04 - Decision Tree Adult Income.ipynb

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   "source": [
    "# -----------------------------------------------------------------\n",
    "# Decision Tree Classifier\n",
    "# Predict the income of an adult based on the census data\n",
    "# -----------------------------------------------------------------\n",
    "\n",
    "# Import libraries\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read dataset\n",
    "data = pd.read_csv('04 - decisiontreeAdultIncome.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "wc                0\n",
       "education         0\n",
       "marital status    0\n",
       "race              0\n",
       "gender            0\n",
       "hours per week    0\n",
       "IncomeClass       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Null values\n",
    "data.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Dummy variables\n",
    "data.dtypes\n",
    "data_prep = pd.get_dummies(data, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X and Y Variables\n",
    "X = data_prep.iloc[:, :-1]\n",
    "Y = data_prep.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the X and Y dataset into training and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = \\\n",
    "train_test_split(X, Y, test_size = 0.3, random_state = 1234, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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,\n",
       "                       random_state=1234, splitter='best')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import and train classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier(random_state=1234)\n",
    "dtc.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the model\n",
    "Y_predict = dtc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test, Y_predict)\n",
    "score = dtc.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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