Telco Customer Churn.ipynb

{
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    "churn.png": {
     "image/png": 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jbBQfS5dwhvuR+px+6c2ReRvX9dk8bidCyAEANoSxkAxsq3IiEY4Mjw527jt04tjTL9RPdPdqnwVod23to0ztDq5QEjXTxcKS1xgAuN5jJyVVVY95y0piCGNOVZS4KitxVZYTcjwejo2ND411dHUcf3bbvhN/eKmdqETrXqiU66Btt6RxGRSdHQ10yEzPjUV6O//9T/ywZN2D99QWLZhfJEWjEw5/3pbkaaZpxAPB8HhH1yhnt3He0hKXLIqqGIkqQIjCUFgsd4h2IVhB6XT9zNZtmtog3AUlwvq7Hiu+5JNfWL306juvqN14zeaC6gVrXf7CYl6w20PDfbHB5pMRVZHpU07abdK6TEauU66NfFq/Kpat56//s3+tWLD5xtvySir/QrA7b0QY5537dwhxvOD0FlfUlCxcVUkUOTjW1RJSZEn7xErWuIGp/mg/2r6x7C33DHyeCGuawWtUkX31PbdX1WzZuNFVWLCaE4RyVZbtciLBBXr6gp37jwy07943IcXjqZ9LLY4EAEQBIAwAoeSfweQnAgAxakD1FgkxG+fQtJ1PqhCnze1y115xWWXF6pXLBJejjBDIj41P4L4Tp4M9h45HpFhMSP5buu3xZLtDmvan/h5J/v8iRcDpYgNGp07T5n7dp+/hNzz6wCWCy3UHwmgZUQlPFCWOeX455rmMLvQm04+AIopSZHR8YqixpaP53Z2nOj480K4qSlDTL+38RKn5MSJnljqZTQ40ek1ymx76Rt7CzTducuYV3MDbHddijl8ICNk1MTo1ONzXenbvO0dOvPH7jsjYENEoLFVDVNo1GQSAQLK/qTWZmnuavGervKYJg2u++m1H7SVXrbG78z6NeeGTCKGKdF8QHOjuOvT84zub97zerypyql9Sst2pPmhtLZTsb4xar2bn8qIjLD11xSfJypZ08RzJuEfqdCkPzt1Xcmv8aj3CClNGwSIsSScIz1ooiLGD49v+/R/z7V5vvhiJCvFg0BabCDo4m1AoOB2lUjTmlGJxpxyLu6VEwqVIkiM2NoHDQ8OqKss8IZO7MUJIjk0EwsG+gTGKbLVkFaYWgO4iuO5b3+QWXnNlKWcT5hNVRYlQuPf0y2/2H/z1k7IRYX3+zWcvc/p9f4swvhqyuRNnAFVRlPGOrva9P/n12x17D3VoFncg+fewlrC2PPK/XQsuu2E+JwgkMjbc9fxf399rkrC0SsAsaU0bk9LFa7hrv/rted7iyod5m+1OhLmVoH8fjYjRUKB5zxsfHnjmh6fjoYBKEVY82a8Ig6AjmvWYYBwOzYawptnZvf/6rLewdsndHMc/hDDeAuz3oIxzE1XtOXXg6Ee//e7e0c7msKZfUcrWtOs1qqO0ZnvQdd5jWFOD+Jnnf+P3lBRdiRBejDDMV1UVE0XBqqJwqqxwqixziiQLiijZFEkSVEmyKZIsqLIsqIoiEEXhCCEcIQQhQAQQIkRVEqGB4f6B0/UtHR8dPBsPhjClgjDjQygXghgR+T0//0978aK6JVgQNiEEdTCZAM1LVMIRVcWqovAA4AGEfERVOaKomKgKp6oqR1QVy3FRkWIxiajquXtnCKmqJMVjE8HRUP9gV9vuvSf7TpyJKZKkbTcdeOcYxokeeuYXbv+8qhswxncDwHwAQJwtv3vdQ/e85CrK377r334UYZAWueM//7nU7vU8jDC+MtdkBQCAOY4rrKtdsOGhR64Yae4eCY8MxGHmcb8AAPIn/uqHi6vXbvksx9sWIYTA6Svo/pOnDn440t709ra/+0wvg4hYH/pwwkyMDwEAuu1bP1trd3n+HCF8CyDkS7embS6Pb8GmGzZFxoZjh194vCkVxqPceVZoQp2jmM9UnwqqF+K7vv3bOoc77wsI43sBobqMvghhXDx/2YLaS65qDwx0tciJuELFIOmPqtOvj13QffL1/zO/sPvnVd2EML4XAawHQCWAoCgXlzeIqqpSPB6af8Vla1fefVtLy/ZdO08898pJjbGngtl6frXurnbdt76JF91w1QLeZnsMIbQlOfFFkNn7wXQqRJJj8UDNlo1reo+e3Hnwiaf3hAYGY5q2p/5kLQRy8z9/y+arrLgZc9w/AcCiVHwSIXSp4HSuWXTtVvfgmeZXG15/J6Fd1BVrVgrFSxfehDn+RjD3wDnLCeJQUcW6JZd+6n9t2PGTv9+hUdSpj3T1l/9xUfXqTf9LsDuvSa1NBPgyjLmtBfMWepZd98knGt7fFjdxWqWaIK1pZHXb3/3cWbXy0rswL3wRIbQFTN/yR8jhzS9YsPnG1YPNJ4a6T+4bgZmZOGWdOGqujXpan+777h+u5gT7XyKELk96KhnD7vH5ajdcs7T39MH+geYTY9ThCU1QOXP9LmTQHQEAfGn7S4XukqKvYIz/HCF0FSBUBghcOfslCCFOEBw2j7vAXVRQVbR4QYWvsnyic++hnjQLxHBQL/nsp9Cqe25fKDgc30UY3QcILU66qDkle4Qxx9lsLofXU+qvqVpQvGSh0n3waIsUi0smYm3oks9+yu8pLvxrhNCVMP2uEEYIFXE22+q8suLDZ3d9NCzHE1Onp1f/1Z8uza+tfowT+PUwhxf/SNwJKObjXf4ib3R8pHe0qyWgdYM2Pfj1hUuuvvOrdpfnSkBImLZ+EPIAQAJh7lTrR28F0pz+mjsJ1fy3R3+5o6yoZsnXMcf/KZrMusFnuvZsTo8bc1yk+8TeHlWR6XtXRgH3XJHXVJ8++S9Puq7+0j88xvHC3ybJyjEbu3Lk5fvCowO9w2fPjKqKLGn6IGpidUb9ylled3w+yOrTv/+ZTXA57kMIfRkAlsHs3qiZcUEc7qLCFQuuueLuTX/y2VVw7t0T640hr+MyIgBAy2+7MU9w2H+UTCqYN/f0jrDgdJZWrlv1qUsfe2iDpt0Oo/YTRbEDQvP0CRHVFtTW/HXB/Jq85Pc4fZXlXn915RWcYLt8TjcvgoBE3AAEI2deYcnqWx663O71eVLtqLvs+polV93xebs7bwsgpHOFghRgjIvAfLoaM4+K0RefPLjalV/8b4jjvpbM256VTfB2h6Nk0eq6eeuuLNEYJn0pVs8NnA1ZTTs9ffSXuyrKFq35Nub4vwOE1uTCxnmb3VF36XUr8yvne+AC3yWb86v0hXW1KL+megNC6AGYTIh2Xp4DIYR4V75/Re3ll20tXrKwENjJ5AxTfRQumI+95WX3AkLX5tL9MyV9BcG36LqtDwsOh5tqu4PV9oH6prgiijuNFj1nE6646f/+zV8XLarz5ddU52395lev8pYVP4pw2ljN7Pgq5gSQhNRmwhfULF669QvfupK3O93ly9aXb7zvKw+5C0o2IV2yApDFRGC4vSEB7LQmqfFgZbaYYVz5lfPRA9/b5vnys8c/ZXO4foEQvg8ACmdpdMhXWlVdu+GqOpvLw6eJnUGu41VbPvNN/k+eOrTRlV/0c4TxVwCgIpe2VjR/2ZKqNZsrOZtdMNjg9bIz5OyZ1Vy6hKlEcC5nvu9BhPEDc62sWKTF2wQnUdXergNHhnQC7LpY/9C9QuXalT80Ui5zupvwgtvmdnV27j88APoPggkAoJ7Dx6W6qy8fceX7r0EIFegMCCc4nSsX33D1uiU3X7upqK72M5xNqJ3T3VHFQII+APnc1HO8YPMUlhflV9Xh6jWbF5cuWnU55gX98AAhcmik/6OPfvvdvWI0hIGdxYIViJ82RgAAq299mLv6S/9YkFda+RjHC/+UdPFz4t4jzHGcIEBwqGdgvLc9SAXctc9YJGDfC8zmTiDc/+8vOGo3XH0Db7P/C0Lo6iR553ROEcY4v3pBUe+pA62R8ZEosO87GvUrJ/GsOXcJ3YUFlYDQKpiD0yczENyuiryKslrMcakULmZS0GAAwItvuLoWEFoGFwgII1vpyqUbYPrjaPrxsA0AeEWS8NEnn2+Ljk/8ByFk3IDE7Xav5wp3YcFdWOAr5lrKk5hrSl1NC+a6vQWLLv/E7fPWXnETJ9g8Rt8hxiLtfWcOnQkN99kgfcJD3WyXlz3wp/zGe79c684v/v8wx38HJvOY5bT/eaXVVVWrNtXY3XmpkmC0q2qUsjvjtnzmZ++5CmsW3cXxwncBoU1zadOegtKypdfctZQTBNYJr154Jad5zuaqc+icSuAqUAaVR+YinmX3uEtcRQV+mJ4tQC/v1NSA2zyulXDhHogDQgi7CvJTCfDSGmrzOzvltl0ffSBFok8DITG40FA4gLgDQMV6uzaHMDYcX1VR4oH+ztPHXv1Ne7Lvbh3SNkwet/aOR/nVtz683O7x/VXSZZoTF58TbPaa9VtXz1t7eTlC2GbCoLNJ4DdZIOSHrzk8hSU3JeNV52VjnX/pdeuL5i8toMiK7iOfYSzx4lFYBEgeZFmlI2eLyG7LdxXkF8L0ZHksw5+2+2GOq7uQhAUIYZvLWQTnLtB6DGI4PABwu/79xyODDc2vyKJ4GM5vlZ2Z6iruBCJlHwUgqqpERgeaz7z3/MFAfxcP7FzirJxn004PvcUVeP3dn18sON3/gDD+4lzHIz3F5RXVa7bUufyFbs3a0kszk6kSQQAAt33rcT6vpHIDxtxfAsDy8zWn7vzi4nV3fm4Db3OkNgzWAYhREZVZqay5IKzp770IOAEh94U0HMxxbt5u0yaRM8pOem7RTz5fuKA5w4iqYk2b6dv+zBPDfT/7TUugp/9ZRZLbL1jDZR4gbp9UWdn0mxA1Fhzra9r92odn3nt+JM28GbmC6Ka/+F6xw+P7BkLo7vOy3jDHVa3etLJs6bpyzPFag7YbGLYZNxEBAMzfeA0qW7ymCvO2bwBCm89vmALjyuUbVy7YclMtzDwMsjNcRT1Szoq05tQYb/g/f+nAHK660ArL7vXkuwsLCillpRfHmlpACOGSC05YhGBgZ69kZbDEAIAH65vkY89u+zAyMvoqUZTRC9LuhB2IZMu607HA6HD9ey/sOvrSr9thepZS1gajGzNZe/sj9qL5y76GMPep89l/T2FZ2ZKr71zl8hd5KaKygf4JtVECw6n/vfSauwTB5X4UIXTbBbEnT55v1c0PXJZXWu2jiFgvbXLOyuzNpTGiqg3rqhDHb4YLnMbGVZBf7KuuLME872aQlf4AI/DDBc4vjhDi/fOqCqh2s65mTFsYDa+9E2zd/sEbiUj0CBAinddGSwJA3Jm1ugqN9A+ceO13u45s+2WbFI/SOcQFYKdVZiqSjfd/5X6O5x9LqrPzq0RWbFxVt+mGWswLmSisdIaN5q294lKMucfARNofoqpi7vvGcQXVC+evu/PRNYLT5cqAsGatsnJNWFONuOJPv2izez2rMIcvv9CxX8HldFeuW7WgoHZeQRoZOzXAV/3l/6pAGNeyxkiKxYciI6NnZFEMnIeFz+WVleZr2pquAMDUAjjy++d6Bk43bpNFqfv8SSsEJOEAImanrkY6Gjv3P/2D3SfeeLJLFuM8zKwiRJOUXuI49OAPX79EcLi+CIAKL8S6szndntW3PHhlXnGFj1pvNPkaKcWZMVmb/aswec9KF9HA6LHuEx89IcWjQ3PRN97udNWs37p26VV3LkijsvRy9oPOJnPeCGvazrb4xmsqebvtYQAoYYY4RHFiort3nyJK4Vw1IDQwFNZRKah0xdKF5auXl2OeZxEV7RLyNZs2Xo0QLmYNZnRsvK39wwPvJ0LhWS8GRZLVsfbOidGz7eOKJCkzCQAQ4lCqraxTMb0CGyQeDCn7H//NoWBf/9vJtC7nIXYlGJ4M6iERCcUadmw7tufX/+9A60dvDStSggf9UmAk3WfhlptteUXlDyPMrQCDgxNZTAwHBro/kMXEnLjOeSVVlZsf/vMtmvljuYdGdQemff7k6cOXI4S26tkuIUQc62594ciLv/zlkW2/2sfxwpwoS4QQcheUli67/p7LatZvrWBspmaVVkakheeCrO74/ndcDn/eLQjjG/R+x0RXz6HDv332NTEaHc/VILbu2NMXGR6JMv1ut8u99JbrVxfU1RTA9DQ29OIRytes9LgK/bfp3QCPjQdGB043DqmSLM/avhMJuXPf4aH2PfsH5VhcnslXBBGV8JrdyqioAZ3tUh1uPhs58vvnXk4EQ8eTxQbmVl2JtozUlSzG5Z6T+7p2/uz/fLTv999v7G84GlYVhdNRjdqHt3rZAggAkMse/LMtWBA2g04qFUKIHAuMHW3Yse1Hncf2fKgqUmJODBtjrmrN5nXr7/5C6i6iM6OwBJV0kRPsnwSAfJ0+SePdZ1858MyPXjn9zjMD89ZduQRxfNbvdeOhiUQiHNB1KTHH8QVVCxauuvnTl+ZXzC+gPBfWq4x0T6fSklauY0to2a03cOWrl6/lBOHvdReLqsqt23d/ONbeGSGKmpMbsERVScMb7w4SVYV1D96zCGGM6C2hdPmShWvuu3Nw70+fmIiNT8RZBCC4nOrVf/nV2zmbbS1rfKRYbHCkta0tHgwqyYD4bKPqIEVjqpxIqIQQvX+jzTbBKqnEKhWVMmzc/O7OQZvL9f2Nn3uQc+b7NiCM7dTYqXIiEU+EozGnPy+PE4Ts7iLIPEDUxayErkiiQlSFAACoiqyGRwcjvWcODbYfeL9/6OzpCTEWTRBVkamFSxcU0d6o1j6+lTQkpiy//l6np6D4DoS5RSwjIEQVg4N92w8++6Nn2g6+H73hG/92G+aErE+yFUlUVUVWBYeLZ7uGHs/aOx65SYpHImfee+GUKkt0uhm9pIzTHro//OO3yvHkMzGHDsGcavnozQ/aD+0MEVVxlS1evTJZOCObhQmDrafGRztbQouvvLXSU1jKJD5OsNmr1my67MrP/62y/5kfvjfcVi8TVdVuKCLMvMlPp0bSZk4xTImdC8KatiAu/9oX5ttczl/DZPoV5s42erZje8uOPb2Cw+5jru4sMN7ZE4qNB5STL7zaV7F2ZUHpiqWFCCFEB0KX3HzdpsGGps76V94OJfNXaZMH2jY88sB8X2XF/ZjjylhtD/UPnT7z8htn3IWFRTnMnoEM1g0A+6KhXlyAAJVWRZUV8eQLr3YONTR9+5q/+cZjvqqKqwCAJ4RgVRTlQP/A4KkXXjuVV17qX33/XVdkRVhpYlfHXnnibF/D0YAqJuSJga5YZGwoDvq5lOj0LKmMAKnMB9rEd3RSQ2X1LQ+tw7ywSicoTcRIqKl596tvNO95fVxwuv3+8tqlnGDzZklWSl/94ZHoxEhi8ZW3ViPMMe9OOb35hevv+vzNcjweadn7drOciEmUUafNKOL0FW5IqivEMCwlNNTb0LT79QGiKnbM8U5fWfUihFFWm6oYjUhDLacCR19+osflL7Qt3HJzFW+zM8mP4232qtWbrtzCC3DouZ++3d94TE5mdaAJy+gJkinSyqXCQo9s++9Ch9/3r8lX7yyo0bHx0yeef+XtYG+/VFBXw5Mc2XwiGBKJqkJ4aETc//PftVz7t9+we8tLPDRpcQIvbPzcg9ePt3cP9R47GdEGPktXLM1feO2V9/IO+xLGglATwVBb2559e4YaW6O1Wwo4IBckYyt9CU/vwal2AcgAIA6caRp//ovf+EX1xnVvuYsLy5SE5Brv6FIHzjSKAODY+NhDa7L3bZPqSgeR8WGx99SBgKrIenmUtOSlVVDa3OExYOcPn0pr4vQVgrugZD3G3ALmAlSUSGCg+9jBP/ykDQDc1as3V9qc7kKEsjPseGhCPP3Ocz2EKKRsydp8X9k8r17Qx11YVrXx/q/cLjhdrzTveaMxHppI9dMBxu8K1Uli4FeDTik5RRLHAgNdfaGhXhUAnEXzlxYKTrcfAGW1RgMDXeGeUwfG5URMOfnGk92+0mpn6eLVRZjjMdv15fiK5ZdceekDX1NPv/Pcjo7DO9qkeCxVeJXOpAomSWvGv8M5MCAAAPTwc7/2e0qLv4IQuokpw1UixoOhM6defO33TW+9368qCjfp6uRGYYWHR+OqoqhEVdX+U2cCh554qjHY2x8kDD/LXVRYsvnLj27Nqyx3p1wsd3Ghe8OjD1zrKS66EmFMu7JEFqWxgTNNu449/UITAHAkh203pc/NKzPajVI0Bp2Q44lY+579vae3vdHU8Ma7rQNnGgdSxo85LCGEMt9CUurK8FY7otOp6BEVTVD0J0oRlzYHk7r8untcvM2xEBBixnkUMT7SfWLviVTsqHTRqmre4cwqbRAhhMSCY/G+hsPBvjOHg6173+kTIyHRIFCNPcUVdWvv/Nyd6+56bK2/vMYH5y4Apyu6ygHGiwDYOeSkRGwsMNA1kfqO+RuvqcOYz+qolqgKCQ71Rkc7miIAQIbb6kPHX/vt2Ym+jgmi6odwJklrw3WXffpP719/9xfXuPyFHjC+WGpU0TznLuHUF3/qtz92+CrK78cc9zXWgBKVJGLjE0dOv/LWU8eeeqFXkSTXpNHn8JQSnQs2y/GE0rJj9yAWeGXdg/cs9lVV5GuVFkIIlyxfvOrSxx7s/uA/frJDlWXbsltuqCtbsexm3mGvYOzKsdGzHTsO/ur3e+KB0OS4EYLJ+VBYiElYCPRr3RHKLSSazYmVLnpqmjDPi5ANYSkckKjLLPHSRMVKBBeFmRVZUnnEtUSWUllT7lXtJVtLWe78lBCUxEj7oR39KZLwV9RWCnZHVoSlypI60tYQiAfHJQAgTR+82u8rrbbXbriqirc7eT3S8haVL1x+3T1Of0VtQePOl/d2n9jXKSdiio7CmKrliACV6bi5IIvxSHCwN5YirMKaJfMwx2VFWIlIWBztbA4moqEpV73twPvDDq8f1tz22YX5VQuKaM9FC19Z9ZpVn/h0UUH1gqKu4x8dOfPuc01gcP1EZ40w3cOcxLAK62qvxxz3p6y4FVHVcHRs4kDT2++/ePR3f2iXE6Lr3M5BUA6NftruLUVjcvO7O3s5gRfX3H/X8rzK8mkxLU4QbPOv2Hz5WFtn98Dpxp66q7ZsdfrzFrPiVhPdfXv2/eyJNwfrm+LJHZEjJKfqyjj9BkJ6x/egI5/pSdcjuWn1AjlBiGeusBCQuJOZkYFiWFYBVdrlS2hUVJxBWDFGDGtauhZ3QUkJwrhMRxEpUiwyMdhyKgEAbofH53b5i0owb8sqk4giJZSuE3tHU2M43tMWOfnWU+02lxtXrNhYztscuoPi8Pora9Zdcbu/rKaqcvnGXcdf/+/D4ZGB1LzSxVqUtXc84iFA7AYxp8Bwe0M4pV7ySiorEcdlZd/x8ERi6OyZAKXSlfrtL3ZLsWhkze2PLC6pW16FOE5XcNjdeZW1G65+qGj+spWVKzbuat7zxuGOw7t6weAGP0xPb63ddKdIK1vCmvolX97x0jJO4L8ICGacyBBFDYVHRrefeeWtF48/s21AiscdQB1Xo9zmfZ7mComRqNj45vZOxHGJdZ/+5HpPSbH2AiGye90FK+665brKdavbCubXXJKsgTgNsfGJpoO/fvKl7kPHwsldma5BN5chd5abZ5b8UkUvUZIgQCf+NUX0ScLK7MG0zE9mE03bRwQws5CqRAXSU8opwohXxRjB9mnuIAAQzubI0yseQVRViofGR1JGXbxgRaHdk+fPNn6lSJLae+rAhHbd9TccnTj84i8aVoYC4fkbr1kgON26JMMJdk9+9YItrvziqvJl65a17ntv+/FXnjiTjGJoFYZcNH9ZmZ47SFRVSoQDgYnedhEAHP6KWq8jz1+IEM7qhDARDojDZ8+EWK576753emPB8Yk1tz48Ublq02LB4dQle8zxzrziiktd/sI6b3FF6Xhv+3OB/s4J0E9TPqOwCq20ZqWwHnzqF27e4XgAELoCqOR8RFUjkdGxd44/s+3p489uG04OtvZIfrIxOdIp6JwKmTbAiXAk0fD6u22e4iK8/PabLrV7PV7NqSGfV1620F1UWCU4HTMWuZxIjDW9s+OVlvd2jcH0d3s4pzxLgKRxxVjpdM2SFtFRWtPcDQBQObstkalLSKKuyYA7ZES6rCKjUY26CusE2OMUUWmvAxAAIJjjBAB25lKiKmJ4dChFWHxB9cJCm9OTdfwqNNIfiQZGRZqI+xuOjkUnRgPh0YHxxVtvX+YpKMkHHRcKIcQ5vL75dvfyCm9xxcKSBctffed7f/EeZaicM6+wQo+wFEkMhUb6h2UxjgCAK1+yrpjjbbZs+yVGI2J0YkSE6cn4ZAAQiapKvWcOhiPjQ2Mrbrh/YMGm61d7isrLDAwT8zZHia+85rK6S689dOyV39QbBOBVSnzMCLxns7NMLXZfdcVVCKE7AcBPdToeHZt468jvn/vt8We3DTACbYgKxM6FwprKhpgIhaMnnnv59FBDc5MiydOCopjn7CyyAgDoOXzi3eNPb2uFmbmncE6fGKI0Ie1JEtG9n8PYrWiCU3RIQqtowrzdlpnCkvnJ9McmPUfKsCWqLdoafqlPGM4VWY0Cu7bktNM1g9AKEFURx3taB1Ibp6eoLF+wO7O7CU5UGO8+G9Qb40B/Z+Doy0/UH3jmRzuHOxrb0g4P5uxOX+Ga6tWb7196zV3zgXo36sovWooQ9jMD7vHI6NDZ070p5V+8cEU5J2Tn5qqSqEYnRuKa9TbNlgAgQVQ1Pt7TNnr4hcePHnj2x2/0nNq/X0rEIkbfizne7S4oLQH2pVLWsytmAD5rt+bBJx93czx3BSC0dEafY/GTB37x3z89+fwrQ8B+L5U0RFByaPi0gU5zO0IDQ8FDv3lmT2x8YghIepKMDI+ePf3S6yciI6MYZmYHQDkmWpKGuQhkWO3H4EROYiibKABEeLs9nonCIlF3Bg+cEWtuWOQZ1RAXi6gMq85gXihBCDGfgymyHG8/uKM7dTrlLa4oFJzuLBUWkFhwLK6z3iQAEBPhQKRlz+vtu3/1nXca3t/2rhgNp3sehXi7s7x69eYVcO5WvAMA7A63dxnCOJ8dcwoOt+x5sztl7L6yeSWYF+zZ9UsliphQNGRFr5spFz4emgg17369dc+v/9879e+98GpkbKibEMLc8BQxHh/rakmFVeinZqzEf8z8YJm6hFM/6K0oWwIIbZjpCpJ479ETvzrz6tuBpISlH66mjElGOMtjdHMxEtowE30nTodGz7Y3ugrySzBvfILStmffvr4TZ2KEEIdOwJrkrO3nAut60kQF47dzZuJeiBGU17pnsuB0xhE2qbAkAUjcYfpmB5r5O/WIk3YJE5p/q71MSl+yRHd/+79LMC8sZJ2kEaLK0bGhs8PtDREAcPkr53tcvkJ/uoynhn48UdO5uQlFlqSBpuOBid72gb6Goy3r7/zc1f6qumUGcTOcLGvmSsUZ80qrXIjjSxBCM0hIVeR4LDA6HJ0YUQDAhjDG7oKSIo7nbTmyJXquUhtLAgAkVZHlse7W4KHnfzrWfmhn/YZ7v7S1bMnazbzN7k7xBVEVKTQ62Nfy0VsjST6g546ua6gNvOfklBBhjqtGCC1gxH1adn33x0dg5qNirbqabCxCcg6N3sgFEpNSVoyMjg0RoipGiUSlWDw0cLpxNBEKp57E0Jf4SNJ1yh1hTQ+0Tt8BJgKJNCSVTn2pGqKV9RaL4LSLZueDRN1mY1d0G7VzQz+xiTM+M26yA7tOI/IWlxVihCt1AtNif/Px46qiYADA+ZV1eQ6vfxbVghDAzKKiMzZJAEgAIXI8NCE3f/BqYKStvnfd3Y+tr1m/davN5S2iiUuREtHuk/t6k4ZNAAD5K2o9SDd+lQiM9ZztTKmSmvVbS+wujzfbC6O83cn7K2o9mONBVWRt/2jSSmg2EZIIB0nfmYOht9rOvLDujs81LLv+nlsdXn85AkDRwFhP/fbnP5Li0RTnCDC9uKwE0++cKXCuEC7KxSnh5E5AANNejBSP9yTCYQzsh5ygWbQq5rAIGOUyjS99g3rGrWmEUCId0cixeFSKRIhmp9YaGwCAihBSIWdki3QVliorylBjS1AnbpUuBkiTHNL5DhUACG+3S0nXzRiiDUjCnuG9WURXadZzN7R3sujgusw4eJjahRHmBUDsd3aqIsfbD77fnBqDvNIqj83tnVWdScwJSNMeWUeNTF27UFVFHelsin7w82+/v+b2R/qWXHXHtZ6CklpOsLkAISwlYoH+hmP7Gne81K8xbNJ17MNwdGKk25FXEMEc59bEipV4KNDTvPv1htR/Kpq/tIC3O5yz6Zfdk2crmr/UNdR6Og7Tn0lNxbE0hDy1ARJCsBgN4wPP/uhQ/fsv1q+783NrOV4QOo7uaW8/+H6/hum1T8voWgrIKIbFZ7m1IKKqaOaVEQAxFO4jk6/tWQ9ziVb2IcyJuVJYCBDR3FdS9HbyRDA8DsT4eoAsSaIsSohyYRXQPizGOVSHyEx8y1TBTTMBefr0ZepnOJsgpw0pEjRZCUfms+kji7RY5aJYmRjo5x0zTj/FeCTsIkUj6JxLMdXq2MRofcfhD1Kn1eAtLPPaXJ6sFRZCCPnKqt3AfmJE3zFLES8BABBjEXzouZ8e6T19oHPVzQ9eUjR/6TKOF+x9DUeP7/399/amTgaT9qkCgNq465VdK2643+UuKF6GOd5NAECOR8e6ju99v7/h6Hgy3kV8pfP8nGCfFWG5fAXOypWX5g+1nh410TdtZfJUu7nQcJ+0+1ff+XD6IduME26aoNIWq+CzNSuiKBECEEQzXcLo5LMV3RxGU+oHc1jM+N5PeoMwJK3B+qaOlZ+8zTADZ3hwaDA6OpagXFgt4coIYzl58pUbaShJJJXNwIRSIjong+mUFmLFBVKjxzsdvNEN5pS6goQtm1dJrNNLOnahUDu6AjNLns9wBVO/4Oy+d/tW3/zgu4LLvRhhbhFCyAOEKFIi1t248+UXUj/n8Pg4b2llgWB3erNebhih/OoFfofXz8dDEwkwvmOWoFxxDABcX/2R/r76I28BwHsaW6H7hgGAO/H67ztb977z8+XX31vjL5tXohIVjXU2jx579bdd2riQp6gsnxPssyqpZ/f4HJXLNxS1fvhWV2ikPwrGV1K0D7dT7dUG0QXKu1Ipl35GfBwMnulkrbBCg8Mt+fOqDiKBX5Q8yQBVUcYG65tPqrKCGGRFnzxImOMFwCiXKQ9YJcGnpSXpPny8Nzo6djKvvPRKYAQ+pVg8ONzU2hkeHolRxqY1IGnywADnyp0l8UBQlOIJ2anv6uoZeybFKrWqC2vmBAAAbC5nESDkMFRXCQcQeVb1cGmVRb971CMqFdIUST347I8TmOPfqrv02jGbO28zQqhEFuNo+Gx94/HXf9eUVFdqYc1il7ewrHC2q82Zl++cf+m1hQ3vbwubOI3VKhGsUVDaDzZS05GxIenQcz9tBoCUa5siBBUAFF9ZNe/yF/qzfZJzztXlcfGCFcWLt95WfuKN3wflRJzokJa2QKyqUU0cRVh0rjaWajbFA1nHsPb//Lf9V/zZnzzp9PuDmOdWKqIUD/YPnjj61AsNRFU5yti0f59iaCzwQg6NnrWTK/SpYWx8Qjz5/KtPLrv9piG7x12hKoqqiJKMOeyWEyIZbmrtqn/93YbYeCCuWUAzjq0xx0u5PCUMDQzFE6GwCOWlmjg8IdGx8aAJkjJzgsgKxGsDmyrH8y5ktCYS9oxOBjM4JKEPAVjEbIqU9z/1X+H9T/3XHgA4lNxIU7UM3UklIueVVtkdefn+2XaAtzn5pVffWdN7+tBAcLA7msaotW4hrURYB1OEcchAn/oqmu/ClSsu9dtcXm8uJsfpK3AvvPzm+RN9HSPth3ZGzqUqm6GUZIbKSnlYrPTdrLtdCrDT65CcEVbb7n1K2+59hzd+7tPtxUsXzZ/o6lVPPPfyeGR4VEj505oBpU+IRACQsn5sm5nbRLsc8vFnt7X0nTjdV7piSZEUjaNYIIBsLpcvEQzxgw3NiXggqC2HTqhTEhEARMxhCeXwwGCktT3Se/TEoKek0Onw+ZwIIRQZGhk7+eJrJ3SUh9EkEx1lZRTvIoYBd5WbJCuZz9VcpZs7FfQfzBopR9CZ/ykj8RSV220ub/5sO4E5DhfNX1ay+paHavc//V+BpBIBYJ9Ya40TNAqJT/53uhAsbTN0+hn62Qoqmr/ULzhcebmYIIQwKqhaULz6loeXx4Ljwb76w+E0G45M9Y3T9IvOHEt7Pqz+zU0+rEO/eSYIAI1w7vYqTy0S0AzutOAqJ/ASQjm9gGk0oFOLlhAiDdY3jQ/WNwWSA2oHgHE4V0oqFRPgNAprWrAR87wECOes7fFAUDr5wmudQw0tww5/HsYcJ0eGR4NnP9jbwyAs2QRhEQOyot2pyX9vdEtcFIAkcqKuSBqSgQwIFxl8PyuFjcTbHHxeSaXX5nTlpDCF4HDZFm65aVEiEggde+U3p+VEXG8Naokr1XaJIi2e4T7RG66WsKbNva9sXn62mSeYhMwLfMmiVfO2fOYvyOEXHhe7jn8UVRWZPsGj+0dnB5E1biKi1KPMIC1DlcXniBhAJyYhw/Q0qNPiSZxNkNIpLEWSpXggGHT683yY53mTJGWktLQZLjE1wNpTQW2wMPUzUxkCsMCLuVNYk3d6Aj190UBPXwBmHvPLBidnikEsgBjM18wrDkhfXUEiJ+rKDBGZ+f9JmrlX9YLgeaVV7rzSqgKEZxfn0Z4WuvKL8pZff98ahHDi2MtPnJASMb2EiiyVpCUt+goQMEIBKhWDxACg+strbO7C0kLM8TktWszb7LbiBcvrLn/0r+zuV3+7vemDVxtkMcHKdksH1JGmf5zGdQUdEpd1iHnafONZLj5WrGHa8wSYmeto6qiXEwQJpbn3ExufGP3oR798Jzo2ETB5SpjuRIr2ven3bKxnInR6k8SkOsyJO2uGXOkxlUzIab1rDUZqh91AKYM3gxcexOhUq2j+UpunqLwyl78QIYzcBcX5K264b+OG+76y3JGX74Dp+cdoNcJ66sJKUsh6AJ6gTh8TABCvWb/V6/D6iyHLzBNpgvCcr3xe1WUPfv32RVfeWoMxJxiQLKuP2tRBdIog1sVg3Vgsn+WCMJK8WCNXecpvnaYOeIdDTHdKqCqKMtbeGVRlWZmFutIjAmAolJTfLVBtn0YYvM2eQBgrOVnwxu8g6SycrBQrRneVZgeVm1VR1BwpMXMu7fQxTLkj006si2qW2J15+fNz3WCEMHL5iwpW3/LgdZ7CUtuRbb/cM97bHieqQgfVgXHYoE37wwE7qR3rEnCKFPny5ZcU2F2eMlNTqigKAAGMOQzI3I14hDB25hUUXf7Z//0ARjjasOuVBlWWtOtO+3fCUEnIQOzI1J96says08voPazFDIKi//eUG8PbbYo5lZI+qwM691RC744Sy1VQNactqibORj/UVuggIWe3iTl9SzhzAvUyGiRAJ58540QJZktcROLNZhM1M4fnQ1nRz0mmjIW3O7C/cr5XcLhq034ZUVVVlhXM8RzCGJtkLcTbna7FV956o6+8xr/vyf98pa/+cIKoaiK5plLukUy5e7SvgAyUMH1HC9vdeaq/vKaME+wVZprZcXjnmYnejsCiKz6xwlNU5keYM6vKkN2TV7j5s3/xOcQLv6p/7/kzmmITWpWkwsyTTb2YJesdYU5jWMSABCQqqEb7t1qyILzdLkOu3uMhpitE0rC6QhGWqnEfWIQ1FfDm7TbJzLMioqgKIECGix7N8Ou1pZ9EHdeVvkVt9sQwA3WFAWIu3aKoqqKomOMwXDxgkdZUPKlq1ea8guqFi8FEihAxGo72Nxzr8JVVFeaVVhdzgs28rSCEShetumztHY8Oi5HQeyMdjQlCiKSJV2nXHmjmDgzUCL3xTKmy5Tfcm+/KL5qPME4bvyKqqsZDE7F9T32/ebD1ZGT9J7+4uqhmSSkn2Eyn3bC5PAXrbn/kjtBQ73jn0d2dOgo/3aVfvZAN/d+nkTbOcDGkOwGRDWJXdB05mbMJCkq/dggg04n+WEfiem6hXlYHbdaAVF4mOqaQ4B12CZk4JRSj0aicEBNZGBodt4obkJVscMqStcoikmAYu4qHxmNGRQly5PbNJqY6zb0uXbza5vQVrDTpNqk9p/b3fPS7/zjY33CkS4rHxEwbNG/tFTddcs+XrnD6izwws9AErxP3oTcp2m5mxDKrV20qFBzuOjNtkuLReHRiNAYA0Hbg/ZFdP/vHoz2n9ncrkqiY52PMeYrKFqy/+/PXe4vLU4U06CIT0xN1sufEaN0yxQDOweIgOnEXkUFU01KEcDabavJ5iwmlgAggXTmpR1wysFP1xoFdoWWqP7zdnvYtoSorshxPiIZGjQAQ0jU0SefAglWTT9bx/c2ML6POHQKIug1LzouxiJhMsXIhyYq1kc64e8cJNrmoZrFXsDtNlTJTZVGe6O8Mdx7ZPbr/6R+e7jz6QYcYDWdUHRpznDBv7eXXL7/uk8uBnbiOdY2BZUusQxcZACSXv0jxlddUcoKwxBRhJWLxWGA09YqDjHQ0hvf8+v+d6j1zsEdVFNOn3pgXHIU1i9esvOnTq2F6JWttsksOZhb3VRlKTGu3LDeZ5JKwdI+QGaSlnQCVt9vTJ/BDZlkfCABKd0oGoH+qqW2v0YlNAgAkweGQ013JkBPxuBSPx03Gd/R2IJERdNdzCY0UVmYTLNrSngwmQhNxQshcx6Zm6xIqACCXLVkLBfMWLUQYm7p/pUiSHOjriAAADLacDB76w0+b2g++3y7GIhmRluB0+Zdec/dNBdULimDynl/KuGk1wroeQCsRmQ5NrLvzcy6H178EIVxkpj2JcCAaGOgKa20hMNAV2fu7753sqz/URVTzpGVzeYpq1l+5qWThqlKqb1q1Rd90Zx06GK3ZWdUlZL38ZyVm0/tMG3hO4Ek6hYWmfhcyY/Tp3EHDG9DAvuLAJAjOxIGBKiuKKitKekZGAMav/kWGstJzCdNlbDAxywgg7E17STQyMRo16xIi00p51iqLpf7VmkuuElz+wqtMfdlknCc80d8ZTf2nsZ6z4SMv/aq59aO3WqR4NBP3EHmLyhevu/OxTUmjpg2bVlpIRyUyXztUr9lSzNsd6031ixASGRsODLc1BKjxkce6WwMHn/3x8YHmE50ZuYbF5fOXbL11FZx7ApXqI4u0zJx+GnpVOAcLRe8hK2tnmFaSHPOcmRNCyOA0zoy6gjTKkHWHbAYx8CbcWSkej0mxWCzD2Bsra4GkE79IFwfIbDJVNQQAEhHtuiXnZ3Eocj7UFdMQOMGuli9Zm8cJ9mvNxa8keaK3fZhe5+M9baEjL/6i6ez+95oUSZQzcA1tVas3bapYdkk5w32yA/vNXbpDLnXpNXdz7sLShRhzG0ypRjEeDwx2jycLTIDWVomqikOtp0dOvfn0yeBg96BpleV0+8uWrFtetmRtKdUvByOexeofQAYXivEsFwbLxTLaGab5rJjnc5hiOKPUwenUltFHAQCFswlqOitUJVlWJEnOsO1GwUmWWs3Z6aAiSUOEoDhEjGNXOYw3zfX3EwAgBfMWoPyquo0IoVKT4yANtp4cZAXEg0O9ocPPP366/eD7Z0zH7xBCDo+/dOk1d6/REJbWdaIqMjFJa8baXXbtXV7B4bwMECow04x4OBgeaasfpuJlU0pekaV41/EPe5p2v35UjIZCGQTgqyqWXTIPzuWhZ7mFrCITGa8RnMuFAexnBHpZBgBznPmAe9pQFwIwvsrAOjFjEa3RZ4pwMcep6d5BqoqqEPOBTALsBHe02pJBPw1LNsUqtApLJHGHalZdZSaakJlHzHMRz4KSBSux4HDfZZ64E1Lr3nd6GUF8CQASgYGuiYPP/ezIQOOxJrPfydnsrvKla1f7yqoLdEjLlsaoZ4ybr7SqhOOE28y6g+GR/tG2A1OZP+kwTgIA4olIKNS8+/XG/sbjZ8z2zeHNLy5ZuLLWmZefx1BZdN/oPHkZLaXZPs1JZ3C6ZAUAZPIVQUZFDMy6g3pGoRffISbIa1ofkAl3VoxEoolQOJqFOlTTuIiZ5IoyD5kHCHtMq6vQcG8kkyDteQy8T8Pa2x+pwBx3tdn41Vh3a3d0YlSi3KZpT2kmetvGDv7hp3si48NDJkUWtnv9hbUbrp5PEZY2AK89XaMrx0zDJ/73D5wOb/41gNBCU1ObiEVHOhr7YsExiaGwtM+DooGBrpG2A9uPh4b7TcWzMMfxvvJ5VcV1y+kyXim3MKUgtSeH6ZRWzgkLTBCDYZ4mkworkxgJmWUfzKjEyYKdJi4/E1UlRFXTGTRB04PuNGnNcKXTqKtsyGqyAsZ4ISGi+WyiI53NExkQ1uwvsmaB4vnLcF5J5SMAYCpPlKrIcvOeNxtg5guNlAqJAkCEEBIaaD7ec/rtZ9+VxUTUzHfbnG5f1apNyxDmXJrgtNHJIbPklc3lRYW1SwqxYPssGFVT0SAWGJ1o3PnKWZh+WVVLVql7h2GiqqG2A9sbR9rrj6uKbOqAwZ1fXOKvmF+c7AerX1rS4tMR8lwRVqaLcTppocwMKoexEjP3tAw/CGOTT4pQpplAjVLE0OrLbL4o8worA4SG+qIZXhxNV+0n12F+dNNf/mcF5vh7gSpHp2vYwfHxhvdf7Ibpp3Xa6yVTj+LlRDx4dv+79f2NR/fp1eOb5hYKNnteaVV5QfWCQoYS0ZKVocpafcuDvLuw5FaEkKnTQVlMJAaaTrQPtpwMMvqlLSoxRVrx0MRE94m9x6ITo2fN/A6nr7DIX1FTnCzgaqc+NoqsDGsPng/CMqOudJbU+XMNckByGTVelWVZkSQly3FLR6TpiCCL8SJKRj+b0dwhch6JCgAA1tz6Gc5bVP4ZQKjSbGvb9r93grCzfmofoU+R1nhP21D7wfcPiNGQqZM1m8vjLVu8pkzjKrEMWy9IjQEALbr8lkKOt33R3KwSEg+Oj59+59kzML1GAas+pPbuYaTjyJ6z0fHhs0RVJTO/yldeU+Irr/FRfWNVd9b2KyMOupjegV0ootL72VkbVSIUisQmguH01oX0SArA+G2k0Z+ZCywxPg4ETLkAqiIrWTzLOV8uIQIAdMk9X6zDHH8HAJgqRy9Gw6H6919sBULoC450eatUvCcCANGBphOdY12th8GEyrI5PZ6C6oXFlCHTMSy9su3JwqrVtyOElpuaJ1WRe+sPNfY3HqPVFZ1iaUZqpdBw79hIZ9NxKREbMPO7vIVlhd7CMo9GTdmofumV/jMtXf4YCSvXpDc7AkQmzctYOWWSyG4WBBsYASCmbnInwsGYIibkzCjkvBAVAABs+cxf8HaP7z5AqM7sb+4+se9kaLg/oWPYEh2cPqeyzg6P97Y1yFJiPN3v4B1Op7ekooDjBVsao2apEfTgD16t4QThc2AycUE8FJg4+caTZ2DmTXOF4RLOyMvVcWjnSSkW6TKz1hx5+T5HXr4LzuWpFwz6ZVSDEP1PIiz0MWyw2ftjc0JUU1+KkOnvToQDUVlKKBkMt5lEgjkb0uU33LcKYe5mAPCbil0FRkcad73cKMUjRCfOo/dsKyKLidBYd+vZRCTYllYhYI5zePxed2GZm0FYRkoErbjxU/a8snlfRBivNLPOCSGk69iHJ4daT4eBXQSCzgISo0mr/dDOnng4cJaoatoL0Ha312Nzex0awrLpkFaqTxxkeL3BUlgXBWOh2V9JOM/udCw0EZXFhJLF95M5Iq2pRf/wT94qEhyuhxFCK82scaIqSsfhXccGm08GJwsEm1Ih0zJ6DLac6kqEg6YC1A6v31M4b5GfUiICwy3UXiTFlz3wtRuTBwim0r9GxoYGTr71VD3MLGbKKojKUlgxAIhFx0eaVEWeSEsmvMDnlVT6bC6P9rKowFBYei6v5RJeeLGHMtV8F5C4zPttipSQiaKQuSDD2XbCU1B6Ncb4FgAwVZAhNNzfd/bA+x3x0LgCMyu70EqE9RA9PtLROCon4oNASFo32e72enxl1b40Rj2thPvtf//LOrs77zGEUJUpdaUqctOuV/aNdbVEgf2wWi+rSoLu62hnU4OqKBNmxtKdX+RJEpZe7UXW5dH/0QqLXFSUhT5G7Z4dsab5ajSXruDUgv+TJw+uwYLwMCBUa454xUT7oR0nBhqPBgghRrXzjOoTxOVEPCaL8WFVVQLpficn2AWb22vXGDXHUCBTKmT59ff6yhavehRz3GYweT1jpLO5qfnDNzsVSUQ6JGyUt25aWqjGXa+cJSb6BQDg8hd7bC5vqoALr/PB1IdFWshSWBeGPkkGZEUuJInZnZ78pEtiiiEKqhe4EcYo4xHJbb+mfv/nfrWziLc77kcIX2vWsIfb6htaPnyzPREJqRrD0Us9pJeQUgQAESEcA0LSnrLaXB63p6DUA+fq9/EMNTKlsNZ/8gt3Cg73vYCQz0yfpFgkWL/9hYMTfR1xmJlETwZzCTanHtmPdjaH5US8lRCSNo7l8Po9NodLgOlFOPQ+GT/RwR8Hi/+4e4QZkhW5UMQlON2lgJCp+Igrv8TrLal0IWRaQ85pH4rrliOb27cOIXwnTN6yTh+HC44Ptx/c0TDc1hBhGDYrY4cuWQFAQlWVCUJIOO2ywBg7vH6XM6/AruM6TZHYNV/+p+UuX+HtCGNTd8kIIWrHkQ/2tR/cOajKEk6jrhKMPtEP7EUAkMRYqBWIicC71+cWHC6tiuL13FzI4rb7RU9YUixhNg0vuQjZbTaXV897XwS7cwUCMLWL21xuByfY8ByPh2lc97XveDDHrQeETGXeVGQp0Xfm0KnGD17t0hQHBWDfANcGo/UqF4myGB8lRDUV67G5vU5XfpHDyHUSHC5b2dK1V/B2x0aTtkrGe842N+56pTkyNqRSJKxQfaJLiMV1VJYMAJIiS8PJ/24IZ16Bz+b2Chr1qCVk1ulnRqR10RNWIhgKzSJv+MVPWpPTdFGQLeK4MkDIYebfugtK/TanR8gkSDcHpDXlSrjyi0oQwqvNqRBVDfR1tDXufLkhOj6sMAzbSIXoJaSUE+HgKFHVcVOE5XQ7nL4phcVUWQs231hq9/iWI4RNvYOUYtFQ+8EdJ3pPHwwAEO3FTPoBd0JHXdEl46Ye3QcHepoJIWnfTHK8INicHiEZKsDAvtmODcjKkLguesLylBaXYJ7/nxJruyAPhAEAtn7hW26EcInZGBYAgNNX4MjAJZwzVK3ehHmbvRphtNHUJhgJjbcd3HGi48gHIzDzyJ/OzKCXR39GjrTB5hMDRFHMPtFxOvMKHDA9c8G0mE/F8g0L7C7PCjPfpyqKPNB07GTT7tc6FUmkKzLrZbDVKkVJh6wUAFBlMR5J/j0t8qvq/Em3UEtARuT18VBYmOMxMvmK/o8bc3qCZgrVay+vQAjlZfIz3qIybxZB91z0b9qOvPa2zzowLywFQAvSGrYsJUY7GpsadmxrA3blGrp4rQjsgh8zylqdfPOpCUWW+oCQeHrC8rpc/kKHpj9Y60IJTrfNW1w+jxPs88wMSHRiuP/sge2N4z1tcQ0RmA206/Vp6lP//rYeIBA3tS6Ky/N4uwNTZMwZkNR5z9aQFXiHnUcYVxm7KZgz53ZMM3qjJy3n9V5TIhROqJKs+3hUESUlEZqqxnLB3ELB4fLDZNYA03DkFTgRwrleQxmPQX7VAj/GXHp1RQiJTowMnHnvhaPBwR4Jpr9nm1HWnqFCRAMlogKAIsbCZ1VVTfv2zuZ0Ox1ev4NSIFMqJK+4wmP3+MoQxmnnRBYT0f7GY/WtH73dBzPLxss6MTmWaqT7M5Vuqefkvhgh6jjolN+avi7yXZxgRxTPIEZfL4psDebNedKdsBv9G7vH48NcRru43qkbyTEXmUJ0bCIqJ0RRf7GJUmx8Ig7sU8Lz5h7yNlsRIHBn8jN2d54DEEY5IKjZPNxGvN3pA4TXmjDsSNfxj460H9o5zCArOiODUYUiSU+JxALjnURVh9PGegSbTcdtSiksO8a8ywwJh0f6e5o+eK0hEQkSysVVKAJmFVbRKxU3o6qNLCZOm7m2gWamVEpHVB8PhWVqNauqYuouEwKik9d9LsrJm1lHKlEUGaUPqNMZT9OlkZmToHUyhUhGT21UWVQATJb5QhmrKbP9RAhjD0qTeZMQooZH+ruPvfzEKTkRA2CfoEmgXzVJpIyaWfxzoq+9jxBlNIsNdppRK2KCqEr61ERiLDzeeXT3kZ6T+8Yoe9Z7VpSuTJxeUVM1PDqwk0w+R0qzLmQZJhNXqgzSQgbK6uKOYamKohBCJgwnJBoNyaIoR0bGDE9fgn2DAVWUFUhf5stIjZleZEpCFAH0n2EoohSPB0Ph6NhETBEl0dCSp78lZCXoyzBXV+YQY5EhIBDJ5GcGmk4MqLK5fF/x4HiCqIQYqKps5iK1wHlIEwtVZUlsO7jj4ER/Z5wRt2KpK92K5QZGTRp2vDSsKnIvADGRpmdGNagpIw4M9iRCw31dqiyNG/QpPtzeeOLkm0+dUSSRwMzDA9EkWaXcRpbtTK3HU289fUiRxONGbiFRFHm0u6U/EQ2LBms44yc5FwVhJYIhkajqaaN/M9Hd1yVGolJocHjUiNGHGpuH5cl0J0RH0qbL5pkR6l9/ZzAp/RU2YYmxeCAQGmvvDEixaMyo7WIoHNeR4unc2pwprqYPXm8lqtJjVmWpsqz0njk0aLZGX2CgK5JMF00bQzq3PW3/VEWWCSFGGxoJj/S3nnzzyUaY+a5Or/4jy23Sy6k/1f6++sOJRDh4lKjqYJoNIhwPjkc1Pz+NuMRoSD39zh8Oj3a3vsp6eKwqcry/8dj2/U/94L3gYE+cIiq9axmsE08tWalGG/6Zd58LDTQe+54sxo8x17wkxruOf7iv+YPXmqTJgrOseFg2G9I5V/p8hqxo6csJAilfs0IUnI4bEEICQ12NHX/6xXf6T9YHbS4nV7NpwzKE8Yw2DzW2dJx56Y3GyMhY1MRkpf57JrX8ZrR9pKUNVt97p4932NezTteiI6PdZ159+9DAmcZw1brVJXmV5WWY53jK3VXHOrr6jj3zUr0UjSZg5oNU1qU+CabXeMyWtKYFQXtPH1TW3PYZP2ezX4IQSntyO3T2dFf9e8+3lS1eU+AtKveBwfWGwEDXaP17z7fEwxNxxtzEKFXDmh89QkMAgJZec5fD4fUtRxgvZhqSLMX2/u4/nuhvOBqG6emHCUOJRDWfmAFpqXpxGqevYKRk4cqNmBNq9UTBeE9be8PObWeCgz0RmHmdQgIAMTzSFxrpaGzAHHdCsDlGEcayKkuBwGDPwfrtL/7h6LZfHRtpb4jD9OIOhHIFWRXMo4w1JeuM9TRXtb/hyBhRlSMI41ZFlsajEyOtoeH+lr76wwdPvvX0jtPvPNsY6OuMJkug6T0eTzemuuv5QhIWmujqATkW784rLz3N2W0eBMhOgIAiimPhweH6I79/7sWGN7f3yPE4Cg0OxxHmwv6aqmKMESaKqsrxRHzgTEPr/p//9/7BxuZxIEQC9l0TluHTO0s6w59xymFzOXuLlywo5nh+Ppp0SwghRBIj0eHOvQd3nfjDS82qJOOhxpaxooV1bldhfj7mMAeEEKIoSnhwePj4H1463H/i9Ajo3/2JzWaCM+lPcKC7pXL5RoWz2ZcAQrZUtWaS+h2EqKoiS6MdTR37n/7BwcHW0yExEorVbrimFvM8p6N+lIb3t53qOv5hvyKJ9HMW1s1xKQPCwgCAB1tPRatWbWq3Od11COFSQGjSeAlRFFkKnnnv+V+eevOpdlWR05EVnTYmlibYziSsgeYTicrlG4dcvsJFiOMKULI8FCFEJUSVo+MjA427XtnfsueNbsqwJa0bSggRw6ODsZ5TB3o7Du863rr33Z1Nu155v2H7tuOdRz8YiU6MpILsrIuvIkVQUYq0REb8ysh9wwCAE5EQ6j19KNJ1bE9n2/7tx5t3v3ayefcb9W0H3u8ZajkVjIcDMiGEAPuyKn06ybpOYbie0QUiLO07IwFx2IEx5wIgLkLADYR4CCFuoqp5MJnedrL0NUJ2R57HWbSwzu/M9ztGW9tD453dkeQA0WyeSmEbAoBw8u+pPyOaSaOVlp4bojXwyVvJCNk4gbff+YN/uSavrHQrIFQ10dXT2fDG9sbmd3cECCFumCzf7XT68tzLbr9pQc3mjYswxtxQU8vAyRdebQ709Ec0rgltOCEACCY/Yc2CSzDanqmLS/eHAwAB84Jj7W2PVC/eesudDm/+JYosCaosCYos2aLjw9G2/e91tO57dywemuAAwMHxgm3VLQ/Vrb7l4bU2l9uBuUniIqqiSrFYov3wzpYjL/6iMTw6EEkuUG164SAABJJ/hpKf1DzFGW4Yk7AAgMccJ2x6+JtldZded6vLX3g3wlx1PDTe3rLnzfeObPtlZyIStCfXkj257lKPnEVKWYU1bYlR4y0yjFu7pqeeoCCMnZd+6muLFm6+8R6nr2Az4riieHB8eKDlVMuZd59r6as/HAFCUo+EU/e/YhqyjCT/niLvVIwqlVLZCedKwjuSKkt76inqfF9U8700EbPcNu11i1TOLqfm97uT4+pJ/t2hUXxEs661bUnZZIyxKRgKiAtNWKlBsGsGwa355KUMHs7VOOOpI2kA9m3eOIOkwpoJi2uMXs2KsM5NYKribZ5m4lJEmyp3ZIOZr9QJFUvRSuaIZnKDGpKNUbtTLghL+54tZQAuzULMg8k3ht5k31KGkirdxNk9PqFm/daS/Kr5XgCA0HB/tL/h6GhwoCumTAbmWZVnghRZaecqnsYFA5j5wNamabubYUxuzTyARmHRhBXWGFTchGFjehPWrAntOKbG0qUZu5Ta05YRi2oIRrtGscZetNV26O9SKIUV0Wx2UYanoVD9Yo2xtm90XUU3tS4EzRizUkuHKQWbYNgik7D480hYhDJSvXLx9EU3npK7PExPrUobvbbWmmhSzpsNwhPG76SLr2pJU9vWVNuxAWHRSdT0gr25urahPVkijH7ROaESMPN2uAwAXCIciDfvfi2scyhAx1W0LoFk4P5lW2fSaE6Ipv20qo2ZuMaglx1Wb+xYQX2sIUxMKRHW0x+ZIhNtLnTQKDRMuWIxapOjx1w1ERLRs1NJs1EkNOtcpQhL1ijrRJpxTbvhnk/CMlpgdEl2kRoI0Bg1BzNzQdOLRNQhLb241WwKsNKloESYnqgMqEnUa7vKMOh0R+k5q5hjwuASGgLWnkjRRTH1CERhqEij2+OZXJ7VIww6syZO/g4tSYgMwmKpD9XEaa3eGIrJ+U8k/0xtVjyliozeK6qanwPGSSdH2ZMIxnfJzMQI060NEWaW61KodcJ6dC2lOfQiFxthpdvNOcq4U5PCKsLIMgo60EefhChZHrMaLU56N6UXEM8wbvo76MuLCQPZTnI8F2AwH7Qy0F4HMENYdIwxwTAiSaePJIu5UCgVkNC0TbumWIbNuv2tZmjYdIiCpxQ3bdh6ClS7bolm7OnfwVPrTabUI+uSaCZ9QjqeEGYoPZmxWdOHYazYpCnxcCEVlp47KFEGkOqwwHCpEGVsLBnOOunJiNXT7DiKZocTqQlUNEZBu7KIWoQqpE9rkvEEZzkXMmXs2pO11HzYYGaFYlZclDBITqtq9FwFOp5CMlxL9FvB1PqgDVvrrhi5g2arGtHEKTFilyq1JoiOEqEJJkW4WluhTz5ZB1CsHFdG4RE67qydw1R4AzFEg3ats95oSjpXKUyfdl9IhYV0Jhcx3EUJ9EtcswiEfmohprlHQzJsMzIgW1YiOD13EAy+RxvzYLmzs1VZrP6wJD+i5L6WrATGLqtHWiqwn4vQge1Md3+gXFRMbXxAES1mGBgri4Heuzr6dyNqPGmjxppx1BKlADPfM7Lyx8sUYWkD4JxGzbPCI5LmO8zE44jJNaIYzC1PtYdQ4R7p4xTDMgrypiZYYixCVvJ6o2ArTVpSmgmDDIiLJhlEkRNrEkUdV1YvFiankdCqzs6ea7VLK1mVsYFwOu4g/b9VRpzSKFZnFC9CBm4h3XZ640OUSygZrBNVx5iIwZpGjFABSkNYrPUi6VyjwBpS5hgbBusAi5VZItMXH7SCBJ355QzWDatNHwuFpWckesF4o9JArMFUKClKD1Kmb/WMCJdFtrRrxVFEi0y4mIqBEc3lw26tQpAZi1Iy2EBQGsIyKu6gdygCOn/qbXxIM3Z0qIADdqEJrQJJt7ERE+tC2w7QcZ0kHWUkU22i7wjSmQ/04rk0SaizPGyi3UJ6bnmNrRp5P6qJQyRdAr3Qp4T0Ua2eWuJAv6y1XoBXpQaH/szG4FluoQzTr18oOrugXttVxm6kpNmNSA7GH6XZPQnlUmnJigPj1/asGAhLaaU7Jcxk49M7hZYNlIjC+P3ZXLGgXVTEGAeVWhdG7WG1BTFsgRUTpUnC6FG9mas8oBP4JxqVyGqXCvrXgNRMPR3+ApEU0lFGLNWFGcHqdISlN2G5NHrt5KUgM4jWqPaantGxiEvJZoJnuYHIDPcmndrVU1qsRSvrkAVhnIYSE6pGj6yMXCeVGt90j9AzIX9Fsy6IgduETK5XoDYHerMgOmNNdMY0m7WT+g5MrVVVR1SwfjfJckOAC5mPm17UmLF7ZJPsS++om6RxBbN5RU63i5bpmApapyNblbFDzmZ3nE1/6H5wBv1Lt67S7f6Kwa5r1E+kY8TYZFv1xjjbcdZb02aKL7A2rHQbK9LxToD6GZXx37JZO8gEaSIddU107DOj1OAXC2EBGCf4yiTJl9HAkByQVTrS0pPs6cabGBBurtueKQkbbSJgkrSA4U6wjDNTsjAiLaO1ZPTiYjabQqbrwqg9ZrOIpNu4QeewIJd2C5D+agtA+lPJi1ZhgYErgQz+nom7ZuZGOMmRkYNJkkVpYh/EgLwA5rZ2IavNmPG/M50XBPopoNU0G0o2yibd7g9pdvtZp202QV5Ghm12HMxugJDjNYNMqj3IgCwvepcwk84jEz9LTAzQXE8eyrD9Zhfb+awGjdIYnVkSNuu2m91YZrvzI5PjDJAbdxuZ2IiNxsbs5oQyMH4yx3YLJhTWrNp0MRAWZEBKmU5UzgZqDnYcMNm2C1H5B2W4gWTbx1wr30zngWS5dnI1jpkoo2xBLrD95rQ9FxNhzXXbyMewzRdi4ZklgNmMxVwTMroIxxl9zOb+fwQpXCxtJn8EY0v+B66TC0UW5CIcS4uk/kgIy8Ifz/qxjNKCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWLFiwYMGCBQsWZof/H1EsFxnW7gFnAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "743fb2d0",
   "metadata": {},
   "source": [
    "# Telco Customer Churn Prediction Project\n",
    "\n",
    "Did you know that attracting a new customer costs five times as much as keeping an existing one?\n",
    "\n",
    "![churn.png](attachment:churn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cabbaceb",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "<b>What is Customer Churn?</b>\n",
    "\n",
    "Customer churn is defined as when customers or subscribers discontinue doing business with a firm or service.\n",
    "\n",
    "Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. The telecommunications business has an annual churn rate of 15-25 percent in this highly competitive market.\n",
    "\n",
    "Individualized customer retention is tough because most firms have a large number of customers and can't afford to devote much time to each of them. The costs would be too great, outweighing the additional revenue. However, if a corporation could forecast which customers are likely to leave ahead of time, it could focus customer retention efforts only on these \"high risk\" clients. The ultimate goal is to expand its coverage area and retrieve more customers loyalty. The core to succeed in this market lies in the customer itself.\n",
    "\n",
    "Customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers.\n",
    "\n",
    "\n",
    "<b>To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.</b>\n",
    "To detect early signs of potential churn, one must first develop a holistic view of the customers and their interactions across numerous channels, including store/branch visits, product purchase histories, customer service calls, Web-based transactions, and social media interactions, to mention a few.\n",
    "\n",
    "As a result, by addressing churn, these businesses may not only preserve their market position, but also grow and thrive. More customers they have in their network, the lower the cost of initiation and the larger the profit. As a result, the company's key focus for success is reducing client attrition and implementing effective retention strategy.\n",
    "\n",
    "\n",
    "## Dataset Description\n",
    "\n",
    "The raw data contains 7043 rows (customers) and 21 columns (features).\n",
    "\n",
    "* customer ID: Customer ID\n",
    "* gender: Whether the customer is a male or a female\n",
    "* SeniorCitizen: Whether the customer is a senior citizen or not (1, 0)\n",
    "* Partner: Whether the customer has a partner or not (Yes, No)\n",
    "* Dependents: Whether the customer has dependents or not (Yes, No)\n",
    "* tenure: Number of months the customer has stayed with the company\n",
    "* PhoneService: Whether the customer has a phone service or not (Yes, No)\n",
    "* MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "* InternetService: Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "* OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service)\n",
    "* OnlineBackup: Whether the customer has online backup or not (Yes, No, No internet service)\n",
    "* DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "* TechSupport: Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "* StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "* StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "* Contract: The contract term of the customer (Month-to-month, One year, Two year)\n",
    "* PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)\n",
    "* PaymentMethod: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
    "* MonthlyCharges: The amount charged to the customer monthly\n",
    "* TotalCharges: The total amount charged to the customer\n",
    "* Churn: Whether the customer churned or not (Yes or No)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c075f5",
   "metadata": {},
   "source": [
    "## Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "488b6475",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style = 'white')\n",
    "sns.color_palette(\"husl\", 8)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3e50e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d56f519",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e3d1a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdad0be3",
   "metadata": {},
   "source": [
    "## Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e48c1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de57866c",
   "metadata": {},
   "source": [
    "The data set includes information about:\n",
    "\n",
    "* <b>Customers who left within the last month</b> – the column is called Churn\n",
    "\n",
    "* <b>Services that each customer has signed up for</b> – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies\n",
    "\n",
    "* <b>Customer account information</b> - how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges\n",
    "\n",
    "* <b>Demographic info about customers</b> – gender, age range, and if they have partners and dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4877947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b9071d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de1bd459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a612669",
   "metadata": {},
   "source": [
    "From the above information, we can understand\n",
    "* There are no null values in all the colums\n",
    "* There are many columns with object data type, i.e, categorial features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78d4fea",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac5ee6d",
   "metadata": {},
   "source": [
    "<b>customerID</b> is not going to be helpful in prediction whether the customer will churn or not. So,we can drop customerID column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4aac148",
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['customerID'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7f01ad",
   "metadata": {},
   "source": [
    "### Encoding\n",
    "\n",
    "<b>Encoding</b> is a technique of <b>converting categorical variables into numerical values</b> so that it could be easily fitted to a machine learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e01839c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_uniques(df, columns):\n",
    "    '''\n",
    "    Returns dict with key as column and value as list of unique values present in the column.\n",
    "    '''\n",
    "    return { column : list(df[column].unique()) for column in columns }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04c2cfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_categorical_columns(df):\n",
    "    '''\n",
    "    Returns list of categorical columns in the given dataset.\n",
    "    '''\n",
    "    return [ column for column in df.columns if df.dtypes[column] == 'object' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a92b1b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gender': ['Female', 'Male'],\n",
       " 'Partner': ['Yes', 'No'],\n",
       " 'Dependents': ['No', 'Yes'],\n",
       " 'PhoneService': ['No', 'Yes'],\n",
       " 'MultipleLines': ['No phone service', 'No', 'Yes'],\n",
       " 'InternetService': ['DSL', 'Fiber optic', 'No'],\n",
       " 'OnlineSecurity': ['No', 'Yes', 'No internet service'],\n",
       " 'OnlineBackup': ['Yes', 'No', 'No internet service'],\n",
       " 'DeviceProtection': ['No', 'Yes', 'No internet service'],\n",
       " 'TechSupport': ['No', 'Yes', 'No internet service'],\n",
       " 'StreamingTV': ['No', 'Yes', 'No internet service'],\n",
       " 'StreamingMovies': ['No', 'Yes', 'No internet service'],\n",
       " 'Contract': ['Month-to-month', 'One year', 'Two year'],\n",
       " 'PaperlessBilling': ['Yes', 'No'],\n",
       " 'PaymentMethod': ['Electronic check',\n",
       "  'Mailed check',\n",
       "  'Bank transfer (automatic)',\n",
       "  'Credit card (automatic)'],\n",
       " 'TotalCharges': ['29.85',\n",
       "  '1889.5',\n",
       "  '108.15',\n",
       "  '1840.75',\n",
       "  '151.65',\n",
       "  '820.5',\n",
       "  '1949.4',\n",
       "  '301.9',\n",
       "  '3046.05',\n",
       "  '3487.95',\n",
       "  '587.45',\n",
       "  '326.8',\n",
       "  '5681.1',\n",
       "  '5036.3',\n",
       "  '2686.05',\n",
       "  '7895.15',\n",
       "  '1022.95',\n",
       "  '7382.25',\n",
       "  '528.35',\n",
       "  '1862.9',\n",
       "  '39.65',\n",
       "  '202.25',\n",
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       "  ...],\n",
       " 'Churn': ['No', 'Yes']}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_uniques(df, get_categorical_columns(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a60e0f9",
   "metadata": {},
   "source": [
    "From above output, we can observe <b>TotalCharges attribute which is of type object (string) but the actual values are floating point</b>. So, we have to change the data type of TotalCharges column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "977883e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing TotalCharges attribute dataset into floating point\n",
    "# df['TotalCharges'].astype(np.float64)  # generates an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9196f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TotalCharges'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "efe7572f",
   "metadata": {},
   "outputs": [
    {
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       " ...]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['TotalCharges'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a945d876",
   "metadata": {},
   "source": [
    "If we notice, in the above output we have an empty string, which basically means means unknown value or Null value. So, we have to replace it Null or Nan value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35d0dc8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalCharges'] = df['TotalCharges'].replace(' ', np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "102b0e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      29.85\n",
       "1    1889.50\n",
       "2     108.15\n",
       "3    1840.75\n",
       "4     151.65\n",
       "Name: TotalCharges, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing TotalCharges attribute dataset into floating point\n",
    "df['TotalCharges'] = df['TotalCharges'].astype(np.float64)  # Now it works!\n",
    "\n",
    "df['TotalCharges'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eca0a5c",
   "metadata": {},
   "source": [
    "So, now that we have replaced empty string with Null value and changed the data type of TotalCharges column. Let's count the Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b38603da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               0\n",
       "SeniorCitizen        0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "tenure               0\n",
       "PhoneService         0\n",
       "MultipleLines        0\n",
       "InternetService      0\n",
       "OnlineSecurity       0\n",
       "OnlineBackup         0\n",
       "DeviceProtection     0\n",
       "TechSupport          0\n",
       "StreamingTV          0\n",
       "StreamingMovies      0\n",
       "Contract             0\n",
       "PaperlessBilling     0\n",
       "PaymentMethod        0\n",
       "MonthlyCharges       0\n",
       "TotalCharges        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea177a45",
   "metadata": {},
   "source": [
    "From above output, we can see there are <b>11 Null values in TotalCharges column</b>.\n",
    "\n",
    "So, Let's <b>replace the Null values with Mean</b> of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f0403c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalCharges'] = df['TotalCharges'].fillna(df['TotalCharges'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e7223ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbcfc877",
   "metadata": {},
   "source": [
    "Now, we don't have any Null values in our data frame.\n",
    "\n",
    "Let's take a look at the unique values in columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "122567ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gender': ['Female', 'Male'],\n",
       " 'Partner': ['Yes', 'No'],\n",
       " 'Dependents': ['No', 'Yes'],\n",
       " 'PhoneService': ['No', 'Yes'],\n",
       " 'MultipleLines': ['No phone service', 'No', 'Yes'],\n",
       " 'InternetService': ['DSL', 'Fiber optic', 'No'],\n",
       " 'OnlineSecurity': ['No', 'Yes', 'No internet service'],\n",
       " 'OnlineBackup': ['Yes', 'No', 'No internet service'],\n",
       " 'DeviceProtection': ['No', 'Yes', 'No internet service'],\n",
       " 'TechSupport': ['No', 'Yes', 'No internet service'],\n",
       " 'StreamingTV': ['No', 'Yes', 'No internet service'],\n",
       " 'StreamingMovies': ['No', 'Yes', 'No internet service'],\n",
       " 'Contract': ['Month-to-month', 'One year', 'Two year'],\n",
       " 'PaperlessBilling': ['Yes', 'No'],\n",
       " 'PaymentMethod': ['Electronic check',\n",
       "  'Mailed check',\n",
       "  'Bank transfer (automatic)',\n",
       "  'Credit card (automatic)'],\n",
       " 'Churn': ['No', 'Yes']}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_uniques(df, get_categorical_columns(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2d4bb7",
   "metadata": {},
   "source": [
    "From the above output, we can observe:\n",
    "* The TotalCharges column is no longer in the output, which means its not a categorical column, i.e, numerical column.\n",
    "* For some attibutes like <b>MultipleLines, OnlineSecurity, OnlineBackup</b> etc..., they have values like <b>No phone services and No internet service<b/> which is same as <b>No</b>\n",
    "    \n",
    "Let's replace No phone services and No internet service, with No."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "53c0a3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns with 'No phone service'\n",
    "df['MultipleLines'] = df['MultipleLines'].replace('No phone service', 'No')\n",
    "\n",
    "# Columns with 'No internet service'\n",
    "no_internet_columns = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
    "for column in no_internet_columns:\n",
    "    df[column] = df[column].replace('No internet service', 'No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "98db4d14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gender': ['Female', 'Male'],\n",
       " 'Partner': ['Yes', 'No'],\n",
       " 'Dependents': ['No', 'Yes'],\n",
       " 'PhoneService': ['No', 'Yes'],\n",
       " 'MultipleLines': ['No', 'Yes'],\n",
       " 'InternetService': ['DSL', 'Fiber optic', 'No'],\n",
       " 'OnlineSecurity': ['No', 'Yes'],\n",
       " 'OnlineBackup': ['Yes', 'No'],\n",
       " 'DeviceProtection': ['No', 'Yes'],\n",
       " 'TechSupport': ['No', 'Yes'],\n",
       " 'StreamingTV': ['No', 'Yes'],\n",
       " 'StreamingMovies': ['No', 'Yes'],\n",
       " 'Contract': ['Month-to-month', 'One year', 'Two year'],\n",
       " 'PaperlessBilling': ['Yes', 'No'],\n",
       " 'PaymentMethod': ['Electronic check',\n",
       "  'Mailed check',\n",
       "  'Bank transfer (automatic)',\n",
       "  'Credit card (automatic)'],\n",
       " 'Churn': ['No', 'Yes']}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_uniques(df, get_categorical_columns(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ff9f59",
   "metadata": {},
   "source": [
    "We have successfully replaced No phone services and No internet service with No and now they are Truely Binary.\n",
    "\n",
    "Next step is to encode the columns. For that, first let's divide the columns into 3 categories:\n",
    "* Binary features => Binary data is discrete data that can be in only one of two categories — either yes or no, 1 or 0, off or on, etc.\n",
    "* Ordinal features => Ordinal data are discrete integers that can be ranked or sorted.\n",
    "* Nominal features => Nominal data is made of discrete values with no numerical relationship between the different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b75b9998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Gender': ['Female', 'Male'],\n",
       " 'Partner': ['Yes', 'No'],\n",
       " 'Dependents': ['No', 'Yes'],\n",
       " 'PhoneService': ['No', 'Yes'],\n",
       " 'MultipleLines': ['No', 'Yes'],\n",
       " 'InternetService': ['DSL', 'Fiber optic', 'No'],\n",
       " 'OnlineSecurity': ['No', 'Yes'],\n",
       " 'OnlineBackup': ['Yes', 'No'],\n",
       " 'DeviceProtection': ['No', 'Yes'],\n",
       " 'TechSupport': ['No', 'Yes'],\n",
       " 'StreamingTV': ['No', 'Yes'],\n",
       " 'StreamingMovies': ['No', 'Yes'],\n",
       " 'Contract': ['Month-to-month', 'One year', 'Two year'],\n",
       " 'PaperlessBilling': ['Yes', 'No'],\n",
       " 'PaymentMethod': ['Electronic check',\n",
       "  'Mailed check',\n",
       "  'Bank transfer (automatic)',\n",
       "  'Credit card (automatic)'],\n",
       " 'Churn': ['No', 'Yes']}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's rename gender -> Gender. So, we have all the columns in CamelCase\n",
    "df = df.rename(columns = {'gender': 'Gender', 'tenure': 'Tenure'})\n",
    "get_uniques(df, get_categorical_columns(df)) # Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26438db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classifying columns into three feature types to easily encode them\n",
    "binary_features = ['Gender', 'Partner', 'Dependents', 'PhoneService', \n",
    "                   'MultipleLines', 'OnlineSecurity', 'OnlineBackup', \n",
    "                   'DeviceProtection', 'TechSupport', 'StreamingTV',\n",
    "                   'StreamingMovies', 'PaperlessBilling'\n",
    "                  ]\n",
    "\n",
    "ordinal_features = ['InternetService', 'Contract']\n",
    "\n",
    "nominal_features = ['PaymentMethod']\n",
    "\n",
    "target_column = 'Churn'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d3c768",
   "metadata": {},
   "source": [
    "Let's define a ordering to ordinal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a28af8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "internet_ordering = ['No', 'DSL', 'Fiber optic']\n",
    "contract_ordering = ['Month-to-month', 'One year', 'Two year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2fdc8ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_encode(df, column, positive_value):\n",
    "    '''\n",
    "        Returns a new dataframe by changing given column values to 1 and 0s\n",
    "        based on the codition, if they are equal to positive then 1 else 0.\n",
    "    '''\n",
    "    df = df.copy()\n",
    "    df[column] = df[column].apply(lambda x : 1 if x == positive_value else 0)\n",
    "    return df \n",
    "\n",
    "def ordinal_encode(df, column, ordering):\n",
    "    '''\n",
    "        Returns a new dataframe by Assgining numeric values to the actual values\n",
    "        for given column based on the given ordering.\n",
    "    '''\n",
    "    df = df.copy()\n",
    "    df[column] = df[column].apply(lambda x : ordering.index(x))\n",
    "    return df \n",
    "\n",
    "def onehot_encode(df, column):\n",
    "    '''\n",
    "        Returns a new dataframe, by adding new columns based on the values of the given column, \n",
    "        it gives value 1 to new column for the record with that value else 0\n",
    "    '''\n",
    "    df = df.copy()\n",
    "    dummies = pd.get_dummies(df[column])\n",
    "    df = pd.concat([df, dummies], axis = 1)\n",
    "    df = df.drop(column, axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5845184",
   "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>Gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DSL</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DSL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>One year</td>\n",
       "      <td>0</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DSL</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>1</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DSL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>One year</td>\n",
       "      <td>0</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              0             DSL               0             1   \n",
       "1              0             DSL               1             0   \n",
       "2              0             DSL               1             1   \n",
       "3              0             DSL               1             0   \n",
       "4              0     Fiber optic               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  \\\n",
       "0                 0            0            0                0   \n",
       "1                 1            0            0                0   \n",
       "2                 0            0            0                0   \n",
       "3                 1            1            0                0   \n",
       "4                 0            0            0                0   \n",
       "\n",
       "         Contract  PaperlessBilling              PaymentMethod  \\\n",
       "0  Month-to-month                 1           Electronic check   \n",
       "1        One year                 0               Mailed check   \n",
       "2  Month-to-month                 1               Mailed check   \n",
       "3        One year                 0  Bank transfer (automatic)   \n",
       "4  Month-to-month                 1           Electronic check   \n",
       "\n",
       "   MonthlyCharges  TotalCharges Churn  \n",
       "0           29.85         29.85    No  \n",
       "1           56.95       1889.50    No  \n",
       "2           53.85        108.15   Yes  \n",
       "3           42.30       1840.75    No  \n",
       "4           70.70        151.65   Yes  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the binary features have Yes or No values except Gender (Yes or No), So let's do it separately\n",
    "df = binary_encode(df, 'Gender', 'Male')\n",
    "\n",
    "for feature in binary_features[1:]:\n",
    "    df = binary_encode(df, feature, 'Yes')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d105c7d6",
   "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>Gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              0                1               0             1   \n",
       "1              0                1               1             0   \n",
       "2              0                1               1             1   \n",
       "3              0                1               1             0   \n",
       "4              0                2               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 1            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 1            1            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
       "0                 1           Electronic check           29.85         29.85   \n",
       "1                 0               Mailed check           56.95       1889.50   \n",
       "2                 1               Mailed check           53.85        108.15   \n",
       "3                 0  Bank transfer (automatic)           42.30       1840.75   \n",
       "4                 1           Electronic check           70.70        151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding ordinal features\n",
    "df = ordinal_encode(df, 'InternetService', internet_ordering)\n",
    "df = ordinal_encode(df, 'Contract', contract_ordering)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6149b253",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "<p>5 rows × 23 columns</p>\n",
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      ],
      "text/plain": [
       "   Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  ...  \\\n",
       "0              0                1               0             1  ...   \n",
       "1              0                1               1             0  ...   \n",
       "2              0                1               1             1  ...   \n",
       "3              0                1               1             0  ...   \n",
       "4              0                2               0             0  ...   \n",
       "\n",
       "   StreamingMovies  Contract  PaperlessBilling  MonthlyCharges  TotalCharges  \\\n",
       "0                0         0                 1           29.85         29.85   \n",
       "1                0         1                 0           56.95       1889.50   \n",
       "2                0         0                 1           53.85        108.15   \n",
       "3                0         1                 0           42.30       1840.75   \n",
       "4                0         0                 1           70.70        151.65   \n",
       "\n",
       "   Churn  Bank transfer (automatic)  Credit card (automatic) Electronic check  \\\n",
       "0     No                          0                        0                1   \n",
       "1     No                          0                        0                0   \n",
       "2    Yes                          0                        0                0   \n",
       "3     No                          1                        0                0   \n",
       "4    Yes                          0                        0                1   \n",
       "\n",
       "   Mailed check  \n",
       "0             0  \n",
       "1             1  \n",
       "2             1  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding Nominal Encoding\n",
    "for feature in nominal_features:\n",
    "    df = onehot_encode(df, feature)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7416953f",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Gender  SeniorCitizen  Partner  Dependents  Tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  ...  \\\n",
       "0              0                1               0             1  ...   \n",
       "1              0                1               1             0  ...   \n",
       "2              0                1               1             1  ...   \n",
       "3              0                1               1             0  ...   \n",
       "4              0                2               0             0  ...   \n",
       "\n",
       "   StreamingMovies  Contract  PaperlessBilling  MonthlyCharges  TotalCharges  \\\n",
       "0                0         0                 1           29.85         29.85   \n",
       "1                0         1                 0           56.95       1889.50   \n",
       "2                0         0                 1           53.85        108.15   \n",
       "3                0         1                 0           42.30       1840.75   \n",
       "4                0         0                 1           70.70        151.65   \n",
       "\n",
       "   Churn  Bank transfer (automatic)  Credit card (automatic)  \\\n",
       "0      0                          0                        0   \n",
       "1      0                          0                        0   \n",
       "2      1                          0                        0   \n",
       "3      0                          1                        0   \n",
       "4      1                          0                        0   \n",
       "\n",
       "   Electronic check  Mailed check  \n",
       "0                 1             0  \n",
       "1                 0             1  \n",
       "2                 0             1  \n",
       "3                 0             0  \n",
       "4                 1             0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we have only one Binary feature, which is our Target feature, Churn. Let's encode also encode Churn.\n",
    "df = binary_encode(df, 'Churn', 'Yes')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca271e94",
   "metadata": {},
   "source": [
    "### EDA | Data Visualization\n",
    "\n",
    "Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3107a42d",
   "metadata": {},
   "source": [
    "Let's look at the distribution of Churn values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f44ff70d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Churn vs Non Churn')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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styOcue82HmVDbim3zjyfwecliga3Uzjtk5iHHnqIdevWNTk8X7NmDStXruSzzz5j0aJFHDlyhE8++QSAPXv2cPPNN/Pll19isVhYsmRJs23m5uaSnp7ebJDB2LFjiYuLA6CoqIjHH3+cZcuWUVFRwY8//njSOquqqpgzZw6LFy9Go9Hw888/Nz6/qKiIdevWne5bbiIYDOL2ePnqx8PcNW+1CHaIOFxe/vrBJp7/YBOuuoahrELLTjvcJpOJZ555psnh+fr165kyZQoGgwGNRsPMmTP56aefAIiLi6Nfv34ApKamUlPTPAwqlQqdTnfS101PT6dbt26oVCp69+59WofrAwYMaPx7amoqSUlJjc9vqY5TqXV5KK108eg7P/Gvr/fiD4g2yFDbtLuUW59fSV5hDXXiXLxFZ9T8OGrUqMbDc6Dx0Pm3/P6GD/q3oZUkCVmWWbFiBdOnT2f69Om88sor9O/fn9zcXI5vsJ83bx7r168HQKPRNNvOsT+Pf81j9Hp9499bquN0ybJMVY2THQcqueulNew/Wn3azxXant1Rx/2vrmHR6oPUe8V5+PHO+NrCscPzsrIyhg8fztKlS6mrq8Pv9/PZZ58xfPjwEz53woQJLF68mMWLF3PXXXcxZMgQ4uLieP311wkEGn44a9euZeHChfTp0+eE27FYLFRXV2O32/F6vaxdu/ZM38Yp+Xx+XO56Fq05zNwPNouWWoUKyvDR8j08/u6POFxecZj+G2fcInHs8PyWW25h3LhxOBwOZs6cid/vZ9SoUVx//fWUlJSc1rYkSeLNN99k7ty5TJ06FY1Gg81m49133yU+Pp6DBw+2+Dyz2czs2bO58sorSUpKIjMz80zfxknVOt0EZYn/+dcWth+oaNVtC20j95Cd2/93Jc/+10g62YzookTHF9GJ5TgV9hoc7gDP/N9GKqqVM+uJcHp0UWoevvECMlLiOvxAFBHuX8iyTGl5FUWV9cz9YDN14hwubEkS3DKtPxNH9OjQl8tEuIFAIECF3cHOQzW89ul2AhEyE0pHd8mwHvxxRmaHPUTv8OGur/ficHpYt6OUvy/ZHepyhFY2IrMz916b1SH34B063G5PHW6Pl2Xrj7JgxYFQlyO0kWEZSfz5+sEdLuAdNtxOpwunu54VW0r45Nv9oS5HaGMXnJfIgzcM6VAzsXbIMXQut4fqWjc/7qoQwe4gNu4u5bn3N1Ln7Tj9FTpcuD11dVTaHWzPc/B/X4pz7I5ky94y/vqPjdR3kIB3qHDX13spKbNzoNjD24t2hrocIQQ27ynjb4t3dog9eIcJt8/nZ9/BI1Q64ZUF2+mYLQ0CwPL1R1i67lDEDzjpEOEOBoNs3roTW2wC//OvnyNmRQ/h7L2/NJeNu0sjeg/eIcL987ZczktP4+m/bxIDQIRG8z7azKEiR8TO7BLx4d63P4/evVP4n39tobjSFepyBAXxB2Semb8eV4R+4Ud0uIuLSzGZLXzy3QExuktoUa3bxzPzcyJyPHjEhru21kl+YSn7i9ws/eFwqMsRFGxffhX/XJYbcQ1sERnuQCDA6h82kNIrhTc/2xHqcoQwsHhNHjvyKvFG0Pl3RIZ7/YatjMoexosfbcUdRovXC6H1wr82UeuOnJViIi7cBUUlxNhiWJ5zlNxD9lCXI4QRd52fuRHUgy2iwl1XX8+OnXuJMsbw8Tf7Ql2OEIb2Hqliec6RiLj+HVHhXrHqB0aNHMq8j7eKCReEs/aPL3OpdftCXcY5i5hw792fR0KnTnz9Uz4FZa277JHQsXj9QeZ9uDns994REe76ei/rN/5MSkpP/v2dGMIpnLudeZX8tKM4rFvPIyLca37cwCUTxvHOol3Uh/EPQ1CWv32+I6xP78I+3OUVldR5vBTZveTsOr350gXhdNS6ffz7271h27klrOeckWWZZd98zxXTp3DPK62/6sipOAo2U5W3pvHfAV8d/roaUi58kPLcL/G6ykCWsXQdTGyfCwGoPrKeqoPfo9Ia6DJ4FlpjLAAFOfNJ6DcVnTmx3d+HcGJfrM1jxtg+YTkHeljPobYzdy8lpZX4dEkh74kmBwMc/fEtLN2G4K0tBUmiU8ZlBP1eDq9+kc5Z12Kw9SBvxVx6jvszzpJd1NcUkNBvKrVF26mrzieh39SQvgehZWMGJXP77wZiCLOAh+1hud/v55uV6xgxbLAi5kGzH/wetc5ETI/hJGRcRsJ5UwDw1zuQg35UmobFCSWVGjngI+ivQ1KpCQa8VOWtJq7vRaEsXziJtVsLqaj2hLqMMxa24d6xay+Dzu/Pyk1HsTtCu+xPwOuiKm8NCf2mAQ1roEkqNcU/f8yR1fMwxvUiypQAQHz6pRz96W2cJTuJSRmFff9KYnpmN4ZfUB5Zhjc/2xZ2595hGW6fz8+qtesZOmQgC1aGfr7x6iM5mBL7ERUd1+T2zoN+T+9LniDg81C57zsAzJ0z6Tn2XroOm03Q78VTlY85eRBlu76gIGd+k3N4QTl2HqykJMzmAwjLcG/dnsvgQefzTc4RqmvrQ10OtUXbsHQb0vhvV9le/HU1AKg0OsxdBlJfU9jseeW5S0joNwV3xQGC/nqSh/4BV9levC4x9lyJPly+B3dd+PRcC7twe70+vl21liFZ57NwdV6oyyHgdeNzV2Cw9Wy8rbZ4O5X7vkOWZYIBP7VF2zDG927yPGdpLhq9Fb01GTnoR5JUSJIEgBwIn1+gjiRnV0lYjTIMu3Bv3ZFLet8+bNlTqoi9ts9diUZnQVL9uthcQr+pBP11HFkzj/x1r6CP6UpMyqjG+4MBP/b9K4hPmwiAMb4vPk8Vh1Y+j9YYi87Sud3fh3Bqsgwff7MHT314fPmG1aWwYDDI3Bff5E+33MBf/7mVvflVoS5J6GA0ahX/fHIiJmNUqEs5pbDacx86cpS42Fgcbr8IthAS/kCQL9bmhUWf87AK9+p1GxgzagSL1x4OdSlCB/bdhvxQl3BawibclfZqCgqL6ZrcmbVbm7c8C0J7Ka/2cKjYEeoyTilswr1h8zayBvTnh+1F+PzBUJcjdHBL1uYp/rJYWITb7/ezZl0OWVkDWP1zUajLEQTW7yhGpZJCXcZJhUW4D+cXYjGbMBgM5B6qDHU5gkC9L8BPO4oJBJV7FBkW4f552y4GDcxk3dYisTqnoBjfbcynrl65reaKD7fP52fjlu0MGpgpDskFRcnNq0SjUW6ElFvZL/IO52OLsaLRaMW1bUFR/AGZnQpeg07x4d78804yzktl0+6yUJciCM2s2Vqo2FZzRYfb7/fz87ZdpKens2lPeajLEYRmNu8pRavQQ3NlVvWLwqJSZDlI9+REth8Q4RaUp8bppbhCmeO8FR3u/QcP0717VwrLHGE11E7oWHJ2lShyCmRFh3v7zj1knJfK1gPi2ragXLsP2RU5BZNiw11XX09+QSE9e/Rkhwi3oGB786uI0iovSsqr6BcFhSWAROfEOA4UVIe6HEE4IYfLi1OBCwcqNtxH8guxxVjw+oLUOCNnQXQhMu05ory14BUb7rzD+aT07M7h4ppQlyIIp7R1f4XiVgVVbLiPHC2ke9cuHChQ/rhZQcgrrCYQUFaLuSLD7XK5qa110SkxkYNFYs8tKF9RuUtxnVmUVc0vysorUUkSiQlxHC4Se25B+RwuL0qba1SR4S4tr0BGxmoxht0qD0LHVV4d2mWtjqfIcOcfLSI+Lhan24tfYecxgnAihWW1oS6hCUWGu7S8gk7xsVTWuENdiiCctrwiB0EFdUNVZLjt9hri4mxhuWyq0HGVVLqoV9B85ooLdzAYpLqmBluMlTK7ss5hBOFkal1esec+GbfbgyzLmMxmyhTWQCEIJ+NwK6snpeLC7ah1IkkqdDo9DpeyPixBOBmn24ekoNmOFRfuWqcLkNFqNIrrzicIJ1Pr9qJRKydSyqnkF16vD2TQaDTUe5XTOCEIp+L0+ES4T8bn8yHLMhqtRlEtj4JwKsGgjD+gnEUKlBduvx8ZGa1WK/bcQthR0nRLygu3t2HPrRWH5UIYUlL/ck2oCzheXX09KpUKtUqlqG/BSPLA9YPo290W6jIiiiw3HG3qtOpQl9JIgeH2olKp8AcCaNQKuq4QQYZmdOH9Dxfg84mrEa3FXlnN76+6jDhrz1CX0khx4ZZlGUmS8AcCRCnoWzBSGHQaZFmmuFis4NKaNBoNpmhjqMtoQnHhjtJqCQaDBPwBxQ1+jwSxFj1BGZ585O5QlyK0McWlR6eL+uWSgl/suduAzaITyyB3EIoLd1RUFLIsE/CLw/K2EGvRK6qLpNB2FBdujVqNJDVc79ZHiXC3NptZp+g1pYXWo7hzbo2mIdAejxubWRfiaiJPvNVAlKblL0054Kd2x/ftWk9HIKm1mPplI6m17fq6igu3VqtFklS4XS7irLGhLifiJMY1b9HduWUj5aXFjBg2jIqlb4WgqsimMpiIPm8E7X02pLhwm6KNSBI4nS4SEjuHupyIEx9jaHbbhrUrMZmteB1iTba2IKnUhKIVU3EnXyaTEVmGWpeLuBZ+EYVzE2NqfqrjrKmhU+cuBF3V7V9QRyDC3cBkikaWZZxON7EWfajLiThmY1STfwf8fjweN9FmC7JTeetdRYKGc20RbvQ6HRq1mhqHA5tF7Llbk0atQnvc5UWP24VKpcIUHU1QhLtNqKOtyMH2HwqquHBLkkRMjAWXy4Onzksnmwh4a7FZdPiOGyPvcTcs+mA0Ggg4q0JRVsTTmGOR2r05TYHhBoiNjaHe66Ws3E7PLtZQlxMxYs36ZiPt3C4XkiRhNEbjF+FuE2pzHGjav+1akeHu0a0rbncdlfZKeiaZQ11OxLBZmjemedwugsEghuhoseduIxprAipN1Kkf2MoUGe6uXRKRZZnKSju9kkW4W4vNrG82jNZVWwuyjC7aTMBZHZrCIpzWlhiS11VkuOPjGzqvlFdU0rOzJcTVRI44q75Zf31HtR290YharSFY5wxRZZFNY4kPyesqM9xxtoY9t72KBFu0GPrZShLjjEjHjRqpqbJjibHhdYmlktuK2hSaWW8UmRq9TkdcbAwut4eyimoxJVArSYhp3vXUUV2F1RYrGtPakNpgCsnrKjLcAN27dcHt8lBQWMj5feJCXU5EaGkgjtNRg9kaIxrT2ohKH5pgg4LDnd63N566egoKixgowt0qLNFNW2yDwSDOWgcms5WgCHeb0CX1IugLzbJYig13965dkCSJgsISenWzifPuc6SSwKBvOuSw3uNBAqJN0chOMWikLeiSU1FpQzN0WbGJSUpMICpKi9vtpqyimvQeYvjnubBE65qthuFxu0CSiDYYxWF5G9H3zERSh2bwpWLDrVKpOK9vbxwOJ4UFBeK8+xzZLDoCx4Xb7XKCBMZoo2hQayO6pF4he23Fhhsgo19fPHV1HDlawLCMTqEuJ6zZzPpmow49bhcEZQxG0TutLajNcSHba4PCw929axeg4bw7IcZIghhEctZsFj0qVdNr3B5XQ9dTveid1ib0XVKRA6FbEkvR4U7sFI/VYsLt9nDw0BGyM8XMLGcr1qIjStv0x+2otqOJikKrNxJw1YSossil65qGKip0cxIoOtySJHHB4IFU1TjYfzCPMQNFuM9Wos2IWtX0x11TVYXVGoO/zgmycpaejRSGHv2RVKGLmKLDDZDZry9yIMiR/AK6JJjE+O6zlBDbvHdaTbUdS2wsPqfYa7c2Sa1Fm9A1pDUoPtzdunYmOtqI21PHvgOHGD0wOdQlhaWWpqxy1lRjsdpEY1obMKScD4HQLrSo+HCr1WqGD83Cbq9h9559TBreXayYcRaOnxhRlmVqHTWYLDGid1obiM4YjRQV2qNMxYcbYEBmOsFggMKiEoIBHwNSE0JdUtgxGZv2TvN56wkEAkSbTARF77TWpVIT3feCZiPw2r2MkL76aeqW3JlOCfHUOl3s2LGTaSN7hLqksGLUa1q8xi1JEtFGo9hztzJD9wxFNFCGRbglSeKiC0dSU1PL7n0HyOgVT3yMmPb4dMVa9Pj8Ta+3ul0ukCDaaCBQK8LdmqIzRiFpQ//7GRbhBsjMSP+lr7mH3L37mTRc7L1PV4xZR/C4XbfH7QJZxhBtwu8S4W41koro9OEhvQR2TOgrOE1Gg56RwwZTUVHF9h25TBzeA406bMoPqViLHpV0fO80J/Iv4Ra901qPLrlvyM+1jwmrdAwfOgh/IEClvYqysnIuGd491CWFBZtZ32zIrNPhQJIkdEazuBTWikwZo5BCNMTzeGEV7s5JnejTuyf2qmpyNm7m6gmpYpz3aUiIMaA9btne6qpKoi1Wgn4vsj80kwlEHLUGU+bYhoX/FCDsknHpxWNxuTwUl5RRXlHBxcPE3vtUWlq211FlJyYmFp/oU95qTOdlh2RlkRMJu3D36dWDXindsVfVkLNhk9h7n4Y4a/OW29qaaswxNvzifLvVWIbPQKVTTvfosEuFJElMnTQel8tNSWk55eXlXCL23icV08LEiLWOGixiYsRWE9W5N1EhWnzgREI3kvwc9OnVg969elBSWs76DZu4ZupkVm4qwFMf2r68StXSsr11Hk/Dsr217ds7bWVeNf/ZVYEE6DQS/++CLizYWU5x7a/n/SVOL5mJ0Tw5vgdf7bPz6c5yzDo1j4zpTpK54b08tuIwcwYn0V0h/R2sw2cgabSnfmA7Crs9NzTde5eWVXAkP5/rJvYNdVmKpNWomp22NO2d1n7L9hbU1PPe5hL+MqEHb0zrwzWZnfjL9/k8Oq47b0zrwxvT+nDniC6YotTcNqxheO+CneW8Mz2VK/rFs2RvwxfR2sM1dLfqFBNsjTWB6NTBimlIOyYsww3QO6U7aam9qKisYt2POUwY0o3uiWJdsePZzDp8vuZzpzWEu32X7dWqJe4ekUzsL/3c+8YZqKrz4/tlbjdfIMiLPxTyxws6k/DLNMwaSaLeH8TtC6JVSdT5g3yWW8F1A5Qz7ZZl+AxFdFo5nvIqOk2SJDFz+iTq6+px1DpZv2Ez/zWzf6jLUhxbC8v2etxuAIzR0fhd1e1WS6IpiqFdG76AZVnm3U3FDOtqRvtLZ6TlB6qIM2gY2f3X9eFuykrkgeWH+OGIg+nnxfPJjnKmpcVi1CpjL6kymLGcfyGSWlmH5BDG4YaG694TLhxJaVkFW3fkEmfWMGpAl1CXpSi2FsZxH+udpjeaQtKgVucL8tyaoxTVerk7+9fx+Z/nVvL785uO+BvVw8pbl6Xy7MU98fgD7Cl3c2GvGN7eWMxjKw6zMLeivctvwjLicpQ6Bjmsww1w0YWjMBoMOF0uVq1ex5zpGRj1YdlO2CZiLbrmy/Y6nYD8y8SI7RvuMqeXe7/OQyVJPH9JCqaohj3wgUoPAVkmMzH6hM/928YSZg9O4udiJx5fgKfH92BTYS1Fjvr2Kr8JTUwi1sETUWnbf+3t0xH24TYa9Fw541IqKqsoKCzm8OEj/L/LxeH5MbFWfbPeaTVVlegNRlRqNcE6V7vV4vYFePCbQ4zsbuHhMd3Q/aahb0epiwFJphP2y84pcBBn1NInzoAvIKOWpMbH1gfkFp/T1mwX/wGVAg/Hjwn7cAMMPL8fqb1TKK+ws+aHn8jsZSP7fDGZIkBSbHSzKY1rqu1YbbF423nutCV77JS5fPyY7+C2JQca/3fU+Smq9ZJoajko3kCQj7eXc+Oghka0rC4mylw+/rBoH0mmKFJs7d9qru/RH0OPDCS1Ms79WyLJ8vHD+MNTeUUlc198G1uMhZSe3bhsyiRuf+F7qmpDc8imFH+9bRQZvZqu1vLBGy9ittq4+MIxlL3/YIgqC2OSii5zXkIf4gkQTyUi9twACfFxXHXFZErLKigqLmX7zl3c8/uBoS4r5GyWFnqn1dRgtlpF77SzZB44AY1Z+WvGR0y4AYYOHsCAzPMoLa0gZ+MWEixqpo5KCXVZIWWJbhruYDCIy1mL2RLTrh1YIoVKZ8R24fVo9Cdu+FOKiAq3SqXiqiumEBWlpbbWxbLlK7j24lT6pXTMFUJVEhh1Ta8c1Hs8QMPif+3d9TQSWEZdpchr2i2JqHADWC1mrrt6OhWVVVTaq/j6u1U8fMOQDjnnmiVa19j765hfe6cZxQwsZ0jXJRXroItQRyljMoZTibhwA2Sc15eLx4+msKiEQ4eP8vO2bTz+h6FEdbChoTaLDr+/hTW5AaPRKOZOOwNSlIH4y+9DraAhnacSkb/tkiQxZeI4+qX3paSknE1btuNyVHLn1QNCXVq7irXoOf5SiNvlbJwYUTSonT7bpD+BAmY0PRMRGW4AjUbDrGtmYLVaqLRX8+3K1aQmR/O78X1CXVq7sVn0qI+7xl3ndhOUQ9M7LVxF9x+LodcAdNHhNTApYsMNYDJF88ebr8Hn8+FwOFm8ZBlTR3ZnUgeZWDHWrG+2bG9NtR2tVotWZyDgcoSosvChsSURe8ktRBnDK9gQ4eGGhsElN113JRUVVdirqvns86VcPymNUQMjf4BJp1hDC8v22rHE2PB7xLK9p6TSEDfjXlBrFDNd8ZmI+HADZGakcc3vplJcUk55hZ1FX3zFrZf3Z3C6csYEt4UEWwsTI1ZXYbGJiRFPh3XMNagt8WjDpHX8eB0i3ADZwwYzY+olFBWVUlxSzuKly7nv2kHNumZGkhNNjCjmTjs1Y+oQLFmXoDdZQ13KWesw4ZYkiQnjspl48RgKi0ooLCph2fIV/PdNQyJ21VBrC8v2Oh01mK0xBGtF77QT0XVJJXbqHWF12aslHSbccOwS2YWMGTmUowXFHDpylC+/+oYHZ2UxIjMp1OW1OpOhpWV7/RijxbK9J6KN7ULC7x4mgIRKgVMnnYkON6uBSqVi5vRJ+P1+fszZgizLLFy8lNtmXorZGMU3OfmhLrFVtLRsr9vlQpJUmIxGgkXisPx4alMMCdc8jsfnx2oL38PxYzpcuAHUajXXXDkNg8HAd9+vIyjLLFj4BddNn4LZqOWzVQdDXeI5O7Zs729nPm2Y9bShX7noetqUFGUg/urH8ARkbPGR0Q4T3scd50ClUjFj6sVcNvkiCotKKSur4N//Wcylw5P504yMZhMchBubWU+w2Z67Ye40ozEav2hQ+5VKQ/zMB/Gq9NjiI+cKSocNNzScg18yfjS/v3Jaw2WySjv//s9i0rvqeeaPw5qds4aTWIuO47+f6twuZBn0ouvpb0jETruDgCkeW6fIanfp0OGGhoCPGjGEW264isrKasrLK/l8yTI8NaW8fM9oena2nHojCmSzNF+2t7amBklCLNt7jKTCNuVWpKRUrAmRFWwQ4W40aEAG99z2BwKBIKVlFaz5YT0bNmzguf8awYWDlT2dTkviW1i2t6aqEpMlhoDfixzwhagyhVA39D4LJKZissWHZQ+0UxHh/o2ePbry57vmkNgpnqMFxeTu2c9/Fi3hhkl9uPvqARh04dP+mBjbcu80q82Gv4P3TpM0UcRf+TBuYwLWuETUCp7k8FyIcB8n1hbDHX+6kVHDB3O0oJiCohL+9clCYvUe3rx/HP17h0dLapy1eQcMR3U1Fmtsh25MUxnMJFz7JA7JSEJyD7RRypxzvDWIcLdAp4vid1dMYdbvL8dur6GouJRvV67h+zVreGhWFnOmZyh+4gdbC8v2OmtrMMd03IkRNbYkEm+cS5nTR+eevdFow7fB9HQo+zc0hCRJYtiQgTx83/8j6ZfD9H378/jgo0/p3Uni1fvGktZduTNgmo1Nf3H9Ph/1Hg/RpvZftlcJdF1SSZz1LPlFJfRM6xexh+K/FT4nkSHSKSGeO//rJlavzeGLZStw1Lr4avkK0lJ78dgfstm8t5z/+3I31QqaH12rUaFpYdleji3bW3koRJWFhmnQxVjHXsvR/Hz69h94ysYzp9PJiy++yMaNG1Gr1VgsFh566CGcTievv/46//znP9up8nMj9tynQaPRMOHCkdx/9x+xWkwcLShmx669vP+vf2OkmrceGMfl43o3W5MrVFpattfjdqHqYBMjqnRG4q54APWgKRQXl9DnvP6nDHYwGGTOnDlYrVY+//xzFi9ezG233cacOXOorq5un8Jbidhzn4GuXZK47445rF6Xw7Jvv8dur6aurp6du/YwdnQ2lw4fxzuf72LznrKQ1mmzNF+21+1yIdPQ9dTbAc65dV1SiZtxH/sP7CcuOkjPPn1P63k5OTkUFxdz5513Ng4cGT58OHPnzsXlcmG325kzZw75+fmkpKTw6quvUlZWxg033MDKlSsBeO211wC44447GD58OP3796e8vJwHHniA+fPno9frOXjwIGlpabzwwgtEtVGjngj3GYqK0nLx+FFkDezPkq++Y/PWndQ4aqmqrqFXz27cevkIql2pfLh8P1v2hibksS0s21vndjVOjOiJ6HBLWEbMwDR0Gls2rOf8C0ZgMp9+R6Tc3FzS09ObjQgbO3YsOTk5FBUV8fbbb5OcnMxVV13Fjz/+SJ8+J56Xr6qqijlz5jBs2DBycnL4+eefWbZsGZ06deKqq65i3bp1jB8//qzf7cmIcJ+luNgYbrxuJqNHXsCChV+Rf7QIt9vDoSMFpKX24tbLs3DWpfHh8n1s3F3arrXZLHo06qa/nE5nLTJyRPdOU0fHEHvZXThVRvbtzmX4uIvPeNimSqVCpzvxzCvp6el069YNgN69e1NVderPcsCAX2fdTU1NJSkpqfH5NTVt1+dAnHOfA0mS6J3Sg/vvmsN1V0/H5/eTf7SQzT/v4IOPPmXn9s3Muawvr907hlEDurTbOXmcRdd8YsRflu2VVCqC9e52qaM9RaePIOmWF9hfYkfS6jj/ghFnNR67f//+5Obmcvz6mPPmzUOWZTSaX/eHkiQhy3Ljn8f4/f4mz9Xrfz2S+u0Xx/HPa20i3K1Ao9Ew/IJBPP7gnVx/zeUEg0GOHi1my9ad/Ovj//Dzlg1cNa4r//fYxdwwOb3F3mOtKTE2ulnDkaPKTkxsHL4Im/E0qlMPOl3/DJrsq9m8YQP9Bg0lKbnbWW9vyJAhxMXF8frrrxMIBABYu3YtCxcuxG5vefYai8VCdXU1drsdr9fL2rVrz/r1W5M4LG9FUVFahg4ewKDzM9i2I5cvv17J0YJi7FU17D94mLjYGDL79+Ple0azP7+KL3/MZ9PuUoLHj808RwkxLfdOa+h6Wt2qrxUqKoMJ65hrMZw3grUrvqFz1x6MGH/JOfcRlySJN998k7lz5zJ16lQ0Gg02m413332X2traFp9jNpuZPXs2V155JUlJSWRmZp5TDa0lYtbnViK/38/uvQdZteYnDuQdQa1SER9vw2gwkJbai/79M7DFWNiwq4S124rZtr8Cf+Dcpxt+9+GL6BzfdBXKN557nPTzBzGkTzKVnz1/zq8RMpIK86CLsY6+htztWykqKiB7/ERiYsOjW3B7EnvuNqTRaMjMSCMzI43SsgpyNm1l3Y+bKCuzU15pZ0fuXmKsFlJ7p3DTpBQSrh/Mlj0lrN1WwtZ95Xjq/ad+kRZYopteWjm2bK/JbAnrZXv13fsRc8kcqlwevly0gAtGjWPAiNEROaKrNYg9dzurr/eSu2c/P6zfzIG8I8iyjMGgJ8ZqwWKOJrV3T3r16kXXLp0oLKtl24FKdhysJDevElfdqcOukmDR/1zWZCYZt8vJ688+xqVXXE2yM4/qHz5ry7fYyiSMfbIwDZ8Blk58/+1yuvboSebgYag1Yt90MuLTaWc6XRSDBmQwaEAGTqeLfQcOsXHLdnbvPUhRcSmHjhRg3bQNo9FA58QEuiZ35roJ3Um+fjCllU725ldzqLiW/JJajpQ4qHF6m2zfYmpYtlen+rXvtMflQpIkTEYDgZLqdn7HZ0dSa4nOHIN5+Azc3gArV6/CGG3iwsmXEW0Kv6V9QkGEO4RMpmiyBvYna2B/3J46Dhw8zI5de9m99wDlFXaO5Bei3boTq8WMwaCnc1InEhPiGNIrlolDetMp3oY/IHO01EFBmZOy6jpUkoTfH0Sn/TXcbpcTAGO08udOUxnMmAdPwjz4UkqKClixcCEarZYxl0whsUv4TZoRSiLcCmE06Dm/fzrn909HlmUq7dUUFBazZ99BcvccoNJeTGFRCcGgjFarwWgwYDDosVrNJMTZsMVYSdJJDL1gULPplY6tyW0wRuNQaLi1Cd0xDZ6EKWM0e3dsY/F7b5PcoycXXTaThMTOoS4vLIlwK5AkScTH2YiPszHw/H7Iskyt00VlZRUVlVXkFxRxJL+QouJSSsvK2X9AAiSCwSBjRl5AlLbpcEaP20UwGESnsGV7NdZORGeMwtB/LEQZ2bZ5A9tff5HMrKH87uY/YbbGhLrEsCbCHQYkScJiNmExm0jp2Y0LBp8PNLSC1zpdDf/XulCrVeh0zQch1FT9Ztled2g7sWjjkjH0HYohbQRaWyJ7d21n238+o7ammuwJl3DT7X9Gpw/vZXyUQoQ7jKlUKqwWM1bLyRuYaqrsWG2xIVm2V2W0oE/uS1S3fhj6DoUoAwf35LJz2XIKj+TROz2DEeMvpmfvvqL1u5WJT7MDqK2uwmKz4WvrcdwqDVGJPdAn90WTfB765L6oDSYqSwrYf/gwez/+mJKio3Tv1YcBF4xg+rU3YTC2bVfcjkyEuwOoddTQs0/f1jnfllSoTTFozHFoLPFoLPGorJ3Qdu6DIbEHrho7JSVF5B86zOFv11BVWYFKkuidnsGwcRPoltIbY7Tp3OsQTkmEO8IdW7ZXGxWFITmVhOufIeiuJeiuRnY7QA4gSSpkSQJJhYSELKkab5NUaiSTDY0lHq0lDm20BZ/HjdvpwOWsxV7rpKqqioLvVlJ05DA+nxeNVkvXnr0YeMEIEpO7kpjcjagwXcA+nIlwRzhvfR1+v58jB/fjdDjQGQzo9Qb0BiM6fReQJJBl5OP+b7wtKOMsL6XGnktVZQVV9goCfh+S1HC5TZaDxCd2oWefvgwcmk18YmdiYuPCfvnbSCDCHeEkSSKxS1fKS4ooLylCQgUSJ+iP3dATWZYbMh8MykgSRJssWG2xJCZ35bwBg7DExBJtMhNtNmO1xRKlaz7zixB6om95ByHLMj5vPXUeD/V1HoKBYGPIJUmCX/6UkBqDH6XTYYg2dYhpgCORCLcgRChxYiQIEUqEWxAilAi3IEQoEW5BiFAi3IIQoUS4BSFCiXALQoQS4RaECCXCLQgRSoRbECKUCLcgRCgRbkGIUCLcghChRLgFIUKJcAtChBLhFoQIJcItCBFKhFsQIpQItyBEKBFuQYhQItyCEKFEuAUhQolwC0KEEuEWhAglwi0IEer/AzWS3+sa5AISAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart of churn\n",
    "churn_rate = df.Churn.value_counts() / len(df.Churn)\n",
    "labels = 'Non-Churn', 'Churn'\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(churn_rate, labels=labels, autopct='%.f%%', shadow=True, explode=[0.1, 0])  \n",
    "ax.set_title('Churn vs Non Churn', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd71ec9c",
   "metadata": {},
   "source": [
    "<b>27%</b> Chrun rate describes that the given data is a imbalanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf22038",
   "metadata": {},
   "source": [
    "### Analyising Numerical Features\n",
    "\n",
    "Let's analyse two numerical features, Tenure, Monthly Charges and Total Charges wrt Churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a5f425d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MmzcPn8/HPffcw5gxY5g/fz6PPPIIo0ePZvny5Tz55JNUV1czcuRI5s2bB8DTTz/NokWL+OMf/0hycjIvvfQSAAsWLGDRokXMmDGDyMhIli9fDsDixYtZtGgRGRkZGI1GXnzxRTQaGaJBCCGEuFSKqqpqRxeio+Tl5XHbbbfx0Ucf0bt3744uTofzlRdTe2p/h5zbPHAsepu95Q2FEEJckZa+++TPaiGEEEKESDAQQgghRIgEAyGEEEKESDAQQgghRIgEAyGEEEKESDAQQgghRIgEAyGEEEKESDAQQgghRIhMKCBa5Cs7h6/sHIpGhz42GV1UXEcXSQghRJhIMBAX5XWcpXLvB6AG6xZodUSPm4E+JqljCyaEECIspCpBNMtXdo7KLz5EGxlL7K33ETPpe2iMFir3bMJfWdrRxRNCCBEGEgzEBanBAFX7P0JjiiA6dToaowVtRDTR42aiaPVU7vsQNRjs6GIKIYRoYxIMxAV5Ck8RdFdjHXEDGqM5tFxriSRi5E0EayrwFBzvwBIKIYQIBwkGoglVVak9cxBthA19Qt8m6w32fmgj46g5+YU8NRBCiG5GgoFowucsIFBZgnnAGBRFabJeURQsQ1IJ1lTKUwMhhOhmJBiIJmpPH0QxmDH2GtLsNgZ7P7RR8dSe2o+qqu1YOiGEEOEkwUA0EvR58JXkYuo9DEXbfG9WRVEw972KgKscf4WjHUsohBAinCQYiEZ8jlxQVQz2fi1ua0gaCBqtVCcIIUQ3IsFANOItzkExmNDZ7C1uq9EbMdj74Sk8gRoMtEPphBBChJsEAxGiBgJ4HWcxJPRFUVr3v4ax1xBUrxtfSX6YSyeEEKI9SDAQIZ5zJ1H93lZVIzQwJPRB0RtxFxwLY8mEEEK0FwkGIqT29EFQNOjje7d6H0WjxZg0EG9xDmpAqhOEEKKrk2AgQtw5h9HH9UKjM1zSfgZ7fwj48TkLwlMwIYQQ7UaCgQDAX12Ov7wIfVzKJe+rj+sFGh1ex9kwlEwIIUR7kmAgAPDkHQW4rOmUFa0OQ3xKXXWCDHYkhBBdmgQDAYA77whodOii4i9rf31CX4K1VQSqy9u2YEIIIdqVBAMB1AUDg73vRUc7vBhD/WRLXkdOWxZLCCFEO5NgIAj6vXgKT2FMHnTZx9CarWgj4/AWSzAQQoiuTIKBwFt4EoJ+jEmXHwwADPa++MuLCPq8bVQyIYQQ7U2CgcCdewQAQ/LAKzqOPi4FVBV/WWFbFEsIIUQHkGAgcOcdQR/bC6058oqOo7clgkaLt1SGRxZCiK5KgkEPp6oq7vxjGHsPu+JjKVod+pgkfBIMhBCiywprMNiwYQPTp09nypQprF69usn67OxsZs+eTVpaGosXL8bv9wNQUFDA3LlzmTp1Kg899BAulwuAyspKHnjgAaZNm8bcuXNxOByNjlddXc3tt9/Orl27wnlZ3UqgqpRgTeUVNTw8nz4uhUCVk6Cntk2OJ4QQon2FLRgUFRWxYsUK1qxZQ3p6OmvXruXEiRONtlm4cCFLlizhgw8+QFVV1q1bB8AzzzzDvffeS2ZmJqNGjWLVqlUArFy5ktTUVDZt2sScOXNYunRpo+M9++yzVFZWhuuSuiXPudMAGJMGtMnxGkZOlKcGQgjRNYUtGGzfvp0JEyZgs9mwWCykpaWRmZkZWp+fn4/b7Wbs2LEAzJ49m8zMTHw+H1lZWaSlpTVaDrBlyxZmzZoFwMyZM9m6dSs+nw+AjRs3EhERwbBhV/5IvCfxnjsNKJc0o+LF6KLjUXQGaWcghBBdVNiCQXFxMQkJCaHPdrudoqKiZtcnJCRQVFREWVkZVqsVnU7XaPk399HpdFitVpxOJwUFBfzP//wPv/jFL8J1Od2Wp+gU+rhkNAZzmxxPUTToY3vJEwMhhOiiwhYMgsEgiqKEPquq2uhzc+u/uR3Q5PP5+wAsXryYp556CpPJ1JaX0CN4z53GkNg21QgN9HG96oZHrq1q0+MKIYQIv7AFg6SkpEaNAx0OB3a7vdn1JSUl2O12YmNjqaqqIhAINNnPbrdTUlICgN/vx+VyUVZWxqlTp1i8eDF33XUXhw8f5sknn2Tnzp3hurRuI1Bbhb+yBGPSlY1f8E36mGQAfM5zbXpcIYQQ4Re2YDBx4kR27NiB0+mktraWzZs3M2nSpND6lJQUjEYje/fuBSAjI4NJkyah1+tJTU1l48aNAKSnp4f2mzx5Munp6UBdm4LU1FSGDx/Op59+SkZGBhkZGYwaNYrnnnuOCRMmhOvSug1v0RkADIn92/S42qhYFJ1BBjoSQoguKGzBIDExkccee4x58+Zx9913M3PmTMaMGcP8+fM5dOgQAMuXL2fZsmVMnTqVmpoa5s2bB8DTTz/NunXrmD59Onv27OHRRx8FYMGCBezfv58ZM2awZs0alixZEq7i9wihHgltXJWgKBp0MYn4nBIMhBCiq1HUhor6HigvL4/bbruNjz76iN69e3d0cdpdccbL1OYcpt8jrwPgKy+m9tT+Njl2zcl91BzbTeyt89AYW27YaB44Fr3N3uJ2QgghrkxL330y8mEP5jl3qs2fFjTQx9a3MyiTdgZCCNGVSDDooYJ+L77SgjbvkdBAF5UAGi0+aWcghBBdigSDHspXWgBqEENCn7AcX9Fq0duknYEQQnQ1Egx6KJ8jFyBswQBAF5NEoLKUoM8btnMIIYRoW7qOLoDoGN6SXKgfpTC0zB+gqqbtvsSDEQmASlVRHprYizfu1PoD6NvszEIIIS5Xq4LBww8/zPe//30mTpwY7vKIduJ15KKPTUbRff117POrnD3XhqMVBs3YUSjPP0u1N/qim0YM7rGdY4QQolNpVVXCHXfcwapVq0hLS+ONN96gvLw8zMUS4eYryQ1rNQIAGh1+kw19bUl4zyOEEKLNtCoY3Hnnnfzv//4vq1atorS0lHvuuYeFCxdy8ODBcJdPhEHQ78VXVoQ+PszBAPCaE9C7yyAYCPu5hBBCXLlWNz4MBoPk5ORw5swZAoEAcXFx/OpXv+J3v/tdOMsnwsBXkh/WHgnn81riUdQgercz7OcSQghx5VrVxmDFihWsX7+ePn36cO+99/Lyyy+j1+upqanhlltu4ZFHHgl3OUUb8paEv0dCA585vu5ctSX4LAktbC2EEKKjtSoYOJ1OXn/9dYYPH95oucVi4cUXXwxLwUT4+By5oNGGRicMJ1VrwGeIRl/jgLgRYT+fEEKIK9OqqoRAINAkFDQ8JbjpppvavlQirLwl9T0StO3TQdBniUdfWwpqsF3OJ4QQ4vJd9InB008/TVFREXv37sXp/LqO2O/3k5ubG/bCifDwOnLDNkfCBc9njsdSfhKduxy/ObbdziuEEOLSXTQY3HPPPRw/fpyjR4+SlpYWWq7Vahk7dmy4yybCIOj34i8vxjpqUrud02f5up2BBAMhhOjcLhoMRo8ezejRo7nxxhtJTExsrzKJMPI7C+t6JMSltNs5gzozfn1E/XgGQ9vtvEIIIS7dRYPBggULePnll/npT396wfUbNmwIS6FE+HhL8wHQt2MwAPCZEzBWF4CqgqK067mFEEK03kWDwfz58wF46qmn2qUwIvx8pQUA7dIj4XxeSzzmyjNovZUEjBcfHlkIIUTHuWivhFGjRgFw/fXXk5yczPXXX09NTQ1ZWVmMGCFdz7oiX2k+2qh4NAZT+563YTyDGhkeWQghOrNWdVdcsmQJr7/+OidPnuTJJ58kLy+PJ554ItxlE2HgK81v1/YFDQL6CAI6EwaZN0EIITq1VgWDw4cP86tf/YoPP/yQb33rWyxbtoz8/Pxwl020MVVV8ZYWoI/r1fLGbU1R8Jnj6xogqjKTohBCdFatCgaqqqLRaPj888+ZMGECAG63O6wFE20vUF2G6q1t94aHDbzmBLT+WrQ+V4ecXwghRMtaFQz69u3L/PnzycvL4/rrr+c///M/GTZsWLjLJtqYL9QjoQOeGPD1eAYyDbMQQnRerZorYdmyZXz44Ydcd9116PV6UlNTufvuu8NcNNHWGoJBR7QxAPAboghq9BhqS3BH9++QMgghhLi4Vj0xsFgspKamUllZyZdffsmYMWM4depUuMsm2pi3tABFb0Ib2UGjDyoKXks8eumZIIQQnVarnhi8/PLL/OUvfyEuLi60TFEUPvroo7AVTLQ9X2l+3eRJSqvyYHjKYI7HVF2Ixu8mqGvfLpNCCCFa1qpgkJGRwebNm2VY5C7OV1qAsXfHDknsNScAde0MPJG9O7QsQgghmmrVn47JyckSCrq4oM+Dv8KBIbZj2hc08JtsqIoWQ42jQ8shhBDiwlr1xOCGG27ghRde4LbbbsNk+vrx78iRI8NWMNG2/GVFgIo+rn2HQm5C0eA1x0nPBCGE6KRaFQzWr18PQGZmZmiZtDHoWnzOhjkSOqar4vl85ngiSr9CCXhRtYaOLo4QQojztCoYfPzxx+Euhwgzn7MQaP/Jky7Ea0nAWlo3b4InsuODihBCiK+1qo2By+Xi17/+NT/84Q8pLy9nyZIluFwyel1X4nMWoI2woTFaOroo+EyxqIoGfW1xRxdFCCHEN7QqGDz33HNERkZSWlqK0WikurqaJUuWhLtsog35nIWd4mkBABotXnM8Bpc0QBRCiM6mVcEgOzubxx57DJ1Oh9lsZvny5WRnZ7e434YNG5g+fTpTpkxh9erVFzzu7NmzSUtLY/Hixfj9fgAKCgqYO3cuU6dO5aGHHgo9naisrOSBBx5g2rRpzJ07F4ej7ouluLiYH/3oR9x5553MmTOnVWXraTpVMKCuOkHvrUDxezq6KEIIIc7TqmCg0TTeLBAINFn2TUVFRaxYsYI1a9aQnp7O2rVrOXHiRKNtFi5cyJIlS/jggw9QVZV169YB8Mwzz3DvvfeSmZnJqFGjWLVqFQArV64kNTWVTZs2MWfOHJYuXQrAihUrSEtL4x//+AcPP/wwzzzzTOuuvocIemoIuMo7VTDwWewAGGrlqYEQQnQmrQoG48aN47//+79xu91s27aNn//854wfP/6i+2zfvp0JEyZgs9mwWCykpaU16tWQn5+P2+1m7NixAMyePZvMzEx8Ph9ZWVmkpaU1Wg6wZcsWZs2aBcDMmTPZunUrPp+PpUuX8t3vfheAvLw8oqKiLu0udHM+5zmgc/RIaOAzxRBUdDKegRBCdDKtCgaPP/44FouFyMhIVq5cyfDhw/nFL35x0X2Ki4tJSEgIfbbb7RQVFTW7PiEhgaKiIsrKyrBareh0ukbLv7mPTqfDarXidDrRaDRoNBqmTp3KsmXLuO+++1p5+T3D110VO88TAxQNPks8hhppgCiEEJ1Ji90VP/zwQ9544w2OHj2KyWRi2LBhXHvttRiNxovuFwwGURQl9FlV1Uafm1v/ze2AJp/P3+f8Ko3MzEyys7P5yU9+wqZNm7DZbC1dXo/Q0FVRF5PUwSVpzGtJINJxDo2/tqOLIoQQot5Fg8GmTZtYsWIFjzzyCMOHD0dRFA4dOsTSpUvxeDxMmTKl2X2TkpLYs2dP6LPD4cButzda39B4EKCkpAS73U5sbCxVVVUEAgG0Wm2j/ex2OyUlJSQlJeH3+3G5XNhsNrZs2cK4ceOIiIhgxIgR9OrVi9zcXAkG9XzOArRR8Wj0Fw9z7c3b0M7AJU8NhBCis7hoVcJf//pX3nrrLWbOnMngwYMZNGgQd999N3/605948803L3rgiRMnsmPHDpxOJ7W1tWzevJlJkyaF1qekpGA0Gtm7dy9QN1HTpEmT0Ov1pKamsnHjRgDS09ND+02ePJn09HQANm7cSGpqKnq9nvfeey/UcPHEiROUlJQwcODAy7sj3VBn65HQwG+0EdQaMdQUtbyxEEKIdnHRYOByuejVq2mDtQEDBuDxXLybWWJiIo899hjz5s3j7rvvZubMmYwZM4b58+dz6NAhAJYvX86yZcuYOnUqNTU1zJs3D4Cnn36adevWMX36dPbs2cOjjz4KwIIFC9i/fz8zZsxgzZo1obEUnnjiCT777DPuvPNOfvnLX/Liiy8SERFxyTejuwp3MHD5oLBaobBaQ43vEnZUFLwWOwZXMaqqhq18QgghWu+iVQlarbbZda35h3zWrFmhXgQNXn/99dD74cOH88477zTZLyUlhbfffrvJcpvNxquvvtpkeWJiIm+88UaL5emJAjVVBN3VYQkGZys17C7QcbxMi0pdOxAFlb5RQcYm+rkqLkAzzUNCPBGJmKpyCZQVQnLHzvwohBCilXMliK4rHJMnqSrsLNDxyVk9Fj2M7+Wnd2QQgIJqDUdKtWQcN7KvKMDUgV7izc2HyIZ2Br78I3BVapuVUQghxOW5aDA4evQo1157bZPlqqri9XrDVijRdtp68iRVhY2nDBwo1jEizs/MQV705z1YGhobYFIfH/uLdWzJ0fPmQRN3DvEyLDZwweMF9Rb8hkj8BUfapHxCCCGuzEWDwYcffthe5RBh4nMWgKJBb7O3vHEr7C7UcaBYx8QUH5P7+C5YVaBR4NpEP0Nj/Lxz1Mi7R43c3NfLxBT/BY/ptSSiKzyB6veh6PRtUk4hhBCX56LBICVF6ny7Op+zEJ3NjqK98i/cs5UaPs7RMyzW32woOJ/VAD8Y6eH9Ewa2nDXgCyhMusB+nohELOUnqM39CsuAq6+4nEIIIS5fq0Y+FF1XW/VI8AYg/ZiBGJPKzEHeFkNBA50G7hri5eoEP5/n69ma2zSgeC0JoNVRe+KLKy6nEEKIKyPBoBtTVbXNgsH2fD3VPg2zBnsxXmKTVUWB6YO8jLXXhYM9hd84gEaHPnkINRIMhBCiw0kw6MYC1eWoPvcV90io8CjsKtAxMt5PSn3vg0ulKDB1oJehMX42n9FztLRxV1h975H4nAWhxpJCCCE6hgSDbqytJk/6JEePAtzc91JGL2pKo9RVK/SyBsk4biCv6uv//fR9rgKg5uS+KzqHEEKIKyPBoBtri66KJTUKX5XquL6Xn2jjlY9OqNfCnOEeIo0qfz9ipLS2rrGCNioBfWwvak7sveJzCCGEuHwSDLoxn7MAtDp0UfGXfYzdhXq0isq45Ct7WnC+CD18b4QHBVibbcRVf2jL4Gtx53xJ0Otus3MJIYS4NBIMujGfsxB9TBKKpvmhrS/G5YNDDi2jEwJEtPHwAjEmle8M91DtVXj3qBGfP4hl8HWoAR+1pw+07cmEEEK0mgSDbsznLLiiaoQvzukIqArX92q7pwXn6xUZZOZgL3lVWt744AzGPiPQmCJwHdsdlvMJIYRomQSDbkoNBvCXFV12MAgEYe85PYNtgYvOdXClrooPMKmPl8+/KuWdLaexDL6OmuN7UIMXHkJZCCFEeEkw6Kb8laWoAR/6mMsLBsfLtNT4Fa5LCs/TgvPdmOJn4ohY3t6UTa5xCMHaaty52WE/rxBCiKYkGHRToa6KcZc3rPUhhxarPsgA2+WNW3ApFAXunzqAEf1j+e8tHlStHtfRXWE/rxBCiKYkGHRTvtKGYHDpgxtVe+FEWV2jQ00rhz6+Ugadhid+dD0RkVaO+npRlb0LVQ1fFYYQQogLk2DQTflK81EMZrQRtkve98sSHSoKoxMuPBtiuNgijSy5fzyHfH1Qq0upOnu8Xc8vhBBCgkG35XMWYojrhdLa2Y7qqSocLNbRyxog3tL+f7H3S4rijnvuIqAqbH3vPQJBeWoghBDtSYJBN+Urzb+s9gWOWgVHrYbRCR3XK+DaMQOpiRtGYuWX/PX9LzusHEII0RNJMOiGgj4P/sqSy+qqeKRUB6gMi23faoRvGnjjHcRpXez9bCebd+V0aFmEEKInkWDQDYXmSLiMJwZHSrX0jQpiNbR1qS5NxNBxoNUzNbGIVe8c4NCJko4tkBBC9BASDLqhr2dVvLQeCfmltZTUahge2/GDC2lMEVgGXcMo7WlS4i08/9ZuChzVHV0sIYTo9iQYdEOhroqXWJWQdawMgGFxHR8MAKxX3UjQVcYT02xoNAq/fmMnVTXeji6WEEJ0axIMuiGfswBtZBwag+mS9ss66qR3ZIBIQ+foCWAZOg7FYEafs4snfnQ9Rc5afvM/Wfj84R90SQgheioJBt2Qr7QAwyUObHSu1MVZR22nqEZooNEbsY64AdeR7YzoHcHD3xnLwRMlrHrngAx+JIQQYSLBoJtRVbV+VsVLCwZZXxUBMKQTBQMA65hbUL1uXEd3cmtqH74/ZRj/yjrLun8d6+iiCSFEtyTBoJsJ1lQSdLsueSjkrK/O0SvWRIypc/0lbuozAp0tkaqDWwD4/pRh3HJdb/438whbvsjr2MIJIUQ3JMGgm/GW5gOX1lWxxu3j0MlSxg6yhalUl09RFCJH34z7zGF8FcUoisLD3xnLqEFxvPy3fXx5qrSjiyiEEN2KBINu5uuuiq3vkXDguAN/IMjYQdHhKtYVsY65GYCqA58AoNdpWfyj60mMtbD0zV3kSzdGIYRoMxIMuhlfaQGKVo8uOqHV+2R9VUSEWc+QXtYwluzy6W12zAPHUrX/X6jBujYQVouBX82fgEaj8MzrO6mo9nRwKYUQonvQdXQBRNvyOQvQxSahaLSt2j4YVMnKLuK6YXZ02o7LiWowSLGzpvn1QycTOLWSwn3b0Q24DgCNovCze67mv/93L0+/toPH516HQd+66z6f2aQj0tLBQz0KIUQnIcGgm/GVFqCP793q7U/ml1Ne5eG6EYlhLFXL/EGVL44WN79BsBcDjNG4dmeS7+3TaNWtqX34YGcOy9fsJW18v0ueUfLaYXYJBkIIUS+sfyJu2LCB6dOnM2XKFFavXt1kfXZ2NrNnzyYtLY3Fixfj99dN3FNQUMDcuXOZOnUqDz30EC6XC4DKykoeeOABpk2bxty5c3E4HAAUFxdz//33c9ddd/Gtb32LHTt2hPOyOi01GMBXVnRJYxg0fBlfO8wermK1DY2Wil7jsTiPoqtt3OBwcG8bE0cnczKvgp2HCzuogEII0T2ELRgUFRWxYsUK1qxZQ3p6OmvXruXEiRONtlm4cCFLlizhgw8+QFVV1q1bB8AzzzzDvffeS2ZmJqNGjWLVqlUArFy5ktTUVDZt2sScOXNYunQpAC+88AK33norGRkZvPjiizz++OMEAp2rP3578JcXQ9B/SWMY7DvqYGBKNLZIYxhL1jYqe00ARcGWv73JurFDExg5MI4vjjr46rT0VBBCiMsVtmCwfft2JkyYgM1mw2KxkJaWRmZmZmh9fn4+brebsWPHAjB79mwyMzPx+XxkZWWRlpbWaDnAli1bmDVrFgAzZ85k69at+Hw+7rjjDmbOnAlAv3798Hg81NQ0X1/dXYXmSGjlE4Mat48jZ5yd/2lBPb8xmqqEMUTn70TxuxutUxSFSWNT6GO38ukX+RSWuDqolEII0bWFLRgUFxeTkPB1y3i73U5RUVGz6xMSEigqKqKsrAyr1YpOp2u0/Jv76HQ6rFYrTqeTtLQ0oqPrutq98cYbjBgxgsjIyHBdWqflddaPYRDbujEMDhwvIRBUu0wwACjvOxltwE10YVaTdRqNwpQJ/YiM0LNpxxmZcEkIIS5D2IJBMBhs1AhMVdVGn5tb/83tgGYbk6mqikbz9SW89dZbrF27lhdeeKGtLqNL8ZUWojFb0VpaF4r2HS3GbNQyvH9smEvWdtxRfamN6octdxuoTSdTMhl0zJg4gEAgyMbtp/H5e16VkhBCXImwBYOkpKRQ40AAh8OB3W5vdn1JSQl2u53Y2FiqqqpCbQTO389ut1NSUgKA3+/H5XJhs9mAunYGf//731m9ejXJyZc23XB34XPmt/ppAcC+Y8WMHpSAXte1hrMo6zsZg7sUq+PwBdfHRJmYMr4fpeVuPsrKlQmXhBDiEoTtG2HixIns2LEDp9NJbW0tmzdvZtKkSaH1KSkpGI1G9u7dC0BGRgaTJk1Cr9eTmprKxo0bAUhPTw/tN3nyZNLT0wHYuHEjqamp6PV63nrrLXbt2sX//d//kZSUFK5L6vR8pQWtbl9QUFLNudIarh3W+oGQOovq+FF4TXHE5nwMzXzp90uO4oYxyZzMryAru+iC2wghhGgqbMEgMTGRxx57jHnz5nH33Xczc+ZMxowZw/z58zl06BAAy5cvZ9myZUydOpWamhrmzZsHwNNPP826deuYPn06e/bs4dFHHwVgwYIF7N+/nxkzZrBmzRqWLFmCqqr84Q9/wOl0ct9993HXXXdx1113NWrP0BMEPbUEqsta3SPhwLG6pzVju1D7ghCNlrJ+t2CqysVS1vwsi2OHJDCsXwxZXxVxMq+8/conhBBdWFgHOJo1a1aoF0GD119/PfR++PDhvPPOO032S0lJ4e23326y3Gaz8eqrrzZZnpXVtCFaT9MwR0JrxzA4cLyEeJuZXvER4SxW2FQmjyPu9GZiz3xETeywC26jKAo3X9ub8ioP/8rKJcpqJMFmbueSCiFE19K1KpdFs7wluQCtGvUwEFQ5eMLB1UPiL3mUwM5C1ehw9r0ZS/lJTOWnm91Op9UwbWJ/jAYtm7afwe3xt2MphRCi65Fg0E34SvJAo0Mf03Ibi9P5FVTV+Bg7pOu1LzhfRcoE/Horcac/uOh2ESY9027oT43bxwe7cggGpTGiEEI0R4JBN+F15KKPS0bRtlw7dOB4XfuCMV08GKhaI85+txJRdhxz2YmLbpsYa2Hytb3JK65mxyEZNlkIIZojwaCb8JbkYYjv0/KGwP7jDvomRRIbZQpzqcKvImUiPmM08Sc3NttDocGI/rGMHhTH/uMOjp0ta6cSCiFE1yLBoBsI+jz4y4paFQy8vgBfnXZ2+WqEBqpWj7P/HZgrc4gozW5x+xuvTiE5PoJP9ubiKK9thxIKIUTXIsGgG/CV5gMq+oSWGx4eyXHi9QW4upsEA4CK5OvxmuOJP/E+BC8+0qFWozB1Qj9MBh2btp+mVhojCiFEIxIMugFvSR5Aq54YHDhegkajMGpQXLiL1X40WhyDZ2KsKSK6YFeLm1tMeqbe0J8at58PduYQCDYdWlkIIXoqCQbdgM+RCxot+tiWeyQcOOZgaB8bFpO+HUrWflzxo6ixDSLudCYaf8tVBA2NEfMd1bzz8fF2KKEQQnQNYR3gSLQPb0ku+thkFO3Fv+yra30czy1jzu1D26lkrWfQQv8o3xUdQxk7He2WVxiQn0lg9MwWt+8/JhKPK4bNu84yenACN1/bclWMEEJ0dxIMugGvIxdj4oAWtzt8soSgSudseOj3Upa954oPExndH/PJzyn3m/CbbC1uf1MUlPRO5Pdr99HHbmVQ75b3EUKI7kyqErq4oM+Dv7y4VSMeHjjmwGjQMqxf15lm+VJVJ4xC1eqJLNrXYvdFAK0GHr5zEFERBp5/azcV1Z52KKUQQnReEgy6OF9pAahBDAmtaHh4wsHIgXFdbprlS6FqjVQljMHgLsVccaZV+0RH6Hnix9dTVuXhhbf3EAhIY0QhRM/Vfb8hegivIwegxWBQWlFLblE1Vw/uhNUIbcwd1Q+vOR6r4yAaX02r9hnSJ4af3XM1B0+U8Ob7X4W5hEII0XlJMOjivMU5oNWhj0u56HYNwyCPHdr9gwGKQmVSKooaJKroi1ZVKQDcNq4vM28aQMbWk3yyNzfMhRRCiM5JgkEX5y3KwRDfB0Wjveh2+445iLYa6J8c1U4l61gBg5WqhFEYXecwVea0er/77xzFqEFxvLJuPyfyysNXQCGE6KQkGHRx3uIcDIn9L7qNqqocOObg6iEJaDRdc5rly1FrG4zXHE9k0X603qpW7aPTaviv+8YRZTVKY0QhRI8kwaAL81eXE3CVY2whGOScq6KsysM1PaEa4XyKQkXy9aAoRBfsBrV1jQptkUae+NE4yqs8/Pave/BLY0QhRA8iwaAL8xbXNzy097vodvuPFQNw9RB72MvU2QT1FiqTUtF7yrA6DrZ6vyF9Yvj5nKs5dLKEP2ccRm1lOwUhhOjqZICjLsxbfAZoORjsO+YgJcFKQoy5HUrV+XgiU6ixDSai7AR+Ywzu6Ivfrwa3pvbldEEl6Z+exB5jYfYtg8NcUiGE6HjyxKAL8xbnoLXGorU036DQ5w9w+GRpz6tG+IYq+xi85niiivaic5e1er8fzxzJjVf34s33v+TTL/LCWEIhhOgcJBh0Yd6iMxgSL/7Xb/aZummWe0Q3xYtRNJT3mkBQa8SW/3mrxzfQaBT+3/evZeTAOFb+7QsOnnCEuaBCCNGxJBh0UWrAh7ckvxXtCxxoNAqjB8e3U8k6L1Vnorz3TSjBADF521AC3lbtZ9BrefLH19MrwcrSN3dzprAyzCUVQoiOI8Ggi/KW5EPQ32KPhH3HHAzrG9Ptplm+XH5jNOUpN6D1ubDlfYYSaN2MjlaLgV/99AbMRh1L/rSdvOLWdX8UQoiuRoJBF+UtOg2Awd6/2W0qXV5O5pX3+PYF3+Sz2KlIHo/eXYYt7zNUb22r9kuIMfPsv09EVWHxHz+nwFEd5pIKIUT7k2DQRXkKT6IYTOjjejW7zcETDlQVxg7ted0UW+KJTKGi13j0bieVmX8g4Kpo1X59EiN57qGJ+AMqT/zxcwpKJBwIIboXCQZdlKfwJMakgRcdCnn/MQcWk46hfW3tV7AuxBPZm4qUGwg4C8j/nyfwOQtatV+/pCiWPnQjXl+Qxas+p7DEFeaSCiFE+5Fg0AWpAT/eojMYkwdddLv9xxyMHhSPViv/mZvjsfYictrDBN0u8t9chOv4nlbt1z85iqUPTcTjC/DLVZ9xuqB1TxyEEKKzk2+MLsjryEX1ey8aDApLXBQ5a6SbYivoEweQ8uPfoIu2U7RuGaUfv43qb7lR4oBe0Sx96EYA/uuVz9h3tDjcRRVCiLCTYNAFeQpPAlw0GHxR/yV1zTBpX9Aa+pgkev1wKZHX3EHFjnTy3ngcd/6xFvcb0Cua5Y9MIjHWwjN/3sm/dp9th9IKIUT4yJDIXZCn8CQaowVdTHKz2+zJLiI5LoJe8RHtWLKuSQ0GKXbWD3g0YR6m5KvxfPoXCt76JbqhN2IYPweNNe6ix/jPe6/lj+sP8vLafZzKr+DOSQPRKK2bydJs0hFpMVzpZQghRJuQYNAFeQpPYEwehNLMF4/HF+DgiRKmjO/b7Dbia/6gGnrCUicZzXX/SUzOx8Sc+BTv8Z1UJo+jrO8t+CzNDxQ16ZreBFWVDZ+d4uAJB7df3xeToeVfsWuH2SUYCCE6DQkGXUzQ78VbfJbo8TOb3ebwyRK8vgCpIxLbsWTdS1BnonTQdCp6TSD27CdEFe7GVrATV+xQKnpNwBU3HFVrbLSPVqNw63V9SIyxsO1AAWv/dYzbU/uSYrd20FUIIcSlC2sbgw0bNjB9+nSmTJnC6tWrm6zPzs5m9uzZpKWlsXjxYvx+PwAFBQXMnTuXqVOn8tBDD+Fy1XUHq6ys5IEHHmDatGnMnTsXh6PxuPWff/45P/zhD8N5SR3OW3y2bsTD5OZn+tuTXYRBr2XUIBkG+Ur5zbEUD/s2p29YTMmANAyuInod/iuDtj1Nr4NvElm4p9G8C4qiMGpQPN++eTBajUL61pN8dqAAnz/QgVchhBCtF7ZgUFRUxIoVK1izZg3p6emsXbuWEydONNpm4cKFLFmyhA8++ABVVVm3bh0AzzzzDPfeey+ZmZmMGjWKVatWAbBy5UpSU1PZtGkTc+bMYenSpQAEg0H+8pe/8P/+3/8jGAyG65I6BU99gzhTrwsHA1VV2ZNdxJjB8Rj1zY9xIC5NwBiFc8AUTt+wmNxrHqKi1/UYq3JJzv4/Bm1bQt+slcSfeB9LaTaK34091sJ3bx/KyIFxHDju4P82H5U5FoQQXULYgsH27duZMGECNpsNi8VCWloamZmZofX5+fm43W7Gjh0LwOzZs8nMzMTn85GVlUVaWlqj5QBbtmxh1qxZAMycOZOtW7fi8/k4efIkJ0+e5Nlnnw3X5XQa7txstFHx6KIv3A2xoMTFudIaqUa4BAYt9I/yte7HFiSxXz8iUmehTv0vfJN/RnDYLRgMWmJyt9L7wJ8ZvO0pBu9byfDCjfx4ZBX/MTUFs0Hhn5+f5sMdJzD4qxod06C6O/oWCCFESNjaGBQXF5OQ8PWXl91u5+DBg82uT0hIoKioiLKyMqxWKzqdrtHyb+6j0+mwWq04nU6GDBnC0qVL2bVrV7gup1NQVRV37hFM/a5qdpvdX54DkGBwKfxeyrJbN7DRhcVCfCzEjsVQW4q+xoGhxoH++Kdoj29hqKLh8Yh4TpmT+NDRm5XplQyNVbmxt4+kCJWkuMltdilCCHGlwhYMgsFgoxbxqqo2+tzc+m9uBzTbsl5VVTSanjMUg7+8iEC1E1PvEc1us/NwIQN6RZEYa2nHkgkANDq8EYl4IxJxAUrQh76mBENNMQZXMSO8BxlhPYgHA4e9vcnK7kuVKZE7bCVMibW3qgeDEEKEW9j+JUpKSmLPnq//CnM4HNjt9kbrz288WFJSgt1uJzY2lqqqKgKBAFqtttF+drudkpISkpKS8Pv9uFwubDZbuC6h03HnHgHA3PfCwaC8ykP2GSffu2NYexZLNEPV6PFak/Fa68ab0Pjd9SGhiLHVuVxnOIVX1fLVthRe2DqYhDE3MH50b0YNjMMg7UOEEB0kbH9uT5w4kR07duB0OqmtrWXz5s1MmjQptD4lJQWj0cjevXsByMjIYNKkSej1elJTU9m4cSMA6enpof0mT55Meno6ABs3biQ1NRW9Xh+uS+h03LnZaIwW9Al9Lrh+91fnUFWYMKr5gY9ExwnqTLij+lKZPI6SwbMo6/1v+Gz9GW0t4wfGj7kpezkH/vdlFv7qbZ798042bj/NybxyfP7u3aBWCNG5hO2JQWJiIo899hjz5s3D5/Nxzz33MGbMGObPn88jjzzC6NGjWb58OU8++STV1dWMHDmSefPmAfD000+zaNEi/vjHP5KcnMxLL70EwIIFC1i0aBEzZswgMjKS5cuXh6v4nZI77wjG3sNRlAvnuZ2HC7HHmBnQK6qdSyYumaIJVTv0nngTxhoH5fs/ZtLxLCYHj1BQtJOPTw7ldW9/0Orp3yuKQSnRpCRYSYy1kBQXQWKsBYtJJ4NYCSHaVFgrNWfNmhXqRdDg9ddfD70fPnw477zzTpP9UlJSePvtt5sst9lsvPrqq82eb/z48YwfP/4KStx5BWoq8ZXkYR114YZqtR4/+485mHZDf/mi6GIUjRbL4GuxDL6WQG0V1Ye3od/7AT/gc75v2E+e7Tq2e4fx+QEX1bWNJ3cy6DRERRiIijASFWHAbNJh1Gsx6LUYDVoMOg1Ggw6dVkGn1dT/KGgbXjV1y7Sh9fXrNF8v02oV9FpNo30Meg1mo4QSIbojae3URTS0LzD1GX7B9V8cLcbnD0o1QhfUaK4GtDDoZgwDJ6PN/wrf4Q/pd2Yb/ZTt6K69Cf/wKZSq0TjKaympqKXK5aWqxkd1rZfKGi8lFbV4fQE8vgA+fxCvL4A/oIal3IoCFpMei1GHxaTDbNITYdLVfTbribQY6n/0REUYiIyo+3z++BoyT4QQnY8Egy6i9sxBFL0RU68hF1y/bX8+0VYDVw2IbeeSiSvVdK6GBgkw4F50SWnEnt1C1NHPULI/xZAwGnO/W7HH9MEe03Lvk6CqEgzW/QTqXxuW9UuK5GR+xdfrVJVgsK7XUDBYt2/gvO0b9vUHgni9dQHE4wvg8QaoqHJT5Azg9QZwewMEghcOJDpt3dMGs1FHcpyFhBgL0VYDtkgjURFGbFYjUVYDNquRaKsBvU4aYgrRniQYdBG1p/Zj6jsSRde0sWWN20fWV0XcPq4PWm3P6b7ZU/jNcRQP+zalA6Zgy92GLe9zIh0HccUMxdn/Nmptg+r+fG+GRlHQaBW4wPdrYlwE5dXeNi+zqqr4AkHcHj81bj+1nvN/AvWvPsqqPOQ5qqmo9jT7ZMNi0hFtrQ8MEXUBwhZpJMZqxBZlwmY1ElO/TKo3hLhyEgy6AF95ET5nIVHXTb3g+t1fFeH1BZh0Te92LploTwFDJKWDplPW7xai83cQk7uVPvv+SE30AJz976AmduhFA0J7UhQFg06LQaclKsLY7HbXDrNjj7Wgqio1bj8V1R4qqr2UV3vq3rvqPlfUfy5y1nA0p4wKlwf1AjnCoNd+HRoiv/6JiTTVvbcaiYmqe5UQIcSFSTDoAmpPHQDAPHDsBddv25dPfLSJEf2lGqErahiSufV0EPtvBK+agP/MbszHP6X3gdcIxvQhMOxW1MThrQ4IFk3bPy24FP7A+e0r6qoZ4qJNxEWbLrpfMKhSVeOl0uWlwuWl0uWhsrrhfd3nfEc12WecVLm8XOhZhEGvwRZpqnvaUP80wmSsa7xpMtQ13jTqtRgNutD7huUWkx6zsa49hdGglYAhuhUJBl1Azan9aKPi0celNFlXVePli6NFzLxpIBqN/OPUJV3RkMxG6HMb5socIkqPoN/5P/iMNlxxI/BYe7UYEOIm3HSZ520bHl+AgydKrvg4JoMOU6wO+wVG/AwGVWq99VUabh819dUbESY91bVeKqq95BVXU13rxeura7DZXPuIC1GUuvObjTqirYZQtUfDEwpbpBF7jJl4mxlt/Uit0uhSdGYSDDo5NRjAfeYQESMmXvCvku0HC/EHVCZd0zQ0iB5Co6XWNpDa6P6YKs8SUZqNrWAHPkNUXUCI7N1pqhg6gkajEGHSE2HSA+bQ8mH9YjiaU3bBfQLBugaWfn+w7jUQxOf/+rWhx4fXH8RX/+rxBahx+zh7roojbidub+OptjWKEmpkOaxfLEP72OiXHEX/5CgZ6VJ0KhIMOjlP/nGCnppmqxH+tTuH3nYrg3vb2rVcohNSNLij++OO6oupMpcI5xFshbvwl36FK3Y47qg+0MzgWKIxrUZBq9Fe0dTl/kCQGrcfV62P8moP5VV1P2VVbj7clUPmjjNAXXDpY7cyMCWagSk2BvWOZlBKNBZTzxnVVXQuEgw6OdfxLFA0mPuPbrIut6iKIzll/GTWSKnjFF9TNLij++GO6ouxKo+I0iNEn8siovQramKHUxvVFzTyF2q46bQNg08ZSI6PaLTu6iHxqCqcLqjkZH45p/Ir2H/MwSd784C6Bzx9EiMZ1jeGYf1iGNo3hr5JUWilulC0AwkGnZiqqriyd2AeMBqt2dpk/eZdOWg1Crdcd+G5E0QPpyh4ovrgieyNsbqAiNJsoor2Yi05RE30QGpjBnV0CXssrUaDPdZCrwQrN17dK7S8rNLNyfwKjp8t4+jZMnYeLuTD3WcBMBu1DO4dw9C+Nob1i2FYv1hioy7eSFOIyyHBoBPzFp3GX16EbeLsJut8/iCf7M3l+pFJ2CKb7w4mBIqCJzIFj7UX+loHlrLjRDiPEOE8Sm2gAIttHDUxQ6SaoROIiTKRGmUidUQiUPfHQWGJi6NnyziWUxcWMraeDI35EG8zh54qDOsXw6Detiuq/hACJBh0aq7sHaBoiBh2fZN1WV+do6Lay5Tx/TqgZKJLUhR8FjsVFjtabzXm8hNEFByh95l9+Iw2KpNTqUwah88S39El7fa+2U3zYnRaDSMHxDFyQBwAPn+AnHNVnC6o4FR+BUdznHx+sACoaxvRNzGyvr1CXVuFeJu5SVWj9IoQFyPBoJNSVZXq7O2Y+49Ca2k6W+KGz05hjzFzzTB7B5ROdHUBg5Vq+1gSx/07x/YdILpwD7FnPiLuzL/wRCRRHT+K6oSR9T0a5ElCW2uLbppx0Wbios2MuyqJGrePImcN50prKHLW8Om+fD7akwuA2airn5HTQmJsBPYYMxNGJUswEM2SYNBJeYtz8JedwzbhribrTuSWc/hkKfffOUoaI4kromj1VCdeQ3XiNeg8FViL9mMt+ZLYnI+Iy/kXfkNkfVfIAdTaBuCJSJaGi52QxaRnQK9oBvSKBurGbnBWujnnrKGo1EWRs4YzhZUAKMCmHWcYOTCO4fVtFVISrDIOigiRYNBJVR/+tL4aoek00umfnsRs1DFlfN8OKJnorvzGaMr7Tqa872Q0PhcRJdlEOI9gLj9DZHHd6JtBrQGvJRFPRCLeiCS8EYn4TDH4jdEEdeYePV5CZ6LRKMTb6gZVGjWwrgrC7fVT5KyhqLSGWo+fzw4U8MHOHAAizPpGbRWG9Y3BKk8UeiwJBp2Q6vdRdeATIoZdjzYiutE6R1ktnx3IZ9a/DZR+ziJsgvoIqpJTqUpOBUDnLsNcfhpT5VkMrnNEOI8Rfa7xaI1BjQG/MYqAPoKA3kJQZ278qjUR1NX9BHQmgloTao0GJeBD1egkVISZyaCjX1IU/ZKiuHaYnXibmXxHNUfrGzUeOeNk7YdHaRj0sbfdGur9MLxfDH0TI2WSth5CgkEn5Dqyk2BtFZHXTGmyLmPrSVRVZeZNAzugZKKn8ptiqEqKoSrp2tAyja8GQ00xOnc5Ok8Fek/dq8ZXg85bhcZVhNZfi8bvRrngbAXALhgCqIqmLjCcHx70EQT0VgKGuh+/ITL02W+w1j+hkC+qy6XRKPRJjKRPYiS3X1/39LHG7eNEXnldWMgpY092ER9l1bVVMBm0DOkTw/D+dU8UhvaLISZSukt2RxIMOqHKLz5AF5OEeUDjQY1KymvZuP00t6T2IfECY8ILcakufQKn8+khLgVofjjuABBQg+Bzg8+N4neDzwN+N4rPg8WokH+uAk3AUx8iPGgCdWHCUONA6z2N1ue6YLBQFQ0BfURdYDBYCRgi699/89VKQB8hIaIVLCY9YwYnMGZwAlDXCLrIWcORnDKO5jg5mlPG+k9OhOaSSIy1hKofhveLZUCvaPQ6uc9dnQSDTsbryMWdm03srfehfOMfsrX/Ooaqqnx/yvAOKp3odq5oAqcrl3j1TUTb3c2uDwJBNQjeGhRPNXiqUTyu+te69wZPVd3niuK616C/yXFURQOGCFSjFUyRqEYrOkcctlpzk1AR0Fu6fYi4lO6SGkXhqv6xXFU/e6vXF+BsURUn8+u6Sx46UcLWfflAXdfK/smRDOptY1BKNINSbE3GWZGukp2fBINOpmLXBhStnsgxtzRafq7UxYe7cph6Q395WiC6j8sOJkZQjGCKg/OfZqsqStCPJuBG43ejCXjqX91o/B60fjeaSgcafx6+fC/2ZkJEQF9fdWFs/PSh8ZOIKII6U5dsG9EW3SUTbGYSbGbGj0yiusYb6i55zuniX7vP8kH9U4VIi4GkOAtJcREkxVm44/q+Egw6OQkGnYiv7BxVBz8hKnVqk0aHb77/JVqthu/cPrSDSidEF6AoqFo9Aa2egCHyopsOGn8j+8+40Hqq0Hmr0Hov/GqoLkTnrUJRg02OEdToCOgj8Ruj8Jts+Iw2/CYbfqMNX/1rwGDt9k8grBYDVouBQfWTuQUCQRzltXVBodRFgaOa47nlALz/2WlGD4rn6iHxXD0kgb5JkTLXSycjwaATKfvsHRStDtsNjYdA3v3VObYfLGTe9BEyNroQbcSoU+gbqwNi6n8uTAV8ahB8tSjuavBUff3qqUbjrsRYW4HJlQclXzapylA1WjBFo5rrfjDbMLji6atEUxSIxmeyEdRZuuSTh+ZotZr6JwQRQELdgG21Ps6VuvB4Axw7W87ur84BYIs0MmZwPNcOs5M6IpFoqwzx3tEkGHQSPmcB1Yc+JXrcdHSRX/8j5fb4eXX9QfokRnL35MEdWEIhupkrbl9hBo0ZLAnQULunqigBL1p/DVpfLRp/DVp/DRpfLdqaSrQVRWj8tXhQMQENA5oHNXr8xujznjREf/3kwWjDb4ru0uFBURQiLQYiLQauHWbHHmuh2FnDgeMODhwv4cAJB1v35aNRYMSAOMaPTGL8qCR6xTedPE6EnwSDTkBVVUo/fAtFpyf6hm81WvfXTdk4ymr5zc9ukta+QnR2ioKqM+LXGfGbmnkKoaokRtXNW+FwutB5ytG5y+u6e7rLsZSdQOetbFJ1EQoPDVUW9YEhFB6MUQT1EV0mPNhjLdwxvh93jO+HqqqcyCtn15fn2HX4HH/Z8CV/2fAlfRKtTBiVzORretMvuenQ8CI8JBh0Aq6vPqfmxF5ib/8ROqsttHz7wQI2bDvFrH8byMj60cuEEF2cooDBQmTKANTYQJPVoaoLdxVKbQXUVqDUlqPUVqCtrUDnrsBccRxqK5t041Q1OjBFoZqjUE1R9e+jQ68NyyyaiHa62NZRFIUhfWIY0ieGH0wdQZGzhl1fFrLr8Dne/eQEf//oOP2SIpl0TW8mXZNSX0UhwkWCQQcL1FRSsvkNjL2GED1uemh5YYmLl9fuY2hfGz+eObIDSyiECAdN0EtZ9r5Wbm0GnRkik6ChTaUaRON314//UPdTV31Ri7amGk2lA62/9oKNJmuMEfTVR9dXX0TXNZ40nv/ZFrYhrlvTVVIBJoxMZsLIZCpdXvZkF7Hry3O8vSmbtzdlMzAlmgkjky6rTYJ0l2yZBIMOpAYDODa8QtBdQ8KMh1DqJ6cpr/LwzJ93olEUfnHfOKlCEEI0pWgI6i0E9RfpvqyqKEHf1+HBV4vWX0tMXDT+0rqRKk2Vueh81U12DWhN+Czx+MxxeM11rz5zPD5LPH5D5GX3tLicrpK2SCNpE/pxgyuZE7nlHMstY83mo/zf5qOk2K0M7RvDwJRojPqWJ/i6dphdgkELJBh0IOdHf6XmxF7ip87HYK9rhlRd6+Pp13bgKK/l1w/cIGMWCCEun6Kgag34tQYwft0Fut+NN+Fy1g0sFQS8AT+4K1Hqf6itQHE5MbhKMbrywHGo0ZMHVatHtcajRiahRiWiRtW9Yra1+JTBGqwkJVohv6KZYbIvIirCwLXD7Vw73I6z0s3xs2Ucyy3n4z25fPpFHv2ToxjaN4a+SZHoZF6HyybBoINU7H6fit3vEzVuOlHXTQXqhjx+9i+7OFtUyVM/mSDtCoQQ4dFijwwzGFLqfmIANYjWV4PWV43W60Lrq0bnrUR37gi6vK+rQ4KKrm4iLWMUvvOqJVTt13+hG+Is6O1XAVc2CVxslInxo5K5fmQSRc4ajueWczy3nJP5FRj0Ggal2BjSx0aK3YqmizTI7CwkGLQzVVUp27KG8u3rsQy9nrjbfwTA0Rwnz7+1m1pPgMU/Hs+1w+0dW1AhhGigaEKTWfGNdn9KwIvOU1kXFDyV6LwVGKsLMFecCW0T0Jnre09Eg2qv6+KpJrXJwE+KooTGTLhxTC/yiqs5nlvGibxyss84sZh0DOwVzYBe0aTYpdFia0gwaEcBVwWOTX+i5uguIsfeTvy0B/AG4P82fsl7W04QbzPz3w9PlG45QoguQ9Ua6toiWOIbLdf43fVdMSvQecrReyowuM6B8wiGY1sZrDHgsSbhsfY67ycZVXf5g7hpNAp9kyLpmxTJ5GuDnCms5ERuOUdyyjh8qhSDXsO+ow5uvLoXY4cmyOyQzZBg0A7UgI+qg1twfrKaoLeW2Fvvw5I6k8ydZ/n7x8dxlNUyZXw/fjxrJFbzlT1eE0KIziCoM+HVJeGNSDpvYYBEsxe/1kyVowhjdQGRxQewFewMbeI1xeGJ7NUoMPhNMZfcQ0Kn1TC4t43BvW34A0Fyi6o4VVDBl6dL2fVl3aiLA3tFM3ZoAlcNiGV4/1gZdbFeWIPBhg0b+OMf/4jf7+eHP/whc+fObbQ+OzubxYsX43K5SE1N5ZlnnkGn01FQUMDChQspLS1lwIABLF++nIiICCorK3n88cfJzc0lNjaWlStXkpCQgNfrZfHixRw+fBiTycTy5csZNGhQOC+tVXzlRVR/+TmVezMJVJViTBlK1TVz+ftplU+f+5CKai/D+8Xw6PeuCU1zKoQQ3ZZGC9Y4gvarcMTU/xGkqug85RirCzFWF4R+rI7DoXEaAjoT3ogkvOZ4/KZYfOZYfPWvfmN0i1USOq2GAfXVCWOHJlBd62Pf0WL2H3Pwj20nWb/lBADJ8REM71fXw6FvUhT9kiKJjTL1uLkcwhYMioqKWLFiBevXr8dgMPC9732P8ePHM3jw18P6Lly4kOeee46xY8fyxBNPsG7dOu69916eeeYZ7r33XmbMmMEf/vAHVq1axcKFC1m5ciWpqam89tprpKens3TpUlauXMnbb7+N2Wxm06ZNZGVl8ctf/pJ169aF69IuylN0hsovNlOT8xWB0lwAKiMHsDf2Vj48EYnr0El0Wg3jRyUxdUI/rh6S0OP+pxNCiBBFwW+KwW+KwRV/1deLAx6M1edCQcHgOoel7Dg6T+OBnVRFi79+6uyAwYpfX/9qiCSoNxPUmgjqjPWvJnDpGJgYx6CUwcy5bSgeX6CuuuGMkyM5TvYfc/DJ3rzQ8a1mPb0SIrDHWEiMtWCPtRATaSIqwhD6sVoMaDXd59/xsAWD7du3M2HCBGw2GwBpaWlkZmby85//HID8/Hzcbjdjx44FYPbs2fzud79jzpw5ZGVl8Yc//CG0/Ac/+AELFy5ky5YtrF69GoCZM2fy61//Gp/Px5YtW1iwYAEA48aNw+l0UlBQQK9evS5axkCgbtSxc+fOtd11v7kKu+sYuf44TvmH8pWvN5WqhcRYDaN667hqQCKjBsVhMekBL/n5+W127itV6SjGUVbWIecuOFfUI8/d0eeXa++ga1fdmAw999qNphLM7qajPjZh1OI19sEb1+frZQ1dK2sr6n7cFeBxoXhdUF2M4j0F3loUNYACaOt/Gpz+CE4DoIBWh6LTo2i09Nbq6KPVMSVCR9CqxRfQ4Ako1PrAU6LiORfE7QtSoCrkAyoKQTSo9e+1Gi1anQZF0aBoNChaLVqNBo1GAxoNiqKgKKCgnPce0NR/hlB1SaM/FhUFs8XM6DtmoRjapk1Ew3dew3fgN4UtGBQXF5OQ8PXjcbvdzsGDB5tdn5CQQFFREWVlZVitVnQ6XaPl39xHp9NhtVpxOp0XPNa5c+daDAYOhwOgSRVHOJwCdoT9LEIIIbqll15t80M6HA769evXZHnYgkEwGGyUelRVbfS5ufXf3A5o9lG7qqpoNJom+zQsb8moUaNYvXo1CQkJaLUtj5glhBBCdHWBQACHw8GoUaMuuD5swSApKYk9e74eQMPhcGC32xutb/iLHaCkpAS73U5sbCxVVVUEAgG0Wm2j/ex2OyUlJSQlJeH3+3G5XNhsNhITEykuLqZv376NjtUSk8lEampqW12yEEII0SVc6ElBg7CNGTlx4kR27NiB0+mktraWzZs3M2nSpND6lJQUjEYje/fuBSAjI4NJkyah1+tJTU1l48aNAKSnp4f2mzx5Munp6QBs3LiR1NRU9Ho9kydPJiMjA4A9e/ZgNBpbrEYQQgghRFOKqqqXPmB1K23YsIE//elP+Hw+7rnnHubPn8/8+fN55JFHGD16NEeOHOHJJ5+kurqakSNHsmzZMgwGA/n5+SxatIjS0lKSk5N56aWXiI6Opry8nEWLFpGbm0tkZCTLly+nd+/eeDwelixZwuHDhzEYDDz33HOMHCkzEgohhBCXKqzBQAghhBBdi0w/JYQQQogQCQZCCCGECJFgIIQQQogQCQZCCCGECJFg0MNt2LCB6dOnM2XKlNBw0z3JK6+8wowZM5gxYwYvvPACUDec96xZs5gyZQorVqwIbZudnc3s2bNJS0tj8eLF+P1+AAoKCpg7dy5Tp07loYcewuVydci1hNNvf/tbFi1aBMj9+aaPP/6Y2bNnM23aNJ577jlA7tH5MjIyQr9jv/3tbwG5Pw2qq6uZOXMmeXl1czO01X2prKzkgQceYNq0acydO7fRmEGtoooe69y5c+ott9yilpWVqS6XS501a5Z6/Pjxji5Wu/n888/V7373u6rH41G9Xq86b948dcOGDerkyZPVs2fPqj6fT/3JT36ibtmyRVVVVZ0xY4a6b98+VVVV9Ze//KW6evVqVVVV9YEHHlDff/99VVVV9ZVXXlFfeOGFDrmecNm+fbs6fvx49b/+67/U2tpauT/nOXv2rHrTTTephYWFqtfrVb///e+rW7ZskXtUr6amRh03bpxaWlqq+nw+9Z577lE/+ugjuT+qqu7fv1+dOXOmOnLkSDU3N7dNf7eeeeYZ9U9/+pOqqqr63nvvqQsWLLiksskTgx7s/ImuLBZLaKKrniIhIYFFixZhMBjQ6/UMGjSIM2fO0K9fP/r06YNOp2PWrFlkZmZecNKvzMxMfD4fWVlZpKWlNVreXZSXl7NixQoefPBBAA4ePCj35zwffvgh06dPJykpCb1ez4oVKzCbzXKP6gUCAYLBILW1tfj9fvx+P1arVe4PsG7dOp5++unQKL1t+bu1ZcsWZs2aBdRNOLh161Z8Pl+ryxa2IZFF59fSRFfd3ZAhQ0Lvz5w5w6ZNm/jBD37Q5J4UFRVd1qRf3cGSJUt47LHHKCwsBC78/0xPvj85OTno9XoefPBBCgsLufnmmxkyZIjco3pWq5UFCxYwbdo0zGYz48aNk/+H6i1durTR57a8L81NOJiYmNiqsskTgx6spYmueorjx4/zk5/8hF/84hf06dPngvekLSb96mr+/ve/k5yczA033BBa1tx96In3B+r+It6xYwfPP/88a9eu5eDBg+Tm5so9qnfkyBHeffddPvnkE7Zt24ZGo+HMmTNyfy4gnL9baisnFmwgTwx6sJYmuuoJ9u7dyyOPPMITTzzBjBkz2L17d6OGOg335HIm/erqNm7ciMPh4K677qKiooKamhry8/MbzUTak+8PQHx8PDfccAOxsbEA3H777WRmZso9qvfZZ59xww03EBcXB9Q97n7jjTfk/lzAN6//Su5LcxMOtpY8MejBWproqrsrLCzkZz/7GcuXL2fGjBkAXH311Zw+fZqcnBwCgQDvv/8+kyZNuqxJv7q6N998k/fff5+MjAweeeQRbr31Vv785z/L/TnPLbfcwmeffUZlZSWBQIBt27YxdepUuUf1hg8fzvbt26mpqUFVVT7++GP5HWtGW96X5iYcbC2ZK6GHu9BEVz3Fc889x7vvvhuarhvge9/7Hv3792fZsmV4PB4mT57ML3/5SxRFueRJv7qT9evXs3v3bn7zm9+wY8cOuT/neeedd3jrrbfw+XzceOONPPnkk+zatUvuUb3XXnuN9evXo9frGT16NE8//TRffPGF3J96t956K3/961/p3bt3m/1uNTfhYGtJMBBCCCFEiFQlCCGEECJEgoEQQgghQiQYCCGEECJEgoEQQgghQiQYCCGEECJEgoEQQgghQmTkQyF6mOeee46srCwATp48SUpKCiaTCYC1a9eG3jfYsmULBw4cYMGCBRc97n333ReaAhbgvffe429/+xtutxufz8d1113HwoULiYqK4ve//z1lZWUsWbIkDFcohLgSEgyE6GGefPLJ0Ptbb72V5cuXM3r06Ga3P3ToEBUVFZd0jldffZWtW7fyhz/8gfj4eHw+H88//zwPPvgga9asueyyCyHCT4KBEAKAP/zhD/zzn/9Eq9UyYMAAnnrqKQoKCvjb3/5GIBAgMjKSf//3f+dXv/oVOTk5lJeXExERwfLlyxk4cGDoODU1NfzpT3/ivffeIz4+HgC9Xs8vfvELPvzwQ7xeLwCnTp3ivvvuw+FwEB8fz0svvYTdbueTTz7hT3/6E16vF6fTyd13382jjz7Krl27WLp0KRaLBZfLxbvvvstbb73FO++8Q0REBKmpqXz00Ud8/PHHeL1eli9fTlZWFoFAgKuuuoonn3wSq9XKmjVr+Nvf/oZer8doNPLrX/+awYMHd8g9F6IzkjYGQgjeffddtm3bxjvvvMOGDRsYMmQIixYt4uqrr+Z73/se06dP57HHHmPr1q1ERUWxdu1aPvjgA0aNGsXq1asbHevUqVOYTCb69+/faLnZbObOO+/EYDAAkJuby8svv0xmZiZRUVH8/e9/R1VV/vKXv/Cb3/yG9evXs3btWl577TWcTidQNxPmiy++yIYNG9i1axfr16/nnXfeYf369bhcrtC5XnvtNbRaLevXr+cf//gHdrud5cuXEwgEeP755/nzn//Mu+++y3e+853QGPRCiDryxEAIwdatW5k9ezYWiwWAefPm8eqrr4b+um8wdepU+vTpw9tvv01OTg67d+/mmmuuabSNRqMhGAy2eM4bb7wxNCvh8OHDcTqdKIrCq6++ypYtW3j//fc5efIkqqpSW1sLQHJyMikpKQB8+umnTJ06laioKADmzp3Lzp07gbp2EVVVVWzfvh0An89HXFwcWq2WqVOn8r3vfY+bb76Zm266icmTJ1/ubROiW5JgIIRoMud7MBjE7/c32W7NmjWsW7eOuXPnMmvWLGw2G3l5eY22GTx4MH6/nzNnzjR6auDxePj5z3/Oc889B4BO9/U/Pw3zy9fU1PCtb32L22+/ndTUVL797W/zr3/9i4YpXRqCS8P+50/1cv5UvsFgkCeeeCL0pe9yufB4PAAsX76cY8eOsX37dl577TUyMjJ4+eWXL/meCdFdSVWCEIJ/+7d/491336WmpgaAt99+m3HjxmEwGNBqtaGQ8Nlnn/Gtb32LOXPmMGDAAD7++GMCgUCjYxkMBubPn8/ixYspKSkBwOv18vzzz1NbW0tiYmKz5cjJyaG6uppHH32UW2+9lV27duH1ei/4BGLy5Mls3ryZqqoqoG6WwwY33XQTq1evDu371FNP8dJLL+F0Opk8eTI2m40f/ehHPProoxw6dOjKbp4Q3Yw8MRBCcM8991BYWMicOXMIBoP069eP5cuXAzBhwgQef/xxnn32WX7yk5+wZMmS0Jfw2LFjOXbsWJPjPfjgg5jNZu6//36g7mnB9ddfz6pVqy5ajmHDhnHzzTczbdo0DAYDQ4cOZfDgweTk5ITaJjS44YYb+M53vsN3v/tdTCYTQ4YMwWw2A/Af//Ef/Pa3v+Vb3/oWgUCAESNGsGjRIqxWKw899BA/+tGPMJlMaLXa0BMMIUQdmXZZCNElHTp0iH379jFv3jwA3nzzTQ4cOMDKlSs7tmBCdHESDIQQXVJ1dTVPPPEEp06dQlEUkpOTefbZZy9aVSGEaJkEAyGEEEKESONDIYQQQoRIMBBCCCFEiAQDIYQQQoRIMBBCCCFEiAQDIYQQQoT8f7R6cJgiwAzlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['Tenure', 'MonthlyCharges', 'TotalCharges']:\n",
    "    fig = plt.figure(figsize=(8,5))\n",
    "    sns.distplot(df[df.Churn == 0][col],bins=10, label='Non-Churn',kde=True)\n",
    "    sns.distplot(df[df.Churn == 1][col],bins=10, label='Churn',kde=True)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6e6c0d",
   "metadata": {},
   "source": [
    "**Tenure**: Customer with less tenure is more likely to churn.\n",
    "\n",
    "**Monthly Charges**: Customer with low monthly charges is less likely to churn; however, the churn trend between churn customers and non-churn customers gets similar as monthly charges go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b6a33885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers\n",
    "for col in ['Tenure', 'MonthlyCharges']:\n",
    "    fig = plt.figure(figsize=(8,3))\n",
    "    sns.boxplot(df[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ec1991",
   "metadata": {},
   "source": [
    "**Outliers:** The box plots show there is no outliers in this data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d2cad7ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JZOJoejHv7MnkXF4VAJ6uDjio7aipb6GhqRW1yo7/mzaY224IRa2S07o/RWfdZ32ruYifLDO3EhdHewK0GktH6THhwV6s/c14nnz9C1a9fJB198Si85JTRkL0lJKKBl5472tOnitD5+XMvbdFMnaYX/v/Q4PByNncKhI/P8/bSRnsO5rHk0vH08/Hdr5P9aQunfN/6KGHSElJMXcW0UMycyoI6++JUtk3JvfpqohBPqy7J5aq2ib+8NIBcuQqACF6xP6v8nlw06dk5VWyLD6KV1dNY07cwA4/gNvZKRka4sWqO8fwp3tjqWvU8+hLB2W9DjPpUvn/7Gc/45VXXmHGjBm88cYbVFVVmTmWMJeGJj05hTVWvZLfTzF8oDcbHojDZDKx6qWDZGRXWDqSEH2WwWjizY9O89y2Ywzwd+Ovv5/K7Ikh2Nt1Xj3RYTqeeSAOe6WCx145REGpXK3T3bpU/rfccgtvv/02r7zyCuXl5cyfP5+VK1dy8uRJc+cT3excfhVGE31+pH9nQvq5s/HBSbi6qHn8bykcPlNk6UhC9DnNegMb3jzMB5+e4+bYAfz5/on4ebt0+fFBvq5seCAOpULBn988TGNz69UfJLqsy5f6GY1GcnJyyM7OxmAw4O3tzR//+Ef++te/mjOf6GaXV/Kz5fIH8PN24dkHJ9HfV8P6f3zJ9k/OYgNjX4XoEfWNev74eiqHzxSx9NYI7p8/4qp7+z/Gz9uFP/xqNPnFtby4/bj8H+1GXRrwt3nzZj788EOCgoJYuHAhL7zwAiqVioaGBqZOncry5cvNnVN0k8ycSgK0GrmUhraVADc8EMdL20/w713pnC+o4rd3jMLJQcbBCnG96hpaeOK1VC4WVPP7haOZMuqnXWEVHabjlzcP5a2d6Ywb7veTn0+06dJ3uYqKCl5//XXCw8M73O/s7Mxzzz1nlmCi+5lMJjJyKhgd7mvpKL2Go9qe3y8axaBAd9786DQrSz5n9V1jZYSxENehrlHPE6+lkn2phtV3jWXsML9ued74qYNJOVXI3/+Xxuihvmic+ubMpD2pS8dhDAbDD4r/8t5+XFxc96cSZlFc0UB1XQvhNn7I//sUCgW33RDKU/fEUlHTxG+f38+eL3PkEKMQ16CuUc8Tr6aQfamax349ptuKH8BOqeCB+SOoqWvmrZ2yXHd36HTP/8knn6S4uJhjx45RUfHtqOjW1lby8vLMHk50r4xvzveHD7DNkf5XEx2mY8vvbuCF/3zNi9uPc/h0EQ/eHi0TAglxFR2Lv/v2+L8rNNCDOXED2XHwAjPGBTMo0KPbX8OWdFr+8+fPJysri8zMTGbMmNF+v52dHdHR0ebOJrpZZk4Fjmo7+tvIrH7XQ+fZth7A/w6c518fp/PQpk+5f34U4yP8r7rqpBC2qK5Rz1ozF/9lC2aEk3w0j227M1j7m/Fmex1b0Gn5R0ZGEhkZycSJE/H1lfPE1i4zp5LBQZ7YXceoW1uiVCq4dUooI8N0PPfOMf785hFGheu459ZIm5oVUYirqWvU8+RrKVy8VM1jd5q3+AE0Tiribwjl37vSycipINxG5yvpDp22wMMPPwzA3Xffzdy5c3/wS1iPZr2BCwXVNn+J37UI9nfj+d9O4e55EWRkV/DgX/bx5ken5XpjIWgb1b/21RQuFFSzavEYxg43b/FfNnfSQNw1at7eld4jr9dXdbrnv3TpUgCeeOKJHgkjzOd8fhUGo0nK/xrZ2ymZN3kQk6MDePPjM3zw6TmSj+YRf0MoN8cOwFEuCxQ2qLqumbWvppJbXMuqxWMYF+HfY6/t5GDP/BvDeON/aZy+UM7wgd499tp9Sad7/hEREQCMHTsWf39/xo4dS0NDA0eOHGHo0KE9ElB0D5nc56fxdHNkxYJR/GX5JIL9XPnHjtPc/ee9fLAvS44ECJtSWdPEY68cIr+klieWjOvR4r9sZmwwrs4qEj471+Ov3Vd06eTv2rVref311zl//jxr1qwhPz+f1atXmzub6EaZOZX4ejnj6epo6ShWLTzYi/XLJrLxwTgG9nPnzY/PcNef9vB6winyimstHU8IsyqrauSxVw5SWtnAk0vHMypcZ5Ecjmp7bp4Qwpeni7gk8/5fly6Vf1paGn/84x/Zu3cvt912Gxs2bKCgoMDc2UQ3ysipkL3+bjQsxJt1907gL8snMXqIjp0pF7n/2X2sfuUQn32VT5McDRB9TG5RDY++fJCKmmaeuieWqFCtRfPMmRiCnVLJ/w5csGgOa9WlE5YmkwmlUsmhQ4dYtmwZAE1NTWYNJrpPWVUj5dVNUv5mEB7sRfivvKiqbeaTI7kkpWazadsx1Co7xgzzZdKIAEYP1eGolrEBwnodP1vCM/86gkplx9P3TWBwkOW/l3i6OXLDqEA+OZLLopnhMmX5NerSd6T+/fuzdOlS8vPzGTt2LL///e8ZMmSIubOJbnL5fL9cFmM+Hq4OzL9xMPE3hHLmYjkHT1zi0MlLHDpxCbXKjqhQH0YN0TE6XIe/j4vMGSCsxu4vctj6wQkCdRrW/mY8Oi9nS0dqN2/KID45kkvykVxunRJq6ThWpUvlv2HDBvbu3cvo0aNRqVTExMRw6623mjma6C4ZORWo7JWE9HO3dJQ+T6lUEDHIh4hBPiy9NZLTF8pIPVXIVxklHE0vBsDP2/mbHwR8iRjkjbOjzFMueh+D0cS/d7Zd4TIyTMuji8fg0svm1B/g78bQAV4kpWYzb/Ig+aH6GnSp/J2dnYmJiaG6uprTp08TFRXFhQsXGD58uLnziW6QmVNJaKAHKnuZ3Kcn2SkVRIVq28+NFpbV81VGMccyS0g+msfOlGzslArCB3gxMkxLdJiW0CBP7JTyDUxYVkVNE89tO8bJc2XcHDuAe2+L7LWTg82MDWbzu19z6nyZxcchWJMulf8LL7zAP/7xD7y9v72eUqFQkJycbLZgonvoW42cy69i9sQQS0exef4+LsyOG8jsuIHoWw2cuVDB12dL+PpsKW8nZfB2UgYaJxVRg32IDtMxMkyLn7eLpWMLG/NlWiEvvX+ChuZWHv7FSG4a29/SkTo1cUQAryekkZSaI+V/DbpU/omJiezZs0em+LVCFy9Vo281ymC/XkZlb8eIMC0jwrT8mrZJU05klfJ1ZinHz5aQcrIQAH9vF6LDtIwZ5suIwVrUKjuL5hZ9V3VdM//YcZp9R/MY2M+d9YtGEeznZulYV+WgsuPGMUHsPHSRqtpmWYiri7pU/v7+/lL8Viojp201Rhns17u5axyYPDKQySMDMZlM5JfU8fXZEo6fLeXTY3nsSs3GUW3HqHAd4yP8GTPUF42MbhbdwGAwsudwLm99fIaG5lZ+cVMYv/jZEKs6TThz/AD+9/kF9n+Vz61TBlk6jlXoUvnHxsby7LPPMm3aNBwdv50kRs75934Z2ZVoPZ3w8XCydBTRRQqFgiBfV4J8Xbll0iD0rQZOnivji7QiDp8uJOVkYdvAwoHejI/wZ1yEHzrP3jMCW1gHk8nEF2lF/HvXGfKK64gY5M2y+Cir2Nv/viBfV0KDPPj0WJ6Ufxd1qfw//PBDAJKSktrvk3P+1iE9u4JhA2Sv35qp7O0YHe7L6HBf7ouPIiuvki9PF/FFWiGvJZzitYRTDAp0JzbSnwmR/QiSJZtFJ/StBg4cv8SHn2aRU1RLgFbDqjvHMCHSupetnjoqkNcT08gpqrHKH2B6WpfKf9++febOIcygtLKRsqpGwqX8+wylUsGQYC+GBHuxeNYwLpXW8UVaIamnCnl7VwZv78ogQKthQpQ/sZH+hAZ6WPU3dNE9TCYT5/Or+fx4AclHcqmpbyHYz5UVC0YxZWRArx3Jfy0mjwzkjR2n+fRoHr+eI0elr6ZL5V9fX89zzz3H+fPneeGFF3j++ed59NFHcXHpfCTyjh072Lp1K62trdx5550sWrSow/b09HQef/xx6uvriYmJ4amnnsLe/ttIW7Zswc7OjoceegiAmpoaHnnkEfLy8vDy8mLLli1otTK680oun+8fKuXfZ/XTaoifOpj4qYMpr27ky9NFpJ4s5INPz/F+chY+Hk7ERrb9IDAsxFsuI7Qh+lYDGdmVpKYV8kVaIaWVjSiVCsYN92Nm7ABGhmn71A+GHq4OjBqi47Ov8lk8axhK+bfeqS6V//r169HpdJSXl+Pg4EBdXR1r167lueeeu+JjiouL2bx5Mx9++CFqtZo77riDcePGERr67SxMK1euZP369URHR7N69Wq2b9/OwoULqa2tZcOGDXz88cfcfffd7V+/ZcsWYmJieO2110hISODpp59my5Yt1//u+7iM7Aoc1HYM6CeHwGyBt7sTsyaEMGtCCDX1LRw5U0TqqUKSUrPZceAC7ho1MUPbTh9Eh2llOtQ+RN9qoKi8gezCGjJyKsjMqeR8fjWtBiNqeyUjh+hYOD2cscP9cHPpu3/vU0cH8pf0Yk6dL2PEYNkx7EyXyj89PZ0NGzbw2Wef4eTkxKZNm5gzZ06nj0lJSWH8+PF4eHgAMGPGDJKSknjwwQcBKCgooKmpiejoaADi4+P561//ysKFC0lOTmbAgAHcddddHZ5z//79bNu2DYA5c+awbt069Ho9KlXvmnWqt0jPriAsyBP7PnBIT1wbNxc108b0Z9qY/jQ2t3Iso5jUk4V8kVZE8pE8lAoI6+/JqHBfRofrGBToIUcFehl9q5G6hhZqGlqorW+htqGFmno9td+5XV7TxKXSOkoqGjCa2h6ntlcSGuTBLZMGMjTEi+jBWhwdbGNtiXER/jg52PPpsTwp/6vo0r8IpbJjeRgMhh/c930lJSUdDsnrdDpOnjx5xe1arZbi4rbpTy9PHfziiy9e8Tnt7e3RaDRUVFTIZYg/oqmllQsF1cRPlfmubZ2Tgz1xIwKIGxGAwWAkK6+KYxklfJVZzLt7MnhndwYuTiqGDvBi+EBvhod4ExokM0J2J6PRRF2jnuq6Zqrqmqmua6a6tpnq+rYi71DwDXpq61to7GRlSJW9EldnNR6uDgwO8uSGUUEEaF0I1LkS7O9ms393Dio7Jkb149DJApbFR8mCWp3o0iczZswY/vKXv9DU1MSBAwd4++23GTduXKePMRqNHc4nmUymDrevtr0rLq82KH7oXF4VBqNJBvuJDuzslIQP8CJ8gBeLZoZTXdfM8bOlnDxXxukL5e3rD6jtlYQFezIsxJvBQR4MDvLAy82xT50j7k5Nza0UltdTUtFAaVUjJZWNlFa2/bm0spGqumaMl3fNv8fFSYWbsxpXFxXuGgcCfV2/ua3G1Vndvs31m/vcnNU4qO3k7+IKpsa0rfT3ZVoRU0YFWjpOr9Wl8n/kkUd47bXXcHV1ZcuWLcTFxXH//fd3+hg/Pz+OHj3afru0tBSdTtdhe2lpafvtsrKyDtt/jE6no6ysDD8/P1pbW6mvr28/rSA6Ss9uG+w3pL/M7CeuzF3jwJRRge3fJKtqmzlzsZzTF8s5c6Gc/+7Lai8tT1cHQoM8GBzkSWigO8H+bmg9nGymhFoNRkoqGigoraOgtJ5LpXUUlNZxqbSOsuqOS5yr7JVoPZzQejoxaogOL3dH3DVqPDQOuGsc8NA44KZpK/K+MNK+N4kY6IOPhxP7v8qX8u/EVct/7969vPHGG2RmZuLo6MiQIUMYNWoUDg6dT6E4YcIEXnzxRSoqKnBycmLPnj386U9/at8eEBCAg4MDx44dY/To0SQmJjJ58uROn3PKlCkkJCSwbNkydu7cSUxMjJzvv4KM7EoCtBrcNTLVpeg6D1cHJkT1Y0JUP6Dt9FH2pRrO5lVyLq+Kc/lVHE0vxvTNTqyLoz39/dwI9ndjgF/bIecB/m5WPftgTX0LBSV15JfUUlBaR35J26+i8noM39l7d3FSEajVEBnqQ4BWQz8fDb7ezmg9nfDQONjMD0W9jVKp4IZRgXy4/xyVtU14ujpe/UE2qNPy37VrF5s3b2b58uWEh4ejUCg4deoUTz/9NM3NzUyfPv2Kj/X19WXFihUsXrwYvV7P/PnziYqKYunSpSxfvpzIyEg2bdrEmjVrqKurY/jw4SxevLjTsA8//DCrVq1i9uzZuLq6smnTput7132cyWQiPbuCccP9LB1FWDlHtX37aYLLGpr0XLxUQ05RDdmFNeQU1nDg63ySmr49R+3l5thWiFoX+vm0/R6g1eDn7YzK3rLrE5hMJmob9JRUNFBc2UBRWX17yReU1lFT39L+tfZ2Svx9XOjv50pspD8BWk37+3JzUUvB91JTRwfy331ZHDhewC2TZMa/H6MwmUw/fiIKWLBgAc899xz9+vXrcP/FixdZvXo17777rtkDdof8/HymTZtGcnIygYF9/zBQQWkdy55J5sHbo5kxPtjScYQNMJlMlFc3tf8wkFNUw6Wyei6V1lPb8G2ZKhRtpw+83dumnPZ2d0Tr4YSXuxPuLmpcnFRonFS4OKlwdlR1eeCawWiiuaWV5hZDx4F1tc1U1jVTXddCRXUTJZUNFFc0/GAwnYerAwFaDYG6tl8BWg0BOg2+ns5yWN5KPbTpUxzVdvxleedHlPuyzrqv0z3/+vr6HxQ/QEhICM3Nzd2bUnSb9IvlAAwdIOf7Rc9QKBT4eLQVeszQjlff1DW0cOmbvevCsnrKqtpmnswvqeNEVikNTVce1e6gtkNtr0SpVKBQKFAqFCiVbb+MBiNNLQaa9Qb0rcZOsrVd+ujp6oivlzORoT74ejmj83Ru+93LGY2TnD7sayaPDOCtnekUVzTg6yVrX3xfp+VvZ3flw3OdHDAQFpaeXdl2PlInc7wLy9M4qwnrrybsCoNPG5r0lFc3UVPfQn2TnvrGb3/VNerRtxoxmkwYjd/8+ubP9nZKHFR2OKjtcFDb46hu+7OzowpPjQPurg64a9S4uTjIHAY2aFJ0W/kfPF7Az28cbOk4vY5cBNkHpWdXEB7sKdNbCqvg7Nh2iF+I7uTn7cKQ/p58LuX/ozot/8zMTEaNGvWD+00mEy0tLT/yCGFpdQ0t5BXXMmVkgKWjCCGERU0aGcDfE9PIL6mVI6Hf02n57927t6dyiG6SkVMJIJP7CCFsXtyIfrzxvzQOfF3Aghnhlo7Tq3Ra/gEBsvdobU5fKMdOqZDJfYQQNs/b3YnhA735/HgBd0wfIpdmfodcw9LHpJ0vIzTIw2YW8hBCiM5MHhlIfkkd2YU1lo7Sq0j59yFNza1k5VURMdDb0lGEEKJXmBDpj1Kp4POvCywdpVeR8u9DMnIqMBhNRIb6WDqKEEL0Cu4aB6LDtHx+vEAuUf8OKf8+JO18OUqlgqEy2E8IIdpNjg6gpKKBzNxKS0fpNaT8+5C0C+UMCnCXa6aFEOI7xkf4Y2+n5IAc+m8n5d9HNOsNZOZUEjFIDvkLIcR3uTipiBmq4+CJgg4rM9oyKf8+4mxOJa0GIxGDZLCfEEJ83+SRgVTUNHPmQrmlo/QKUv59xIlzpSgVMCxEyl8IIb5vzFBfHNV2fPZ1vqWj9ApS/n3EibOlDA7ylNXJhBDiRzg62DN2uB8pJy/RarjyKpC2Qsq/D6hv1HM2r4roMK2lowghRK81OTqA2gY9x8+WWjqKxUn59wGnzpdhNJoYIeUvhBBXNCpch4uTigPHZdS/lH8fcOJsKQ5qO8KDZT5/IYS4EpW9HRMi/Uk9VUiL3mDpOBYl5d8HHM8qZfhAb1T2dpaOIoQQvdqk6AAam1s5ml5s6SgWJeVv5cqqGskvqSN6sBzyF0KIq4kK9cFdo+ZzGz/0L+Vv5S4PXJHBfkIIcXV2dkomRvXjyJliGpr0lo5jMVL+Vu5oRjFebg4M8HezdBQhhLAKk0cG0qI3cPh0kaWjWIyUvxVrNRj5OrOE0eG+KBQKS8cRQgirMHSAFz4eTnz6le1O+CPlb8XSL1bQ0NTKmGG+lo4ihBBWQ6lUMHV0IMczS6ioabJ0HIuQ8rdiR9KLsbdTMEIG+wkhxDW5MSYIown2H7PNvX8pfyt25EwREQN9ZAlfIYS4RoE6V4YEe7LvaC4mk+2t9GfW8t+xYwezZs1i+vTpbNu27Qfb09PTiY+PZ8aMGTz++OO0trYCcOnSJRYtWsTMmTO57777qK+vB+Dw4cOMGzeOefPmMW/ePB577DFzxu/VisrryS+pI0YO+QshxHWZFhNETlEtFwqqLR2lx5mt/IuLi9m8eTPvvPMOCQkJvPfee5w7d67D16xcuZK1a9eye/duTCYT27dvB+Cpp55i4cKFJCUlERERwSuvvAJAWloaS5YsITExkcTERDZs2GCu+L3e4TNto1THDJXyF0KI6zEpOgCVvZJPjuRaOkqPM1v5p6SkMH78eDw8PHB2dmbGjBkkJSW1by8oKKCpqYno6GgA4uPjSUpKQq/Xc+TIEWbMmNHhfoBTp05x8OBB5s6dy7JlyygsLDRX/F4v9VQh/f1c6afVWDqKEEJYJY2zmtgIf/Yfy6fZxqb7NVv5l5SUoNV+OxBNp9NRXFx8xe1arZbi4mIqKyvRaDTY29t3uB/A1dWVX/3qV+zYsYMpU6awYsUKc8Xv1Sprmzh9oZyJUf0sHUUIIaza9PHB1DXqSTl5ydJRepTZyt9oNHa49txkMnW4faXt3/86oP32unXrmD59OgALFizg3Llz1NbWmust9FpfnCrEZIIJUv5CCPGTRA7ywd/bhd1f5Fg6So8yW/n7+flRWvrtmsmlpaXodLorbi8rK0On0+Hl5UVtbS0Gg6HD44xGI1u3bm2//zI7O9tbzObQyUsEaF0I9nO1dBQhhLBqSqWC6eODOX2hnPwS29mZNFv5T5gwgdTUVCoqKmhsbGTPnj1Mnjy5fXtAQAAODg4cO3YMgMTERCZPnoxKpSImJoadO3cCkJCQwOTJk1Eqlezdu5fdu3e33z9ixAicnZ3N9RZ6peq6Zk6dL2dCVD+Z1U8IIbrBtDFB2CkVNrX3b7by9/X1ZcWKFSxevJhbb72VOXPmEBUVxdKlSzl16hQAmzZtYsOGDcycOZOGhgYWL14MwJNPPsn27duZNWsWR48e5be//S0AGzdu5K233mL27Nl88MEHrF+/3lzxe60v0oowGk1yvl8IIbqJp6sjsZH+7D2cS1Nzq6Xj9AiFyQZmN8jPz2fatGkkJycTGBho6Tg/yaqXD1JZ08TfVk2TPX8hhOgmpy+Us+rlgzwwfwQzYwdYOk636Kz7ZIY/K1JUXs/pC+XcOCZIil8IIbrRsBAvBga4s+PgBZuY8U/K34p8ejQPhQKmjg6ydBQhhOhTFAoFc+MGkltUy6nzZZaOY3ZS/lbCaDSRfDSPqFAfdJ62NchRCCF6wuSRAbi5qEn47Lylo5idlL+VOHOxnOKKBm6M6W/pKEII0SepVXbMiRvIkTPFZBfWWDqOWUn5W4ndX+bg5GDHhEh/S0cRQog+a05cCE4Odvw3OcvSUcxKyt8KVNY0cfB4AdPG9MfRwd7ScYQQos9ydVYzMzaEA8fzKSqvt3Qcs5HytwJJqdm0GkzMiRto6ShCCNHnzZs8EKVSyX/39d29fyn/Xk7famRnajYxQ30JkBX8hBDC7LzdnZgxPphPDudyqazO0nHMQsq/lzt4ooCq2mbmyl6/EEL0mF/cFIa9vZJtSRmWjmIWUv69mNFo4r/7sgjy1RAdpr36A4QQQnQLTzdHbpk0kM+/LuBCQbWl43Q7Kf9e7NCJS+QW1XLHz4agVMqMfkII0ZPipw5G46Tinx+d7nOz/kn591IGo4l392YQ5OvKxBEBlo4jhBA2R+OkYsGMIRw/W0rKqUJLx+lWUv691IHjBeQV17Fg+hDsZK9fCCEsYvaEEEL6ufH3hFM09qEV/6T8e6Gmllbe2nmGAf5usnSvEEJYkJ2dkmXxUZRVN/GfPZmWjtNtpPx7ofeTsyitbOTe2yLlXL8QQljYsBBvfja2PwmfnSP9YoWl43QLKf9epqC0jg8/PccNowOJGORj6ThCCCGAu+dF4OPpzHPvHKOhSW/pOD+ZlH8vYjCaeOn946hVSpbMGW7pOEIIIb7h7KjidwtGUVrZwKv/75TVj/6X8u9F/pt8lrTz5SydF4mnm6Ol4wghhPiO4QO9+b+bhrDvaB47Dl6wdJyfRMq/lzhzsZx39mQyZWQg08YEWTqOEEKIH7Fg+hDGDffjjcQ0vs4ssXSc6ybl3wsUldez4V9H0Hk6cf/8KBQKGeQnhBC9kVKp4HcLRxHk68qGfx0hM8c6BwBK+VtYdV0zT76WisFgZO1vxuPsqLJ0JCGEEJ1wdlTx1D2xeGgcePK1VLLyKi0d6ZpJ+VtQZU0Ta19NpayqkSeWjCfI19XSkYQQQnSBt7sT6++bgIuzmjV/S+GrDOs6BSDlbyEFpXWsfPEAl8rqePyucQwN8bJ0JCGEENdA5+nMM/fH4evlzFN/TyXx8/NWcxWAlL8F7D+Wx++2fEZTSytP3zeRUeE6S0cSQghxHbSeTmx8cBJjhvnx98Q0/vj6F5RXN1o61lVJ+fegovJ6nvnXEZ575yuC/dx47uEphPX3tHQsIYQQP4GTgz2P3zWWZfFRpF0o576N+9j+yVmaWnrvWgD2lg5gCy6V1fHRwYvsSslGqVTwy5vDmT91MHZ28rOXEEL0BQqFgtkTQxg5RMs//neaf+9K56ODF5g1MYSZ4wfg4epg6YgdmLX8d+zYwdatW2ltbeXOO+9k0aJFHbanp6fz+OOPU19fT0xMDE899RT29vZcunSJlStXUl5eTkhICJs2bcLFxYWamhoeeeQR8vLy8PLyYsuWLWi1WnO+hetWUdPE4dNFHDp5iRNZpSgVCqaODmLRzHB8PJwsHU8IIYQZ9PPRsGbJOE5fKGd78lm2JWXwnz2ZjAjTMjGqH9FhWnSezpaOicJkptEJxcXFLFiwgA8//BC1Ws0dd9zB888/T2hoaPvXzJkzh/Xr1xMdHc3q1auJiIhg4cKF3Hvvvdxyyy3Mnj2bl19+mYaGBlauXMm6devw8/PjnnvuISEhgf3797Nly5arZsnPz2fatGkkJycTGBjYbe/RZDLR0NRKeXUjZVVN5BbXkl1YTUZ2BQWl9QD4eTtzw6ggZsYG4+0upS+EELYkr7iW5CO5HDxxieKKBgB0Xs6E+LvR38+VYL+23308nNA4qbp1npfOus9se/4pKSmMHz8eDw8PAGbMmEFSUhIPPvggAAUFBTQ1NREdHQ1AfHw8f/3rX7n99ts5cuQIL7/8cvv9v/zlL1m5ciX79+9n27ZtQNsPDuvWrUOv16NSdX5tvMFgAKCoqKhb3pvJZOL5d77i4qVqmloMHba5OasJCXAnZrw3wwd5E6jVoFAoaKwtJ7+2W15eCCGElVAAN0W7M22EGwWl9WRkV5CVV8X5izkcOtaAyfjt/re7i5onl8bi5qLulte+3HmXO/C7zFb+JSUlHQ7J63Q6Tp48ecXtWq2W4uJiKisr0Wg02Nvbd7j/+4+xt7dHo9FQUVGBr69vp1lKS0sBfnDawVxO9MirCCGE6Gtu29H9z1laWkpwcHCH+8xW/kajscPhC5PJ1OH2lbZ//+uAKx4GMZlMKJVXHzQXERHBtm3b0Gq12NnZXetbEUIIIayOwWCgtLSUiIiIH2wzW/n7+flx9OjR9tulpaXodLoO2y/vkQOUlZWh0+nw8vKitrYWg8GAnZ1dh8fpdDrKysrw8/OjtbWV+vr69tMKnXF0dCQmJqb73pwQQghhBb6/x3+Z2a41mzBhAqmpqVRUVNDY2MiePXuYPHly+/aAgAAcHBw4duwYAImJiUyePBmVSkVMTAw7d+4EICEhof1xU6ZMISEhAYCdO3cSExNz1fP9QgghhOjIbKP9oe1Sv1dffRW9Xs/8+fNZunQpS5cuZfny5URGRpKRkcGaNWuoq6tj+PDhbNiwAbVaTUFBAatWraK8vBx/f3+ef/553N3dqaqqYtWqVeTl5eHq6sqmTZu6dfS+EEIIYQvMWv5CCCGE6H1kijkhhBDCxkj5CyGEEDZGyl8IIYSwMVL+QgghhI2R8hdCCCFsjJS/YMeOHcyaNYvp06e3r50grs1LL73E7NmzmT17Ns8++yzQtr7F3LlzmT59Ops3b7ZwQuuzceNGVq1aBchn+VPs27eP+Ph4br75ZtavXw/I5/lTJCYmtv9f37hxI2Cln6dJ2LSioiLT1KlTTZWVlab6+nrT3LlzTVlZWZaOZVUOHTpk+sUvfmFqbm42tbS0mBYvXmzasWOHacqUKabc3FyTXq83LVmyxLR//35LR7UaKSkppnHjxpkeffRRU2Njo3yW1yk3N9cUFxdnKiwsNLW0tJgWLFhg2r9/v3ye16mhocE0ZswYU3l5uUmv15vmz59vSk5OtsrPU/b8bdx3V190dnZuX31RdJ1Wq2XVqlWo1WpUKhWDBg0iOzub4OBggoKCsLe3Z+7cufK5dlFVVRWbN29m2bJlAJw8eVI+y+u0d+9eZs2ahZ+fHyqVis2bN+Pk5CSf53UyGAwYjUYaGxtpbW2ltbUVjUZjlZ+n2eb2F9bhaqsviqsbPHhw+5+zs7PZtWsXv/zlL3/wuV5enVJ0bu3ataxYsYLCwkLgx/+NymfZNTk5OahUKpYtW0ZhYSE33HADgwcPls/zOmk0Gh5++GFuvvlmnJycGDNmjNX++5Q9fxt3tdUXRddlZWWxZMkS/vCHPxAUFCSf63V4//338ff3JzY2tv0++Td6/QwGA6mpqfz5z3/mvffe4+TJk+Tl5cnneZ0yMjL44IMP+PTTTzlw4ABKpZLs7Gyr/Dxlz9/GXW31RdE1x44dY/ny5axevZrZs2dz+PDhDqtWyufaNTt37qS0tJR58+ZRXV1NQ0MDBQUFHZbils+y63x8fIiNjcXLywuAm266iaSkJPk8r9PBgweJjY3F29sbgPj4eN544w2r/Dxlz9/GXW31RXF1hYWFPPDAA2zatInZs2cDMGLECC5evEhOTg4Gg4GPPvpIPtcu+Oc//8lHH31EYmIiy5cv58Ybb+Tvf/+7fJbXaerUqRw8eJCamhoMBgMHDhxg5syZ8nlep/DwcFJSUmhoaMBkMrFv3z6r/b8ue/42ztfXlxUrVrB48eL21RejoqIsHcuqvPHGGzQ3N/PMM8+033fHHXfwzDPP8NBDD9Hc3MyUKVOYOXOmBVNaLwcHB/ksr9OIESO4++67WbhwIXq9nokTJ7JgwQIGDhwon+d1iIuL48yZM8THx6NSqYiMjOShhx5i4sSJVvd5yqp+QgghhI2Rw/5CCCGEjZHyF0IIIWyMlL8QQghhY6T8hRBCCBsj5S+EEELYGLnUTwjxo9avX8+RI0cAOH/+PAEBATg6OgLw3nvvtf9ZCGF95FI/IcRV3XjjjbzwwgtERkZaOooQohvInr8Q4pq8//77vPvuuxiNRjw8PHjiiScYNGgQq1atQqPRkJmZSVFREUOGDGHjxo24uLgwZMgQUlNT26eZvXw7KyuLp59+GmdnZ+rr6/nggw84ePAgW7duRa/X4+joyKOPPsrIkSMt/K6F6Fuk/IUQXXb48GESEhLYtm0bTk5OHDx4kAcffJBdu3YBkJaWxltvvYVCoeD//u//SEpK4uc//3mnz5mVlcUnn3xCQEAA2dnZbN68mbfeegtPT0+ysrK466672LNnD87Ozj3xFoWwCVL+Qogu279/Pzk5Odxxxx3t99XU1FBVVQXApEmTUKvVAISFhVFdXX3V5/T39ycgIACAQ4cOUVJSwq9//ev27QqFgtzcXMLDw7vvjQhh46T8hRBdZjQamTdvHitXrmy/XVJSgru7O0CHQYAKhYIfG1LU0tLS4fZ39+iNRiOxsbFs2bKl/b7CwkKrWCVNCGsil/oJIbosLi6Ojz/+mJKSEgDeffdd7rzzzqs+zsvLi1OnTgHw0UcfXfHrYmNjOXToEOfPnwfgs88+45ZbbqGpqakb0gshLpM9fyFEl8XFxbF06VKWLFmCQqFAo9Hw0ksvoVAoOn3cmjVrWLduHW5ubkyYMAGtVvujXxcaGsq6dev43e9+h8lkwt7enq1bt+Li4mKOtyOEzZJL/YQQQggbI4f9hRBCCBsj5S+EEELYGCl/IYQQwsZI+QshhBA2RspfCCGEsDFS/kIIIYSNkfIXQgghbMz/B3osLkHOuBaUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution\n",
    "for col in ['Tenure', 'MonthlyCharges']:\n",
    "    fig = plt.figure(figsize=(8,3))\n",
    "    sns.kdeplot(df[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8064278",
   "metadata": {},
   "source": [
    " **Skewness:** The density plots show they are not normal distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5235822a",
   "metadata": {},
   "source": [
    "### Analyising categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e52f5e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['Gender', 'SeniorCitizen', 'Partner', 'Dependents']:\n",
    "    plt.figure(figsize=(8,5))\n",
    "    sns.countplot(x=col, hue='Churn', data=df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da5ff62",
   "metadata": {},
   "source": [
    "**Gender:** The churn rate is similar between male and female, indicating Gender may not be a good predictor.\n",
    "\n",
    "**Senior Citizen:** Customer who is senior citizen is more likely to churn.\n",
    "\n",
    "**Partner:** Customer who does not have partner is more likely to churn.\n",
    "\n",
    "**Dependents:** Customer who does have dependents is more likely to churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d79a18ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa284052f70>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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79m33lQsAcC1r0zlcAED7BQzupUuXVFxcrJqaGp8bkHPLRgBom4DBzcvL09mzZxUfH88NyAHgKgQMbkVFhfbs2aOQED7+DACuRsCK9u3bV01NTSZmAQBH83uEu3HjRklfv6ts2rRpGjlypMLDw73rOYcLAG3jN7inTp2SJPXs2VM9e/bk3WUAcJX8BnfZsmUtnmtoaGjXpz8AAK5wDrehoUG/+c1vtHfvXu9zjz32mJ566inO6QJAO/gN7po1a1RbW6vBgwd7n1u8eLEuXLigtWvXGhkOAJzEb3APHDigVatWqW/fvt7nYmNjtWLFCu3bt8/IcADgJH6DGx4erm7durV4vmfPnpzHBYB28BvckJAQ1dbWtni+traWc7gA0A5+gztmzBgtXLhQHo/H+5zH49HChQuVkpJiZDgAcBK/wX3ooYd03XXXadiwYZo8ebIyMzM1bNgw9erVS7NnzzY5IwA4gt/rcENCQrRkyRLNnDlT77//vkJCQpSYmNjio84BAK0T8OY1/fr1U79+/UzMAgCOxi3AAMAQggsAhhBcADCE4AKAIQQXAAwhuABgCMEFAEMILgAYQnABwBCCCwCGEFwAMITgAoAhBBcADCG4AGAIwQUAQwguABhCcAHAEIILAIYQXFzzSktLlZeXp9LS0mCPAocL+JlmgNNt2rRJFRUV8ng8SkpKCvY4cDCOcHHN83g8Po+AXQguABhCcAHAEIILAIYQXAAwhOACgCEEFwAMIbgAYAjBBQBDCC6MsJoagz1Cl8XPzjl4ay+McIWFq+r384I9xne6fOFL72NnnNH9vyuCPQI6CEe4AGAIwQUAQwguABhia3ALCwuVlpamlJQUFRQU+N1u3rx52rFjh52jAEDQ2RbcyspKrV69Wlu3btWuXbu0bds2nT59usU2M2fO1J49e+waAwA6DduCW1JSoqSkJEVHRysqKkqpqakqKiry2aawsFAjR47UqFGj7BoDADoN2y4Lq6qqUkxMjHfZ7Xbr2LFjPts8+uijkqSysjK7xgCATsO24DY3N8vlcnmXLcvyWe4I5eXlAbcZMmRIh77mtaaj/jLk93B1OCjpOq70v3XbghsXF6cjR454l6urq+V2uzv0NRISEhQZGdmh+4QvQtk58HtwBtvO4SYnJ+vw4cOqqalRXV2diouLNWLECLteDmi37mGhPo+AXWwLbmxsrHJzc5WVlaXx48drzJgxSkxMVHZ2to4fP27XywJtlnnHDbo9pqcy77gh2KPA4Wy9l0J6errS09N9nnvllVdabLd8+XI7xwCuaPD3e2vw93sHewxcA3inGQAYQnABwBCCCwCGEFwAMITgAoAhBBcADCG4AGAIwQUAQwguABhCcAHAEIILAIYQXAAwhOACgCEEFwAMIbgAYAjBBQBDCC4AGEJwAcAQggsAhhBcADCE4AKAIQQXAAwhuABgCMEFAEMILgAYQnABwBCCCwCGEFwAMITgAoAhBBcADCG4AGAIwQUAQwguABhCcAHAEIILoFMoLS1VXl6eSktLgz2KbcKCPQAASNKmTZtUUVEhj8ejpKSkYI9jC45wAXQKHo/H59GJCC4AGEJwAcAQggsAhhBcADCE4ALXmKbLzcEeoUvqiJ8bl4UB15iw0BCte+1gsMdo4cLFS97HzjjfrF8Mv+p9cIQLAIYQXAAwhOACgCEEF0CnEBYe4fPoRAQXQKdw+6D7FBN3k24fdF+wR7ENVykA6BS+/4N4ff8H8cEew1Yc4QKAIQQXAAwhuABgCMEFAEMILgAYQnABwBCCCwCG2BrcwsJCpaWlKSUlRQUFBS3Wf/jhh8rIyFBqaqoWLFigpqYmO8cBgKCyLbiVlZVavXq1tm7dql27dmnbtm06ffq0zzZz587VM888oz179siyLG3fvt2ucQAg6Gx7p1lJSYmSkpIUHR0tSUpNTVVRUZHmzJkjSfr888916dIlDRw4UJKUkZGhNWvWaOrUqQH3bVmWJKmhoaFVs/QICW/7NwDV19d36P6aIqI6dH/Xio7+PUhSRLirw/fpdG35PURERMjlavkzti24VVVViomJ8S673W4dO3bM7/qYmBhVVla2at+NjY2SpFOnTrVq+3Hfc/bbBe1SXl7esTscmN6x+7tG/Kujfw+SBvXv1uH7dLq2/PeQkJCgyMjIFs/bFtzm5mafwluW5bMcaP2V9OjRQ/Hx8QoPD2/11wCAKRER333HM9uCGxcXpyNHjniXq6ur5Xa7fdZXV1d7l7/88kuf9VcSEhKi6667ruOGBQADbPtHs+TkZB0+fFg1NTWqq6tTcXGxRowY4V3fr18/RUZGqqysTJK0e/dun/UA4DQu65t/gbJBYWGhNmzYoMbGRmVmZio7O1vZ2dnKycnRnXfeqRMnTmjhwoWqra3VHXfcoWXLlvk9FAeArs7W4AIA/h/vNAMAQwguABhCcAHAEIILAIYQ3CAJdGMfmFNbW6sxY8bos88+C/Yo16zf/e53Gj16tEaPHq0VK1YEexzbENwgaM2NfWDG3//+dz3wwAM6c+ZMsEe5ZpWUlOjgwYPauXOndu3apffff1979+4N9li2ILhB8N839omKivLe2Afmbd++Xc8++2yr3+WIjhcTE6Mnn3xSERERCg8P180336wvvvgi2GPZwra39sK/QDf2gTnPPfdcsEe45t1yyy3eP585c0Zvv/22Xn/99SBOZB+OcIPgam7cAzhVRUWFHnnkEc2bN0833XRTsMexBcENgm/fuOfbN/YBrjVlZWWaPn26nnjiCU2YMCHY49iG4AZBoBv7ANeSf/3rX5o9e7ZeeOEFjR49Otjj2IpzuEEQGxur3NxcZWVleW/sk5iYGOyxgKD4wx/+oPr6ei1fvtz73JQpU/TAAw8EcSp7cPMaADCEUwoAYAjBBQBDCC4AGEJwAcAQggsAhhBcdDmXL1/Wxo0blZGRoXHjxiktLU0rV65UQ0NDu/Z38eJFZWVlddh8Hb0/OAfBRZeTn5+vo0ePavPmzdq9e7feeOMNffTRR1qwYEG79nfhwgUdP368w+br6P3BObgOF13KZ599pjFjxujgwYPq2bOn9/nq6mq99957Sk5O1qJFi3TixAm5XC7dc889ysvLU1hYmO68807NmDFDhw4dUlVVlR599FFNnTpV06ZN05EjRxQfH68dO3ZowIABGjlypE6cOKEXXnhBJ0+e1LZt29TY2KgLFy4oOztbU6dOlSRt2LBBO3fuVFhYmH74wx9q+fLlmjVrls/+QkNDg/XjQmdjAV1IUVGRNXHiRL/r582bZy1ZssRqbm626uvrrUceecTasGGDZVmWFR8fb23ZssWyLMs6fvy4lZCQYF26dMn69NNPrYEDB3r3ER8fb+3cudOyLMuqra21Jk+ebNXU1FiWZVlHjx71brtv3z4rJSXFOn/+vGVZlvXb3/7WWrduXYv9Ad/grb3oUkJCQtTc3Ox3/bvvvqvXX39dLpdLERERmjJlijZv3qwZM2ZIkkaOHClJuuOOO9TQ0CCPx/Od+xk6dKgkqUePHlq/fr3eeecdnTlzRidOnPB+zeHDh3X//ferd+/ekqSnnnpKkvjkCPjFOVx0KYmJifrnP/+p2tpan+crKys1Y8aMFre+bG5uVlNTk3c5MjJSkrzbWH7OqEVFRUmSzp49q/Hjx+vzzz/XkCFD9Pjjj3u3CQ0N9Xmtr776itjiigguupTY2Filp6dr/vz53ujW1tYqPz9f0dHRGj58uF577TVZlqWGhgZt375dycnJV9xnWFiYLl++/J3xLS8v1/XXX69Zs2Zp+PDh+vOf/yzp6yslkpOTtXfvXu8ca9eu1aZNm664P1zbOKWALufZZ5/VunXrNGXKFIWGhqqhoUE//elP9dhjj+k///mPli5dqvT0dDU2Nuqee+7RzJkzr7i/mJgYJSYmavTo0S0+0HPYsGF64403dP/998vlcumuu+7S9ddfr48//lj33nuvTp8+7b2rVf/+/bVkyRJ1797dZ399+vSx7WeBroWrFADAEE4pAIAhBBcADCG4AGAIwQUAQwguABhCcAHAEIILAIYQXAAw5P8AA95H5lxVqioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", palette=\"Set2\")\n",
    "g1=sns.catplot(x=\"Contract\", y=\"Churn\", data=df,kind=\"bar\")\n",
    "g1.set_ylabels(\"Churn Probability\")\n",
    "\n",
    "g2=sns.catplot(x=\"InternetService\", y=\"Churn\", data=df,kind=\"bar\")\n",
    "g2.set_ylabels(\"Churn Probability\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31e5928",
   "metadata": {},
   "source": [
    "Having month-to-month contract and fiber obtic internet have a really huge effect on churn probability."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def74559",
   "metadata": {},
   "source": [
    "### Correlation of Churn with other attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7254565f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Contract                    -0.396713\n",
       "Tenure                      -0.352229\n",
       "TotalCharges                -0.199428\n",
       "OnlineSecurity              -0.171226\n",
       "TechSupport                 -0.164674\n",
       "Dependents                  -0.164221\n",
       "Partner                     -0.150448\n",
       "Credit card (automatic)     -0.134302\n",
       "Bank transfer (automatic)   -0.117937\n",
       "Mailed check                -0.091683\n",
       "OnlineBackup                -0.082255\n",
       "DeviceProtection            -0.066160\n",
       "Gender                      -0.008612\n",
       "PhoneService                 0.011942\n",
       "MultipleLines                0.040102\n",
       "StreamingMovies              0.061382\n",
       "StreamingTV                  0.063228\n",
       "SeniorCitizen                0.150889\n",
       "PaperlessBilling             0.191825\n",
       "MonthlyCharges               0.193356\n",
       "Electronic check             0.301919\n",
       "InternetService              0.316846\n",
       "Churn                        1.000000\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Churn'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34e473d",
   "metadata": {},
   "source": [
    "Customers having month-to-month contract, having fiber optic internet service and using electronic payment are tend to churn more whereas people having two-year contract and having internet service are tend to not churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2cb26d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "df.corr()['Churn'].sort_values(ascending = False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87837346",
   "metadata": {},
   "source": [
    "Month to month contracts, absence of online security and tech support seem to be positively correlated with churn. While, tenure, two year contracts seem to be negatively correlated with churn.\n",
    "\n",
    "Interestingly, services such as Online security, streaming TV, online backup, tech support, etc. without internet connection seem to be negatively related to churn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cde0725",
   "metadata": {},
   "source": [
    "### Scaling and Splitting data\n",
    "\n",
    "#### Scaling\n",
    "Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units.\n",
    "\n",
    "#### Splitting\n",
    "The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model.\n",
    "* <b>Train Dataset:</b> Used to fit the machine learning model.\n",
    "* <b>Test Dataset:</b> Used to evaluate the fit machine learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4216025c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Churn'] # target column\n",
    "X = df.drop('Churn', axis = 1) # all the other columns except churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f8f421a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "54e4e602",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae59e35",
   "metadata": {},
   "source": [
    "## Machine Learning Models\n",
    "\n",
    "### KNN - K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e595a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy according to K=3 is : 0.7397065783246569\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 3) #set K neighbor as 3\n",
    "knn.fit(X_train,y_train)\n",
    "predicted_y = knn.predict(X_test)\n",
    "print(\"KNN accuracy according to K=3 is :\",knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca60298",
   "metadata": {},
   "source": [
    "We assume K = 3 for first iteration, but actually we don't know what is the optimal K value that gives maximum accuracy. So we can write a for loop that iterates for example 25 times and gives the accuracy at each iteartion. So that we can find the optimal K value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "572932bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_array = []\n",
    "for each in range(1,25):\n",
    "    knn_loop = KNeighborsClassifier(n_neighbors = each) #set K neighbor as 3\n",
    "    knn_loop.fit(X_train,y_train)\n",
    "    score_array.append(knn_loop.score(X_test,y_test))\n",
    "    \n",
    "plt.plot(range(1,25),score_array)\n",
    "plt.xlabel(\"Range\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd42309",
   "metadata": {},
   "source": [
    "As you can see above, if we use K = 21, then we get maximum score of %78.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "112ba9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy according to K=21 is : 0.7875059157595835\n"
     ]
    }
   ],
   "source": [
    "knn_model = KNeighborsClassifier(n_neighbors = 21) #set K neighbor as 21\n",
    "knn_model.fit(X_train,y_train)\n",
    "predicted_y = knn_model.predict(X_test)\n",
    "accuracy_knn = knn_model.score(X_test,y_test)\n",
    "print(\"KNN accuracy according to K=21 is :\",accuracy_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301f347a",
   "metadata": {},
   "source": [
    "### Logistic Regression Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "55192fdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy is : 0.8130619971604354\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(X_train,y_train)\n",
    "accuracy_lr = lr_model.score(X_test,y_test)\n",
    "print(\"Logistic Regression accuracy is :\",accuracy_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e177986",
   "metadata": {},
   "source": [
    "### SVM(Support Vector Machine) Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f8e3d3bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy is : 0.8054898248935163\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_model = SVC(random_state = 1)\n",
    "svc_model.fit(X_train,y_train)\n",
    "accuracy_svc = svc_model.score(X_test,y_test)\n",
    "print(\"SVM accuracy is :\",accuracy_svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b257fe9",
   "metadata": {},
   "source": [
    "### Naive Bayes Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "57016a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes accuracy is : 0.77520113582584\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb_model = GaussianNB()\n",
    "nb_model.fit(X_train,y_train)\n",
    "accuracy_nb = nb_model.score(X_test,y_test)\n",
    "print(\"Naive Bayes accuracy is :\",accuracy_nb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "787e950a",
   "metadata": {},
   "source": [
    "### Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9a3871b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy is : 0.7311878845243729\n"
     ]
    }
   ],
   "source": [
    "# %%Decision Tree Classification\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_model = DecisionTreeClassifier()\n",
    "dt_model.fit(X_train,y_train)\n",
    "accuracy_dt = dt_model.score(X_test,y_test)\n",
    "print(\"Decision Tree accuracy is :\",accuracy_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef2c0d5",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8caa8916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy for 5 trees is : 0.7666824420255561\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model_initial = RandomForestClassifier(n_estimators = 5, random_state = 1)\n",
    "rf_model_initial.fit(X_train,y_train)\n",
    "print(\"Random Forest accuracy for 5 trees is :\",rf_model_initial.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829ab203",
   "metadata": {},
   "source": [
    "The tree number is set to 5 initially. But, to find the appropriate tree number. Let's try to find the best number with trying 1 to 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7df92f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_array = []\n",
    "for each in range(1,50):\n",
    "    rf_loop = RandomForestClassifier(n_estimators = each, random_state = 1) #set K neighbor as 3\n",
    "    rf_loop.fit(X_train,y_train)\n",
    "    score_array.append(rf_loop.score(X_test,y_test))\n",
    "    \n",
    "plt.plot(range(1,50),score_array)\n",
    "plt.xlabel(\"Range\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74270983",
   "metadata": {},
   "source": [
    "As we can see, the highest accuracy is at n_estimators = 48."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5538d945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy for 33 trees is : 0.7865593942262187\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators = 48, random_state = 1) #set tree number as 48\n",
    "rf_model.fit(X_train,y_train)\n",
    "accuracy_rf = rf_model.score(X_test,y_test)\n",
    "print(\"Random Forest accuracy for 33 trees is :\",accuracy_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf3deb9",
   "metadata": {},
   "source": [
    "### Accuracies of ML Algorithms\n",
    "\n",
    "| Algorithm |Acuuracy(%)  | \n",
    "| :---:   | :---: |\n",
    "| Desicion Tree | 73.1  | \n",
    "   | Naive Bayes| 78.2   | \n",
    "| KNN (k=21)| 79.4   | \n",
    "| Random forest | 80.4  | \n",
    "| SVM | 80.7   | \n",
    "| Logistic Regression | 81.9   | \n",
    "\n",
    "From the above table, Logistic regression and SVC classificagtion algorithms have the highest accuracy.But as I mentioned before, our data is imbalanced. So it is important to look at the confusion matrix according to these two algorithms. With imbalanced datasets, the highest accuracy does not give the best model. Assume we have 1000 total rows, 10 rows are churn and 990 rows are non-churn. If we find all these 10 churn rows as non-churn, then the accuracy will be still %99. Althogh it is a wrong model, if we do not look at the confusion matrix, then we can not see the mistake.\n",
    "\n",
    "Confusion matrix gives us FN(false negative), FP(false positive), TN(true negative) and TP(true positive) values.\n",
    "\n",
    "![](https://drive.google.com/uc?export=view&id=1Ks14UX9YOnXvSVZx4ucfU1bIgNpiYMdz)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523b1eed",
   "metadata": {},
   "source": [
    "Learn about confusion matrix - https://www.youtube.com/watch?v=Kdsp6soqA7o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "055a13a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%Confusion Matrix libraries\n",
    "from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, classification_report\n",
    "\n",
    "#for Logistic Regression\n",
    "cm_lr = confusion_matrix(y_test,lr_model.predict(X_test))\n",
    "\n",
    "# %% confusion matrix visualization\n",
    "f, ax = plt.subplots(figsize = (5,5))\n",
    "sns.heatmap(cm_lr, annot = True, linewidths = 0.5, color = \"red\", fmt = \".0f\", ax=ax)\n",
    "plt.xlabel(\"y_predicted\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Confusion Matrix of Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eee6e52",
   "metadata": {},
   "source": [
    "For logistic regression confusion matrix;\n",
    "```\n",
    "TN = 1394\n",
    "FP = 156\n",
    "FN = 239\n",
    "TP = 324\n",
    "```\n",
    "This means; there are total 1394+156 = 1550 actual non-churn values and the algorithm predict 1394 of them as non-churn and 156 of them churn. Also there are total 239 + 324 = 563 actual churn values and the algorithm predict 239 of them as non-churn and 324 of them as churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "23efd9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for SVM\n",
    "cm_lr = confusion_matrix(y_test,svc_model.predict(X_test))\n",
    "\n",
    "# %% confusion matrix visualization\n",
    "f, ax = plt.subplots(figsize = (5,5))\n",
    "sns.heatmap(cm_lr, annot = True, linewidths = 0.5, color = \"red\", fmt = \".0f\", ax=ax)\n",
    "plt.xlabel(\"y_predicted\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Confusion Matrix of Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd491b19",
   "metadata": {},
   "source": [
    "Acuracy should not be used as solely metric for imbalance datasets. There are some other metrics named as recall and precision.\n",
    "\n",
    "![](https://drive.google.com/uc?export=view&id=1_4ZejjBAy3WUo-7OkS0hJpzBEaRr3otQ)\n",
    "\n",
    "Sometimes we get high recall and low precision or vice versa. There is another metric that combines both precision and recall like below. We will use F1 score to identify the best algorithm score.\n",
    "\n",
    "![](https://drive.google.com/uc?export=view&id=1sbGivo8TRDQr-iEBrM4HdHTuhVJD8kms)\n",
    "\n",
    "Let's write a function that calculates and print both accuracy, recall, precision and weighted F1 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7baf3cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the function that prints all scores\n",
    "def print_scores(headline, y_true, y_pred):\n",
    "    print(headline)\n",
    "    acc_score = accuracy_score(y_true, y_pred)\n",
    "    print(\"accuracy: \",acc_score)\n",
    "    pre_score = precision_score(y_true, y_pred)\n",
    "    print(\"precision: \",pre_score)\n",
    "    rec_score = recall_score(y_true, y_pred)                            \n",
    "    print(\"recall: \",rec_score)\n",
    "    f_score = f1_score(y_true, y_pred, average='weighted')\n",
    "    print(\"f1_score: \",f_score)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "df8241e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.90      0.88      1550\n",
      "           1       0.68      0.58      0.62       563\n",
      "\n",
      "    accuracy                           0.81      2113\n",
      "   macro avg       0.76      0.74      0.75      2113\n",
      "weighted avg       0.81      0.81      0.81      2113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We can also use classification_report function from skleran library to show all these metrics.\n",
    "\n",
    "report = classification_report(y_test, lr_model.predict(X_test))\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "12504c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression;\n",
      "accuracy:  0.8130619971604354\n",
      "precision:  0.675\n",
      "recall:  0.5754884547069272\n",
      "f1_score:  0.8080612082641923\n",
      "\n",
      "SVC;\n",
      "accuracy:  0.8054898248935163\n",
      "precision:  0.6844660194174758\n",
      "recall:  0.5008880994671403\n",
      "f1_score:  0.794944985997414\n",
      "\n",
      "KNN;\n",
      "accuracy:  0.7875059157595835\n",
      "precision:  0.6158536585365854\n",
      "recall:  0.5381882770870338\n",
      "f1_score:  0.7827345560527701\n",
      "\n",
      "Naive Bayes;\n",
      "accuracy:  0.77520113582584\n",
      "precision:  0.5582010582010583\n",
      "recall:  0.7495559502664298\n",
      "f1_score:  0.7841853913779209\n",
      "\n",
      "Decision Tree;\n",
      "accuracy:  0.7311878845243729\n",
      "precision:  0.49581239530988275\n",
      "recall:  0.5257548845470693\n",
      "f1_score:  0.7336368942882013\n",
      "\n",
      "Random Forest;\n",
      "accuracy:  0.7865593942262187\n",
      "precision:  0.6308411214953271\n",
      "recall:  0.47957371225577267\n",
      "f1_score:  0.7764748626361726\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Printing results for all ML algorithms\n",
    "\n",
    "print_scores(\"Logistic Regression;\",y_test, lr_model.predict(X_test))\n",
    "print_scores(\"SVC;\",y_test, svc_model.predict(X_test))\n",
    "print_scores(\"KNN;\",y_test, knn_model.predict(X_test))\n",
    "print_scores(\"Naive Bayes;\",y_test, nb_model.predict(X_test))\n",
    "print_scores(\"Decision Tree;\",y_test, dt_model.predict(X_test))\n",
    "print_scores(\"Random Forest;\",y_test, rf_model.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae18a79",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "* Since data set is imbalanced, we prefered to use F1 score rather than accuracy.\n",
    "* Logistic Regression gives the highest F1 Score, so it is the best model.\n",
    "* Naive Bayes is the worst model because it gives the lowest F1 score.\n",
    "* Sex has no impact on churn.\n",
    "* People having month-to-month contract tend to churn more than people having long term conracts.\n",
    "* As the tenure increases, the probability of churn decreases.\n",
    "* As tmonthly charges increases, the probability of churn increases."
   ]
  }
 ],
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