model_selection.ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ed102d",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "375c6b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba11842d",
   "metadata": {},
   "source": [
    "## importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41aabbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       10000 non-null  int64  \n",
      " 1   gender    10000 non-null  int64  \n",
      " 2   marital   10000 non-null  float64\n",
      " 3   dep       10000 non-null  int64  \n",
      " 4   Income    10000 non-null  float64\n",
      " 5   Job yrs   10000 non-null  int64  \n",
      " 6   Town yrs  10000 non-null  int64  \n",
      " 7   Yrs Ed    10000 non-null  int64  \n",
      " 8   Dri Lic   10000 non-null  int64  \n",
      " 9   Own Home  10000 non-null  int64  \n",
      " 10  # Cred C  10000 non-null  int64  \n",
      " 11  Churn     10000 non-null  int64  \n",
      "dtypes: float64(2), int64(10)\n",
      "memory usage: 937.6 KB\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_excel(\"./Expenditure-churn (3).xlsx\")\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "709b9ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,:-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f95812",
   "metadata": {},
   "source": [
    "## Splitting the dataset into Training and Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7036e7b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aec3eb4",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5355a6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "svc = StandardScaler()\n",
    "X_train = svc.fit_transform(X_train)\n",
    "X_test = svc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad436e30",
   "metadata": {},
   "source": [
    "## Training the Kernel SVM model on the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de1035ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel='rbf', random_state=0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb00c4f",
   "metadata": {},
   "source": [
    "## Making the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b2b8519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1671   11]\n",
      " [  38  780]] \n",
      " Accuracy:  98.04\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "y_pred = classifier.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm, '\\n Accuracy: ', accuracy_score(y_test, y_pred)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0e8350",
   "metadata": {},
   "source": [
    "## Applying Grid Search to find the best model and best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3016e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy: 99.54666666666667\n",
      "Best Parameters: {'C': 1, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = [{'C': [0.25, .5, .75, 1], 'kernel':['linear']},\n",
    "              {'C': [0.25, .5, .75, 1], 'kernel':['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\n",
    "grid_search = GridSearchCV(estimator=classifier,\n",
    "                           param_grid=parameters,\n",
    "                           scoring='accuracy',\n",
    "                           cv=10,\n",
    "                           n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "best_accuracy = grid_search.best_score_\n",
    "best_parameters = grid_search.best_params_\n",
    "print(f\"Best Accuracy: {best_accuracy*100}\")\n",
    "print(\"Best Parameters:\", best_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d61b6bf6",
   "metadata": {},
   "outputs": [
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       "((7500, 11), (7500,))"
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     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234601c9",
   "metadata": {},
   "source": [
    "## Fit a new SVC using the new parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ee8cfd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1681    1]\n",
      " [  10  808]] \n",
      " Accuracy:  99.56\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel='linear', random_state=0)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Making a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "y_pred = classifier.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm, '\\n Accuracy: ', accuracy_score(y_test, y_pred)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629a2263",
   "metadata": {},
   "source": [
    "## Precision and Recall and the F1 Score\n",
    "The F1 score is the harmonic mean of precision and recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7178cba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  99.87639060568603\n",
      "Recall:     98.77750611246944\n"
     ]
    }
   ],
   "source": [
    "# precison and recall\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score\n",
    "\n",
    "# precison\n",
    "print(\"Precision: \", precision_score(y_test, y_pred)*100)\n",
    "# Recall\n",
    "print(\"Recall:    \", recall_score(y_test, y_pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "28f74647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score:  99.3239090350338\n"
     ]
    }
   ],
   "source": [
    "# f1 Score\n",
    "print(\"F1 Score: \", f1_score(y_test, y_pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "36bfb9b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.3590265401824\n"
     ]
    }
   ],
   "source": [
    "# roc_auc score\n",
    "\n",
    "print(roc_auc_score(y_test, y_pred)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9ab6da",
   "metadata": {},
   "source": [
    "## Applying K-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2615f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.55 %\n",
      "Standard Deviation: 0.19 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "63f7f666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99733333, 0.99466667, 0.99866667, 0.99466667, 0.992     ,\n",
       "       0.996     , 0.99733333, 0.99333333, 0.996     , 0.99466667])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracies"
   ]
  }
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