Scikit-Learn-Python.ipynb

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   "source": [
    "This is a notebook made using the Kaggle scikit-learn tutorial."
   ]
  },
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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>699</th>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Humblen, Mr. Adolf Mathias Nicolai Olsen</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>348121</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>F G63</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>707</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kelly, Mrs. Florence \"Fannie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>223596</td>\n",
       "      <td>13.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wick, Miss. Mary Natalie</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>36928</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>C7</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>543</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Sigrid Elisabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3101278</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "699          700         0       3   \n",
       "706          707         1       2   \n",
       "318          319         1       1   \n",
       "542          543         0       3   \n",
       "85            86         1       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "699           Humblen, Mr. Adolf Mathias Nicolai Olsen    male  42.0      0   \n",
       "706                      Kelly, Mrs. Florence \"Fannie\"  female  45.0      0   \n",
       "318                           Wick, Miss. Mary Natalie  female  31.0      0   \n",
       "542                  Andersson, Miss. Sigrid Elisabeth  female  11.0      4   \n",
       "85   Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...  female  33.0      3   \n",
       "\n",
       "     Parch   Ticket      Fare  Cabin Embarked  \n",
       "699      0   348121    7.6500  F G63        S  \n",
       "706      0   223596   13.5000    NaN        S  \n",
       "318      2    36928  164.8667     C7        S  \n",
       "542      2   347082   31.2750    NaN        S  \n",
       "85       0  3101278   15.8500    NaN        S  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#importing packages\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt # data visualization\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "\n",
    "# Importing csv files\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "train = pd.read_csv(\"train.csv\") \n",
    "test = pd.read_csv(\"test.csv\")\n",
    "train.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "_uuid": "230fef719d60d7cd85329c37b5e396ac76be6f07"
   },
   "source": [
    "**Data preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Lname</th>\n",
       "      <th>NamePrefix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "      <td>19</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>267</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Cabin  Lname  \\\n",
       "0            1         0       3    1    4      1      0     0      7    100   \n",
       "1            2         1       1    0    0      1      0     3      2    182   \n",
       "2            3         1       3    0    7      0      0     0      7    329   \n",
       "3            4         1       1    0    7      1      0     3      2    267   \n",
       "4            5         0       3    1    7      0      0     1      7     15   \n",
       "\n",
       "   NamePrefix  \n",
       "0          19  \n",
       "1          20  \n",
       "2          16  \n",
       "3          20  \n",
       "4          19  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "def encode_features(df_train, df_test): #label encoding for sklearn algorithms\n",
    "    features = ['Fare', 'Cabin', 'Age', 'Sex', 'Lname', 'NamePrefix']\n",
    "    df_combined = pd.concat([df_train[features], df_test[features]])\n",
    "    for feature in features:\n",
    "        le = preprocessing.LabelEncoder()\n",
    "        le = le.fit(df_combined[feature])\n",
    "        df_train[feature] = le.transform(df_train[feature])\n",
    "        df_test[feature] = le.transform(df_test[feature])\n",
    "    return df_train, df_test\n",
    "\n",
    "def simplify_ages(df): # Binning ages \n",
    "    df.Age = df.Age.fillna(-0.5)\n",
    "    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)\n",
    "    group_names = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']\n",
    "    categories = pd.cut(df.Age, bins, labels=group_names)\n",
    "    df.Age = categories\n",
    "    return df\n",
    "\n",
    "def simplify_cabins(df): # Storing first letter of cabins\n",
    "    df.Cabin = df.Cabin.fillna('N')\n",
    "    df.Cabin = df.Cabin.apply(lambda x: x[0])\n",
    "    returins = (-1, 0, 8, 15, 31, 1000)\n",
    "    group_nan df\n",
    "\n",
    "def simplify_fares(df): # Binning fares\n",
    "    df.Fare = df.Fare.fillna(-0.5)\n",
    "    bmes = ['Unknown', '1_quartile', '2_quartile', '3_quartile', '4_quartile']\n",
    "    categories = pd.cut(df.Fare, bins, labels=group_names)\n",
    "    df.Fare = categories\n",
    "    return df\n",
    "\n",
    "def format_name(df): # Keeping title\n",
    "    df['Lname'] = df.Name.apply(lambda x: x.split(' ')[0])\n",
    "    df['NamePrefix'] = df.Name.apply(lambda x: x.split(' ')[1])\n",
    "    return df    \n",
    "    \n",
    "def drop_features(df): # Dropping useless features\n",
    "    return df.drop(['Ticket', 'Name', 'Embarked'], axis=1)\n",
    "\n",
    "def transform_features(df):\n",
    "    df = simplify_ages(df)\n",
    "    df = simplify_cabins(df)\n",
    "    df = simplify_fares(df)\n",
    "    df = format_name(df)\n",
    "    df = drop_features(df)\n",
    "    return df\n",
    "\n",
    "train = transform_features(train)\n",
    "test = transform_features(test)\n",
    "train_vis = train.copy(deep=True) # data for visualization\n",
    "train, test = encode_features(train, test)\n",
    "train.head()"
   ]
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   },
   "source": [
    "**Data Visualization**"
   ]
  },
  {
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\", y=\"Survived\", data=train_vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "3910456c-1c4a-4504-a28c-29227c131f35",
    "_uuid": "e9883856b85d4eb2dafa691a23835fcfb2871170"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f43a92e898>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=train_vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "a691bb0d-0dfa-4cdf-9093-0190913641bb",
    "_uuid": "746ee969ffe9a6ea8bd0f645a8b7f48fbcf373f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f43a91d390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Age\", y=\"Survived\", hue=\"Sex\", data=train_vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "4dbceab6-3293-454f-bf04-5298b84a7d16",
    "_uuid": "0b612987ff7bc38a6b2c5c0c6874831e08c20a3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f43c78b9e8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Pclass\", y=\"Survived\", hue=\"Fare\", data=train_vis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3ea528a0-ec88-425f-823c-15d09206e6cd",
    "_uuid": "e974e24d9f421bafb754897a833ed92c95e43f5d"
   },
   "source": [
    "**Setting up training and testing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "0d84353e-11ec-4823-b674-5b5454d0d054",
    "_uuid": "2421934e6837a6b2a75663efde501fc221a997a7"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_all = train.drop(['Survived', 'PassengerId'], axis=1)\n",
    "y_all = train['Survived']\n",
    "\n",
    "num_test = 0.20\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "08ce9fa0-d6db-4b7e-abc7-fa0c8497ea05",
    "_uuid": "934be0aa9da83714fd0c426e2e9184a2ee7b8909"
   },
   "source": [
    "**Fiting and tuning an algorithm GradientBoostingClassifier** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf = DecisionTreeClassifier()\n",
    "#clf = RandomForestClassifier(n_estimators=10000)\n",
    "clf = GradientBoostingClassifier(n_estimators=1000, max_depth=10,min_samples_split=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=10,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=3,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "              n_iter_no_change=None, presort='auto', random_state=None,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluating**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CSAX16xdaEe3bh2si1LwulRJFhlMxO5FKkFjE75BDwo2Frr4aaukrMBWxdj2ZWVczm21mc83s+mKWNzOz0WY22cymmVm3OOPJRrq6WKSCFiwIg9NPPBGmBwyA665TkiiD2I6UmdUEHgSOAfKBiWY20t1nJqx2I/Ccuz9sZq2BUUDzuGLKVupuEimHDRvgwQdDZdcaNeDMM9MdUdaKs0XRAZjr7vPd/TdgBNC9yDoONIyebwt8E2M8IpIrZs2Czp3hiitCMb8ZM8LYhJRLnG2vJsCihOl8oGORdW4G3jazy4CtgaOL25CZDQAGADRr1qzSA80EJZ36qnEJkXKYOzcU8nvyydCSMEt3RFktzhZFcf8zXmS6NzDM3ZsC3YAnzWyLmNx9kLvnuXveDjvsEEOo6VfSqa8alxBJ0aefwtCh4flJJ4WxibPOUpKoBHG2KPKBXROmm7Jl19L5QFcAd//YzOoB2wM/xBhXxtJYhEg5rF4Nt9wCd90Fu+4aflnVqwcNG5b+WklJnIliItDKzFoAi4FeQNHfxguBo4BhZrYPUA9YEmNMaVPaVdXqYhIph7FjoX9/mDMn3EjorrtUxC8GsXU9uft64FLgLWAW4eymGWZ2q5mdHK12NXCBmU0FhgP93L1o91S1UNpV1epiEimjxYvhqKNg/Xp4910YMiSnS4HHKdYTid19FOGU18R5f014PhM4NM4YMom6lkQqweefw377hSJ+L78cKr5uvXW6o6rWVOspRrpns0glWroU+vaFtm1DlxPAiScqSVQBJYoY6apqkUrgDs89F4r4jRgBN90EHYueaS9x0jXsMVN3k0gFnXNOuB4iLw/eey90O0mVUqIQkcyTWMTviCNCd9OVV6o+U5qo66mSaVxCpILmz4ejj4Zhw8L0+efDNdcoSaSREkUl07iESDlt2AD33Re6liZODIX8JCMoRcdA4xIiZTRzJpx3HnzyCZxwAjzyCDRtmu6oJKJEUQl0cyGRClqwAObNC39IvXqpPlOGUduuEqi7SaQcJk6EwYPD8xNOCGMTvXsrSWQgtSgqibqbRFL066/w17/CvffCbruFi+jq1YMGDdIdmZRALQoRqTpjxoRTXe++Gy64ACZPVhG/LKAWhYhUjfx8OOaY0Ip4//1Qo0mygloUIhKvqVPDv02bwquvwrRpShJZRolCROKxZEk4s6NdO/jggzCvWzfYaqv0xiVlpq4nEalc7qF43+WXw08/hbvPHXxwuqOSCkgpUZhZHaCZu8+NOR4RyXZ9+8LTT4cKr489Bm3apDsiqaBSu57M7ATgc+CdaLqdmb0cd2AikkU2biws5HfkkXDPPTBunJJENZFKi+JWoCMwGsDdp5hZy1ijyiCl3esadDW25Li5c8Oprn37hjIc55+f7oikkqUymL3O3VcUmVct72tdnNLudQ26Glty1Pr1cNddoYjf5MlQp066I5KYpNKimGVmZwA1zKwFcAUwPt6wMouuuhYpYvp0OPdcmDQJuneHhx6CXXZJd1QSk1RaFJcCBwIbgZeANYRkISK5auFC+PrrcHbTyy8rSVRzqbQojnP364DrCmaY2amEpCEiueKTT8LFcwMGhOsh5s+HbbZJd1RSBVJpUdxYzLwbKjsQEclQv/wCAweGayHuuAPWrg3zlSRyRoktCjM7DugKNDGzexIWNSR0Q4lIdff+++GMpvnz4aKL4O9/h7p10x2VVLFkXU8/ANMJYxIzEuavBK6PMygRyQD5+XDccdCiRSjBcfjh6Y5I0qTEROHuk4HJZva0u6+pwphEJJ0mT4YDDghF/F57DY44AurXT3dUkkapjFE0MbMRZjbNzL4seMQemYhUre+/h549oX37wiJ+XbsqSUhKiWIY8G/AgOOB54ARMcYkIlXJHZ56Clq3hldegdtug0MOSXdUkkFSSRRbuftbAO4+z91vBFRMXqS66NMnlN/Ya69QhuCGG6B27XRHJRkkleso1pqZAfPM7EJgMbBjvGGJSKw2bgSz8Dj22HDq6yWXQM2a6Y5MMlAqLYqrgG2Ay4FDgQuA8+IMSkRi9OWXocLr0KFh+txzw70jlCSkBKW2KNz9k+jpSqAvgJk1jTOodEusGKvKsFJtrF8fyn/fdBPUq6dBaklZ0haFmR1kZqeY2fbRdBsze4JqXhQwsWKsKsNKtTBtGnTqBNddB8cfDzNn6oMtKUt2ZfbtQA9gKnBjdLOiK4B/ABdWTXjpo4qxUq3k58OiRfD889CjRxibEElRsq6n7sD+7r7azH4HfBNNz05142bWFfgnUBMY4u5/L2adM4CbCfe4mOru+pkjUhn++9/QkrjwwsIifltvne6oJAsl63pa4+6rAdz9R+CLMiaJmsCDhGsvWgO9zax1kXVaAX8GDnX3NsCVZYxfRIpatQquuAIOOwzuvruwiJ+ShJRTshbF7mZWUErcgOYJ07j7qaVsuwMw193nA5jZCEIrZWbCOhcAD7r78mibP5QxfhFJ9PbboQz4woXhdNf/9/9UxE8qLFmi6FFk+oEybrsJsChhOp9w7+1EewKY2ThC99TN7v5m0Q2Z2QBgAECzZs3KGIZIjli0CE44AfbYA8aODS0KkUqQrCjgexXcdnGjZUXvtV0LaAV0AZoCH5rZvkXv0e3ug4BBAHl5eRW+X3fi6a/F0SmxklU+/RQOPBB23RVGjYLOncPpryKVJJUL7sorH9g1YbopYUC86Dqvuvs6d18AzCYkjlglnv5aHJ0SK1nhu+/g9NMhL6+wiN8xxyhJSKVLpYRHeU0EWplZC0LZj15A0a/fV4DewLDoWo09gfkxxrSJTn+VrOUOTzwBV10Fv/4axiFUxE9ilHKiMLO67r421fXdfb2ZXQq8RRh/GOruM8zsVmCSu4+Mlh1rZjOBDcC17r6sbG8hNbraWqqNXr3guefg0ENhyBDYe+90RyTVnLkn7/I3sw7AY8C27t7MzPYH+rv7ZVURYFF5eXk+adKkMr+uS5fNE0SfPuHkEJGskFjE7/HHYeVKuPhiqBFn77FUJ2b2qbvnlee1qbQo7gdOJHQT4e5TzSwry4yru0my0hdfQP/+0K9f+Pecc9IdkeSYVH6O1HD3r4vM2xBHMCKSYN26MP6w//6hNtM226Q7IslRqbQoFkXdTx5dbX0ZoFuhisRpypRQ/nvKFDjtNPjXv2DnndMdleSoVBLFRYTup2bA98C70TwRict334XHiy/CqaUVQRCJVyqJYr2794o9EpFc99FHoYjfxRdD164wbx5stVW6oxJJaYxiopmNMrNzzKxB7BGJ5JqVK+HSS8MV1ffdV1jET0lCMkSpicLd9wBuAw4EPjezV8xMLQyRyvDWW7DvvvDQQ6Hi62efqYifZJyUTsJ29/+6++VAe+Bn4OlYoxLJBYsWwYknhpbDRx+F1oTObJIMVGqiMLNtzOxMM3sNmAAsAbKiXsCgQeFCu4KL7UTSzh0mTAjPd90V3ngDJk9WCQ7JaKm0KKYDnYA73L2lu1/t7p/EHFel0L2vJaN8+224DWnHjoVF/I4+WkX8JOOlctbT7u6+MfZIYqKrsSXt3GHYMBg4ENasgX/8I9RpEskSJSYKM7vb3a8GXjSzLQpCpXCHOxEBOOMMeOGFcFbTkCGw557pjkikTJK1KJ6N/i3rne1EZMOGUMCvRg046ST4wx/gj39UET/JSiV+at09GnFjH3d/L/EB7FM14YlkoVmzQuvhscfC9Nlnw0UXKUlI1krlk3teMfPOr+xARLLeunVw221hYGz2bNh223RHJFIpko1R9CTcla6Fmb2UsKgBsKL4V4nkqMmTQxnwadOgZ0+4/37Yccd0RyVSKZKNUUwAlhHudf1gwvyVwOQ4gxLJOt9/D0uXwiuvQPfu6Y5GpFKVmCjcfQGwgFAtVkSKGjsWPv8cLrkkFPGbOxfq1093VCKVrsQxCjP7IPp3uZn9mPBYbmY/Vl2IZaOrsSV2P/8cKrwecUToYioo4qckIdVUssHsgtudbg/skPAomM5IuhpbYjVqFLRpA48+Gi6gUxE/yQHJup4KrsbeFfjG3X8zs8OAtsBThOKAGUlXY0ssFi0K4w977RUuoOvYMd0RiVSJVE6PfYVwG9Q9gCcI11A8E2tUIpnCHcaPD8933RXefju0IpQkJIekkig2uvs64FTgPne/DGgSb1hlo3EJicU338App8DBBxcW8TvySKhTJ71xiVSxVBLFejM7HegLvB7Nqx1fSGWncQmpVO6hJlPr1qEFcdddKuInOS2V6rHnARcTyozPN7MWwPB4wyo7jUtIpTntNHjppXBW05Ah0LJluiMSSatSE4W7Tzezy4GWZrY3MNfd/xZ/aCJVKLGI3ymnwLHHwgUXqD6TCKnd4a4zMBd4DBgKfGlmaodL9TF9euhaKiji17evKr2KJEjlL+FeoJu7H+ruhwAnAP+MNyyRKvDbb3DLLdC+PcybB40apTsikYyUyhhFHXefWTDh7rPMTKd9SHb79NNQxG/69HD2w333wQ4Zex2pSFqlkig+M7NHgSej6TNRUUDJdsuWwYoV8NprcOKJ6Y5GJKOlkiguBC4H/gQYMBb4V5xBicRi9OhQxO/yy8Ng9Zw5UK9euqMSyXhJE4WZ7QfsAbzs7ndUTUgileynn+BPfwpXZu69dxiorltXSUIkRcmqx/6FUL7jTOAdMyvuTncime2118KFc0OGwDXXhLEJFfETKZNkLYozgbbu/ouZ7QCMIpweK5IdFi2CHj1CK+KVV+Cgg9IdkUhWSnZ67Fp3/wXA3ZeUsq5IZnCH//43PC8o4jdpkpKESAUk+/Lf3cxeih4vA3skTL+U5HWbmFlXM5ttZnPN7Pok651mZm5meWV9AyKb5OfDySeHi+cKivh16aIifiIVlKzrqUeR6QfKsmEzq0m41/YxQD4w0cxGJl6TEa3XgHBW1Sdl2b7IJhs3wuDBcO21sH493HMPHHZYuqMSqTaS3bjovQpuuwOhLtR8ADMbAXQHZhZZ7/+AO4BrKrg/yVU9eoQxiD/8ISSM3XdPd0Qi1Uqc4w5NgEUJ0/kUuY+FmR0A7Orur5OEmQ0ws0lmNmnJkiWVH6lkn/XrQ0sCQqIYPBjefVdJQiQGcSYKK2aeb1poVoNQR+rq0jbk7oPcPc/d83ZQmQWZNi3cTGjw4DB91lnQv3+o/ioilS7lRGFmZT35PJ9wv+0CTYFvEqYbAPsCY8zsK6ATMFID2lKitWvhppvgwAPh669Vm0mkiqRSZryDmX0OzImm9zezVEp4TARamVmLqIhgL2BkwUJ3/8ndt3f35u7eHBgPnOzuk8rzRqSamzgxVHm99Vbo3RtmzYJTT013VCI5IZUWxf3AicAyAHefChxZ2ovcfT1wKfAWMAt4zt1nmNmtZnZy+UOWnLR8OaxaBaNGwRNPQOPG6Y5IJGekUhSwhrt/bZv3/25IZePuPopwRXfivL+WsG6XVLYpOeT990MRvyuuCEX8vvxS5TdE0iCVFsUiM+sAuJnVNLMrgS9jjkty2YoV4TakRx0Fjz4axiZASUIkTVJJFBcBA4FmwPeEQeeL4gxKctirr4YifkOHhoqvKuInknaldj25+w+EgWiReC1cCKefDvvsAyNHQp5OgBPJBKUmCjMbTML1DwXcfUAsEUlucYePPoLOnaFZs3DRXKdOqs8kkkFS6Xp6F3gveowDdgTWxhmU5IiFC+GEE+DwwwuL+B1+uJKESIZJpevp2cRpM3sSeCe2iKT627gRHnkErrsutCjuv19F/EQyWCqnxxbVAtitsgORHHLqqWHQ+phjwu1JmzdPd0QikkQqYxTLKRyjqAH8CJR4bwmRYq1fDzVqhEfPntC9O/Trp/pMIlkgaaKwcJXd/sDiaNZGd99iYFskqalT4bzzwrURF14YSnCISNZIOpgdJYWX3X1D9FCSkNStWQM33hhOc83Ph513TndEIlIOqZz1NMHM2sceiVQvEybAAQfA3/4GZ54Zividckq6oxKRciix68nMakWF/Q4DLjCzecAvhPtMuLsreUjJfv4ZVq+GN9+E445LdzQiUgHJxigmAO0B/QyU1Lz9NsyYAVddBUcfDbNnq/yGSDWQLFEYgLvPq6JYJFstXw4DB8JD7kQuAAATqElEQVSwYdCmDVx8cUgQShIi1UKyRLGDmQ0saaG73xNDPJJtXnoJLrkEliyBP/8Z/vpXJQiRaiZZoqgJbEPx974WCSU4evWCffcNNxQ64IB0RyQiMUiWKL5191urLBLJDu4wdiwccUQo4vf++9CxI9Sune7IRCQmyU6PVUtCNvf113D88dClS2ERv8MOU5IQqeaSJYqjqiwKyWwbN8IDD4SB6o8+gn/9K5QFF5GcUGLXk7v/WJWBSAY75RR47bVwPcSjj8JuqgkpkkvKUz1WcsG6dVCzZiji17s3nHYa9O2rIn4iOSiVEh6Saz77DDp0CPeMgJAozj5bSUIkRylRSKHVq8O1EB06wHffwa67pjsiEckA6nqSYPx4OOcc+PLLUBL8rrugUaN0RyUiGUCJQoJffgnjEu+8E+o0iYhElChy2ZtvhiJ+V18NRx0FX3wBdeqkOyoRyTAao8hFy5aFbqbjj4fHH4fffgvzlSREpBhKFLnEHV54AVq3hmeeCXefmzhRCUJEklLXUy5ZuBD69IG2bcO9I/bfP90RiUgWyLpEMXt2KDWUaMoUaNcuLeFkPncYPRr+8IdwRfWYMeH011pZ918vImmSdV1Pq1dvOa9du/BDWYpYsACOPTYMVBcU8TvkECUJESmTrPvGqF8//CiWJDZsCEX8/vKXUIbj4YdVxE9Eyi3rEoWkoHt3+M9/oFu3UIZDV1iLSAUoUVQXiUX8+vYN9Zn69FF9JhGpsFjHKMysq5nNNrO5ZnZ9McsHmtlMM5tmZu+ZmepXl8ekSZCXF7qYAHr2hDPPVJIQkUoRW6Iws5rAg8DxQGugt5m1LrLaZCDP3dsCLwB3xBVPtbR6NVx3XbgV6ZIluk+EiMQizhZFB2Cuu89399+AEUD3xBXcfbS7/xpNjgeaxhhP9fLxx+E6iDvuCEX8Zs6EE09Md1QiUg3FOUbRBFiUMJ0PdEyy/vnAG8UtMLMBwACAunXbVlZ82W316nCL0nffDae/iojEJM5EUVwHuRe7otlZQB5wRHHL3X0QMAigQYO8YreRE0aNCkX8rr02XEA3axbUrp3uqESkmouz6ykfSDwvsynwTdGVzOxo4AbgZHdfG2M82WvpUjjrLDjhBHj66cIifkoSIlIF4kwUE4FWZtbCzOoAvYCRiSuY2QHAo4Qk8UOMsWQndxgxAvbZB557Dm66CSZMUBE/EalSsXU9uft6M7sUeAuoCQx19xlmdiswyd1HAncC2wDPWziVc6G7nxxXTFln4cJQDnz//eGxx2C//dIdkYjkIHPPri7/Bg3yfOXKSekOIz7u8N57hXeZGz8eDjooXEwnIlJOZvapu+eV57VZVxSwWps3L5zBdMwxhUX8OnVSkhCRtFKiyAQbNsA994SupU8/hUcfVRE/EckYqvWUCU46Cd54I1ww9/DD0FTXHYpI5lCiSJfffgv3hahRA/r1C4X8evVSfSYRyTjqekqHCRPgwAPhoYfC9BlnhGqvShIikoGUKKrSr7/C1VfDwQfD8uWwxx7pjkhEpFTqeqoqH30UromYPx/++Ef4xz9g223THZWISKmUKKpKwY2FRo+GLl3SHY2ISMqUKOL02muhcN+f/gRHHhlKgdfSIReR7KIxijgsWRJuQ3ryyTB8eGERPyUJEclCShSVyR2eeSYU8XvhBbj1VvjkExXxE5Gspp+4lWnhQjj3XDjggFDEr02bdEckIlJhalFU1MaN8NZb4fluu8GHH8K4cUoSIlJtKFFUxJw54U5zXbvC2LFhXocOKuInItWKEkV5rF8Pd94JbdvClCmhm0lF/ESkmtIYRXmceGLoburePZTh2GWXdEckkpHWrVtHfn4+a9asSXcoOaNevXo0bdqU2pV4q2TduChVa9eGe1TXqBHOaNq4EU4/XfWZRJJYsGABDRo0oHHjxpj+VmLn7ixbtoyVK1fSokWLzZbpxkVxGz8e2reHBx8M06edFgr56YMvktSaNWuUJKqQmdG4ceNKb8EpUSTzyy9w1VVwyCGwciW0apXuiESyjpJE1YrjeGuMoiQffhiK+C1YABdfDLffDg0bpjsqEZEqpxZFSdavD2MSH3wQupyUJESy1ssvv4yZ8cUXX2yaN2bMGE488cTN1uvXrx8vvPACEAbir7/+elq1asW+++5Lhw4deOONNyocy+23307Lli3Za6+9eKvgGqwi3nvvPdq3b0+7du047LDDmDt37qZlzz33HK1bt6ZNmzb06dOnwvGkQoki0SuvhJYDhCJ+M2bA4YenNyYRqbDhw4dz2GGHMWLEiJRf87//+798++23TJ8+nenTp/Paa6+xcuXKCsUxc+ZMRowYwYwZM3jzzTe5+OKL2bBhwxbrXXTRRTz99NNMmTKFPn36cNtttwEwZ84cbr/9dsaNG8eMGTO47777KhRPqtT1BPD993DZZfD882HQ+uqrQ30mFfETqTRXXhkuO6pM7dpBad+Vq1atYty4cYwePZqTTz6Zm2++udTt/vrrrwwePJgFCxZQt25dAHbaaSfOOOOMCsX76quv0qtXL+rWrUuLFi1o2bIlEyZM4OCDD95sPTPj559/BuCnn35il+gU/MGDB3PJJZfQqFEjAHbccccKxZOq3P4mdIenngqf4FWr4G9/g2uvDV1OIlItvPLKK3Tt2pU999yT3/3ud3z22We0b98+6Wvmzp1Ls2bNaJhCl/NVV13F6NGjt5jfq1cvrr/++s3mLV68mE6dOm2abtq0KYsXL97itUOGDKFbt27Ur1+fhg0bMn78eAC+/PJLAA499FA2bNjAzTffTNeuXUuNsaJyO1EsXAj9+0NeXri6eu+90x2RSLVVRb0kWxg+fDhXXnklEL68hw8fTvv27Us8O6isZw3de++9Ka9b3HVrxe3v3nvvZdSoUXTs2JE777yTgQMHMmTIENavX8+cOXMYM2YM+fn5dO7cmenTp7PddtuVKeayyr1EUVDE7/jjQxG/ceNCtVfVZxKpdpYtW8b777/P9OnTMTM2bNiAmXHHHXfQuHFjli9fvtn6P/74I9tvvz0tW7Zk4cKFrFy5kgYNGiTdR1laFE2bNmXRokWbpvPz8zd1KxVYsmQJU6dOpWPHjgD07NlzU6uhadOmdOrUidq1a9OiRQv22msv5syZw0EHHZT6QSkPd8+qxzbbHOjlNnu2e+fO7uA+Zkz5tyMiKZk5c2Za9//II4/4gAEDNpt3+OGH+9ixY33NmjXevHnzTTF+9dVX3qxZM1+xYoW7u1977bXer18/X7t2rbu7f/PNN/7kk09WKJ7p06d727Ztfc2aNT5//nxv0aKFr1+/frN11q1b540bN/bZs2e7u/uQIUP81FNPdXf3N954w88++2x3d1+yZIk3bdrUly5dusV+ijvuwCQv5/dubrQo1q+Hu++Gm26C+vXh3//W2UwiOWD48OFb/Krv0aMHzzzzDJ07d+app57i3HPPZc2aNdSuXZshQ4aw7bbbAnDbbbdx44030rp1a+rVq8fWW2/NrbfeWqF42rRpwxlnnEHr1q2pVasWDz74IDWj3oxu3boxZMgQdtllFwYPHkyPHj2oUaMGjRo1YujQoQAcd9xxvP3227Ru3ZqaNWty55130rhx4wrFlIrcqPV03HHw9ttw6qnhmoidd44nOBHZzKxZs9hnn33SHUbOKe64V6TWU/VtUaxZE85eqlkTBgwIjx490h2ViEjWqZ4X3I0bF06wLiji16OHkoSISDlVr0SxahVcfnm4idCaNaAmr0jaZVv3draL43hXn0TxwQew777wwANw6aUwfTocc0y6oxLJafXq1WPZsmVKFlXEo/tR1KtXr1K3W73GKLbaKlR9PfTQdEciIoTz/vPz81myZEm6Q8kZBXe4q0zZfdbTSy/BF1/AX/4Spjds0IVzIiLFyNg73JlZVzObbWZzzez6YpbXNbNno+WfmFnzlDb83XfhLnM9esDLL8Nvv4X5ShIiIpUutkRhZjWBB4HjgdZAbzNrXWS184Hl7t4SuBf4R2nb3XbdsjBI/frroST4f/8bKr2KiEgs4mxRdADmuvt8d/8NGAF0L7JOd+Dx6PkLwFFWSkWundZ+HQatp06F669XpVcRkZjFOZjdBFiUMJ0PdCxpHXdfb2Y/AY2BpYkrmdkAYEA0udY++mi6Kr0CsD1FjlUO07EopGNRSMei0F7lfWGciaK4lkHRkfNU1sHdBwGDAMxsUnkHZKobHYtCOhaFdCwK6VgUMrMy1j4qFGfXUz6wa8J0U+CbktYxs1rAtsCPMcYkIiJlFGeimAi0MrMWZlYH6AWMLLLOSOCc6PlpwPuebefriohUc7F1PUVjDpcCbwE1gaHuPsPMbiXURR8JPAY8aWZzCS2JXilselBcMWchHYtCOhaFdCwK6VgUKvexyLoL7kREpGpVn1pPIiISCyUKERFJKmMTRWzlP7JQCsdioJnNNLNpZvaeme2WjjirQmnHImG908zMzazanhqZyrEwszOiz8YMM3umqmOsKin8jTQzs9FmNjn6O+mWjjjjZmZDzewHM5tewnIzs/uj4zTNzNqntOHy3mw7zgdh8HsesDtQB5gKtC6yzsXAI9HzXsCz6Y47jcfiSGCr6PlFuXwsovUaAGOB8UBeuuNO4+eiFTAZaBRN75juuNN4LAYBF0XPWwNfpTvumI7F4UB7YHoJy7sBbxCuYesEfJLKdjO1RRFL+Y8sVeqxcPfR7v5rNDmecM1KdZTK5wLg/4A7gDVVGVwVS+VYXAA86O7LAdz9hyqOsaqkciwcaBg935Ytr+mqFtx9LMmvResOPOHBeGA7M/t9advN1ERRXPmPJiWt4+7rgYLyH9VNKsci0fmEXwzVUanHwswOAHZ199erMrA0SOVzsSewp5mNM7PxZta1yqKrWqkci5uBs8wsHxgFXFY1oWWcsn6fAJl746JKK/9RDaT8Ps3sLCAPOCLWiNIn6bEwsxqEKsT9qiqgNErlc1GL0P3UhdDK/NDM9nX3FTHHVtVSORa9gWHufreZHUy4fmtfd98Yf3gZpVzfm5naolD5j0KpHAvM7GjgBuBkd19bRbFVtdKORQNgX2CMmX1F6IMdWU0HtFP9G3nV3de5+wJgNiFxVDepHIvzgecA3P1joB6hYGCuSen7pKhMTRQq/1Go1GMRdbc8SkgS1bUfGko5Fu7+k7tv7+7N3b05YbzmZHcvdzG0DJbK38grhBMdMLPtCV1R86s0yqqRyrFYCBwFYGb7EBJFLt6fdSRwdnT2UyfgJ3f/trQXZWTXk8dX/iPrpHgs7gS2AZ6PxvMXuvvJaQs6Jikei5yQ4rF4CzjWzGYCG4Br3X1Z+qKOR4rH4mpgsJldRehq6Vcdf1ia2XBCV+P20XjMTUBtAHd/hDA+0w2YC/wKnJvSdqvhsRIRkUqUqV1PIiKSIZQoREQkKSUKERFJSolCRESSUqIQEZGklCgk45jZBjObkvBonmTd5iVVyizjPsdE1UenRiUv9irHNi40s7Oj5/3MbJeEZUPMrHUlxznRzNql8JorzWyriu5bcpcShWSi1e7eLuHxVRXt90x3359QbPLOsr7Y3R9x9yeiyX7ALgnL+rv7zEqJsjDOh0gtzisBJQopNyUKyQpRy+FDM/ssehxSzDptzGxC1AqZZmatovlnJcx/1MxqlrK7sUDL6LVHRfcw+Dyq9V83mv93K7wHyF3RvJvN7BozO41Qc+vpaJ/1o5ZAnpldZGZ3JMTcz8z+Vc44PyahoJuZPWxmkyzce+KWaN7lhIQ12sxGR/OONbOPo+P4vJltU8p+JMcpUUgmqp/Q7fRyNO8H4Bh3bw/0BO4v5nUXAv9093aEL+r8qFxDT+DQaP4G4MxS9n8S8LmZ1QOGAT3dfT9CJYOLzOx3wP8Abdy9LXBb4ovd/QVgEuGXfzt3X52w+AXg1ITpnsCz5YyzK6FMR4Eb3D0PaAscYWZt3f1+Qi2fI939yKiUx43A0dGxnAQMLGU/kuMysoSH5LzV0ZdlotrAA1Gf/AZC3aKiPgZuMLOmwEvuPsfMjgIOBCZG5U3qE5JOcZ42s9XAV4Qy1HsBC9z9y2j548AlwAOEe10MMbP/ACmXNHf3JWY2P6qzMyfax7hou2WJc2tCuYrEO5SdYWYDCH/XvyfcoGdakdd2iuaPi/ZTh3DcREqkRCHZ4irge2B/Qkt4i5sSufszZvYJcALwlpn1J5RVftzd/5zCPs5MLCBoZsXe3ySqLdSBUGSuF3Ap8IcyvJdngTOAL4CX3d0tfGunHCfhLm5/Bx4ETjWzFsA1wEHuvtzMhhEK3xVlwDvu3rsM8UqOU9eTZIttgW+j+wf0Jfya3oyZ7Q7Mj7pbRhK6YN4DTjOzHaN1fmep31P8C6C5mbWMpvsCH0R9+tu6+yjCQHFxZx6tJJQ9L85LwCmEeyQ8G80rU5zuvo7QhdQp6rZqCPwC/GRmOwHHlxDLeODQgvdkZluZWXGtM5FNlCgkWzwEnGNm4wndTr8Us05PYLqZTQH2JtzycSbhC/VtM5sGvEPolimVu68hVNd83sw+BzYCjxC+dF+PtvcBobVT1DDgkYLB7CLbXQ7MBHZz9wnRvDLHGY193A1c4+5TCffHngEMJXRnFRgEvGFmo919CeGMrOHRfsYTjpVIiVQ9VkREklKLQkREklKiEBGRpJQoREQkKSUKERFJSolCRESSUqIQEZGklChERCSp/w+pJB8IFO70HAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "# calculate the fpr and tpr for all thresholds of the classification\n",
    "probs = clf.predict_proba(X_test)\n",
    "preds = probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.63359857, 0.54265785, 0.53352523]),\n",
       " 'score_time': array([0.        , 0.        , 0.00451136]),\n",
       " 'test_score': array([0.80672269, 0.79831933, 0.80508475])}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "cross_validate(clf, X_train, y_train, cv=3,return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "08ce9fa0-d6db-4b7e-abc7-fa0c8497ea05",
    "_uuid": "934be0aa9da83714fd0c426e2e9184a2ee7b8909"
   },
   "source": [
    "**Fiting and tuning an algorithm RandomForestClassifier** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf = DecisionTreeClassifier()\n",
    "clf = RandomForestClassifier(n_estimators=1000,criterion='entropy')\n",
    "#clf = GradientBoostingClassifier(n_estimators=100, max_depth=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\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=1000, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluating**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "import sklearn.metrics as metrics\n",
    "# calculate the fpr and tpr for all thresholds of the classification\n",
    "probs = clf.predict_proba(X_test)\n",
    "preds = probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "08ce9fa0-d6db-4b7e-abc7-fa0c8497ea05",
    "_uuid": "934be0aa9da83714fd0c426e2e9184a2ee7b8909"
   },
   "source": [
    "**Fiting and tuning an algorithm DecisionTreeClassifier** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(min_samples_split=2)\n",
    "#clf = RandomForestClassifier(n_estimators=10000)\n",
    "#clf = GradientBoostingClassifier(n_estimators=100, max_depth=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, 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, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluating**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "import sklearn.metrics as metrics\n",
    "# calculate the fpr and tpr for all thresholds of the classification\n",
    "probs = clf.predict_proba(X_test)\n",
    "preds = probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0., 0., 0.]),\n",
       " 'score_time': array([0., 0., 0.]),\n",
       " 'test_score': array([0.78151261, 0.77310924, 0.77966102])}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "cross_validate(clf, X_train, y_train, cv=3,return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "213fba13-e3ca-4817-9a54-4a157caf90be",
    "_uuid": "cc25238af8d33607a52fbbea3bf7200ff64ee593"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=5, max_features='sqrt', 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=9, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import make_scorer, accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Choose the type of classifier. \n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "parameters = {'n_estimators': [4, 6, 9], \n",
    "              'max_features': ['log2', 'sqrt','auto'], \n",
    "              'criterion': ['entropy', 'gini'],\n",
    "              'max_depth': [2, 3, 5, 10], \n",
    "              'min_samples_split': [2, 3, 5],\n",
    "              'min_samples_leaf': [1,5,8]\n",
    "             }\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "acc_scorer = make_scorer(accuracy_score)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=acc_scorer)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "clf = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "e32a4efc-59ad-42c2-ba1f-3daf172c7a9b",
    "_uuid": "820325154d9031e5fb51b0a9443f2974103982c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.810055865922\n"
     ]
    }
   ],
   "source": [
    "predictions = clf.predict(X_test)\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b84624c7-b4db-4ff5-9cbc-13f541e55ba2",
    "_uuid": "a14b84407d9c5264085fbc8e47f840401ce6dcfa"
   },
   "source": [
    "**Validating with K-fold**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "3e9356b9-64cb-4809-a871-3854b10038fb",
    "_uuid": "df8f75d7a34d5476d09873d93585aa3a720f144f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 accuracy: 0.7444444444444445\n",
      "Fold 2 accuracy: 0.8651685393258427\n",
      "Fold 3 accuracy: 0.7752808988764045\n",
      "Fold 4 accuracy: 0.8539325842696629\n",
      "Fold 5 accuracy: 0.8539325842696629\n",
      "Fold 6 accuracy: 0.8089887640449438\n",
      "Fold 7 accuracy: 0.7865168539325843\n",
      "Fold 8 accuracy: 0.7865168539325843\n",
      "Fold 9 accuracy: 0.8539325842696629\n",
      "Fold 10 accuracy: 0.8202247191011236\n",
      "Mean Accuracy: 0.8148938826466917\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "\n",
    "def run_kfold(clf):\n",
    "    kf = KFold(891, n_folds=10)\n",
    "    outcomes = []\n",
    "    fold = 0\n",
    "    for train_index, test_index in kf:\n",
    "        fold += 1\n",
    "        X_train, X_test = X_all.values[train_index], X_all.values[test_index]\n",
    "        y_train, y_test = y_all.values[train_index], y_all.values[test_index]\n",
    "        clf.fit(X_train, y_train)\n",
    "        predictions = clf.predict(X_test)\n",
    "        accuracy = accuracy_score(y_test, predictions)\n",
    "        outcomes.append(accuracy)\n",
    "        print(\"Fold {0} accuracy: {1}\".format(fold, accuracy))     \n",
    "    mean_outcome = np.mean(outcomes)\n",
    "    print(\"Mean Accuracy: {0}\".format(mean_outcome)) \n",
    "\n",
    "run_kfold(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d6494237-6714-4940-bc66-90aae8d5ffb4",
    "_uuid": "88c4a07e8f15efe142cea2451920e2eaba57e2e3"
   },
   "source": [
    "**Predicting on test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "d4030eb0-0dd9-48a8-a4ac-305bd24e30c0",
    "_uuid": "a208901ae9d8c850fe2b9ed274ff60fafe5cee63"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "    <tr>\n",
       "      <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",
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      "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = test['PassengerId']\n",
    "predictions = clf.predict(test.drop('PassengerId', axis=1))\n",
    "\n",
    "\n",
    "output = pd.DataFrame({ 'PassengerId' : ids, 'Survived': predictions })\n",
    "output.to_csv('titanic-predictions.csv', index = False)\n",
    "output.head()"
   ]
  }
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