my_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40d4b4ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-22 22:27:44.305097: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-12-22 22:27:44.456860: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-12-22 22:27:44.456888: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-12-22 22:27:45.049472: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-22 22:27:45.049538: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-22 22:27:45.049545: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "# coping code from gradiant decent calculation\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#sigmoid function\n",
    "import math\n",
    "def sigmoid_numpy(X):\n",
    "   return 1/(1+np.exp(-X))\n",
    "\n",
    "#log loss function\n",
    "def log_loss(y_true, y_predicted):\n",
    "    epsilon = 1e-15\n",
    "    y_predicted_new = [max(i,epsilon) for i in y_predicted]\n",
    "    y_predicted_new = [min(i,1-epsilon) for i in y_predicted_new]\n",
    "    y_predicted_new = np.array(y_predicted_new)\n",
    "    return -np.mean(y_true*np.log(y_predicted_new)+(1-y_true)*np.log(1-y_predicted_new))\n",
    "\n",
    "#the gradient decent function\n",
    "def gradient_decent(age, affordability, y_true, epotch, loss_threshold):\n",
    "    #w1, w2, bias0\n",
    "    w1,w2,bias = 1,1,0\n",
    "    rate = 0.5\n",
    "    n = len(age)\n",
    "    \n",
    "    for i in range(epotch):\n",
    "        weighted_sum = w1* age + w2* affordability + bias #calculating the weighted sum\n",
    "        y_predicted = sigmoid_numpy(weighted_sum)\n",
    "        \n",
    "        loss = log_loss(y_true, y_predicted) # the distance between true and predicted\n",
    "        \n",
    "        w1d = (1/n)*np.dot(np.transpose(age),(y_predicted-y_true)) #derivative of age\n",
    "        w2d = (1/n)*np.dot(np.transpose(affordability),(y_predicted-y_true)) #derivative of affordability\n",
    "        \n",
    "        bias_d = np.mean(y_predicted-y_true) #derivative of bias or intercept\n",
    "        \n",
    "        w1 = w1 - rate * w1d # Subtract the respective derivative with learning rate\n",
    "        w2 = w2 - rate * w2d # Subtract the respective derivative with learning rate\n",
    "        bias = bias - rate * bias_d # Subtract the respective derivative with learning rate\n",
    "        \n",
    "        print (f'Epotch:{i}, w1:{w1}, w2:{w2}, bias:{bias}, loss:{loss}') # printing log\n",
    "        \n",
    "        if loss<= loss_threshold:\n",
    "            break\n",
    "    return w1,w2,bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e54d95c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myNN:\n",
    "    def __init__(self):\n",
    "        self.w1 = self.w2 = 1\n",
    "        self.bias = 0\n",
    "    \n",
    "    def fit(self, X_train, y_train, epotch, loss_threshold):\n",
    "        self.w1,self.w2,self.bias = gradient_decent(X_train['age'],X_train['affordibility'],y_train, epotch, loss_threshold)\n",
    "    \n",
    "    def predict(self, X_test):\n",
    "        weighted_sum = self.w1*X_test['age']+ self.w2*X_test['affordibility']+self.bias\n",
    "        return sigmoid_numpy(weighted_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2f35982b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('insurance_data.csv')\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[['age', 'affordibility']],df.bought_insurance, test_size=0.2, random_state=25)\n",
    "\n",
    "X_train_scaled = X_train.copy()\n",
    "X_train_scaled['age'] = X_train['age'] / 100\n",
    "\n",
    "X_test_scaled = X_test.copy()\n",
    "X_test_scaled['age'] = X_test['age'] / 100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c08b992e",
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   "id": "60704df2",
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   "execution_count": null,
   "id": "02e2240b",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd41cd31",
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  {
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   "execution_count": null,
   "id": "17601b18",
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67bfce67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epotch:1, w1:0.9556229728273669, w2:0.9058873696677865, bias:-0.2122349122718517, loss:0.681264778737757\n",
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      "Epotch:85, w1:1.995889765679638, w2:1.2551984708420285, bias:-1.5464795429674643, loss:0.5466386828110088\n",
      "Epotch:86, w1:2.0096769928183367, w2:1.2581405625993878, bias:-1.554409536192387, loss:0.5461130291389041\n",
      "Epotch:87, w1:2.023445915002755, w2:1.2610290989843815, bias:-1.5622915600184968, loss:0.545590563023641\n",
      "Epotch:88, w1:2.037196382383489, w2:1.2638651346193672, bias:-1.5701263314476384, loss:0.5450712351014357\n",
      "Epotch:89, w1:2.0509282490633733, w2:1.2666497026227028, bias:-1.577914553678509, loss:0.5445549975424178\n",
      "Epotch:90, w1:2.0646413730163813, w2:1.2693838150830055, bias:-1.585656916406187, loss:0.5440418039927909\n",
      "Epotch:91, w1:2.0783356160084976, w2:1.2720684635220503, bias:-1.5933540961140944, loss:0.5435316095193146\n",
      "Epotch:92, w1:2.092010843520487, w2:1.2747046193465692, bias:-1.6010067563586592, loss:0.5430243705560023\n",
      "Epotch:93, w1:2.1056669246724873, w2:1.2772932342892063, bias:-1.6086155480469115, loss:0.5425200448529465\n",
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      "Epotch:95, w1:2.1329211421337653, w2:1.282331552660889, bias:-1.62370406775359, loss:0.5415199705154982\n",
      "Epotch:96, w1:2.1465190342258187, w2:1.2847830650067702, bias:-1.6311850367430925, loss:0.5410241435291299\n",
      "Epotch:97, w1:2.1600972913844236, w2:1.2871906551145709, bias:-1.638624619627673, loss:0.5405310730102341\n",
      "Epotch:98, w1:2.173655799855084, w2:1.2895551825993699, bias:-1.6460234079994696, loss:0.5400407225900913\n",
      "Epotch:99, w1:2.1871944491052364, w2:1.2918774898345378, bias:-1.6533819823304428, loss:0.5395530569489538\n",
      "Epotch:100, w1:2.200713131760032, w2:1.2941584023238903, bias:-1.6607009122062801, loss:0.5390680417774752\n",
      "Epotch:101, w1:2.2142117435395288, w2:1.2963987290649803, bias:-1.667980756554761, loss:0.5385856437396539\n",
      "Epotch:102, w1:2.227690183197251, w2:1.298599262903754, bias:-1.675222063868738, loss:0.5381058304372313\n",
      "Epotch:103, w1:2.241148352460086, w2:1.300760780880796, bias:-1.6824253724238831, loss:0.5376285703754836\n",
      "Epotch:104, w1:2.2545861559694704, w2:1.302884044569378, bias:-1.68959121049134, loss:0.5371538329303491\n",
      "Epotch:105, w1:2.268003501223843, w2:1.3049698004055226, bias:-1.696720096545423, loss:0.5366815883168389\n",
      "Epotch:106, w1:2.281400298522321, w2:1.30701878001029, bias:-1.7038125394664934, loss:0.5362118075586769\n",
      "Epotch:107, w1:2.294776460909571, w2:1.3090317005044871, bias:-1.7108690387391425, loss:0.5357444624591193\n",
      "Epotch:108, w1:2.308131904121845, w2:1.3110092648159952, bias:-1.717890084645807, loss:0.5352795255729065\n",
      "Epotch:109, w1:2.3214665465341486, w2:1.3129521619799067, bias:-1.7248761584559342, loss:0.5348169701793002\n",
      "Epotch:110, w1:2.334780309108516, w2:1.3148610674316565, bias:-1.7318277326108187, loss:0.5343567702561651\n",
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      "Epotch:112, w1:2.361344891223853, w2:1.318579538653317, bias:-1.7456292286588566, loss:0.5334433360772121\n",
      "Epotch:113, w1:2.3745955651733888, w2:1.3203903898394993, bias:-1.752480052898938, loss:0.5329900530506113\n",
      "Epotch:114, w1:2.387825068005961, w2:1.3221698206861112, bias:-1.759298182518741, loss:0.532539027907722\n",
      "Epotch:115, w1:2.4010333328795768, w2:1.3239184427944621, bias:-1.7660840484474298, loss:0.5320902377642434\n",
      "Epotch:116, w1:2.4142202952505927, w2:1.3256368557876617, bias:-1.772838073810153, loss:0.5316436602986057\n",
      "Epotch:117, w1:2.427385892828985, w2:1.3273256475595065, bias:-1.7795606740855376, loss:0.5311992737322607\n",
      "Epotch:118, w1:2.440530065534521, w2:1.328985394517677, bias:-1.7862522572596642, loss:0.5307570568107249\n",
      "Epotch:119, w1:2.4536527554538115, w2:1.3306166618213877, bias:-1.7929132239766132, loss:0.5303169887853427\n",
      "Epotch:120, w1:2.46675390679823, w2:1.332220003613633, bias:-1.7995439676856702, loss:0.5298790493957434\n",
      "Epotch:121, w1:2.479833465862666, w2:1.3337959632481655, bias:-1.8061448747852695, loss:0.5294432188529623\n",
      "Epotch:122, w1:2.4928913809851023, w2:1.3353450735113397, bias:-1.8127163247637639, loss:0.5290094778232023\n",
      "Epotch:123, w1:2.505927602506996, w2:1.336867856838949, bias:-1.8192586903370926, loss:0.5285778074122077\n",
      "Epotch:124, w1:2.518942082734439, w2:1.3383648255281848, bias:-1.82577233758343, loss:0.5281481891502279\n",
      "Epotch:125, w1:2.5319347759000843, w2:1.3398364819448387, bias:-1.832257626074885, loss:0.5277206049775486\n",
      "Epotch:126, w1:2.5449056381258233, w2:1.341283318725866, bias:-1.8387149090063277, loss:0.5272950372305655\n",
      "Epotch:127, w1:2.557854627386187, w2:1.3427058189774277, bias:-1.8451445333214114, loss:0.5268714686283816\n",
      "Epotch:128, w1:2.570781703472466, w2:1.3441044564685243, bias:-1.851546839835858, loss:0.5264498822599072\n",
      "Epotch:129, w1:2.583686827957526, w2:1.345479695820328, bias:-1.857922163358077, loss:0.5260302615714416\n",
      "Epotch:130, w1:2.596569964161304, w2:1.346831992691322, bias:-1.8642708328071762, loss:0.5256125903547197\n",
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      "Epotch:132, w1:2.6222701335377554, w2:1.3494695378936876, bias:-1.8768894964063034, loss:0.524783033162003\n",
      "Epotch:133, w1:2.635087101784378, w2:1.3507556543381913, bias:-1.8831601199749577, loss:0.5243711163952075\n",
      "Epotch:134, w1:2.647881951833145, w2:1.3520205648706964, bias:-1.8894053485265268, loss:0.5239610874976152\n",
      "Epotch:135, w1:2.660654655244622, w2:1.353264682973678, bias:-1.8956254832169885, loss:0.5235529318238407\n",
      "Epotch:136, w1:2.6734051851329177, w2:1.3544884141953148, bias:-1.901820819969824, loss:0.5231466350109902\n",
      "Epotch:137, w1:2.686133516135547, w2:1.3556921563080242, bias:-1.9079916495774736, loss:0.5227421829694884\n",
      "Epotch:138, w1:2.6988396243838655, w2:1.3568762994635601, bias:-1.9141382578006458, loss:0.5223395618742417\n",
      "Epotch:139, w1:2.7115234874740595, w2:1.3580412263447545, bias:-1.920260925465534, loss:0.5219387581561296\n",
      "Epotch:140, w1:2.724185084438686, w2:1.3591873123139828, bias:-1.9263599285589843, loss:0.521539758493806\n",
      "Epotch:141, w1:2.736824395718745, w2:1.3603149255584348, bias:-1.9324355383216678, loss:0.5211425498058037\n",
      "Epotch:142, w1:2.7494414031362733, w2:1.3614244272322646, bias:-1.9384880213393003, loss:0.5207471192429267\n",
      "Epotch:143, w1:2.7620360898674554, w2:1.3625161715956964, bias:-1.9445176396319575, loss:0.5203534541809204\n",
      "Epotch:144, w1:2.7746084404162294, w2:1.363590506151157, bias:-1.9505246507415273, loss:0.5199615422134104\n",
      "Epotch:145, w1:2.787158440588389, w2:1.364647771776508, bias:-1.9565093078173448, loss:0.5195713711450981\n",
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      "Epotch:148, w1:2.824674215900416, w2:1.3677204672010856, bias:-1.974331622196247, loss:0.5184111844124737\n",
      "Epotch:149, w1:2.8371346977812824, w2:1.3687127378995887, bias:-1.9802293096782038, loss:0.5180278589849453\n",
      "Epotch:150, w1:2.8495727769689085, w2:1.3696895491572745, bias:-1.986105845859897, loss:0.5176462164249294\n",
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      "Epotch:152, w1:2.8743817007948973, w2:1.3715980026795769, bias:-1.9977963744644796, loss:0.5168879358432925\n",
      "Epotch:153, w1:2.886752535009837, w2:1.3725302353021163, bias:-2.003610812429271, loss:0.5165112762092271\n",
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      "Epotch:155, w1:2.9114269301875644, w2:1.3743521464077455, bias:-2.015179121702366, loss:0.515762865430428\n",
      "Epotch:156, w1:2.9237304871985708, w2:1.3752423822955708, bias:-2.0209334165758692, loss:0.5153910936190104\n",
      "Epotch:157, w1:2.9360116162467342, w2:1.376119167892991, bias:-2.0266680814520357, loss:0.515020930661866\n",
      "Epotch:158, w1:2.9482703179108047, w2:1.3769827688798149, bias:-2.032383319414142, loss:0.5146523665893518\n",
      "Epotch:159, w1:2.960506593755025, w2:1.3778334459072408, bias:-2.038079330182483, loss:0.514285391569821\n",
      "Epotch:160, w1:2.9727204463094963, w2:1.3786714546948804, bias:-2.0437563101772027, loss:0.5139199959056945\n",
      "Epotch:161, w1:2.984911879050898, w2:1.3794970461257903, bias:-2.0494144525798648, loss:0.5135561700296716\n",
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      "Epotch:222, w1:3.6868006917488327, w2:1.4139034480432113, bias:-2.3649671962933168, loss:0.49401197695156235\n",
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      "Epotch:355, w1:4.959488518337886, w2:1.453942254870665, bias:-2.919624323972772, loss:0.46476172226543927\n",
      "Epotch:356, w1:4.967889809631988, w2:1.4542177833837202, bias:-2.9232947697566276, loss:0.4645929968835612\n",
      "Epotch:357, w1:4.976275442145338, w2:1.4544934182693938, bias:-2.9269588746695274, loss:0.46442489213089116\n",
      "Epotch:358, w1:4.984645453211936, w2:1.4547691629980217, bias:-2.93061665698442, loss:0.464257405334892\n",
      "Epotch:359, w1:4.9929998800871696, w2:1.4550450209253871, bias:-2.934268134850457, loss:0.46409053383601956\n",
      "Epotch:360, w1:5.001338759947489, w2:1.4553209952950035, bias:-2.9379133262946424, loss:0.46392427498765465\n",
      "Epotch:361, w1:5.00966212989009, w2:1.4555970892403576, bias:-2.9415522492234514, loss:0.46375862615603475\n",
      "Epotch:362, w1:5.0179700269326, w2:1.45587330578711, bias:-2.9451849214244237, loss:0.46359358472018636\n",
      "Epotch:363, w1:5.026262488012782, w2:1.456149647855258, bias:-2.94881136056773, loss:0.4634291480718573\n",
      "Epotch:364, w1:5.0345395499882475, w2:1.456426118261257, bias:-2.952431584207713, loss:0.46326531361545026\n",
      "Epotch:365, w1:5.042801249636176, w2:1.4567027197201048, bias:-2.956045609784402, loss:0.46310207876795667\n",
      "Epotch:366, w1:5.051047623653049, w2:1.4569794548473887, bias:-2.9596534546250037, loss:0.46293944095888917\n"
     ]
    }
   ],
   "source": [
    "costomModel = myNN()\n",
    "costomModel.fit(X_train_scaled, y_train, epotch=8000, loss_threshold=0.4631)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce9ab8a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     0.705020\n",
       "10    0.355836\n",
       "21    0.161599\n",
       "11    0.477919\n",
       "14    0.725586\n",
       "9     0.828987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costomModel.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19865118",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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