Sesi 13.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6092dc65",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09402683",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = [\"Sunny\", \"Sunny\", \"Overcast\", \"Rainy\", \"Rainy\", \"Rainy\", \"Overcast\", \"Sunny\", \"Sunny\", \"Rainy\", \"Sunny\", \"Overcast\", \"Overcast\", \"Rainy\"]\n",
    "\n",
    "temp = [\"Hot\", \"Hot\", \"Hot\", \"Mild\", \"Cool\", \"Cool\", \"Cool\", \"Mild\", \"Cool\", \"Mild\", \"Mild\", \"Mild\", \"Hot\", \"Mild\"]\n",
    "\n",
    "play = [\"No\", \"No\", \"Yes\", \"Yes\", \"Yes\", \"No\", \"Yes\", \"No\", \"Yes\", \"Yes\", \"Yes\", \"Yes\", \"Yes\", \"No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2a2b6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 0 1 1 1 0 2 2 1 2 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "weather_encoded = le.fit_transform(weather)\n",
    "\n",
    "print(weather_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c1e6de2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 2 0 0 0 2 0 2 2 2 1 2]\n",
      "[0 0 1 1 1 0 1 0 1 1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "temp_encoded = le.fit_transform(temp)\n",
    "label = le.fit_transform(play)\n",
    "\n",
    "print(temp_encoded)\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d2e9ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 1),\n",
       " (2, 1),\n",
       " (0, 1),\n",
       " (1, 2),\n",
       " (1, 0),\n",
       " (1, 0),\n",
       " (0, 0),\n",
       " (2, 2),\n",
       " (2, 0),\n",
       " (1, 2),\n",
       " (2, 2),\n",
       " (0, 2),\n",
       " (0, 1),\n",
       " (1, 2)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(zip(weather_encoded,temp_encoded))\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "391f170b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "model = GaussianNB()\n",
    "\n",
    "model.fit(features,label)\n",
    "\n",
    "predicted = model.predict([[0,2]])\n",
    "predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfaa27e6",
   "metadata": {},
   "source": [
    "# Bayes Multiple Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbb60eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "wine = datasets.load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5caa4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(wine.data,wine.target,test_size=0.3,random_state=109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8c49f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(X_train,y_train)\n",
    "\n",
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f162be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9074074074074074"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71fc2ac",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b84e3547",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e536b993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnant</th>\n",
       "      <th>glucose</th>\n",
       "      <th>bp</th>\n",
       "      <th>skin</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree</th>\n",
       "      <th>age</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pregnancies</td>\n",
       "      <td>Glucose</td>\n",
       "      <td>BloodPressure</td>\n",
       "      <td>SkinThickness</td>\n",
       "      <td>Insulin</td>\n",
       "      <td>BMI</td>\n",
       "      <td>DiabetesPedigreeFunction</td>\n",
       "      <td>Age</td>\n",
       "      <td>Outcome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pregnant  glucose             bp           skin  insulin   bmi  \\\n",
       "0  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI   \n",
       "1            6      148             72             35        0  33.6   \n",
       "2            1       85             66             29        0  26.6   \n",
       "3            8      183             64              0        0  23.3   \n",
       "4            1       89             66             23       94  28.1   \n",
       "\n",
       "                   pedigree  age    label  \n",
       "0  DiabetesPedigreeFunction  Age  Outcome  \n",
       "1                     0.627   50        1  \n",
       "2                     0.351   31        0  \n",
       "3                     0.672   32        1  \n",
       "4                     0.167   21        0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = [\"pregnant\",\"glucose\",\"bp\",\"skin\",\"insulin\",\"bmi\",\"pedigree\",\"age\",\"label\"]\n",
    "\n",
    "pima = pd.read_csv(\"https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/diabetes.csv\",header=None,names=col_names)\n",
    "pima.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "766965f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 769 entries, 0 to 768\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   pregnant  769 non-null    object\n",
      " 1   glucose   769 non-null    object\n",
      " 2   bp        769 non-null    object\n",
      " 3   skin      769 non-null    object\n",
      " 4   insulin   769 non-null    object\n",
      " 5   bmi       769 non-null    object\n",
      " 6   pedigree  769 non-null    object\n",
      " 7   age       769 non-null    object\n",
      " 8   label     769 non-null    object\n",
      "dtypes: object(9)\n",
      "memory usage: 54.2+ KB\n"
     ]
    }
   ],
   "source": [
    "pima.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d3ffa7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "numer = [\"pregnant\",\"insulin\",\"bmi\",\"age\",\"glucose\",\"bp\",\"pedigree\",\"label\"]\n",
    "\n",
    "for col in numer:\n",
    "    pima[col] = pd.to_numeric(pima[col],errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0fecf144",
   "metadata": {},
   "outputs": [],
   "source": [
    "pima.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "648b320d",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = [\"pregnant\",\"insulin\",\"bmi\",\"age\",\"glucose\",\"bp\",\"pedigree\"]\n",
    "\n",
    "X = pima[feature_cols]\n",
    "y = pima.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a5d841f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ff97da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37311578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6536796536796536"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "de2903ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    158\n",
       "1.0     73\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_pred).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5b0f88",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44bed23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eee3acf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "459fd4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "be1b2d5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24371671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width  species\n",
       "0           5.1          3.5           1.4          0.2        0\n",
       "1           4.9          3.0           1.4          0.2        0\n",
       "2           4.7          3.2           1.3          0.2        0\n",
       "3           4.6          3.1           1.5          0.2        0\n",
       "4           5.0          3.6           1.4          0.2        0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"sepal length\":iris.data[:,0],\n",
    "    \"sepal width\":iris.data[:,1],\n",
    "    \"petal length\":iris.data[:,2],\n",
    "    \"petal width\":iris.data[:,3],\n",
    "    \"species\":iris.target,\n",
    "})\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "658bd6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(\"species\",axis=1)\n",
    "y = data[\"species\"]\n",
    " \n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad8a43ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c51fbc3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c1bc025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[3,5,4,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0d94290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "petal width (cm)     0.459231\n",
       "petal length (cm)    0.410238\n",
       "sepal length (cm)    0.100167\n",
       "sepal width (cm)     0.030363\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.Series(clf.feature_importances_,index=iris.feature_names).sort_values(ascending=False)\n",
    "feature_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ac4621a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.barplot(x=feature_imp,y=feature_imp.index)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d24f0891",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop([\"sepal width\",\"species\"],axis=1)\n",
    "y = data[\"species\"]\n",
    " \n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8cccf65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4f7d1f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9111111111111111"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93793ec4",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04b8ca0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = datasets.load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2c84d43a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3f7290d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fdb2c7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a84f1be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,\n",
       "        3.001e-01, 1.471e-01, 2.419e-01, 7.871e-02, 1.095e+00, 9.053e-01,\n",
       "        8.589e+00, 1.534e+02, 6.399e-03, 4.904e-02, 5.373e-02, 1.587e-02,\n",
       "        3.003e-02, 6.193e-03, 2.538e+01, 1.733e+01, 1.846e+02, 2.019e+03,\n",
       "        1.622e-01, 6.656e-01, 7.119e-01, 2.654e-01, 4.601e-01, 1.189e-01],\n",
       "       [2.057e+01, 1.777e+01, 1.329e+02, 1.326e+03, 8.474e-02, 7.864e-02,\n",
       "        8.690e-02, 7.017e-02, 1.812e-01, 5.667e-02, 5.435e-01, 7.339e-01,\n",
       "        3.398e+00, 7.408e+01, 5.225e-03, 1.308e-02, 1.860e-02, 1.340e-02,\n",
       "        1.389e-02, 3.532e-03, 2.499e+01, 2.341e+01, 1.588e+02, 1.956e+03,\n",
       "        1.238e-01, 1.866e-01, 2.416e-01, 1.860e-01, 2.750e-01, 8.902e-02],\n",
       "       [1.969e+01, 2.125e+01, 1.300e+02, 1.203e+03, 1.096e-01, 1.599e-01,\n",
       "        1.974e-01, 1.279e-01, 2.069e-01, 5.999e-02, 7.456e-01, 7.869e-01,\n",
       "        4.585e+00, 9.403e+01, 6.150e-03, 4.006e-02, 3.832e-02, 2.058e-02,\n",
       "        2.250e-02, 4.571e-03, 2.357e+01, 2.553e+01, 1.525e+02, 1.709e+03,\n",
       "        1.444e-01, 4.245e-01, 4.504e-01, 2.430e-01, 3.613e-01, 8.758e-02],\n",
       "       [1.142e+01, 2.038e+01, 7.758e+01, 3.861e+02, 1.425e-01, 2.839e-01,\n",
       "        2.414e-01, 1.052e-01, 2.597e-01, 9.744e-02, 4.956e-01, 1.156e+00,\n",
       "        3.445e+00, 2.723e+01, 9.110e-03, 7.458e-02, 5.661e-02, 1.867e-02,\n",
       "        5.963e-02, 9.208e-03, 1.491e+01, 2.650e+01, 9.887e+01, 5.677e+02,\n",
       "        2.098e-01, 8.663e-01, 6.869e-01, 2.575e-01, 6.638e-01, 1.730e-01],\n",
       "       [2.029e+01, 1.434e+01, 1.351e+02, 1.297e+03, 1.003e-01, 1.328e-01,\n",
       "        1.980e-01, 1.043e-01, 1.809e-01, 5.883e-02, 7.572e-01, 7.813e-01,\n",
       "        5.438e+00, 9.444e+01, 1.149e-02, 2.461e-02, 5.688e-02, 1.885e-02,\n",
       "        1.756e-02, 5.115e-03, 2.254e+01, 1.667e+01, 1.522e+02, 1.575e+03,\n",
       "        1.374e-01, 2.050e-01, 4.000e-01, 1.625e-01, 2.364e-01, 7.678e-02]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e93a10f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "00316c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(cancer.data,cancer.target,test_size=0.3,random_state=109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "43779979",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC(kernel=\"linear\")\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2d2e36c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e5578029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9811320754716981"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "300493ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9629629629629629"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2d92cf",
   "metadata": {},
   "source": [
    "# Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b4dda09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/Final_Dataset/train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0dab27c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d965e86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d68aefc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_12148/1192414524.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  train.fillna(train.mean(),inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.fillna(train.mean(),inplace=True)\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "43cc752e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Gender.fillna(train.Gender.mode()[0],inplace=True)\n",
    "train.Married.fillna(train.Married.mode()[0],inplace=True)\n",
    "train.Dependents.fillna(train.Dependents.mode()[0],inplace=True)\n",
    "train.Self_Employed.fillna(train.Self_Employed.mode()[0],inplace=True)\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "63478829",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "train.Loan_Amount_Term = np.log(train.Loan_Amount_Term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8bf274c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(\"Loan_Status\",axis=1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c0d603fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)\n",
    "train = pd.get_dummies(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f823d125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_ID_LP001002</th>\n",
       "      <th>Loan_ID_LP001003</th>\n",
       "      <th>Loan_ID_LP001005</th>\n",
       "      <th>Loan_ID_LP001006</th>\n",
       "      <th>Loan_ID_LP001008</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Dependents_3+</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 634 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162          5.886104   \n",
       "1             4583             1508.0  128.000000          5.886104   \n",
       "2             3000                0.0   66.000000          5.886104   \n",
       "3             2583             2358.0  120.000000          5.886104   \n",
       "4             6000                0.0  141.000000          5.886104   \n",
       "\n",
       "   Credit_History  Loan_ID_LP001002  Loan_ID_LP001003  Loan_ID_LP001005  \\\n",
       "0             1.0                 1                 0                 0   \n",
       "1             1.0                 0                 1                 0   \n",
       "2             1.0                 0                 0                 1   \n",
       "3             1.0                 0                 0                 0   \n",
       "4             1.0                 0                 0                 0   \n",
       "\n",
       "   Loan_ID_LP001006  Loan_ID_LP001008  ...  Dependents_1  Dependents_2  \\\n",
       "0                 0                 0  ...             0             0   \n",
       "1                 0                 0  ...             1             0   \n",
       "2                 0                 0  ...             0             0   \n",
       "3                 1                 0  ...             0             0   \n",
       "4                 0                 1  ...             0             0   \n",
       "\n",
       "   Dependents_3+  Education_Graduate  Education_Not Graduate  \\\n",
       "0              0                   1                       0   \n",
       "1              0                   1                       0   \n",
       "2              0                   1                       0   \n",
       "3              0                   0                       1   \n",
       "4              0                   1                       0   \n",
       "\n",
       "   Self_Employed_No  Self_Employed_Yes  Property_Area_Rural  \\\n",
       "0                 1                  0                    0   \n",
       "1                 1                  0                    1   \n",
       "2                 0                  1                    0   \n",
       "3                 1                  0                    0   \n",
       "4                 1                  0                    0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  \n",
       "0                        0                    1  \n",
       "1                        0                    0  \n",
       "2                        0                    1  \n",
       "3                        0                    1  \n",
       "4                        0                    1  \n",
       "\n",
       "[5 rows x 634 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "09ab467a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Y\n",
       "1    N\n",
       "2    Y\n",
       "3    Y\n",
       "4    Y\n",
       "Name: Loan_Status, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "96165859",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_cv,y_train,y_cv = train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dd2d93a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1be794b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c422feec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d2a48a9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8373983739837398\n",
      "[[19 20]\n",
      " [ 0 84]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_cv,pred_cv))\n",
    "print(confusion_matrix(y_cv,pred_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "490b7612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "84dd0b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv1 = dt.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ee346051",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7560975609756098\n",
      "[[20 19]\n",
      " [11 73]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_cv,pred_cv1))\n",
    "print(confusion_matrix(y_cv,pred_cv1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "59eb3a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "be64b42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv2 = rf.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a9ca67ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8211382113821138\n",
      "[[17 22]\n",
      " [ 0 84]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_cv,pred_cv2))\n",
    "print(confusion_matrix(y_cv,pred_cv2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3efca5e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVM\n",
    "\n",
    "svm = svm.SVC()\n",
    "svm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c913cb16",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv3 = svm.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f4905170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6829268292682927\n",
      "[[ 0 39]\n",
      " [ 0 84]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_cv,pred_cv3))\n",
    "print(confusion_matrix(y_cv,pred_cv3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f5d8efd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3d7917ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv4 = nb.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "406729c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8130081300813008\n",
      "[[20 19]\n",
      " [ 4 80]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_cv,pred_cv4))\n",
    "print(confusion_matrix(y_cv,pred_cv4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "324a47da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "kNN = KNeighborsClassifier()\n",
    "kNN.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6e6a60a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv5 = kNN.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bce164b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6097560975609756\n",
      "[[10 29]\n",
      " [19 65]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_cv,pred_cv5))\n",
    "print(confusion_matrix(y_cv,pred_cv5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d76b9213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression 0.8373983739837398\n",
      "Decision Tree 0.7560975609756098\n",
      "Random Forest 0.8211382113821138\n",
      "SVM 0.6829268292682927\n",
      "Naive Bayes 0.8130081300813008\n",
      "KNN 0.6097560975609756\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression\",accuracy_score(y_cv,pred_cv))\n",
    "print(\"Decision Tree\",accuracy_score(y_cv,pred_cv1))\n",
    "print(\"Random Forest\",accuracy_score(y_cv,pred_cv2))\n",
    "print(\"SVM\",accuracy_score(y_cv,pred_cv3))\n",
    "print(\"Naive Bayes\",accuracy_score(y_cv,pred_cv4))\n",
    "print(\"KNN\",accuracy_score(y_cv,pred_cv5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e5bb914a",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame(pred_cv2,columns=[\"predictions\"]).to_csv(\"H8_NB_Credit_Predictions.csv\")"
   ]
  }
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