JazmynFuller_HW4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW4 Support Vector Machine and Decision Trees\n",
    "\n",
    "# Due on 11/25 23:59 pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will use the same affair dataset from HW3, but will skip the EDA phrase we have done enough of it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Everything removing outliers, create dummies variabes had been done for you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>had_affair</th>\n",
       "      <th>occ2</th>\n",
       "      <th>occ3</th>\n",
       "      <th>occ4</th>\n",
       "      <th>occ5</th>\n",
       "      <th>occ6</th>\n",
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       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  religious  educ  had_affair  \\\n",
       "0            3.0  32.0          9.0       3.0        3.0  17.0           1   \n",
       "1            3.0  27.0         13.0       3.0        1.0  14.0           1   \n",
       "2            4.0  22.0          2.5       0.0        1.0  16.0           1   \n",
       "3            4.0  37.0         16.5       4.0        3.0  16.0           1   \n",
       "4            5.0  27.0          9.0       1.0        1.0  14.0           1   \n",
       "\n",
       "   occ2  occ3  occ4  occ5  occ6  \n",
       "0     1     0     0     0     0  \n",
       "1     0     1     0     0     0  \n",
       "2     0     1     0     0     0  \n",
       "3     0     0     0     1     0  \n",
       "4     0     1     0     0     0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember the affair data set from HW3, we will use that dataset again\n",
    "# but we will directly load it from the API\n",
    "orig_df = pd.read_csv(\"affairs2.csv\")\n",
    "# Set up our target class label\n",
    "orig_df['had_affair'] = orig_df['affairs'].apply(lambda x: 1 if x != 0 else 0)\n",
    "orig_df = orig_df.drop('affairs',axis=1)\n",
    "# remove NA\n",
    "orig_df.dropna(inplace=True)\n",
    "# create dummies variable for occupation\n",
    "occ = pd.get_dummies(orig_df['occupation'],drop_first=True)\n",
    "# we include rate_marriage feature as well. In HW3, we did not include that variable\n",
    "features = ['rate_marriage','age','yrs_married','children','religious','educ', 'had_affair']\n",
    "df = orig_df\n",
    "df = pd.concat([orig_df[features], occ], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rate_marriage    0\n",
       "age              0\n",
       "yrs_married      0\n",
       "children         0\n",
       "religious        0\n",
       "educ             0\n",
       "had_affair       0\n",
       "occ2             0\n",
       "occ3             0\n",
       "occ4             0\n",
       "occ5             0\n",
       "occ6             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sure there is no missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we are ready to build models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: Build a classification model using SVC using linear kernel with usual steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "model = svm.SVC(kernel='linear')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "target = df['had_affair']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(df.drop('had_affair',axis=1), \n",
    "                                                    df['had_affair'], train_size=0.80, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.93      0.81       850\n",
      "           1       0.64      0.27      0.38       424\n",
      "\n",
      "    accuracy                           0.71      1274\n",
      "   macro avg       0.68      0.60      0.59      1274\n",
      "weighted avg       0.69      0.71      0.67      1274\n",
      "\n",
      "Accuracy Score: 0.71 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "educ_children = df[['educ', 'children']].as_matrix\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the model from sklearn import svm, create the SVC object \n",
    "#model = svm.SVC()\n",
    "#Call Train test split\n",
    "#print out model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2. Now try different value of C-parameter and rerun your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=32, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
      "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
      "    shrinking=True, tol=0.001, verbose=False)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.92      0.81       850\n",
      "           1       0.64      0.27      0.38       424\n",
      "\n",
      "    accuracy                           0.71      1274\n",
      "   macro avg       0.68      0.60      0.59      1274\n",
      "weighted avg       0.69      0.71      0.66      1274\n",
      "\n",
      "Accuracy Score: 0.71 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='linear', C=2**5)\n",
    "model.fit(X_train,Y_train)\n",
    "print (model)\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=0.0009765625, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
      "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
      "    shrinking=True, tol=0.001, verbose=False)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      1.00      0.80       850\n",
      "           1       0.67      0.01      0.02       424\n",
      "\n",
      "    accuracy                           0.67      1274\n",
      "   macro avg       0.67      0.50      0.41      1274\n",
      "weighted avg       0.67      0.67      0.54      1274\n",
      "\n",
      "Accuracy Score: 0.67 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='linear', C=4**-5)\n",
    "model.fit(X_train,Y_train)\n",
    "print(model)\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3. Try using rbf as your kernel and use Gamma of 2**-5, 0.1, 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=32, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=0.03125, kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.86      0.79       850\n",
      "           1       0.56      0.35      0.43       424\n",
      "\n",
      "    accuracy                           0.69      1274\n",
      "   macro avg       0.64      0.61      0.61      1274\n",
      "weighted avg       0.67      0.69      0.67      1274\n",
      "\n",
      "Accuracy Score: 0.692 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf', C=2**5, gamma=2**-5)\n",
    "model.fit(X_train,Y_train)\n",
    "print(model)\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.91      0.81       850\n",
      "           1       0.64      0.31      0.42       424\n",
      "\n",
      "    accuracy                           0.71      1274\n",
      "   macro avg       0.68      0.61      0.61      1274\n",
      "weighted avg       0.70      0.71      0.68      1274\n",
      "\n",
      "Accuracy Score: 0.711 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf', C=1, gamma=2**-5)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      1.00      0.80       850\n",
      "           1       0.00      0.00      0.00       424\n",
      "\n",
      "    accuracy                           0.67      1274\n",
      "   macro avg       0.33      0.50      0.40      1274\n",
      "weighted avg       0.45      0.67      0.53      1274\n",
      "\n",
      "Accuracy Score: 0.667 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf', C=2**-5, gamma=0.1)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.89      0.80       850\n",
      "           1       0.59      0.31      0.41       424\n",
      "\n",
      "    accuracy                           0.70      1274\n",
      "   macro avg       0.66      0.60      0.60      1274\n",
      "weighted avg       0.68      0.70      0.67      1274\n",
      "\n",
      "Accuracy Score: 0.7 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf',C=1, gamma=0.1)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.81      0.76       850\n",
      "           1       0.47      0.33      0.39       424\n",
      "\n",
      "    accuracy                           0.65      1274\n",
      "   macro avg       0.59      0.57      0.57      1274\n",
      "weighted avg       0.63      0.65      0.64      1274\n",
      "\n",
      "Accuracy Score: 0.653 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf', C=2**5, gamma=1)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.87      0.77       850\n",
      "           1       0.47      0.24      0.32       424\n",
      "\n",
      "    accuracy                           0.66      1274\n",
      "   macro avg       0.58      0.55      0.55      1274\n",
      "weighted avg       0.62      0.66      0.62      1274\n",
      "\n",
      "Accuracy Score: 0.658 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf', C=1, gamma=1)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.87      0.76       850\n",
      "           1       0.42      0.19      0.26       424\n",
      "\n",
      "    accuracy                           0.64      1274\n",
      "   macro avg       0.55      0.53      0.51      1274\n",
      "weighted avg       0.59      0.64      0.60      1274\n",
      "\n",
      "Accuracy Score: 0.642 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf',C=2**5, gamma=2)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.88      0.77       850\n",
      "           1       0.40      0.16      0.23       424\n",
      "\n",
      "    accuracy                           0.64      1274\n",
      "   macro avg       0.54      0.52      0.50      1274\n",
      "weighted avg       0.59      0.64      0.59      1274\n",
      "\n",
      "Accuracy Score: 0.642 \n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf',C=1, gamma=2)\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "# Needed for classification report\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test,predictions))\n",
    "print('Accuracy Score: %s ' % round((accuracy_score(Y_test, predictions)),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4. So out of all the models you try in Question 2 and 3, what is the best choice for the kernel, C and gamma parameters?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the rbf:\n",
    "The C-parameter that gave a better accuracy score was 1, merely a larger number in general gave a better score for both rbf and linear. This means that there was more room for misclassification, than the higher C-parameter values. Lastly, the gamma parameter that gives the higher accuracy score, are the ones very close to zero. However, this also means our model is underfitting. \n",
    "model = svm.SVC(kernel='rbf', C=1, gamma=2**-5)\n",
    "\n",
    "#### For the linear:\n",
    "The linear model with the C-parameter being equal to 32 gave a good accuracy score. Which means that we had a lot less misclassifcation, but we can also be overfitting our model.\n",
    "\n",
    "Both accuracy scores were very close, so either of these two options would be good,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now we will try to fit the same dataset with Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5. Build a Decision Tree Classifier using default parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('had_affair',axis=1)\n",
    "Y = df['had_affair']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier(max_depth=3)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1196  100]\n",
      " [ 447  167]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.92      0.81      1296\n",
      "           1       0.63      0.27      0.38       614\n",
      "\n",
      "    accuracy                           0.71      1910\n",
      "   macro avg       0.68      0.60      0.60      1910\n",
      "weighted avg       0.69      0.71      0.67      1910\n",
      "\n",
      "0.7136125654450262\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6. Now try using max_depth = 2, 3, 4 and crierion = 'gini' and 'entropy' to build 3 X 2 = 6 different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1171  125]\n",
      " [ 418  196]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.90      0.81      1296\n",
      "           1       0.61      0.32      0.42       614\n",
      "\n",
      "    accuracy                           0.72      1910\n",
      "   macro avg       0.67      0.61      0.62      1910\n",
      "weighted avg       0.70      0.72      0.69      1910\n",
      "\n",
      "0.7157068062827225\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=2, criterion='gini')\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1171  125]\n",
      " [ 418  196]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.90      0.81      1296\n",
      "           1       0.61      0.32      0.42       614\n",
      "\n",
      "    accuracy                           0.72      1910\n",
      "   macro avg       0.67      0.61      0.62      1910\n",
      "weighted avg       0.70      0.72      0.69      1910\n",
      "\n",
      "0.7157068062827225\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=2, criterion='entropy')\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1196  100]\n",
      " [ 447  167]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.92      0.81      1296\n",
      "           1       0.63      0.27      0.38       614\n",
      "\n",
      "    accuracy                           0.71      1910\n",
      "   macro avg       0.68      0.60      0.60      1910\n",
      "weighted avg       0.69      0.71      0.67      1910\n",
      "\n",
      "0.7136125654450262\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=3, criterion='gini')\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1196  100]\n",
      " [ 447  167]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.92      0.81      1296\n",
      "           1       0.63      0.27      0.38       614\n",
      "\n",
      "    accuracy                           0.71      1910\n",
      "   macro avg       0.68      0.60      0.60      1910\n",
      "weighted avg       0.69      0.71      0.67      1910\n",
      "\n",
      "0.7136125654450262\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=3, criterion='entropy')\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1168  128]\n",
      " [ 418  196]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.90      0.81      1296\n",
      "           1       0.60      0.32      0.42       614\n",
      "\n",
      "    accuracy                           0.71      1910\n",
      "   macro avg       0.67      0.61      0.61      1910\n",
      "weighted avg       0.69      0.71      0.68      1910\n",
      "\n",
      "0.7141361256544503\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=4, criterion='gini')\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1172  124]\n",
      " [ 418  196]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.90      0.81      1296\n",
      "           1       0.61      0.32      0.42       614\n",
      "\n",
      "    accuracy                           0.72      1910\n",
      "   macro avg       0.67      0.61      0.62      1910\n",
      "weighted avg       0.70      0.72      0.69      1910\n",
      "\n",
      "0.7162303664921466\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=4, criterion='entropy')\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7. What is your obsevation from Question 6? Does the choice of the criterion important in this case? What about the max_depth? What is the best choice of max_depth and criterion?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the values for max_depth and criterion we chose resulted in very good and similar accuracy results, therefore, I don't think they're that significant in this case. However, the best choice for max_depth is 4, while the choice for criterion, because both gini and entropy yield the exact same performance and accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree Visualization\n",
    "\n",
    "Scikit learn actually has some built-in visualization capabilities for decision trees, you won't use this often and it requires you to install the pydot library, but here is an example of what it looks like and the code to execute this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jazmyn/anaconda3/lib/python3.7/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'yrs_married',\n",
       " 'children',\n",
       " 'religious',\n",
       " 'educ',\n",
       " 'had_affair',\n",
       " 'occ2',\n",
       " 'occ3',\n",
       " 'occ4',\n",
       " 'occ5',\n",
       " 'occ6']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image  \n",
    "from sklearn.externals.six import StringIO  \n",
    "from sklearn.tree import export_graphviz\n",
    "import io\n",
    "import pydot \n",
    "\n",
    "features = list(df.columns[1:])\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "export_graphviz(model, out_file=dot_data, feature_names=features)\n",
    "(graph, ) = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8, now pick three models, with max_depth = 2, 3 and 4. You can pick the which ever criterions you want and visual the three trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hint \n",
    "# model1 = DecisionTreeClassifier(max_depth=..., criterion= ...)\n",
    "# model1.fit(X_train, y_train)\n",
    "# model2 = DecisionTreeClassifier(max_depth=..., criterion= ...)\n",
    "# model2.fit(X_train, y_train)\n",
    "# model3 = DecisionTreeClassifier(max_depth=..., criterion= ...)\n",
    "# model3.fit(X_train, y_train)\n",
    "# Then display all 3 trees\n",
    "# \n",
    "\n",
    "#dot_data = StringIO()  \n",
    "#export_graphviz(model1, out_file=dot_data, feature_names=features)\n",
    "#graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "#Image(graph[0].create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = DecisionTreeClassifier(max_depth=2, criterion='entropy')\n",
    "model1.fit(X_train, y_train)\n",
    "\n",
    "dot_data = StringIO()  \n",
    "export_graphviz(model1, out_file=dot_data, feature_names=features)\n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph[0].create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = DecisionTreeClassifier(max_depth=3, criterion='entropy')\n",
    "model1.fit(X_train, y_train)\n",
    "\n",
    "dot_data = StringIO()  \n",
    "export_graphviz(model1, out_file=dot_data, feature_names=features)\n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph[0].create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = DecisionTreeClassifier(max_depth=4, criterion='entropy')\n",
    "model1.fit(X_train, y_train)\n",
    "\n",
    "dot_data = StringIO()  \n",
    "export_graphviz(model1, out_file=dot_data, feature_names=features)\n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph[0].create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 9. Now build a Random Forest Classifier with, say, 100 trees. Check the model performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', 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, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.90      0.81      1296\n",
      "           1       0.61      0.32      0.42       614\n",
      "\n",
      "    accuracy                           0.72      1910\n",
      "   macro avg       0.67      0.61      0.62      1910\n",
      "weighted avg       0.70      0.72      0.69      1910\n",
      "\n",
      "0.72\n"
     ]
    }
   ],
   "source": [
    "rfc_pred = rfc.predict(X_test)\n",
    "print(classification_report(y_test,predictions))\n",
    "print(round(accuracy_score(y_test, predictions),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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