01_Decision_Tree.ipynb

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   "source": [
    "## Decision Tree Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the Decision Tree Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn import tree\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",
    "\n",
    "print(wine.info())\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": [
    "\n",
    "#### 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 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = tree.DecisionTreeClassifier()\n",
    "model = model.fit(train_data, train_target)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply the Model to the Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(test_data) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_name =\"Quality\"\n",
    "cm = pd.crosstab(test_target, prediction,     \\\n",
    "                 rownames=[row_name], colnames=[''])\n",
    "print(' '*(len(row_name)+3),\"Predicted \", row_name)\n",
    "print(cm)\n"
   ]
  }
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