Outlier_Detection_1_Univariate_Techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion Related With Outliers And Impact On Machine Learning!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Which Machine LEarning Models Are Sensitive To Outliers?\n",
    "1. Naivye Bayes Classifier--- Not Sensitive To Outliers\n",
    "2. SVM--------                Not Sensitive To Outliers          \n",
    "3. Linear Regression----------  Sensitive To Outliers\n",
    "4. Logistic Regression-------   Sensitive To Outliers\n",
    "5. Decision Tree Regressor or Classifier---- Not Sensitive\n",
    "6. Ensemble(RF,XGboost,GB)------- Not Sensitive\n",
    "7. KNN--------------------------- Not Sensitive \n",
    "8. Kmeans------------------------ Sensitive\n",
    "9. Hierarichal------------------- Sensitive \n",
    "10. PCA-------------------------- Sensitive \n",
    "11. Neural Networks-------------- Sensitive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4045b1d48>"
      ]
     },
     "execution_count": 7,
     "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(df['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e405dc3888>"
      ]
     },
     "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": [
    "sns.distplot(df['Age'].fillna(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "execution_count": 11,
     "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=df.Age.hist(bins=50)\n",
    "figure.set_title('Age')\n",
    "figure.set_xlabel('Age')\n",
    "figure.set_ylabel('No of passenger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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=df.boxplot(column=\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If The Data Is Normally Distributed We use this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-13.880374349943303\n",
      "73.27860964406094\n",
      "29.69911764705882\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Assuming Age follows A Gaussian Distribution we will calculate the boundaries which differentiates the outliers\n",
    "\n",
    "uppper_boundary=df['Age'].mean() + 3* df['Age'].std()\n",
    "lower_boundary=df['Age'].mean() - 3* df['Age'].std()\n",
    "print(lower_boundary), print(uppper_boundary),print(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If Features Are Skewed We Use the below Technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ml+x8Bwjg94A+SbMBDvSuZjMzGzuKFIVZ6ftLe7V/mKxI+JkFM7MWUeTuo55GdizpJuCTwLaIODm1XQP8AfBSWu3zEXF3WnY1cAnZO6Evj4h7GzmumZk1rsx3LX8LmFun/W8iYlb6DBaEmcB84KS0zY2SxpUYm5mZ1VFaUYiIB4DtBVefB/RGxK40Cusm4LSyYjMzs/r2WxQknZO+p4/wMS+T9LikmyRNSG2TgZ/UrLM5tZmZWRMpIuovkNZHxOzB74Z2Lk0D7qq5ptAJvEx2gfrLwKSIuFjSDcAPI+KWtN4y4O6IuL3OPhcCCwE6Ozvn9Pb2NhIaANu2v8rWncXXP2Xy0Q0fq2oDAwN0dHRUHUZTtFOu0F75tlOuUF6+PT096yKiq96yoS40/1zS/cB0Sav3XhgRvz3cQCJi6+C0pG/y9us9NwNTa1adAmzZzz6WAksBurq6oru7e7hh5K5bsYolG4rcgJVseKNuc/+1ZzUcQ7P09fVxML/VWNJOuUJ75dtOuUI1+Q71F/EsYDbZuxOWjMTBJE2KiBfT7KeAJ9L0auBWSV8FTgBmAGtH4phmZlbcfotCRPwCeFDShyPiJUlHZc0xUGTHkr4DdAPHStpM9pxDt6RZZN1H/cBn0rE2SloJPEn2Mp9LI2JP42mZmVkjivSddEr6PjARkKSXgAUR8cRQG0XEeXWalw2x/mJgcYF4zMysJEVuSV0KXBkRJ0bEu4GrUpuZmbWYIkXhyIi4f3AmIvqAI0uLyMzMKlOk++g5SX9CdsEZ4ALg+fJCMjOzqhQ5U7gYOA64I32OBX6/zKDMzKwaRQbE20H2fmYzM2txZQ6IZ2ZmY4yLgpmZ5VwUzMwsd8CiIGmKpDslvSRpq6TbJU1pRnBmZtZcRc4UbiYbm2gS2XDW/5jazMysxRQpCsdFxM0RsTt9vkV2i6qZmbWYIkXhZUkXSBqXPhcAPy87MDMza76iD6+dC/wMeBH4dGozM7MWU+ThtReAYb9Qx8zMxp79FgVJfzrEdhERXy4hHjMzq9BQZwr13j15JHAJ8C6ydyybmVkLGerNa/krONNb1z5LNhBeLyP0ek4zMxtdhrymIGkicCVwPrAcmJ0GyDMzsxY01DWFvwZ+h+wta6cUfTezmZmNXUPdknoVcALwRWCLpNfS53VJrzUnPDMza6ahril4sDwzszbjP/xmZpYrrShIuknSNklP1LRNlLRG0rPpe0LNsqslbZL0jKQzy4rLzMz2r8wzhW8Bc/dqWwTcFxEzgPvSPJJmAvOBk9I2N0oaV2JsZmZWR2lFISIeALbv1TyP7NZW0vfZNe29EbErIp4HNgGnlRWbmZnVp4gob+fSNOCuiDg5zb8SEcfULN8RERMkXQ88GBG3pPZlwD0RcVudfS4EFgJ0dnbO6e3tbTi+bdtfZevOhjfPnTL56IPfSckGBgbo6OioOoymaKdcob3ybadcobx8e3p61kVEV71lBxwQr0lUp61utYqIpWTPTtDV1RXd3d0NH/S6FatYsuHgf4L+8xuPoVn6+vo4mN9qLGmnXKG98m2nXKGafJt999FWSZMA0ve21L4ZmFqz3hRgS5NjMzNre80uCquBBWl6AbCqpn2+pMMkTQdmAGubHJuZWdsrrftI0neAbuBYSZuBLwHXAislXQK8AJwDEBEbJa0EngR2A5dGxJ6yYjMzs/pKKwoRcd5+Fn10P+svBhaXFY+ZmR2Yn2g2M7Oci4KZmeVcFMzMLOeiYGZmORcFMzPLuSiYmVnORcHMzHIuCmZmlnNRMDOznIuCmZnlXBTMzCznomBmZjkXBTMzy7komJlZzkXBzMxyLgpmZpZzUTAzs5yLgpmZ5VwUzMws56JgZmY5FwUzM8u5KJiZWe7QKg4qqR94HdgD7I6ILkkTgX8ApgH9wLkRsaOK+MzM2lWVZwo9ETErIrrS/CLgvoiYAdyX5s3MrIlGU/fRPGB5ml4OnF1hLGZmbUkR0fyDSs8DO4AAvhERSyW9EhHH1KyzIyIm1Nl2IbAQoLOzc05vb2/DcWzb/ipbdza8+QGdMvno8nY+TAMDA3R0dFQdRlO0U67QXvm2U65QXr49PT3ranppfkkl1xSAMyJii6TjgTWSni66YUQsBZYCdHV1RXd3d8NBXLdiFUs2lPcT9J/fXdq+h6uvr4+D+a3GknbKFdor33bKFarJt5Luo4jYkr63AXcCpwFbJU0CSN/bqojNzKydNb0oSDpS0lGD08DHgSeA1cCCtNoCYFWzYzMza3dVdB91AndKGjz+rRHxPUkPASslXQK8AJxTQWxmZm2t6UUhIp4DPlin/efAR5sdj5mZvW003ZJqZmYVq+ruo7Y2bdF367b3X3tWkyMxM/tlPlMwM7Oci4KZmeVcFMzMLOeiYGZmORcFMzPL+e6jMcB3K5lZs/hMwczMci4KZmaWc1EwM7Oci4KZmeVcFMzMLOeiYGZmOd+SOors79bT4a7vW1XNrFEuCiUa7h95M7OqufvIzMxyLgpmZpZzUTAzs5yvKdiI8sVvs7HNRaGNNHLhe39/zH0R3aw1uSiYlcRnTTYWjbqiIGku8LfAOODvIuLaikOyMWy4f5iHOgPyH3NrB6OqKEgaB9wA/CawGXhI0uqIeLLayMzMqtHsM85RVRSA04BNEfEcgKReYB7gojAM9f5PdNUpuxl9/3NX18UyktdEfH3FWokiouoYcpI+DcyNiP+S5i8EPhQRl9WssxBYmGY/ADxzEIc8Fnj5ILYfS5xr62qnfNspVygv3xMj4rh6C0bbPx1Vp+2XqlZELAWWjsjBpIcjomsk9jXaOdfW1U75tlOuUE2+o+3htc3A1Jr5KcCWimIxM2s7o60oPATMkDRd0juA+cDqimMyM2sbo6r7KCJ2S7oMuJfsltSbImJjiYcckW6oMcK5tq52yredcoUK8h1VF5rNzKxao637yMzMKuSiYGZmubYsCpLmSnpG0iZJi6qOZyRIuknSNklP1LRNlLRG0rPpe0LNsqtT/s9IOrOaqBsjaaqk+yU9JWmjpM+m9pbLV9LhktZKeizl+mepveVyHSRpnKRHJN2V5ls5135JGyQ9Kunh1FZtvhHRVh+yC9g/At4DvAN4DJhZdVwjkNdvALOBJ2ravgIsStOLgL9K0zNT3ocB09PvMa7qHIaR6yRgdpo+CvjXlFPL5Uv27E5Hmh4P/AtweivmWpPzlcCtwF1pvpVz7QeO3aut0nzb8UwhH0ojIn4BDA6lMaZFxAPA9r2a5wHL0/Ry4Oya9t6I2BURzwObyH6XMSEiXoyI9Wn6deApYDItmG9kBtLs+PQJWjBXAElTgLOAv6tpbslch1Bpvu1YFCYDP6mZ35zaWlFnRLwI2R9S4PjU3jK/gaRpwKlk/4JuyXxTd8qjwDZgTUS0bK7A14D/AbxV09aquUJW4L8vaV0awgcqzndUPafQJAccSqMNtMRvIKkDuB24IiJek+qlla1ap23M5BsRe4BZko4B7pR08hCrj9lcJX0S2BYR6yR1F9mkTtuYyLXGGRGxRdLxwBpJTw+xblPybcczhXYaSmOrpEkA6Xtbah/zv4Gk8WQFYUVE3JGaWzZfgIh4BegD5tKauZ4B/LakfrJu3Y9IuoXWzBWAiNiSvrcBd5J1B1WabzsWhXYaSmM1sCBNLwBW1bTPl3SYpOnADGBtBfE1RNkpwTLgqYj4as2ilstX0nHpDAFJRwAfA56mBXONiKsjYkpETCP77/J/R8QFtGCuAJKOlHTU4DTwceAJqs636qvvVXyAT5DdsfIj4AtVxzNCOX0HeBF4k+xfFJcA7wLuA55N3xNr1v9Cyv8Z4D9VHf8wc/11stPmx4FH0+cTrZgv8GvAIynXJ4A/Te0tl+teeXfz9t1HLZkr2R2Qj6XPxsG/RVXn62EuzMws147dR2Zmth8uCmZmlnNRMDOznIuCmZnlXBTMzCzXjk80mx0USXuADTVNZ0dEf0XhmI0o35JqNkySBiKiY5jbiOy/t7cOuLJZhdx9ZHaQJHVIuk/S+jQ2/rzUPi298+FGYD0wVdIfS3pI0uOD70YwG01cFMyG74j0UpRHJd0J/BvwqYiYDfQAS/T26HwfAP4+Ik5N0zPIxreZBcyR9BsVxG+2X76mYDZ8OyNi1uBMGpzvL9If+LfIhjPuTIt/HBEPpumPp88jab6DrEg80JSozQpwUTA7eOcDxwFzIuLNNMrn4WnZGzXrCfjLiPhGk+MzK8zdR2YH72iy9wC8KakHOHE/690LXJzeA4GkyWkcfbNRw2cKZgdvBfCP6cXrj5INbb2PiPi+pF8FfpguOQwAF/D2ePlmlfMtqWZmlnP3kZmZ5VwUzMws56JgZmY5FwUzM8u5KJiZWc5FwczMci4KZmaW+/8LCZpaDqjitgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=df.Fare.hist(bins=50)\n",
    "figure.set_title('Fare')\n",
    "figure.set_xlabel('Fare')\n",
    "figure.set_ylabel('No of passenger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e40808a788>"
      ]
     },
     "execution_count": 24,
     "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": [
    "df.boxplot(column=\"Fare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Lets compute the Interquantile range to calculate the boundaries\n",
    "IQR=df.Fare.quantile(0.75)-df.Fare.quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26.724\n",
      "65.6344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bridge=df['Fare'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['Fare'].quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge), print(upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-61.358399999999996\n",
      "100.2688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Extreme outliers\n",
    "lower_bridge=df['Fare'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge=df['Fare'].quantile(0.75)+(IQR*3)\n",
    "print(lower_bridge), print(upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Age']>=73,'Age']=73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Fare']>=100,'Fare']=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "execution_count": 40,
     "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=data.Age.hist(bins=50)\n",
    "figure.set_title('Fare')\n",
    "figure.set_xlabel('Fare')\n",
    "figure.set_ylabel('No of passenger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "execution_count": 39,
     "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=data.Fare.hist(bins=50)\n",
    "figure.set_title('Fare')\n",
    "figure.set_xlabel('Fare')\n",
    "figure.set_ylabel('No of passenger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(data[['Age','Fare']].fillna(0),data['Survived'],test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_score: 0.6716417910447762\n",
      "roc_auc_score: 0.7115520907158045\n"
     ]
    }
   ],
   "source": [
    "### Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier=LogisticRegression()\n",
    "classifier.fit(X_train,y_train)\n",
    "y_pred=classifier.predict(X_test)\n",
    "y_pred1=classifier.predict_proba(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score\n",
    "print(\"Accuracy_score: {}\".format(accuracy_score(y_test,y_pred)))\n",
    "print(\"roc_auc_score: {}\".format(roc_auc_score(y_test,y_pred1[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}