Pima Indians Diabetes Analysis.ipynb

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    "# Foundations of Data Science Project - Diabetes Analysis\n",
    "\n",
    "---------------\n",
    "## Context\n",
    "---------------\n",
    "\n",
    "Diabetes is one of the most frequent diseases worldwide and the number of diabetic patients are growing over the years. The main cause of diabetes remains unknown, yet scientists believe that both genetic factors and environmental lifestyle play a major role in diabetes.\n",
    "\n",
    "A few years ago research was done on a tribe in America which is called the Pima tribe (also known as the Pima Indians). In this tribe, it was found that the ladies are prone to diabetes very early. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients were females at least 21 years old of Pima Indian heritage. \n",
    "\n",
    "-----------------\n",
    "## Objective\n",
    "-----------------\n",
    "\n",
    "Here, we are analyzing different aspects of Diabetes in the Pima Indians tribe by doing Exploratory Data Analysis.\n",
    "\n",
    "-------------------------\n",
    "## Data Dictionary\n",
    "-------------------------\n",
    "\n",
    "The dataset has the following information:\n",
    "\n",
    "* Pregnancies: Number of times pregnant\n",
    "* Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test\n",
    "* BloodPressure: Diastolic blood pressure (mm Hg)\n",
    "* SkinThickness: Triceps skin fold thickness (mm)\n",
    "* Insulin: 2-Hour serum insulin (mu U/ml)\n",
    "* BMI: Body mass index (weight in kg/(height in m)^2)\n",
    "* DiabetesPedigreeFunction: A function that scores the likelihood of diabetes based on family history.\n",
    "* Age: Age in years\n",
    "* Outcome: Class variable (0: a person is not diabetic or 1: a person is diabetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vVR3oeVo_bv5"
   },
   "source": [
    "## Q 1: Import the necessary libraries and briefly explain the use of each library (3 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "sQPI1Bzz_bv5"
   },
   "outputs": [],
   "source": [
    "# Remove _____ & write the appropriate library name\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OV6ExTxy_bv6"
   },
   "source": [
    "#### Write your Answer here: "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "mMa-2YFV_bv7"
   },
   "source": [
    "Ans 1: Numpy is used for working with arrays.  Pandas is used for data manipulation and analysis.  Seaborn is used for visualizations.  Matplotlib.pyplot is used for plots and visualizations.  %matplotlib inline enables inline plotting which allows the plots/graphs to be displayed in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8WpI8LC7_bv7"
   },
   "source": [
    "## Q 2: Read the given dataset (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "OTzXe_vA_bv7"
   },
   "outputs": [],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H3SziaVY_bv8"
   },
   "source": [
    "## Q3. Show the last 10 records of the dataset. How many columns are there? (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "c4UHQy0q_bv8"
   },
   "outputs": [
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       "      <th>SkinThickness</th>\n",
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       "      <td>27</td>\n",
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       "      <td>23</td>\n",
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       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "758            1      106             76             20       79  37.5   \n",
       "759            6      190             92             20       79  35.5   \n",
       "760            2       88             58             26       16  28.4   \n",
       "761            9      170             74             31       79  44.0   \n",
       "762            9       89             62             20       79  22.5   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27       79  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60             20       79  30.1   \n",
       "767            1       93             70             31       79  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "758                     0.197   26        0  \n",
       "759                     0.278   66        1  \n",
       "760                     0.766   22        0  \n",
       "761                     0.403   43        1  \n",
       "762                     0.142   33        0  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  "
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     "metadata": {},
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   ],
   "source": [
    "# Remove ______ and write the appropriate number in the function\n",
    "\n",
    "pima.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xqzI9UYB_bv9"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "Lh7sxkKp_bv9"
   },
   "source": [
    "Ans 3: There are 9 columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xDnQFTsR_bv9"
   },
   "source": [
    "## Q4. Show the first 10 records of the dataset (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "fhOmMaSn_bv-"
   },
   "outputs": [
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       "      <td>20</td>\n",
       "      <td>79</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin        BMI  \\\n",
       "0            6      148             72             35       79  33.600000   \n",
       "1            1       85             66             29       79  26.600000   \n",
       "2            8      183             64             20       79  23.300000   \n",
       "3            1       89             66             23       94  28.100000   \n",
       "4            0      137             40             35      168  43.100000   \n",
       "5            5      116             74             20       79  25.600000   \n",
       "6            3       78             50             32       88  31.000000   \n",
       "7           10      115             69             20       79  35.300000   \n",
       "8            2      197             70             45      543  30.500000   \n",
       "9            8      125             96             20       79  31.992578   \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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name and the number of rows to get in the output\n",
    "\n",
    "pima.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OzzsF3Pg_bv-"
   },
   "source": [
    "## Q5. What do you understand by the dimension of the dataset? Find the dimension of the `pima` dataframe. (3 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "9QQGBlaw_bv-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5_7DfrKa_bv_"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "WW0P9jMc_bv_"
   },
   "source": [
    "Ans 5: This is a 768x9 dataset.  There are 768 rows and 9 columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V5duvKqA_bv_"
   },
   "source": [
    "## Q6. What do you understand by the size of the dataset? Find the size of the `pima` dataframe. (3 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "yX1yh0Bx_bv_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6912"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oILSLGBL_bwA"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "zdtFn47Q_bwA"
   },
   "source": [
    "Ans 6: This is equivalent to the total number of elements in this dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DyG29Cqz_bwA"
   },
   "source": [
    "## Q7. What are the data types of all the variables in the data set? (2 Marks)\n",
    "**Hint: Use the info() function to get all the information about the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "INEhm7-M_bwA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OqnMczju_bwB"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "RdARzto8_bwB"
   },
   "source": [
    "Ans 7: The following columns are of integer data type: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, Age, and Outcome.  The BMI and DiabetesPedigreeFunction columns are of float data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wHYJkmek_bwB"
   },
   "source": [
    "## Q8. What do we mean by missing values? Are there any missing values in the `pima` dataframe? (4 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "OiThIwyD_bwB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CpcpowUT_bwC"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "b8D07NoL_bwC"
   },
   "source": [
    "Ans 8: Missing values are null values that are represented by NaN.  In other words, there are no values in cells that are missing.  There are no missing values in the pima dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RmTpaojz_bwC"
   },
   "source": [
    "## Q9. What do the summary statistics of the data represent? Find the summary statistics for all variables except 'Outcome' in the `pima` data. Take one column/variable from the output table and explain all its statistical measures. (5 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "JuJBDhb-_bwC"
   },
   "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>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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.675781</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>26.447917</td>\n",
       "      <td>118.270833</td>\n",
       "      <td>32.450805</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.436252</td>\n",
       "      <td>12.117203</td>\n",
       "      <td>9.733872</td>\n",
       "      <td>93.243829</td>\n",
       "      <td>6.875374</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</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>79.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</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",
       "    </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",
       "    </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  121.675781      72.250000      26.447917  118.270833   \n",
       "std       3.369578   30.436252      12.117203       9.733872   93.243829   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.750000      64.000000      20.000000   79.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   79.000000   \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  \n",
       "count  768.000000                768.000000  768.000000  \n",
       "mean    32.450805                  0.471876   33.240885  \n",
       "std      6.875374                  0.331329   11.760232  \n",
       "min     18.200000                  0.078000   21.000000  \n",
       "25%     27.500000                  0.243750   24.000000  \n",
       "50%     32.000000                  0.372500   29.000000  \n",
       "75%     36.600000                  0.626250   41.000000  \n",
       "max     67.100000                  2.420000   81.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima.iloc[: , 0 : 8].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-aYGSoyf_bwC"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "y20K1lOx_bwC"
   },
   "source": [
    "Ans 9: There is a sample size of 768n patients recorded.  The average age of the patients is 33.240885 years old.  The standard deviation of the sample of data is 11.760232.  This is the measure of how spread out the numbers are from the mean.  In this sample of data, the lowest age is 21 years old and the highest age is 81 years old.  The 25th percentile is 24 years old, which means that 25% of the observations lie below 24.  The 50th percentile, also known as the median, is 29 years old.  50% of the observations lie below 29.  Lastly, the 75th percentile is 41 years old, which means that 75% of the observations lie below 41."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3gO4yNoS_bwD"
   },
   "source": [
    "## Q 10. Plot the distribution plot for the variable 'BloodPressure'. Write detailed observations from the plot. (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "k4ulBXrW_bwD"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate library name\n",
    "\n",
    "sns.displot(pima['BloodPressure'], kind = 'kde')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFmj2sAi_bwD"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "LGKnjmYV_bwD"
   },
   "source": [
    "Ans 10: The distribution of the variable 'BloodPressure' is unimodal and almost symmetric, otherwise described as a normal distribution.  The center of this distribution is 72.25 mm Hg.  The range of this distribution is 98 mm Hg, which is found by subtracting the min value from the max value.  It appears most of the data falls between 60 to 80 mm Hg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H82v-Oiq_bwD"
   },
   "source": [
    "## Q 11. What is the 'BMI' of the person having the highest 'Glucose'? (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "-xXHlCS5_bwD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "661    42.9\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima[pima['Glucose'] == pima['Glucose'].max()]['BMI']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JiUByWjP_bwE"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "pE2GYrtU_bwE"
   },
   "source": [
    "Ans 11: The 'BMI' of the person having the highest 'Glucose' is 42.9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0xN_VJOl_bwE"
   },
   "source": [
    "## Q12.\n",
    "### 12.1 What is the mean of the variable 'BMI'? \n",
    "### 12.2 What is the median of the variable 'BMI'? \n",
    "### 12.3 What is the mode of the variable 'BMI'?\n",
    "### 12.4 Are the three measures of central tendency equal?\n",
    "\n",
    "### (4 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "-aKQ7UMC_bwE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.45080515543617\n",
      "32.0\n",
      "32.0\n"
     ]
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "m1 = pima['BMI'].mean()  # mean\n",
    "print(m1)\n",
    "\n",
    "m2 = pima['BMI'].median()  # median\n",
    "print(m2)\n",
    "\n",
    "m3 = pima['BMI'].mode()[0]  # mode\n",
    "print(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F381SOUJ_bwE"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "NNYE_suf_bwE"
   },
   "source": [
    "Ans 12: The mean (average value) is 32.45080515543617, the median (value that appears in the middle of a list of values from the data set that are put in order) is 32.0, and the mode (value in the data set that occurs most often) is 32.0.  The median and mode are equal but the mean is very slightly larger."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQgUfx28_bwE"
   },
   "source": [
    "## Q13. How many women's 'Glucose' levels are above the mean level of 'Glucose'? (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "elzY2lG0_bwE"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "343"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "pima[pima['Glucose'] > pima['Glucose'].mean()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zNopQzBy_bwF"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "KP5Ex2Mj_bwF"
   },
   "source": [
    "Ans 13: 343 women's 'Glucose' levels are above the mean level of 'Glucose'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TUFPGMGX_bwF"
   },
   "source": [
    "## Q14. How many women have their 'BloodPressure' equal to the median of 'BloodPressure' and their 'BMI' less than the median of 'BMI'? (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "wn118oGx_bwF"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>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>14</th>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>19</td>\n",
       "      <td>175</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.587</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>4</td>\n",
       "      <td>134</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.277</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.283</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>5</td>\n",
       "      <td>111</td>\n",
       "      <td>72</td>\n",
       "      <td>28</td>\n",
       "      <td>79</td>\n",
       "      <td>23.9</td>\n",
       "      <td>0.407</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>8</td>\n",
       "      <td>112</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0.840</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>72</td>\n",
       "      <td>21</td>\n",
       "      <td>168</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.123</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>8</td>\n",
       "      <td>118</td>\n",
       "      <td>72</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1.476</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>6</td>\n",
       "      <td>124</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.368</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>82</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.821</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>4</td>\n",
       "      <td>116</td>\n",
       "      <td>72</td>\n",
       "      <td>12</td>\n",
       "      <td>87</td>\n",
       "      <td>22.1</td>\n",
       "      <td>0.463</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>4</td>\n",
       "      <td>115</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.376</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>12</td>\n",
       "      <td>70</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.658</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>9</td>\n",
       "      <td>120</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>56</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0.733</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>79</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.294</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>72</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.547</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.297</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>4</td>\n",
       "      <td>154</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>126</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0.338</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.207</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>13</td>\n",
       "      <td>104</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.465</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>3</td>\n",
       "      <td>103</td>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>152</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.730</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>10</td>\n",
       "      <td>94</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.595</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "14             5      166             72             19      175  25.8   \n",
       "93             4      134             72             20       79  23.8   \n",
       "103            1       81             72             18       40  26.6   \n",
       "205            5      111             72             28       79  23.9   \n",
       "299            8      112             72             20       79  23.6   \n",
       "325            1      157             72             21      168  25.6   \n",
       "330            8      118             72             19       79  23.1   \n",
       "366            6      124             72             20       79  27.6   \n",
       "380            1      107             72             30       82  30.8   \n",
       "393            4      116             72             12       87  22.1   \n",
       "406            4      115             72             20       79  28.9   \n",
       "446            1      100             72             12       70  25.3   \n",
       "460            9      120             72             22       56  20.8   \n",
       "488            4       99             72             17       79  25.6   \n",
       "497            2       81             72             15       76  30.1   \n",
       "510           12       84             72             31       79  29.7   \n",
       "568            4      154             72             29      126  31.3   \n",
       "615            3      106             72             20       79  25.8   \n",
       "635           13      104             72             20       79  31.2   \n",
       "644            3      103             72             30      152  27.6   \n",
       "717           10       94             72             18       79  23.1   \n",
       "765            5      121             72             23      112  26.2   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "14                      0.587   51        1  \n",
       "93                      0.277   60        1  \n",
       "103                     0.283   24        0  \n",
       "205                     0.407   27        0  \n",
       "299                     0.840   58        0  \n",
       "325                     0.123   24        0  \n",
       "330                     1.476   46        0  \n",
       "366                     0.368   29        1  \n",
       "380                     0.821   24        0  \n",
       "393                     0.463   37        0  \n",
       "406                     0.376   46        1  \n",
       "446                     0.658   28        0  \n",
       "460                     0.733   48        0  \n",
       "488                     0.294   28        0  \n",
       "497                     0.547   25        0  \n",
       "510                     0.297   46        1  \n",
       "568                     0.338   37        0  \n",
       "615                     0.207   27        0  \n",
       "635                     0.465   38        1  \n",
       "644                     0.730   27        0  \n",
       "717                     0.595   56        0  \n",
       "765                     0.245   30        0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate column name\n",
    "\n",
    "pima[(pima['BloodPressure'] == pima['BloodPressure'].median()) & (pima['BMI'] < pima['BMI'].median())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KgU-UehX_bwF"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "-b8kksU3_bwF"
   },
   "source": [
    "Ans 14: 22 women have their 'BloodPressure' equal to the median of 'BloodPressure' and their 'BMI' less than the median of 'BMI'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O8KX8Nva_bwF"
   },
   "source": [
    "## Q15. Create a pairplot for the variables 'Glucose', 'SkinThickness', and 'DiabetesPedigreeFunction'. Write your observations from the plot. (3 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "dDj12snn_bwG"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 593.25x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "sns.pairplot(data = pima, vars = ['Glucose', 'SkinThickness', 'DiabetesPedigreeFunction'], hue = 'Outcome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S7CMbCwL_bwG"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "S2bPFIk9_bwG"
   },
   "source": [
    "Ans 15: There is a very weak positive correlation between the variables being compared.  In general, patients who are diabetic have higher glucose levels regardless of their SkinThickness and DiabetesPedigreeFunction.  The patient's SkinThickness generally seems to be very slightly higher in patients who have diabetes compared to those who don't as the DiabetesPedigreeFunction increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pTzCIQOb_bwG"
   },
   "source": [
    "## Q16. Plot the scatterplot between 'Glucose' and 'Insulin'. Write your observations from the plot. (4 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "CNHLuzOD_bwG",
    "scrolled": false
   },
   "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": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "sns.scatterplot(x = 'Glucose', y = 'Insulin', data = pima)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1AhKhAbf_bwG"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "YScevRlr_bwG"
   },
   "source": [
    "Ans 16: There is a weak/moderate, positive association between 'Glucose' and 'Insulin'.  This means that as the plasma glucose concentration over 2 hours in an oral glucose tolerance test increases, the 2-hour serum insulin level increases as well.  There are also some outliers prevalent in this graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NzGHKjXv_bwG"
   },
   "source": [
    "## Q 17. Plot the boxplot for the 'Age' variable. Are there outliers? (2 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "zmWjERJM_bwH"
   },
   "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": [
    "# Remove _____ & write the appropriate function and column name \n",
    "\n",
    "plt.boxplot(pima['Age'])\n",
    "\n",
    "plt.title('Boxplot of Age')\n",
    "plt.ylabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vm5bRpLz_bwH"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "ID7pFghO_bwH"
   },
   "source": [
    "Ans 17: Yes, there are outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M6A_z8kr_bwH"
   },
   "source": [
    "## Q18. Plot histograms for the 'Age' variable to understand the number of women in different age groups given whether they have diabetes or not. Explain both histograms and compare them. (5 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "5DpqaiXH_bwH"
   },
   "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": [
    "# Remove _____ & write the appropriate function and column name\n",
    "\n",
    "plt.hist(pima[pima['Outcome'] == 1]['Age'], bins = 5)\n",
    "plt.title('Distribution of Age for Women who has Diabetes')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ultPz45S_bwH"
   },
   "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": [
    "# Remove _____ & write the appropriate function and column name\n",
    "\n",
    "plt.hist(pima[pima['Outcome'] == 0]['Age'], bins = 5)\n",
    "plt.title('Distribution of Age for Women who do not have Diabetes')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "InfhuW8o_bwH"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "D6iAgeF-_bwI"
   },
   "source": [
    "Ans 18: Both of the histograms are negatively right-skewed.  Neither graphs appear to have any outliers.  There are more women who don't have diabetes compared to those who do in this study in the younger ages (20s to mid-30s).  As the women get older, the number of women with diabetes compared to the number of women who do not have diabetes evens out more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EpTEjZ-q_bwI"
   },
   "source": [
    "## Q 19. What is the Interquartile Range of all the variables? Why is this used? Which plot visualizes the same? (5 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "JVOqHx1W_bwI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                  5.0000\n",
      "Glucose                     40.5000\n",
      "BloodPressure               16.0000\n",
      "SkinThickness               12.0000\n",
      "Insulin                     48.2500\n",
      "BMI                          9.1000\n",
      "DiabetesPedigreeFunction     0.3825\n",
      "Age                         17.0000\n",
      "Outcome                      1.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate variable name\n",
    "\n",
    "Q1 = pima.quantile(0.25)\n",
    "Q3 = pima.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WWtc2nex_bwI"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "2vLWPyd-_bwI"
   },
   "source": [
    "Ans 19: The interquartile range (IQR) for each variable is as follows: Pregnancies (5 times), Glucose (40.5), BloodPressure (16 mm Hg), SkinThickness (12 mm), Insulin (48.25 mu U/ml), BMI (9.1), DiabetesPedigreeFunction (0.3825), Age (17 years), and Outcome (1).  The IQR measures the range of the middle 50% of the data for each variable.  This can be used to determine the variability where most of the values lie.  The box plot visualizes the IQR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KjldKKMs_bwI"
   },
   "source": [
    "## Q 20. Find and visualize the correlation matrix. Write your observations from the plot. (3 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "dvBVRRM8_bwI"
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128022</td>\n",
       "      <td>0.208987</td>\n",
       "      <td>0.009393</td>\n",
       "      <td>-0.018780</td>\n",
       "      <td>0.021546</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.128022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219765</td>\n",
       "      <td>0.158060</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0.231464</td>\n",
       "      <td>0.137158</td>\n",
       "      <td>0.266673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.208987</td>\n",
       "      <td>0.219765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130403</td>\n",
       "      <td>0.010492</td>\n",
       "      <td>0.281222</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.326791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>0.009393</td>\n",
       "      <td>0.158060</td>\n",
       "      <td>0.130403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.532552</td>\n",
       "      <td>0.157196</td>\n",
       "      <td>0.020582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.018780</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0.010492</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.189919</td>\n",
       "      <td>0.158243</td>\n",
       "      <td>0.037676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.021546</td>\n",
       "      <td>0.231464</td>\n",
       "      <td>0.281222</td>\n",
       "      <td>0.532552</td>\n",
       "      <td>0.189919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.153508</td>\n",
       "      <td>0.025748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137158</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.157196</td>\n",
       "      <td>0.158243</td>\n",
       "      <td>0.153508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.266673</td>\n",
       "      <td>0.326791</td>\n",
       "      <td>0.020582</td>\n",
       "      <td>0.037676</td>\n",
       "      <td>0.025748</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.128022       0.208987       0.009393   \n",
       "Glucose                      0.128022  1.000000       0.219765       0.158060   \n",
       "BloodPressure                0.208987  0.219765       1.000000       0.130403   \n",
       "SkinThickness                0.009393  0.158060       0.130403       1.000000   \n",
       "Insulin                     -0.018780  0.396137       0.010492       0.245410   \n",
       "BMI                          0.021546  0.231464       0.281222       0.532552   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137158       0.000471       0.157196   \n",
       "Age                          0.544341  0.266673       0.326791       0.020582   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.018780  0.021546                 -0.033523   \n",
       "Glucose                   0.396137  0.231464                  0.137158   \n",
       "BloodPressure             0.010492  0.281222                  0.000471   \n",
       "SkinThickness             0.245410  0.532552                  0.157196   \n",
       "Insulin                   1.000000  0.189919                  0.158243   \n",
       "BMI                       0.189919  1.000000                  0.153508   \n",
       "DiabetesPedigreeFunction  0.158243  0.153508                  1.000000   \n",
       "Age                       0.037676  0.025748                  0.033561   \n",
       "\n",
       "                               Age  \n",
       "Pregnancies               0.544341  \n",
       "Glucose                   0.266673  \n",
       "BloodPressure             0.326791  \n",
       "SkinThickness             0.020582  \n",
       "Insulin                   0.037676  \n",
       "BMI                       0.025748  \n",
       "DiabetesPedigreeFunction  0.033561  \n",
       "Age                       1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name and run the code\n",
    "\n",
    "corr_matrix = pima.iloc[ : ,0 : 8].corr()\n",
    "\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Ju8Gymxw_bwJ"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove _____ & write the appropriate function name\n",
    "\n",
    "plt.figure(figsize = (8, 8))\n",
    "sns.heatmap(corr_matrix, annot = True)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Woaq6vqT_bwJ"
   },
   "source": [
    "#### Write your Answer here: \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "id": "m3AKvSrM_bwJ"
   },
   "source": [
    "Ans 20: All of the variables have positive correlations except Pregnancies and Insulin and Pregnancies and DiabetesPedigreeFunction.  Most of the variables have a very weak positive correlation, however, some of which having almost no correlation such as Pregnancies and SkinThickness and BloodPressure and DiabetesPedigree Function.  The variables with the strongest positive correlation is Pregnancies and Age with a correlation coefficient of 0.54, followed by SkinThickness and BMI with a correlation coefficient of 0.53."
   ]
  }
 ],
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    "Woaq6vqT_bwJ"
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   "name": "MIT_FDS_Project_Diabetes_Analysis_Questions-1.ipynb",
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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