Loan_Approval_Prediction_Model.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "17NySOZHXz-Z8fGG-i6zpH1BGn8MGsPF_",
      "authorship_tag": "ABX9TyPclUQWykoxCjRa4jXEqfTj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<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>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loan Approval Prediction Model (Regression)\n",
        "This is the link of the dataset :- [gdrive](https://drive.google.com/file/d/1LIvIdqdHDFEGnfzIgEh4L6GFirzsE3US/view?usp=sharing)\n",
        "\n",
        "![LOAN PREDICTION.PNG](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAd4AAAIDCAYAAACq1n+vAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAInWSURBVHhe7fvxTxtZmu+Pf/+b/JYfIuWH3I0UKf1t3buZ/mEg2mHQzbb1jRpfpa8zYkHKlYXyJUNmfD3ICETvAMqsjKJBoF0z2RvIDoLM7YA2CNTNjkdh/OnQ8o5bWO5mvqaFjJWWLSE93+epOmWfKlcZQwhUivdLOsJVrjp1znme87zPc8r8vwgAAAAApwaEFwAAADhFILwAAADAKQLhBQAAAE4RCC8AAABwikB4AQAAgFMEwgsAAACcIhBeAAAA4BSB8AIAAACnSMvCm8vlUFBQUFBQzn15W44kvNVq9cTKSTQe+AvYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCju+E99uX/0R/f/0KffJ/vnP9Xi8I0sEDNgUABB0fCe/39Ieff0gXrn9Ily+x8P4OwnsegU0BAEHHR8Jbpf3//Jq+/eGP9EsI77kFNgUABB1fCa9ZILznGdgUABB0ILzAV8CmAICgE2jhLT6PUfKVOgDvBS075N4L6rs9TZkDdXwi5GjqToKW99XhO6C0kqDQjFsfy7QcS9DSO3w2AMAf+Et4976n4l9XqJ+F9++n/syfy+7XqXKo8D7toXhaHYD3glYdMpPsor6H/TSwXlVnTobKfpkqJyrmDg6qVHqjPtvYpdTdHkrtqEMAQGDxkfB+R//6syt0gUW3Xj6m33ztdq1Z3kp493OUXlml5fUtKuqBdo/Pr/P5lU3KWtlHtUDpbJloZ4vPr1I6x5/BO6Elh6xu0EB4lvLyN7pIJXXaSSm7ydeoA84os68KVJGPYuMCC2Bu07BnRhO74utcvT6295r4SGbXuEf+CJ71Cl5+ZfGmQBl+tkUxs6HaUIDwAnBO8JHwHr0cW3h3FincmaDl7TIHvjnqvT1BaYmF1U1KPV6kjHF+lsIS3I3r5yjcHqHxhRwVORhPdXfSeEa+ACdNKw5ZWqhnuplHPTTlcUt6RBeyTYrfnaOifEyP0gedMUqmC1TaXqWB9ijN78kXWtbp8JG+9msUfmoqr2e9sv0dnaQ03yP1xh8umud1+NkXRjaNj8VnPdQ2uEr5vV3KPInRjasQXgDOA+dSeLMzIYp/qQ6Y/JMI9T7XsljZDtxjgQ2PknG7CK8VXJnKy0QtCIOT5VCHPMjxwmey/m53e5ZCgxv1jFOjmfBa4icY/mAYui68bj5ymPBmZ3psC7I8i2nDoqD2bO7HrVFaq2XFnPGGIbwAnAfOpfCmR67Yzst1RlDd4yylu5/iqUVaW1+kOAdGN+GV4AnhfTccZtPKeoIuXu+ktlshVTrp8iUrY7XTqvDW/aQuvJ4+wnjVmx7ppIFnG+w79ZJ1tqv2bL7vkvIvA7zjBeC8cC6Ft/S8n8JPCuqoSmtDXWamwkExZJ0/2KLxjyC8p01zm5Zp/l6jyIoYh1KWPeukx7RXArlZ6jiC8Db6yM268HrUK/dEnml+4faOt/bsMi1F+VnWI95sUPwjCC8A54FgC++zHrrcbmVGZom/LHNALNDSwy4KxyYoHuW/j7fMrco3mzR+u4f6xiZoYDBBfSFNeKMv6j+6gfC+M5ratDDnvq0si6TOCS17VLAohtojNDAUo0iSbdpdF94PklvGJYIuvPPd/ea/9LCPzN/rpJD4yIMYDURDdZt71XuwS8sjEQrdH6XhoX4K9U5Tpv47KpNXE3TDenZhkSJSz9go9T3gelj05+FWAASeQAsveP/wq031rWYAAHgbILzAV/hWeJ+x8OrbyAAAcEwgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0Dl14UVBQUFBQTnv5W1BxgtODNgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0/CO8e3+m3/zDT+nypSt0gcvF/3qfHn9Vdr9WFQTp4AGbAgCCjn+EN/+cEskV+ir/PRX/+jXN/OwaXfjZv9G3bteqgiAdPGBTAEDQ8Y/wOspf/uVTuvA/n0B4zxmwKQAg6PhTePf+SL9su0I/mfnG/XtVEKSDB2wKTp29MlXURwBOA/8J7w/fGNvMF3/2hP7yg8v3Wmmp8bsblLwfoRvtH3EJUWRkkTL76ru3JkfJ7mnKqiPw9rRi00p2keK9IWXTCPU+3qDigfryLSmtJGjg87I6OmG2Vyl+r4ciT7bUCYW0XWt/OtlDqW11cKbs0vz9UVqryucyLQ9GaOqknd3LbruL1PvwBZXUoU7mcReNv1IHHrQ+hmWafzhJGWnH/iZN3Y9SeGyViuaXJ4w+nuA84y/hZdH9/f/3p3Txp7+mL/ZcvneUQxu/s0jhzgQt76hjppJbpamXu+robdmk+KVRSqsj8PYcZtPKqwlq6561LZ5K6Tmaf30y0az4tIfCT0/KP3QKNBVmX3yjDnXSo7ZnpkeuUNwXTrVLqbssYNb8ecOZ4QktcEx4/tydcxe5nTkKX7pJ4xl1bLH3gnqvHj4+LY/hHgv8yCZ/qNKaLCzePh42wTGe4NziI+Et0xdDP6UL/+9f0O+/kR9YmWW/SdZ7WOMzyZvUt3JIQN7PUXpllZbXt+pZ0x6fK1SplNukZf4uo0+UgzJl03z9yiZl9zfswutZV5mKmVWaT7+LgB4smtu0TEvRECUPybosu61l6uNdym5SnoXDtJ1mH2Fni9bEbnx93iG8nnXt71JmZZHSLkG04Z4DvnZ9miK3Rml+fYO0qqhS2KS1qSi1DS3S2rq0UYnGlx5tNeqy/E+dcyI+57ymWqB0ljN57qu0LZ2rZ/XOscnXpoxdKCrb+ne8SMls2Os6qFJenTt07KU92piktx3zVIQ3NkrxqC7MVR6bfoqP9NRF1eOZDcLrNjeZ0vMYX8d1pBcpfitKSb0tbve8KVBGzefa+FrX6YZ1bZeL8Hq0CwQb/wjvd/9Gn6h/JaqXj+k3X7tcq0rzxu/SfPchq0tePfdFJ3milakk24APF81JzhnIB50xSqYLxvmB9ijN78kXPPHHQhSe4UmyV6C1sS66aAlv07r6aXyBJ6lRB2hGc5tu0vDV5jsMpc9j1JvkQL9XpvzKKPU9M4NeeuQmtT2cNuyTX0nQjXuL5jamtSvC54uZOeprv1YT3qZ1sa3n0zkqOYJl8VkPtQ2u8j0skE/6qWNsk7NEXsTtsR+FOVPnukq6xvCCU7a3Q+xTJXnXyPWJaFy+59LWgy1KdrNQ5XapxAI69ZD9zaFXVN2k1ONFyhj9maUwPzMv50XIQlEaf2r67vIg+3GtPyEKcV1r1j2dE6peu1DUxUzNA2NsWEBT07TMVRVXpmlqPVerf2DdbJzr2DvHxLkTIO3lTLS00F+rR85JdprlxZElqt7P1ITXa27KQu4B+xOPeWWPx1N2JKy2NJnPF65HaYrFtJiZ5jGNUO+Q2LtAS7GuWobu3i6H8Hq2CwQd/wjvMcphwmtz8mqO5scmaHiwhz4wtpaIsjM9tq2s/JOYudUkk0tdI2RnQuYkrkqgmDMDmXBQz3ib1jW0YZ4Eh3KY8Np2GOT9vdj0fpcSyxxN3VXv6wwKlHowa7yDt2dAfB1nWqbd2LZfmmeF/JNIa3Vp99Qx613T7wn305KRdTbZVmUf8d5qrre18jJBkQXt/fOXozTwUomSE0PYREzUeImQ6c8XX75l9eea9jxzTMx6PYRXu9eNyj4LCQus1Sev/hy61SxzkBcb4/f4Wdyf5cEYLfHiVV4H6O0Vmj3Tc27us/ANbqgfVklf677VWmyQeyz7MmwP3Y6CvV328fR8Bgg8ARZeniNjHzZM0NqEZtIjnTTwbIPW1uvFyEodwlub6M7gpQlBq3WB5hy2mHLdxagJF9vj7xLG1mXdDpyVyiW24F8Psvbzpq2PVpdO3R9M9EDbRGSaCm+9rdK2yONVrT0bxisRG5xFxrv7KZ6SrWvZPlXtEd+1LQB1341QqmCeFepjYBeKWrsa5oFJNsX3cWabWuG2TUVrffLqT9Mx0eapLDj6xkYpkjR/lKYLbyvP9JqbUm8tm7a1y/ueRuGt36Pb0b1dzvH0eAYIPIEWXsrNUsfdacrq21jahC4976eI2m4zsDIVh1jWJzqvvkOc0Vj1FbguFbxarQs05zCbSrC8EVu1vw+rBTx5Bxylec0Mlh28gr/YLfzEUp0qrQ3dPGJdOnIPB1arujcbFA9ZWXNz4W2bqffbU6heTxpb1zWkXn0cBK4rZPVHssWP1L3i9x9pGbzMjaj5q2F5Xj2T5swy1knjr+WzfaFTbxfX2679UMyo02ynle2XFloUXq/MWZunRjvk18DqefX52OSZY/Us3n1uSj+5D7V1i96uVuez/Z66H3q1yz6ens8AgSfYwsuUXs1S7+0Qtd1SpTNEkadWYNqlZV7th3hSDw/1U6h3mjIyEXkCfaBW14K+wq6kJ6gjxBnFGK/CB0ept33CnHhedb2aoBtaXaA5rdhU3reG2Y51m3bR8LoSjl3O+O7Ie7cJit+PUGRqy9hKlPeMppgIWsA8KND8vU4Kxfj6BzEajnGmYgVDr7rGLGFyofCC+m730ABnaL38N2k4gdBEeKubvKDrpDALQfJl2butLBbZVD+19SZomP0vcidBSyqI13jDdfFz+8YmaGCQfZQXisa9ImS3+mmA+yi+G+7sp3k1DUQYww/En7mf0S7qeLRZ+7/WIovGDfb38fVdW78r8n6zU/op7ZigtX22y7N+FnPxf26fjGNNeL37k37URTe6OfPkTN54j21hE147+nz0emblFS9S2rl9CzmquM3N8ioN8AKu/v+7DhFtMp8v1xY/jntqwuvdLn08PZ8BAk/ghRe8X8Cm7wgRMg/ht2ek54SdVVo6oX9BA+CoQHiBr4BN3xEivGpr2Yk9IwUAvGsgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0Dl14UVBQUFBQTnv5W1BxgtODNgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0/CO8e3+mx32f0OVLV+gCl4v/9T49/qrsfq0qCNLBAzYFAAQd/whvfoV+M7NCX+W/p+Jfv6aZn12jCz/7N/rW7VpVEKSDB2wKAAg6/hFeR/lq6mMI7zkENgUABB1/Ce8PX9PvP/s1JX7xKV2++gn945+w1XzegE0BAEHHZ8L7Df0hycL72TB92naNfvzZH2nf7TpVEKSDB2wKALAo7VXVp2DhL+HVy9owXbh0n36/6/KdKoc1vrjQT32fl9WRxsEWJW9PUlodHo8cTd1J0PK+OmyF7DSFpyAszTjMpulkFyVfqwOdvRfUd3+RiuqwJbKzFBpcpZJ8PlDlENLJHkptqwMHlewcDdwJUVunlCjFF3JUUd+1UrdBq9e9DfubNHU/SuGx1cbx0p//apLCTwrq4CzZpfn7o7SmYnBpJUGhmROeR+9g3Jv5yqmh+7jNnvYx1Xmbdhefx6j3mcNnCovUa7XhKOwtUt+jLXWg2OW6Hr5w1FWmpYfuffErvhHe/c1/o9/87o/0l7/Kj6u+oy/GP6ELV4fpix/cr5dyqPA+7aELH01SxjGpSs/76eKl0bcUXg60+2WqHGXCpkfpwsimOgBuHCq8I1fowr3FhkmcSXbShbtzRxPegyqV9tVsZduEn+6an5sgz4+7Oc4+C38nT35rIXZQpszCC8oY1e9S6m4r/tbqdW9DldYGI+S+/tukuD6GvvFXGRcWgx11KHZ7oz6fCO9m3D195TRx+Hjdno4x1Xirdh8UKNXN9Vra6zw+AiVOnBrasTNH4asfUuSpXqF3X/yKb4S3+s1z6v///IgF0fx3ogvXP6XfvOU7XhHegZFR6tUDapWDS3SUA4w20ThIZtMbtLyySumclSFXKf8qR8X9HKUXVjmAOo+5/te5ugAc7FJmfZXr2KSsngXL9Vzvcpqv/RLCexiHC28PxUdkQmrLW56MvWznAU00KoUtWpNxX9+iorU42mNbFMpUzKzSfJp9QmyW5cVTYZPWpqLUNrRIa+ublJegzgErnzF9Yi1T9x/PoPRqgi662pZ9a32R4reilFzfoLXXqi5pi81fXK6rFijN7bOQdmb31EHN3zYo4xVwLN+rjQH3KV1/RnpbG0N51vo0RW6N0rz1nRGoN6iU2zTGwfmcohqf+pxx0GRelaT90jaZFzb7sFBYz+PPJo7A+qagfccBWl1fs5OH7UpZsa20ScbNGhMP+zSg2q2OhMr2Vuu+0syWjOtYHmLjUlbzbcO2jngkz7P+CkcR3i+tcXLEM+ZQu+8sUrhTEp4qpcdC1PtcXVfrj6POHZe5Klnsg1FK144VIrwjfG0sROO1nS9HX7ye4yP8I7xa2S+7n3eWVoQ3ni7TfDRR24YoPpWgnWNDsVGNM+wcT6dpPlOgEjvAVDdnA8Y2ixizk0Ijs7TM31XYmI3Hytiydd3NASu3a9bxcJIMXWCRH+7soSmejKXtDRq+fQ3CewitCG+KRXW8e5byxhm2HwtxakfL1nZXKZnaoOxOmfIrCWob3DC3fDnwfNDZT+MLKujJJJZ7qmW1fblFpT1zF6O4Mk1T67zQ2ivQ8mCIBtZNB/IUXrb1eChC8afmc/WdkMoe+0Q4Qctct5F98LWpx4uU2ZZFwCyFw2ZfGq4zgkzdX0x/lk8SlKKmX+3kaDn1Qo2FhhH8uC7jGbwwuT1h+KTtGXrWKJnR3ioNcFsy1ncSqK+zIKV5bmzzd+1RmldikXnMbVmQ8eEgNxOj8Vfm+NRpNq+6KPJojsVE2adbvSLg57WFua51654QDRv1OgKrJiDFZz1s30Ue813Kss1SfL237W5S28NpXlSYz72hdk4axt2DzKMuGs+oAyoY90hcaclXPG3pNZaH2zifitSeRduz1HEpVN/JyExSWLbjLR+Xc0cQ3st3JmnN8s9O03eEw+1uUuEko+NOhOPlpjn3vGKkvCK6xz7HvlDMrdLU5yqTlR0kbbxqWOP4huf7HY7hhg9rfXHE3PjtHpp36eNZ40vhbbW0Jrz84fUkRcQJ33DweCDvB8RQlvDWqfDEy8xw8DS+kGsi2haJ27Fp7MrLBEUWtNUfO93Ay6pxPqS/I0PGeygtCS+Peel5zBhjys1SJCnvgRzbpIIIqoiJHniGNoyvDBxByW2rWV4niChb33kKr2BkeIs0Ncb+0Bmi8KMNtYJ39zdT7CToW985rvMM1jlK3h6l5V33oCdkZ0KctagDJv8kojIPj7YYNN9qNuqUG6s8pvp2/wEHuNhq/X22g8Z5pQf8qpG9GILBz7PtGrBt24x6HffU2pWjqVssmB7D0Nx2cq/HuHvB4hayFnEsbB2O949Nn+dlS8+xPNzGenvyqR4af8oiacwFebZa6BxLeK9p42Ta3ZhrR7J7gVLherz0ipHyu5eOoVVe/KrzCmN+W4sKHX0c2T9CUSue12OxM+Z+oMbET5wP4ZXJPRij4ZGo2p7QJ1qZlod6qHdo1thimx9SwaXBMb2P5TmRx6u0JltVqsiWmZy3BXNHIAONtCq8dMCB894oDbNdl4wsTBMNnpDh7hgln4pNpiniGniYJsKb5UAW5uwotcL2nIq2JrwO0mOdnLHJJ93fGF4MxLv7KZ6SrW3Z5vQQAE/hZXYlax6lXskeH281BD9nO+u+6HiGjebCW3u+tKt3mpY1f197VXC0odm8GqU1bUeg1lZ+ni1o1uxTn2sGtXZxe11+q9Ga7fRxaDYmOmWavydZv3pPrn6A1NLzvGzZbCwPsbHRHmM3j+fCfdk14Sy8e5Iy1Q0a6FZ2rI0hY7OnY0w1RLT1d7I132nJ7hb2+qUOtxhpfPdqjpJD/RS+HaWk8aMIidf1XUobjnHMP41S5OlG7Vl1P1c4fNgvnBPhZXYWqXdErVZtE40nb3iuto2TeXRT3eN0zCbHnFF3jGnGlaAiRc5zhmU5Zv5Jjy+dwE+0LLxM5dUk9T6xrq+LhmF3K9vbW2xZeNtqv5Q1/cMSh9KCFkzH7NlAjd0tx3s4Ds5DXdpCT8vM+Fk1gTnYovGPdAHQrttdpHAtw5DMUHu2Jlxrg7pfmsgPCOu/YFVtMbZJzb65dcEYw1uzlFVHzvGqzyduc2jC/v5N/2zQbF7drG/ZygIq1E9L8i5OnqdlVJK93DCylV2alx/oWH2statMS9Eu+w/FjFdAHrZrKryOzJnjixuVdc6oRkYpUmtnk+fpvuJpyyZjqZ1zs7Egz+sb4kWjUkrjVdpIgnqt7FL3cdvvEBxjqiHjVM9Opa2dyo9bsbuFjItWv1eMtNXFCwZpq2TWXpm0Q3jN9n1IF6+qZ7nEXFum7RPOj/Da0CddldKPunjFOsHZU4IGHnTVAsQ8ZyVGQDBodlzlVW8/tfUmaFi2Ge8kaMlwOPlxQRd1RM3MbIBXdTd05wMNHEV47dSF1/j3hdscgMZGaeCzBGe/9cBjBnKFBENjq4ox3tF2UjiWoOTLMuWf9Zt2G2KbxuqraBH7jvYeGtD/VUjY4/t71b8S3TL/hrUspcgB8gYLzIC8j3zD197uob6xCT5OUF+oLoS264ys0fSfgXsxGue+G75Z3aJkb4SzSfZZblvHwxdm/3QOCrT0kP06NkFxFqd6W3Tfd2LOhRvdPA84OymxwOnbdPp8qmRnqfdWlAakD9yWgc+1LMPAe16l7oaoNxZjweB7u/mz9e8nIqh3eZ484DHnudIh//Kn3kNb4zK+zs/R7Vh4QX23u3gsODO8EzX+DcbLdvKO1/6DHK9xZ/tc9/AzWSi1yyKiLsyt+YqHLeU6t7FsxcaCvAu1REeQhebVWD1Oufo495GbbxtTDZlj8cfsl4NO32nF7hamsM/XvnaPkZXX0/xZ5qr4Qhf1cX2y4DK2od1oEF5G3ve212OxvIc27KHGLe+5ODg7Ai284P0DNg06jkxIx5FhnyXZmcn36v9Cg0TxpfVveMEFwgt8BWwadER49Z0jDUeGfZZUILrgHQLhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQOXXhRUFBQUFBOe/lbUHGC04M2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHT8Kbz5f6NPrl6jxH+4fKcVBOngAZsCAIKO/4T3h29o5mfX6MKlK/TLL1y+1wqCdPCATQEAQcd3wvuX33XT5f91nz6F8J5LYFMAQNDxl/B+84Q+uXqffv/XP9IvIbznEtgUABB0/CO8P3xNjz++Rv3LZT6G8J5XYFMAQNDxjfD+ZeYTunDpQ/rb//4x/fi//5Qus/BebvuU+pe+c71eymGNL60kqO1WqFYisVla3q6qb8+I7DSFp95i0A/U34BymE3TyX6a31UHzcaipXHapfn7o7SmXCKd7KHUtvn5xHg1SeEnBXXQIra229t4Iuj1H6d97wh9/GXuhmZOeBHm5RO7i9Q7skEVdfje8Lax5C0RGw18XlZHx6ClOXoGnEK7fCO8+9/8kf795boqvzXe8X76eJ2++EYyYPd7Dmt88WkPxdPqQNjboqnuEJ87Q/FNj9KFkU11cFR2KXV3lPQuBY1DhXeEg/OO+bnBvhrNvqsj41mvLz1ypYV7jsgx7G1vu72Nb88mxe/OUVEdvZ0/niy28T+oUumN+nwiNJk7O3MU1sfkfeGMbSd+Gn5qrYKPisMPfcPpxFjfCK+9/JESJ/DvRK7B1znJDnYps75KyyublN1X56oFSmd5JbezxedXKZ2zr+pKuU3j/Fqm7nSl7Cbl35Qpm5a6tqior5r2c5Tm65fTOSp9aZ8srdfFx+uLFL8VpeT6Bq29VtdzG9ek7nXHM99TWhXeUnaD5odCFJnisXD03fU7NzsfIrw124jdnGPLPpLRd092tyhdqB9XtrcoL4dGcNyo1ZVxCGgxs2Gct3ysse2qjdvcfrGzsy2Wb+ljsMfnCmWue5Xm01pgFL9en6bIrVGaZx9KS/uP2L4GDsRPnddUKf9K2unSZqNtLKrW87Qxs43/Gx5f7Tu3eVIpuPi+s36jPx5zx0LFhLzbfOcFQF6Ngf5ss9/iTzyOXmMjbWk1tqh215Cxc7ZTaBJLvH3BfbyFRvs2sZ1gxRsei7xDeFuuy80PmZqNnc+0kHoa5rDMGY6X+/KMRUo7/LdpXG6o7xA/OUF8KrytlWMJL23R+Eej5ormYIuS3Wz83C6V2KGmHk6SkQzLRAxFafwpG2qvQMuDIQo/M41Q+jxGvUk25l6Z8iuj1KfOp0duUtvDaXYiOZ+gG/cWqSRfVDdpuLOHpthRS9sbNHz7Wm2yHLWuimTs4QQt8/WlfW7o3gvquzfLwYWDbG6Vpj73x5bh29Cq8BJPpsxMhAZWeCx4PCr6hHJ+V/WwczPhfT1JocFV0zbpORYwR3CtbtBAeJby5gGtDbJdY6tqu7JM8/fYTvIMEbbrPJHTBbb/Kg20R2l+z7iIMo/ZPxdy7GMcAGZiNP6Kb2jol7TxGrUNLVJ2R67roRuPtswKxP5R7gv7idQdf7hoLij5mR909tP4AgcU9SwDySL3uA3c7oz4kGSUR22fjSqln07TfIbvlXHtjtCUsVUsbe6iyKM5wzfFh9u6621rC3Nd69Y9IRpW9drG31gQmPOk+KyH2gbN/mdXpikl1++uUjK1wedU/YNqq9joe6yhPw1zR0fm+9WQ2VejTZ00njG/KvLzptZlDMw4MLBu3ptJio34GXw+/ZQDviYEBjzvU48XeXEmC6BZClu+Is9qj7BtHM/anqUOK2YwlfUEhVKO+dwkljTzBa/xdrev2K6TQo9c/G1nkcKdPIZGn+aor/1aTXiPVJebHx463+x9j9/uofnavOV4yX2fdxHsVuOyVV9TPzlBzqHw5miKV1pyuvIyQZEFzcC8ghx4yYPtzIqr7CS3Zikr996dpEzNuAVKPZDzYmA9W7I/I6S/Q6utUo9el+nI1mcmO00dQ6tUfHf+ceq0LLyMu31N9O887dxEeGVRFE7l7IJuQ8RWCY2I8IM5SsX6aUkCsPiPJgR6VpKdkVcd/EF8Sgu0dMCTXwm3vV/2Nhr3Kd/MciCzBELIP4mR8cpPnjm0YZ5soPlWcyvtc6PCgUoWDGa7HW3msVqOhWptu6g9j3Kz1KbqdRde8X+1iHGjygFSgrjVJ6/+OOeOjmO+i784t1Ar+/wcDtzm+TItPeCgnW1h4hkiI8FcPdvzWZo/GQu3+gLIwjuWNPcF1/H2tG8zf+Ox/NI8LeSfRMy2H6Mupx8eNt/c+v5B0lwQGH6jtUvnKHHZrK+Jn5wg50949xYpYoio+X3k8SqtybaCKsZ2j0wOW+BiJ7kkxuC/f5cwtkfq9/Aqi6+wG7huPHmGbRLXAsPR63JziuKrOUoO9VP4Nmctmfdfgd+F8Hra2REY7ONe5RXyLA3HotRxN0FLjuTDIDPJwaJgTGLJhCRL6XvOK+uUJUCMQwhq7RIf652mZa1Na6+4Lv0aA3sb9YCVHumkgWfa/VyMDNfxTDv2gHec9tUp0/JQD/UOzRp+LFvkZrtNP13TgmhtbPl5toCnCZFt/PV5Ysw9Bywg4W7ObJ+KXacpYvXJqz8uc6eGQwylDmvOZlM8fzljSq1w/6ei9bn8hjPg1AQLTheFYy8o7xQMXgzEu/spnlrk9sn2pXp2k2eJP3VIRsjZb8hauGlIX9xjCX9s4guu4+1p32b+ps8PrT3HqKvBDw+Zb837bm+Xjv27ug9419fET06Q8yW8B7vGdlEvB0eD1+zoY/VJavyaTYo40kdaNsqTvCP6gkWRV7pRXolq9rJ+AedlYOMZLOLWJMo/6VEGPkZdxmdt9W+1T+BVZn01+f5yVOE1M9dGbN952ZnHc75bE96xa/Vx18dWtgG1YF7jYIvG2TbxaML81fEBB5MoH3drvqMFCKHuk3xvaILSNhuaf+z9Epu7B6/S836KqNcTBh7PtMP3q4WnwTHaV4frCs+p7XbWjUc31b3S5pv1DOyAM42Q2g2Q52nZkSxabliZiz7+tXbJPOkyszcLbofRTivLkcW05fue/XHMHR1PMTTnnrWAKC1owquNRT7VVW+3BddREzzxE+v1luezhIKxzRkf6qLx1+qUjmcsOcQXXMfby77N/a3+C3jO0IduqrYfva4GP9TvdZtvLn23drGOI7ze9bn4yRs3p3k7gi28z3rocnv934na7vRTcl1zTnaebKqf2noTNDyWoMgdXmmJk8jkuNVPAw9iFOfz4c5+mrf8bZdXsncivMqfoPj9CEWmtgzjybuE+mTRDMzPSI91sXCP0vBgjAY4O71hicCR6+I+8eS/wUFsYHCR/p/XvNLnPsXHeOXd3UV9n+t9ez85ivDKO6dIexf1xhI077zN9p2HnRlrPMfZLyqveDK2s+gt5KjweYJCxvWj1Hu7h5Kv3Sdf9nGnbStPxOfGY60xHPisLTGhLgQcBLOz1HsrSgNiv94IDVj2s7Vd7K9Ey0ALXrKQHIlQ6D77FvtViLMOY9Pj1URNzBphf3zURTe6OUt/vEql47SvhllX+OEE+zbX98ASIGkzL3BjMepj3x7o5s/P1AQSIbjL8+ABjy23ueP2JKXVr5f18a/ofSi8oL7bPB5DbIs7UfNfjgqLbBfx/VEa+IznaLcakyb90edOLeALMt+NhbWC67DEMP+M2yhzd4jbG7OypF1aivF8jXG/pQ/datx13mzSOPtNn4wdj01fSBNej2cJIu66UNppEku8fKHZeLvat5m/FWj+XieFuN9xjo3GeCixP3JdDj/8y6HzrWq8Rzb6zs/teFjfZUiPdbovVJhmcdmrPrufsB2v99gTpBMg0MJ7bJyrUnBqvDObglNEApye6WiIEDizGVBDXlNYP+A6ETDevgTC64ZzVQpODQhvEHBmOhosBHpGCnRky1b/weUJgPH2JRBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDonLrwoqCgoKCgnPfytiDjBScGbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQcdHwvtHSly9QhcuaeWnv6WvXK81C4J08IBNAQBBx1fC+0sW2/7/+z0V/6rKbtnlunpBkA4esCkAIOj4Tnh/+YXbd+4FQTp4wKYAgKDjO+H9259+TBdlm/nqj+gf/uVr2ne91iwI0sEDNgUABB1fCe8//rSb/vHlN8Y281f/0s0C/Ak9/k+3a82CIB08YFMAQNDxkfA6y+Fbz4c1vrSSoLZbIVuJvyyrbzWy0xSeOuGAf6D+GuRo6k6ClvfVIfDkMJumk/00v6sObGN8VMq0PBihqax83qX5+6O0VjW+IHo1SeEnBXXgoJVn7i5S78gGVdRhOtlDqW11cJo42tES0j+tjyfedn38jtO+d4Vu8+wshQZXqWQenQyefnOM2LC7Qcn7EbrR/hGXEEVGFinzlrFFYuXA5y6xsRUcPtMaPP9iCVo6y5h45DafHP4R3t11+s1nT+iL/zR/WHUSGW/xaQ/F0+qgGelRujCyqQ5OBuezK/tlqpyhod8XDhXeERaCHfNzy/b14o1lk11K3a3X6+0Pct0oHfrInTkK352jojpMj1x5u3YeF0c7WoL7Hn5qrWxOuu2O8TtO+94Vus0PqlTat1ZhJ0MzXz1SbNhZpHAnC7Xlq0wlt0pTL+s2Ow7SPt3uR8LhM63hmHOnTotz+R3hH+Etf03/+otP6LK835Vy/RPqX/jG/VpV3kp493OUXlml5XSOSl9qk26Pzxfqk66U3aS8dXiwS5l1vmdlk7LWSu2gTNn0Bp9bpXTOXDGWshs0P8Qr0akNWlvfoiJPquJrfo7xLeNWD1Up/4qvke+sdp1DoW5VeN3GuEa1QJltLXDubtlsWtneMmxq/W0IAq7Cy3ZeX6T4rSgl1/mZr1WgcbNlq8Lbkq9tUKYhOHFbrOcLbwqUzmrZCvc3u8d/VTvyO1s2/7Qo5TaN82sZs65KYZPWpqLUNrTIY8pteaPa/qX4uLTFMc5ufeexT2d3jbrnX25p2azL+B2xfQ2wQOYz5tzTrzHGkRdVVptrY2q0jZ/h9jyb8HK/9PG0YoXuZ2I717431t/UVxlbbOB715zP0sgkb1LfSt1nnBh935cYskhp5TeVgked1rN47PI14VVxyLyi3ifBZbzdfEbwsl1R3Z/ZKdjnnI6bX6l2FcUWC3y/YwicNrf1s8F+Lr54yvhHeLWyv9f834is0orwms5uFTaOGKy6ScOdPTTFTlHa3qDh29fqk65hxa+cQ90zni7wPZs0NbXKgbVK6afTNJ/hc+zEU90RmpJtOXaAzEyEBlbKVOK+VNiRak7meHb8dg/NG84n13RS6NEiZXfY8WZ66MajLfniXNGq8DaOsfm9QXWDBsKzlDcPaG2Q7RtbVSJQpvl7nDGwH9QFsRXh5SCzxzYO8738PCMjOtiiZPcozefYlmL/h5OUFv9qVXi9fI3buPQgavrITo6WUy9UXywKRjusrfF8qosu3JolY9ecyTxi/5JhlHZcDVF8gQOW4Z+dNJ4xryl9HqPeJM8H7kt+ZZT6nnE7qtyvlQSFZrZqYyptv3xvmtLbcl2CbtxbNINys763Ryie4uCoLSqEhvE7avscFFemaWqd790r0PJgiAbWzeelR0IU4vascZuLmVnOECfqbQtFafwpB2B1T9iqV7e5bj+VYUo/i5xZJp9uUoXncOrxIi/uVP2Wr6m+jzv708xXdd/be0F992ZZlLheyWI/d77u2KX5bs1PXUiP3KS26CTNWwv3XW5zaoNjimm/tkG1tW9lzkYf5qiv/ZryRWnPqH1XQo2L63i7+IyX7YrPevj5q3ye49uTGN246tKXw+LjyCwvFAqOMVT9fujip45+9t42faHBF08ZXwpvq+VYwssrsspLdhT9HZ6e8XoEQ7mnbcb7eRU2oEwuK8Das+365HJ79gdJEVhH8K+u0oAWvM8LLQsv472jIWKrFkEiwg/mKBXrN98nSSBRweeowusMSmLLyILKBgS25cBLnsR64GaOLrw5St4epeVd74CQT3EgN4SGRbh7klK8UBt/zYcHmxTXhUBrh7TXfF6Opu5OUqYWvDj7eKCEu6FNetv5vltm/5v2PcxZrDptxz5+x2qfC7JVK8Hfand65JptvLMzLBIudjHmmLVg8RDe2r1uyJa0EcBVnzz708xXNd/LTlPHEC/oPc3ujBE5mh+boOHBHvpAtd2w15fGRzsikHv1mCL90q/LP4motrrYyDEXnONt9xkv25m+s6afD2t9UciYecfHCKWcaxGFl5+69bP3ufito5+nTOCF183Z5XzdURh90nkEw4Z7DMq0PNRDvUOzNM/CLttJ1vPsz65PGO9nOyYVcQDVg8Q54WSEl8lMUphnqUxkEajKeoL6eMKJYFn3vK3wyvMjj1e1hd2GuXXsCMBHF15mV7KqUeq928XP0LdsFXuL1CsLiNws9Uo02ubMiwOU9LNWp1Noas9j3/o7zuK0dq9xJmNkCA1t0tte73+rfbdjH79jtU8jm+K5xFlOaoW/n4rW2p0esQfo2pyT5w1tqLMCP+eSao9uc61drrZjAYt393NWL9ursmWp6vDsTzNftfte8dUcJYf6KXw7SknnfiqTHvuwsR55rmp7Q3vZP8LdMc7UxVbTFPHoV22M3Gyk6vYab7vPeNlOG2sDe78t6u1Q1Ozifr2Fl5+23M9T5lwKL72e5JVl/deU+Sc99Un3aqK+xXvAK6eQMjbfc6O2XckYKzd2Jm11n3l0s/Y8eXZ9paw5jcuzzczB6Vhct2cACy5HFV7vbGSLxnlixaNqS1YywSgfc3Zorcbrk9Kxhcc+cNHyBxtio4SxTW0gthzTrpN6pewu2gJwesyegdXw8jXjWP1l1jijqfuFhWyZxyjOCz8js5e2Sf84s5+X97uCpxCUaSkarf86XLCex9foOzteAc2z785n2nCM33HaV8Nsi5VBlRZ04b2iZeNVWo51mrsB8ryPtGyMRakj+sIUdN3mWrtKz/upQ9/pknu5nbWsTPzsIzUmnv1p5qvSD2VfvY8HG+47XtLmu9OUVe9SDeS5qu1uQlPL+HixZgmv9Kv+y/0qrQ3drAnSfDePvfIhWbia4+I93naf8bKdnOd+Wo98s0Hxj1z8uuX4aMfLTxv72aVeZ8g1mi8Kb9zs824ItvA+66HL7W7/TlTlYNjFk26UhgdjNMArzBtWEJEAeKeTQrFR6rs3ScMxy9hVyjzuobbuBJ+LUuizDZ6wXM+jLl4FynYPZ1YPuurG32Enb++i3pj5fmieV8jmT+fNeoxnc90dD19Q3nBMcQTrGgHC64YtK7SNsTqnkX3caRNQWRjdeFy/MD2mAjJT5EByI9RP4+scMaqbNB7qpHBskbKOuWhdNzDI37Ets6l+autlnxhLUOROgpaMtrG973VSx/1JWuPqKq84mLRz4F3I2TNXL1+rblGyN0K9Q+xXykfc/EDepV3UfESC4cUHSkgECciWsAiaEMi7v/gd8xnx+xGKTKmsutb3BCV5rsi7M2uM9IAmfuzad1l06M90YBu/47RPI/+MRVHm0ZDMyXqmJEE4/CBBfYN8b5Tn+aNN81553i1+9gNesHCbw528SKnFZM3m32l9OCjQ0kOu4z4vaqQdT/mGN3zt7R7qG5vgfvBzQprwevXH01frsaHymjPSO/3cNq63u4v6Plf3Oii9mqXe21pc6wyZ7WJ0nzYoLPK1UucoDXzGfe5W/sL9Eh8NxXiMeDyM8VPvYksvR6mDbRTnONeX5D6oOeQ13k6f8bQdtyXSHqEBbkvfA+4jC6lNoA2842M9hjbi6afKfmHpJ/tCWNs9ss9l7sN1t/a8GwItvOD9AzYFb4sz66shwngOF7PAf0B4ga+ATcHbYs9+NJwZKQBnBIQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6py68KCgoKCgo5728Lch4wYkBmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0PGX8O79mX7zsx/RxUtX6MKla/RffvZb+tOey3WqIEgHD9gUABB0fCS839G//uwaXf9fT+hP+e+p+Ndv6N9n+DOE91wBmwIAgo5/hPfrf6YfX+qmmTx//qFM+z84vncpCNLBAzYFAAQd/wjvF8N04dKv6B8ff0qXja3mD+nTf/ma9t2uVQVBOnjApgCAoOMz4b1CPxn/IxU52y2+HKbrVgbsdj0XBOngAZsCAM6MgzKV9tXnd4h/hHfzn1hor9Avv7DO/ZF+aTtuLIc2/mCX1h5Fqa0zZJTQ/Wla21XfeZBO9lBqWx0I26sUv9dDkSdb6oSTHE3d4fpvaeXuLGXVt2/Fq0kKPymog3dFjpLd0yfT3hOgmU0zU132cbbK7WnK0C7N3x+ltaq6WKO0kqDQzPEcXe4d+LysjrzJr4xS5G6UUq/FHxK0/C4n74H6KxzHR/T7ndi+8x7TU2d3kXpHNqhiHLyDMZZ+u45LmZYHIzT1TiZI4/jW/UidOCUa4t47wH0uva0td2npYT/NO6ZA/lk/DawcPm8byExS34L9vuJCP/U5273/gvqGLH88Ov4R3h/+TP/YpmW8y5Lx/i/617+6XKvKYY3PPg7Zg9LOBqVeNlfe9MgViqfVARVoKsxO8UYdurJJ8btzVFRHJ0p6lC6MbKqDdwW3/9Io1bp8xrTmkLuUusuBYkcdGridUxxUqdTUht4Un/ZQ+Okhq7XtWQrFVmuTsLJfpkozcXsrHP52DB+RPtV93I79uyZjetrszFFY6/eJjzGPo6ed37wrezrG1+FHp4k97r0bvObSW9uywL7RPUd5qw7n8RHIPOpv8Hdp98XrvBjSxd3hj0fFP8Ir5ZvnFP3pNWPL+cLVjym68I37dao0bzyvJrubB41SbpOWV1ZpLVN3hpoDcracWZ+myK1Rml/fIO0SB02E902BMoUyFTOr/JxNysqqbj9HaX7mcq3CKuVf5agkz5PzafmsvnIJqsXMhtHmdE6twKoFSmf11RjXlynUJm/D9cJBmbJpq00bwRLebZdxNOyg0opa3zfsY6Kzs0VrykZ5R7Bo8JldvnYqSm1Di7S2vkVFfmaRs96SfGfZhutrsAHT3DYu7ZP6NJ9Mb3OfDB/ZqLUro4+JUZf9GaXsBs0PhSgytVFrr0Xjd03GVLB82VGPTqWgxlK/Zo/vY3vU2mzZptlccAS62hgLxlyVMVNzzDjX2Hev+iuFTc2Gm5R3LNIq21uUt5pYexbHBI/YUvMRx3g12lvzYxc/8sLNb2Q8io7YYos7gue4uwivi21LWR6b+i1Gm2thzMsXmswlC5stNVznhwfFZz3UltxiE2/ScGc/Le2pLzza5W6jLRqPNsZyYzHK1w50TlLGutYpvC3MBR1/Ca9V9sru5x3lsMYXF6LU0TtN8yxEzoyn9HmMepPsSPws2d7pe2Y6RF142UH3eLDDs5Tha0q6w9lg4VWBcM0qlhBKUOSV0hQ7XTEzTeFQhHqHVvmZBVqKddF4Ri6SyddFkUdzPJGlLQlq6140DWoT3iqlxziDn2HD8v1rI10UljZXN2igXXMIWTUPmlsgmcfsMAs8Ifc4WMzEaPyVdMJRz1gXXQyM8F4zAld2R/rbQzceqdcD2jhmkhEaWGF/4L6nny5S2rnFtbNI4c4ELW/LgmmO+tqv1YKFq89U2TeMrewtrlNW7lrbZHK2R2hcbMABaKq7U9ncyzaHtM/pk+LTyseSab5nm79rj9K8EXTYzk+V7xvPjtCUbCVy9paZkWfw/UZ75VpFw3dNxnTvBfVFJ1n8+Vp5HfNQ+azO7iolUxt8r/Jr5ZfS5rYw93ndaluIho3+N5kLtkCnjbERaHto3Oj/Jk1NrfI1Hn037uuk0CNHfxpsKNfWqYtSmZYemPO5tJOj5dQLyhtXaLye5Pknc5zbn56j+bQZC9ztrfejeRssvOsRO1mxhft4Z7Qed+4tmsLmOe56HxnHHOi9PUFpviyfYtvUtmGrtCZb8DKuXr7QZC7V0cZAw2t+eMPtGerifke4H+par3Z52EjO97otDER4eWwqPH4hjiOGD+v+6DFezfCn8LZYWmm8rLiXeRLG73dR2+0EpbIyIjmauquJFRUo9cB8L2tf+bWyjewivK+V8bSAbzoYr8SsQPqltbXldLwqLcdCNCVd0++vSsCdq0/0A85UP+I+8Md8ioPlumnpzCMl6HK9NeEEuV62sdzqCeJWs/TTsl1tHCVw9tCU4QPuZGdCFP9SHTD5JxFlJ2+fkfrrAUVrh2NVXHmZMK/zsk0L7Ttsq9lov8OYFQ4wIqjWeft2sh37d95jmmXRshYRQv5JzPRZN0RUZMGg2eOi1mbKzVKb0f8mc8FDeGVM25q8v7f3vbmPNIqCST0m5Ch5e5SWd73tI4uzcCpnF05Pezva42iDZHxmTFFZ+BHq+UCyPwP207Ca357jrvfRfQ70PmdxkkU+L/qM2LG3SBHVFi9f8J5LOk6bM579PITCHIW02ObVLlcbMZkkzz2X99z6nMiyLxljofmj53g1IfDCa2OXncVYQXPw+ruEXSzXze2OYwmv1zXs6Hbh1QSuNsnM82uaE9TaoN/vCOLGcy3BtCaBPjHkes72l/U+vipQpVk9PuDEhFe3iz6ObwqcqUzwxO6icIwzFsfks9vfnHSmnbg+D5+p21LQ2uEca+s6L9vINYe0r8Hf9L4x9SBRpuWhHuodmjXaLFvIVr+OLbzas9MjnTTwTGs/l6y1vWfBgT3cHaPk01X+fpoimj1Ctt9eWOPUZC7YxrLerrp9dLz63txHGusxsfnE7ialHo9Sr2Tmj7dcxKDKmfosDcei1HE3QUvSTU97O9pjawPXk7auV8J7rHrMMTWa7znu9j56z4H6wl5f7Hv5QrN66jhtwnj28xBsPuLdLlcbycL6voqdDmxz4oAXMveilPrSfewE937aCbDw8uBm7O9YjJfu0RccLDmziEZpXh8bdV167Jo2iI4g50qTa9jRWxPem/WV2QE7QEhlxq8mtBXqFo2HOChZW+bSl9qq0Nz2iY/0aystuX6C0nr/jc8u9Zwn4dXGQ7bO9AkjlJ73az/Ik+2rm8pO3j7TGOhUOxyBoH6dl220v4xb+4x+3dJ+NW/zMT1I8HXa6j/z6GatLrlm4KV71mb/zntMZZwi6vWMgd4XhdEWKxOQxaFuDy2jkaz1hpGhNZkLvGh2E17ZHryhZ0NGO7z63txHvDJnW2DV+rk2qNel0Mdhe5Y6DNt42dvxOxSbH7nhXY+tX7Z65Lu68LqPO3+lxb3GOWC9FmOMH4CNUvx2fffHyxe855KOve0VIy41mR9vmuwGOeabp4/q9Vo24kVixMP+9TmleMMZ+fVrdFGbC57j5UGghTf7LEEd8q9EtX87sVY3zO4qxe/IO9cJit+PUGTKXL1WXk1SRzsHn4UcH2sT05NNGr7+kf3fW6x/J2LhvDxmBUVtAgi1ySHnQ9Qbi1Eft2Wgmz8/U42sbrIDdnLms0iy+1iR9zW3+yk+luC/sXpfhMwkfSBbz5pTVbKz1HsrSgNjXG8vr1A/N52wkp6gDg5oUk/fIK/e29nJjW/OnlaFVwKWTQSNcdS28nXbsR2swL4UY1vHJmh4qJ86uqcp45zHvKKdv9fJwYX94kGMV8W8erUmr4fPOAPdfLdqhwQCY6Gn0K5zt00L7WO/TnPWcaM7QQOPV6nEdda3FfUgYV4Xfsh1DfK1DzQR32ERbO9in+MM3jnctu+ajOnBLi2PRCh0f9Roa4gzlIa2Fhap1/DXURr4jH22W90rAnA3xm1KmP3kIJ42Am6TuSDjynbpuD9Ja7vaGHM/5X1gG4+HZDChzzZ4vL363qQ/tbmWoORL+zZheqyTxuXfe6pblGRbif3FLzoevmiIDcXPEzwW3Bbuc+/tHkq+NgfFay7K71Bu8FwcX+djmx+54+U3tvng8Eeb8LqOO9erxz2eA0sPZceF/TzKf22ZfZntcEV718t4+UKzuVRDsyW34bJ6p+/eT7bRdee815DFmfWbAMGjXW42ys5EPV+VNAgvI+97b1hzu+l4uRNg4T1htl+woXjC6SW1WQ+qx0YmhsvK+YhU1tmZbL93fz85VZuCs0EEQMvS65zMXAAeeI77UTD/xdIX/9t9YvDi7bn8KO/0gPCeOc5V+HFQ26DOd2zvIRDecwALgJ6l1zmJuQA88Rz3I/CaM2Prl+3g2EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgc+rCi4KCgoKCct7L24KMF5wYsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOj4RniLS7+gi5eu0AVb+ZRmvnG/XgqCdPCATQEAQcc3wlv9oUzFv36vynf0xWc/pQs/+if60w8u16qCIB08YFMAQNDxj/Dq5a/P6R+uXqN/WPje/XtVEKSDB2wKAAg6vhTeP42304W25tmuFATp4AGbAgCCjv+EN/+EPrl0jfqXy+7fa6Xlxh9sUfJ2jJb21PHbsLtIvSMbVDEOcjR1J0HL+8bByXGg/gryvOSmOgg+Ldt07wX13Z6mjD5WxySd7KHUtjrIzlJocJVK6vBE8GzjO/IfAICv8Znwlunf//c1uvDxP9NXh2S7UlptfOl5P/U+jFE4VVBn3oKdOQrfnaOiOqzsl6lyAsG/zibFtfqN541AeJ1kkl3U97CfBtar6szxSY9coXhaHRxUqbT/9nXqFJ/21Ot3cPL+AwDwO/4S3q//mX4i2e7Lw7NdKa01vkBT4QStVflv96Q9Q9rLUbrAgTa3Scsrq5ThzyZVyr/KUelglzJ8fjktn9VXDuEtvubv1GeS69f5+pVNylpZzEGZsukNo/50rqxOetTPbUyvT1Pk1ijNr29QepvbowlvKbtJ+TdSnzxji4p6X9yezRQzzmdzsN/eovy+/dm1MdhRFwkedb5LWrJpdYMGwrOUl7/RRVt26hyjfM2kPLZZHoOdrYbxsAsv91mus9hnH5FxWtfGW/zGOS4e9ZeyGzQ/FKLI1Aat6XUoav7TpH2CZZ+1zK46436O3hTYj8tsd619Vh/06xjX+wEA7xxfCe8X4x/SxZ/9G33r8p1baanxmclaplta4AzppZbNpEepLRyj8fUClTjgTXWHaPiVfL9LqbtdFHk0x0JUpvxKgtq6F02xtQmvXNdDKRGr6iYNd/bQeJrr2t6kqalVvqZK6afTNJ+x6o/QlLGlKfd1UujRImV3ONDP9NCNR1sc9HkRsLdqiEpmr0ylN3ypJrzpkZvU9nCaBdls0417SnS8nj0WovAMB/u9Aq2NdFH4mRlgRWgu3+NnyLOneqgj3E/xFbn3BQ2E1OJEtue7eQGQ2zXb/nCS0trQvStasalhR5XpZh710JR2S3okRCFu6xqPUTEzS+HOCbPdMo6hKI0/NcdjeZDHRhuPmvDq9t1Z5PsTxhgUc6uUfLpJFR7r1ONFylj1ywLAuq89QuMLOSoatu6k8Qyf50VAZiZCAytsT7apPbvV/Mfrfqb4rIfaBk1fya5MU4p91Dy3Svk9tuGTfuoY47bJxezTF65HaYrFtJiZ5j5HqHdIrivQUqyrVmfp85jxCiPPbcqvjFKfGgsAwLvHV8J71HJ446u0HOuneevdblVlSupQgtRFfRs3N0ttsVUOYFpANJB6QmaA1wOzdl3lJYvzjHd7KhzgJACbAd5Rf5XFtlan91azTSDk/SBnxnLo+mypMzxX7+vBBsU/YlHljw1CE31RyxrXhur9iSxoWdeXo/ZFyzviUJsecL/1nYtteSdrvXOXvl3TxogoOxMy222zGyPjc2uWsvzRS3hr97phLJJYIMOmDZz1y/iFn5pi5r3V7BBe1/vFzglatjXDtP1aTcQLlOLF05JktyK8NZ+W+tV5gW1Yq/OuvvvD9z8wxwIA8O4JtvByUO64+hG13QrVyt9cvVlb9UuQCj3R3vvWgp8ELD2wacHZFiDrgVOCqxVo65RpmYWsd2jW2DqWLUczADuE1ya2rQqv2UY5dH22I5Ab9V4yr28QGm3xkR6p9yfyeJXWuN1WkW35d81hNq2sJ+ji9U7Npp10+VK0trhKj0RIf5VfGxvp59CGOis0GQ81bvbxVuytUry7n+KpRR6TRYqrxU/DeLNvWTY5jvDW76+3s47znFZPg/Bq1+l1/h1n8ppt19a1VyYAgHdKoIU386i+tVZDz5AkSFnbtYxkGTeSW/xJApYm0JJlhVTmsLvoKrz0epJuGNmywhBtDnBa1pl5dFMFYO0+A76uVid/VpmYgSaMXsLr/uwtGg/x4kG2q4UC16P62orwSp2yfVlD6tQWIu+K5jYt0/y9ushaiBiHlNpK3+qZuuxUdNL4a/4o/ZSM3+pDjhdlKtNPj2lZsiaA8qO8Dn0nQe5ln6kt1g54jD9qTXjdM2fNDzzvL9NStMu2nS6/G1iK8n3WAuPNBsWtVwTi04cKr9zP42g2z+QUbAsAMAmu8FY5GOnbyjW0oCNB6m6MBh4kaHiIg+ztSUobQiUBK0S9sRj1DU3QQDd/fmZFuV0O/p3UcX+S1nb5M2c/5lZelTKPe6itm+uKRSn02QYH9SqlWfzDDydoeDDBz+nShFfbArQJr3nPDa5ngDPOkiaM8o7XEBEDPai6PZsFSd7x3e6n+FiC/8ZoSXUhPfYhDb8yP3sKL9eZTfVTWy/XyfdH7iRoqbZQeHc0tSkvHvRt5RoigPIulz+K8IbZnn2DExRnwep4pN59Sj9v9bMNYuZ4dPbTvBqPyiteZLSzOC7kqCILK2vr/aBASw+5jvujFL8fochTvuHNJo3f7qG+MfYLtmkfL25qwqtt2evCK++KI+1d7E/yvtg8ZaL5T7P7C/KvU3z/0Cj13oma//pknOM2j/E5/pvMKGF/NUGXawsmL+FldjlzvyPvf3mcpG9TW43jCgB4JwRXeFtBhFcTnToSsPSMFJwWb2tT1+1hQYRNzygBAOCMOPfC+4GxtezEmZGC0+LthVffFdBwZpQAAHBGnG/hBb4DNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCzqkLLwoKCgoKynkvbwsyXnBiwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKDjI+Et05+S3fRfrl6hC5e4XP+E+he+cbmuXhCkgwdsCgAIOv4R3s1/ouuXPqHfbJaN42/n79PFS/fp97su16qCIB08YFMAQNDxj/D+x69ZaIfp339Qx1//M/0YwnvugE0BAEHHP8L7wzf0+59/TP/l4/v0y89+RZ/81x/R3z/+M+27XasKgnTwgE0BAEHHP8Kb/zf69Po1+snPf02Jz35N/f/jQ7r4syf0FysDdikI0sEDNgUABB3fCO+3v+umC5+w0FrnflinX166Qr/8wn6dXlpq/M4GJe9H6Mb1K3Txv4Uo8miDigfqu3fOLs3fH6W1qnwu0/JghKayxhcnh60vmzR+f5GK6uh9pBWbll4vUrw3RH9z9Qpdbo/QwNMtKp2aTV14NUnhJwV1kKOpOwla3leHJ4H0zbV/78inAADvFN8Ib3HhPl340TD94a/q+D/+iX5yqZ3+cbPxWqsc2vidRQp3chDcUcdMKT1H868NJTwFdil1t4dS1vPflKlyogIh9Y9SWh2J8MbvzgVaeCuvJqite5YylrAdVCm7Mmuz8amTHqULI5vqgNu4f8J25vrDT3fVgYMT9ykAwLvGN8JrvOP9xSd0Wf6VSMrVH9Enybd7x5tJ3qS+lSYiu5+j9MoqLa9v2bPgPT6/zudXNilrBfhqgdLZMov5Fp9fpXSOP1vI9YUqlXKbxncZ/mxiF97K9hblteYUMxv2ulhE8urcWqYeaEvZTcpzgM2mpU1WW/l4nTO/W1FKrm/Q2mu5XhNeZ5scwtTwbMa6Vn82vSlwf8p8vTYe1rjp1zFudR6V5jYt01I0RMkmGZ5rHzyoFLZoTfqh29/TlmybVznOrHcpI/ek5bP6yiG8xdf8nfpMcr3Tlw7Els6xcq+/UtiktakotQ0t0tq6+IG6XFH3qSbtE9x83e2c3J/dtfvNUeoEAByKf4RXK/t77uedpXnjHdmmEysb3hZRmaPe2xOUlgBW3aTU40XKGOdnKRyepbxx/RyFQ1Eaf8pBZq9Ay4MhCj9TwZ0Db1s4RuPrBSqxME91h2j4lVRmb0N65ArFjfS0Sukxvj/JgZTrSqemaZmrKq5M09R6rlb/wLoZ9NMjN6nt4TSluU35lQTduLdoBPbKHj8rzH3YK1NpX67VhJfb9EFnjJJpbtP2Kg20R2l+T77wePazHmobXOVzHGSf9FPH2CZVVD0XrkdpioWsmJnmMYhQ75BcV6ClWBeNZ+QiXuQ87qH4grSd75/hsTD6f3Sa25T7d0nP8O149sGN3VVKpjYou2OOadvgRq2/3rbsosijORYjdU+32ta3Ca9mc/al4c4eGjdssElTU6t8PY//02maz1j1R2hq27qvk0KPFrlNMoY9dOPRFtfBtuVnhWa2qMR2dma3dZ/yuF9Qvj6fYxvmuN9PeVy8/F/8/GqI4ivS5hfsNxEKDc1yn7nOqR6KLKiFwt4L6otOGj4p/hV/+H6/4gDgNPGl8LZa3kZ4szMcXL5UB0z+CQvKcy1T4+yzZAibCvQSkPRt3CqL2a1ZMpIvDrwXtYyHcrPUFlvlQG5vQy1I6ve6IFuVEmyt7cV6cBVyNHXLEh+pXxciu/DqWZjRX89nm3Wu1YJ6gVLhflqSDK1BVNR54Uu1BSp1qsWAwcEGxY3+H53jC2+TPhhZ5wZnjFxYBG3tEmHb4/Zr49aKLflGWo6FaEqa6yG8lZcszjPe/amwkGZmIppwavXLmGpt8tpqtguv+/1i+4GX9oWQp/87/FwWfeOv1UFhjkKqn1kWdmvRJeSfxMyxAAAcSoCFl4PG2LWGgGNhFzPOlp72mMGNg3C8u5/iKdnak61cTXiHNoxrTTQR4MAYqv24hqkFL3swrD3TKeKKbIrbwJltaoUFYirqIbxS59GFV/rn/WynoGntbhAV7TpLEKTOXs6cLXGT8sohcC1y2GJqnjNQ9wDfpA9vOLN3Ci8Larg7xtnfKp+fpog2bt621IVds0vDGJnPrfmUjTItD/VQL2eR89ye+SG1INLba2C35ZGFV7vf6euC81ytrQ7/SI9odcp3qp/pkU4aeKbZm0vW2FEBABxGoIVXgmtHaJIy+nuxnS3zHd7zfu2XqFVaG1LbpnrgPdii8Y9UMJeg8xHXZQVeqTv6wszyJPBqGZ9kOjeSss0nQlEPXPVgx/W2J2jZapdRpz2wlxZaFV6up7a2aEF4XZ8t7065ndZwvOGMVcZNvmsQFRfhlTpDE5S2xkbQPx+Bw2wqY3tZfrmt1S/vI7OcPXr2wQVjPKyMb2/RJrxetkzd5ezPyvIOOMMOqYz61YSWJct1yuavJ+mGnvkbbWEbhefM1xdM5tFNZRftPgO7Lb0y51aEV3y9Q7+f2+Hp/y0Kr9wfsV61CMe0NwDnkWALL1N6NU3h9k5quxWits5OutE9SWsSLw4KtPSwi8KxCYpH+e/jLTNAvtmk8ds91Dc2QQODCeoLacJ7q58GHsQoPpagcGc/zVtxS4L13Rh/l6DhIQ5ytycprYStyAJ6gwP0+PouZ+CdtW27irwv7eyhAa4rcmeC1jiA55/xvdFRroPridWzJdt2n0P8rPoHBhcp6wjWHxiCYVIXXvdnU+EF9XG/B8ZGqZf/JjNKzVlULo8dJrxcZ3aWem9F+X4et94IDXyuBeUj0IpN8wsJ6rBsyn87YnOUlfH26oMbhUW+pp9tOUoDn7E9uzXhdbWl9D1EvbEY9Q1xHznz7n2mHKDKPhPqZF9iG1RlsWVtx1eNd99t3WLPKIU+22BBr1L6EfvbwwkaZv8aeNClCae2ja/bslZ/gpIv7T9cq/tUk/uVr3fcH6X4/QhFnnK7vfx/d5HHov6+1kt4Zft+eSRCIa5TxinUO03NhhsAUCfwwntiODIBGxKstewSHJ9TtakbnrYUYdMzSgAAOB4Q3lYR4bW2lp04sktwfPwgvO62dGaUAABwPCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6py68KCgoKCgo5728Lch4wYkBmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgo6PhLdMf0p+SpcvXaELXC7/j9/Sn/bcrqsXBOngAZsCAIKOf4T3i2G6eKmbHn9dpuoP39DMz67Rxf+9Tvtu16qCIB08YFMAQNDxjfB++7tuuvA/n9C3+vGlX9Efyo3XWgVBOnjApgCAoOMb4d1/+Ssz4/1PPlYZ7wU+nsk3XmsVBOngAZsCAIKOb4RXxPb3P/+YxfcKXbj6MSV+91v6BMJ77oBNAQBBxz/Ca5W972n/B86Al3/FAjxMX/Bn1+u4HNb44vMYJV+pg3fN3gvquz1NmQN1fJb4oQ3H5DCbph/HaGlXHZw3mtn19SQNLMjA5GjqToKW983TLeGoN53sodS2OvAlOUp2T1NWHTWw+4IGHm1SRR0C4Df8I7xfP6ffzP+Zvv3r91T8zxXqb7tCf/ubP7tfq8qhwvu0h+JpdfCOySS7qO9hPw2sV9WZs+M0+33SHCq8IywKO+rgXLFJ8btzVFRHdqq0Nlgfl8p+mSpHWHw5/SU9csXn/sNjcWmUvJu4S6m7/bR0lMUHAKeIf4T3m+cU/am815V/J/qQfvKL5/SXJtmulLcS3v0cpVdWaXl9i4p6kNrj8+t8fmWTstbErRYonS0T7Wzx+VVK5/izTnWDBsKzlJe/0UUqqdNCZXuL8vu7lJFnpXNU4meVcptGPRmHgBQzGw31l7KbXK86oDJlXxXMlby0s1BtqKuU3aD5oRBFpjZozdm394C3EV638aODKuXV+bVMPVU2xvUNj2dabN1knNz84YDtmd21j72c48+WjS2sa/Rn1/xJUSlw3Xvyidv6Su531CXXr09T5NYoza9vUHrbsbirrtLArdlaBlh8zffJh8P8lnHzF0N4v/QYG7d549kf5sCqh9utP/+o86xWj1y/YRdevn7N0absTIgGXp79IhgAN/wjvFYpl93Pu5RjC+/OIoU7E7S8XeZgPUe9tycoLXO0ukmpx4uUMc7PUljE1Lh+jsLtERpfyFGRJ/lUdyeNZ+QLk9JCPdPNPOqhKa1ZEsQu35vl4MzBdKqHOsL9FF8pUGn7BQ2EJtXWdJXSYyEKz3Dg2CvQ2kgXhZ+ZgdouNFrWkx6lDzpjlExLXRx426M0L8GOxSQzE6GBlTKV9o6W+fiB4wmv9/gVV6Zpap3txueXBzkYKzulR25S28NpFrEy5VcSdOOefcFk0MwfrobqdmTfCA3VbRxZMAWj+KyH2gZXKb/H55/0U8eY2v6U+0c2jWuEup9KptZJoUeLlJW6ZnroxqMtY/FQ2mMb8/MzbNPSG+O2OrlZahvaUAdShxqjQ/zWwMVfTJ91GRuveePZH9kJkrp5nHj8008XKS0ie+R55rDvWBddtIRXXvEY84vryq3S1OcFOUv0Jc+PJI8dAD7Ef8J7hHJc4ZXVcPxLdcDkn0So97m+upZAxxM/rCa3BARtm6/yMkHhpyqDOchxgLAElNmepdDgRu39km3bTuqJvqgF+LUhFSAlYwnPmcFHOOAV/UdcJ39sJrwXtGBn9Ek9p2mm73OOJbxNxs9Ctl9LLCKW3ezbqWxDziY9h+wQfxARH3+tDgpzFDLsYta5Vlv4FCjFiy5j+9NTqDTRFKRftec02Wq2+YJDeL38VsPpL15j4zlvPPtTpqUHvBDNemSerc4zN/tawpudpo6hVSo6HyGLEa1NAPiJcym89sBiXmdMcM4q4t39FE8t0tr6IsWtYOwICBLorABWWU/Qxeud1HYrpEonXb6ksk+mQXi1YFATEWf92jusVoVX76tXv98HjiW8TcYvm2LbcmabWtmgtamoh/CKWClb67ToD7Y21Wxcb4OJQxBdbecQXpvY6p8dtCi8ut/qOP3Fa2w8541nf5g3BVpOTdDAvS4Kx15QXhYiR51nzvOOsS2+mqPkUD+Fb0cpmVEKXFsAAeA/zqXwlp7zJH2itqTkhylDXeaWFk/0kHX+YIvGPzokIPCKfv5eXWQtRIxDKbOeloSX+Fkhzo6sLUQOGmG1vZce07YHeRXfYbXDFmztfZXP7+v7rWMJr+f4maJhZZ2lhSMKb4v+4C68nO1F+bzlZm84S7NeLewu1uwr/rccu6bacojwau9xbbyepA9qvnA84dX9xWtsPOeNZ3+YWsbPGXKqyzx/5HnmYl9LeLX6JROu7RDwvRfHILzAnwRbeJ/10OV2KxM1S/xlmSdogZYeygp8guJR/vt4y9wafrNJ47d7qG+MV+iDCerjyV4LCNoWcS0gyKpa21auIcGkc8K4Nz32IQ1b/9JUC8omesCuZKZ5xd5P8bEE/43RkhXfWGxD7REaGIpRJMnt6q4HFv0dlm2RsbNIkfYu6o0laP7t7XuqHCq8PJ5/06nb1Hyn7jV++Wf91BEdpeGhBA3HVIbG2LaHvYTXyx9EaLoXawLhLrxMQf7FjEVtbJR6+W8tG2NRXmbRknYN3IvRON9fF17917i68FYp/aiLbnQnaODxat0XBdtW7C7NczZZ29J281snDn/xHBuvedOkP0uxCEX4+mHOSDu6p8kYgqPOM6aSnqCOkGnfvkEez3ZzflVeT1PkjpyXudFFfZ+b1ze8PgLARwRaeMH7B2x6HCS7btx5Ob9gPIC/gfACXwGbHg95vdGHDM9k/wVnxS47UQD4BAgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0Tl14UVBQUFBQznt5W5DxghMDNgUABB0IL/AVsCkAIOhAeIGvgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoHP2wrv7Z5r5+cd08eqv6Qv9/N4f6R8//pAuXLpCF65+TNGFb+z3cUGQDh6wKQAg6Jyt8H71W/rx1Wt0+fo1FthhTXjL9O//+xpd/NkT+mr3e/r25TD97aVP6PF/avdyQZAOHrApACDonK3w/vAdffX19/SX33Xbhbe8Qv2c6f5yzbr2a3r836/QJ7/7rn4vFwTp4AGbAgCCztkKryrfOoU3/4Q+ufQpzXxjXfMdzfzPK3Rh6I+1e6QgSAcP2BQAEHQgvMBXwKYAgKDjT+H12mr+P0fbai4+j1HylTo4K/ZeUN/tacocqOOz5JA2ZKa6qO1WqLFI+9U175pmNvVD+1ohvzJKkbtRSr1WJwx2af7+KLFLv3NK7Pfj4vfZWQoNrlLJPH22iO9p/pdO9lBqWx0cxu4LGni0SRV1CMD7ztkK7w9lKv71e/rT1KcsvL+iP/DnYlm+035cVeZrjvnjquLTHoqn1cEZkUl2Ud/DfhpYP4WI25RdSt0dpdaGQ67lwLijDk+R1hzy7Np3KNssdrFVF5E4rTYXaCrMdjaErkql/bP2O0V6lMJPd9UBH45cOcLclLHrp6V9dQjAe87ZCu9//Jouyr8LaeXiz1eoKN+dwL8TNRXe/RylV1ZpeX2LinomuMfn1/n8yiZlrYleLVA6Wyba2eLzq5TO8edWqG7QQHiW8vI3umjLPCrbW5Tf36WMtCGdoxK3oZTbNOrP6MH5gK9xtoeqlH/F96ijWvsEaX+BA66trjJl1xcpfitKyfUNWntdD4DuuItEMbPh6L/ZjqKM5QI/i2N88bU6ln5lzOcUM872e/M2wnui7WNbr8k1Tv/QaHjeLt8zFaW2oUVaa7jPpc3vwgdzs9RmCb/4TlO/cGBcv2u/Rs5JG5WP1nBru0cbK4VNbVw2Kf9GCe+X7Jdp6ae9/9bz17RnZmdCNPDSJ4sIAN6SsxXeVgpnvK7nuRxbeHcWKdyZoOVtzqYzc9R7e4LSMqerm5R6vEgZ4/wshUU0jevnKNweofEFDtocVKa6O2m8hb3N0kI908086qEprbkSeC7fm+XgxoFtqoc6wv0UXylQafsFDYQmza1pbs9wJ9/HAlHa3qD47R6aNwKmBHEte5X2jWyanzmz+KAzRsm01LVKA+1Rmt/j4LfH7Q5zn/fKLWRBjSKRecxjKf3f4/bOyFam1CHXdVJoZJZFrEAVDtKpu9c4wK5Sfq9ASzH+7s6o0f5iZprC9+yLDzeOK7wn2j55PWDYhv0gt0pTnxfkrEaV0mMhCs+wYHA9ayNdFH7GIl7lsV1JUIjPl3icK7pQOdv8jnyw8pKf/0S1V+65O0dF+ezhFzbk+quhuh/y80JDdR+NLCgR92q7VxtdxsX0/2lKcx15/u6GNfavJ43t8Txfl0/P0XxaPfNLbn9yy/wMwHuO/4W3STmu8MrqOf6lOmDyTyLU+1xNcEG26AyhUuKmBzBGgpu+bebKQY4DjxJQQbYgBzdqW5C2rTapP/qiJkprQ2aAtgVRoRZ8JIh7C+8F6zNj9NW40HFPUxwiUeVArYvmAS8CjKxKrotQqtZEx30S7GvBskApazybcCzhPen2Zaepg8W56LU+keeF50xBFOR5H7Gt5TPX6e4b9me/Kx8Un6+d1+/x9AsNxzPSIzdp3HpPXZjjBYx5v2fbm7XRMS72rWaeK7fMfpY+j1E4lXMsWhjJ5LX2A/A+cy6F1z7ptWC1t0rx7n6Kp2RLTLZm3YOed3CtU1lP0MXrndqPgDrp8qV6ltEgvFpQSY+YAdoWRIVa8JQgromYfr8jwNbHwHFPUxwCJfX3TtOybFNb5RVnkM7rnMe2cWrt+ccS3nfQvuKrOUoO9VP4dpSSsket4/QH2qT4JXWvp2/Yn/2ufNDmM/o9nn6h4XiG5YcG8p2637PtzdroaK+9Dn3sq5wBz9JwLEoddxO0ZC2aNOEH4H3nXApv6TkH1FomWeUMs8vcEuPgUMswD7Zo/KOjBb06ZZq/17iVJ2IcUulXK8Ir224dQ/UsOf/E2u7bpfnuev2SWVy07vcMsBLcErTslcXZcAgU8ViEJswf7FgYn53XOY5t46QHV2+OJbwn3T5bPRs0oNveQJ43Smtv1CGLQm2b2tM3xGb1Z78rHzTqtc7r9/D1JyW8nm1v1kb+3DZTt62n8Opjvz1LHZpfXxyrtx+A95lgC++zHrrcrv3bCZf4SxaugwItPeyicGyC4lH++3jLFLc3mzR+u4f6xiZoYDBBfRxca0FP2wr2Cno1ZHWubSvXkEDayQLBH9NjH9Kw9a9OWlAT6gGvary77IiOcgbAfx++oLwKTKWXo9QR6qc4ZwZ9SW6PFqD0d2F6gC0uROkG3zMwuEhZ85QHpkjMa12sZGep91aUBmRseiM08Ll8KdfpvzZ13GcbJy24NqFV4X2X7au8nqbIHR5bqau7i/qMuuxU5J3wbbkmwX9j9cysiW9Y4z++zt+/Kx+ULVlerBnsLtbvaeIXNeT67sVDhdez7c3aWOV+hTr5ngQleQ7atrG1sS9+zovT3gQNj41SL49D8rW5UmzYigfgPSbQwvuuKaVnOUBM2EvDD3H8iV/bftY2ff+RbFz7bUEgKNNS1OXHYAC8p0B4ga+ATd+efKpHyyYDwP4L6nPbQQLgPQXCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEnVMXXhQUFBQUlPNe3hZkvODEgE0BAEEHwgt8BWwKAAg6EF7gK2BTAEDQgfACXwGbAgCCDoQX+ArYFAAQdCC8wFfApgCAoAPhBb4CNgUABB0IL/AVsCkAIOicvfDu/plmfv4xXbz6a/rC+V1+nf7xf3xIF372b/St8zsuCNLBAzYFAASdsxXer35LP756jS5fv0YXLg3bhLf4f39Bly99yN9doQv/8wmE95wAmwIAgs7ZCu8P39FXX39Pf/ldd4PwVve+oa/yVfpiCMJ7noBNAQBB52yFV5Vv3YRXFQjv+QI2BQAEHQgv8BWwKQAg6ARfeHc2KHk/QjeuX6GL/y1EkUcbVDxQ3x2LHCW7pynLn0orCQrNqDZInS3Um0720/yuOrDYX6WB+4tU5I+2Or14q/b7m1ZsWnq9SPHeEP3N1St0uT1CA0+3qPQWYyJjPvB5mT+VaXkwQlNiXMGrzt1F6h3ZoIo6rOO4340W/eTEOShTOtVPke4JWtb9LztLocFVKqnD1vCYA67kaOpOgpb31aFOdprCUy3MYTdeTVL4ScH42NKcCSLH8SMvPxD0+jx93IsWfN+Vui95otm6gbOYSyfA2QrvD2Uq/vV7+tPUpyy8v6I/8Odi2f7dH/43C+8n/0x/4s/7P2j3cjm08TuLFO7kSb+jjplSeo7mX1fV0XHYpPilUUrLx4Mqld4YJ1lRRyn81OnJjaRHeiiltcdgZ47Cd+cM4bXV6coupe6q5weQw2xaeTVBbd2zlLECOY9XdmXWZuOjUnzaU7fdmzJVjMncZJx1ezmp3e9Bi35y0lTWPcRJ/G3/qPPBYw54UNn3GBMeiwsjm+rgiOj37r+ggeSW+fnccLw44OkHzvqa+bgXh/m+K5ovedHET2Tuxo86CD7gbIX3P35NFy+xsGrl4s9XqMjffft/JAu2f/fjx1/b7j+s8ZnkTepb8QoqVcq/ylFxP0fphVXKqMuKmQ1aXlmldE4yIAWvErPpVT6/Sdn9jbqjvClQplClSmGT1qai1Da0SGvrm5RvEogOFV5Vp0HtuRuqPXy8ztnerSgl1zdo7bUK4NIHbvPy+paWzTv6Vy5QOqv1Sb7PFI6woj0dmtu0TEvRECW9lsd73NcCL9gyqzSfbjY2zM4Wrcn5zC7lNeGtbG9RvuoxzhbKXnmuw+kr5v3q4GCXMuum/TJscy8/KeU2jXrWuC01nH0xjjVflrqd7RJqzxRfNU9VtjdpfihEkSnuS9phc7ne8gv1DKs90uYah8wBqnr7V/F1rp5RW/ZI87kv6wG1lOXxqHWPn/Wq3s5KQdlKt6EejNkOmT3zo3PMnRjP2edrVhYprb53G3/jOhYSs8+aTQVjLOxxwt5+ZpfbJNUZ47trH1M5Z42B5pOttMPsPx8380/hSH7gUl8THxfc2lr3fffYWsPLlwS3+WrYeqP2zPqYb9T7Y11/iP39wtkK71uW5o2XFZyLyNWQ7zspNMLZkgQINlrmMa+eFthh9th4MzEafyUeU6X0WIjCM2zYvQKtjXXxYkE5ijX5OVCb211bVNprvupLj4T4GeIoWlkYpTZLeLWAkklGaGClwHVyUHvKgYInUGVvi6bCnMXzc4xMxcrqtyVIz1Hv7QlKG47u6N8PGzTQPkkZq23bssV4lK2k06G5TQ9ZHfPYfdDZT+MLPKElEO+9oL7oJKV5bErbqxR/aG7nO8esr/1aTXjTI1eMFXTDOOtIULoqdmRf4cA01d1J4xnzK+t+Y5HwIEpTHJhKOzlaTr3goNToJ8VnPdQ2uEp58bkn/dQxtmnaxNkXtlfHvcWagBmZS8qx/VbdpOHOHvOZ2xzQbvfQvPg/B+7MjPgSP3/PpS+a733QGaMkB2UZr4H2KM0bgtbCHDjYovGQm39p89DRvuHb12q+bl+Qsp2tNu2uUjK1QdmdMuV57Nosn9XmSR2XMVffWKRHblIb+8S8Er3S5zHqTbK4sT3yK6PU98zygxCFHk7SmuEjs+wv1rzisXg6TfM8p0qG7SM0tU2UT3VRZMESpyqtybYrn6/5iszjbc7M2yMUGpplUWB7T/XU7vFuB7f34bThw9L/G8oHmvrnMfygMa54+7h3Wy3fb4ytdZr4ktd8FVtfj9D4ujXmIRqW2Gzrj8ynw+3vF8658EaoFruqHGi0wEYH7LCxVarI+TCv/NRp47wz6KjPrW01tyq84kQ8ebKOSWW0uy4+2Rmu70t1wOSfRKj3uUxmR/+YfIqddN2sL/OoqzaR/MTbCu+FoQ11IGPTY+tj/kmM5JWi25g5hdc5zjZ0sWIqLxMu9+coeXuUlncd9rP5SY6mbo3SWi0wFSgV7qclyVAcfbEFc/aN+XuWKNaRdoT0d2GcUX6gtmA9t+QcwquLmTFOck+Lc8Ddv+rz0K191r2ewmshi5Y9bodHW008xlzDsE/N9jz+d7XFgoz/g1njfWN65JptvGQsBl7a661wsJfAb44RL2zDs+YY7S1SxIolDl8RIR1/rQ4KcyxO0odm7bD8STD9xTz09s9j+YGzPk8fb6WtjbGnRhNf8pqvYuuLuq1zs9QmsZk/2vtzuP39QoCFl+01dq1hstSpBwQDcbTeaVrWBVG2uxwOaAv++uTnz60Jr8tiwCv4vSnwqm2CFwRdFI7x6s1wdvsEsU9M0xHNdjj6J1gBQQ8SPuOwxdQ8r3Y9f4/jCMbpkU4aeKbZk4tkj95jpn/nCEQ6Tp/QbG+re3eTUo9HqfcuZ0OPt2qZWt1PnAsJzWaOvhhkJqnjEQdQj90KvR8GWh2eAdfL95jaPa3OAVf/qvepWfs8hZeDbLibs/Cnq2y/aYp4tLWG25hr2G3Pz/m7BM1r/rG2bm6Lp0fswlFve5mWh3qol7NWuU+2Oq36rMWGvgBxjp2tn/Kd0Ydm7dDbq/ukt38eyw+c9Xn6eCtt1fzYSRNf8pqv8mzbQkKro6E/h9jfLwRaeGXSdsj2l/7OdWdLvStzOodslU1QuraSY4zPcp6zEqsOXqWGPYS3rYVfVh5JeLW2yFZW3akTtKzmdel5v/aLP86KhhozjTpm1hQf6VdZsf84zKay8r4svwDXxkbe/VgTVA/GMjYRtQ1moO5pHLObtUBlDx71cbbhGZT0+xmtjWuDyhY2P5F31nzeasobXv1b27WOvpgUjO3AuNjYypp0XrMwc5ZsBZv8k/pWpmfA9fI9pn5Pi3Og5l9RbdtV80OX9ln3psfqW5nGvNUDq5WhirBbbX01Yc+CLNzGXMMuZDL+Uft/Gaj75Tp963g5xu0zxpyFQsvYMo9u1uuTBVFslOK3tYzQ4Svuwtu8HfX2ylha4tjEP4/jB876PH28lbZqNm/A25e85qvhY9pupMSAG1oGb0uurHsYN/v7hWALL1N6NU3h9k5quxWits5OutE9SWuGbSV7Utt6ikp2lnpvRWlgjLPMXl61fm46QSU9wQLeT/GxBPUN8mqqnQVavuDJbzmAvFcZD3VyZpqg5EtvUXMV3v0X1Nut3mfU6tylpViEIrEJGh7qp47u6foPwBaidIPbMzC4SNmDAi09lIx4guJR/ltb5TX2z4Czpg8+0reK/EUrNs0vJKjDsin/7YjNUVYmsm4P4WCXljlzCd0fNcYw1KvGkMds/l4nB0keswcxGo5xhmC9pxIBUKJmG2fzlIkEpeiLWiCoByXt/uoWJdmHeofYflx/x8MXpn2dflJ4QX23OXiMsV/x36RlZGdfFCVukx6E7FSN3yl0RLm/6pnmLkmTgLu7WO8L98PakhT0e1qaAwIH/RsfcSCtxULdD+X9XpfZvsEYDbBNbowp8WSxDcm/hg3FKJLk+detgn5hkcdFnjtKA58lOPtV52vjyLaxnuU15hq6fQ12Vyl+x7wnfp/n25Q5f0REwg+kr+a86nik3r1LHzizDT/kZwwmaOCBtSAW5BWALtiMjK81txl34WU826FtTRuCZglvE/88jh8wtvqa+LhnW2tj6xF7FJ6+5DVf5dkj0zzWiXostIR7hxdj7V3Uy/Np/uvD7e8XAi+8Z8L2Cxpm8baV1KZHsDxdXH+U4yN8a1OfYNvGBO8Me6bZKuaOxBrMAw4BwnuuUNtEjh/l+AnYtBmOXw6Dd4Y902wR2eKVd/AAHAKEF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOqcuvCgoKCgoKOe9vC3IeMGJAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAILO2Qvv7p9p5ucf08Wrv6YvaufL9Kfk/6K/vX6FLlzicvVH9A8zX9O+fh8XBOngAZsCAILO2QrvV7+lH1+9RpevX2OBHdaE9zv6/We/pT98/R0V//o9ffUv3XTxUjf963favVwQpIMHbAoACDpnK7w/fEdfff09/eV33Q7hdZRvntDfs/DO5O3nEaSDB2wKAAg6Zyu8qnzbVHjL9MXQT+nCx/9Mf3F8hyAdPGBTcFRKe1X16YzZL1PpQH0GoAm+F17Jhi9e5Wz3m8bvDmt88XmM2m6F6qV3lJa31ZenzQlMyNLnMep4vKWOgknLDrn3gvpuT1PmrALd9irF7/VQ5IlP7XHkccnR1J0ELe+rw2OxSUl9vt2KUO/jDSqelI1eTVL4SUEdKPYWqe8R2yA7TR3JTXXSYpfm709SWh3Z2F2k3pENqqjDkyDzKEbze/zhTY5SsQi1dfIYcImMLFL2jXnNW5GdpdDgKpXU4bF5B30HR8PXwvuXhV/Q3179mBJflG3nrXKo8D7tobg+6/Y3KB6aPINgvUnxu3NUVEfH4mCLxm/HqC+aoDWfLPDfBa06ZCbZRX0P+2lg/SwGo0BTYRapkwim74gG32+BCmdslbeaG41+nk/1UPjprjp6S9KjdGHELq6lhX6zny7fifCm7o66C+/OHIXfdk7qyPyMSn1lWnoQoviXZfUFt/H1Ii29PgE/PahSaf8E6jnpvoMjc7bC+0PZ+PHUn6Y+ZeH9Ff2BPxfL5nf7XwzT3176EUUXvjGuMYr6zipHFl5mbbCHUjs8GbKblN/fpczKIqX5WCjlNml5ZZXWMlqgqBYoneVJtLNlfJfO1SeU4HaPre48378+TZFbozS/vkHpbZ488r0+f3a3SH+kG5X1BIVSBeNv74LehirlX+WouJ+j9MIqZVS9xcxGY3sPypRNu5z3ES05ZHWDBsKzPIb8N7poywBatisHsbwaI9t5Bw33HnDdmj2dtxrPfyPjvMr3bdXtbPjRrlHf/MstM9sw6pLrNilbyzRNe5bkO37uclo+q68Et3scdf//shs0P8SZ1tQGra1vUfEH5cM1pO/sS+rIovian6U+67j6kisuC8zCHIWUINr9nsfolWrDHvtugWNBZpXm0+aAVgpbtCb9l/Zb/W8QVxE5Flb5/jDhdY6/Ep+8mtdOH3B9vtFOnr/KJzLKvwxeT1KvscDYpOGrHmKvcBtPGXtjDsszVVtkPGx2Ftvb7Gin5fFt1nePeeH06xPbxTinnK3w/sev6aL8u5BWLv58hYrV7+hff2Y/L+XHj7+23X9k4ZWMt92cqOmRm9QWnWRHNANb8VkPtQ2uUn6PnftJP3WMbZpOK04aitL4U3a2vQItD4Yo/Mx0SNn67U2yQ+6VKb8ySn3qvK1ubmdpb9UQigxfV+IsKZ/qokhNPKu8GIjQVNMt8DLNW5murKy7Zylbc3wJLp0c3GZ5wvJE4/OZx9zvBZ7I0peZGI2/khurlH46TfN8TYkn3FT3Yc88G1pxSMlyrEw386iHprRbnHb1slFxZZqm1mWMTJu6Zc6uPiFZh25Px23pkRCFHk7S2rYEulkKd05QWq4RP2qPUDzFAZuDN1U5QHdy2zm4lbbZL2/30LwRyMWe7B+P5jiwS5sT1Na9aIoZ2z7ZzYKf43vEhvwc17o5QGZmIjSwwu3jNlZ+4AVKu7bTsy1bls6tRnmuuSjVcfclLxzCywu9NR6P3uemr6dH9Pq1a1k0P+jsp/EFFhnZqt1dpWRqg7JW/622OsV1/wX1WceHCa9zjOT4aoj6XOZ1s+d/0BmjZJrn0Db7QHvU3FpmMsl+1TeeZ4+6KDQ0R2tiJ8dwuY+ntPMatQ2JrxVoKcbz+c6o4RvFzDSF76nFpbTZubDRaHl8m/Tda14Y8+rhNCcO5pjcsNoEjsXZCu9bllaE90a4n3qjXFho5B3vfNZypCsU/9L4yORoijOYtZqYFSjF9y3JStPp7FWecLdY+OSeu/q2Nd/zQM476xYcAamqMjb5vLdIkcOcOMcBXN5jKfIpDqo1oZBJGyFOhk2kfXp9BxzUY6u2IFvhYCyB+ahbkafBoQ55wOPe7S0iDXb1sJGFbK+WOJA0boc28QmnPTXSI9ds45qd4eD1km0lfhTmLEOdr7xMUEh/X/klB8ek2NgpgFVajoWMxYXcU1+wMXyPW92Cc9Gp+0yGhWE8Y3zUcBHeFnzJDo/LRz00MDZBw7Eo3bjeyeJfH9dmwnBhaMM4a6MqCwdug36dJq6l57H6PGhFePUx8pzXGoc8X2xrjrG5GNbH38isUxM00BvihRMvxGQYPMfTMfYilIYvCOJ3Wh+stkh2vC47Glxem2Pc8vi20HfnvDDmVc2fzLmhuRc4IoEXXi9xsTsSO+kl3ZG0iSBOagsK1rX89+8Sxnaj4fxGMbfq7HUL2iRQWMHPLqJuSEZ8jS63az9aaf+QLtQmsGPSSnt7p2lZb5ex5VSm5aEe6h2aNdosW5FeY3OWHGZT2Wq/yAG9/gOeTrp8qZ55NNjVw0ZZeffIK/jUCp+biroIbxOfcLGnRXpEWwQx4oNG3Y5gVztvUQvqpljUBb/eJ7kn8nhV68uGsfXZEEiZBt+3Fnj6os+Gw48ET1/ywjEuvGAMRV/UhKapMOiiKQvNbs4sn0pfpynieh0vSAa13zvIlnaDeG/S8PWJRtES5Nh1XjMtPV8bY76+Vze6k1cTdGOM7/McT8fY83PqvmH6Q0MfdrfqdbQivPr4Num717ywzyutTeBYQHgNyrQUZae15s4bXolaP8ISJ/1Iy5p4knUYwUTu4YCvxU7rl6SuwutcTUumFhul+G09I3PBNSPWt6edAZNX3yEONnqdxmdug7bizzy66Tk2Z0lzm5Zp/l5dZC2s999Co13dbGQGDkvcSgtuwtvEJ/Sg5kCer79GWI510vhr/qgHTeH1JHVw8LNELP+ERdW4T9p2s56RSoYfUpm23CMB3ELaIsVZNyO+b2TDNUyfiY/017Z+7Wh+dFClinGrly8xb9wWi85xqfJ4dLE9zGvTYzwWVr9kHnkIgzFvrV0L8X/rOhawi9Z1kqXp2be8gmm3/yq78uUo3bB2Q5xj5DmvmzzfQ3izM9H66w55D6u/M2WkHR1GBus1no453IrwutDq+Hr33XteQHhPlvMrvOKkEhAtCvLvKbJNNkq9/Ddp/UpJnPRWPw08iFF8LEHhzn6at4Lx7irF70Q4i5yg+P0IRabMH8001C0BiDPcG90JGuCMxRRRERE9SLuTf+KREXMQbjOC8C7Nd1tboCaVLK/Ab0WNLb+BXr7/c5k8ZhvCDydomDOFgQcSEM3r/URTm0pW0/BukpGgK+9S+WPD2HvYKP+sn4PNKA0PJWg45vHLWy+fOER4ww8S1DfIz4t2Uccj9VuB3UUKa9mf2EPe9xlt4Od3PHxB+VoQDlFvLEZ93OaBbv78zHK4Kmck/dTWy21mX4zcSdCSBOuGupkdFoz2Lq6HM35rSDOT9IEecG3U/ajIAfeiEnh3X9qk8es99gWNgcu4cDvCD1TbOMCH2rmOoRhFktI3da1khLWtVaawyOPdz/NtlAY+4zlnXVfl54Y6KRxbpP9nmX3YtrCQts5RH39f2w3pnqaM9ctz5xjJvI5N0pQ1r2/HaMkaZq/ns4DVt4Ct+MILo3v6orrM8yxq/iuRSzvcx1PGXhtPL+F1s7NOq+PbpO9e80Le8dbnFYT3bQm08J4Ih6wyj4/5LynWVlkpPcvBlEVRL59bkeD8cCo2fYc07nYcFQlq+g7GyaHvDLzvFF++qP2C/0ypbtHSy4YVCABNgfAehghvs1XmcZFtQ+0HU8Dk/RdePTM4DiK89h2Mk0Ftuzu26QEApw+EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOqcuvCgoKCgoKOe9vC3IeMGJAZsCAIIOhBf4CtgUABB0ILzAV8CmAICgA+EFvgI2BQAEHQgv8BWwKQAg6EB4ga+ATQEAQQfCC3wFbAoACDoQXuArYFMAQNCB8AJfAZsCAILO2Qvv7p9p5ucf08Wrv6YvaufL9KeZ+/ST61fowiUuV39E/zDzNe3r93FBkA4esCkAIOicrfB+9Vv68dVrdPn6NRbYYU14v6M/JP+Z/vD1d1T86/f01b9008VL3fSv32n3ckGQDh6wKQAg6Jyt8P7wHX319ff0l991O4TXUb7+Z/oxhPdcAJsCAILO2QqvKt96CO9XC7+mxGe/or/njPgnv/kztprPAbApACDo+Fp4//J/f8vC+2v65T/8lC7+9Nf0xZ79ewTp4AGbAgCCjq+Ft1Z+WKdfXrpC/7Dwve38YY0vPo9R8pU6OGUq2TkauBOitk4pUYov5KiivqMD9fcwWr0uQBzqkG9ylIpF1LiGKDKySNk36js/jetBmdKpfop0T9DyrjpnsEnJhy+oqI5Ok8zjLur7vKyO3gOy0xSeOsOFmPiJq6+UaXkwQlNZdXgMJDb1PiuoI0VhkXoHV6mkDt047n2tkk52UfK1OrBgO0RSjmdqHOee887ZCu8PZePHU3+a+pSF91f0B/5cLPP58p/pX5NP6Iv/5GM+9+0X/0Q/uXSNfvmFdi+XQ4X3aQ/F0+rgNNl/QX2do7S2r445CGcWXlCmKge7lLo7Soc3q9XrgkVzm5Zp6UGI4l/WxaP0epGWXhsD27K9T8MvKusJCs249WWT4nfnTl94915QbzRGfd2zlFenfE96lC6MbKqDM4CfH35qWzXVeVOmytss4A4KlOruoZo2OY+9OO59LZIeuUYXOXalrcWscIgdjnPPeedshfc/fk0X5d+FtHLx5ytU/OEb+v0vPqH/ctU6/yH9ffLo73ibBtj9HKVXVml5fYuK+gTa4/PrfH5lk7KWcFYLlM5ysN/Z4vOrlM4dkjW8mqCLrk5Xpuz6IsVvRSm5vkFrr9Wkbnimy3VWGxSVAl+7pw4Odilj3L9BmR117j2luU03afgqT3B1pFPKbtD8EGfAUzxelk15wZNNb9hs1nhdlfKvcvVsQR9n435zXD1tXhv7ur9Utjfrz0gX6jsdBk2E16UuA5d+cEPNdss94sdp+ay+ciGfitDAOt+T6qHxjDopNPHtUnaT8iww5hhsUd5c36h7dqmU436+3DL759Z28euCdRMj11g+79VXa15Kf770Dt7ybGnvWkYTRrd2WTTpp1DM2MdX5tfaVJTahhbZT2QcjNM1KtvaeOh4xRU3dhYp3DlJGfbB9FiIep/b2+SJ132e/mOdPzw+pEdYxF/OUij6oj4nDhXeFu45yricA85WeFsp5bL7eS7HFl7DcRO0vM0Zd2aOem9PUFomUXWTUo8XKWOcn6VwWGUHO3MUbo/Q+EKOijxxp7o77cHLCdczHopQ/OkGZXfsK+PKHt8f5mfvlam0zw/1eGbDddIGzZHrfZMsMEpTHIBKOzlaTr14fzIaF5rblAPNoy4KDc3RWo77qwc+FojMDIvLCo8Xj1lFgtLTaZrPFHhcxGYRmtp2u86xs6CNcyYp1/H9exy0ny5SWg9mAttuuLPHHPvtDYrf7qF5CWy2Zzijs4fwetUlfXbrh7Ej0kmhR4vsYxxYZ3roxqMt+aKRgy0atzLdvUXqja3WRUn6G4rS+FMOiNzP5cEQhZ+ZYpYeCVHo4SStWb7ZqeaJmg/x1CplRFi92s7PitxbrAVjYxdAUjNuT7J7lObFhtInfoY1//R6hm9fcw34xWc91Da4Svk97veTfuoY2zT742yXjvqucQ6b4hWeMfu/NtJl9r/KtluRXYst5SdGLTXSI1caY8veC+qLcl94vErbqxR/uHjozkaFFxcddyIU4n7aF2jNabjPa0yPGB8MEWXb5Z9GKfJUpdGtCG+ze7zi7TnG/8LbpBxXeLMzsl2pDpj8k4h9tclBu2QInwrIMmm1YFl5mfDegrIwspRFmhpLUKSTJ/ajDbXS89hCdj7TeZ20QXP+et9ylLw9Ssu7wfDkVhzSyGhSEzTQG6K2bhYGZQove1c4cIoQWt/Zr/MaZwlYLAJZ73EVPwg90fb4OBh+kDTFz6stXsLbrC4Lez+k3WbAM6iu0oBHJi119y5Y/l2lNXk/aYg34/Bto55bsySvL2ULUe+DzJuBlzweck94rhbAvduuP6tM8/eiNL9nXh+ptYfh66Vet3oaA36Opm6N0lpNCAuUCvfTkiyKHO2y4einPMuYw9Jf/Z4DXjh8xNmkfGbx8JrnbsKb5cWPviDPP4nR4a+opf0R21ax+Pea7HRxYb30wH6f15geNT5YIiq2W46FaFze3Woi6ta2w+45NN6eQ86l8DonjVxnTLA9XqV29/OKWbaXZKvXXXibTUgv0mOdNGz80MsR6L2e2bLwMruSNY9S790uijx2bLG9ZxzZIV9N0A3OeAS7vcu0PNRDvUOzNM9BQrZ+re/s1zUZ5zecAYrA3+MsKMaZgiPrqfmNhRZs7M/QcRde77q8+uEQXs8t7AJNha7R38iP0W6p8t+u0QdWdiz9HdowPxtwPZfM8UiP2AWh1kbHfGg2DpSZpA551vYshQY3DN+U6yOPV2sBXIpsSTetp0a9fSbaODjnqY7zO2sON9yj1W9d44Kb8KZHOmngWb1PUmqvgzxx2pHH53X9fm/htd/nNaYGR4gPdRFl3vBY3OGxWNH82qVth93jGW/PMedSeEvP+ylcW1nzqnyoy1yp8kSrrbhle+4jNQG9Jq0Xu1uOdynqGcYv/2TCJGjZWoB6PdN53e4ihWvbdrKy1LIRTRDWBu2T+H2jqU3lXZUjEhlbblqWaa7yBQ4AWiaTeXSzNl7263ZpvtvMxATJHGrv57Vxzae6Gn3pNYsKi5YVyPJPOPiprMPL9zwF0rMur344A7ZHvZbw2WAxDidoTYZAfFsyPKuvuVnqUO/qJGDWsyjxuU7Th53zock4WM+KW3NMkOvVYslAni3FpZ5G4S3TUpT7bU3fN5yhhlT7G0RUw3MO85wLcQZtvcMt8HXWPONr2lx/IGeOTc2+b0xfkrgSUdv0BtaYHnA2b7lbA43C2xqO+7zGVLD+MrX4wPHTDZuICuwPoavuW/4Wh93jGW/PMcEW3mc9dLldW+lzib/kgHBQoKWHksVMUDzKf61VIK/Wxm/3UN8YZzmDCerjCWnMLZm0jh8ONBXePa5HtkGtLEO2mrWVZnEhSjdC/fwM+VcYj2c6rzMyny4OiqOcgcVonJ3dmPjVLUr2RjgjmqDhWA91nNG/qpwUzW1apvSjaH1cpXRPU8YKmjuLFGnvot5YguZz5vvg8EMeFx7XgQeacNqu48DwksWbxzkei1Jfkm1rBIxdWopFKMI+MjzUTx3ynIZYVaXMYx5ztok19lZW3Ex4h69/VG+/lLuytetVl1c/JPCqLVYDN+GVIKdtK2vUREJ8+xb72IMYxccSFO7sp3kVI0Vcwg/YJwfNedLxSL1PlEWgPh+ajIMgz7poa1uVsql+autN0LC8irmToCUjcMv7VtPHhwdjNMDjbu1m2Ci8oD6eMwNjnMXx36RlmIZ2aTSZw5XMNIVvs/2l/7djtFTTCPmtRifHiQQlJW5oyA6WuZDma6730LxUxQvD5ZEIhe5z+7ntoV7TZ2QeX3Trh4Es/NT9R8J5n8eYusYHvvdeY8YuNIgok2dfvuF47aFz6D1e8fYcE2jhfdeU0rPs5OzQevncmrXgOJy1Tc8dTbJEt+1UEAwqK5OuCzJwOkB4ga+ATU8ZZyaokR65qbI6EDg4foKzA8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKAD4QW+AjYFAAQdCC/wFbApACDoQHiBr4BNAQBBB8ILfAVsCgAIOhBe4CtgUwBA0IHwAl8BmwIAgg6EF/gK2BQAEHQgvMBXwKYAgKBz6sKLgoKCgoJy3svb0rLwAgAAAODtgfACAAAApwiEFwAAADhFILwAAADAKQLhBQAAAE4RCC8AAABwikB4AQAAgFMEwgsAAACcIhBeAAAA4NQg+v8DsNAfjrarXwYAAAAASUVORK5CYII=)\n",
        "\n",
        "_Source GeeksforGeeks_   "
      ],
      "metadata": {
        "id": "Aixd9CsjS4-Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing the Modules & load the data\n"
      ],
      "metadata": {
        "id": "3u3IKHW6jYA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZx91kYpvOqG",
        "outputId": "1f21a8f2-01bd-41ef-fc8b-cc3de4060400"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.6/16.6 MB\u001b[0m \u001b[31m82.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m305.1/305.1 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.7/138.7 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m381.9/381.9 kB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.5/60.5 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IJNJ5pJBYlTd"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import seaborn as sn\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score,log_loss"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load csv\n",
        "data = pd.read_csv('/content/drive/MyDrive/LoanApprovalPrediction.csv')"
      ],
      "metadata": {
        "id": "naFm6bzI9lXw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning"
      ],
      "metadata": {
        "id": "F0pZVXIJjmCW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the data\n",
        "data.head(n=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "3DB0GLEgnXpd",
        "outputId": "86a5052c-8da9-4bfa-f6f8-5963b8851fb9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Loan_ID  Gender Married  Dependents     Education Self_Employed  \\\n",
              "0   LP001002    Male      No         0.0      Graduate            No   \n",
              "1   LP001003    Male     Yes         1.0      Graduate            No   \n",
              "2   LP001005    Male     Yes         0.0      Graduate           Yes   \n",
              "3   LP001006    Male     Yes         0.0  Not Graduate            No   \n",
              "4   LP001008    Male      No         0.0      Graduate            No   \n",
              "5   LP001011    Male     Yes         2.0      Graduate           Yes   \n",
              "6   LP001013    Male     Yes         0.0  Not Graduate            No   \n",
              "7   LP001014    Male     Yes         3.0      Graduate            No   \n",
              "8   LP001018    Male     Yes         2.0      Graduate            No   \n",
              "9   LP001020    Male     Yes         1.0      Graduate            No   \n",
              "10  LP001024    Male     Yes         2.0      Graduate            No   \n",
              "11  LP001027    Male     Yes         2.0      Graduate           Yes   \n",
              "12  LP001028    Male     Yes         2.0      Graduate            No   \n",
              "13  LP001029    Male      No         0.0      Graduate            No   \n",
              "14  LP001030    Male     Yes         2.0      Graduate            No   \n",
              "15  LP001032    Male      No         0.0      Graduate            No   \n",
              "16  LP001034    Male      No         1.0  Not Graduate            No   \n",
              "17  LP001036  Female      No         0.0      Graduate            No   \n",
              "18  LP001038    Male     Yes         0.0  Not Graduate            No   \n",
              "19  LP001041    Male     Yes         0.0      Graduate           Yes   \n",
              "\n",
              "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0              5849                0.0         NaN             360.0   \n",
              "1              4583             1508.0       128.0             360.0   \n",
              "2              3000                0.0        66.0             360.0   \n",
              "3              2583             2358.0       120.0             360.0   \n",
              "4              6000                0.0       141.0             360.0   \n",
              "5              5417             4196.0       267.0             360.0   \n",
              "6              2333             1516.0        95.0             360.0   \n",
              "7              3036             2504.0       158.0             360.0   \n",
              "8              4006             1526.0       168.0             360.0   \n",
              "9             12841            10968.0       349.0             360.0   \n",
              "10             3200              700.0        70.0             360.0   \n",
              "11             2500             1840.0       109.0             360.0   \n",
              "12             3073             8106.0       200.0             360.0   \n",
              "13             1853             2840.0       114.0             360.0   \n",
              "14             1299             1086.0        17.0             120.0   \n",
              "15             4950                0.0       125.0             360.0   \n",
              "16             3596                0.0       100.0             240.0   \n",
              "17             3510                0.0        76.0             360.0   \n",
              "18             4887                0.0       133.0             360.0   \n",
              "19             2600             3500.0       115.0               NaN   \n",
              "\n",
              "    Credit_History Property_Area Loan_Status  \n",
              "0              1.0         Urban           Y  \n",
              "1              1.0         Rural           N  \n",
              "2              1.0         Urban           Y  \n",
              "3              1.0         Urban           Y  \n",
              "4              1.0         Urban           Y  \n",
              "5              1.0         Urban           Y  \n",
              "6              1.0         Urban           Y  \n",
              "7              0.0     Semiurban           N  \n",
              "8              1.0         Urban           Y  \n",
              "9              1.0     Semiurban           N  \n",
              "10             1.0         Urban           Y  \n",
              "11             1.0         Urban           Y  \n",
              "12             1.0         Urban           Y  \n",
              "13             1.0         Rural           N  \n",
              "14             1.0         Urban           Y  \n",
              "15             1.0         Urban           Y  \n",
              "16             NaN         Urban           Y  \n",
              "17             0.0         Urban           N  \n",
              "18             1.0         Rural           N  \n",
              "19             1.0         Urban           Y  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
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              "      <th>LoanAmount</th>\n",
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              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LP001013</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2333</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LP001014</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3036</td>\n",
              "      <td>2504.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LP001018</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4006</td>\n",
              "      <td>1526.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LP001020</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>12841</td>\n",
              "      <td>10968.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LP001024</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3200</td>\n",
              "      <td>700.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LP001027</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2500</td>\n",
              "      <td>1840.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LP001028</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3073</td>\n",
              "      <td>8106.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LP001029</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1853</td>\n",
              "      <td>2840.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>LP001030</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1299</td>\n",
              "      <td>1086.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LP001032</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4950</td>\n",
              "      <td>0.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LP001034</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LP001036</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3510</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LP001038</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4887</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>LP001041</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2600</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d0845842-df1e-4582-8aad-33ba77520c06 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d0845842-df1e-4582-8aad-33ba77520c06');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-801d08f4-646d-437a-94b3-6607282a225e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-801d08f4-646d-437a-94b3-6607282a225e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-801d08f4-646d-437a-94b3-6607282a225e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Replacing NaN(Null) Values\n",
        "\n",
        "We are treating missing values in all features one by one.\n",
        "We can consider these methods to fill the missing values:\n",
        "\n",
        "*   For numerical variables: imputation using mean or median\n",
        "*   For categorical variables: imputation using mode\n",
        "\n"
      ],
      "metadata": {
        "id": "X8R0kj4NpmAd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all the null values in the\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyGWgGz7apy0",
        "outputId": "163646e8-82f2-4e92-eca4-f0cb7c7ce6b4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender                0\n",
              "Married               0\n",
              "Dependents           12\n",
              "Education             0\n",
              "Self_Employed         0\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           21\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       49\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill empty values of Dependant,Loan Amount,Loan_Amount_Term as it's numeric and float dtype\n",
        "data['Dependents'].fillna(data['Dependents'].median(),inplace=True)\n",
        "data['LoanAmount'].fillna(data['LoanAmount'].median(),inplace=True)\n",
        "data['Loan_Amount_Term'].fillna(data['Loan_Amount_Term'].median(),inplace=True)"
      ],
      "metadata": {
        "id": "U7SP9I0F3R--"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Fill Empty Credit History with mode as its categorical\n",
        "data['Credit_History'].fillna(data['Credit_History'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "-gPR--3n4ni1"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Drop unwanted Columns"
      ],
      "metadata": {
        "id": "mP2MqiuPFGVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping Loan_ID column\n",
        "data.drop(['Loan_ID'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "Y0BoBOGmlkku"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Get data info\n",
        "def table_info(data):\n",
        "  print(f'Num Rows :- {data.shape[0]} , Num Colm :- {data.shape[1]}')\n",
        "  print(\"\\nTable DataTypes :\\n\",data.dtypes)\n",
        "  print(\"\\nColumn names :\",data.columns.values)"
      ],
      "metadata": {
        "id": "FWR-opXwC29t"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table_info(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFzAZBr6cfvZ",
        "outputId": "4795c96b-4aa0-437b-a36f-7ace3d60b829"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num Rows :- 598 , Num Colm :- 12\n",
            "\n",
            "Table DataTypes :\n",
            " Gender                object\n",
            "Married               object\n",
            "Dependents           float64\n",
            "Education             object\n",
            "Self_Employed         object\n",
            "ApplicantIncome        int64\n",
            "CoapplicantIncome    float64\n",
            "LoanAmount           float64\n",
            "Loan_Amount_Term     float64\n",
            "Credit_History       float64\n",
            "Property_Area         object\n",
            "Loan_Status           object\n",
            "dtype: object\n",
            "\n",
            "Column names : ['Gender' 'Married' 'Dependents' 'Education' 'Self_Employed'\n",
            " 'ApplicantIncome' 'CoapplicantIncome' 'LoanAmount' 'Loan_Amount_Term'\n",
            " 'Credit_History' 'Property_Area' 'Loan_Status']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data['Dependents']=data['Dependents'].astype(int)\n",
        "\n",
        "data['ApplicantIncome']=data['ApplicantIncome'].astype(int)\n",
        "\n",
        "data['CoapplicantIncome']=data['CoapplicantIncome'].astype(int)\n",
        "\n",
        "data['LoanAmount']=data['LoanAmount'].astype(int)\n",
        "\n",
        "data['Loan_Amount_Term']=data['Loan_Amount_Term'].astype(int)\n",
        "\n",
        "data['Credit_History'] = data['Credit_History'].astype(int)\n",
        "table_info(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DsI9WjmDpGU8",
        "outputId": "a948376c-f62e-4adc-9861-4c24878cc775"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num Rows :- 598 , Num Colm :- 12\n",
            "\n",
            "Table DataTypes :\n",
            " Gender               object\n",
            "Married              object\n",
            "Dependents            int64\n",
            "Education            object\n",
            "Self_Employed        object\n",
            "ApplicantIncome       int64\n",
            "CoapplicantIncome     int64\n",
            "LoanAmount            int64\n",
            "Loan_Amount_Term      int64\n",
            "Credit_History        int64\n",
            "Property_Area        object\n",
            "Loan_Status          object\n",
            "dtype: object\n",
            "\n",
            "Column names : ['Gender' 'Married' 'Dependents' 'Education' 'Self_Employed'\n",
            " 'ApplicantIncome' 'CoapplicantIncome' 'LoanAmount' 'Loan_Amount_Term'\n",
            " 'Credit_History' 'Property_Area' 'Loan_Status']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for no empty value in our table\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8XwKHEN7A8F",
        "outputId": "432903d8-88d0-413a-fc44-96068ae2b91a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Loan_Amount_Term     0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Visualication"
      ],
      "metadata": {
        "id": "nhSm7FlhE7kI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Categorical & Numerical data Visualization"
      ],
      "metadata": {
        "id": "zejaZ8eiOqAR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "object_list = (data.dtypes=='object')\n",
        "object_list = (object_list[object_list].index.values)\n",
        "plt.figure(figsize=(60,80))\n",
        "for i in range(int(len(object_list))):\n",
        "  plt.ylabel('Count of Person')\n",
        "  plt.xlabel(f'{object_list[i]} Category')\n",
        "  ax = plt.subplot(4,3,i+1)\n",
        "  data[object_list[i]].value_counts().plot.bar(title=object_list[i])\n",
        "plt.subplot(4,3,7)\n",
        "plt.xlabel('Count of Dependents')\n",
        "plt.ylabel('Cout of Person')\n",
        "data['Dependents'].value_counts().plot.bar(title='Dependents')\n",
        "plt.subplot(4,3,8)\n",
        "plt.xlabel('Loan Amount (in Thousand)')\n",
        "plt.ylabel('Count of Person')\n",
        "plt.hist(data['LoanAmount'],label ='Loan Amount (in thousands)')\n",
        "plt.subplot(4,3,9)\n",
        "plt.xlabel('Terms (in months)')\n",
        "plt.ylabel('Count of Person')\n",
        "data['Loan_Amount_Term'].value_counts().plot.bar(title='Loan_Amount_Term')\n",
        "plt.subplot(4,3,10)\n",
        "plt.xlabel('Applicant income Amount')\n",
        "plt.ylabel('Count of Person')\n",
        "sns.distplot(data['ApplicantIncome']) # The function distplot will be removed in future so use seaborn version below 0.14.0\n",
        "plt.subplot(4,3,11)\n",
        "plt.xlabel('Coapplicant income Amount')\n",
        "plt.ylabel('Count of Person')\n",
        "sns.distplot(data['CoapplicantIncome'])# The function distplot will be removed in future so use seaborn version below 0.14.0\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "iu_jXgwSplk1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1c985557-f808-458c-dc50-0ef0a907b4f8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x8000 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### data v/s target Variable data visualization"
      ],
      "metadata": {
        "id": "MPQ1EE22Nsw3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.figure(figsize=(20,10))\n",
        "gender = pd.crosstab(data['Gender'],data['Loan_Status'])\n",
        "gender.div(gender.sum(1).astype(int),axis=0).plot.bar(stacked=True,align='edge')\n",
        "\n",
        "married = pd.crosstab(data['Married'],data['Loan_Status'])\n",
        "married.div(married.sum(1).astype(int),axis=0).plot.bar(stacked=True ,align='edge')\n",
        "\n",
        "Self = pd.crosstab(data['Self_Employed'],data['Loan_Status'])\n",
        "Self.div(Self.sum(1).astype(int),axis=0).plot.bar(stacked=True)\n",
        "\n",
        "Education = pd.crosstab(data['Education'],data['Loan_Status'])\n",
        "Education.div(Education.sum(1).astype(int),axis=0).plot.bar(stacked=True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "dKQsm4QLF0KF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f620f6f2-3f88-4403-dc8e-5438bff400a7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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VVSkhIUGSFB8fr759+yojI0MuLi4KDQ2tt72Xl5ckNVgOAAC6J7tjJC4uTufOnVNqaqpKS0sVHh6u3Nxc20GtJSUlcnDgxK4AAKB5LFar1Wp6iFuprKyUp6enKioq5OHhYXqc9pXmaXoCtKe0CtMToD3x/u5euuH7u7m/v9mFAQAAjCJGAACAUcQIAAAwihgBAABG2f1tGrSv4KtbTI+AdnTK9AAAYAB7RgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGBUD9MDAEB3FXx1i+kR0I5OmR6gA2PPCAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIzq0ZKN1qxZo9dff12lpaUKCwvTqlWrNGLEiEbX3bBhgzZv3qxDhw5JkiIiIpSent7k+gBaxmq16tq1a6qtrTU9Srfh6OioHj16yGKxmB4F6NTsjpGcnBwlJydr3bp1ioqKUmZmpmJjY1VUVKQ+ffo0WH/Pnj2aNGmS7rvvPrm4uGjZsmV67LHHdPjwYfXt27dVXgTQ3dXU1Ojs2bO6fPmy6VG6HTc3NwUEBMjJycn0KECnZbFarVZ7NoiKitK9996r1atXS5Lq6uoUFBSkX/3qV5o3b94tt6+trVXv3r21evVqxcfHN+s5Kysr5enpqYqKCnl4eNgzbqcXPG+H6RHQjk4tHWP3NnV1dTp+/LgcHR3l6+srJycn/qXeDqxWq2pqanTu3DnV1tZq0KBBcnCw75Nv3t/dS0ve351dc39/27VnpKamRgUFBUpJSbEtc3BwUExMjPLz85v1GJcvX9a3336rf/iHf2hynerqalVXV9v+XFlZac+YQLdSU1Nj+0eBm5ub6XG6FVdXV/Xs2VPFxcWqqamRi4uL6ZGATsmujD9//rxqa2vl5+dXb7mfn59KS0ub9RgvvviiAgMDFRMT0+Q6GRkZ8vT0tN2CgoLsGRPoluz9VzlaBz934Pa167to6dKlys7O1rvvvnvTf0GkpKSooqLCdjt9+nQ7TgkAANqTXR/T+Pj4yNHRUWVlZfWWl5WVyd/f/6bbLl++XEuXLtUHH3ygoUOH3nRdZ2dnOTs72zMaAADopOzaM+Lk5KSIiAjl5eXZltXV1SkvL0/R0dFNbvfaa69pyZIlys3NVWRkZMunBWC3qVOnaty4cabHaNKGDRsUFhamXr16ycvLS8OGDVNGRobt/pbOn5aWpvDw8NYbFECbsfurvcnJyZoyZYoiIyM1YsQIZWZmqqqqSgkJCZKk+Ph49e3b1/aXybJly5SamqotW7YoODjYdmxJr1691KtXr1Z8KQA6m6ysLM2ePVtvvvmmHnroIVVXV+vPf/6z7bxEALoHu48ZiYuL0/Lly5Wamqrw8HAVFhYqNzfXdlBrSUmJzp49a1v/N7/5jWpqavTzn/9cAQEBttvy5ctb71UAaJEPP/xQI0aMkLOzswICAjRv3jxdu3bNdn9ubq7uv/9+eXl5ydvbWz/72c/0xRdf2O4/deqULBaL3nnnHT388MNyc3NTWFhYs79d94c//EG//OUvlZiYqIEDB+qee+7RpEmT9Oqrr0q6vndj06ZN+q//+i9ZLBZZLBbt2bNH0vWD4QcPHiw3NzcNGDBACxcu1LfffitJ2rhxoxYvXqzPPvvMtt3GjRtt8xYWFtpm+Oabb+o97tdff63JkyfL19dXrq6uGjRokN5+++3b+CkDuJUWnYF15syZmjlzZqP33XhD33Dq1KmWPAWANva3v/1No0eP1tSpU7V582YdO3ZM06ZNk4uLi9LS0iRJVVVVSk5O1tChQ3Xp0iWlpqZq/PjxKiwsrPctkvnz52v58uUaNGiQ5s+fr0mTJunEiRPq0ePmf8X4+/vrww8/VHFxse64444G98+ZM0dHjx5VZWWlLQhunBbA3d1dGzduVGBgoD7//HNNmzZN7u7umjt3ruLi4nTo0CHl5ubqgw8+kCR5eno2ON6tMQsXLtSRI0f0/vvvy8fHRydOnNCVK1ea9TMF0DItihEAnd/atWsVFBSk1atXy2Kx6K677tKZM2f04osvKjU1VQ4ODpo4cWK9bbKysuTr66sjR44oNDTUtnzOnDkaM+b6CZ0WL16se+65RydOnNBdd9110xkWLVqkCRMmKDg4WIMHD1Z0dLRGjx6tn//853JwcFCvXr3k6uqq6urqBgfJL1iwwPbfwcHBmjNnjrKzszV37ly5urqqV69e6tGjxy0Prv97JSUlGjZsmO34tuDgYLu2B2A/viAPdFNHjx5VdHR0vbO1jhw5UpcuXdJf//pXSdLx48c1adIkDRgwQB4eHrZfzCUlJfUe6/vfkAsICJAklZeX33KGgIAA5efn6/PPP9esWbN07do1TZkyRaNGjVJdXd1Nt83JydHIkSPl7++vXr16acGCBQ3maokZM2YoOztb4eHhmjt3rvbt23fbjwng5ogRAE0aO3asLly4oA0bNmj//v3av3+/pOtnff2+nj172v77RtzcKia+LzQ0VP/8z/+s3/3ud9q9e7d2796tDz/8sMn18/PzNXnyZI0ePVrvvfeePv30U82fP7/BXH/vxkdL378Kxo3jTG54/PHHVVxcrKSkJJ05c0aPPvqo5syZ0+zXAsB+xAjQTYWEhCg/P7/eL+a9e/fK3d1d/fr101dffaWioiItWLBAjz76qEJCQvT111+3+Vx33323pOvHq0jXTynw91ci3rdvn+644w7Nnz9fkZGRGjRokIqLi+ut09h2vr6+klTvIPvvH8z6/fWmTJmi3/3ud8rMzNT69etv+3UBaBrHjADdQEVFRYNfus8995wyMzP1q1/9SjNnzlRRUZEWLVqk5ORkOTg4qHfv3vL29tb69esVEBCgkpKSZl0M0x4zZsxQYGCgHnnkEfXr109nz57VK6+8Il9fX9u5i4KDg7Vr1y4VFRXJ29tbnp6eGjRokEpKSpSdna17771XO3bs0LvvvlvvsYODg3Xy5EkVFhaqX79+cnd3l6urq3784x9r6dKl6t+/v8rLy+sdeyJJqampioiI0D333KPq6mq99957CgkJadXXDaA+9owA3cCePXs0bNiwerclS5Zo586dOnDggMLCwjR9+nQlJibafjk7ODgoOztbBQUFCg0NVVJSkl5//fVWnSsmJkZ/+tOf9Itf/EKDBw/WxIkT5eLiory8PHl7e0uSpk2bpjvvvFORkZHy9fXV3r179cQTTygpKUkzZ85UeHi49u3bp4ULF9Z77IkTJ2rUqFF6+OGH5evrq3//93+XdP0g3GvXrikiIkKzZ8/WK6+8Um87JycnpaSkaOjQoXrwwQfl6Oio7OzsVn3dAOqzWL+/j7aDau4liLsiLjHevbTkEuNXr17VyZMn1b9/f64aa8Dt/Px5f3cvLXl/d3bN/f3NnhEAAGAUMQKgzTz++OO2Sz/8/S09Pd30eAA6CA5gBdBm3nrrrSbPXnrjTKoAQIwAaDN9+/Y1PQKAToCPaQAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACj+GovgJtq71OW23vK7KlTp2rTpk3KyMiodyG/7du3a/z48eoEV7wAuj32jADo9FxcXLRs2TJ9/fXXpkcB0ALECIBOLyYmRv7+/srIyDA9CoAWIEYAdHqOjo5KT0/XqlWr9Ne//tX0OADsRIwA6BLGjx+v8PBwLVq0yPQoAOxEjADoMpYtW6ZNmzbp6NGjpkcBYAdiBECX8eCDDyo2NlYpKSmmRwFgB77aC6BLWbp0qcLDw3XnnXeaHgVAM7FnBECXMmTIEE2ePFlvvvmm6VEANBMxAqDLefnll1VXV2d6DADNxMc0AG7K3jOitreNGzc2WBYcHKzq6ur2HwZAi7BnBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAozgdPICbS/Ns5+eraPaqVqtVP/3pT+Xo6Khdu3bVu2/t2rV66aWXdOjQIfXr16+1pwTQitgzAqDTslgsevvtt7V//37967/+q235yZMnNXfuXK1atYoQAToBYgRApxYUFKQ33nhDc+bM0cmTJ2W1WpWYmKjHHntMTz/9tOnxADQDH9MA6PSmTJmid999V88884wmTJigQ4cO6fDhw6bHAtBMxAiALmH9+vW655579NFHH+k///M/5evra3okAM3ExzQAuoQ+ffro+eefV0hIiMaNG2d6HAB2IEYAdBk9evRQjx7s8AU6G2IEAAAYRYwAAACjiBEAAGAUH64CuDk7zohqWlpamtLS0kyPAcBO7BkBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAnQRVqvV9AjdEj934PYRI0An17NnT0nS5cuXDU/SPd34ud/43wGA/TjPCNDJOTo6ysvLS+Xl5ZIkNzc3WSwWw1N1fVarVZcvX1Z5ebm8vLzk6OhoeiSg0yJGgC7A399fkmxBgvbj5eVl+/kDaBliBOgCLBaLAgIC1KdPH3377bemx+k2evbsyR4RoBW0KEbWrFmj119/XaWlpQoLC9OqVas0YsSIJtf/j//4Dy1cuFCnTp3SoEGDtGzZMo0ePbrFQwNonKOjI78cAXQ6dh/AmpOTo+TkZC1atEgHDx5UWFiYYmNjm9w9vG/fPk2aNEmJiYn69NNPNW7cOI0bN06HDh267eEBAEDnZ3eMrFy5UtOmTVNCQoLuvvturVu3Tm5ubsrKymp0/TfeeEOjRo3S//t//08hISFasmSJhg8frtWrV9/28AAAoPOzK0ZqampUUFCgmJiY7x7AwUExMTHKz89vdJv8/Px660tSbGxsk+sDAIDuxa5jRs6fP6/a2lr5+fnVW+7n56djx441uk1paWmj65eWljb5PNXV1aqurrb9uaLi+iXMKysr7Rm3S6ir5twR3Ul3/P94d8b7u3vpju/vG6/5VicH7JDfpsnIyNDixYsbLA8KCjIwDdB+PDNNTwCgrXTn9/fFixfl6enZ5P12xYiPj48cHR1VVlZWb3lZWVmT37P39/e3a31JSklJUXJysu3PdXV1unDhgry9vbvVyZwqKysVFBSk06dPy8PDw/Q4AFoR7290B1arVRcvXlRgYOBN17MrRpycnBQREaG8vDyNGzdO0vVQyMvL08yZMxvdJjo6Wnl5eZo9e7Zt2e7duxUdHd3k8zg7O8vZ2bneMi8vL3tG7VI8PDz4ywroonh/o6u72R6RG+z+mCY5OVlTpkxRZGSkRowYoczMTFVVVSkhIUGSFB8fr759+yojI0OSNGvWLD300ENasWKFxowZo+zsbH3yySdav369vU8NAAC6ILtjJC4uTufOnVNqaqpKS0sVHh6u3Nxc20GqJSUlcnD47ks69913n7Zs2aIFCxbopZde0qBBg7R9+3aFhoa23qsAAACdlsXK9a87rOrqamVkZCglJaXBx1YAOjfe38B3iBEAAGCU3WdgBQAAaE3ECAAAMIoYAQAARhEjANAOrly5osuXvzv9e3FxsTIzM/XHP/7R4FRAx0CMAEA7ePLJJ7V582ZJ0jfffKOoqCitWLFCTz75pH7zm98Yng4wixjpwKxW6y0vLgSgczh48KAeeOABSdK2bdvk5+en4uJibd68WW+++abh6QCziJEOaPPmzRoyZIhcXV3l6uqqoUOH6t/+7d9MjwXgNly+fFnu7u6SpD/+8Y+aMGGCHBwc9OMf/1jFxcWGpwPMIkY6mJUrV2rGjBkaPXq0tm7dqq1bt2rUqFGaPn26fv3rX5seD0ALDRw4UNu3b9fp06e1a9cuPfbYY5Kk8vJyrk2Dbo+TnnUw/fv31+LFixUfH19v+aZNm5SWlqaTJ08amgzA7di2bZueeuop1dbW6pFHHtHu3bslSRkZGfroo4/0/vvvG54QMIcY6WBcXFx06NAhDRw4sN7y48ePa8iQIbp69aqhyQDcrtLSUp09e1ZhYWG2a3gdOHBAHh4euuuuuwxPB5jDxzQdzMCBA7V169YGy3NycjRo0CADEwFoLf7+/nJ3d9fu3bt15coVSdK9995LiKDbs/uqvWhbixcvVlxcnD766CONHDlSkrR3717l5eU1GikAOoevvvpKv/zlL/W///u/slgsOn78uAYMGKDExET17t1bK1asMD0iYAx7RjqYiRMnav/+/fL29tb27du1fft2+fj46MCBAxo/frzp8QC0UFJSknr27KmSkhK5ubnZlsfFxSk3N9fgZIB5HDMCAO3A399fu3btUlhYmNzd3fXZZ59pwIAB+vLLLzV06FBdunTJ9IiAMXxM00E4ODjIYrHcdB2LxaJr166100QAWlNVVVW9PSI3XLhwQc7OzgYmAjoOYqSDePfdd5u8Lz8/X2+++abq6uracSIAreHMmTMKDAzUAw88oM2bN2vJkiWSrv/joq6uTq+99poefvhhw1MCZvExTQdWVFSkefPm6b//+781efJkvfzyy7rjjjtMjwXADr1799aaNWsUFhamRx55RMOHD9f//M//6IknntDhw4d14cIF7d27Vz/60Y9MjwoYwwGsHdCZM2c0bdo0DRkyRNeuXVNhYaE2bdpEiACd0Kuvvqrnn39eaWlpOnLkiO6//349+eSTqqqq0oQJE/Tpp58SIuj22DPSgVRUVCg9PV2rVq1SeHi4li1bZruwFoDO6+TJk0pMTNSRI0e0fv16PfHEE6ZHAjoUYqSDeO2117Rs2TL5+/srPT1dTz75pOmRALSy1atXKykpSSEhIerRo/4hewcPHjQ0FWAeMdJBODg4yNXVVTExMXJ0dGxyvXfeeacdpwLQWoqLi5WQkKBDhw7p+eefbxAjixYtMjQZYB7fpukg4uPjb/nVXgCd04YNG/Qv//IviomJ0eHDh+Xr62t6JKBDYc8IALShUaNG6cCBA8rMzGxwNW4A17FnBADaUG1trf785z+rX79+pkcBOiz2jAAAAKM4zwgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAB1WcHCwMjMzb+sx0tLSFB4e3irzAGgbxAiAm5o6daosFoumT5/e4L4XXnhBFotFU6dObZPn/r//+z8999xzbfLYADoOYgTALQUFBSk7O1tXrlyxLbt69aq2bNmiH/7wh7f12N9++22DZTU1NZIkX19fubm53dbjA+j4iBEAtzR8+HAFBQXVu1DjO++8ox/+8IcaNmyYbVlubq7uv/9+eXl5ydvbWz/72c/0xRdf2O4/deqULBaLcnJy9NBDD8nFxUW///3vNXXqVI0bN06vvvqqAgMDdeedd0pq+DHNN998o2effVa+vr7y8PDQI488os8++6zerEuXLpWfn5/c3d2VmJioq1evttFPBUBrIUYANMszzzyjt99+2/bnrKwsJSQk1FunqqpKycnJ+uSTT5SXlycHBweNHz9edXV19dabN2+eZs2apaNHjyo2NlaSlJeXp6KiIu3evVvvvfdeozP84he/UHl5ud5//30VFBRo+PDhevTRR3XhwgVJ0tatW5WWlqb09HR98sknCggI0Nq1a1vzxwCgDXBtGgDN8k//9E9KSUlRcXGxJGnv3r3Kzs7Wnj17bOtMnDix3jZZWVny9fXVkSNHFBoaals+e/ZsTZgwod66P/jBD/TWW2/Jycmp0ef/+OOPdeDAAZWXl8vZ2VmStHz5cm3fvl3btm3Tc889p8zMTCUmJioxMVGS9Morr+iDDz5g7wjQwbFnBECz+Pr6asyYMdq4caPefvttjRkzRj4+PvXWOX78uCZNmqQBAwbIw8NDwcHBkqSSkpJ660VGRjZ4/CFDhjQZIpL02Wef6dKlS/L29lavXr1st5MnT9o+Cjp69KiioqLqbRcdHd2SlwugHbFnBECzPfPMM5o5c6Ykac2aNQ3uHzt2rO644w5t2LBBgYGBqqurU2hoqO2A1Bt+8IMfNNi2sWXfd+nSJQUEBNTbE3ODl5dX818EgA6HGAHQbKNGjVJNTY0sFovtWI8bvvrqKxUVFWnDhg164IEHJF3/aKW1DB8+XKWlperRo4dtj8vfCwkJ0f79+xUfH29b9qc//anVZgDQNogRAM3m6Oioo0eP2v77+3r37i1vb2+tX79eAQEBKikp0bx581rtuWNiYhQdHa1x48bptdde0+DBg3XmzBnt2LFD48ePV2RkpGbNmqWpU6cqMjJSI0eO1O9//3sdPnxYAwYMaLU5ALQ+jhkBYBcPDw95eHg0WO7g4KDs7GwVFBQoNDRUSUlJev3111vteS0Wi3bu3KkHH3xQCQkJGjx4sP7xH/9RxcXF8vPzkyTFxcVp4cKFmjt3riIiIlRcXKwZM2a02gwA2obFarVaTQ8BAAC6L/aMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBR/x80XIeidjDsXgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [0,2500,4000,6000,81000]\n",
        "group=['Low','Average','High','Very high']\n",
        "data['Income_bins']=pd.cut(data['ApplicantIncome'],bins,labels=group)\n",
        "income_bin = pd.crosstab(data['Income_bins'],data['Loan_Status'])\n",
        "income_bin.div(income_bin.sum(1).astype(float),axis=0).plot.bar(stacked=True)\n",
        "\n",
        "data=data.drop('Income_bins',1)"
      ],
      "metadata": {
        "id": "0oocAPzrXinS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "7d63edd0-85a8-434b-ff0f-d24edd28f278"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matrix = data.corr()\n",
        "sns.heatmap(matrix,vmax=0.8,cmap='BuPu',square=True,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "6-2MAckIKlFn",
        "outputId": "8b11ce49-1bac-4982-e1d6-a31ac26ce0d9"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Gender']=data.Gender.apply(lambda x:1 if x=='Male' else 0 )\n",
        "data['Education'] = data.Education.apply(lambda x:0 if x=='Graduate' else 1)\n",
        "data['Married'] = data.Education.apply(lambda x:0 if x=='Yes' else 1)\n",
        "data['Self_Employed'] = data.Education.apply(lambda x:0 if x=='Yes' else 1)\n",
        "Prop_area = {'Urban':0,'Semiurban':1,'Rural':2}\n",
        "data['Property_Area'] = data['Property_Area'].map(Prop_area)\n",
        "data.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "1nfy_aexTgsp",
        "outputId": "45288dd4-d52f-4231-9f65-95813a4cda8f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "0        1        1           0          0              1             5849   \n",
              "1        1        1           1          0              1             4583   \n",
              "2        1        1           0          0              1             3000   \n",
              "3        1        1           0          1              1             2583   \n",
              "4        1        1           0          0              1             6000   \n",
              "5        1        1           2          0              1             5417   \n",
              "6        1        1           0          1              1             2333   \n",
              "7        1        1           3          0              1             3036   \n",
              "8        1        1           2          0              1             4006   \n",
              "9        1        1           1          0              1            12841   \n",
              "10       1        1           2          0              1             3200   \n",
              "11       1        1           2          0              1             2500   \n",
              "12       1        1           2          0              1             3073   \n",
              "13       1        1           0          0              1             1853   \n",
              "14       1        1           2          0              1             1299   \n",
              "15       1        1           0          0              1             4950   \n",
              "16       1        1           1          1              1             3596   \n",
              "17       0        1           0          0              1             3510   \n",
              "18       1        1           0          1              1             4887   \n",
              "19       1        1           0          0              1             2600   \n",
              "\n",
              "    CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "0                   0         127               360               1   \n",
              "1                1508         128               360               1   \n",
              "2                   0          66               360               1   \n",
              "3                2358         120               360               1   \n",
              "4                   0         141               360               1   \n",
              "5                4196         267               360               1   \n",
              "6                1516          95               360               1   \n",
              "7                2504         158               360               0   \n",
              "8                1526         168               360               1   \n",
              "9               10968         349               360               1   \n",
              "10                700          70               360               1   \n",
              "11               1840         109               360               1   \n",
              "12               8106         200               360               1   \n",
              "13               2840         114               360               1   \n",
              "14               1086          17               120               1   \n",
              "15                  0         125               360               1   \n",
              "16                  0         100               240               1   \n",
              "17                  0          76               360               0   \n",
              "18                  0         133               360               1   \n",
              "19               3500         115               360               1   \n",
              "\n",
              "    Property_Area Loan_Status  \n",
              "0               0           Y  \n",
              "1               2           N  \n",
              "2               0           Y  \n",
              "3               0           Y  \n",
              "4               0           Y  \n",
              "5               0           Y  \n",
              "6               0           Y  \n",
              "7               1           N  \n",
              "8               0           Y  \n",
              "9               1           N  \n",
              "10              0           Y  \n",
              "11              0           Y  \n",
              "12              0           Y  \n",
              "13              2           N  \n",
              "14              0           Y  \n",
              "15              0           Y  \n",
              "16              0           Y  \n",
              "17              0           N  \n",
              "18              2           N  \n",
              "19              0           Y  "
            ],
            "text/html": [
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              "      <th>5</th>\n",
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              "      <th>14</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
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              "      <td>Y</td>\n",
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              "  </tbody>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7909506c-9996-4d7e-bd5e-cfb30a572c60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "l1yNDHKojMyg",
        "outputId": "f718aa1c-1b17-4c9f-9ebf-448bc83ded33"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Gender  Married  Dependents   Education  Self_Employed  ...  \\\n",
              "count  598.000000    598.0  598.000000  598.000000          598.0  ...   \n",
              "mean     0.814381      1.0    0.740803    0.222408            1.0  ...   \n",
              "std      0.389124      0.0    1.003198    0.416212            0.0  ...   \n",
              "min      0.000000      1.0    0.000000    0.000000            1.0  ...   \n",
              "25%      1.000000      1.0    0.000000    0.000000            1.0  ...   \n",
              "50%      1.000000      1.0    0.000000    0.000000            1.0  ...   \n",
              "75%      1.000000      1.0    1.000000    0.000000            1.0  ...   \n",
              "max      1.000000      1.0    3.000000    1.000000            1.0  ...   \n",
              "\n",
              "       CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "count         598.000000  598.000000        598.000000      598.000000   \n",
              "mean         1631.498328  144.337793        342.341137        0.856187   \n",
              "std          2953.316188   81.303985         64.494977        0.351193   \n",
              "min             0.000000    9.000000         12.000000        0.000000   \n",
              "25%             0.000000  100.000000        360.000000        1.000000   \n",
              "50%          1211.500000  127.000000        360.000000        1.000000   \n",
              "75%          2324.000000  163.500000        360.000000        1.000000   \n",
              "max         41667.000000  650.000000        480.000000        1.000000   \n",
              "\n",
              "       Property_Area  \n",
              "count     598.000000  \n",
              "mean        0.961538  \n",
              "std         0.789499  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         1.000000  \n",
              "75%         2.000000  \n",
              "max         2.000000  \n",
              "\n",
              "[8 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-323d64c2-5666-4b66-b207-ec10da79dda0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>...</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.0</td>\n",
              "      <td>...</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>598.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.814381</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.740803</td>\n",
              "      <td>0.222408</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1631.498328</td>\n",
              "      <td>144.337793</td>\n",
              "      <td>342.341137</td>\n",
              "      <td>0.856187</td>\n",
              "      <td>0.961538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.389124</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.003198</td>\n",
              "      <td>0.416212</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2953.316188</td>\n",
              "      <td>81.303985</td>\n",
              "      <td>64.494977</td>\n",
              "      <td>0.351193</td>\n",
              "      <td>0.789499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1211.500000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2324.000000</td>\n",
              "      <td>163.500000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-323d64c2-5666-4b66-b207-ec10da79dda0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-323d64c2-5666-4b66-b207-ec10da79dda0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-323d64c2-5666-4b66-b207-ec10da79dda0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cb8661ca-2a24-4413-8cd8-678afd7518b5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb8661ca-2a24-4413-8cd8-678afd7518b5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cb8661ca-2a24-4413-8cd8-678afd7518b5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train test split for the model"
      ],
      "metadata": {
        "id": "zWkfZY2pVdro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=data.drop('Loan_Status',1)\n",
        "y=data.Loan_Status.apply(lambda x:0 if x=='Y' else 1) # Can use pd.get_dummies to reduce code\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.7,test_size=0.3,random_state=42)\n",
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lsJOlC3OKppu",
        "outputId": "7583a773-4d5e-4713-e94a-ff4c9e093f48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "108       1        1         0.0          0              1           5316.0   \n",
              "272       1        1         0.0          0              1          14583.0   \n",
              "597       0        1         0.0          0              1           4583.0   \n",
              "478       1        1         0.0          1              1           2699.0   \n",
              "596       1        1         2.0          0              1           7583.0   \n",
              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "108                0.0       136.0             360.0             1.0   \n",
              "272                0.0       436.0             360.0             1.0   \n",
              "597                0.0       133.0             360.0             0.0   \n",
              "478             2785.0        96.0             360.0             1.0   \n",
              "596                0.0       187.0             360.0             1.0   \n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-5c393cae-caf6-473d-a416-38a261ba7908 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5c393cae-caf6-473d-a416-38a261ba7908');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart {\n",
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              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f2336c48-dfc6-45d3-bb1f-e1adf049b9ac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a test Models to identify loss ,accuracy etc."
      ],
      "metadata": {
        "id": "qgbNYr2p8qX-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Regression Models\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.linear_model import LinearRegression ,LogisticRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "RM = RandomForestRegressor()\n",
        "LR = LinearRegression()\n",
        "DT= DecisionTreeRegressor()\n",
        "LG = LogisticRegression()\n",
        "RM.fit(X_train, y_train) # Random Forest training\n",
        "LR.fit(X_train, y_train) # Linear Regg training\n",
        "DT.fit(X_train, y_train) # Decision Tree Training\n",
        "LG.fit(X_train, y_train) # Log Regg Training"
      ],
      "metadata": {
        "id": "TG8vJloRC47X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "5500e959-cdd7-46ae-ef2a-6bca11a5bb99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing the Models\n",
        "import numpy as np\n",
        "from sklearn.metrics import r2_score , accuracy_score , log_loss ,confusion_matrix,ConfusionMatrixDisplay\n",
        "RM_pred = RM.predict(X_test)\n",
        "LG_pred = LG.predict(X_test)\n",
        "LR_pred= LR.predict(X_test)\n",
        "DT_pred = DT.predict(X_test)\n",
        "models_scores = {\"Linear\":accuracy_score(y_test,LR_pred.astype(np.int32)),\n",
        "\"Random\":accuracy_score(y_test,RM_pred.astype(np.int32)),\n",
        " \"Logistic\":accuracy_score(y_test,LG_pred.astype(np.int32)),\n",
        "\"Decision \":accuracy_score(y_test,DT_pred.astype(np.int32))}"
      ],
      "metadata": {
        "id": "4N0jv1RD0BkW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kH6USAxRP_L",
        "outputId": "9f974ac7-67b2-4c1a-b697-a1fa42ef82fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Linear': 0.7055555555555556,\n",
              " 'Random': 0.75,\n",
              " 'Logistic': 0.7944444444444444,\n",
              " 'Decision ': 0.7222222222222222}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear regression Confusion matrix\n",
        "confusion_matrix(y_test,LR_pred.astype(np.int32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7vo5t9QEdic",
        "outputId": "1c34ff00-60d1-4d85-e168-ccc4b8178d1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[123,   1],\n",
              "       [ 52,   4]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression Confusion Matrix\n",
        "confusion_matrix(y_test,LG_pred.astype(np.int32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jge8IyXWQtUw",
        "outputId": "5798f42d-7118-41ba-d6c0-4263adb06a8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[121,   3],\n",
              "       [ 34,  22]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,RM_pred.astype(np.int32))"
      ],
      "metadata": {
        "id": "fGT-PxUjRl63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2726799-9594-4a56-aed7-a06ed5508019"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[123,   1],\n",
              "       [ 44,  12]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,DT_pred.astype(np.int32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFxmyWKfiERY",
        "outputId": "b8d5f41e-89d4-47b8-ca7f-7a22515010f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[95, 29],\n",
              "       [21, 35]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Accuracy in percent')\n",
        "plt.ylim(top=1)\n",
        "plt.bar(range(len(models_scores)), models_scores.values(), tick_label=list(models_scores.keys()))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "7UszL6zW68Gd",
        "outputId": "98ff1c08-d126-44ee-b179-8ffe8af9775a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Logistic Regression few Non Default paratmeter to Optimize the accuracy"
      ],
      "metadata": {
        "id": "Nr-8tUvPfzpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Parameter Efficient Training By GridSearchCV\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "parameter = {'penalty':['l1','l2','elasticnet'],'C':[1,2,5,10,20,25,30,40,50],'max_iter':[100,150,200]}\n",
        "Eff_log_reg=GridSearchCV(estimator=LG,param_grid=parameter,scoring='accuracy',cv=5)"
      ],
      "metadata": {
        "id": "Holx1p4MibpY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Log_Model = Eff_log_reg.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "--fcnGhni1X0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Eff_log_reg.best_params_)\n"
      ],
      "metadata": {
        "id": "mGJuQa4bjURG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cabbf481-5454-4d60-e16a-6bff9700fa0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': 30, 'max_iter': 100, 'penalty': 'l2'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Eff_log_reg.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J24K124cq-Lz",
        "outputId": "d8f86d2b-9d44-4992-b900-6fac6e6002cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8135111876075731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Parameter Efficient Training By LogisticRegessionCV\n",
        "from sklearn.linear_model import LogisticRegressionCV\n",
        "LGCV = LogisticRegressionCV(solver='liblinear',cv=5)\n",
        "LGCV.fit(X_train,y_train)\n",
        "LGCV.bes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "BP6bQzTVuVkF",
        "outputId": "14bafaa4-31ad-4fb2-d4e6-e40c121bf029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegressionCV(cv=5, solver='liblinear')"
            ],
            "text/html": [
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          "metadata": {},
          "execution_count": 75
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      "metadata": {
        "id": "6N2jNawjBOOc"
      },
      "execution_count": null,
      "outputs": []
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}