aprendizadoDeMaquina.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Aplicando Machine Learning ao desastre</h1>\n",
    "\n",
    "<h4><a href='https://www.youtube.com/watch?v=Z5kqCBDcMnQ'>Conteúdo de auxilio de Krish Narukulla</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Antes de qualquer coisa, devo explicar uma coisa. A ferramenta Sckit Learn não funciona com dados literais('A', 'B'), sendo necessário que todas a váriaveis estejam em formato numérico para que assim evite problemas.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Bibliotecas de maninupação de dados\n",
    "import pandas as pd #biblioteca para criação de DataFrames\n",
    "import numpy as np #algebra\n",
    "\n",
    "#bibliotecas de machine learning\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#Bibliotecas de visualização\n",
    "\n",
    "import seaborn as sns #graficos mais atraentes\n",
    "import matplotlib.pyplot as plt #plotagem\n",
    "sns.set_style('whitegrid')\n",
    "import random #seleção de valores aleatórios para plotagem\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lendo o arquivo\n",
    "df1 = pd.read_csv(\"data/train.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Limpando colunas que não afetam a análise</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1.drop(['Ticket','Cabin','PassengerId'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df1.pop('Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "variaveisNumericas = list(df1.dtypes[df1.dtypes!= 'object'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1       3  22.0      1      0   7.2500\n",
       "1            2       1  38.0      1      0  71.2833\n",
       "2            3       3  26.0      0      0   7.9250\n",
       "3            4       1  35.0      1      0  53.1000\n",
       "4            5       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[variaveisNumericas].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tratando as celulas que não tem nenhum valor, iremos colocar a média das idades para preencher essas lacunas\n",
    "df1['Age'].fillna(df1.Age.mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model =  RandomForestClassifier(n_estimators=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=2000, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(df1[variaveisNumericas],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia do Treino 100.0 %\n"
     ]
    }
   ],
   "source": [
    "treinoAcu = accuracy_score(y, model.predict(df1[variaveisNumericas]))\n",
    "treinoAcu =  treinoAcu * 100\n",
    "print(\"Acurácia do Treino\",treinoAcu,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTEste = pd.read_csv(\"data/test.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare\n",
       "0          892       3  34.5      0      0   7.8292\n",
       "1          893       3  47.0      1      0   7.0000\n",
       "2          894       2  62.0      0      0   9.6875\n",
       "3          895       3  27.0      0      0   8.6625\n",
       "4          896       3  22.0      1      1  12.2875"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTEste[variaveisNumericas].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTEste['Age'].fillna(dfTEste.Age.mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTEste = dfTEste[variaveisNumericas].fillna(dfTEste.mean()).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(dfTEste[variaveisNumericas])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Criando o data frame com o resultado.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "submissao = pd.DataFrame({\n",
    "                        'PassengerID': dfTEste['PassengerId'],\n",
    "                        'Survived': y_pred\n",
    "                         })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerID  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissao.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "submissao.to_csv('data/titanic.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissao['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "vivos = submissao[submissao['Survived'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A quantidade de pessoas que conseguiram sobreviver é  PassengerID    158\n",
      "Survived       158\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"A quantidade de pessoas que conseguiram sobreviver é \", vivos.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "classePlot =  sns.countplot(data = submissao,x = 'Survived', hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}