200968268_L2_EX1.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "dfeb85e5",
   "metadata": {},
   "source": [
    "% 1. Title: Pima Indians Diabetes Database\n",
    "% \n",
    "% 2. Sources:\n",
    "%    (a) Original owners: National Institute of Diabetes and Digestive and\n",
    "%                         Kidney Diseases\n",
    "%    (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu)\n",
    "%                           Research Center, RMI Group Leader\n",
    "%                           Applied Physics Laboratory\n",
    "%                           The Johns Hopkins University\n",
    "%                           Johns Hopkins Road\n",
    "%                           Laurel, MD 20707\n",
    "%                           (301) 953-6231\n",
    "%    (c) Date received: 9 May 1990\n",
    "% \n",
    "% 3. Past Usage:\n",
    "%     1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \\&\n",
    "%        Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast\n",
    "%        the onset of diabetes mellitus.  In {\\it Proceedings of the Symposium\n",
    "%        on Computer Applications and Medical Care} (pp. 261--265).  IEEE\n",
    "%        Computer Society Press.\n",
    "% \n",
    "%        The diagnostic, binary-valued variable investigated is whether the\n",
    "%        patient shows signs of diabetes according to World Health Organization\n",
    "%        criteria (i.e., if the 2 hour post-load plasma glucose was at least \n",
    "%        200 mg/dl at any survey  examination or if found during routine medical\n",
    "%        care).   The population lives near Phoenix, Arizona, USA.\n",
    "% \n",
    "%        Results: Their ADAP algorithm makes a real-valued prediction between\n",
    "%        0 and 1.  This was transformed into a binary decision using a cutoff of \n",
    "%        0.448.  Using 576 training instances, the sensitivity and specificity\n",
    "%        of their algorithm was 76% on the remaining 192 instances.\n",
    "% \n",
    "% 4. Relevant Information:\n",
    "%       Several constraints were placed on the selection of these instances from\n",
    "%       a larger database.  In particular, all patients here are females at\n",
    "%       least 21 years old of Pima Indian heritage.  ADAP is an adaptive learning\n",
    "%       routine that generates and executes digital analogs of perceptron-like\n",
    "%       devices.  It is a unique algorithm; see the paper for details.\n",
    "% \n",
    "% 5. Number of Instances: 768\n",
    "% \n",
    "% 6. Number of Attributes: 8 plus class \n",
    "% \n",
    "% 7. For Each Attribute: (all numeric-valued)\n",
    "%    1. Number of times pregnant\n",
    "%    2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "%    3. Diastolic blood pressure (mm Hg)\n",
    "%    4. Triceps skin fold thickness (mm)\n",
    "%    5. 2-Hour serum insulin (mu U/ml)\n",
    "%    6. Body mass index (weight in kg/(height in m)^2)\n",
    "%    7. Diabetes pedigree function\n",
    "%    8. Age (years)\n",
    "%    9. Class variable (0 or 1)\n",
    "% \n",
    "% 8. Missing Attribute Values: None\n",
    "% \n",
    "% 9. Class Distribution: (class value 1 is interpreted as \"tested positive for\n",
    "%    diabetes\")\n",
    "% \n",
    "%    Class Value  Number of instances\n",
    "%    0            500\n",
    "%    1            268\n",
    "% \n",
    "% 10. Brief statistical analysis:\n",
    "% \n",
    "%     Attribute number:    Mean:   Standard Deviation:\n",
    "%     1.                     3.8     3.4\n",
    "%     2.                   120.9    32.0\n",
    "%     3.                    69.1    19.4\n",
    "%     4.                    20.5    16.0\n",
    "%     5.                    79.8   115.2\n",
    "%     6.                    32.0     7.9\n",
    "%     7.                     0.5     0.3\n",
    "%     8.                    33.2    11.8\n",
    "% \n",
    "% \n",
    "%\n",
    "%\n",
    "%\n",
    "%\n",
    "% Relabeled values in attribute 'class'\n",
    "%    From: 0                       To: tested_negative     \n",
    "%    From: 1                       To: tested_positive     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2947249",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # importing libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "568e017a",
   "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>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>insu</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>tested_positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>tested_negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>tested_positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>tested_negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>tested_positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   preg  plas  pres  skin  insu  mass   pedi  age            class\n",
       "0     6   148    72    35     0  33.6  0.627   50  tested_positive\n",
       "1     1    85    66    29     0  26.6  0.351   31  tested_negative\n",
       "2     8   183    64     0     0  23.3  0.672   32  tested_positive\n",
       "3     1    89    66    23    94  28.1  0.167   21  tested_negative\n",
       "4     0   137    40    35   168  43.1  2.288   33  tested_positive"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diab=pd.read_csv(\"D:\\\\Sem 4\\\\ML\\\\week2\\\\diabetes.csv\")\n",
    "diab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e514f320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: class, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diab[\"class\"] = diab[\"class\"].map({\"tested_positive\": 1, \"tested_negative\": 0})\n",
    "diab[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "69bf6210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "preg     2.771620\n",
       "plas    25.181793\n",
       "pres    12.639425\n",
       "skin    13.659627\n",
       "insu    84.505079\n",
       "mass     5.842270\n",
       "pedi     0.247309\n",
       "age      9.586405\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_dev = np.mean(abs(diab.loc[:, diab.columns != 'class'] - np.mean(diab.loc[:, diab.columns != 'class'])))\n",
    "mean_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b699f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "preg      3.367384\n",
       "plas     31.951796\n",
       "pres     19.343202\n",
       "skin     15.941829\n",
       "insu    115.168949\n",
       "mass      7.879026\n",
       "pedi      0.331113\n",
       "age      11.752573\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev = np.std(diab.loc[:, diab.columns != 'class'])\n",
    "std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2b7aeec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diab[\"age\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7d5962f0",
   "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>age</th>\n",
       "      <th>class</th>\n",
       "      <th>mass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>0.036242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mass</th>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age     class      mass\n",
       "age    1.000000  0.238356  0.036242\n",
       "class  0.238356  1.000000  0.292695\n",
       "mass   0.036242  0.292695  1.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=diab[[\"age\", \"class\", \"mass\"]].corr() \n",
    "corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "73faafce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x187e7beb790>,\n",
       "  <matplotlib.axis.XTick at 0x187e7beb760>],\n",
       " [Text(0, 0, ''), Text(0, 0, '')])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = diab.groupby(\"class\", as_index = False).count()\n",
    "x = base[\"class\"]\n",
    "y = base[\"age\"]\n",
    "plt.bar(x,y)\n",
    "plt.xticks([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c3e713a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the classes are not balanced, the 1(aka 'tested_positive') class is almost half of the 'tested_negative' class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "930656f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\yaswa\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = diab.loc[:, diab.columns != 'class']\n",
    "y1 = diab[\"class\"]\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x1,y1,test_size=0.3) \n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression() \n",
    "model.fit(x_train,y_train)# fitting data points to model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "27fdb26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "85cb418e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[136,  17],\n",
       "       [ 33,  45]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "98205a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4fd6173b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy =  0.7835497835497836\n",
      "TPR =  0.5769230769230769\n",
      "TNR =  0.8888888888888888\n"
     ]
    }
   ],
   "source": [
    "tn = cm[0,0]\n",
    "fp = cm[0,1]\n",
    "fn = cm[1,0]\n",
    "tp = cm[1,1]\n",
    "acc = (tp+tn)/(tp+tn+fp+fn)\n",
    "tpr = tp/(fn+tp)\n",
    "tnr = tn/(tn+fp)\n",
    "\n",
    "print(\"accuracy = \", acc)\n",
    "print(\"TPR = \", tpr)\n",
    "print(\"TNR = \", tnr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "690dec9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "falsepr, truepr, _ = roc_curve(y_test, y_pred)\n",
    "plt.plot(falsepr,truepr)\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3efd583b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7329059829059829"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r =  roc_auc_score(y_test,y_pred)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b95b482e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#in general, a curve moving towards the top and left of and roc curve means the classifier is better\n",
    "#a 45 degree line corresponds to random predictions\n",
    "#The area under the curve is 0.73, indicating it is a decent model\n",
    "#The accuracy could be improved by scaling the input values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49323f39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b844b471",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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