notebooks/nn.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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       "  <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",
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       "      <td>Klaber, Mr. Herman</td>\n",
       "      <td>male</td>\n",
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       "      <td>113028</td>\n",
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       "      <td>de Messemaeker, Mrs. Guillaume Joseph (Emma)</td>\n",
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       "      <td>3</td>\n",
       "      <td>Gronnestad, Mr. Daniel Danielsen</td>\n",
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       "      <th>626</th>\n",
       "      <td>627</td>\n",
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       "      <td>2</td>\n",
       "      <td>Kirkland, Rev. Charles Leonard</td>\n",
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       "      <td>219533</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>599</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Mr. Hanna</td>\n",
       "      <td>male</td>\n",
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       "     PassengerId  Survived  Pclass  \\\n",
       "711          712         0       1   \n",
       "559          560         1       3   \n",
       "769          770         0       3   \n",
       "626          627         0       2   \n",
       "598          599         0       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "711                            Klaber, Mr. Herman    male   NaN      0      0   \n",
       "559  de Messemaeker, Mrs. Guillaume Joseph (Emma)  female  36.0      1      0   \n",
       "769              Gronnestad, Mr. Daniel Danielsen    male  32.0      0      0   \n",
       "626                Kirkland, Rev. Charles Leonard    male  57.0      0      0   \n",
       "598                             Boulos, Mr. Hanna    male   NaN      0      0   \n",
       "\n",
       "     Ticket     Fare Cabin Embarked  \n",
       "711  113028  26.5500  C124        S  \n",
       "559  345572  17.4000   NaN        S  \n",
       "769    8471   8.3625   NaN        S  \n",
       "626  219533  12.3500   NaN        Q  \n",
       "598    2664   7.2250   NaN        C  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('../data/raw/train.csv')\n",
    "train_df.sample(5)"
   ]
  },
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   "execution_count": 3,
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>204</th>\n",
       "      <td>1096</td>\n",
       "      <td>2</td>\n",
       "      <td>Andrew, Mr. Frank Thomas</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 34050</td>\n",
       "      <td>10.5000</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
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       "      <td>Johansson Palmquist, Mr. Oskar Leander</td>\n",
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       "      <td>26.0</td>\n",
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       "      <th>205</th>\n",
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       "      <td>1</td>\n",
       "      <td>Omont, Mr. Alfred Fernand</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Astor, Col. John Jacob</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>227.5250</td>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1061</td>\n",
       "      <td>3</td>\n",
       "      <td>Hellstrom, Miss. Hilda Maria</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "     PassengerId  Pclass                                    Name     Sex  \\\n",
       "204         1096       2                Andrew, Mr. Frank Thomas    male   \n",
       "103          995       3  Johansson Palmquist, Mr. Oskar Leander    male   \n",
       "205         1097       1               Omont, Mr. Alfred Fernand    male   \n",
       "202         1094       1                  Astor, Col. John Jacob    male   \n",
       "169         1061       3            Hellstrom, Miss. Hilda Maria  female   \n",
       "\n",
       "      Age  SibSp  Parch      Ticket      Fare    Cabin Embarked  \n",
       "204  25.0      0      0  C.A. 34050   10.5000      NaN        S  \n",
       "103  26.0      0      0      347070    7.7750      NaN        S  \n",
       "205   NaN      0      0  F.C. 12998   25.7417      NaN        C  \n",
       "202  47.0      1      0    PC 17757  227.5250  C62 C64        C  \n",
       "169  22.0      0      0        7548    8.9625      NaN        S  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test_df = pd.read_csv('../data/raw/test.csv')\n",
    "test_df.sample(5)"
   ]
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   "execution_count": 4,
   "metadata": {
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   "source": [
    "# dummies for unseen data\n",
    "# http://stackoverflow.com/a/37451867/436721\n",
    "train_df[\"SibSp\"] = train_df[\"SibSp\"].astype('category',categories=[0,1,2,3,4,5,6,7,8,9])\n",
    "train_df[\"Parch\"] = train_df[\"Parch\"].astype('category',categories=[0,1,2,3,4,5,6,7,8,9])\n",
    "\n",
    "test_df[\"SibSp\"] = test_df[\"SibSp\"].astype('category',categories=[0,1,2,3,4,5,6,7,8,9])\n",
    "test_df[\"Parch\"] = test_df[\"Parch\"].astype('category',categories=[0,1,2,3,4,5,6,7,8,9])"
   ]
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   "cell_type": "code",
   "execution_count": 5,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>class_1</th>\n",
       "      <th>class_2</th>\n",
       "      <th>class_3</th>\n",
       "      <th>sib_0</th>\n",
       "      <th>sib_1</th>\n",
       "      <th>sib_2</th>\n",
       "      <th>...</th>\n",
       "      <th>parch_3</th>\n",
       "      <th>parch_4</th>\n",
       "      <th>parch_5</th>\n",
       "      <th>parch_6</th>\n",
       "      <th>parch_7</th>\n",
       "      <th>parch_8</th>\n",
       "      <th>parch_9</th>\n",
       "      <th>emb_C</th>\n",
       "      <th>emb_Q</th>\n",
       "      <th>emb_S</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
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      "text/plain": [
       "   Survived   Age  sex_female  sex_male  class_1  class_2  class_3  sib_0  \\\n",
       "0         0  22.0           0         1        0        0        1      0   \n",
       "1         1  38.0           1         0        1        0        0      0   \n",
       "2         1  26.0           1         0        0        0        1      1   \n",
       "3         1  35.0           1         0        1        0        0      0   \n",
       "4         0  35.0           0         1        0        0        1      1   \n",
       "\n",
       "   sib_1  sib_2  ...    parch_3  parch_4  parch_5  parch_6  parch_7  parch_8  \\\n",
       "0      1      0  ...          0        0        0        0        0        0   \n",
       "1      1      0  ...          0        0        0        0        0        0   \n",
       "2      0      0  ...          0        0        0        0        0        0   \n",
       "3      1      0  ...          0        0        0        0        0        0   \n",
       "4      0      0  ...          0        0        0        0        0        0   \n",
       "\n",
       "   parch_9  emb_C  emb_Q  emb_S  \n",
       "0        0      0      0      1  \n",
       "1        0      1      0      0  \n",
       "2        0      0      0      1  \n",
       "3        0      0      0      1  \n",
       "4        0      0      0      1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def xtract(df,test=False):\n",
    "    cpy = df.copy()\n",
    "      \n",
    "    cpy = pd.concat([df,pd.get_dummies(df[\"Sex\"],prefix='sex')],axis=1)\n",
    "    cpy = pd.concat([cpy,pd.get_dummies(df[\"Pclass\"],prefix='class')],axis=1)\n",
    "    cpy = pd.concat([cpy,pd.get_dummies(df[\"SibSp\"],prefix='sib')],axis=1)\n",
    "    cpy = pd.concat([cpy,pd.get_dummies(df[\"Parch\"],prefix='parch')],axis=1)\n",
    "    cpy = pd.concat([cpy,pd.get_dummies(df[\"Embarked\"],prefix='emb')],axis=1)\n",
    "    \n",
    "    # only columns we'll actually use\n",
    "    if test:\n",
    "        cpy = cpy[ [col for col in list(cpy) if col.startswith('sex')  or col.startswith('class') or col.startswith('sib') or col.startswith('parch') or col.startswith('emb')  or col == \"Age\" or col == \"Survived\" or col == \"PassengerId\" ] ]\n",
    "        return cpy\n",
    "    else:\n",
    "        cpy = cpy[ [col for col in list(cpy) if col.startswith('sex')  or col.startswith('class') or col.startswith('sib') or col.startswith('parch') or col.startswith('emb') or col == \"Age\" or col == \"Survived\" ] ]\n",
    "        return cpy.dropna()\n",
    "    \n",
    "        \n",
    "train_df_clean = xtract(train_df)\n",
    "train_df_clean.head()"
   ]
  },
  {
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   "execution_count": 6,
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       "      <th>parch_7</th>\n",
       "      <th>parch_8</th>\n",
       "      <th>parch_9</th>\n",
       "      <th>emb_C</th>\n",
       "      <th>emb_Q</th>\n",
       "      <th>emb_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>1247</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1053</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>1137</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId   Age  sex_female  sex_male  class_1  class_2  class_3  \\\n",
       "205         1097   NaN           0         1        1        0        0   \n",
       "0            892  34.5           0         1        0        0        1   \n",
       "355         1247  50.0           0         1        1        0        0   \n",
       "161         1053   7.0           0         1        0        0        1   \n",
       "245         1137  41.0           0         1        1        0        0   \n",
       "\n",
       "     sib_0  sib_1  sib_2  ...    parch_3  parch_4  parch_5  parch_6  parch_7  \\\n",
       "205      1      0      0  ...          0        0        0        0        0   \n",
       "0        1      0      0  ...          0        0        0        0        0   \n",
       "355      1      0      0  ...          0        0        0        0        0   \n",
       "161      0      1      0  ...          0        0        0        0        0   \n",
       "245      0      1      0  ...          0        0        0        0        0   \n",
       "\n",
       "     parch_8  parch_9  emb_C  emb_Q  emb_S  \n",
       "205        0        0      1      0      0  \n",
       "0          0        0      0      1      0  \n",
       "355        0        0      0      0      1  \n",
       "161        0        0      1      0      0  \n",
       "245        0        0      0      0      1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_clean = xtract(test_df,test=True)\n",
    "test_df_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "\n",
    "for row in train_df_clean.values:\n",
    "    X.append(row[1:])\n",
    "    y.append(row[0])\n",
    "    \n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# comment this block to see the difference\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.70833333333333337"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = MLPClassifier(random_state=1,solver='lbfgs')\n",
    "clf.fit(X_train,y_train)\n",
    "metrics.accuracy_score(y_test,clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "passenger_ids = []\n",
    "X_out = []\n",
    "\n",
    "for row in test_df_clean.values:\n",
    "    passenger_ids.append(row[0])\n",
    "    X_out.append(row[1:])\n",
    "    \n",
    "X_out = np.array(X_out)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 29)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = Imputer()\n",
    "imp.fit(X_train)\n",
    "X_out = imp.transform(X_out)\n",
    "X_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_out = clf.predict(X_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>892.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0        892.0       0.0\n",
       "1        893.0       1.0\n",
       "2        894.0       1.0\n",
       "3        895.0       1.0\n",
       "4        896.0       1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df = pd.DataFrame({\n",
    "    'PassengerId': passenger_ids,\n",
    "    'Survived': y_out\n",
    "})\n",
    "out_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         1\n",
       "3          895         1\n",
       "4          896         1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df[\"PassengerId\"] = out_df[\"PassengerId\"].apply(lambda dbl: int(dbl))\n",
    "out_df[\"Survived\"] = out_df[\"Survived\"].apply(lambda dbl: int(dbl))\n",
    "out_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "out_df.to_csv(\"../data/interim/nn.csv\", index=False)"
   ]
  }
 ],
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