Fintech.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6cd170b8",
   "metadata": {},
   "source": [
    "# <center>Application of AI Models for Credit Risk Assessment in P2P Lending</center>"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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wBhiAAguQtjjEFBatzp53MGuZCTXShCWnBmtRxcM1no/mVFqKEkg8ggEwMJwB8y4POHVjOonGkEeD/eqHkpiGzmzCcj8H1GCdMHk0knvMKA4g24k0YZ5OqBAMJwNQYE6iNoJp5dHq8bxMIacpbsycOokJS04NbvWI5J5wEYJHMAAGRjFQuMj9Q2Yz/vAcUqc1kRrGPTqEj3JbI0epQL+88yPNbrmTvxrsHgagwHrYWxPrERY5TLXjXGDVb2bWUSYsOTW41aOTOzPoQHjAABjoZIDf2QE7Vz2QSofo5BpjiiQeqEP4KLc10kzuhPHMpq4SAHp05jdh2klpYYQZgAILU7cs0KkqNMzTsY5yeszkOtaEJacGt3rM5M4kZiycYAAMhBnQd7bfUyjqT5JDmslNsIkkpw6hIWFo5M4KzFR15iuYbyHeHY8FBqDACuRsOuSUFBpWfR8d4veYyXW4CUtODW7yFDKbPzbVycsZMAoGwEATA7k72+MvFPKElzFmcjPERJJTh9CQMDTS1C5OWDW5ANBja34Tr52UH0YrA1BgrYytx3ukg8YkT6F7MySH12AT6YSlWA12eszSprOa0IyCEwyAgQAD+qrWssPEJGehYiGqPNSas4DPfSgyQ8yuNFLDNEZ7/FEaqWdE58+9KWUzQ+D0MAAF5mFpL4yWEWZ/GpY8Jjj3lcgPNpG6BxOWnBpc/rOQhVTVIbPWdeWq/QAABu7HAN3Q3NCvyUeFrcHJI2BOGZHLlstZTavbyIU4kRpW7jnXeS5K5881bGYww5symGmf7IQCO2/2tXow30HDyOPEm7AmrUYVycjlbEpbSNI0RF2R0RQOMBgAAwUGzAvbxJvI5PTjTaTfafZQDneGmDBTuGhkuYE06o/SSLONctEhScolnjMKBXbeXJNcICP3DgTQhg7xYFKURua+HvmRrZk5Xr+L0HMmQGBSqzkk/GAADLQy0HRVm+CcPjDBhfYI78EQOFedknAktwlQ/vkdhyWbJ0m2xmiPP0oj9TtyjK7V02cu25P9UGDnzb6WNYV30OCc1DCROrMJa1JguQZMSUTleCfkJIOPJpuGyIhhdBQ8YAAMeBjgdznZhUDCCMMMERj+SHjuJJtGhUEA0xDg9Ggi/U6dU8dqjPb4ozRSKLAqwNSUuiV4nAxAgTmJ2ghGkoKMcnMEE4aIEqP8sayNElJkS488iWnrKBPmcfJUHryJ4UlggwEw0MOA5zrn+U18cnIY2QV8YYjChVEIoaFACMVqQ2QLixtP5lRLIz0KzIziTv0i8DgZgAJzErURTEuHanM6xPxqZcKcTrOHamwsSqfVeTSm6tFJ4AEDYCDMAL+kyS5nI5gwzCiBcT6aqUz1oxPqWI3xezzZNEZ7dEWNSR6NhALLcTXHDwU2h+eRVbSSqGbXIXMU2HUfz8QbmQwITPXRTAInGAADYQb0lV9NpUOESuAZTHDByWO1XQhMQzrEI91SlE6us3kwnVHVEhpQ9uh+4PEzAAXm52oXpFYSns6cURomPKmW6TTbEEjxaIaYJUQgPRYyjM1TLQQAGAADggFxeYvR3KOIKiiwlEHjTU+uHPebgeTkSG4TQBsEE0Pk54bAVF+cYkUg+bXhRApY7lHnh6eJASiwJro2AicJ0tQQqRbzA5hIxcFkc4zp5ABhE54MATAfCawNE59z6nAPCbls8IMBMOBkgC5vJz7BKCqsQgIZqEMeSzaNmgbByNCwwhAHJxj3eGxncv7RrpqWcgqjGgiAkwEoMCdRgIEBMAAGwAAYAANgYBgDUGDDqEQiMAAGwAAYAANgAAw4GYACcxIFGBgAA2AADIABMAAGhjEABTaMSiQCA2AADIABMAAGwICTASgwJ1GAgQEwAAbAABgAA2BgGANQYMOoRCIwAAbAABgAA2AADDgZgAJzEgUYGAADYAAMgAEwAAaGMQAFNoxKJAIDYAAMgAEwAAbAgJMBKDAnUYCBATAABsAAGAADYGAYA1Bgw6hEIjAABsAAGAADYAAMOBmAAnMSBRgYAANgAAyAATAABoYxAAU2jEokAgNgAAyAATAABsCAkwEoMCdRgIEBMAAGwAAYAANgYBgDUGDDqEQiMAAGwAAYAANgAAw4GYACcxIFGBgAA2AADIABMAAGhjEABTaMSiQCA2AADIABMAAGwICTASgwJ1GAgQEwAAbAABgAA2BgGANQYMOoRCIwAAbAABgAA2AADDgZgAJzEgUYGAADYAAMgAEwAAaGMQAFNoxKJAIDYAAMgAEwAAbAgJMBKDAnUYCBATAABsAAGAADYGAYA1Bgw6hEIjAABsAAGAADYAAMOBmAAnMSBRgYAANgAAyAATAABoYxAAU2jEokAgNgAAyAATAABsCAkwEoMCdRp8K+/Oob8d+pb4K+wQAYAAOPYUCc219+9c1jXv1BLwoFdtvJ1htYeG775ngxMAAGwMCZDIhTWjye+U7oOssAFFiWmqMHxL4tPx79pmgeDIABMHA6A+Ujmo+e/qbonzMABcbZuI/Nd6zfvs/7403AABgAA9sz4D+cOXL710KDXgagwLxMHYTjezVmH/SyaBUMgAEwcBYDsWOZR531vug2xwAUWI6ZU/18l/bbp7KAvsEAGAADmzHQfyDzDJu9HNqJMAAFFmFt5xi+RUfZO78vegMDYAAMbM7AqKOY59n8ldGehwEoMA9Lx2D4/rzCPoYINAoGwAAYWM3AFYcwz7n6/VC/lwEosF4Gt4rnm/NSe6u3RjNgAAyAgX0YuPTs5cn3eWV0EmMACizG245RfGdOs3ckAj2BATAABqYzMO3U5YWmvyUKjmQACmwkm2tz8W052V774qgOBsAAGFjFwOTDVpRb9daoO4QBKLAhNK5PIrblqsf1RKADMAAGwMAUBlYds7zulBdFkasYgAK7itnJefme3MGe/PooBwbAABiYw8AOByzvYc5bo8oVDECBXcHqgpx8Q25lL+ACJcEAGAADoxnY6lzlzYx+UeSbxwAU2Dyur6vEd2Or/fvvv+X/tYY78de9OzKDATAABi5lwHnKtcJ+98OP/L/WcI6/9PWR/DoGoMCu43ZeZr4Vm2yuvYTdlMcJnscIKoGBhzLw99effv36w+f//vL3h9Iw6LWdJ1sTjKsuYTfl4eBBr4s0sxmAApvN+BX1+FZssoXqMh+bElbBV7w+coIBMPCZgb+/vvssv/7w69ef/vzZ//P//uV/Ic7eCCk+VE8zP0AordyjP6FAFt8Dg/syAAW279z4OxO70flo6q2y05m5DPO/1yDkn9/uJPo8kIzvvn796f9e/7jdp4J//Pn1p6/fXvy7r19/eb+PXfw+ib1//N+HQPnD16+/uQjaDFRQYH9mb/fr1x/+d7PO92qnfIg5R3NKK+d3ptWwvbhDN24GoMDcVO0K1LvR6SmLrfKos4QJm03k39j/3S/kF3/87n9f/5jd2iX1Ku/79avpJ1OVbJ8/t9yDPfGyTURdMpeBpHkF9qYvf/36w9c3WfABkhwh5tnldOYElsfvLCFgjhcCZEcGoMB2nJWmnsRW9D+WNZZ/1F+RkE0v2AsW1ypXXdI+9LMHY+gvX79/5/iskMSb+r/uPIo98bI3U2AvfANjO6Vo0knlNzzqyoPxVxTI4gthcFMGoMA2nRh/W2If+h/9GsuJ9Jf2v90ApLhWhRaRj0f/aOb9+4d8Na7G3K/5IPZeL/Gyd1Ngr9eHQG/8FDpgH56Uwn+UeURVE8ZfWiBP4he9fmYACuwzE8f+r9iH/kenrgrAPD3M41tcq/zzzz/+/umXpYRSOfLeTXSSAvv69Tf6zba/v/jvX9PLch4Kk/Eg9h6gwAoTjaHPDHiOryZR1QT2VDcxn9vH/57EABTYSbNl9mruRo8zIK1aQwptmO9yibOgIVI9/qf39R8fu6Sny5L+Sf3xt1Tq4+PH5y9hTqH5KPbEyzopumwyQ4lJhf880eLPQoYyPi0od2o1CakYOFfa43/aNN3jfaHAjp9Hz+bMYVoVVQ9e9DCPd3Gt6m8/AvDd/1m9/fwl6Tv+wcz5hygbAz+kUvqTAfwLFv+yZfVY8Il3/MOvvb+PLwLvzZ542QYF1jnLr09/Gpd/jq3/wdW/f/qZKV+Qn/5I7+v1Kiqwj9WllPrH0Oc/ktLakv7jt/TBlYwTFKE4qWJaKhYlSjc9FnY/hrZlAAps26nxNta0SwW4R1HFYnkD3jfsxIlrVWsI8WfEtAITGeg6ScZ3+d+pCgR+fJD7+tOtzP9up57vc7oTzYPJswjUUXdiT7ysU4GJKM/yeJtl/utZn79Q/itJ7o8r5v+KkL+8DwnF81H31y+xzj+Gfi76Ich8LXH5KBjgj6KoueSWOvkBFVNRPVG8equ9lDYUDzIABRYkbp+w1o3K8TEV1RPFq0/iUFyQWkMIgLi0slcRv5ksERYL5BehkF89Ckw2k7va1ZwIcu7NnnhZjwKTxPJVQbZaHh+z/Gspsrle+YPSSZ/mR+lyEcIfxWJ+q/v+rfdtiDq3DK2iXCT8nErHqhW31sEPqB4tFYvl1VvttbyheowBKLAYb7tEte5Sge/RUuFY6mESieJalRpC3Wf83hUfePjdJmwe9Xq9woH8IhQl4gpMvaO4mAsz8Sj2xMuKOdUsXTTLYt7FivXLHb1g+OoSYogPiQb041tL73/JBf2tv/94/xqXkvgXnmZ7iodOp5iE6oyi6jFjCkMoMpIBKLCRbM7PFduoFBVWUT2BVH0SXeJa5ZeHcUnwj0NKuNDt8nq9/sb//nTxN4yHA19v/6ifuLT+8efgX6EpGGj6qzhF7L3ZEy9bUWBDZ5n+Slu9Jt9Uy7vc4Ror/RqWUEtvse+rq6rAnC1x0kQ5rlDF0KTN31yGTqdOLRULp+oxo/ltEbCaASiw1TPQVz+2USmqR0iFY6l636u7o/kNIe4n/cjvCX5/mL+6LgCkToRfX+QCQIGv9zuS36/u11VAdWfrflTMh+NR7ImXLRMlJlGDBcA/yyKwsCb5UJoz8QoCwD90lRWYCCy0xL/JSRL42lM/iv1YZBtZdDrFJFRnFFWPGRvxiFZ8DECB+XjaFRXbqBQVVlE9gVR9EqniTtKqizziTnoL5N/GqHF+wbA/XRgOFApM9ENlGwz1naY159u7WL8VdCf2xMtKPfHO+xu4ZXnUZ/l91viUcbnzh19b/3Lle6wQUk4Fxiv+66Xf03IA50GU47qNh7wTuc8THU1ffvVNp5aKhfMGAvY+TKITJwNQYE6iNoUFdikP6RFS4VjewAxa+Q1BckEb4vJ4Ff6EWkaI0IUtrkldS3gosH43NxH2fmt++rXu9u8Qj2JPvCyfF038hbP8PnFcu7wVNWXfe6xY1VcoMC6zPn0q/vyvv4sfp4pONJ8bePjRFJNQnVG8gYC9AYVooY0BKLA2vnZDB3YpDwmrqJ5A3sAMPsW1+qZ+vv4kSujOEN283XYZ1cWz0YUdDhypwN5v4k99tsuvT7/uVvh3zW/HnnhZmlCxMNLjhbP8PndcgXEJZU/oe6zQPTycp60vvPe0IrZOhSkWTVpXOulo6hRS4XBqIGas5A61QwxAgYVo2yYotlEpqkdI9cRSAzOIFNcq/3Wccvn6vcJlGbtjwoH1i7DcMY2+35dh+aUV2L3ZE0tlpAJjy6M+y+/Tx+XO29J6z/mvyX+P3UKBmX3SWt3IoHMpLKE6A6mBmLERlWjFxwAUmI+nXVGxjUpRPSqqJ5YamMGruFaDGqLlFqlfk/n3LnylyAe9j7zfwbEfPlLGR7EnXrZBgbUsj2EKbL/fA/vu/a/pr//N/rTOtjDoXOoUUuFwaiBmbEEimmhhAAqsha39sLGNSlE9KqonlhqYwai4Vv0KTP6CC/1b17Wuw4H1u7lWWv91nYHf/eJFHsWeeNmyArtwlt81NP8GJjoUn7j0Ly8KQEHfF4Y+rYd8Sx+BjTKUL7M9bDqXwhKqJ5Cqh409WEQXDQxAgTWQtSE0vFcpsJNWhEAAACAASURBVEdIhWOp+gxKxaXlV2Cv9z/qmP5uiE//9B79v7+//vHn119+/k2pt6suHPj+t1Hwq5dqlo2P6/Dnn5AGMoj8j2JPvGxZgcWXR3WW83JHF6XfYhz494EZyybX0ru/wphYW9s90rnUI6TCsVQ9bGxHKBqqMQAFVmNo7/HwXqXAsIrqCaTqM9gV12qDAmv545BvCqwjkEso4yIsEia+yvA/JWDYvi8Wz2FP/9KbQdrPupaW0NuPm/kvBSpbLI/KLL/LGrEMeGyuQ/L76/K0ouKnRZdviQdS3WR89/Wnfzv8T//7+pv7+3FxgV89SOdSWEX1BFL1sHE1P8g/nAEosOGUTk0Y3qsU2COkwrFUfQZZPRpCXDziguGP4qoLB/L7zLgIi4SJN+XtmTYpiUJWkdMT8pHt/do2e0jOHdjzK7CPLz3RF6zM8ntasQyqOvu7r19EtSC2ULcw9GlCCy1Z//oQNfBh+BT/x+KZbdGhdOhfBvblV9/Mpgz1uhmAAuumcGkCfmrE7LCK6gmkVmeQ16Uhfr57/sSutI8b5f07x8etTO/091cgsHIRUnLLEG+aa5X8HjklcnpC3loLkfApQyiwh72IAruoz4Lc+ZncrAj7+vWXv7/9m6QzFJhvpj6tutDfh/K2nC58oEMJCuxClpH6nQEosHc+Tnvip0bM7hFS4Vje6uWUv/0Djl9bf424o4V//P3T73vxrwuf/ozh578Q6+2Xw96ztQbyH2yJ6/M9sfXk/BqRtKPvm8SD2Hu9Xj4CtQwdPstcu9vLIC1I+j8Dvn796fM/G/oP9muI9FtiabkUVldhKMXmWuKql8R9zjD+DxVrIa/w8UOp54eJ4VjeQMxeQRtqdjEABdZF3/Lg2EblUWEV1RPIG1jOIRoAA2AgyID4RCoE3ydh+v79GArshx9zEo2fijE7OIkIW8cAFNg67kdUjm1UHtUjpMKxvIERNCAHGAADKxjgAkv8vtq/2mEf5Mx/3n5F12ZNOpRyCulqPzUQNsz3gnNnBqDAdp6dem/hvUqBYRXVGUgN1F8SCDAABvZkgCuw9I9C8p/Ii38a8g++H3wvelM6ka5WWrn81EDMWEQbynYxAAXWRd8OwbHtSlGdQiocTg3swCF6AANgIMJA9s8E0C+oMWPjH0G+Xi86kXIK6Wo/NRAzItOHmNUMQIGtnoHu+rHtSlFhCdUZSA10E4AEYAAMrGPAJcK+3v+vBKMT6WqllctPDcSMdSsAleMMQIHFudskMrZdKapTSIXDqYFNaEQbYAAMxBn42/99+rtXvmNfvNIfFsZfx5r/1XshxehIjBnxuUPkOgagwNZxP6hybLtSVFhCdQZSA4NoQBowAAbAQJwBOpGEMJr2SA3EjPibI3IdA1Bg67gfVDm2XSmqU0iFw6mBQTQgDRgAA2AgzgCdSNMkFy9E1cNG/M0RuY4BKLB13A+qHN6xFBhWUT2BVH0QDUgDBsAAGIgzQCcSF0bTbKoeNuJvjsh1DECBreN+UOXwjqXAHiEVjqXqg2hAGjAABsBAkAE6jvBPEgUZRFiIASiwEG07BfGzI2aHVVRPIG91JzrRCxgAA49jgB9H07578UK8gZj9uDm7xQtDgR0/jbHtyqN6hFQ4ljdw/BzgBcAAGDiZAX4ccWE0zeYNxOyT6X9u71Bgx899bLvyqLCK6gnkDRw/B3gBMAAGTmaAjqNpkksUogbCxsn0P7d3KLDj5z68YymwR0j1xFIDx88BXgAMgIGTGaCzSAijaY/UQNg4mf7n9g4Fdvzch3csBfaoqJ5YauD4OcALgAEwcDIDdBZNk1yiEDUQNk6m/7m9Q4EdP/fhHUuBPSqqJ5YaOH4O8AJgAAyczACdRUIYTXukBsLGyfQ/t3cosOPnPrxjKbBHRfXEUgPHzwFeAAyAgZMZoLNomuQShaiBsHEy/c/tHQrsDnMf3rQpsEdF9cRS23eYA7wDGAADxzJAZ5EQRtMeqYGYcSzxT28cCuwOKyC2aSmqR0X1xFIDd5gDvAMYAAPHMkBn0TTJJQpRAzHjWOKf3jgU2B1WQGzTUlSPiuqJpQbuMAd4BzAABo5lgM4iIYzmPFL1sHEs8U9vHArsDisgvG9TYI+K6omltu8wB3gHMAAGjmWAzqI5kktUoeph41jin944FNgdVkB431Jgj5AKx1L1O8wB3gEMgIEzGaCDCP8o5JkTeHDXUGAHTx61zk+QmB1WUT2BvFV6FxhgAAyAgZkM8INIfJ2a88gbiNkz6UKtgQxAgQ0kc1mq2KblUT1CKhzLG1jGHQqDATDwbAboIJqjt3QVaiBsPHsCD357KLCDJ49aD+9bCgyrqM5AaoDeBQYYAANgYCYDdAppbTTHQw2EjZl0odZABqDABpK5LFV431Jgp5AKh1MDy7hDYTAABp7NAJ1Cc/SWrkINhI1nT+DBbw8FdvDkUevhfUuBYQnVGUgN0LvAAANgAAzMZIBOIa2N5niogbAxky7UGsgAFNhAMpelCu9bCuwUUuFwamAZdygMBsDAsxmgU2iO3tJVqIGw8ewJPPjtocAOnjxqPbxvKTAsoToDqQF6FxhgAAyAgZkM0CmktdEcDzUQNmbShVoDGYACG0jmslThfUuBnUIqHE4NLOMOhcEAGHg2A3QKzdFbugo1EDaePYEHvz0U2MGTR62H9y0FhiVUZyA1QO8CAwyAATAwkwE6hbQ2muOhBsLGTLpQayADUGADyVyWKrxvKbBTSIXDqYFl3KEwGAADz2aATqE5ektXoQZixrNn7+y3hwI7e/6o+9jWpaiwhOoMpAboRWCAATAABmYyQKeQ1kYTPFQ9bMzkCrXGMgAFNpbPZdnCuzcFdgopCv/PX31BtsfgbS/jDoXBABh4KgP8CJqgt3QJ3kDMfurU3eG9ocDuMIuv1yu2dXmURzBVMf/5qy/Sf1UkAaiHm8wEXgMMgIFzGKDzR2ujOR5qIGycQzY6lQxAgUlGDn0O714KJEnUY5ACI6OajRo4lHm0DQbAwLkM0PkzR2/pKtRA2DiXfHQOBXaTNRDevRRYlUoeAAkvbeTCqYGbzAReAwyAgXMYoPNHayOP5z9/9YUHVsBQA2HjHLLRqWQACkwycuhzePdSYE4hNfm18NIekZAaOJR5tA0GwMC5DND5UxBJ5hCdbOao30kNhI1zyUfnUGA3WQPh3UuBQhjFHulUqhqUnxq4yUzgNcAAGDiHATp/nJpJn2zOwByMGggb55CNTiUDUGCSkUOfw7uXAkkS9Rj6eKp6qIFDmUfbYAAMnMsAnT85hZT8hXOsHFgdpQbCxrnko3MosJusgfDupcAe4UWxhXOqOnSTmcBrgAEwcA4DdADmpFL14MoFOv3UQNg4h2x0KhmAApOMHPoc3r0USCqqx6ieVh7AoVOAtsEAGDiOAToAhWDynFQJIwJbH6mBsHEc52iYGIACIyrONsK7lwJ7hBfF+o+tKvLs+UD3YAAMnMAAHYCknKpHkwBQYMygBsLGCTSjR5sBKDCbl+O84d1LgaSiegxxNg15PG4u0DAYAAOnMEAHYPiwigkviqIGwsYpVKNPzQAUmObkSE9491Jgj/Ci2PAp5gk8cmLQNBgAA7sy8OVX33hOnjKGtFTMoBM4bOzKLvqqMwAFVufoCER491Igqageo3xUjRo9YkbQJBgAAzszMOo4igmvFEXHb4+xM8norcwAFFiZn5NGe/Zwiu3RXil21KHmyXPS3KBXMAAG9mDAc7Y0YdYqsD1IRRdBBqDAgsRtGPY0BUan5IZzgZbAABjYjQE6McYaUGC7TfRB/UCBHTRZlVYfq8DoPK0QhGEwAAaexwCdDxcZUGDPW1PD3hgKbBiVyxNBgaUTdvlEoAEwAAaWM3CR3tJpocCWz/W5DUCBnTt3snMoMHE4SoLwDAbAwAMYEOfA1Y9QYA9YU1e9IhTYVczOzwsFljtq588FKoIBMDCZgdz2v9oPBTZ5ou9UDgrsPrMJBVY9au8z2XgTMAAGPjNQ3fiXAqDAPs8D/reZASiwZsq2DYACc56z284gGgMDYMDPgHO/Xw2DAvNPGZCCASgwQcjBj1BgrUftwZON1sHAUxlo3eZX46HAnroSB7w3FNgAEjdJAQUWPmo3mUG0AQbAQIGB8Aa/NBAKrDBlGCozAAVW5uekUSiw/nP2pPlGr2DgGQz07+tLM0CBPWMZXvKWUGCX0LokKRTYqHN2yfShKBgAA4KBUTv60jxQYGLW8OhnAArMz9XuSCiw4efs7lOO/sDAHRkYvpEvTRhWYP0n9pdffXPH+X/QO0GB3Weyh+znzn+c+9KTbmHy+6wSvAkY2JWBhRu8pzQU2K4L6oC+oMAOmCRni1BgPceoM9Y5F4CBATDgZ8C5+/aELVRgfoaB3JMBKLA95yXYVb8IwzcwzykfnB6EgQEwwBjw7LX9MVBgbEphtjEABdbG1+ZoKLDJ5/Xm6wHtgYE9GZi8Ty8tBwW25xo7oisosCOmydskFNilR20huXeGgAMDD2agsIPOHYICe/CK7n11KLBeBreKhwJbfo5vtR7QDBjYgYHlu/LSBqDAdlhjh/YABXboxMm2+7VXyoDfAxtyWMvpwTMYeCQDQ3bT5kkWKrB0aD9yZd3kpaHAzp7IUcKL8kCBjT3uz15e6B4MhBgYu4k2z7ZcgdHpHZorBK1kAApsJfs9tWnXjTWgwC467nvmGrFg4AgGLto7m6fdR4Glu+CIpYImEwNQYEeuhLGqi2eDArv6uD9ywaFpMFBk4Opds3P+3RQY/qL84lLdaxAKbK/58HTDBdNwGwpszlnvmWhgwMDmDMzZLJtX2VCBQYRtvnGoPSgwouIMY7jkEgmhwCYf92csO3QJBt4ZmLxNdi4XU2Di4L3i8X3G8LQjA1BgO85KrqcrdqnO2SPCdj4oN+8tN+nwg4F9GNh8Ey1pb1sFhi9h+2ycXCdQYDlmtvNrqXSRBwpsyTmeim637NAQGGAMLNwa25beWYFBhLHFu6MJBbbjrOieLhJbZloosB3Oer0G4AEDyxnYYWvs1sPmCgwibPmuKTQABVYgZ5chUydd54QC2+qI32UVoo8nMbDVFti8mf0VGETYtnsXCmzbqflo7DqxZWaGAtvzxP9YELDAwGUM7Ln4d+4KCuyyxXj/xFBgu8+xKZIudUKB7Xzc775e0d+ZDOy85jfv7QgFhs9ge+5LKLA95+VfXV2qtHLJocA2P/FTe1svXDR3CANHLPXNmzxFgUGEbbgpocA2nJSPlnIi6VI/FNjmJ75o72O5wAIDbgbEKsJjmAEoMPeiA1AyAAUmGdnn+VKZVUgOBRY+i9cG7rN00cm2DKxdoresfpACw2ew3TYmFNhuM/LRT0EkXToEBXb0PfGxgGCBAcbA0at65+ahwNgqg9nGABRYG18z0ZfKrEJyKLCdj3t/bzPXKmpty4B/wQAZYwAKbNvFv39jUGCbzlFBIV09BAUWO4i3jdp0iaOtKxnYdjXer7GzFBh+EHnltmvODQXWTNmcgKtlViE/FNjNLolf/Pt/pDeas3RRZS0DN1u9+79OQIEVjt8JQ2vXJ6pzBqDAOBsb2RP2YaFEWITtf1w+rcNf/Pt/iP82WuVoZRwDT1vY+7wvFNi4Vfy4TFBgO055QRvNGYIC2+d87+xEyC963HHdo6cQA50rBOGdDBynwPCDyNA+uyQICuwSWjuTzpFZhSpQYJ2H8j7hJLmE0blEEb6cgX3W2MM7gQJbvhfObQAKbMe5K2ijOUNQYLe5VITwoscd1320p39+/+0/v/82Gn1YXFqZv/jv/3ebJXr6i0CBHbaFdmoXCmyn2fjcyxyZVagCBXb6rfCve1r9ElhSYDS/n1fc7v+bNJbz/9/9ZaL9/eevvvjFf/8/+u8eS/QGbwEFFl3RiHtBgW23CArCaNoQ3dCtxg3O0zu9An3xEoY5rct3glNgNcGWv9SQBkh1CeNOa/XcdzlRgeFXwYZszP4kUGD9HA7OME1mFQqZN7THee4xesvOhfCix+pUDl7Tn9M1iaex4M8tHPm/QnjR4y0X7XEvBQV25Kbao2kosD3mgXVREEbThqo3dA5w3Ol574ZJcgkjN32mn61NlzlWOQ3P5nqHzUAkuYRx79V7yttBgW22XU5qBwpsu9maJrMKhcyb2OM85dB8Qp9CddGjZx5NDG2V4apoSUJ6nZ2NdLsL4cUfn7CSN39HKLCdd9DmvUGBbTdBBWE0bci8gD3Ozc/KR7VHkksYnnnMYZZIpdaif/3jbwv/6Wy7HQHmjc5VF7cftaT3fFlzvsrOaSd5odBuy/6Z/UCBbTfvhT0zbSh3AVf9ex6Rz+xKCC96rE5iAaDlyyaeguSqDtErrD0Lync2V13cfuba3uqtyxNnjk47yQuF1q52VE8MQIGtWQl6Y1Afemi+p3AHl4e2Ohkf3gxJLmGUZ7AwSkplH6OqrmIA2oxXG+b1bDq56uL2wxf5Dq9vzlfBOf88NyvS2tajNATjagagwK5mWObXy508CUqPC43CNVwe2uFARA+f/uKo2t8EVp5Hc3QH4RVTVEOi5E6OPhfu5vIQF17cxoJfy0B51vTowoOdl369XvxR29EFjrgGBqDAGsjqh+pVvq3HvIOrzrVHIaoTAzdTYEMk1PAkgQNB38d+D1dd3KZJh7GEAf8MJuS2Z75uLLDCEdLEABRYE11dYL2+d/ZUxZYJWHICoqhm4AYKbLhgmpCwekC03tYcz1UXt/XswzOTAT5HHnvnY1/3Vl3SAPQwAAXWw15DrF7Zm3tMgVV1zjz4UCvHQE5+/eLf/6M6gznAnB9BThBJk0voM8JzSZsYrrq4nVsG8M9hwJysgnPzk1+3p9cwPKMYgAIbxWQlj17Wm3tyN3HZP+fIQ5UyAzkFVp678uilCmyyKlpbrnA3V4e48OJ2eT1g9FIGqrMmAJuf/Lq9yt2G4Q4GoMA6yHOH6jW9v6d8H+dGLz3pkNzJwBEKbK0MWlhd3MdNj1x1cdu5MAC7goGmGfzdDz/uf/iLDt0XHYDNDECBNVMWCBAL+ojHnMYq+6844JCzlYHhCmzsB7CF6meH0q0XNsdz1cXt1hUC/EAG+AR57CPOf9Fk4NZDiIcBKDAPS70YsZqPeCwrrdzowHMNqWIMDJdfv//+WyiwsdLNc0/nMFx4cTu2WhDVz0BupnL+I85/0WTvFYj4DANQYBlihrrFaj7iMaexyv7+4wwZOhmAAhurlq7IlrubPX6uurjduWwQHmbAM2scc8T5L5oceh8i2QcDUGAfXFxnidV8xGNZaeVGw6cYAkcxAAV2hWYam5Pfx602V13cHrV+kKeVgdYZPOL8F01edzk+PDMU2IwFIFbzEY85jVX2tx5ewA9nYLgCG/sjyH9+/+1YNXNottZrm/BcdXF7+EJCQicDNDVO44jznzc54458ag0osEkzzxf0EXZZaeVGnWcWYBcxMFx+4ZfALlJ4ztvahHHhxe2LFhXSlhkw56jgPOL8501OuiMfWQYKbNK08wV9hJ3TWGV/+ajC6NUMQIFdJJiGpy3c0NUhrrq4ffXqQn6Tgep8ccARh79octId+cgyUGCTpl2s6SMey2LLHDVPKDinMQAFNlwqXZSQ38qtNldd3J62zFCIM9A0fUec/KLJSXfkI8tAgU2adrGmj3g0NVbZyQ8m2JMZyMmvrf4xoosEzYlpm25uDuaqS9iTlxzK/eevvuBTU7WPOPlFk5PuyEeWgQKbNO1iTR/xWBZb5ihO5IUM5BSYOVNOJ34N/zptV72tCwAhvOhx4fJ7bOnCNOmhI05+0eSkO/KRZaDAJk27WNNHPDovaQ577Cm8w4tDgV2nlq7IrK9nv4cklzB2WIdP68E/ayf+k0RffvXNpDvykWWgwCZN+xGSSzTJpZXTftrhu9X7QoFdoZOuy9l0cwuwEF70uNWCfEgzYmrKj+KMPeJx0h35yDJQYJOm/YidJpp0qi4Oe8iZu+FrQn5dJ5Wuy1y+rcujpLqEseHivHdL5WkSo+KMPeJx0h35yDJQYJOm/YidJprk0qrVvveZu+HbQYFdp5Ouyyyu56ZHIbzoccPFee+WmmZNnLFHPE66Ix9ZBgps0rQfsdNEk62qy8Tf+/Dd5+2gwEgn6T89QEO7GU2XtwCT5BLGPmvyIZ2IeSk/ijP2iMdJd+Qjy0CBTZr2I3aaaNJUVDHnQ87iha85XIFpHdPvuUIAObu6ovSQnOULuzAqhBc9LlyEDyxdmCBzSJyx+z9OuiCfWgYKbN7M77/ZRIcxsVWOeuAZPeGVh8uv4f8YUdJJQySLU3IJ2JDSVyQx72mnk1SXMCYsOZRwzpGAiTN2/8d5F+QjK0GBzZv2/Teb6LCspTpHcYIPZAAKTOgt8/EK/dSfU9zQTY9CeNHjwKWFVJqBpjkSYHHG7v8474J8ZCUosHnTvv9mEx12aixPuD7d4AkwAAVmSi7t7BdMwzOIG7rpkSSXMAJLCCFVBpqmJgcWZ+z+j/MuyEdWggKbN+37bzbRoUdCjcJUjz8ACgwMV2Bau/R7RmmXnk5G9TA2T+62rvqF8OKPhdWCoVYGqhPhBIgD9ojHeRfkIytBgc2b9sB+e71egaiBIaMElj9P6+EI/HD5db9fAuOibax4GpLNeX+bMK66uI190c+ASXiPc+DJHEgVu03mXZCPrAQFNm/ap+2ZQKFciF85jUX2n57PyQAFxgWWxx4imwYm6bnUueri9nPW/xVv2jMjhdjcMTvHDwU277J3V4ICc1PVDQxss9ieCRTKhYzVVU3ZUktXHK83ywkF5lFdAjNQP/WnKtzZ1SGuurh9s0U+53WqbHcCcsfsHH/sNum+95CgxAAUWImdsWOBbZYaCASOCmnSTGPB6RUSA3PO3xOr5OTXL/79P8LTIcTKqMd+pTKqk5Snv5+BGXqudi68uH3iel7Vcw///thRx3IgT/gqGXsJIptgAApMEHLhY3jbxP5vl0A5HRK+xfsDUzNiPlYd0NvWzSmwHv7HCh3K1q9XKNUoo7+lURn8t7hGctXF7W0X7T6NaTIv9egDdo6HTtFAOYqFcQUDUGBXsGrn7Fn9gdghIT0XeWcs9W+yuc8hvrYTKLBONTZKQnXm6bn4ueri9tqVuXn1HsLDsXSmTTboCG2tS4EwLmIACuwiYu20rRvgy6++oUSB2P6QThXVE07NEwPa2PyUn9DecAXWKWhy4Z0C5a9//G0uc7+/v7chGcL3Oldd3J6w/I4r8eVX34R57g+kM22mQcdmoCjFwriIASiwi4i10/bsgUBsf0iPhOqMpeZtKt+9x90EQxoeLr8u+nso/vn9t50apV9mlTN0tjckvOeC58KL20OW2T2S0HnSw3NnLPUw06CTMlCUYmFcxAAU2EXE2mk790AgvDOkU0X1hFPnNpUZ7z1uC+dbPESBlcXTwNEhQiqcpOd256qL286FdGMYnRPpPOkhuT+WzrRpBr1+7JeJeTjsKxiAAruC1WzOwMYTuQIZekJ6JFR/bOpcMOB5vPGNwl8NCmyg/KJUYQnVGdhzwXPVxW2+Wp5mi4MiHSY9JPfH9hzFgViTgaY8IgMehzMABTac0lLCptWfwDpdIEk4pF9F9WTIMaA5yXnufesMV2CkQoYbYXUyvBNPwnC3nYHhO56rLmHfewvot8sdBekwCTM8JDB8DgcCNQ9Dkui08PQwAAXWw15z7JA9EEgSDunRT/2xqe1mllWAPqZv4Bkuv677JbDY74F5pNKlmE45FQjvueaF8KLHGyx15yuoff/mSIdJD8P9seFzOBD49vI/PwxJotPC08MAFFgPe82xo/ZAIE84pF9IhTOknptZzgc4j/IjYDdWYJfqqtbkASEVDum540lyCeOIxdzTZH67v42kw6SH4c7Y8AkcCHx7888Po/J8zof/HcAAFNgAEv0pBu6BQKpYSFg/9Qemhv30+pE9J/4msWcpMOdnsFZ5NA0fFlWtgeFrXggvetxkuV7Rhn+/0++hh+ntD4wdv4GoHC0DU+VKwN/KABRYK2Nd+LF7IJAtENIvpMIZUrddjBeDr7gV5uTMya8N/zGiaSJpTqFWRdWK77npSXUJY86anFaluKezg+kw6aG3MzZw9gZCsu//eo3NViiEIT8DUGB+rgYgh++BQMLWkLB+6g9MrQ7gvZZi2uUxqlBOgfVwPkfB3KZKq7Ry4nuueSG86HHUqluep7aPS+PpMOmhtzO29eAN4EvvDwVWZmfRKBTYVOKHbyr6uh7I7AzpudQ7Y1OHM2do+R3jbAAKbBMl59RVfljPNU+SSxjORbUtbMgJkA6THno7Y53nbRhWZak1czUhAP0MQIH1c9iWoXUb8H+YqFApkNYZ0qmiesKpw8KLXzG07VVEjQ1XYJsImhPb8KsrJzJ80wvhRY+0bI4zRu1uOknC3PYHUg/DDQ9LgaKetMB0MgAF1klgc/h1OyGQ2RPSI6E6Y6m9ZpYHBex5Yw2XX5f+PRQniqrWnp3SygnruexJdQljz5Wc62rQ9v1IQydJD7edsdTDWOPjJYtWoGgxHwbHMAAFNoZHf5ard0IgfzmkU0X1hFNjfnqvQObuiVV+KLBWhTQB71RXHljPTS+EFz2uWqutda/Yv/SrGj3E9sfSaTbKaOIqULQpP8AxBqDAYrzFo+bshECVXEiPhOqMpZbidA+NbL1OLsJDgU1QVLESHoFVxfRc9iS5hHHRUhyVdug2NZKlk6SH2P5YOs36DeMNa65A0VpKjA9gAApsAIlNKWbuhEAtHdKpojrDUz9NDE8Aj7p4YnmGK7CY2kCUyUBVYHkA4fteCC/+GFtsV0dN2K13+gYWpksf7FVPuBYC/QxAgfm5GoOsrnsNCBfWqQKeTgnVGZ4aDjNwaeDVl5OZf7j8wi+BmUKqx+nRWGVMWIH97ocfueritrmcFjov3ZsieTpGeljtjw2cl1CsFwAAIABJREFUvTpEvJf/UaeqevzJgQwzAAUWpi4YWF33GhCs9HOYztbq6ZRQneGp2x4GJsTOvMagwHq00bTYssCqjvbc91x1cXvmKi3UmrAfdYl0jPSw2h/bevBqvH4vv0dnq3r8yYEMMwAFFqYuGFhd9xoQrPRzmM7W6umUUJ3hqdseBmbGFi6eUUNQYNNUVH+hqtIqAMJXPldd3B61AsN5Zu5EUSsdI2FK+wNbT10TL16q6dFMWHY25Qc4xgAUWIy3eFR50Zuj8WKhvwdZ99CponrCUzM9DMyPDV9RnsDhCqxfZyBDgYGCxioP9dz6XHhx27PAhmPmb0BdMR0jPZR2xupDNeDR7+X3TC7nb+zhSCiw2Qtg8k4IlNMhPRKqMzY1M3uSBtUbfpkNl1/4JbCCeBo1VFZaudGeK5+rLm4PX5DlhIO20YA06RjpobQzVh+qAU8PEa3lemoh1s8AFJifq2HImZuhtZaJ71RRPeGpn2HUr0hUvqWaRs9VYKQzRsmas/LQ6/uNniufqy5uNy22MHjFJqvUTMdID6Wdsea52uqsvGR+uLWQ8x9iyRfEiJcBKDAvUwNxM/dDoJYO6ZFQnbHUzED+V6UKX2kU2KTAfmL/LzcLV+uYguC4uvRu+QtU5IbCtz5XXdymhXSRsWpnlevSGRLmsz+Qeugxyq9ZGA0ULWTD0EAGoMAGkulNNXM/BGrpkNz9PcFPzXjJPQEXu//C8ispMXOyrpMpOVXB/ddV3zYzf/2q3XPxc+HF7djaK0dtvufSGdJDZn8snWM9RpjnQNFwLQQ2MQAF1kTXGPDM/RCopUPMy3uOk5oZQ/1OWcq3mh49SIFVtQUBtpVK1zVG7141ei5+rrq4rddVj2en/ZTtJZ0hPWT2x9I51mNk37A2EChaS4nxMQxAgY3hsSnLzP0QqKVD5oitXJXUTxPDZ4GdV+ApCqwqLATgOq3Tk/mvf/xtT3g5VjCQeyxc/OwnzD+ZMK66uO1cbGXYWfsrHSAmS9Oc+lANeMK0z6wVbvKZgVBgC+Z95n4I1NIhOW00x5/6WTBP00uWr73hCqysEgKjOSVR9gcKzQnZVoRx+ZVsU0lw4cXt8jIrj07fEwMKpgPEpGiaUx+qAU+Yi5m1wk0+MxAKbMG8z9wPgVo6ZI7SylVJ/SyYp0UlzSuwSX4lJvk9bXI7VseUZVZ5dGwnA7NdKsL++f23ZVr++sffmhKBzyzZGslVF7fNBVZ2LtoKY8qmA0TzM9OjD9WAJ0zHzFrhJp8ZCAW2YN5n7odALR1i3t/TnKmfBfO0uiS/FAMKrDpBY5VKVUwUAAM7GZ4qtT08LSUs0OJXYKaY4KqL23xdVe3Vm2BA/XSAmBRNc+pDNeAJczGzVrjJZwZCgS2Y95n7IVBLh1Tv8ksBqZ8F87RHyXRH7qzAyhrCM0pyZE/jUhFW5UerBPruRYbGdP4T3Xus/TFdpAPEpGiaUx+qAU+Yjpm1wk0+MxAKbMG8z9wPgVo65FKBVU2e+lkwTzuVzCmw1GOVQw0YonWq6sEJGNLM1Umu+4lkmSVTJZD2+ukn+zfxUxT/9MXtwqevnVb9mF7SAWLSOMepT9SYJ0xHoFy4FgKbGIACa6JrDHjyfgiUEyH6/p7pSc2Mof7wLEKH6bfxz0u/XinrhtbR/n4mZFgiwnpUAldd3NYKTK+l23jSAdJDY2esOE5jjz3T0VqxpxZimxiAAmuiaxi4dUv0/DMRgVo6xH+1D0emZoZRf5dEv/j3/yi/SnkiOvVKq8Aq4zubmRmeXmR4xQI/Pdc/V13c5gqsvIpuMJoOkB4aO2P1cRrwhCdiZq1wk48NhAJbM/Uzd0Wglg4pX+eXjqZm1szTXarqCcppCP3GGlmQC7EhXWJnzxUirMxbWAFw1SVsPdF39aQDJMxhf6A+TgOe8OzMrBVu8rGBUGBrpn7mrgjU0iH6Cp/moWbWTNXtqv7++29b34nrobJWiI3y/KfYw38iKajjcxQTASmDEF70yPPf2E6nR4zAUVF0gvUY4TkKFA3XQmArA1BgrYyNwc/cFYFaOmSa3tKFqJkx1CNLiAESRkIo9D9WMxNgN2OsCONMiinySwER+Hq9SHIlQwPu7Umnh5/AK5B0gvUY4WkKFA3XQmArA1BgrYyNwc/cFYFaOkQLo5me1M8Y6pElyoDnbxDlMsJjJ1FVRe6mvaif1Dk99hhEgp6fsizQeO75xX//P/74NDsdHWUCrx7Vx2nAE564mbXCTT42EApszdTP3BWBWjpkpt7StVI/a6YKVRkDpBKWGD365rrYISKM+GRkv5laJbwN4yHDQDo6NHszPfo4DXgy71d3z6xV7waIdwagwN75mPU0c1cEaukQrYpmelI/syYHdbIMkFBYYlynovozd/5EMvGZ5f31SoqhAMCQyUA6OmbqLV1LH6cBj/l2HufMWp5+gOEMQIFxNubZM3dFoJYOmam3dK3Uz7zpQSWLgYGqK/wDzX6pdF2GHhH21z/+1qIcvl4G0tGhVdFMjz5OA54wETNrhZt8bCAU2Jqpn7krArV0iFZFMz2pnzVThaqfGRilwMLyq0fiXCe8eOZEEfd4bMivz0ts/P+mo2Om3tK19HEa8ISpmVkr3ORjA6HA1kz9zF0RqKVDZuotXSv1s2aqUJUx0C/CeuRXTN9oDXSpkgs0yQiGOZiBdHRoVTTTo4/TgCfMS2utcCEEBhiAAguQNiZk2sZoLWTitSqa6UktjeEdWToY6Fdg/Rm0ogp49hFhHbOB0DoD6eiYqbd0LfNEbXXWX9VCtFbp+cdXrPrwVRiAAqsQdN3wtL0RKKRDZuotXSv1c91cILOTgX791J8hoLfMkAkirFzCyTlgPQyko0OromkefZbGPDESArVihRAVYwAKLMbbgKhpeyNQSIdoVTTTk/oZQDpSdDMQkFD9P3nkRU05FXOmtLHYahT1rJH4xa/uZehNkI6OaXpLF9JnaczjfeF3XKDWewI8XcsAFNi1/Bayz9wbgVo6ZKbkErVSMwUyMTSNARIWTmOs/Cp/VdJax+O5Imeqm6No2mSh0JdffaNV0UyPPkgDnvA8zqwVbvLJgVBgy2Z/5t4I1NIhQhXNfKRmls0WCjMGcsJC+4fLr4vU0nVpBSeMRZiXM5DOjZl6S9eis6vHCDMVKBquhcAAA1BgAdLGhMzcG4FaOmSm5NK1Uj9jqEeWbgaEsDAfT5Ff9L3K88GsFcOZ6WYdCdoYSIeGVkUzPfogDXjaXpuhZ9ZiZWF6GYAC8zI1HDdzbwRq6RCtimZ6Uj/DZwEJexjg8oLbpGm4c4hNAsjMRqMxI+WMxeaiqM8enhEbYyAdGjP1lq6lD9KAJ/b6r9drZq1wk08OhAJbNvsz90aglg6Zqbd0rdTPstlC4XYGSHyMMpLKqWbLiSGPPyX3IJ2YlLCdPEQMYCAdGloVzfTogzTgCXMxs1a4yScHQoEtm/2ZeyNQS4doVTTTk/pZNlso3MhAVSe1ApLicUY55VEONvDXwvDHHhsXzkh4OjRm6i1dSx+kAU+YlJm1wk0+ORAKbNnsz9wbgVo6ZKbe0rVSP8tmC4UbGXBKJScs8CtlOXXl9I8SYY20AT6SgXRoaFU006MP0oAnTMrMWuEmnxwIBbZs9mfujUAtHaJV0UxP6mfZbKFwIwNOaeWBBeTXEP2UenMqthyskTbARzKQDo2ZekvX0gdpwBMmpbVWuBACYwxAgcV4GxM1bXu0FjLxM/WWrpVaGsM7slzPgEdaeTAx+TVEPFHpnLry+K9nGhWyDKRDQ6uimR7zLG11Zt+wONBaBf8kUZHOSwahwC6h1Zl02g4JFNIhWhXN9KR+nMQCtpwBj7qqYkgDVZEmwKOQPJjwF7Xls/DwBtKhMVNv6Vr6IA14YvM4rVCsPUS9Xi8osJXLYNoOCRTSITP1lq6V+lk5W6jdwoApieY7PQLLgwmIsBa2gL2EgXRoaFU006MP0oAnxs60QrH2EAUFtngNTNshgUI6RKuimZ7Uz+IJQ/kWBsJ6q/PTF6/rUVdOTErrBP/z+29bqAL2Egbu8U8ShX84qM/wqueSaUDSPAP4Bpbn5vqR6n7QgFhTOk/MM1NyiVrUcIwBRM1ngCshvz1QfgU+XJUFllOEzacaFTUD6cSY+blL16JTq9PQb+fxBIp60gIzkAEosIFkNqeauUMCtXSIUEWTH1M/zSwjYB0DfuFF4qY1pIwvK6rAKJXLxa4jG5XfGEjHhVZFMz36CA143t6q5WFmrZa+gP1gAArsg4v51swdEqilQyZLLlEu9TN/mlAxzADpFY8x9uvX8A9gJLnEu7xeL/zMMbxCrgtMx8VMvaVr6SM04AlTNLNWuMmHB0KBrVwAM3dIoJYOEZJo8mPqZ+WEoXaIASFZzMfh8itVIdk00KD+Q2QgaBID6bjQqmimRx+hAU+Yr5m1wk0+PBAKbOUCmLlDArV0yGTJJcqlflZOGGpHGSDVoo30DUn7+z1J1SXtxbP1qzH8W0PRhTAvLh0XM/WWrqWP0IAnTNnMWuEmHx4IBbZyAczcIYFaOkRIosmPqZ+VE4ba3QyQEhKZyD/K0MJLZw5LMdE8HjdkIB0XWhXN9OgjNOAJc9taK1wIgWEGoMDC1I0JnLZJWguZ+MmSS5RLLY3hHVl2YkBro06PR36lEjERthN56MVmIB0XM/WWrmWeoq1O+/Uc3mmFHL0AYjMABWbzMs3buklm/t0wujchiSY/pn6mTQ0KTWOgU2+J8NZfKQuIsGnMoFCYgXRcaFU006OP0IAnxsC0QrH2EJUYgAJbvBKm7ZNAIR0yWXKJcqmfxROG8hcwICRUz2Or/Eq1WkXYBRwg5WAG0nExU2/pWvoIDXhivEwrFGsPUYkBKLDFK2HaPgkU0iFCEk1+TP0snjCUv4CBHsnFY2PyK/D3VlzAAVIOZiAdF1oVzfToIzTgifEyrVCsPUQlBqDAFq+EafskUEiHTJZcolzqZ/GEofwFDHAVFbbD8itVbPoMdgEHSDmYgXRczNRbupY+QgOeGC/TCsXaQ1RiAAps8UqYtk8ChXSIkEQzH6mZxROG8hcwEFZdFNgpv5o+g11AAFKOZ4BODC2M5niogU4jRk2gaKwQonoYgALrYW9A7LR9EiikQ2ZKLl6LdzKAdKTYjAESUgGjX3ulov5vYJuRh3ZsBvihMUdyiSq8gR7bfr2aN1CxlhLj4xmAAhvPaVPGafskUMgM4cJoji3aaKIX4FMYCGiv9OEqFqijnArsFD7Rpzg3hDya8CgaCD/GpjJQLlYIUT0MQIH1sDcgduY+CdTSIXNUF1XRDQwgHSn2Y0BLoqpn1NevVMijwPajDR1lGdBHxwTVxUvoBgKe7OvVBmbWqvWC8SwDUGBZauYMzNwngVo6hLTR1YYunTxz5gVV5jNQlVwcMFZ+pX9lqCzC5hOCij0M5A4QLpIutXMNNPnDDDRVSeBwLQSGGYACC1M3JnDmPgnU0iFXC6/ff/+tLkqeMaQjy64McI2Vs5NOyo2G/USJ0GHkh3EcA3RuaONS7ZWS66IBT5jz1lrhQgjsYQAKrIe9MbHTtkprIRN/tQIzi5JzDOPIsjEDZQmVGi9jYqMbU4LWggzQuWEaV4sws2irM/bmrVXC/9RKrD1EEQNQYETFMmPabgkU0iHXKTBdS3uWTRIKT2dACCmqL/xDHv/5/beUH8ZtGNAHiPZcp8N0rYAnNhfTCsXaQxQxAAVGVCwzpu2WQCEdcpEC04W0Z9kMofBODAyRXDwJ/dhxp7dEL2MY0MeI9lwkwnShgCfGwrRCsfYQRQxAgREVy4xpuyVQSIcMV2C6hOlZNj0ovBkDXDz12yS/8Blss3ke1o55nmjncB2mSwQ8MRamFYq1hyhiAAqMqFhmTNstgUI6ZKAC08lznmVzg8KbMdAvuXgGLr+gwDab6pHt5A4W4R8rwkTy2GOMhUCtWCFEdTIABdZJ4IDwabslUEiHjFJgOnPOM4BipLgLA1w/ddpCfkGB3WWN2O+RO160f5QO05kDHvtlat5phWqNYLzCABRYhaAJw9N2S6CQDulXYDpnwTOBf5Q4iIFO1UXhWn5BgR20DGKtFs4ZPdSvw3TOgGfCm6auYoUQ1ckAFFgngQPCd96WurdOBaYT5jwDmEWK2zFAEqrHgPy63bpoeKHcgaP9PSJMZ4t5Gl6MQQO1WDTMeQxAgc3jOldp5m4J1NIhMRGm8xQ8Oa7gfzgDPcIrxZryCx/AHrWuCiePHorpMJ0n4AlPysxa4SYR+Hq9oMDWL4OZuyVQS4cEFJhOkvOsnw90sDcDYRGW016QX3tP+FXd5Y4g7Q+IMJ0k4Am/eWutcCEEdjIABdZJ4JjwaRumtZCJb1JgZoaccwybyHJrBmIKrCC/oMBuvV5KL5c7iEx/kw4zM7Q6S60Xx6YVKnaBwToDUGB1jiYgpm2Y1kIm3qnAzNiCcwLPKHEPBlpFGOTXPeb9irconEh6yC/CdGzAE3vfaYVi7SGKMwAFxtlYZk/bM4FCOsSjwHRUwbOMdxQ+lgG/CCvIr2PfHo0PZqBwOukhjw7TUQFP7CWnFYq1hyjOABQYZ2OZPW3PBArpkLIC0/iyZxnpKHw4A1URRu+nRRgNwQADiYHyMaVHyzpM4wOe2NRMKxRrD1GcASgwzsYye9qeCRTSIQUFpsFlzzLGUfguDOR02F3eD+8xj4HyYaVHCyJMgwOe2JtPKxRrD1GcASgwzsYye+aeCdQSIaYCE5jq4zKuURgMgAEwkGegenYJgKnDBCbwmG+wMjKzVqUVDNcYgAKrMTRlfOaeCdTSIUKEaUDZM4VUFAEDYAAMRBgoH196VIgwDQh4In3/HDOzVrhJBCYGoMC2WAkz90yglg4hBaaHqp4tGEcTYAAMgIE8A9VzTANIh+mhgCffWmVkZq1KKxiuMQAFVmNoyvjMPROoNSpkCpcoAgbAABgYw8Cooy+QJ/wCM2uFm0RgYgAKbIuVMHnPBMr1h2xBNJoAA2AADLQw0H/0BTK0NCixk8vJ8nhuYQAKrIWty7CT90ygXGfIZcwhMRgAA2DgWgY6T79AeM/7tJbrqYXYTgagwDoJHBY+c9u01urBDyMIicAAGAADixjoOQMDseG3nFkr3CQCiQEoMKJisTF55wTKBUIWc4ryYAAMgIFxDATOwEBIT7+Ty/W0itjX6wUFtssymLxzAuVaQ3ZhFn2AATAABgYx0HoMBvA9nU4u19MqYqHANloD83dOoKI/ZCNm0QoYAANgYBwD/mMwgOxsc37FzoYfHo5vYLssgPk7J1DRE7ILoegDDIABMHAZA57DMIDp7Hd+xc6GHx4OBbbLAliycwJFyyG7sIk+wAAYAAMXM1A+DAOj/f0uKdrf9mMzQIHtMvVLdk6gaCFkFyrRBxgAA2BgCgOF8zAw1N/ykqL9bT82AxTYLlO/aucE6uqQXUhEH2AADICB6QzoIzHgGdL1qrpDmn9gEiiwXSZ94c4JlOYhuzCIPsAAGAADixjgR2LAHtX1wtKjXuFReaDAdpnutTsnUD2F7EIf+gADYAAMLGVgh1M00MNSzp5eHApsoxXQunnGtt5a/cuvvhnbALKBATAABo5mYPkp2trA0WzfoHkosI0mcfnm8TewEWtoBQyAATCwEwOrDlJ/XULuRNsTe4EC22jWaVf4jeHde0oPL4qEYAAMgIE7MVA9SK942WpRDbiiDeT0MwAF5ufqcqTeHlXPRT3l6l5UDmnBABgAA/djYPJBmitX8N+P87PeCApso/kq7JPc0NXdp7pXV0F+MAAGwMCNGZhzkOauiYL/xpwf8WpQYBtNU2Gf5IY26h6tgAEwAAbAwDoGctdEwb+uWVT+xAAU2EbroLBPckMbdY9WwAAYAANgYB0DuWui4F/XLCp/YgAKbKN1UNgnuaGNukcrYAAMgAEwsI6B3DVR8K9rFpU/MQAFttE6KOyT3NBG3aMVMAAGwAAYWMdA7poo+Nc1i8qfGIACW7YOfvnFb8R/hX3SOrTsrVAYDIABMAAGrmSg9Too4MUd9MsvfnNl48gtGYACk4wMf9ZLPOcp7JNRQ8PfDgnBABgAA2DgCgZGHfu5PLmbyPRf8YLICQU2fg2Yy9fpzG2V6/zj3x8ZwQAYAANgoJGB6w75XGbnrWTCGl8OcJsBKDCblyavuUBjztxWmeNvemuAwQAYAANgoIeBOQd7rkrsktJRPQw8PBYKLL4A9ELs9+S2ymR/nBREggEwAAbAQJGByed5rlz/haUzFN8bg5IBKDDJSPVZr7mBntxWWeivEgIAGAADYAAMlBlYeIbnSg+8uXSqMhsYTQxAgTWsBL3IhntyW2W5v4EmQMEAGAADYOAzA8tP71wDw+8vnfAzB/hfmwEoMJsX7tWr6jpPbqts4ue0wAYDYAAMgIECA5uc27k2rrvIdOYCS08eggIrzb5eRld7cltlK3+JMoyBATAABp7NwFbHdaGZq68znf/Z68J4eygwg5Tk0qtngqewW3YbyhKHATAABsDAUxnY7aAu9DPhRtMlnrou7PeGAjN44UtWL6BLPbz0EbZBH1xgAAyAgecxcMSJzZu89C7Tyan085ZG9o2hwCQ1tEq0oZfUcI8uur9HMohnMAAGwMDDGNj/oNYdDr+/dEJdlDwPWyD260KBvfFCi6Nq6KU2ylMtvSfgjUc8gAEwAAaewcCeB3K1q1EXls5TLU2AZyyQ0ltCgX2wQ8ui1dBLsMfTWn0T/AePsMAAGAADj2FgkxO4tY2eS0rHtlYn/GOWif2iUGD/4oUWRKehl2arp7OBheH2EoMXDIABMHBTBhaet52lWy8mje9sgMJvujRcrwUF9okmWgpjDb1kc57/+p/f0n+BHnpiA+UKIa5FBxAYAANg4HwGCifhnKGek59i/+t/fpu7mLT/ovc6fy0E3wAK7Cr5xVeqXsfk4dsg2TzQaYskzqiLYMGViDAwAAbAwDkMXHR+OtP2n/kiQ0GHOVvqhJ0z8yM7hQKbocD40ixorzTEwU77l1/8Rm8nZ+xw2MjliVxgAAyAgS0ZGH5yOhPqo/6XX/zGGcth6boxs4VvIp6/1d5yki9vCgpstgJL63Lsus+putY9MAR/+ZpFATAABsDAagaGnJatScTFQSd/a54vv/qGYkXO//qf3way9Yesns819aHAtlBgtBl6/q8Z/SWsf1fEMqxZy6gKBsAAGJjCQOxg7IzSUokujkBmit3k4pgyb9sVgQJboMAKGyntiqbtVNhI+L9mtttwaAgMgIHzGWg6okeByxdHUxV+a2gFtuTiOH9RRN4ACmyBAvvyq2/EXhL7gT/qfcVHhS3S6tgJnsgyRAwYAANg4CgGJpyluoQ44cX5T486kP/YkWDJEDmXyK8vv/rmqMkf1iwU2BYKLO0BsTGaHvUuwkYatkuQCAyAATDAGIj9uogpjJqc5jnfdFMIsE7Y1M9AMGP3QebTFdiqjZQWrl79ySM2SeExl2HgxmhN9csvfvOgDYRXBQNg4HkM4OJovReq+GdeHP/2+++/ffh/a/eS/olkTlQ5/dWFfjXg4csJrz+fgdfr9bsffsR/YGAaA8tvjZtdHL/84jfT5m6rQlBg3+6wl5IqcmosE3a1rnLm/+UXv5l/AaPiwxmAAtvqUnlIM7g4nJeCBwYF9twvYelnfJ5VMh9jiq1Vv+BVff3E5MPVAF5/PgNQYA8RPVu9Ji6O6o3gBCQmt5rcac3gG9gn6bnzXnIu4uWwxCG+gc3XH6gIBTbtwkAhYoBOvOVn79ENPFl+/e6HH6HAPhTYPl+Vj9tRdBhBDYCB+QxAgZEsgDGTATr3jjuxN2mYCJw5a1vVggL7149faSlAh7VuTqJu/tWLimDg999/CwW21aXyqGbo9Gs9Nh+OJ94e+xtgaZtAgX38AhxfEw/fHs7X54xBCoCBVQxAgT1K9Oz2svwYdJ6cD4dxxnabzcn9QIF9KDD6hTBaHw/fJ+XXJ5bwu1+rlAfqJgagwCZfGygnGOCHYfnYfPgoJ+rhX7/SEoICe1NgWoThh5L6yBC7CDoADKxlAApMCAI8zmdAnIr62Hy4R/AD+QUFJrUXv0X0cnn4/jH/WTHOGGwwsIoBKLD5ggMVTQZwceiLUnNiUvdMJ76BZXWYXjeP/R5mUrHqukVdMCAYgAJ75u2151ubp6XWJU/wmFTsOWuruoICyyqwdMqba+ghUiz37uL+wyMYWMsAFNiq+wN1cwzkDs/HCi/82NFcKlBgFQWWrpbcdrqlFCu87NqLFtXBgMkAFJh5uMO5nIHCWXo/KVZ42eUTsW0DUGAuBUbnfmGRHa3Gyu+FP+1ICwDGhgxAgW17waCx3/3wY/l0PVqKlV8Ns19lAAqsTYGl66e87Gh0561FTZaNDa9btAQGBANQYNWDHoAdGCgftml051vD/PNY5kvtwPYRPUCBRRQYvwDM9Wc6124ts6Wck78gbDCwOQNQYEdcNmiSGMgdvKb/lIuD3g6GnwEosF4FRpeTuXmczlF7zFnOhNGLwAADZzEABeY/8YHcjQHzNHY6d7g4duPzrH6gwIYpMHFpObfQWpjoGY9g4EQGoMDOunXQbY6BtdeBs3quefgDDECBXaXAxE3mXNyXwkRLeAQD92AACixw9CNkfwYuvQ78yfcn6twOocAmKbDcVeffBn5krhb8YOCWDECBnXsDofMYA/7rwI+MdYKoHgagwBYrsFveiHgpMDCTASiwnjsAsWAADKxiAAoMCgwMgIGzGYACW3V/oC4YAAM9DECBnX33zPzSgFpgYE8GoMB67gDEggEwsIoBKDAoMDAABs5mAAps1f2BumAADPQwAAV29t2z5zcJdAUGZjIABdZzByAWDICBVQxAgUGBgQEwcDYDUGCr7g/UBQNgoIcBKLCz756ZXxpQCwzsyQAUWM8dgFgwAAZWMQAFBgUPhn3eAAARf0lEQVQGBsDA2QxAga26P1AXDICBHgagwM6+e/b8JoGuwMBMBqDAeu4AxIIBMLCKASgwKDAwAAbOZgAKbNX9gbpgAAz0MAAFdvbdM/NLA2qBgT0ZgALruQMQCwbAwCoGoMCgwMAAGDibASiwVfcH6oIBMNDDABTY2XfPnt8k0BUYmMkAFFjPHYBYMAAGVjEABQYFBgbAwNkMQIGtuj9QFwyAgR4GoMDOvntmfmlALTCwJwNQYD13AGLBABhYxQAUGBQYGAADZzMABbbq/kBdMAAGehiAAjv77tnzmwS6AgMzGYAC67kDEAsGwMAqBqDAoMDAABg4mwEosFX3B+qCATDQwwAU2Nl3z8wvDagFBvZkAAqs5w5ALBgAA6sYgAKDAgMDYOBsBqDAVt0fqAsGwEAPA1BgZ989e36TQFdgYCYDUGA9dwBiwQAYWMUAFBgUGBgAA2czAAW26v5AXTAABnoYgAI7++6Z+aUBtcDAngxAgfXcAYgFA2BgFQNQYFBgYAAMnM0AFNiq+wN1wQAY6GEACix+9/z0+f/5Pwx8jpD/68kgY35+9gQ6MT/99JMT+fvvv20CJ7zZv3A6G+BRzpAE44HcbkpiglsJySWhrkyA0xlI4u/fj3R22w9bpcB++umnwOFLs1MwcmmdFcsws26uYpO/XJenSj1wj2mLVk2M6XR2UoCJ0vzRrOh3Ot+dEhaaJAw3eKtNsX5wGSkaMB95w7/74cdywh6wiN3tEQosqMDEqvLcIiLEfMzlMcHCacZyjAlITidMaKlCQj7Ek1dtHijscqwAi8dyLB8VgZ5HCveANYbCTUPjyx6RpAwWE1pWVzxzNe1MwHwFxqloOtN5YNkWaQVYjPJHjuT+ZPPRnK2jqh6eqh/Ms5l2oQTHF2Dp1k9gDeNJCrYOrHp4tiZwVaPwzKZdLsdDnEgN40mqNoVzJDlNw480wzd0QoGNUWDlq0vfc3wlaVvcXhqQ84hAUVePkkckJL82/EiKFSHlR4riRjmEj/IobnNM1eaBVZtnq4IFgMeWbRGYe9RJckjy+0M4ksJ3ME5RYJxAj80vDI3no9zmSO7nmoNjcraILT+KJAVwGSlGC4+5Ejwkh9FUCCRPUrVFbPlRZBsCFjkLj7lyOsSJFDCdp+BJsRogctKjQJL/aAMKLKLAxFJIj+WryAzJOUWqHMz0F2LFEH8UqfgQt50wHiJUoMigH0Vsa3hOCutCZY9uI+fheXIY088DPbaZRDjNPAIjHv0hHCmSrH28pQIT9wonP9kCQI8cSc5k8CGPLcILjzqbCS7D9GjZUy1hApJTZBZIMVp+FLHlR50qh3ciNazsMcuZIR4kx5hJCk6KFRjyC8MJE1GbP0KBDVNgubs/XU5i9VQf+ZVWBQtALpb7hS0ymO/iwYi06VEHmp6m2HJmncqsWHDqDDkPT5LDaD+P4nZCcg+3dR7u4Uhuc4y2OZJsDRNS2ASsch6qwJruBpoabpgZCgA+5LTNEqZTJ9QwgeEAMUSPCUOPwuAZNFKPkqecR4xWHylt1dCpciFOpIbRzyvNIRrldcPIQhI+VLZ1dY33YHTU/h4osGYFppcCeQo3EGGSoZEFQGEo5SkA+JAuyj0cmWz/KEdqm2fWowUPD9QtUaAHxjEUKAwPRoSEdQmvlWydWSQvwFKszkkeM3k5SodQNlOga/w0z2MVWPU2FTcQn8FqbAKLDOXHcv6mUbM9/dNDDeNVCt1yWCBJOdxfN+XReJHfD9OpWknjpXU2Pip4KwzpPMIjYsuZxahIddYjFFiXAhN3ZOHKESvMRHIMB3C/efkVAHyI5zRtDhaFCkNmKu7ksdxftXmg6EfECqQGc4CIpUeO0RkIJgweJYZyjzwk2TmkWGCFrnRO7vHnL0QVhgr5Jww9WYHp24hPk7iN+JAOJLATRngyRCAvURjySAQqocF8SIyKIf4o+uFDziQ8gwgvPPIobvMQ7uc2x4gmE0wA+CPPw+clYcQof+RJdFE+yqN0CY407UJ4YchMdZATCqxNgYmlEL4gzWtJJ0+wnJ8n4RiPn2O4zfPQfW86eVTZ5uFlpBjlgdSMwNBjGcxHKUQbThgP7AzpfC/qRLehPQTmBodpO4fk/uU2FBi/cvgkcn/5+uRInqH1KhWxKdx0hiuWs/FRXkLYHKbfkY+KQHrkGJ2BYMIQUfTIYeQUBsf4ZzNFiVSiYT3KPbwu95eT8CinbSY3nc6E+8OgwOIKjC4evkTIKQyOyV26HMPDud+MLQD4EM+Zszk+Z+dic36eJ4fRfh6VbI3hHoHnQ0IoiyF6LGcgmDB4lBjKPfIQc0JFoAfPMSmcewpVOEwQJaIEUjS58PFpCqx8+/JpEjcQHxLXJ0cKWAHJo8gW4eVuU5QOoWymUcDzITPWU9GTxIPRDfAoYZuNcYzIxoc8c1TAiyH9SKXFEPk9s8zBpi2Sm49m4LlOKLDBCkxcWnQticVEfjIKgMJQCi8A+BDVKhs8RNvlWHNUJzE9IlZgxKj5WAjhQ2asEB85jPZ7MvMojs+tFo4XjZkhuZzcL3LSo8ZwDy/H/RS+g3GoAuN8ClvfKBygbzt+AQskT8WHeAjHJNuP1LFme5SwijcBwknZksFH+RD3C5vDNBV8VASmRw7Q4WaIDhREiceUlhcSaQtDAqlLi555Kl2XgzWSaomhwiOFaKMQxdvQgYd6oMAaFBhfHOLiKQwlJAfwW03frwnJ84+K5TnLtqhIj+Wo3CiFVw2eQYD5UM4uhIih6mOuhPbzVHpUezherAQNTp5qCAfwJNyfq8UxZjkK1Ehea6H9QAWmb2u6n/g0iWuJDxFeYNKjH+kJ59k0no+Wu6LYQggfIrw2OEwXFaPVR50/5+GpzEksAHhODtP9cyTZhRBzyO9MJQS+/EhdaSMXqJE38ECBjVdgdGPxOym3qkw/D8xJNDNQl+Ywkbb8yAOTXcYXRnWqnIcnERg+lLMLIWKo+pgrof08lR7VHo7X86XxegEITDkhHxWB6dEEcGeyRRtmqlXOZyowfX+nC4nPnbii+FD52vYjRQl6FBnSI41yQyD5UM4uhPChXHiOOsLzJFWbojwGz5bw3MNtPcrzc2R5KimqEGIOCWeqIpyUXFMqkOKRBwpbINOjwNzmEQrMq8DMZZFz6qsohzT9ItzEmE4R2HNr6vw6udOjU5kekU1gxKh+FHghbvRozqMzlz08TxlJozxE9EkYbpTxYrT8yNMmm+P5KPenJrmHI5fbN1Ng5u3CyecA7td3JEfqO1KM8kedlo86bX8SPzKVLuALQ7ztMkyMFh55To/NUxGeO5NtDpEzGSJKjOrHAj43JPz6kVfRo6aHh+RsHZhDnu6HArtEgelrVS8p02NeZiZSOM3AHgXWGcv74a1yf9nmUcluwguwzmZ6RJTnkefx4AWxeqmIJDx/sjlAj5Y9PDbZHC9G+ZCwBXLt46EKrOny4PzzQO7XNkf6FZjII5L4H/15/Ej9FuILkDNVGSZGc49+KgjJU5nOBDCHyJkMnkqQIJA9pIkq4pEXKgxxmNPm2ZwhJ8KgwFwKjK8Gpy3upEKUQOrHUbE6c9nD65aR5dFwHh7YqlRESzwVH+L+ZPNRj80zePCdCkyU4NWddiGDGNKtUgmNXOh5sgIzL1eaJnEnkT8ZYpQenTDC5wx/Hj9Sv6+o7kzFYSKDKMFHeVRV9PBAsnkGcjorcrwIqTbD62pw0ygH85a4X5fgSI/Ns3nwh2KgwNYosKbriq/FqhARmXmsGKo+9sTy5OE8PDDZPC23q0gO4IGmyBCA8mMhcy6QhyQ7htR5PB5Ri4eIIZOccsM6wwTPwxWYvoxpTsW1RP5kiNH0KDA9V6lIZZZrLSpy6vaqAE2XbownEaN8SFcXYP3Iw8VoGjKd1SETQKl4URMpABRIhgDQIwE8rHJw1aYSAZKryfcBQIHVFRhfCmUBVEAWhqpX1KjYaiEB4HXFUNNjTx4em2xduhVTzaABBQ+vXoCJIR4Vfi+RRJTgjxzJ/UJjiaH0yGPJNpGrnFBg+v5LMyVuGpo+PSqG6FFkaHqkJLqcziPA5r3rwZhU8HKeJBzDY6vJBVg/FjJrsCinATxbsodgPEl0OdGMTtLk4dmaAs8CQ4E9SIHxNa1tfX1yjB71e3ry8FinbTbGYzWAjyZbY3IeHZvziAw5WM4vwj3iiUJETvI7k4jw8v8dwpPPsdcqME0O9/D7gPurNg+s3sQJrHMWkmiw9ojw1keRsBou8J5HM6cnkDDVDBpAsWRoTM5DIabE1FFVPAc47XKVXGNmcp7KBBScPFbbPFCP3sYDBdamwKo3Cl83/KLK+asJxR3JcwZiRRv8UWcrj2p8ztOZh4dX7XAPOnMulfDrwILnuliRWT/yrvhozs8xnYtQpBr+uLMC4/cZp9pj82uG47mf2xyTbD4qZJwGC4+IDTwGEoqQ8mOhpXIgHzWTNAH4FJvZuLOamYPFlIkheuQ5qzZFcUNE8SFuC5h4cT1a9vDM2uaxevQ2HiiwSQpM3GFNlxBfi02BoijPo22dmWP0qN8zJA9PkrMLLfGQHIxj/EpXRJUfdekyPo3qKDGzJkA4eSE+lPNzjCjnJ0ckuegRCowuJD6b4oIU17lAikdK2GPEcooo89HTlRkonLk8HNaD0bGezDzKieewnM3TCluEiFH+yJHcn2w+WrV1OPfwcO6/mQ0F5lVgTZdHWj0ihJaU8Hseh8RSEtPQbXCYHvV7KI8/xERSHm2YeOGkKOHnjx4MxyebosqGDiRPIZAw2khR2p/zUBUByPkFjOswPbTQM1+B+dWMuDCI6qpRCBRD4pEyC396pFHTMEN6nFSlNQkFaqMplQ5PnnISihoC40mcmSmE8FpPE4YMDhY2YXIG4XMA8peRNFo1KKFp8HATcA8nFFhdgS28WlAaDICBKgNLFNg9LgC8BRgAAwsZgAKDAgMDYOBsBqDAFl4hKA0GwECYASiws++e6ucBAMDA7RmAAgtfAAgEA2BgIQNQYFBgYAAMnM0AFNjCKwSlwQAYCDMABXb23XP7zxt4QTBQZQAKLHwBIBAMgIGFDECBQYGBATBwNgNQYAuvEJQGA2AgzAAU2Nl3T/XzAABg4PYMQIGFLwAEggEwsJABKDAoMDAABs5mAAps4RWC0mAADIQZgAI7++65/ecNvCAYqDIABRa+ABAIBsDAQgagwKDAwAAYOJsBKLCFVwhKgwEwEGYACuzsu6f6eQAAMHB7BqDAwhcAAsEAGFjIABQYFBgYAANnMwAFtvAKQWkwAAbCDECBnX333P7zBl4QDFQZgAILXwAIBANgYCEDUGBQYGAADJzNABTYwisEpcEAGAgzAAV29t1T/TwAABi4PQNQYOELAIFgAAwsZAAKDAoMDICBsxmAAlt4haA0GAADYQagwM6+e27/eQMvCAaqDECBhS8ABIIBMLCQASgwKDAwAAbOZgAKbOEVgtJgAAyEGYACO/vuqX4eAAAM3J4BKLDwBYBAMAAGFjIABQYFBgbAwNkMQIEtvEJQGgyAgTADUGBn3z23/7yBFwQDVQagwMIXAALBABhYyAAUGBQYGAADZzMABbbwCkFpMAAGwgxAgZ1991Q/DwAABm7PABRY+AJAIBgAAwsZgAKDAgMDYOBsBqDAFl4hKA0GwECYASiws++e23/ewAuCgSoDUGDhCwCBYAAMLGQACgwKDAyAgbMZgAJbeIWgNBgAA2EGoMDOvnuqnwcAAAO3ZwAKLHwBIBAMgIGFDECBQYGBATBwNgNQYAuvEJQGA2AgzAAU2Nl3z+0/b+AFwUCVASiw8AWAQDAABhYyAAUGBQYGwMDZDECBLbxCUBoMgIEwA1BgZ9891c8DAICB2zMABRa+ABAIBsDAQgagwKDAwAAYOJsBKLCFVwhKgwEwEGYACuzsu+f2nzfwgmCgygAUWPgCQCAYAAMLGYACgwIDA2DgbAagwBZeISgNBsBAmIH/Dx31vgRjosPmAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "5ddd0d85",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24394b86",
   "metadata": {},
   "source": [
    "<a id='Table_Contents'></a><br>\n",
    "### Table of Contents\n",
    "1. [Introduction](#intro)\n",
    " * Problem Statement\n",
    " * Project Objective\n",
    " * Project Question\n",
    " * Project Hypothesis\n",
    " \n",
    " \n",
    "2. [Data](#imports_data)\n",
    " * Data Source\n",
    " * Dataset Description\n",
    " * Importing Packages\n",
    " * Dataset Inspection\n",
    " * Data preprocessing\n",
    " \n",
    " \n",
    "3. [Exploratory Data Analysis](#EDA)\n",
    " * Descriptive Analysis \n",
    " * Customer Demographics\n",
    " * Loan History and Behaviour\n",
    " * Loan servicing behaviour\n",
    " \n",
    "\n",
    "4. [Model Building](#model)\n",
    " * Encoding Data\n",
    " * Logistic Regression\n",
    " * OLS\n",
    " * LinearDiscriminantAnalysis\n",
    " * Support Vector Machine (SVM)\n",
    " * Random Forest Classifier\n",
    " * XGBoost\n",
    " * LightGBM\n",
    " \n",
    " \n",
    "5. [Model Evaluation](#evaluate)\n",
    "\n",
    "6. [Conclusion](#conclude)\n",
    "\n",
    "7. [Recommendation](#recommend)\n",
    "\n",
    "8. [Reference](#reference)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255ce1da",
   "metadata": {},
   "source": [
    "<a id='intro'></a><br>\n",
    "## 1. Introduction\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5eda9a7",
   "metadata": {},
   "source": [
    "### Problem Statement\n",
    "Because bank lending is the most common source of external funding for many Small and Medium Enterprises and entrepreneurs, there is a growing demand for loans to people and SMEs. People and businesses require loans for a variety of reasons, including cash emergencies, debt consolidation, and the start-up and/or expansion of a company. A substantial number of individuals and organizations in Nigeria are unable to obtain credit, and SMEs face similar challenges due to issues such as a lack of collateral, poor record-keeping, poor creditworthiness, and weak project proposals. For this reason, peer-to-peer lending platforms, which allow people and businesses to borrow and lend money without the involvement of a traditional financial institution have evolved over time.\n",
    "\n",
    "However, P2P lending platforms are linked to high credit risks since borrowers have a high tendency to default, either by not repaying disbursed loans on time or at all. For P2PL organizations, managing loan default risk is crucial and it is advisable for them to deploy models that can accurately predict loan default tendencies to help mitigate these risks.\n",
    "\n",
    "Building algorithms that identify specific consumer categories and allocate weights based on risk levels, in my opinion, would assist an investor in lessening the risks associated with P2P lending. This research will analyze how Artificial Intelligence (AI) models can help Financial Technology Institutions in Nigeria improve the P2P lending process by reducing the chances of borrowers not meeting their repayment commitments when they are due."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f9558d",
   "metadata": {},
   "source": [
    "### Project Objective\n",
    "The overall aim of this project is to create a predictive loan risk model that will guide P2P lending platform investors to make informed decisions about borrowers with high default risks based on certain characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758e4d31",
   "metadata": {},
   "source": [
    "### Project Questions\n",
    "The major questions stated below should represent the bulk of this research's primary findings to meet the research's main goals and objectives. Among them are:\n",
    "\n",
    "1. What are the key contributing factors that increase a borrower’s likelihood of defaulting on P2P lending platforms?\n",
    "2. Is the risk of loan default larger for first-time loans or repeat loans?\n",
    "3. How accurately is the model performing to predict the probability of borrowers that will default?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77be79b",
   "metadata": {},
   "source": [
    "### Project Hypothesis\n",
    "Null hypothesis: There are no statistically significant factors that contributes to increase a borrower’s likelihood of defaulting on P2P lending platforms in Nigeria\n",
    "\n",
    "Null hypothesis: The probability of a borrower defaulting when taking a first-time loan is higher than when he/she takes subsequent loans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facd104f",
   "metadata": {},
   "source": [
    "<a id='imports_data'></a><br>\n",
    "## 2. Data\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ff7c1e",
   "metadata": {},
   "source": [
    "### Data Source\n",
    "Data used is a secondary loan history data from a P2P lending organisation provided by Data Science Nigeria (DSN) and hosted on the Zindi platform (Odegua 2020). The database contains information on consumer demographics, loan history, payment information, and if the customer defaulted based on several factors."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "76326082",
   "metadata": {},
   "source": [
    "### Data Description\n",
    "There are 3 different datasets from the data source. They include:<br>\n",
    "\n",
    "a)\tDemographic data <br>\n",
    "This contains preliminary information about the customers that used the P2P lending services. Features include the customer identification, date of birth of the customer, type of primary bank account, location (longitude and latitude), name of the bank clients uses, location of the bank branch used by the clients, type of employment that customer has, highest level of education. The data is made up of 9 features and 4346 samples.\n",
    "\n",
    "b)\tLoan Performance data <br>\n",
    "This includes the repeat loan that the consumer has taken, the performance of which we must predict. In essence, we must forecast whether this loan would default based on the customer's demographics and history of lending. Feature information include the customer’s id, The id associated with the particular loan, The number of the loan a customer has collected so far, Date that loan was approved, Date that loan application was created, Loan value taken, Total repayment required to settle the loan. This is the capital loan value disbursed plus interest and fees, Term of loan, customer that referred a particular person, whether the loan was settled on time or not. This is represented by good for “settled on time” and bad for “did not settle loan on time”. The data contains 10 features and 4368 samples.\n",
    "\n",
    "c)\tPrevious loans data <br>\n",
    "This dataset contains all information about previous loans that the customer had prior to the loan as well as the information on pay-back mechanism.  Features include loan information in the performance data with additional information on the first payment due date and the actual date that he/she paid the first payment as defined previously. The data is made up of 12 features and 18183 samples.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f92d8a4",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2892074b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import timedelta\n",
    "import seaborn as sns\n",
    "from datetime import date\n",
    "\n",
    "#Logistic Regression Model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, confusion_matrix\n",
    "import statsmodels.api as sm\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sns.set_theme(context='notebook', style='darkgrid', palette='deep', font='sans-serif', \n",
    "                  font_scale=1, color_codes=True, rc=None)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc80cea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('merged.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af3caca",
   "metadata": {},
   "source": [
    "### Dataset Inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c341d186",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerid</th>\n",
       "      <th>systemloanid</th>\n",
       "      <th>loannumber</th>\n",
       "      <th>approveddate</th>\n",
       "      <th>creationdate</th>\n",
       "      <th>loanamount</th>\n",
       "      <th>totaldue</th>\n",
       "      <th>termdays</th>\n",
       "      <th>referredby</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>...</th>\n",
       "      <th>longitude_gps</th>\n",
       "      <th>latitude_gps</th>\n",
       "      <th>bank_name_clients</th>\n",
       "      <th>bank_branch_clients</th>\n",
       "      <th>employment_status_clients</th>\n",
       "      <th>level_of_education_clients</th>\n",
       "      <th>good_bad_flag</th>\n",
       "      <th>closeddate</th>\n",
       "      <th>firstduedate</th>\n",
       "      <th>firstrepaiddate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8a1088a0484472eb01484669e3ce4e0b</td>\n",
       "      <td>301960241.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-07-02 19:19:01</td>\n",
       "      <td>2017-07-02 18:17:53</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-24 20:50:43</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>2017-07-24 20:40:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8a1088a0484472eb01484669e3ce4e0b</td>\n",
       "      <td>301999397.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017-07-27 19:44:08</td>\n",
       "      <td>2017-07-27 18:44:00</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-11-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.351667</td>\n",
       "      <td>6.657019</td>\n",
       "      <td>GT Bank</td>\n",
       "      <td>OJUELEGBA</td>\n",
       "      <td>Permanent</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Bad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8a1a1e7e4f707f8b014f797718316cad</td>\n",
       "      <td>301620412.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-13 15:29:37</td>\n",
       "      <td>2016-05-13 14:29:31</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-15 00:03:33</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>2016-06-14 10:41:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8a1a1e7e4f707f8b014f797718316cad</td>\n",
       "      <td>301632940.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-06-15 11:22:38</td>\n",
       "      <td>2016-06-15 10:22:32</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-15 13:59:02</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>2016-07-15 13:43:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8a1a1e7e4f707f8b014f797718316cad</td>\n",
       "      <td>301916386.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-05-23 14:21:42</td>\n",
       "      <td>2017-05-23 13:20:30</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-21 21:58:00</td>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>2017-06-21 21:47:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         customerid  systemloanid  loannumber  \\\n",
       "0  8a1088a0484472eb01484669e3ce4e0b   301960241.0         1.0   \n",
       "1  8a1088a0484472eb01484669e3ce4e0b   301999397.0         2.0   \n",
       "2  8a1a1e7e4f707f8b014f797718316cad   301620412.0         1.0   \n",
       "3  8a1a1e7e4f707f8b014f797718316cad   301632940.0         2.0   \n",
       "4  8a1a1e7e4f707f8b014f797718316cad   301916386.0         3.0   \n",
       "\n",
       "          approveddate         creationdate  loanamount  totaldue  termdays  \\\n",
       "0  2017-07-02 19:19:01  2017-07-02 18:17:53     10000.0   11500.0      15.0   \n",
       "1  2017-07-27 19:44:08  2017-07-27 18:44:00     10000.0   11500.0      15.0   \n",
       "2  2016-05-13 15:29:37  2016-05-13 14:29:31     10000.0   13000.0      30.0   \n",
       "3  2016-06-15 11:22:38  2016-06-15 10:22:32     10000.0   13000.0      30.0   \n",
       "4  2017-05-23 14:21:42  2017-05-23 13:20:30     20000.0   24500.0      30.0   \n",
       "\n",
       "  referredby   birthdate  ... longitude_gps  latitude_gps  bank_name_clients  \\\n",
       "0        NaN         NaN  ...           NaN           NaN                NaN   \n",
       "1        NaN  1989-11-09  ...      3.351667      6.657019            GT Bank   \n",
       "2        NaN         NaN  ...           NaN           NaN                NaN   \n",
       "3        NaN         NaN  ...           NaN           NaN                NaN   \n",
       "4        NaN         NaN  ...           NaN           NaN                NaN   \n",
       "\n",
       "  bank_branch_clients employment_status_clients level_of_education_clients  \\\n",
       "0                 NaN                       NaN                        NaN   \n",
       "1           OJUELEGBA                 Permanent                    Primary   \n",
       "2                 NaN                       NaN                        NaN   \n",
       "3                 NaN                       NaN                        NaN   \n",
       "4                 NaN                       NaN                        NaN   \n",
       "\n",
       "  good_bad_flag           closeddate firstduedate      firstrepaiddate  \n",
       "0           NaN  2017-07-24 20:50:43   2017-07-18  2017-07-24 20:40:34  \n",
       "1           Bad                  NaN          NaN                  NaN  \n",
       "2           NaN  2016-06-15 00:03:33   2016-06-13  2016-06-14 10:41:13  \n",
       "3           NaN  2016-07-15 13:59:02   2016-07-15  2016-07-15 13:43:58  \n",
       "4           NaN  2017-06-21 21:58:00   2017-06-22  2017-06-21 21:47:51  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e01197d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23628 entries, 0 to 23627\n",
      "Data columns (total 21 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   customerid                  23628 non-null  object \n",
      " 1   systemloanid                22559 non-null  float64\n",
      " 2   loannumber                  22559 non-null  float64\n",
      " 3   approveddate                22559 non-null  object \n",
      " 4   creationdate                22559 non-null  object \n",
      " 5   loanamount                  22559 non-null  float64\n",
      " 6   totaldue                    22559 non-null  float64\n",
      " 7   termdays                    22559 non-null  float64\n",
      " 8   referredby                  1615 non-null   object \n",
      " 9   birthdate                   4346 non-null   object \n",
      " 10  bank_account_type           4346 non-null   object \n",
      " 11  longitude_gps               4346 non-null   float64\n",
      " 12  latitude_gps                4346 non-null   float64\n",
      " 13  bank_name_clients           4346 non-null   object \n",
      " 14  bank_branch_clients         51 non-null     object \n",
      " 15  employment_status_clients   3698 non-null   object \n",
      " 16  level_of_education_clients  587 non-null    object \n",
      " 17  good_bad_flag               4376 non-null   object \n",
      " 18  closeddate                  18183 non-null  object \n",
      " 19  firstduedate                18183 non-null  object \n",
      " 20  firstrepaiddate             18183 non-null  object \n",
      "dtypes: float64(7), object(14)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info() #check data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "130f25b9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerid                        0\n",
       "systemloanid                   1069\n",
       "loannumber                     1069\n",
       "approveddate                   1069\n",
       "creationdate                   1069\n",
       "loanamount                     1069\n",
       "totaldue                       1069\n",
       "termdays                       1069\n",
       "referredby                    22013\n",
       "birthdate                     19282\n",
       "bank_account_type             19282\n",
       "longitude_gps                 19282\n",
       "latitude_gps                  19282\n",
       "bank_name_clients             19282\n",
       "bank_branch_clients           23577\n",
       "employment_status_clients     19930\n",
       "level_of_education_clients    23041\n",
       "good_bad_flag                 19252\n",
       "closeddate                     5445\n",
       "firstduedate                   5445\n",
       "firstrepaiddate                5445\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum() #checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7117b1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerid', 'systemloanid', 'loannumber', 'approveddate',\n",
       "       'creationdate', 'loanamount', 'totaldue', 'termdays', 'referredby',\n",
       "       'birthdate', 'bank_account_type', 'longitude_gps', 'latitude_gps',\n",
       "       'bank_name_clients', 'bank_branch_clients', 'employment_status_clients',\n",
       "       'level_of_education_clients', 'good_bad_flag', 'closeddate',\n",
       "       'firstduedate', 'firstrepaiddate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ec8267",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70b2191",
   "metadata": {},
   "source": [
    "Converting the date columns to datetime object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d0d09e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = ['approveddate', 'creationdate', 'birthdate', 'closeddate', 'firstduedate', 'firstrepaiddate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df44aa69",
   "metadata": {},
   "outputs": [],
   "source": [
    "for date in dates:\n",
    "    data[date] = pd.to_datetime(data[date])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6399f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017-07-24 20:50:43\n",
       "1                       NaT\n",
       "2       2016-06-15 00:03:33\n",
       "3       2016-07-15 13:59:02\n",
       "4       2017-06-21 21:58:00\n",
       "                ...        \n",
       "23623   2017-07-05 20:22:42\n",
       "23624                   NaT\n",
       "23625                   NaT\n",
       "23626                   NaT\n",
       "23627                   NaT\n",
       "Name: closeddate, Length: 23628, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['closeddate']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e55b71",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087fd83a",
   "metadata": {},
   "source": [
    "Extracting year, month, day, hour, minute and seconds features from the dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39294335",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data = data.drop(np.where(data['systemloanid'].isnull())[0], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6f2756f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# interest feature\n",
    "\n",
    "drop_data['interest'] = drop_data['totaldue'] - drop_data['loanamount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02d75a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#interest rate\n",
    "drop_data['interest_rate'] = round(drop_data['interest'] / (drop_data['loanamount'] * (drop_data['termdays']/365)), 2) #* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d529a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_fill = ['birthdate', 'bank_account_type', 'longitude_gps', 'latitude_gps', 'bank_name_clients', \n",
    "               'bank_branch_clients', 'employment_status_clients', 'level_of_education_clients', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ed54645",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in col_to_fill:\n",
    "    drop_data[col_to_fill] = drop_data[col_to_fill].fillna(method = 'bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce144740",
   "metadata": {},
   "outputs": [],
   "source": [
    "for rows in drop_data['termdays']:\n",
    "    drop_data['test'] = drop_data['approveddate'] + timedelta(days=rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c3deaba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017-08-01 19:19:01\n",
       "1       2017-08-26 19:44:08\n",
       "2       2016-06-12 15:29:37\n",
       "3       2016-07-15 11:22:38\n",
       "4       2017-06-22 14:21:42\n",
       "                ...        \n",
       "23620   2017-06-19 09:52:31\n",
       "23621   2017-07-25 15:51:04\n",
       "23622   2017-08-22 18:39:10\n",
       "23623   2017-07-06 11:04:47\n",
       "23624   2017-08-05 19:42:39\n",
       "Name: test, Length: 22559, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06c88818",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['closeddate'] = np.where(drop_data['closeddate'].isnull(),drop_data['test'], drop_data['closeddate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b049969",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['firstduedate'] = np.where(drop_data['firstduedate'].isnull(),drop_data['test'], drop_data['firstduedate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c0f9c1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['firstrepaiddate'] = np.where(drop_data['firstrepaiddate'].isnull(),drop_data['test'], \n",
    "                                        drop_data['firstrepaiddate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9f8750e",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['approved_year'] = drop_data['approveddate'].dt.year\n",
    "drop_data['approved_Month'] = drop_data['approveddate'].dt.month\n",
    "drop_data['approved_day'] = drop_data['approveddate'].dt.day\n",
    "drop_data['approved_hour'] = drop_data['approveddate'].dt.hour\n",
    "drop_data['approved_minute'] = drop_data['approveddate'].dt.minute\n",
    "drop_data['approved_second'] = drop_data['approveddate'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e5d24c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['creation_year'] = drop_data['creationdate'].dt.year\n",
    "drop_data['creation_Month'] = drop_data['creationdate'].dt.month\n",
    "drop_data['creation_day'] = drop_data['creationdate'].dt.day\n",
    "drop_data['creation_hour'] = drop_data['creationdate'].dt.hour\n",
    "drop_data['creation_minute'] = drop_data['creationdate'].dt.minute\n",
    "drop_data['creation_second'] = drop_data['creationdate'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cae2d156",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['closed_year'] = drop_data['closeddate'].dt.year\n",
    "drop_data['closed_Month'] = drop_data['closeddate'].dt.month\n",
    "drop_data['closed_day'] = drop_data['closeddate'].dt.day\n",
    "drop_data['closed_hour'] = drop_data['closeddate'].dt.hour\n",
    "drop_data['closed_minute'] = drop_data['closeddate'].dt.minute\n",
    "drop_data['closed_second'] = drop_data['closeddate'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ac3244be",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['firstdue_year'] = drop_data['firstduedate'].dt.year\n",
    "drop_data['firstdue_Month'] = drop_data['firstduedate'].dt.month\n",
    "drop_data['firstdue_day'] = drop_data['firstduedate'].dt.day\n",
    "drop_data['firstdue_hour'] = drop_data['firstduedate'].dt.hour\n",
    "drop_data['firstdue_minute'] = drop_data['firstduedate'].dt.minute\n",
    "drop_data['firstdue_second'] = drop_data['firstduedate'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c149758",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['firstrepaid_year'] = drop_data['firstrepaiddate'].dt.year#.astype(int)\n",
    "drop_data['firstrepaid_Month'] = drop_data['firstrepaiddate'].dt.month\n",
    "drop_data['firstrepaid_day'] = drop_data['firstrepaiddate'].dt.day\n",
    "drop_data['firstrepaid_hour'] = drop_data['firstrepaiddate'].dt.hour\n",
    "drop_data['firstrepaid_minute'] = drop_data['firstrepaiddate'].dt.minute\n",
    "drop_data['firstrepaid_second'] = drop_data['firstrepaiddate'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff345805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([23614, 23615, 23616, 23617, 23618, 23619, 23620, 23621, 23622,\n",
       "            23623, 23624],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = drop_data[drop_data['employment_status_clients'].isnull()].index\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c18e1541",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data = drop_data.drop(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5d28120d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#response time\n",
    "drop_data['response_time'] = (drop_data['approveddate'] - drop_data['creationdate']).astype('timedelta64[h]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc285f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['proposed_paytime'] = (drop_data['firstduedate'] - drop_data['approveddate']).astype('timedelta64[D]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9686baac",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['first_paytime'] = (drop_data['firstrepaiddate'] - drop_data['approveddate']).astype('timedelta64[D]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c030f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['final_paytime'] = (drop_data['closeddate'] - drop_data['approveddate']).astype('timedelta64[D]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bf53f4a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['level_of_education_clients'] = drop_data['level_of_education_clients'].fillna(\n",
    "    drop_data['level_of_education_clients'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1779df55",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_drop = ['referredby', 'bank_branch_clients', 'good_bad_flag', 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "06ca5c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data = drop_data.drop(col_to_drop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "06b2dd7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['first_time_returning'] = np.where(drop_data['loannumber'] == 1, 'First-time', 'Returning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1e55d2d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['target'] = np.where(drop_data['final_paytime'] > drop_data['termdays'], 'Bad','Good')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5af0e273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>customerid</th>\n",
       "      <th>systemloanid</th>\n",
       "      <th>loannumber</th>\n",
       "      <th>approveddate</th>\n",
       "      <th>creationdate</th>\n",
       "      <th>loanamount</th>\n",
       "      <th>totaldue</th>\n",
       "      <th>termdays</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>bank_account_type</th>\n",
       "      <th>...</th>\n",
       "      <th>firstrepaid_day</th>\n",
       "      <th>firstrepaid_hour</th>\n",
       "      <th>firstrepaid_minute</th>\n",
       "      <th>firstrepaid_second</th>\n",
       "      <th>response_time</th>\n",
       "      <th>proposed_paytime</th>\n",
       "      <th>first_paytime</th>\n",
       "      <th>final_paytime</th>\n",
       "      <th>first_time_returning</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8a1088a0484472eb01484669e3ce4e0b</td>\n",
       "      <td>301960241.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-07-02 19:19:01</td>\n",
       "      <td>2017-07-02 18:17:53</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1989-11-09</td>\n",
       "      <td>Savings</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>First-time</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8a1088a0484472eb01484669e3ce4e0b</td>\n",
       "      <td>301999397.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017-07-27 19:44:08</td>\n",
       "      <td>2017-07-27 18:44:00</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1989-11-09</td>\n",
       "      <td>Savings</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8a1a1e7e4f707f8b014f797718316cad</td>\n",
       "      <td>301620412.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-13 15:29:37</td>\n",
       "      <td>2016-05-13 14:29:31</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1979-10-18</td>\n",
       "      <td>Other</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>First-time</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8a1a1e7e4f707f8b014f797718316cad</td>\n",
       "      <td>301632940.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-06-15 11:22:38</td>\n",
       "      <td>2016-06-15 10:22:32</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1979-10-18</td>\n",
       "      <td>Other</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8a1a1e7e4f707f8b014f797718316cad</td>\n",
       "      <td>301916386.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-05-23 14:21:42</td>\n",
       "      <td>2017-05-23 13:20:30</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1979-10-18</td>\n",
       "      <td>Other</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "      <td>51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         customerid  systemloanid  loannumber  \\\n",
       "0  8a1088a0484472eb01484669e3ce4e0b   301960241.0         1.0   \n",
       "1  8a1088a0484472eb01484669e3ce4e0b   301999397.0         2.0   \n",
       "2  8a1a1e7e4f707f8b014f797718316cad   301620412.0         1.0   \n",
       "3  8a1a1e7e4f707f8b014f797718316cad   301632940.0         2.0   \n",
       "4  8a1a1e7e4f707f8b014f797718316cad   301916386.0         3.0   \n",
       "\n",
       "         approveddate        creationdate  loanamount  totaldue  termdays  \\\n",
       "0 2017-07-02 19:19:01 2017-07-02 18:17:53     10000.0   11500.0      15.0   \n",
       "1 2017-07-27 19:44:08 2017-07-27 18:44:00     10000.0   11500.0      15.0   \n",
       "2 2016-05-13 15:29:37 2016-05-13 14:29:31     10000.0   13000.0      30.0   \n",
       "3 2016-06-15 11:22:38 2016-06-15 10:22:32     10000.0   13000.0      30.0   \n",
       "4 2017-05-23 14:21:42 2017-05-23 13:20:30     20000.0   24500.0      30.0   \n",
       "\n",
       "   birthdate bank_account_type  ...  firstrepaid_day  firstrepaid_hour  \\\n",
       "0 1989-11-09           Savings  ...               24                20   \n",
       "1 1989-11-09           Savings  ...               26                19   \n",
       "2 1979-10-18             Other  ...               14                10   \n",
       "3 1979-10-18             Other  ...               15                13   \n",
       "4 1979-10-18             Other  ...               21                21   \n",
       "\n",
       "  firstrepaid_minute firstrepaid_second response_time proposed_paytime  \\\n",
       "0                 40                 34           1.0             15.0   \n",
       "1                 44                  8           1.0             30.0   \n",
       "2                 41                 13           1.0             30.0   \n",
       "3                 43                 58           1.0             29.0   \n",
       "4                 47                 51           1.0             29.0   \n",
       "\n",
       "  first_paytime final_paytime  first_time_returning  target  \n",
       "0          22.0          22.0            First-time     Bad  \n",
       "1          30.0          30.0             Returning     Bad  \n",
       "2          31.0          32.0            First-time     Bad  \n",
       "3          30.0          30.0             Returning    Good  \n",
       "4          29.0          29.0             Returning    Good  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4bf6c1b6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerid                    0\n",
       "systemloanid                  0\n",
       "loannumber                    0\n",
       "approveddate                  0\n",
       "creationdate                  0\n",
       "loanamount                    0\n",
       "totaldue                      0\n",
       "termdays                      0\n",
       "birthdate                     0\n",
       "bank_account_type             0\n",
       "longitude_gps                 0\n",
       "latitude_gps                  0\n",
       "bank_name_clients             0\n",
       "employment_status_clients     0\n",
       "level_of_education_clients    0\n",
       "closeddate                    0\n",
       "firstduedate                  0\n",
       "firstrepaiddate               0\n",
       "interest                      0\n",
       "interest_rate                 0\n",
       "approved_year                 0\n",
       "approved_Month                0\n",
       "approved_day                  0\n",
       "approved_hour                 0\n",
       "approved_minute               0\n",
       "approved_second               0\n",
       "creation_year                 0\n",
       "creation_Month                0\n",
       "creation_day                  0\n",
       "creation_hour                 0\n",
       "creation_minute               0\n",
       "creation_second               0\n",
       "closed_year                   0\n",
       "closed_Month                  0\n",
       "closed_day                    0\n",
       "closed_hour                   0\n",
       "closed_minute                 0\n",
       "closed_second                 0\n",
       "firstdue_year                 0\n",
       "firstdue_Month                0\n",
       "firstdue_day                  0\n",
       "firstdue_hour                 0\n",
       "firstdue_minute               0\n",
       "firstdue_second               0\n",
       "firstrepaid_year              0\n",
       "firstrepaid_Month             0\n",
       "firstrepaid_day               0\n",
       "firstrepaid_hour              0\n",
       "firstrepaid_minute            0\n",
       "firstrepaid_second            0\n",
       "response_time                 0\n",
       "proposed_paytime              0\n",
       "first_paytime                 0\n",
       "final_paytime                 0\n",
       "first_time_returning          0\n",
       "target                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd165fb4",
   "metadata": {},
   "source": [
    "<a id='EDA'></a><br>\n",
    "## 3. Exploratory Data Analysis\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd004c02",
   "metadata": {},
   "source": [
    "### Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4b1f4b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['loannumber', 'loanamount', 'totaldue', 'termdays', 'interest', 'interest_rate', 'response_time',\n",
    "                   'proposed_paytime','first_paytime', 'final_paytime', 'age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "219c41f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loannumber</th>\n",
       "      <td>4.380078</td>\n",
       "      <td>3.354294</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loanamount</th>\n",
       "      <td>16755.011531</td>\n",
       "      <td>9628.926377</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>60000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totaldue</th>\n",
       "      <td>19900.049876</td>\n",
       "      <td>10780.727309</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>12250.00</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>68100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>termdays</th>\n",
       "      <td>27.192656</td>\n",
       "      <td>11.106949</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interest</th>\n",
       "      <td>3145.038345</td>\n",
       "      <td>1541.904931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>9000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interest_rate</th>\n",
       "      <td>2.949165</td>\n",
       "      <td>0.878581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>response_time</th>\n",
       "      <td>1.050115</td>\n",
       "      <td>0.959678</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>42.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proposed_paytime</th>\n",
       "      <td>25.621031</td>\n",
       "      <td>6.784751</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>34.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_paytime</th>\n",
       "      <td>24.018893</td>\n",
       "      <td>11.263387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>final_paytime</th>\n",
       "      <td>24.664848</td>\n",
       "      <td>12.183260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>32.174517</td>\n",
       "      <td>6.216430</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean           std     min       25%       50%  \\\n",
       "loannumber            4.380078      3.354294     1.0      2.00      3.00   \n",
       "loanamount        16755.011531   9628.926377  3000.0  10000.00  10000.00   \n",
       "totaldue          19900.049876  10780.727309  3450.0  12250.00  13000.00   \n",
       "termdays             27.192656     11.106949    15.0     15.00     30.00   \n",
       "interest           3145.038345   1541.904931     0.0   1500.00   3000.00   \n",
       "interest_rate         2.949165      0.878581     0.0      2.31      3.65   \n",
       "response_time         1.050115      0.959678     1.0      1.00      1.00   \n",
       "proposed_paytime     25.621031      6.784751    13.0     16.00     29.00   \n",
       "first_paytime        24.018893     11.263387     0.0     15.00     28.00   \n",
       "final_paytime        24.664848     12.183260     0.0     15.00     28.00   \n",
       "age                  32.174517      6.216430    20.0     28.00     31.00   \n",
       "\n",
       "                       75%       max  \n",
       "loannumber            6.00     27.00  \n",
       "loanamount        20000.00  60000.00  \n",
       "totaldue          24500.00  68100.00  \n",
       "termdays             30.00     90.00  \n",
       "interest           4000.00   9000.00  \n",
       "interest_rate         3.65      3.65  \n",
       "response_time         1.00     42.00  \n",
       "proposed_paytime     30.00     34.00  \n",
       "first_paytime        30.00    380.00  \n",
       "final_paytime        30.00    380.00  \n",
       "age                  36.00     55.00  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data[numeric_features].describe().T.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b539bc7f",
   "metadata": {},
   "source": [
    "Descriptive statistics give us an overview of how distributed our variables are. Based on the results, we can make the following conclusions about each of the variables:\n",
    "\n",
    "1.\tA customer can borrow at least 3000. The highest amount of money to be loaned out is 60,000 with an average value of approximately 16,500.\n",
    "2.\tAge range falls between 20 – 55 years with an average age of 32 years.\n",
    "3.\tThe time duration given by the organization to pay back loan spans from 15 days to a maximum of approximately 3 months (90 days).\n",
    "4.\tWhen a customer applies for loans, it takes an average of a little over one hour to approve loan from the time application was created.\n",
    "5.\tInterest rate seems fair as the highest rate is on 3.65% while the lowest rate is 0%\n",
    "6.\tThe average time it takes a customer to pay back loan is approximately 25 days. Despite the time duration given by the organization to pay back loans, customers response to this term is not encouraging as there are cases where customer exceeded the maximum time duration of 3 months. (Max = 381 days).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4bbaf7",
   "metadata": {},
   "source": [
    "### Distribution Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3466ed2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['loanamount'])\n",
    "plt.xlabel('Loan Amount')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Laon Amount Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d18e33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['interest'])\n",
    "plt.xlabel('Interest')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Interest Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b7cbf371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['termdays'])\n",
    "plt.xlabel('Loan Term')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Loan Term Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9c7bcbfe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['totaldue'])\n",
    "plt.xlabel('Total')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Total Paid Amount Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f8a6230e",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_data['age'] = drop_data.apply(lambda x: x['creationdate'].year - x['birthdate'].year - ((x['creationdate'].month, x['creationdate'].day) < (x['birthdate'].month, x['birthdate'].day)), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2d74b55a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        27\n",
       "1        27\n",
       "2        36\n",
       "3        36\n",
       "4        37\n",
       "         ..\n",
       "23609    29\n",
       "23610    29\n",
       "23611    29\n",
       "23612    29\n",
       "23613    29\n",
       "Name: age, Length: 22548, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fa93768e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['age'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Age Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "80a8b1c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iEgBMlca1T9BbunQp3bv/9fLZDzzwABMnTrQsnIiIBBZTu6e2bdtGz5496d69Oy6Xi/Xr1+P1egGYPXu2pQFFRCRwmCqNpUuXsnv37u9W6NKFiooKXn31VStziYhIADK1e+rw4cOUlJQAEBUVxcqVKxk/frylwUREJPCYeqXR0tJCc3Oz73Zra6tlgUREJHCZeqXxj//4jzz77LOMHz8em81GSUkJiYmJVmcTEZEAY6o05s2bx6ZNm9i1axehoaGMGTOGyZMnW51NREQCjKnSCAkJYdq0ab6PYRURkc7JVGl8+umnvPrqq3z77bcYhuEb/+KLLywLJiIigcdUafzHf/wHmZmZDBw4EJvNZnUmEREJUKZKo0ePHiQlJVmdRUREApypt9w+9thj7Nmzx+osIiIS4Ey90tizZw8bN24kLCyMsLAwDMPAZrPpmIaISCdjqjTWr19vcQwREQkGpnZPxcTEcOzYMfLz8+nduzeHDx8mJibG6mwiIhJgTJXG2rVr2bx5M06nk6amJt566y1Wr15tdTYREQkwpkrjv/7rv3j77beJiIigV69e5Ofn+y5gKCIinYep0ggNDaVr166+2z169CA09NaHQ1auXMnYsWNJTU1l3bp1AJSXl5OWlkZSUhK5ubm+ZauqqkhPTyc5OZn58+f7LopYU1PD1KlTSUlJYebMmTQ2NnboAYqIyJ1jqjSio6PZvXs3NpuN5uZm1qxZc8tjGgcPHmT//v1s27aNjz76iA0bNvCnP/2JrKws8vLyKC0tpbKy0vdW3rlz57Jw4UJ27NiBYRjk5+cDf/3UQKfTyeDBg8nLy/s/PmQREbldpkpjwYIFrFu3juPHjzNkyBD++7//mwULFtx0nccff5z33nuP0NBQLly4gMfj4fLly/Tr14/Y2FhCQ0NJS0vD6XRy9uxZmpqaGDJkCADp6ek4nU5aWlo4dOgQycnJbcZFRMQ/TL3ltk+fPrz77rtcvXoVj8dDZGSkqTsPCwtj1apV/Od//icpKSmcO3cOu93u+77D4aC2tvaGcbvdTm1tLXV1dURGRvp2hV0bFxER/zBVGteOR3zf9OnTb7nu7Nmz+cUvfsHzzz9PdXV1m2tXXTtJ0Ov1tjt+7e/rdfTaV1FR5gquPXZ799te1x+CLa9Y828WbM+DYMobTFnBmrymSuPLL7/0fd3c3MyhQ4eIj4+/6TonT56kubmZRx99lIiICJKSknA6nYSEhPiWcblcOBwO+vbti8vl8o2fP38eh8NB7969qa+vx+PxEBIS4lu+Iy5caMDrNW694PfY7d1xueo7vJ6/3Im8wfYf4l5wp59jnfF5e7cEU1a4/bxduthu+su2qWMay5Yt8/1544032LJlCxcuXLjpOn/5y1/Izs6mubmZ5uZmdu3axeTJkzl9+jRnzpzB4/FQUlJCQkICMTExhIeHU1FRAUBRUREJCQmEhYURFxdHaWkpAFu3biUhIcHsYxcRkTvM1CuN7+vTpw9nz5696TKJiYkcPXqUCRMmEBISQlJSEqmpqfTu3ZtZs2bhdrtJTEwkJSUFgJycHLKzs2loaGDQoEG+D3xatGgRmZmZrFmzhujoaFasWHE7kUVE5A7o8DENwzCorKwkKirqluvNmjWLWbNmtRmLj49n27ZtNyw7YMAACgoKbhiPiYlhw4YNZmKKiIjFOnxMA747b2PevHmWBBIRkcBlqjSWLVtmdQ4REQkCpkojIyPjpm91fe+99+5YIBERCVymSmPw4MGcPHmSiRMnEhYWRlFREa2traSmplqdT0REAoip0vjiiy94//33fedY/OQnP2HixIm+y3uIiEjnYOo8jYsXL+J2u323GxsbaWpqsiyUiIgEJlOvNMaNG8ekSZMYM2YMhmGwfft233kUIiLSeZgqjV/+8pcMHDiQ/fv3Ex4ezpIlS3j88cetztZpde8RQbfwjp93qcuAiIjVTP9k6tOnD/379yc9PZ0//vGPVmbq9LqFh5L266K7vt3iN8bf9W2KSHAxdUzjo48+4pVXXuEPf/gD9fX1vPDCC74PSRIRkc7DVGls3LiRDz/8kMjISKKioigsLOTdd9+1OpuIiAQYU6XRpUuXNh+8FB0d3eYS5yIi0jmYKo2ePXtSVVXlOyt827Zt/O3f/q2lwUREJPCYOhCelZXFL3/5S77++mtGjhxJeHg4eXl5VmcTEZEAY6o0mpqaKCoqorq6Go/Hw4MPPkhYWJjV2UREJMCY2j318ssvExISwsMPP8yPfvQjFYaISCdlqjQeeeQRiouLqamp4dKlS74/IiLSuZjaPbVr1y6cTmebMZvNRlVVlSWhREQkMJkqjWPHjlmdQ0REgsBNd08tWLDA9/XFixctDyMiIoHtpqVRWVnp+/rZZ5+1PIyIiAS2m5aGYRjtfi0iIp2TqXdPATf9jHAREekcbnog3Ov18u2332IYBh6Px/f1NT179rQ6n4iIBJCblsaXX37JiBEjfEUxfPhw3/f0llsRkc7npqXxpz/96W7lEBGRIGD6mIaIiIhKQ0RETLO0NN566y1SU1NJTU3l9ddfB6C8vJy0tDSSkpLIzc31LVtVVUV6ejrJycnMnz+f1tZWAGpqapg6dSopKSnMnDmTxsZGKyOLiMhNWFYa5eXl/M///A8ff/wxW7du5Y9//CMlJSVkZWWRl5dHaWkplZWV7NmzB4C5c+eycOFCduzYgWEYvs8gX7x4MVOmTMHpdDJ48GB9joeIiB9ZVhp2u53MzEy6du1KWFgYDz/8MNXV1fTr14/Y2FhCQ0NJS0vD6XRy9uxZmpqaGDJkCADp6ek4nU5aWlo4dOgQycnJbcZFRMQ/TF2w8Hb079/f93V1dTXbt2/nn//5n7Hb7b5xh8NBbW0t586dazNut9upra2lrq6OyMhIQkND24x3RFRU5K0X+gF2e/fbXlfEDCueY8H2vA2mvMGUFazJa1lpXHPixAn+9V//lXnz5hESEkJ1dbXve4ZhYLPZ8Hq9bc44vzZ+7e/rdfTM9AsXGvB6O34JFLu9Oy5XfYfXuxOC7Ykpt+9OP8f8+by9HcGUN5iywu3n7dLFdtNfti0tjYqKCmbPnk1WVhapqakcPHgQl8vl+77L5cLhcNC3b9824+fPn8fhcNC7d2/q6+vxeDyEhIT4lhe5FzS3ePzySqPJ3Ur95at3fLvSOVhWGt988w0vvvgiubm5xMfHA/DYY49x+vRpzpw5w/33309JSQn/9E//RExMDOHh4VRUVDBs2DCKiopISEggLCyMuLg4SktLSUtLY+vWrSQkJFgVWeSu6hoWQtqvi+76dovfGE/w/L4sgcay0njnnXdwu90sX77cNzZ58mSWL1/OrFmzcLvdJCYmkpKSAkBOTg7Z2dk0NDQwaNAgpk2bBsCiRYvIzMxkzZo1REdHs2LFCqsii4jILVhWGtnZ2WRnZ7f7vW3btt0wNmDAAAoKCm4Yj4mJYcOGDXc8n4iIdJzOCBcREdNUGiIiYppKQ0RETFNpiIiIaSoNERExTaUhIiKmqTRERMQ0lYaIiJim0hAREdNUGiIiYppKQ0RETFNpiIiIaSoNERExTaUhIiKmqTRERMQ0yz8jPFhZ9VGcIiLBTKXxA/z1UZzw3cdxiogEIu2eEhER01QaIiJimnZPiXQy/jpe1+Rupf7y1bu+XbmzVBoinYy/jtcVvzGe+ru+VbnTtHtKRERMU2mIiIhpKg0RETFNpSEiIqapNERExDSVhoiImGZpaTQ0NDBu3Dj+8pe/AFBeXk5aWhpJSUnk5ub6lquqqiI9PZ3k5GTmz59Pa2srADU1NUydOpWUlBRmzpxJY2OjlXFFROQWLCuNI0eO8Mwzz1BdXQ1AU1MTWVlZ5OXlUVpaSmVlJXv27AFg7ty5LFy4kB07dmAYBvn5+QAsXryYKVOm4HQ6GTx4MHl5eVbFFREREywrjfz8fBYtWoTD4QDg6NGj9OvXj9jYWEJDQ0lLS8PpdHL27FmampoYMmQIAOnp6TidTlpaWjh06BDJycltxkVExH8sOyP83//939vcPnfuHHa73Xfb4XBQW1t7w7jdbqe2tpa6ujoiIyMJDQ1tMy4iIv5z1y4j4vV6sdlsvtuGYWCz2X5w/Nrf1/v+bTOioiJvP7SI3FHtXfMqmD63JpiygjV571pp9O3bF5fL5bvtcrlwOBw3jJ8/fx6Hw0Hv3r2pr6/H4/EQEhLiW76jLlxowOs1OrxesD05RIKBy9X26lN2e/cbxgJVMGWF28/bpYvtpr9s37W33D722GOcPn2aM2fO4PF4KCkpISEhgZiYGMLDw6moqACgqKiIhIQEwsLCiIuLo7S0FICtW7eSkJBwt+KKiEg77torjfDwcJYvX86sWbNwu90kJiaSkpICQE5ODtnZ2TQ0NDBo0CCmTZsGwKJFi8jMzGTNmjVER0ezYsWKuxVXRETaYXlplJWV+b6Oj49n27ZtNywzYMAACgoKbhiPiYlhw4YNluYTERHzdEa4iIiYptIQERHTVBoiImKaSkNERExTaYiIiGkqDRERMU2lISIipqk0RETENJWGiIiYptIQERHTVBoiImKaSkNERExTaYiIiGkqDRERMe2ufZ6GiHRuzS0ev33ca5O7lfrLVy3fTmeg0hCRu6JrWAhpvy7yy7aL3xhP8HxQa2DT7ikRETFNpSEiIqapNERExDQd0xCRe94PHYTvqI7ex714AF6lISL3PH8dhL8XD8Br95SIiJim0hAREdNUGiIiYppKQ0RETFNpiIiIaXr3lIiIRe7UW31vd9tWUGmIiFjE39fbskJQ7J4qLi5m7NixJCUlsWnTJn/HERHptAL+lUZtbS25ubkUFhbStWtXJk+ezPDhw/n7v/97f0cTEel0Ar40ysvLGTFiBD179gQgOTkZp9PJSy+9ZGr9Ll1st71tR6+I2173/8pf29Zj7hzb7mzb9ee2/fmYb+fn363WsRmGYdxuoLvh97//PVeuXGHOnDkAbNmyhaNHj7J06VI/JxMR6XwC/piG1+vFZvtr8xmG0ea2iIjcPQFfGn379sXlcvluu1wuHA6HHxOJiHReAV8aTzzxBPv27ePixYtcvXqVnTt3kpCQ4O9YIiKdUsAfCO/Tpw9z5sxh2rRptLS08PTTT/PjH//Y37FERDqlgD8QLiIigSPgd0+JiEjgUGmIiIhpKg0RETFNpSEiIqYF/Lun7pa33nqL7du3A5CYmMi8efMoLy9n2bJluN1ufvrTn/rOSve39rK+8sorVFRUEBHx3SULXnrpJcaMGePPmD4rV65kx44d2Gw2nn76aaZPnx6wc9te1kCeW4DXXnuNuro6li9fHrDzer3r8wby3GZkZHDx4kVCQ7/7MblkyRIaGxsDcn7by/rBBx9YM7eGGJ999pkxadIkw+12G83Nzca0adOM4uJiIzEx0fj666+NlpYWY8aMGcbu3bv9HbXdrDt37jTGjRtn1NbW+jveDQ4cOGBMnjzZaGlpMa5evWqMGjXKqKqqCsi5bS/ryZMnA3ZuDcMwysvLjeHDhxu/+c1vjKtXrwbkvF7v+ryGYQTs3Hq9XmPkyJFGS0uLbyxQ57e9rIZh3dxq9xRgt9vJzMyka9euhIWF8fDDD1NdXU2/fv2IjY0lNDSUtLQ0nE6nv6O2m7WmpoaamhqysrJIS0tj1apVeL1ef0cF4PHHH+e9994jNDSUCxcu4PF4uHz5ckDObXtZu3XrFrBze+nSJXJzc3n++ecBOHr0aEDO6zXfz3v16tWAndtTp04BMGPGDJ588kk2btwYsPPbXlYr51alAfTv358hQ4YAUF1dzfbt27HZbNjtdt8yDoeD2tpaPyX8q/ay/uQnP2HEiBG8+uqr5Ofn8/nnn1NQUODfoNcJCwtj1apVpKamEh8fz7lz5wJybuHGrK2trQE7twsXLmTOnDn06NEDIKDnFW7Me/78+YCd28uXLxMfH8/q1atZv349H3zwATU1NQE5v+1lLSoqsmxuVRrXOXHiBDNmzGDevHnExsYG9IUSr8/60EMPsXr1ahwOBxEREWRkZLBnzx5/R2xj9uzZ7Nu3j2+++Ybq6uqAntvrs+7bty8g53bLli1ER0cTHx/vGwvki3u2lzc2NjYg5xZg6NChvP7663Tv3p3evXvz9NNPs2rVqoCc3/aynjp1yrK51YHw/6+iooLZs2eTlZVFamoqBw8eDNgLJX4/6/Hjx6muriY5ORn47sl87YCYv508eZLm5mYeffRRIiIiSEpKwul0EhIS4lsmUOa2vaylpaX07Nkz4Oa2tLQUl8vF+PHj+fbbb7ly5Qpnz54NyHmF9vO++OKLPPnkkwE3twCff/45LS0tvpIzDIOYmJiA/JnQXtazZ8+yY8cOa+b2jh8lCUI1NTXG8OHDjfLyct9YU1OTkZCQYFRXVxutra3Gs88+a5SWlvox5Xfay1pVVWUkJCQYly5dMpqbm40ZM2YYxcXFfkz5V7t37zbS09MNt9ttuN1uY/r06UZJSUlAzm17Wd98882AndtrPvroI+M3v/lNwD5nv+9a3kB+3paVlRkTJkwwmpqajPr6eiMtLc344osvAnJ+28t64MABy+Y2MGrdz9555x3cbjfLly/3jU2ePJnly5cza9Ys3G43iYmJpKSk+DHld34o67/8y7/wzDPP0NraSlJSEuPGjfNjyr9KTEzk6NGjTJgwgZCQEJKSkkhNTaV3794BN7ftZX3ppZfo1atXQM7t94WHhwfkc/aHDBgwIGCft6NGjeLIkSNMmDABr9fLlClTGDp0aEDOb3tZH3/8ccvmVhcsFBER03QgXERETFNpiIiIaSoNERExTaUhIiKmqTRERMQ0lYaIRVpaWhg5ciTPPfecv6OI3DEqDRGLfPLJJwwYMIDKykpOnjzp7zgid4TO0xCxSEZGBmPHjuXEiRO0trayZMkSANauXUtBQQH33XcfcXFx7Nq1i7KyMpqbm8nJyeHQoUN4PB4GDhxIdnY2kZGRfn4kIn+lVxoiFvjqq684fPgwKSkpTJgwgaKiIurq6ti7dy+FhYUUFBRQWFhIY2Ojb521a9cSEhJCYWEh27Ztw+FwkJOT48dHIXIjXUZExAKbN29m1KhR9OrVi169enH//feTn5+Py+UiJSXFd3nwqVOnsn//fgB2795NfX095eXlwHfHRKKiovz2GETao9IQucOuXLlCUVERXbt2ZfTo0QA0NDSwceNGUlNTuX6P8PVXpfV6vWRlZZGYmAhAY2Mjbrf77oYXuQXtnhK5w4qLi+nZsyd79+6lrKyMsrIyPv30U65cucKgQYPYuXMn9fX1AG0+GGfkyJFs2rSJ5uZmvF4vCxYsYMWKFf56GCLtUmmI3GGbN29m+vTpbV5F9OjRg4yMDNavX8/EiROZNGkS6enp1NfXExERAcALL7xATEwMTz31FGPHjsUwDDIzM/31METapXdPidxFx44d4/Dhw0ybNg2AdevWceTIEX73u9/5N5iISSoNkbuooaGBrKwsTp06hc1mIzo6mqVLl9KnTx9/RxMxRaUhIiKm6ZiGiIiYptIQERHTVBoiImKaSkNERExTaYiIiGkqDRERMe3/AZ1S3+v4w1K+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=drop_data['age'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Age Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "35f67b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['interest_rate'])\n",
    "plt.xlabel('Interest Rate')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Interest Rate Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ce57b105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['final_paytime'])\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Pay Time Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2fe957ba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(drop_data['response_time'])\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Response Time Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1499d962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerid', 'systemloanid', 'loannumber', 'approveddate',\n",
       "       'creationdate', 'loanamount', 'totaldue', 'termdays', 'birthdate',\n",
       "       'bank_account_type', 'longitude_gps', 'latitude_gps',\n",
       "       'bank_name_clients', 'employment_status_clients',\n",
       "       'level_of_education_clients', 'closeddate', 'firstduedate',\n",
       "       'firstrepaiddate', 'interest', 'interest_rate', 'approved_year',\n",
       "       'approved_Month', 'approved_day', 'approved_hour', 'approved_minute',\n",
       "       'approved_second', 'creation_year', 'creation_Month', 'creation_day',\n",
       "       'creation_hour', 'creation_minute', 'creation_second', 'closed_year',\n",
       "       'closed_Month', 'closed_day', 'closed_hour', 'closed_minute',\n",
       "       'closed_second', 'firstdue_year', 'firstdue_Month', 'firstdue_day',\n",
       "       'firstdue_hour', 'firstdue_minute', 'firstdue_second',\n",
       "       'firstrepaid_year', 'firstrepaid_Month', 'firstrepaid_day',\n",
       "       'firstrepaid_hour', 'firstrepaid_minute', 'firstrepaid_second',\n",
       "       'response_time', 'proposed_paytime', 'first_paytime', 'final_paytime',\n",
       "       'first_time_returning', 'target', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ed5cbc",
   "metadata": {},
   "source": [
    "### Customer Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4dd5afff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "drop_data['employment_status_clients'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Employment Status')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d60324bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "drop_data['bank_account_type'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Account Type')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Bank Account Type')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb72e6c",
   "metadata": {},
   "source": [
    "From our data, most of the clients are graduates who have a permanent source of income, probably working in a corporate organization and have a savings account. Very few clients are contractual employees, attained at least a primary level of education and are retired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1c4aaef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(7,4))\n",
    "drop_data['bank_name_clients'].value_counts().plot(kind='barh')\n",
    "plt.xlabel('Number of Customers')\n",
    "plt.ylabel('Bank')\n",
    "plt.title('Most Preferred Bank by Customers')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3400a72b",
   "metadata": {},
   "source": [
    "GT Bank, First bank and Access bank are the top 3 most prefereed bank among people that seek loan in this P2P lending platform while Unity and Standard Chartered bank are the least preferred bank."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb902eb8",
   "metadata": {},
   "source": [
    "### Loan History and behaviour"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31e755c",
   "metadata": {},
   "source": [
    "From our data, there was a rise in borrowing trend in 2017 compared to the previous year of 2016. Taking a deeper dive to know the share of these loans among customers in terms of educational level and employment status, we could make the following conclusions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "443a1347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='approved_year'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drop_data.groupby('approved_year').mean()['loanamount'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb403d3e",
   "metadata": {},
   "source": [
    "Among the six categories of employment status, it is striking that about 75% of customers that have a permanent source of income borrow less than 30,000. This could be attributed to the fact that they already have a source of income. This category also has the highest amount of loan being borrowed. The unemployed category also exhibited the same trend as the permanently employed customers. Reasons could be persons in this category might not be enlightened about the loan scheme. The customers in the contract, retired, self-employed and student category also share similar characteristics as regards their borrowing behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e01e9958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot(x='employment_status_clients',\n",
    "    y='loanamount',\n",
    "    data=drop_data)\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Loan Amount')\n",
    "plt.title('Share of Loan by Employment Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7146ea",
   "metadata": {},
   "source": [
    "Judging through the perspectives of educational level, it is shown in fig 10that all four categories of customers share the similar characteristics. Although, some difference is seen in highest amount of funds borrowed. Customers in the post graduate and graduate level tend to borrow higher amounts compared to the secondary and primary categories. Customers in the primary level borrow the least number of funds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "72a5f441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot(x='level_of_education_clients',\n",
    "    y='loanamount',\n",
    "    data=drop_data)\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Loan Amount')\n",
    "plt.title('Share of Loan by Educational Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb48ec2",
   "metadata": {},
   "source": [
    "### Loan Servicing Behaviour"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02495991",
   "metadata": {},
   "source": [
    "Customers in the contract, student and retired category showed strong commitment to servicing their funds. These categories make sure they pay their debt before the maximum 3 months threshold set by the organization. However, other categories aside these default in paying their debs especially those ones that have a permanent job. This might be as a result of high interest rate from accrued high amount of money borrowed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c414ba04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot(x='employment_status_clients',\n",
    "    y='final_paytime',\n",
    "    data=drop_data)\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Payment Time')\n",
    "plt.title('Share of Loan Servicing by Employment Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97a4aca",
   "metadata": {},
   "source": [
    "From the figure below, we can see that all categories of customers in this space does not comply with the stipulated time for paying of their debt especially customers who are graduates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a178c9f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot(x='level_of_education_clients',\n",
    "    y='final_paytime',\n",
    "    data=drop_data)\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Payment Time')\n",
    "plt.title('Share of Loan Servicing by Education Level')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b895be",
   "metadata": {},
   "source": [
    "There are more returning cusotomers than first time customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8d6dfd8f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returning     18207\n",
       "First-time     4341\n",
       "Name: first_time_returning, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data['first_time_returning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a10378a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b40f8d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bf17430a",
   "metadata": {},
   "source": [
    "<a id='model'></a><br>\n",
    "## 4. Model Building\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db0d46b",
   "metadata": {},
   "source": [
    "### Encoding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "446391ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Re-assign data for model training\n",
    "model_data = drop_data\n",
    "\n",
    "#List of columns to be dropped\n",
    "columns_drop = ['customerid', 'systemloanid', 'approveddate',\n",
    "       'creationdate', 'birthdate', 'longitude_gps', 'latitude_gps',\n",
    "       'closeddate', 'firstduedate', 'firstrepaiddate', 'target']\n",
    "\n",
    "#Droping some feature variables and assigning the target variable\n",
    "model_data1 = model_data.drop(columns_drop, axis = 1)\n",
    "y = model_data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e4e1de1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Instantiating label econder and standard scaler\n",
    "encoder = LabelEncoder()\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#Encoding the categorical variables\n",
    "columns = ['bank_account_type', 'bank_name_clients', 'employment_status_clients', 'level_of_education_clients', 'first_time_returning']\n",
    "for col in columns:\n",
    "    model_data1[f'{col}_encode'] = encoder.fit_transform(model_data1[col])\n",
    "    \n",
    "#Re-assigning the features\n",
    "X1 = model_data1.drop(columns, axis = 1)\n",
    "\n",
    "#Applying Standard scaler to the feature variables and label encoder to the target variable\n",
    "#X_scaled =  scaler.fit_transform(X)\n",
    "y_encoded = encoder.fit_transform(y)\n",
    "\n",
    "\n",
    "#Splitting the dataset into train and test set\n",
    "X, x, y_train, y_test = train_test_split(X1, y_encoded, test_size=0.15, random_state=101)\n",
    "\n",
    "X_train = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "X_test = pd.DataFrame(scaler.transform(x), columns=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2fb1ec95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22548,)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_encoded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6adb7c49",
   "metadata": {},
   "source": [
    "# Model training with unbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dd7f3f49",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAEMCAYAAAAyF0T+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAd90lEQVR4nO3df2xV533H8fc11xCCHRHTe4FaiDKaNhFsJZ3XJPvDKFGN+WHXrQsT2AIttCNCoWJZ5sYxDMdMJBF1XJYRM9R2jRaIgssW3xEZ0ypbklVhDWUNGR1iWxY6wJO5tmmwPUxs37M/ot6FpClgG198/H5JyJznnAPfrziHD+d5zsWRIAgCJEnSmJaV6QIkSdLwGeiSJIWAgS5JUggY6JIkhYCBLklSCBjokiSFgIEuSVIIRDNdwHCdP99LKuVH6ceiadNy6OzsyXQZ0rjjvTd2ZWVFuPXWKb9235gP9FQqMNDHMP/spMzw3gsfp9wlSQoBA12SpBAw0CVJCgEDXZKkEDDQJUkKAQNdkqQQMNAlSQqBMf859DDLvWUyN00K9x9RLJab6RKum75LA3RfuJjpMiSNE1eVFj09PaxcuZK/+qu/4u2336ahoSG9r729nc997nPs3r2bnTt38rd/+7fccsstAPzBH/wBlZWVtLW1UVVVRWdnJ3PmzKG+vp4pU6Zw4cIF/vRP/5TTp0+Tl5fHjh07iMVi16fTMeimSVFKH05kugwN0YGnyujOdBGSxo0rTrkfO3aMVatWcerUKQAWLlxIIpEgkUjw3e9+l5ycHB599FEAjh8/TkNDQ3p/ZWUlAHV1dVRUVNDa2sr8+fNpbGwEYMeOHRQUFHDw4EFWrFjBtm3brlObkiSF2xUDvampidraWuLx+Ef2bd++nZUrV/KpT30KeD/Qd+/eTWlpKVu3buXSpUv09/dz5MgRiouLASgvL6e1tRWAV155hdLSUgBKSkp47bXX6O/vH6neJEkaN6445f5xT82nTp3ijTfeSO/v7e3ljjvuoKqqitmzZ1NdXU1jYyOVlZXk5OQQjb7/W8ViMdrb2wE4d+5ceoo9Go2Sk5NDV1cX06dPv+oGpk3LuepjpdEW5ncENLZ5bYbPkN+42rdvHxUVFUycOBGAKVOm8J3vfCe9f+3atdTU1FBRUUEkErns3A9v/0oQBGRlXduL952dPaH9JgPecGNfMukqum48sViu1+YYlZUV+dgH2SF/bO3ll19m6dKl6e22tjb279+f3g6CgGg0Sl5eHt3d3QwODgKQTCbT0/fxeJyOjg4ABgYG6O3tZerUqUMtSZKkcWtIgd7V1UVfXx+zZs1Kj910001861vf4vTp0wRBwN69eykqKiI7O5uCggJaWloAaG5uprCwEHj/Bbvm5mYAWlpaKCgoIDs7e5gtSZI0/gwp0M+cOcOMGTMuG8vLy2Pr1q2sX7+exYsXEwQB999/PwC1tbU0NTWxdOlSfvrTn/LHf/zHAGzcuJE333yTZcuW8fzzz7Nly5bhdSNJ0jgVCYJgTC9Ah30N3c+hj10HnipznVI3JNfQx67rsoYuSZJuHAa6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhcFWB3tPTQ0lJCWfOnAHg0UcfZdGiRZSVlVFWVsaPfvQjAE6cOEF5eTnFxcVs2rSJgYEBANra2qisrGTx4sWsX7+e3t5eAC5cuMC6detYsmQJlZWVJJPJ69GjJEmhd8VAP3bsGKtWreLUqVPpsePHj7Nnzx4SiQSJRIKioiIAqqqq2LJlC4cOHSIIApqamgCoq6ujoqKC1tZW5s+fT2NjIwA7duygoKCAgwcPsmLFCrZt23YdWpQkKfyuGOhNTU3U1tYSj8cBuHjxIm1tbdTU1FBaWsrTTz9NKpXi7Nmz9PX1sWDBAgDKy8tpbW2lv7+fI0eOUFxcfNk4wCuvvEJpaSkAJSUlvPbaa/T391+PPiVJCrXolQ748FNzR0cHd999N7W1teTm5vLAAw+wf/9+brvtNmKxWPq4WCxGe3s758+fJycnh2g0etk4wLlz59LnRKNRcnJy6OrqYvr06VfdwLRpOVd9rDTaYrHcTJcg/Vpem+FzxUD/sFmzZvHMM8+kt1evXk1zczNz584lEomkx4MgIBKJpL9+0Ie3P3hOVta1vafX2dlDKhVc0zljhTfc2JdMdme6BOkjYrFcr80xKisr8rEPstf8lvvJkyc5dOhQejsIAqLRKDNmzLjspbaOjg7i8Th5eXl0d3czODgIQDKZTE/fx+NxOjo6ABgYGKC3t5epU6dea0mSJI171xzoQRDw+OOP8+6779Lf38++ffsoKioiPz+fSZMmcfToUQASiQSFhYVkZ2dTUFBAS0sLAM3NzRQWFgKwcOFCmpubAWhpaaGgoIDs7OwRak2SpPHjmqfcb7/9dtatW8eqVasYGBhg0aJFlJSUAFBfX8/mzZvp6elh3rx5rFmzBoDa2lqqq6vZtWsXM2fOpKGhAYCNGzdSXV3NsmXLyM3Npb6+fgRbkyRp/IgEQTCmF6DDvoZe+nAi02VoiA48VeY6pW5IrqGPXSO6hi5Jkm48BrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFwVYHe09NDSUkJZ86cAWDfvn2UlJRQWlrKo48+ynvvvQfAzp07uffeeykrK6OsrIy9e/cC0NbWRmVlJYsXL2b9+vX09vYCcOHCBdatW8eSJUuorKwkmUxejx4lSQq9Kwb6sWPHWLVqFadOnQLgnXfe4Xvf+x4vvPACf//3f08qleL5558H4Pjx4zQ0NJBIJEgkElRWVgJQV1dHRUUFra2tzJ8/n8bGRgB27NhBQUEBBw8eZMWKFWzbtu06tSlJUrhdMdCbmpqora0lHo8DMHHiRGpra8nJySESifCZz3yGtrY24P1A3717N6WlpWzdupVLly7R39/PkSNHKC4uBqC8vJzW1lYAXnnlFUpLSwEoKSnhtddeo7+//7o0KklSmEWvdMCHn5rz8/PJz88HoKuri7179/LEE0/Q29vLHXfcQVVVFbNnz6a6uprGxkYqKyvJyckhGn3/t4rFYrS3twNw7tw5YrHY+4VEo+Tk5NDV1cX06dOvuoFp03Ku+lhptMViuZkuQfq1vDbD54qB/nHa29v5+te/zle/+lXuuusuAL7zne+k969du5aamhoqKiqIRCKXnfvh7V8JgoCsrGt7T6+zs4dUKrjG6scGb7ixL5nsznQJ0kfEYrlem2NUVlbkYx9kh/SW+9tvv83KlSv5yle+woMPPgi8/+Lb/v3708cEQUA0GiUvL4/u7m4GBwcBSCaT6en7eDxOR0cHAAMDA/T29jJ16tShlCRJ0rh2zYHe09PD1772NTZu3MjatWvT4zfddBPf+ta3OH36NEEQsHfvXoqKisjOzqagoICWlhYAmpubKSwsBGDhwoU0NzcD0NLSQkFBAdnZ2SPQliRJ48s1T7nv37+fjo4Ovv/97/P9738fgPvuu4+NGzeydetW1q9fT39/P5///Oe5//77AaitraW6uppdu3Yxc+ZMGhoaANi4cSPV1dUsW7aM3Nxc6uvrR7A1SZLGj0gQBGN6ATrsa+ilDycyXYaG6MBTZa5T6obkGvrYNeJr6JIk6cZioEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAIGuiRJIWCgS5IUAga6JEkhYKBLkhQCBrokSSFgoEuSFAJXFeg9PT2UlJRw5swZAF5//XVKS0tZtGgR3/72t9PHnThxgvLycoqLi9m0aRMDAwMAtLW1UVlZyeLFi1m/fj29vb0AXLhwgXXr1rFkyRIqKytJJpMj3Z8kSePCFQP92LFjrFq1ilOnTgHQ19dHTU0NjY2NtLS0cPz4cV599VUAqqqq2LJlC4cOHSIIApqamgCoq6ujoqKC1tZW5s+fT2NjIwA7duygoKCAgwcPsmLFCrZt23ad2pQkKdyuGOhNTU3U1tYSj8cBeOutt5g9ezazZs0iGo1SWlpKa2srZ8+epa+vjwULFgBQXl5Oa2sr/f39HDlyhOLi4svGAV555RVKS0sBKCkp4bXXXqO/v/969ClJUqhFr3TAh5+az507RywWS2/H43Ha29s/Mh6LxWhvb+f8+fPk5OQQjUYvG//wrxWNRsnJyaGrq4vp06dfdQPTpuVc9bHSaIvFcjNdgvRreW2GzxUD/cNSqRSRSCS9HQQBkUjkY8d/9fWDPrz9wXOysq7tPb3Ozh5SqeCazhkrvOHGvmSyO9MlSB8Ri+V6bY5RWVmRj32Qvea33GfMmHHZy2vJZJJ4PP6R8Y6ODuLxOHl5eXR3dzM4OHjZ8fD+031HRwcAAwMD9Pb2MnXq1GstSZKkce+aA/1zn/sc77zzDr/4xS8YHBzkpZdeorCwkPz8fCZNmsTRo0cBSCQSFBYWkp2dTUFBAS0tLQA0NzdTWFgIwMKFC2lubgagpaWFgoICsrOzR6g1SZLGj2uecp80aRJPPvkk3/jGN7h06RILFy5k8eLFANTX17N582Z6enqYN28ea9asAaC2tpbq6mp27drFzJkzaWhoAGDjxo1UV1ezbNkycnNzqa+vH8HWJEkaPyJBEIzpBeiwr6GXPpzIdBkaogNPlblOqRuSa+hj14iuoUuSpBuPgS5JUggY6JIkhYCBLklSCBjokiSFgIEuSVIIGOiSJIWAgS5JUggY6JIkhYCBLklSCBjokiSFgIEuSVIIGOiSJIWAgS5JUggY6JIkhYCBLklSCBjokiSFgIEuSVIIGOiSJIWAgS5JUghEh3riD37wA/bs2ZPePnPmDGVlZVy8eJGjR48yefJkADZs2EBRUREnTpxg06ZN9Pb2UlBQQF1dHdFolLa2Nqqqqujs7GTOnDnU19czZcqU4XcmSdI4MuQn9BUrVpBIJEgkEtTX1zNt2jQ2bNjA8ePH2bNnT3pfUVERAFVVVWzZsoVDhw4RBAFNTU0A1NXVUVFRQWtrK/Pnz6exsXFkOpMkaRwZkSn3xx57jIceeojJkyfT1tZGTU0NpaWlPP3006RSKc6ePUtfXx8LFiwAoLy8nNbWVvr7+zly5AjFxcWXjUuSpGsz5Cn3X3n99dfp6+tjyZIlnD59mrvvvpva2lpyc3N54IEH2L9/P7fddhuxWCx9TiwWo729nfPnz5OTk0M0Gr1s/FpMm5Yz3Bak6yYWy810CdKv5bUZPsMO9BdeeIH7778fgFmzZvHMM8+k961evZrm5mbmzp1LJBJJjwdBQCQSSX/9oA9vX0lnZw+pVDCMDm5c3nBjXzLZnekSpI+IxXK9NseorKzIxz7IDmvK/b333uPIkSPcd999AJw8eZJDhw6l9wdBQDQaZcaMGSSTyfR4R0cH8XicvLw8uru7GRwcBCCZTBKPx4dTkiRJ49KwAv3kyZN86lOf4uabbwbeD/DHH3+cd999l/7+fvbt20dRURH5+flMmjSJo0ePApBIJCgsLCQ7O5uCggJaWloAaG5uprCwcJgtSZI0/gxryv306dPMmDEjvX377bezbt06Vq1axcDAAIsWLaKkpASA+vp6Nm/eTE9PD/PmzWPNmjUA1NbWUl1dza5du5g5cyYNDQ3DKUmSpHEpEgTBmF6ADvsaeunDiUyXoSE68FSZ65S6IbmGPnZdtzV0SZJ0YzDQJUkKAQNdkqQQMNAlSQoBA12SpBAw0CVJCgEDXZKkEDDQJUkKAQNdkqQQMNAlSQoBA12SpBAw0CVJCgEDXZKkEDDQJUkKAQNdkqQQMNAlSQoBA12SpBAw0CVJCgEDXZKkEDDQJUkKgehwTl69ejVdXV1Eo+//Mlu3bqW3t5cnnniCS5cusWTJEh566CEATpw4waZNm+jt7aWgoIC6ujqi0ShtbW1UVVXR2dnJnDlzqK+vZ8qUKcPvTJKkcWTIT+hBEHDq1CkSiUT6x2c/+1lqampobGykpaWF48eP8+qrrwJQVVXFli1bOHToEEEQ0NTUBEBdXR0VFRW0trYyf/58GhsbR6YzSZLGkSEH+n/9138BsHbtWr70pS+xZ88e3nrrLWbPns2sWbOIRqOUlpbS2trK2bNn6evrY8GCBQCUl5fT2tpKf38/R44cobi4+LJxSZJ0bYY85X7hwgXuuece/uzP/oz+/n7WrFnD17/+dWKxWPqYeDxOe3s7586du2w8FovR3t7O+fPnycnJSU/Z/2r8WkybljPUFqTrLhbLzXQJ0q/ltRk+Qw70O++8kzvvvDO9vXz5cp5++ml+93d/Nz0WBAGRSIRUKkUkEvnI+K++ftCHt6+ks7OHVCoYYhc3Nm+4sS+Z7M50CdJHxGK5XptjVFZW5GMfZIc85f7Tn/6Uw4cPp7eDICA/P59kMpkeSyaTxONxZsyYcdl4R0cH8XicvLw8uru7GRwcvOx4SZJ0bYYc6N3d3Wzfvp1Lly7R09PDiy++yJ/8yZ/wzjvv8Itf/ILBwUFeeuklCgsLyc/PZ9KkSRw9ehSARCJBYWEh2dnZFBQU0NLSAkBzczOFhYUj05kkSePIkKfc7733Xo4dO8aXv/xlUqkUFRUV3HnnnTz55JN84xvf4NKlSyxcuJDFixcDUF9fz+bNm+np6WHevHmsWbMGgNraWqqrq9m1axczZ86koaFhZDqTpCHKvWUyN00a1qd6b3hhXtLruzRA94WLmS5j1EWCIBjTC9BhX0MvfTiR6TI0RAeeKnOdcozy3hvbwnzvXZc1dEmSdOMw0CVJCgEDXZKkEDDQJUkKAQNdkqQQMNAlSQoBA12SpBAw0CVJCgEDXZKkEDDQJUkKAQNdkqQQMNAlSQoBA12SpBAw0CVJCgEDXZKkEDDQJUkKAQNdkqQQMNAlSQoBA12SpBAw0CVJCoHocE7euXMnBw8eBGDhwoV885vf5NFHH+Xo0aNMnjwZgA0bNlBUVMSJEyfYtGkTvb29FBQUUFdXRzQapa2tjaqqKjo7O5kzZw719fVMmTJl+J1JkjSODPkJ/fXXX+fHP/4xL774Is3Nzfz85z/nRz/6EcePH2fPnj0kEgkSiQRFRUUAVFVVsWXLFg4dOkQQBDQ1NQFQV1dHRUUFra2tzJ8/n8bGxpHpTJKkcWTIgR6LxaiurmbixIlkZ2czd+5c2traaGtro6amhtLSUp5++mlSqRRnz56lr6+PBQsWAFBeXk5rayv9/f0cOXKE4uLiy8YlSdK1GfKU+2233Zb++alTpzh48CB79+7ljTfeoLa2ltzcXB544AH279/PbbfdRiwWSx8fi8Vob2/n/Pnz5OTkEI1GLxu/FtOm5Qy1Bem6i8VyM12CNC6Nx3tvWGvoAP/xH//BAw88wDe/+U1+67d+i2eeeSa9b/Xq1TQ3NzN37lwikUh6PAgCIpFI+usHfXj7Sjo7e0ilguE1cYMajxdk2CST3ZkuQUPgvTf2hfXey8qKfOyD7LDecj969Ch/+Id/yMMPP8xXvvIVTp48yaFDh9L7gyAgGo0yY8YMkslkeryjo4N4PE5eXh7d3d0MDg4CkEwmicfjwylJkqRxaciB/j//8z88+OCD1NfXs2zZMuD9AH/88cd599136e/vZ9++fRQVFZGfn8+kSZM4evQoAIlEgsLCQrKzsykoKKClpQWA5uZmCgsLR6AtSZLGlyFPuX/ve9/j0qVLPPnkk+mxlStXsm7dOlatWsXAwACLFi2ipKQEgPr6ejZv3kxPTw/z5s1jzZo1ANTW1lJdXc2uXbuYOXMmDQ0Nw2xJkqTxJxIEwZhegA77Gnrpw4lMl6EhOvBUWWjX8cLOe29sC/O9d93W0CVJ0o3BQJckKQQMdEmSQsBAlyQpBAx0SZJCwECXJCkEDHRJkkLAQJckKQQMdEmSQsBAlyQpBAx0SZJCwECXJCkEDHRJkkLAQJckKQQMdEmSQsBAlyQpBAx0SZJCwECXJCkEDHRJkkLAQJckKQRuiEA/cOAAS5cuZdGiRezduzfT5UiSNOZEM11Ae3s73/72t/m7v/s7Jk6cyMqVK7nrrrv49Kc/nenSJEkaMzIe6K+//jp33303U6dOBaC4uJjW1lY2bNhwVednZUWuY3WZF791cqZL0DCE/foMM++9sS2s995v6ivjgX7u3DlisVh6Ox6P89Zbb131+bfeOuV6lHXD+N7mRZkuQcMwbVpOpkvQEHnvjW3j8d7L+Bp6KpUiEvn/f3EEQXDZtiRJurKMB/qMGTNIJpPp7WQySTwez2BFkiSNPRkP9N///d/n8OHDdHV1cfHiRX74wx9SWFiY6bIkSRpTMr6GPn36dB566CHWrFlDf38/y5cv53d+53cyXZYkSWNKJAiCINNFSJKk4cn4lLskSRo+A12SpBAw0CVJCgEDXZKkEDDQJUkKAQNdkqQQyPjn0DU+HDly5Dfu/73f+71RqkSSwsnPoWtUrF69GoBf/vKX/Pd//zef//znycrK4mc/+xmf+cxneOGFFzJcoRRO991332/8/hgvv/zyKFaj68kndI2K5557DoA/+qM/YufOncyePRuAs2fPsmXLlkyWJoXac889RxAEPPPMM8yaNYvy8nImTJjAgQMHOHPmTKbL0wgy0DWq2tra0mEO8MlPfpK2trYMViSFW35+PgAnT57kiSeeSI+vXbuW8vLyTJWl68BA16iaN28ejzzyCEuWLCEIAg4cOEBBQUGmy5LGhcOHD3PPPfcA8OqrrzJhwoQMV6SR5Bq6RtV7773Hnj17eOONN4hEItxzzz1UVFQQjfpvS+l6+rd/+zceeeQRkskkQRCQn5/P9u3b+fSnP53p0jRCDHSNun//93/nJz/5CYODg9x1113ccccdmS5JGjfOnz9PJBJh6tSpmS5FI8xA16hqbm5m586dfPGLXySVSvHyyy+zfv16li9fnunSpFB788032b17N//7v/9LEASkUina2tr4h3/4h0yXphFioGtUlZWV8eyzz3LrrbcC0NXVxZo1a3jppZcyXJkUbkuXLuVrX/saL774IqtXr+aHP/wh06ZNo6amJtOlaYS4cKlRlUql0mEOkJeX9xs/IytpZEycOJGvfvWrnD17lltuuYXt27dTWlqa6bI0gvyvXzWqPvvZz7Jt2zZOnjzJyZMn2bZtG7fffnumy5JCb9KkSfzyl79kzpw5HDt2jAkTJjA4OJjpsjSCnHLXqOrr6+Mv//Iv+ed//meCIOCuu+7iwQcfJCcnJ9OlSaF28OBBmpqa2LlzJ8uXLycrK4vbb7+dp556KtOlaYQY6Bp1XV1dvPnmm6RSKRYsWMAnPvGJTJckhdrbb79Nbm4un/jEJ/jud7/L4cOHmTx5Mg0NDdx0002ZLk8jxEDXqPqnf/onampqWLBgAalUip/97Gds27aNe++9N9OlSaH0N3/zN/z1X/81EyZM4Atf+ALvvPMOS5cu5Y033uDmm29m+/btmS5RI8RA16gqLy/nL/7iL5g1axYAp0+fZsOGDSQSiQxXJoXTsmXL+MEPfsDFixf54he/yI9//GOmTJnC4OAgX/7ylzlw4ECmS9QI8S13jaqBgYF0mAPMmjWLVCqVwYqkcItGo9x8883cfPPNzJo1iylTpgAwYcIE/4fGkPEtd42qT37ykzz77LP09PTQ09PDs88+m/7mEZJGXlbW//817//dHm5OuWtUdXZ28ud//uf85Cc/IZVKcffdd7Np0ybi8XimS5NC6c477+S3f/u3AfjXf/3X9M+DIODnP/85//Iv/5LJ8jSCnG/RqHn++eeJxWLs2LGD5cuX09XVxYkTJ7h48WKmS5NCa/fu3ZkuQaPEQNeo2L17N4cPH6a2thZ4/7uuPffcc/zjP/4ju3fv5vHHH89whVI4feELX8h0CRolBrpGRXNzM/v370+/kJOVlUV+fj4rV65k8eLFGa5OksY+X4rTqJgwYUI6zAHWr18PvP8G7gfHJUlDY6BrVKRSKXp6etLbxcXFAHR3d1/2Fq4kaWj8m1SjorS0lEceeeSyUO/t7aWmpoYvfelLGaxMksLBj61pVAwODvLYY4/x0ksvMXfuXCKRCP/5n/9JWVkZjz32WKbLk6Qxz0DXqGpvb+ett94CYP78+cycOTPDFUlSOBjokiSFgGvokiSFgIEuSVIIGOiSJIWAgS5JUggY6JIkhcD/Acg2jdhctqMSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the distribution of the target variable\n",
    "plt.figure(figsize=(8, 4))\n",
    "model_data['target'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f3cac1",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4e4d5861",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Instantiating logistic regression with 0 random state\n",
    "lr = LogisticRegression(random_state=0)\n",
    "\n",
    "#Training the modeal\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "#Generating predictions from the trained model\n",
    "y_pred = lr.predict(X_test) #Return the predictions                                                                                      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7c08bc",
   "metadata": {},
   "source": [
    "## Risk in first time VS repeat loans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9c79fe",
   "metadata": {},
   "source": [
    "Given that a borrower can apply for a loan for the first time or subsequent times, the probability of a borrower defaulting whether it is a first-time or subsequent loans, according to this study, is defined as follows:\n",
    "\n",
    "\n",
    "Pr (G|F) =   \"number of good loan | first-time loan\" /\"Total number of first-time loan\"      …………………………………... (1)\n",
    "\n",
    "Pr (G|R) =   \"number of good loan | returning loan\" /\"Total number of returning loan\"       ...………………………………... (2)\n",
    "\n",
    "Pr (B|F) =   \"number of bad loan | first-time loan\" /\"Total number of first-time loan\"       …………………………………... (3)\n",
    "\n",
    "Pr (B|R) =   \"number of bad loan | returning loan\" /\"Total number of returning loan\"       …………………………………... (4)\n",
    "\n",
    "\n",
    "Where:\n",
    "B = Bad loan\n",
    "G = Good loan\n",
    "F = First-time loan\n",
    "R = Returning loan\n",
    "\n",
    "The likelihood that a first-time and returning loan will be good or bad is determined using the aforementioned equations (1) through (4). The values of the probabilities are presented in the probability distribution of customer status for good and bad loan as displays below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cb66cc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding y_test and y_pred to the test dataset\n",
    "x['actual'] = y_test\n",
    "x['predictions'] = y_pred#.value_counts()\n",
    "\n",
    "\n",
    "#Creating a dictionary for the columns to be added to the created data frame\n",
    "pred_counts_returning = x[x['first_time_returning_encode'] == 1]['predictions'].value_counts()\n",
    "actual_counts_returning = x[x['first_time_returning_encode'] == 1]['actual'].value_counts()\n",
    "\n",
    "pred_counts_first = x[x['first_time_returning_encode'] == 0]['predictions'].value_counts()\n",
    "actual_counts_first = x[x['first_time_returning_encode'] == 0]['actual'].value_counts()\n",
    "\n",
    "no_of_first = len(x[x['first_time_returning_encode'] == 0])\n",
    "no_of_returning = len(x[x['first_time_returning_encode'] == 1])\n",
    "\n",
    "# dic = {\n",
    "#     'Status': ['First time', 'Returning'],\n",
    "# #     'Good': [pred_counts_first[1], pred_counts_returning[1]], \n",
    "# #     'Bad': [pred_counts_first[0], pred_counts_returning[0]],\n",
    "#     'Prob_Good': [round(pred_counts_first[1]/no_of_first, 3), round(pred_counts_returning[1]/no_of_returning, 3)],\n",
    "#     'Prob_Bad': [round(pred_counts_first[0]/no_of_first, 3), round(pred_counts_returning[0]/no_of_returning, 3)]\n",
    "#         }\n",
    "\n",
    "dic = {\n",
    "    'Status': ['Prob_Good', 'Prob_Bad'],\n",
    "#     'Good': [pred_counts_first[1], pred_counts_returning[1]], \n",
    "#     'Bad': [pred_counts_first[0], pred_counts_returning[0]],\n",
    "    'First time': [round(pred_counts_first[1]/no_of_first, 3), round(pred_counts_first[0]/no_of_first, 3)],\n",
    "    'Returning': [round(pred_counts_returning[1]/no_of_returning, 3), round(pred_counts_returning[0]/no_of_returning, 3)]\n",
    "        }\n",
    "\n",
    "#Creating a data frame containing store the predicted, actual and loan status of first time and returning customers\n",
    "status_df = pd.DataFrame(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3032bed8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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68ccf0aFDBxw5cgTNmjXDoEGD0KZNG+zZswdFRUVSm3v37sW9e/dQVFSE+Ph4eHp6GlSLsbGxXhj16dMHUVFRcHFxgaOjo0FtXL58GV999RUGDx6sN/7cuXPw8/PDW2+9hWHDhmHgwIE4efLkE/stzZYtWwAAp0+fxrVr19C8eXPY2triwoULyM/PR2FhoV5wlNR2+/bt8e2330IIgYKCAsTHx8PDw8OgGoAHofznn38iNTUVwINrpUeOHEGbNm1KXa5Xr144fPgwtm3bJv3iOXXqFJYsWSL9AWRrayt9Ntu3b5eWXb9+PSZMmID27dtj7NixaN++Pc6cOaO3nvXr14eJiQmSkpIAPLgZb+fOnWVat7Zt2+LAgQPSncw///wz/P39kZeXV+pyNWvWRHx8vN43MO7evYu0tDTpbMDDOv/55x+cPHkSY8aMgZeXF27duoVr165Joffoz+3hURrw4IbGh0eWpW1TJenQoQN++OEH6c7/TZs2wdrautgZjsc5OjoiJCQEI0eOxM2bN6Xxf/31F3777TcYGRmV+tmX1m+HDh2wYcMGAMDNmzefeCc4AOzfvx/Dhw+Hr68vAOD333/X2+9LWt+EhATk5+cjPz9f75smT3Ps2DEAD/6w379/P3r06IHg4GDUq1cPe/fulc46Nm3aFP/3f/8H4MF++ccffxjcB/Bge9u/fz+uX78OANI18ObNm2P//v0YMGAAunfvDjs7OyQnJz91nSsjnmZXgKCgIPz9998IDg6GTqdD3bp19W4qqlGjBgIDA5GXl4d+/frB3d0dDRo0QFJSEnx8fKDT6dCpUyfcu3cP2dnZACD9cvvnn3/w7rvvSqeOn6Zz585YuHAhCgsL0aNHD3Tq1AmTJ09G7969S1zm0UsKRkZGMDc3x+jRo/Hee+/pzde4cWP4+PigZ8+esLCwQJUqVTB58uRi/T7N9evX0b17d6hUKixcuBDW1tZo164dWrduDR8fH9jb28PNzU36hd+iRQssXboUI0aMQP/+/aV2Jk+ejJkzZ0KtVqOwsBAdOnTARx99ZNDnBDwImMWLF2PGjBnIy8uDSqXCnDlzUK9ePemU7ZNYW1sjLi4O8+fPx4oVK2BkZISqVati1qxZ0p3skydPRmRkJCwtLeHh4SHdhNS9e3ccPnwYvr6+qFq1Kl577TVpnbp164b+/fsjJiYGX331FWbOnImYmBgUFRVh+PDhcHd3LzEkHtegQQNERkZi9OjR0tmgZcuWoVq1aqUuZ2VlhbVr1+KLL75AVFQUqlatCjMzMwwbNkw6/d21a1eMHTsW06dPx9ChQ9GjRw9YWFjA0dERrq6uuHr1Ktq2bas338cff4yIiAjpKL5Vq1YASt+mStKuXTsMHDgQAwYMgE6ng62trfRzeJrw8HBs27YNn332GXJzc5GVlQUrKyv4+vqib9++MDU1LfGzB1Biv9OmTcOECRPg4+MDJyenEr/xEh4ejuHDh8PCwgLVq1dH69atpUsfJenduzeuXbsGPz+/p/7Rcu3aNWlf1ul0MDMzQ0xMDCwtLTF48GBMnTpV+optixYtpNBeuHAhJkyYgP/+97+oU6cO6tevX2If0dHR+PLLL6XhTp06YeHChZg2bRpGjBiBoqIiVKlSBcuXL0eNGjUwfPhwREVFYfHixTA1NYWrq+tT17kyUonKes6BXojjx49j8uTJ2L59e7HTYERE9GLwyJye2fjx43H48GFER0czyImIKhCPzImIiBSON8AREREpHMOciIhI4RjmRERECscwJyIiUjhF381+58596HS8f0+J7OyqIyMju6LLIHrlcN9TLiMjFWxsnvwsB0WHuU4nGOYKxp8dUcXgvlf58DQ7ERGRwjHMiYiIFE7Rp9mJiKhiCCGQnX0PubnZ0OlevRebyMnExAw2NvYwNjY8ohnmRERUZnfuaKBSqWBr6whjYxM+0rmcCCFw//4/uHNHg5o1XzN4OZ5mJyKiMisoyIO1tR1MTEwZ5OVIpVKhWjVLaLUFZVqOYU5ERM9AQKVihMjhWf444k+CiIhI4WS9Zp6YmIhly5ZBq9ViwIAB6Nu3r97006dPY+rUqSgsLMRrr72G+fPnw9LSUs6SiIhIJjUsq6KKefnHSl6+Fln/5JY6z99/38S//x2IN9+srzd+3ryFSExMQOPGTdC+fUeD+ps9+3MMHjwUTk7616xXrVqBVq3aoHnzlpg7dwa6d++Jxo3fKdvKyES2ME9LS0N0dDQ2b94MMzMz9O7dG25ubmjQoIE0z6xZsxAWFoaOHTti7ty5WLVqFcLDw+UqiYiIZFTF3ATqz7aWe7uJXwQgy4D5ata0x5o164uNHzLkozL199tvRzFoUGix8cePH0PLlu8CACIippSpTbnJFubJyclwd3eHtbU1AMDb2xs7duzAiBEjpHl0Oh3u378PAMjNzYWVlZVc5SiOXH/hviwKCvlVFiJ6MWbNmo6WLd9Fy5bv4rPPPoWVlTXMzc0xYkQ4oqJmoaioCGZmZpg4cRr27duL27c1GDt2JJYujYWVlTUA4Mcft+P8+bOYN28mZs9egOjoKAwePBQA8M03/4GpqSn+/vsm2rXzRNWqVfHrrz9DCIEFCxbD1tYOBw8mY9Wq5dBqtXjttdoYP36S1HZ5kC0t0tPTYW9vLw07ODggNTVVb56IiAgMHjwYs2fPRtWqVREfH1+mPuzsqpdLrS8rOf7CfVkkfhEAe/saFV0G0SupPPa99HQjmJi8uNuuntaXsbERbt/WYNCgPtI4b28f9Os3ACqVCkZGKhgbG+HatavYvHkpatWqhRkzpqFv3/54//2u+P77RJw7dwqDBg3G1q2bsHBhDOzsbKW21Gp//PBDIoYMGYZGjRpCpXrQHgCcOXMa3333f7CysoKPTxeEhYVj7dp1mDFjGn76aRe8vHywYsWXWLp0JSwtLbFly0YsX/4lJk2aWuL6GBkZlennJFuY63Q6vTvyhBB6w3l5eZg0aRLWrFkDFxcXrF69GuPHj8fKlSsN7iMjI7vSPmP4VQg6jcaQE2dEVJ7s7WuUy76n0+mg1erKoSLDPK2voiIdata0x+rV+qfZtVodhHjwHo+iIh1sbGzh4OAErVYHd/d2WLBgHpKTD6BdO0+0a9dB6qeoqPj6CSGk8Q//DwD1678FOzsHAICVlTVatmwNrVYHBwcn3L17D6mpqbh16xY++eTBkbxOVwRLS6tS10mn0xX7ORkZqUo8iJUtzJ2cnHD06FFpWKPRwMHBQRr+448/YG5uDhcXFwBAr169sHjxYrnKISIigrm5ufT/Tp26oFkzFxw48Cvi49cjJWU/xo+fXOY2TUz0o9TY2FhvWKcrgotLc8ybFw0AyM/PR25u6Tf0lZVs50g8PDyQkpKCzMxM5ObmIikpCZ6entL0unXr4tatW/jzzz8BAHv27IGzs7Nc5RAREemZOnUCzp49g+7de2LIkI9w/vw5AA/CuKio+H09xsYmTxz/NO+80wynT5/EtWtXAQBr1nyNpUsXPVftj5PtyNzR0RHh4eEICQlBYWEhgoKC4OLigtDQUISFhcHZ2Rlz5szBqFGjIISAnZ0dZs+eLVc5REQks7x8LRK/CJClXTn07z8I8+bNxJo1sTAxMcWYMREAAA+PDhgzZiQWLoxBrVq1pfnd3NpiwYI5mDz58zL1Y2dXExERUzF16gTodEWwt3fE1KmR5bouKiGEYi86V/Zr5pX9BjheMyd68crrmvmtW1fh5FS3HCqiJ3nS51sh18yJSqPTFlTqm/y0Bfm4c69sz1YmInpWDHOqEEYmZvhzVs+KLkM29SdtAsAwJ6IXg89mJyIiUjiGORERkcIxzImIiBSOYU5ERKRwvAGOiIjKhY2VGUzMzJ8+Yxnx2yFPxzAnIqJyYWJmLsu3VAz5dsjj7zMX4sFbOX18/PDhh8NKXO7TT4chJmZFudQ5cGCfJ76C9UVgmBMRUaXw+PvMb9/WoHfvHnj/fS+8+Wa9Jy5z/Pixcuu/ooIcYJgTEVEldfv2bQghYGFhgbi4Nfjpp10oKtLBzc0dH38chsWLFwAAQkMHIDZ2Ldq3b4X9+x+8IOyHHxJx/PgxTJo0HUFBarzzTjNcuHAeU6ZEYv782ahf/y388cd52NraYcaMubC0tJKWX7VqBW7f1uD69WtIS7sFP78ADBjwIbRaLebPn43U1BOwt3eASqXCgAEfwtW11XOvK2+AIyKiSuH2bQ0GDuyDPn164oMP3kds7DLMnr0Af/55CefPn0Vs7DdYvXodNBoNkpJ+xKhRYwEAsbFrn9q2u7sHvvtuM2xsbHHx4gX06tUXcXHxqF69OpKSfiw2/8WLFxAdvRQrV67Bt9+uRVZWFhISNiIvLxfr12/CxInTcPbsmXJbdx6ZExFRpfDwNLtOp8OXX0bjypXLaN3aDV99tQRnzpzChx/2BwDk5+fB0dGpTG2/804z6f82NrZo2LAxAKB+/Qb4559/is3v6toKpqamsLGxhaWlJe7fz8aRI4egVveASqWCk9NrePfd1s+xtvoY5kREVKkYGRnhk09GYtCgPvjuuzjodEX417/+jd69+wEAsrKyir1z/CEhBFQqFbRa/Te1PfoedDMzs2LLPO7ReVQqFYQQMDIyhhC6Z16v0vA0OxERVTomJiYYPnwU1qxZhYYNG2Pnzh+Qk5MDrVaLCRM+w759ewA8eHf5w+C2trbG5cuXIITA/v2/lHtNrVq1we7dSRBC4PZtDY4fPwaVSlUubfPInIiIyoW2IP//f42s/Nt9Fu7uHmjWzBm//34cHTt2xtChA6HTFcHNzQM+Pn4AgPbtPTFwYB+sWhWHjz4agXHjwmFrawcXlxa4d+9uOa4FEBAQiIsXLyAkpBfs7GrCyek1vSP+58H3mb+kXoX3mVf2t6bxfe30MuL7zCtOcvJ+CCHQrl0HZGdnY9Cgvli16htYWloVm5fvMyciInoJvflmPcyYMRWxscsAAEOGDHtikD8LhjkREdELUKtWbSxbtkqWtnkDHBERPQOVbHdmv+qe5eo3w5yIiMrMzKwK7t69Da228JnCh55MCIH79/+BiYnZ02d+BE+zExFRmdnY2CM7+x4yM9Og0xVVdDmViomJGWxs7Mu2jEy1EBFRJaZSqVCjhjVq1LCu6FIIPM1ORESkeLIemScmJmLZsmXQarUYMGAA+vbtK007e/YsIiIipOHMzExYWVlh+/btcpZERERU6cgW5mlpaYiOjsbmzZthZmaG3r17w83NDQ0aNAAANGnSBFu3PngoSm5uLoKDgzF9+nS5yiEiIqq0ZDvNnpycDHd3d1hbW8PCwgLe3t7YsWPHE+ddsWIFWrdujVatnv+drkRERK8a2Y7M09PTYW//v7vxHBwckJqaWmy+rKwsxMfHIzExscx9lPRYO6KXgb19jYougeiJuG1WPrKFuU6n03sbzMPXyj1u27Zt6NKlC+zs7MrcR2V/NjspG5/NTi+j8no2O714pT2bXbbT7E5OTtBoNNKwRqOBg4NDsfl2794NX19fucogIiKq9GQLcw8PD6SkpCAzMxO5ublISkqCp6en3jxCCJw+fRotW7aUqwwiIqJKT7Ywd3R0RHh4OEJCQtC9e3f4+fnBxcUFoaGhOHnyJIAHX0czNTUtt/e5EhERvYpk/Z65Wq2GWq3WGxcbGyv9387ODgcOHJCzBCIiokqPT4AjIiJSOIY5ERGRwjHMiYiIFI5hTkREpHAMcyIiIoVjmBMRESkcw5yIiEjhGOZEREQKxzAnIiJSOIY5ERGRwjHMiYiIFI5hTkREpHAMcyIiIoVjmBMRESkcw5yIiEjhGOZEREQKxzAnIiJSOIY5ERGRwjHMiYiIFI5hTkREpHAMcyIiIoVjmBMRESkcw5yIiEjhZA3zxMRE+Pr6wsvLC+vWrSs2/c8//0T//v3h7++PDz/8EPfu3ZOzHCIiokpJtjBPS0tDdHQ01q9fj4SEBGzYsAEXL16Upgsh8PHHHyM0NBTbtm1DkyZNsHLlSrnKISIiqrRkC/Pk5GS4u7vD2toaFhYW8Pb2xo4dO6Tpp0+fhoWFBTw9PQEAH330Efr27StXOURERJWWbGGenp4Oe3t7adjBwQFpaWnS8LVr11CzZk1MnDgRPXr0wLRp02BhYSFXOURERJWWiVwN63Q6qFQqaVgIoTes1Wpx+PBhfPvtt3B2dsaiRYswd+5czJ071+A+7Oyql2vNROXJ3r5GRZdA9ETcNisf2cLcyckJR48elYY1Gg0cHBykYXt7e9StWxfOzs4AAD8/P4SFhZWpj4yMbOh0onwKfslwZ1M+jSaroksgKsbevga3TYUyMlKVeBAr22l2Dw8PpKSkIDMzE7m5uUhKSpKujwNAy5YtkZmZiXPnzgEA9u7di6ZNm8pVDhERUaUl25G5o6MjwsPDERISgsLCQgQFBcHFxQWhoaEICwuDs7Mzli5dismTJyM3NxdOTk6IioqSqxwiIqJKS7YwBwC1Wg21Wq03LjY2Vvp/8+bNsXHjRjlLICIiqvT4BDgiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwsoZ5YmIifH194eXlhXXr1hWb/uWXX6JTp04ICAhAQEDAE+chIiKi0pnI1XBaWhqio6OxefNmmJmZoXfv3nBzc0ODBg2keU6dOoWFCxeiZcuWcpVBRERU6cl2ZJ6cnAx3d3dYW1vDwsIC3t7e2LFjh948p06dwooVK6BWqxEZGYn8/Hy5yiEiIqq0DDoy37t3Lzp16gSVSmVww+np6bC3t5eGHRwckJqaKg3fv38fTZo0wdixY1G3bl1ERETgq6++Qnh4uMF92NlVN3heohfN3r5GRZdA9ETcNisfg8I8Li4OM2fORFBQEIKDg/VCuiQ6nU4v/IUQesPVqlVDbGysNDx48GBMnDixTGGekZENnU4YPL+ScGdTPo0mq6JLICrG3r4Gt02FMjJSlXgQa9Bp9tWrV2PNmjXIycnBv/71L4wcORIpKSmlLuPk5ASNRiMNazQaODg4SMM3b97Exo0bpWEhBExMZLuET0REVGkZfM28Tp06CA8PR0REBE6dOoXRo0dDrVbrnTp/lIeHB1JSUpCZmYnc3FwkJSXB09NTml6lShXMnz8f169fhxAC69atQ9euXZ9/jYiIiF4xBh0KX716FfHx8di6dSsaNWqEiRMnolOnTvj9998xatQo7N27t9gyjo6OCA8PR0hICAoLCxEUFAQXFxeEhoYiLCwMzs7OiIyMxMcff4zCwkK4urpi0KBB5b6CRERElZ1BYR4cHIwePXrg22+/xZtvvimNb9myJdq0aVPicmq1Gmq1Wm/co9fJvb294e3tXcaSiYiI6FEGnWafMmUKJkyYoBfkCQkJAIC5c+fKURcREREZqNQj871790Kr1WLx4sWoUqUKhHhw57hWq0VMTAy6d+/+ImokIiKiUpQa5mfPnsXBgweRkZGBb7755n8LmZhg4MCBctdGREREBig1zIcPH47hw4dj3bp16Nu374uqiYiIiMqg1DDfunUrAgICkJ+fj9WrVxebzrvPiYiIKl6pYX716lUAwIULF15IMURERFR2pYZ5WFgYAGDOnDkvpBgiIiIqu1LD/PHviD8uMTGxXIshIiKisis1zKdMmfKi6iAiIqJnVGqY29nZ4a233sLp06dfVD1ERERURqWGeVRUFFasWIFPP/202DSVSoU9e/bIVhgREREZptQwX7FiBQA88UUqRERE9HIw6EUrOTk5WLZsGQ4cOABTU1N4enoiNDQUZmZmctdHRERET2HQi1Y+//xz3Lp1C2PHjsXIkSNx4cIFzJw5U+7aiIiIyAAGHZmfOXNG72tobm5uCAgIkK0oIiIiMpxBR+ZWVla4e/euNJyTk4MaNWrIVRMRERGVQalH5g9PpZuYmCAwMBBeXl4wMjLC3r170aBBgxdSIBEREZWu1DC3trYGALRq1QqtWrWSxvv5+claFBERERmu1DAfMWJEidNycnLKvRgiIiIqO4NugNu9ezeWLFmCnJwcCCGg0+lw9+5dHD9+XO76iIiI6CkMCvOoqCiMGjUK3333HUJDQ7F7925Uq1ZN7tqIiIjIAAbdzV61alX4+vqiRYsWMDc3x/Tp07Fv3z6ZSyMiIiJDGBTm5ubmKCgoQJ06dXD27FkYGRlBpVLJXRsREREZwKAw79y5M4YOHQpPT0+sWbMGn376KWxsbJ66XGJiInx9feHl5YV169aVON++ffvQuXNnw6smIiIiiUHXzD/66CP4+/vD0dERX331FY4cOfLUr6elpaUhOjoamzdvhpmZGXr37g03N7di30+/ffs25s2b9+xrQERE9Ioz6MgcAC5duoR58+bhxx9/RJMmTWBnZ1fq/MnJyXB3d4e1tTUsLCzg7e2NHTt2FJtv8uTJpX4FjoiIiEpn0JH58uXLsW3bNnh7e0On02HKlCkICQlB3759S1wmPT0d9vb20rCDgwNSU1P15vnmm2/wzjvvoHnz5s9UvJ1d9WdajuhFsLfnI4/p5cRts/IxKMy3b9+O+Ph4VK/+IDwHDx6MPn36lBrmOp1O7yY5IYTe8B9//IGkpCSsWbMGt27deqbiMzKyodOJZ1r2ZcedTfk0mqyKLoGoGHv7Gtw2FcrISFXiQazBd7M/+r1yKysrmJubl7qMk5MTNBqNNKzRaODg4CAN79ixAxqNBj179sTQoUORnp6OPn36GFIOERERPaLUI/OkpCQAQL169fDJJ58gODgYxsbGSEhIQLNmzUpt2MPDAzExMcjMzETVqlWRlJSEGTNmSNPDwsIQFhYGALhx4wZCQkKwfv36510fIiKiV06pYR4XF6c3vHr1aun/GRkZpTbs6OiI8PBwhISEoLCwEEFBQXBxcUFoaCjCwsLg7Oz8HGUTERHRQ2UKc61WCyEETE1NDWpcrVZDrVbrjYuNjS023+uvv469e/ca1CYRERHpM+iaeUZGBoYMGYIWLVrAxcUFISEhSEtLk7s2IiIiMoBBYR4ZGYkWLVogOTkZycnJaNWqFaZPny5zaURERGQIg8L8ypUrGDFiBCwtLWFjY4OwsDBcu3ZN7tqIiIjIAAaFuVarRX5+vjScm5vLF60QERG9JAx6aIyvry8GDhyIwMBAqFQqbNq0Cd7e3nLXRkRERAYwKMyHDx8OJycn/Prrr9DpdAgMDERQUJDctREREZEBDArzAQMGYO3atejZs6fc9RAREVEZGXTNPCsrCzk5OXLXQkRERM/AoCPzqlWrolOnTmjUqBEsLCyk8cuXL5etMCIiIjLMU8P8jz/+wPvvv4/27dvDycnpRdREREREZVBqmG/atAnz5s1D3bp1ce3aNSxYsAAdOnR4UbURERGRAZ76bPbExEQ4Ojri+PHjiI6OZpgTERG9ZJ56A5yjoyMAoGXLlrhz547sBREREVHZlBrmjz/lzdjYWNZiiIiIqOwM+mraQ3yEKxER0cun1Gvm58+fh6urqzScl5cHV1dXCCGgUqnw22+/yV4gERERla7UMN+1a9eLqoOIiIieUalhXrt27RdVBxERET2jMl0zJyIiopcPw5yIiEjhGOZEREQKxzAnIiJSOIY5ERGRwska5omJifD19YWXlxfWrVtXbPquXbugVqvxwQcfICIiAgUFBXKWQ0REVCnJFuZpaWmIjo7G+vXrkZCQgA0bNuDixYvS9JycHERGRmL16tX4/vvvkZ+fjy1btshVDhERUaUlW5gnJyfD3d0d1tbWsLCwgLe3N3bs2CFNt7CwwN69e1GzZk3k5uYiIyMDlpaWcpVDRERUaZX60JjnkZ6eDnt7e2nYwcEBqampevOYmpri559/xrhx4+Dg4ID27duXqQ87u+rlUiuRHOzta1R0CURPxG2z8pEtzHU6nd6LWR4+z/1xHTt2xKFDh7Bw4UJMnz4dX3zxhcF9ZGRkQ6cT5VLvy4Y7m/JpNFkVXQJRMfb2NbhtKpSRkarEg1jZTrM7OTlBo9FIwxqNBg4ODtLw3bt3sX//fmlYrVbj/PnzcpVDRERUackW5h4eHkhJSUFmZiZyc3ORlJQET09PaboQAmPHjsXNmzcBADt27NB7QxsREREZRrbT7I6OjggPD0dISAgKCwsRFBQEFxcXhIaGIiwsDM7OzpgxYwaGDRsGlUqFBg0a4PPPP5erHCIiokpLtjAHHpw6V6vVeuNiY2Ol/3fp0gVdunSRswQiIqJKj0+AIyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFM6noAoiI6MXRaQtgb1+josuQjbYgH3fuFVR0GS8cw5yI6BViZGKGP2f1rOgyZFN/0iYAr16Yy3qaPTExEb6+vvDy8sK6deuKTd+9ezcCAgLg7++PTz75BPfu3ZOzHCIiokpJtjBPS0tDdHQ01q9fj4SEBGzYsAEXL16UpmdnZ2P69OlYuXIltm3bhkaNGiEmJkaucoiIiCot2cI8OTkZ7u7usLa2hoWFBby9vbFjxw5pemFhIaZNmwZHR0cAQKNGjfD333/LVQ4REVGlJVuYp6enw97eXhp2cHBAWlqaNGxjY4OuXbsCAPLy8rBy5Up06dJFrnKIiIgqLdlugNPpdFCpVNKwEEJv+KGsrCwMHz4cjRs3Ro8ePcrUh51d9eeuk0gulfmO4cqsoLAIZqbGFV0GPYdXcd+TLcydnJxw9OhRaVij0cDBwUFvnvT0dHz44Ydwd3fHxIkTy9xHRkY2dDrx3LW+jF7FjbGy0WiyKroEegb29jWg/mxrRZchm8QvAiq6BNlV1n3PyEhV4kGsbKfZPTw8kJKSgszMTOTm5iIpKQmenp7S9KKiInz00Ufw8fHBpEmTnnjUTkRERE8n25G5o6MjwsPDERISgsLCQgQFBcHFxQWhoaEICwvDrVu3cObMGRQVFWHnzp0AgGbNmmHWrFlylURERFQpyfrQGLVaDbVarTcuNjYWAODs7Ixz587J2T0REdErgc9mJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBSOYU5ERKRwDHMiIiKFY5gTEREpHMOciIhI4RjmRERECidrmCcmJsLX1xdeXl5Yt25difONGzcOmzdvlrMUIiKiSku2ME9LS0N0dDTWr1+PhIQEbNiwARcvXiw2z0cffYSdO3fKVQYREVGlJ1uYJycnw93dHdbW1rCwsIC3tzd27NihN09iYiLef/99+Pj4yFUGERFRpWciV8Pp6emwt7eXhh0cHJCamqo3z5AhQwAAx44de6Y+7OyqP3uBRDKzt69R0SUQvZJexX1PtjDX6XRQqVTSsBBCb7g8ZGRkQ6cT5drmy+JV3BgrG40mq6JLoGfAfU/5Kuu+Z2SkKvEgVrbT7E5OTtBoNNKwRqOBg4ODXN0RERG9smQLcw8PD6SkpCAzMxO5ublISkqCp6enXN0RERG9smQLc0dHR4SHhyMkJATdu3eHn58fXFxcEBoaipMnT8rVLRER0StHtmvmAKBWq6FWq/XGxcbGFptv7ty5cpZBRERUqfEJcERERArHMCciIlI4hjkREZHCMcyJiIgUjmFORESkcAxzIiIihWOYExERKRzDnIiISOEY5kRERArHMCciIlI4hjkREZHCMcyJiIgUjmFORESkcAxzIiIihWOYExERKRzDnIiISOEY5kRERArHMCciIlI4hjkREZHCMcyJiIgUjmFORESkcAxzIiIihWOYExERKZysYZ6YmAhfX194eXlh3bp1xaafPXsWgYGB8Pb2xqRJk6DVauUsh4iIqFKSLczT0tIQHR2N9evXIyEhARs2bMDFixf15hk7diymTp2KnTt3QgiB+Ph4ucohIiKqtEzkajg5ORnu7u6wtrYGAHh7e2PHjh0YMWIEAOCvv/5CXl4eWrRoAQAIDAzEkiVL0KdPH4P7MDJSlXfZLxUHm6oVXYKsTKzsK7oEWVX27bMy476nbJV13yttvWQL8/T0dNjb/2+DcXBwQGpqaonT7e3tkZaWVqY+bGyqPX+hL7FVk70qugRZ1RmxvKJLkJWdXfWKLoGeEfc9ZXsV9z3ZTrPrdDqoVP/7K0IIoTf8tOlERERkGNnC3MnJCRqNRhrWaDRwcHAocfrt27f1phMREZFhZAtzDw8PpKSkIDMzE7m5uUhKSoKnp6c0vXbt2jA3N8exY8cAAFu3btWbTkRERIZRCSGEXI0nJiZixYoVKCwsRFBQEEJDQxEaGoqwsDA4Ozvj3LlzmDx5MrKzs9G0aVPMmTMHZmZmcpVDRERUKcka5kRERCQ/PgGOiIhI4RjmRERECscwJyIiUjiGORERkcIxzImIiBROtse5krLcuHED3bp1w1tvvQWVSoXCwkI4ODhgzpw5cHJyMqiNRo0a4fz582Xq9/r161iwYAFOnz4NY2Nj2NraYsyYMXj33XefZTX03LhxAyEhIdi7d+9zt0X0olTEvhgREYGDBw/CysoKOp0OVapUwYwZM9C4cWOD24iJiQEAfPrppwYvQ+WHR+YkcXBwwNatW5GQkIDvv/8ejRo1QlRUlGz93blzB3369EH79u2xe/du7Ny5E+PGjcPIkSNx+/Zt2foletm96H0RAMLCwrB161YkJiZi2LBhmDx5sqz9UfnikTmVyM3NDQsXLkTnzp3h4uKCs2fPYv369di3bx9Wr14NlUqFpk2bYsqUKahW7cFLb6ZMmYLU1FTY2Nhg9uzZqFWrVontb9iwAa6urggODpbGtWzZEhEREcjNzQUA/PTTT1i0aBF0Oh3eeOMNREZGombNmjhx4gRmzZqF/Px82NjYIDIyEnXr1sWZM2cwadIkACjTUQXRy0zuffFxWVlZqFmzJgBAq9Vi+vTpuHDhAm7fvo1GjRph4cKFqFKlCr7++mvEx8fDxsYGlpaWcHFxkWX96el4ZE5PVFhYiJ07d0qvqPX09MTOnTtx+/ZtLF++HHFxcUhMTETVqlXx5ZdfSsu1bt0aW7duRdeuXTFr1qxS+zhx4gRat25dbLyfnx/eeOMNZGRkYOrUqVi6dCkSExPh6uqKyMhIFBQUYPTo0ZgyZQq2bduG3r17Y/To0QCA8ePHY8yYMdiyZQtef/318vtAiCrIi9gXAWDJkiUICAiAl5cXpkyZgr59+wIAjh8/DlNTU2zYsAG7du1CVlYWfv75Z5w8eRKbNm3Cli1bsHr1aty6dUuW9SfDMMxJkp6ejoCAAAQEBMDf3x9CCHz22WcAgObNmwMAjhw5gk6dOsHGxgYA0KtXLxw8eBAAUKVKFfj7+wMAAgICcPjw4af2+eib8saNG4eAgAB07doVq1atQmpqKlxcXKRQftjXlStX9I4CfHx8cO3aNfz1119IT09Hu3btAACBgYHl8bEQvXAVsS8+PM2elJSEb775BiNHjsT169fRunVr9OnTB+vWrcOsWbNw5coV5OTk4PDhw+jYsSOqVasGCwsLdOvWTY6PggzE0+wkeXid7knMzc0BPHh17aOEENBqtQAAIyMjvfEmJqVvXs7Ozvjtt9+kI4CH1wRjYmKQk5NTYl+Pj384zcLCAo8+ndjY2LjU/oleVi96X3ycq6sr6tSpg9OnT+OPP/7AkiVLEBISgsDAQNy5c0d6ZfWj+5uJiQkKCgrK1A+VHx6ZU5m0adMGe/fuxd27dwEA8fHxcHNzAwDk5ORgz549AIBNmzbBw8Oj1Lb+/e9/49ixY9i8ebP0S+H27ds4ceIEjIyM0Lx5c/z++++4ceMGgAfX2N3c3FC/fn3cvXsXqampAIAffvgBtWrVgo2NDWrVqoV9+/YBALZv317eq0/00ijPffFxf/31F27cuIHGjRsjJSUFPj4+6NmzJywtLXHo0CEUFRWhbdu2+Omnn5CVlYX8/Hzs2rWrXNePyoZH5lQmjRs3xrBhw9C/f38UFhaiadOm+PzzzwEAlpaW2L17NxYvXgxHR0fMmTOn1LZsbW3x3//+F1988QVWrVqFoqIimJqawt/fHyEhITA3N0dkZCRGjBiBwsJC1KpVC7NmzYKZmRmio6MxY8YM5ObmwsrKCtHR0QCA+fPnY8KECVi0aJF0jZGoMirPfRF4cM187dq1MDY2Rn5+PsaPH48333wTwcHBGDNmDL7//nuYmprC1dUVN27cQHBwMAYMGICgoCBYWlqW6QY7Kn98axoREZHC8cicZDVv3jwkJycXG9+sWTOD7rAlovLBfbFy45E5ERGRwvEGOCIiIoVjmBMRESkcw5yIiEjhGOZEr6ATJ06gf//+UKvV8PPzw5AhQ3DhwgUAwODBg5GZmfnUNgydj4jkx7vZiV4xBQUFGDZsGP7zn/+gadOmAICtW7ciNDQUe/bswYEDBwxqx9D5iEh+PDInesXk5uYiKysLOTk50jh/f39MmTJFeu3lgAED8Pfff+Onn35C7969ERgYiPfeew+LFi0CAEyYMEFvvs6dO+PkyZNSew+HtVotpk2bBrVajcDAQISFheH+/fsvbmWJXhE8Mid6xVhZWWHs2LEYMmQIatasCVdXV7i5ueGDDz7A+++/j82bN2Pt2rWwsbHBuHHjMHfuXLz55ptIS0tDp06dEBISgjlz5kjz2draltjXiRMncPjwYfzwww9QqVSYP38+zp8/D1dX1xe4xkSVH8Oc6BU0aNAgBAcH48iRIzhy5AhiY2MRGxuLjRs3SvOoVCosX74c+/btw/bt23Hp0iUIIaR3zRuiYcOGMDY2RnBwMNq3bw9vb2++85pIBjzNTvSKOXbsGL7++mtUr14dnTp1wrhx4/D9999DpVLpXQfPyclBjx49cPr0abzzzjsYN24cTExMUNJzph4d//DtWZaWlti6dSvGjx8PY2NjjBo1CuvWrZN3BYleQTwyJ3rF2NraYtmyZWjRogVatWoFANBoNMjOzpaOpLVaLa5evYrs7GyMGjUKZmZmSEhIQEFBgfTqzYfzPWzz1KlTcHFxwaFDh6DRaAAAP/30E/7zn/9g9erVaN26NYQQOHXqVMWsOFElxjAnesXUq1cPS5cuRXR0NG7dugVzc3PUqFEDs2fPRv369dGtWzf0798fixcvxnvvvQcfHx+YmZmhYcOGaNCgAa5evYo6depI88XExGDMmDGYPn06NmzYgKZNm0p3yXt6euKXX36Bn58fLCwsYGVlhRkzZlTwJ0BU+fDZ7ERERArHa+ZEREQKxzAnIiJSOIY5ERGRwjHMiYiIFI5hTkREpHAMcyIiIoVjmBMRESnc/wMcZWLo+caXsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "status_plot = status_df.set_index('Status')\n",
    "\n",
    "status_plot.plot(kind='bar', figsize=(8,5))\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Probability Distribution of Customer Status for Good and Bad Loan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90626a2",
   "metadata": {},
   "source": [
    "The distribution provides insight into the project question: \n",
    "\n",
    "**\"Is the risk of loan default larger for first-time borrowers or repeat borrowers?\"**\n",
    "\n",
    "For context’s sake, a loan is said to be good when its servicing by the borrower occurs within the stipulated time given by the P2P platform. In this case, we can say the loan is “settled on time” while a loan is considered bad when the opposite scenarios happen. In this case, the loan was not settled loan on time. It is evident from the probabilities shown in the graph that borrowers at first-time loans are at a lower risk of defaulting on their loans than repeat borrowers are. This is because the likelihood that a loan will be good given that a it is a first-time loan is marginally higher than the likelihood that a loan will be good given that it is a repeat borrower. In a similar vein, the likelihood that a loan will default if the borrower is a first-time borrower is lower than the likelihood that the borrower would default if they are a recurring borrower. This outcome led us to reject the null hypothesis and accept the alternative hypothesis which states that the probability of a borrower defaulting when taking a first-time loan is lower than when he/she takes subsequent loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "1fda982d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 723,    1],\n",
       "       [   1, 2658]], dtype=int64)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c83c5fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Checking out the metrics: mae = 0.00059, mse = 0.00059 and r2 = 0.996'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Regression Metrics\n",
    "#Mean Absolute Error\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "#Mean Squared Error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "#R2 Score\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "f'Checking out the metrics: mae = {round(mae, 5)}, mse = {round(mse, 5)} and r2 = {round(r2, 3)}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d5f7cde6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a5cb51",
   "metadata": {},
   "source": [
    "## Key contributory factors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17fcf407",
   "metadata": {},
   "source": [
    "To answer the research question of what factors can influence the likelihood of defaulting in a P2P lending platform, the following hypothesis statement were stated using the threshold p-value of less than or equal to 0.05.\n",
    "\n",
    "**Null hypothesis:** There are no statistically significant factors that contributes to increase a borrower’s likelihood of defaulting on P2P lending platforms in Nigeria\n",
    "\n",
    "Acceptance and rejection of the hypothesis is based the p-value. A p-value less than or equal to 0.05 indicates that the null hypothesis will be rejected which alternatively means that the alternative hypothesis will be accepted. The vice-versa happens when the p-value is greater than the threshold of 0.05.\n",
    "\n",
    "To test the hypothesis, the data was run through an OLS regression model using the stats model’s library. The target variable “Default or not default” was set against the all of the features and fitted to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "022952de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'target~loannumber+loanamount+totaldue+termdays+interest+interest_rate+approved_year+approved_Month+approved_day+approved_hour+approved_minute+approved_second+creation_year+creation_Month+creation_day+creation_hour+creation_minute+creation_second+closed_year+closed_Month+closed_day+closed_hour+closed_minute+closed_second+firstdue_year+firstdue_Month+firstdue_day+firstdue_hour+firstdue_minute+firstdue_second+firstrepaid_year+firstrepaid_Month+firstrepaid_day+firstrepaid_hour+firstrepaid_minute+firstrepaid_second+response_time+proposed_paytime+first_paytime+final_paytime+age+bank_account_type_encode+bank_name_clients_encode+employment_status_clients_encode+level_of_education_clients_encode+first_time_returning_encode'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_name = 'target'\n",
    "\n",
    "X_name = list(X.columns)\n",
    "\n",
    "formular = y_name + \"~\" + \"+\".join(X_name)\n",
    "formular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "48499e63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ols_data = X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "43e17ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ols_data['target'] = y_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b502388c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>loannumber</th>\n",
       "      <th>loanamount</th>\n",
       "      <th>totaldue</th>\n",
       "      <th>termdays</th>\n",
       "      <th>interest</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>approved_year</th>\n",
       "      <th>approved_Month</th>\n",
       "      <th>approved_day</th>\n",
       "      <th>approved_hour</th>\n",
       "      <th>...</th>\n",
       "      <th>proposed_paytime</th>\n",
       "      <th>first_paytime</th>\n",
       "      <th>final_paytime</th>\n",
       "      <th>age</th>\n",
       "      <th>bank_account_type_encode</th>\n",
       "      <th>bank_name_clients_encode</th>\n",
       "      <th>employment_status_clients_encode</th>\n",
       "      <th>level_of_education_clients_encode</th>\n",
       "      <th>first_time_returning_encode</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loannumber  loanamount  totaldue  termdays  interest  interest_rate  \\\n",
       "0         1.0     10000.0   11500.0      15.0    1500.0           3.65   \n",
       "1         2.0     10000.0   11500.0      15.0    1500.0           3.65   \n",
       "2         1.0     10000.0   13000.0      30.0    3000.0           3.65   \n",
       "3         2.0     10000.0   13000.0      30.0    3000.0           3.65   \n",
       "4         3.0     20000.0   24500.0      30.0    4500.0           2.74   \n",
       "\n",
       "   approved_year  approved_Month  approved_day  approved_hour  ...  \\\n",
       "0           2017               7             2             19  ...   \n",
       "1           2017               7            27             19  ...   \n",
       "2           2016               5            13             15  ...   \n",
       "3           2016               6            15             11  ...   \n",
       "4           2017               5            23             14  ...   \n",
       "\n",
       "   proposed_paytime  first_paytime  final_paytime  age  \\\n",
       "0              15.0           22.0           22.0   27   \n",
       "1              30.0           30.0           30.0   27   \n",
       "2              30.0           31.0           32.0   36   \n",
       "3              29.0           30.0           30.0   36   \n",
       "4              29.0           29.0           29.0   37   \n",
       "\n",
       "   bank_account_type_encode  bank_name_clients_encode  \\\n",
       "0                         2                         6   \n",
       "1                         2                         6   \n",
       "2                         1                        11   \n",
       "3                         1                        11   \n",
       "4                         1                        11   \n",
       "\n",
       "   employment_status_clients_encode  level_of_education_clients_encode  \\\n",
       "0                                 1                                  2   \n",
       "1                                 1                                  2   \n",
       "2                                 1                                  0   \n",
       "3                                 1                                  0   \n",
       "4                                 1                                  0   \n",
       "\n",
       "   first_time_returning_encode  target  \n",
       "0                            0       0  \n",
       "1                            1       0  \n",
       "2                            0       0  \n",
       "3                            1       1  \n",
       "4                            1       1  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0a025d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4161fb31",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 target   R-squared:                       0.302\n",
      "Model:                            OLS   Adj. R-squared:                  0.301\n",
      "Method:                 Least Squares   F-statistic:                     221.2\n",
      "Date:                Sun, 28 Aug 2022   Prob (F-statistic):               0.00\n",
      "Time:                        17:56:36   Log-Likelihood:                -7617.2\n",
      "No. Observations:               22548   AIC:                         1.532e+04\n",
      "Df Residuals:                   22503   BIC:                         1.569e+04\n",
      "Df Model:                          44                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================================\n",
      "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Intercept                           -30.8732     19.177     -1.610      0.107     -68.461       6.714\n",
      "loannumber                           -0.0058      0.001     -4.072      0.000      -0.009      -0.003\n",
      "loanamount                        -4.355e+07   4.29e+07     -1.015      0.310   -1.28e+08    4.06e+07\n",
      "totaldue                           4.355e+07   4.29e+07      1.015      0.310   -4.06e+07    1.28e+08\n",
      "termdays                              0.0138      0.001     24.197      0.000       0.013       0.015\n",
      "interest                          -4.355e+07   4.29e+07     -1.015      0.310   -1.28e+08    4.06e+07\n",
      "interest_rate                        -0.0467      0.008     -5.729      0.000      -0.063      -0.031\n",
      "approved_year                     -4.451e+06   4.39e+06     -1.015      0.310    -1.3e+07    4.15e+06\n",
      "approved_Month                        1.3422      2.751      0.488      0.626      -4.049       6.734\n",
      "approved_day                          0.0518      0.091      0.572      0.568      -0.126       0.229\n",
      "approved_hour                         0.0020      0.004      0.507      0.612      -0.006       0.010\n",
      "approved_minute                       0.0002      0.000      0.470      0.638      -0.001       0.001\n",
      "approved_second                   -9.382e-05      0.000     -0.636      0.525      -0.000       0.000\n",
      "creation_year                      4.451e+06   4.39e+06      1.015      0.310   -4.15e+06     1.3e+07\n",
      "creation_Month                       -1.5953      2.750     -0.580      0.562      -6.986       3.796\n",
      "creation_day                         -0.0603      0.091     -0.666      0.505      -0.238       0.117\n",
      "creation_hour                        -0.0025      0.004     -0.649      0.516      -0.010       0.005\n",
      "creation_minute                      -0.0005      0.000     -1.140      0.254      -0.001       0.000\n",
      "creation_second                   -3.778e-05      0.000     -0.278      0.781      -0.000       0.000\n",
      "closed_year                          10.3987      4.191      2.481      0.013       2.185      18.613\n",
      "closed_Month                          0.8652      0.348      2.483      0.013       0.182       1.548\n",
      "closed_day                            0.0308      0.011      2.679      0.007       0.008       0.053\n",
      "closed_hour                          -0.0005      0.001     -0.607      0.544      -0.002       0.001\n",
      "closed_minute                       4.19e-05      0.000      0.294      0.769      -0.000       0.000\n",
      "closed_second                      9.972e-05      0.000      0.672      0.502      -0.000       0.000\n",
      "firstdue_year                        -4.7791      2.158     -2.214      0.027      -9.010      -0.548\n",
      "firstdue_Month                       -0.3897      0.180     -2.170      0.030      -0.742      -0.038\n",
      "firstdue_day                         -0.0136      0.006     -2.306      0.021      -0.025      -0.002\n",
      "firstdue_hour                         0.0051      0.001      5.586      0.000       0.003       0.007\n",
      "firstdue_minute                       0.0003      0.000      0.934      0.351      -0.000       0.001\n",
      "firstdue_second                       0.0004      0.000      1.048      0.295      -0.000       0.001\n",
      "firstrepaid_year                     -2.5598      4.355     -0.588      0.557     -11.097       5.977\n",
      "firstrepaid_Month                    -0.2192      0.362     -0.605      0.545      -0.929       0.491\n",
      "firstrepaid_day                      -0.0087      0.012     -0.731      0.465      -0.032       0.015\n",
      "firstrepaid_hour                     -0.0019      0.001     -2.053      0.040      -0.004   -8.39e-05\n",
      "firstrepaid_minute                    0.0002      0.000      1.680      0.093   -4.01e-05       0.001\n",
      "firstrepaid_second                    0.0003      0.000      1.946      0.052   -2.07e-06       0.001\n",
      "response_time                        -0.0041      0.005     -0.894      0.371      -0.013       0.005\n",
      "proposed_paytime                      0.0118      0.006      1.982      0.048       0.000       0.023\n",
      "first_paytime                         0.0065      0.012      0.540      0.589      -0.017       0.030\n",
      "final_paytime                        -0.0484      0.012     -4.199      0.000      -0.071      -0.026\n",
      "age                                   0.0014      0.000      3.765      0.000       0.001       0.002\n",
      "bank_account_type_encode              0.0041      0.005      0.805      0.421      -0.006       0.014\n",
      "bank_name_clients_encode           9.481e-05      0.000      0.193      0.847      -0.001       0.001\n",
      "employment_status_clients_encode     -0.0041      0.002     -1.760      0.079      -0.009       0.000\n",
      "level_of_education_clients_encode    -0.0004      0.002     -0.206      0.837      -0.005       0.004\n",
      "first_time_returning_encode          -0.0114      0.007     -1.636      0.102      -0.025       0.002\n",
      "==============================================================================\n",
      "Omnibus:                     4232.612   Durbin-Watson:                   1.856\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            72868.908\n",
      "Skew:                          -0.415   Prob(JB):                         0.00\n",
      "Kurtosis:                      11.768   Cond. No.                     2.72e+16\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 2.78e-20. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "model = ols(formula = formular, data = ols_data)\n",
    "fitted = model.fit()\n",
    "\n",
    "print(fitted.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cde697",
   "metadata": {},
   "source": [
    "The summary table of the model is shown above. The table shows the coefficients of each of the features, the standard error from calculating the coefficients, the t-statistics, p-value as well as the confidence interval. From the output generated, the p-value for each of the features were evaluated and compared with the threshold p-value. The result shows that ‘number of loans taken’, ‘length of contract’, ‘interest rate’, ‘duration it takes to pay loan after the first due date’, ‘proposed time duration to make the first payment or instalment’, ‘time duration it took the borrower to finish paying the loan’ and ‘age of the borrower as at the time of loan application’. Hence, the stated features are contributory factors to determine loan default or not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc52abac",
   "metadata": {},
   "source": [
    "# LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "18e29dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the LDA model\n",
    "lda_model = LinearDiscriminantAnalysis()\n",
    "lda_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "39144a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8744928811509227\n"
     ]
    }
   ],
   "source": [
    "#Define method to evaluate model\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "\n",
    "#evaluate model\n",
    "scores = cross_val_score(lda_model, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "print(np.mean(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "311276fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict which class the new observation belongs to\n",
    "X_test_lda = X_test#.drop(['actual', 'predictions'], axis = 1)\n",
    "y_pred_lda = lda_model.predict(X_test_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "bb47ce23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       1.00      0.40      0.58       724\n",
      "         Bad       0.86      1.00      0.93      2659\n",
      "\n",
      "    accuracy                           0.87      3383\n",
      "   macro avg       0.93      0.70      0.75      3383\n",
      "weighted avg       0.89      0.87      0.85      3383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lda, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "d0865afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 293,  431],\n",
       "       [   0, 2659]], dtype=int64)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "dbe0f1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Checking out the metrics: mae = 0.1274, mse = 0.1274 and r2 = 0.243'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LDA Metrics\n",
    "#Mean Absolute Error\n",
    "mae_lda = mean_absolute_error(y_test, y_pred_lda)\n",
    "\n",
    "#Mean Squared Error\n",
    "mse_lda = mean_squared_error(y_test, y_pred_lda)\n",
    "\n",
    "#R2 Score\n",
    "r2_lda = r2_score(y_test, y_pred_lda)\n",
    "\n",
    "f'Checking out the metrics: mae = {round(mae_lda, 5)}, mse = {round(mse_lda, 5)} and r2 = {round(r2_lda, 3)}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c85ebecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "clf = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "pred_clf = clf.predict(X_test_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f41d5feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Checking out the metrics: mae = 0.11587, mse = 0.11587 and r2 = 0.311'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RandomForestClassifier Metrics\n",
    "#Mean Absolute Error\n",
    "mae_clf = mean_absolute_error(y_test, pred_clf)\n",
    "\n",
    "#Mean Squared Error\n",
    "mse_clf = mean_squared_error(y_test, pred_clf)\n",
    "\n",
    "#R2 Score\n",
    "r2_clf = r2_score(y_test, pred_clf)\n",
    "\n",
    "f'Checking out the metrics: mae = {round(mae_clf, 5)}, mse = {round(mse_clf, 5)} and r2 = {round(r2_clf, 3)}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5223a822",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       1.00      1.00      1.00       724\n",
      "         Bad       1.00      1.00      1.00      2659\n",
      "\n",
      "    accuracy                           1.00      3383\n",
      "   macro avg       1.00      1.00      1.00      3383\n",
      "weighted avg       1.00      1.00      1.00      3383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "target_names = ['Good','Bad'] # target values\n",
    "\n",
    "# Print classification report after a train/test split:\n",
    "print(classification_report(y_test, y_pred, target_names = target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "02534204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       1.00      0.46      0.63       724\n",
      "         Bad       0.87      1.00      0.93      2659\n",
      "\n",
      "    accuracy                           0.88      3383\n",
      "   macro avg       0.94      0.73      0.78      3383\n",
      "weighted avg       0.90      0.88      0.87      3383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_clf, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "ccc2d4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 332,  392],\n",
       "       [   0, 2659]], dtype=int64)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694ffd9f",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "519d2256",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "014c32de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e3dea3da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_model = XGBClassifier()\n",
    "xg_model.fit(X_train, y_train)\n",
    "# plot feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812d861c",
   "metadata": {},
   "source": [
    "To delve more in-depth into contributory factors that increase a borrower’s likelihood of defaulting on P2P lending platforms in Nigeria, the XGBoost feature importance class was explored. Feature Importance refers to techniques that calculate a score for all the input features for a given model — the scores simply represent the “importance” of each feature. A higher score means that the specific feature will have a larger effect on the model that is being used to predict a certain variable.\n",
    "\t\t\t\t\n",
    "The figure below shows the order of importance from the highest to the lowest. From the plot, the feature with the most contribution to the target variable is the time duration it takes to pay off the loan, followed by the length of contract.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "9fe8df2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(15,6))\n",
    "plot_importance(xg_model, height=0.5)\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "690a5708",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_xgb = xg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "db49e878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       1.00      1.00      1.00       724\n",
      "         Bad       1.00      1.00      1.00      2659\n",
      "\n",
      "    accuracy                           1.00      3383\n",
      "   macro avg       1.00      1.00      1.00      3383\n",
      "weighted avg       1.00      1.00      1.00      3383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_xgb, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "626c0c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 724,    0],\n",
       "       [   0, 2659]], dtype=int64)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1748dd2b",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "975cfa06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as ltb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "88e0c64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4ff21f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lgm = ltb.LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "470184d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lgm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "a93af809",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lgm = model_lgm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "0c63c62e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       1.00      1.00      1.00       724\n",
      "         Bad       1.00      1.00      1.00      2659\n",
      "\n",
      "    accuracy                           1.00      3383\n",
      "   macro avg       1.00      1.00      1.00      3383\n",
      "weighted avg       1.00      1.00      1.00      3383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_lgm, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "05073907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 724,    0],\n",
       "       [   1, 2658]], dtype=int64)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred_lgm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65651286",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "4a523ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1d42381b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_svm = svm.SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "f0988639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "0c3eef25",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_svm = model_lgm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e63dd30b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       1.00      1.00      1.00       724\n",
      "         Bad       1.00      1.00      1.00      2659\n",
      "\n",
      "    accuracy                           1.00      3383\n",
      "   macro avg       1.00      1.00      1.00      3383\n",
      "weighted avg       1.00      1.00      1.00      3383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_svm, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde4fe27",
   "metadata": {},
   "source": [
    "<a id='evaluate'></a><br>\n",
    "## 5. Model Evaluation\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecee4354",
   "metadata": {},
   "source": [
    "To further evaluate the error made by our choice ML model, the Root Mean Squared error was looked into. A common metric for determining how accurately a model predicts quantitative data is the Root Mean Square Error (RMSE). RMSE is defined as the square root of the mean of the square of all errors. Mathematically, it is represented as:\n",
    "\n",
    "RMSE=√(∑_(i = 1)^n▒〖(y ̂_i-y_i )^2/n  〗)\n",
    "\n",
    "where y_i are the observations, y ̂ represent the predicted values of a variable, and n the number of observations available for analysis. As a prominent statistic used to test a model's inaccuracy in making predictions, it reveals the model's absolute fit to the data. Additionally, it offers the average model prediction error in units of the relevant variable. Lower values are preferable because RMSE scores are negatively oriented.\n",
    "\n",
    "For our study, the RMSE score for the train and test set was calculated and the results are shown in the chart below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "70f195c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(model, x, y):\n",
    "    # your code here\n",
    "    y_pred = model.predict(x)\n",
    "    rmse = np.sqrt(mean_squared_error(y_pred, y))\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "7f8b4cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [lr, lda_model, clf, xg_model, model_lgm, model_svm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "d849eb0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_train = [test_model(model, X_train, y_train) for model in models]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e9183dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.03148635507168932,\n",
       " 0.35306418363629344,\n",
       " 0.3421056328430716,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.20972942951079135]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "03497c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_test = [test_model(model, X_test, y_test) for model in models]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "3d455b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.024314424738609272,\n",
       " 0.35693376760209455,\n",
       " 0.3404019463405298,\n",
       " 0.0,\n",
       " 0.017192894613320565,\n",
       " 0.23946931178303613]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "bce8ec15",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_df = pd.DataFrame({\n",
    "            'Model': ['Logistic R', 'LDA', 'Random Forest', 'XGBoost', 'LightGBM', 'SVM'],\n",
    "            'Train_RMSE' : eval_train,\n",
    "             'Test_RMSE': eval_test}).set_index('Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "a7b432b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Train_RMSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic R</th>\n",
       "      <td>0.031486</td>\n",
       "      <td>0.024314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.353064</td>\n",
       "      <td>0.356934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.342106</td>\n",
       "      <td>0.340402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LightGBM</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.209729</td>\n",
       "      <td>0.239469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Train_RMSE  Test_RMSE\n",
       "Model                               \n",
       "Logistic R       0.031486   0.024314\n",
       "LDA              0.353064   0.356934\n",
       "Random Forest    0.342106   0.340402\n",
       "XGBoost          0.000000   0.000000\n",
       "LightGBM         0.000000   0.017193\n",
       "SVM              0.209729   0.239469"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "b217d705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_df.plot()\n",
    "plt.xticks = models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5810c086",
   "metadata": {},
   "source": [
    "Our results revealed that the XGBoost classifier performed better, with the train and test RMSE scores at 0. This indicate that the model is able to accurately predict if a loan is good or bad. The Logistic Regression and LightGBM model had rather low train and test RMSE scores as seen in the graph. However, the Logistic regression model has a smaller error rate between the train and test set compared to the LightGBM model making it a more preferred model.\n",
    "\n",
    "On the other hand, compared to its counterpart models, the LDA and Random Forest models had the worst-performance with relatively high RMSE scores for both the train and test sets. The SVM is the model that had the highest error rate between the train and test set when compared to other models. Even though it has an accuracy of 1.0, this wide margin indicates that there was overfitting meaning the model might not be able to correctly classify the outcome variable correctly when exposed to unseen data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece85be4",
   "metadata": {},
   "source": [
    "<a id='conclude'></a><br>\n",
    "## 6. Conclusion\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f21013",
   "metadata": {},
   "source": [
    "The study reveals that ‘number of loans taken’, ‘length of contract’, ‘interest rate’, ‘duration it takes to pay loan after the first due date’, ‘proposed time duration to make the first payment or instalment’, ‘time duration it took the borrower to finish paying the loan’ and ‘age of the borrower as at the time of loan application’ are all contributory factors that influences the default rate of borrowers in a P2P setting. Findings from the research also revealed that the probability of a borrower defaulting when taking a first-time loan is lower than when he/she takes subsequent loans Also, the XGBoost classification model will be the model of interest for this study given that it does well in predicting our target variables with high accuracy and very minimal errors. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c785e691",
   "metadata": {},
   "source": [
    "<a id='recommend'></a><br>\n",
    "## 7. Recommendation\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc145c3",
   "metadata": {},
   "source": [
    "Having broadly discussed the P2P lending and the adoption of AI models in reducing the credit risk, the following recommendations are hereby proffered;\n",
    "\n",
    "1.\tThe P2P lending platforms in the country should always be ready to adopt hybrid models that contains a combination of the best performing models used on the data for minimizing the credit risk. In order to avoid loan defaults by the borrowers either due to delay in payment or the lack of payment, there is a need for FinTech institutions in the country to stay updated. These platforms should be willing to learn from countries that have adopted the P2P lending where the marketplace is huge and prospers with little or no hitches. Examples of some of these countries are the UK and the US where P2P lending has expanded to some certain level. Learning from them and adopting their techniques can go a long way in minimizing credit risk.\n",
    "\n",
    "2.\tAnother method which can be helpful in reducing this risk after the adoption of AI models is to have an effective regulatory framework for P2P lending in the country. Where there are regulatory frameworks which holds the borrowers accountable in cases of default, then risk is minimized to some certain extent. The regulatory framework will assist AI models in minimising this risk.\n",
    "\n",
    "3.\tSince these P2P lending platforms are the ones in charge of using AI models to determine the worthiness of the borrowers, then they should in all cases serve as advisors to investors and lenders. The investors should be provided with all necessary information of the borrowers in order to determine whether or not a borrower is worthy of the investment. This strategy would as a matter of fact work better where these platforms are given the full capacity to make the decisions on who is appropriate or not.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1c007d",
   "metadata": {},
   "source": [
    "<a id='reference'></a><br>\n",
    "## 8. Reference\n",
    "[Back to Table of Contents](#Table_Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82cedce",
   "metadata": {},
   "source": [
    "Odegua, R. (2020) Predicting bank loan default with extreme gradient boosting. arXiv preprint arXiv:2002.02011."
   ]
  }
 ],
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