fruit_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbc81aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import numpy.matlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from collections import Counter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12540410",
   "metadata": {},
   "source": [
    "## Dataset overview\n",
    "\n",
    "In this problem we are building 15-way multi-class classifiers for a fruit classification task. The training and validation sets are stored in the .pkl files provided. See the hw5.pdf file we have provided for details.\n",
    "\n",
    "In each dataset, we provide the following:\n",
    "- 'images': the raw RGB images\n",
    "- 'feats': 15 dimensional condensed representations of each image (that were precomputed by using a pretrained vision transformer + PCA)\n",
    "- 'labels': an integer value between 0 and 14. The integer to class mapping is provided below in the variable 'idx_to_class'.\n",
    "\n",
    "We first create a simple python dictionary that maps integers to textual class labels that can be useful for debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "132b41aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'Apple', 1: 'Banana', 2: 'Carambola', 3: 'Guava', 4: 'Kiwi', 5: 'Mango', 6: 'Orange', 7: 'Peach', 8: 'Pear', 9: 'Persimmon', 10: 'Pitaya', 11: 'Plum', 12: 'Pomegranate', 13: 'Tomatoes', 14: 'Muskmelon'}\n"
     ]
    }
   ],
   "source": [
    "idx_to_class = {0: 'Apple', 1: 'Banana', 2: 'Carambola', 3: 'Guava', 4: 'Kiwi', 5: 'Mango', 6: 'Orange', 7: 'Peach', 8: 'Pear', 9: 'Persimmon', 10: 'Pitaya', 11: 'Plum', 12: 'Pomegranate', 13: 'Tomatoes', 14: 'Muskmelon'}\n",
    "target_names = list(idx_to_class.values())\n",
    "print(idx_to_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b4d929",
   "metadata": {},
   "source": [
    "Next, we load the .pkl files that load as python dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad2a9dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train.pkl', 'rb') as f:\n",
    "    train_dataset = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50f990b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('val.pkl', 'rb') as f:\n",
    "    val_dataset = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97f572f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test_data.pkl','rb') as f:\n",
    "    test_dataset = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "884a562e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot the images this way. This will be quite useful for debugging.\n",
    "plt.figure()\n",
    "plt.title('Example Image')\n",
    "plt.imshow(val_dataset['images'][2005])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab956a98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples Features shape: (15,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Examples Features shape:\", train_dataset['feats'][0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a55eeddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example image label: Orange\n"
     ]
    }
   ],
   "source": [
    "print(\"Example image label:\", idx_to_class[val_dataset['labels'][2114]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "072bc26a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['images', 'feats', 'labels'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2964a736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['images', 'feats', 'labels'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f86496a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2116"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_dataset['feats'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67d888dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Dataset\n",
    "X_train = train_dataset['feats']\n",
    "y_train = train_dataset['labels']\n",
    "\n",
    "# Validation Dataset\n",
    "X_valid = val_dataset['feats']\n",
    "y_valid = val_dataset['labels']\n",
    "\n",
    "#Test Dataset\n",
    "X_test = test_dataset['feats']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "320eb640",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_valid,y_valid, X_test = np.array(X_train), np.array(y_train), np.array(X_valid), np.array(y_valid), np.array(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f3e0c1",
   "metadata": {},
   "source": [
    "## Part 2(a): Logistic Regression\n",
    "\n",
    "Implement Logistic Regression from scratch and report your classifier's accuracy on the training and validation sets. **You should use your implementation from prior homeworks and turn it in again with updates, if any.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "473a0443",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Implement multinomial logistic regression\n",
    "class LogisticRegression:\n",
    "    def __init__(self, learning_rate=0.01, num_iterations=1000):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.num_iterations = num_iterations\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Initialize parameters\n",
    "        num_samples, num_features = X.shape\n",
    "        num_classes = len(np.unique(y))\n",
    "        self.weights = np.zeros((num_classes, num_features))\n",
    "        self.bias = np.zeros((1, num_classes))\n",
    "        \n",
    "        # One-hot encode the labels\n",
    "        y_onehot = np.zeros((num_samples, num_classes))\n",
    "        y_onehot[np.arange(num_samples), y] = 1\n",
    "        \n",
    "        # Optimization with gradient descent\n",
    "        for i in range(self.num_iterations):\n",
    "            z = np.dot(X, self.weights.T) + self.bias\n",
    "            A = self.softmax(z)\n",
    "            loss = self.cross_entropy_loss(y_onehot, A)\n",
    "            dz = A - y_onehot\n",
    "            dw = np.dot(dz.T, X)\n",
    "            db = np.sum(dz, axis=0, keepdims=True)\n",
    "            self.weights -= self.learning_rate * dw\n",
    "            self.bias -= self.learning_rate * db\n",
    "            \n",
    "            if i % 100 == 0:\n",
    "                print(f\"Loss after iteration {i}: {loss}\")\n",
    "                \n",
    "    def predict(self, X):\n",
    "        z = np.dot(X, self.weights.T) + self.bias\n",
    "        A = self.softmax(z)\n",
    "        return np.argmax(A, axis=1)\n",
    "    \n",
    "    def softmax(self, z):\n",
    "        return np.exp(z) / np.sum(np.exp(z), axis=1, keepdims=True)\n",
    "    \n",
    "    def cross_entropy_loss(self, y, A):\n",
    "        num_samples = y.shape[0]\n",
    "        loss = -1/num_samples * np.sum(y * np.log(A))\n",
    "        return loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57520a74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss after iteration 0: 2.7080502011022087\n",
      "Loss after iteration 100: 3.6183928000484467\n",
      "Loss after iteration 200: 0.6079408640270438\n",
      "Loss after iteration 300: 0.7049865542059661\n",
      "Loss after iteration 400: 0.682983263892733\n",
      "Loss after iteration 500: 0.6981128570270975\n",
      "Loss after iteration 600: 0.7442464953517783\n",
      "Loss after iteration 700: 0.7638848107654738\n",
      "Loss after iteration 800: 0.7264520828167546\n",
      "Loss after iteration 900: 0.670570970871221\n",
      "Training Accuracy of Logistic Regression: 89.42891859052247\n",
      "Validation Accuracy of Logistic Regression: 88.13799621928166\n"
     ]
    }
   ],
   "source": [
    "# TODO: Report accuracy on training and validation sets\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred_train = log.predict(X_train)\n",
    "accuracy = np.mean(y_pred_train == y_train) * 100\n",
    "print(f\"Training Accuracy of Logistic Regression: {accuracy}\")\n",
    "y_pred_valid = log.predict(X_valid)\n",
    "accuracy = np.mean(y_pred_valid == y_valid) * 100\n",
    "print(f\"Validation Accuracy of Logistic Regression: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d87b7142",
   "metadata": {},
   "outputs": [],
   "source": [
    "def repeated_label(y):\n",
    "        counter = Counter(y)\n",
    "        repeat = counter.most_common(1)[0][0]\n",
    "        return repeat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d5064b",
   "metadata": {},
   "source": [
    "## Part 2(b): Random Forest/GBDT \n",
    "\n",
    "Implement a random forest or GBDT from scratch and report your classifier's accuracy on the training and validation sets. **You should use your implementation from prior homeworks and turn it in again with updates, if any.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d09f8bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_sample(X, y):\n",
    "    n_samples = X.shape[0]\n",
    "    idxs = np.random.choice(n_samples, n_samples, replace=True)\n",
    "    return X[idxs], y[idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "589a156e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TreeNode:\n",
    "    '''\n",
    "    Data structure that are used for storing a node on a tree.\n",
    "    \n",
    "    A tree is presented by a set of nested TreeNodes,\n",
    "    with one TreeNode pointing two child TreeNodes,\n",
    "    until a tree leaf is reached.\n",
    "    \n",
    "    A node on a tree can be either a leaf node or a non-leaf node.\n",
    "    '''\n",
    "    def __init__(self, feature=None, threshold=None, left=None, right=None, *, value=None):\n",
    "        self.feature = feature\n",
    "        self.threshold = threshold\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "        self.value = value\n",
    "\n",
    "    def forward(self):\n",
    "        return self.value is not None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2ac9717",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tree:\n",
    "    '''\n",
    "    Class of a single decision tree\n",
    "\n",
    "    Parameters:\n",
    "        n_threads: The number of threads used for fitting and predicting.\n",
    "        max_depth: The maximum depth of the tree.\n",
    "        min_sample_split: The minimum number of samples required to further split a node.\n",
    "    '''\n",
    "    def __init__(self, min_samples_split=2, max_depth=100, n_feats=None):\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.max_depth = max_depth\n",
    "        self.n_feats = n_feats\n",
    "        self.root = None\n",
    "    \n",
    "    def path(self, x, node):\n",
    "        if node.forward():\n",
    "            return node.value\n",
    "\n",
    "        if x[node.feature] <= node.threshold:\n",
    "            return self.path(x, node.left)\n",
    "        return self.path(x, node.right)\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        self.n_feats = X.shape[1] if not self.n_feats else min(self.n_feats, X.shape[1])\n",
    "        self.root = self.construct_tree(X, y)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return np.array([self.path(x, self.root) for x in X])\n",
    "\n",
    "    def construct_tree(self, X, y, depth=0):\n",
    "        n_samples, n_features = X.shape\n",
    "        n_labels = len(np.unique(y))\n",
    "        if (depth >= self.max_depth\n",
    "                or n_labels == 1\n",
    "                or n_samples < self.min_samples_split):\n",
    "            leaf_value = repeated_label(y)\n",
    "            return TreeNode(value=leaf_value)\n",
    "\n",
    "        feat_idxs = np.random.choice(n_features, self.n_feats, replace=False)\n",
    "\n",
    "        # greedily select the best split according to information gain\n",
    "        best_feat, best_thresh = self.find_decision_rule(X, y, feat_idxs)\n",
    "        \n",
    "        # grow the children that result from the split\n",
    "        left_idxs, right_idxs = self._best_split(X[:, best_feat], best_thresh)\n",
    "        left = self.construct_tree(X[left_idxs, :], y[left_idxs], depth+1)\n",
    "        right = self.construct_tree(X[right_idxs, :], y[right_idxs], depth+1)\n",
    "        return TreeNode(best_feat, best_thresh, left, right)\n",
    "\n",
    "    def _best_split(self, X_column, split_thresh):\n",
    "        left_idxs = np.argwhere(X_column <= split_thresh).flatten()\n",
    "        right_idxs = np.argwhere(X_column > split_thresh).flatten()\n",
    "        return left_idxs, right_idxs\n",
    "    \n",
    "    def find_decision_rule(self, X, y, feat_idxs):\n",
    "        best_gain = -1\n",
    "        split_idx, split_thresh = None, None\n",
    "        for feat_idx in feat_idxs:\n",
    "            X_column = X[:, feat_idx]\n",
    "            thresholds = np.unique(X_column)\n",
    "            for threshold in thresholds:\n",
    "                gain = self._information_gain(y, X_column, threshold)\n",
    "\n",
    "                if gain > best_gain:\n",
    "                    best_gain = gain\n",
    "                    split_idx = feat_idx\n",
    "                    split_thresh = threshold\n",
    "\n",
    "        return split_idx, split_thresh\n",
    "\n",
    "    def entropy(self,y):\n",
    "        hist = np.bincount(y)\n",
    "        ps = hist / len(y)\n",
    "        return -np.sum([p * np.log2(p) for p in ps if p > 0])\n",
    "    \n",
    "    def _information_gain(self, y, X_column, split_thresh):\n",
    "        parent_entropy = self.entropy(y)\n",
    "        left_idxs, right_idxs = self._best_split(X_column, split_thresh)\n",
    "\n",
    "        if len(left_idxs) == 0 or len(right_idxs) == 0:\n",
    "            return 0\n",
    "\n",
    "        # compute the weighted avg. of the loss for the children\n",
    "        n = len(y)\n",
    "        n_l, n_r = len(left_idxs), len(right_idxs)\n",
    "        e_l, e_r = self.entropy(y[left_idxs]), self.entropy(y[right_idxs])\n",
    "        child_entropy = (n_l / n) * e_l + (n_r / n) * e_r\n",
    "\n",
    "        # information gain is difference in loss before vs. after split\n",
    "        ig = parent_entropy - child_entropy\n",
    "        return ig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b89fbdf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomForest:\n",
    "    \n",
    "    def __init__(self, n_trees=10, min_samples_split=2,\n",
    "                 max_depth=100, n_feats=None):\n",
    "        self.n_trees = n_trees\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.max_depth = max_depth\n",
    "        self.n_feats = n_feats\n",
    "        self.trees = []\n",
    "\n",
    "    def fit(self, X_train, y_train):\n",
    "        self.trees = []\n",
    "        for _ in range(self.n_trees):\n",
    "            tree = Tree(min_samples_split=self.min_samples_split,\n",
    "                max_depth=self.max_depth, n_feats=self.n_feats)\n",
    "            X_samp, y_samp = new_sample(X_train, y_train)\n",
    "            tree.fit(X_samp, y_samp)\n",
    "            self.trees.append(tree)\n",
    "\n",
    "    def predict(self, X):\n",
    "        tree_preds = np.array([tree.predict(X) for tree in self.trees])\n",
    "        tree_preds = np.swapaxes(tree_preds, 0, 1)\n",
    "        y_pred = [repeated_label(tree_pred) for tree_pred in tree_preds]\n",
    "        return np.array(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84a6a566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Random Forest: 98.96719319562575\n",
      "Validation Accuracy of Random Forest: 88.23251417769377\n"
     ]
    }
   ],
   "source": [
    "# TODO: Report accuracy on training and validation sets\n",
    "# Reshape features if necessary\n",
    "\n",
    "rf = RandomForest(n_trees = 5)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_train = rf.predict(X_train)\n",
    "accuracy = np.mean(y_pred_train == y_train) * 100\n",
    "print(f\"Training Accuracy of Random Forest: {accuracy}\")\n",
    "y_pred_valid = rf.predict(X_valid)\n",
    "accuracy = np.mean(y_pred_valid == y_valid) * 100\n",
    "print(f\"Validation Accuracy of Random Forest: {accuracy}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb37252",
   "metadata": {},
   "source": [
    "## Part 2(c): K-NN\n",
    "\n",
    "Implement a K-NN classifier from scratch and report your classifier's accuracy on the training and validation sets. **You should use your implementation from prior homeworks and turn it in again with updates, if any.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "605810c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Implement a kNN classifier\n",
    "class KNN:\n",
    "    def __init__(self, k):\n",
    "        self.k = k\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.X_train = X\n",
    "        self.y_train = y\n",
    "\n",
    "    def predict(self, X):\n",
    "        y_pred = np.zeros(X.shape[0], dtype=int)\n",
    "        for i in range(X.shape[0]):\n",
    "            distances = np.sqrt(np.sum((self.X_train - X[i])**2, axis=1))\n",
    "            nearest_neighbors = self.y_train[np.argsort(distances)[:self.k]]\n",
    "            y_pred[i] = np.bincount(nearest_neighbors).argmax()\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9291e0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of KNN: 95.71688942891859\n",
      "Validation Accuracy of KNN: 93.47826086956522\n"
     ]
    }
   ],
   "source": [
    "#TODO: Report accuracy on training and validation sets\n",
    "knn = KNN(k=5)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred_train = knn.predict(X_train)\n",
    "accuracy = np.mean(y_pred_train == y_train) * 100\n",
    "print(f\"Training Accuracy of KNN: {accuracy}\")\n",
    "y_pred_valid = knn.predict(X_valid)\n",
    "accuracy = np.mean(y_pred_valid == y_valid) * 100\n",
    "print(f\"Validation Accuracy of KNN: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f634e12f",
   "metadata": {},
   "source": [
    "## Part 2(d): MLP\n",
    "\n",
    "Train an MLP classifier and report your classifier's accuracy on the training and validation sets. **You are allowed to use sklearn.neural_network.MLPClassifier for this section.** Make sure to run in all code you used to train and test your MLP classifier. See https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc111892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of MLP: 99.96962332928311\n",
      "Validation Accuracy of MLP: 95.41587901701322\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# TODO: Train an MLPClassifier and report training and validation set accuracies\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(100, 50), activation='relu', solver='adam', max_iter=500, random_state=42)\n",
    "mlp.fit(X_train, y_train)\n",
    "y_pred_train = mlp.predict(X_train)\n",
    "accuracy = np.mean(y_pred_train == y_train) * 100\n",
    "print(f\"Training Accuracy of MLP: {accuracy}\")\n",
    "y_pred_valid = mlp.predict(X_valid)\n",
    "accuracy = np.mean(y_pred_valid == y_valid) * 100\n",
    "print(f\"Validation Accuracy of MLP: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d975c24",
   "metadata": {},
   "source": [
    "## Part 2(f): Bakeoff: Choose the best Classifier\n",
    "\n",
    "For this section, you are free to use a classifier of your choice (you are allowed to use any libraries of your choice). You are also free to use the training and validation sets in any way you choose i.e. you can merge them and perform k-fold cross validation. <br>\n",
    "\n",
    "To evaluate your classifier, we will on March 15th provide you with a pickle file ('test.pkl') that will be in the same format as the 'train.pkl' and 'val.pkl' but **will not contain the labels**. You will need to generate predictions for each of the inputs we provide in 'test.pkl' in a text file. <br>\n",
    "\n",
    "For your submission, you will need to provide us with a  **test_predictions.txt** file containing the class predictions of your classifier. We will compute the accuracy of the predictions you provide for your evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f39a54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extra libraries\n",
    "import pandas as pd\n",
    "#Importing Models and metrics\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, f1_score, make_scorer, precision_score, recall_score\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, cross_validate, train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ff92009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((11992, 15), (11992,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining both train and valid sets for higher data\n",
    "X_train = np.append(X_train, X_valid, axis=0)\n",
    "y_train = np.append(y_train, y_valid)\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "559c792c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1977,  533,  361, 3299, 1451,  687,  526,  464,  517,  342,  410,\n",
       "        411,  347,  356,  311])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_counts = np.bincount(y_train)\n",
    "label_counts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e98488f9",
   "metadata": {},
   "source": [
    "We will use `weighted` averaging for metrics as the classes are imbalanced"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ed041bd5",
   "metadata": {},
   "source": [
    " k-fold cross validation splits the train and validation sets randomly but sometimes it may lead to poor train set. So insead we will use stratified k-fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbfe8249",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3c29408d",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "579bbb4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 1}\n",
      "0.9395431609674729\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>split3_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.009989</td>\n",
       "      <td>0.003053</td>\n",
       "      <td>0.030674</td>\n",
       "      <td>0.002365</td>\n",
       "      <td>1</td>\n",
       "      <td>{'n_neighbors': 1}</td>\n",
       "      <td>0.936667</td>\n",
       "      <td>0.940833</td>\n",
       "      <td>0.934946</td>\n",
       "      <td>0.939950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939766</td>\n",
       "      <td>0.941762</td>\n",
       "      <td>0.935291</td>\n",
       "      <td>0.944234</td>\n",
       "      <td>0.934574</td>\n",
       "      <td>0.935906</td>\n",
       "      <td>0.948806</td>\n",
       "      <td>0.939065</td>\n",
       "      <td>0.004546</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009726</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.043112</td>\n",
       "      <td>0.004051</td>\n",
       "      <td>3</td>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.921601</td>\n",
       "      <td>0.935780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934889</td>\n",
       "      <td>0.942907</td>\n",
       "      <td>0.933738</td>\n",
       "      <td>0.933358</td>\n",
       "      <td>0.935449</td>\n",
       "      <td>0.936947</td>\n",
       "      <td>0.943273</td>\n",
       "      <td>0.934478</td>\n",
       "      <td>0.006159</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010041</td>\n",
       "      <td>0.000445</td>\n",
       "      <td>0.050449</td>\n",
       "      <td>0.001433</td>\n",
       "      <td>5</td>\n",
       "      <td>{'n_neighbors': 5}</td>\n",
       "      <td>0.935833</td>\n",
       "      <td>0.934167</td>\n",
       "      <td>0.909925</td>\n",
       "      <td>0.929108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.927426</td>\n",
       "      <td>0.936659</td>\n",
       "      <td>0.927576</td>\n",
       "      <td>0.928479</td>\n",
       "      <td>0.938587</td>\n",
       "      <td>0.932821</td>\n",
       "      <td>0.945490</td>\n",
       "      <td>0.931312</td>\n",
       "      <td>0.009341</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009260</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.049866</td>\n",
       "      <td>0.001498</td>\n",
       "      <td>7</td>\n",
       "      <td>{'n_neighbors': 7}</td>\n",
       "      <td>0.935000</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.914929</td>\n",
       "      <td>0.935780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934121</td>\n",
       "      <td>0.930647</td>\n",
       "      <td>0.925041</td>\n",
       "      <td>0.922338</td>\n",
       "      <td>0.935243</td>\n",
       "      <td>0.930110</td>\n",
       "      <td>0.938044</td>\n",
       "      <td>0.929407</td>\n",
       "      <td>0.006980</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008601</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.050881</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>9</td>\n",
       "      <td>{'n_neighbors': 9}</td>\n",
       "      <td>0.930833</td>\n",
       "      <td>0.928333</td>\n",
       "      <td>0.914095</td>\n",
       "      <td>0.931610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929628</td>\n",
       "      <td>0.932890</td>\n",
       "      <td>0.931065</td>\n",
       "      <td>0.917426</td>\n",
       "      <td>0.936357</td>\n",
       "      <td>0.928465</td>\n",
       "      <td>0.933534</td>\n",
       "      <td>0.927830</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.052569</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>11</td>\n",
       "      <td>{'n_neighbors': 11}</td>\n",
       "      <td>0.928333</td>\n",
       "      <td>0.929167</td>\n",
       "      <td>0.904921</td>\n",
       "      <td>0.925771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923615</td>\n",
       "      <td>0.926945</td>\n",
       "      <td>0.920586</td>\n",
       "      <td>0.917915</td>\n",
       "      <td>0.937499</td>\n",
       "      <td>0.930131</td>\n",
       "      <td>0.924012</td>\n",
       "      <td>0.923839</td>\n",
       "      <td>0.008670</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.008928</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.057068</td>\n",
       "      <td>0.003178</td>\n",
       "      <td>13</td>\n",
       "      <td>{'n_neighbors': 13}</td>\n",
       "      <td>0.925833</td>\n",
       "      <td>0.925833</td>\n",
       "      <td>0.900751</td>\n",
       "      <td>0.923269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920740</td>\n",
       "      <td>0.927233</td>\n",
       "      <td>0.917123</td>\n",
       "      <td>0.909333</td>\n",
       "      <td>0.932312</td>\n",
       "      <td>0.927562</td>\n",
       "      <td>0.926979</td>\n",
       "      <td>0.920807</td>\n",
       "      <td>0.009644</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.009705</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.061759</td>\n",
       "      <td>0.002652</td>\n",
       "      <td>15</td>\n",
       "      <td>{'n_neighbors': 15}</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.901585</td>\n",
       "      <td>0.922435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920182</td>\n",
       "      <td>0.926511</td>\n",
       "      <td>0.917320</td>\n",
       "      <td>0.908534</td>\n",
       "      <td>0.926769</td>\n",
       "      <td>0.921623</td>\n",
       "      <td>0.924703</td>\n",
       "      <td>0.919347</td>\n",
       "      <td>0.008548</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.009989      0.003053         0.030674        0.002365   \n",
       "1       0.009726      0.001629         0.043112        0.004051   \n",
       "2       0.010041      0.000445         0.050449        0.001433   \n",
       "3       0.009260      0.000471         0.049866        0.001498   \n",
       "4       0.008601      0.000215         0.050881        0.000723   \n",
       "5       0.008573      0.000250         0.052569        0.001005   \n",
       "6       0.008928      0.000191         0.057068        0.003178   \n",
       "7       0.009705      0.000564         0.061759        0.002652   \n",
       "\n",
       "  param_n_neighbors               params  split0_test_Accuracy  \\\n",
       "0                 1   {'n_neighbors': 1}              0.936667   \n",
       "1                 3   {'n_neighbors': 3}              0.932500   \n",
       "2                 5   {'n_neighbors': 5}              0.935833   \n",
       "3                 7   {'n_neighbors': 7}              0.935000   \n",
       "4                 9   {'n_neighbors': 9}              0.930833   \n",
       "5                11  {'n_neighbors': 11}              0.928333   \n",
       "6                13  {'n_neighbors': 13}              0.925833   \n",
       "7                15  {'n_neighbors': 15}              0.926667   \n",
       "\n",
       "   split1_test_Accuracy  split2_test_Accuracy  split3_test_Accuracy  ...  \\\n",
       "0              0.940833              0.934946              0.939950  ...   \n",
       "1              0.933333              0.921601              0.935780  ...   \n",
       "2              0.934167              0.909925              0.929108  ...   \n",
       "3              0.932500              0.914929              0.935780  ...   \n",
       "4              0.928333              0.914095              0.931610  ...   \n",
       "5              0.929167              0.904921              0.925771  ...   \n",
       "6              0.925833              0.900751              0.923269  ...   \n",
       "7              0.925000              0.901585              0.922435  ...   \n",
       "\n",
       "   split3_test_F1-score  split4_test_F1-score  split5_test_F1-score  \\\n",
       "0              0.939766              0.941762              0.935291   \n",
       "1              0.934889              0.942907              0.933738   \n",
       "2              0.927426              0.936659              0.927576   \n",
       "3              0.934121              0.930647              0.925041   \n",
       "4              0.929628              0.932890              0.931065   \n",
       "5              0.923615              0.926945              0.920586   \n",
       "6              0.920740              0.927233              0.917123   \n",
       "7              0.920182              0.926511              0.917320   \n",
       "\n",
       "   split6_test_F1-score  split7_test_F1-score  split8_test_F1-score  \\\n",
       "0              0.944234              0.934574              0.935906   \n",
       "1              0.933358              0.935449              0.936947   \n",
       "2              0.928479              0.938587              0.932821   \n",
       "3              0.922338              0.935243              0.930110   \n",
       "4              0.917426              0.936357              0.928465   \n",
       "5              0.917915              0.937499              0.930131   \n",
       "6              0.909333              0.932312              0.927562   \n",
       "7              0.908534              0.926769              0.921623   \n",
       "\n",
       "   split9_test_F1-score  mean_test_F1-score  std_test_F1-score  \\\n",
       "0              0.948806            0.939065           0.004546   \n",
       "1              0.943273            0.934478           0.006159   \n",
       "2              0.945490            0.931312           0.009341   \n",
       "3              0.938044            0.929407           0.006980   \n",
       "4              0.933534            0.927830           0.007011   \n",
       "5              0.924012            0.923839           0.008670   \n",
       "6              0.926979            0.920807           0.009644   \n",
       "7              0.924703            0.919347           0.008548   \n",
       "\n",
       "   rank_test_F1-score  \n",
       "0                   1  \n",
       "1                   2  \n",
       "2                   3  \n",
       "3                   4  \n",
       "4                   5  \n",
       "5                   6  \n",
       "6                   7  \n",
       "7                   8  \n",
       "\n",
       "[8 rows x 58 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_knn = KNeighborsClassifier()\n",
    "grid_knn = GridSearchCV(\n",
    "    estimator = mod_knn,\n",
    "    param_grid={'n_neighbors': [1,3,5,7,9,11,13,15]},\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy' #Inorder to rank the output\n",
    ")\n",
    "grid_knn.fit(X_train, y_train)\n",
    "mod_knn_scores = pd.DataFrame(grid_knn.cv_results_)\n",
    "print(grid_knn.best_params_)\n",
    "print(grid_knn.best_score_)\n",
    "mod_knn_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "28d9a008",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "885d94e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 40}\n",
      "0.9652267167083679\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>split3_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.338777</td>\n",
       "      <td>0.009018</td>\n",
       "      <td>0.106316</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>10</td>\n",
       "      <td>{'C': 10}</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.960833</td>\n",
       "      <td>0.964971</td>\n",
       "      <td>0.964971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.964683</td>\n",
       "      <td>0.957050</td>\n",
       "      <td>0.961796</td>\n",
       "      <td>0.959334</td>\n",
       "      <td>0.966573</td>\n",
       "      <td>0.964661</td>\n",
       "      <td>0.965099</td>\n",
       "      <td>0.962741</td>\n",
       "      <td>0.002863</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.347353</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.105709</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>20</td>\n",
       "      <td>{'C': 20}</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.962469</td>\n",
       "      <td>0.964971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.964847</td>\n",
       "      <td>0.965534</td>\n",
       "      <td>0.960891</td>\n",
       "      <td>0.959966</td>\n",
       "      <td>0.968984</td>\n",
       "      <td>0.967211</td>\n",
       "      <td>0.969071</td>\n",
       "      <td>0.964648</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.373390</td>\n",
       "      <td>0.026872</td>\n",
       "      <td>0.108097</td>\n",
       "      <td>0.006941</td>\n",
       "      <td>30</td>\n",
       "      <td>{'C': 30}</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.961635</td>\n",
       "      <td>0.967473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967337</td>\n",
       "      <td>0.968066</td>\n",
       "      <td>0.957335</td>\n",
       "      <td>0.962499</td>\n",
       "      <td>0.966319</td>\n",
       "      <td>0.967178</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.964897</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.373816</td>\n",
       "      <td>0.033203</td>\n",
       "      <td>0.103540</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>40</td>\n",
       "      <td>{'C': 40}</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.960801</td>\n",
       "      <td>0.968307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968168</td>\n",
       "      <td>0.968027</td>\n",
       "      <td>0.959128</td>\n",
       "      <td>0.961560</td>\n",
       "      <td>0.967305</td>\n",
       "      <td>0.968020</td>\n",
       "      <td>0.965761</td>\n",
       "      <td>0.965085</td>\n",
       "      <td>0.003136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.349398</td>\n",
       "      <td>0.009538</td>\n",
       "      <td>0.101916</td>\n",
       "      <td>0.003950</td>\n",
       "      <td>50</td>\n",
       "      <td>{'C': 50}</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.958299</td>\n",
       "      <td>0.967473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967385</td>\n",
       "      <td>0.966359</td>\n",
       "      <td>0.956418</td>\n",
       "      <td>0.959151</td>\n",
       "      <td>0.965608</td>\n",
       "      <td>0.967136</td>\n",
       "      <td>0.967376</td>\n",
       "      <td>0.963897</td>\n",
       "      <td>0.003945</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.368553</td>\n",
       "      <td>0.022036</td>\n",
       "      <td>0.105720</td>\n",
       "      <td>0.006259</td>\n",
       "      <td>60</td>\n",
       "      <td>{'C': 60}</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.957465</td>\n",
       "      <td>0.966639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966527</td>\n",
       "      <td>0.964693</td>\n",
       "      <td>0.955723</td>\n",
       "      <td>0.957541</td>\n",
       "      <td>0.963858</td>\n",
       "      <td>0.967115</td>\n",
       "      <td>0.967357</td>\n",
       "      <td>0.963324</td>\n",
       "      <td>0.004283</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.370797</td>\n",
       "      <td>0.025769</td>\n",
       "      <td>0.104207</td>\n",
       "      <td>0.005836</td>\n",
       "      <td>70</td>\n",
       "      <td>{'C': 70}</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.956631</td>\n",
       "      <td>0.968307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968188</td>\n",
       "      <td>0.964693</td>\n",
       "      <td>0.956571</td>\n",
       "      <td>0.955940</td>\n",
       "      <td>0.963040</td>\n",
       "      <td>0.965440</td>\n",
       "      <td>0.968226</td>\n",
       "      <td>0.963020</td>\n",
       "      <td>0.004502</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.355661</td>\n",
       "      <td>0.017624</td>\n",
       "      <td>0.099381</td>\n",
       "      <td>0.002473</td>\n",
       "      <td>80</td>\n",
       "      <td>{'C': 80}</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.957465</td>\n",
       "      <td>0.967473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967380</td>\n",
       "      <td>0.963836</td>\n",
       "      <td>0.957416</td>\n",
       "      <td>0.956670</td>\n",
       "      <td>0.963810</td>\n",
       "      <td>0.966320</td>\n",
       "      <td>0.967433</td>\n",
       "      <td>0.963257</td>\n",
       "      <td>0.004079</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.351023</td>\n",
       "      <td>0.002814</td>\n",
       "      <td>0.099132</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>90</td>\n",
       "      <td>{'C': 90}</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.958299</td>\n",
       "      <td>0.968307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968188</td>\n",
       "      <td>0.963836</td>\n",
       "      <td>0.958323</td>\n",
       "      <td>0.957459</td>\n",
       "      <td>0.963810</td>\n",
       "      <td>0.966374</td>\n",
       "      <td>0.967418</td>\n",
       "      <td>0.963514</td>\n",
       "      <td>0.003762</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.373175</td>\n",
       "      <td>0.025499</td>\n",
       "      <td>0.103286</td>\n",
       "      <td>0.005787</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 100}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.964167</td>\n",
       "      <td>0.957465</td>\n",
       "      <td>0.968307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968188</td>\n",
       "      <td>0.964693</td>\n",
       "      <td>0.958323</td>\n",
       "      <td>0.957459</td>\n",
       "      <td>0.963947</td>\n",
       "      <td>0.966374</td>\n",
       "      <td>0.968280</td>\n",
       "      <td>0.963613</td>\n",
       "      <td>0.004023</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       0.338777      0.009018         0.106316        0.002395      10   \n",
       "1       0.347353      0.011198         0.105709        0.006329      20   \n",
       "2       0.373390      0.026872         0.108097        0.006941      30   \n",
       "3       0.373816      0.033203         0.103540        0.005560      40   \n",
       "4       0.349398      0.009538         0.101916        0.003950      50   \n",
       "5       0.368553      0.022036         0.105720        0.006259      60   \n",
       "6       0.370797      0.025769         0.104207        0.005836      70   \n",
       "7       0.355661      0.017624         0.099381        0.002473      80   \n",
       "8       0.351023      0.002814         0.099132        0.002052      90   \n",
       "9       0.373175      0.025499         0.103286        0.005787     100   \n",
       "\n",
       "       params  split0_test_Accuracy  split1_test_Accuracy  \\\n",
       "0   {'C': 10}              0.962500              0.960833   \n",
       "1   {'C': 20}              0.961667              0.965833   \n",
       "2   {'C': 30}              0.965833              0.965833   \n",
       "3   {'C': 40}              0.965000              0.966667   \n",
       "4   {'C': 50}              0.965000              0.965833   \n",
       "5   {'C': 60}              0.965833              0.966667   \n",
       "6   {'C': 70}              0.965000              0.965833   \n",
       "7   {'C': 80}              0.965833              0.965833   \n",
       "8   {'C': 90}              0.965833              0.965000   \n",
       "9  {'C': 100}              0.966667              0.964167   \n",
       "\n",
       "   split2_test_Accuracy  split3_test_Accuracy  ...  split3_test_F1-score  \\\n",
       "0              0.964971              0.964971  ...              0.964683   \n",
       "1              0.962469              0.964971  ...              0.964847   \n",
       "2              0.961635              0.967473  ...              0.967337   \n",
       "3              0.960801              0.968307  ...              0.968168   \n",
       "4              0.958299              0.967473  ...              0.967385   \n",
       "5              0.957465              0.966639  ...              0.966527   \n",
       "6              0.956631              0.968307  ...              0.968188   \n",
       "7              0.957465              0.967473  ...              0.967380   \n",
       "8              0.958299              0.968307  ...              0.968188   \n",
       "9              0.957465              0.968307  ...              0.968188   \n",
       "\n",
       "   split4_test_F1-score  split5_test_F1-score  split6_test_F1-score  \\\n",
       "0              0.957050              0.961796              0.959334   \n",
       "1              0.965534              0.960891              0.959966   \n",
       "2              0.968066              0.957335              0.962499   \n",
       "3              0.968027              0.959128              0.961560   \n",
       "4              0.966359              0.956418              0.959151   \n",
       "5              0.964693              0.955723              0.957541   \n",
       "6              0.964693              0.956571              0.955940   \n",
       "7              0.963836              0.957416              0.956670   \n",
       "8              0.963836              0.958323              0.957459   \n",
       "9              0.964693              0.958323              0.957459   \n",
       "\n",
       "   split7_test_F1-score  split8_test_F1-score  split9_test_F1-score  \\\n",
       "0              0.966573              0.964661              0.965099   \n",
       "1              0.968984              0.967211              0.969071   \n",
       "2              0.966319              0.967178              0.966667   \n",
       "3              0.967305              0.968020              0.965761   \n",
       "4              0.965608              0.967136              0.967376   \n",
       "5              0.963858              0.967115              0.967357   \n",
       "6              0.963040              0.965440              0.968226   \n",
       "7              0.963810              0.966320              0.967433   \n",
       "8              0.963810              0.966374              0.967418   \n",
       "9              0.963947              0.966374              0.968280   \n",
       "\n",
       "   mean_test_F1-score  std_test_F1-score  rank_test_F1-score  \n",
       "0            0.962741           0.002863                  10  \n",
       "1            0.964648           0.003102                   3  \n",
       "2            0.964897           0.003163                   2  \n",
       "3            0.965085           0.003136                   1  \n",
       "4            0.963897           0.003945                   4  \n",
       "5            0.963324           0.004283                   7  \n",
       "6            0.963020           0.004502                   9  \n",
       "7            0.963257           0.004079                   8  \n",
       "8            0.963514           0.003762                   6  \n",
       "9            0.963613           0.004023                   5  \n",
       "\n",
       "[10 rows x 58 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm2 = SVC(kernel='rbf')\n",
    "gridsvm = GridSearchCV(\n",
    "    estimator = svm2,\n",
    "    param_grid={'C':[10,20,30,40,50,60, 70, 80, 90, 100]},\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "gridsvm.fit(X_train, y_train)\n",
    "modsvm_scores = pd.DataFrame(gridsvm.cv_results_)\n",
    "print(gridsvm.best_params_)\n",
    "print(gridsvm.best_score_)\n",
    "modsvm_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1cff084f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 30, 'kernel': 'rbf'}\n",
      "0.9650599110369751\n"
     ]
    },
    {
     "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.848301</td>\n",
       "      <td>0.030550</td>\n",
       "      <td>0.247105</td>\n",
       "      <td>0.007256</td>\n",
       "      <td>0.1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.903333</td>\n",
       "      <td>0.879900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.904059</td>\n",
       "      <td>0.902190</td>\n",
       "      <td>0.899466</td>\n",
       "      <td>0.892729</td>\n",
       "      <td>0.900537</td>\n",
       "      <td>0.879513</td>\n",
       "      <td>0.904838</td>\n",
       "      <td>0.895284</td>\n",
       "      <td>0.010004</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.400596</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>0.147288</td>\n",
       "      <td>0.006505</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.947500</td>\n",
       "      <td>0.945833</td>\n",
       "      <td>0.938282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.954666</td>\n",
       "      <td>0.947076</td>\n",
       "      <td>0.950700</td>\n",
       "      <td>0.937462</td>\n",
       "      <td>0.956409</td>\n",
       "      <td>0.946897</td>\n",
       "      <td>0.957063</td>\n",
       "      <td>0.948057</td>\n",
       "      <td>0.006508</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.342081</td>\n",
       "      <td>0.005797</td>\n",
       "      <td>0.108109</td>\n",
       "      <td>0.002969</td>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.960833</td>\n",
       "      <td>0.964971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.964683</td>\n",
       "      <td>0.957050</td>\n",
       "      <td>0.961796</td>\n",
       "      <td>0.959334</td>\n",
       "      <td>0.966573</td>\n",
       "      <td>0.964661</td>\n",
       "      <td>0.965099</td>\n",
       "      <td>0.962741</td>\n",
       "      <td>0.002863</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.339932</td>\n",
       "      <td>0.006264</td>\n",
       "      <td>0.100335</td>\n",
       "      <td>0.001519</td>\n",
       "      <td>30</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 30, 'kernel': 'rbf'}</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.961635</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967337</td>\n",
       "      <td>0.968066</td>\n",
       "      <td>0.957335</td>\n",
       "      <td>0.962499</td>\n",
       "      <td>0.966319</td>\n",
       "      <td>0.967178</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.964897</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.345567</td>\n",
       "      <td>0.006965</td>\n",
       "      <td>0.100962</td>\n",
       "      <td>0.004283</td>\n",
       "      <td>50</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 50, 'kernel': 'rbf'}</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>0.958299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967385</td>\n",
       "      <td>0.966359</td>\n",
       "      <td>0.956418</td>\n",
       "      <td>0.959151</td>\n",
       "      <td>0.965608</td>\n",
       "      <td>0.967136</td>\n",
       "      <td>0.967376</td>\n",
       "      <td>0.963897</td>\n",
       "      <td>0.003945</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.360294</td>\n",
       "      <td>0.009485</td>\n",
       "      <td>0.098695</td>\n",
       "      <td>0.002579</td>\n",
       "      <td>100</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 100, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.964167</td>\n",
       "      <td>0.957465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968188</td>\n",
       "      <td>0.964693</td>\n",
       "      <td>0.958323</td>\n",
       "      <td>0.957459</td>\n",
       "      <td>0.963947</td>\n",
       "      <td>0.966374</td>\n",
       "      <td>0.968280</td>\n",
       "      <td>0.963613</td>\n",
       "      <td>0.004023</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       0.848301      0.030550         0.247105        0.007256     0.1   \n",
       "1       0.400596      0.006615         0.147288        0.006505       1   \n",
       "2       0.342081      0.005797         0.108109        0.002969      10   \n",
       "3       0.339932      0.006264         0.100335        0.001519      30   \n",
       "4       0.345567      0.006965         0.100962        0.004283      50   \n",
       "5       0.360294      0.009485         0.098695        0.002579     100   \n",
       "\n",
       "  param_kernel                       params  split0_test_Accuracy  \\\n",
       "0          rbf  {'C': 0.1, 'kernel': 'rbf'}              0.900000   \n",
       "1          rbf    {'C': 1, 'kernel': 'rbf'}              0.947500   \n",
       "2          rbf   {'C': 10, 'kernel': 'rbf'}              0.962500   \n",
       "3          rbf   {'C': 30, 'kernel': 'rbf'}              0.965833   \n",
       "4          rbf   {'C': 50, 'kernel': 'rbf'}              0.965000   \n",
       "5          rbf  {'C': 100, 'kernel': 'rbf'}              0.966667   \n",
       "\n",
       "   split1_test_Accuracy  split2_test_Accuracy  ...  split3_test_F1-score  \\\n",
       "0              0.903333              0.879900  ...              0.904059   \n",
       "1              0.945833              0.938282  ...              0.954666   \n",
       "2              0.960833              0.964971  ...              0.964683   \n",
       "3              0.965833              0.961635  ...              0.967337   \n",
       "4              0.965833              0.958299  ...              0.967385   \n",
       "5              0.964167              0.957465  ...              0.968188   \n",
       "\n",
       "   split4_test_F1-score  split5_test_F1-score  split6_test_F1-score  \\\n",
       "0              0.902190              0.899466              0.892729   \n",
       "1              0.947076              0.950700              0.937462   \n",
       "2              0.957050              0.961796              0.959334   \n",
       "3              0.968066              0.957335              0.962499   \n",
       "4              0.966359              0.956418              0.959151   \n",
       "5              0.964693              0.958323              0.957459   \n",
       "\n",
       "   split7_test_F1-score  split8_test_F1-score  split9_test_F1-score  \\\n",
       "0              0.900537              0.879513              0.904838   \n",
       "1              0.956409              0.946897              0.957063   \n",
       "2              0.966573              0.964661              0.965099   \n",
       "3              0.966319              0.967178              0.966667   \n",
       "4              0.965608              0.967136              0.967376   \n",
       "5              0.963947              0.966374              0.968280   \n",
       "\n",
       "   mean_test_F1-score  std_test_F1-score  rank_test_F1-score  \n",
       "0            0.895284           0.010004                   6  \n",
       "1            0.948057           0.006508                   5  \n",
       "2            0.962741           0.002863                   4  \n",
       "3            0.964897           0.003163                   1  \n",
       "4            0.963897           0.003945                   2  \n",
       "5            0.963613           0.004023                   3  \n",
       "\n",
       "[6 rows x 59 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_svm = SVC()\n",
    "grid_svm = GridSearchCV(\n",
    "    estimator = mod_svm,\n",
    "    param_grid={'kernel': ['rbf'], 'C':[0.1,1,10,30,50,100]},\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_svm.fit(X_train, y_train)\n",
    "mod_svm_scores = pd.DataFrame(grid_svm.cv_results_)\n",
    "print(grid_svm.best_params_)\n",
    "print(grid_svm.best_score_)\n",
    "mod_svm_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e3f1bd27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'break_ties': False,\n",
       " 'cache_size': 200,\n",
       " 'class_weight': None,\n",
       " 'coef0': 0.0,\n",
       " 'decision_function_shape': 'ovr',\n",
       " 'degree': 3,\n",
       " 'gamma': 'scale',\n",
       " 'kernel': 'rbf',\n",
       " 'max_iter': -1,\n",
       " 'probability': False,\n",
       " 'random_state': None,\n",
       " 'shrinking': True,\n",
       " 'tol': 0.001,\n",
       " 'verbose': False}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_svm.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "80259744",
   "metadata": {},
   "source": [
    "### Ensemble"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7f6ec263",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7be5eab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 100}\n",
      "0.9310371837642479\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>split3_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.045044</td>\n",
       "      <td>0.069567</td>\n",
       "      <td>0.013473</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>50</td>\n",
       "      <td>{'n_estimators': 50}</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>0.919167</td>\n",
       "      <td>0.919933</td>\n",
       "      <td>0.929942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.927750</td>\n",
       "      <td>0.927144</td>\n",
       "      <td>0.931403</td>\n",
       "      <td>0.924984</td>\n",
       "      <td>0.939610</td>\n",
       "      <td>0.920124</td>\n",
       "      <td>0.925614</td>\n",
       "      <td>0.925688</td>\n",
       "      <td>0.006363</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.905382</td>\n",
       "      <td>0.020884</td>\n",
       "      <td>0.020505</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>100</td>\n",
       "      <td>{'n_estimators': 100}</td>\n",
       "      <td>0.931667</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.920767</td>\n",
       "      <td>0.934112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.932768</td>\n",
       "      <td>0.931172</td>\n",
       "      <td>0.927310</td>\n",
       "      <td>0.934149</td>\n",
       "      <td>0.933354</td>\n",
       "      <td>0.923976</td>\n",
       "      <td>0.932486</td>\n",
       "      <td>0.929552</td>\n",
       "      <td>0.004406</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.893165</td>\n",
       "      <td>0.047785</td>\n",
       "      <td>0.041102</td>\n",
       "      <td>0.001603</td>\n",
       "      <td>200</td>\n",
       "      <td>{'n_estimators': 200}</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.919167</td>\n",
       "      <td>0.919933</td>\n",
       "      <td>0.932444</td>\n",
       "      <td>...</td>\n",
       "      <td>0.930592</td>\n",
       "      <td>0.933083</td>\n",
       "      <td>0.929063</td>\n",
       "      <td>0.927194</td>\n",
       "      <td>0.933083</td>\n",
       "      <td>0.926641</td>\n",
       "      <td>0.937060</td>\n",
       "      <td>0.928378</td>\n",
       "      <td>0.006117</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       1.045044      0.069567         0.013473        0.002264   \n",
       "1       1.905382      0.020884         0.020505        0.000344   \n",
       "2       3.893165      0.047785         0.041102        0.001603   \n",
       "\n",
       "  param_n_estimators                 params  split0_test_Accuracy  \\\n",
       "0                 50   {'n_estimators': 50}              0.926667   \n",
       "1                100  {'n_estimators': 100}              0.931667   \n",
       "2                200  {'n_estimators': 200}              0.932500   \n",
       "\n",
       "   split1_test_Accuracy  split2_test_Accuracy  split3_test_Accuracy  ...  \\\n",
       "0              0.919167              0.919933              0.929942  ...   \n",
       "1              0.932500              0.920767              0.934112  ...   \n",
       "2              0.919167              0.919933              0.932444  ...   \n",
       "\n",
       "   split3_test_F1-score  split4_test_F1-score  split5_test_F1-score  \\\n",
       "0              0.927750              0.927144              0.931403   \n",
       "1              0.932768              0.931172              0.927310   \n",
       "2              0.930592              0.933083              0.929063   \n",
       "\n",
       "   split6_test_F1-score  split7_test_F1-score  split8_test_F1-score  \\\n",
       "0              0.924984              0.939610              0.920124   \n",
       "1              0.934149              0.933354              0.923976   \n",
       "2              0.927194              0.933083              0.926641   \n",
       "\n",
       "   split9_test_F1-score  mean_test_F1-score  std_test_F1-score  \\\n",
       "0              0.925614            0.925688           0.006363   \n",
       "1              0.932486            0.929552           0.004406   \n",
       "2              0.937060            0.928378           0.006117   \n",
       "\n",
       "   rank_test_F1-score  \n",
       "0                   3  \n",
       "1                   1  \n",
       "2                   2  \n",
       "\n",
       "[3 rows x 58 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_rf = RandomForestClassifier()\n",
    "grid_rf = GridSearchCV(\n",
    "    estimator = mod_rf,\n",
    "    param_grid={'n_estimators': [50, 100, 200]},\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_rf.fit(X_train, y_train)\n",
    "mod_rf_scores = pd.DataFrame(grid_rf.cv_results_)\n",
    "print(grid_rf.best_params_)\n",
    "print(grid_rf.best_score_)\n",
    "mod_rf_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "17963e85",
   "metadata": {},
   "source": [
    "#### Gradient Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ed279a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'init': None,\n",
       " 'learning_rate': 0.1,\n",
       " 'loss': 'log_loss',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_iter_no_change': None,\n",
       " 'random_state': None,\n",
       " 'subsample': 1.0,\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbdt = GradientBoostingClassifier()\n",
    "gbdt.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5f7be8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 200}\n",
      "0.9217819710870169\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>split3_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.812142</td>\n",
       "      <td>0.378932</td>\n",
       "      <td>0.011452</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>50</td>\n",
       "      <td>{'n_estimators': 50}</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>0.875730</td>\n",
       "      <td>0.907423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.905325</td>\n",
       "      <td>0.910404</td>\n",
       "      <td>0.897376</td>\n",
       "      <td>0.895230</td>\n",
       "      <td>0.896067</td>\n",
       "      <td>0.880223</td>\n",
       "      <td>0.902205</td>\n",
       "      <td>0.893132</td>\n",
       "      <td>0.011117</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.225979</td>\n",
       "      <td>0.597779</td>\n",
       "      <td>0.019805</td>\n",
       "      <td>0.001230</td>\n",
       "      <td>100</td>\n",
       "      <td>{'n_estimators': 100}</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.901667</td>\n",
       "      <td>0.899083</td>\n",
       "      <td>0.920767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919412</td>\n",
       "      <td>0.918038</td>\n",
       "      <td>0.916916</td>\n",
       "      <td>0.910883</td>\n",
       "      <td>0.915456</td>\n",
       "      <td>0.896440</td>\n",
       "      <td>0.916665</td>\n",
       "      <td>0.909747</td>\n",
       "      <td>0.008430</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.902350</td>\n",
       "      <td>1.773949</td>\n",
       "      <td>0.033473</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>200</td>\n",
       "      <td>{'n_estimators': 200}</td>\n",
       "      <td>0.9175</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.912427</td>\n",
       "      <td>0.925771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.924611</td>\n",
       "      <td>0.929323</td>\n",
       "      <td>0.929352</td>\n",
       "      <td>0.922590</td>\n",
       "      <td>0.924953</td>\n",
       "      <td>0.907418</td>\n",
       "      <td>0.925958</td>\n",
       "      <td>0.921012</td>\n",
       "      <td>0.007037</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0      26.812142      0.378932         0.011452        0.000606   \n",
       "1      53.225979      0.597779         0.019805        0.001230   \n",
       "2     103.902350      1.773949         0.033473        0.001021   \n",
       "\n",
       "  param_n_estimators                 params  split0_test_Accuracy  \\\n",
       "0                 50   {'n_estimators': 50}                0.8875   \n",
       "1                100  {'n_estimators': 100}                0.9050   \n",
       "2                200  {'n_estimators': 200}                0.9175   \n",
       "\n",
       "   split1_test_Accuracy  split2_test_Accuracy  split3_test_Accuracy  ...  \\\n",
       "0              0.887500              0.875730              0.907423  ...   \n",
       "1              0.901667              0.899083              0.920767  ...   \n",
       "2              0.916667              0.912427              0.925771  ...   \n",
       "\n",
       "   split3_test_F1-score  split4_test_F1-score  split5_test_F1-score  \\\n",
       "0              0.905325              0.910404              0.897376   \n",
       "1              0.919412              0.918038              0.916916   \n",
       "2              0.924611              0.929323              0.929352   \n",
       "\n",
       "   split6_test_F1-score  split7_test_F1-score  split8_test_F1-score  \\\n",
       "0              0.895230              0.896067              0.880223   \n",
       "1              0.910883              0.915456              0.896440   \n",
       "2              0.922590              0.924953              0.907418   \n",
       "\n",
       "   split9_test_F1-score  mean_test_F1-score  std_test_F1-score  \\\n",
       "0              0.902205            0.893132           0.011117   \n",
       "1              0.916665            0.909747           0.008430   \n",
       "2              0.925958            0.921012           0.007037   \n",
       "\n",
       "   rank_test_F1-score  \n",
       "0                   3  \n",
       "1                   2  \n",
       "2                   1  \n",
       "\n",
       "[3 rows x 58 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_gbdt = GradientBoostingClassifier()\n",
    "grid_gbdt = GridSearchCV(\n",
    "    estimator = mod_gbdt,\n",
    "    param_grid={'n_estimators': [50, 100, 200]},\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_gbdt.fit(X_train, y_train)\n",
    "mod_gbdt_scores = pd.DataFrame(grid_gbdt.cv_results_)\n",
    "print(grid_gbdt.best_params_)\n",
    "print(grid_gbdt.best_score_)\n",
    "mod_gbdt_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "963ed85d",
   "metadata": {},
   "source": [
    "#### XG-Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2dae24d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:logistic',\n",
       " 'use_label_encoder': None,\n",
       " 'base_score': None,\n",
       " 'booster': None,\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': None,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'gpu_id': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': None,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'num_parallel_tree': None,\n",
       " 'predictor': None,\n",
       " 'random_state': None,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': None,\n",
       " 'tree_method': None,\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier()\n",
    "xgb.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "363b0fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'n_estimators': 300}\n",
      "0.9411276063386154\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.749584</td>\n",
       "      <td>1.369605</td>\n",
       "      <td>0.008659</td>\n",
       "      <td>0.002881</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>0.934167</td>\n",
       "      <td>0.925771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.940471</td>\n",
       "      <td>0.934489</td>\n",
       "      <td>0.937850</td>\n",
       "      <td>0.929827</td>\n",
       "      <td>0.933560</td>\n",
       "      <td>0.920579</td>\n",
       "      <td>0.932533</td>\n",
       "      <td>0.931422</td>\n",
       "      <td>0.005699</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.693647</td>\n",
       "      <td>2.052870</td>\n",
       "      <td>0.011193</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.1</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 200}</td>\n",
       "      <td>0.931667</td>\n",
       "      <td>0.943333</td>\n",
       "      <td>0.931610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946901</td>\n",
       "      <td>0.939638</td>\n",
       "      <td>0.941295</td>\n",
       "      <td>0.935700</td>\n",
       "      <td>0.945486</td>\n",
       "      <td>0.935200</td>\n",
       "      <td>0.936567</td>\n",
       "      <td>0.938604</td>\n",
       "      <td>0.005210</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.717268</td>\n",
       "      <td>2.416299</td>\n",
       "      <td>0.013510</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.1</td>\n",
       "      <td>300</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 300}</td>\n",
       "      <td>0.934167</td>\n",
       "      <td>0.945833</td>\n",
       "      <td>0.933278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.951353</td>\n",
       "      <td>0.943032</td>\n",
       "      <td>0.944592</td>\n",
       "      <td>0.937368</td>\n",
       "      <td>0.942877</td>\n",
       "      <td>0.935102</td>\n",
       "      <td>0.940039</td>\n",
       "      <td>0.940637</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.164219</td>\n",
       "      <td>0.556875</td>\n",
       "      <td>0.008406</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>{'learning_rate': 0.01, 'n_estimators': 100}</td>\n",
       "      <td>0.872500</td>\n",
       "      <td>0.878333</td>\n",
       "      <td>0.869058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889485</td>\n",
       "      <td>0.889441</td>\n",
       "      <td>0.887566</td>\n",
       "      <td>0.883767</td>\n",
       "      <td>0.884376</td>\n",
       "      <td>0.871919</td>\n",
       "      <td>0.876715</td>\n",
       "      <td>0.879477</td>\n",
       "      <td>0.008081</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.488621</td>\n",
       "      <td>0.550093</td>\n",
       "      <td>0.010881</td>\n",
       "      <td>0.002230</td>\n",
       "      <td>0.01</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.01, 'n_estimators': 200}</td>\n",
       "      <td>0.891667</td>\n",
       "      <td>0.901667</td>\n",
       "      <td>0.881568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898609</td>\n",
       "      <td>0.899444</td>\n",
       "      <td>0.910552</td>\n",
       "      <td>0.897367</td>\n",
       "      <td>0.896992</td>\n",
       "      <td>0.885774</td>\n",
       "      <td>0.888157</td>\n",
       "      <td>0.894616</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.430641</td>\n",
       "      <td>2.490333</td>\n",
       "      <td>0.013134</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.01</td>\n",
       "      <td>300</td>\n",
       "      <td>{'learning_rate': 0.01, 'n_estimators': 300}</td>\n",
       "      <td>0.903333</td>\n",
       "      <td>0.908333</td>\n",
       "      <td>0.884904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912566</td>\n",
       "      <td>0.914786</td>\n",
       "      <td>0.913461</td>\n",
       "      <td>0.912327</td>\n",
       "      <td>0.906495</td>\n",
       "      <td>0.892967</td>\n",
       "      <td>0.902510</td>\n",
       "      <td>0.904599</td>\n",
       "      <td>0.009635</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.998370</td>\n",
       "      <td>1.051811</td>\n",
       "      <td>0.007056</td>\n",
       "      <td>0.001377</td>\n",
       "      <td>0.05</td>\n",
       "      <td>100</td>\n",
       "      <td>{'learning_rate': 0.05, 'n_estimators': 100}</td>\n",
       "      <td>0.915000</td>\n",
       "      <td>0.919167</td>\n",
       "      <td>0.901585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.926229</td>\n",
       "      <td>0.925890</td>\n",
       "      <td>0.922470</td>\n",
       "      <td>0.921687</td>\n",
       "      <td>0.919162</td>\n",
       "      <td>0.902062</td>\n",
       "      <td>0.918139</td>\n",
       "      <td>0.916747</td>\n",
       "      <td>0.008526</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21.184623</td>\n",
       "      <td>5.477631</td>\n",
       "      <td>0.012676</td>\n",
       "      <td>0.003085</td>\n",
       "      <td>0.05</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 0.05, 'n_estimators': 200}</td>\n",
       "      <td>0.929167</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.930776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941251</td>\n",
       "      <td>0.934507</td>\n",
       "      <td>0.933637</td>\n",
       "      <td>0.929787</td>\n",
       "      <td>0.935202</td>\n",
       "      <td>0.918816</td>\n",
       "      <td>0.933399</td>\n",
       "      <td>0.931754</td>\n",
       "      <td>0.005458</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.782510</td>\n",
       "      <td>7.922213</td>\n",
       "      <td>0.015401</td>\n",
       "      <td>0.004942</td>\n",
       "      <td>0.05</td>\n",
       "      <td>300</td>\n",
       "      <td>{'learning_rate': 0.05, 'n_estimators': 300}</td>\n",
       "      <td>0.928333</td>\n",
       "      <td>0.939167</td>\n",
       "      <td>0.935780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.945485</td>\n",
       "      <td>0.935531</td>\n",
       "      <td>0.936244</td>\n",
       "      <td>0.931417</td>\n",
       "      <td>0.934485</td>\n",
       "      <td>0.926584</td>\n",
       "      <td>0.937824</td>\n",
       "      <td>0.934932</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.689226</td>\n",
       "      <td>1.202018</td>\n",
       "      <td>0.007109</td>\n",
       "      <td>0.002164</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'learning_rate': 1, 'n_estimators': 100}</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.920833</td>\n",
       "      <td>0.919933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.938841</td>\n",
       "      <td>0.930799</td>\n",
       "      <td>0.938875</td>\n",
       "      <td>0.926281</td>\n",
       "      <td>0.929911</td>\n",
       "      <td>0.921976</td>\n",
       "      <td>0.927887</td>\n",
       "      <td>0.927926</td>\n",
       "      <td>0.006510</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.123433</td>\n",
       "      <td>1.516539</td>\n",
       "      <td>0.006556</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>{'learning_rate': 1, 'n_estimators': 200}</td>\n",
       "      <td>0.927500</td>\n",
       "      <td>0.922500</td>\n",
       "      <td>0.923269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941275</td>\n",
       "      <td>0.931647</td>\n",
       "      <td>0.939010</td>\n",
       "      <td>0.928015</td>\n",
       "      <td>0.932405</td>\n",
       "      <td>0.923670</td>\n",
       "      <td>0.925876</td>\n",
       "      <td>0.929439</td>\n",
       "      <td>0.006246</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.094863</td>\n",
       "      <td>1.732112</td>\n",
       "      <td>0.008459</td>\n",
       "      <td>0.002595</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>{'learning_rate': 1, 'n_estimators': 300}</td>\n",
       "      <td>0.927500</td>\n",
       "      <td>0.923333</td>\n",
       "      <td>0.923269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.940486</td>\n",
       "      <td>0.932531</td>\n",
       "      <td>0.938867</td>\n",
       "      <td>0.927143</td>\n",
       "      <td>0.930924</td>\n",
       "      <td>0.924740</td>\n",
       "      <td>0.929237</td>\n",
       "      <td>0.929744</td>\n",
       "      <td>0.005762</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        9.749584      1.369605         0.008659        0.002881   \n",
       "1       15.693647      2.052870         0.011193        0.002080   \n",
       "2       20.717268      2.416299         0.013510        0.001459   \n",
       "3       10.164219      0.556875         0.008406        0.001552   \n",
       "4       20.488621      0.550093         0.010881        0.002230   \n",
       "5       31.430641      2.490333         0.013134        0.003202   \n",
       "6        9.998370      1.051811         0.007056        0.001377   \n",
       "7       21.184623      5.477631         0.012676        0.003085   \n",
       "8       32.782510      7.922213         0.015401        0.004942   \n",
       "9        4.689226      1.202018         0.007109        0.002164   \n",
       "10       6.123433      1.516539         0.006556        0.001171   \n",
       "11       8.094863      1.732112         0.008459        0.002595   \n",
       "\n",
       "   param_learning_rate param_n_estimators  \\\n",
       "0                  0.1                100   \n",
       "1                  0.1                200   \n",
       "2                  0.1                300   \n",
       "3                 0.01                100   \n",
       "4                 0.01                200   \n",
       "5                 0.01                300   \n",
       "6                 0.05                100   \n",
       "7                 0.05                200   \n",
       "8                 0.05                300   \n",
       "9                    1                100   \n",
       "10                   1                200   \n",
       "11                   1                300   \n",
       "\n",
       "                                          params  split0_test_Accuracy  \\\n",
       "0    {'learning_rate': 0.1, 'n_estimators': 100}              0.926667   \n",
       "1    {'learning_rate': 0.1, 'n_estimators': 200}              0.931667   \n",
       "2    {'learning_rate': 0.1, 'n_estimators': 300}              0.934167   \n",
       "3   {'learning_rate': 0.01, 'n_estimators': 100}              0.872500   \n",
       "4   {'learning_rate': 0.01, 'n_estimators': 200}              0.891667   \n",
       "5   {'learning_rate': 0.01, 'n_estimators': 300}              0.903333   \n",
       "6   {'learning_rate': 0.05, 'n_estimators': 100}              0.915000   \n",
       "7   {'learning_rate': 0.05, 'n_estimators': 200}              0.929167   \n",
       "8   {'learning_rate': 0.05, 'n_estimators': 300}              0.928333   \n",
       "9      {'learning_rate': 1, 'n_estimators': 100}              0.925000   \n",
       "10     {'learning_rate': 1, 'n_estimators': 200}              0.927500   \n",
       "11     {'learning_rate': 1, 'n_estimators': 300}              0.927500   \n",
       "\n",
       "    split1_test_Accuracy  split2_test_Accuracy  ...  split3_test_F1-score  \\\n",
       "0               0.934167              0.925771  ...              0.940471   \n",
       "1               0.943333              0.931610  ...              0.946901   \n",
       "2               0.945833              0.933278  ...              0.951353   \n",
       "3               0.878333              0.869058  ...              0.889485   \n",
       "4               0.901667              0.881568  ...              0.898609   \n",
       "5               0.908333              0.884904  ...              0.912566   \n",
       "6               0.919167              0.901585  ...              0.926229   \n",
       "7               0.932500              0.930776  ...              0.941251   \n",
       "8               0.939167              0.935780  ...              0.945485   \n",
       "9               0.920833              0.919933  ...              0.938841   \n",
       "10              0.922500              0.923269  ...              0.941275   \n",
       "11              0.923333              0.923269  ...              0.940486   \n",
       "\n",
       "    split4_test_F1-score  split5_test_F1-score  split6_test_F1-score  \\\n",
       "0               0.934489              0.937850              0.929827   \n",
       "1               0.939638              0.941295              0.935700   \n",
       "2               0.943032              0.944592              0.937368   \n",
       "3               0.889441              0.887566              0.883767   \n",
       "4               0.899444              0.910552              0.897367   \n",
       "5               0.914786              0.913461              0.912327   \n",
       "6               0.925890              0.922470              0.921687   \n",
       "7               0.934507              0.933637              0.929787   \n",
       "8               0.935531              0.936244              0.931417   \n",
       "9               0.930799              0.938875              0.926281   \n",
       "10              0.931647              0.939010              0.928015   \n",
       "11              0.932531              0.938867              0.927143   \n",
       "\n",
       "    split7_test_F1-score  split8_test_F1-score  split9_test_F1-score  \\\n",
       "0               0.933560              0.920579              0.932533   \n",
       "1               0.945486              0.935200              0.936567   \n",
       "2               0.942877              0.935102              0.940039   \n",
       "3               0.884376              0.871919              0.876715   \n",
       "4               0.896992              0.885774              0.888157   \n",
       "5               0.906495              0.892967              0.902510   \n",
       "6               0.919162              0.902062              0.918139   \n",
       "7               0.935202              0.918816              0.933399   \n",
       "8               0.934485              0.926584              0.937824   \n",
       "9               0.929911              0.921976              0.927887   \n",
       "10              0.932405              0.923670              0.925876   \n",
       "11              0.930924              0.924740              0.929237   \n",
       "\n",
       "    mean_test_F1-score  std_test_F1-score  rank_test_F1-score  \n",
       "0             0.931422           0.005699                   5  \n",
       "1             0.938604           0.005210                   2  \n",
       "2             0.940637           0.005560                   1  \n",
       "3             0.879477           0.008081                  12  \n",
       "4             0.894616           0.008323                  11  \n",
       "5             0.904599           0.009635                  10  \n",
       "6             0.916747           0.008526                   9  \n",
       "7             0.931754           0.005458                   4  \n",
       "8             0.934932           0.005164                   3  \n",
       "9             0.927926           0.006510                   8  \n",
       "10            0.929439           0.006246                   7  \n",
       "11            0.929744           0.005762                   6  \n",
       "\n",
       "[12 rows x 59 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_xgb = XGBClassifier()\n",
    "grid_xgb = GridSearchCV(\n",
    "    estimator = mod_xgb,\n",
    "    param_grid={'n_estimators':[100,200,300], 'learning_rate':[0.1,0.01,0.05,1] },\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_xgb.fit(X_train, y_train)\n",
    "mod_xgb_scores = pd.DataFrame(grid_xgb.cv_results_)\n",
    "print(grid_xgb.best_params_)\n",
    "print(grid_xgb.best_score_)\n",
    "mod_xgb_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42c16ff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'n_estimators': 700}\n",
      "0.9442128162357519\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.8552</td>\n",
       "      <td>6.589666</td>\n",
       "      <td>0.015493</td>\n",
       "      <td>0.005534</td>\n",
       "      <td>0.1</td>\n",
       "      <td>700</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 700}</td>\n",
       "      <td>0.940833</td>\n",
       "      <td>0.9475</td>\n",
       "      <td>0.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95395</td>\n",
       "      <td>0.944621</td>\n",
       "      <td>0.947276</td>\n",
       "      <td>0.942318</td>\n",
       "      <td>0.940588</td>\n",
       "      <td>0.936745</td>\n",
       "      <td>0.947329</td>\n",
       "      <td>0.943765</td>\n",
       "      <td>0.005043</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        22.8552      6.589666         0.015493        0.005534   \n",
       "\n",
       "  param_learning_rate param_n_estimators  \\\n",
       "0                 0.1                700   \n",
       "\n",
       "                                        params  split0_test_Accuracy  \\\n",
       "0  {'learning_rate': 0.1, 'n_estimators': 700}              0.940833   \n",
       "\n",
       "   split1_test_Accuracy  split2_test_Accuracy  ...  split3_test_F1-score  \\\n",
       "0                0.9475              0.937448  ...               0.95395   \n",
       "\n",
       "   split4_test_F1-score  split5_test_F1-score  split6_test_F1-score  \\\n",
       "0              0.944621              0.947276              0.942318   \n",
       "\n",
       "   split7_test_F1-score  split8_test_F1-score  split9_test_F1-score  \\\n",
       "0              0.940588              0.936745              0.947329   \n",
       "\n",
       "   mean_test_F1-score  std_test_F1-score  rank_test_F1-score  \n",
       "0            0.943765           0.005043                   1  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_xgb = XGBClassifier()\n",
    "grid_xgb = GridSearchCV(\n",
    "    estimator = mod_xgb,\n",
    "    param_grid={'n_estimators':[700], 'learning_rate':[0.1] },\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_xgb.fit(X_train, y_train)\n",
    "mod_xgb_scores = pd.DataFrame(grid_xgb.cv_results_)\n",
    "print(grid_xgb.best_params_)\n",
    "print(grid_xgb.best_score_)\n",
    "mod_xgb_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e0c283cd",
   "metadata": {},
   "source": [
    "#### Multi-Layer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6be78276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.01, 'hidden_layer_sizes': (100, 50), 'learning_rate': 'constant'}\n",
      "0.9576379621907144\n"
     ]
    },
    {
     "data": {
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       "    }\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.489487</td>\n",
       "      <td>0.507677</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>(100, 50)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'alpha': 0.0001, 'hidden_layer_sizes': (100, ...</td>\n",
       "      <td>0.949167</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.963103</td>\n",
       "      <td>0.953158</td>\n",
       "      <td>0.955639</td>\n",
       "      <td>0.949472</td>\n",
       "      <td>0.962056</td>\n",
       "      <td>0.955211</td>\n",
       "      <td>0.963755</td>\n",
       "      <td>0.956529</td>\n",
       "      <td>0.005402</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.459113</td>\n",
       "      <td>0.614640</td>\n",
       "      <td>0.002838</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>(100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.0001, 'hidden_layer_sizes': (100, ...</td>\n",
       "      <td>0.949167</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.963103</td>\n",
       "      <td>0.953158</td>\n",
       "      <td>0.955639</td>\n",
       "      <td>0.949472</td>\n",
       "      <td>0.962056</td>\n",
       "      <td>0.955211</td>\n",
       "      <td>0.963755</td>\n",
       "      <td>0.956529</td>\n",
       "      <td>0.005402</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.981420</td>\n",
       "      <td>0.985941</td>\n",
       "      <td>0.002580</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>(50, 100, 50)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'alpha': 0.0001, 'hidden_layer_sizes': (50, 1...</td>\n",
       "      <td>0.949167</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960979</td>\n",
       "      <td>0.956025</td>\n",
       "      <td>0.961915</td>\n",
       "      <td>0.943577</td>\n",
       "      <td>0.956102</td>\n",
       "      <td>0.953836</td>\n",
       "      <td>0.959755</td>\n",
       "      <td>0.955562</td>\n",
       "      <td>0.005715</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.983308</td>\n",
       "      <td>0.986798</td>\n",
       "      <td>0.002599</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>(50, 100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.0001, 'hidden_layer_sizes': (50, 1...</td>\n",
       "      <td>0.949167</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960979</td>\n",
       "      <td>0.956025</td>\n",
       "      <td>0.961915</td>\n",
       "      <td>0.943577</td>\n",
       "      <td>0.956102</td>\n",
       "      <td>0.953836</td>\n",
       "      <td>0.959755</td>\n",
       "      <td>0.955562</td>\n",
       "      <td>0.005715</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.335895</td>\n",
       "      <td>0.657416</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(100, 50)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (100, 5...</td>\n",
       "      <td>0.951667</td>\n",
       "      <td>0.968333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962469</td>\n",
       "      <td>0.951634</td>\n",
       "      <td>0.958251</td>\n",
       "      <td>0.947656</td>\n",
       "      <td>0.964436</td>\n",
       "      <td>0.955994</td>\n",
       "      <td>0.961632</td>\n",
       "      <td>0.957459</td>\n",
       "      <td>0.006360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.375929</td>\n",
       "      <td>0.641758</td>\n",
       "      <td>0.002304</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (100, 5...</td>\n",
       "      <td>0.951667</td>\n",
       "      <td>0.968333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962469</td>\n",
       "      <td>0.951634</td>\n",
       "      <td>0.958251</td>\n",
       "      <td>0.947656</td>\n",
       "      <td>0.964436</td>\n",
       "      <td>0.955994</td>\n",
       "      <td>0.961632</td>\n",
       "      <td>0.957459</td>\n",
       "      <td>0.006360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.967783</td>\n",
       "      <td>1.133061</td>\n",
       "      <td>0.002937</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(50, 100, 50)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (50, 10...</td>\n",
       "      <td>0.940833</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960083</td>\n",
       "      <td>0.960597</td>\n",
       "      <td>0.958631</td>\n",
       "      <td>0.959973</td>\n",
       "      <td>0.955788</td>\n",
       "      <td>0.953860</td>\n",
       "      <td>0.964336</td>\n",
       "      <td>0.955979</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.997540</td>\n",
       "      <td>1.185212</td>\n",
       "      <td>0.002963</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(50, 100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (50, 10...</td>\n",
       "      <td>0.940833</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960083</td>\n",
       "      <td>0.960597</td>\n",
       "      <td>0.958631</td>\n",
       "      <td>0.959973</td>\n",
       "      <td>0.955788</td>\n",
       "      <td>0.953860</td>\n",
       "      <td>0.964336</td>\n",
       "      <td>0.955979</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.628980</td>\n",
       "      <td>1.143805</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>0.01</td>\n",
       "      <td>(100, 50)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'alpha': 0.01, 'hidden_layer_sizes': (100, 50...</td>\n",
       "      <td>0.955833</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.950602</td>\n",
       "      <td>0.958201</td>\n",
       "      <td>0.954872</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>0.956197</td>\n",
       "      <td>0.960672</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.004088</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.718727</td>\n",
       "      <td>1.129325</td>\n",
       "      <td>0.003815</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.01</td>\n",
       "      <td>(100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.01, 'hidden_layer_sizes': (100, 50...</td>\n",
       "      <td>0.955833</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.950602</td>\n",
       "      <td>0.958201</td>\n",
       "      <td>0.954872</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>0.956197</td>\n",
       "      <td>0.960672</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.004088</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.575788</td>\n",
       "      <td>2.191041</td>\n",
       "      <td>0.006078</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>0.01</td>\n",
       "      <td>(50, 100, 50)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'alpha': 0.01, 'hidden_layer_sizes': (50, 100...</td>\n",
       "      <td>0.944167</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958038</td>\n",
       "      <td>0.955613</td>\n",
       "      <td>0.955460</td>\n",
       "      <td>0.949138</td>\n",
       "      <td>0.955841</td>\n",
       "      <td>0.953852</td>\n",
       "      <td>0.958197</td>\n",
       "      <td>0.955037</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.492851</td>\n",
       "      <td>2.257792</td>\n",
       "      <td>0.005535</td>\n",
       "      <td>0.001612</td>\n",
       "      <td>0.01</td>\n",
       "      <td>(50, 100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.01, 'hidden_layer_sizes': (50, 100...</td>\n",
       "      <td>0.944167</td>\n",
       "      <td>0.965833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958038</td>\n",
       "      <td>0.955613</td>\n",
       "      <td>0.955460</td>\n",
       "      <td>0.949138</td>\n",
       "      <td>0.955841</td>\n",
       "      <td>0.953852</td>\n",
       "      <td>0.958197</td>\n",
       "      <td>0.955037</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0        7.489487      0.507677         0.002927        0.000832      0.0001   \n",
       "1        7.459113      0.614640         0.002838        0.000840      0.0001   \n",
       "2        5.981420      0.985941         0.002580        0.000321      0.0001   \n",
       "3        5.983308      0.986798         0.002599        0.000249      0.0001   \n",
       "4        7.335895      0.657416         0.002534        0.000204       0.001   \n",
       "5        7.375929      0.641758         0.002304        0.000060       0.001   \n",
       "6        6.967783      1.133061         0.002937        0.000497       0.001   \n",
       "7        6.997540      1.185212         0.002963        0.000544       0.001   \n",
       "8        7.628980      1.143805         0.003748        0.000753        0.01   \n",
       "9        7.718727      1.129325         0.003815        0.000650        0.01   \n",
       "10       9.575788      2.191041         0.006078        0.001143        0.01   \n",
       "11       9.492851      2.257792         0.005535        0.001612        0.01   \n",
       "\n",
       "   param_hidden_layer_sizes param_learning_rate  \\\n",
       "0                 (100, 50)            constant   \n",
       "1                 (100, 50)            adaptive   \n",
       "2             (50, 100, 50)            constant   \n",
       "3             (50, 100, 50)            adaptive   \n",
       "4                 (100, 50)            constant   \n",
       "5                 (100, 50)            adaptive   \n",
       "6             (50, 100, 50)            constant   \n",
       "7             (50, 100, 50)            adaptive   \n",
       "8                 (100, 50)            constant   \n",
       "9                 (100, 50)            adaptive   \n",
       "10            (50, 100, 50)            constant   \n",
       "11            (50, 100, 50)            adaptive   \n",
       "\n",
       "                                               params  split0_test_Accuracy  \\\n",
       "0   {'alpha': 0.0001, 'hidden_layer_sizes': (100, ...              0.949167   \n",
       "1   {'alpha': 0.0001, 'hidden_layer_sizes': (100, ...              0.949167   \n",
       "2   {'alpha': 0.0001, 'hidden_layer_sizes': (50, 1...              0.949167   \n",
       "3   {'alpha': 0.0001, 'hidden_layer_sizes': (50, 1...              0.949167   \n",
       "4   {'alpha': 0.001, 'hidden_layer_sizes': (100, 5...              0.951667   \n",
       "5   {'alpha': 0.001, 'hidden_layer_sizes': (100, 5...              0.951667   \n",
       "6   {'alpha': 0.001, 'hidden_layer_sizes': (50, 10...              0.940833   \n",
       "7   {'alpha': 0.001, 'hidden_layer_sizes': (50, 10...              0.940833   \n",
       "8   {'alpha': 0.01, 'hidden_layer_sizes': (100, 50...              0.955833   \n",
       "9   {'alpha': 0.01, 'hidden_layer_sizes': (100, 50...              0.955833   \n",
       "10  {'alpha': 0.01, 'hidden_layer_sizes': (50, 100...              0.944167   \n",
       "11  {'alpha': 0.01, 'hidden_layer_sizes': (50, 100...              0.944167   \n",
       "\n",
       "    split1_test_Accuracy  ...  split3_test_F1-score  split4_test_F1-score  \\\n",
       "0               0.961667  ...              0.963103              0.953158   \n",
       "1               0.961667  ...              0.963103              0.953158   \n",
       "2               0.961667  ...              0.960979              0.956025   \n",
       "3               0.961667  ...              0.960979              0.956025   \n",
       "4               0.968333  ...              0.962469              0.951634   \n",
       "5               0.968333  ...              0.962469              0.951634   \n",
       "6               0.958333  ...              0.960083              0.960597   \n",
       "7               0.958333  ...              0.960083              0.960597   \n",
       "8               0.965000  ...              0.962500              0.950602   \n",
       "9               0.965000  ...              0.962500              0.950602   \n",
       "10              0.965833  ...              0.958038              0.955613   \n",
       "11              0.965833  ...              0.958038              0.955613   \n",
       "\n",
       "    split5_test_F1-score  split6_test_F1-score  split7_test_F1-score  \\\n",
       "0               0.955639              0.949472              0.962056   \n",
       "1               0.955639              0.949472              0.962056   \n",
       "2               0.961915              0.943577              0.956102   \n",
       "3               0.961915              0.943577              0.956102   \n",
       "4               0.958251              0.947656              0.964436   \n",
       "5               0.958251              0.947656              0.964436   \n",
       "6               0.958631              0.959973              0.955788   \n",
       "7               0.958631              0.959973              0.955788   \n",
       "8               0.958201              0.954872              0.957449   \n",
       "9               0.958201              0.954872              0.957449   \n",
       "10              0.955460              0.949138              0.955841   \n",
       "11              0.955460              0.949138              0.955841   \n",
       "\n",
       "    split8_test_F1-score  split9_test_F1-score  mean_test_F1-score  \\\n",
       "0               0.955211              0.963755            0.956529   \n",
       "1               0.955211              0.963755            0.956529   \n",
       "2               0.953836              0.959755            0.955562   \n",
       "3               0.953836              0.959755            0.955562   \n",
       "4               0.955994              0.961632            0.957459   \n",
       "5               0.955994              0.961632            0.957459   \n",
       "6               0.953860              0.964336            0.955979   \n",
       "7               0.953860              0.964336            0.955979   \n",
       "8               0.956197              0.960672            0.957447   \n",
       "9               0.956197              0.960672            0.957447   \n",
       "10              0.953852              0.958197            0.955037   \n",
       "11              0.953852              0.958197            0.955037   \n",
       "\n",
       "    std_test_F1-score  rank_test_F1-score  \n",
       "0            0.005402                   5  \n",
       "1            0.005402                   5  \n",
       "2            0.005715                   9  \n",
       "3            0.005715                   9  \n",
       "4            0.006360                   1  \n",
       "5            0.006360                   1  \n",
       "6            0.006597                   7  \n",
       "7            0.006597                   7  \n",
       "8            0.004088                   3  \n",
       "9            0.004088                   3  \n",
       "10           0.005381                  11  \n",
       "11           0.005381                  11  \n",
       "\n",
       "[12 rows x 60 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_mlp = MLPClassifier(activation='relu', solver='adam', max_iter=500, random_state=42)\n",
    "parameters = {\n",
    "    'hidden_layer_sizes':[(100,50),(50,100,50)],\n",
    "    'alpha':[0.0001,0.001,0.01],\n",
    "    'learning_rate':['constant','adaptive']\n",
    "}\n",
    "grid_mlp = GridSearchCV(\n",
    "    estimator = mod_mlp,\n",
    "    param_grid=parameters,\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_mlp.fit(X_train, y_train)\n",
    "mod_mlp_scores = pd.DataFrame(grid_mlp.cv_results_)\n",
    "print(grid_mlp.best_params_)\n",
    "print(grid_mlp.best_score_)\n",
    "mod_mlp_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dcb905d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.001, 'hidden_layer_sizes': (300, 250, 200, 150, 100, 50), 'learning_rate': 'adaptive'}\n",
      "0.9583046983597441\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.473368</td>\n",
       "      <td>5.292844</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>0.002566</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(300, 250, 200, 150, 100, 50)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (300, 2...</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.963091</td>\n",
       "      <td>0.962327</td>\n",
       "      <td>0.953342</td>\n",
       "      <td>0.951374</td>\n",
       "      <td>0.955364</td>\n",
       "      <td>0.958443</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.957849</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0      17.473368      5.292844         0.010134        0.002566       0.001   \n",
       "\n",
       "        param_hidden_layer_sizes param_learning_rate  \\\n",
       "0  (300, 250, 200, 150, 100, 50)            adaptive   \n",
       "\n",
       "                                              params  split0_test_Accuracy  \\\n",
       "0  {'alpha': 0.001, 'hidden_layer_sizes': (300, 2...                 0.965   \n",
       "\n",
       "   split1_test_Accuracy  ...  split3_test_F1-score  split4_test_F1-score  \\\n",
       "0              0.961667  ...              0.963091              0.962327   \n",
       "\n",
       "   split5_test_F1-score  split6_test_F1-score  split7_test_F1-score  \\\n",
       "0              0.953342              0.951374              0.955364   \n",
       "\n",
       "   split8_test_F1-score  split9_test_F1-score  mean_test_F1-score  \\\n",
       "0              0.958443              0.950413            0.957849   \n",
       "\n",
       "   std_test_F1-score  rank_test_F1-score  \n",
       "0           0.004854                   1  \n",
       "\n",
       "[1 rows x 60 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_mlp = MLPClassifier(activation='relu', solver='adam', max_iter=500, random_state=42)\n",
    "parameters = {\n",
    "    'hidden_layer_sizes':[(150,100,50)],\n",
    "    'alpha':[0.001],\n",
    "    'learning_rate':['adaptive']\n",
    "}\n",
    "grid_mlp = GridSearchCV(\n",
    "    estimator = mod_mlp,\n",
    "    param_grid=parameters,\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_mlp.fit(X_train, y_train)\n",
    "mod_mlp_scores = pd.DataFrame(grid_mlp.cv_results_)\n",
    "print(grid_mlp.best_params_)\n",
    "print(grid_mlp.best_score_)\n",
    "mod_mlp_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c16dd742",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.001, 'hidden_layer_sizes': (150, 100, 50)}\n",
      "0.9561376841812621\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.030969</td>\n",
       "      <td>0.548975</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(150, 100, 50)</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (150, 1...</td>\n",
       "      <td>0.950833</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.951626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962385</td>\n",
       "      <td>0.953202</td>\n",
       "      <td>0.952812</td>\n",
       "      <td>0.956471</td>\n",
       "      <td>0.956417</td>\n",
       "      <td>0.952831</td>\n",
       "      <td>0.964781</td>\n",
       "      <td>0.955955</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.241681</td>\n",
       "      <td>0.201647</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>{'alpha': 0.001, 'hidden_layer_sizes': (100,)}</td>\n",
       "      <td>0.945000</td>\n",
       "      <td>0.944167</td>\n",
       "      <td>0.946622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.957745</td>\n",
       "      <td>0.946340</td>\n",
       "      <td>0.956588</td>\n",
       "      <td>0.940059</td>\n",
       "      <td>0.955014</td>\n",
       "      <td>0.945872</td>\n",
       "      <td>0.952123</td>\n",
       "      <td>0.948919</td>\n",
       "      <td>0.005693</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.775695</td>\n",
       "      <td>0.427714</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>(150, 100, 50)</td>\n",
       "      <td>{'alpha': 0.0001, 'hidden_layer_sizes': (150, ...</td>\n",
       "      <td>0.945000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.950792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966438</td>\n",
       "      <td>0.953070</td>\n",
       "      <td>0.945916</td>\n",
       "      <td>0.950318</td>\n",
       "      <td>0.956425</td>\n",
       "      <td>0.939975</td>\n",
       "      <td>0.962512</td>\n",
       "      <td>0.952012</td>\n",
       "      <td>0.007656</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.097260</td>\n",
       "      <td>0.175770</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>{'alpha': 0.0001, 'hidden_layer_sizes': (100,)}</td>\n",
       "      <td>0.941667</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.945788</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955289</td>\n",
       "      <td>0.935557</td>\n",
       "      <td>0.949946</td>\n",
       "      <td>0.945459</td>\n",
       "      <td>0.952526</td>\n",
       "      <td>0.943204</td>\n",
       "      <td>0.957077</td>\n",
       "      <td>0.945645</td>\n",
       "      <td>0.008162</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0       2.030969      0.548975         0.001686        0.000285       0.001   \n",
       "1       1.241681      0.201647         0.001199        0.000067       0.001   \n",
       "2       1.775695      0.427714         0.001583        0.000423      0.0001   \n",
       "3       1.097260      0.175770         0.001208        0.000117      0.0001   \n",
       "\n",
       "  param_hidden_layer_sizes                                             params  \\\n",
       "0           (150, 100, 50)  {'alpha': 0.001, 'hidden_layer_sizes': (150, 1...   \n",
       "1                   (100,)     {'alpha': 0.001, 'hidden_layer_sizes': (100,)}   \n",
       "2           (150, 100, 50)  {'alpha': 0.0001, 'hidden_layer_sizes': (150, ...   \n",
       "3                   (100,)    {'alpha': 0.0001, 'hidden_layer_sizes': (100,)}   \n",
       "\n",
       "   split0_test_Accuracy  split1_test_Accuracy  split2_test_Accuracy  ...  \\\n",
       "0              0.950833              0.958333              0.951626  ...   \n",
       "1              0.945000              0.944167              0.946622  ...   \n",
       "2              0.945000              0.950000              0.950792  ...   \n",
       "3              0.941667              0.930000              0.945788  ...   \n",
       "\n",
       "   split3_test_F1-score  split4_test_F1-score  split5_test_F1-score  \\\n",
       "0              0.962385              0.953202              0.952812   \n",
       "1              0.957745              0.946340              0.956588   \n",
       "2              0.966438              0.953070              0.945916   \n",
       "3              0.955289              0.935557              0.949946   \n",
       "\n",
       "   split6_test_F1-score  split7_test_F1-score  split8_test_F1-score  \\\n",
       "0              0.956471              0.956417              0.952831   \n",
       "1              0.940059              0.955014              0.945872   \n",
       "2              0.950318              0.956425              0.939975   \n",
       "3              0.945459              0.952526              0.943204   \n",
       "\n",
       "   split9_test_F1-score  mean_test_F1-score  std_test_F1-score  \\\n",
       "0              0.964781            0.955955           0.004453   \n",
       "1              0.952123            0.948919           0.005693   \n",
       "2              0.962512            0.952012           0.007656   \n",
       "3              0.957077            0.945645           0.008162   \n",
       "\n",
       "   rank_test_F1-score  \n",
       "0                   1  \n",
       "1                   3  \n",
       "2                   2  \n",
       "3                   4  \n",
       "\n",
       "[4 rows x 33 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_mlp = MLPClassifier(learning_rate='adaptive', activation='relu', solver='adam', max_iter=500, early_stopping=True)\n",
    "grid_mlp = GridSearchCV(\n",
    "    estimator = mod_mlp,\n",
    "    param_grid={'hidden_layer_sizes':[(150,100,50),(100,)], 'alpha':[0.001,0.0001] },\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_mlp.fit(X_train, y_train)\n",
    "mod_mlp_scores = pd.DataFrame(grid_mlp.cv_results_)\n",
    "print(grid_mlp.best_params_)\n",
    "print(grid_mlp.best_score_)\n",
    "mod_mlp_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bdcbcd92",
   "metadata": {},
   "source": [
    "## Final Model\n",
    "A voting classifier based on SVM and MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb7213c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_svm = SVC(kernel='rbf',C=35)\n",
    "final_xgb = XGBClassifier(n_estimators=1000, learning_rate=0.1)\n",
    "final_mlp = MLPClassifier(hidden_layer_sizes=(150,100,50), alpha=0.001,learning_rate='adaptive', activation='relu', solver='adam', max_iter=500, early_stopping=True)\n",
    "mod_final = VotingClassifier(estimators=[('SVC', final_svm),('MLP', final_mlp),('XGB',final_xgb)], voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d136f803",
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_Accuracy</th>\n",
       "      <th>test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.836510</td>\n",
       "      <td>0.162778</td>\n",
       "      <td>0.961667</td>\n",
       "      <td>0.961260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.258949</td>\n",
       "      <td>0.162258</td>\n",
       "      <td>0.964167</td>\n",
       "      <td>0.963678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.701907</td>\n",
       "      <td>0.159990</td>\n",
       "      <td>0.959967</td>\n",
       "      <td>0.959783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.984801</td>\n",
       "      <td>0.170234</td>\n",
       "      <td>0.964971</td>\n",
       "      <td>0.964469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.279162</td>\n",
       "      <td>0.158354</td>\n",
       "      <td>0.966639</td>\n",
       "      <td>0.966230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.727736</td>\n",
       "      <td>0.175340</td>\n",
       "      <td>0.959967</td>\n",
       "      <td>0.958900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.318046</td>\n",
       "      <td>0.172352</td>\n",
       "      <td>0.958299</td>\n",
       "      <td>0.958058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38.924497</td>\n",
       "      <td>0.164107</td>\n",
       "      <td>0.962469</td>\n",
       "      <td>0.961813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.562625</td>\n",
       "      <td>0.160791</td>\n",
       "      <td>0.957465</td>\n",
       "      <td>0.956949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38.873427</td>\n",
       "      <td>0.160687</td>\n",
       "      <td>0.965805</td>\n",
       "      <td>0.965302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fit_time  score_time  test_Accuracy  test_F1-score\n",
       "0  44.836510    0.162778       0.961667       0.961260\n",
       "1  41.258949    0.162258       0.964167       0.963678\n",
       "2  39.701907    0.159990       0.959967       0.959783\n",
       "3  40.984801    0.170234       0.964971       0.964469\n",
       "4  40.279162    0.158354       0.966639       0.966230\n",
       "5  40.727736    0.175340       0.959967       0.958900\n",
       "6  39.318046    0.172352       0.958299       0.958058\n",
       "7  38.924497    0.164107       0.962469       0.961813\n",
       "8  40.562625    0.160791       0.957465       0.956949\n",
       "9  38.873427    0.160687       0.965805       0.965302"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_final = VotingClassifier(estimators=[('SVC', final_svm),('MLP', final_mlp),('XGB',final_xgb),('KNN',final_knn)], voting='hard')\n",
    "#Incase of a tie between models, the above order is consider\n",
    "final_scores = cross_validate(\n",
    "    mod_final, X_train, y_train, cv = kfold, scoring={'Accuracy':make_scorer(accuracy_score),'F1-score':make_scorer(f1_score, average='weighted')}\n",
    ")\n",
    "mod_final_scores = pd.DataFrame(final_scores)\n",
    "mod_final_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ce4fa531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9621412983041422"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_final_scores['test_Accuracy'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b31d64ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8bda7d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9624843684868696\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Apple       0.93      0.93      0.93       351\n",
      "      Banana       0.99      1.00      1.00       125\n",
      "   Carambola       1.00      1.00      1.00        84\n",
      "       Guava       0.99      1.00      0.99       674\n",
      "        Kiwi       0.96      0.99      0.97       288\n",
      "       Mango       0.94      0.91      0.92       129\n",
      "      Orange       0.96      0.95      0.95       110\n",
      "       Peach       0.97      0.94      0.96        72\n",
      "        Pear       0.84      0.87      0.86       106\n",
      "   Persimmon       0.99      1.00      0.99        69\n",
      "      Pitaya       1.00      1.00      1.00        95\n",
      "        Plum       1.00      1.00      1.00        83\n",
      " Pomegranate       0.93      0.87      0.90        61\n",
      "    Tomatoes       1.00      0.96      0.98        78\n",
      "   Muskmelon       0.91      0.86      0.89        74\n",
      "\n",
      "    accuracy                           0.96      2399\n",
      "   macro avg       0.96      0.95      0.96      2399\n",
      "weighted avg       0.96      0.96      0.96      2399\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhonicap/miniconda3/envs/titanic/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd591b7bfd0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod_final.fit(X_train, y_train)\n",
    "final_pred = mod_final.predict(X_test)\n",
    "print(accuracy_score(y_test, final_pred))\n",
    "print(classification_report(y_test, final_pred, target_names = target_names))\n",
    "plot_confusion_matrix(mod_final, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "33d0f3a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9562317632346811\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Apple       0.91      0.93      0.92       351\n",
      "      Banana       0.99      0.99      0.99       125\n",
      "   Carambola       0.95      0.99      0.97        84\n",
      "       Guava       0.99      0.99      0.99       674\n",
      "        Kiwi       0.98      0.98      0.98       288\n",
      "       Mango       0.94      0.87      0.90       129\n",
      "      Orange       0.95      0.97      0.96       110\n",
      "       Peach       0.97      0.96      0.97        72\n",
      "        Pear       0.82      0.88      0.85       106\n",
      "   Persimmon       0.96      0.97      0.96        69\n",
      "      Pitaya       1.00      1.00      1.00        95\n",
      "        Plum       1.00      1.00      1.00        83\n",
      " Pomegranate       0.91      0.87      0.89        61\n",
      "    Tomatoes       0.97      0.95      0.96        78\n",
      "   Muskmelon       0.91      0.84      0.87        74\n",
      "\n",
      "    accuracy                           0.96      2399\n",
      "   macro avg       0.95      0.95      0.95      2399\n",
      "weighted avg       0.96      0.96      0.96      2399\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhonicap/miniconda3/envs/titanic/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd592b8b5b0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_mlp.fit(X_train, y_train)\n",
    "final_pred = final_mlp.predict(X_test)\n",
    "print(accuracy_score(y_test, final_pred))\n",
    "print(classification_report(y_test, final_pred, target_names = target_names))\n",
    "plot_confusion_matrix(final_mlp, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6b306ee5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9453939141308879\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Apple       0.90      0.91      0.91       351\n",
      "      Banana       0.98      0.97      0.98       125\n",
      "   Carambola       0.98      0.96      0.97        84\n",
      "       Guava       0.98      0.99      0.99       674\n",
      "        Kiwi       0.95      0.98      0.97       288\n",
      "       Mango       0.89      0.88      0.89       129\n",
      "      Orange       0.89      0.90      0.90       110\n",
      "       Peach       0.99      0.92      0.95        72\n",
      "        Pear       0.79      0.80      0.79       106\n",
      "   Persimmon       0.97      0.91      0.94        69\n",
      "      Pitaya       1.00      1.00      1.00        95\n",
      "        Plum       1.00      0.99      0.99        83\n",
      " Pomegranate       0.93      0.90      0.92        61\n",
      "    Tomatoes       1.00      0.94      0.97        78\n",
      "   Muskmelon       0.90      0.85      0.88        74\n",
      "\n",
      "    accuracy                           0.95      2399\n",
      "   macro avg       0.94      0.93      0.93      2399\n",
      "weighted avg       0.95      0.95      0.95      2399\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhonicap/miniconda3/envs/titanic/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd592875000>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_xgb.fit(X_train, y_train)\n",
    "final_pred = final_xgb.predict(X_test)\n",
    "print(accuracy_score(y_test, final_pred))\n",
    "print(classification_report(y_test, final_pred, target_names = target_names))\n",
    "plot_confusion_matrix(final_xgb, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0dc699a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9420591913297207\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Apple       0.90      0.90      0.90       351\n",
      "      Banana       0.98      0.99      0.99       125\n",
      "   Carambola       1.00      1.00      1.00        84\n",
      "       Guava       0.98      1.00      0.99       674\n",
      "        Kiwi       0.96      0.97      0.96       288\n",
      "       Mango       0.95      0.87      0.91       129\n",
      "      Orange       0.90      0.92      0.91       110\n",
      "       Peach       0.88      0.96      0.92        72\n",
      "        Pear       0.79      0.77      0.78       106\n",
      "   Persimmon       0.90      0.93      0.91        69\n",
      "      Pitaya       0.99      1.00      0.99        95\n",
      "        Plum       1.00      1.00      1.00        83\n",
      " Pomegranate       0.92      0.80      0.86        61\n",
      "    Tomatoes       0.97      0.96      0.97        78\n",
      "   Muskmelon       0.84      0.76      0.79        74\n",
      "\n",
      "    accuracy                           0.94      2399\n",
      "   macro avg       0.93      0.92      0.93      2399\n",
      "weighted avg       0.94      0.94      0.94      2399\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhonicap/miniconda3/envs/titanic/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd5918ce950>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "final_pred = knn.predict(X_test)\n",
    "print(accuracy_score(y_test, final_pred))\n",
    "print(classification_report(y_test, final_pred, target_names = target_names))\n",
    "plot_confusion_matrix(knn, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fecd9202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100, 'kernel': 'rbf'}\n",
      "0.9971507599013576\n"
     ]
    },
    {
     "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_Accuracy</th>\n",
       "      <th>split1_test_Accuracy</th>\n",
       "      <th>split2_test_Accuracy</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_F1-score</th>\n",
       "      <th>split4_test_F1-score</th>\n",
       "      <th>split5_test_F1-score</th>\n",
       "      <th>split6_test_F1-score</th>\n",
       "      <th>split7_test_F1-score</th>\n",
       "      <th>split8_test_F1-score</th>\n",
       "      <th>split9_test_F1-score</th>\n",
       "      <th>mean_test_F1-score</th>\n",
       "      <th>std_test_F1-score</th>\n",
       "      <th>rank_test_F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.594585</td>\n",
       "      <td>0.064863</td>\n",
       "      <td>3.289709</td>\n",
       "      <td>0.026630</td>\n",
       "      <td>0.1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.952920</td>\n",
       "      <td>0.948879</td>\n",
       "      <td>0.955749</td>\n",
       "      <td>...</td>\n",
       "      <td>0.953620</td>\n",
       "      <td>0.953018</td>\n",
       "      <td>0.952335</td>\n",
       "      <td>0.949198</td>\n",
       "      <td>0.950284</td>\n",
       "      <td>0.951434</td>\n",
       "      <td>0.954843</td>\n",
       "      <td>0.952090</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.579618</td>\n",
       "      <td>0.024678</td>\n",
       "      <td>1.689541</td>\n",
       "      <td>0.015236</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.977571</td>\n",
       "      <td>0.974944</td>\n",
       "      <td>0.979390</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982077</td>\n",
       "      <td>0.977015</td>\n",
       "      <td>0.980108</td>\n",
       "      <td>0.976669</td>\n",
       "      <td>0.979568</td>\n",
       "      <td>0.977370</td>\n",
       "      <td>0.983317</td>\n",
       "      <td>0.978754</td>\n",
       "      <td>0.002490</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.595652</td>\n",
       "      <td>0.018093</td>\n",
       "      <td>0.868947</td>\n",
       "      <td>0.010961</td>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>0.991109</td>\n",
       "      <td>0.991311</td>\n",
       "      <td>0.992120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.994097</td>\n",
       "      <td>0.992062</td>\n",
       "      <td>0.992875</td>\n",
       "      <td>0.993333</td>\n",
       "      <td>0.994319</td>\n",
       "      <td>0.994732</td>\n",
       "      <td>0.995946</td>\n",
       "      <td>0.993175</td>\n",
       "      <td>0.001518</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.500054</td>\n",
       "      <td>0.017015</td>\n",
       "      <td>0.709652</td>\n",
       "      <td>0.006671</td>\n",
       "      <td>30</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 30, 'kernel': 'rbf'}</td>\n",
       "      <td>0.994140</td>\n",
       "      <td>0.994342</td>\n",
       "      <td>0.994140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.995115</td>\n",
       "      <td>0.995526</td>\n",
       "      <td>0.995937</td>\n",
       "      <td>0.995958</td>\n",
       "      <td>0.995128</td>\n",
       "      <td>0.996962</td>\n",
       "      <td>0.998177</td>\n",
       "      <td>0.995536</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.511563</td>\n",
       "      <td>0.022620</td>\n",
       "      <td>0.689486</td>\n",
       "      <td>0.008900</td>\n",
       "      <td>50</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 50, 'kernel': 'rbf'}</td>\n",
       "      <td>0.995555</td>\n",
       "      <td>0.994342</td>\n",
       "      <td>0.994746</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996145</td>\n",
       "      <td>0.996754</td>\n",
       "      <td>0.996549</td>\n",
       "      <td>0.996971</td>\n",
       "      <td>0.996962</td>\n",
       "      <td>0.998384</td>\n",
       "      <td>0.998785</td>\n",
       "      <td>0.996513</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.544707</td>\n",
       "      <td>0.020126</td>\n",
       "      <td>0.677138</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>100</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 100, 'kernel': 'rbf'}</td>\n",
       "      <td>0.996363</td>\n",
       "      <td>0.994746</td>\n",
       "      <td>0.994948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996754</td>\n",
       "      <td>0.997567</td>\n",
       "      <td>0.997362</td>\n",
       "      <td>0.998181</td>\n",
       "      <td>0.997570</td>\n",
       "      <td>0.999192</td>\n",
       "      <td>0.998783</td>\n",
       "      <td>0.997142</td>\n",
       "      <td>0.001410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       6.594585      0.064863         3.289709        0.026630     0.1   \n",
       "1       2.579618      0.024678         1.689541        0.015236       1   \n",
       "2       1.595652      0.018093         0.868947        0.010961      10   \n",
       "3       1.500054      0.017015         0.709652        0.006671      30   \n",
       "4       1.511563      0.022620         0.689486        0.008900      50   \n",
       "5       1.544707      0.020126         0.677138        0.009452     100   \n",
       "\n",
       "  param_kernel                       params  split0_test_Accuracy  \\\n",
       "0          rbf  {'C': 0.1, 'kernel': 'rbf'}              0.952920   \n",
       "1          rbf    {'C': 1, 'kernel': 'rbf'}              0.977571   \n",
       "2          rbf   {'C': 10, 'kernel': 'rbf'}              0.991109   \n",
       "3          rbf   {'C': 30, 'kernel': 'rbf'}              0.994140   \n",
       "4          rbf   {'C': 50, 'kernel': 'rbf'}              0.995555   \n",
       "5          rbf  {'C': 100, 'kernel': 'rbf'}              0.996363   \n",
       "\n",
       "   split1_test_Accuracy  split2_test_Accuracy  ...  split3_test_F1-score  \\\n",
       "0              0.948879              0.955749  ...              0.953620   \n",
       "1              0.974944              0.979390  ...              0.982077   \n",
       "2              0.991311              0.992120  ...              0.994097   \n",
       "3              0.994342              0.994140  ...              0.995115   \n",
       "4              0.994342              0.994746  ...              0.996145   \n",
       "5              0.994746              0.994948  ...              0.996754   \n",
       "\n",
       "   split4_test_F1-score  split5_test_F1-score  split6_test_F1-score  \\\n",
       "0              0.953018              0.952335              0.949198   \n",
       "1              0.977015              0.980108              0.976669   \n",
       "2              0.992062              0.992875              0.993333   \n",
       "3              0.995526              0.995937              0.995958   \n",
       "4              0.996754              0.996549              0.996971   \n",
       "5              0.997567              0.997362              0.998181   \n",
       "\n",
       "   split7_test_F1-score  split8_test_F1-score  split9_test_F1-score  \\\n",
       "0              0.950284              0.951434              0.954843   \n",
       "1              0.979568              0.977370              0.983317   \n",
       "2              0.994319              0.994732              0.995946   \n",
       "3              0.995128              0.996962              0.998177   \n",
       "4              0.996962              0.998384              0.998785   \n",
       "5              0.997570              0.999192              0.998783   \n",
       "\n",
       "   mean_test_F1-score  std_test_F1-score  rank_test_F1-score  \n",
       "0            0.952090           0.002178                   6  \n",
       "1            0.978754           0.002490                   5  \n",
       "2            0.993175           0.001518                   4  \n",
       "3            0.995536           0.001232                   3  \n",
       "4            0.996513           0.001351                   2  \n",
       "5            0.997142           0.001410                   1  \n",
       "\n",
       "[6 rows x 59 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "mod_svm = SVC()\n",
    "grid_svm = GridSearchCV(\n",
    "    estimator = mod_svm,\n",
    "    param_grid={'kernel': ['rbf'], 'C':[0.1,1,10,30,50,100]},\n",
    "    cv = kfold,\n",
    "    scoring = {'Accuracy':make_scorer(accuracy_score),'Precision':make_scorer(precision_score, average='weighted'), 'Recall':make_scorer(recall_score, average='weighted'),'F1-score':make_scorer(f1_score, average='weighted')},\n",
    "    refit = 'Accuracy', #Inorder to rank the output\n",
    ")\n",
    "grid_svm.fit(X_train_resampled, y_train_resampled)\n",
    "mod_svm_scores = pd.DataFrame(grid_svm.cv_results_)\n",
    "print(grid_svm.best_params_)\n",
    "print(grid_svm.best_score_)\n",
    "mod_svm_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a8ce1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 9, 3, ..., 5, 8, 9])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "mod_final.fit(X_train_resampled, y_train_resampled)\n",
    "pred = mod_final.predict(X_test)\n",
    "pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1d5aeff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2116"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "da4f4377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c234b9d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'Apple', 1: 'Banana', 2: 'Carambola', 3: 'Guava', 4: 'Kiwi', 5: 'Mango', 6: 'Orange', 7: 'Peach', 8: 'Pear', 9: 'Persimmon', 10: 'Pitaya', 11: 'Plum', 12: 'Pomegranate', 13: 'Tomatoes', 14: 'Muskmelon'}\n"
     ]
    }
   ],
   "source": [
    "print(idx_to_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8a575503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot the images this way. This will be quite useful for debugging.\n",
    "x=2003\n",
    "print(pred[x])\n",
    "plt.figure()\n",
    "plt.title('Example Image')\n",
    "plt.imshow(test_dataset['images'][x])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c70d568d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Train a classifier and generate predictions on the test set and put your predictions in pred.\n",
    "# ...\n",
    "\n",
    "# Make sure that pred contains a 1-dimensional integer array of class labels for the test set and make sure that\n",
    "# every integer is in the range 0 through 14. If you have an entry in this array outside of the integer\n",
    "# range of 0 through 14, it will be assumed to be an error. Also, if your array is not the length of the test\n",
    "# set that will be assumed to be an error. Specifically, suppose n_mine is the length of the array of integers\n",
    "# you turn in. If the test set has n_test images (meaning \n",
    "# it is an n_test X m matrix), then if your array is less than n_test long (so n_mine < n_test, then any missing \n",
    "# images will be assumed to be an error, so there will be n_test - n_mine errors. If your array is longer than \n",
    "# n_test images long, so that n_mine > n_test, then we will take the final n_mine - n_test images, whatever\n",
    "# your predictions are for those images, and assume that those n_mine - n_test images are in error. Bottom line:\n",
    "# make sure your array is exactly the right length, meaning you should have n_mine == n_test. Meaning,\n",
    "# the array pred should be a one-dimensional integer array with integers in the 0-14 range\n",
    "# of length n_mine. You can make sure that pred is of integer type by doing `pred = pred.astype(np.int32)` but\n",
    "# after you do this, make sure to examine the result to ensure that they are integers in the right range.\n",
    "#\n",
    "# Note that the saved text file should have one prediction per ASCII line (this can be done by ensuring that pred \n",
    "# is of shape (n_test,) and then simply using np.savetxt).\n",
    "np.savetxt('test_predictions.txt', pred)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}