Fare.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling the Outliers for Fare Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of Passengers')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=dataset['Fare'].hist(bins=50)\n",
    "figure.set_title('Fare')\n",
    "figure.set_xlabel('Fare')\n",
    "figure.set_ylabel('No of Passengers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e92614c88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EAGOuOxlCWFrV9jZJXzWzpZJOS3pP2X3PhBBeiLevlbRM0q/jt7d16P9ftgK4I4AxH90r6WVJH1RhGe0/Zfe9UXbbJH0nhPBAgmMDpow1YMxH50h6KYTwlqTPSJpoXfcpSRvi978W//7XuxMaIzApAhjz0dcl9ZjZL1VYfnijVqcQwh8k9anwVxCeVeGvW1yU2CiBSfA2NABwwgwYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcPI/0qRl8aL4GwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dataset['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here the Data is not Normally distributed and is following the Right Skewed distribution that means we can use the Interquartile Range to measure the boundaries for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0896"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR= dataset['Fare'].quantile(0.75) - dataset['Fare'].quantile(0.25)\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26.724\n",
      "65.6344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge= dataset['Fare'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge= dataset['Fare'].quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge), print(upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here the maximum value of outliers is very high compare to upper boundary that indicates we need to calculate the extreme outliers boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-61.358399999999996\n",
      "100.2688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the extreme boundaries\n",
    "lower_bridge= dataset['Fare'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge= dataset['Fare'].quantile(0.75)+(IQR*3)\n",
    "print(lower_bridge), print(upper_bridge)"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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