task_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is the Pima-Indians Diabetes task I did for pre-interview phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding nessacsary libs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</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>1</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>0</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>1</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>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the dataset\n",
    "dataset = pd.read_csv('diabetes.csv')\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some general info on the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's good to plot some of the featuristics of the dataset to get deeper insight of the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship/correlation of the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_data = dataset.corr(method='pearson')\n",
    "sns.heatmap(correlation_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation plot, it can be seen that features such as age/pregnancy, glucose/outcome and some others are correlated. This is nothing unexpected because age affects pregnancy, and vice versa. Also glucose levels have a direct effect on Outcome(being diabetic/healthy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Pregnancies'}>,\n",
       "        <AxesSubplot:title={'center':'Glucose'}>,\n",
       "        <AxesSubplot:title={'center':'BloodPressure'}>],\n",
       "       [<AxesSubplot:title={'center':'SkinThickness'}>,\n",
       "        <AxesSubplot:title={'center':'Insulin'}>,\n",
       "        <AxesSubplot:title={'center':'BMI'}>],\n",
       "       [<AxesSubplot:title={'center':'DiabetesPedigreeFunction'}>,\n",
       "        <AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'Outcome'}>]], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(bins=50, figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it can be seen above, there are a lot of 0 values in most of the features. We should do something about this, as these rows will affect the result of the model, for example the model can't decide correctly if some person is diabetic or not, if it has been trained with more than 370 samples saying insuling level was zero! (Because insulin levels have direct effect on the diabetes test results).\n",
    "\n",
    "There are two approaches for this, remove rows containing zero values, or fill the values with mean of the column. The first one is extreme and destructive, as it removes almost 80% of the dataset in case of Insulin/SkinThickness. We use a hybrid approach, remove zero values in columns with low zero frequency(BMI, Glucose, BloodPressure) and fill the zero values in columns with high zero frequency(Insulin, Age)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "med_insulin = dataset['Insulin'].median()\n",
    "med_skin = dataset['SkinThickness'].median()\n",
    "\n",
    "# filling the Insulin and SkinThickness with their column median value\n",
    "dataset['Insulin'] = dataset['Insulin'].replace(0, med_insulin)\n",
    "dataset['SkinThickness'] = dataset['SkinThickness'].replace(0, med_skin)\n",
    "\n",
    "#removing zero values of glucose/BMI/BloodPressure as there is not too many sample of them\n",
    "dataset = dataset[(dataset['BloodPressure']!=0) & (dataset['BMI']!=0) & (dataset['Glucose']!=0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Pregnancies'}>,\n",
       "        <AxesSubplot:title={'center':'Glucose'}>,\n",
       "        <AxesSubplot:title={'center':'BloodPressure'}>],\n",
       "       [<AxesSubplot:title={'center':'SkinThickness'}>,\n",
       "        <AxesSubplot:title={'center':'Insulin'}>,\n",
       "        <AxesSubplot:title={'center':'BMI'}>],\n",
       "       [<AxesSubplot:title={'center':'DiabetesPedigreeFunction'}>,\n",
       "        <AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'Outcome'}>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(bins=50, figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has been asked to 'Make the dataset imbalance with 10% of the True (1) label' so we first counted true/false outcomes and then up-sampled the true samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it can be seen, from the population, there are 268 diabetic and 500 normal. To make the dataset imbalance in a way that the data is imbalanced with 10% of the diabetic people, there are 2 approaches possible. \n",
    "\n",
    "1- We can under-sample the diabet-free class, more accurately if we remove samples from those 500 would acquire us the named goal, but this approach would not be best, hence we would lose data. \n",
    "\n",
    "2- Second approach is over-sample from the diabetic class where we could add sample randomly(which may cause over-fitting since we are adding exact copies to our dataset) or from the value domains(mean, max/min, ...) of the existing diabetic samples.\n",
    "\n",
    "**We will use the second approach**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Pregnancies'}>,\n",
       "        <AxesSubplot:title={'center':'Glucose'}>,\n",
       "        <AxesSubplot:title={'center':'BloodPressure'}>],\n",
       "       [<AxesSubplot:title={'center':'SkinThickness'}>,\n",
       "        <AxesSubplot:title={'center':'Insulin'}>,\n",
       "        <AxesSubplot:title={'center':'BMI'}>],\n",
       "       [<AxesSubplot:title={'center':'DiabetesPedigreeFunction'}>,\n",
       "        <AxesSubplot:title={'center':'Age'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "# we want 10% more of the True(1) label\n",
    "# so first we over-sample the True samples to be as much as False sample (both become equal)\n",
    "# next we under-sample False samples with a ratio of 0.9 so True samples would be 10% more\n",
    "ros = RandomOverSampler(sampling_strategy=0.9)\n",
    "\n",
    "X_over, y_over = ros.fit_resample(X, y)\n",
    "\n",
    "y_over.hist(bins=50, figsize=(5, 5))\n",
    "X_over.hist(bins=50, figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data set is imbalanced with 10% of the True label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature engineering & feature selection steps has been asked to be explained and implemented(if there is a need).\n",
    "\n",
    "Feature engineering method is used when our data represents some faulty values, something doesn't seem quite reasonable or the existing data cannot be used directly and effectively in our model. In that case we use our domain knowledge to edit the data and/or create a new column from that column(s). (For example the above operations we did on the dataset, such as cleaning, removing outlier values, filling null values in dataset and ... could be called engineering of the features; Other processes such as generating new columns were not deemed necassary in this specific task).\n",
    "\n",
    "About feature selection, it is more often used when there are too many features(and some of them may even be misleading), in that case, using this amount of features and data may even make our model results worse; Hence, there is feature selection. We pick some of the features we deem more valuable and worthwhile to the model, and dispose of the rest. In this notebook, first we train our models without excluding a set of features and afterwards we pick a set of features and train/test our models on that.\n",
    "\n",
    "In the case of Pima Indians Diabetes dataset, the features provide enough and valuable data(since the information was obtained from a credible medical research) and there aren't too many of them. In fact all of the features are related to the matter and removing them would be harmful. So I believe cleaning the data, filling the N/A, Zero values, ... and making the dataset more balanced(with a ratio of 10% towards the false label) would do the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Model\n",
    "\n",
    "There were several approaches in regards to what model should be used; I used the following models to get different results and be able to compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting dataset in training/test sets with a 70:30 ratio\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_over, y_over, test_size=0.30, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes accuracy is 71.96%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import metrics\n",
    "naive=GaussianNB()\n",
    "naive.fit(X_train,y_train)\n",
    "predicted_naive=naive.predict(X_test)\n",
    "accuracy_naive=metrics.accuracy_score(y_test,predicted_naive)\n",
    "print(f'Naive Bayes accuracy is {round(accuracy_naive * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(naive, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy is 73.06%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier(n_neighbors=13)\n",
    "knn.fit(X_train,y_train)\n",
    "predicted_knn=knn.predict(X_test)\n",
    "accuracy_knn=metrics.accuracy_score(y_test,predicted_knn)\n",
    "print(f'KNN accuracy is {round(accuracy_knn * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(knn, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desicion Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree accuracy is 78.97%\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "d_tree=tree.DecisionTreeClassifier()\n",
    "d_tree.fit(X_train,y_train)\n",
    "predicted_tree=d_tree.predict(X_test)\n",
    "accuracy_tree=metrics.accuracy_score(y_test,predicted_tree)\n",
    "print(f'Decision tree accuracy is {round(accuracy_tree * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(d_tree, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression accuracy is 74.54%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logisticRegr= LogisticRegression(max_iter=200)\n",
    "logisticRegr.fit(X_train,y_train)\n",
    "predict_lg = logisticRegr.predict(X_test)\n",
    "accuracy_lg=metrics.accuracy_score(y_test,predict_lg)\n",
    "print(f'Logistic regression accuracy is {round(accuracy_lg * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(logisticRegr, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear discriminant analysis accuracy is 73.43%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "lda=LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train,y_train)\n",
    "predict_lda=lda.predict(X_test)\n",
    "accuracy_lda=metrics.accuracy_score(y_test,predict_lda)\n",
    "print(f'Linear discriminant analysis accuracy is {round(accuracy_lda * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lda, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = [accuracy_naive, accuracy_knn, accuracy_tree, accuracy_lg, accuracy_lda]\n",
    "x = ['Naive', 'KNN', 'DTree', 'LG', 'LDA']\n",
    "\n",
    "plt.bar(x, accuracies)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above visualization shows that all of the models have performed quite equally on the task. With the best of them being D-Tree with 78.97% of accuracy and the least accuracy being Naive Bayes with 71.96% of accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Models + Feature Selection\n",
    "For the sake of completeness I have included the following code, which selects the most essential features (Insulin, Glucose, SkinThickness, BMI, Age and Outcome) and tries to train and test with this part of dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes accuracy is 70.48%\n",
      "KNN accuracy is 71.59%\n",
      "Decision tree accuracy is 79.7%\n",
      "Logistic regression accuracy is 70.48%\n",
      "Linear discriminant analysis accuracy is 71.22%\n"
     ]
    }
   ],
   "source": [
    "X_over = X_over[['Glucose', 'Insulin', 'SkinThickness', 'BMI', 'Age']]\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_over, y_over, test_size=0.30, random_state=10)\n",
    "# Naive Bayes\n",
    "naive=GaussianNB()\n",
    "naive.fit(X_train,y_train)\n",
    "predicted_naive=naive.predict(X_test)\n",
    "accuracy_naive=metrics.accuracy_score(y_test,predicted_naive)\n",
    "print(f'Naive Bayes accuracy is {round(accuracy_naive * 100, 2)}%')\n",
    "# KNN\n",
    "knn=KNeighborsClassifier(n_neighbors=13)\n",
    "knn.fit(X_train,y_train)\n",
    "predicted_knn=knn.predict(X_test)\n",
    "accuracy_knn=metrics.accuracy_score(y_test,predicted_knn)\n",
    "print(f'KNN accuracy is {round(accuracy_knn * 100, 2)}%')\n",
    "# Decision Tree\n",
    "d_tree=tree.DecisionTreeClassifier()\n",
    "d_tree.fit(X_train,y_train)\n",
    "predicted_tree=d_tree.predict(X_test)\n",
    "accuracy_tree=metrics.accuracy_score(y_test,predicted_tree)\n",
    "print(f'Decision tree accuracy is {round(accuracy_tree * 100, 2)}%')\n",
    "# Logistic Regression\n",
    "logisticRegr= LogisticRegression(max_iter=200)\n",
    "logisticRegr.fit(X_train,y_train)\n",
    "predict_lg = logisticRegr.predict(X_test)\n",
    "accuracy_lg=metrics.accuracy_score(y_test,predict_lg)\n",
    "print(f'Logistic regression accuracy is {round(accuracy_lg * 100, 2)}%')\n",
    "# Linear Discriminant Analysis\n",
    "lda=LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train,y_train)\n",
    "predict_lda=lda.predict(X_test)\n",
    "accuracy_lda=metrics.accuracy_score(y_test,predict_lda)\n",
    "print(f'Linear discriminant analysis accuracy is {round(accuracy_lda * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that feature selection improved our model in case of Desicion Tree(by around 1%), and in other cases it either not changed the result, or decreased the accuracy by a very small factor. So in this dataset we can say feature selection was not quite helpful(this is also what we expected before)."
   ]
  }
 ],
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