Age.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### How to Handle Outliers in Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('titanic.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling the Outliers for the Age Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of Passengers')"
      ]
     },
     "execution_count": 3,
     "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.Age.hist(bins=50)\n",
    "figure.set_title('Age')\n",
    "figure.set_xlabel('Age')\n",
    "figure.set_ylabel('No of Passengers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=dataset.boxplot(column='Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e9247c5c0>"
      ]
     },
     "execution_count": 5,
     "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": [
    "sns.distplot(dataset['Age'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here the Age columns in normally distributed that means we have to use empricial formula to calculate the Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-13.88037434994331\n",
      "73.27860964406095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_boundary=dataset['Age'].mean()+3*dataset['Age'].std()\n",
    "lower_boundary=dataset['Age'].mean()-3*dataset['Age'].std()\n",
    "print(lower_boundary),print(upper_boundary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Age'].describe()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}