Notebooks/01a_Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "764e3a9c",
   "metadata": {},
   "source": [
    "## <span style=\"color : green\">Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23402566",
   "metadata": {},
   "source": [
    "# <center> Table of Contents </center>\n",
    "\n",
    "1. Find categorical and numerical using numpy\n",
    "2. Five number summary\n",
    "1. box plot\n",
    "1. Outliers with z-score\n",
    "1. Correlation between independent variables with dependent variable\n",
    "1. Skewness and its plot\n",
    "1. Univariate, bivariate and multivariate plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2287f036",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a502265",
   "metadata": {},
   "source": [
    "# Find categorical and numerical using numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4a3bc37b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "Id                                                                       \n",
       "1             5.1           3.5            1.4           0.2  Iris-setosa\n",
       "2             4.9           3.0            1.4           0.2  Iris-setosa\n",
       "3             4.7           3.2            1.3           0.2  Iris-setosa\n",
       "4             4.6           3.1            1.5           0.2  Iris-setosa\n",
       "5             5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../Datasets/Iris.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0e18e9a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Id                                                          \n",
       "1             5.1           3.5            1.4           0.2\n",
       "2             4.9           3.0            1.4           0.2\n",
       "3             4.7           3.2            1.3           0.2\n",
       "4             4.6           3.1            1.5           0.2\n",
       "5             5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_data = df.select_dtypes(include=np.number)\n",
    "numeric_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6ce7394f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 150 entries, 1 to 150\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      " 4   Species        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 7.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "323ab66c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        Species\n",
       "Id             \n",
       "1   Iris-setosa\n",
       "2   Iris-setosa\n",
       "3   Iris-setosa\n",
       "4   Iris-setosa\n",
       "5   Iris-setosa"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = df.select_dtypes(include=np.object)\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9070d4",
   "metadata": {},
   "source": [
    "# Five number summary\n",
    "\n",
    "\n",
    "| 1st Quartile | Medium | 3rd Quartile | Minimum | Maximum |\n",
    "| ------------ | ------ |  --| ------ |---- |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1e5f6e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st Quartile - 1.7\n",
      "2nd Quartile - 3.2\n",
      "3rd Quartile - 5.1\n"
     ]
    }
   ],
   "source": [
    "_1quart =  np.quantile(numeric_data, 0.25)\n",
    "median = np.quantile(numeric_data, 0.5)\n",
    "_3quart =  np.quantile(numeric_data, 0.75)\n",
    "\n",
    "print(f'1st Quartile - {_1quart}')\n",
    "print(f'2nd Quartile - {median}')\n",
    "print(f'3rd Quartile - {_3quart}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c987d8fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    4.3\n",
       "SepalWidthCm     2.0\n",
       "PetalLengthCm    1.0\n",
       "PetalWidthCm     0.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(numeric_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1620707c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    7.9\n",
       "SepalWidthCm     4.4\n",
       "PetalLengthCm    6.9\n",
       "PetalWidthCm     2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(numeric_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbc7dcf",
   "metadata": {},
   "source": [
    "# box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9d1339de",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hVbW+qjZU1cVV9UeztJmqqmur6vzh9vKdUy4AACxsi+fRZnOSP2mtfbGq9ktyXlV9rLX25RntPt1ae3L/EgEAYNcx5wx2a+3K1toXh/s/SLIhyd12dmEAALArms8M9k2qammSByf5/Cy7H1lVFyS5IsmLW2sXz3L8sUmOTZIlS5Zkenr61tbLArRp0yZfS1iAXJvQ14oVKyZdwk3Wr18/6RLYhmqtza9h1b5JPpVkTWvtfTP2/UySn7TWNlXVkUle01q7z7b6W758eTv33HO3s2wWkunp6UxNTU26DGAG1yYsPEtXnZGNJxw16TLooKrOa60tn23fvN5FpKr2TvLeJG+fGa6TpLX2/dbapuH+mUn2rqqDdqBmAADYJc3nXUQqydokG1prf7uVNncZ2qWqHj70e3XPQgEAYFcwnzXYj0ryrCQXVtX5w7aXJrlHkrTWTk5ydJLnVdXmJNcneXqb79oTAADYjcwZsFtrZyepOdqclOSkXkUBAMCuyic5AgBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHiyddAADAzvbAP/9orr3+hkmXkSRZuuqMSZeQJNn/jnvnglc8ftJl7JYEbABgt3ft9Tdk4wlHTbqMTE9PZ2pqatJlJFk4QX93ZIkIAAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHcwbsqjqkqtZX1Yaquriq/miWNlVVr62qS6rqS1X1kJ1TLgAALGyL59Fmc5I/aa19sar2S3JeVX2stfblsTZPSnKf4faIJG8c/gUAgD3KnDPYrbUrW2tfHO7/IMmGJHeb0eypSd7aRj6X5ICqumv3agEAYIGbzwz2TapqaZIHJ/n8jF13S3LZ2OPLh21Xzjj+2CTHJsmSJUsyPT1966plQdq0aZOvJSxArk34qf2WrcoDTl816TJGTp90ASP7LUump/eZdBm7pXkH7KraN8l7k7yotfb9mbtnOaTdYkNrpyQ5JUmWL1/epqam5l8pC9b09HR8LWHhcW3CT/1g1QnZeMJRky5jQV2XS1edkannTE26jN3SvN5FpKr2zihcv7219r5Zmlye5JCxx3dPcsWOlwcAALuW+byLSCVZm2RDa+1vt9LsA0mePbybyK8kuba1duVW2gIAwG5rPktEHpXkWUkurKrzh20vTXKPJGmtnZzkzCRHJrkkyQ+TPLd7pQAAsAuYM2C31s7O7Gusx9u0JMf3KgoAAHZVPskRAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADpaPOkCALilqpp0CTdprU26BOhi6aozJl3CyIcXRh3733HvSZew2xKwARagHqF26aozsvGEozpUA7u+hXItuC73DJaIAABARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0NGfArqpTq+rbVXXRVvZPVdW1VXX+cHt5/zIBAGDXsHgebU5LclKSt26jzadba0/uUhEAAOzC5pzBbq2dleSa26AWAADY5c1nBns+HllVFyS5IsmLW2sXz9aoqo5NcmySLFmyJNPT052GZ5I2bdrkawmD4z9xXa67YdJV/NTSVWdMuoTss3fy+iP2mXQZsGD4mbn76xGwv5jk0Nbapqo6Msn7k9xntoattVOSnJIky5cvb1NTUx2GZ9Kmp6fjawkj1334jGw84ahJl5Fk4VybS1edsSDqgAXhw66HPcEOv4tIa+37rbVNw/0zk+xdVQftcGUAALAL2uGAXVV3qaoa7j986PPqHe0XAAB2RXMuEamqdUmmkhxUVZcneUWSvZOktXZykqOTPK+qNie5PsnTW2ttp1UMAAAL2JwBu7W2co79J2X0Nn4AALDH80mOAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB3N+VHpAAAkVdWnnxN3vI/W2o53wk5jBhsAYB5aazt8W79+fZd+WNgEbAAA6EjABgCAjgRsAADoSMAGAICOBGwAAOhIwAYAgI4EbAAA6EjABgCAjgRsAADoSMAGAICOBGwAAOhIwAYAgI4EbAAA6EjABgCAjgRsAADoaPGkCwDYney3bFUecPqqSZfxU6dPuoBkv2VJctSkywC4zQjYAB39YMMJ2XjCwgiT09PTmZqamnQZWbrqjEmXAHCbskQEAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKCjxZMuAGB3s3TVGZMu4ac+PPla9r/j3pMuAeA2JWADdLTxhKMmXcJNlq46Y0HVA7CnsEQEAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADqaM2BX1alV9e2qumgr+6uqXltVl1TVl6rqIf3LBACAXcN8ZrBPS/LEbex/UpL7DLdjk7xxx8sCAIBd05wBu7V2VpJrttHkqUne2kY+l+SAqrprrwIBAGBX0mMN9t2SXDb2+PJhGwAA7HEWd+ijZtnWZm1YdWxGy0iyZMmSTE9PdxieSdu0aZOvJXS2YsWKLv3UiTvex/r163e8EyCJn5l7ih4B+/Ikh4w9vnuSK2Zr2Fo7JckpSbJ8+fI2NTXVYXgmbXp6Or6W0Fdrs85T3CquTVh4XJd7hh5LRD6Q5NnDu4n8SpJrW2tXdugXAAB2OXPOYFfVuiRTSQ6qqsuTvCLJ3knSWjs5yZlJjkxySZIfJnnuzioWAAAWujkDdmtt5Rz7W5Lju1UEAAC7MJ/kCAAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdhst3Xr1uWwww7LEUcckcMOOyzr1q2bdEkAABO3eNIFsGtat25dVq9enbVr1+bGG2/MokWLcswxxyRJVq5cOeHqAAAmxww222XNmjVZu3ZtVqxYkcWLF2fFihVZu3Zt1qxZM+nSAAAmSsBmu2zYsCGHH374zbYdfvjh2bBhw4QqAgBYGARstsuyZcty9tln32zb2WefnWXLlk2oIgCAhUHAZrusXr06xxxzTNavX5/Nmzdn/fr1OeaYY7J69epJlwYAMFFe5Mh22fJCxhe+8IXZsGFDli1bljVr1niBIwCwxxOw2W4rV67MypUrMz09nampqUmXAwCwIFgiAgAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQ0bwCdlU9sar+o6ouqapVs+yfqqprq+r84fby/qUCAMDCN2fArqpFSV6f5ElJ7p9kZVXdf5amn26tPWi4vapznSxA69aty2GHHZYjjjgihx12WNatWzfpkgAAJm4+H5X+8CSXtNa+niRV9c4kT03y5Z1ZGAvbunXrsnr16qxduzY33nhjFi1alGOOOSbJ6CPUAQD2VPNZInK3JJeNPb582DbTI6vqgqr616r6pS7VsWCtWbMma9euzYoVK7J48eKsWLEia9euzZo1ayZdGgDARM1nBrtm2dZmPP5ikkNba5uq6sgk709yn1t0VHVskmOTZMmSJZmenr5VxbJwbNiwITfeeGOmp6ezadOmTE9P58Ybb8yGDRt8XWGB2HJtAguH63LPMJ+AfXmSQ8Ye3z3JFeMNWmvfH7t/ZlW9oaoOaq1dNaPdKUlOSZLly5e3qamp7a2bCVu2bFkWLVqUqampTE9PZ2pqKuvXr8+yZcvi6woLw5ZrE1g4XJd7hvksETknyX2q6heq6nZJnp7kA+MNquouVVXD/YcP/V7du1gWjtWrV+eYY47J+vXrs3nz5qxfvz7HHHNMVq9ePenSAAAmas4Z7Nba5qp6QZKPJFmU5NTW2sVVddyw/+QkRyd5XlVtTnJ9kqe31mYuI2E3suWFjC984QuzYcOGLFu2LGvWrPECRwBgjzefJSJprZ2Z5MwZ204eu39SkpP6lsZCt3LlyqxcudKfuwAAxvgkRwAA6EjABgCAjgRsAADoSMAGAICOBGwAAOhIwAYAgI4EbAAA6EjABgCAjgRsAADoSMAGAICOBGwAAOho8aQLYLKqatIl3ExrbdIlAADsEDPYe7jW2g7fDv2zD3XpR7gGAHYHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBARwI2AAB0JGADAEBHAjYAAHQkYAMAQEeLJ10A2++Bf/7RXHv9DZMuI0mydNUZky4hSbL/HffOBa94/KTLAAD2YAL2Luza62/IxhOOmnQZmZ6eztTU1KTLSLJwgj4AsOeyRAQAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOlo86QLYfvstW5UHnL5q0mWMnD7pAkb2W5YkR026DABgDyZg78IufM6Fky4hSbJ01RnZeIJQCwCQWCICAABdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANDR4kkXwGRVVZ9+TuzSTVprfToCAJgQM9h7uNbaDt/Wr1/fpR/hGgDYHQjYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J2AAA0NG8AnZVPbGq/qOqLqmqVbPsr6p67bD/S1X1kP6lAgDAwjdnwK6qRUlen+RJSe6fZGVV3X9Gsycluc9wOzbJGzvXCQAAu4T5zGA/PMklrbWvt9b+vyTvTPLUGW2emuStbeRzSQ6oqrt2rhUAABa8+QTsuyW5bOzx5cO2W9sGAAB2e4vn0aZm2da2o02q6tiMlpBkyZIlmZ6ensfwLHSbNm3ytYQFyLUJC4/rcs8wn4B9eZJDxh7fPckV29EmrbVTkpySJMuXL29TU1O3plYWqOnp6fhawsLj2oSFx3W5Z5jPEpFzktynqn6hqm6X5OlJPjCjzQeSPHt4N5FfSXJta+3KzrUCAMCCN+cMdmttc1W9IMlHkixKcmpr7eKqOm7Yf3KSM5McmeSSJD9M8ty5+j3vvPOuqqpLd6R4FoyDklw16SKAW3BtwsLjutx9HLq1HdXaLZZKw61SVee21pZPug7g5lybsPC4LvcMPskRAAA6ErABAKAjAZseTpl0AcCsXJuw8Lgu9wDWYAMAQEdmsAEAoCMBew9QVZu2se+zO3Hcl+6svmGhm9R1Nx9VdXBVvWc7j52uKu+AwG5hZ1+nVfWqqnrcrTzmV6tq1Rxttvsa5rZhicgeoKo2tdb2nbFtUWvtxtt6XNhTTOq6mzHe4tba5s59Tid5cWvt3Hm2v03PGW6NCf58dF3s5sxg70Gqaqqq1lfVO5JcOGzbNPx716o6q6rOr6qLqurRsxz/S1X1haHNl6rqPsP2Z45tf1NVLaqqE5Lccdj29qHdHw99X1RVLxq27VNVZ1TVBcP2pw3bX15V5wzbTqmquk2eJOhsR667qtq/qjZW1V7D4ztV1WVVtXdV3auqPlxV51XVp6vqfkOb06rqb6tqfZITq+qxQ//nV9W/V9V+VbW0qi4a2i+qqr+uqguH6/qFw/YjhvYXVtWpVXX7Wc5t5bD/oqo6cWz7pmHm7vNJHrlTnljoaCdep6dV1dHD9o3Dz7azk/xWVR1ZVV+pqrOr6rVV9aGh3e9W1UnD/dOGfZ+tqq+P9TWfa9jP0Qma85Mc2e08PMlhrbVvzNj+O0k+0lpbU1WLktxplmOPS/Ka1trbq+p2SRZV1bIkT0vyqNbaDVX1hiTPaK2tqqoXtNYelCRV9dCMPuHzEUkqyeer6lNJ7pnkitbaUUO7/YexTmqtvWrY9o9Jnpzkg72eBLiNbdd111q7tqouSPLYJOuTPGVof0NVnZLkuNba16rqEUnekOR/DIf+YpLHtdZurKoPJjm+tfaZqto3yY9m1HBskl9I8uDhk3sPrKo7JDktyRGtta9W1VuTPC/J3285qKoOTnJikocm+W6Sj1bVr7XW3p9knyQXtdZevp3PF0zCzrhOZ47xo9ba4cM19rUkj2mtfaOq1m2jrrsmOTzJ/ZJ8IMnMpSG3uIaH7X6OTpAZ7D3PF2b55pEk5yR5blW9MskDWms/mKXNvyV5aVX9WZJDW2vXJzkiox+w51TV+cPje85y7OFJ/rm1dl1rbVOS9yV5dEYzBY+rqhOr6tGttWuH9iuq6vNVdWFGoeGXtveEYQHYkevuXRn9EpskT0/yriEo/z9J3j1cd2/K6IfwFu8e+/PzZ5L8bVX9YZIDZlky8rgkJ2/Z3lq7Jsl9k3yjtfbVoc3pSR4z47iHJZlurX1nOPbtY21uTPLeWc4FFrKu1+lWxtiy/X5Jvj423rYC9vtbaz9prX05yZJZ9s92DSd+jk6UgL3nuW62ja21szL64fjfSf6xqp5dVb8+9qfl5a21dyT51STXJ/lIVf2PjGajT2+tPWi43be19spZhpj1T1PDD/CHZhS0/9/hT1p3yGg27ujW2gOSvDnJHXborGGytvu6y2jG6knDrNRDk3wyo+/d3xu77h7UWls223ittROS/H6SOyb5XA1LScZUkpkvxpnPn5K31eZH1peyC+p9nW5rjFuzXOPHY/dnO+4W17Cfo5MnYJMkqapDk3y7tfbmJGuTPKS19s9jP7zPrap7ZvQb92sz+mbyy0k+keToqrrz0M+BQ19JckNV7T3cPyvJrw1r0/ZJ8utJPj38mfmHrbW3JfnrJA/JT78JXDXM1B29058AmID5XHfDX3y+kOQ1ST7UWruxtfb9JN+oqt8a+qmqeuBWxrhXa+3C1tqJSc7NaOZs3EeTHFdVi4f2Byb5SpKlVXXvoc2zknxqxnGfT/LYqjpo+LP5ylnawC5ve6/TObr9SpJ7VtXS4fHTttF2LrNdw36OTpg12GwxleRPq+qGJJuSPHuWNk9L8syhzTeTvKq1dk1VvSyj9Zd7JbkhyfFJLs3o06q+VFVfbK09o6pOy+gbUJL8Q2vt36vqCUn+qqp+Mhz7vNba96rqzRnNam/M6M9zsDuaytzXXTL6s/K7h/ZbPCPJG4frb+8k70xywSzHvqiqVmS0bOPLSf41N19O8g8Zrdn+0lDHm1trJ1XVczNagrI4o2vw5PFOW2tXVtVLMlpzWknObK39y3xPHHYhU9n+63RWrbXrq+r5ST5cVVflpz8bt8fWrmE/RyfI2/QBANzGqmrf1tqm4d09Xp/ka621v5t0XfRhiQgAwG3vfw0vUr44yf4ZvViZ3YQZbAAA6MgMNgAAdCRgAwBARwI2AAB0JGADLDBVtbqqLq6qLw0fZPGIjn2fWVUH9OoPgFvyPtgAC0hVPTLJkzP6MIsfV9VBSW7Xq//W2pG9+gJgdmawARaWuya5qrX24yRprV3VWruiqjZW1YlV9YXhdu8kqaqfr6r3VtU5w+1Rw/Z9q+otVXXhMBP+m8P2jUNoT1U9c+jr/Kp6U1UtGm6nVdVFw7H/e0LPA8AuS8AGWFg+muSQqvpqVb2hqh47tu/7rbWHJzkpyd8P216T5O9aaw9L8psZfapbkvzfJNe21h7QWvvlJJ8cH6SqlmX06ayPaq09KKNPenxGkgcluVtr7bDW2gOSvGUnnCPAbs0SEYAFZPhkt4cmeXSSFUneVVWrht3rxv7d8olvj0ty/9GHwSVJfqaq9hu2P32s3+/OGOqIJA9Ncs5w7B2TfDvJB5Pcs6pel+SMjAI/ALeCgA2wwLTWbkwynWS6qi5M8pwtu8abDf/uleSRrbXrx/sYPn55W58kVklOb6295BY7qh6Y5AlJjk/y20l+bztOA2CPZYkIwAJSVfetqvuMbXpQkkuH+08b+/ffhvsfTfKCseMftJXtPztjqE8kObqq7jzsP7CqDh3WZ+/VWntvRstMHrKj5wSwpzGDDbCw7JvkdcNb6W1OckmSYzN6Z5HbV9XnM5ocWTm0/8Mkr6+qL2X0Pf2sJMclefWw/aKM1lf/eZL3bRmktfblqnpZko9W1V5Jbshoxvr6JG8ZtiXJLWa4Adi2am1bf0EEYCGoqo1JlrfWrpp0LQBsmyUiAADQkRlsAADoyAw2AAB0JGADAEBHAjYAAHQkYAMAQEcCNgAAdCRgAwBAR/8/uWyF5vYmdRwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = [ x for x in df.columns if x != 'Species']\n",
    "\n",
    "for attr in features :\n",
    "    df.boxplot(column=attr, by='Species', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "291e54fb",
   "metadata": {},
   "source": [
    "# Outliers with z-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8545a127",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "\n",
    "z_scores = numeric_data.apply(zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "75fd88a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "threshold = 3  # Denotes the 3 to -3 \n",
    "\n",
    "df_no_outliers = numeric_data[((z_scores < threshold) & (z_scores > - threshold)).all(axis=1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6e884815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Dataframe:\n",
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Original Dataframe:\")\n",
    "print(numeric_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1009ffcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Id                                                          \n",
       "1             5.1           3.5            1.4           0.2\n",
       "2             4.9           3.0            1.4           0.2\n",
       "3             4.7           3.2            1.3           0.2\n",
       "4             4.6           3.1            1.5           0.2\n",
       "5             5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ee414368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataframe with Outliers Filtered:\n",
      "(149, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nDataframe with Outliers Filtered:\")\n",
    "print(df_no_outliers.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fb7b8006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Id                                                          \n",
       "1             5.1           3.5            1.4           0.2\n",
       "2             4.9           3.0            1.4           0.2\n",
       "3             4.7           3.2            1.3           0.2\n",
       "4             4.6           3.1            1.5           0.2\n",
       "5             5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_outliers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e1fd00",
   "metadata": {},
   "source": [
    "# Correlation between independent variables with dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ec250f0c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame._add_numeric_operations.<locals>.max of SepalLengthCm    0.782561\n",
       "SepalWidthCm    -0.419446\n",
       "PetalLengthCm    0.949043\n",
       "PetalWidthCm     0.956464\n",
       "Species          1.000000\n",
       "Name: Species, dtype: float64>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "\n",
    "encoder = LabelEncoder()\n",
    "df['Species'] = encoder.fit_transform(df['Species'])\n",
    "\n",
    "corr = df.corr(method='pearson')\n",
    "corr['Species'].max"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8654b0f",
   "metadata": {},
   "source": [
    "# Skewness and its plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a0a734ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right-skewed features: []\n",
      "Left-skewed features: []\n",
      "No skew features: [{'SepalLengthCm': 0.3149109566369728}, {'SepalWidthCm': 0.3340526621720866}, {'PetalLengthCm': -0.27446425247378287}, {'PetalWidthCm': -0.10499656214412734}, {'Species': 0.0}]\n"
     ]
    }
   ],
   "source": [
    "skewness = df.skew()\n",
    "\n",
    "right_skewed = []\n",
    "left_skewed = []\n",
    "no_skew = []\n",
    "\n",
    "for feature, skew in skewness.iteritems():\n",
    "    if skew > 0.5:\n",
    "        right_skewed.append(dict([feature, skew]))\n",
    "    elif skew < -0.5:\n",
    "        left_skewed.append(dict(zip([feature], [skew])))\n",
    "    else:\n",
    "        no_skew.append(dict(zip([feature], [skew])))\n",
    "print(\"Right-skewed features:\", right_skewed)\n",
    "print(\"Left-skewed features:\", left_skewed)\n",
    "print(\"No skew features:\", no_skew)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f21f2d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAU/0lEQVR4nO3df7DldX3f8edLdi1WjS71QjbLEhKkjpROFntddUkclNBZsSOQatBpCKamS1rJQJPaUDOTYJ1pncRf06ZjXJVxk1gLRi2EIIYgaHQRvCDC0sVgHCI/dnYvOAkwOhrg3T/Od4fL9f447N7v+Zx77vMxc+Z8z+d8v+fz/sx39nW/+z2f7/ekqpAkjd6zWhcgSWuVASxJjRjAktSIASxJjRjAktTIutYFDGP79u117bXXti5Dkg5VFmpcFUfADz30UOsSJGnFrYoAlqRJZABLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1MtEBvGnzcSRp8ti0+bjWw5c05lbF/YAP1YP338e5H97dpO/LL9jWpF9Jq8dEHwFL0jgzgCWpkd4DOMkRSb6e5Oru9VFJrktyT/e8oe8aJGkcjeII+CJg75zXlwDXV9WJwPXda0lac3oN4CTHAq8HPjqn+SxgV7e8Czi7zxokaVz1fQT8QeA/A0/OaTumqvYBdM9HL7Rhkh1JZpLMzM7O9lymJI1ebwGc5F8BB6rq1kPZvqp2VtV0VU1PTU2tcHWS1F6f84BPBd6Q5EzgSODHkvwJsD/Jxqral2QjcKDHGiRpbPV2BFxV/6Wqjq2q44E3A1+oql8CrgLO71Y7H7iyrxokaZy1mAf8HuCMJPcAZ3SvJWnNGcmlyFV1I3Bjt/wwcPoo+pWkceaVcJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUSG8BnOTIJLck+UaSu5K8q2u/NMkDSW7vHmf2VYMkjbN1PX72D4DXVtVjSdYDX07yue69D1TVe3vsW5LGXm8BXFUFPNa9XN89qq/+JGm16fUccJIjktwOHACuq6qbu7cuTHJHksuSbFhk2x1JZpLMzM7O9lmmJDXRawBX1RNVtQU4Ftia5GTgQ8AJwBZgH/C+RbbdWVXTVTU9NTXVZ5mS1MRIZkFU1d8BNwLbq2p/F8xPAh8Bto6iBkkaN33OgphK8sJu+TnAzwN3J9k4Z7VzgD191SBJ46zPWRAbgV1JjmAQ9FdU1dVJ/jjJFgZfyN0LXNBjDZI0tvqcBXEHcMoC7ef11ackrSZeCSdJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktRIbwGc5MgktyT5RpK7kryraz8qyXVJ7umeN/RVgySNsz6PgH8AvLaqfgbYAmxP8krgEuD6qjoRuL57LUlrTm8BXAOPdS/Xd48CzgJ2de27gLP7qkGSxlmv54CTHJHkduAAcF1V3QwcU1X7ALrno/usQZLGVa8BXFVPVNUW4Fhga5KTh902yY4kM0lmZmdne6tRkloZySyIqvo74EZgO7A/yUaA7vnAItvsrKrpqpqempoaRZmSNFJ9zoKYSvLCbvk5wM8DdwNXAed3q50PXNlXDZI0ztb1+NkbgV1JjmAQ9FdU1dVJbgKuSPI24DvAm3qsQZLGVm8BXFV3AKcs0P4wcHpf/UrSauGVcJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAGsibBp83EkafLYtPm41sPXKrWudQHSSnjw/vs498O7m/R9+QXbmvSr1c8jYElqxACWpEZ6C+Akm5PckGRvkruSXNS1X5rkgSS3d48z+6pBksZZn+eAHwd+s6puS/J84NYk13XvfaCq3ttj35I09noL4KraB+zrlh9NshfY1Fd/krTajOQccJLjgVOAm7umC5PckeSyJBsW2WZHkpkkM7Ozs6MoU5JGqvcATvI84NPAxVX1CPAh4ARgC4Mj5PcttF1V7ayq6aqanpqa6rtMSRq5XgM4yXoG4fuJqvoMQFXtr6onqupJ4CPA1j5rkKRx1ecsiAAfA/ZW1fvntG+cs9o5wJ6+apCkcdbnLIhTgfOAO5Pc3rW9E3hLki1AAfcCF/RYgySNrT5nQXwZyAJvXdNXn5K0mnglnCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1MlQAJzl1mDZJ0vCGPQL+n0O2SZKGtOSVcEleBWwDppL8xpy3fgw4os/CJGnSLXcp8rOB53XrPX9O+yPAG/sqSpLWgiUDuKq+CHwxycer6m9HVJMkrQnD3oznHyXZCRw/d5uqem0fRUnSWjBsAH8K+EPgo8AT/ZUjSWvHsAH8eFV9qNdKJGmNGXYa2p8l+Q9JNiY56uCj18okacINewR8fvf8jjltBfz0ypYjSWvHUAFcVT/VdyGStNYMFcBJfnmh9qr6o5UtR5LWjmFPQbx8zvKRwOnAbYABLEmHaNhTEL8+93WSFwB/3EtFkrRGHOrtKL8HnLiShUjSWjPsOeA/YzDrAQY34XkpcEVfRUnSWjDsOeD3zll+HPjbqrq/h3okac0Y6hREd1OeuxncEW0D8MM+i5KktWDYX8T4ReAW4E3ALwI3J/F2lJJ0GIY9BfHbwMur6gBAkingL4E/7aswSZp0w86CeNbB8O08vNy2STYnuSHJ3iR3Jbmoaz8qyXVJ7umeNxxi7ZK0qg0bwNcm+XyStyZ5K/DnwDXLbPM48JtV9VLglcDbk5wEXAJcX1UnAtd3ryVpzVnuN+FeDBxTVe9I8gvAzwIBbgI+sdS2VbUP2NctP5pkL7AJOAs4rVttF3Aj8FuHPgRJWp2WOwL+IPAoQFV9pqp+o6r+I4Oj3w8O20mS44FTgJsZBPrBYN4HHL3INjuSzCSZmZ2dHbYrSVo1lgvg46vqjvmNVTXD4OeJlpXkecCngYur6pFhC6uqnVU1XVXTU1NTw24mSavGcgF85BLvPWe5D0+ynkH4fqKqPtM170+ysXt/I3Bgse0laZItF8BfS/Lv5jcmeRtw61IbJgnwMWBvVb1/zltX8dQN3s8Hrhy+XEmaHMvNA74Y+GySf8NTgTsNPBs4Z5ltTwXOA+5McnvX9k7gPcAVXYh/h8HFHZK05iwZwFW1H9iW5DXAyV3zn1fVF5b74Kr6MoMZEws5/RlVKUkTaNj7Ad8A3NBzLZK0phzq/YAlSYfJAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWpk2F/EkLSYZ61jcOX9aP3EsZt54L7vjLxfrRwDWDpcTz7OuR/ePfJuL79g28j71MryFIQkNWIAS1IjBrAkNWIAS1IjBrAkNeIsCGm1ajT9DZwCt1IMYGm1ajT9DZwCt1I8BSFJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjfQWwEkuS3IgyZ45bZcmeSDJ7d3jzL76l6Rx1+cR8MeB7Qu0f6CqtnSPa3rsX5LGWm8BXFVfAr7b1+dL0mrX4hzwhUnu6E5RbGjQvySNhVEH8IeAE4AtwD7gfYutmGRHkpkkM7OzsyMqT9JQulthtnhs2nxc69GvmJHejrKq9h9cTvIR4Ool1t0J7ASYnp6u/quTNDRvhbkiRnoEnGTjnJfnAHsWW1eSJl1vR8BJPgmcBrwoyf3A7wKnJdkCFHAvcEFf/UvSuOstgKvqLQs0f6yv/iRptfFKOElqxACWpEb8UU5Jq8sE/Rq0ASxpdZmgKXCegpCkRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRnoL4CSXJTmQZM+ctqOSXJfknu55Q1/9S9K46/MI+OPA9nltlwDXV9WJwPXda0lak3oL4Kr6EvDdec1nAbu65V3A2X31L0njbtTngI+pqn0A3fPRi62YZEeSmSQzs7OzIytQh27T5uNI0uQhrUbrWhewmKraCewEmJ6ersblaAgP3n8f5354d5O+L79gW5N+pcMx6iPg/Uk2AnTPB0bcvySNjVEH8FXA+d3y+cCVI+5fksZGn9PQPgncBLwkyf1J3ga8BzgjyT3AGd1rSVqTejsHXFVvWeSt0/vqU5JWE6+Ek6RGDGBJamRsp6Gtes9a12R+6k8cu5kH7vvOyPuV9MwZwH158vEmc2KdDyutHp6CkKRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRG1rXoNMm9wKPAE8DjVTXdog5JaqlJAHdeU1UPNexfkpryFIQkNdIqgAv4iyS3Jtmx0ApJdiSZSTIzOzs74vIkqX+tAvjUqnoZ8Drg7UlePX+FqtpZVdNVNT01NTX6CiWpZ00CuKoe7J4PAJ8FtraoQ5JaGnkAJ3lukucfXAb+JbBn1HVIUmstZkEcA3w2ycH+/3dVXdugDklqauQBXFXfBn5m1P1K0rhxGpokNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNdIkgJNsT/LNJN9KckmLGiSptZEHcJIjgP8FvA44CXhLkpNGXYcktdbiCHgr8K2q+nZV/RD4P8BZDeqQpKZSVaPtMHkjsL2qfrV7fR7wiqq6cN56O4Ad3cuXAN8EXgQ8NMJyW3Gck2MtjBEc53Ieqqrt8xvXHX49z1gWaPuRvwJVtRPY+bQNk5mqmu6rsHHhOCfHWhgjOM5D1eIUxP3A5jmvjwUebFCHJDXVIoC/BpyY5KeSPBt4M3BVgzokqamRn4KoqseTXAh8HjgCuKyq7hpy853LrzIRHOfkWAtjBMd5SEb+JZwkacAr4SSpEQNYkhoZywBe7lLlDPyP7v07krysRZ2Ha4hxnpbk75Pc3j1+p0WdhyPJZUkOJNmzyPurfl8OMcZVvx8BkmxOckOSvUnuSnLRAutMwv4cZpwrs0+raqweDL6Y+xvgp4FnA98ATpq3zpnA5xjMKX4lcHPrunsa52nA1a1rPcxxvhp4GbBnkfcnYV8uN8ZVvx+7cWwEXtYtPx/46wn9tznMOFdkn47jEfAwlyqfBfxRDXwVeGGSjaMu9DCtiUuyq+pLwHeXWGXV78shxjgRqmpfVd3WLT8K7AU2zVttEvbnMONcEeMYwJuA++a8vp8fHfww64y7YcfwqiTfSPK5JP9sNKWN1CTsy2FM1H5McjxwCnDzvLcman8uMU5YgX3a4lLk5QxzqfJQlzOPuWHGcBvwk1X1WJIzgf8LnNh3YSM2CftyORO1H5M8D/g0cHFVPTL/7QU2WZX7c5lxrsg+Hccj4GEuVZ6Ey5mXHUNVPVJVj3XL1wDrk7xodCWOxCTsyyVN0n5Msp5BKH2iqj6zwCoTsT+XG+dK7dNxDOBhLlW+Cvjl7hvXVwJ/X1X7Rl3oYVp2nEl+PEm65a0M9tfDI6+0X5OwL5c0KfuxG8PHgL1V9f5FVlv1+3OYca7UPh27UxC1yKXKSX6te/8PgWsYfNv6LeB7wK+0qvdQDTnONwL/PsnjwPeBN1f3FexqkeSTDL4xflGS+4HfBdbD5OzLIca46vdj51TgPODOJLd3be8EjoPJ2Z8MN84V2adeiixJjYzjKQhJWhMMYElqxACWpEYMYElqxACWpEYMYDWT5InuTlJ7knwqyT9eYt0t3RVHy33maUmu7uahPpRkQ9e+MUkl+dk5684m+SdJPprkpAU+661J/qBbPnvuOkluTLLgjzMm2ZrkSxnc6e7u7vMXHZvWLgNYLX2/qrZU1cnAD4FfW2LdLQzmlw6lm5N5M/Cqrmkb8PXumSQvYfBT4Q9X1a9W1f9b5iPPBn4kpOdLcgzwKeC3quolwEuBaxncVUt6GgNY4+KvgBcneW4G99f9WpKvJzmru1LwvwLndkfM53ZHmbu7dXZ3gTrfV+gCt3t+P08P5N3w9KPZJL+S5K+TfJHBhHySbAPeAPx+1/8J3We8Kckt3fo/17W9HdhVVTfB4A9BVf1pVe1PcmmSXUn+Ism9SX4hye8luTPJtd3lr1pDDGA1l2Qd8DrgTuC3gS9U1cuB1wC/z+Cqst8BLu+OmC8H7gZeXVWndO/9twU+ejdPBfBWBjdMOXifgm0MAnpuHRuBdzEI3jPojnirajeDS2zf0fX/N90m66pqK3Axg6vfAE4Gbl1iuCcAr2dw28Y/AW6oqn/O4Gqq1y+xnSbQ2F2KrDXlOXMu9fwrBtff7wbekOQ/de1H0l0COs8LgF1JTmRwt62Fjh5vAU5J8lxgfXfnqm8neTGDAH7fvPVfAdxYVbMASS4H/ukS9R+8ScutwPFLrDfX56rqH5LcyeAS9Gu79jufwWdoQhjAaun7VbVlbkN3g5N/XVXfnNf+innbvpvB0eM5Gdyz9cb5H15V30vyLeDfMrh9IMBXGZxLPhr45vxteGa3TvxB9/wET/1bugv4F8CVS21TVU8m+Yc59w94Ev89rjmegtC4+Tzw63PuNHVK1/4oT/8i6wXAA93yW5f4vK8wOEVwU/f6JuAi4KsL3DzlZuC0bmbEeuBNc96b3/9i/gA4f+4fjCS/lOTHh9hWa4wBrHHzbganE+7I4Ecu39213wCcdPBLOOD3gP+e5CsM/iu/mK8w+N29gwF8G4N71O6ev2J328RLu3X/kqeOmmHwk1Hv6L70O2H+tnM+Yz+DW4u+t5uGthf4OWD+Db0l74YmSa14BCxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjfx/tfVGTNMg+hQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in features:\n",
    "    sns.displot(df[feature])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce672983",
   "metadata": {},
   "source": [
    "# Univariate, bivariate and multivariate plot\n",
    "\n",
    "### Univariate plots:\n",
    "\n",
    "Histogram<br>\n",
    "Box plot<br>\n",
    "Violin plot<br>\n",
    "Swarm plot<br>\n",
    "Bar plot<br>\n",
    "Pie chart\n",
    "\n",
    "### Bivariate plots:\n",
    "\n",
    "Scatter plot<br>\n",
    "Line plot<br>\n",
    "Heatmap\n",
    "\n",
    "### Multivariate plots:\n",
    "\n",
    "Pair plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2fdcee2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}