Python Lab MST (18BCS6201).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Importing Modules </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading data files from the system.\n",
    "Df = pd.read_csv(r\"C:\\Users\\Helenasec\\A4.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>0.627</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0.672</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>2.288</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  BloodPressure  DiabetesPedigreeFunction  Outcome\n",
       "0      148             72                     0.627        1\n",
       "1       85             66                     0.351        0\n",
       "2      183             64                     0.672        1\n",
       "3       89             66                     0.167        0\n",
       "4      137             40                     2.288        1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 4 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Glucose                   132 non-null    int64  \n",
      " 1   BloodPressure             132 non-null    int64  \n",
      " 2   DiabetesPedigreeFunction  132 non-null    float64\n",
      " 3   Outcome                   132 non-null    int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 4.2 KB\n"
     ]
    }
   ],
   "source": [
    "Df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Data Preprocessing </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Checking for null values </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "DiabetesPedigreeFunction    0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning total null/missing values in 'df'.\n",
    "Df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No missing values found\n"
     ]
    }
   ],
   "source": [
    "print(\"No missing values found\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Checking for outliers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26154db7ee0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting boxplots.\n",
    "plt.figure(figsize=(20, 12))\n",
    "plt.subplot(2,3,1)\n",
    "sns.boxplot(y= 'Glucose',  data = Df, color=\"0.25\")\n",
    "plt.subplot(2,3,2)\n",
    "sns.boxplot(y= 'BloodPressure', data = Df, palette=\"Greens\")\n",
    "plt.subplot(2,3,3)\n",
    "sns.boxplot(y= 'DiabetesPedigreeFunction', data = Df, color=\"Orange\")\n",
    "plt.subplot(2,3,4)\n",
    "sns.boxplot(y= 'Outcome', data = Df, palette=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Data Visulaization </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x2880 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a heatmap.\n",
    "plt.figure(figsize = (40,40))\n",
    "sns.heatmap(Df.corr(), annot = True,cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000026154E015B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000026152887700>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000026152821970>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000026154E19BB0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Df.hist(figsize=(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26153741460>"
      ]
     },
     "execution_count": 84,
     "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": [
    "sns.distplot(Df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Data Preprocessing </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Building models and comapring</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping unnecessary attributes.\n",
    "X = Df.drop(['Outcome'], axis=1)\n",
    "Y= df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries.\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,Y, train_size=0.7, test_size=0.3, random_state =50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Logistic Regression </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'LR'.\n",
    "Lr = LogisticRegression()\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Lr.fit(X_train, y_train)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Lr.predict(X_test)\n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_lr = round(Lr.score(X_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Stochastic Gradient Descent </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'SGD'.\n",
    "Sgd = linear_model.SGDClassifier(max_iter=5, tol=None)\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Sgd.fit(X_train, y_train)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Sgd.predict(X_test)\n",
    "\n",
    "#Calculating score using '.score()' function.\n",
    "Sgd.score(X_train, y_train)\n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_sgd = round(Sgd.score(X_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Gaussian Naive Bayes </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'GNB'.\n",
    "Gaussian = GaussianNB() \n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Gaussian.fit(X_train, y_train)  \n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Gaussian.predict(X_test) \n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_gaussian = round(Gaussian.score(X_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Model Evaluation </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.39\n",
      "64.13\n",
      "72.83\n"
     ]
    }
   ],
   "source": [
    "print(Acc_lr)\n",
    "print(Acc_sgd)\n",
    "print(Acc_gaussian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes has greatest score of 72.83 so we choose this model\n"
     ]
    }
   ],
   "source": [
    "print(\"Gaussian Naive Bayes has greatest score of 72.83 so we choose this model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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