02_Univariate_Analysis.ipynb

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      "name": "02 Univariate Analysis.ipynb",
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        "<a href=\"https://colab.research.google.com/github/sandipanpaul21/Machine-Learning-in-Python-Code/blob/master/02_Univariate_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "**UNIVARIATE ANALYSIS**\n",
        "- *Uni Means ONE.* So Univariate Analysis is One Variable Analysis\n",
        "\n",
        "Mainly in this part, we will deal with Numerical Measures. So in total we have 14 Numerical Measures\n",
        "\n",
        "**Measure of Central Tendency :** \n",
        "  1. Mean\n",
        "  2. Median\n",
        "  3. Mode\n",
        "\n",
        "**Measure of Data Spread :**\n",
        "  4. Quartile\n",
        "  5. Percentile\n",
        "  6. Range\n",
        "  7. IQR \n",
        "  8. Boxplot\n",
        "  9. Variance\n",
        "  10. Standard Deviation\n",
        "\n",
        "**Variation between Variables :**\n",
        "  11. Covariance\n",
        "  12. Correlation Coefficient (Pearson and Spearman)\n",
        "\n",
        "**Measure Distribution and Peakness :**\n",
        "  13. Skewness \n",
        "  14. Kurtosis\n",
        "\n",
        "**CENTRAL TENDECY**\n",
        "  - In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution.\n",
        "  - It may also be called *a center or location of the distribution.* \n",
        "  - The most common measures of central tendency are the arithmetic mean, the median, and the mode. \n",
        "  - A middle tendency can be calculated for either a finite set of values or for a theoretical distribution, such as the normal distribution.\n",
        "\n",
        "1. **Mean**\n",
        "  - Measure of Central Tendency\n",
        "  - (Sum of data values)/(Data Count) \n",
        "  - AFFECTED BY OUTLIERS\n",
        "\n",
        "2. **Median**\n",
        "  - Also a Measure of Central Tendency\n",
        "  - It is the observation/value at the middle when the data is sorted in ascending order\n",
        "  - MIGHT OR MIGHT NOT AFFECT BY OUTLIERS\n",
        "\n",
        "3. **Mode**\n",
        "  - Most frequently observed variable (categorical column) in the column\n",
        "\n",
        "4. **Quartile**\n",
        "  - Firstly, Data is sorted in Ascending Order\n",
        "    1. *First Quartile* = Lower Quartile = 25% of the data [0-25% of Data)\n",
        "    2. *Second Quartile* = Median = 50% of the data (0-50% of Data)\n",
        "    3. *Third Quartile* = Upper Quartile = 75% of the data (0-75% of Data)\n",
        "\n",
        "5. **Percentile**\n",
        "  - Nth percentile of an observation variable is the value that cuts off the first n percent of the data values when it is sorted in ascending order\n",
        "\n",
        "6. **Range**\n",
        "  - Measures how far apart the entire data spread is in terms of values \n",
        "  - Range = (Largest Value - Smallest Value)\n",
        "  - AFFECTED BY OUTLIERS\n",
        "\n",
        "7. **Inter Quartile Range**\n",
        "  - IQR = (Upper Quartile - Lower Quartile) = (75% of Data - 25% of Data)\n",
        "\n",
        "8. **Box Plot**\n",
        "  - Graphical representation of\n",
        "    1.  Three quantiles (First, Second and Third Quantile Respectively) \n",
        "    2.  Smallest value \n",
        "    3.  Largest value \n",
        "\n",
        "9. **Standard Deviation**\n",
        "  - Square root of the Variance\n",
        "\n",
        "10. **Variance**\n",
        "  - Numerical Measure of how the data value is dispersed around the mean\n",
        "  - AFFECTED BY OUTLIERS\n",
        "\n",
        "11. **Covariance**\n",
        "  - To check linearity between two variables. It can be +ve or -ve relationship.\n",
        "\n",
        "12. **Correlation Coefficient**\n",
        "  - Strength of relationship between two variables\n",
        "      1. *Pearson Correlation Coefficient* - Works on linear relationship\n",
        "      2. *Spearman Correlation Coefficient* - Works on both linear & non linear relationship\n",
        "\n",
        "13. **Skewness**\n",
        "  - Spreadness of the Distribution\n",
        "\n",
        "14. **Kurtosis**\n",
        "  - Peakness of the Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PHSawDp7KnM"
      },
      "source": [
        "# Libraries \n",
        "from sklearn import datasets \n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TnWNSFE68OV"
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      "source": [
        "# Load the Datasets (For indepth analysis please refer to Part O1 All About Datasets)\n",
        "\n",
        "# Iris Dataset for Classification\n",
        "# Load Dataset\n",
        "iris = datasets.load_iris()\n",
        "# Convert to DataFrame\n",
        "iris_pd = pd.DataFrame(iris.data)\n",
        "# Feature Name\n",
        "iris_pd.columns = iris.feature_names\n",
        "# Target Variable\n",
        "iris_pd[\"Class\"] = iris.target\n",
        "# iris_pd is the final dataset\n",
        "\n",
        "# Boston Dataset for Regression\n",
        "boston = datasets.load_boston()\n",
        "boston_pd = pd.DataFrame(boston.data)\n",
        "boston_pd.columns = boston.feature_names\n",
        "boston_pd[\"MEDV\"] = boston.target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CniJCQqnmR_T"
      },
      "source": [
        "**1. MEAN**\n",
        "\n",
        "- The mean is the mathematical average of a set of two or more numbers. It is also known as Arithmetic Mean.\n",
        "- Summing the numbers in a set and dividing by the total number gives you the arithmetic mean.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "# By default, axis = 0\n",
        "# Column Wise Mean, axis = 0\n",
        "# Row Wise Mean, axis = 1\n",
        "\n",
        "iris_pd.mean()\n",
        "# Inference: \n",
        "# First column \"sepal length (cm)\" mean is 5.8 and second column \"sepal length (cm)\" mean is 3.05"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    5.843333\n",
              "sepal width (cm)     3.057333\n",
              "petal length (cm)    3.758000\n",
              "petal width (cm)     1.199333\n",
              "Class                1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f44yecCqNMsi",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bb84a7f-946b-4261-9558-70971a0e51be"
      },
      "source": [
        "# if we mention axis = 0 also, it will give same output\n",
        "iris_pd.mean(axis = 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    5.843333\n",
              "sepal width (cm)     3.057333\n",
              "petal length (cm)    3.758000\n",
              "petal width (cm)     1.199333\n",
              "Class                1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weJlJrmYkRS5",
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      },
      "source": [
        "# If we want to see row wise Mean i.e, total rows in IRIS dataset is 150 (0 - 149)\n",
        "print(\"Number of Rows in IRIS Dataset : \",len(iris_pd))\n",
        "iris_pd.mean(axis = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Rows in IRIS Dataset :  150\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2.04\n",
              "1      1.90\n",
              "2      1.88\n",
              "3      1.88\n",
              "4      2.04\n",
              "       ... \n",
              "145    3.84\n",
              "146    3.54\n",
              "147    3.74\n",
              "148    3.86\n",
              "149    3.56\n",
              "Length: 150, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2GbvxO3nXgO"
      },
      "source": [
        "**2. MEDIAN**\n",
        "- The median is the middle number in a sorted, ascending or descending, list of numbers and can be more descriptive of that data set than the average.\n",
        "- The median is sometimes used as opposed to the mean when there are *outliers in the sequence that might skew the average of the values.*\n",
        "- If there is an *odd amount of numbers,* the median value is the number that is in the middle, with the same amount of numbers below and above.\n",
        "- If there is an *even amount of numbers* in the list, the middle pair must be determined, added together, and divided by two to find the median value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ui5P9tkMK9c",
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        "outputId": "261c0f28-0a55-463a-b8a2-0448ecc842e1"
      },
      "source": [
        "# MEDIAN\n",
        "# Here also by default axis = 0 means Column Wise Computation\n",
        "# axis = 1 mean Row Wise Computation\n",
        "print(\"MEDIAN of IRIS Dataset are as follows\")\n",
        "iris_pd.median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEDIAN of IRIS Dataset are as follows\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    5.80\n",
              "sepal width (cm)     3.00\n",
              "petal length (cm)    4.35\n",
              "petal width (cm)     1.30\n",
              "Class                1.00\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "MJau1AzLMdn5",
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        "outputId": "c7ad00e3-65f5-47bc-c9f9-02c6c40a3712"
      },
      "source": [
        "# If we want to analyze both mean and median at a same time\n",
        "pd.DataFrame([iris_pd.mean(),iris_pd.median()],index= [\"MEAN\",\"MEDIAN\"])\n",
        "\n",
        "# Inference :\n",
        "# Almost all Mean and Median Values are similar"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MEAN</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEDIAN</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        sepal length (cm)  sepal width (cm)  ...  petal width (cm)  Class\n",
              "MEAN             5.843333          3.057333  ...          1.199333    1.0\n",
              "MEDIAN           5.800000          3.000000  ...          1.300000    1.0\n",
              "\n",
              "[2 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 131
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        "outputId": "285d873d-76ea-44f2-cba6-fd6859236e10"
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      "source": [
        "# Now check for Boston dataset\n",
        "pd.DataFrame([boston_pd.mean(),boston_pd.median()],index= [\"MEAN\",\"MEDIAN\"])\n",
        "\n",
        "# Inference :\n",
        "# Columns like CRIM, ZN, TAX have lot of difference between MEAN and MEDIAN\n",
        "# SO ARE THERE ANY OUTLIERS in those column? i.e, use Boxplot for Outlier Detection\n",
        "# Columns like INDUS, CHAS, NOX etc have similar MEAN and MEDIAN"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MEAN</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.06917</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEDIAN</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            CRIM         ZN      INDUS  ...           B      LSTAT       MEDV\n",
              "MEAN    3.613524  11.363636  11.136779  ...  356.674032  12.653063  22.532806\n",
              "MEDIAN  0.256510   0.000000   9.690000  ...  391.440000  11.360000  21.200000\n",
              "\n",
              "[2 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18O9yXlUoNC8"
      },
      "source": [
        "**3. MODE**\n",
        "- In categorical varible, the mode is the most commonly observed value in a set of data.\n",
        "- For the normal distribution, the mode is also the same value as the mean and median."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ob-oo3LZNIJ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f08041be-7885-4706-ab6d-7ebd3d925358"
      },
      "source": [
        "# MODE \n",
        "iris_pd['Class'].mode()\n",
        "\n",
        "# Inference\n",
        "# 0 rank means most frequent, i.e, in column Class \"0\" is most frequent\n",
        "# 1 rank means second most frequent, i.e, in column Class \"1\" is second most frequent and so on \n",
        "# NOTE: Class is Int Type so need to do Data Type Conversion "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    2\n",
              "Name: Class, dtype: category\n",
              "Categories (3, int64): [0, 1, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9Xs8Hx4Qe3v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6275259-c01c-49eb-9f90-73db67bd4a49"
      },
      "source": [
        "# First Convert Numerical Column to Categorical Column\n",
        "print(\"Previous Data Type\")\n",
        "print(iris_pd.dtypes)\n",
        "\n",
        "# Conveting only one column i.e, \"Class\" Column\n",
        "iris_pd['Class'] = iris_pd['Class'].astype('category')\n",
        "print(\"\\nConverted Data Type\")\n",
        "print(iris_pd.dtypes)\n",
        "\n",
        "# Inference : Class Datatype is changed from Int to Category \n",
        "\n",
        "print(\"\\n\")\n",
        "# To check the count also for each category i.e, Frequency Count\n",
        "print(\"Frequency Count of Class Type\")\n",
        "iris_pd.Class.value_counts()\n",
        "\n",
        "# Inference : All category have equal number of counts / distributions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Previous Data Type\n",
            "sepal length (cm)     float64\n",
            "sepal width (cm)      float64\n",
            "petal length (cm)     float64\n",
            "petal width (cm)      float64\n",
            "Class                category\n",
            "dtype: object\n",
            "\n",
            "Converted Data Type\n",
            "sepal length (cm)     float64\n",
            "sepal width (cm)      float64\n",
            "petal length (cm)     float64\n",
            "petal width (cm)      float64\n",
            "Class                category\n",
            "dtype: object\n",
            "\n",
            "\n",
            "Frequency Count of Class Type\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    50\n",
              "1    50\n",
              "0    50\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcZAVtG1pcHy"
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      "source": [
        "**INTERQUARTILE RANGE(IQR)**\n",
        "- Imagine all the data in a set as points on a number line. \n",
        "- Once plotted on that number line, the smallest data point and the biggest data point in the set of data create the boundaries (i.e. a *lower bound and an upper bound*) of an interval of space on the number line that contains all data points in the set. \n",
        "- The interquartile range (IQR) is the length of the middle 50% of that interval of space(25% of Data to 75% of Data).\n",
        "--------------------------------------------------------\n",
        "*What if I Have an Even Set of Numbers?*\n",
        "\n",
        "*Example question: Find the IQR for the following data set: 3, 5, 7, 8, 9, 11, 15, 16, 20, 21.*\n",
        "\n",
        "**Step 1: Put the numbers in order.**\n",
        "\n",
        "3, 5, 7, 8, 9, 11, 15, 16, 20, 21.\n",
        "\n",
        "**Step 2: Make a mark in the center of the data:**\n",
        "\n",
        "3, 5, 7, 8, 9, | 11, 15, 16, 20, 21.\n",
        "\n",
        "**Step 3: Place parentheses around the numbers above and below the mark you made in Step 2–it makes Q1 and Q3 easier to spot.**\n",
        "\n",
        "(3, 5, 7, 8, 9) | (11, 15, 16, 20, 21).\n",
        "\n",
        "**Step 4: Find Q1 and Q3**\n",
        "\n",
        "Q1 is the median (the middle) of the lower half of the data, and Q3 is the median (the middle) of the upper half of the data.\n",
        "\n",
        "(3, 5, 7, 8, 9), | (11, 15, 16, 20, 21)\n",
        "\n",
        "Q1 = 7 and Q3 = 16.\n",
        "\n",
        "\n",
        "**Step 5: Subtract Q1 from Q3.**\n",
        "\n",
        "16 – 7 = 9.\n",
        "This is your IQR.\n",
        "\n",
        "--------------------------------------------------------\n",
        "*What if I Have an Odd Set of Numbers?*\n",
        "\n",
        "*Example question: Find the IQR for the following data*\n",
        "\n",
        "**Step 1: Put the numbers in order.**\n",
        "\n",
        "1, 2, 5, 6, 7, 9, 12, 15, 18, 19, 27.\n",
        "\n",
        "**Step 2: Find the median.**\n",
        "\n",
        "1, 2, 5, 6, 7, 9, 12, 15, 18, 19, 27.\n",
        "\n",
        "**Step 3: Place parentheses around the numbers above and below the median.**\n",
        "\n",
        "Not necessary statistically, but it makes Q1 and Q3 easier to spot.\n",
        "\n",
        "(1, 2, 5, 6, 7), 9, (12, 15, 18, 19, 27).\n",
        "\n",
        "**Step 4: Find Q1 and Q3**\n",
        "\n",
        "Think of Q1 as a median in the lower half of the data and think of Q3 as a median for the upper half of data.\n",
        "\n",
        "(1, 2, 5, 6, 7),  9, ( 12, 15, 18, 19, 27). Q1 = 5 and Q3 = 18.\n",
        "\n",
        "**Step 5: Subtract Q1 from Q3 to find the interquartile range.**\n",
        "\n",
        "18 – 5 = 13."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNzWGnrQRfoO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "70c7a054-f554-42f7-d35a-33a58e4a11b7"
      },
      "source": [
        "# 4) Range 6) Quartile 7) IQR\n",
        "# Range = Maximum Value - Minimum Value. Example in \"NOX\" Column, Range = 0.87 - 0.38 = 0.49 \n",
        "# Quartile = 3 Quartiles, First is 25%, Second is 50% and third is 75%\n",
        "# Example in \"NOX\" Column, First Quartile = 0.44, Second Quantile = 0.53 and Third Quantile = 0.62\n",
        "# IQR = 75% - 25%, Example in \"NOX\" Column, IQR = 0.62 - 0.44 = 0.18\n",
        "\n",
        "boston_pd.describe()\n",
        "\n",
        "# Inference :\n",
        "# If we take first column CRIM, min to 75% data range is 3 i.e, (0-3)\n",
        "# But from 75% to max, range is 85 i.e,(88-3)\n",
        "# OUTLIER CHANCES"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677083</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             CRIM          ZN       INDUS  ...           B       LSTAT        MEDV\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  356.674032   12.653063   22.532806\n",
              "std      8.601545   23.322453    6.860353  ...   91.294864    7.141062    9.197104\n",
              "min      0.006320    0.000000    0.460000  ...    0.320000    1.730000    5.000000\n",
              "25%      0.082045    0.000000    5.190000  ...  375.377500    6.950000   17.025000\n",
              "50%      0.256510    0.000000    9.690000  ...  391.440000   11.360000   21.200000\n",
              "75%      3.677083   12.500000   18.100000  ...  396.225000   16.955000   25.000000\n",
              "max     88.976200  100.000000   27.740000  ...  396.900000   37.970000   50.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhXKouU6ny0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "de49fd0f-36bd-4fb7-e233-8a9b6f3d50ce"
      },
      "source": [
        "# 5) Percentile\n",
        "boston_pd['CRIM'].quantile([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])\n",
        "\n",
        "# Inference : \n",
        "# Till 90% of data, range is 10 i.e, (0-10)\n",
        "# But for last 10% data, range drastically changes to 78 i.e, (10-88)\n",
        "# OUTLIER CHANCES"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     0.006320\n",
              "0.1     0.038195\n",
              "0.2     0.064170\n",
              "0.3     0.099245\n",
              "0.4     0.150380\n",
              "0.5     0.256510\n",
              "0.6     0.550070\n",
              "0.7     1.728440\n",
              "0.8     5.581070\n",
              "0.9    10.753000\n",
              "1.0    88.976200\n",
              "Name: CRIM, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YaQg9Twfn0TQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "6d1ffe1d-c66f-4e6b-d92d-aecf2e693bd5"
      },
      "source": [
        "# 8) Boxplot\n",
        "import seaborn as sns\n",
        "print(\"Box Plot for IRIS Dataset\")\n",
        "sns.boxplot(data = iris_pd)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Box Plot for IRIS Dataset\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe2dec53c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SANjJ9z0pTcN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "505c6f46-56df-44f8-fb38-274999bd1b12"
      },
      "source": [
        "# As we can observe on X axis, Column Names are not clear\n",
        "# So change the axis\n",
        "sns.boxplot(data = iris_pd, orient = 'h')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe2dde3898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUzX5VOWpnsy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a529b649-9b19-4357-f853-4ec22155855a"
      },
      "source": [
        "# Now if we want to change the colour\n",
        "sns.boxplot(data = iris_pd, orient = 'h', palette= \"Set2\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe2d863b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0ONairop0eZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "b7da07c6-772b-4b77-fe75-256b6b0cab2a"
      },
      "source": [
        "# Suppose we want to compare two variables\n",
        "sns.boxplot(data = iris_pd, orient= 'h', palette= 'Set2', order = ['sepal length (cm)',\n",
        "                                                                   'sepal width (cm)'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe2d7fe6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUgZJjgBNN-3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "adc1de96-1813-49e3-966e-08ee92c2f02d"
      },
      "source": [
        "# For CRIM in boston dataset\n",
        "sns.boxplot(data = boston_pd, orient = 'h', palette = 'Set2', order = ['CRIM'])\n",
        "\n",
        "# Inference :\n",
        "# There are lot of outliers in the CRIM column\n",
        "# Treat Outliers for this column "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe2d728550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/++15SQ8qfw+/Y66Rjow+AKC6Try9AwCogegDQCBEHwACIfoAEAjRB4BAiD4ABEL0ASCQ/wPhr0cnL2NqvAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XdxITT90K7w"
      },
      "source": [
        "**Standard Deviation**\n",
        "\n",
        "- The Standard Deviation is a measure of how spread out numbers are. Symbol is σ (the greek letter sigma)\n",
        "\n",
        "- The formula is easy: it is the square root of the Variance. \n",
        "\n",
        "**Variance**\n",
        "\n",
        "- The average of the squared differences from the Mean.\n",
        "\n",
        "**To calculate the variance follow these steps:**\n",
        "\n",
        "1. Work out the Mean (the simple average of the numbers)\n",
        "2. Then for each number: subtract the Mean and square the result (the squared difference).\n",
        "3. Then work out the average of those squared differences. (Why Square?)\n",
        "\n",
        "**EXAMPLE : \n",
        "The heights are: 10, 20, 30, 40 and 50. Find out the Mean, the Variance, and the Standard Deviation.**\n",
        "\n",
        "*Mean =*\t(10+20+30+40+50)/5 = 150/5 = 30m\n",
        "\n",
        "*Variance =*\n",
        "\n",
        "If we don't square, then result is 0\n",
        "\n",
        "[(30-10)+(30-20)+(30-30)+(30-40)+(30-50)]/5 = [20+10+0-10-20]/5 = 0\n",
        "\n",
        "That's why we square\n",
        "\n",
        "(30-10)^2+(30-20)^2+(30-30)^2+(30-40)^2+(30-50)^2/5 = (400 + 100 + 0 + 100 + 400 )/5 = 1000/5 = 200\n",
        "\n",
        "*Standard Deviation =*\n",
        "\n",
        "Sqaure Root(500) = 22.36 (Same scale of the data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiKeti5eq1WK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0dba456f-06ba-44b4-ea8b-591d7d933e2e"
      },
      "source": [
        "# 9) Variance\n",
        "import statistics\n",
        "print(\"Variance for TAX in Boston Dataset is \", statistics.variance(boston_pd.TAX))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variance for TAX in Boston Dataset is  28404.759488122727\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KsO02GNqe71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "4243798f-4ed8-47eb-cc18-f28ff56ca8de"
      },
      "source": [
        "# Want to print \"Variance\" for full dataset\n",
        "print(\"Variance for Boston Dataset Column Wise\")\n",
        "boston_pd.var()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variance for Boston Dataset Column Wise\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM          73.986578\n",
              "ZN           543.936814\n",
              "INDUS         47.064442\n",
              "CHAS           0.064513\n",
              "NOX            0.013428\n",
              "RM             0.493671\n",
              "AGE          792.358399\n",
              "DIS            4.434015\n",
              "RAD           75.816366\n",
              "TAX        28404.759488\n",
              "PTRATIO        4.686989\n",
              "B           8334.752263\n",
              "LSTAT         50.994760\n",
              "MEDV          84.586724\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGVzhBe_w2No"
      },
      "source": [
        "**10. STANDARD DEVIATION**\n",
        "- Standard Deviation = Square root(Variance) \n",
        "- SMALL SD means the values in the dataset are close to mean of the dataset on average\n",
        "- LARGE SD means that the value in the dataset are far away from the mean of dataset on average\n",
        "- In short, Standard Deviation measures how concentrated the data is around the mean\n",
        "- More Concentration -> Smaller Standard Deviation\n",
        "\n",
        "**A SMALL SD can be the goal in certain situations where results are restricted**\n",
        "- Example, In Product Manufacutring and Quality Control companies. Suppose a particular type of car part that has to be 2 cm in diameter to properly fit cannot have a very BIG SD in manufacturing process. A BIG SD would mean most material would end up in trash because they don't fight right\n",
        "\n",
        "**A HIGH SD reflects large amount of variation in the group**\n",
        "- Example, if we look at the salaries for everyone in the company from intern to CEO, SD may be very large. On other hand if we see or observe only intern group, SD of salary may be low\n",
        "\n",
        "**Inference :**\n",
        "1. SD can be low or high depend on the scenario\n",
        "2. OUTLIERS may affect the SD as the formula contains the mean\n",
        "3. SD cannot be negative and lowest possible value is 0\n",
        "4. 0 is only possible when every single entity is having same number (No Deviation)\n",
        "5. SD have the same unit as original data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7kSF39urgZu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "65ade192-05ce-45df-96e7-de155fd7f1eb"
      },
      "source": [
        "# 10) Standard Deviation\n",
        "print(\"Standard Deivation for Boston Dataset is\")\n",
        "boston_pd.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Standard Deivation for Boston Dataset is\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM         8.601545\n",
              "ZN          23.322453\n",
              "INDUS        6.860353\n",
              "CHAS         0.253994\n",
              "NOX          0.115878\n",
              "RM           0.702617\n",
              "AGE         28.148861\n",
              "DIS          2.105710\n",
              "RAD          8.707259\n",
              "TAX        168.537116\n",
              "PTRATIO      2.164946\n",
              "B           91.294864\n",
              "LSTAT        7.141062\n",
              "MEDV         9.197104\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcglxpOOrUVu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "daf93761-5e1a-4f17-fce0-a67e3f3c35b3"
      },
      "source": [
        "# Want to check standard column of one column ?\n",
        "print(\"SD of TAX column is \",boston_pd.TAX.std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SD of TAX column is  168.53711605495926\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8rySG3qZAWk"
      },
      "source": [
        "**11. COVAIRANCE**\n",
        "\n",
        "- Covariance is a statistical tool that is used to determine the relationship between the movement of two variables.\n",
        "\n",
        "- When two stocks tend to move together, they are seen as having a positive covariance; when they move inversely, the covariance is negative.\n",
        "\n",
        "- Covariance tells us how columns are related to each other.\n",
        "  \n",
        "  1. *If its positive value,* they are positively related. Coefficient/Value is positive means both variable tend to increase/decrease together\n",
        "  2. *Coefficient/Value is negative* means if one variable increase other variable will decrease or vice-versa. \n",
        "  3. If it is close to 0, they are unrelated\n",
        "\n",
        "**Problem -** It give us sign (+ or -) but not the strength i.e, how much they are related.  For Strength of relation, USE CORRELATION COEFFICIENT\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNUabL6TrqmA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5630f65f-6946-40ea-c076-155aebcae4a3"
      },
      "source": [
        "# 11) Covariance\n",
        "iris_pd.cov() \n",
        "\n",
        "# Inference :\n",
        "# Try to remove one variable, if both the variables are highly correlated"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <td>0.685694</td>\n",
              "      <td>-0.042434</td>\n",
              "      <td>1.274315</td>\n",
              "      <td>0.516271</td>\n",
              "      <td>0.530872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <td>-0.042434</td>\n",
              "      <td>0.189979</td>\n",
              "      <td>-0.329656</td>\n",
              "      <td>-0.121639</td>\n",
              "      <td>-0.152349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal length (cm)</th>\n",
              "      <td>1.274315</td>\n",
              "      <td>-0.329656</td>\n",
              "      <td>3.116278</td>\n",
              "      <td>1.295609</td>\n",
              "      <td>1.372483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal width (cm)</th>\n",
              "      <td>0.516271</td>\n",
              "      <td>-0.121639</td>\n",
              "      <td>1.295609</td>\n",
              "      <td>0.581006</td>\n",
              "      <td>0.597315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>0.530872</td>\n",
              "      <td>-0.152349</td>\n",
              "      <td>1.372483</td>\n",
              "      <td>0.597315</td>\n",
              "      <td>0.671141</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   sepal length (cm)  ...     Class\n",
              "sepal length (cm)           0.685694  ...  0.530872\n",
              "sepal width (cm)           -0.042434  ... -0.152349\n",
              "petal length (cm)           1.274315  ...  1.372483\n",
              "petal width (cm)            0.516271  ...  0.597315\n",
              "Class                       0.530872  ...  0.671141\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w19eCIZbHlx3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "f703809a-b15c-4a83-d693-a87e2febcd8a"
      },
      "source": [
        "# Want to make it colourful\n",
        "sns.heatmap(iris_pd.cov(),annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f91f59b50>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKAOvq6oFQIR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "64f4c412-2015-4743-b368-c39b9f5501e9"
      },
      "source": [
        "# Change the colour to something light colour\n",
        "sns.heatmap(iris_pd.cov(),annot = True,cmap = 'Set3')\n",
        "\n",
        "# Inference : \n",
        "# Negatively related - sepal length (cm) & sepal width (cm)\n",
        "# Positively Related - petal length (cm) & sepal length (cm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe29b65828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XAF+GTxIC+Avh1E5m9gTBs1kvk5ROMAvFUCvm2X+erJxzzpUZM/uYYGqnoso8AjxSmnq9G9A551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zk+RMsIqrKrcfGOwQXMQ+2GRbvECKkyMfIJZxji35geaXgyco55xwH/LZVvEMokncDOuecizxPVs455yLPk5VzzrnI82TlnHMu8jxZOeecizxPVs455yLPk5VzzrnI82TlnHMu8jxZOeecizxPVs455yLPk5VzzrnI82TlnHMu8jxZOeecKzOSDpD0oaTFkhZJuqqAMpL0kKTlkhZI6lVcvf7Udeecc2UpHRhpZvMk1QXmSnrPzBbHlPkV0D78OQJ4PPy3UN6ycs45V2bMbK2ZzQtfbwO+AlrmKXYm8JwFPgNSJTUvql5PVs4550pM0nBJc2J+hhdRti3QE5iZZ1VLYFXM+9XkT2i5eDegc865EjOzscDY4spJqgP8F7jazLbu7369ZeWcc65MSapKkKheNLNXCyjyA3BAzPtW4bJCecvKOeccB/1cp0zqkSTgaeArM7u/kGITgRGSXiIYWJFmZmuLqteTlXPOubLUF7gA+FLS/HDZX4DWAGb2BPA2cAqwHNgB/L64Sj1ZOeecKzNm9jGgYsoYcEVp6vVrVs455yLPW1YJxMy4887XmTr1K2rUqMY99wylS5dW+cotXLiKG298iV279jJgQGduumkwQTd0YPz4j/jb395kxozbaNCgDhMnzmXcuA8Bo3bt6owaNYROnVpU4JHtn2nTlnDnna+TmZnJOeccwfDhx+daP3v2N9x11xssXbqW++8/n5NP7p697r773mLq1K8AuPzyEzjllJ4VGntZMzM+Hf8i38/7girVqjHwymE0PrBtvnKTRv+dHZu3YBkZNDukI/3+cCFJyUnM/vd/WTFrHkpKomZKXQaOGEbtBvUr/kDKQEnPxawXJ7Bs6ifs3r6dS1/MGSS3YOI7fDVlKklJSdRIqcfAyy+lbpNGFXgElUvkWlaSBkp6ax+2ayFpQiHrPpLUJ3z9l5jlbSUtLGH9V0u6sLRxFVDPCEmX7G89+2LatCWsWLGBd9+9kdGjz2HUqP8WWG7UqP8yevS5vPvujaxYsYFp05Zkr1u7djOffLKUFi1yPoBatWrACy9czptvXs9llw3i5ptfKfdjKSsZGZncfvurPPXUMCZN+jNvvfU5y5f/mKtM8+b1ufvuoZx2Wu5E9NFHi1m8eDWvv34tL7/8J55+eio//7yrIsMvc6vmLSBt7Y8MfeRe+l/2ez4e+2yB5QaNvIJz7r+Dcx68i11p2/h2xiwAup95Cuc8cCdDxoymde8ezH3ljYoMv0yV9Fy0OawHv/7brfmWN2zXhrPuHcU5D9zJgUf24bPn/1PeIVdqkUtW+8rM1pjZkBIU/UvxRXKTVAW4BPhXqQPLbzxwZRnUU2pTpixk8ODeSKJHjzZs3bqTdety3/6wbt1Wfv55Fz16tEESgwf3ZsqUnHx+990Tuf7604lpaNGrVztSUmoB0KNHG378cUuFHE9ZWLDge9q0acgBBzSkWrUqnHpqT6ZMWZSrTKtWDejUqQVJSbm74Zcv/4k+fQ6iSpVkatWqTseOzXMl9l+iFbPn0WFAXyTRtMPB7N6+g+2b8/9/VqtVE4DMjAwy0tPJ+oXIWg6Qvnt30RcuIq6k56Jph4OpXT813/KWh3amavXq2WW2b9xU7jFXZqVOVpJqS5ok6QtJCyX9JlzeW9JUSXMl/S/r0Rlhq+YfkuaH5Q8Plx8uaYakzyV9KqljMfudJKlb+PpzSbeEr2+XNCy2lSSppqSXJH0l6TWgZrj8HqBmGMuLYdXJksaFD1x8V1LNAnZ/HDDPzNLDeg6W9H54DuZJOihsEU6V9IakbyXdI+k8SbMkfSnpIAAz2wGsyDoPFemnn9Jo1iznj6pZsxR++imtmDKp2WXef38hTZqkFNnFN2HCTPr371TGkZefvMfbtGn+c1KYTp1aMH36Enbu3MOmTT8zc+byX1SiLsj2TZup3ahh9vvaDRuwY+PmAstOuv0+nrvkSqrVrMGBRx6WvXzWixN4Yfg1fD1tBn2GnlXuMZeX0pyL4iyZMpXWvbqVVWgJaV9aVicDa8ysu5l1Bd4JbwB7GBhiZr0JWg93xmxTy8x6AJeH6wCWAMeYWU/gFuCuYvY7HThGUgrBgxL7hsuPAablKXsZsMPMOgO3Ar0BzOwGYKeZ9TCz88Ky7YFHzawLsAU4u4B99wXmxrx/MdymO3A0kHV/QHfgj0BngqGbHczscOApcrem5oRx5xL7GJOxY98p8mRUtJ079/Dkk1O46qqTCi3z2WfLmTBhFtddd1oFRhY//fp1ZMCAzgwd+jAjR75Ajx5tSEqqNJ0VxTr1luu54Kl/kLE3nTULc55Revh5Qzh/7AO0738UCye/H8cIo2HZ1E9Y/80Kup95SrxD+UXblwEWXwJjJP0NeMvMpkvqCnQF3gsvxCeT8wEO8G8AM5smqZ6kVKAu8Kyk9oABVYvZ73TgT8B3wCRgkKRaQDszWxo+gypLf+ChcJ8LJC0oot7vzCzrXoC5QNsCyjQneBgj4VOEW5rZa2H9u8LlALOzbmyT9A3wbrj9l8CxMfWtA/I1P3I/xuQtKyLmEnvxxY95+eXgsVyHHnpArm/+P/6YRtOmKbnKN22akqfMFpo2TeH77zeyevUmzjxzTPa2Z531AK+8chWNG9djyZI1/PWvLzNu3DDq169dFqFXiLzH+9NP+c9JUS677AQuu+wEAEaOfIF27RqXeYzlbeHk91ny/lQAGh/cju0bNmav275xE7UaFj5Aokq1arQ9vCcrZs2jVfeuudYdfMzRTL5zDIf9glpX+3MuCrL6i0V8/t83OWP0X0iuWtxHnCtKqZOVmS0L5x45BbhD0hTgNWCRmR1V2GYFvB8NfGhmvw4TzUfF7Ho20Af4FngPaAQMI3eLZ1/sjnmdQdhlmMdOoEYp68qMeZ9J7nNdI6yz3J13Xj/OO68fEAwIeOGFTzj11J588cX31K1bgyZN6uUq36RJPerUqcH8+Svp3r01r78+lwsu6EfHjs2ZMeO27HLHHXcHEyZcTYMGdVizZjNXXvkM997721/ch/Whhx7AihUbWLVqI02bpjBp0ueMGXN+ibbNyMhk69ad1K9fmyVL1rB06Vr69u1QzhGXva6/OoGuvwoS7sq581k0+X0O6nck677+hmq1aua7HrN35y727NpF7fqpZGZksHLuFzTvHBx32pofSWnRLKhr9jxSWxb5IO3IKe25KMqGb1cy/cl/cspfr6NmSr3iN3BFKnWyktQC2GRmL0jaAvwBuAdoLOkoM5sRdgt2MLOsK9W/AT6U1I/gsRppYXde1rOgLi5uv2a2R9Iq4BzgdqAx8PfwJ69pwO+AD8JWX2xn8V5JVc1sbykO+yvg4DCObZJWSxpsZq9Lqk7QkiyNDsAnpdxmvw0Y0JmpU79i0KC7qVmzKnfdNTR73ZlnjuGNN0YCcOutZ2cPXe/fv1Ox16AeffRdtmzZwW23BY8AS05O4tVXrym/AylDVaokc8stZ/GHP4wlI8M4++zDad++Gf/4xzt07dqK44/vyoIF3zNixDNs3bqTDz9czMMP/49Jk/5MenoG5533KAB16lTnvvt+R5Uqpf1ViJbWvbrz/bwFvHTF9VSpXp2BV/whe92EkTczZMxo9u7ezf/ufpCMvXsxM1p07cwhJx0HwMwXXmHLmrVIok7jRvT/v4vidSj7rSTnAuCz5/7D8ukzSN+9hxeGXU2nEwbQ5ze/5rPnXmLvrt28Nyb8HWnUgJNv/GX8XUSRghuJS7GBdBJwH0FrYS9wmZnNkdSDoOsthSAJPmhm4yR9BMwHBhB09V1iZrMkHQU8C2wn6NY738zaShoIXGdm+S58SBoNHG9mR4dJ8wegdzjJV1uCbsmu4SCJfxJcQ8qaS+WKMM6/AWcA84CbsrYJ678OqGNmo/Lstw3wvJn1D9+3B54kaN3tJUigrWPjDo/7unCfA/OsmwcMMrONFKpsugFd5XH/Qr9HxxXs2q5H7vfAy4x5a0r0mZPcq0VcBnmWOlmVegcxH9rluqNyFo4q/LOZfb2f9fQErjWzC4ou6cnK5ebJyhUmEZJV4gxd2n83EAy02F+NgJvLoB7nnEsY5f64JTMbWN77qAhmthRYWgb1vFcG4TjnXELxlpVzzrnI82TlnHMu8vyp684555hc/44SlTuNx8o5koJ5y8o551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zkebJyzjkXeZ6snHPOlSlJ4yWty5q9vYD1AyWlhbO2z8+a+b0ofp+Vc865svYM8AjwXBFlphc0u0ZhvGXlnHOuTJnZNGBTWdbpyco551w8HCXpC0mTJXUprrAnK+eccyUmabikOTE/w/ehmnlAGzPrDjwMvF7cBn7NyjnnXImZ2Vhg7H7WsTXm9duSHpPUyMw2FLaNt6ycc85VKEnNJCl8fThBLtpY1DbesnLOOVemJP0bGAg0krQauBWoCmBmTwBDgMskpQM7gaFmZkXV6cnKOedcmTKz3xaz/hGCoe0l5t2AzjnnIs9bVhGVOem1eIcQCQ+2GRbvECLj2hYvxTuEyMicsS3eIURL1yPjHUG582TlnHOOUxbvLlnBduUbR2G8G9A551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zkebJyzjkXeZ6snHPORZ4nK+ecc5Hnyco551zk+UzBzjnnGJ1xTYnK3VrOcRTGW1bOOeciz5OVc865yPNk5ZxzrkxJGi9pnaSFhayXpIckLZe0QFKv4ur0a1aOm15axkeLN9OgTlXe/HP+35kpCzfy0OSVJEkkJ4kbB7ej94EpcYi0fJgZn45/ke/nfUGVatUYeOUwGh/YNl+5SaP/zo7NW7CMDJod0pF+f7iQpOQkZv/7v6yYNQ8lJVEzpS4DRwyjdoP6FX8gZWj37gzOu2wme/ZmkpFhnHRsM/40rH2uMrM/38RdD37F0m+2cf/t3Tn5uOZxirZ8Jfrfxz56BngEeK6Q9b8C2oc/RwCPh/8WqtxaVpIultSiBOWekTSkpMvLIK6/xLxuW1jmL2C7qyVdWAb7HyHpkv2tpywNPqwpY4d3KXT9ke1Tef26nrx2XU/uHNqem19eXoHRlb9V8xaQtvZHhj5yL/0v+z0fj322wHKDRl7BOfffwTkP3sWutG18O2MWAN3PPIVzHriTIWNG07p3D+a+8kZFhl8uqlVL4tlHDmfi8/14/bm+TP9sPfMXbs5VpnmzGtx986GcNqhyJqksif73sS/MbBqwqYgiZwLPWeAzIFVSkb9I5dkNeDFQbLKKg78UXyQ3SVWAS4B/lcH+xwNXlkE9Zeawg1JIrVV4I7t29WQkAbBjTwaqqMAqyIrZ8+gwoC+SaNrhYHZv38H2zVvylatWqyYAmRkZZKSnQ3hOspYDpO/eXSnOjyRqh78T6elGerpl/w5kadW8Fp0OrkdSUmU44sIl+t9HXpKGS5oT8zN8H6ppCayKeb86XFaoEnUDSmoLvAPMBXoBi4ALzWyHpN7A/UAdYANBkuoL9AFelLQTOAq4HjgdqAl8CvyfmVkJ959vH2a2VtJHwEzgWCAVuNTMpkuqRdAM7QosJUiaVwBDgJqS5ofHcBOQLGkccDTwA3Cmme3ME8JxwDwzSw/jORh4AmgMZADnAAcAtwFbgEOBl4EvgavCYx5sZt+E52yFpMPNbFZJjj8K3luwgQfeXsmmbXt5fNgh8Q6nTG3ftJnajRpmv6/dsAE7Nm6mdv3UfGUn3X4f65Z/S+ue3TjwyMOyl896cQLLpn5CtVo1Of22Gyok7vKWkWGc9ftP+H71Dn53dmu6d8l/PlygMv995GVmY4GxFb3f0rSsOgKPmVlnYCtwuaSqwMPAEDPrTdBquNPMJgBzgPPMrEf44f+ImR1mZl0JPrxPK8lOC9tHTJEqZlrUCZEAACAASURBVHY4cDU5twBcDmw2s0OAm4HeAGZ2A7AzjOm8sGx74FEz60KQaM4uIIy+BIk6y4vhNt0JktzacHl34I9AZ+ACoEMY21Pkbk3NAY4pyfFHxaBujXj7ht48fElnHpq8Mt7hxM2pt1zPBU/9g4y96axZuDh7+eHnDeH8sQ/Qvv9RLJz8fhwjLDvJyeKN5/ox9Y1jWbA4jWXfbIt3SJHlfx+l9gPBF/wsrcJlhSpNslplZp+Er18A+hEksK7Ae2Fr5a/hTgtyrKSZkr4kaKkU3gmcW3H7eDX8dy7QNnzdD3gJwMwWAguKqP87M5tfQB2xmgPrASTVBVqa2Wth/bvMbEdYbraZrTWz3cA3wLvh8i/z1LuOArpIY5vXY99ZUkTI8XPYQSms3riLzT/vjXco+2Xh5PeZMPJmJoy8mVr1U9m+YWP2uu0bN1GrYeEDJKpUq0bbw3uyYta8fOsOPuZovvtsTrnEHC/16lbliF4NmP7Z+niHEnmV5e+jAkwELgxHBR4JpJnZ2qI2KM1owLxddgYIWGRmRxW1oaQawGNAHzNbJWkUUKOE+y1uH7vDfzPYt9GNu2NeZxC0+vLaScnija0rM+Z9Zp7YaoR15hLbvM6cdGmJukgrwsr1O2ndqAaSWLT6Z/akG6m1f9kDSbv+6gS6/uoEAFbOnc+iye9zUL8jWff1N1SrVTNfF+DenbvYs2sXteunkpmRwcq5X9C8cwcA0tb8SEqLZkFds+eR2vKXP+Bg0+bdVKmSRL26Vdm1K4NPZ29k2PkHxjusSKqMfx/7S9K/gYFAI0mrCXq9qgKY2RPA28ApwHJgB/D74uoszRltLekoM5sB/A74mOB6UOOs5WGXXQczWwRsA+qG22Z90G+QVIfg2tGEEu63qH0U5hPgXOBDSYcQXEPKsldSVTMrzVefr4CDAcxsm6TVkgab2euSqgPJpagLoEMYYySMfH4Js5ansWV7OgNvm8WIk1qTnhnkyqFHN+fdBRt5Y846qiaL6lWTuP/Cjvkutv+Ste7Vne/nLeClK66nSvXqDLziD9nrJoy8mSFjRrN3927+d/eDZOzdi5nRomtnDjnpOABmvvAKW9asRRJ1Gjei//9dFK9DKTPrNu7mhtsXkJEZDO0/+bhmHNuvCf8Yu4yunVM4/pimLFi8hRE3zGPrtnQ+/HgdDz+1nEn/+kX1bpdIov997Asz+20x641gHEGJlSZZLQWukDQeWAw8bmZ7wuHlD0lKCet7kGDwwjPAEzEDLMYBC4Efgdkl3Wkx+yjMY8CzkhYDS8KyaeG6scACSfMIBliUxGTg+Zj3FwBPSrod2EswwKI0+gKjSrlNuRlzQaci1w87vhXDji+sd/eXTxLHDCv4roQhY0YDUCs1hbPuHVVgmRP/HKnBnWWi08H1eP25fvmWXzW8Q/brboekMm3icRUZVlwk+t9HVKgkA/LC0YBvhYMjIk9SMlDVzHZJOgh4H+hoZnv2o87XgD+b2df7GVtP4Fozu6CoclHqBoynB9sMi3cIkXFti5fiHUJkZM7wwR6xkk59er+bcrdNXFiiz5xbz+gal2ZjZe1YrUXQBViV4JrX5fuTqEI3EAy02K9kBTQiGKHonHOuhEqUrMxsBcGIvF8EM9tGcJ9XWda5lKArdH/rea8MwnHOuYTiD7J1zjkXeZ6snHPORV5lvWblnHOuFG76/J8lK3jGmPINpBDesnLOORd5nqycc85Fnicr55xzkefJyjnnXOR5snLOORd5nqycc85Fnicr55xzkefJyjnnXOR5snLOORd5nqycc85Fnicr55xzkefJyjnnXOR5snLOORd5nqycc85Fnicr55xzkefJyjnnXJmSdLKkpZKWS7qhgPUXS1ovaX7484fi6vTJFyNqarV8/78JamO8A4iMzBnb4h1CZGTOSY13CJGSdGq8I8ghKRl4FBgErAZmS5poZovzFP2PmY0oab2erJxzzjH96D+WqNyxxRc5HFhuZt8CSHoJOBPIm6xKxbsBnXPOlaWWwKqY96vDZXmdLWmBpAmSDiiuUk9WzjnnSkzScElzYn6G70M1bwJtzawb8B7wbHEbeDegc865EjOzscDYIor8AMS2lFqFy2LriL0Y/RRwb3H79ZaVc865sjQbaC+pnaRqwFBgYmwBSc1j3p4BfFVcpd6ycs45V2bMLF3SCOB/QDIw3swWSbodmGNmE4E/SToDSAc2ARcXV68nK+ecc2XKzN4G3s6z7JaY1zcCN5amTu8GdM45F3merJxzzkWeJyvnnHOR58nKOedc5Hmycs45F3merJxzzkWeJyvnnHOR58nKOedc5Hmycs45F3merJxzzkWeJyvnnHOR588GdM45x7GDlpawZPtyjaMw3rJyzjkXeZ6snHPORZ4nK+ecc5Hn16wS1KLFc3l5wlgyMzPpe/SJnHziObnWf/rZ+7z6+nhSUxoCMHDAafQ7+iRWrf6Wf730KLt27SQpKYlfnXQufXr3j8chlBkz49PxL/L9vC+oUq0aA68cRuMD2+YrN+vFCSyb+gm7t2/n0hdzZvVeMPEdvpoylaSkJGqk1GPg5ZdSt0mjCjyC8nHTS8v4aPFmGtSpypt/7pVv/ZSFG3lo8kqSJJKTxI2D29H7wJQ4RFo+dFBHkk4eDElJZM6biX3yQe713Q8jadBpsC0NgMxZn2CfzwQg6YTTUPvOIGHfLiPzndcrPP7KpsKSlaSLgXfNbE0x5Z4B3jKzCaWs/4/ADjN7Ls/ytmF9XSX1AFqEE4MhaRTws5n9vZi6BUwBBpvZ1tLEVUBd7wPnmNnm/alnf2RmZvDvlx/nqhF3UD+1IXffdw3dDj2CFs1b5yrXu9cx/Pbcy3Itq1a1OhdfeC1Nm7Rky5aN3HXv1RzSuRe1atWpyEMoU6vmLSBt7Y8MfeRe1n39DR+PfZZf33NrvnJtDutBl1NO4KURf861vGG7Npx17yiqVq/Oonem8Nnz/2HQyCsqKvxyM/iwpvyuXwtu+NeyAtcf2T6V47o0QBJL12znmueW8PYNvSs4ynIikXTKWWQ8/yRsTSN52NVkLF0EG37KVcwWzSdz8mu5t23VFh3Qlowngo+V5N+PQG0OwlZ+U1HRV0oV2Q14MdCivCo3syfyJqoC9ABO2YfqTwG+2N9EFXoeuLwM6tlnK1Yso0mj5jRu1IwqVapyWK/+LFjwWYm2bdq0JU2btAQgNbUhdeumsO3ntPIMt9ytmD2PDgP6IommHQ5m9/YdbN+8JV+5ph0Opnb91HzLWx7amarVq2eX2b5xU7nHXBEOOyiF1FqFf5+tXT2Z4Hsc7NiTgSoqsIrQsjW2aSNs2QSZGWQu+hx16lLCjQ2qVIHkZEgO/rXt28o13ESwTy2rsLXyDjAX6AUsAi40sx2SegP3A3WADQRJqi/QB3hR0k7gKOB64HSgJvAp8H9mZoXsrwkw2cx6S+oOzAfamNn3kr4BDgX+TNhKCmMYH27+blhHNeB2oKakfsDd4fpDJH0EtAYeNLOHCgjhPCC730fShcB1gAELzOyCsEW4E+gJNAEuAS4Mj3WmmV0cbj4RmA7cWcjpLXeb0zZSv37j7Pep9Rvx3Yr8w1Y/n/8py5cvokmTFpxz9jAaxGwD8N2KpWSkp9O4UfNyj7k8bd+0mdqNGma/r92wATs2bi4wMRVnyZSptO7VrSzDi7T3FmzggbdXsmnbXh4fdki8wykzqpsCW2O+sGxNQy1bk/cDSp27kdzmQGzjejL/NzHYZvVKbMU3JI8cBYDN/gQ2rKuw2Cur/WlZdQQeM7POwFbgcklVgYeBIWaWlTDuDLv05gDnmVkPM9sJPGJmh5lZV4KEdVphOzKzdUANSfWAY8K6jpHUBlhnZjvybPJP4Eoz6x5Txx7gFuA/YQz/CVd1Ak4CDgduDY8hr74EiRlJXYC/AseF9V8VU64+QXK6hiApPQB0AQ4NuyAJu/+qS2pIhHXrejh33jaem//yCJ079eTZ5x/ItT4tbRPPPHc/F55/NUlJPk4HYNnUT1j/zQq6n7kvjfdfpkHdGvH2Db15+JLOPDR5ZbzDqVC2bBEZ/7iDjCfGYN8uI2nw0GBF/YbQqAkZ999Oxv23o7YHQ+t2cY21MtifT5lVZvZJ+PoFoB9BAusKvCdpPsGHeqtCtj9W0kxJXwLHEXyoF+VTgqTRH7gr/PcYglZKNkmpQKqZTQsXPV9MvZPMbLeZbQDWAU0LKNPAzLLa8ccBr4TlMbPYPp83w9bhl8BPZvalmWUStDzbxpRbRwFdopKGS5ojac5bk14qJux9Vz+lIZs3r89+v2XzBuqn5M6dderUo2rVIG/3O/pEVn6/PHvdzp07eOTx2zjj9As4sF2ncouzPC2c/D4TRt7MhJE3U6t+Kts3bMxet33jJmo1rF+q+lZ/sYjP//smJ994NclVC/q+U7kddlAKqzfuYvPPe+MdSpmwbWlQL6ZlXS8lWBZr5w7IyAjKz5uJmgcfdep8KPbDSti7B/buIXP5EtSqbQVFXnntT7LK2yI2QMCisOXSw8wONbMT824oqQbwGEEL7FBgHFCjmP1NI0hObYA3gO4ECXJ6URuVwO6Y1xkU3DWaLqkk5yqrrsw89WbmqbcGQZdhLmY21sz6mFmf004dWoLd7Zs2bTqwbv0aNmz4kfT0vcyeN41u3Y7IVSYtLScHf/HlTJo3OwCA9PS9PDHuDo484jh69+xXbjGWt66/OoEhY0YzZMxo2h7ei2VTP8HM+GnZcqrVqlmqLsAN365k+pP/5OQbrqZmSr1yjDpaVq7fSVbP/aLVP7Mn3UitXUkGGP+wCjVsBKkNICmZpC49saWLcpepUzf7pTp2yenqS9uC2hwESoKkpOB1noEZrvT25zertaSjzGwG8DvgY2Ap0Dhredil1sHMFgHbgKz/3azEtEFSHWAIUNzov6zrPNPMLFPSJoKBDzfGFjKzLZK2SOpnZh8TXG/KEhtDaSwFDgSWAx8Ar0m638w2SmqQp3VVpHBkYTNgxT7EUSaSk5P5zbl/5KFHbyHTMjn6yEG0aN6GiW+9QJvW7ene7Qg++GgiC76cRVJyErVr1eWi868GYO68j/l6+SK2b9/GjM/eB+CiC67hgFYHxutw9lvrXt35ft4CXrrieqpUr87AK/6QvW7CyJsZMmY0AJ899x+WT59B+u49vDDsajqdMIA+v/k1nz33Ent37ea9MY8CUKdRA06+8Zq4HEtZGvn8EmYtT2PL9nQG3jaLESe1Jj0zSE5Dj27Ouws28sacdVRNFtWrJnH/hR2zB1z84lkmmW+/SvL5w0Eic/4sWP8TSQNPwtasxpYtIumIY1CHLpCZie3cQcbrQW+ILf4CtTuY5MuCy9q2fCm2bHFcD6cyUCFjGoreKGeAxRygN7AYuCAcYNEDeAhIIUiGD5rZOElnE3TfZQ2wuAn4LfAjsAxYaWajihq6LmkVMNrMxkr6CzDUzLqF60aRf4CFEQywOCUcut4A+B9QlWCARWdihq5LWgicZmYr8uz3ZmCtmT0Vvr+IYIBIBvC5mV0cG3fscPmwfOy6PsCNZnZ2Uef4w/e+Lv1/TCX0efONxRdKEFevHBfvECIjc07pB79UZlVuHVMG3xLeKuFnzmnF7kvSycA/gGTgKTO7J8/66sBzBPljI/CbvJ+7+ercj2SV/WFc2UlqDjxnZoPKoK5/ABPNbEpR5TxZBTxZ5fBklcOTVW5RSlaSkgkaIIOA1cBs4LdmtjimzOVANzP7o6ShwK/N7DdF1evDuErAzNYC48LRiPtrYXGJyjnnfsEOB5ab2bfhKOyXgDPzlDkTeDZ8PQE4XsX0Ie9TsjKzFYnSqspiZi+XxU3BZuZfj51zv1ixo5bDn+F5irQEVsW8Xx0uK7CMmaUDaUCRt/NUkqE7zjnnKoKZjSXmIQkVxbsBnXPOlaUfgANi3rcKlxVYRlIVggF5RV6g9paVc8457l9YspkCri3+AtBsoL2kdgRJaSjB7U2xJgIXATMIbl36oLDH7WXxZOWcc67MmFm6pBEEtwklA+PNbJGk24E5ZjYReBp4XtJyYBNBQiuSJyvnnHNlKpyG6e08y26Jeb0LOCfvdkXxa1bOOeciz5OVc865yPNk5ZxzLvI8WTnnnIs8T1bOOeciz5OVc865yPNk5ZxzLvI8WTnnnIs8T1bOOeciz5OVc865yPNk5ZxzLvI8WTnnnIs8T1bOOeciz5OVc865yPNk5ZxzLvJUzOSMLoFJGm5mY+MdRxT4ucjh5yKHn4uK4y0rV5Th8Q4gQvxc5PBzkcPPRQXxZOWccy7yPFk555yLPE9WrijeF5/Dz0UOPxc5/FxUEB9g4ZxzLvK8ZeWccy7yPFk555yLPE9WzjnnIq9KvANw0SDpKOB84BigObATWAhMAl4ws7Q4hlfhJPUhOBctyDkX75nZ5rgGFgd+LnKTVJ+cc7HCzDLjHFJC8AEWDkmTgTXAG8AcYB1QA+gAHAucDtxvZhPjFmQFkfR74ErgO2Auuc9FX4IP6pvN7Pu4BVlB/FzkkJQCXAH8FqgGrCc4F02Bz4DHzOzD+EVY+XnLygFcYGYb8iz7GZgX/oyR1Kjiw4qLWkBfM9tZ0EpJPYD2QKX/gMbPRawJwHPAMWa2JXaFpN7ABZIONLOn4xJdAvCWlctHUj1ivsiY2aY4huOcc96ycjkk/R9wG7ALyPoWY8CBcQsqTiS1I+gCa0vuxH1GvGKKFz8XuUnqRv5z8WrcAkoQ3rJy2SR9DRxVQJdgwpH0BfA08CWQfQHdzKbGLag48XORQ9J4oBuwiJxzYWZ2SfyiSgzesnKxvgF2xDuIiNhlZg/FO4iI8HOR40gzOyTeQSQib1m5bJJ6Av8EZgK7s5ab2Z/iFlScSPodweCBd8l9LubFLag48XORQ9LTwBgzWxzvWBKNt6xcrCeBD8jT3ZOgDgUuAI4jprsnfJ9o/FzkeA6YIelHgsQtgm7AbvENq/LzlpXLJulzM+sZ7ziiQNJy4BAz2xPvWOLNz0WO8FxcS/7rdyvjFlSC8JaVizVZ0nDgTXJ39yTi0PWFQCrBjbCJzs9FjvWJcHN8FHnLymWT9F0Bi83MEnHo+kcEo75mkztxJ9xwbT8XOSQ9RpC4836h86Hr5cxbVi6bmbWLdwwRcmu8A4gQPxc5ahIkqRNjlhngyaqcecvKZZN0BfBi1uNkwgd2/tbMHotvZBUvvBF2rZntCt/XBJqa2Yq4BhYHfi5cFPgUIS7WsNjnnoVP1R4Wx3ji6RVyj4jMCJclIj8XIUnPSkqNeV8/vFHYlTNPVi5WsiRlvZGUTPCE6URUJXb0W/jazwUJfy66FfCFzkfQVgBPVi7WO8B/JB0v6Xjg3+GyRLReUvYAAklnAon6GCo/FzmSwu5xACQ1wK/9Vwi/ZuWySUoChgMnhIveA54ys4z4RRUfkg4CXiSYZA9gNcFUKt/EL6r48HORQ9KFwF/I6QY9B7jTzJ6PX1SJwZOVc0WQVAfAzH6Odyzx5uciIOkQcp7e8YE/eqlieLJySHoTGAu8Y2Z786w7ELiYYPruSn8hWdL5wL8Km6o8bGU0N7OPKzayiufnIoekOsUl6ZKUcfvO+1odBCP+rgUelLSJnCm72wHLgUfM7I04xleRGgKfS5pLMJV71rk4GBhAcK3mhviFV6H8XOR4Q9J84A1grplth+wvc8cC5wLjCGYUduXAW1YuF0ltgebATmCZmSXclCHhKMjjgL7knIuvgMlmlghTuGfzc5FD0inAeQTnogGwF1gKTAKeNrMf4xhepefJyjnnXOT50HXnnHOR58nKOedc5Hmycs45F3k+GtBlk9QXGAW0IfjdyJoFNRGnCKkOnA20JebvxMxuj1dM8eLnIrdw0ElTcp+LhBpsEg+erFysp4FrCIYpJ9xTK/J4A0gjOBe7iylb2fm5CEm6kmDKlJ/IebivEcz35cqRjwZ02STNNLMj4h1HFEhaaGZd4x1HFPi5yBFOa3+EmW2MdyyJxltWDkm9wpcfSrqPYCK52FlQ58UlsPj6VNKhZvZlvAOJAD8XOVYRtDJdBfOWlUPSh0WsNjM7roj1lYqkLwm6daoA7YFvCRJ31vW7hOnu8XORQ9K14csuQEeCG4Fjv9DdH4+4Eom3rBxmdiwEj44xs29j14WPk0kkp8U7gAjxc5Gjbvjv9+FPNXLm9PJv/BXAW1Yum6R5ZtYrz7K5ZtY7XjHFi6TnzeyC4pYlAj8XOSSdY2avFLfMlT1vWTkkdSLo3kiRdFbMqnoEDy5NRF1i34TDlRMuaYf8XOS4kZy5rIpa5sqYJysHQR/8aUAqcHrM8m0ET2RPGJJuJJhcr6akrVmLgT0E06gkDD8XOST9CjgFaCnpoZhV9YD0+ESVWLwb0GWTdJSZzYh3HFEg6W4zuzHecUSBnwuQ1B3oCdwG3BKzahvwoZltjktgCcSTlcsm6WHyXyxOA+YkynxWMcP4C5SIw/gLOSdpwEozS6hWhaSqeScodRXDk5XLJmks0Imc/vezge8IJuH71syujldsFSVmGH8NoA/wBUHXVzeCpH1UvGKLF0mfAb2ABQTn4lBgIZACXGZm78YxvAoRM4y/QIk0jD9e/JqVi9UN6GtmGQCSHgemA/2AhLghNGYY/6tAr6wbYSV1JXhuYiJaA1xqZosAJB0C3A78meAG8kqfrMgZxn9F+O/z4b/n40PXK4QnKxerPlCHnDv0awMNzCxDUqI9E65j7BMbzGyhpM7xDCiOOmQlKgAzWyypk5l9KymecVUYM1sJIGmQmfWMWfX/JM0DbohPZInDk5WLdS8wX9JHBN09/YG7JNUG3o9nYHGwQNJTwAvh+/MIusES0aKwlf1S+P43wOLwaeyJdv1Gkvqa2Sfhm6PxqZYqhF+zcrlIag4cHr6dbWZr4hlPvEiqAVxGkLABpgGPm9mu+EUVH5JqApcTdAcDfAI8BuwCapnZz/GKraJJ6g2MJ7heJ2AzcEkiDrypaJ6sXC6SWpIznxUAZjYtfhE5Fz2SUgDMzB9qW0G8G9Blk/Q3gi6eReSeqydhkpWkl83s3MJGfyXiqK8CJuUEIJEm5ZR0vpm9EPNA26zlgD/ItiJ4snKxBhMMLEi0wRSxrgr/9Ye45vBJOYPBRpDzQFtXwbwb0GWTNBk4J5GuQRRG0qXANDP7Ot6xxJtPyplDUo1EvG4ZBd6ycrF2EIwGnELuuXr+FL+Q4qY18KSktgQtimnAdDObH8+g4sQn5cyxUNJPBPcfTgc+9utWFcNbVi6bpIsKWm5mz1Z0LFERjoQbBlwHtDSz5DiHVOEKmZwzoSbljCWpNXAM0Jfg4bZbzKxHfKOq/DxZuVzCD+fWZrY03rHEk6S/EnwY1QE+Bz4maFmtjWtgLq4ktSJIVAOA7sAmgtbV3XENLAF4snLZJJ0O/B2oZmbtJPUAbjezM+IcWoULn0qQTjB9+VRgRqIOPJHUFLgLaGFmvwoft3SUmT0d59AqnKRMYDZwV6I83Dkq/M5rF2sUwQ3BWwDC6zMJMzw5Vjhj8gnALGAQ8KWkj+MbVdw8A/wPaBG+XwZU+ocaF6In8BzwO0kzJD0XDsZx5cwHWLhYe80sLc/z3jILK1yZhQ+uzeru6QOsIrignogamdnL4WSMmFm6pIQcwm5mX0j6BviG4PfjfILfkYRrZVY0T1Yu1iJJvwOSJbUH/gR8GueY4uUeghGADxE8dirRnoEXa7ukhoQ3SUs6kpyHHScUSXOA6gR/F9OB/lkPuXXly69ZuWySagE3AScSPPfsf8Bov68ksYWTLz4MdCWYx6oxMMTMEu7BvpIam9n6eMeRiDxZOeeKJakK0JHgS8zSBG9pujjwZOWQ9CZFz4KacKMBHUg6q6j1ZvZqRcXinF+zchAMV3cur9OLWGcET7RwrkJ4y8q5GN7KdAXxVmb8ecvKudy8lekK4q3MOPOWlXPOucjzlpVzBQjvM7sbOASokbU8kSYcdAWTdCrQhdy/F7fHL6LE4MnK+XWagv0TuBV4ADgW+D0J9ngyv06Tn6QngFoEvxNPAUMIHsnlypl3AzokDShqvZlNrahYokLSXDPrLelLMzs0dlm8Y6sokv5ZxGozs0sqLJiIkLTAzLrF/FsHmGxmx8Q7tsrOW1YuIZNRCeyWlAR8LWkE8APBdCEJw8x+H+8YImhn+O8OSS2AjUDzOMaTMDxZuWx+nSaXqwi6e/4EjAaOAwqcnDIR+HWabG9JSgXuA+YRdJ8/Fd+QEsP/b+9uQqyu4jCOfx+IzCAJKiqLWgQFZi9OSilCVEiGtCoJWkS0CVyFu14psKBNBAYVQWkFkVCLKKggLOwFeplKS1A3GkTLsKGYSHla/M+9938d09Xcc+k8HxiY/7mb32bmd855/vecbAPGULkCY5DT3EnJaWw/UbWwiiQto9vymqtdSy3/ldPYbu5qDElLBveaSVpC17znW73rbJKaCozjtJba/oRuEnPE9pPApso1VSFptaR9wF66u6x+lNRMXnWCdbbvA363/RSwFriyck21fDX4xfbfto/2x2LxZBsw+prPaXpeBbbY3gMgaT3dG4LXVq2qjuZzGkkXAZcASyWtojvQF2AZ3aozFlmaVfQlpxk5PmhUALY/l3SsZkEVJaeB24H7gUuB53rjfwCP1CioNcmsYoHkNCDpeWAp8BbdP+d7gHngTQDbs/Wqm6zkNCOS7rL9Tu06WpRmFUOSVtNtdZ1Tho4CD9j+rl5VdUjafYqPbfvWiRVTmaRZ2zOnG2tB2Q58Glhu+w5JK4C1tnOt/SLLNmD0JacpbN9Su4baktOc1Gvl59HyfBB4G0izWmRpVtGXnKaQdCHwDG3PoJPTLHS+7V2SHgawfUzS8dpFtSDNKvo+k/Qy4znNp5JmoK2cBthB4zNo2zuBnclpxvwp6TzKWZqSbqLbLo9FlswqhpLT3KC3RAAAAgBJREFUjEj6xvYaSd/bXlXGfrB9fe3aJi05zUiZuG0HVgI/ARcAd9veW7WwBmRlFUPJacZkBj2SnKawPVsOfr6KLsM7YPufymU1Ic0qhpLTjNkKvAdcIekLygy6bknVJKcpJJ0FbAHW001k9kh6yfZ83cr+/3LcUvTtAD4Clpfng8BD1aqpqORzNwPrgAeBqxve6skqc+R1ugN9twMvlN/fqFpRI7Kyir7MoAtJm4EPbf8s6TFgRtK2xl4yGcgqc2Sl7RW9592S9lerpiFZWUVfZtAjj9ueK981u40un3mxck1VZJU5Zrb8XQAg6Ubg24r1NCMrq+jLDHpksKLcBLxi+wNJ22oWVEtymjE3AF9K+qU8XwYcKCf023ZzX6CflLy6HmMknUHedELS+3Snzm8AZuhOHv/a9nVVC6tA0i5gjnIuInAvcK7tzfWqqkPS5af63PaRSdXSmjSrGOrlNHODnAZoMqeRdDawEdhn+5Cki4FrbH9cubSJk7T/hJzmpGMRiymZVfQlpyls/2X7XduHyvNvLTaqIjlNVJdmFX0LchrgzIr1xHQY5DSHJR2muxl3jaR9klp90SImLC9YRN+v5WzADcCz5e6iTGhiY+0CIpJZxVBymoiYVmlWEREx9bLFExERUy/NKiIipl6aVURETL00q4iImHppVhERMfX+BVxGmM+GDa4XAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4P9dzWgZ2FD"
      },
      "source": [
        "**12. Correlation Coefficient**\n",
        "\n",
        "  - Covariance find the direction of relationship but there is no range set for strength of relationship\n",
        "  - Correlation find the exact value of strength in the relationship and the direction as well\n",
        "  - *Range is from -1 to +1*\n",
        "  - *+ve value suggest* they are positively correlated\n",
        "  - *-ve value suggest* they are negatively correlated\n",
        "\n",
        "**2 Methods are use to calculate Correlation Coefficient** \n",
        "  1. *Pearson correlation coefficient:* Assume data follows Normal Distribution and works well with linear data\n",
        "  2. *Spearman correlation coefficient:* Don't assume any distribution. Works well with both linear & non-linear\n",
        "\n",
        "By default while calculating, pearson method is used"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mu4WyE1iDTh8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "279418d7-f013-4117-c9d7-65782e1423d1"
      },
      "source": [
        "# Pearson Correlation\n",
        "sns.heatmap(iris_pd.corr(),annot=True,cmap = 'viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe29a9be10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uREepFLDbdr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "e2d70628-0806-4fa2-cb60-929254387129"
      },
      "source": [
        "# Now purposefully we are mentioning 'Pearson Method'\n",
        "sns.heatmap(iris_pd.corr(method= 'pearson'),annot= True, cmap = 'YlGnBu')\n",
        "  \n",
        "# Inference : Results are same"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe299e2f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_j2gd_UADgjM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "3ef35316-a1a2-4891-ce30-56e6e80db8a2"
      },
      "source": [
        "# Now with Spearman Correlation Coefficient \n",
        "\n",
        "sns.heatmap(iris_pd.corr(method = 'spearman'),annot= True, cmap = 'Blues')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe29b24e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFf-_L9KM0XC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "ae93a5f6-8d7e-492f-a333-59ca332542a0"
      },
      "source": [
        "# Correlation Plot for Boston Dataset\n",
        "sns.heatmap(boston_pd.corr(method='spearman'),annot = True,cmap = 'Blues')\n",
        "\n",
        "# Problem - Increase the size of Plot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe298fd780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nz3KF-XdLypc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "8335ca52-5c12-4520-e7f3-d015bb7bdc97"
      },
      "source": [
        "# Increasing the size of the plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(11,11)) # Sample figsize in inches, change the number accordingly\n",
        "sns.heatmap(boston_pd.corr(method = 'spearman'), annot= True, cmap = 'Blues',ax = ax)\n",
        "\n",
        "# MEDV and LSTAT is having -0.85, we might need to remove one variable"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe297e1f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0aTsCe9ubLQo"
      },
      "source": [
        "**13. SKEWNESS**\n",
        "\n",
        "  - In the curve of a distribution, the data on the right side of the curve may taper differently from the data on the left side. These taperings are known as \"tails.\" \n",
        "  - *Negative skew refers* to a longer or fatter tail on the left side of the distribution, while *positive skew refers* to a longer or fatter tail on the right.\n",
        "  - The mean of positively skewed data will be greater than the median. \n",
        "  - In a distribution that is negatively skewed, the exact opposite is the case: the mean of negatively skewed data will be less than the median. \n",
        "  - If the data graphs symmetrically, the distribution has zero skewness, regardless of how long or fat the tails are.\n",
        "  - If *Skewness = 0,* data is perfectly symmetrical\n",
        "  - If *Skewness is less than -1 or greater +1,* distribution is highly skewed\n",
        "  - If *Skewness is between -1 and -0.5 or between 0.5 and 1,* distribution is moderately skewed\n",
        "  - If *Skewness is between -0.5 and 0.5* then it is approximately symmetric  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9_Da8N9DkqI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "919d4c5e-7ff7-41f8-c09f-ffb0006ce245"
      },
      "source": [
        "# 13) Skewness\n",
        "iris_pd.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    0.314911\n",
              "sepal width (cm)     0.318966\n",
              "petal length (cm)   -0.274884\n",
              "petal width (cm)    -0.102967\n",
              "Class                0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srbdjPsDQTIc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "1d6bafe5-296a-428e-a8ff-97362e2b7039"
      },
      "source": [
        "# For Boston dataset\n",
        "boston_pd.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       5.223149\n",
              "ZN         2.225666\n",
              "INDUS      0.295022\n",
              "CHAS       3.405904\n",
              "NOX        0.729308\n",
              "RM         0.403612\n",
              "AGE       -0.598963\n",
              "DIS        1.011781\n",
              "RAD        1.004815\n",
              "TAX        0.669956\n",
              "PTRATIO   -0.802325\n",
              "B         -2.890374\n",
              "LSTAT      0.906460\n",
              "MEDV       1.108098\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2KRIG4loaxr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e28cede0-23cf-47af-8c6a-be07c30f45ef"
      },
      "source": [
        "# Randomly take one variable\n",
        "print(\"Skewness of 'B' Column is\",boston_pd['B'].skew()) \n",
        "\n",
        "# Inference : -2.89 which is highly skewed. Use Transformation to remove the skewness"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skewness of 'B' Column is -2.8903737121414492\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tX7MPUfqo00r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d0524595-b49c-4633-aad0-02a4c583c670"
      },
      "source": [
        "# Plot the \"B\" variable\n",
        "sns.distplot(boston_pd['B'])\n",
        "\n",
        "# Inference - Left Skewed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe27c2b400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El2-cqsuQZkP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "caffd2d6-265e-4952-9a7a-57608b890182"
      },
      "source": [
        "# Check for another one variable\n",
        "print(\"Skewness of 'CRIM' Column is \",boston_pd['CRIM'].skew())\n",
        "\n",
        "# Inference : 5.22 which is highly skewed. \n",
        "# Use transformation - 1. Log  2. Square Root  3. Box Cox  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skewness of 'CRIM' Column is  5.223148798243851\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6q1su3yYQkrm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "72bc0882-1a08-4522-de1c-bf45c116c3a6"
      },
      "source": [
        "# Plot the Skewness\n",
        "sns.distplot(boston_pd['CRIM'])\n",
        "\n",
        "# Right Skewed "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe27c2b748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlpjIiFHRPOo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "2f951f52-15e7-41c5-a7f0-f16204c6be3f"
      },
      "source": [
        "# Log Transformation\n",
        "import numpy as np\n",
        "CRIM_Log = np.log(boston_pd['CRIM'])\n",
        "print('After LOG TRANSFORMATION, Skewness is :',CRIM_Log.skew())\n",
        "sns.distplot(CRIM_Log)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After LOG TRANSFORMATION, Skewness is : 0.4059344988496048\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe27a93400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWfAmXbCDv9_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "c307504c-9b70-4440-d81c-adc9cd1fb50f"
      },
      "source": [
        "# Square Root Transformation\n",
        "CRIM_sqrt = np.sqrt(boston_pd['CRIM'])\n",
        "print('After Square Root Transformation, Skewness is ',CRIM_sqrt.skew())\n",
        "sns.distplot(CRIM_sqrt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After Square Root Transformation, Skewness is  2.024382103123676\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe279fe278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXOV3ybnSycn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "c51139a1-c0b4-478c-e7ab-fa0f0ad7666b"
      },
      "source": [
        "# Box Cox Transformation\n",
        "from scipy import stats\n",
        "CRIM_BoxCox = stats.boxcox(boston_pd['CRIM'])[0]\n",
        "Variable = pd.Series(CRIM_BoxCox)\n",
        "\n",
        "print(\"After BoxCox Transformation, Skewness is \",Variable.skew())\n",
        "sns.distplot(Variable)\n",
        "\n",
        "# Inference :\n",
        "# So the best one is BoxCox Transformation because of LOWEST SKEWNESS out of all three"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After BoxCox Transformation, Skewness is  0.09364917268984103\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe2989e0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWsRTY1wpX0R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "ac2f91fe-c1b4-4cc3-a597-39e456f0b9d4"
      },
      "source": [
        "# All the Skewness Value Calculated\n",
        "print(\"Comparing all Skewness values\")\n",
        "pd.DataFrame([boston_pd['CRIM'].skew(),CRIM_Log.skew(),CRIM_sqrt.skew(),Variable.skew()],\n",
        "             index=['Normal','Log','Square Root','Box Cox'],\n",
        "             columns = [\"Skewness\"])\n",
        "\n",
        "# Inference : Best transformations can be Log or Box Cox Transformation "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Comparing all Skewness values\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>Skewness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Normal</th>\n",
              "      <td>5.223149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Log</th>\n",
              "      <td>0.405934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Square Root</th>\n",
              "      <td>2.024382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Box Cox</th>\n",
              "      <td>0.093649</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Skewness\n",
              "Normal       5.223149\n",
              "Log          0.405934\n",
              "Square Root  2.024382\n",
              "Box Cox      0.093649"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYOTI1xedr3u"
      },
      "source": [
        "**14. KURTOSIS**\n",
        "\n",
        "- Kurtosis is a statistical measure used to describe the degree to which scores cluster in the tails or the peak of a frequency distribution. \n",
        "- The peak is the tallest part of the distribution, and the tails are the ends of the distribution.\n",
        "- There are three types of kurtosis: mesokurtic, leptokurtic, and platykurtic.\n",
        "    1. *Mesokurtic:* Distributions that are moderate in breadth and curves with a medium peaked height.\n",
        "    2. *Leptokurtic:* More values in the distribution tails and more values close to the mean (i.e. sharply peaked with heavy tails)\n",
        "    3. *Platykurtic:* Fewer values in the tails and fewer values close to the mean (i.e. the curve has a flat peak and has more dispersed scores with lighter tails).\n",
        "\n",
        "**What kurtosis is normal?**\n",
        "\n",
        "- When kurtosis is equal to 3, the distribution is mesokurtic.\n",
        "- This means the kurtosis is the same as the normal distribution, it is mesokurtic (medium peak).\n",
        "- The kurtosis of a mesokurtic distribution is neither high nor low, rather it is considered to be a baseline for the two other classifications.\n",
        "\n",
        "**Valur of Kurtosis Suggests**\n",
        "- Positive Value means lots of data on tails\n",
        "- Negative Value means little data on tails\n",
        "- Standard Normal Distribution has a Kurtosis of 3\n",
        "  1. Kurtosis < 3 - Platykurtic\n",
        "  2. Kurtosis = 3 - Mesokurtic\n",
        "  3. Kurtosis > 3 - Lepokurtic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuNxztLeDzCr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "8a6e6e60-fd2e-433d-e45a-04a429d64afb"
      },
      "source": [
        "# 14) Kurtosis\n",
        "\n",
        "print(\"Before Transformation, Kurtosis is : \",boston_pd['CRIM'].kurtosis())\n",
        "print(\"After Log Transformation, Kurtosis : \",CRIM_Log.kurtosis())\n",
        "print(\"After Square Root Transformation, Kurtosis : \",CRIM_sqrt.kurtosis())\n",
        "print(\"After Box Cox Transformation, Kurtosis : \",Variable.kurtosis())\n",
        "\n",
        "# So the best one is Log Transformation or Box Cox Transformation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Transformation, Kurtosis is :  37.13050912952203\n",
            "After Log Transformation, Kurtosis :  -1.0096975215321273\n",
            "After Square Root Transformation, Kurtosis :  5.050975884674759\n",
            "After Box Cox Transformation, Kurtosis :  -1.06942877456582\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2u2wzX92pVT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "5d1660c8-3864-4ded-8332-fae196607ef7"
      },
      "source": [
        "# Revision\n",
        "\n",
        "print(\"NUMERICAL MEASURES FOR IRIS DATASET\")\n",
        "pd.DataFrame([iris_pd.mean(),iris_pd.median(),iris_pd.var(),iris_pd.std(),\n",
        "              iris_pd.skew(),iris_pd.kurtosis()],\n",
        "             index = [\"MEAN\",\"MEDIAN\",\"VARIANCE\",\"STANDARD DEVIATION\",\"SKEWNESS\",\"KURTOSIS\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NUMERICAL MEASURES FOR IRIS DATASET\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MEAN</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEDIAN</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VARIANCE</th>\n",
              "      <td>0.685694</td>\n",
              "      <td>0.189979</td>\n",
              "      <td>3.116278</td>\n",
              "      <td>0.581006</td>\n",
              "      <td>0.671141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STANDARD DEVIATION</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "      <td>0.819232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SKEWNESS</th>\n",
              "      <td>0.314911</td>\n",
              "      <td>0.318966</td>\n",
              "      <td>-0.274884</td>\n",
              "      <td>-0.102967</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KURTOSIS</th>\n",
              "      <td>-0.552064</td>\n",
              "      <td>0.228249</td>\n",
              "      <td>-1.402103</td>\n",
              "      <td>-1.340604</td>\n",
              "      <td>-1.510135</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    sepal length (cm)  ...     Class\n",
              "MEAN                         5.843333  ...  1.000000\n",
              "MEDIAN                       5.800000  ...  1.000000\n",
              "VARIANCE                     0.685694  ...  0.671141\n",
              "STANDARD DEVIATION           0.828066  ...  0.819232\n",
              "SKEWNESS                     0.314911  ...  0.000000\n",
              "KURTOSIS                    -0.552064  ... -1.510135\n",
              "\n",
              "[6 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    }
  ]
}