Assignment 1.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DMML ASSIGNMENT 1 - SHANKAR RAM V (MCS202114)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. IMPORTING NECESSARY PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (0.0)\r\n",
      "Requirement already satisfied: imbalanced-learn in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (from imblearn) (0.9.0)\r\n",
      "Requirement already satisfied: scipy>=1.1.0 in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.7.3)\r\n",
      "Requirement already satisfied: scikit-learn>=1.0.1 in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.0.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (3.1.0)\r\n",
      "Requirement already satisfied: joblib>=0.11 in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.1.0)\r\n",
      "Requirement already satisfied: numpy>=1.14.6 in /home/h1lo/Downloads/yes/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.22.2)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"decision_trees\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. READING AND MODIFYING THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('bank-additional-full.csv', sep = ';')\n",
    "\n",
    "#print(data['y'].value_counts())\n",
    "\n",
    "data.drop(['duration'], axis = 1,inplace = True)\n",
    "data.drop(['month'], axis = 1, inplace = True)\n",
    "data.drop(['day_of_week'], axis = 1, inplace = True)\n",
    "data.drop(['contact'], axis = 1, inplace = True)\n",
    "data.drop(['campaign'], axis = 1, inplace = True)\n",
    "\n",
    "#Changing categorical attributes to numerical attributes using Label Encoding.\n",
    "categorical_cols = ['job', 'marital', 'education', 'default', 'poutcome', 'housing', 'default', 'loan']\n",
    "data = pd.get_dummies(data, columns = categorical_cols, drop_first = True)\n",
    "\n",
    "X = data.drop(['y'], axis = 1)\n",
    "y = data['y']\n",
    "\n",
    "feature_names = X.columns\n",
    "\n",
    "#Splitting the data into training and test samples (Default 75-25 split)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "\n",
    "oversample = SMOTE()\n",
    "X_train, y_train = oversample.fit_resample(np.array(X_train), np.array(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. FITTING A DECISION TREE CLASSIFIER TO THIS DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, max_leaf_nodes=16, random_state=42)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DT = DecisionTreeClassifier(max_depth = 3, max_leaf_nodes = 16, random_state=42)\n",
    "DT.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h1lo/Downloads/yes/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.94      0.87      0.90      9144\n",
      "         yes       0.35      0.58      0.44      1153\n",
      "\n",
      "    accuracy                           0.83     10297\n",
      "   macro avg       0.65      0.72      0.67     10297\n",
      "weighted avg       0.88      0.83      0.85     10297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = DT.predict(X_test)\n",
    "y_test = np.array(y_test).flatten()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<title>3</title>\n",
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       "<title>2&#45;&gt;3</title>\n",
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       "<title>2&#45;&gt;4</title>\n",
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       "<!-- 3&#45;&gt;6 -->\n",
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       "<title>3&#45;&gt;6</title>\n",
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       "<!-- 4&#45;&gt;7 -->\n",
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       "<title>4&#45;&gt;7</title>\n",
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       "<!-- 8 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>8</title>\n",
       "<path fill=\"#eb9c64\" stroke=\"black\" d=\"M1394.5,-68C1394.5,-68 1251.5,-68 1251.5,-68 1245.5,-68 1239.5,-62 1239.5,-56 1239.5,-56 1239.5,-12 1239.5,-12 1239.5,-6 1245.5,0 1251.5,0 1251.5,0 1394.5,0 1394.5,0 1400.5,0 1406.5,-6 1406.5,-12 1406.5,-12 1406.5,-56 1406.5,-56 1406.5,-62 1400.5,-68 1394.5,-68\"/>\n",
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       "<text text-anchor=\"middle\" x=\"1323\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7044</text>\n",
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       "<!-- 4&#45;&gt;8 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>4&#45;&gt;8</title>\n",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x7fede05aff40>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Source\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(\n",
    "        DT,\n",
    "        out_file=os.path.join(IMAGES_PATH, \"BM_tree.dot\"),\n",
    "        feature_names=feature_names,\n",
    "        class_names=['N','Y'],\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )\n",
    "\n",
    "Source.from_file(os.path.join(IMAGES_PATH, \"BM_tree.dot\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. FITTING A RANDOM FOREST CLASSIFIER AND A BAGGING MODEL TO THIS DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_leaf_nodes=16, n_estimators=125, random_state=42)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators = 125, max_leaf_nodes = 16, random_state = 42)\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h1lo/Downloads/yes/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.94      0.88      0.91      9144\n",
      "         yes       0.38      0.58      0.46      1153\n",
      "\n",
      "    accuracy                           0.85     10297\n",
      "   macro avg       0.66      0.73      0.68     10297\n",
      "weighted avg       0.88      0.85      0.86     10297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "y_test = np.array(y_test).flatten()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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azaynCYhu+lhi1pbka8DQFvYfA7xQuuqYWbkq5eW2pAclrZVUn27P5cRGSVogqUHSbEmDcmKSNEXSsnS7XJJy4oPTMg3pOY5vrS5Zk+TVwP9Ken/6fjdJ44DLgasynsPMurGIbFsbnBsRVen2XgBJtcCdwCVAP2AOcEtOmfHAKSTrae8PnAycnROfCcwF+gMXAbdL2rFYJTIlyYi4PK3Y74G+wGxgGjAtIn6S5Rxm1n01P7vdAQM3pwHzI+K2iFgLTAYOkNS8euI44IqIWBwRrwJXAGcASBoKHARMiog1EXEHydKzo4t9YOZbgCLiIpJ1cA8FDgd2jIhL2vDlzKy7CiCUbYNaSXNytvEFzvpdSXWS/iTpuHTfMOCpTR8bsZqky29YS/H0dW5sUUSsKhBvUVtvJg+ShcIBGttY1sy6sTZcStdFxMhWjvlP4BlgPTAWuEfSCKAKWJp37EqgOn1dlb7PjVWl/ZL5seb4wGIVyXqf5NaSfgQsJ8m884Dlkn4sqTLLOcysOxPRlG3LIiIei4hVEbEuIq4H/gScSMt31NQAza3D/HgNUB8RkaFsi7Jebl8FjAHOJLkdaK/09anATzOew8y6sy17o2QAAuaTDMoAIKkvsGe6n/x4+jo3NkRSdYF4i7ImyU8Cn4+ImyJiUbrdBHyBJHmaWU8WpRu4kbS9pBMkVUrqnT7xdwxwP3AXMFzS6PQqdiIwLyIWpMVvAC6QNFDSLsCFwAyAiFgIPAlMSs99KskI+B3F6pO1T3I18GoL+18F1mQ8h5l1Z6V7nKYPcCmwD8nYxwLglIh4DkDSaGAqcCPwGEmfZbPpwBCSUWuAa9J9zcaSJM0VwMvAmIjI7+PcTNYkeSVJ9j0jItakFd2G5F6lKzOew8y6tdI8cZMmrUOKxGeRJNCWYkHyGHWLc0pExIvAcW2pT7GZyX+dt+s44FVJ89L3+6Xl+7blA82sm2rq7ApsGcVaksvy3udft/+zxHUxs3LVfJ9kN1RsZvLPd2RFzKy8dddJdz0zuZmVRk9PkpI+D3wa2B3YKjcWEUNKXC8zKzfd9HI76xM3Xyd5UPxvJDOT/wp4mmQWjuu2UN3MrIwosm3lJuvN5GcB4yPim8AGYGpEfJwkcQ4qWtLMur8QNGXcykzWJLkr8Hj6eg3vPP84k1amGTKzHqKbrt+QNUkuIZkmDeAl4Ij09V6U5dc2s5Lr4UnyAeDj6etrgR9Imk0yI/CdW6JiZlZmummSzDq6PZ40oUbENEkrgPeT3GA+vVhBM+sBeuLN5Lkioomch44i4hY2X1fCzHq4chy5zqLYs9sHZT1JRDxRmuqYWdnqaUmSZBWy5okuiwmgV8lqZGZlqce1JIE9OqwWRSycty0n7DKis6thbVBRmT83inVpGzeW5jw9rU8yIl7qyIqYWRkr05HrLDzBhZmVhpOkmVlh6qaT7ma9mdzMrLgS30wuaW9JayXdmLNvlKQFkhokzZY0KCcmSVMkLUu3y9P1tpvjg9MyDek5js9SDydJM2u3rDMAtXEE/CfAXzd9hlRL8oTfJSQzkM1h8/u1xwOnkCwTuz9wMnB2TnwmMBfoD1wE3C5px9Yq0aYkKalW0mGStm5LOTPrAULZtgwkjQXeAv6Qs/s0YH5E3BYRa4HJwAGSmhcFGwdcERGLI+JVklnKzkjPNxQ4CJgUEWsi4g6SFRVbnaAn63yS1ZJuBd4E/gwMTPdPkzQ5yznMrJsr0eW2pBrgWyRrZucaBjy16eMiVgMvpPvfFU9f58YWRcSqAvGCsrYkp5AkxoPYfJ3t3wCnZjyHmXVjbbjcrpU0J2cbn3eqbwPXRsQrefurgJV5+1YC1QXiK4GqtF+ytbIFZR3d/jhwakQ8KW3Wq/AsyULgZtaTRZtGt+siYmRLAUkjgOOBA1sI1/POXLbNaoBVBeI1QH1EhKTWyhaUtSW5A+9eYhaSLNyY8Rxm1p2V5nL7OJIlYl6WtAT4GjBa0hPAfJJBGQAk9QX2TPeTH09f58aGSKouEC8oa5L8K+/MJwnvfNWzSfoozaynK02SvJok8Y1It2nAvcAJwF3AcEmjJVUCE4F5EbEgLXsDcIGkgZJ2IenTnAEQEQuBJ4FJkiolnUoyAn5HaxXKern9X8D9koalZS5IXx8KHJPxHGbWjZVigouIaAAaNp0zuUxeGxFL0/ejganAjcBjwNic4tNJuv/+nr6/hs3nux1LkjRXAC8DY5rPW0zW+ST/LOlIkqbvC8Ao4AngiIj4e9HCZmb/ooiYnPd+FrBPgWMDmJBuLcVfJLmcb5PMjyWmyXBcWz/AzHqInvzstqR+xeIRsbw01TGzstS20e2ykrUlWUfx3xOedNesp+vJLUngA3nv+5Dcx3QOcHFJa2RmZUf0zJnJN4mIh1rYPUvSIuBM4OaS1srMyk9PTpJFPIlvATKzts/wUzb+5SQpqQr4KpD/fKWZ9UQ9eeBG0io2b0wL2BZYDXx2C9TLzMpMT29Jnpv3vglYCjwWEStKWyUzK0s9NUlK6g30BX4VEa9t+SqZWdnpxqsltjrBRURsBL5PctuPmVmLtsDyDV1C1lmA/gIcvCUrYmZlrsQLgXUVWfskfwb8j6Tdgb+RDNhsEhFPlLpiZlZeeuRjiZKuI7nNp/lm8R+0cFjgxxLNerYybSVm0VpLchzwDWCPDqiLmZUppVt31FqSFEBEvNQBdTGzctZDW5LQbb+6mZVSOY5cZ5ElSS5JVmQsLCLcJ2nW0/XgJDkeeGsL18PMylkPn3T3noh4c4vXxMzKWwlbkpJuJFlLqy+wBLg8Iq5JY6OAnwC7kywGdkbzuImSy97vkUzhCHAt8J/p+jdIGgz8HDiMZDGwc9N1cwpq7WbybtqANrNSK/ETN98FBkdEDcly1pdKOlhSLXAncAnQD5gD3JJTbjxwCsma2vsDJ5Msfd1sJjAX6A9cBNwuacdiFWktSXbXUX0zK7USPnETEfMjYl3emfcETgPmR8RtEbEWmAwcIKl5BcVxwBURsTgiXgWuAM4AkDQUOAiYFBFrIuIOkuVnRxerS9EkGREVvtQ2syza0JKslTQnZxvf4vmkn0pqABYArwP3AcOAp5qPiYjVJMtcD0t3bRZPX+fGFkXEqgLxFrV3ZnIzs6Sdl33gpi4iRrZ6yogvSToPOIJkvex1QBXJNI25VgLV6euq9H1urCrtq8yPNccHFqtH1gkuzMwKal4IrNSzAEVEY0Q8AuxKsvBgPVCTd1gN0Nw6zI/XAPXpwE1rZVvkJGlmpbFlZwHqTdInOZ9kUAYASX1z9pMfT1/nxoZIqi4Qb5GTpJmVhCIyba2eR9pJ0lhJVZJ6SToB+DTwAHAXMFzSaEmVwERgXkQsSIvfAFwgaaCkXYALgRkAEbGQZPHCSZIqJZ1KMgJ+R7H6uE/SzNqvtLMABcml9TSShtxLwFcj4m4ASaOBqcCNJPdJjs0pOx0YQjJqDXBNuq/ZWJKkuYLkPskxEZHfx7kZJ0kzK4lSPbudJq1ji8RnAfsUiAUwId1air9IMgiUmZOkmZVET34s0cysdd30+TwnSTNrvzJd5CsLJ0kzKw0nSTOzljXfTN4dOUmaWUmoqXtmSSdJM2u/HrxaorVDRUVw5e8Wsuz1PkwcN4Qhw9bw5e8tZqvKJho3iqnf3JXnntwWgD3et4YvT1lM3+pGmprEeSfuzYZ1fiCqI/Wt3shXv7eIQUPXEAE//M8hLJhbzcc/t4SPfe4NGjeKx2dvz3VTducDn6hj9Fmvbyq7xz4NnPex4Sx6tm8nfoPO5VuASkDSuSRzu+0HzIyIMzry8zvaKWfW8co/Ktm2qhGAMy9+jRt/MIA5s2s45INv84WLX2PCmL2o6BVMuPJlvv/l3Vn0zDZU77CRxg2eyrOjfXHiS8x5aHsu+4+h9O7TxNaVTex/+EoO/9AKvnTifmxYX8F2/TcAMPvuWmbfXQvA4Pc2MHH6wh6dIIFu25Ls6KbKa8ClwHUd/Lkdrnbn9Rw66m1+e3O/TfsioG91kjD71jSy/I0+ABx87Cr++Wwli57ZBoBVK3rT1OQk2ZG2rdrI8ENXcf+tySTVGzdUsHpVb0767JvcOm0XNqxP/qmsXNbnXWWP/dgyHrqnf4fWtyvaErMAdQUd2pKMiDsBJI0kmfqo2/rif7/GNZfuzLZV71yDTJs4kO/MXMRZE19HCs7/+N4A7DpkHRHisptfYLv+jTx09/bc9tOdOqvqPdJ7dlvHyuW9ueDyRQx5XwP/eLov0741iIF7rGX4IasYd+ErbFhXwTXf3Z2F86o2K3vsScv477OHdlLNu4ggaQV0Q12y00vS+OZZizewrvUCXcxhx7/NW3W9ef7v2262/+Rxy5g+aRdOH7kv0ycP5IIfvAJAr97B8ENXM+XcQVx4yl4c+ZGVjDiq6BR3VmK9egd7DVvNvTcN4NyP7cfahgr+7Yuv0atXUFWzkfNPG8Y1392db175PLnXle89oJ61ayt4aeG2hU/eQ6gp21ZuumSSjIirI2JkRIzsw9adXZ022/eQ1Rz+4be5/rFn+OZVL3HAUfVMuPIlPvTJ5Txy33YA/PGe7Rg6ogGApa/3Yd6jfXl7eW/Wrangrw/UsNd+azrzK/Q4da9vRd2SrXjuqaSV+Mjv+rHX8AbqlmzFn+7vB4iF86qIJtiu38ZN5XypndhSk+52BV0ySZa7n393Z04fuS/jDtuX754ziKceqeLy8wax7I0+7H/EagBGHFXPa/9MfgH87cFq9th3LVtv00RFr2D/I+p5eWFlZ36FHmdF3VYsfX1rBu6R/HIaceTbvPyPbXj09zsw4oi3ARi4xxp69wlWLk96qaTg6I86SQLJpXbWrcz4FqAO9KOv78o530ou4davq+BHX0+6ZetX9ubO6Tty5X0LiRCPP1DN43/In2XetrSrJg9iwo9eoE+fJl5/uZIfThjC2jUVnD9lEVf9dh4bN4grvj6E5kVEhx+6irolW7HkFf9Cg/JsJWah6MDMLqk3SWKeRDJwcxawMSI2FipTo35xmEZ1UA2tFCoqnTTKyV/W3sfKpmXtup2ievtd48BjvpLp2IfvmfC3LAuBdRUdfbl9MbAG+AZwevr64g6ug5ltAd21T7KjbwGaTLKYuJl1JwE0lmEGzMB9kmZWEuXYSszCo9tmVholGt2WtLWkayW9JGmVpLmSPpoTHyVpgaQGSbMlDcqJSdIUScvS7XJJyokPTss0pOc4vrX6OEmaWUmUsE+yN/AKyWJg2wGXALemCa4WuDPd1w+YA9ySU3Y8cArJetr7AycDZ+fEZwJzgf7ARcDtknYsVhknSTNrv2jD1tqpIlZHxOSIeDEimiLiN8A/gYOB04D5EXFbRKwlGeM4QFLz6onjgCsiYnFEvApcQTKpDpKGAgcBkyJiTUTcQbL07Ohi9XGSNLN2E6DGyLQBtc2PHafb+KLnlgYAQ4H5wDDgqeZYRKwGXkj3kx9PX+fGFkXEqgLxFnngxsxKQtnvua7Lep+kpD7ATcD1EbFAUhWwNO+wlUB1+roqfZ8bq0r7JfNjzfGBxergJGlm7bcFZiaXVAH8AlgPnJvurgfyH0erAVYViNcA9RERklor2yJfbptZCZT22e205XctMAAYHREb0tB8kkGZ5uP6Anum+98VT1/nxoZIqi4Qb5GTpJmVRImfuLkKeB/wsYjInRLrLmC4pNGSKoGJwLyIWJDGbwAukDRQ0i7AhcAMgIhYCDwJTJJUKelUkhHwO4pVxJfbZlYaJZoHIr3v8WxgHbAk5zbHsyPiJkmjganAjcBjwNic4tOBISSj1gDXpPuajSVJmiuAl4ExEZHfx7kZJ0kza7+geeS6/aeKeInmqZZajs8C9ikQC2BCurUUfxE4ri31cZI0s9Lopo8lOkmaWUm04RagsuIkaWal4SRpZlZAAGW4yFcWTpJm1m4ifLltZlZUU/dsSjpJmln7+XLbzKw4X26bmRXjJGlmVkj2ySvKjZOkmbWfV0s0MyvOfZJmZsU4SZqZFRBAk5OkmVkBHrgxMyvOSdLMrIAAGrvnIzdOkmZWAgHhJGlmVlg3vdz2aolm1n7No9tZtlZIOlfSHEnrJM3Ii42StEBSg6TZ6aJhzTFJmiJpWbpdrpxVxCQNTss0pOc4PstXc5I0s9Io3brbrwGXAtfl7pRUC9wJXAL0A+YAt+QcMh44hWQt7f2Bk0lWXWw2E5gL9AcuAm6XtGNrlXGSNLPSKFGSjIg7I+JXwLK80GnA/Ii4LSLWApOBAyQ1r5w4DrgiIhZHxKvAFcAZAJKGAgcBkyJiTUTcQbLs7OjW6uM+STNrvwhobMx6dK2kOTnvr46IqzOUGwY89c5HxmpJL6T7F+TH09fDcsouiohVBeIFOUmaWWlkH7ipi4iR/8InVAFL8/atBKpz4ivzYlVpv2R+rDk+sLUPdZI0s9LY8qPb9UBN3r4aYFWBeA1QHxEhqbWyBblP0sxKIOPIdvue755PMigDgKS+wJ7p/nfF09e5sSGSqgvEC3KSNLP2C4hoyrS1RlJvSZVAL6CXpEpJvYG7gOGSRqfxicC8iFiQFr0BuEDSQEm7ABcCMwAiYiHwJDApPd+pJCPgd7RWHydJMyuNxqZsW+suBtYA3wBOT19fHBFLSUajLwNWAIcBY3PKTQfuIRm1fhq4N93XbCwwMi37PWBMes6i3CdpZu0XUbIlZSNiMsntPS3FZgH7FIgFMCHdWoq/CBzX1vo4SZpZaXTTxxKdJM2sJKJELcmuxknSzErAk+6amRXm5RvMzAoLILI/llhWnCTNrP3Ck+6amRUVvtw2Myuim7YkFV18RErSUuClzq7HFlAL1HV2JaxNuuvf2aCIaHXy2WIk/Y7k55NFXUR8pD2f15G6fJLsriTN+Reni7JO4r+znsnPbpuZFeEkaWZWhJNk58kyXb11Lf4764HcJ2lmVoRbkmZmRThJmpkV4STZwST1k3SXpNWSXpL0mc6ukxUm6VxJcyStkzSjs+tjHc9P3HS8nwDrgQHACOBeSU9FRKsLElmneA24FDgB2KaT62KdwAM3HShd3W0FMDxdmAhJvwBejYhvdGrlrChJlwK7RsQZnV0X61i+3O5YQ4HG5gSZegoY1kn1MbNWOEl2rCpgZd6+lUB1C8eaWRfgJNmx6oGavH01wKpOqIuZZeAk2bEWAr0l7Z2z7wDAgzZmXZSTZAeKiNXAncC3JPWV9H7gE8AvOrdmVoik3pIqgV5AL0mVknxXSA/iJNnxvkRyK8mbwEzgHN/+06VdDKwBvgGcnr6+uFNrZB3KtwCZmRXhlqSZWRFOkmZmRThJmpkV4SRpZlaEk6SZWRFOkmZmRThJ9jCSxkiKnPdnSKrvpLr8ptgcjZKOkxSSsq7njKQHJU1tZ70Gp5/r5WPNSbIrkDQj/UcZkjZIWiTpf9Kp1ba0W4AhWQ+W9KKkr23B+ph1KX68quuYBfw70Ac4GrgG6Auck39g+lhcY5TgSYCIWEPyFImZtcAtya5jXUQsiYhXIuJm4CbgFABJkyU9nV4avwCsA/pK2k7S1ZLelLRK0kP5l4iSPpcuE9Eg6TckM6Lnxt91uS3pJEmPSVojaZmke9Jnlh8EBgHfb2755pQ5Mv38BkmvSrpKUk1OfNu0xVwv6Q1J/9XWH5Ck/pJmSlqc1m2+pM+3cGhvST+WtCLdvi+pIuc8W0makp5ntaS/SjqhrfWxnsFJsutaQ9KqbLYH8BngkyQzB60D7gUGAicDBwJ/BB6QtDOApMOAGSTrRY8A7gG+VexDJX0EuBv4PXAw8AHgIZL/V04DFqfn2DndkLQf8H/Ar9O6nZZ+3nU5p/4f4EPAaGBUWt9jMv80EpXAE+n3HQb8GJguaVTecZ9N63sEcDYwHvhqTvznwLEkP8/9gOuBeyQd0Mb6WE8QEd46eSNJZL/JeX8oUAfckr6fDGwABuQc80GS+Sm3yTvXk8CE9PXNwO/z4tckf+2b3p8B1Oe8/xPwyyJ1fRH4Wt6+G4Br8/aNAALYiWSy4XXAZ3PiVcBbwIwin3Vceo7aIsf8Ergm5/2DJFPSKWffxcDi9PWeQBOwe955fgX8NH09OP3ckZ39/4a3zt/cJ9l1fCS97O1N0oK8GzgvJ744It7IeX8wsC2wVFLueSpJEgHA+0haj7keBb5QpB4HkiTttjgY2EvSp3L2NVdqT6AB2Cr9bAAiol7S39vyIZJ6kczG8ymSFvTW6XkfzDv0LxGR21/7KPDt9PL/oLRuz+T93LYGHmhLfaxncJLsOv5Iclm4AXgtIjbkxVfnva8A3iAZ5Mn3dvqnWohtCRUkLdQfthB7FXhviT7na8CFwFeAv5O0pL9D0lrNqoKklXgIyc86lwew7F2cJLuOhoh4vg3HP0EyCNMUEYsKHPMMcHjevvz3+eaS9Bn+rEB8PckEtPl1GVao/pKeJ0lIhwOL0n19geHAC63UJ9dRwD0R8Yv0HCJZXO2tvOMOk6Sc1uThJL943pY0l+SXx3siYnYbPtt6KA/clK9ZJP2Hd0v6qKQ9JB0h6b8lNbcu/xc4XtI3Je0t6Szg1FbOexnwSUmXStpX0jBJ50vaNo2/CBwtaWDOTd5TgEMlTZN0oKS9JJ0saTokl9bAtcAUSR+SNIxkUCc/2bZmITBK0lGS9gGmkgxo5dsF+JGk90oaA3ydtJUbyUqVNwEzlNxYP0TSSElfk3RaG+tjPYCTZJlKW0knkvSj/Qx4DriV5NL2tfSYv5D0P54DzCMZdZ7cynnvI0mkHyVpVT5EMsLdlB4yEdiNpAW4NC0zj2SkenB6/FPAd0m6A5p9DZgN3JX++TRJF0NbXAo8Dvw2LbuaJOHlu4kkAT9G8rO5ls27Aj5PMsJ9ObAA+E1a/5faWB/rATwzuZlZEW5JmpkV4SRpZlaEk6SZWRFOkmZmRThJmpkV4SRpZlaEk6SZWRFOkmZmRThJmpkV8f8B2wLA845fvmUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=42),\n",
       "                  max_samples=120, n_estimators=150, random_state=42)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=150,\n",
    "    max_samples=120, bootstrap=True, random_state=42)\n",
    "bag_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h1lo/Downloads/yes/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but BaggingClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.94      0.89      0.91      9144\n",
      "         yes       0.39      0.57      0.46      1153\n",
      "\n",
      "    accuracy                           0.85     10297\n",
      "   macro avg       0.67      0.73      0.69     10297\n",
      "weighted avg       0.88      0.85      0.86     10297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bag_clf.predict(X_test)\n",
    "y_test = np.array(y_test).flatten()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The Random Forest and Bagging models have similar accuracy on this data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "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": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. FITTING A NAIVE BAYES CLASSIFIER TO THIS DATA (W/ LAPLACIAN SMOOTHING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "NB = GaussianNB()\n",
    "NB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h1lo/Downloads/yes/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but GaussianNB was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.94      0.79      0.86      9144\n",
      "         yes       0.27      0.60      0.37      1153\n",
      "\n",
      "    accuracy                           0.77     10297\n",
      "   macro avg       0.60      0.70      0.62     10297\n",
      "weighted avg       0.86      0.77      0.81     10297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = NB.predict(X_test)\n",
    "y_test = np.array(y_test).flatten()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. BOOSTING THE NAIVE BAYES CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=GaussianNB(), learning_rate=0.2,\n",
       "                   n_estimators=100, random_state=42)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_nb = AdaBoostClassifier(\n",
    "    GaussianNB(), n_estimators = 100, \n",
    "    algorithm=\"SAMME.R\", learning_rate=0.2, random_state=42)\n",
    "ada_nb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h1lo/Downloads/yes/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but AdaBoostClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.92      0.83      0.88      9144\n",
      "         yes       0.26      0.46      0.33      1153\n",
      "\n",
      "    accuracy                           0.79     10297\n",
      "   macro avg       0.59      0.65      0.60     10297\n",
      "weighted avg       0.85      0.79      0.81     10297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = ada_nb.predict(X_test)\n",
    "y_test = np.array(y_test).flatten()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "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": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (DMML)",
   "language": "python",
   "name": "dmml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}