Notebooks/10_Ensemble.ipynb

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        "### **Random Forest and Adaboost using Ensemble Learning approach**\n"
      ]
    },
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      "source": [
        "## <span style=\"color : green\"> Ensemble Learning </span>\n",
        "\n",
        "# <center> Table of Contents </center>\n",
        "\n",
        "1. Train a Random Forest model and AdaBoost model with different parameters and find the best parameters.\n",
        "1. Display the confusion matrix ( graph ) for the models\n",
        "1. Evaluate the models using accuracy score and classification report \n"
      ]
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        "## **Description**:\n",
        "Random Forest is a machine learning algorithm that is used for classification, regression, and other tasks. It works by combining multiple decision trees and creating an ensemble of trees.\n",
        "Here's a simplified explanation of how the algorithm works:\n",
        "-\tRandomly select a subset of the data (sampling with replacement)\n",
        "-\tBuild a decision tree based on the selected subset of the data\n",
        "-\tRepeat the process (step 1 and 2) to create multiple decision trees\n",
        "-\tCombine the predictions of all the decision trees to create a final prediction\n",
        "\n",
        "\n"
      ]
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    {
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      "execution_count": null,
      "metadata": {
        "id": "EiraYpi_BXdo"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import  confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmxytRkNC8Lo"
      },
      "source": [
        "### **Random Forest Classifier**"
      ]
    },
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              "age               0\n",
              "fnlwgt            0\n",
              "education_num     0\n",
              "capital_gain      0\n",
              "capital_loss      0\n",
              "hours_per_week    0\n",
              "income_level      0\n",
              "dtype: int64"
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          "execution_count": 2,
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      "source": [
        "#1. Read the dataset and do necessary preprocessing[data imputation in null values, use encoding techniques to convert categorical to numerical]\n",
        "df=pd.read_csv(\"income.csv\")\n",
        "df.isnull().sum()"
      ]
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      "source": [
        "#2. Choose independent variable (X) and dependent variable (Y) from given dataset\n",
        "x=df.iloc[:,:-1]\n",
        "y=df.income_level\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=3)"
      ]
    },
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      "source": [
        "3. Create different model of Random Forest using different parameters[Minimum of 4]\n",
        "4. Find the best parameters for the model and calculate the Ypredict value\n",
        "5. Display the confusion table and calculate the accuracy, precision and recall, f-score etc.\n",
        "for the different parameters of Random Forest"
      ]
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        "model_1 = RandomForestClassifier(criterion='gini',max_features='sqrt')\n",
        "model_1.fit(x_train,y_train)\n",
        "y1_predicted = model_1.predict(x_test)\n",
        "model_1.score(x_test,y_test)"
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              "     TP   FN    FP    TN  Accuracy  Precision    Recall  F1_Score\n",
              "0  6612  788  1151  1218  0.801515   0.851733  0.893514  0.514141"
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        "cm_1 = confusion_matrix(y_test,y1_predicted)\n",
        "acc_1=(cm_1[0][0]+cm_1[1][1])/np.sum(cm_1)\n",
        "pre_1=cm_1[0][0]/(cm_1[0][0]+cm_1[1][0])\n",
        "rec_1=cm_1[0][0]/(cm_1[0][0]+cm_1[0][1])\n",
        "f1_sc_1=cm_1[1][1]/(cm_1[1][1]+cm_1[1][0])\n",
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        "model_2 = RandomForestClassifier(criterion='gini',max_features='log2')\n",
        "model_2.fit(x_train,y_train)\n",
        "y2_predicted = model_2.predict(x_test)\n",
        "model_2.score(x_test,y_test)"
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              "     TP   FN    FP    TN  Accuracy  Precision    Recall  F1_Score\n",
              "0  6603  797  1145  1224  0.801208    0.85222  0.892297  0.516674"
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        "cm_2 = confusion_matrix(y_test,y2_predicted)\n",
        "acc_2=(cm_2[0][0]+cm_2[1][1])/np.sum(cm_2)\n",
        "pre_2=cm_2[0][0]/(cm_2[0][0]+cm_2[1][0])\n",
        "rec_2=cm_2[0][0]/(cm_2[0][0]+cm_2[0][1])\n",
        "f1_sc_2=cm_2[1][1]/(cm_2[1][1]+cm_2[1][0])\n",
        "met_2=pd.DataFrame([[cm_2[0][0],cm_2[0][1],cm_2[1][0],cm_2[1][1],acc_2,pre_2,rec_2,f1_sc_2]],columns=['TP','FN','FP','TN','Accuracy','Precision','Recall','F1_Score'])\n",
        "met_2"
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        "model_3 = RandomForestClassifier(criterion='entropy',max_features='sqrt')\n",
        "model_3.fit(x_train,y_train)\n",
        "y3_predicted = model_3.predict(x_test)\n",
        "model_3.score(x_test,y_test)"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP</th>\n",
              "      <th>FN</th>\n",
              "      <th>FP</th>\n",
              "      <th>TN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6609</td>\n",
              "      <td>791</td>\n",
              "      <td>1141</td>\n",
              "      <td>1228</td>\n",
              "      <td>0.802232</td>\n",
              "      <td>0.852774</td>\n",
              "      <td>0.893108</td>\n",
              "      <td>0.518362</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-1c0f688d-2dc2-4318-b4aa-1e527663290f 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-1c0f688d-2dc2-4318-b4aa-1e527663290f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     TP   FN    FP    TN  Accuracy  Precision    Recall  F1_Score\n",
              "0  6609  791  1141  1228  0.802232   0.852774  0.893108  0.518362"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm_3 = confusion_matrix(y_test,y3_predicted)\n",
        "acc_3=(cm_3[0][0]+cm_3[1][1])/np.sum(cm_3)\n",
        "pre_3=cm_3[0][0]/(cm_3[0][0]+cm_3[1][0])\n",
        "rec_3=cm_3[0][0]/(cm_3[0][0]+cm_3[0][1])\n",
        "f1_sc_3=cm_3[1][1]/(cm_3[1][1]+cm_3[1][0])\n",
        "met_3=pd.DataFrame([[cm_3[0][0],cm_3[0][1],cm_3[1][0],cm_3[1][1],acc_3,pre_3,rec_3,f1_sc_3]],columns=['TP','FN','FP','TN','Accuracy','Precision','Recall','F1_Score'])\n",
        "met_3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVwD79ZIGC62",
        "outputId": "49ec04c0-ffce-4003-ccde-a67549bc9a85"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8158460436073293"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_4 = RandomForestClassifier(criterion='log_loss',max_features=None)\n",
        "model_4.fit(x_train,y_train)\n",
        "y4_predicted = model_4.predict(x_test)\n",
        "model_4.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "fV2g7fu_Oeg4",
        "outputId": "0361abf6-6ab5-40ee-af30-47d7a7756fba"
      },
      "outputs": [
        {
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              "      <th>TP</th>\n",
              "      <th>FN</th>\n",
              "      <th>FP</th>\n",
              "      <th>TN</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "     TP   FN    FP    TN  Accuracy  Precision    Recall  F1_Score\n",
              "0  6757  643  1156  1213  0.815846   0.853911  0.913108   0.51203"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm_4 = confusion_matrix(y_test,y4_predicted)\n",
        "acc_4=(cm_4[0][0]+cm_4[1][1])/np.sum(cm_4)\n",
        "pre_4=cm_4[0][0]/(cm_4[0][0]+cm_4[1][0])\n",
        "rec_4=cm_4[0][0]/(cm_4[0][0]+cm_4[0][1])\n",
        "f1_sc_4=cm_4[1][1]/(cm_4[1][1]+cm_4[1][0])\n",
        "met_4=pd.DataFrame([[cm_4[0][0],cm_4[0][1],cm_4[1][0],cm_4[1][1],acc_4,pre_4,rec_4,f1_sc_4]],columns=['TP','FN','FP','TN','Accuracy','Precision','Recall','F1_Score'])\n",
        "met_4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GybIsCi3PwSy"
      },
      "source": [
        "### **AdaBoost Classifier**\n",
        "\n",
        "- Adaptive Boosting (AdaBoost) is a popular ensemble learning algorithm that combines multiple weak classifiers to create a stronger classifier. In AdaBoost, each classifier is trained on the same dataset, but the weights of the data points are adjusted in each iteration to give more weight to the misclassified points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EY4C5ZwWP2do",
        "outputId": "db850edc-cec2-4d64-f4a7-d4b0e97767b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age               0\n",
              "fnlwgt            0\n",
              "education_num     0\n",
              "capital_gain      0\n",
              "capital_loss      0\n",
              "hours_per_week    0\n",
              "income_level      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#1. Read the dataset and do necessary preprocessing[data imputation in null values, use encoding techniques to convert categorical to numerical]\n",
        "df=pd.read_csv(\"income.csv\")\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oH6kg1ocP8qO"
      },
      "outputs": [],
      "source": [
        "#2. Choose independent variable (X) and dependent variable (Y) from given dataset\n",
        "x=df.iloc[:,:-1]\n",
        "y=df.income_level\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=105)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1qwmn7TQDrw"
      },
      "source": [
        "3. Create different model of AdaBoost using different parameters[Minimum of 4]\n",
        "4. Find the best parameters for the model and calculate the Ypredict value\n",
        "5. Display the confusion table and calculate the accuracy, precision and recall, f-score etc.\n",
        "for the different parameters of AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RO6Lfrm5QG_I",
        "outputId": "ca2644f3-782e-49e5-860d-c33f05297b77"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8347834988228069"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "clf_1 = AdaBoostClassifier(n_estimators=100,algorithm='SAMME',random_state=0)\n",
        "clf_1.fit(x_train,y_train)\n",
        "y1c_predicted = clf_1.predict(x_test)\n",
        "clf_1.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "vA8ya_JQRpV3",
        "outputId": "60153ff6-a08d-4b77-af2b-3d8242f38193"
      },
      "outputs": [
        {
          "data": {
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              "      <th>TP</th>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-f3d6e963-c70d-44fe-94ba-5f30e6df5684 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-f3d6e963-c70d-44fe-94ba-5f30e6df5684');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     TP   FN    FP   TN  Accuracy  Precision    Recall  F1_Score\n",
              "0  7164  279  1335  991  0.834783   0.842923  0.962515  0.426053"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm_1c = confusion_matrix(y_test,y1c_predicted)\n",
        "acc_1c=(cm_1c[0][0]+cm_1c[1][1])/np.sum(cm_1c)\n",
        "pre_1c=cm_1c[0][0]/(cm_1c[0][0]+cm_1c[1][0])\n",
        "rec_1c=cm_1c[0][0]/(cm_1c[0][0]+cm_1c[0][1])\n",
        "f1_sc_1c=cm_1c[1][1]/(cm_1c[1][1]+cm_1c[1][0])\n",
        "met_1c=pd.DataFrame([[cm_1c[0][0],cm_1c[0][1],cm_1c[1][0],cm_1c[1][1],acc_1c,pre_1c,rec_1c,f1_sc_1c]],columns=['TP','FN','FP','TN','Accuracy','Precision','Recall','F1_Score'])\n",
        "met_1c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k78iF9VdSD_x",
        "outputId": "39787948-7a6f-4f90-a500-e0ca01e8c74b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8415395639267069"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_2 = AdaBoostClassifier(n_estimators=100,algorithm='SAMME.R',random_state=0)\n",
        "clf_2.fit(x_train,y_train)\n",
        "y2c_predicted = clf_2.predict(x_test)\n",
        "clf_2.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "6KNPAYhQSMAX",
        "outputId": "94906e9f-9e33-428d-c51f-383a91d6f46d"
      },
      "outputs": [
        {
          "data": {
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              "      <th>TP</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "     TP   FN    FP    TN  Accuracy  Precision    Recall  F1_Score\n",
              "0  7198  245  1303  1023   0.84154   0.846724  0.967083  0.439811"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm_2c = confusion_matrix(y_test,y2c_predicted)\n",
        "acc_2c=(cm_2c[0][0]+cm_2c[1][1])/np.sum(cm_2c)\n",
        "pre_2c=cm_2c[0][0]/(cm_2c[0][0]+cm_2c[1][0])\n",
        "rec_2c=cm_2c[0][0]/(cm_2c[0][0]+cm_2c[0][1])\n",
        "f1_sc_2c=cm_2c[1][1]/(cm_2c[1][1]+cm_2c[1][0])\n",
        "met_2c=pd.DataFrame([[cm_2c[0][0],cm_2c[0][1],cm_2c[1][0],cm_2c[1][1],acc_2c,pre_2c,rec_2c,f1_sc_2c]],columns=['TP','FN','FP','TN','Accuracy','Precision','Recall','F1_Score'])\n",
        "met_2c"
      ]
    }
  ],
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      "display_name": "Python 3",
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