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05c8520
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Parent(s):
794a152
Upload Loan_Approval_Prediction_Model.ipynb
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Loan_Approval_Prediction_Model.ipynb
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| 1 |
+
{
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| 2 |
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"nbformat": 4,
|
| 3 |
+
"nbformat_minor": 0,
|
| 4 |
+
"metadata": {
|
| 5 |
+
"colab": {
|
| 6 |
+
"provenance": [],
|
| 7 |
+
"mount_file_id": "17NySOZHXz-Z8fGG-i6zpH1BGn8MGsPF_",
|
| 8 |
+
"authorship_tag": "ABX9TyNj0zZ+MNSd0XgS6OnnIvik",
|
| 9 |
+
"include_colab_link": true
|
| 10 |
+
},
|
| 11 |
+
"kernelspec": {
|
| 12 |
+
"name": "python3",
|
| 13 |
+
"display_name": "Python 3"
|
| 14 |
+
},
|
| 15 |
+
"language_info": {
|
| 16 |
+
"name": "python"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"cells": [
|
| 20 |
+
{
|
| 21 |
+
"cell_type": "markdown",
|
| 22 |
+
"metadata": {
|
| 23 |
+
"id": "view-in-github",
|
| 24 |
+
"colab_type": "text"
|
| 25 |
+
},
|
| 26 |
+
"source": [
|
| 27 |
+
"<a href=\"https://colab.research.google.com/github/pravincoder/Tensorflow_models/blob/main/Loan_Approval_Prediction_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
| 28 |
+
]
|
| 29 |
+
},
|
| 30 |
+
{
|
| 31 |
+
"cell_type": "markdown",
|
| 32 |
+
"source": [
|
| 33 |
+
"# Loan Approval Prediction Model\n",
|
| 34 |
+
"This is the link of the dataset :- [gdrive](https://drive.google.com/file/d/1LIvIdqdHDFEGnfzIgEh4L6GFirzsE3US/view?usp=sharing)\n",
|
| 35 |
+
"\n",
|
| 36 |
+
"\n",
|
| 37 |
+
"\n",
|
| 38 |
+
"_Source GeeksforGeeks_ "
|
| 39 |
+
],
|
| 40 |
+
"metadata": {
|
| 41 |
+
"id": "Aixd9CsjS4-Z"
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
{
|
| 45 |
+
"cell_type": "markdown",
|
| 46 |
+
"source": [
|
| 47 |
+
"## Importing the Modules & load the data\n"
|
| 48 |
+
],
|
| 49 |
+
"metadata": {
|
| 50 |
+
"id": "3u3IKHW6jYA5"
|
| 51 |
+
}
|
| 52 |
+
},
|
| 53 |
+
{
|
| 54 |
+
"cell_type": "code",
|
| 55 |
+
"execution_count": 3,
|
| 56 |
+
"metadata": {
|
| 57 |
+
"id": "IJNJ5pJBYlTd"
|
| 58 |
+
},
|
| 59 |
+
"outputs": [],
|
| 60 |
+
"source": [
|
| 61 |
+
"# Imports\n",
|
| 62 |
+
"import pandas as pd\n",
|
| 63 |
+
"import numpy as np\n",
|
| 64 |
+
"import seaborn as sn\n",
|
| 65 |
+
"import tensorflow as tf\n",
|
| 66 |
+
"from sklearn.model_selection import train_test_split"
|
| 67 |
+
]
|
| 68 |
+
},
|
| 69 |
+
{
|
| 70 |
+
"cell_type": "code",
|
| 71 |
+
"source": [
|
| 72 |
+
"# Load csv\n",
|
| 73 |
+
"data = pd.read_csv('/content/drive/MyDrive/LoanApprovalPrediction.csv')"
|
| 74 |
+
],
|
| 75 |
+
"metadata": {
|
| 76 |
+
"id": "naFm6bzI9lXw"
|
| 77 |
+
},
|
| 78 |
+
"execution_count": 4,
|
| 79 |
+
"outputs": []
|
| 80 |
+
},
|
| 81 |
+
{
|
| 82 |
+
"cell_type": "markdown",
|
| 83 |
+
"source": [
|
| 84 |
+
"## Data Cleaning"
|
| 85 |
+
],
|
| 86 |
+
"metadata": {
|
| 87 |
+
"id": "F0pZVXIJjmCW"
|
| 88 |
+
}
|
| 89 |
+
},
|
| 90 |
+
{
|
| 91 |
+
"cell_type": "code",
|
| 92 |
+
"source": [
|
| 93 |
+
"# Read the data\n",
|
| 94 |
+
"data.head()"
|
| 95 |
+
],
|
| 96 |
+
"metadata": {
|
| 97 |
+
"colab": {
|
| 98 |
+
"base_uri": "https://localhost:8080/",
|
| 99 |
+
"height": 288
|
| 100 |
+
},
|
| 101 |
+
"id": "3DB0GLEgnXpd",
|
| 102 |
+
"outputId": "a9fd2529-d4c7-4efd-f69a-560ba74f1873"
|
| 103 |
+
},
|
| 104 |
+
"execution_count": 5,
|
| 105 |
+
"outputs": [
|
| 106 |
+
{
|
| 107 |
+
"output_type": "execute_result",
|
| 108 |
+
"data": {
|
| 109 |
+
"text/plain": [
|
| 110 |
+
" Loan_ID Gender Married Dependents Education Self_Employed \\\n",
|
| 111 |
+
"0 LP001002 Male No 0.0 Graduate No \n",
|
| 112 |
+
"1 LP001003 Male Yes 1.0 Graduate No \n",
|
| 113 |
+
"2 LP001005 Male Yes 0.0 Graduate Yes \n",
|
| 114 |
+
"3 LP001006 Male Yes 0.0 Not Graduate No \n",
|
| 115 |
+
"4 LP001008 Male No 0.0 Graduate No \n",
|
| 116 |
+
"\n",
|
| 117 |
+
" ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\n",
|
| 118 |
+
"0 5849 0.0 NaN 360.0 \n",
|
| 119 |
+
"1 4583 1508.0 128.0 360.0 \n",
|
| 120 |
+
"2 3000 0.0 66.0 360.0 \n",
|
| 121 |
+
"3 2583 2358.0 120.0 360.0 \n",
|
| 122 |
+
"4 6000 0.0 141.0 360.0 \n",
|
| 123 |
+
"\n",
|
| 124 |
+
" Credit_History Property_Area Loan_Status \n",
|
| 125 |
+
"0 1.0 Urban Y \n",
|
| 126 |
+
"1 1.0 Rural N \n",
|
| 127 |
+
"2 1.0 Urban Y \n",
|
| 128 |
+
"3 1.0 Urban Y \n",
|
| 129 |
+
"4 1.0 Urban Y "
|
| 130 |
+
],
|
| 131 |
+
"text/html": [
|
| 132 |
+
"\n",
|
| 133 |
+
"\n",
|
| 134 |
+
" <div id=\"df-5b22b868-a97e-44cc-a69e-b64d305d232d\">\n",
|
| 135 |
+
" <div class=\"colab-df-container\">\n",
|
| 136 |
+
" <div>\n",
|
| 137 |
+
"<style scoped>\n",
|
| 138 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 139 |
+
" vertical-align: middle;\n",
|
| 140 |
+
" }\n",
|
| 141 |
+
"\n",
|
| 142 |
+
" .dataframe tbody tr th {\n",
|
| 143 |
+
" vertical-align: top;\n",
|
| 144 |
+
" }\n",
|
| 145 |
+
"\n",
|
| 146 |
+
" .dataframe thead th {\n",
|
| 147 |
+
" text-align: right;\n",
|
| 148 |
+
" }\n",
|
| 149 |
+
"</style>\n",
|
| 150 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 151 |
+
" <thead>\n",
|
| 152 |
+
" <tr style=\"text-align: right;\">\n",
|
| 153 |
+
" <th></th>\n",
|
| 154 |
+
" <th>Loan_ID</th>\n",
|
| 155 |
+
" <th>Gender</th>\n",
|
| 156 |
+
" <th>Married</th>\n",
|
| 157 |
+
" <th>Dependents</th>\n",
|
| 158 |
+
" <th>Education</th>\n",
|
| 159 |
+
" <th>Self_Employed</th>\n",
|
| 160 |
+
" <th>ApplicantIncome</th>\n",
|
| 161 |
+
" <th>CoapplicantIncome</th>\n",
|
| 162 |
+
" <th>LoanAmount</th>\n",
|
| 163 |
+
" <th>Loan_Amount_Term</th>\n",
|
| 164 |
+
" <th>Credit_History</th>\n",
|
| 165 |
+
" <th>Property_Area</th>\n",
|
| 166 |
+
" <th>Loan_Status</th>\n",
|
| 167 |
+
" </tr>\n",
|
| 168 |
+
" </thead>\n",
|
| 169 |
+
" <tbody>\n",
|
| 170 |
+
" <tr>\n",
|
| 171 |
+
" <th>0</th>\n",
|
| 172 |
+
" <td>LP001002</td>\n",
|
| 173 |
+
" <td>Male</td>\n",
|
| 174 |
+
" <td>No</td>\n",
|
| 175 |
+
" <td>0.0</td>\n",
|
| 176 |
+
" <td>Graduate</td>\n",
|
| 177 |
+
" <td>No</td>\n",
|
| 178 |
+
" <td>5849</td>\n",
|
| 179 |
+
" <td>0.0</td>\n",
|
| 180 |
+
" <td>NaN</td>\n",
|
| 181 |
+
" <td>360.0</td>\n",
|
| 182 |
+
" <td>1.0</td>\n",
|
| 183 |
+
" <td>Urban</td>\n",
|
| 184 |
+
" <td>Y</td>\n",
|
| 185 |
+
" </tr>\n",
|
| 186 |
+
" <tr>\n",
|
| 187 |
+
" <th>1</th>\n",
|
| 188 |
+
" <td>LP001003</td>\n",
|
| 189 |
+
" <td>Male</td>\n",
|
| 190 |
+
" <td>Yes</td>\n",
|
| 191 |
+
" <td>1.0</td>\n",
|
| 192 |
+
" <td>Graduate</td>\n",
|
| 193 |
+
" <td>No</td>\n",
|
| 194 |
+
" <td>4583</td>\n",
|
| 195 |
+
" <td>1508.0</td>\n",
|
| 196 |
+
" <td>128.0</td>\n",
|
| 197 |
+
" <td>360.0</td>\n",
|
| 198 |
+
" <td>1.0</td>\n",
|
| 199 |
+
" <td>Rural</td>\n",
|
| 200 |
+
" <td>N</td>\n",
|
| 201 |
+
" </tr>\n",
|
| 202 |
+
" <tr>\n",
|
| 203 |
+
" <th>2</th>\n",
|
| 204 |
+
" <td>LP001005</td>\n",
|
| 205 |
+
" <td>Male</td>\n",
|
| 206 |
+
" <td>Yes</td>\n",
|
| 207 |
+
" <td>0.0</td>\n",
|
| 208 |
+
" <td>Graduate</td>\n",
|
| 209 |
+
" <td>Yes</td>\n",
|
| 210 |
+
" <td>3000</td>\n",
|
| 211 |
+
" <td>0.0</td>\n",
|
| 212 |
+
" <td>66.0</td>\n",
|
| 213 |
+
" <td>360.0</td>\n",
|
| 214 |
+
" <td>1.0</td>\n",
|
| 215 |
+
" <td>Urban</td>\n",
|
| 216 |
+
" <td>Y</td>\n",
|
| 217 |
+
" </tr>\n",
|
| 218 |
+
" <tr>\n",
|
| 219 |
+
" <th>3</th>\n",
|
| 220 |
+
" <td>LP001006</td>\n",
|
| 221 |
+
" <td>Male</td>\n",
|
| 222 |
+
" <td>Yes</td>\n",
|
| 223 |
+
" <td>0.0</td>\n",
|
| 224 |
+
" <td>Not Graduate</td>\n",
|
| 225 |
+
" <td>No</td>\n",
|
| 226 |
+
" <td>2583</td>\n",
|
| 227 |
+
" <td>2358.0</td>\n",
|
| 228 |
+
" <td>120.0</td>\n",
|
| 229 |
+
" <td>360.0</td>\n",
|
| 230 |
+
" <td>1.0</td>\n",
|
| 231 |
+
" <td>Urban</td>\n",
|
| 232 |
+
" <td>Y</td>\n",
|
| 233 |
+
" </tr>\n",
|
| 234 |
+
" <tr>\n",
|
| 235 |
+
" <th>4</th>\n",
|
| 236 |
+
" <td>LP001008</td>\n",
|
| 237 |
+
" <td>Male</td>\n",
|
| 238 |
+
" <td>No</td>\n",
|
| 239 |
+
" <td>0.0</td>\n",
|
| 240 |
+
" <td>Graduate</td>\n",
|
| 241 |
+
" <td>No</td>\n",
|
| 242 |
+
" <td>6000</td>\n",
|
| 243 |
+
" <td>0.0</td>\n",
|
| 244 |
+
" <td>141.0</td>\n",
|
| 245 |
+
" <td>360.0</td>\n",
|
| 246 |
+
" <td>1.0</td>\n",
|
| 247 |
+
" <td>Urban</td>\n",
|
| 248 |
+
" <td>Y</td>\n",
|
| 249 |
+
" </tr>\n",
|
| 250 |
+
" </tbody>\n",
|
| 251 |
+
"</table>\n",
|
| 252 |
+
"</div>\n",
|
| 253 |
+
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b22b868-a97e-44cc-a69e-b64d305d232d')\"\n",
|
| 254 |
+
" title=\"Convert this dataframe to an interactive table.\"\n",
|
| 255 |
+
" style=\"display:none;\">\n",
|
| 256 |
+
"\n",
|
| 257 |
+
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
| 258 |
+
" width=\"24px\">\n",
|
| 259 |
+
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
|
| 260 |
+
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
|
| 261 |
+
" </svg>\n",
|
| 262 |
+
" </button>\n",
|
| 263 |
+
"\n",
|
| 264 |
+
"\n",
|
| 265 |
+
"\n",
|
| 266 |
+
" <div id=\"df-b37c8fa9-dbcd-450f-9341-50c75e7abbf6\">\n",
|
| 267 |
+
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b37c8fa9-dbcd-450f-9341-50c75e7abbf6')\"\n",
|
| 268 |
+
" title=\"Suggest charts.\"\n",
|
| 269 |
+
" style=\"display:none;\">\n",
|
| 270 |
+
"\n",
|
| 271 |
+
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
| 272 |
+
" width=\"24px\">\n",
|
| 273 |
+
" <g>\n",
|
| 274 |
+
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
| 275 |
+
" </g>\n",
|
| 276 |
+
"</svg>\n",
|
| 277 |
+
" </button>\n",
|
| 278 |
+
" </div>\n",
|
| 279 |
+
"\n",
|
| 280 |
+
"<style>\n",
|
| 281 |
+
" .colab-df-quickchart {\n",
|
| 282 |
+
" background-color: #E8F0FE;\n",
|
| 283 |
+
" border: none;\n",
|
| 284 |
+
" border-radius: 50%;\n",
|
| 285 |
+
" cursor: pointer;\n",
|
| 286 |
+
" display: none;\n",
|
| 287 |
+
" fill: #1967D2;\n",
|
| 288 |
+
" height: 32px;\n",
|
| 289 |
+
" padding: 0 0 0 0;\n",
|
| 290 |
+
" width: 32px;\n",
|
| 291 |
+
" }\n",
|
| 292 |
+
"\n",
|
| 293 |
+
" .colab-df-quickchart:hover {\n",
|
| 294 |
+
" background-color: #E2EBFA;\n",
|
| 295 |
+
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
| 296 |
+
" fill: #174EA6;\n",
|
| 297 |
+
" }\n",
|
| 298 |
+
"\n",
|
| 299 |
+
" [theme=dark] .colab-df-quickchart {\n",
|
| 300 |
+
" background-color: #3B4455;\n",
|
| 301 |
+
" fill: #D2E3FC;\n",
|
| 302 |
+
" }\n",
|
| 303 |
+
"\n",
|
| 304 |
+
" [theme=dark] .colab-df-quickchart:hover {\n",
|
| 305 |
+
" background-color: #434B5C;\n",
|
| 306 |
+
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
| 307 |
+
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
| 308 |
+
" fill: #FFFFFF;\n",
|
| 309 |
+
" }\n",
|
| 310 |
+
"</style>\n",
|
| 311 |
+
"\n",
|
| 312 |
+
" <script>\n",
|
| 313 |
+
" async function quickchart(key) {\n",
|
| 314 |
+
" const containerElement = document.querySelector('#' + key);\n",
|
| 315 |
+
" const charts = await google.colab.kernel.invokeFunction(\n",
|
| 316 |
+
" 'suggestCharts', [key], {});\n",
|
| 317 |
+
" }\n",
|
| 318 |
+
" </script>\n",
|
| 319 |
+
"\n",
|
| 320 |
+
" <script>\n",
|
| 321 |
+
"\n",
|
| 322 |
+
"function displayQuickchartButton(domScope) {\n",
|
| 323 |
+
" let quickchartButtonEl =\n",
|
| 324 |
+
" domScope.querySelector('#df-b37c8fa9-dbcd-450f-9341-50c75e7abbf6 button.colab-df-quickchart');\n",
|
| 325 |
+
" quickchartButtonEl.style.display =\n",
|
| 326 |
+
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
| 327 |
+
"}\n",
|
| 328 |
+
"\n",
|
| 329 |
+
" displayQuickchartButton(document);\n",
|
| 330 |
+
" </script>\n",
|
| 331 |
+
" <style>\n",
|
| 332 |
+
" .colab-df-container {\n",
|
| 333 |
+
" display:flex;\n",
|
| 334 |
+
" flex-wrap:wrap;\n",
|
| 335 |
+
" gap: 12px;\n",
|
| 336 |
+
" }\n",
|
| 337 |
+
"\n",
|
| 338 |
+
" .colab-df-convert {\n",
|
| 339 |
+
" background-color: #E8F0FE;\n",
|
| 340 |
+
" border: none;\n",
|
| 341 |
+
" border-radius: 50%;\n",
|
| 342 |
+
" cursor: pointer;\n",
|
| 343 |
+
" display: none;\n",
|
| 344 |
+
" fill: #1967D2;\n",
|
| 345 |
+
" height: 32px;\n",
|
| 346 |
+
" padding: 0 0 0 0;\n",
|
| 347 |
+
" width: 32px;\n",
|
| 348 |
+
" }\n",
|
| 349 |
+
"\n",
|
| 350 |
+
" .colab-df-convert:hover {\n",
|
| 351 |
+
" background-color: #E2EBFA;\n",
|
| 352 |
+
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
| 353 |
+
" fill: #174EA6;\n",
|
| 354 |
+
" }\n",
|
| 355 |
+
"\n",
|
| 356 |
+
" [theme=dark] .colab-df-convert {\n",
|
| 357 |
+
" background-color: #3B4455;\n",
|
| 358 |
+
" fill: #D2E3FC;\n",
|
| 359 |
+
" }\n",
|
| 360 |
+
"\n",
|
| 361 |
+
" [theme=dark] .colab-df-convert:hover {\n",
|
| 362 |
+
" background-color: #434B5C;\n",
|
| 363 |
+
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
| 364 |
+
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
| 365 |
+
" fill: #FFFFFF;\n",
|
| 366 |
+
" }\n",
|
| 367 |
+
" </style>\n",
|
| 368 |
+
"\n",
|
| 369 |
+
" <script>\n",
|
| 370 |
+
" const buttonEl =\n",
|
| 371 |
+
" document.querySelector('#df-5b22b868-a97e-44cc-a69e-b64d305d232d button.colab-df-convert');\n",
|
| 372 |
+
" buttonEl.style.display =\n",
|
| 373 |
+
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
| 374 |
+
"\n",
|
| 375 |
+
" async function convertToInteractive(key) {\n",
|
| 376 |
+
" const element = document.querySelector('#df-5b22b868-a97e-44cc-a69e-b64d305d232d');\n",
|
| 377 |
+
" const dataTable =\n",
|
| 378 |
+
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
| 379 |
+
" [key], {});\n",
|
| 380 |
+
" if (!dataTable) return;\n",
|
| 381 |
+
"\n",
|
| 382 |
+
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
| 383 |
+
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
| 384 |
+
" + ' to learn more about interactive tables.';\n",
|
| 385 |
+
" element.innerHTML = '';\n",
|
| 386 |
+
" dataTable['output_type'] = 'display_data';\n",
|
| 387 |
+
" await google.colab.output.renderOutput(dataTable, element);\n",
|
| 388 |
+
" const docLink = document.createElement('div');\n",
|
| 389 |
+
" docLink.innerHTML = docLinkHtml;\n",
|
| 390 |
+
" element.appendChild(docLink);\n",
|
| 391 |
+
" }\n",
|
| 392 |
+
" </script>\n",
|
| 393 |
+
" </div>\n",
|
| 394 |
+
" </div>\n"
|
| 395 |
+
]
|
| 396 |
+
},
|
| 397 |
+
"metadata": {},
|
| 398 |
+
"execution_count": 5
|
| 399 |
+
}
|
| 400 |
+
]
|
| 401 |
+
},
|
| 402 |
+
{
|
| 403 |
+
"cell_type": "code",
|
| 404 |
+
"source": [
|
| 405 |
+
"# Get data info\n",
|
| 406 |
+
"def table_info(data):\n",
|
| 407 |
+
" print(f'Num Rows :- {data.shape[0]} , Num Colm :- {data.shape[1]}')\n",
|
| 408 |
+
" print(\"\\nTable DataTypes :\\n\",data.dtypes)\n",
|
| 409 |
+
" print(\"\\nColumn names :\",data.columns.values)"
|
| 410 |
+
],
|
| 411 |
+
"metadata": {
|
| 412 |
+
"id": "FWR-opXwC29t"
|
| 413 |
+
},
|
| 414 |
+
"execution_count": 21,
|
| 415 |
+
"outputs": []
|
| 416 |
+
},
|
| 417 |
+
{
|
| 418 |
+
"cell_type": "code",
|
| 419 |
+
"source": [
|
| 420 |
+
"table_info(data)"
|
| 421 |
+
],
|
| 422 |
+
"metadata": {
|
| 423 |
+
"colab": {
|
| 424 |
+
"base_uri": "https://localhost:8080/"
|
| 425 |
+
},
|
| 426 |
+
"id": "DsI9WjmDpGU8",
|
| 427 |
+
"outputId": "cebad37b-3013-4c83-89e8-82c6b6baa0d9"
|
| 428 |
+
},
|
| 429 |
+
"execution_count": 22,
|
| 430 |
+
"outputs": [
|
| 431 |
+
{
|
| 432 |
+
"output_type": "stream",
|
| 433 |
+
"name": "stdout",
|
| 434 |
+
"text": [
|
| 435 |
+
"Num Rows :- 598 , Num Colm :- 13\n",
|
| 436 |
+
"\n",
|
| 437 |
+
"Table DataTypes :\n",
|
| 438 |
+
" Loan_ID object\n",
|
| 439 |
+
"Gender object\n",
|
| 440 |
+
"Married object\n",
|
| 441 |
+
"Dependents float64\n",
|
| 442 |
+
"Education object\n",
|
| 443 |
+
"Self_Employed object\n",
|
| 444 |
+
"ApplicantIncome int64\n",
|
| 445 |
+
"CoapplicantIncome float64\n",
|
| 446 |
+
"LoanAmount float64\n",
|
| 447 |
+
"Loan_Amount_Term float64\n",
|
| 448 |
+
"Credit_History float64\n",
|
| 449 |
+
"Property_Area object\n",
|
| 450 |
+
"Loan_Status object\n",
|
| 451 |
+
"dtype: object\n",
|
| 452 |
+
"\n",
|
| 453 |
+
"Column names : ['Loan_ID' 'Gender' 'Married' 'Dependents' 'Education' 'Self_Employed'\n",
|
| 454 |
+
" 'ApplicantIncome' 'CoapplicantIncome' 'LoanAmount' 'Loan_Amount_Term'\n",
|
| 455 |
+
" 'Credit_History' 'Property_Area' 'Loan_Status']\n"
|
| 456 |
+
]
|
| 457 |
+
}
|
| 458 |
+
]
|
| 459 |
+
},
|
| 460 |
+
{
|
| 461 |
+
"cell_type": "markdown",
|
| 462 |
+
"source": [
|
| 463 |
+
"### Problem :-Now that we have seen the data we can clearly see an issue of 2 datatypes in the dataset , so we"
|
| 464 |
+
],
|
| 465 |
+
"metadata": {
|
| 466 |
+
"id": "X8R0kj4NpmAd"
|
| 467 |
+
}
|
| 468 |
+
},
|
| 469 |
+
{
|
| 470 |
+
"cell_type": "code",
|
| 471 |
+
"source": [],
|
| 472 |
+
"metadata": {
|
| 473 |
+
"id": "iu_jXgwSplk1"
|
| 474 |
+
},
|
| 475 |
+
"execution_count": null,
|
| 476 |
+
"outputs": []
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"cell_type": "code",
|
| 480 |
+
"source": [
|
| 481 |
+
"# Dropping Loan_ID column\n",
|
| 482 |
+
"data.drop(['Loan_ID'],axis=1,inplace=True)"
|
| 483 |
+
],
|
| 484 |
+
"metadata": {
|
| 485 |
+
"id": "dKQsm4QLF0KF"
|
| 486 |
+
},
|
| 487 |
+
"execution_count": null,
|
| 488 |
+
"outputs": []
|
| 489 |
+
},
|
| 490 |
+
{
|
| 491 |
+
"cell_type": "code",
|
| 492 |
+
"source": [
|
| 493 |
+
"#"
|
| 494 |
+
],
|
| 495 |
+
"metadata": {
|
| 496 |
+
"id": "0oocAPzrXinS"
|
| 497 |
+
},
|
| 498 |
+
"execution_count": null,
|
| 499 |
+
"outputs": []
|
| 500 |
+
}
|
| 501 |
+
]
|
| 502 |
+
}
|