diff --git a/Human_Activity_Recognition.ipynb b/Human_Activity_Recognition.ipynb
index 607a61f..36f2c8e 100644
--- a/Human_Activity_Recognition.ipynb
+++ b/Human_Activity_Recognition.ipynb
@@ -43,7 +43,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 9,
       "metadata": {
         "id": "dYKHNjzfCQf8"
       },
@@ -57,7 +57,16 @@
         "warnings.filterwarnings(\"ignore\")\n",
         "from collections import Counter\n",
         "from sklearn.decomposition import PCA\n",
-        "from sklearn.manifold import TSNE\n"
+        "from sklearn.manifold import TSNE\n",
+        "from sklearn.model_selection import RandomizedSearchCV\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.metrics import classification_report\n",
+        "\n"
       ]
     },
     {
@@ -71,7 +80,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "iiAnW8-qGWN2",
-        "outputId": "a8ae65c5-ae16-4222-b36d-9de8b838325a"
+        "outputId": "4cc33170-c006-470c-ad96-638d1d19adb1"
       },
       "execution_count": 2,
       "outputs": [
@@ -116,9 +125,9 @@
           "height": 367
         },
         "id": "vVsNZ4xDHZS5",
-        "outputId": "16ea8e4f-eb02-4d56-e721-1affe864f9bc"
+        "outputId": "31bf6dda-dae2-4635-ebc9-19cabd777b24"
       },
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -208,7 +217,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-99042a6d-cef2-45a4-a69c-3f892655d6a5\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-6f312e8c-e98a-4601-a4a9-9ceb960ab87c\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -450,7 +459,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99042a6d-cef2-45a4-a69c-3f892655d6a5')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f312e8c-e98a-4601-a4a9-9ceb960ab87c')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -502,12 +511,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-99042a6d-cef2-45a4-a69c-3f892655d6a5 button.colab-df-convert');\n",
+              "        document.querySelector('#df-6f312e8c-e98a-4601-a4a9-9ceb960ab87c button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-99042a6d-cef2-45a4-a69c-3f892655d6a5');\n",
+              "        const element = document.querySelector('#df-6f312e8c-e98a-4601-a4a9-9ceb960ab87c');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -527,8 +536,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-f181b23e-8509-496d-898c-828944dc1261\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f181b23e-8509-496d-898c-828944dc1261')\"\n",
+              "    <div id=\"df-bf21e1ce-a8ad-4858-832a-7aff9e8e72e1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bf21e1ce-a8ad-4858-832a-7aff9e8e72e1')\"\n",
               "                title=\"Suggest charts\"\n",
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               "\n",
@@ -647,7 +656,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-f181b23e-8509-496d-898c-828944dc1261 button');\n",
+              "            document.querySelector('#df-bf21e1ce-a8ad-4858-832a-7aff9e8e72e1 button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -677,9 +686,9 @@
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         },
         "id": "N3gV5fSKH1_K",
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+        "outputId": "d5e81a7c-52c2-442a-8e52-96d4d0a2e48f"
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-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
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@@ -842,9 +851,9 @@
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         "id": "iuY0f1v1H6wD",
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+        "outputId": "228839a1-0a5e-40f7-bc95-de308698af28"
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-      "execution_count": 6,
+      "execution_count": null,
       "outputs": [
         {
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@@ -868,9 +877,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "f_guNNdbIgzE",
-        "outputId": "8d942fe3-8132-4f11-9e43-a1c4f46b044b"
+        "outputId": "92627191-5860-4b13-e9b8-db786fb222b9"
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-      "execution_count": 7,
+      "execution_count": null,
       "outputs": [
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@@ -897,9 +906,9 @@
           "height": 446
         },
         "id": "8qdi496vIxf2",
-        "outputId": "637b43b4-892c-403a-9e0e-ddc128008a55"
+        "outputId": "e0c410e3-a4fe-4f27-c127-b4e2947a4f70"
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-      "execution_count": 8,
+      "execution_count": null,
       "outputs": [
         {
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@@ -925,9 +934,9 @@
           "height": 677
         },
         "id": "ymigtCajJTOX",
-        "outputId": "4552d6bd-fe2d-4089-e5bf-3a4770b39886"
+        "outputId": "c373b258-7746-4e9f-b510-ae7b77a2069a"
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       "outputs": [
         {
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@@ -957,7 +966,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-39bec2bc-ac1a-4a48-ba2d-39d8758ae689\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-3cf8a5aa-4151-4788-9ccb-26b860e2c572\" class=\"colab-df-container\">\n",
               "    <div>\n",
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@@ -1066,7 +1075,7 @@
               "    <div class=\"colab-df-buttons\">\n",
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               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39bec2bc-ac1a-4a48-ba2d-39d8758ae689')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cf8a5aa-4151-4788-9ccb-26b860e2c572')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1118,12 +1127,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-39bec2bc-ac1a-4a48-ba2d-39d8758ae689 button.colab-df-convert');\n",
+              "        document.querySelector('#df-3cf8a5aa-4151-4788-9ccb-26b860e2c572 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-39bec2bc-ac1a-4a48-ba2d-39d8758ae689');\n",
+              "        const element = document.querySelector('#df-3cf8a5aa-4151-4788-9ccb-26b860e2c572');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1143,8 +1152,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-6d7a38fe-55ab-4f34-8442-13dcfd5d9f8a\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d7a38fe-55ab-4f34-8442-13dcfd5d9f8a')\"\n",
+              "    <div id=\"df-024cda10-8c3c-498c-ac8f-c0979a9ca676\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-024cda10-8c3c-498c-ac8f-c0979a9ca676')\"\n",
               "                title=\"Suggest charts\"\n",
               "                style=\"display:none;\">\n",
               "\n",
@@ -1263,7 +1272,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-6d7a38fe-55ab-4f34-8442-13dcfd5d9f8a button');\n",
+              "            document.querySelector('#df-024cda10-8c3c-498c-ac8f-c0979a9ca676 button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -1279,7 +1288,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 9
+          "execution_count": 4
         }
       ]
     },
@@ -1301,9 +1310,9 @@
           "height": 400
         },
         "id": "cvYCUL_VLPSf",
-        "outputId": "48571726-ee44-402a-b5f7-1f47b0e6b48e"
+        "outputId": "b6ab8b1f-6894-437b-83d9-8382373fde92"
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       "outputs": [
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@@ -1351,9 +1360,9 @@
           "height": 410
         },
         "id": "XTIm6OPnN39n",
-        "outputId": "a986e30f-788d-4305-9e87-1c47730bd9ff"
+        "outputId": "ee61bb16-1990-43d0-974c-94855f8ca3ce"
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       "outputs": [
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@@ -1384,9 +1393,9 @@
           "height": 641
         },
         "id": "IUp37UpiONND",
-        "outputId": "269de97e-bed1-495e-98c1-adbe5bc8a90f"
+        "outputId": "3cd98d91-bd3d-431a-df82-679ba01031ee"
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+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1418,9 +1427,9 @@
           "height": 722
         },
         "id": "jggDx5R_PkWi",
-        "outputId": "35ff01db-ecd5-4d77-8477-ee56d3e70929"
+        "outputId": "8271d277-399c-4c78-e1c5-0b23a3ee4d72"
       },
-      "execution_count": 13,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1452,9 +1461,9 @@
           "height": 792
         },
         "id": "_JKdunPhQIla",
-        "outputId": "8bfedb76-b8f8-47d8-b4ef-3fafac09c5ba"
+        "outputId": "3911b8ed-1771-4a9a-aa38-ae6305e5a14f"
       },
-      "execution_count": 14,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1486,7 +1495,7 @@
       "metadata": {
         "id": "b649U3yASpaq"
       },
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -1504,7 +1513,7 @@
         "id": "i0cF6wJxKDcV",
         "outputId": "b82d3309-c7b1-47e1-c83d-455d798c4259"
       },
-      "execution_count": 16,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1527,7 +1536,7 @@
       "metadata": {
         "id": "kxK0pEoDKlAH"
       },
-      "execution_count": 17,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -1545,7 +1554,7 @@
         "id": "niI3Y3Z6K8sc",
         "outputId": "e1b4ff44-8f4a-42c9-9122-d235c2055c56"
       },
-      "execution_count": 18,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1558,6 +1567,1884 @@
           "metadata": {}
         }
       ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train = train.drop(['subject', 'Activity'], axis = 1)\n",
+        "y_train = train.Activity\n",
+        "\n",
+        "X_test = test.drop(['subject', 'Activity'], axis = 1)\n",
+        "y_test = test.Activity"
+      ],
+      "metadata": {
+        "id": "wly502DPcVDS"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Training data size : ', X_train.shape)\n",
+        "print('Test data size : ', X_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wQj_TWxUdF3D",
+        "outputId": "c269f1a1-9ed3-40c1-896c-f06574622957"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training data size :  (7352, 561)\n",
+            "Test data size :  (999, 561)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###ML Model 1: Logistic Regression"
+      ],
+      "metadata": {
+        "id": "ifp3fC8wb8LL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "parameters = {'max_iter': [100, 200, 500]}\n",
+        "lr_classifier = LogisticRegression()\n",
+        "lr_classifier_rs = RandomizedSearchCV(lr_classifier, param_distributions= parameters, cv = 5, random_state=42)\n",
+        "lr_classifier_rs.fit(X_train, y_train)\n",
+        "y_pred_lr = lr_classifier_rs.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "97BsfJD7cNI1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Calculating Model Accuracy\n",
+        "lr_accuracy = accuracy_score(y_true=y_test, y_pred=y_pred_lr)\n",
+        "print(\"Accuracy using Logistic Regression : \", lr_accuracy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WfePGfvAdO1j",
+        "outputId": "f105040e-ac06-4407-82fc-7553a65e5f1d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy using Logistic Regression :  0.954954954954955\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plotting the confusion matrix\n",
+        "def plot_confusion_matrix(cm,labels):\n",
+        "    fig, ax = plt.subplots(figsize=(10,6)) # for plotting confusion matrix as image\n",
+        "    im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
+        "    ax.figure.colorbar(im, ax=ax)\n",
+        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
+        "    yticks=np.arange(cm.shape[0]),\n",
+        "    xticklabels=labels, yticklabels=labels,\n",
+        "    ylabel='True label',\n",
+        "    xlabel='Predicted label')\n",
+        "    plt.xticks(rotation = 90)\n",
+        "    thresh = cm.max() / 2.\n",
+        "    for i in range(cm.shape[0]):\n",
+        "        for j in range(cm.shape[1]):\n",
+        "            ax.text(j, i, int(cm[i, j]),ha=\"center\", va=\"center\",color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+        "    fig.tight_layout()"
+      ],
+      "metadata": {
+        "id": "lsEa7JC4dRNf"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cmt = confusion_matrix(y_test.values,y_pred_lr)\n",
+        "cmt"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tXJ4jH6LdnIz",
+        "outputId": "77cbe415-92f1-433e-d31e-cb31593ad3c8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[183,   0,   0,   0,   0,   0],\n",
+              "       [  0, 146,  21,   0,   0,   3],\n",
+              "       [  0,   8, 170,   0,   0,   0],\n",
+              "       [  0,   0,   0, 185,   0,   0],\n",
+              "       [  0,   0,   0,   0, 134,   0],\n",
+              "       [  0,   0,   0,  13,   0, 136]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_confusion_matrix(cmt, np.unique(y_pred_lr))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "HpNnTHECdpvR",
+        "outputId": "e6f3fe4b-2316-4113-90e0-da069107305b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Searching for best random search attributes\n",
+        "def get_best_randomsearch_results(model):\n",
+        "    print(\"Best estimator : \", model.best_estimator_)\n",
+        "    print(\"Best set of parameters : \", model.best_params_)\n"
+      ],
+      "metadata": {
+        "id": "rwQFI9usdx56"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "get_best_randomsearch_results(lr_classifier_rs)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s8z2abKoI_p2",
+        "outputId": "26f79ab7-fe47-440a-b2f4-f68c47cac617"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best estimator :  LogisticRegression(max_iter=500)\n",
+            "Best set of parameters :  {'max_iter': 500}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###ML Model 2: Support Vector Machine"
+      ],
+      "metadata": {
+        "id": "a1QbShGkHES1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "parameters = {\n",
+        "    'kernel': ['linear', 'rbf', 'poly', 'sigmoid'],\n",
+        "    'C': [100, 50]\n",
+        "}\n",
+        "\n",
+        "svm_rs = RandomizedSearchCV(SVC(), param_distributions=parameters, cv = 3, random_state=42)\n",
+        "svm_rs.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        },
+        "id": "_YEUoaCoHDuW",
+        "outputId": "03cdad31-dac6-4ed3-a499-6f3d43ffb280"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomizedSearchCV(cv=3, estimator=SVC(),\n",
+              "                   param_distributions={'C': [100, 50],\n",
+              "                                        'kernel': ['linear', 'rbf', 'poly',\n",
+              "                                                   'sigmoid']},\n",
+              "                   random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=SVC(),\n",
+              "                   param_distributions={&#x27;C&#x27;: [100, 50],\n",
+              "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;,\n",
+              "                                                   &#x27;sigmoid&#x27;]},\n",
+              "                   random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=3, estimator=SVC(),\n",
+              "                   param_distributions={&#x27;C&#x27;: [100, 50],\n",
+              "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;,\n",
+              "                                                   &#x27;sigmoid&#x27;]},\n",
+              "                   random_state=42)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: SVC</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=50)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=50)</pre></div> </div></div></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = svm_rs.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "6oiQEcCFHUkN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "kernel_svm_accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
+        "print(\"Accuracy using Kernel SVM : \", kernel_svm_accuracy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z_ycNXOQHW0A",
+        "outputId": "4eab5c47-608f-47f7-88af-8ccd3b16b67f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy using Kernel SVM :  0.965965965965966\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm = confusion_matrix(y_test.values,y_pred)\n",
+        "plot_confusion_matrix(cm, np.unique(y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "slqdaKiwHZL_",
+        "outputId": "1161d83d-24da-49cf-8de2-7e7f98dfd48f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "get_best_randomsearch_results(svm_rs)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GNMLcNiLH5xl",
+        "outputId": "ee22d7fb-50cf-43b4-e944-5bfa5542f468"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best estimator :  SVC(C=50)\n",
+            "Best set of parameters :  {'kernel': 'rbf', 'C': 50}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###ML Model 3: Decision Tree"
+      ],
+      "metadata": {
+        "id": "iIkHS8CuITfI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "parameters = {'max_depth': np.arange(2, 10, 2)}\n",
+        "\n",
+        "dt_classifier = DecisionTreeClassifier()\n",
+        "dt_classifier_rs = RandomizedSearchCV(dt_classifier, param_distributions = parameters, random_state = 42)\n",
+        "dt_classifier_rs.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        },
+        "id": "zJAIfp8pIRME",
+        "outputId": "5f234b79-e738-484d-fc9f-a11b5d110deb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomizedSearchCV(estimator=DecisionTreeClassifier(),\n",
+              "                   param_distributions={'max_depth': array([2, 4, 6, 8])},\n",
+              "                   random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-2 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-2 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-2 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=DecisionTreeClassifier(),\n",
+              "                   param_distributions={&#x27;max_depth&#x27;: array([2, 4, 6, 8])},\n",
+              "                   random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(estimator=DecisionTreeClassifier(),\n",
+              "                   param_distributions={&#x27;max_depth&#x27;: array([2, 4, 6, 8])},\n",
+              "                   random_state=42)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=np.int64(6))</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=np.int64(6))</pre></div> </div></div></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = dt_classifier_rs.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "gxwBRgcWIbFf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dt_accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
+        "print(\"Accuracy using Decision tree : \", dt_accuracy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "T6bTANNbIdyj",
+        "outputId": "f3c8c880-b79d-48b8-ce36-254d57aa362f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy using Decision tree :  0.8358358358358359\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm = confusion_matrix(y_test.values,y_pred)\n",
+        "plot_confusion_matrix(cm, np.unique(y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "fQacuvXwIfyB",
+        "outputId": "92e366ec-cc8f-4faa-8071-c9e1aef1044d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "get_best_randomsearch_results(dt_classifier_rs)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0PXiaB7pImW1",
+        "outputId": "3d64a863-2d62-48b0-edf9-5023fd29e689"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best estimator :  DecisionTreeClassifier(max_depth=np.int64(6))\n",
+            "Best set of parameters :  {'max_depth': np.int64(6)}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###ML Model 4: Random Forest Classifier"
+      ],
+      "metadata": {
+        "id": "Uts5oQO9utD-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "parameters = {\n",
+        "    'n_estimators':np.arange(20, 101, 10),\n",
+        "    'max_depth': np.arange(2, 17, 2)\n",
+        "}\n",
+        "rf_classifier = RandomForestClassifier()\n",
+        "rf_classifier_rs = RandomizedSearchCV(rf_classifier, param_distributions=parameters,random_state = 42)\n",
+        "rf_classifier_rs.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        },
+        "id": "S2bUGHcqu1WV",
+        "outputId": "6a2fa32d-19a2-4c18-db14-4a2e26f8e9f3"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
+              "                   param_distributions={'max_depth': array([ 2,  4,  6,  8, 10, 12, 14, 16]),\n",
+              "                                        'n_estimators': array([ 20,  30,  40,  50,  60,  70,  80,  90, 100])},\n",
+              "                   random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
+              "                   param_distributions={&#x27;max_depth&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16]),\n",
+              "                                        &#x27;n_estimators&#x27;: array([ 20,  30,  40,  50,  60,  70,  80,  90, 100])},\n",
+              "                   random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
+              "                   param_distributions={&#x27;max_depth&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16]),\n",
+              "                                        &#x27;n_estimators&#x27;: array([ 20,  30,  40,  50,  60,  70,  80,  90, 100])},\n",
+              "                   random_state=42)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=np.int64(14), n_estimators=np.int64(100))</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=np.int64(14), n_estimators=np.int64(100))</pre></div> </div></div></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "get_best_randomsearch_results(rf_classifier_rs)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z6_RFSW6vBJG",
+        "outputId": "9d304193-8986-477e-b703-4b59b6332803"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best estimator :  RandomForestClassifier(max_depth=np.int64(14), n_estimators=np.int64(100))\n",
+            "Best set of parameters :  {'n_estimators': np.int64(100), 'max_depth': np.int64(14)}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = rf_classifier_rs.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "EJmcWt7evFsh"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rf_accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
+        "print(\"Accuracy using Random forest : \", rf_accuracy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KMdG7vlavHPd",
+        "outputId": "7e2be960-4616-4586-8e1f-cea281940218"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy using Random forest :  0.9159159159159159\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cms = confusion_matrix(y_test.values,y_pred)\n",
+        "plot_confusion_matrix(cms, np.unique(y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "Z9_cLxDavJS0",
+        "outputId": "744db400-5c13-4779-df1b-b371b9f95781"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
     }
   ]
 }
\ No newline at end of file