Outliers and its Impact.ipynb

{
 "cells": [
  {
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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": [
    "df=pd.read_csv('titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2872b731910>"
      ]
     },
     "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(df['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28728954df0>"
      ]
     },
     "execution_count": 6,
     "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 ->3 std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "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": [
    "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARcUlEQVR4nO3df6xXd33H8ee7lAYscxRbvmH+QjNSb8W00xuntlkuZa1lLoNt4kricrNgbgjbnVmXTDaSGbNgMFkaTWMTyXC7Zg7FHw0ICqV3fF1ITBVEI/XqcFpr1yuoLY1go1x47497YAgX7rnc7/d++fT7fCTknPP5nvM9b5LDi08+33POJzITSVJ5rut0AZKkq2OAS1KhDHBJKpQBLkmFMsAlqVDXz+TJbr755ly8ePFMnlKq5dSpU9x4442dLkOa0KFDh36ambdc3D6jAb548WIOHjw4k6eUamk2m/T19XW6DGlCEfHDidodQpGkQhngklQoA1ySCmWAS1KhDHBJKlStAI+Iv4mIJyLiSERsi4g5EbEgIvZFxNFqeVO7i5Vabdu2bSxdupTly5ezdOlStm3b1umSpNomvY0wIl4O/DVwW2a+EBHbgfuB24DhzNwcERuADcD72lqt1ELbtm1j48aNbN26lTNnzjBr1izWrl0LwJo1azpcnTS5ukMo1wNzI+J64CXAM8BKYKj6fAhY1frypPbZtGkTW7duZdmyZVx//fUsW7aMrVu3smnTpk6XJtUyaQ88M/83Iv4ZeAp4AXg0Mx+NiEZmjlb7jEbEwomOj4gBYACg0WjQbDZbVrw0HSMjI5w5c4Zms8nJkydpNpucOXOGkZERr1MVoc4Qyk2M97ZfA5wAPhMR7657gszcAmwB6O3tTZ9207Wip6eHWbNm0dfXd/5JzP3799PT0+NTmSpCnSGU3wd+kJk/yczTwOeBtwHHImIRQLU83r4ypdbbuHEja9euZf/+/YyNjbF//37Wrl3Lxo0bO12aVEudd6E8BbwlIl7C+BDKcuAgcAroBzZXyx3tKlJqh3M/VA4ODjIyMkJPTw+bNm3yB0wVI+rMiRkRHwD+DBgDDgPvAeYB24FXMR7yqzPz2St9T29vb/oyK12LfJmVrmURcSgzey9ur/U2wsx8P/D+i5p/yXhvXJLUAT6JKUmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgKur+TpZlWxGZ6WXriW+TlalsweuruXrZFU6A1xda2RkhLvuuuvX2u666y5GRkY6VJE0NQa4ulZPTw8HDhz4tbYDBw7Q09PToYqkqTHA1bV8naxK54+Y6lq+Tlalq/U62VbxdbK6Vvk6WV3LLvc6WYdQ1NUGBweZM2cOy5YtY86cOQwODna6JKk2A1xda3BwkIcffpj58+cTEcyfP5+HH37YEFcxJh1CiYhbgU9f0PRa4B+BT1Tti4EngXdl5nNX+i6HUHQtmT17NrNmzeLs2bOcPn2a2bNnc91113HmzBlOnz7d6fKk8656CCUzv5uZd2TmHcCbgF8AjwAbgOHMXAIMV9tSMcbGxhgbG2Pz5s186UtfYvPmzefbpBJMdQhlOfA/mflDYCUwVLUPAataWZg0E1asWMEDDzzAnDlzeOCBB1ixYkWnS5Jqm+pthPcD597208jMUYDMHI2IhRMdEBEDwABAo9Gg2WxeZalS6+3atYv169dz9913s379enbt2gXgdaoi1L6NMCJuAJ4BXp+ZxyLiRGbOv+Dz5zLzpit9h2PgupacG/POzPNj4BFxfkxcula04jbCFcDXM/NYtX0sIhZVX74IOD79MqWZs27dOsbGxliwYAEACxYsYGxsjHXr1nW4MqmeqQT4Gv5/+ARgJ9BfrfcDO1pVlDQTHnroIdavX8+JEycAOHHiBOvXr+ehhx7qcGVSPbWGUCLiJcCPgNdm5vNV28uA7cCrgKeA1Zn57JW+xyEUXat8ElPXsssNodT6ETMzfwG87KK2nzF+V4okqQN8ElOSCmWAq6s5J6ZK5utk1bWcE1OlsweuruWcmCqdPXB1rZGRET74wQ+yfPlyMpOIYPny5c6JqWLYA1fXmjt3Lo899hjr1q3jC1/4AuvWreOxxx5j7ty5nS5NqsUAV9c6deoU8+bNY/Xq1cyZM4fVq1czb948Tp061enSpFoMcHW1Bx98kMHBQd7+9rczODjIgw8+2OmSpNoMcHWtiODw4cMcOXKE4eFhjhw5wuHDh4mITpcm1eKkxnpRmqkQnsl/P+peTmqsrpKZtf7ce++958M+Irj33ntrH2t4q9MMcHW1vXv3cvbsWV79vl2cPXuWvXv3drokqTYDXJIKZYBLUqEMcEkqlAEuSYWqFeARMT8iPhsR34mIkYh4a0QsiIh9EXG0Wl5xQmNJUmvV7YF/BNiTma8DbgdGgA3AcGYuAYarbUnSDJk0wCPipcDvAVsBMvNXmXkCWAkMVbsNAavaVaQk6VJ1Xif7WuAnwL9GxO3AIeC9QCMzRwEyczQiFk50cEQMAAMAjUaDZrPZirqllvPaVGnqBPj1wBuBwcx8PCI+whSGSzJzC7AFxh+ld+ZvXZP27HZWehWnzhj408DTmfl4tf1ZxgP9WEQsAqiWx9tToiRpIpMGeGb+GPhRRNxaNS0Hvg3sBPqrtn5gR1sqlCRNqO6UaoPAJyPiBuD7wF8wHv7bI2It8BSwuj0lSpImUivAM/MbwCWvMmS8Ny5J6gCfxJSkQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFarWhA4R8STwc+AMMJaZvRGxAPg0sBh4EnhXZj7XnjIlSRebSg98WWbekZnnZubZAAxn5hJgmCnMVC9Jmr7pDKGsBIaq9SFg1fTLkSTVVXdS4wQejYgEPpaZW4BGZo4CZOZoRCyc6MCIGAAGABqNBs1mc/pVS23gtanS1A3wOzPzmSqk90XEd+qeoAr7LQC9vb3Z19c39SqldtuzG69NlabWEEpmPlMtjwOPAG8GjkXEIoBqebxdRUqSLjVpgEfEjRHxG+fWgXuBI8BOoL/arR/Y0a4iJUmXqjOE0gAeiYhz+/9HZu6JiK8B2yNiLfAUsLp9ZUqSLjZpgGfm94HbJ2j/GbC8HUVJkibnk5iSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVqnaAR8SsiDgcEbuq7QURsS8ijlbLm9pXpiTpYlPpgb8XGLlgewMwnJlLgOFqW5I0Q2oFeES8AngH8C8XNK8Ehqr1IWBVa0uTJF1J3R74h4G/A85e0NbIzFGAarmwxbVJkq5g0jkxI+IPgeOZeSgi+qZ6gogYAAYAGo0GzWZzql8hzQivTZWmzqz0dwJ/FBF/AMwBXhoR/w4ci4hFmTkaEYuA4xMdnJlbgC0Avb292dfX15rKpVbasxuvTZVm0iGUzPz7zHxFZi4G7gf+MzPfDewE+qvd+oEdbatSknSJ6dwHvhm4JyKOAvdU25KkGVJnCOW8zGwCzWr9Z8Dy1pckSapjSgEudcLtH3iU51843fbzLN6wu63f/5tzZ/PN99/b1nOouxjguuY9/8Jpntz8jraeo9lstv1HzHb/B6Hu47tQJKlQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhJg3wiJgTEV+NiG9GxBMR8YGqfUFE7IuIo9XypvaXK0k6p04P/JfA3Zl5O3AHcF9EvAXYAAxn5hJguNqWJM2QOpMaZ2aerDZnV38SWAkMVe1DwKq2VChJmlCtGXkiYhZwCPht4KOZ+XhENDJzFCAzRyNi4WWOHQAGABqNBs1msyWFq7u0+7o5efLkjFybXv9qpVoBnplngDsiYj7wSEQsrXuCzNwCbAHo7e3Ndk9bpRehPbvbPt3ZTEypNhN/D3WXKd2FkpknGJ+V/j7gWEQsAqiWx1tenSTpsurchXJL1fMmIuYCvw98B9gJ9Fe79QM72lWkJOlSdYZQFgFD1Tj4dcD2zNwVEV8BtkfEWuApYHUb65QkXSQyc8ZO1tvbmwcPHpyx8+nF4Q1Db+h0CS3zrf5vdboEFSgiDmVm78XttX7ElDrp5yObeXLzO9p6jpn4EXPxht1t/X51Hx+ll6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVB1plR7ZUTsj4iRiHgiIt5btS+IiH0RcbRa3tT+ciVJ59TpgY8Bf5uZPcBbgL+MiNuADcBwZi4BhqttSdIMmTTAM3M0M79erf8cGAFeDqwEhqrdhoBV7SpSknSpKU2pFhGLgd8BHgcamTkK4yEfEQsvc8wAMADQaDRoNpvTKFfdakamI9vT3nPcOBuvf7VU7UmNI2Ie8GVgU2Z+PiJOZOb8Cz5/LjOvOA7upMa6Vi3esLvt825KV+tykxrXugslImYDnwM+mZmfr5qPRcSi6vNFwPFWFStJmlydu1AC2AqMZOaDF3y0E+iv1vuBHa0vT5J0OXXGwO8E/hz4VkR8o2r7B2AzsD0i1gJPAavbU6IkaSKTBnhmHgDiMh8vb205kqS6fBJTkgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklSoOlOqfTwijkfEkQvaFkTEvog4Wi2vOJmxJKn16vTA/w2476K2DcBwZi4BhqttSdIMmjTAM/O/gGcval4JDFXrQ8CqFtclSZpEnUmNJ9LIzFGAzByNiIWX2zEiBoABgEajQbPZvMpTSu3ltanSXG2A15aZW4AtAL29vdnX19fuU0pTt2c3XpsqzdXehXIsIhYBVMvjrStJklTH1Qb4TqC/Wu8HdrSmHElSXXVuI9wGfAW4NSKejoi1wGbgnog4CtxTbUuSZtCkY+CZueYyHy1vcS2SpCnwSUxJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqGmFeARcV9EfDcivhcRG1pVlCRpclcd4BExC/gosAK4DVgTEbe1qjBJ0pVNOqXaFbwZ+F5mfh8gIj4FrAS+3YrCpOmIiKkf86Gpnyczp36Q1CLTCfCXAz+6YPtp4Hcv3ikiBoABgEajQbPZnMYppXr2798/pf1PnjzJvHnzpnwer2d10nQCfKIuziXdkczcAmwB6O3tzb6+vmmcUmqPZrOJ16ZKM50fMZ8GXnnB9iuAZ6ZXjiSprukE+NeAJRHxmoi4Abgf2NmasiRJk7nqIZTMHIuIvwL2ArOAj2fmEy2rTJJ0RdMZAyczvwh8sUW1SJKmwCcxJalQBrgkFcoAl6RCxUw+SRYRPwF+OGMnlOq7Gfhpp4uQLuPVmXnLxY0zGuDStSoiDmZmb6frkKbCIRRJKpQBLkmFMsClcVs6XYA0VY6BS1Kh7IFLUqEMcEkqlAGurhERfxwRGRGv63QtUisY4Ooma4ADjL/6WCqeAa6uEBHzgDuBtVQBHhHXRcTDEfFEROyKiC9GxDurz94UEV+OiEMRsTciFnWwfGlCBri6xSpgT2b+N/BsRLwR+BNgMfAG4D3AWwEiYjbwEPDOzHwT8HFgUyeKlq5kWu8DlwqyBvhwtf6pans28JnMPAv8OCLOzYR8K7AU2FfNbj8LGJ3ZcqXJGeB60YuIlwF3A0sjIhkP5AQeudwhwBOZ+dYZKlG6Kg6hqBu8E/hEZr46Mxdn5iuBHzD+9sE/rcbCG0Bftf93gVsi4vyQSkS8vhOFS1digKsbrOHS3vbngN8CngaOAB8DHgeez8xfMR76H4qIbwLfAN42c+VK9fgovbpaRMzLzJPVMMtXgTsz88edrkuqwzFwdbtdETEfuAH4J8NbJbEHLkmFcgxckgplgEtSoQxwSSqUAS5JhTLAJalQ/weDsDVlSw5DnQAAAABJRU5ErkJggg==\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": 9,
   "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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-13.88037434994331\n",
      "73.27860964406095\n",
      "29.69911764705882\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Assuming Age follows A Gaussian Distribution we will calculate the boundaries which differentiates the outliers\n",
    "\n",
    "upper_boundary=df['Age'].mean() + 3* df['Age'].std()\n",
    "lower_boundary=df['Age'].mean() - 3* df['Age'].std()\n",
    "print(lower_boundary), print(upper_boundary),print(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If Features Are Skewed We Use the below Technique"
   ]
  },
  {
   "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": [
    "# Fare is skewed\n",
    "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2872bb46af0>"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26.724\n",
      "65.6344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-61.358399999999996\n",
      "100.2688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Age']>=73,'Age']=73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Fare']>=100,'Fare']=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of passenger')"
      ]
     },
     "execution_count": 21,
     "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": 24,
   "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=0.2,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_score: 0.7206703910614525\n",
      "roc_auc_score: 0.7490777338603425\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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}