HD_exercise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['datetime', 'axes', 'plt']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pylab as plt\n",
    "from datetime import datetime\n",
    "from dateutil.parser import parse\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, export_graphviz\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning with Decision Trees\n",
    "June 18, 2019\n",
    "\n",
    "This notebook gives the source code for the decision tree exercise at Tech & AI To Improve Human Capital. Participants filled out a survey (72 responses) based on this fictional scenario:\n",
    "\n",
    "**A new cohort of 100 staff have just been hired, and will start this summer. We need to decide how many bike racks or parking spaces to build for them. To do so, we'll use data on current staff to build a machine learning model. For fun, we'll also predict whether staff prefer classical music or heavy metal.**\n",
    "\n",
    "The notebook is in Python and makes use of SciKitLearn, a widely used library for machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load and prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>exercise_regime</th>\n",
       "      <th>purchase_history</th>\n",
       "      <th>mode_of_travel</th>\n",
       "      <th>music_choice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DC</td>\n",
       "      <td>25-31</td>\n",
       "      <td>Female</td>\n",
       "      <td>Keep fit through jogging or gym.</td>\n",
       "      <td>Gardening supplies, Theater tickets</td>\n",
       "      <td>Walk or bike</td>\n",
       "      <td>Classical music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>56-61</td>\n",
       "      <td>Male</td>\n",
       "      <td>Keep fit through jogging or gym.</td>\n",
       "      <td>Gardening supplies, Dog food / cat food</td>\n",
       "      <td>Train</td>\n",
       "      <td>Classical music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>32-38</td>\n",
       "      <td>Female</td>\n",
       "      <td>Keep fit through jogging or gym.</td>\n",
       "      <td>Gardening supplies, Bluetooth speaker, Dog foo...</td>\n",
       "      <td>Train</td>\n",
       "      <td>Classical music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>39-45</td>\n",
       "      <td>Female</td>\n",
       "      <td>Keep fit through jogging or gym.</td>\n",
       "      <td>Gardening supplies, Bluetooth speaker, Dog foo...</td>\n",
       "      <td>Car</td>\n",
       "      <td>Heavy metal / rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>32-38</td>\n",
       "      <td>Female</td>\n",
       "      <td>Netflix is my most strenuous exercise.</td>\n",
       "      <td>Gardening supplies, Bluetooth speaker, Theater...</td>\n",
       "      <td>Train</td>\n",
       "      <td>Heavy metal / rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      state    age  gender                         exercise_regime  \\\n",
       "0        DC  25-31  Female        Keep fit through jogging or gym.   \n",
       "1  Virginia  56-61    Male        Keep fit through jogging or gym.   \n",
       "2  Maryland  32-38  Female        Keep fit through jogging or gym.   \n",
       "3  Virginia  39-45  Female        Keep fit through jogging or gym.   \n",
       "4  Maryland  32-38  Female  Netflix is my most strenuous exercise.   \n",
       "\n",
       "                                    purchase_history mode_of_travel  \\\n",
       "0                Gardening supplies, Theater tickets   Walk or bike   \n",
       "1            Gardening supplies, Dog food / cat food          Train   \n",
       "2  Gardening supplies, Bluetooth speaker, Dog foo...          Train   \n",
       "3  Gardening supplies, Bluetooth speaker, Dog foo...            Car   \n",
       "4  Gardening supplies, Bluetooth speaker, Theater...          Train   \n",
       "\n",
       "         music_choice  \n",
       "0     Classical music  \n",
       "1     Classical music  \n",
       "2     Classical music  \n",
       "3  Heavy metal / rock  \n",
       "4  Heavy metal / rock  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data was collected on Google Forms and downloaded as a CSV file.\n",
    "# We'll load it as a dataframe ('df'), clean up the column names, and take a look.\n",
    "\n",
    "df = pd.read_csv('HD_ML_survey.csv')\n",
    "df.columns = ['state','age','gender','exercise_regime','purchase_history','mode_of_travel','music_choice']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = df[df.apply(lambda row: not row.astype(str).str.contains('Prefer').any(), axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Inspect the data\n",
    "* Before building a model, visualize the data to understand what's in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a248a0ac8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Our respondents are from a broad set of ages\n",
    "\n",
    "pd.Series(df.age.value_counts(sort=False),index=['25-31','32-38','39-45','46-52','52-56','56-61']).plot(kind='bar',title='Number of respondents by age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There are slightly more women than men, and the majority live in DC.\n",
    "\n",
    "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=[9,4])\n",
    "pies = ['gender','state']\n",
    "\n",
    "for n,i in enumerate(pies):\n",
    "    ax = axes.flatten()[n]\n",
    "    df[i].value_counts().plot(kind='pie',autopct='%1.0f%%',ax=ax)\n",
    "    ax.set(ylabel='',title=i)\n",
    "\n",
    "fig.suptitle(\"Demographic questions\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.exercise_regime.value_counts().plot(kind = 'pie', autopct='%1.0f%%',title='Exercise regime')\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### --> We'll use supervised learning methods, so having a target variable is crucial. We have two target variables. We'll build one model to predict mode of transport, and a second model to predict music choice. The classes are fairly evenly balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,2,sharey=True)\n",
    "df.mode_of_travel.value_counts().plot(kind='bar',ax=axes[1],title='Mode of transport',color='orange')\n",
    "df.music_choice.value_counts().plot(kind='bar',ax=axes[0],title='Music choice');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Create a machine-readable set of features\n",
    "We need to transform the data a bit before our algorithm can work with it:\n",
    "* For categorical features (eg. State = DC, Maryland or Virginia), transform them into dummy variables.\n",
    "* This will give us a new column for each possible value, encoded as 0 or 1. (So if you live in DC, you'll be marked '1' in the DC column and '0' in the Virginia and Maryland columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the purchases data from string (eg. 'yoga mat, theater tickets') to a list.  \n",
    "\n",
    "df.purchase_history = df.purchase_history.astype(str)\n",
    "df.purchase_history = [cell.split(',') for cell in df.purchase_history]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummies for purchase history\n",
    "\n",
    "items = ['Theater tickets','Yoga mat','Bluetooth speaker / headphones',\n",
    "             'Gardening or home improvement gear',' Presents for my kids']\n",
    "\n",
    "for item in items:\n",
    "    df[item] = [item in cell for cell in df.purchase_history]\n",
    "    df[item] = df[item].map({False:0,True:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform gender, household_type, state, food and exercise to dummies\n",
    "\n",
    "for feature in ['gender','state','exercise_regime']:\n",
    "    dummy_feature = pd.get_dummies(df[feature])\n",
    "    df = pd.concat([df,dummy_feature], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take the mid-point of the age range\n",
    "df['age_continuous'] = df.age.map({'25-31':28,'32-38':35,'39-45':42,'46-52':49,'52-56':54,'56-61':58,'Prefer not to say':45})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map the TRANSPORT and MUSIC_CHOICE columns (our target variables) to 0s/1s\n",
    "\n",
    "y_transport = df.mode_of_travel.map({'Train':0,'Car':0,'Walk or bike':1})\n",
    "y_music = df.music_choice.map({'Classical music':0,'Heavy metal / rock':1})\n",
    "\n",
    "# Note, we're using the following binary values:\n",
    "\n",
    "# TRANSPORT -- Train or car: 0, Walk or bike, 1\n",
    "# MUSIC -- Classical: 0, Heavy metal / rock: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['state', 'age_continuous', 'gender', 'Theater tickets', 'Yoga mat',\n",
    "       'Bluetooth speaker / headphones', 'Gardening or home improvement gear',\n",
    "       ' Presents for my kids', 'Female', 'Male',\n",
    "       'Female', 'Male', 'DC', 'Maryland', 'Virginia',\n",
    "       'I play team sports / run races.', 'Keep fit through jogging or gym.',\n",
    "       'Netflix is my most strenuous exercise.']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Build decision tree models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.1 TRANSPORT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In machine learning, the set of predictive features is typically denoted X. The target variable is denoted y.\n",
    "# This reflects mathematical notation where y = f(X)   [outcome y is a function of input features x1 ... xN]\n",
    "\n",
    "# Here are the feature we'll use:\n",
    "\n",
    "X = ['age_continuous', 'Female', 'Male', 'DC', 'Maryland', 'Virginia', 'Theater tickets', 'Yoga mat',\n",
    "       'Bluetooth speaker / headphones', 'Gardening or home improvement gear',\n",
    "       ' Presents for my kids','I play team sports / run races.', 'Keep fit through jogging or gym.',\n",
    "       'Netflix is my most strenuous exercise.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_continuous</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>DC</th>\n",
       "      <th>Maryland</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Theater tickets</th>\n",
       "      <th>Yoga mat</th>\n",
       "      <th>Bluetooth speaker / headphones</th>\n",
       "      <th>Gardening or home improvement gear</th>\n",
       "      <th>Presents for my kids</th>\n",
       "      <th>I play team sports / run races.</th>\n",
       "      <th>Keep fit through jogging or gym.</th>\n",
       "      <th>Netflix is my most strenuous exercise.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>58</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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_continuous  Female  Male  DC  Maryland  Virginia  Theater tickets  \\\n",
       "0              28       1     0   1         0         0                0   \n",
       "1              58       0     1   0         0         1                0   \n",
       "\n",
       "   Yoga mat  Bluetooth speaker / headphones  \\\n",
       "0         0                               0   \n",
       "1         0                               0   \n",
       "\n",
       "   Gardening or home improvement gear   Presents for my kids  \\\n",
       "0                                   0                      0   \n",
       "1                                   0                      0   \n",
       "\n",
       "   I play team sports / run races.  Keep fit through jogging or gym.  \\\n",
       "0                                0                                 1   \n",
       "1                                0                                 1   \n",
       "\n",
       "   Netflix is my most strenuous exercise.  \n",
       "0                                       0  \n",
       "1                                       0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[X].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets. We take 70% of data to train the model, 30% to evaluate it.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_transport, X_test_transport, y_train_transport, y_test_transport = train_test_split(df[X],y_transport,test_size=0.31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows and columns in X_train: (47, 14)\n",
      "rows and columns in y_train: (47,)\n",
      "rows and columns in X_test: (22, 14)\n",
      "rows and columns in y_test: (22,)\n"
     ]
    }
   ],
   "source": [
    "# X_train is the features for our training set, y_train is the corresponding labels (eg. 'classical fan','heavy metal lover')\n",
    "\n",
    "print(\"rows and columns in X_train: {}\".format(X_train_transport.shape))\n",
    "print(\"rows and columns in y_train: {}\".format(y_train_transport.shape))\n",
    "print(\"rows and columns in X_test: {}\".format(X_test_transport.shape))\n",
    "print(\"rows and columns in y_test: {}\".format(y_test_transport.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\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,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First set up the model that we'll use:\n",
    "\n",
    "tree_transport = DecisionTreeClassifier(max_depth=3)\n",
    "\n",
    "# Then pass it the training data:\n",
    "\n",
    "tree_transport.fit(X_train_transport,y_train_transport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the model\n",
    "\n",
    "export_graphviz(tree_transport, out_file='tree.dot', \n",
    "                feature_names = X,\n",
    "                class_names = ['Train/car','walk/bike'],\n",
    "                rounded = True, proportion = False, \n",
    "                precision = 2, filled = True)\n",
    "\n",
    "#from IPython.display import Image\n",
    "#Image('tree1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.2 MUSIC CHOICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_music, X_test_music, y_train_music, y_test_music = train_test_split(df[X],y_music,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=2, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_music = DecisionTreeClassifier(max_depth=4, min_samples_leaf=2)\n",
    "tree_music.fit(X_train_music,y_train_music)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(tree_music, out_file='tree2.dot', \n",
    "                feature_names = X,\n",
    "                class_names = ['Classical','Metal'],\n",
    "                rounded = True, proportion = False, \n",
    "                precision = 2, filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Image('tree2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Predict against test set, evaluate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANSPORT: First 10 predictions: [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "# We built a decision tree classifier with the training data. Now let's run the test data through it.\n",
    "# We'll output the predictions it made and look at them:\n",
    "\n",
    "predictions_transport = tree_transport.predict(X_test_transport)\n",
    "\n",
    "print(\"TRANSPORT: First 10 predictions: {}\".format(predictions_transport[:10].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = len(predictions_transport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANSPORT - We predicted 17 right out of 22 examples. That's a 77.3 % accuracy rate.\n"
     ]
    }
   ],
   "source": [
    "# Is the model any good? Let's check how many of the predicted value equal the actual values.\n",
    "\n",
    "n_correct = sum(predictions_transport == y_test_transport)\n",
    "accuracy = n_correct / n_samples * 100\n",
    "\n",
    "print(\"TRANSPORT - We predicted {} right out of {} examples. That's a {:.1f} % accuracy rate.\".format(\n",
    "    n_correct,n_samples,accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MUSIC - We predicted 13 right out of 22 examples. That's a 59.1 % accuracy rate.\n"
     ]
    }
   ],
   "source": [
    "# Check accuracy for music\n",
    "\n",
    "predictions_music = tree_music.predict(X_test_music)\n",
    "\n",
    "print(\"MUSIC - We predicted {} right out of {} examples. That's a {:.1f} % accuracy rate.\".format(\n",
    "    sum(predictions_music == y_test_music),n_samples,sum(predictions_music == y_test_music)/n_samples*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> Note that the transport model addresses an easy task, whereas predicting musical taste seems difficult. What could boost the prediction accuracy? Two approaches: get more data to train your models on, or improve the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Will a more complex model give us better accuracy?\n",
    "* Random Forest is an algorithm that builds a decisions of decision trees, each with a slightly different subset of the training data, then makes predictions by averaging the results of each tree. \n",
    "* Building a large 'forest' can boost prediction accuracy. But a complex model risks overfitting the data, and benefits of adding more trees peaks after a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score       # This is a variant on train-test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = np.arange(1,40,1)           # We'll build a Random Forest with 1 tree, 2 trees, 3 trees, etc, up to 50 trees.\n",
    "results = []                             # We'll test the prediction accuracy of each model, and store the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in n_estimators:\n",
    "    transport_rf = RandomForestClassifier(n_estimators=i,random_state=10)\n",
    "    scores = cross_val_score(transport_rf,df[X],y_transport,cv=5)\n",
    "    results.append(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.6)\n",
    "f, ax = plt.subplots(figsize=[8,6])\n",
    "sns.lineplot(n_estimators,results,color='orange',ax=ax,linewidth=5)\n",
    "fig = plt.gca()\n",
    "fig.set(title=\"Mode of transport classifier: Random forest\",ylabel='% predictions correct', xlabel='number of trees');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> performance rises with number of trees, then levels off. It may be worth using a RF with say 10-15 trees. Note that on large and complex datasets, it can be worth using a large number of trees (100s or even 1000s)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Demo of parameter tuning\n",
    "ML models have parameters, eg. for a decision-tree you can specify the maximum depth and the minimum number of samples to include in any split. We'll tune the model by trying different parameters to find the combination that works best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's specify the set of parameters to look at, and the values to search among.\n",
    "# ---> we'll try any number of trees between 2 and 40, and several options for max depth and minimum samples per leaf.\n",
    "\n",
    "param_grid = {'n_estimators':np.arange(2,40),'max_depth':[3,4,5,6],'min_samples_leaf':[2,3,4,5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'max_depth': [3, 4, 5, 6],\n",
       "                         'min_samples_leaf': [2, 3, 4, 5],\n",
       "                         'n_estimators': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll build a new model for each combination of the parameters (this is a Grid Search), and store the results.\n",
    "\n",
    "grid_search = GridSearchCV(RandomForestClassifier(),param_grid,cv=5)\n",
    "grid_search.fit(X_train_transport,y_train_transport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters were: {'max_depth': 3, 'min_samples_leaf': 2, 'n_estimators': 14}\n",
      "The best model classified 85.1 percent of examples correctly.\n"
     ]
    }
   ],
   "source": [
    "print(\"The best parameters were: {}\".format(grid_search.best_params_))\n",
    "print(\"The best model classified {} percent of examples correctly.\".format((grid_search.best_score_*100).round(1)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Demo of feature importance\n",
    "* Sometimes we want to know which features the model made use of. This helps inform us about what set of rules just got constructed. It's also informative in its own right: eg. in education policy, you might not need to predict student performance, but finding out which factors influence most strongly is of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/pandas/plotting/_core.py:185: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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VR4FTgO0naz+FY4GPAucN+c17WqrqJuCGgfkjLwPOTLIWsEVVfQ94K7Ap3ajGhH2UZOuquriqjqC7jbDdZO2bbwOvbKMCJHlwkgdMUfZiutseAIMTjb8P7NuOsy3dbZXLWvsd2pyQLYBdWps5wFpVdRLwTuCxQ8419LWSZCu620g7As9K8rjJ2k9w3IPGwmba/J+B/f5PbVV1M3Blkr9ubdICjCSpB5b275z8K93chDGHAPPaRMRL6SZ5QnfL43kZMtlzEm8Hzqru0zxvAl6V5OGTtH8RsCjJhXRv3scvzYUMqqoFwM3ce1RoWewHfCDJQmAHunknawOfbbeSLgCOrKobmbyP3tAmcF5Edxvtm3S3hu5KctGwCadVdRrdPI0ftXN9makD4geBg9N9nHvw5/pvwNrtOCcA+1fVErrbTVfS3cr6IN1IGnQjXme0n8WngX8Ycq7/81pJNwzySbq5N9fSBd9jk6w/rP0E13As3a2rha2/xn+iaaLa9gUOaPtcQjdyRpK9khw+YY9Jkla63HPXY82VZHO6CZTbTXYrSRrTzTmZP+oyJGlGLW9kSLKgqiaaPvEna/xfiE3ycuBc4B0GE0mSRm+N/ONdg6rqeJbjlpAkSVqx1viRE0mS1C+GE0mS1CuGE0mS1Ctr/JwTaVnstBPM98M6krRSOHIiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6xXAiSZJ6ZZ1RFyCtihYsgGTUVUj3qBp1BdKK48iJJEnqFcOJJEnqFcOJJEnqFcOJJEnqFcOJJEnqFcOJJEnqFcOJeiPJH5NcOPCYuxLPtX+So1fW8SVJy86/c6I+ub2qdhh1EZKk0XLkRL2WZO0kH0hyXpKFSf62bd8tyZlJvpTkZ0n+Jcm+SX6c5OIkW7d2z0lybpILknw3yQOHnGOzJCe1c5yX5IkzfZ2SpHsYTtQnGwzc0vlq23YAcFNV7QzsDLw6yUPbc48B/g54NPAyYNuq2gU4Fnh9a3M28Piq2hH4IvDWIef9CHBkO8c+bX9J0oh4W0d9Muy2ztOB7ZO8oK3PBrYB7gDOq6pfAyS5HDittbkYeGpbfghwQpI/A9YDrhxy3t2BR+Sev0e/SZJZVXXLYKMkBwIHdmtbLsv1SZKmwXCivgvw+qr69r02JrsBSwY23T2wfjf3vLaPAj5UVae0fQ4bco61gCdU1e2TFVJVxwDHdOef5zeZSNJK4m0d9d23gYOTrAuQZNskGy3F/rOBa9ryfhO0OQ143dhKEiflStIIGU7Ud8cClwLnJ1kEfJylG/E7DDgxyVnAdRO0OQSY1ybcXgoctBz1SpKWU8rv2ZaWWndbZ/6oy5D+xH/KtSpIsqCq5k3VzpETSZLUK4YTSZLUK4YTSZLUK4YTSZLUK4YTSZLUK/4RNmkZ7LQTzPfDOpK0UjhyIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSesVwIkmSemWdURcgrYoWLIBk1FVoVVU16gqkfnPkRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRCtVOmcnedbAthcm+dYo65pIklcmedCo65CkNZnhRCtVVRVwEPChJOsn2Qh4H/Da0VY2oVcChhNJGiH/QqxWuqpalORrwNuAjYDjq+ryJG8FXt6afbyqjgJI8m7gRcDVwPXAj6rqw0kOAg4A1gN+Bry8qm4fPFeS9wIPATYHtgXeAOwKPAO4Cti7qu5q59gD2AA4GzgYeCGwA3BCktuBXarqjpXSKZKkCTlyopnybuAlwLOA9yfZBdgX2AV4AvCaJNsneTzwbOAxwD7AzgPHOLGqdq6qxwCXA/tPcK6H0gWPfYDPA9+qqkcBdwPPbG0+UlU7A48GZgPPrKoTgAuBF1XVDuODSZIDk8xPMh9+tzx9IUmahCMnmhFVdVuSE4Bbq2pJkl2Bk6rqDwBJTgaeBGwInFxVS4AlSU4dOMz2SQ4HNgVmAacy3Dfa6MjF7dzfadsvBua25acleQuwPjAHWAB8c4prOAY4pqt3nt+OIkkrieFEM+nu9gCY6GvzJvs6veOBZ7XbRK8CHj9BuyUD5xsc/bgbWCfJhsDRwGOr6pp2K2j96VyAJGnl87aORuX7wPOSbJBkY2Bv4Cy6+R97JblPkll0t2fGbAT8Jsm6dLeIltUGdEHlunaOfQaeu4VuVEaSNCKOnGgkqurHSb4AnNc2/XtVXQzQPma8EFjcnr+ptXkX8GPgl8AilnG0o6p+n+Qz7RhXAecOPH0ccKwTYiVpdNJ90lPqjyQbV9Wt7WPHZwP7VdXCUdc1qJtzMn/UZWgV5T+7WlMlWVBV86Zq58iJ+uiTSR5GNzLyqb4FE0nSymU4Ue9U1YtGXYMkaXScECtJknrFcCJJknrFcCJJknrFOSfSMthpJ5jvh3UkaaVw5ESSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPWK4USSJPXKOqMuQFoVLVgAyairmFzVqCuQpGXjyIkkSeoVw4kkSeoVw4kkSeoVw4kkSeoVw4kkSeoVw4kkSeoVw4kkSeoVw4l6IclzkzxiYP3wJLuPsiZJ0mgYTtQXzwX+FE6q6l1V9d0R1iNJGhHDyRoiyclJFiS5JMmBbdsBSX6W5Iwkn0hydNu+WZKTkpzXHk+c5LgbJzkuycVJFibZp21/cdu2KMkRA+1vTfK+JBclOSfJA5P8JbAX8IEkFybZOsmnk7yg7bM4ybuTnN+OuV3bfliSQweOvSjJ3Lb8pra+KMkb2ra5SRYNtD80yWFt+ZAkl7Zr+OIK6XRJ0jLxz9evOV5ZVdcn2QA4L8nXgXcCjwVuAU4HLmptPwIcWVVnJ9kS+Dbw8AmO+07gpqp6NECS+ybZHDgC2Am4ATgtyXOr6mRgI+CcqnpHkvcDr66q9yY5BTi1qr7cjjP+PNdV1WOTvAY4FHjVRBeaZCfgFcDjgADnJjmz1TKRvwceWlVLkmw6wXEPBA7s1rac5FCSpOXhyMma45AkFwHnAFsALwPOrKrrq+pO4MSBtrsDRye5EDgF2CTJrAmOuzvwsbGVqroB2Bk4o6p+V1V3AZ8Dntya3AGc2pYXAHOnWf9XlmKfJwFfrarbqurWtu+uU+yzEPhckpcCdw1rUFXHVNW8qpoHm02zbEnS0nLkZA2QZDe6EPGEqvpDkjOAy5h4NGSt1vb26RweGP8Vc5N9Jd6dVX/6Sro/Mv3X4JIh+9zFvQP2+lOcf6L2AHvSBai9gHcmeWQLVpKkGebIyZphNnBDCybbAY8HNgSe0m7DrAPsM9D+NOB1YytJdpjk2OPb3hc4tx17TpK1gRcDZ05R4y3ARKMzE1lMd1uKJI8FHtq2fx94bpINk2wEPA84C/gt8IAk909yH+DZbd+1gC2q6nvAW4FNgY2XshZJ0gpiOFkzfAtYJ8lC4D10t3auAf6JLkh8F7gUuKm1PwSY1yaHXgocNMmx3wvct008vQh4alX9GvgH4Ht081jOr6r/mqLGLwJvSXJBkq2neV0nAfdrt58OBn4GUFXnA58Gftyu79iquqDdvjq8bTsV+Gk7ztrAZ5NcDFxAN9/mxmnWIElawXLPCLvWNEk2rqpb28jJV4FPVdVXR13XqiCZVzB/1GVMyv+1JfVNkgXdvL3JOXKyZjusjTosAq4ETh5xPZIkOSF2TVZVh07dqpPkFcDfjdv8g6p67YqtSpK0pjOcaFqq6jjguFHXIUla/XlbR5Ik9YrhRFoGO+3UTTjt80OSVlWGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CuGE0mS1CvrjLoAaVW0YAEko67iHlWjrkCSVhxHTiRJUq8YTiRJUq8YTiRJUq8YTiRJUq8YTiRJUq8YTiRJUq8YTiRJUq8YTrRKS1JJ/nNgfZ0kv0ty6hT77TZVG0nSaBhOtKq7DXhUkg3a+l8B14ywHknScjKcaHXwTWDPtvxi4AtjTyTZJckPk1zQ/vuw8Tsn2SjJp5Kc19rtPUN1S5KGMJxodfBF4G+SrA9sD5w78NxPgSdX1Y7Au4B/GrL/O4DTq2pn4KnAB5JsNL5RkgOTzE8yH363wi9CktTxu3W0yquqhUnm0o2afGPc07OBzyTZBihg3SGHeDqwV5JD2/r6wJbAT8ad5xjgGIBknt9mI0krieFEq4tTgA8CuwH3H9j+HuB7VfW8FmDOGLJvgH2q6rKVW6IkaTq8raPVxaeAw6vq4nHbZ3PPBNn9J9j328Drk+57hpPsuFIqlCRNi+FEq4Wq+lVVfWTIU+8H/jnJD4C1J9j9PXS3exYmWdTWJUkjkipvnUtLq5tzMn/UZfyJ/xtLWhUkWVBV86Zq58iJJEnqFcOJJEnqFcOJJEnqFcOJJEnqFcOJJEnqFcOJtAx22qn7hExfHpK0OjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXjGcSJKkXkn5t6+lpZbkFuCyUdfRQ3OA60ZdRE/ZNxOzb4ZbHftlq6rabKpG68xEJdJq6LKqmjfqIvomyXz7ZTj7ZmL2zXBrcr94W0eSJPWK4USSJPWK4URaNseMuoCesl8mZt9MzL4Zbo3tFyfESpKkXnHkRJIk9YrhRJIk9YrhRJpEkmcmuSzJL5L8/ZDn75PkhPb8uUnmznyVM28a/fLkJOcnuSvJC0ZR46hMo2/elOTSJAuT/HeSrUZR50ybRr8clOTiJBcmOTvJI0ZR5yhM1TcD7V6QpJKs9h8vNpxIE0iyNvAx4FnAI4AXD/kH8wDghqr6C+BI4IiZrXLmTbNffgnsD3x+ZqsbrWn2zQXAvKraHvgy8P6ZrXLmTbNfPl9Vj66qHej65EMzXOZITLNvSDILOAQ4d2YrHA3DiTSxXYBfVNUVVXUH8EVg73Ft9gY+05a/DDwtSWawxlGYsl+qanFVLQTuHkWBIzSdvvleVf2hrZ4DPGSGaxyF6fTLzQOrGwFryqc1pvPvDMB76ELb/85kcaNiOJEm9mDg6oH1X7VtQ9tU1V3ATcD9Z6S60ZlOv6yplrZvDgC+uVIr6odp9UuS1ya5nO5N+JAZqm3UpuybJDsCW1TVqTNZ2CgZTqSJDRsBGf/b3HTarG7WxGuermn3TZKXAvOAD6zUivphWv1SVR+rqq2BtwH/uNKr6odJ+ybJWnS3jN88YxX1gOFEmtivgC0G1h8CXDtRmyTrALOB62ekutGZTr+sqabVN0l2B94B7FVVS2aotlFa2tfMF4HnrtSK+mOqvpkFPAo4I8li4PHAKav7pFjDiTSx84Btkjw0yXrA3wCnjGtzCrBfW34BcHqt/n/ZcDr9sqaasm/aEP3H6YLJ/4ygxlGYTr9sM7C6J/DzGaxvlCbtm6q6qarmVNXcqppLN09pr6qaP5pyZ4bhRJpAm0PyOuDbwE+AL1XVJUkOT7JXa/ZJ4P5JfgG8CZjwY4Cri+n0S5Kdk/wK+Gvg40kuGV3FM2ear5kPABsDJ7aPza72wW6a/fK6JJckuZDu/6X9JjjcamWafbPG8c/XS5KkXnHkRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRJIk9YrhRJIk9cr/B6BCYO+PAAAAA0lEQVS+2IpyhriRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importance = pd.Series(tree_music.feature_importances_,index=df[X].columns)\n",
    "feat_importance.nlargest(5).plot(kind='barh',title='Music model: Feature importances',colors='b')\n",
    "plt.gca().invert_yaxis();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "rf_transport = RandomForestClassifier()\n",
    "rf_transport.fit(X_train_transport,y_train_transport,);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/pandas/plotting/_core.py:185: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "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": [
    "feat_importance = pd.Series(rf_transport.feature_importances_,index=df[X].columns)\n",
    "feat_importance.nlargest(7).plot(kind='barh',title='Transport model: Feature importances',colors='g')\n",
    "plt.gca().invert_yaxis();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thanks for participating! Contacts: Nick Jones (njones@worldbank.org), Mersedeh Tariverdi (mtariverdi@worldbank.org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}