Titanic Prediction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Titanic Prediction","provenance":[],"mount_file_id":"1AMXo5WcGfXnE3vu-26qdFN6VLESt1rl1","authorship_tag":"ABX9TyNzM6LDTtrD9sOK3lzafC3Y"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"Gb0AjvwV_msr","colab_type":"code","outputId":"c667be44-beb3-45f8-e456-243840fd5a0c","executionInfo":{"status":"ok","timestamp":1584383749569,"user_tz":-330,"elapsed":1273,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["cd 'drive/My Drive/PROJECTS/Predicting the Survival of Titanic Passengers'"],"execution_count":27,"outputs":[{"output_type":"stream","text":["[Errno 2] No such file or directory: 'drive/My Drive/PROJECTS/Predicting the Survival of Titanic Passengers'\n","/content/drive/My Drive/PROJECTS/Predicting the Survival of Titanic Passengers\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"I25SGOFwAg0e","colab_type":"code","colab":{}},"source":["# linear algebra\n","import numpy as np \n","\n","# data processing\n","import pandas as pd \n","\n","# data visualization\n","import seaborn as sns\n","%matplotlib inline\n","from matplotlib import pyplot as plt\n","from matplotlib import style\n","\n","# Algorithms\n","from sklearn import linear_model\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.linear_model import Perceptron\n","from sklearn.linear_model import SGDClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.svm import SVC, LinearSVC\n","from sklearn.naive_bayes import GaussianNB\n","\n","import re\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4Jcx-YplA5sz","colab_type":"code","colab":{}},"source":["test_df = pd.read_csv(\"test.csv\")\n","train_df = pd.read_csv(\"train.csv\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GyQ1u3HlA6qR","colab_type":"code","outputId":"4a1f9f0a-c126-4284-8e58-1c82edbe07a3","executionInfo":{"status":"ok","timestamp":1584383749570,"user_tz":-330,"elapsed":1261,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":306}},"source":["train_df.info()\n"],"execution_count":30,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n","PassengerId    891 non-null int64\n","Survived       891 non-null int64\n","Pclass         891 non-null int64\n","Name           891 non-null object\n","Sex            891 non-null object\n","Age            714 non-null float64\n","SibSp          891 non-null int64\n","Parch          891 non-null int64\n","Ticket         891 non-null object\n","Fare           891 non-null float64\n","Cabin          204 non-null object\n","Embarked       889 non-null object\n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TWpmOkddA9rr","colab_type":"code","outputId":"873680f8-2e62-44bf-82c2-3af2ff320b47","executionInfo":{"status":"ok","timestamp":1584383749571,"user_tz":-330,"elapsed":1258,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["train_df.describe()\n"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>714.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>446.000000</td>\n","      <td>0.383838</td>\n","      <td>2.308642</td>\n","      <td>29.699118</td>\n","      <td>0.523008</td>\n","      <td>0.381594</td>\n","      <td>32.204208</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>257.353842</td>\n","      <td>0.486592</td>\n","      <td>0.836071</td>\n","      <td>14.526497</td>\n","      <td>1.102743</td>\n","      <td>0.806057</td>\n","      <td>49.693429</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.420000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>223.500000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>20.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.910400</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>446.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>668.500000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>38.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>891.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>6.000000</td>\n","      <td>512.329200</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n","count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n","mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n","std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n","min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n","25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n","50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n","75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n","max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n","\n","[8 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"kLBtqoU3Bn5R","colab_type":"code","outputId":"2cb0fa83-b6f2-4560-c798-012547e579e3","executionInfo":{"status":"ok","timestamp":1584383749571,"user_tz":-330,"elapsed":1254,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["train_df.head(8)\n"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Moran, Mr. James</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>330877</td>\n","      <td>8.4583</td>\n","      <td>NaN</td>\n","      <td>Q</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>McCarthy, Mr. Timothy J</td>\n","      <td>male</td>\n","      <td>54.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>17463</td>\n","      <td>51.8625</td>\n","      <td>E46</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Palsson, Master. Gosta Leonard</td>\n","      <td>male</td>\n","      <td>2.0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>349909</td>\n","      <td>21.0750</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n","0            1         0       3  ...   7.2500   NaN         S\n","1            2         1       1  ...  71.2833   C85         C\n","2            3         1       3  ...   7.9250   NaN         S\n","3            4         1       1  ...  53.1000  C123         S\n","4            5         0       3  ...   8.0500   NaN         S\n","5            6         0       3  ...   8.4583   NaN         Q\n","6            7         0       1  ...  51.8625   E46         S\n","7            8         0       3  ...  21.0750   NaN         S\n","\n","[8 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"pH118F0fB5-F","colab_type":"code","outputId":"1a27f0ed-0850-4141-a970-d95de1957e8e","executionInfo":{"status":"ok","timestamp":1584383749572,"user_tz":-330,"elapsed":1250,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["total = train_df.isnull().sum().sort_values(ascending=False)\n","percent_1 = train_df.isnull().sum()/train_df.isnull().count()*100\n","percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n","missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])\n","missing_data.head(5)"],"execution_count":33,"outputs":[{"output_type":"execute_result","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>Total</th>\n","      <th>%</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Cabin</th>\n","      <td>687</td>\n","      <td>77.1</td>\n","    </tr>\n","    <tr>\n","      <th>Age</th>\n","      <td>177</td>\n","      <td>19.9</td>\n","    </tr>\n","    <tr>\n","      <th>Embarked</th>\n","      <td>2</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>Fare</th>\n","      <td>0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>Ticket</th>\n","      <td>0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Total     %\n","Cabin       687  77.1\n","Age         177  19.9\n","Embarked      2   0.2\n","Fare          0   0.0\n","Ticket        0   0.0"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"lnzjVdBxDQfr","colab_type":"code","outputId":"5c999650-3b17-44d6-89e4-b71650328e23","executionInfo":{"status":"ok","timestamp":1584383750175,"user_tz":-330,"elapsed":1849,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["train_df.columns.values\n"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n","       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"code","metadata":{"id":"uWhWxTW2DkGC","colab_type":"code","outputId":"044f82f1-fbbf-4493-f3b1-47ad5860a165","executionInfo":{"status":"ok","timestamp":1584383750176,"user_tz":-330,"elapsed":1845,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["survived = 'survived'\n","not_survived = 'not survived'\n","fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))\n","women = train_df[train_df['Sex']=='female']\n","men = train_df[train_df['Sex']=='male']\n","ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False)\n","ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[0], kde =False)\n","ax.legend()\n","ax.set_title('Female')\n","ax = sns.distplot(men[men['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[1], kde = False)\n","ax = sns.distplot(men[men['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[1], kde = False)\n","ax.legend()\n","_ = ax.set_title('Male')"],"execution_count":35,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlAAAAEWCAYAAACpC6mpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLdi/iikyHlE9/ul3tP4mChe1Yg9rDkX7V8UHkUfE/sDtwOcz8/fANcDRwF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SNeo2r+TVb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\nqSADlCRJUkEGKEmSpIIMUJIkSQX9f3K7ccDkkV0hAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 720x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"iirysaGXkvQr","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":519},"outputId":"a57366cb-da86-4fe7-b1d8-77c9553c416e","executionInfo":{"status":"ok","timestamp":1584383752360,"user_tz":-330,"elapsed":4023,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n","grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n","grid.add_legend();"],"execution_count":36,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:243: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n","  warnings.warn(msg, UserWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAgAAAAHUCAYAAABMP5BeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nc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apHkuwzxSJXVNX/3Sz7fuB0ehX4HwOvqKoHJ3WhnQF8tKo+lWR34Kcq7yTrmDpZPlxV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\npg3yAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 514.88x475.2 with 6 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"nSK0WtrDEHEh","colab_type":"code","colab":{}},"source":["train_df = train_df.drop(['PassengerId'], axis=1)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ukpp161unfzv","colab_type":"code","colab":{}},"source":["deck = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"U\": 8}\n","data = [train_df, test_df]\n","\n","for dataset in data:\n","    dataset['Cabin'] = dataset['Cabin'].fillna(\"U0\")\n","    dataset['Deck'] = dataset['Cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n","    dataset['Deck'] = dataset['Deck'].map(deck)\n","    dataset['Deck'] = dataset['Deck'].fillna(0)\n","    dataset['Deck'] = dataset['Deck'].astype(int)\n","# we can now drop the cabin feature\n","train_df = train_df.drop(['Cabin'], axis=1)\n","test_df = test_df.drop(['Cabin'], axis=1)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"u5wSUqKNnkxU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":419},"outputId":"5437c6bf-f479-4469-a50f-36501e0748d4","executionInfo":{"status":"ok","timestamp":1584383752361,"user_tz":-330,"elapsed":4010,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["train_df"],"execution_count":39,"outputs":[{"output_type":"execute_result","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>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>Embarked</th>\n","      <th>Deck</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\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>S</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\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>C</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\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>S</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\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>S</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\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>S</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>886</th>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>Montvila, Rev. Juozas</td>\n","      <td>male</td>\n","      <td>27.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>211536</td>\n","      <td>13.0000</td>\n","      <td>S</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>887</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Graham, Miss. Margaret Edith</td>\n","      <td>female</td>\n","      <td>19.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>112053</td>\n","      <td>30.0000</td>\n","      <td>S</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>888</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>W./C. 6607</td>\n","      <td>23.4500</td>\n","      <td>S</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>889</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Behr, Mr. Karl Howell</td>\n","      <td>male</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>111369</td>\n","      <td>30.0000</td>\n","      <td>C</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>890</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Dooley, Mr. Patrick</td>\n","      <td>male</td>\n","      <td>32.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>370376</td>\n","      <td>7.7500</td>\n","      <td>Q</td>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>891 rows × 11 columns</p>\n","</div>"],"text/plain":["     Survived  Pclass  ... Embarked Deck\n","0           0       3  ...        S    8\n","1           1       1  ...        C    3\n","2           1       3  ...        S    8\n","3           1       1  ...        S    3\n","4           0       3  ...        S    8\n","..        ...     ...  ...      ...  ...\n","886         0       2  ...        S    8\n","887         1       1  ...        S    2\n","888         0       3  ...        S    8\n","889         1       1  ...        C    3\n","890         0       3  ...        Q    8\n","\n","[891 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"ZYEhkbXyoJDw","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"b042d8e7-9a65-4f52-b459-c589bf01dc29","executionInfo":{"status":"ok","timestamp":1584383951113,"user_tz":-330,"elapsed":1072,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["data = [train_df, test_df]\n","\n","for dataset in data:\n","    mean = train_df[\"Age\"].mean()\n","    std = test_df[\"Age\"].std()\n","    is_null = dataset[\"Age\"].isnull().sum()\n","    # compute random numbers between the mean, std and is_null\n","    rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n","    # fill NaN values in Age column with random values generated\n","    age_slice = dataset[\"Age\"].copy()\n","    age_slice[np.isnan(age_slice)] = rand_age\n","    dataset[\"Age\"] = age_slice\n","    dataset[\"Age\"] = train_df[\"Age\"].astype(int)\n","train_df[\"Age\"].isnull().sum()"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"m2LZlRnDpHyC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":102},"outputId":"c3ed5700-f54b-40f9-c142-4bc1cefc17e8","executionInfo":{"status":"ok","timestamp":1584383981423,"user_tz":-330,"elapsed":1092,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["train_df['Embarked'].describe()\n"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count     889\n","unique      3\n","top         S\n","freq      644\n","Name: Embarked, dtype: object"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"Dq8cWl-xpPLw","colab_type":"code","colab":{}},"source":["common_value = 'S'\n","data = [train_df, test_df]\n","\n","for dataset in data:\n","    dataset['Embarked'] = dataset['Embarked'].fillna(common_value)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hnrsyd-mpUbg","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":289},"outputId":"75f329d0-21f8-42c3-8979-ad59e9aec058","executionInfo":{"status":"ok","timestamp":1584384011799,"user_tz":-330,"elapsed":926,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["train_df.info()\n"],"execution_count":43,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 11 columns):\n","Survived    891 non-null int64\n","Pclass      891 non-null int64\n","Name        891 non-null object\n","Sex         891 non-null object\n","Age         891 non-null int64\n","SibSp       891 non-null int64\n","Parch       891 non-null int64\n","Ticket      891 non-null object\n","Fare        891 non-null float64\n","Embarked    891 non-null object\n","Deck        891 non-null int64\n","dtypes: float64(1), int64(6), object(4)\n","memory usage: 76.7+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eJzxVSqUpWo-","colab_type":"code","colab":{}},"source":["data = [train_df, test_df]\n","\n","for dataset in data:\n","    dataset['Fare'] = dataset['Fare'].fillna(0)\n","    dataset['Fare'] = dataset['Fare'].astype(int)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"EUzITOrjpkEo","colab_type":"code","colab":{}},"source":["data = [train_df, test_df]\n","titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n","\n","for dataset in data:\n","    # extract titles\n","    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n","    # replace titles with a more common title or as Rare\n","    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\\\n","                                            'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n","    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n","    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n","    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n","    # convert titles into numbers\n","    dataset['Title'] = dataset['Title'].map(titles)\n","    # filling NaN with 0, to get safe\n","    dataset['Title'] = dataset['Title'].fillna(0)\n","train_df = train_df.drop(['Name'], axis=1)\n","test_df = test_df.drop(['Name'], axis=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uO83wgpfqkRw","colab_type":"code","colab":{}},"source":["genders = {\"male\": 0, \"female\": 1}\n","data = [train_df, test_df]\n","\n","for dataset in data:\n","    dataset['Sex'] = dataset['Sex'].map(genders)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wQXNvG7Xq2ET","colab_type":"code","colab":{}},"source":["train_df = train_df.drop(['Ticket'], axis=1)\n","test_df = test_df.drop(['Ticket'], axis=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XM4xCVDuq62f","colab_type":"code","colab":{}},"source":["ports = {\"S\": 0, \"C\": 1, \"Q\": 2}\n","data = [train_df, test_df]\n","\n","for dataset in data:\n","    dataset['Embarked'] = dataset['Embarked'].map(ports)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"J830Zyu1q9AK","colab_type":"code","colab":{}},"source":["data = [train_df, test_df]\n","for dataset in data:\n","    dataset['Age'] = dataset['Age'].astype(int)\n","    dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0\n","    dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n","    dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n","    dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n","    dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n","    dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n","    dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n","    dataset.loc[ dataset['Age'] > 66, 'Age'] = 6\n","\n","# let's see how it's distributed train_df['Age'].value_counts()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4qOjCPySrYOY","colab_type":"code","colab":{}},"source":["data = [train_df, test_df]\n","\n","for dataset in data:\n","    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n","    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n","    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n","    dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare'] <= 99), 'Fare']   = 3\n","    dataset.loc[(dataset['Fare'] > 99) & (dataset['Fare'] <= 250), 'Fare']   = 4\n","    dataset.loc[ dataset['Fare'] > 250, 'Fare'] = 5\n","    dataset['Fare'] = dataset['Fare'].astype(int)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"HbWZ7GcHsA2t","colab_type":"code","colab":{}},"source":["data = [train_df, test_df]\n","for dataset in data:\n","    dataset['Age_Class']= dataset['Age']* dataset['Pclass']"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dBcNfTI6sRxl","colab_type":"code","colab":{}},"source":["X_train = train_df.drop(\"Survived\", axis=1)\n","Y_train = train_df[\"Survived\"]\n","X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Av7zADePs95O","colab_type":"code","colab":{}},"source":["sgd = linear_model.SGDClassifier(max_iter=5, tol=None)\n","sgd.fit(X_train, Y_train)\n","Y_pred = sgd.predict(X_test)\n","\n","sgd.score(X_train, Y_train)\n","\n","acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"fBfDHQa5trNy","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"1307002d-fa40-4e1b-ae44-8055be080593","executionInfo":{"status":"ok","timestamp":1584386673166,"user_tz":-330,"elapsed":2265,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_sgd"],"execution_count":56,"outputs":[{"output_type":"execute_result","data":{"text/plain":["76.99"]},"metadata":{"tags":[]},"execution_count":56}]},{"cell_type":"code","metadata":{"id":"w1_BTS0YzgDU","colab_type":"code","colab":{}},"source":["random_forest = RandomForestClassifier(n_estimators=100)\n","random_forest.fit(X_train, Y_train)\n","\n","Y_prediction = random_forest.predict(X_test)\n","\n","random_forest.score(X_train, Y_train)\n","acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AvdbixP-zub9","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"2bee1ba4-5e47-4748-b413-2fa9ad0b8762","executionInfo":{"status":"ok","timestamp":1584386736130,"user_tz":-330,"elapsed":1024,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_random_forest"],"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/plain":["92.48"]},"metadata":{"tags":[]},"execution_count":58}]},{"cell_type":"code","metadata":{"id":"75boi6UG2JSO","colab_type":"code","colab":{}},"source":["logreg = LogisticRegression()\n","logreg.fit(X_train, Y_train)\n","\n","Y_pred = logreg.predict(X_test)\n","\n","acc_log = round(logreg.score(X_train, Y_train) * 100, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RkioP9aU3Fud","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"2fdae648-8801-45fb-e8a9-363a25807329","executionInfo":{"status":"ok","timestamp":1584387619439,"user_tz":-330,"elapsed":1089,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_log"],"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["81.03"]},"metadata":{"tags":[]},"execution_count":63}]},{"cell_type":"code","metadata":{"id":"qlApAyQ53HXw","colab_type":"code","colab":{}},"source":["knn = KNeighborsClassifier(n_neighbors = 3) \n","knn.fit(X_train, Y_train)  \n","\n","Y_pred = knn.predict(X_test) \n","\n","acc_knn = round(knn.score(X_train, Y_train) * 100, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LFT5Y3Nf4I37","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"464a4777-feb1-469d-b1ce-cdfa5ddf838c","executionInfo":{"status":"ok","timestamp":1584387916097,"user_tz":-330,"elapsed":1103,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_knn"],"execution_count":67,"outputs":[{"output_type":"execute_result","data":{"text/plain":["87.54"]},"metadata":{"tags":[]},"execution_count":67}]},{"cell_type":"code","metadata":{"id":"XTx5hLlf4Py0","colab_type":"code","colab":{}},"source":["gaussian = GaussianNB() \n","gaussian.fit(X_train, Y_train) \n","\n","Y_pred = gaussian.predict(X_test)  \n","\n","acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"s98-Sp_a4V_w","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"00376465-7076-4a66-fb2c-076cd99087cf","executionInfo":{"status":"ok","timestamp":1584387946655,"user_tz":-330,"elapsed":886,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_gaussian"],"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":["78.79"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"id":"mWYUEqQm4wjV","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":71},"outputId":"e6b70e73-9b62-489e-8925-9cf57aee72be","executionInfo":{"status":"ok","timestamp":1584388053109,"user_tz":-330,"elapsed":1029,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["perceptron = Perceptron(max_iter=5)\n","perceptron.fit(X_train, Y_train)\n","\n","Y_pred = perceptron.predict(X_test)\n","\n","acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)"],"execution_count":74,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n","  ConvergenceWarning)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"-_RKuiZt4yul","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"6d38eb29-8371-4f81-ef60-bc065cd1f79c","executionInfo":{"status":"ok","timestamp":1584388061129,"user_tz":-330,"elapsed":1021,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_perceptron"],"execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["80.13"]},"metadata":{"tags":[]},"execution_count":75}]},{"cell_type":"code","metadata":{"id":"z5VTDWvU4XTl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"outputId":"3da65e53-3d48-4740-d4b5-e59a9cc30c00","executionInfo":{"status":"ok","timestamp":1584387955863,"user_tz":-330,"elapsed":924,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["linear_svc = LinearSVC()\n","linear_svc.fit(X_train, Y_train)\n","\n","Y_pred = linear_svc.predict(X_test)\n","\n","acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)"],"execution_count":70,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n","  \"the number of iterations.\", ConvergenceWarning)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"yPIoVsP94kFc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"3dbdb5f8-9be3-44ed-e421-ffcb105b6d1a","executionInfo":{"status":"ok","timestamp":1584388000173,"user_tz":-330,"elapsed":1075,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_linear_svc"],"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["80.25"]},"metadata":{"tags":[]},"execution_count":72}]},{"cell_type":"code","metadata":{"id":"HW4I2jGt4Zi7","colab_type":"code","colab":{}},"source":["decision_tree = DecisionTreeClassifier() \n","decision_tree.fit(X_train, Y_train)  \n","\n","Y_pred = decision_tree.predict(X_test)  \n","\n","acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RocH7uBJ4ews","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"6b110780-6544-4a60-9f87-932b8bf5e294","executionInfo":{"status":"ok","timestamp":1584388013032,"user_tz":-330,"elapsed":629,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["acc_decision_tree"],"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["92.48"]},"metadata":{"tags":[]},"execution_count":73}]},{"cell_type":"code","metadata":{"id":"Tr96qa8E4nky","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":328},"outputId":"c42bdfca-57ec-4d0d-b51a-29ac3e32dfbe","executionInfo":{"status":"ok","timestamp":1584388076898,"user_tz":-330,"elapsed":983,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["results = pd.DataFrame({\n","    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n","              'Random Forest', 'Naive Bayes', 'Perceptron', \n","              'Stochastic Gradient Decent', \n","              'Decision Tree'],\n","    'Score': [acc_linear_svc, acc_knn, acc_log, \n","              acc_random_forest, acc_gaussian, acc_perceptron, \n","              acc_sgd, acc_decision_tree]})\n","result_df = results.sort_values(by='Score', ascending=False)\n","result_df = result_df.set_index('Score')\n","result_df.head(9)"],"execution_count":76,"outputs":[{"output_type":"execute_result","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>Model</th>\n","    </tr>\n","    <tr>\n","      <th>Score</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>92.48</th>\n","      <td>Random Forest</td>\n","    </tr>\n","    <tr>\n","      <th>92.48</th>\n","      <td>Decision Tree</td>\n","    </tr>\n","    <tr>\n","      <th>87.54</th>\n","      <td>KNN</td>\n","    </tr>\n","    <tr>\n","      <th>81.03</th>\n","      <td>Logistic Regression</td>\n","    </tr>\n","    <tr>\n","      <th>80.25</th>\n","      <td>Support Vector Machines</td>\n","    </tr>\n","    <tr>\n","      <th>80.13</th>\n","      <td>Perceptron</td>\n","    </tr>\n","    <tr>\n","      <th>78.79</th>\n","      <td>Naive Bayes</td>\n","    </tr>\n","    <tr>\n","      <th>76.99</th>\n","      <td>Stochastic Gradient Decent</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                            Model\n","Score                            \n","92.48               Random Forest\n","92.48               Decision Tree\n","87.54                         KNN\n","81.03         Logistic Regression\n","80.25     Support Vector Machines\n","80.13                  Perceptron\n","78.79                 Naive Bayes\n","76.99  Stochastic Gradient Decent"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"Ryp1TCtQ43FQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":390},"outputId":"f1071a4d-c87d-4635-b1d9-80683116be35","executionInfo":{"status":"ok","timestamp":1584388105236,"user_tz":-330,"elapsed":1024,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["importances = pd.DataFrame({'feature':X_train.columns,'importance':np.round(random_forest.feature_importances_,3)})\n","importances = importances.sort_values('importance',ascending=False).set_index('feature')\n","importances.head(15)"],"execution_count":77,"outputs":[{"output_type":"execute_result","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>importance</th>\n","    </tr>\n","    <tr>\n","      <th>feature</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Title</th>\n","      <td>0.209</td>\n","    </tr>\n","    <tr>\n","      <th>Sex</th>\n","      <td>0.182</td>\n","    </tr>\n","    <tr>\n","      <th>Fare</th>\n","      <td>0.097</td>\n","    </tr>\n","    <tr>\n","      <th>Deck</th>\n","      <td>0.095</td>\n","    </tr>\n","    <tr>\n","      <th>Age_Class</th>\n","      <td>0.090</td>\n","    </tr>\n","    <tr>\n","      <th>SibSp</th>\n","      <td>0.083</td>\n","    </tr>\n","    <tr>\n","      <th>Pclass</th>\n","      <td>0.081</td>\n","    </tr>\n","    <tr>\n","      <th>Age</th>\n","      <td>0.066</td>\n","    </tr>\n","    <tr>\n","      <th>Embarked</th>\n","      <td>0.055</td>\n","    </tr>\n","    <tr>\n","      <th>Parch</th>\n","      <td>0.041</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           importance\n","feature              \n","Title           0.209\n","Sex             0.182\n","Fare            0.097\n","Deck            0.095\n","Age_Class       0.090\n","SibSp           0.083\n","Pclass          0.081\n","Age             0.066\n","Embarked        0.055\n","Parch           0.041"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"id":"h4c7g47-49_V","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":337},"outputId":"2f0b2f97-84a8-4703-afd0-1bcf202e622e","executionInfo":{"status":"ok","timestamp":1584388124527,"user_tz":-330,"elapsed":1036,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["importances.plot.bar()\n"],"execution_count":78,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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bHbk8AM4OaKYwcAlwBvJ5v0/RJJ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\nLHsyAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"kUH5uPOv5Csr","colab_type":"code","colab":{}},"source":["train_df  = train_df.drop(\"Parch\", axis=1)\n","test_df  = test_df.drop(\"Parch\", axis=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RoG6lbw85J06","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"b69faf25-3d9b-4439-ae23-8f99dc5f8ef0","executionInfo":{"status":"ok","timestamp":1584388160943,"user_tz":-330,"elapsed":1065,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["random_forest = RandomForestClassifier(n_estimators=100, oob_score = True)\n","random_forest.fit(X_train, Y_train)\n","Y_prediction = random_forest.predict(X_test)\n","\n","random_forest.score(X_train, Y_train)\n","\n","acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n","print(round(acc_random_forest,2,), \"%\")"],"execution_count":80,"outputs":[{"output_type":"stream","text":["92.48 %\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XsR-7GEO5LlH","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":198},"outputId":"ebdc0ad2-6c44-45a9-d035-42659b0d62c2","executionInfo":{"status":"error","timestamp":1584391685214,"user_tz":-330,"elapsed":3280077,"user":{"displayName":"SAJ MARU","photoUrl":"","userId":"12446872909316284903"}}},"source":["param_grid = { \"criterion\" : [\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35], \"n_estimators\": [100, 400, 700, 1000, 1500]}\n","from sklearn.model_selection import GridSearchCV, cross_val_score\n","rf = RandomForestClassifier(n_estimators=100, max_features='auto', oob_score=True, random_state=1, n_jobs=-1)\n","clf = GridSearchCV(estimator=rf, param_grid=param_grid, n_jobs=-1)\n","clf.fit(X_train, Y_train)\n","clf.bestparam"],"execution_count":82,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-82-86506c2b3bda>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbestparam\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'bestparam'"]}]},{"cell_type":"code","metadata":{"id":"GPCfRASC6HdI","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}