td>2007-03-21</td>\n",
  49 |        "      <td>93.635</td>\n",
  50 |        "    </tr>\n",
  51 |        "    <tr>\n",
  52 |        "      <th>1</th>\n",
  53 |        "      <td>2007-03-22</td>\n",
  54 |        "      <td>93.685</td>\n",
  55 |        "    </tr>\n",
  56 |        "    <tr>\n",
  57 |        "      <th>2</th>\n",
  58 |        "      <td>2007-03-23</td>\n",
  59 |        "      <td>93.570</td>\n",
  60 |        "    </tr>\n",
  61 |        "    <tr>\n",
  62 |        "      <th>3</th>\n",
  63 |        "      <td>2007-03-26</td>\n",
  64 |        "      <td>93.500</td>\n",
  65 |        "    </tr>\n",
  66 |        "    <tr>\n",
  67 |        "      <th>4</th>\n",
  68 |        "      <td>2007-03-27</td>\n",
  69 |        "      <td>93.320</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>...</th>\n",
  73 |        "      <td>...</td>\n",
  74 |        "      <td>...</td>\n",
  75 |        "    </tr>\n",
  76 |        "    <tr>\n",
  77 |        "      <th>60</th>\n",
  78 |        "      <td>2007-06-15</td>\n",
  79 |        "      <td>93.020</td>\n",
  80 |        "    </tr>\n",
  81 |        "    <tr>\n",
  82 |        "      <th>61</th>\n",
  83 |        "      <td>2007-06-18</td>\n",
  84 |        "      <td>92.995</td>\n",
  85 |        "    </tr>\n",
  86 |        "    <tr>\n",
  87 |        "      <th>62</th>\n",
  88 |        "      <td>2007-06-19</td>\n",
  89 |        "      <td>93.010</td>\n",
  90 |        "    </tr>\n",
  91 |        "    <tr>\n",
  92 |        "      <th>63</th>\n",
  93 |        "      <td>2007-06-20</td>\n",
  94 |        "      <td>93.060</td>\n",
  95 |        "    </tr>\n",
  96 |        "    <tr>\n",
  97 |        "      <th>64</th>\n",
  98 |        "      <td>2007-06-21</td>\n",
  99 |        "      <td>93.095</td>\n",
 100 |        "    </tr>\n",
 101 |        "  </tbody>\n",
 102 |        "</table>\n",
 103 |        "<p>65 rows × 2 columns</p>\n",
 104 |        "</div>"
 105 |       ],
 106 |       "text/plain": [
 107 |        "       Date_1  Prices_1\n",
 108 |        "0  2007-03-21    93.635\n",
 109 |        "1  2007-03-22    93.685\n",
 110 |        "2  2007-03-23    93.570\n",
 111 |        "3  2007-03-26    93.500\n",
 112 |        "4  2007-03-27    93.320\n",
 113 |        "..        ...       ...\n",
 114 |        "60 2007-06-15    93.020\n",
 115 |        "61 2007-06-18    92.995\n",
 116 |        "62 2007-06-19    93.010\n",
 117 |        "63 2007-06-20    93.060\n",
 118 |        "64 2007-06-21    93.095\n",
 119 |        "\n",
 120 |        "[65 rows x 2 columns]"
 121 |       ]
 122 |      },
 123 |      "execution_count": 35,
 124 |      "metadata": {},
 125 |      "output_type": "execute_result"
 126 |     }
 127 |    ],
 128 |    "source": [
 129 |     "## We are given the Quotation Prices of the Contracts\n",
 130 |     "df=pd.read_excel('C:/Users/adib-/Desktop/AdibHW2.xlsx')\n",
 131 |     "df"
 132 |    ]
 133 |   },
 134 |   {
 135 |    "cell_type": "code",
 136 |    "execution_count": 36,
 137 |    "metadata": {},
 138 |    "outputs": [],
 139 |    "source": [
 140 |     "df['difference']=df['Prices_1'].diff()"
 141 |    ]
 142 |   },
 143 |   {
 144 |    "cell_type": "code",
 145 |    "execution_count": 37,
 146 |    "metadata": {},
 147 |    "outputs": [
 148 |     {
 149 |      "data": {
 150 |       "text/html": [
 151 |        "<div>\n",
 152 |        "<style scoped>\n",
 153 |        "    .dataframe tbody tr th:only-of-type {\n",
 154 |        "        vertical-align: middle;\n",
 155 |        "    }\n",
 156 |        "\n",
 157 |        "    .dataframe tbody tr th {\n",
 158 |        "        vertical-align: top;\n",
 159 |        "    }\n",
 160 |        "\n",
 161 |        "    .dataframe thead th {\n",
 162 |        "        text-align: right;\n",
 163 |        "    }\n",
 164 |        "</style>\n",
 165 |        "<table border=\"1\" class=\"dataframe\">\n",
 166 |        "  <thead>\n",
 167 |        "    <tr style=\"text-align: right;\">\n",
 168 |        "      <th></th>\n",
 169 |        "      <th>Date_1</th>\n",
 170 |        "      <th>Prices_1</th>\n",
 171 |        "      <th>difference</th>\n",
 172 |        "    </tr>\n",
 173 |        "  </thead>\n",
 174 |        "  <tbody>\n",
 175 |        "    <tr>\n",
 176 |        "      <th>0</th>\n",
 177 |        "      <td>2007-03-21</td>\n",
 178 |        "      <td>93.635</td>\n",
 179 |        "      <td>NaN</td>\n",
 180 |        "    </tr>\n",
 181 |        "    <tr>\n",
 182 |        "      <th>1</th>\n",
 183 |        "      <td>2007-03-22</td>\n",
 184 |        "      <td>93.685</td>\n",
 185 |        "      <td>0.050</td>\n",
 186 |        "    </tr>\n",
 187 |        "    <tr>\n",
 188 |        "      <th>2</th>\n",
 189 |        "      <td>2007-03-23</td>\n",
 190 |        "      <td>93.570</td>\n",
 191 |        "      <td>-0.115</td>\n",
 192 |        "    </tr>\n",
 193 |        "    <tr>\n",
 194 |        "      <th>3</th>\n",
 195 |        "      <td>2007-03-26</td>\n",
 196 |        "      <td>93.500</td>\n",
 197 |        "      <td>-0.070</td>\n",
 198 |        "    </tr>\n",
 199 |        "    <tr>\n",
 200 |        "      <th>4</th>\n",
 201 |        "      <td>2007-03-27</td>\n",
 202 |        "      <td>93.320</td>\n",
 203 |        "      <td>-0.180</td>\n",
 204 |        "    </tr>\n",
 205 |        "    <tr>\n",
 206 |        "      <th>...</th>\n",
 207 |        "      <td>...</td>\n",
 208 |        "      <td>...</td>\n",
 209 |        "      <td>...</td>\n",
 210 |        "    </tr>\n",
 211 |        "    <tr>\n",
 212 |        "      <th>60</th>\n",
 213 |        "      <td>2007-06-15</td>\n",
 214 |        "      <td>93.020</td>\n",
 215 |        "      <td>-0.125</td>\n",
 216 |        "    </tr>\n",
 217 |        "    <tr>\n",
 218 |        "      <th>61</th>\n",
 219 |        "      <td>2007-06-18</td>\n",
 220 |        "      <td>92.995</td>\n",
 221 |        "      <td>-0.025</td>\n",
 222 |        "    </tr>\n",
 223 |        "    <tr>\n",
 224 |        "      <th>62</th>\n",
 225 |        "      <td>2007-06-19</td>\n",
 226 |        "      <td>93.010</td>\n",
 227 |        "      <td>0.015</td>\n",
 228 |        "    </tr>\n",
 229 |        "    <tr>\n",
 230 |        "      <th>63</th>\n",
 231 |        "      <td>2007-06-20</td>\n",
 232 |        "      <td>93.060</td>\n",
 233 |        "      <td>0.050</td>\n",
 234 |        "    </tr>\n",
 235 |        "    <tr>\n",
 236 |        "      <th>64</th>\n",
 237 |        "      <td>2007-06-21</td>\n",
 238 |        "      <td>93.095</td>\n",
 239 |        "      <td>0.035</td>\n",
 240 |        "    </tr>\n",
 241 |        "  </tbody>\n",
 242 |        "</table>\n",
 243 |        "<p>65 rows × 3 columns</p>\n",
 244 |        "</div>"
 245 |       ],
 246 |       "text/plain": [
 247 |        "       Date_1  Prices_1  difference\n",
 248 |        "0  2007-03-21    93.635         NaN\n",
 249 |        "1  2007-03-22    93.685       0.050\n",
 250 |        "2  2007-03-23    93.570      -0.115\n",
 251 |        "3  2007-03-26    93.500      -0.070\n",
 252 |        "4  2007-03-27    93.320      -0.180\n",
 253 |        "..        ...       ...         ...\n",
 254 |        "60 2007-06-15    93.020      -0.125\n",
 255 |        "61 2007-06-18    92.995      -0.025\n",
 256 |        "62 2007-06-19    93.010       0.015\n",
 257 |        "63 2007-06-20    93.060       0.050\n",
 258 |        "64 2007-06-21    93.095       0.035\n",
 259 |        "\n",
 260 |        "[65 rows x 3 columns]"
 261 |       ]
 262 |      },
 263 |      "execution_count": 37,
 264 |      "metadata": {},
 265 |      "output_type": "execute_result"
 266 |     }
 267 |    ],
 268 |    "source": [
 269 |     "df"
 270 |    ]
 271 |   },
 272 |   {
 273 |    "cell_type": "code",
 274 |    "execution_count": 38,
 275 |    "metadata": {},
 276 |    "outputs": [
 277 |     {
 278 |      "data": {
 279 |       "text/html": [
 280 |        "<div>\n",
 281 |        "<style scoped>\n",
 282 |        "    .dataframe tbody tr th:only-of-type {\n",
 283 |        "        vertical-align: middle;\n",
 284 |        "    }\n",
 285 |        "\n",
 286 |        "    .dataframe tbody tr th {\n",
 287 |        "        vertical-align: top;\n",
 288 |        "    }\n",
 289 |        "\n",
 290 |        "    .dataframe thead th {\n",
 291 |        "        text-align: right;\n",
 292 |        "    }\n",
 293 |        "</style>\n",
 294 |        "<table border=\"1\" class=\"dataframe\">\n",
 295 |        "  <thead>\n",
 296 |        "    <tr style=\"text-align: right;\">\n",
 297 |        "      <th></th>\n",
 298 |        "      <th>Date_1</th>\n",
 299 |        "      <th>Prices_1</th>\n",
 300 |        "      <th>difference</th>\n",
 301 |        "    </tr>\n",
 302 |        "  </thead>\n",
 303 |        "  <tbody>\n",
 304 |        "    <tr>\n",
 305 |        "      <th>0</th>\n",
 306 |        "      <td>2007-03-21</td>\n",
 307 |        "      <td>93.635</td>\n",
 308 |        "      <td>NaN</td>\n",
 309 |        "    </tr>\n",
 310 |        "    <tr>\n",
 311 |        "      <th>1</th>\n",
 312 |        "      <td>2007-03-22</td>\n",
 313 |        "      <td>93.685</td>\n",
 314 |        "      <td>0.050</td>\n",
 315 |        "    </tr>\n",
 316 |        "    <tr>\n",
 317 |        "      <th>2</th>\n",
 318 |        "      <td>2007-03-23</td>\n",
 319 |        "      <td>93.570</td>\n",
 320 |        "      <td>-0.115</td>\n",
 321 |        "    </tr>\n",
 322 |        "    <tr>\n",
 323 |        "      <th>3</th>\n",
 324 |        "      <td>2007-03-26</td>\n",
 325 |        "      <td>93.500</td>\n",
 326 |        "      <td>-0.070</td>\n",
 327 |        "    </tr>\n",
 328 |        "    <tr>\n",
 329 |        "      <th>4</th>\n",
 330 |        "      <td>2007-03-27</td>\n",
 331 |        "      <td>93.320</td>\n",
 332 |        "      <td>-0.180</td>\n",
 333 |        "    </tr>\n",
 334 |        "    <tr>\n",
 335 |        "      <th>...</th>\n",
 336 |        "      <td>...</td>\n",
 337 |        "      <td>...</td>\n",
 338 |        "      <td>...</td>\n",
 339 |        "    </tr>\n",
 340 |        "    <tr>\n",
 341 |        "      <th>60</th>\n",
 342 |        "      <td>2007-06-15</td>\n",
 343 |        "      <td>93.020</td>\n",
 344 |        "      <td>-0.125</td>\n",
 345 |        "    </tr>\n",
 346 |        "    <tr>\n",
 347 |        "      <th>61</th>\n",
 348 |        "      <td>2007-06-18</td>\n",
 349 |        "      <td>92.995</td>\n",
 350 |        "      <td>-0.025</td>\n",
 351 |        "    </tr>\n",
 352 |        "    <tr>\n",
 353 |        "      <th>62</th>\n",
 354 |        "      <td>2007-06-19</td>\n",
 355 |        "      <td>93.010</td>\n",
 356 |        "      <td>0.015</td>\n",
 357 |        "    </tr>\n",
 358 |        "    <tr>\n",
 359 |        "      <th>63</th>\n",
 360 |        "      <td>2007-06-20</td>\n",
 361 |        "      <td>93.060</td>\n",
 362 |        "      <td>0.050</td>\n",
 363 |        "    </tr>\n",
 364 |        "    <tr>\n",
 365 |        "      <th>64</th>\n",
 366 |        "      <td>2007-06-21</td>\n",
 367 |        "      <td>93.095</td>\n",
 368 |        "      <td>0.035</td>\n",
 369 |        "    </tr>\n",
 370 |        "  </tbody>\n",
 371 |        "</table>\n",
 372 |        "<p>65 rows × 3 columns</p>\n",
 373 |        "</div>"
 374 |       ],
 375 |       "text/plain": [
 376 |        "       Date_1  Prices_1  difference\n",
 377 |        "0  2007-03-21    93.635         NaN\n",
 378 |        "1  2007-03-22    93.685       0.050\n",
 379 |        "2  2007-03-23    93.570      -0.115\n",
 380 |        "3  2007-03-26    93.500      -0.070\n",
 381 |        "4  2007-03-27    93.320      -0.180\n",
 382 |        "..        ...       ...         ...\n",
 383 |        "60 2007-06-15    93.020      -0.125\n",
 384 |        "61 2007-06-18    92.995      -0.025\n",
 385 |        "62 2007-06-19    93.010       0.015\n",
 386 |        "63 2007-06-20    93.060       0.050\n",
 387 |        "64 2007-06-21    93.095       0.035\n",
 388 |        "\n",
 389 |        "[65 rows x 3 columns]"
 390 |       ]
 391 |      },
 392 |      "execution_count": 38,
 393 |      "metadata": {},
 394 |      "output_type": "execute_result"
 395 |     }
 396 |    ],
 397 |    "source": [
 398 |     "df"
 399 |    ]
 400 |   },
 401 |   {
 402 |    "cell_type": "code",
 403 |    "execution_count": 39,
 404 |    "metadata": {},
 405 |    "outputs": [
 406 |     {
 407 |      "data": {
 408 |       "text/html": [
 409 |        "<div>\n",
 410 |        "<style scoped>\n",
 411 |        "    .dataframe tbody tr th:only-of-type {\n",
 412 |        "        vertical-align: middle;\n",
 413 |        "    }\n",
 414 |        "\n",
 415 |        "    .dataframe tbody tr th {\n",
 416 |        "        vertical-align: top;\n",
 417 |        "    }\n",
 418 |        "\n",
 419 |        "    .dataframe thead th {\n",
 420 |        "        text-align: right;\n",
 421 |        "    }\n",
 422 |        "</style>\n",
 423 |        "<table border=\"1\" class=\"dataframe\">\n",
 424 |        "  <thead>\n",
 425 |        "    <tr style=\"text-align: right;\">\n",
 426 |        "      <th></th>\n",
 427 |        "      <th>Date_1</th>\n",
 428 |        "      <th>Prices_1</th>\n",
 429 |        "      <th>difference</th>\n",
 430 |        "      <th>Daily Gain/Loss</th>\n",
 431 |        "    </tr>\n",
 432 |        "  </thead>\n",
 433 |        "  <tbody>\n",
 434 |        "    <tr>\n",
 435 |        "      <th>0</th>\n",
 436 |        "      <td>2007-03-21</td>\n",
 437 |        "      <td>93.635</td>\n",
 438 |        "      <td>NaN</td>\n",
 439 |        "      <td>NaN</td>\n",
 440 |        "    </tr>\n",
 441 |        "    <tr>\n",
 442 |        "      <th>1</th>\n",
 443 |        "      <td>2007-03-22</td>\n",
 444 |        "      <td>93.685</td>\n",
 445 |        "      <td>0.050</td>\n",
 446 |        "      <td>12500.0</td>\n",
 447 |        "    </tr>\n",
 448 |        "    <tr>\n",
 449 |        "      <th>2</th>\n",
 450 |        "      <td>2007-03-23</td>\n",
 451 |        "      <td>93.570</td>\n",
 452 |        "      <td>-0.115</td>\n",
 453 |        "      <td>-28750.0</td>\n",
 454 |        "    </tr>\n",
 455 |        "    <tr>\n",
 456 |        "      <th>3</th>\n",
 457 |        "      <td>2007-03-26</td>\n",
 458 |        "      <td>93.500</td>\n",
 459 |        "      <td>-0.070</td>\n",
 460 |        "      <td>-17500.0</td>\n",
 461 |        "    </tr>\n",
 462 |        "    <tr>\n",
 463 |        "      <th>4</th>\n",
 464 |        "      <td>2007-03-27</td>\n",
 465 |        "      <td>93.320</td>\n",
 466 |        "      <td>-0.180</td>\n",
 467 |        "      <td>-45000.0</td>\n",
 468 |        "    </tr>\n",
 469 |        "    <tr>\n",
 470 |        "      <th>...</th>\n",
 471 |        "      <td>...</td>\n",
 472 |        "      <td>...</td>\n",
 473 |        "      <td>...</td>\n",
 474 |        "      <td>...</td>\n",
 475 |        "    </tr>\n",
 476 |        "    <tr>\n",
 477 |        "      <th>60</th>\n",
 478 |        "      <td>2007-06-15</td>\n",
 479 |        "      <td>93.020</td>\n",
 480 |        "      <td>-0.125</td>\n",
 481 |        "      <td>-31250.0</td>\n",
 482 |        "    </tr>\n",
 483 |        "    <tr>\n",
 484 |        "      <th>61</th>\n",
 485 |        "      <td>2007-06-18</td>\n",
 486 |        "      <td>92.995</td>\n",
 487 |        "      <td>-0.025</td>\n",
 488 |        "      <td>-6250.0</td>\n",
 489 |        "    </tr>\n",
 490 |        "    <tr>\n",
 491 |        "      <th>62</th>\n",
 492 |        "      <td>2007-06-19</td>\n",
 493 |        "      <td>93.010</td>\n",
 494 |        "      <td>0.015</td>\n",
 495 |        "      <td>3750.0</td>\n",
 496 |        "    </tr>\n",
 497 |        "    <tr>\n",
 498 |        "      <th>63</th>\n",
 499 |        "      <td>2007-06-20</td>\n",
 500 |        "      <td>93.060</td>\n",
 501 |        "      <td>0.050</td>\n",
 502 |        "      <td>12500.0</td>\n",
 503 |        "    </tr>\n",
 504 |        "    <tr>\n",
 505 |        "      <th>64</th>\n",
 506 |        "      <td>2007-06-21</td>\n",
 507 |        "      <td>93.095</td>\n",
 508 |        "      <td>0.035</td>\n",
 509 |        "      <td>8750.0</td>\n",
 510 |        "    </tr>\n",
 511 |        "  </tbody>\n",
 512 |        "</table>\n",
 513 |        "<p>65 rows × 4 columns</p>\n",
 514 |        "</div>"
 515 |       ],
 516 |       "text/plain": [
 517 |        "       Date_1  Prices_1  difference  Daily Gain/Loss\n",
 518 |        "0  2007-03-21    93.635         NaN              NaN\n",
 519 |        "1  2007-03-22    93.685       0.050          12500.0\n",
 520 |        "2  2007-03-23    93.570      -0.115         -28750.0\n",
 521 |        "3  2007-03-26    93.500      -0.070         -17500.0\n",
 522 |        "4  2007-03-27    93.320      -0.180         -45000.0\n",
 523 |        "..        ...       ...         ...              ...\n",
 524 |        "60 2007-06-15    93.020      -0.125         -31250.0\n",
 525 |        "61 2007-06-18    92.995      -0.025          -6250.0\n",
 526 |        "62 2007-06-19    93.010       0.015           3750.0\n",
 527 |        "63 2007-06-20    93.060       0.050          12500.0\n",
 528 |        "64 2007-06-21    93.095       0.035           8750.0\n",
 529 |        "\n",
 530 |        "[65 rows x 4 columns]"
 531 |       ]
 532 |      },
 533 |      "execution_count": 39,
 534 |      "metadata": {},
 535 |      "output_type": "execute_result"
 536 |     }
 537 |    ],
 538 |    "source": [
 539 |     "df['Daily Gain/Loss']=df['difference']/100 *100*0.25*1000000 ## 3 month LIBOR so that's why 0.25\n",
 540 |     "df"
 541 |    ]
 542 |   },
 543 |   {
 544 |    "cell_type": "code",
 545 |    "execution_count": 40,
 546 |    "metadata": {},
 547 |    "outputs": [
 548 |     {
 549 |      "data": {
 550 |       "text/plain": [
 551 |        "-135000.00000000157"
 552 |       ]
 553 |      },
 554 |      "execution_count": 40,
 555 |      "metadata": {},
 556 |      "output_type": "execute_result"
 557 |     }
 558 |    ],
 559 |    "source": [
 560 |     "df['Daily Gain/Loss'].sum()"
 561 |    ]
 562 |   },
 563 |   {
 564 |    "cell_type": "code",
 565 |    "execution_count": 41,
 566 |    "metadata": {},
 567 |    "outputs": [],
 568 |    "source": [
 569 |     "df['Margin Balance']=None"
 570 |    ]
 571 |   },
 572 |   {
 573 |    "cell_type": "code",
 574 |    "execution_count": 42,
 575 |    "metadata": {},
 576 |    "outputs": [],
 577 |    "source": [
 578 |     "df.iloc[0,4]=148500"
 579 |    ]
 580 |   },
 581 |   {
 582 |    "cell_type": "code",
 583 |    "execution_count": 43,
 584 |    "metadata": {},
 585 |    "outputs": [
 586 |     {
 587 |      "data": {
 588 |       "text/html": [
 589 |        "<div>\n",
 590 |        "<style scoped>\n",
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 593 |        "    }\n",
 594 |        "\n",
 595 |        "    .dataframe tbody tr th {\n",
 596 |        "        vertical-align: top;\n",
 597 |        "    }\n",
 598 |        "\n",
 599 |        "    .dataframe thead th {\n",
 600 |        "        text-align: right;\n",
 601 |        "    }\n",
 602 |        "</style>\n",
 603 |        "<table border=\"1\" class=\"dataframe\">\n",
 604 |        "  <thead>\n",
 605 |        "    <tr style=\"text-align: right;\">\n",
 606 |        "      <th></th>\n",
 607 |        "      <th>Date_1</th>\n",
 608 |        "      <th>Prices_1</th>\n",
 609 |        "      <th>difference</th>\n",
 610 |        "      <th>Daily Gain/Loss</th>\n",
 611 |        "      <th>Margin Balance</th>\n",
 612 |        "    </tr>\n",
 613 |        "  </thead>\n",
 614 |        "  <tbody>\n",
 615 |        "    <tr>\n",
 616 |        "      <th>0</th>\n",
 617 |        "      <td>2007-03-21</td>\n",
 618 |        "      <td>93.635</td>\n",
 619 |        "      <td>NaN</td>\n",
 620 |        "      <td>NaN</td>\n",
 621 |        "      <td>148500</td>\n",
 622 |        "    </tr>\n",
 623 |        "    <tr>\n",
 624 |        "      <th>1</th>\n",
 625 |        "      <td>2007-03-22</td>\n",
 626 |        "      <td>93.685</td>\n",
 627 |        "      <td>0.050</td>\n",
 628 |        "      <td>12500.0</td>\n",
 629 |        "      <td>None</td>\n",
 630 |        "    </tr>\n",
 631 |        "    <tr>\n",
 632 |        "      <th>2</th>\n",
 633 |        "      <td>2007-03-23</td>\n",
 634 |        "      <td>93.570</td>\n",
 635 |        "      <td>-0.115</td>\n",
 636 |        "      <td>-28750.0</td>\n",
 637 |        "      <td>None</td>\n",
 638 |        "    </tr>\n",
 639 |        "    <tr>\n",
 640 |        "      <th>3</th>\n",
 641 |        "      <td>2007-03-26</td>\n",
 642 |        "      <td>93.500</td>\n",
 643 |        "      <td>-0.070</td>\n",
 644 |        "      <td>-17500.0</td>\n",
 645 |        "      <td>None</td>\n",
 646 |        "    </tr>\n",
 647 |        "    <tr>\n",
 648 |        "      <th>4</th>\n",
 649 |        "      <td>2007-03-27</td>\n",
 650 |        "      <td>93.320</td>\n",
 651 |        "      <td>-0.180</td>\n",
 652 |        "      <td>-45000.0</td>\n",
 653 |        "      <td>None</td>\n",
 654 |        "    </tr>\n",
 655 |        "    <tr>\n",
 656 |        "      <th>...</th>\n",
 657 |        "      <td>...</td>\n",
 658 |        "      <td>...</td>\n",
 659 |        "      <td>...</td>\n",
 660 |        "      <td>...</td>\n",
 661 |        "      <td>...</td>\n",
 662 |        "    </tr>\n",
 663 |        "    <tr>\n",
 664 |        "      <th>60</th>\n",
 665 |        "      <td>2007-06-15</td>\n",
 666 |        "      <td>93.020</td>\n",
 667 |        "      <td>-0.125</td>\n",
 668 |        "      <td>-31250.0</td>\n",
 669 |        "      <td>None</td>\n",
 670 |        "    </tr>\n",
 671 |        "    <tr>\n",
 672 |        "      <th>61</th>\n",
 673 |        "      <td>2007-06-18</td>\n",
 674 |        "      <td>92.995</td>\n",
 675 |        "      <td>-0.025</td>\n",
 676 |        "      <td>-6250.0</td>\n",
 677 |        "      <td>None</td>\n",
 678 |        "    </tr>\n",
 679 |        "    <tr>\n",
 680 |        "      <th>62</th>\n",
 681 |        "      <td>2007-06-19</td>\n",
 682 |        "      <td>93.010</td>\n",
 683 |        "      <td>0.015</td>\n",
 684 |        "      <td>3750.0</td>\n",
 685 |        "      <td>None</td>\n",
 686 |        "    </tr>\n",
 687 |        "    <tr>\n",
 688 |        "      <th>63</th>\n",
 689 |        "      <td>2007-06-20</td>\n",
 690 |        "      <td>93.060</td>\n",
 691 |        "      <td>0.050</td>\n",
 692 |        "      <td>12500.0</td>\n",
 693 |        "      <td>None</td>\n",
 694 |        "    </tr>\n",
 695 |        "    <tr>\n",
 696 |        "      <th>64</th>\n",
 697 |        "      <td>2007-06-21</td>\n",
 698 |        "      <td>93.095</td>\n",
 699 |        "      <td>0.035</td>\n",
 700 |        "      <td>8750.0</td>\n",
 701 |        "      <td>None</td>\n",
 702 |        "    </tr>\n",
 703 |        "  </tbody>\n",
 704 |        "</table>\n",
 705 |        "<p>65 rows × 5 columns</p>\n",
 706 |        "</div>"
 707 |       ],
 708 |       "text/plain": [
 709 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance\n",
 710 |        "0  2007-03-21    93.635         NaN              NaN         148500\n",
 711 |        "1  2007-03-22    93.685       0.050          12500.0           None\n",
 712 |        "2  2007-03-23    93.570      -0.115         -28750.0           None\n",
 713 |        "3  2007-03-26    93.500      -0.070         -17500.0           None\n",
 714 |        "4  2007-03-27    93.320      -0.180         -45000.0           None\n",
 715 |        "..        ...       ...         ...              ...            ...\n",
 716 |        "60 2007-06-15    93.020      -0.125         -31250.0           None\n",
 717 |        "61 2007-06-18    92.995      -0.025          -6250.0           None\n",
 718 |        "62 2007-06-19    93.010       0.015           3750.0           None\n",
 719 |        "63 2007-06-20    93.060       0.050          12500.0           None\n",
 720 |        "64 2007-06-21    93.095       0.035           8750.0           None\n",
 721 |        "\n",
 722 |        "[65 rows x 5 columns]"
 723 |       ]
 724 |      },
 725 |      "execution_count": 43,
 726 |      "metadata": {},
 727 |      "output_type": "execute_result"
 728 |     }
 729 |    ],
 730 |    "source": [
 731 |     "df"
 732 |    ]
 733 |   },
 734 |   {
 735 |    "cell_type": "code",
 736 |    "execution_count": 44,
 737 |    "metadata": {},
 738 |    "outputs": [],
 739 |    "source": [
 740 |     "df1=df.copy()\n",
 741 |     "df2=df.copy()"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "code",
 746 |    "execution_count": 45,
 747 |    "metadata": {},
 748 |    "outputs": [
 749 |     {
 750 |      "name": "stderr",
 751 |      "output_type": "stream",
 752 |      "text": [
 753 |       "C:\\Users\\adib-\\AppData\\Local\\Temp\\ipykernel_15484\\1135165224.py:2: SettingWithCopyWarning: \n",
 754 |       "A value is trying to be set on a copy of a slice from a DataFrame\n",
 755 |       "\n",
 756 |       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
 757 |       "  df1['Margin Balance'][i]=df1['Margin Balance'][i-1]+df1['Daily Gain/Loss'][i]\n",
 758 |       "C:\\Users\\adib-\\AppData\\Local\\Temp\\ipykernel_15484\\1135165224.py:4: SettingWithCopyWarning: \n",
 759 |       "A value is trying to be set on a copy of a slice from a DataFrame\n",
 760 |       "\n",
 761 |       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
 762 |       "  df1['Margin Balance'][i]=df1['Margin Balance'][i]+(148500-df1['Margin Balance'][i])\n"
 763 |      ]
 764 |     }
 765 |    ],
 766 |    "source": [
 767 |     "for i in range(1,len(df1['Margin Balance'])):\n",
 768 |     "    df1['Margin Balance'][i]=df1['Margin Balance'][i-1]+df1['Daily Gain/Loss'][i]\n",
 769 |     "    if df1['Margin Balance'][i]<111000:\n",
 770 |     "        df1['Margin Balance'][i]=df1['Margin Balance'][i]+(148500-df1['Margin Balance'][i])\n",
 771 |     "    \n",
 772 |     "\n",
 773 |     "\n"
 774 |    ]
 775 |   },
 776 |   {
 777 |    "cell_type": "code",
 778 |    "execution_count": 46,
 779 |    "metadata": {},
 780 |    "outputs": [
 781 |     {
 782 |      "data": {
 783 |       "text/html": [
 784 |        "<div>\n",
 785 |        "<style scoped>\n",
 786 |        "    .dataframe tbody tr th:only-of-type {\n",
 787 |        "        vertical-align: middle;\n",
 788 |        "    }\n",
 789 |        "\n",
 790 |        "    .dataframe tbody tr th {\n",
 791 |        "        vertical-align: top;\n",
 792 |        "    }\n",
 793 |        "\n",
 794 |        "    .dataframe thead th {\n",
 795 |        "        text-align: right;\n",
 796 |        "    }\n",
 797 |        "</style>\n",
 798 |        "<table border=\"1\" class=\"dataframe\">\n",
 799 |        "  <thead>\n",
 800 |        "    <tr style=\"text-align: right;\">\n",
 801 |        "      <th></th>\n",
 802 |        "      <th>Date_1</th>\n",
 803 |        "      <th>Prices_1</th>\n",
 804 |        "      <th>difference</th>\n",
 805 |        "      <th>Daily Gain/Loss</th>\n",
 806 |        "      <th>Margin Balance</th>\n",
 807 |        "    </tr>\n",
 808 |        "  </thead>\n",
 809 |        "  <tbody>\n",
 810 |        "    <tr>\n",
 811 |        "      <th>0</th>\n",
 812 |        "      <td>2007-03-21</td>\n",
 813 |        "      <td>93.635</td>\n",
 814 |        "      <td>NaN</td>\n",
 815 |        "      <td>NaN</td>\n",
 816 |        "      <td>148500</td>\n",
 817 |        "    </tr>\n",
 818 |        "    <tr>\n",
 819 |        "      <th>1</th>\n",
 820 |        "      <td>2007-03-22</td>\n",
 821 |        "      <td>93.685</td>\n",
 822 |        "      <td>0.050</td>\n",
 823 |        "      <td>12500.0</td>\n",
 824 |        "      <td>161000.0</td>\n",
 825 |        "    </tr>\n",
 826 |        "    <tr>\n",
 827 |        "      <th>2</th>\n",
 828 |        "      <td>2007-03-23</td>\n",
 829 |        "      <td>93.570</td>\n",
 830 |        "      <td>-0.115</td>\n",
 831 |        "      <td>-28750.0</td>\n",
 832 |        "      <td>132250.0</td>\n",
 833 |        "    </tr>\n",
 834 |        "    <tr>\n",
 835 |        "      <th>3</th>\n",
 836 |        "      <td>2007-03-26</td>\n",
 837 |        "      <td>93.500</td>\n",
 838 |        "      <td>-0.070</td>\n",
 839 |        "      <td>-17500.0</td>\n",
 840 |        "      <td>114750.0</td>\n",
 841 |        "    </tr>\n",
 842 |        "    <tr>\n",
 843 |        "      <th>4</th>\n",
 844 |        "      <td>2007-03-27</td>\n",
 845 |        "      <td>93.320</td>\n",
 846 |        "      <td>-0.180</td>\n",
 847 |        "      <td>-45000.0</td>\n",
 848 |        "      <td>148500.0</td>\n",
 849 |        "    </tr>\n",
 850 |        "    <tr>\n",
 851 |        "      <th>...</th>\n",
 852 |        "      <td>...</td>\n",
 853 |        "      <td>...</td>\n",
 854 |        "      <td>...</td>\n",
 855 |        "      <td>...</td>\n",
 856 |        "      <td>...</td>\n",
 857 |        "    </tr>\n",
 858 |        "    <tr>\n",
 859 |        "      <th>60</th>\n",
 860 |        "      <td>2007-06-15</td>\n",
 861 |        "      <td>93.020</td>\n",
 862 |        "      <td>-0.125</td>\n",
 863 |        "      <td>-31250.0</td>\n",
 864 |        "      <td>197250.0</td>\n",
 865 |        "    </tr>\n",
 866 |        "    <tr>\n",
 867 |        "      <th>61</th>\n",
 868 |        "      <td>2007-06-18</td>\n",
 869 |        "      <td>92.995</td>\n",
 870 |        "      <td>-0.025</td>\n",
 871 |        "      <td>-6250.0</td>\n",
 872 |        "      <td>191000.0</td>\n",
 873 |        "    </tr>\n",
 874 |        "    <tr>\n",
 875 |        "      <th>62</th>\n",
 876 |        "      <td>2007-06-19</td>\n",
 877 |        "      <td>93.010</td>\n",
 878 |        "      <td>0.015</td>\n",
 879 |        "      <td>3750.0</td>\n",
 880 |        "      <td>194750.0</td>\n",
 881 |        "    </tr>\n",
 882 |        "    <tr>\n",
 883 |        "      <th>63</th>\n",
 884 |        "      <td>2007-06-20</td>\n",
 885 |        "      <td>93.060</td>\n",
 886 |        "      <td>0.050</td>\n",
 887 |        "      <td>12500.0</td>\n",
 888 |        "      <td>207250.0</td>\n",
 889 |        "    </tr>\n",
 890 |        "    <tr>\n",
 891 |        "      <th>64</th>\n",
 892 |        "      <td>2007-06-21</td>\n",
 893 |        "      <td>93.095</td>\n",
 894 |        "      <td>0.035</td>\n",
 895 |        "      <td>8750.0</td>\n",
 896 |        "      <td>216000.0</td>\n",
 897 |        "    </tr>\n",
 898 |        "  </tbody>\n",
 899 |        "</table>\n",
 900 |        "<p>65 rows × 5 columns</p>\n",
 901 |        "</div>"
 902 |       ],
 903 |       "text/plain": [
 904 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance\n",
 905 |        "0  2007-03-21    93.635         NaN              NaN         148500\n",
 906 |        "1  2007-03-22    93.685       0.050          12500.0       161000.0\n",
 907 |        "2  2007-03-23    93.570      -0.115         -28750.0       132250.0\n",
 908 |        "3  2007-03-26    93.500      -0.070         -17500.0       114750.0\n",
 909 |        "4  2007-03-27    93.320      -0.180         -45000.0       148500.0\n",
 910 |        "..        ...       ...         ...              ...            ...\n",
 911 |        "60 2007-06-15    93.020      -0.125         -31250.0       197250.0\n",
 912 |        "61 2007-06-18    92.995      -0.025          -6250.0       191000.0\n",
 913 |        "62 2007-06-19    93.010       0.015           3750.0       194750.0\n",
 914 |        "63 2007-06-20    93.060       0.050          12500.0       207250.0\n",
 915 |        "64 2007-06-21    93.095       0.035           8750.0       216000.0\n",
 916 |        "\n",
 917 |        "[65 rows x 5 columns]"
 918 |       ]
 919 |      },
 920 |      "execution_count": 46,
 921 |      "metadata": {},
 922 |      "output_type": "execute_result"
 923 |     }
 924 |    ],
 925 |    "source": [
 926 |     "df1"
 927 |    ]
 928 |   },
 929 |   {
 930 |    "cell_type": "code",
 931 |    "execution_count": 47,
 932 |    "metadata": {},
 933 |    "outputs": [
 934 |     {
 935 |      "data": {
 936 |       "text/html": [
 937 |        "<div>\n",
 938 |        "<style scoped>\n",
 939 |        "    .dataframe tbody tr th:only-of-type {\n",
 940 |        "        vertical-align: middle;\n",
 941 |        "    }\n",
 942 |        "\n",
 943 |        "    .dataframe tbody tr th {\n",
 944 |        "        vertical-align: top;\n",
 945 |        "    }\n",
 946 |        "\n",
 947 |        "    .dataframe thead th {\n",
 948 |        "        text-align: right;\n",
 949 |        "    }\n",
 950 |        "</style>\n",
 951 |        "<table border=\"1\" class=\"dataframe\">\n",
 952 |        "  <thead>\n",
 953 |        "    <tr style=\"text-align: right;\">\n",
 954 |        "      <th></th>\n",
 955 |        "      <th>Date_1</th>\n",
 956 |        "      <th>Prices_1</th>\n",
 957 |        "      <th>difference</th>\n",
 958 |        "      <th>Daily Gain/Loss</th>\n",
 959 |        "      <th>Margin Balance</th>\n",
 960 |        "      <th>Cash Flow</th>\n",
 961 |        "    </tr>\n",
 962 |        "  </thead>\n",
 963 |        "  <tbody>\n",
 964 |        "    <tr>\n",
 965 |        "      <th>0</th>\n",
 966 |        "      <td>2007-03-21</td>\n",
 967 |        "      <td>93.635</td>\n",
 968 |        "      <td>NaN</td>\n",
 969 |        "      <td>NaN</td>\n",
 970 |        "      <td>148500</td>\n",
 971 |        "      <td>NaN</td>\n",
 972 |        "    </tr>\n",
 973 |        "    <tr>\n",
 974 |        "      <th>1</th>\n",
 975 |        "      <td>2007-03-22</td>\n",
 976 |        "      <td>93.685</td>\n",
 977 |        "      <td>0.050</td>\n",
 978 |        "      <td>12500.0</td>\n",
 979 |        "      <td>161000.0</td>\n",
 980 |        "      <td>12500.0</td>\n",
 981 |        "    </tr>\n",
 982 |        "    <tr>\n",
 983 |        "      <th>2</th>\n",
 984 |        "      <td>2007-03-23</td>\n",
 985 |        "      <td>93.570</td>\n",
 986 |        "      <td>-0.115</td>\n",
 987 |        "      <td>-28750.0</td>\n",
 988 |        "      <td>132250.0</td>\n",
 989 |        "      <td>-28750.0</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>3</th>\n",
 993 |        "      <td>2007-03-26</td>\n",
 994 |        "      <td>93.500</td>\n",
 995 |        "      <td>-0.070</td>\n",
 996 |        "      <td>-17500.0</td>\n",
 997 |        "      <td>114750.0</td>\n",
 998 |        "      <td>-17500.0</td>\n",
 999 |        "    </tr>\n",
1000 |        "    <tr>\n",
1001 |        "      <th>4</th>\n",
1002 |        "      <td>2007-03-27</td>\n",
1003 |        "      <td>93.320</td>\n",
1004 |        "      <td>-0.180</td>\n",
1005 |        "      <td>-45000.0</td>\n",
1006 |        "      <td>148500.0</td>\n",
1007 |        "      <td>33750.0</td>\n",
1008 |        "    </tr>\n",
1009 |        "    <tr>\n",
1010 |        "      <th>...</th>\n",
1011 |        "      <td>...</td>\n",
1012 |        "      <td>...</td>\n",
1013 |        "      <td>...</td>\n",
1014 |        "      <td>...</td>\n",
1015 |        "      <td>...</td>\n",
1016 |        "      <td>...</td>\n",
1017 |        "    </tr>\n",
1018 |        "    <tr>\n",
1019 |        "      <th>60</th>\n",
1020 |        "      <td>2007-06-15</td>\n",
1021 |        "      <td>93.020</td>\n",
1022 |        "      <td>-0.125</td>\n",
1023 |        "      <td>-31250.0</td>\n",
1024 |        "      <td>197250.0</td>\n",
1025 |        "      <td>-31250.0</td>\n",
1026 |        "    </tr>\n",
1027 |        "    <tr>\n",
1028 |        "      <th>61</th>\n",
1029 |        "      <td>2007-06-18</td>\n",
1030 |        "      <td>92.995</td>\n",
1031 |        "      <td>-0.025</td>\n",
1032 |        "      <td>-6250.0</td>\n",
1033 |        "      <td>191000.0</td>\n",
1034 |        "      <td>-6250.0</td>\n",
1035 |        "    </tr>\n",
1036 |        "    <tr>\n",
1037 |        "      <th>62</th>\n",
1038 |        "      <td>2007-06-19</td>\n",
1039 |        "      <td>93.010</td>\n",
1040 |        "      <td>0.015</td>\n",
1041 |        "      <td>3750.0</td>\n",
1042 |        "      <td>194750.0</td>\n",
1043 |        "      <td>3750.0</td>\n",
1044 |        "    </tr>\n",
1045 |        "    <tr>\n",
1046 |        "      <th>63</th>\n",
1047 |        "      <td>2007-06-20</td>\n",
1048 |        "      <td>93.060</td>\n",
1049 |        "      <td>0.050</td>\n",
1050 |        "      <td>12500.0</td>\n",
1051 |        "      <td>207250.0</td>\n",
1052 |        "      <td>12500.0</td>\n",
1053 |        "    </tr>\n",
1054 |        "    <tr>\n",
1055 |        "      <th>64</th>\n",
1056 |        "      <td>2007-06-21</td>\n",
1057 |        "      <td>93.095</td>\n",
1058 |        "      <td>0.035</td>\n",
1059 |        "      <td>8750.0</td>\n",
1060 |        "      <td>216000.0</td>\n",
1061 |        "      <td>8750.0</td>\n",
1062 |        "    </tr>\n",
1063 |        "  </tbody>\n",
1064 |        "</table>\n",
1065 |        "<p>65 rows × 6 columns</p>\n",
1066 |        "</div>"
1067 |       ],
1068 |       "text/plain": [
1069 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance Cash Flow\n",
1070 |        "0  2007-03-21    93.635         NaN              NaN         148500       NaN\n",
1071 |        "1  2007-03-22    93.685       0.050          12500.0       161000.0   12500.0\n",
1072 |        "2  2007-03-23    93.570      -0.115         -28750.0       132250.0  -28750.0\n",
1073 |        "3  2007-03-26    93.500      -0.070         -17500.0       114750.0  -17500.0\n",
1074 |        "4  2007-03-27    93.320      -0.180         -45000.0       148500.0   33750.0\n",
1075 |        "..        ...       ...         ...              ...            ...       ...\n",
1076 |        "60 2007-06-15    93.020      -0.125         -31250.0       197250.0  -31250.0\n",
1077 |        "61 2007-06-18    92.995      -0.025          -6250.0       191000.0   -6250.0\n",
1078 |        "62 2007-06-19    93.010       0.015           3750.0       194750.0    3750.0\n",
1079 |        "63 2007-06-20    93.060       0.050          12500.0       207250.0   12500.0\n",
1080 |        "64 2007-06-21    93.095       0.035           8750.0       216000.0    8750.0\n",
1081 |        "\n",
1082 |        "[65 rows x 6 columns]"
1083 |       ]
1084 |      },
1085 |      "execution_count": 47,
1086 |      "metadata": {},
1087 |      "output_type": "execute_result"
1088 |     }
1089 |    ],
1090 |    "source": [
1091 |     "df1['Cash Flow']=df1['Margin Balance'].diff()\n",
1092 |     "df1"
1093 |    ]
1094 |   },
1095 |   {
1096 |    "cell_type": "code",
1097 |    "execution_count": 48,
1098 |    "metadata": {},
1099 |    "outputs": [
1100 |     {
1101 |      "data": {
1102 |       "text/html": [
1103 |        "<div>\n",
1104 |        "<style scoped>\n",
1105 |        "    .dataframe tbody tr th:only-of-type {\n",
1106 |        "        vertical-align: middle;\n",
1107 |        "    }\n",
1108 |        "\n",
1109 |        "    .dataframe tbody tr th {\n",
1110 |        "        vertical-align: top;\n",
1111 |        "    }\n",
1112 |        "\n",
1113 |        "    .dataframe thead th {\n",
1114 |        "        text-align: right;\n",
1115 |        "    }\n",
1116 |        "</style>\n",
1117 |        "<table border=\"1\" class=\"dataframe\">\n",
1118 |        "  <thead>\n",
1119 |        "    <tr style=\"text-align: right;\">\n",
1120 |        "      <th></th>\n",
1121 |        "      <th>Date_1</th>\n",
1122 |        "      <th>Prices_1</th>\n",
1123 |        "      <th>difference</th>\n",
1124 |        "      <th>Daily Gain/Loss</th>\n",
1125 |        "      <th>Margin Balance</th>\n",
1126 |        "    </tr>\n",
1127 |        "  </thead>\n",
1128 |        "  <tbody>\n",
1129 |        "    <tr>\n",
1130 |        "      <th>0</th>\n",
1131 |        "      <td>2007-03-21</td>\n",
1132 |        "      <td>93.635</td>\n",
1133 |        "      <td>NaN</td>\n",
1134 |        "      <td>NaN</td>\n",
1135 |        "      <td>148500</td>\n",
1136 |        "    </tr>\n",
1137 |        "    <tr>\n",
1138 |        "      <th>1</th>\n",
1139 |        "      <td>2007-03-22</td>\n",
1140 |        "      <td>93.685</td>\n",
1141 |        "      <td>0.050</td>\n",
1142 |        "      <td>12500.0</td>\n",
1143 |        "      <td>None</td>\n",
1144 |        "    </tr>\n",
1145 |        "    <tr>\n",
1146 |        "      <th>2</th>\n",
1147 |        "      <td>2007-03-23</td>\n",
1148 |        "      <td>93.570</td>\n",
1149 |        "      <td>-0.115</td>\n",
1150 |        "      <td>-28750.0</td>\n",
1151 |        "      <td>None</td>\n",
1152 |        "    </tr>\n",
1153 |        "    <tr>\n",
1154 |        "      <th>3</th>\n",
1155 |        "      <td>2007-03-26</td>\n",
1156 |        "      <td>93.500</td>\n",
1157 |        "      <td>-0.070</td>\n",
1158 |        "      <td>-17500.0</td>\n",
1159 |        "      <td>None</td>\n",
1160 |        "    </tr>\n",
1161 |        "    <tr>\n",
1162 |        "      <th>4</th>\n",
1163 |        "      <td>2007-03-27</td>\n",
1164 |        "      <td>93.320</td>\n",
1165 |        "      <td>-0.180</td>\n",
1166 |        "      <td>-45000.0</td>\n",
1167 |        "      <td>None</td>\n",
1168 |        "    </tr>\n",
1169 |        "    <tr>\n",
1170 |        "      <th>...</th>\n",
1171 |        "      <td>...</td>\n",
1172 |        "      <td>...</td>\n",
1173 |        "      <td>...</td>\n",
1174 |        "      <td>...</td>\n",
1175 |        "      <td>...</td>\n",
1176 |        "    </tr>\n",
1177 |        "    <tr>\n",
1178 |        "      <th>60</th>\n",
1179 |        "      <td>2007-06-15</td>\n",
1180 |        "      <td>93.020</td>\n",
1181 |        "      <td>-0.125</td>\n",
1182 |        "      <td>-31250.0</td>\n",
1183 |        "      <td>None</td>\n",
1184 |        "    </tr>\n",
1185 |        "    <tr>\n",
1186 |        "      <th>61</th>\n",
1187 |        "      <td>2007-06-18</td>\n",
1188 |        "      <td>92.995</td>\n",
1189 |        "      <td>-0.025</td>\n",
1190 |        "      <td>-6250.0</td>\n",
1191 |        "      <td>None</td>\n",
1192 |        "    </tr>\n",
1193 |        "    <tr>\n",
1194 |        "      <th>62</th>\n",
1195 |        "      <td>2007-06-19</td>\n",
1196 |        "      <td>93.010</td>\n",
1197 |        "      <td>0.015</td>\n",
1198 |        "      <td>3750.0</td>\n",
1199 |        "      <td>None</td>\n",
1200 |        "    </tr>\n",
1201 |        "    <tr>\n",
1202 |        "      <th>63</th>\n",
1203 |        "      <td>2007-06-20</td>\n",
1204 |        "      <td>93.060</td>\n",
1205 |        "      <td>0.050</td>\n",
1206 |        "      <td>12500.0</td>\n",
1207 |        "      <td>None</td>\n",
1208 |        "    </tr>\n",
1209 |        "    <tr>\n",
1210 |        "      <th>64</th>\n",
1211 |        "      <td>2007-06-21</td>\n",
1212 |        "      <td>93.095</td>\n",
1213 |        "      <td>0.035</td>\n",
1214 |        "      <td>8750.0</td>\n",
1215 |        "      <td>None</td>\n",
1216 |        "    </tr>\n",
1217 |        "  </tbody>\n",
1218 |        "</table>\n",
1219 |        "<p>65 rows × 5 columns</p>\n",
1220 |        "</div>"
1221 |       ],
1222 |       "text/plain": [
1223 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance\n",
1224 |        "0  2007-03-21    93.635         NaN              NaN         148500\n",
1225 |        "1  2007-03-22    93.685       0.050          12500.0           None\n",
1226 |        "2  2007-03-23    93.570      -0.115         -28750.0           None\n",
1227 |        "3  2007-03-26    93.500      -0.070         -17500.0           None\n",
1228 |        "4  2007-03-27    93.320      -0.180         -45000.0           None\n",
1229 |        "..        ...       ...         ...              ...            ...\n",
1230 |        "60 2007-06-15    93.020      -0.125         -31250.0           None\n",
1231 |        "61 2007-06-18    92.995      -0.025          -6250.0           None\n",
1232 |        "62 2007-06-19    93.010       0.015           3750.0           None\n",
1233 |        "63 2007-06-20    93.060       0.050          12500.0           None\n",
1234 |        "64 2007-06-21    93.095       0.035           8750.0           None\n",
1235 |        "\n",
1236 |        "[65 rows x 5 columns]"
1237 |       ]
1238 |      },
1239 |      "execution_count": 48,
1240 |      "metadata": {},
1241 |      "output_type": "execute_result"
1242 |     }
1243 |    ],
1244 |    "source": [
1245 |     "## Starting Second Question\n",
1246 |     "df2"
1247 |    ]
1248 |   },
1249 |   {
1250 |    "cell_type": "code",
1251 |    "execution_count": 49,
1252 |    "metadata": {},
1253 |    "outputs": [
1254 |     {
1255 |      "name": "stderr",
1256 |      "output_type": "stream",
1257 |      "text": [
1258 |       "C:\\Users\\adib-\\AppData\\Local\\Temp\\ipykernel_15484\\674684878.py:9: SettingWithCopyWarning: \n",
1259 |       "A value is trying to be set on a copy of a slice from a DataFrame\n",
1260 |       "\n",
1261 |       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1262 |       "  df2['Margin Balance'][i]=df2['Margin Balance'][i-1]\n",
1263 |       "C:\\Users\\adib-\\AppData\\Local\\Temp\\ipykernel_15484\\674684878.py:7: SettingWithCopyWarning: \n",
1264 |       "A value is trying to be set on a copy of a slice from a DataFrame\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 |       "  df2['Margin Balance'][i]=df2['Margin Balance'][i-1]+df2['Daily Gain/Loss'][i]\n",
1268 |       "C:\\Users\\adib-\\AppData\\Local\\Temp\\ipykernel_15484\\674684878.py:11: SettingWithCopyWarning: \n",
1269 |       "A value is trying to be set on a copy of a slice from a DataFrame\n",
1270 |       "\n",
1271 |       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1272 |       "  df2['Margin Balance'][i]=df2['Margin Balance'][i]+(148500-df2['Margin Balance'][i])\n"
1273 |      ]
1274 |     }
1275 |    ],
1276 |    "source": [
1277 |     "# Same loop different condition\n",
1278 |     "## If he decides to take every profit out then the margin balance will experience margin calls more frequently-----\n",
1279 |     "\n",
1280 |     "\n",
1281 |     "for i in range(1,len(df2['Margin Balance'])):\n",
1282 |     "    if df2['Daily Gain/Loss'][i]<0:\n",
1283 |     "        df2['Margin Balance'][i]=df2['Margin Balance'][i-1]+df2['Daily Gain/Loss'][i]\n",
1284 |     "    else:\n",
1285 |     "         df2['Margin Balance'][i]=df2['Margin Balance'][i-1]\n",
1286 |     "    if df2['Margin Balance'][i]<111000:\n",
1287 |     "            df2['Margin Balance'][i]=df2['Margin Balance'][i]+(148500-df2['Margin Balance'][i])\n",
1288 |     "    "
1289 |    ]
1290 |   },
1291 |   {
1292 |    "cell_type": "code",
1293 |    "execution_count": 51,
1294 |    "metadata": {},
1295 |    "outputs": [
1296 |     {
1297 |      "data": {
1298 |       "text/html": [
1299 |        "<div>\n",
1300 |        "<style scoped>\n",
1301 |        "    .dataframe tbody tr th:only-of-type {\n",
1302 |        "        vertical-align: middle;\n",
1303 |        "    }\n",
1304 |        "\n",
1305 |        "    .dataframe tbody tr th {\n",
1306 |        "        vertical-align: top;\n",
1307 |        "    }\n",
1308 |        "\n",
1309 |        "    .dataframe thead th {\n",
1310 |        "        text-align: right;\n",
1311 |        "    }\n",
1312 |        "</style>\n",
1313 |        "<table border=\"1\" class=\"dataframe\">\n",
1314 |        "  <thead>\n",
1315 |        "    <tr style=\"text-align: right;\">\n",
1316 |        "      <th></th>\n",
1317 |        "      <th>Date_1</th>\n",
1318 |        "      <th>Prices_1</th>\n",
1319 |        "      <th>difference</th>\n",
1320 |        "      <th>Daily Gain/Loss</th>\n",
1321 |        "      <th>Margin Balance</th>\n",
1322 |        "    </tr>\n",
1323 |        "  </thead>\n",
1324 |        "  <tbody>\n",
1325 |        "    <tr>\n",
1326 |        "      <th>0</th>\n",
1327 |        "      <td>2007-03-21</td>\n",
1328 |        "      <td>93.635</td>\n",
1329 |        "      <td>NaN</td>\n",
1330 |        "      <td>NaN</td>\n",
1331 |        "      <td>148500</td>\n",
1332 |        "    </tr>\n",
1333 |        "    <tr>\n",
1334 |        "      <th>1</th>\n",
1335 |        "      <td>2007-03-22</td>\n",
1336 |        "      <td>93.685</td>\n",
1337 |        "      <td>0.050</td>\n",
1338 |        "      <td>12500.0</td>\n",
1339 |        "      <td>148500</td>\n",
1340 |        "    </tr>\n",
1341 |        "    <tr>\n",
1342 |        "      <th>2</th>\n",
1343 |        "      <td>2007-03-23</td>\n",
1344 |        "      <td>93.570</td>\n",
1345 |        "      <td>-0.115</td>\n",
1346 |        "      <td>-28750.0</td>\n",
1347 |        "      <td>119750.0</td>\n",
1348 |        "    </tr>\n",
1349 |        "    <tr>\n",
1350 |        "      <th>3</th>\n",
1351 |        "      <td>2007-03-26</td>\n",
1352 |        "      <td>93.500</td>\n",
1353 |        "      <td>-0.070</td>\n",
1354 |        "      <td>-17500.0</td>\n",
1355 |        "      <td>148500.0</td>\n",
1356 |        "    </tr>\n",
1357 |        "    <tr>\n",
1358 |        "      <th>4</th>\n",
1359 |        "      <td>2007-03-27</td>\n",
1360 |        "      <td>93.320</td>\n",
1361 |        "      <td>-0.180</td>\n",
1362 |        "      <td>-45000.0</td>\n",
1363 |        "      <td>148500.0</td>\n",
1364 |        "    </tr>\n",
1365 |        "    <tr>\n",
1366 |        "      <th>...</th>\n",
1367 |        "      <td>...</td>\n",
1368 |        "      <td>...</td>\n",
1369 |        "      <td>...</td>\n",
1370 |        "      <td>...</td>\n",
1371 |        "      <td>...</td>\n",
1372 |        "    </tr>\n",
1373 |        "    <tr>\n",
1374 |        "      <th>60</th>\n",
1375 |        "      <td>2007-06-15</td>\n",
1376 |        "      <td>93.020</td>\n",
1377 |        "      <td>-0.125</td>\n",
1378 |        "      <td>-31250.0</td>\n",
1379 |        "      <td>148500.0</td>\n",
1380 |        "    </tr>\n",
1381 |        "    <tr>\n",
1382 |        "      <th>61</th>\n",
1383 |        "      <td>2007-06-18</td>\n",
1384 |        "      <td>92.995</td>\n",
1385 |        "      <td>-0.025</td>\n",
1386 |        "      <td>-6250.0</td>\n",
1387 |        "      <td>142250.0</td>\n",
1388 |        "    </tr>\n",
1389 |        "    <tr>\n",
1390 |        "      <th>62</th>\n",
1391 |        "      <td>2007-06-19</td>\n",
1392 |        "      <td>93.010</td>\n",
1393 |        "      <td>0.015</td>\n",
1394 |        "      <td>3750.0</td>\n",
1395 |        "      <td>142250.0</td>\n",
1396 |        "    </tr>\n",
1397 |        "    <tr>\n",
1398 |        "      <th>63</th>\n",
1399 |        "      <td>2007-06-20</td>\n",
1400 |        "      <td>93.060</td>\n",
1401 |        "      <td>0.050</td>\n",
1402 |        "      <td>12500.0</td>\n",
1403 |        "      <td>142250.0</td>\n",
1404 |        "    </tr>\n",
1405 |        "    <tr>\n",
1406 |        "      <th>64</th>\n",
1407 |        "      <td>2007-06-21</td>\n",
1408 |        "      <td>93.095</td>\n",
1409 |        "      <td>0.035</td>\n",
1410 |        "      <td>8750.0</td>\n",
1411 |        "      <td>142250.0</td>\n",
1412 |        "    </tr>\n",
1413 |        "  </tbody>\n",
1414 |        "</table>\n",
1415 |        "<p>65 rows × 5 columns</p>\n",
1416 |        "</div>"
1417 |       ],
1418 |       "text/plain": [
1419 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance\n",
1420 |        "0  2007-03-21    93.635         NaN              NaN         148500\n",
1421 |        "1  2007-03-22    93.685       0.050          12500.0         148500\n",
1422 |        "2  2007-03-23    93.570      -0.115         -28750.0       119750.0\n",
1423 |        "3  2007-03-26    93.500      -0.070         -17500.0       148500.0\n",
1424 |        "4  2007-03-27    93.320      -0.180         -45000.0       148500.0\n",
1425 |        "..        ...       ...         ...              ...            ...\n",
1426 |        "60 2007-06-15    93.020      -0.125         -31250.0       148500.0\n",
1427 |        "61 2007-06-18    92.995      -0.025          -6250.0       142250.0\n",
1428 |        "62 2007-06-19    93.010       0.015           3750.0       142250.0\n",
1429 |        "63 2007-06-20    93.060       0.050          12500.0       142250.0\n",
1430 |        "64 2007-06-21    93.095       0.035           8750.0       142250.0\n",
1431 |        "\n",
1432 |        "[65 rows x 5 columns]"
1433 |       ]
1434 |      },
1435 |      "execution_count": 51,
1436 |      "metadata": {},
1437 |      "output_type": "execute_result"
1438 |     }
1439 |    ],
1440 |    "source": [
1441 |     "df2"
1442 |    ]
1443 |   },
1444 |   {
1445 |    "cell_type": "code",
1446 |    "execution_count": 58,
1447 |    "metadata": {},
1448 |    "outputs": [
1449 |     {
1450 |      "data": {
1451 |       "text/html": [
1452 |        "<div>\n",
1453 |        "<style scoped>\n",
1454 |        "    .dataframe tbody tr th:only-of-type {\n",
1455 |        "        vertical-align: middle;\n",
1456 |        "    }\n",
1457 |        "\n",
1458 |        "    .dataframe tbody tr th {\n",
1459 |        "        vertical-align: top;\n",
1460 |        "    }\n",
1461 |        "\n",
1462 |        "    .dataframe thead th {\n",
1463 |        "        text-align: right;\n",
1464 |        "    }\n",
1465 |        "</style>\n",
1466 |        "<table border=\"1\" class=\"dataframe\">\n",
1467 |        "  <thead>\n",
1468 |        "    <tr style=\"text-align: right;\">\n",
1469 |        "      <th></th>\n",
1470 |        "      <th>Date_1</th>\n",
1471 |        "      <th>Prices_1</th>\n",
1472 |        "      <th>difference</th>\n",
1473 |        "      <th>Daily Gain/Loss</th>\n",
1474 |        "      <th>Margin Balance</th>\n",
1475 |        "      <th>Cash Flow</th>\n",
1476 |        "    </tr>\n",
1477 |        "  </thead>\n",
1478 |        "  <tbody>\n",
1479 |        "    <tr>\n",
1480 |        "      <th>0</th>\n",
1481 |        "      <td>2007-03-21</td>\n",
1482 |        "      <td>93.635</td>\n",
1483 |        "      <td>NaN</td>\n",
1484 |        "      <td>NaN</td>\n",
1485 |        "      <td>148500</td>\n",
1486 |        "      <td>NaN</td>\n",
1487 |        "    </tr>\n",
1488 |        "    <tr>\n",
1489 |        "      <th>1</th>\n",
1490 |        "      <td>2007-03-22</td>\n",
1491 |        "      <td>93.685</td>\n",
1492 |        "      <td>0.050</td>\n",
1493 |        "      <td>12500.0</td>\n",
1494 |        "      <td>148500</td>\n",
1495 |        "      <td>0</td>\n",
1496 |        "    </tr>\n",
1497 |        "    <tr>\n",
1498 |        "      <th>2</th>\n",
1499 |        "      <td>2007-03-23</td>\n",
1500 |        "      <td>93.570</td>\n",
1501 |        "      <td>-0.115</td>\n",
1502 |        "      <td>-28750.0</td>\n",
1503 |        "      <td>119750.0</td>\n",
1504 |        "      <td>-28750.0</td>\n",
1505 |        "    </tr>\n",
1506 |        "    <tr>\n",
1507 |        "      <th>3</th>\n",
1508 |        "      <td>2007-03-26</td>\n",
1509 |        "      <td>93.500</td>\n",
1510 |        "      <td>-0.070</td>\n",
1511 |        "      <td>-17500.0</td>\n",
1512 |        "      <td>148500.0</td>\n",
1513 |        "      <td>28750.0</td>\n",
1514 |        "    </tr>\n",
1515 |        "    <tr>\n",
1516 |        "      <th>4</th>\n",
1517 |        "      <td>2007-03-27</td>\n",
1518 |        "      <td>93.320</td>\n",
1519 |        "      <td>-0.180</td>\n",
1520 |        "      <td>-45000.0</td>\n",
1521 |        "      <td>148500.0</td>\n",
1522 |        "      <td>0.0</td>\n",
1523 |        "    </tr>\n",
1524 |        "    <tr>\n",
1525 |        "      <th>...</th>\n",
1526 |        "      <td>...</td>\n",
1527 |        "      <td>...</td>\n",
1528 |        "      <td>...</td>\n",
1529 |        "      <td>...</td>\n",
1530 |        "      <td>...</td>\n",
1531 |        "      <td>...</td>\n",
1532 |        "    </tr>\n",
1533 |        "    <tr>\n",
1534 |        "      <th>60</th>\n",
1535 |        "      <td>2007-06-15</td>\n",
1536 |        "      <td>93.020</td>\n",
1537 |        "      <td>-0.125</td>\n",
1538 |        "      <td>-31250.0</td>\n",
1539 |        "      <td>148500.0</td>\n",
1540 |        "      <td>10000.0</td>\n",
1541 |        "    </tr>\n",
1542 |        "    <tr>\n",
1543 |        "      <th>61</th>\n",
1544 |        "      <td>2007-06-18</td>\n",
1545 |        "      <td>92.995</td>\n",
1546 |        "      <td>-0.025</td>\n",
1547 |        "      <td>-6250.0</td>\n",
1548 |        "      <td>142250.0</td>\n",
1549 |        "      <td>-6250.0</td>\n",
1550 |        "    </tr>\n",
1551 |        "    <tr>\n",
1552 |        "      <th>62</th>\n",
1553 |        "      <td>2007-06-19</td>\n",
1554 |        "      <td>93.010</td>\n",
1555 |        "      <td>0.015</td>\n",
1556 |        "      <td>3750.0</td>\n",
1557 |        "      <td>142250.0</td>\n",
1558 |        "      <td>0.0</td>\n",
1559 |        "    </tr>\n",
1560 |        "    <tr>\n",
1561 |        "      <th>63</th>\n",
1562 |        "      <td>2007-06-20</td>\n",
1563 |        "      <td>93.060</td>\n",
1564 |        "      <td>0.050</td>\n",
1565 |        "      <td>12500.0</td>\n",
1566 |        "      <td>142250.0</td>\n",
1567 |        "      <td>0.0</td>\n",
1568 |        "    </tr>\n",
1569 |        "    <tr>\n",
1570 |        "      <th>64</th>\n",
1571 |        "      <td>2007-06-21</td>\n",
1572 |        "      <td>93.095</td>\n",
1573 |        "      <td>0.035</td>\n",
1574 |        "      <td>8750.0</td>\n",
1575 |        "      <td>142250.0</td>\n",
1576 |        "      <td>0.0</td>\n",
1577 |        "    </tr>\n",
1578 |        "  </tbody>\n",
1579 |        "</table>\n",
1580 |        "<p>65 rows × 6 columns</p>\n",
1581 |        "</div>"
1582 |       ],
1583 |       "text/plain": [
1584 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance Cash Flow\n",
1585 |        "0  2007-03-21    93.635         NaN              NaN         148500       NaN\n",
1586 |        "1  2007-03-22    93.685       0.050          12500.0         148500         0\n",
1587 |        "2  2007-03-23    93.570      -0.115         -28750.0       119750.0  -28750.0\n",
1588 |        "3  2007-03-26    93.500      -0.070         -17500.0       148500.0   28750.0\n",
1589 |        "4  2007-03-27    93.320      -0.180         -45000.0       148500.0       0.0\n",
1590 |        "..        ...       ...         ...              ...            ...       ...\n",
1591 |        "60 2007-06-15    93.020      -0.125         -31250.0       148500.0   10000.0\n",
1592 |        "61 2007-06-18    92.995      -0.025          -6250.0       142250.0   -6250.0\n",
1593 |        "62 2007-06-19    93.010       0.015           3750.0       142250.0       0.0\n",
1594 |        "63 2007-06-20    93.060       0.050          12500.0       142250.0       0.0\n",
1595 |        "64 2007-06-21    93.095       0.035           8750.0       142250.0       0.0\n",
1596 |        "\n",
1597 |        "[65 rows x 6 columns]"
1598 |       ]
1599 |      },
1600 |      "execution_count": 58,
1601 |      "metadata": {},
1602 |      "output_type": "execute_result"
1603 |     }
1604 |    ],
1605 |    "source": [
1606 |     "df2['Cash Flow']=df2['Margin Balance'].diff()\n",
1607 |     "df2"
1608 |    ]
1609 |   },
1610 |   {
1611 |    "cell_type": "code",
1612 |    "execution_count": 54,
1613 |    "metadata": {},
1614 |    "outputs": [
1615 |     {
1616 |      "data": {
1617 |       "text/html": [
1618 |        "<div>\n",
1619 |        "<style scoped>\n",
1620 |        "    .dataframe tbody tr th:only-of-type {\n",
1621 |        "        vertical-align: middle;\n",
1622 |        "    }\n",
1623 |        "\n",
1624 |        "    .dataframe tbody tr th {\n",
1625 |        "        vertical-align: top;\n",
1626 |        "    }\n",
1627 |        "\n",
1628 |        "    .dataframe thead th {\n",
1629 |        "        text-align: right;\n",
1630 |        "    }\n",
1631 |        "</style>\n",
1632 |        "<table border=\"1\" class=\"dataframe\">\n",
1633 |        "  <thead>\n",
1634 |        "    <tr style=\"text-align: right;\">\n",
1635 |        "      <th></th>\n",
1636 |        "      <th>Date_1</th>\n",
1637 |        "      <th>Prices_1</th>\n",
1638 |        "      <th>difference</th>\n",
1639 |        "      <th>Daily Gain/Loss</th>\n",
1640 |        "      <th>Margin Balance</th>\n",
1641 |        "    </tr>\n",
1642 |        "  </thead>\n",
1643 |        "  <tbody>\n",
1644 |        "    <tr>\n",
1645 |        "      <th>0</th>\n",
1646 |        "      <td>2007-03-21</td>\n",
1647 |        "      <td>93.635</td>\n",
1648 |        "      <td>NaN</td>\n",
1649 |        "      <td>NaN</td>\n",
1650 |        "      <td>148500</td>\n",
1651 |        "    </tr>\n",
1652 |        "    <tr>\n",
1653 |        "      <th>1</th>\n",
1654 |        "      <td>2007-03-22</td>\n",
1655 |        "      <td>93.685</td>\n",
1656 |        "      <td>0.050</td>\n",
1657 |        "      <td>12500.0</td>\n",
1658 |        "      <td>148500</td>\n",
1659 |        "    </tr>\n",
1660 |        "    <tr>\n",
1661 |        "      <th>2</th>\n",
1662 |        "      <td>2007-03-23</td>\n",
1663 |        "      <td>93.570</td>\n",
1664 |        "      <td>-0.115</td>\n",
1665 |        "      <td>-28750.0</td>\n",
1666 |        "      <td>119750.0</td>\n",
1667 |        "    </tr>\n",
1668 |        "    <tr>\n",
1669 |        "      <th>3</th>\n",
1670 |        "      <td>2007-03-26</td>\n",
1671 |        "      <td>93.500</td>\n",
1672 |        "      <td>-0.070</td>\n",
1673 |        "      <td>-17500.0</td>\n",
1674 |        "      <td>148500.0</td>\n",
1675 |        "    </tr>\n",
1676 |        "    <tr>\n",
1677 |        "      <th>4</th>\n",
1678 |        "      <td>2007-03-27</td>\n",
1679 |        "      <td>93.320</td>\n",
1680 |        "      <td>-0.180</td>\n",
1681 |        "      <td>-45000.0</td>\n",
1682 |        "      <td>148500.0</td>\n",
1683 |        "    </tr>\n",
1684 |        "    <tr>\n",
1685 |        "      <th>...</th>\n",
1686 |        "      <td>...</td>\n",
1687 |        "      <td>...</td>\n",
1688 |        "      <td>...</td>\n",
1689 |        "      <td>...</td>\n",
1690 |        "      <td>...</td>\n",
1691 |        "    </tr>\n",
1692 |        "    <tr>\n",
1693 |        "      <th>60</th>\n",
1694 |        "      <td>2007-06-15</td>\n",
1695 |        "      <td>93.020</td>\n",
1696 |        "      <td>-0.125</td>\n",
1697 |        "      <td>-31250.0</td>\n",
1698 |        "      <td>148500.0</td>\n",
1699 |        "    </tr>\n",
1700 |        "    <tr>\n",
1701 |        "      <th>61</th>\n",
1702 |        "      <td>2007-06-18</td>\n",
1703 |        "      <td>92.995</td>\n",
1704 |        "      <td>-0.025</td>\n",
1705 |        "      <td>-6250.0</td>\n",
1706 |        "      <td>142250.0</td>\n",
1707 |        "    </tr>\n",
1708 |        "    <tr>\n",
1709 |        "      <th>62</th>\n",
1710 |        "      <td>2007-06-19</td>\n",
1711 |        "      <td>93.010</td>\n",
1712 |        "      <td>0.015</td>\n",
1713 |        "      <td>3750.0</td>\n",
1714 |        "      <td>142250.0</td>\n",
1715 |        "    </tr>\n",
1716 |        "    <tr>\n",
1717 |        "      <th>63</th>\n",
1718 |        "      <td>2007-06-20</td>\n",
1719 |        "      <td>93.060</td>\n",
1720 |        "      <td>0.050</td>\n",
1721 |        "      <td>12500.0</td>\n",
1722 |        "      <td>142250.0</td>\n",
1723 |        "    </tr>\n",
1724 |        "    <tr>\n",
1725 |        "      <th>64</th>\n",
1726 |        "      <td>2007-06-21</td>\n",
1727 |        "      <td>93.095</td>\n",
1728 |        "      <td>0.035</td>\n",
1729 |        "      <td>8750.0</td>\n",
1730 |        "      <td>142250.0</td>\n",
1731 |        "    </tr>\n",
1732 |        "  </tbody>\n",
1733 |        "</table>\n",
1734 |        "<p>65 rows × 5 columns</p>\n",
1735 |        "</div>"
1736 |       ],
1737 |       "text/plain": [
1738 |        "       Date_1  Prices_1  difference  Daily Gain/Loss Margin Balance\n",
1739 |        "0  2007-03-21    93.635         NaN              NaN         148500\n",
1740 |        "1  2007-03-22    93.685       0.050          12500.0         148500\n",
1741 |        "2  2007-03-23    93.570      -0.115         -28750.0       119750.0\n",
1742 |        "3  2007-03-26    93.500      -0.070         -17500.0       148500.0\n",
1743 |        "4  2007-03-27    93.320      -0.180         -45000.0       148500.0\n",
1744 |        "..        ...       ...         ...              ...            ...\n",
1745 |        "60 2007-06-15    93.020      -0.125         -31250.0       148500.0\n",
1746 |        "61 2007-06-18    92.995      -0.025          -6250.0       142250.0\n",
1747 |        "62 2007-06-19    93.010       0.015           3750.0       142250.0\n",
1748 |        "63 2007-06-20    93.060       0.050          12500.0       142250.0\n",
1749 |        "64 2007-06-21    93.095       0.035           8750.0       142250.0\n",
1750 |        "\n",
1751 |        "[65 rows x 5 columns]"
1752 |       ]
1753 |      },
1754 |      "execution_count": 54,
1755 |      "metadata": {},
1756 |      "output_type": "execute_result"
1757 |     }
1758 |    ],
1759 |    "source": [
1760 |     "df2"
1761 |    ]
1762 |   },
1763 |   {
1764 |    "cell_type": "code",
1765 |    "execution_count": null,
1766 |    "metadata": {},
1767 |    "outputs": [],
1768 |    "source": []
1769 |   }
1770 |  ],
1771 |  "metadata": {
1772 |   "kernelspec": {
1773 |    "display_name": "base",
1774 |    "language": "python",
1775 |    "name": "python3"
1776 |   },
1777 |   "language_info": {
1778 |    "codemirror_mode": {
1779 |     "name": "ipython",
1780 |     "version": 3
1781 |    },
1782 |    "file_extension": ".py",
1783 |    "mimetype": "text/x-python",
1784 |    "name": "python",
1785 |    "nbconvert_exporter": "python",
1786 |    "pygments_lexer": "ipython3",
1787 |    "version": "3.9.15"
1788 |   },
1789 |   "orig_nbformat": 4
1790 |  },
1791 |  "nbformat": 4,
1792 |  "nbformat_minor": 2
1793 | }
1794 | 


--------------------------------------------------------------------------------
/Risk Management/README.md:
--------------------------------------------------------------------------------
1 | ## In this folder, I will be putting all short miscellaneous projects in Risk Management.
2 | 


--------------------------------------------------------------------------------
/Risk Management/Var Exercise-Monte Carlo.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## VaR Monte Carlo with as many Tickers symbols as you want."
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 111,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import pandas as pd\n",
 17 |     "import numpy as np\n",
 18 |     "import datetime as dt\n",
 19 |     "import yfinance as yf\n",
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "from scipy.stats import norm"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 112,
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "years=15\n",
 31 |     "\n",
 32 |     "endDate=dt.datetime.now()\n",
 33 |     "startDate=endDate-dt.timedelta(days=365*years)"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 113,
 39 |    "metadata": {},
 40 |    "outputs": [],
 41 |    "source": [
 42 |     "tickers=['TSLA','GOOGL','QQQ','VTI','AMD','AMZN'] ## Put as many as tickers as you want"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 114,
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "## Next get adjusted closing price\n",
 52 |     "\n",
 53 |     "adj_close_df=pd.DataFrame()"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 115,
 59 |    "metadata": {},
 60 |    "outputs": [
 61 |     {
 62 |      "name": "stdout",
 63 |      "output_type": "stream",
 64 |      "text": [
 65 |       "[*********************100%***********************]  1 of 1 completed\n",
 66 |       "[*********************100%***********************]  1 of 1 completed\n",
 67 |       "[*********************100%***********************]  1 of 1 completed\n",
 68 |       "[*********************100%***********************]  1 of 1 completed\n",
 69 |       "[*********************100%***********************]  1 of 1 completed\n",
 70 |       "[*********************100%***********************]  1 of 1 completed\n"
 71 |      ]
 72 |     }
 73 |    ],
 74 |    "source": [
 75 |     "for i in tickers:\n",
 76 |     "    data=yf.download(i,start=startDate,end=endDate)\n",
 77 |     "    adj_close_df[i]=data['Adj Close']"
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": 116,
 83 |    "metadata": {},
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "## We will get the log returns because those are consistent in many aspect than just returns. Secondly, take the first difference as they are not stationary.\n",
 87 |     "log_returns=np.log(adj_close_df/adj_close_df.shift(1))\n"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": 117,
 93 |    "metadata": {},
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "## Removing the first row because after the difference one gets NaN\n",
 97 |     "log_returns=log_returns.dropna()"
 98 |    ]
 99 |   },
100 |   {
101 |    "cell_type": "code",
102 |    "execution_count": 118,
103 |    "metadata": {},
104 |    "outputs": [
105 |     {
106 |      "data": {
107 |       "text/html": [
108 |        "<div>\n",
109 |        "<style scoped>\n",
110 |        "    .dataframe tbody tr th:only-of-type {\n",
111 |        "        vertical-align: middle;\n",
112 |        "    }\n",
113 |        "\n",
114 |        "    .dataframe tbody tr th {\n",
115 |        "        vertical-align: top;\n",
116 |        "    }\n",
117 |        "\n",
118 |        "    .dataframe thead th {\n",
119 |        "        text-align: right;\n",
120 |        "    }\n",
121 |        "</style>\n",
122 |        "<table border=\"1\" class=\"dataframe\">\n",
123 |        "  <thead>\n",
124 |        "    <tr style=\"text-align: right;\">\n",
125 |        "      <th></th>\n",
126 |        "      <th>TSLA</th>\n",
127 |        "      <th>GOOGL</th>\n",
128 |        "      <th>QQQ</th>\n",
129 |        "      <th>VTI</th>\n",
130 |        "      <th>AMD</th>\n",
131 |        "      <th>AMZN</th>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <th>Date</th>\n",
135 |        "      <th></th>\n",
136 |        "      <th></th>\n",
137 |        "      <th></th>\n",
138 |        "      <th></th>\n",
139 |        "      <th></th>\n",
140 |        "      <th></th>\n",
141 |        "    </tr>\n",
142 |        "  </thead>\n",
143 |        "  <tbody>\n",
144 |        "    <tr>\n",
145 |        "      <th>2010-06-30</th>\n",
146 |        "      <td>-0.002515</td>\n",
147 |        "      <td>-0.020708</td>\n",
148 |        "      <td>-0.015335</td>\n",
149 |        "      <td>-0.010974</td>\n",
150 |        "      <td>-0.021622</td>\n",
151 |        "      <td>0.005967</td>\n",
152 |        "    </tr>\n",
153 |        "    <tr>\n",
154 |        "      <th>2010-07-01</th>\n",
155 |        "      <td>-0.081723</td>\n",
156 |        "      <td>-0.012347</td>\n",
157 |        "      <td>-0.002813</td>\n",
158 |        "      <td>-0.004386</td>\n",
159 |        "      <td>0.009517</td>\n",
160 |        "      <td>0.015439</td>\n",
161 |        "    </tr>\n",
162 |        "    <tr>\n",
163 |        "      <th>2010-07-02</th>\n",
164 |        "      <td>-0.134312</td>\n",
165 |        "      <td>-0.006712</td>\n",
166 |        "      <td>-0.002822</td>\n",
167 |        "      <td>-0.004789</td>\n",
168 |        "      <td>-0.030222</td>\n",
169 |        "      <td>-0.016538</td>\n",
170 |        "    </tr>\n",
171 |        "    <tr>\n",
172 |        "      <th>2010-07-06</th>\n",
173 |        "      <td>-0.175470</td>\n",
174 |        "      <td>-0.001100</td>\n",
175 |        "      <td>0.003056</td>\n",
176 |        "      <td>0.003067</td>\n",
177 |        "      <td>-0.018298</td>\n",
178 |        "      <td>0.008394</td>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>2010-07-07</th>\n",
182 |        "      <td>-0.019431</td>\n",
183 |        "      <td>0.031889</td>\n",
184 |        "      <td>0.031426</td>\n",
185 |        "      <td>0.032024</td>\n",
186 |        "      <td>0.048520</td>\n",
187 |        "      <td>0.030160</td>\n",
188 |        "    </tr>\n",
189 |        "    <tr>\n",
190 |        "      <th>...</th>\n",
191 |        "      <td>...</td>\n",
192 |        "      <td>...</td>\n",
193 |        "      <td>...</td>\n",
194 |        "      <td>...</td>\n",
195 |        "      <td>...</td>\n",
196 |        "      <td>...</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>2023-09-25</th>\n",
200 |        "      <td>0.008580</td>\n",
201 |        "      <td>0.006581</td>\n",
202 |        "      <td>0.004739</td>\n",
203 |        "      <td>0.004017</td>\n",
204 |        "      <td>0.012191</td>\n",
205 |        "      <td>0.016514</td>\n",
206 |        "    </tr>\n",
207 |        "    <tr>\n",
208 |        "      <th>2023-09-26</th>\n",
209 |        "      <td>-0.011688</td>\n",
210 |        "      <td>-0.019563</td>\n",
211 |        "      <td>-0.015130</td>\n",
212 |        "      <td>-0.014510</td>\n",
213 |        "      <td>-0.014689</td>\n",
214 |        "      <td>-0.041133</td>\n",
215 |        "    </tr>\n",
216 |        "    <tr>\n",
217 |        "      <th>2023-09-27</th>\n",
218 |        "      <td>-0.014940</td>\n",
219 |        "      <td>0.015206</td>\n",
220 |        "      <td>0.002341</td>\n",
221 |        "      <td>0.001040</td>\n",
222 |        "      <td>0.021750</td>\n",
223 |        "      <td>0.000000</td>\n",
224 |        "    </tr>\n",
225 |        "    <tr>\n",
226 |        "      <th>2023-09-28</th>\n",
227 |        "      <td>0.024155</td>\n",
228 |        "      <td>0.013468</td>\n",
229 |        "      <td>0.008330</td>\n",
230 |        "      <td>0.006594</td>\n",
231 |        "      <td>0.046715</td>\n",
232 |        "      <td>0.000000</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>2023-09-29</th>\n",
236 |        "      <td>0.015465</td>\n",
237 |        "      <td>-0.011020</td>\n",
238 |        "      <td>0.000726</td>\n",
239 |        "      <td>-0.002915</td>\n",
240 |        "      <td>0.000584</td>\n",
241 |        "      <td>0.009008</td>\n",
242 |        "    </tr>\n",
243 |        "  </tbody>\n",
244 |        "</table>\n",
245 |        "<p>3336 rows × 6 columns</p>\n",
246 |        "</div>"
247 |       ],
248 |       "text/plain": [
249 |        "                TSLA     GOOGL       QQQ       VTI       AMD      AMZN\n",
250 |        "Date                                                                  \n",
251 |        "2010-06-30 -0.002515 -0.020708 -0.015335 -0.010974 -0.021622  0.005967\n",
252 |        "2010-07-01 -0.081723 -0.012347 -0.002813 -0.004386  0.009517  0.015439\n",
253 |        "2010-07-02 -0.134312 -0.006712 -0.002822 -0.004789 -0.030222 -0.016538\n",
254 |        "2010-07-06 -0.175470 -0.001100  0.003056  0.003067 -0.018298  0.008394\n",
255 |        "2010-07-07 -0.019431  0.031889  0.031426  0.032024  0.048520  0.030160\n",
256 |        "...              ...       ...       ...       ...       ...       ...\n",
257 |        "2023-09-25  0.008580  0.006581  0.004739  0.004017  0.012191  0.016514\n",
258 |        "2023-09-26 -0.011688 -0.019563 -0.015130 -0.014510 -0.014689 -0.041133\n",
259 |        "2023-09-27 -0.014940  0.015206  0.002341  0.001040  0.021750  0.000000\n",
260 |        "2023-09-28  0.024155  0.013468  0.008330  0.006594  0.046715  0.000000\n",
261 |        "2023-09-29  0.015465 -0.011020  0.000726 -0.002915  0.000584  0.009008\n",
262 |        "\n",
263 |        "[3336 rows x 6 columns]"
264 |       ]
265 |      },
266 |      "execution_count": 118,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "log_returns"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 119,
278 |    "metadata": {},
279 |    "outputs": [],
280 |    "source": [
281 |     "def expected_return(weights,log_returns):\n",
282 |     "    return np.sum(log_returns.mean()*weights)"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 120,
288 |    "metadata": {},
289 |    "outputs": [],
290 |    "source": [
291 |     "def standard_deviation(weights,cov_matrix):\n",
292 |     "    variance=weights.T@cov_matrix@weights\n",
293 |     "    return np.sqrt(variance)\n"
294 |    ]
295 |   },
296 |   {
297 |    "cell_type": "code",
298 |    "execution_count": 121,
299 |    "metadata": {},
300 |    "outputs": [
301 |     {
302 |      "data": {
303 |       "text/html": [
304 |        "<div>\n",
305 |        "<style scoped>\n",
306 |        "    .dataframe tbody tr th:only-of-type {\n",
307 |        "        vertical-align: middle;\n",
308 |        "    }\n",
309 |        "\n",
310 |        "    .dataframe tbody tr th {\n",
311 |        "        vertical-align: top;\n",
312 |        "    }\n",
313 |        "\n",
314 |        "    .dataframe thead th {\n",
315 |        "        text-align: right;\n",
316 |        "    }\n",
317 |        "</style>\n",
318 |        "<table border=\"1\" class=\"dataframe\">\n",
319 |        "  <thead>\n",
320 |        "    <tr style=\"text-align: right;\">\n",
321 |        "      <th></th>\n",
322 |        "      <th>TSLA</th>\n",
323 |        "      <th>GOOGL</th>\n",
324 |        "      <th>QQQ</th>\n",
325 |        "      <th>VTI</th>\n",
326 |        "      <th>AMD</th>\n",
327 |        "      <th>AMZN</th>\n",
328 |        "    </tr>\n",
329 |        "  </thead>\n",
330 |        "  <tbody>\n",
331 |        "    <tr>\n",
332 |        "      <th>TSLA</th>\n",
333 |        "      <td>0.001285</td>\n",
334 |        "      <td>0.000209</td>\n",
335 |        "      <td>0.000235</td>\n",
336 |        "      <td>0.000180</td>\n",
337 |        "      <td>0.000399</td>\n",
338 |        "      <td>0.000266</td>\n",
339 |        "    </tr>\n",
340 |        "    <tr>\n",
341 |        "      <th>GOOGL</th>\n",
342 |        "      <td>0.000209</td>\n",
343 |        "      <td>0.000292</td>\n",
344 |        "      <td>0.000174</td>\n",
345 |        "      <td>0.000134</td>\n",
346 |        "      <td>0.000219</td>\n",
347 |        "      <td>0.000214</td>\n",
348 |        "    </tr>\n",
349 |        "    <tr>\n",
350 |        "      <th>QQQ</th>\n",
351 |        "      <td>0.000235</td>\n",
352 |        "      <td>0.000174</td>\n",
353 |        "      <td>0.000170</td>\n",
354 |        "      <td>0.000135</td>\n",
355 |        "      <td>0.000251</td>\n",
356 |        "      <td>0.000193</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>VTI</th>\n",
360 |        "      <td>0.000180</td>\n",
361 |        "      <td>0.000134</td>\n",
362 |        "      <td>0.000135</td>\n",
363 |        "      <td>0.000125</td>\n",
364 |        "      <td>0.000202</td>\n",
365 |        "      <td>0.000138</td>\n",
366 |        "    </tr>\n",
367 |        "    <tr>\n",
368 |        "      <th>AMD</th>\n",
369 |        "      <td>0.000399</td>\n",
370 |        "      <td>0.000219</td>\n",
371 |        "      <td>0.000251</td>\n",
372 |        "      <td>0.000202</td>\n",
373 |        "      <td>0.001247</td>\n",
374 |        "      <td>0.000274</td>\n",
375 |        "    </tr>\n",
376 |        "    <tr>\n",
377 |        "      <th>AMZN</th>\n",
378 |        "      <td>0.000266</td>\n",
379 |        "      <td>0.000214</td>\n",
380 |        "      <td>0.000193</td>\n",
381 |        "      <td>0.000138</td>\n",
382 |        "      <td>0.000274</td>\n",
383 |        "      <td>0.000430</td>\n",
384 |        "    </tr>\n",
385 |        "  </tbody>\n",
386 |        "</table>\n",
387 |        "</div>"
388 |       ],
389 |       "text/plain": [
390 |        "           TSLA     GOOGL       QQQ       VTI       AMD      AMZN\n",
391 |        "TSLA   0.001285  0.000209  0.000235  0.000180  0.000399  0.000266\n",
392 |        "GOOGL  0.000209  0.000292  0.000174  0.000134  0.000219  0.000214\n",
393 |        "QQQ    0.000235  0.000174  0.000170  0.000135  0.000251  0.000193\n",
394 |        "VTI    0.000180  0.000134  0.000135  0.000125  0.000202  0.000138\n",
395 |        "AMD    0.000399  0.000219  0.000251  0.000202  0.001247  0.000274\n",
396 |        "AMZN   0.000266  0.000214  0.000193  0.000138  0.000274  0.000430"
397 |       ]
398 |      },
399 |      "execution_count": 121,
400 |      "metadata": {},
401 |      "output_type": "execute_result"
402 |     }
403 |    ],
404 |    "source": [
405 |     "cov_matrix=log_returns.cov()\n",
406 |     "cov_matrix"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "code",
411 |    "execution_count": 122,
412 |    "metadata": {},
413 |    "outputs": [
414 |     {
415 |      "data": {
416 |       "text/plain": [
417 |        "array([0.16666667, 0.16666667, 0.16666667, 0.16666667, 0.16666667,\n",
418 |        "       0.16666667])"
419 |       ]
420 |      },
421 |      "execution_count": 122,
422 |      "metadata": {},
423 |      "output_type": "execute_result"
424 |     }
425 |    ],
426 |    "source": [
427 |     "## Create Equally weighted portfolio\n",
428 |     "\n",
429 |     "portfolio_value=1000000\n",
430 |     "weights=np.array([1/len(tickers)]*len(tickers))\n",
431 |     "weights"
432 |    ]
433 |   },
434 |   {
435 |    "cell_type": "code",
436 |    "execution_count": 123,
437 |    "metadata": {},
438 |    "outputs": [],
439 |    "source": [
440 |     "portfolio_expected_return=expected_return(weights,log_returns)\n",
441 |     "portfolio_standard_deviation=standard_deviation(weights,cov_matrix)"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "code",
446 |    "execution_count": 124,
447 |    "metadata": {},
448 |    "outputs": [
449 |     {
450 |      "data": {
451 |       "text/plain": [
452 |        "0.0166627391905645"
453 |       ]
454 |      },
455 |      "execution_count": 124,
456 |      "metadata": {},
457 |      "output_type": "execute_result"
458 |     }
459 |    ],
460 |    "source": [
461 |     "portfolio_standard_deviation"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": 125,
467 |    "metadata": {},
468 |    "outputs": [],
469 |    "source": [
470 |     "def rand_z_score():\n",
471 |     "    return np.random.normal(0,1)"
472 |    ]
473 |   },
474 |   {
475 |    "cell_type": "code",
476 |    "execution_count": 126,
477 |    "metadata": {},
478 |    "outputs": [],
479 |    "source": [
480 |     "days=20\n",
481 |     "\n",
482 |     "def scenario_loss(portfolio_value,portfolio_standard_deviation,z_score,days):\n",
483 |     "    return portfolio_value*portfolio_expected_return*days+portfolio_value*portfolio_standard_deviation*z_score*np.sqrt(days)"
484 |    ]
485 |   },
486 |   {
487 |    "cell_type": "code",
488 |    "execution_count": 127,
489 |    "metadata": {},
490 |    "outputs": [],
491 |    "source": [
492 |     "##Simulation\n",
493 |     "\n",
494 |     "n_iter=10000\n",
495 |     "scenario_return=[]\n",
496 |     "\n",
497 |     "for i in range(n_iter):\n",
498 |     "    z_score=rand_z_score()\n",
499 |     "    scenario_return.append(scenario_loss(portfolio_value,portfolio_standard_deviation,z_score,days))"
500 |    ]
501 |   },
502 |   {
503 |    "cell_type": "code",
504 |    "execution_count": 128,
505 |    "metadata": {},
506 |    "outputs": [],
507 |    "source": [
508 |     "conf_intv=0.95\n",
509 |     "VaR=np.percentile(scenario_return,5)"
510 |    ]
511 |   },
512 |   {
513 |    "cell_type": "code",
514 |    "execution_count": 129,
515 |    "metadata": {},
516 |    "outputs": [
517 |     {
518 |      "data": {
519 |       "text/plain": [
520 |        "-106272.09525042716"
521 |       ]
522 |      },
523 |      "execution_count": 129,
524 |      "metadata": {},
525 |      "output_type": "execute_result"
526 |     }
527 |    ],
528 |    "source": [
529 |     "VaR"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": 130,
535 |    "metadata": {},
536 |    "outputs": [
537 |     {
538 |      "data": {
539 |       "text/plain": [
540 |        "0.9999999999999999"
541 |       ]
542 |      },
543 |      "execution_count": 130,
544 |      "metadata": {},
545 |      "output_type": "execute_result"
546 |     }
547 |    ],
548 |    "source": [
549 |     "weights=np.random.rand(len(tickers))\n",
550 |     "weights=weights/np.sum(weights)\n",
551 |     "sum(weights)"
552 |    ]
553 |   },
554 |   {
555 |    "cell_type": "code",
556 |    "execution_count": 131,
557 |    "metadata": {},
558 |    "outputs": [],
559 |    "source": [
560 |     "portfolio_expected_return=expected_return(weights,log_returns)\n",
561 |     "portfolio_standard_deviation=standard_deviation(weights,cov_matrix)\n",
562 |     "days=20\n",
563 |     "\n",
564 |     "def scenario_loss(portfolio_value,portfolio_standard_deviation,z_score,days):\n",
565 |     "    return portfolio_value*portfolio_expected_return*days+portfolio_value*portfolio_standard_deviation*z_score*np.sqrt(days)"
566 |    ]
567 |   },
568 |   {
569 |    "cell_type": "code",
570 |    "execution_count": 132,
571 |    "metadata": {},
572 |    "outputs": [
573 |     {
574 |      "data": {
575 |       "text/plain": [
576 |        "(6,)"
577 |       ]
578 |      },
579 |      "execution_count": 132,
580 |      "metadata": {},
581 |      "output_type": "execute_result"
582 |     }
583 |    ],
584 |    "source": [
585 |     "weights.shape"
586 |    ]
587 |   },
588 |   {
589 |    "cell_type": "code",
590 |    "execution_count": 133,
591 |    "metadata": {},
592 |    "outputs": [],
593 |    "source": [
594 |     "##Simulation\n",
595 |     "\n",
596 |     "n_iter=10000\n",
597 |     "scenario_return=[]\n",
598 |     "\n",
599 |     "for i in range(n_iter):\n",
600 |     "    z_score=rand_z_score()\n",
601 |     "    scenario_return.append(scenario_loss(portfolio_value,portfolio_standard_deviation,z_score,days))"
602 |    ]
603 |   },
604 |   {
605 |    "cell_type": "code",
606 |    "execution_count": 134,
607 |    "metadata": {},
608 |    "outputs": [
609 |     {
610 |      "data": {
611 |       "text/plain": [
612 |        "-130322.34366072073"
613 |       ]
614 |      },
615 |      "execution_count": 134,
616 |      "metadata": {},
617 |      "output_type": "execute_result"
618 |     }
619 |    ],
620 |    "source": [
621 |     "conf_intv=0.95\n",
622 |     "VaR=np.percentile(scenario_return,5)\n",
623 |     "VaR"
624 |    ]
625 |   },
626 |   {
627 |    "cell_type": "code",
628 |    "execution_count": null,
629 |    "metadata": {},
630 |    "outputs": [],
631 |    "source": []
632 |   }
633 |  ],
634 |  "metadata": {
635 |   "kernelspec": {
636 |    "display_name": "base",
637 |    "language": "python",
638 |    "name": "python3"
639 |   },
640 |   "language_info": {
641 |    "codemirror_mode": {
642 |     "name": "ipython",
643 |     "version": 3
644 |    },
645 |    "file_extension": ".py",
646 |    "mimetype": "text/x-python",
647 |    "name": "python",
648 |    "nbconvert_exporter": "python",
649 |    "pygments_lexer": "ipython3",
650 |    "version": "3.9.15"
651 |   },
652 |   "orig_nbformat": 4
653 |  },
654 |  "nbformat": 4,
655 |  "nbformat_minor": 2
656 | }
657 | 


--------------------------------------------------------------------------------
/SECURITY.md:
--------------------------------------------------------------------------------
 1 | # Security Policy
 2 | 
 3 | ## Supported Versions
 4 | 
 5 | Use this section to tell people about which versions of your project are
 6 | currently being supported with security updates.
 7 | 
 8 | | Version | Supported          |
 9 | | ------- | ------------------ |
10 | | 5.1.x   | :white_check_mark: |
11 | | 5.0.x   | :x:                |
12 | | 4.0.x   | :white_check_mark: |
13 | | < 4.0   | :x:                |
14 | 
15 | ## Reporting a Vulnerability
16 | 
17 | Use this section to tell people how to report a vulnerability.
18 | 
19 | Tell them where to go, how often they can expect to get an update on a
20 | reported vulnerability, what to expect if the vulnerability is accepted or
21 | declined, etc.
22 | 


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