Titanic_DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # TITANIC - KAGGLE CHALLENGE  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titanic rescue prediction using Decision Tree, SVM, Logistic Regression, Random Forest and KNN\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 1: Data Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age\n",
       "0         0       3    male  22.0\n",
       "1         1       1  female  38.0\n",
       "2         1       3  female  26.0\n",
       "3         1       1  female  35.0\n",
       "4         0       3    male  35.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using drop function to exclude collums I won't need. The parameter inplace true will change the table \n",
    "data.drop([\"PassengerId\", \"Name\",\"SibSp\",\"Parch\",\"Ticket\",\"Fare\",\"Cabin\",\"Embarked\"], axis=\"columns\",inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To define X I can simply drop the collumn I don't need, which is the Y \n",
    "# Here I am not using the parameter inplace, thus it is automaticaly set as \"False\" and doesn't change the table\n",
    "X = data.drop([\"Survived\"],axis=\"columns\")\n",
    "# For the variable Y I can simply select the column Survived \n",
    "Y = data[\"Survived\"] #Another way to declare: Y = data.Survived \n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Pclass  Sex   Age\n",
       "0       3    0  22.0\n",
       "1       1    1  38.0\n",
       "2       3    1  26.0\n",
       "3       1    1  35.0\n",
       "4       3    0  35.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The next step: create a pattern\n",
    "#Pattern: Male = 0 / Female = 1\n",
    "#This method map will create synonims in a dictionary\n",
    "X.Sex = X.Sex.map({\"male\":0,\"female\":1})\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "5     NaN\n",
       "6    54.0\n",
       "7     2.0\n",
       "8    27.0\n",
       "9    14.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Some filles with the ages are empy. Checkin out the first 10 lines\n",
    "X.Age[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.000000\n",
       "1    38.000000\n",
       "2    26.000000\n",
       "3    35.000000\n",
       "4    35.000000\n",
       "5    29.699118\n",
       "6    54.000000\n",
       "7     2.000000\n",
       "8    27.000000\n",
       "9    14.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To fix this, we will use method fillna(). Inside the brackets I'll put the information to replace, which will be the mean\n",
    "X.Age = X.Age.fillna(X.Age.mean())\n",
    "X.Age[0:10]\n",
    "#Index 5 was empty and is now showing the mean (29.699118)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[     Pclass  Sex        Age\n",
       " 163       3    0  17.000000\n",
       " 181       2    0  29.699118\n",
       " 56        2    1  21.000000\n",
       " 518       2    1  36.000000\n",
       " 188       3    0  40.000000\n",
       " ..      ...  ...        ...\n",
       " 699       3    0  42.000000\n",
       " 596       2    1  29.699118\n",
       " 316       2    1  24.000000\n",
       " 232       2    0  59.000000\n",
       " 461       3    0  34.000000\n",
       " \n",
       " [712 rows x 3 columns],\n",
       "      Pclass  Sex        Age\n",
       " 255       3    1  29.000000\n",
       " 859       3    0  29.699118\n",
       " 652       3    0  21.000000\n",
       " 769       3    0  32.000000\n",
       " 283       3    0  19.000000\n",
       " ..      ...  ...        ...\n",
       " 170       1    0  61.000000\n",
       " 563       3    0  29.699118\n",
       " 444       3    0  29.699118\n",
       " 719       3    0  33.000000\n",
       " 744       3    0  31.000000\n",
       " \n",
       " [179 rows x 3 columns],\n",
       " 163    0\n",
       " 181    0\n",
       " 56     1\n",
       " 518    1\n",
       " 188    0\n",
       "       ..\n",
       " 699    0\n",
       " 596    1\n",
       " 316    1\n",
       " 232    0\n",
       " 461    0\n",
       " Name: Survived, Length: 712, dtype: int64,\n",
       " 255    1\n",
       " 859    0\n",
       " 652    0\n",
       " 769    0\n",
       " 283    1\n",
       "       ..\n",
       " 170    0\n",
       " 563    0\n",
       " 444    1\n",
       " 719    0\n",
       " 744    1\n",
       " Name: Survived, Length: 179, dtype: int64]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now want data to training (80%) and testing (20%) \n",
    "#Sklearn has a method to automaticaly make this division\"train_test_split\"\n",
    "#Parameters: X, Y, percentage. If I put one, the method will automaticaly calculate the other \n",
    "train_test_split(X,Y,train_size = 0.8) # It is automaticaly setting 0.2 for testing\n",
    "# The table will show X_training [712 rows x 3 columns], X_test [179 rows x 3 columns], Y_training[Length: 712, Y_test[Length: 179]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "712"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now I create 4 variables to place each parameter\n",
    "X_train,X_test,Y_train,Y_real=train_test_split(X,Y,train_size = 0.8)\n",
    "#to see the lenght:\n",
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>11</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>74.000000</td>\n",
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       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age\n",
       "667       3    0  29.699118\n",
       "761       3    0  41.000000\n",
       "106       3    1  21.000000\n",
       "773       3    0  29.699118\n",
       "11        1    1  58.000000\n",
       "..      ...  ...        ...\n",
       "639       3    0  29.699118\n",
       "445       1    0   4.000000\n",
       "812       2    0  35.000000\n",
       "851       3    0  74.000000\n",
       "541       3    1   9.000000\n",
       "\n",
       "[179 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note that X_test is not in order. It presents it ramdomly to prevent model from becoming biased \n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2: Data Analysis  \n",
    "* 2.1. Decision Tree\n",
    "* 2.2. Logistic Regression\n",
    "* 2.3. SVM (SVC)\n",
    "* 2.4. Random Forest \n",
    "* 2.5. KNN \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2.1. Decision Tree   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If it is empty it will create it with as many levels it can. TO avoid this we put max_dep to determine how many levels we want\n",
    "#The sext parameter will be the criteria. If I dont put it will use gini. If I wanted to use entropy I'd have to put it. I want to use gini, so I don't need to put it\n",
    "model_dt = tree.DecisionTreeClassifier(max_depth=3)   #model with deph 3\n",
    "model_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now the method fit will adjust data, trainning the model\n",
    "model_dt.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using predict method to test the model\n",
    "model_dt.predict(X_test) #always gets x and retuns y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Storing the result in a variable \n",
    "Y_pred_dt = model_dt.predict(X_test)\n",
    "Y_pred_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>Survided REAL</th>\n",
       "      <th>Survived PREDICTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survided REAL  Survived PREDICTION\n",
       "667              0                    0\n",
       "761              0                    0\n",
       "106              1                    1\n",
       "773              0                    0\n",
       "11               1                    1\n",
       "..             ...                  ...\n",
       "639              0                    0\n",
       "445              1                    0\n",
       "812              0                    0\n",
       "851              0                    0\n",
       "541              0                    1\n",
       "\n",
       "[179 rows x 2 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe to show Y real and Y predicted:\n",
    "#Using a dictionary to separate columns  \n",
    "result = pd.DataFrame({\"Survided REAL\": Y_real, \"Survived PREDICTION\": Y_pred})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the tree\n",
    "plt.figure(figsize=(40,40))\n",
    "graph_tree = plot_tree (model_dt,feature_names = ['Pclass','Sex','Age'],\n",
    "                        class_names = ['Survived','Not Survived'],\n",
    "                        filled=True,rounded=True,fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Decision Tree - Performance Measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy = true negatives + true positives / true positives + false positives + true negatives + false negatives\n",
    "accuracy_dt = model_dt.score(X_test,Y_real)  \n",
    "accuracy_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is another way to find the accuracy score\n",
    "from sklearn import metrics\n",
    "accuracy = metrics.accuracy_score(Y_real,Y_pred_dt)  \n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8305084745762712"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = true positive / true positive + false positive\n",
    "precision_dt = metrics.precision_score(Y_real,Y_pred_dt)  \n",
    "precision_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6901408450704225"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall = true positive / true positive + false negative\n",
    "recall_dt = metrics.recall_score(Y_real,Y_pred_dt)  \n",
    "recall_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d) Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[98, 10],\n",
       "       [22, 49]], dtype=int64)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "confusion_matrix_dt = metrics.confusion_matrix(Y_real,Y_pred_dt)\n",
    "confusion_matrix_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0,0: 53 => True Negative\n",
    "# 0,1: 0  => False Positive \n",
    "# 1,0: 23 => False Negative \n",
    "# 1,1: 4  => True Positive \n",
    "sns.heatmap(confusion_matrix_dt, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### e) AUC - ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.798774126238915"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_dt = metrics.roc_auc_score(Y_real, Y_pred_dt) # as the documentation explain, the main parameters are: y_true and y_score\n",
    "auc_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree - k-Fold Cross-Validation\n",
    "\n",
    "Now using kfold, a model validation technique which is not using my pre-trained model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8024668884564685"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf_dt = KFold(n_splits=5,shuffle=True)  \n",
    "cv_dt = cross_val_score(model, X, Y, cv=kf_dt)\n",
    "np.mean(cv_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2.2. Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LIBLINEAR -- A Library for Large Linear Classification\n",
    "The solvers implemented in the class Logistic Regression are “liblinear”, “newton-cg”, “lbfgs”, “sag” and “saga”. According to Scikit Documentation: The “liblinear” solver was the one used by default for historical reasons before version 0.22. Since then, default use is lbfgs Algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training \n",
    "model_lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The predict() function enables us to predict the labels of the data values on the basis of the trained model.\n",
    "# Note: predict() function accepts only a single argument which is the data to be tested.\n",
    "y_pred_lg = model_lr.predict(X_test)\n",
    "y_pred_lg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Logistic Regression - Performance Measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy = true negatives + true positives / true positives + false positives + true negatives + false negatives\n",
    "accuracy_lg = model_lr.score(X_test,Y_real) \n",
    "accuracy_lg "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is another way to find the accuracy score \n",
    "acc_lg = metrics.accuracy_score(Y_real,y_pred_lg)\n",
    "acc_lg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = true positive / true positive + false positive\n",
    "precision_lg = metrics.precision_score(Y_real,y_pred_lg)  \n",
    "precision_lg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.676056338028169"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall = true positive / true positive + false negative\n",
    "rec_lg = metrics.recall_score(Y_real, y_pred_lg)\n",
    "rec_lg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d) Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[96, 12],\n",
       "       [23, 48]], dtype=int64)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_lg = metrics.confusion_matrix(Y_real,y_pred_lg)\n",
    "cm_lg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD4CAYAAACt8i4nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUUklEQVR4nO3de7RcZXnH8e8DARNEIQkSIlHAErFQJCJNVVAsAYOAJQtoCiIGmnqsRQUvlWitLO/QomhX8XIk1ajcwq3JchU0RgQVDSThlhA1GBJIyEWQCASQnDNP/zgDHHM5ew6ZfWbOzvfDetfM7L3nneePs355effe747MRJJUnh1aXYAkVZ1BK0klM2glqWQGrSSVzKCVpJINKfsHNj68zMsatJlhL39zq0tQG+p6ZlVsax/9yZyd9njVNv9eI0oPWkkaULXuVlewGYNWUrVkrdUVbMaglVQtNYNWkkqVjmglqWTdXa2uYDMGraRq8WSYJJXMqQNJKpknwySpXJ4Mk6SyOaKVpJJ1b2x1BZsxaCVVSxtOHbh6l6RqqdUabwUi4pyIWBQRiyPi3Pq2ERExJyKW1l+HF/Vj0Eqqlqw13voQEX8FvAcYDxwCnBAR+wPTgLmZORaYW//cJ4NWUrU0b0T7l8C8zHwyM7uAm4GTgBOBGfVjZgCTijoyaCVVStY2NtwioiMi5vdqHb26WgS8OSJGRsQuwHHAK4BRmbm6fswaYFRRTZ4Mk1Qt/bi8KzM7gc6t7FsSERcCPwI2AHcC3ZsckxFRuNC4I1pJ1dKkOVqAzJyema/PzLcAjwK/BdZGxGiA+uu6on4MWknVUutuvBWIiD3rr6+kZ372cmA2MKV+yBRgVlE/Th1IqpbmXkd7bUSMBDYCZ2fm+oi4AJgZEVOBFcDkok4MWknV0sRbcDNzs6eIZuYjwIT+9GPQSqoWF/6WpJK5qIwklSvTJyxIUrkc0UpSydpw9S6DVlK1OKKVpJJ51YEklcypA0kqmVMHklQyg1aSSubUgSSVzJNhklQypw4kqWROHUhSyRzRSlLJDFpJKlkWPitxwPnMMEnV0tXVeCsQER+KiMURsSgiroiIoRGxX0TMi4j7IuKqiNi5qB+DVlK1NOkpuBGxN/BB4LDM/CtgR+BU4ELg4szcn54n404tKsmglVQttVrjrdgQYFhEDAF2AVYDRwHX1PfPACYVdWLQSqqWzIZbRHRExPxereP5bnIVcBHwAD0B+0dgAbA+M5+dd1gJ7F1UkifDJFVLP646yMxOoHNL+yJiOHAisB+wHrgaOPaFlGTQSqqW5l3edTRwf2b+HiAirgMOB3aPiCH1Ue0YYFVRR04dSKqU7O5uuBV4AHhDROwSEQFMAO4FbgJOqR8zBZhV1JFBK6lamnQyLDPn0XPSayFwDz152QmcB3w4Iu4DRgLTi0py6kBStTRxrYPMPB84f5PNy4Dx/enHoJVULbX2uzPMoJVULa51IEklKz7JNeA8GVaS7838Xya965858fT38r2rrn9u+2VXz+Idp72HE09/L1+6pHAOXRXzrc4v8dDKu7jzjrnPbbvwi59k0T03s3DBHK65+lJ22+2lLaywApp7Z1hTGLQlWLpsOdfOvpErLv0K1874GjffehsPrHyI2xbcxU0//xXXzriEWZd9kzPfeXKrS9UA++53Z3L8Caf/2bYfz72FQ8YdxaGvP4alS5cx7bz3t6i6iqhl422AFE4dRMRr6Lk74tnbzFYBszNzSZmFDWbLlj/IwQcdwLChQwE4bNzB/PjmX7D410uZ+q7J7Lxzz2I/I4fv3sIq1Qo/+/k89tlnzJ9tm/PjW557/6t5Czn5pOMHuqxqacMnLPQ5oo2I84ArgQBuq7cAroiIaeWXNzjt/6p9WHjXYtb/8TGeevppfvbL21mz9vcsf2AVC+5axGnvOZczz/5X7lnym1aXqjZz1pmncuMPb2p1GYPbIBzRTgUOysyNvTdGxJeBxcAFW/pSfWGGDoCvfelz/NO7T2tCqYPHX+z7Sv7x9L+n40P/xrChQzlg7KvYYYcd6O7u5rHHHufyzotZtOS3fPTfv8iNV3+bnptOtL37+LQP0tXVxeWXX9fqUga1HIRXHdSAlwMrNtk+ur5vi3ov1LDx4WXtd1HbADj5HRM5+R0TAfjKN77DXnvuwf0PrOToIw8nIjj4wAOICB5d/0dGOIWw3Xv3GZM5/rijOWbi5FaXMvi14VUHRUF7LjA3IpYCD9a3vRLYH3DGvg+PPLqekcN3Z/Wadcy9+Rdc1nkxEcFtC+9i/OsPYfkDK9nY1cXw3XdrdalqsYlveysf/ej7OGrCyTz11NOtLmfwG2w3LGTmjRHxanpuN+t9Muz2zGy/fzbayIc+8TnWP/YYQ4YM4d8+8i+89CW7ctIJb+OTX7iYSe/6Z3baaQhf+ORHnDbYznz/e5dw5FveyB57jGD5svl8+jMXcd7H3s+LXvQibrzhSgDmzVvI2e/3FMgL1oZTB5ElP8hse506UN+GvfzNrS5BbajrmVXbPPLY8KlTG86cF3/mygEZ6XhnmKRqacPLuwxaSdUy2OZoJWmwya72O31k0EqqFke0klSyNpyjdVEZSdXSpFtwI+KAiLizV3ssIs6NiBERMSciltZfhxeVZNBKqpSsZcOtz34yf5OZ4zJzHPB64EngemAaMDczxwJz65/7ZNBKqpau7sZb4yYAv8vMFfSsZjijvn0GMKnoywatpGrpx9RBRHRExPxerWMrvZ4KXFF/PyozV9ffrwFGFZXkyTBJ1dKPqw56L4C1NRGxM/B3wMe38P2MiMIfNGglVUoJywq8HViYmWvrn9dGxOjMXB0Ro4F1RR04dSCpWpq/8PdpPD9tADAbmFJ/PwWYVdSBI1pJ1dLEGxYi4sXAMcB7e22+AJgZEVPpWau7cBFhg1ZSpWRX825YyMwNwMhNtj1Cz1UIDTNoJVVL+90YZtBKqpaiGxFawaCVVC0GrSSVzKkDSSqXUweSVLLsMmglqVxOHUhSudpw3W+DVlLFGLSSVC5HtJJUsuxqdQWbM2glVYojWkkqmUErSWXLaHUFmzFoJVWKI1pJKlnW2m9E66NsJFVKrTsabkUiYveIuCYifh0RSyLijRExIiLmRMTS+uvwon4MWkmVkrXGWwO+CtyYma8BDgGWANOAuZk5Fphb/9wng1ZSpWQtGm59iYjdgLcA0wEy85nMXA+cCMyoHzYDmFRUk0ErqVIyG28F9gN+D3w7Iu6IiEvrD2sclZmr68esAUYVdWTQSqqU/oxoI6IjIub3ah29uhoCHAp8PTNfB2xgk2mCzEygMLK96kBSpTRykutZmdkJdG5l90pgZWbOq3++hp6gXRsRozNzdUSMBtYV/Y4jWkmV0qw52sxcAzwYEQfUN00A7gVmA1Pq26YAs4pqckQrqVKyuXeGfQC4LCJ2BpYBZ9EzQJ0ZEVOBFcDkok4MWkmV0sw7wzLzTuCwLeya0J9+DFpJlVJzrQNJKleTpw6awqCVVCn9uepgoBi0kiqlHReVMWglVYpztJJUMudoJalkDaxhMOAMWkmV4tSBJJWs5skwSSrXdjmi/ZuD3132T2gQmjniyFaXoIryZJgklWy7HNFK0kBqw4sODFpJ1dJda79ltg1aSZXSxFUSm8aglVQpiXO0klSqWhtO0hq0kiql1sQRbUQsBx4HuoGuzDwsIkYAVwH7AsuByZn5aF/9tN+ssSRtgyQabg3628wcl5nPPtJmGjA3M8cCc9nkEeRbYtBKqpRuouH2Ap0IzKi/nwFMKvqCQSupUmr9aBHRERHze7WOTbpL4EcRsaDXvlGZubr+fg0wqqgm52glVUp/Lu/KzE6gs49DjsjMVRGxJzAnIn69yfczIgpPvzmilVQpzZyjzcxV9dd1wPXAeGBtRIwGqL+uK+rHoJVUKbVovPUlIl4cES959j3wNmARMBuYUj9sCjCrqCanDiRVShMv7xoFXB8R0JOVl2fmjRFxOzAzIqYCK4DJRR0ZtJIqpbtJ/WTmMuCQLWx/BJjQn74MWkmVUgtvwZWkUrXhHbgGraRqcfUuSSpZGz6b0aCVVC3bcGttaQxaSZXiiFaSSuYcrSSVzKsOJKlkTh1IUsmcOpCkknU7opWkcjmilaSSGbSSVDKvOpCkknnVgSSVzKkDSSpZsxb+biafGSapUpr1zLBnRcSOEXFHRPyg/nm/iJgXEfdFxFURsXNRHwatpEqp9aM16BxgSa/PFwIXZ+b+wKPA1KIODFpJlZL9aEUiYgxwPHBp/XMARwHX1A+ZAUwq6seglVQpNbLhFhEdETG/V+vYpLuvAB/j+QHwSGB9ZnbVP68E9i6qyZNhkiqlPyfDMrMT6NzSvog4AViXmQsi4q3bUpNBK6lSmnh51+HA30XEccBQ4KXAV4HdI2JIfVQ7BlhV1JFTB5IqpVlXHWTmxzNzTGbuC5wK/CQzTwduAk6pHzYFmFVUk0ErqVL6M0f7Ap0HfDgi7qNnznZ60RecOpBUKWWsdZCZPwV+Wn+/DBjfn+8btJIqxVtwJalk3W24fpdBK6lSHNFKUsm24SRXaQxaSZXSfjFr0EqqGKcOJKlkngyTpJI5R7sdGfXyPfnMf32SkS8bTiZc9/3ZXHHp1bzvY//EWyceQa2W/OGRRzn/nM/z8NpHWl2uBtIOwVE//DxPrfkDvzzjIl52xEEc/Kl3wg5B14Y/seCcb7Bh+dpWVzlotV/Megtuabq7urn40//NKUeewZTjO5h85kns9+p9+e7XLucfJpzJacecxc/m3ErHh89qdakaYPu/5+08vvT5dUjGXfiP3H72Jfzk6E+w8vpf8JoPTWpdcRUwALfg9ptBW5KH1z3Cr+/5LQBPbniK+5cuZ8+99mDDE08+d8ywXYaS2Y7//qosw0aPYK+jx7H8spue35jJkF2HATDkJbvw9Jr1rSmuIkp4wsI2c+pgAIwesxcHHPxqFi28F4Czp3Vw/CkTeeLxDXSc8sEWV6eB9NrPnsGiz17BkF2HPrdt4Ue+xZsu+xi1p59h4xNP8dPjzm9hhYNftuHkwQse0UbEVv+ft/eq5Q8/ueaF/kQlDNtlGBdN/zxf+tRXnxvNXnJBJ8cddjI3XPcjTj3rpBZXqIGy1zGv408PP8b6u+//s+37d7ydW0//D2449AOsuPIWXvvpd7WowmroJhtuA2Vbpg4+vbUdmdmZmYdl5mF77LLXNvzE4DZkyI5cNP1z/N91P+In/3fLZvtvuG4ORx3/1oEvTC0x8q9fzei3HcrE27/K+G98gJcdfhBv+v6/sttB+/DoHb8DYOWsXzLir8e2uNLBbdBNHUTE3VvbBYxqfjnV8qkvf5z7l67gsm9e9dy2V+w3hgfvXwnAkROPYPl9K1pVngbY4i9cxeIv9Pwt7PGmv2Ts+47nV2d+mePu/jq7vmovnli2hj3fcjCP//ahFlc6uNXa8LxH0RztKGAiPY/U7S2AW0upqCLGjX8tJ/z9sSy99z6umPNtAP77i99k0jtPYJ+/eCVZq7F65Vo+f95/trhStVJ217jjo9/ib6afS9aSjX/cwIJzt/gIKzWo/WK2OGh/AOyamXduuiMiflpGQVVx5213c+joIzbb/ouf/KoF1ajdPHzrEh6+dQkAD90wn4dumN/iiqpj0N2wkJlT+9j3zuaXI0nbpllXHUTEUOAW4EX0ZOU1mXl+ROwHXEnPY2wWAGdk5jN99eV1tJIqpYtsuBX4E3BUZh4CjAOOjYg3ABcCF2fm/vRMq251QPosg1ZSpWQ//uuznx5P1D/uVG8JHAVcU98+A5hUVJNBK6lS+nN5V+9r/uuto3dfEbFjRNwJrAPmAL8D1mdmV/2QlcDeRTV5Z5ikSunPbe2Z2Qls9TKPzOwGxkXE7sD1wGteSE0GraRKKeOqg8xcHxE3AW8Edo+IIfVR7RhgVd/fdupAUsU06xbciHhZfSRLRAwDjgGWADcBp9QPmwLMKqrJEa2kSmniiHY0MCMidqRnUDozM38QEfcCV0bE54A7gOlFHRm0kiqlWUuPZubdwOu2sH0ZML4/fRm0kirFhzNKUsnacT1ag1ZSpQy6tQ4kabDpzvabPDBoJVWKUweSVLLBuPC3JA0q7RezBq2kivFkmCSVzKCVpJJ51YEklcyrDiSpZM1a66CZDFpJleIcrSSVzBGtJJWsuw3X7zJoJVWKd4ZJUsna8aoDnxkmqVJqmQ23vkTEKyLipoi4NyIWR8Q59e0jImJORCytvw4vqsmglVQp2Y//CnQBH8nMA4E3AGdHxIHANGBuZo4F5tY/98mglVQpzRrRZubqzFxYf/84PU/A3Rs4EZhRP2wGMKmoJudoJVVKf27BjYgOoKPXps7M7NzCcfvS86DGecCozFxd37UGGFX0OwatpErpz8mweqhuFqy9RcSuwLXAuZn5WET0/n5GROEPGrSSKiWbuKhMROxET8helpnX1TevjYjRmbk6IkYD64r6cY5WUqXUyIZbX6Jn6DodWJKZX+61azYwpf5+CjCrqCZHtJIqpYm34B4OnAHcExF31rd9ArgAmBkRU4EVwOSijgxaSZXSrEVlMvPnQGxl94T+9GXQSqqU7pprHUhSqdrxFlyDVlKluEyiJJXMhb8lqWSOaCWpZJ4Mk6SSOXUgSSVz6kCSSuajbCSpZF5HK0klc0QrSSWrNXGZxGYxaCVViifDJKlkBq0klaz9YhaiHdO/qiKiY0sPftP2zb+L6vNRNgOro/gQbYf8u6g4g1aSSmbQSlLJDNqB5TyctsS/i4rzZJgklcwRrSSVzKCVpJIZtAMkIo6NiN9ExH0RMa3V9aj1IuJ/ImJdRCxqdS0ql0E7ACJiR+AS4O3AgcBpEXFga6tSG/gOcGyri1D5DNqBMR64LzOXZeYzwJXAiS2uSS2WmbcAf2h1HSqfQTsw9gYe7PV5ZX2bpO2AQStJJTNoB8Yq4BW9Po+pb5O0HTBoB8btwNiI2C8idgZOBWa3uCZJA8SgHQCZ2QW8H/ghsASYmZmLW1uVWi0irgB+CRwQESsjYmqra1I5vAVXkkrmiFaSSmbQSlLJDFpJKplBK0klM2glqWQGrSSVzKCVpJL9P5yd7J0ZtsklAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(cm_lg, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### e) AUC - ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7824726134585289"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_lg = metrics.roc_auc_score(Y_real, y_pred_lg) # as the documentation explain, the main parameters are: y_true and y_score\n",
    "auc_lg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression - k-Fold Cross-Validation\n",
    "Now using kfold, a model validation technique which is not using my pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_lg = KFold(n_splits=5,shuffle=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7889900194589167"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_lg = cross_val_score(model_lr, X, Y, cv=kf_lg)\n",
    "np.mean(cv_lg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2.3. Support Vector Machines (SVC)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc = svm.SVC(kernel=\"linear\")\n",
    "model_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training \n",
    "model_svc.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The predict() function enables us to predict the labels of the data values on the basis of the trained model.\n",
    "# Note: predict() function accepts only a single argument which is the data to be tested.\n",
    "Y_pred_svm = model_svc.predict(X_test)\n",
    "Y_pred_svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Classification (SVC) - Performance Measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### a) Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy = true negatives + true positives / true positives + false positives + true negatives + false negatives\n",
    "accuracy_svc = model_svc.score(X_test,Y_real) \n",
    "accuracy_svc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is another way to find the accuracy score\n",
    "from sklearn import metrics\n",
    "acc_svc = metrics.accuracy_score(Y_real,Y_pred_svm)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8214285714285714"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = true positive / true positive + false positive\n",
    "precision_svc = metrics.precision_score(Y_real,Y_pred_svm)  \n",
    "precision_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.647887323943662"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall = true positive / true positive + false negative \n",
    "rec_svc = metrics.recall_score(Y_real, Y_pred_svm)\n",
    "rec_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d) Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[98, 10],\n",
       "       [25, 46]], dtype=int64)"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_svc = metrics.confusion_matrix(Y_real,Y_pred_svm)\n",
    "cm_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_svc, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### e) AUC - ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7776473656755347"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_svc = metrics.roc_auc_score(Y_real, Y_pred_svm) # as the documentation explain, the main parameters are: y_true and y_score\n",
    "auc_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Classification (SVC) - k-Fold Cross-Validation\n",
    "Now using kfold, a model validation technique which is not using my pre-trained model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_svc = KFold(n_splits=5,shuffle=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7889900194589167"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_svc = cross_val_score(model_svc, X, Y, cv=kf_svc)\n",
    "np.mean(cv_lg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2.4. Random Forest  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rf = RandomForestClassifier().fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_forest = model_rf.predict(X_test)\n",
    "y_pred_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest - Performance Measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8435754189944135"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy = true negatives + true positives / true positives + false positives + true negatives + false negatives\n",
    "accuracy_forest = model_rf.score(X_test,Y_real)\n",
    "accuracy_forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8435754189944135"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is another way to find the accuracy score \n",
    "accuracy_forest = metrics.accuracy_score(Y_real,y_pred_forest)\n",
    "accuracy_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = true positive / true positive + false positive\n",
    "precision_forest = metrics.precision_score(Y_real,y_pred_forest)  \n",
    "precision_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7323943661971831"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall = true positive / true positive + false negative \n",
    "recall_forest = metrics.recall_score(Y_real, y_pred_forest)\n",
    "recall_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d) Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[99,  9],\n",
       "       [19, 52]], dtype=int64)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_forest = metrics.confusion_matrix(Y_real,y_pred_forest)\n",
    "cm_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_forest, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### e) AUC - Roc Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8245305164319248"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_forest = metrics.roc_auc_score(Y_real, y_pred_forest) # as the documentation explain, the main parameters are: y_true and y_score\n",
    "auc_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest - k-Fold Cross-Validation\n",
    "Now using kfold, a model validation technique which is not using my pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_forest = KFold(n_splits=5,shuffle=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8080660347749671"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_forest = cross_val_score(model_rf, X, Y, cv=kf_forest)\n",
    "np.mean(cv_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 2.5. KNN (K-Nearest Neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model = KNeighborsRegressor()\n",
    "knn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2, 0. , 0. , 0.2, 0.4, 0.4, 0.4, 0.2, 0.8, 0.4, 0.2, 0.2, 0. ,\n",
       "       0. , 0.6, 0.8, 0.4, 0.6, 0.2, 1. , 0.2, 0. , 0.6, 1. , 0.2, 0.6,\n",
       "       0.4, 0.4, 0. , 0.2, 1. , 0. , 0.2, 0.6, 0.6, 0. , 0. , 0.2, 0.2,\n",
       "       0. , 0. , 0. , 1. , 0. , 0.6, 0.2, 0.2, 0.4, 1. , 0. , 0.2, 0.8,\n",
       "       0. , 0.4, 0.6, 0.2, 0.6, 0.2, 1. , 1. , 1. , 0.4, 0.8, 0.4, 0.4,\n",
       "       0.2, 0.2, 0.4, 0.2, 0.8, 0. , 1. , 0. , 0. , 0.6, 0.2, 1. , 1. ,\n",
       "       0.6, 0.6, 0.6, 0. , 0.4, 0.8, 0.2, 0.4, 0.6, 0.6, 0.6, 0. , 0.2,\n",
       "       0.8, 0.6, 0.2, 0.4, 0.6, 0.8, 0. , 1. , 0.4, 0. , 1. , 0.4, 0.2,\n",
       "       0.2, 0.6, 0.2, 0. , 0.4, 0.6, 0.4, 0.6, 0.2, 0. , 0.2, 0.2, 0.2,\n",
       "       0. , 0.2, 0. , 1. , 0.2, 0.4, 0. , 0.4, 0. , 0.2, 0.6, 0.2, 0.2,\n",
       "       0.2, 0.6, 0.6, 0. , 0.4, 0. , 0. , 0. , 0.4, 0.6, 0. , 0.4, 0. ,\n",
       "       0.2, 1. , 0.2, 1. , 0. , 0.2, 0.4, 1. , 1. , 0.2, 0.6, 0. , 0.2,\n",
       "       1. , 1. , 0.8, 0.2, 0.2, 0.2, 0. , 0.6, 0.6, 0.8, 0.2, 0.4, 1. ,\n",
       "       0.2, 0.4, 0. , 0.4, 0. , 0.2, 1. , 0. , 0. , 0. ])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_knn = knn_model.predict(X_test)\n",
    "y_pred_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors - Performance Measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classification metrics can't handle a mix of binary and continuous targets\n",
    "# Here I must apply method round() from numpy \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# its criteria is to round to 1 when higher than 0.5\n",
    "rounding = np.round(y_pred_knn)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7374301675977654"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NOTE = no parametro tem q ser o y real e depois o pred\n",
    "acc_knn = metrics.accuracy_score(Y_real,rounding)  \n",
    "acc_knn "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Precision = true positive / true positive + false positive\n",
    "precision_knn = metrics.precision_score(Y_real,rounding)  \n",
    "precision_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5915492957746479"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall = true positive / true positive + false negative \n",
    "recall_knn = metrics.recall_score(Y_real, rounding)\n",
    "recall_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d) Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[90, 18],\n",
       "       [29, 42]], dtype=int64)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_knn = metrics.confusion_matrix(Y_real, rounding)\n",
    "cm_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_forest, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### e) AUC - ROC Curve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7124413145539907"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_knn = metrics.roc_auc_score(Y_real, rounding) # as the documentation explain, the main parameters are: y_true and y_score\n",
    "auc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors - k-Fold Cross-Validation\n",
    "Now using kfold, a model validation technique which is not using my pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_neighbors = KFold(n_splits=5,shuffle=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28765620133288194"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_neighbors = cross_val_score(knn_model, X, Y, cv=kf_neighbors)\n",
    "np.mean(cv_neighbors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 3 - Valuation Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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": [
    "plt.title(\"Accuracy Comparison Graph\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "\n",
    "plt.xlabel(\"Algorithms - 1.Decision Tree / 2.Logistic Regression / 3.SVM (SVC) / 4.Random Forest / 5.KNN\")\n",
    "\n",
    "y = [acc_knn,accuracy_dt,accuracy_lg,acc_svc,accuracy_forest]\n",
    "\n",
    "plt.plot([1,2,3,4,5],y, color = \"black\")\n",
    "\n",
    "\n",
    "plt.scatter(1,acc_knn, marker=\"o\", color = \"purple\",label = \"KNN\")\n",
    "\n",
    "plt.scatter(2,accuracy_dt, marker=\"o\", color=\"red\", label=\"Decision Tree\")\n",
    "\n",
    "plt.scatter(3,accuracy_lg, marker=\"o\", color=\"green\", label=\"Logistic Regression\")\n",
    "\n",
    "plt.scatter(4,acc_svc, marker=\"o\", color=\"blue\", label=\"SVM (SVC)\")\n",
    "\n",
    "plt.scatter(5,accuracy_forest, marker=\"o\", color = \"orange\",label = \"Random Forest\")\n",
    "\n",
    " \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n"
   ]
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