02_Random_Forest.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the Random Forest Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
    "\n",
    "wine = pd.read_csv(data_url, delimiter=';')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify the Target Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the quality column out of the data\n",
    "wine_target = wine['quality']\n",
    "wine_data = wine.drop('quality', axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Divide the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "test_size = 0.30\n",
    "seed = 7\n",
    "train_data, test_data, train_target, test_target = model_selection.train_test_split(wine_data,\n",
    "        wine_target, test_size=test_size, \n",
    "        random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit the Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = RandomForestClassifier()\n",
    "model.fit(train_data, train_target)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply the Model to the Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_results = model.predict(test_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(zip(train_data, model.feature_importances_))\n",
    "importances = model.feature_importances_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print(\"Feature ranking:\")\n",
    "col_names = list(train_data.columns.values)\n",
    "for f in range(len(indices)):\n",
    "    ndx = indices[f]\n",
    "    feature = col_names[ndx]\n",
    "    value = importances[ndx]\n",
    "    #feature = col_names[indices[f]]\n",
    "    space = ' '*(20 - len(feature))\n",
    "\n",
    "    print(\"%d.\\t %s %s %f\" % (f + 1, feature, space, value))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ndx = np.argsort(importances)\n",
    "value = pd.DataFrame(importances[ndx])\n",
    "\n",
    "ax = value.plot(kind='barh', figsize=(8, 10), color='#4466ff', zorder=2, width=0.85)\n",
    "\n",
    "# Despine\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Switch off ticks\n",
    "ax.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "column_names = [col_names[x] for x in ndx]\n",
    " \n",
    "ax.set_yticklabels(column_names)\n",
    "plt.show()"
   ]
  }
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