Individual_Notebook_.ipynb

{
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rutujahingankar/Health-insurance-cross-sell-prediction/blob/main/Individual_Notebook_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGcl5Bv9ed6u"
      },
      "source": [
        "# **Problem Statement**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJAmGx75jJk1"
      },
      "source": [
        "Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.\n",
        "\n",
        "An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.\n",
        "\n",
        "For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.\n",
        "\n",
        "Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.\n",
        "\n",
        "Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.\n",
        "\n",
        "Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzGDqdC4fZ-b"
      },
      "source": [
        "# **Attribute Information**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEJxl68MjYbs"
      },
      "source": [
        "1. id :\tUnique ID for the customer\n",
        "\n",
        "2. Gender\t: Gender of the customer\n",
        "\n",
        "3. Age :\tAge of the customer\n",
        "\n",
        "4. Driving_License\t0 : Customer does not have DL, 1 : Customer already has DL\n",
        "\n",
        "5. Region_Code :\tUnique code for the region of the customer\n",
        "\n",
        "6. Previously_Insured\t: 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance\n",
        "\n",
        "7. Vehicle_Age :\tAge of the Vehicle\n",
        "\n",
        "8. Vehicle_Damage\t :1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past.\n",
        "\n",
        "9. Annual_Premium\t: The amount customer needs to pay as premium in the year\n",
        "\n",
        "10. PolicySalesChannel :\tAnonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.\n",
        "\n",
        "11. Vintage :\tNumber of Days, Customer has been associated with the company\n",
        "\n",
        "12. Response :\t1 : Customer is interested, 0 : Customer is not interested"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Dataset"
      ],
      "metadata": {
        "id": "ps0GjhVBdR_N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7W7Rl8yEDDtY"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SVRlkqeDemP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50ee198e-c0be-4565-a552-8236a8134a1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NGDq6hP7DDvY",
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          "height": 206
        },
        "outputId": "d8205c5b-73de-4098-f5b9-129236eb8df0"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
              "0   1    Male   44                1         28.0                   0   \n",
              "1   2    Male   76                1          3.0                   0   \n",
              "2   3    Male   47                1         28.0                   0   \n",
              "3   4    Male   21                1         11.0                   1   \n",
              "4   5  Female   29                1         41.0                   1   \n",
              "\n",
              "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
              "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
              "1    1-2 Year             No         33536.0                  26.0      183   \n",
              "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
              "3    < 1 Year             No         28619.0                 152.0      203   \n",
              "4    < 1 Year             No         27496.0                 152.0       39   \n",
              "\n",
              "   Response  \n",
              "0         1  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         0  "
            ],
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          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "#reading dataset\n",
        "df=pd.read_csv('/content/drive/MyDrive/TRAIN-HEALTH INSURANCE CROSS SELL PREDICTION.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(columns=['id','Driving_License','Policy_Sales_Channel','Vintage','Response'])# independent variable\n",
        "y = df['Response']# dependent variable"
      ],
      "metadata": {
        "id": "70sDv5zojTaw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill any numerical NaNs with mode()\n",
        "\n",
        "fill_mode = lambda col: col.fillna(col.mode())\n",
        "X = X.apply(fill_mode, axis=0)\n",
        "df = df.apply(fill_mode, axis=0)"
      ],
      "metadata": {
        "id": "ZP3ilC7r8hZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Building"
      ],
      "metadata": {
        "id": "yNSb8zTs1Pr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for imbalance in data \n",
        "df['Response'].value_counts()"
      ],
      "metadata": {
        "id": "k4iAuPyljTf7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29d995c9-80a7-476a-f88c-30baee15de90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    334399\n",
              "1     46710\n",
              "Name: Response, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We can clearly see that there is a huge difference between the data set. \n",
        "* Standard ML techniques such as Decision Tree and Logistic Regression have a bias towards the majority class, and they tend to ignore the minority class. So solving this issue we use resampling technique.\n"
      ],
      "metadata": {
        "id": "cj_NpsbapGqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Resampling\n",
        "ros = RandomOverSampler(random_state=0)\n",
        "X_new,y_new= ros.fit_resample(X, y)\n",
        "\n",
        "print(\"After Random Over Sampling Of Minor Class Total Samples are :\", len(y_new))\n",
        "print('Original dataset shape {}'.format(Counter(y)))\n",
        "print('Resampled dataset shape {}'.format(Counter(y_new)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfVYkVvKPePk",
        "outputId": "aaa0bf76-12aa-435f-aecc-83ff226ba431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Random Over Sampling Of Minor Class Total Samples are : 668798\n",
            "Original dataset shape Counter({0: 334399, 1: 46710})\n",
            "Resampled dataset shape Counter({1: 334399, 0: 334399})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the data in train and test sets"
      ],
      "metadata": {
        "id": "hxZSkqHTvSCb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test ,y_train, y_test=  train_test_split(X_new, y_new, random_state=42, test_size=0.3)\n",
        "X_train.shape, X_test.shape , y_train.shape, y_test.shape"
      ],
      "metadata": {
        "id": "LqfFmzFkxUoo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e68186cb-3d64-4f6c-fe69-5bbcc858bd25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((468158, 7), (200640, 7), (468158,), (200640,))"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the Dataset using Standard Scaling Technique.\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "X_train=scaler.fit_transform(X_train)\n",
        "X_test=scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "N4JMuxtAQr9Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "enxfxI_PAkPR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Logistic Regression\n",
        "model= LogisticRegression(random_state=42)\n",
        "model=model.fit(X_train, y_train)\n",
        "#Making prediction\n",
        "pred = model.predict(X_test)\n",
        "prob = model.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "G_kYObX8xUsS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model** **Evaluation**"
      ],
      "metadata": {
        "id": "wlkDPz3FwXKB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "r_lgt= recall_score(y_test, pred)\n",
        "print(\"recall_score : \", r_lgt)\n",
        "\n",
        "p_lgt= precision_score(y_test, pred)\n",
        "print(\"precision_score :\",p_lgt)\n",
        "\n",
        "f1_lgt= f1_score(y_test, pred)\n",
        "print(\"f1_score :\", f1_lgt)\n",
        "\n",
        "A_lgt= accuracy_score(pred, y_test)\n",
        "print(\"accuracy_score :\",A_lgt)\n",
        "\n",
        "acu_lgt = roc_auc_score(pred, y_test)\n",
        "print(\"ROC_AUC Score:\",acu_lgt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JPolsOmc3Qx",
        "outputId": "f060c01a-8c7b-4c68-e826-6c8eee4f6003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall_score :  0.976100453713285\n",
            "precision_score : 0.7071892503658422\n",
            "f1_score : 0.8201651165221027\n",
            "accuracy_score : 0.7855811403508772\n",
            "ROC_AUC Score: 0.8341983171030103\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr, tpr, _ = roc_curve(y_test, prob)\n",
        "\n",
        "plt.title('Logistic Regression ROC curve')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), linestyle=\"--\",color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "NZoisR3i-Wdn",
        "outputId": "94694b19-d1c2-47ef-92e2-61ce0750c97e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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X5qnXPnl+fM7LA9tXLEhOU1GmlOmzbZ9iZJRzi5xPwzfK3srouc++YFlPvtw027KeN1GPUS4Bcsv14n5r99yD1CxsS5nk+HxOb+HTi4iPvUXwyq+4evEsdd1fo5yNiU7+ngtNkXB0dCQ6Ohp3d3fg2T0Lbbm5ueXrgpLQ0NASd69c6fN/FEUhNjGFK7fucTIiluS0TJIepBN/L5U7d1OztTU2NsLW2pzydlY0cilNpXLWVHcsQ9VKNtiWtsDKwhQz08Jz4qB8zsVLVlYWX331FfNnz8TCwoIffviBIUOGcOrUqXz1OT09/blfrgtNkejUqRPBwcG88cYbJCUlsXfvXn7++WdDxxLF2LWY+xz7J4Y/T94k8X4aqekq4FERKGdjgWP50tSrYU/lCqWpZG9N2TIWONqXppytJSbGMomkMAwjIyP27dtHx44dWbx4cb6+TOeFXorEnDlz2L17N/Hx8bz77rvY2dmxfft2/P39GTt2LPXr16d79+6cPXuWN954A4CAgACqVKmij3iihDl1IY41O89zOSoJgNpV7Kju5ECDWuWpWdmOV53KYGZqYuCUQvwnPT2dzz//nMGDB1OtWjU2bdqElZWVXma81kuRmD59OtOnT3/m8eXLl2t+NjExYfbs2fqII0qweykqFv92nFIWpgzt5kZzt0pUsrc2dCwhcnX06FGGDh1KREQEZcqUYfz48ZQqVUpv6y80w01C6JqiKOw4mYSiwLwxXlIcRKGWnJzMjBkz+Pbbb6lcuTI7duygc+fOes9ReI6uCaFjB07f4uLNNPq+4SwFQhR6c+bM4ZtvvuG9994jPDzcIAUCZE9ClBAJ91JZsvEsTuXM6NG6pqHjCJGjpKQk7ty5Q+3atZkyZQpvvvkmXl5eBs0kexKi2FMUhe/WnyE1XcVbLcphaiL/7EXhs3XrVlxdXXnnnXdQFAVbW1uDFwiQIiFKgOB9/3LqYhy+HZypYJvz3beEMJTY2Fh8fX3p0aMHDg4OLFu2TC9nLWlLioQo1rb8dYU1f5ynRX1H+njXMXQcIbI5d+4crq6ubNmyhblz53LixIlCdxGgHJMQxZKiKPz2v39ZveM8zepVYmI/DxlmEoWGWq3GxMQEFxcX3nrrLSZOnIiLi4uhY+VI/mpEsZOVpbB44zlW7ziPV8NXmDr4NSzM5OI4YXhZWVksXrwYNzc3kpKSMDMz44cffii0BQKkSIhiJlOVRdCaE+w8co2ebWvxQT8PjGUKDVEIXLp0iTZt2mhmk0hJSTF0JK1IkRDFRkpaJh//eJS/z8UwoLMLg7q4SoEQBqdWqwkKCsLd3Z2wsDBWrFjBrl27dD7nUkGRYxKiWIi7m0Lg8qPcuvOQgF4N6NSiuqEjCQGAsbEx+/fvp0uXLixcuPCFN2ArbKRIiCLv6q17zPj+b7KyFGYNa05jZwdDRxIlXHp6Op9++ilDhgzJNiFfUSRFQhRp+0/dZGHwGawsTPn0vVZUcyxj6EiihPv7778ZOnQoFy5coGzZsowbN67IFgiQYxKiiFIUhV92XeCrn0NxrlqWoNGeUiCEQT18+JCxY8fi6elJSkoKO3fuZNy4cYaO9dJkT0IUOfcepvPtr6c5eT6Wth6VGd27IeZyiqswsDlz5rBw4UJGjx7N3LlzsbGxMXSkAiFFQhQp/1xNYN6ak9xPzmB4j/r4eL5aqKYwECXL3bt3uXPnDnXq1OGjjz6iW7dutGzZ0tCxCpQUCVEkZGSq+XXPRYL3/YtTeWumvetJnaplDR1LlGAbN24kICAAJycnQkNDsbW1LXYFAqRIiCLg4vVE5q8/TVTsQ9o3rcKQrm6UsTY3dCxRQt2+fZvRo0ezceNGGjVqxE8//VSs92alSIhCKz1Tzdo/zvP7gSuUs7Vitn8LGteV01uF4Zw9e5a2bduSkpLCZ599xsSJEzEzK94zC0uREIXSP1cT+G79aaLjk+nUojrv+rhSyrJ4/zGKwkulUmFqaoqrqyu9evVi4sSJODs7GzqWXkiREIVKWrqKVTsi2H44kgplSzFnZEsa1K5g6FiihMrKymLRokUsWLCA48ePY2dnx7JlywwdS6+kSIhC49zlO3y3/gyxiSn4eL7KwDddsbKQf6LCMC5cuMCwYcM4fPgwHTt2JDU1FTs7O0PH0jv5CxQGl5KWycqQCP44cg3H8tZ8HuBJvRr2ho4lSqjHE/LNnj0ba2trVq1axYABA4r1wennkSIhDCoiMoGvfzlF3N0UerSuSb9OdbE0l3+WwnCMjY05ePAg3bp1Y+HChVSsWNHQkQxK/hqFQajUWXy/OYydR65hV9qCoAAvXF4tZ+hYooRKTU1l7ty5DBs2jOrVqxfpCfkKmhQJoXf3Hqbz9S+nOHUxjs4tq9OvY11sS1sYOpYooQ4dOsTQoUO5dOkSDg4OjB07VgrEE6RICL26nZDMzGVHiIlP5r2e7rzZ8lVDRxIl1IMHD/joo49YtGgR1atXZ8+ePXh7exs6VqEjRULozaUbdwlafYIHKRkE+jfHo27JHusVhjV37lwWL17MuHHjmDNnDtbW1oaOVChJkRB6cT4ykWlLD2Nrbc7c91pRu4rMuyT0LyEhgfj4eJydnfnoo4/o0aMHzZs3N3SsQk3uJyF07tDZW8xc9jf2tpZ8M76NFAihd4qi8Ntvv+Hq6krfvn1RFAVbW1spEFqQIiF0JitLYe3O8wStPkmVijZ8HuCJnY0coBb6FRMTQ8+ePenduzdVqlRhxYoVJfaah/zQ23BTZGQkU6ZMISkpCTs7O4KCgqhevXq2NgkJCXz00UfExMSgUqlo1qwZ06dPx9RURsWKmpS0TILWnOTUhTi8m1ZlVC93zEzlxkBCv86ePUubNm1IS0sjKCiICRMmyPYkj/S2JzFr1iz8/PzYtWsXfn5+zJw585k2S5cupWbNmmzbto3ff/+df/75h927d+sroiggZy/dIWDe/zh9MQ7/7m6M9W0oBULolUqlAsDV1RVfX1/Onj3LpEmTpEDkg16KREJCAhEREfj4+ADg4+NDREQEiYmJ2doZGRmRnJxMVlYWGRkZZGZmlvirHYuSTFUWa3eeZ/r3f2NsYsznAZ50e72m7NoLvVGr1cyfP59evXpx9+5dzMzMWLp0KXXq1DF0tCJLL2U1JiaGihUrYmLy6NukiYkJDg4OxMTEUK7cf1fZjho1ijFjxuDp6Ulqair9+vXDw8MjT+sKDw/Pd87Q0NB8v7aoKqg+37mXyaa/E4m5m0m9qlZ09rAlNfEaoYnXCmT5BUk+5+Lp6tWrfPLJJ4SFhdGqVSuOHz9O+fLlDR1Lr3TxOReqfa+dO3fi7OzMqlWrSE5Oxt/fn507d9KpUyetl+Hm5oaFRd4PjoaGhua5IBV1BdXn3ceus3x3GBZmJkwd3JQW9Z0KIJ1uyOdc/KjVaj799FPmzJmDjY0Na9euxdnZmSZNmhg6ml7l93NOT09/7pdrvQw3OTo6Ehsbi1qtBh59qHFxcTg6OmZrt3btWrp164axsTE2Nja0a9eOY8eO6SOiyIesLIWftv3Dgg1ncK1ejoUfti3UBUIUT8bGxhw9epS3336biIgI+vXrJ0OcBUgvRcLe3h4XFxdCQkIACAkJwcXFJdtQE0DlypU5cOAAABkZGRw5coTatWvrI6LII3WWwry1J9m8/zIdm1cj0L855cpYGjqWKCFSU1OZOnUqkZGRGBkZsXHjRtatW4eDg9zetqDp7eymwMBA1q5dS8eOHVm7di2zZ88GwN/fn7CwMACmTp1KaGgoXbt2pUePHlSvXp0+ffroK6LQkjpL4bv1pzl8Npp+neoS0KsBJiZyyY3Qj7/++gt3d3c+++wzzRdPS0v5gqIrejsmUbNmTYKDg595fPny5Zqfq1atyooVK/QVSeSDoij89Hs4/zsZhW+HOrzToWTc51cY3v3795k8eTJLly6lRo0a7Nu3j3bt2hk6VrEnX/9Envz2v3/5/eBVunrVoF/HuoaOI0qQTz/9lGXLljFhwgTOnTsnBUJPCtXZTaJwi4p9wC+7LtLK3Ylh3dzk4KDQufj4eO7cuYOLiwtTp07lrbfeolmzZoaOVaJovSdx+PBhpk6dysiRIwEICwvjyJEjOgsmChe1OosFG85gaW7CiLfrY2wsBULojqIo/Prrr7i4uNCvXz8URaFMmTJSIAxAqyKxZs0aAgMDqV69OidOnAAeHSiaP3++TsOJwmPtzgucv5bI8LfqU9ZGDhIK3bl16xY9evSgb9++1KhRg9WrV8teqwFpNdy0atUqVq5cSeXKlTUHmmvUqEFkZKROw4nC4XJUEhv//Jd2TarQ1qOKoeOIYuzMmTO0bt2azMxMvvrqK95//33NTA3CMLQqEsnJyZoL3x5XdJVKhZmZme6SiULh7v00Pl11nLI2Fvj3qG/oOKKYyszMxMzMjHr16tGvXz8mTpxIzZo1DR1LoOVwU9OmTVm2bFm2x1avXi3jg8WcWp3F7B+PkvQgnWnvNqO0lXwpEAVLrVbz9ddfU7duXc2EfIsXL5YCUYhotScxffp0Ro4cSXBwMMnJyXTs2BFra2u+//57XecTBrRh7yWu3LzH5IFNqFNV7iYnClZ4eDhDhw7l+PHj+Pj4kJGRYehIIgdaFQkHBwc2btxIWFgYt27dwtHREXd3d4yN5TKL4ioq9gEb9v1LqwZOtHKX+ZhEwVGr1cyZM4e5c+dia2vLunXr8PX1lYPThZRWW/n33nsPIyMj3N3d6dy5Mw0bNsTY2JjRo0frOp8wgJS0TOb8dAxrK1P8u8v1EKJgGRsbc+LECXr37k1ERATvvPOO/BsrxLQqErnNxHr8+PECDSMKh6WbznE7IZnJA5tib2tl6DiiGEhJSWHSpElcvXpVMyHfzz//TIUKFQwdTbzAc4ebHl8HkZmZ+cw1EVFRUTg5yTBEcXPyfCx/ht7knQ7O1K9Zsm7YInTjzz//ZNiwYVy9epVq1aoREBCQr3u+CMN4bpG4ffs28Ojqx8c/P+bo6MiYMWN0l0zoXVqGiiWbzvFKhdL0bi9TtIuXc+/ePT788EOWL19OrVq12L9/P61btzZ0LJFHzy0Sn332GQCNGjWSKbtLgF93XyQuMYW577XE3EwuYBIv57PPPuPHH3/kww8/JDAwkFKlShk6ksgHrc5uelwgHj58yN27d7M9V6WKXIFbHNy685CtB67QvmkV3GvJOLHInzt37hAfH6+ZkK9Xr14l7jaixY1WReLKlStMnDiRCxcuYGRkhKIomrMRzp8/r9OAQj+WbwnDzNSEQW+6GjqKKIIURWHdunWMHTuWqlWrEhoaSpkyZaRAFANand0UGBhIs2bNOH78OKVLl+bEiRP4+vry+eef6zqf0IPDZ6MJvRCHr3cdysotSEUeRUVF0bVrV/r160etWrVYs2aNnNJajGi1J3HhwgV++uknzMzMUBQFGxsbJk2ahI+PD927d9d1RqFDaRkqlm0J41WnMvRoLVMhiLw5ffo0rVu3Rq1W88033zBmzBiZkK+Y0WpPwsLCApVKBUDZsmWJjo4mKyuLpKQknYYTurf5z8sk3k9jxFvucp9qobXHU2i4ubkxcOBAwsLCGDdunBSIYkirrYKHhwd//PEHAB07dsTf358BAwbQvHlznYYTunX7bgbr9lzk9YavUK+GvaHjiCJApVLxxRdf4OzsTGJiImZmZixcuJAaNWoYOprQEa2Gm568kG7ChAnUqlWLlJQU3nrrLZ0FE7q369Q9SluZM+Jtd0NHEUXAuXPnGDp0KCdPnqRHjx6a0QVRvOV5fMHY2JgePXrQq1cvNm3apItMQg+OR9wmMjaddzrUoYy1uaHjiEJMrVYzc+ZMPDw8uHHjBhs2bGDTpk04ODgYOprQgxcWiSNHjvDTTz+xd+9e4NHu5urVq2nfvj2//vqrzgOKgqdSZ/H95jDKlzGlc8vqho4jCjljY2NOnz6Nn58fERER9O7dW85eKkGeO9y0bNkylixZQq1atbh8+TJ9+/bl+PHjmJub88knn9CmTRs9xRQFactfV4hLTMGvtT1mpnKgUTwrOTmZWbNmMWrUKGrUqMHGjRsxN5c9zpLouUVi/fr1rFmzBjc3N86cOUPfvtwdGm4AACAASURBVH2ZPHkygwcP1lM8UdAS7qWyfs9F3GraU9tJrokQz9q7dy/Dhw8nMjKSGjVqMGrUKCkQJdhzh5vu3r2Lm5sbAA0bNsTc3JxBgwbpJZjQjdU7zpOpymJMn4YyZCCySUpKYujQoXTo0AFTU1MOHDjAqFGjDB1LGNgLj0koikJWVhZqtVozvW9WVpbmP1F0xMQn82doFB2bV8OpfGlDxxGFzOeff86qVauYMmUKZ8+excvLy9CRRCHw3OGmlJQUXF3/m8tHURTN74/nb5K5m4qO4H2XMDUxxreDs6GjiEIiNjaW+Ph46tWrx9SpU+nTpw+NGzc2dCxRiDy3SOzbt09fOYSO3Yx7wP9ORtG5RXXKyfxMJZ6iKKxZs4Zx48ZRvXp1zYR8UiDE055bJF555RV95RA6pCgKCzacwdLchN7edQwdRxjYjRs3GDFiBDt37qRly5b8+OOPcnxK5EqrK64LQmRkJFOmTCEpKQk7OzuCgoKoXr36M+127NjBkiVLNMNZK1asoHx5uY3myzhz6Q4RkYn4d3eTvYgS7tSpU7Ru3RpFUfjuu+8ICAjA2Fjm7BK501uRmDVrFn5+fnTv3p2tW7cyc+ZMVq9ena1NWFgYCxcuZNWqVVSoUIEHDx7IqXcvSaXO4offw3Eoa0WnFtUNHUcYSHp6OhYWFri7uzNkyBDGjx+f45c0IZ6ml68QCQkJRERE4OPjA4CPjw8REREkJiZma7dy5UqGDBlChQqP7oxmY2MjN0x/SdsPR3Lj9gOG96gvtyQtgVQqFStXrtRMyGdqasr8+fOlQAit5alIxMTEcObMmTyvJCYmhooVK2qmETYxMcHBwYGYmJhs7a5cuUJUVBT9+vXjrbfeYvHixSiKkuf1iUdS0jJZt+sCDWqX57V6lQwdR+jZmTNnaNasGQsXLsTDwwO1Wm3oSKII0mq4KTo6mgkTJmhuX3r69Gl27tzJwYMHmTt3boGFUavVXLx4kRUrVpCRkcGwYcNwcnKiR48eWi8jPDw83+sPDQ3N92sLo4P/3Cc5TUXjakacOnUqxzbFrc/aKO59VqvVfP/996xatUpz/K99+/bcuHGDGzduGDqe3hT3zzknuuizVkVi5syZtGnThl9++YVmzZoB0KpVK4KCgrRaiaOjI7GxsajVakxMTFCr1cTFxeHo6JitnZOTE506dcLc3Bxzc3Pat2/PuXPn8lQk3Nzc8jVEFRoaioeHR55fV1jdT87gi017cK9Vnrc7t8yxTXHrszZKQp8VRWHOnDn079+fr7/+msjIyGLf56eVhM/5afntc3p6+nO/XGs13BQWFsbw4cMxNjbWnCpnY2PDgwcPtAphb2+Pi4sLISEhAISEhODi4kK5cuWytfPx8eHQoUMoikJmZiZHjx6lbt26Wq1DZLf1wBWS01T496hv6ChCDx4+fMj48eO5cuUKRkZGbNy4kZUrVz7zNyZEXmlVJOzt7bl+/Xq2xy5fvvzMnsDzBAYGsnbtWjp27MjatWuZPXs2AP7+/oSFhQHQpUsX7O3tefPNN+nRowe1atWiV69eWq9DPJKaruKPv6/R1LUi1R3LGDqO0LFdu3ZRr1495s+fz549ewAwMzMzcCpRXGg13DRkyBBGjhzJ8OHDUalUhISE8P333+Pv76/1imrWrElwcPAzjy9fvlzzs7GxMR999BEfffSR1ssVzwred4kHKRn0kQvnirXExEQmTJjAqlWrqFu3LgcPHqRVq1aGjiWKGa2KRK9evbCzs2P9+vU4OjqyZcsW3n//fby9vXWdT+RRbGIKm/dfoU3jytStJkMNxdm8efNYu3Yt06ZNY/r06VhayoWSouBpVSTUajXe3t5SFIqAH7aGYWJixKAuri9uLIqc27dvEx8fj5ubG9OmTeOdd96hYcOGho4lijGtjkm0atWKwMDAEnlKWVFy7vIdjobfpnf72pS3szJ0HFGAFEVh5cqVuLq6MnDgQBRFwcbGRgqE0DmtisRPP/1EqVKlmDhxIu3ateOrr77i4sWLus4m8uiXXRext7WkR+taho4iCtC1a9fo1KkT7777LvXq1WPdunUyIZ/QG62Gm1xdXXF1dWXSpEkcP36ckJAQBg0aRIUKFdi2bZuuMwothF2J55+rCQzpWg8LmX6j2Dh16hSvv/46RkZGLFq0iJEjR8qEfEKv8vyvrUaNGtSsWRMnJydu3bqli0wijxRF4ftN5yhXxoLOMolfsZCWlgaAu7s7/v7+hIeHM2rUKCkQQu+0+hd3//59goODGTRoEN7e3hw/fpxhw4Zx5MgRXecTWrhy6x7Xbz/Ax7MGlhZ6m9hX6EBmZiZz586lTp06JCQkYGpqyjfffEO1atUMHU2UUFptUby8vGjUqBE+Pj4sWLCAMmXkAq3CZP2ei1hZmMhU4EXcqVOnGDJkCGfPnqVPnz4yuaUoFLQqEnv27MHBwUHXWUQ+/HM1gaPht+nfqS42peTeG0WRWq1m2rRpfPnllzg4OLB58+Y8zVcmhC7lWiROnDhB06ZNgUdTeF+5ciXHdi1atNBNMqGVjX/+i00pM7q/XtPQUUQ+GRsbc/78eQYPHsyXX36JnZ2doSMJoZFrkZg9e7ZmQr5p06bl2MbIyIh9+/bpJpl4oajYB5yIiKXvG85yLKKIefDgAdOnT2fMmDHUqlWL3377TeZbEoVSrluWxwUC4H//+59ewoi82XrgCuamxnRp9aqho4g8+OOPPxgxYgQ3b97E1dWVWrVqSYEQhZZWZze99957OT4+evToAg0jtJdwL5V9J27QtkkVbEvLLV6LgoSEBAYOHMibb76JjY0Nhw8fZsSIEYaOJcRzaVUkjh07luPjx48fL9AwQnu/7fuXLAV6tatt6ChCS1988QXr1q1jxowZnDp1So7niSLhuQPZ8+fPBx6du/3458eioqJwcnLSXTKRq7v309h17DreTatSyd7a0HHEc0RHR5OQkED9+vWZNm0afn5+uLu7GzqWEFp7bpG4ffs28OiK3sc/P+bo6MiYMWN0l0zkaufR62Sqsni7rczRVFgpisJPP/3ExIkTqVGjBqGhodjY2EiBEEXOc4vEZ599BkCjRo3o06ePXgKJ50tJy2TH4Uga13XglQqlDR1H5ODq1asMHz6cffv20bp1a3744QeZkE8UWbkWiZs3b1K5cmXg0bUQUVFRObarUqWKbpKJHO09cYOkh+n4yl3nCqVTp07h5eWFiYkJS5cuxd/fX+ZbEkVarkWia9eunD59GoAOHTpgZGT0zDQBRkZGnD9/XrcJhYZKncXvB67iXLUsrq/aGzqOeEJaWhqWlpa4u7vz3nvv8f7778sXKFEs5FokHhcIgAsXLugljHi+A6dvEpuYwrDuboaOIv5fRkYGQUFBLFu2jDNnzmBvb8+XX35p6FhCFJh8XaYbFRWFkZGRZjhK6J46SyF4379UdyzDa66VDB1H8GjqmqFDhxIWFsY777xj6DhC6IRWg6UTJkzg1KlTAGzcuJEuXbrg4+NDcHCwTsOJ/xwJi+Zm3EP6eNfB2FgOghqSWq1m0qRJNG/enISEBLZu3cq6deuwt5chQFH8aFUkjhw5gpvboyGOlStXsmLFCoKDg1m+fLlOw4lHFEVhw95LvFKhNC3d5doUQzM2Nuby5csMHTqUiIgIunXrZuhIQuiMVsNNmZmZmJubExsbS1JSEh4eHgDEx8frNJx45Pg/t4mMvs/7vo0wkb0Ig7h37x7Tpk3j/fffp3bt2mzYsAFTU5lUURR/Wv0rd3Fx4fvvv+fWrVu0adMGgNjYWEqXlvP09WHboasYGxvh1egVQ0cpkbZv386IESOIiYnB3d2d2rVrS4EQJYZWw01z587l0qVLpKen8/777wOPzn7q2rWrTsOJR9OBn/03no7Nq2FhZmLoOCXKnTt36NevHz4+PpQtW5YjR44wfPhwQ8cSQq+0+jpUtWpVvvrqq2yPderUiU6dOukklPjPjr8jMTE2ou8bzoaOUuJ89dVXBAcHExgYyEcffYS5udz5T5Q8Wu8zb9y4ka1btxIbG0vFihXp3r07PXv21GW2Ei/xfhq7j16njUdlytpYGjpOiXDr1i0SEhJwd3dn+vTp9O/fX3PShhAlkVZFYsmSJWzZsoUhQ4bg5OREdHQ0P/zwA3Fxcbnea0K8vN8PXEGVpeDrLXsRuqYoCj/88AMffPABNWvWJDQ0lNKlS0uBECWeVkUiODiYNWvW8Mor/x049fT0pH///lIkdCQtXcWuo9dp7lYJx/IyHbguXblyBX9/f/7880/atm3L8uXLZUI+If6fVkUiNTWVcuXKZXvMzs6OtLQ0nYQSj45FPEzNpPvrNQ0dpVgLDQ3Fy8sLMzMzli1bxrBhw6RACPEErc5u8vLy4oMPPuDq1aukpaVx5coVpkyZgqenp9YrioyMxNfXl44dO+Lr68u1a9dybXv16lUaNGhAUFCQ1ssvTjJVWfxx5BqVHUrjUr3cC9uLvEtNTQWgQYMGjB49moiICPz9/aVACPEUrYrEzJkzsba2plu3bjRq1IgePXpgZWXFjBkztF7RrFmz8PPzY9euXfj5+TFz5swc26nVambNmoW3t7fWyy5u/nfyBrcTUhjStZ5stApYRkYGgYGB1K5dm/j4eExNTZk3b162oVQhxH9eONz04MEDbty4wcyZM/n888+5e/cuZcuWzdMc+QkJCURERLBixQoAfHx8+OSTT0hMTHxmGGvZsmW0adOGlJQUUlJS8tidok9RFHYeuUaViqVp4lLR0HGKlWPHjtG/f3+uXr1K//795T4PQmjhuUVi//79jBs3jrS0NKytrVm0aBHNmzfP80piYmKoWLEiJiaPLgYzMTHBwcGBmJiYbEXiwoULHDp0iNWrV7N48eI8rwcgPDw8X6+DR+PThnYtNp3LN+/h09ROM6miLhWGPuuaSqXiu+++Y926dTg4OPDtt9/i6elJZGQkkZGRho6nFyXhc36a9LlgPLdIzJ8/nw8++ICePXuyYcMGvv32W3799dcCDwGP5oeaMWMGn332maaY5IebmxsWFhZ5fl1oaKhmTipD2v7jUcpYmzO4p6fOr7AuLH3WNUVRSEtLY+TIkfj6+tK6dWtDR9KrkvI5P0n6rL309PTnfrl+bpGIioqif//+APTr14+lS5fmOQCAo6MjsbGxqNVqTExMUKvVxMXF4ejoqGlz584dbty4oZn24P79+yiKwsOHD/nkk0/ytd6iJir2ASciYun7hrNMwfGSkpKSmDp1KuPHj9dMyGdiYlIiv10K8TKeWySysrL+a2hqilqtztdK7O3tcXFxISQkhO7duxMSEoKLi0u2oSYnJyeOHTum+X3BggWkpKQwefLkfK2zKNp64Armpsa82fJVQ0cp0n7//Xfee+89bt++TePGjaldu/ZL7Z0KUZI9t0ikpaXRr18/ze/JycnZfgf4+eeftVpRYGAgU6ZMYfHixZQpU0Zzequ/vz9jx46lfv36ec1erNx9kMb/TkbRvmlV7GzyPlwmIC4ujrFjx7J+/Xrq16/P1q1badKkiaFjCVGkPbdIzJ07N9vvvXr1yveKatasmeOd7HK7cdGYMWPyva6iaPvhSFTqLLq/XsPQUYqsr7/+ms2bN/PJJ58wadIkmZBPiALw3CLx1ltv6StHiZaWoWLH4Uhec61EZQcbQ8cpUqKiokhISKBhw4bMmDGDgQMH4urqauhYQhQbcqJ4IbDvRBQPUjJ5q00tQ0cpMrKysliyZAn16tVj6NChKIqCtbW1FAghCpgUCQNTZyls/esKzlXL4vqqTMGhjX///Ze2bdsyatQomjVrxm+//SZXpguhI3IPRgM7fTGOmIRkBrzpIhs6LZw8eRIvLy8sLCz48ccfeffdd+V9E0KHZE/CwLYfjsSutAXN3SoZOkqh9niKlkaNGjFu3DgiIiIYMmSIFAghdEyrIpGRkcE333xD+/btNVf0HTp0iLVr1+o0XHF39dY9Tp6PpWOLapiZynn8OUlPT2fGjBmaCflMTEz47LPPcHJyMnQ0IUoErYrEp59+yqVLl/jyyy8139xq167NunXrdBquuFu78zzWVmb0aC0HrHNy5MgRGjVqxJw5c/D29pYJ+YQwAK2OSezdu5fdu3dTqlQpzR9qxYoViY2N1Wm44uz67fuciIjFt0MdSluZGTpOoaJSqfjggw/47rvvqFKlCn/88QedOnUydCwhSiStvpqZmZk9MyVHYmIidnZ2OglVEmz68zKW5iZ085I7zz3N1NSUmzdvMmrUKMLDw6VACGFAWhWJTp06MXnyZKKiooBH0x98/PHHdOnSRafhiqsHKRkcOnOLth5VKGMtVwUD3L17lxEjRnDx4kUA1q9fz8KFC7GxkYsLhTAkrYrE+PHjqVy5Mt26deP+/ft07NgRBwcHAgICdJ2vWPrzZBQZqiw6t6xu6CiFwubNm3F1deXHH3/k4MGDADIhnxCFhFbHJMzNzZk6dSpTp04lMTGRsmXLyqmH+ZSVpbDz6DVqvGLLq062ho5jULdv32bMmDH89ttvNGzYkO3bt9O4cWNDxxJCPEGrIvF4mOmx5ORkzc9VqlQp2ETF3MkLsUTFPmSin2wMv/32W7Zt28ann37KBx98gJmZHMAXorDRqkh06NABIyMjFEXRPPZ4T+L8+fO6SVZMbd5/mbI2Fng2fMXQUQzixo0bJCQk0KhRI2bMmMHgwYOpW7euoWMJIXKhVZG4cOFCtt/v3LnDwoULZa7+PIqMvkf4lQTe9amHqUnJOuf/8YR8U6ZMoU6dOpw8eRJra2spEEIUcvnaUlWoUIFp06bx9ddfF3SeYm374UjMzUzo0KyqoaPo1cWLF2ndujWjR4+mZcuWbNy4UY5pCVFE5HuCv6tXr5KamlqQWYq1BykZ/Bl6kzaNK2NTquSc9nrixAm8vLwoVaoUK1euZODAgVIghChCtCoSfn5+2f6wU1NTuXz5spwCmwd7j98gI1ONj2fJuH91cnIy1tbWNG7cmIkTJzJmzBgqVZJJDIUoarQqEr179872u5WVFXXr1qV69eq6yFTsqLMUth+OpF4N+2J/2mtaWhqffPIJK1as4OzZs1SoUOGZ2+AKIYqOFxYJtVrN0aNH+eSTT+Sewfl06MwtYhNTGNSleN817fDhwwwdOpSLFy8yePBgTE3ldiVCFHUvPHBtYmLC4cOHZRz5JfzvZBRlrM1pUd/R0FF0QqVSMWbMGLy8vEhLS2PXrl2sWLGCsmXLGjqaEOIlaXV206BBg1iwYAGZmZm6zlPsRMc/5MylOLybVi22p72ampoSGxvLmDFjCA8P54033jB0JCFEAXnueEBISAg+Pj6sXbuW+Ph4VqxYQbly5bLtVezfv1/XGYu0PcduANDt9RoGTlKwEhMTmTx5Mh988AHOzs78+uuvcr8HIYqh5xaJmTNn4uPjwxdffKGvPMVKRqaaPcev09S1Eva2VoaOU2A2btxIQEAACQkJtGzZEmdnZykQQhRTzy0Sj6fheO211/QSprg5cPoW9x5m0NWreOxFxMTEMHr0aDZt2kTjxo3ZuXMnDRs2NHQsIYQOPbdIZGVlcfTo0WxzNj2tRYsWBR6qOFAUhW2HrlK1kg3utcobOk6BmD9/Ptu3b+fzzz9n4sSJcvaSECXAc//KMzIymDZtWq5FwsjIiH379ukkWFEXEZnI1Vv3COjVoEifGXbt2jXu3r2rmZBvyJAh1KlTx9CxhBB68twiYWVlJUUgn7YdukppKzPaeFQ2dJR8UavVLFq0iKlTp1K3bl1OnDiBtbW1FAghShg52qgDd+6mciQsho7Nq2FpXvSGZM6fP8/rr7/O+++/j5eXF5s2bSrSe0NCiPzT6sC1yJuDZ26SlaXQqUV1Q0fJsxMnTuDp6Unp0qVZs2YN/fr1kwIhRAn23CJx+vTpAltRZGQkU6ZMISkpCTs7O4KCgp6Z+2nRokXs2LEDY2NjzMzMGD9+PF5eXgWWQR+yshR2/H0N56plqWRvbeg4Wnvw4AE2NjY0btyYyZMnM3r0aBwcHAwdSwhhYHobbpo1axZ+fn7s2rULPz8/Zs6c+Uwbd3d3fvvtN80tLcePH09aWpq+IhaIUxfjiE1MKTIXz6WmpjJlyhRq165NXFwcJiYmfPzxx1IghBCAnopEQkICERER+Pj4AODj40NERASJiYnZ2nl5eWFl9eiiM2dnZxRFISkpSR8RC8zuY9exK21Bi/pOho7yQqdOnaJBgwYEBQXh4+MjEzgKIZ6hlyIRExNDxYoVMTExAR5NGujg4EBMTEyur9myZQtVq1YtUvcgiEtM4Vh4DO2bVsHMtPCeE6BSqQgICGD48OGoVCr27t3LDz/8gJ2dnaGjCSEKmUJ56s3x48eZP38+P/30U55fGx4enu/1hoaG5vu1AAf+uU+WApVtHr70snTt33//pW/fvowaNQorK6tCn7cglaS+PiZ9Lhl00mdFD+Lj4xUPDw9FpVIpiqIoKpVK8fDwUBISEp5pe+rUKeX1119XwsPD87SOtLQ05eTJk0paWlq+Mp48eTJfr3tMpc5Shs7ZrUxeePCllqMr8fHxyrvvvqucP39eURRFUavVL93nokj6XDJIn7X3om2nXsZE7O3tcXFxISQkBHg0u6yLiwvlypXL1u7cuXOMHz+e7777jnr16ukjWoEJPR9LbGJKobs9qaIobNiwARcXF9asWcPRo0cBZEI+IYRW9LalCAwMZO3atXTs2JG1a9cye/ZsAPz9/QkLCwNg9uzZpKWlMXPmTLp370737t25ePGiviK+lK0HrlDe1pLmboXnxkLR0dG89dZb+Pr6Uq1aNUJDQxk8eLChYwkhihC9HZOoWbMmwcHBzzy+fPlyzc8bN27UV5wCdf32fc5djmdAZ5dCdWOhBQsWsGvXLr744gvGjRsnE/IJIfJMthoFYNvBq5ibmRSKK6yvXr3K3bt38fDwYMaMGQwdOpRatWoZOpYQoogqPF97i6i0dBV/nbqJZwMnylgb7joDtVrNt99+S/369RkxYgSKolCqVCkpEEKIlyJF4iUdOhtNWoaaN5pVM1iGf/75h1atWjF+/Hjatm3Lli1bZL4lIUSBkOGml6D8/42FKjuUxvXVci9+gQ4cP34cT09PbG1t+eWXX3jnnXekQAghCozsSbyE8KsJXL11j65eNfS+YX7w4AEAHh4eTJ06lYiICPr27SsFQghRoKRIvIRNf17GppQZ7ZpU0ds6U1JS+PDDD6lVq5ZmQr7AwEAqVKigtwxCiJJDikQ+xcQnE3ohlrZNqujtxkL79+/H3d2dL7/8ku7du2NhYaGX9QohSi4pEvm0/9RNFAW6e9XU+bpUKhUjRoygbdu2KIrCvn37WLZsGba2tjpftxCiZJMikQ/qLIWdR67R2NkBh3KldL4+U1NT7t+/z4QJEwgLC6Ndu3Y6X6cQQoAUiXw5Gh5D4v00nZ72eufOHQYNGsSFCxcA+Pnnn/nqq68oVUr3RUkIIR6TIpEP2w5epWK5UjSvX/DzNCmKwrp163B1dWXdunUcP34ckAn5hBCGIVuePIqITOCfqwm82bI6JsYFe7rpzZs36datG35+ftSoUYNTp04xcODAAl2HEELkhRSJPFCrs1gZEoFdaQs6tyz4KcEXL17Mvn37+Prrr/n7779xc3Mr8HUIIUReSJHIg60HrnL+WiKDfVyxsiiY014vX77MyZMnAZg+fTphYWGMHz9ec6tXIYQwJCkSWroclcSaPyJoVq9SgVw8p1ar+eqrr3B3d2fkyJGaCflq1tT9KbVCCKEtKRJaSEnLZN7ak9iVtmCsb6OXnvoiPDycFi1a8MEHH+Dt7c3WrVtlOg0hRKEkE/y9gKIoLP7tHLEJyXw6yvOlpwN/PCGfnZ0dv/76K3369JECIYQotGRP4gUOnY3mr9M36duxLvVq2Od7Offu3QOgSZMmTJ8+nYiICHx9faVACCEKNSkSz5GSlsmyzWHUeMWW3u1q52sZycnJTJgwgdq1axMbG4uxsTEzZ86kfPnyBZxWCCEKnhSJ59j452WSHqbzXk93TPJx7+p9+/ZRv359vvnmG3r16oWVlZUOUgohhO5IkchFSlom2w9dpaW7I3Wr5e2GQiqVCn9/f7y9vTE1NWX//v0sXryYMmXK6CitEELohhSJXOz4+xrJaSp6t6uT59eampqSnJzMpEmTOHv2LK1bt9ZBQiGE0D0pEjnIVKnZeuAKjepUoFYVO61eExcXx4ABAzh//jzwaEK+oKAgGWISQhRpUiRycPDMLZIepNOjda0XtlUUhZ9//hlXV1c2bNiguXpazloSQhQHUiRysP1wJJUdStPI+fm3BI2KisLHx4f+/ftTu3ZtTp8+zYABA/SUUgghdE+KxFMuXk/k0o0kOres/sK9gcWLF7N//36+/fZbDh06hKurq55SCiGEfsgV10/ZeyIKS3MTvJtWzfH5S5cukZSUxGuvvcaMGTMYPnw4r75a8DPCCiFEYSB7Ek/IVKk5ePomr7lWopSlWbbnVCoV8+bNo0GDBowaNUozIZ8UCCFEcSZF4glnLt0hOU1Fa4/K2R4/e/YszZo1Y/LkyXTu3Jlt27bJgWkhRIkgw01POHDmFtZWZjSq898B62PHjuHp6Um5cuUIDg6mZ8+eUiCEECWG7En8v/TMLI6Fx9DCzREzUxOSkpIAaNq0KYGBgZw/f55evXpJgRBClCh6KxKRkZH4+vrSsWNHfH19uXbt2jNt1Go1s2fPxtvbmw4dOhAcHKyveEREpZKarsazvj3jxo3LNiHftGnTKFcub1NzCCFEcaC3IjFr1iz8/PzYtWsXfn5+zJw585k227Zt48aNG+zevZv169ezYMECbt68qZd85yJTyEw8Ty+f1syfPx9fX19KlSqll3ULIURhpZcikZCQQEREBD4+USh/lQAADspJREFUPgD4+PgQERFBYmJitnY7duygd+/eGBsbU65cOby9vdm5c6fO88UmPGDL6i/ZtfIjzM3NOXDgAAsXLsTGxkbn6xZCiMJMLweuY2JiqFixIiYmJgCYmJjg4OBATExMtmGcmJgYnJycNL87Ojpy+/btPK0rPDw8z/kiolJBUeHrN5CxASOwsLAgNDQ0z8spikpKP58kfS4ZpM8Fo9id3eTm5oaFhUWeXuPhATUqzqNVi9d0lKpwCg0NxcPDw9Ax9Er6XDJIn7WXnp7+3C/XehlucnR0JDY2FrVaDTw6QB0XF4ejo+Mz7aKjozW/x8TEUKlSJX1ExNLcRC/rEUKIokQvRcLe3h4XFxdCQkIACAkJwcXF5Zkzhjp16kRwcDBZWVkkJiayd+9eOnbsqI+IQgghcqC3s5sCAwNZu3YtHTt2ZO3atcyePRsAf39/wsLCAOjevTuVK1fmjTfeoE+fPgQEBFClShV9RRRCCPEUvR2TqFmzZo7XPSxfvlzzs4mJiaZ4CCGEMDy54loIIUSupEgIIYTIlRQJIYQQuSo210koigJARkZGvpeRnp5eUHGKDOlzySB9Lhny0+fH28zH29CnGSm5PVPEPHjwgEuXLhk6hhBCFEl16tTJcSqiYlMksrKySE5OxszMTKbzFkIILSmKQmZmJtbW1hgbP3sEotgUCSGEEAVPDlwLIYTIlRQJIYQQuZIiIYQQIldSJIQQQuRKioQQQohcSZEQQgiRKykSQgghclWiikRkZCS+vr507NgRX19frl279kwbtVrN7Nmz8fb2pkOHDjlOb16UaNPnRYsW0aVLF7p27crbb7/NwYMH9R+0AGnT58euXr1KgwYNCAoK0l9AHdC2zzt27KBr1674+PjQtWtX4uPj9Ru0AGnT54SEBIYPH07Xrl3p3LkzgYGBqFQq/YctAEFBQbRr1w5nZ+dcZ5fQyfZLKUEGDBigbNmyRVEURdmyZYsyYMCAZ9ps3rxZGTJkiKJWq5WEhATFy8tLiYqK0nfUAqNNnw8cOKCkpKQoiqIo58+fVzw8PJTU1FS95ixI2vRZURRFpVIp/fv3VyZMmKB8/vnn+oxY4LTp87lz55TOnTsrcXFxiqIoyv3795W0tDS95ixI2vR5zpw5ms82IyND6dWrl7J9+3a95iwoJ06cUKKjo5W2bdsqFy9ezLGNLrZfJWZPIiEhgYiICHx8fADw8fEhIiKCxMTEbO127NhB7969MTY2ply5cnh7e7Nz505DRH5p2vbZy8sLKysrAJydnVEUhaSkJL3nLQja9hlg2bJltGnThurVq+s5ZcHSts8rV65kyJAh/F979x/UdP3HAfwpLijETIzpkOqOrgzMH5PPNk6ZKdy5wS3ABmGnnFBdBhnllWdq8wg4tdIzKT3+MLDLS8Ui0kV4cUpENYRzNuOicCdIgUOkDIfg2F7fP7jv5/Qb4zsbMsjX425/fLYPn8/zNY7Pa5/PPrzfISEhAIDJkycjICBg1POOBE9rnjBhAux2O1wuF65fvw6Hw4Hp06f7IrLXBEGATCYbdp3bcfy6Y5pER0cHpk+fjokTJwIYnAVPKpWio6Pjb+uFhoaKyzKZDBcvXhzVrCPF05pvVF5ejgcffBAzZswYrZgjytOam5qaUFtbi4yMDB+kHFme1my1WtHW1oaVK1di+fLl2Lt3r9uRP8c6T2vOzs7G+fPnERMTIz6ioqJ8EXlU3I7j1x3TJNj/d+rUKezevRs7d+70dZTbyuFwwGAw4K233hIPMncCp9OJX375BSUlJfj4449RU1ODL774wtexbqvKykrMmjULtbW1qKmpQUNDw7i9MuArd0yTkMlksNlscDqdAAb/YDo7O/92+iaTydDe3i4ud3R0jNtP1Z7WDABmsxnr16/Hnj17EB4ePtpRR4wnNV+6dAkXLlzACy+8gNjYWHz00UcoLS2FwWDwVWyvePp7Dg0NhVarhb+/P4KCghAXFweLxeKLyF7ztOYDBw4gMTERfn5+mDx5MmJjY1FXV+eLyKPidhy/7pgmMW3aNERERMBoNAIAjEYjIiIiEBwcfNN6Wq0WR44cgcvlQnd3N6qqqqDRaHwR2Wue1myxWLBu3ToUFhZi9uzZvog6YjypOTQ0FHV1dThx4gROnDiB1atX4+mnn0Z+fr6vYnvF09+zTqdDbW2tODS0yWTCY4895ovIXvO05rCwMNTU1AAYnFznhx9+wCOPPDLqeUfLbTl+efW19zhz7tw5SklJoWXLllFKSgpZrVYiInr++efJYrEQ0eAdL1u2bKG4uDiKi4ujQ4cO+TKy1zyp+amnniKVSkWJiYnio6mpyZexveJJzTcqLCwc93c3eVKz0+mkrVu3klarpYSEBNq6dSs5nU5fxvaKJzW3trZSRkYG6XQ6io+Pp9zcXHI4HL6M/Y/l5+eTWq2miIgIWrhwISUkJBDR7T9+8XwSjDHG3LpjLjcxxhi7ddwkGGOMucVNgjHGmFvcJBhjjLnFTYIxxphb3CTYuJaenj7mR+o9evQonn32WbevNzQ0jNv/xWH/ftwk2JgRGxuLuXPnQi6Xiw+bzTbqOdLT0zFnzhzI5XKoVCqsXbsWnZ2d/3h7iYmJKC4uFpdnzZqF1tZWcVkQBBw/ftyrzEN5//33MXv2bMjlcgiCgBUrVsBsNnv88/+bk92ZuEmwMaWoqAhms1l8+GrEzi1btsBsNuP48eP466+/sG3bNp/k8FZ8fDzMZjNMJhNUKhVeeeUVX0di4ww3CTamXblyBWvWrEF0dDQUCgXWrFnjdlTL1tZWrFq1ClFRUVCpVHj11VfF16xWKzIzM6FUKqHRaFBRUeHR/u+77z5oNBo0NzcDAE6fPg29Xo+oqCjo9XqcPn1aXLesrAxxcXGQy+WIjY3F0aNHxeefeeYZAMDKlSsBAElJSZDL5aioqEBdXR0WL14MYHD48pycnJsyFBQUoKCgAADQ09ODTZs2ISYmBmq1Grt27RLHLxqORCLBk08+CZvNJg6nbbFYkJaWBkEQEBMTg7y8PFy/ft1tTgA4efIkkpKSxDOTpqYmj95HNo55/T/bjI2QpUuX0nfffXfTc93d3VRZWUm9vb3U09NDL7/8MmVlZYmvr1q1ikpLS4mIaN26dbR3715yOp3U19dH9fX1RERkt9tp8eLF9Omnn5LD4aDGxkZSKpXU3Nw8ZI4bt3n58mVKT0+n119/nf744w8SBIE+//xzcjgcdOzYMRIEgbq7u8lut5NcLheHhrDZbPTrr78SEdFnn31GK1asELf/6KOPUktLi7hsMplIrVYTEdFvv/1Gc+fOpZ6eHiIaHGZh0aJFZDabiYgoOzubDAYD2e126urqIr1eTwcPHhyyjsLCQnrttdeIiKi/v5/effddUiqV4rAUZ8+eJbPZTA6Hg9ra2kir1VJJSYnbnI2NjRQdHU1nzpyhgYEBKisro6VLl1J/f/+Q+2f/DnwmwcaUl156CYIgQBAEZGdnY+rUqdBoNLjnnnsQFBSErKws1NfXD/mzEokE7e3t6OzsREBAAARBAABUV1dj5syZ0Ov1kEgkiIyMhEajGXbI6IKCAgiCgKSkJISEhGDjxo2orq7GQw89hOTkZEgkEuh0OoSHh+PkyZMAAD8/PzQ3N6Ovrw9SqfQfDSQ3c+ZMREZGoqqqCgBgMplw9913Y/78+ejq6sI333yDTZs2ITAwENOmTUNGRga+/PJLt9urrKyEIAiYN28ejhw5gsLCQkgkEgDA448/jvnz50MikSAsLAxpaWlu31sAOHz4MNLS0jBv3jxMnDgRy5cvx1133YUzZ87ccp1s/JD4OgBjN9qzZw8WLlwoLl+7dg3btm3Dt99+iytXrgAA7HY7nE7n3+aCWL9+PXbv3o2UlBRMmTIFmZmZSElJwe+//w6LxSI2DWBwaOnExES3Od58802kpqbe9FxnZ+dNE7oAgyPK2mw2BAYGYteuXSguLsbmzZuxYMECbNiwAQ8//PAtvwc6nQ5GoxHJyckwGo3i7Gvt7e0YGBhATEyMuK7L5Rp2tjKtVosdO3agu7sbOTk5aGxshEqlAjA4R/T27dvx008/4dq1a3A6ncOOAtze3o7y8nIcOHBAfM7hcHj1pT4b+7hJsDGtuLgY58+fR2lpKUJCQvDzzz8jOTl5yBnVQkJCxGv3DQ0NyMzMhEKhgEwmg0KhQElJiVdZpFLpTWP1A4Pj9avVagCD08Cq1Wr09fXhvffeg8FgwCeffHLL+4mPj8fbb7+Nixcv4uuvv8bhw4cBADNmzIC/vz9MJpN4NuCp4OBg5OXlQa/XQ6fTQSqVIjc3F5GRkdi5cyeCgoKwf//+Ye+ykslkePHFF5GVlXXLNbHxiy83sTHNbrcjICAA9957L/7880988MEHbtf96quvxC+1p0yZggkTJsDPzw9LlixBS0sLysvL4XA44HA4YLFYYLVabynLE088gZaWFhw7dgwDAwOoqKjAuXPnsGTJEnR1daGqqgq9vb3w9/dHYGAg/PyG/vO6//770dbW5nY/wcHBUCqV2LhxI8LCwsSzEalUikWLFmH79u24evUqXC4XLly4gFOnTnmUPzw8HGq1Gvv27QMw+N5OmjQJkyZNgtVqxcGDB4fNmZqaikOHDuHHH38EEaG3txfV1dW4evWqR/tn4xM3CTamrV69Gv39/YiOjkZaWpr4qX0oZ8+eRWpqKuRyObKysrB582Y88MADCAoKwocffoiKigqo1WrExMRgx44d4p08npo6dSqKiopQUlIClUqFffv2oaioCMHBwXC5XNi/fz/UajWUSiXq6+uRm5s75HbWrl2LN954A4IguL3LSqfT4fvvvxcvNf3XO++8A4fDgYSEBCgUCuTk5ODSpUse1/Dcc8+htLQUly9fxoYNG2A0GrFgwQIYDAYkJCQMm3POnDnIz89HXl4eFAoFli1bhrKyMo/3zcYnnk+CMcaYW3wmwRhjzC1uEowxxtziJsEYY8wtbhKMMcbc4ibBGGPMLW4SjDHG3OImwRhjzC1uEowxxtziJsEYY8yt/wC7Am0niFYvmwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**confusion_matrix**"
      ],
      "metadata": {
        "id": "Db8u8ZG7rHgl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix= confusion_matrix(y_test, pred)\n",
        "print(matrix)\n",
        "sns.heatmap(matrix ,annot=True, fmt='g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "f0QQ1je0xUvP",
        "outputId": "d6d82e6b-4fc4-48fa-d570-f6c7b57eb8ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[59517 40619]\n",
            " [ 2402 98102]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c7b992290>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* A way to evaluate the results is by the confusion matrix, which shows the correct and incorrect predictions for each class."
      ],
      "metadata": {
        "id": "yeDxFIxbGuMx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUBJ9Un8xUyo",
        "outputId": "d94fd5b0-a4ea-411b-dd41-8d282f61fc2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.96      0.73     61919\n",
            "           1       0.98      0.71      0.82    138721\n",
            "\n",
            "    accuracy                           0.79    200640\n",
            "   macro avg       0.79      0.83      0.78    200640\n",
            "weighted avg       0.86      0.79      0.79    200640\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#RandomForest Classifier"
      ],
      "metadata": {
        "id": "S8vUr5C4inMg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RF_model= RandomForestClassifier()\n",
        "RF_model= RF_model.fit(X_train, y_train)\n",
        "#Making prediction\n",
        "rf_pred= RF_model.predict(X_test)\n",
        "rf_proba= RF_model.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "Ukoc7CQkxU40"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**"
      ],
      "metadata": {
        "id": "i_FvcFN1q0_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "r_rf=  recall_score(y_test, rf_pred)\n",
        "print(\"recall_score : \", r_rf)\n",
        "\n",
        "p_rf= precision_score(y_test, rf_pred)\n",
        "print(\"precision_score :\",p_rf)\n",
        "\n",
        "f1_rf= f1_score(y_test, rf_pred)\n",
        "print(\"f1_score :\", f1_rf)\n",
        "\n",
        "A_rf= accuracy_score(y_test, rf_pred)\n",
        "print(\"accuracy_score :\",A_rf)\n",
        "\n",
        "acu_rf = roc_auc_score(rf_pred, y_test)\n",
        "print(\"ROC_AUC Score:\",acu_rf)"
      ],
      "metadata": {
        "id": "uZSBq2BCxU8R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e24e2fd5-f380-47f7-c317-8d4d2f46b09a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall_score :  0.9848961235373717\n",
            "precision_score : 0.8639180296391977\n",
            "f1_score : 0.9204489450535145\n",
            "accuracy_score : 0.9147228867623605\n",
            "ROC_AUC Score: 0.9231397914689912\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, rf_proba)\n",
        "\n",
        "plt.title('Random Forest ROC curve')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), linestyle=\"--\",color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "zs-SMrEQ-p0n",
        "outputId": "66502a1c-3ce7-4735-fc68-aa109a54b3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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tpki0a9eOkJAQ2rZtS3JyMvv372fLli1qx7JZRqNC2IVYfjhxjbALcWToDAB4VX+Wpl5ulCtVlGeKOVG8qCPlShWlbMkiFHFyQPOPnXlB/kMSoqCys7PjwIEDBAYGsmrVqlx9mH4SVikSCxYs4Pvvvyc+Pp7XX3+dUqVK8e233xIcHMzYsWOpV68eXbp04cyZM7Rt2xaAUaNGUalSJWvEy1e0OgPfHoli9+HLxN9Jp+QzTrT0fo5m9d3xql4ORwe59EWIgkar1fLuu+8yaNAgnn/+eXbs2EHRokWtMmRnlSIxc+ZMZs6c+dDza9euNX1vb2/PvHnzrBEn3zp+LobVO84SfycdjyplGBhUh+b13XGwl8IgREF17NgxhgwZQmRkJCVKlGDChAkUK1bMatu3meEmkTODwcj60Eh2HbpMFbcSjOvtTYOarmrHEkJYUEpKCrNmzeLjjz+mYsWK7Nmzh/bt21s9hxQJG3c/Tcf7m05x+mIcQc1fYHCnujKkJEQhsGDBAj766CNGjhzJokWLKFGihCo5pEjYsNR0HfPWHuVSdDLDu9UjqHlVtSMJISwoOTmZ27dvU6NGDaZNm0aHDh3w8/NTNZN8JLVRGToDc9ce41J0Mm/295UCIUQBt2vXLjw9PenduzeKolCyZEnVCwRIkbBJOr2RhRtOcP5qIpP7+dCsvmVPcRNCqCc2NpZevXrRtWtXXF1dWbNmjU1daCjDTTbGaFT4eNtpTl+I443uXvg1yHlSRCFE/nb27FlatWrF/fv3WbhwIVOmTMnxNqJqkSJhY7bsu8Ch8Bv0CqhJh6YvqB1HCGEBBoMBe3t7PDw86NatG5MmTcLDw0PtWI8kw0025ETkLb7cfwl/30r0a1db7ThCiDxmNBpZtWoVdevWJTk5GUdHR9atW2ezBQKkSNiMuKRUlmwJw61scYZ3q2dTY5JCiKd36dIlXn75ZdNsEqmpqWpHMosUCRugNxj58P9Ooygwb9hLFCtiW2OSQojcMxgMLF68GC8vLyIiIli/fj379u2z+JxLeUWKhA34aOtpfr+SwIhXvHB7trjacYQQeUij0XDw4EE6duxIZGQkgwYNylcjBXLgWmWf74nkUPgNerephb+vTGgoREGg1Wp55513GDx4cLYJ+fIj6UmoaN+xq4Qc+IOmXm70alNT7ThCiDzw66+/0qBBA95++22+/vprgHxbIECKhGpuJaSwbtc5qlcqxdie3jKTqxD53P379xk7dizNmzcnNTWVvXv3Mn78eLVjPTXZM6lkzc4I7OzseGvgixQvKgeqhcjvFixYwIoVKxg9ejTnzp0jMDBQ7Uh5Qo5JqODHU9GcjIyld5tauJa23rzwQoi8lZSUxO3bt6lZsyZvvfUWnTt3pmnTpmrHylPSk7Cym/H3WbcrghfcS9Ddv7racYQQubR9+3Y8PDyyTchX0AoESJGwKr3ByCdfnSVNq2dMzwYUcZKOnBD5za1bt+jRowc9evTA3d2dzz77LF+d0vqkZC9lRQdOXuO3P27Tv11talQqrXYcIcQTOnPmDK1atSI1NZVFixYxadIkm5uQL69JkbCSxLvprNl5jufKPcOrreV0VyHyE71ej4ODA56envTo0YNJkyZRq1YttWNZhQw3WYHRqPDB5jAydAamDXwRjabgdk2FKEiMRiPLly/H09PTNCHfmjVrCk2BACkSVrHv+F9EXI5nYEdPqripc59aIcSTuXDhAi1atGDs2LFUrVqVtLQ0tSOpQoqEhSXdS2f1jrPUrVaWLi2qqR1HCPEfDAYD77zzDvXr1ycyMpKNGzfy3Xff4ebmpnY0VcgxCQv7eFs4ACO6eeHoIDVZCFun0Wg4fPgwnTt3ZsWKFZQvX17tSKqSvZYFhV2I5fSFOPq3q83zMswkhM1KS0tj5syZXL16FTs7O3bs2EFISEihLxAgRcJitDoDq3ec5blyxenaUoaZhLBVR44coUGDBixcuJBvvvkGyN8T8uU1KRIW8tWBP7iVkMob3evj6GCvdhwhxL/cu3eP0aNH4+fnR0ZGBj/88ANjx45VO5bNkSJhAanpOkKPXOGlem7Ur1FO7ThCiEdYuHAhq1atYvz48Zw7d46AgAC1I9kkOXBtAXuPXuV+mo5XW9dQO4oQ4h8SEhKIj4+nVq1avPXWW3Tt2pUmTZqoHcumSU8ij6Wm6/j2lyhqVS4tU28IYSMUReGrr77C09OTPn36mCbkkwLx36RI5LEdP/3J7eQ0Xu9UR+0oQgggJiaG7t278+qrr1KpUiXWr19foCfky2tWG26Kiopi2rRpJCcnU6pUKRYvXkyVKlWyLZOQkMBbb71FTEwMer2exo0bM3PmTBwc8seo2J37Wr45fJkmdd2oU7Ws2nGEKPTOnDnDyy+/THp6OosXL2bixIn5Zn9iK6zWk5gzZw59+/Zl37599O3bl9mzZz+0zOrVq6lWrRq7d+/mm2++4ffff+f777+3VsSntvvIFdK0Bvq3q612FCEKNb1eD4Cnpye9evXizJkzvPnmm1IgcsEqRSIhIYHIyEiCgoIACAoKIjIyksTExGzL2dnZkZKSgtFoJCMjA51Ol28uZolPTuPrg5dpVt+dyhXkwjkh1GAwGFi6dCk9evQgKSkJR0dHVq9eTc2aMvNyblmlrMbExFC+fHns7TOvF7C3t8fV1ZWYmBjKlCljWm7kyJGMGTOG5s2bk5aWRr9+/fDx8XmibZ07dy7XOcPCwnL93t0nktDrDfg+b3yq9VhbfsqaV6TNBdOVK1eYP38+ERERNGvWjBMnTvDss8+qHcuqLPF7tqm+1969e6lVqxYbN24kJSWF4OBg9u7dS7t27cxeR926dXF2dn7ibYeFhT1xQcpy7dZdwi//RFDzqgS0rJerdajhadqcX0mbC56sCfkWLFiAi4sLmzdvplatWvj6+qodzapy+3vWarWP/XBtleEmNzc3YmNjMRgMQOYvNS4u7qFZFTdv3kznzp3RaDS4uLjg7+/P8ePHrRHxqYQc+AMHB3t6BkiXVghr02g0HDt2jFdeeYXIyEj69esnZy/lIasUibJly+Lh4UFoaCgAoaGheHh4ZBtqAqhYsSKHDh0CICMjg6NHj1Kjhm1fkHbz9n0OhV+nY7MXKPnMk/dghBBPLi0tjenTpxMVFYWdnR3bt29n69atuLq6qh2twLHa2U1z585l8+bNBAYGsnnzZubNmwdAcHAwERERAEyfPp2wsDA6depE165dqVKlCj179rRWxFzZe+wvsLOjS4uqakcRolD4+eef8fLyYtGiRaYPnkWKFFE5VcFltWMS1apVIyQk5KHn165da/q+cuXKrF+/3lqRnlpquo59x67S3MudsiVl1kghLOnu3btMnTqV1atXU7VqVQ4cOIC/v7/asQo8ueL6Kfx0KprUdD1dZCpwISzunXfeYc2aNUycOJGzZ89KgbASmzq7Kb/5/sQ1qj5XkpqVZY4mISwhPj6e27dv4+HhwfTp0+nWrRuNGzdWO1ahYnZP4pdffmH69OmMGDECgIiICI4ePWqxYLbu8vVkrty4Q9tGldWOIkSBoygK27Ztw8PDg379+qEoCiVKlJACoQKzisSmTZuYO3cuVapU4eTJk0DmgaKlS5daNJwt++HENRwdNLRsWFHtKEIUKDdu3KBr16706dOHqlWr8vnnn8sprSoya7hp48aNbNiwgYoVK5oONFetWpWoqCiLhrNVGToDB09f56V6bjxTzEntOEIUGL/99hstW7ZEp9OxZMkSxo0bZ5qpQajDrCKRkpJiuvAtq6Lr9XocHR0tl8yGHY2IISVNR9tGz6sdRYgCQafT4ejoSJ06dejXrx+TJk2iWjU5IcQWmDXc9OKLL7JmzZpsz33++eeFdnzwwMlruJYpRr3qhWteGCHymsFg4MMPP6R27dqmCflWrVolBcKGmFUkZs6cyQ8//IC/vz8pKSkEBgby3XffMW3aNEvnszn3UjM482c8LRo8h0Yj46RC5Na5c+do2rQpkyZNwtPTk4yMDLUjiUcwa7jJ1dWV7du3ExERwY0bN3Bzc8PLywuNpvBdZnEy8hZGo8JL9dz+e2EhxEMMBgMLFixg4cKFlCxZkq1bt9KrVy85OG2jzNrLv/HGG9jZ2eHl5UX79u1p0KABGo2G0aNHWzqfzfn1bAzPlixCjUql1I4iRL6k0Wg4efIkr776KpGRkfTu3VsKhA0zq0jkNBPriRMn8jSMrUvX6gm/GEeTum7yn1qIJ5Camsqbb77JlStXTBPybdmyhXLlyqkdTfyHxw43ZV0HodPpHromIjo6Gnd3d8sls0Fn/4wnQ2+kSV0ZahLCXD/99BNDhw7lypUrPP/884waNSpX93wR6nhskbh16xaQefVj1vdZ3NzcGDNmjOWS2aCIy/E42GvweKHMfy8sRCF3584dpkyZwtq1a6levToHDx6kZcuWascST+ixRWLRokUAeHt72/yU3dYQGZVAzcqlcHKUi3uE+C+LFi3if//7H1OmTGHu3LkUK1ZM7UgiF8w6uymrQNy/f5+kpKRsr1WqVCnvU9mgdK2ey9fv8Eqr6mpHEcJm3b59m/j4eNOEfD169Ch0txEtaMwqEpcvX2bSpElcuHABOzs7FEUxHbg9f/68RQPaiot/JWEwKni+UFbtKELYHEVR2Lp1K2PHjqVy5cqEhYVRokQJKRAFgFlnN82dO5fGjRtz4sQJnnnmGU6ePEmvXr149913LZ3PZvwelYCdHXhUkeMRQvxTdHQ0nTp1ol+/flSvXp1NmzbJ2X8FiFk9iQsXLvDZZ5/h6OiIoii4uLjw5ptvEhQURJcuXSyd0SZERiXwgltJihctnPNVCfEo4eHhtGzZEoPBwEcffcSYMWNkQr4CxqyehLOzM3q9HoDSpUtz8+ZNjEYjycnJFg1nKxRF4cqNu1SXC+iEADBNoVG3bl1ee+01IiIiGD9+vBSIAsisIuHj48N3330HQGBgIMHBwQwYMIAmTZpYNJytuHM/g3upGVSu4KJ2FCFUpdfref/996lVqxaJiYk4OjqyYsUKqlatqnY0YSFmDTf980K6iRMnUr16dVJTU+nWrZvFgtmS6Nh7AFQuL0VCFF5nz55lyJAhnDp1iq5du5pGF0TB9sQz9Gk0Grp27UqPHj3YsWOHJTLZnGu37gJIT0IUSgaDgdmzZ+Pj48O1a9f48ssv2bFjB66urmpHE1bwn0Xi6NGjfPbZZ+zfvx/I7G5+/vnntG7dmm3btlk8oC24FnuPYkUcKFOiiNpRhLA6jUZDeHg4ffv2JTIykldffVXOXipEHjvctGbNGj755BOqV6/On3/+SZ8+fThx4gROTk7Mnz+fl19+2Uox1RUde5/K5V3kD0MUGikpKcyZM4eRI0dStWpVtm/fjpOT3Kq3MHpskfjiiy/YtGkTdevW5bfffqNPnz5MnTqVQYMGWSmebbgWe5dGnhXUjiGEVezfv59hw4YRFRVF1apVGTlypBSIQuyxw01JSUnUrVsXgAYNGuDk5MTAgQOtEsxW3Lmv5c79DCpXKKF2FCEsKjk5mSFDhtCmTRscHBw4dOgQI0eOVDuWUNl/HpNQFAWj0YjBYDBN72s0Gk1fBZ2c2SQKi3fffZeNGzcybdo0zpw5g5+fn9qRhA147HBTamoqnp6epseKopgeZ83fVNDnbrr2oEhUkiIhCqDY2Fji4+OpU6cO06dPp2fPnjRs2FDtWMKGPLZIHDhwwFo5bFb0rXsUdXbg2VJyZpMoOBRFYdOmTYwfP54qVaqYJuSTAiH+7bFF4rnnnrNWDpt1MyGF58oVlzObRIFx7do1hg8fzt69e2natCn/+9//5P+3yJFZV1znhaioKKZNm0ZycjKlSpVi8eLFVKlS5aHl9uzZwyeffGIazlq/fj3PPvustWI+5HZSGhVdn1Ft+0LkpdOnT9OyZUsURWHZsmWMGjUKjeaJr6kVhYjVisScOXPo27cvXbp0YdeuXcyePZvPP/882zIRERGsWLGCjRs3Uq5cOe7du6fqqXeKohCfnEqDmnKzdpG/abVanJ2d8fLyYvDgwUyYMOGRH9KE+DerfIRISEggMjKSoKAgAIKCgoiMjCQxMTHbchs2bHArVk8AACAASURBVGDw4MGUK5e5U3ZxcVH1hukp6XrStAbKlSqqWgYhnoZer2fDhg2mCfkcHBxYunSpFAhhticqEjExMfz2229PvJGYmBjKly9vmkbY3t4eV1dXYmJisi13+fJloqOj6devH926dWPVqlUoivLE28sr8clpADwrRULkQ7/99huNGzdmxYoV+Pj4YDAY1I4k8iGzhptu3rzJxIkTTbcvDQ8PZ+/evRw+fJiFCxfmWRiDwcDFixdZv349GRkZDB06FHd3d7p27Wr2Os6dO5fr7YeFhWV7fOlGZpFIiP2LsLBbuV6vLft3mwuDgt5mg8HAp59+ysaNG03H/1q3bs21a9e4du2a2vGspqD/nh/FEm02q0jMnj2bl19+mf/7v/+jcePGADRr1ozFixebtRE3NzdiY2MxGAzY29tjMBiIi4vDzc0t23Lu7u60a9cOJycnnJycaN26NWfPnn2iIlG3bt1cDVGFhYXh4+OT7bk4bRSQgF8Tb8qWLHi9iUe1uaArDG1WFIUFCxbQv39/PvzwQ6Kiogp8m/+tMPye/y23bdZqtY/9cG3WcFNERATDhg1Do9GYTpVzcXHh3r17ZoUoW7YsHh4ehIaGAhAaGoqHhwdlymS/X3RQUBBHjhxBURR0Oh3Hjh2jdu3aZm3DEm4np2GvsaOUi1wjIWzb/fv3mTBhApcvX8bOzo7t27ezYcOGh/7GhHhSZhWJsmXL8tdff2V77s8//3yoJ/A4c+fOZfPmzQQGBrJ582bmzZsHQHBwMBEREQB07NiRsmXL0qFDB7p27Ur16tXp0aOH2dvIa7eT0yhbsgj2GjmHXNiuffv2UadOHZYuXcoPP/wAgKOj3Itd5A2zhpsGDx7MiBEjGDZsGHq9ntDQUD799FOCg4PN3lC1atUICQl56Pm1a9eavtdoNLz11lu89dZbZq/Xkm4npVGudDG1YwjxSImJiUycOJGNGzdSu3ZtDh8+TLNmzdSOJQoYs4pEjx49KFWqFF988QVubm7s3LmTcePGERAQYOl8qopPTqP289JdF7bpvffeY/PmzcyYMYOZM2dSpIgMi4q8Z1aRMBgMBAQEFPii8E9Go0LCnTTKlS54B6xF/nXr1i3i4+OpW7cuM2bMoHfv3jRo0EDtWKIAM+uYRLNmzZg7d26hOqUs+b4WvUGRaySETVAUhQ0bNuDp6clrr72Goii4uLhIgRAWZ1aR+OyzzyhWrBiTJk3C39+fJUuWcPHiRUtnU9XtpFQA6UkI1V29epV27drx+uuvU6dOHbZu3SoT8gmrMWu4ydPTE09PT958801OnDhBaGgoAwcOpFy5cuzevdvSGVURn5wOIFNyCFWdPn2aFi1aYGdnx8qVKxkxYoRMyCes6on/t1WtWpVq1arh7u7OjRs3LJHJJtxOftCTkCIhVJCenvkhxcvLi+DgYM6dO8fIkSOlQAirM+t/3N27dwkJCWHgwIEEBARw4sQJhg4dytGjRy2dTzW3k9Mo4mRP8aJyvrmwHp1Ox8KFC6lZsyYJCQk4ODjw0Ucf8fzzz6sdTRRSZg03+fn54e3tTVBQEMuXL6dEiRKWzqW6zGskisrYr7Ca06dPM3jwYM6cOUPPnj1VndxSiCxmFYkffvgBV1dXS2exKfHJaTxbAOdrErbHYDAwY8YMPvjgA1xdXfn666+faL4yISwpxyJx8uRJXnzxRSBzCu/Lly8/crmXXnrJMslUFp+cRhW3gt9jEurTaDScP3+eQYMG8cEHH1CqVCm1IwlhkmORmDdvnmlCvhkzZjxyGTs7Ow4cOGCZZCrS6Q0k3dPKlBzCYu7du8fMmTMZM2YM1atX56uvvpL5loRNyrFIZBUIgB9//NEqYWxFwp2s019lmgOR97777juGDx/O9evX8fT0pHr16lIghM0y6+ymN95445HPjx49Ok/D2Iq7KRkAlHxGvVunioInISGB1157jQ4dOuDi4sIvv/zC8OHD1Y4lxGOZVSSOHz/+yOdPnDiRp2FsRXqGHoAiTmYd1xfCLO+//z5bt25l1qxZnD59usAezxMFy2P3gkuXLgUyz93O+j5LdHQ07u7ulkumovSMzHsBOzvZq5xE5Hc3b94kISGBevXqMWPGDPr27YuXl5fasYQw22OLxK1bmfd1VhTF9H0WNzc3xowZY7lkKtJqM4tEESkSIpcUReGzzz5j0qRJVK1albCwMFxcXKRAiHznsUVi0aJFAHh7e9OzZ0+rBLIFWcNNzjLcJHLhypUrDBs2jAMHDtCyZUvWrVsnF2WKfCvHveD169epWLEikHktRHR09COXq1SpkmWSqUirk56EyJ3Tp0/j5+eHvb09q1evJjg4WOZbEvlajkWiU6dOhIeHA9CmTRvs7OwemibAzs6O8+fPWzahCuSYhHhS6enpFClSBC8vL9544w3GjRtXID9AicInxyKRVSAALly4YJUwtiI9Q4+dHTg7SpEQj5eRkcHixYtZs2YNv/32G2XLluWDDz5QO5YQeSZXg+7R0dHY2dmZhqMKGm2GAWdHexlHFo918uRJhgwZQkREBL1791Y7jhAWYdZg6cSJEzl9+jQA27dvp2PHjgQFBRESEmLRcGrRZhhkqEnkyGAw8Oabb9KkSRMSEhLYtWsXW7dupWzZsmpHEyLPmVUkjh49St26dQHYsGED69evJyQkhLVr11o0nFrSM/RyZpPIkUaj4c8//2TIkCFERkbSuXNntSMJYTFm7Ql1Oh1OTk7ExsaSnJyMj48PAPHx8RYNp5b0DIOc2SSyuXPnDjNmzGDcuHHUqFGDL7/8EgcH+SAhCj6z/pd7eHjw6aefcuPGDV5++WUAYmNjeeaZZyyZTTVaKRLiH7799luGDx9OTEwMXl5e1KhRQwqEKDTMGm5auHAhly5dQqvVMm7cOCDz7KdOnTpZNJxa0jP0ODvKTqCwu337Nv369SMoKIjSpUtz9OhRhg0bpnYsIazKrD1h5cqVWbJkSbbn2rVrR7t27SwSSm1anYHSLjJ1c2G3ZMkSQkJCmDt3Lm+99RZOTk5qRxLC6sz+uLx9+3Z27dpFbGws5cuXp0uXLnTv3t2S2VSTrjVQpKwMNxVGN27cICEhAS8vL2bOnEn//v1NJ20IURiZVSQ++eQTdu7cyeDBg3F3d+fmzZusW7eOuLi4HO81kZ9pM/QyTXghoygK69atY/LkyVSrVo2wsDCeeeYZKRCi0DNrTxgSEsKmTZt47rnnTM81b96c/v37F8giIWc3FS6XL18mODiYn376iVatWrF27Vq5kFKIB8wqEmlpaZQpUybbc6VKlSI9Pd0iodSm1cnFdIVFWFgYfn5+ODo6smbNGoYOHSoFQoh/MOvsJj8/PyZPnsyVK1dIT0/n8uXLTJs2jebNm5u9oaioKHr16kVgYCC9evXi6tWrOS575coV6tevz+LFi81ef14xGBV0eqNcTFfApaWlAVC/fn1Gjx5NZGQkwcHBUiCE+BezisTs2bMpXrw4nTt3xtvbm65du1K0aFFmzZpl9obmzJlD37592bdvH3379mX27NmPXM5gMDBnzhwCAgLMXnde0ppuXSo9iYIoIyODuXPnUqNGDeLj43FwcOC9997LNpQqhPjbf35cvnfvHteuXWP27Nm8++67JCUlUbp06SeaIz8hIYHIyEjWr18PQFBQEPPnzycxMfGhYaw1a9bw8ssvk5qaSmpq6hM25+llTRMuRaLgOX78OP379+fKlSv0799f7vMghBkeWyQOHjzI+PHjSU9Pp3jx4qxcuZImTZo88UZiYmIoX7489vaZO157e3tcXV2JiYnJViQuXLjAkSNH+Pzzz1m1atUTbwfg3LlzuXofZI5PJ9zL7EncvBFNWFhirteVX4SFhakdweL0ej3Lli1j69atuLq68vHHH9O8eXOioqKIiopSO55VFIbf879Jm/PGY4vE0qVLmTx5Mt27d+fLL7/k448/Ztu2bXkeAjLnh5o1axaLFi0yFZPcqFu3Ls7Ozk/8vrCwMHx8fIi6eQe4Re1a1fHxcs91jvwgq80FnaIopKenM2LECHr16kXLli3VjmRVheX3/E/SZvNptdrHfrh+bJGIjo6mf//+APTr14/Vq1c/cQAANzc3YmNjMRgM2NvbYzAYiIuLw83NzbTM7du3uXbtmmnag7t376IoCvfv32f+/Pm52m5uaGW4qUBITk5m+vTpTJgwwTQhn729faH8dCnE03hskTAajX8v6OCAwWDI1UbKli2Lh4cHoaGhdOnShdDQUDw8PLINNbm7u3P8+HHT4+XLl5OamsrUqVNztc3cSjcduJazm/Krb775hjfeeINbt27RsGFDatSo8VS9UyEKs8fuCdPT0+nXr5/pcUpKSrbHAFu2bDFrQ3PnzmXatGmsWrWKEiVKmE5vDQ4OZuzYsdSrV+9Js1uE3N86/4qLi2Ps2LF88cUX1KtXj127duHr66t2LCHytccWiYULF2Z73KNHj1xvqFq1ao+8k11ONy4aM2ZMrrf1NLKGm+T+1vnPhx9+yNdff838+fN58803ZUI+IfLAY4tEt27drJXDZvx9CqwMN+UH0dHRJCQk0KBBA2bNmsVrr72Gp6en2rGEKDDkRPF/MV1M5yw9CVtmNBr55JNPqFOnDkOGDEFRFIoXLy4FQog8JkXiX+RiOtv3xx9/0KpVK0aOHEnjxo356quvZDoNISxExlT+JT1Dj8YOHOylftqiU6dO4efnh7OzM//73/94/fXXpUAIYUGyJ/yXzBlgHWTHY2Oypmjx9vZm/PjxREZGMnjwYPk9CWFhZhWJjIwMPvroI1q3bm26ou/IkSNs3rzZouHUoJV7SdgUrVbLrFmzTBPy2dvbs2jRItzdC/bV8ELYCrOKxDvvvMOlS5f44IMPTJ/catSowdatWy0aTg3pWoOc2WQjjh49ire3NwsWLCAgIEAm5BNCBWbtDffv38/3339PsWLFTH+o5cuXJzY21qLh1JCeoZcL6VSm1+uZPHkyy5Yto1KlSnz33Xe0a9dO7VhCFEpmfTRzdHR8aEqOxMRESpUqZZFQapK70qnPwcGB69evM3LkSM6dOycFQggVmVUk2rVrx9SpU4mOjgYypz94++236dixo0XDqUGOSagjKSmJ4cOHc/HiRQC++OILVqxYgYuLi8rJhCjczCoSEyZMoGLFinTu3Jm7d+8SGBiIq6sro0aNsnQ+q0vP0MsxCSv7+uuv8fT05H//+x+HDx8GkAn5hLARZu0NnZycmD59OtOnTycxMZHSpUsX2FMP0zNkuMlabt26xZgxY/jqq69o0KAB3377LQ0bNlQ7lhDiH8wqElnDTFlSUlJM31eqVClvE6lMm6GXyf2s5OOPP2b37t288847TJ48GUdHR7UjCSH+xawi0aZNG+zs7FAUxfRcVk/i/PnzlkmmEm2GgSLOMtxkKdeuXSMhIQFvb29mzZrFoEGDqF27ttqxhBA5MGtveOHChWyPb9++zYoVKwrkXP3pcuDaIrIm5Js2bRo1a9bk1KlTFC9eXAqEEDYuV1cnlStXjhkzZvDhhx/mdR5V6fRGDEZFjknksYsXL9KyZUtGjx5N06ZN2b59e4E9piVEQZPrcZUrV66QlpaWl1lUlzVNuLOjDDfllZMnT+Ln50exYsXYsGEDr732mhQIIfIRs/aGffv2zfaHnZaWxp9//lngToHV6mSa8LySkpJC8eLFadiwIZMmTWLMmDFUqFBB7VhCiCdkVpF49dVXsz0uWrQotWvXpkqVKpbIpBq5l8TTS09PZ/78+axfv54zZ85Qrly5h26DK4TIP/6zSBgMBo4dO8b8+fML/D2D07UPhpvkYrpc+eWXXxgyZAgXL15k0KBBODjIz1GI/O4/D1zb29vzyy+/FIpxZOlJ5I5er2fMmDH4+fmRnp7Ovn37WL9+PaVLl1Y7mhDiKZl1dtPAgQNZvnw5Op3O0nlUpX1QJOTspifj4OBAbGwsY8aM4dy5c7Rt21btSEKIPPLY8YDQ0FCCgoLYvHkz8fHxrF+/njJlymTrVRw8eNDSGa1Gq8scbpK5m/5bYmIiU6dOZfLkydSqVYtt27bJ/R6EKIAeuzecPXs2QUFBvP/++9bKoyoZbjLP9u3bGTVqFAkJCTRt2pRatWpJgRCigHpskciahqNRo0ZWCaO2dBlueqyYmBhGjx7Njh07aNiwIXv37qVBgwZqxxJCWNBji4TRaOTYsWPZ5mz6t5deeinPQ6nFdDGdDDc90tKlS/n222959913mTRpkpy9JEQh8Ni/8oyMDGbMmJFjkbCzs+PAgQMWCaYGGW562NWrV0lKSjJNyDd48GBq1qypdiwhhJU8tkgULVq0QBWB/6LNMOBgb4eDvYyvGwwGVq5cyfTp06lduzYnT56kePHiUiCEKGRkb/gP6Rl6GWoic/r3Fi1aMG7cOPz8/NixY0ehuE5GCPEwsw5cFxZyf+vMCfmaN2/OM888w6ZNm+jXr58UCCEKsccWifDw8DzbUFRUFNOmTSM5OZlSpUqxePHih+Z+WrlyJXv27EGj0eDo6MiECRPw8/PLswz/JT3DUGjvSnfv3j1cXFxo2LAhU6dOZfTo0bi6uqodSwihMqsNN82ZM4e+ffuyb98++vbty+zZsx9axsvLi6+++sp0S8sJEyaQnp5urYgPehKFa7gpLS2NadOmUaNGDeLi4rC3t+ftt9+WAiGEAKxUJBISEoiMjCQoKAiAoKAgIiMjSUxMzLacn58fRYsWBaBWrVooikJycrI1IgJZxyQKT0/i9OnT1K9fn8WLFxMUFFTgJ3AUQjw5qxSJmJgYypcvj7195g7Y3t4eV1dXYmJicnzPzp07qVy5slXvQVBYjkno9XpGjRrFsGHD0Ov17N+/n3Xr1lGqVCm1owkhbIxNjq2cOHGCpUuX8tlnnz3xe8+dO5fr7SbduYed0YGwsLBcryO/+OOPP+jTpw8jR46kaNGihaLNWQpTW7NImwsHS7TZKkXCzc2N2NhYDAYD9vb2GAwG4uLicHNze2jZ8PBwpkyZwqpVq6hateoTb6tu3bo4Ozs/8fvCwsKws3eigmsZfHx8nvj9ti4hIYEpU6bw5ptvUrt2bfbu3Ut4eHiBbOvjhIWFSZsLAWmz+bRa7WM/XFtluKls2bJ4eHgQGhoKZM4u6+HhQZkyZbItd/bsWSZMmMCyZcuoU6eONaJlo9UZCtwxCUVR+PLLL/Hw8GDTpk0cO3YMQCbkE0KYxWp7irlz57J582YCAwPZvHkz8+bNAyA4OJiIiAgA5s2bR3p6OrNnz6ZLly506dKFixcvWisi2gx9gTq76ebNm3Tr1o1evXrx/PPPExYWxqBBg9SOJYTIR6y2R6xWrRohISEPPb927VrT99u3b7dWnIcoikJ6ATtwvXz5cvbt28f777/P+PHjZUI+IcQTk73GA3oDKEr+nyb8ypUrJCUl4ePjw6xZsxgyZAjVq1dXO5YQIp+SgekHdAYjkH+LhMFg4OOPP6ZevXoMHz4cRVEoVqyYFAghxFORIvFAhj5znqr8eEzi999/p1mzZkyYMIFWrVqxc+dOmW9JCJEn8t8e0UJ0piKRv3oSJ06coHnz5pQsWZL/+7//o3fv3lIghBB5RnoSD2T1JPLLBH/37t0DwMfHh+nTpxMZGUmfPn2kQAgh8pQUiQd0+WS4KTU1lSlTplC9enXThHxz586lXLlyakcTQhRAtr1HtCLTgWtn2+1JHDx4kKFDh3L58mWCg4NzdWW5EEI8CelJPGDLB671ej3Dhw+nVatWKIrCgQMHWLNmDSVLllQ7mhCigJMi8YAtH7h2cHDg7t27TJw4kYiICPz9/dWOJIQoJKRIPGBrB65v377NwIEDuXDhAgBbtmxhyZIlFCtWTOVkQojCRIrEAzrDgyKhck9CURS2bt2Kp6cnW7du5cSJE4BMyCeEUIfseR7IGm5yVvGYxPXr1+ncuTN9+/alatWqnD59mtdee021PEIIIUXigQy9EScHDfYa9a4zWLVqFQcOHODDDz/k119/pW7duqplEUIIkFNgTXR6RZWhpj///JPk5GR8fX2ZOXMmQ4YMoVq1albPIYQQjyI9iQcy9IpVh5oMBgNLlizBy8uLESNGmCbkkwIhhLAlUiQe0BkUq53+eu7cOV566SUmT55MQEAAu3btkuk0hBA2SYabHtDprVMksibkK1WqFNu2baNnz55SIIQQNkt6Eg9k6I0WHW66c+cOgOnYQ2RkJL169ZICIYSwaVIkHrDUgeuUlBQmTpxIjRo1iI2NRaPRMHv2bJ599tk835YQQuQ1GW56wBLHJA4cOEBwcDBRUVG88cYbFC1aNE/XL4QQliY9iQcy9EqeTe6n1+sJDg4mICAABwcHDh48yKpVqyhRokSerF8IIaxFisQDeTnc5ODgQEpKCm+++SZnzpyhZcuWebJeIYSwNikSD2Tolaea3C8uLo4BAwZw/vx5IHNCvsWLF8sQkxAiX5MiARiNCnpD7oabFEVhy5YteHp68uWXX3Lq1CkAOWtJCFEgSJEAMnQG4MnvJREdHU1QUBD9+/enRo0ahIeHM2DAAEtEFEIIVUiRANIzclckVq1axcGDB/n44485cuQInp6elognhBCqkVNggfQMPWDevSQuXbpEcnIyjRo1YtasWQwbNowXXnjB0hGFEEIV0pMAtA96Eo+74lqv1/Pee+9Rv359Ro4caZqQTwqEEKIgkyIBaP/jmMSZM2do3LgxU6dOpX379uzevVsOTAshCgUZbuLv4aZHnd10/PhxmjdvTpkyZQgJCaF79+5SIIQQhYb0JPj7wPU/j0kkJycD8OKLLzJ37lzOnz9Pjx49pEAIIQoVqxWJqKgoevXqRWBgIL169eLq1asPLWMwGJg3bx4BAQG0adOGkJAQq2TTav8uEvfv32f8+PHZJuSbMWMGZcqUsUoWIYSwJVYrEnPmzKFv377s27ePvn37Mnv27IeW2b17N9euXeP777/niy++YPny5Vy/ft3i2bKGm47/8jP16tVj6dKl9OrVi2LFill820IIYcusUiQSEhKIjIwkKCgIgKCgICIjI0lMTMy23J49e3j11VfRaDSUKVOGgIAA9u7da/F8qWlazuxbTq8enXFycuLQoUOsWLECFxcXi29bCCFsmVUOXMfExFC+fHns7TPH/O3t7XF1dSUmJibbME5MTAzu7u6mx25ubty6deuJtnXu3LknzpcQn4Idel57bSDDhw/D2dmZsLCwJ15PflRY2vlP0ubCQdqcNwrc2U1169bF2dn5id7j7a1Q67n3aPZSIwulsk1hYWH4+PioHcOqpM2Fg7TZfFqt9rEfrq0y3OTm5kZsbCwGQ+YBYoPBQFxcHG5ubg8td/PmTdPjmJgYKlSoYPF8Go2dVe5vLYQQ+Y1VikTZsmXx8PAgNDQUgNDQUDw8PB46Y6hdu3aEhIRgNBpJTExk//79BAYGWiOiEEKIR7Da2U1z585l8+bNBAYGsnnzZubNmwdAcHAwERERAHTp0oWKFSvStm1bevbsyahRo6hUqZK1IgohhPgXqx2TqFat2iOve1i7dq3pe3t7e1PxEEIIoT654loIIUSOpEgIIYTIkRQJIYQQOSow10koigJARkZGrteh1WrzKk6+IW0uHKTNhUNu2py1z8zah/6bnZLTK/nMvXv3uHTpktoxhBAiX6pZs+YjpyIqMEXCaDSSkpKCo6OjTOcthBBmUhQFnU5H8eLF0WgePgJRYIqEEEKIvCcHroUQQuRIioQQQogcSZEQQgiRIykSQgghciRFQgghRI6kSAghhMiRFAkhhBA5KlRFIioqil69ehEYGEivXr24evXqQ8sYDAbmzZtHQEAAbdq0eeT05vmJOW1euXIlHTt2pFOnTrzyyiscPnzY+kHzkDltznLlyhXq16/P4sWLrRfQAsxt8549e+jUqRNBQUF06tSJ+Ph46wbNQ+a0OSEhgWHDhtGpUyfat2/P3Llz0ev11g+bBxYvXoy/vz+1atXKcXYJi+y/lEJkwIABys6dOxVFUZSdO3cqAwYMeGiZr7/+Whk8eLBiMBiUhIQExc/PT4mOjrZ21DxjTpsPHTqkpKamKoqiKOfPn1d8fHyUtLQ0q+bMS+a0WVEURa/XK/3791cmTpyovPvuu9aMmOfMafPZs2eV9u3bK3FxcYqiKMrdu3eV9PR0q+bMS+a0ecGCBabfbUZGhtKjRw/l22+/tWrOvHLy5Enl5s2bSqtWrZSLFy8+chlL7L8KTU8iISGByMhIgoKCAAgKCiIyMpLExMRsy+3Zs4dXX30VjUZDmTJlCAgIYO/evWpEfmrmttnPz4+iRYsCUKtWLRRFITk52ep584K5bQZYs2YNL7/8MlWqVLFyyrxlbps3bNjA4MGDKVeuHAAuLi44OztbPW9eMLfNdnZ2pKSkYDQaycjIQKfTUb58eTUiPzVfX1/c3Nweu4wl9l+FpkjExMRQvnx57O3tgcy74Lm6uhITE/PQcu7u7qbHbm5u3Lp1y6pZ84q5bf6nnTt3UrlyZSpUqGCtmHnK3DZfuHCBI0eOMGjQIBVS5i1z23z58mWio6Pp168f3bp1Y9WqVTnO/GnrzG3zyJEjiYqKonnz5qYvHx8fNSJbhSX2X4WmSIj/duLECZYuXcqSJUvUjmJROp2OWbNmMW/ePNNOpjAwGAxcvHiR9evXs2nTJg4dOsSuXbvUjmVRe/fupVatWhw5coRDhw5x6tSpfDsyoJZCUyTc3NyIjY3FYDAAmX8wcXFxD3Xf3NzcuHnzpulxTExMvv1UbW6bAcLDw5kyZQorV66katWq1o6aZ8xp8+3bt7l27RrDhg3D39+fjRs38uWXXzJr1iy1Yj8Vc3/P7u7utGvXDicnJ5555hlat27N2bNn1Yj81Mxt8+bNm+ncuTMajQYXFxf8/f05esFLSgAACUxJREFUfvy4GpGtwhL7r0JTJMqWLYuHhwehoaEAhIaG4uHhQZkyZbIt165dO0JCQjAajSQmJrJ//34CAwPViPzUzG3z2bNnmTBhAsuWLaNOnTpqRM0z5rTZ3d2d48eP8+OPP/Ljjz8ycOBAevbsyfz589WK/VTM/T0HBQVx5MgR09TQx44do3bt2mpEfmrmtrlixYocOnQIyLy5ztGjR6lRo4bV81qLRfZfT3XYO5/5888/lR49eiht27ZVevTooVy+fFlRFEUZOnSocvbsWUVRMs94mT17ttK6dWuldevWyrZt29SM/NTMafMrr7yiNG7cWOncubPp68KFC2rGfirmtPmfli1blu/PbjKnzQaDQXnnnXeUdu3aKR06dFDeeecdxWAwqBn7qZjT5r/++ksZNGiQEhQUpLRv316ZO3euotPp1Iyda/Pnz1f8/PwUDw8PpWnTpkqHDh0URbH8/kvuJyGEECJHhWa4SQghxJOTIiGEECJHUiSEEELkSIqEEEKIHEmREEIIkSMpEiJfGzBggM3P1PvNN98wePDgHF8/depUvr0WRxR8UiSEzfD398fLywtvb2/TV2xsrNVzDBgwgHr16uHt7U3jxo0ZPXo0cXFxuV5f586d+eyzz0yPa9WqxV9//WV67Ovry759+54q86MsX76cOnXq4O3tja+vL7179yY8PNzs9/87pyicpEgIm7J69WrCw8NNX2rN2Dl79mzCw8PZt28fd+/eZdGiRarkeFrt27cnPDycY8eO0bhxY8aNG6d2JJHPSJEQNu3OnTsMHz6cJk2a8OKLLzJ8+PAcZ7X866+/6N+/Pz4+PjRu3Jjx48ebXrt8+TKvv/46jRo1IjAwkD179pi1/VKlShEYGMgff/wBwOnTp+nevTs+Pj50796d06dPm5bdsWMHrVu3xtvbG39/f7755hvT83369AGgX79+AHTp0gVvb2/27NnD8ePHadGiBZA5ffnYsWOzZViwYAELFiwA4N69e0yfPp3mzZvj5+fHRx99ZJq/6HEcHBzo1KkTsbGxpum0z549S69evfD19aV58+a8/fbbZGRk5JgT4KeffqJLly6mnsmFCxfM+jmKfOypr9kWIo+0atVK+eWXX7I9l5iYqOzdu1dJTU1V7t27p4wZM0Z54403TK/3799f+fLLLxVFUZQJEyYoq1atUgwGg5Kenq6cPHlSURRFSUlJUVq0aKF89dVXik6nU37//XelUaNGyh9//PHIHP9cZ0JCgjJgwABl8uTJSlJSkuLr66t8/fXXik6nU3bv3q34+voqiYmJSkpKiuLt7W2aGiI2Nla5dOmSoiiKsn37dqV3796m9desWVO5evWq6fGxY8cUPz8/RVEU5fr164qXl5dy7949RVEyp1lo1qyZEh4eriiKoowcOVKZNWuWkpKSosTHxyvdu3dXtm7d+sh2LFu2TJk0aZKiKIqi1WqV999/X2nUqJFpWoqIiAglPDxc0el0SnR0tNKuXTtl/fr1Oeb8/ffflSZNmii//fabotfrlR07diitWrVStFrtI7cvCgbpSQibMmrUKHx9ffH9//buJiSqNQzg+N/ppDUOilMzKCqB7YZCkzMa5UFXWjLUhA6zLGnjRLgLMxPEgiwUSxRc+LUITYUQmixIaEQQSRd+4kJFKRzMLAr8itHpLuQON65H9La4Cs9vN4cz533OgeGZ9z0vz6Oq3Lp1i5iYGHJycjh+/DgmkwmPx8PQ0NCO31UUBb/fz9LSEhEREaiqCoDP5yM+Pp68vDwURcFms5GTk7NryeiHDx+iqipXr17FYrFQUlKCz+fj1KlTOJ1OFEXB4XCQlJTE+/fvATAYDExPT7OxsYHVav1PheTi4+Ox2Wz09vYCMDg4yLFjx0hJSWF5eZm+vj7u3buH0WjkxIkT3Lhxg9evX+te7+3bt6iqSnJyMl1dXdTW1qIoCgBnzpwhJSUFRVFISEjA7XbrPluAjo4O3G43ycnJHDlyhGvXrnH06FFGRkb2fZ/i8FD+7wCE+Kf6+nouXLgQ+ry+vs6jR4/o7+/nx48fAKyurrK1tfWvXhB37tzh2bNn5OfnEx0dTUFBAfn5+SwsLDA2NhZKGrBdWvrKlSu6cdy/fx+Xy/XbsaWlpd8ausB2RdnPnz9jNBqpqamhubmZ0tJSUlNTKS4u5vTp0/t+Bg6HA6/Xi9PpxOv1hrqv+f1+Njc3ycjICJ0bDAZ37VZ26dIlqqqq+PbtG0VFRUxOTpKeng5s94iurKxkYmKC9fV1tra2dq0C7Pf76e7u5vnz56FjgUDgj17qi4NPkoQ40Jqbm5mbm6OzsxOLxcLU1BROp3PHjmoWiyW0dj88PExBQQF2u524uDjsdjstLS1/FIvVav2tVj9s1+vXNA3YbgOraRobGxs8ffqUsrIy2tra9j3O5cuXefz4MYuLi7x7946Ojg4AYmNjCQ8PZ3BwMDQb2Cuz2UxFRQV5eXk4HA6sVivl5eXYbDaqq6sxmUy0trbuussqLi6OwsJCPB7Pvu9JHF6y3CQOtNXVVSIiIoiKiuL79+/U1dXpnvvmzZvQS+3o6GjCwsIwGAxkZWUxPz9Pd3c3gUCAQCDA2NgYs7Oz+4olMzOT+fl5Xr16xebmJj09PczMzJCVlcXy8jK9vb2sra0RHh6O0WjEYNj553Xy5Ek+ffqkO47ZbCYtLY2SkhISEhJCsxGr1crFixeprKxkZWWFYDDIx48f+fDhw57iT0pKQtM0Ghsbge1nGxkZSWRkJLOzs7S3t+8ap8vl4sWLF4yOjvLr1y/W1tbw+XysrKzsaXxxOEmSEAfa9evX+fnzJ+fPn8ftdof+te9kfHwcl8vFuXPn8Hg8lJaWkpiYiMlkoqmpiZ6eHjRNIyMjg6qqqtBOnr2KiYmhoaGBlpYW0tPTaWxspKGhAbPZTDAYpLW1FU3TSEtLY2hoiPLy8h2vc/v2be7evYuqqrq7rBwOBwMDA6Glpr89efKEQCBAbm4udrudoqIivnz5sud7uHnzJp2dnXz9+pXi4mK8Xi+pqamUlZWRm5u7a5xnz57lwYMHVFRUYLfbyc7O5uXLl3seWxxO0k9CCCGELplJCCGE0CVJQgghhC5JEkIIIXRJkhBCCKFLkoQQQghdkiSEEELokiQhhBBClyQJIYQQuiRJCCGE0PUXUNlpW8J81cQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**confusion_matrix**"
      ],
      "metadata": {
        "id": "xnOTl7agq6Pa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix= confusion_matrix(y_test,rf_pred)\n",
        "print(matrix)\n",
        "sns.heatmap(matrix ,annot=True, fmt='g')"
      ],
      "metadata": {
        "id": "wf5hLuozbBwo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "f9c1b144-2112-417c-ec51-ec2c6238702f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[84544 15592]\n",
            " [ 1518 98986]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c7d2740d0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(rf_pred, y_test))"
      ],
      "metadata": {
        "id": "4LTWhMhkbByN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17eb11fc-9750-440f-cc50-51cd44fe7752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.98      0.91     86062\n",
            "           1       0.98      0.86      0.92    114578\n",
            "\n",
            "    accuracy                           0.91    200640\n",
            "   macro avg       0.91      0.92      0.91    200640\n",
            "weighted avg       0.92      0.91      0.92    200640\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#XGBoost"
      ],
      "metadata": {
        "id": "u2xkZKrAqFw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "XG_model= XGBClassifier()\n",
        "XG_model= XG_model.fit(X_train, y_train)\n",
        "#Making prediction\n",
        "XG_pred = XG_model.predict(X_test)\n",
        "XG_prob = XG_model.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "nUPXIaN-bB0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model** **Evaluation**"
      ],
      "metadata": {
        "id": "DBKtqu2yq99P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "r_XG= recall_score(y_test, XG_pred)\n",
        "print(\"recall_score : \", r_XG)\n",
        "\n",
        "p_XG= precision_score(y_test, XG_pred)\n",
        "print(\"precision_score :\",p_XG)\n",
        "\n",
        "f1_XG= f1_score(y_test, XG_pred)\n",
        "print(\"f1_score :\", f1_XG)\n",
        "\n",
        "A_XG= accuracy_score( y_test, XG_pred)\n",
        "print(\"accuracy_score :\",A_XG)\n",
        "\n",
        "acu_XG = roc_auc_score(XG_pred, y_test)\n",
        "print(\"ROC_AUC Score:\",acu_XG)"
      ],
      "metadata": {
        "id": "rkSS4_robB3G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d40f99b4-72d5-4abc-b224-69bd27d30a13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall_score :  0.9335847329459523\n",
            "precision_score : 0.7339507669683435\n",
            "f1_score : 0.8218178633208523\n",
            "accuracy_score : 0.7972139154704945\n",
            "ROC_AUC Score: 0.8211299735197491\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, XG_prob)\n",
        "\n",
        "plt.title('XGBoost ROC curve')\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), linestyle=\"--\",color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "wBkxdNWE-vkZ",
        "outputId": "df016a82-da6a-4507-856d-3d306b3f7a8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**confusion_matrix**"
      ],
      "metadata": {
        "id": "5UhMliX-rNgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix= confusion_matrix(y_test,XG_pred)\n",
        "print(matrix)\n",
        "sns.heatmap(matrix ,annot=True, fmt='g')"
      ],
      "metadata": {
        "id": "y7cPsD81bB5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "7aa08436-42d7-49c6-d84b-ae9696076e69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[66124 34012]\n",
            " [ 6675 93829]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c7d46a0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(XG_pred, y_test))"
      ],
      "metadata": {
        "id": "eDcDaKxsbB9C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2153ed9-95f9-46e5-9aa1-13368ace6eac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.91      0.76     72799\n",
            "           1       0.93      0.73      0.82    127841\n",
            "\n",
            "    accuracy                           0.80    200640\n",
            "   macro avg       0.80      0.82      0.79    200640\n",
            "weighted avg       0.83      0.80      0.80    200640\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion"
      ],
      "metadata": {
        "id": "PgnI-SVNZLVK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Starting from loading our dataset, we initially checked for null values and duplicates. There were no null values and duplicates so treatment of such was not required.\n",
        "* Through Exploratory Data Analysis,we observed that customers belonging to youngAge are more interested in vehicle response.while Young people below 30 are not interested in vehicle insurance. We observed that customers having vehicles older than 2 years are more likely to be interested in vehicle insurance. Similarly, customers having damaged vehicles are more likely to be interested in vehicle insurance. \n",
        "* The variable such as Age, Previously_insured,Annual_premium are more afecting the target variable.\n",
        "* For Feature Selection, we applied the Mutual Information technique. Here we observed that Previously_Insured is the most important feature and has the highest impact on the dependent feature and there is no correlation between the two.\n",
        "* We observed that the target variable was highly imbalanced.So this issue was solved by using Random Over Sample resampling technique. \n",
        "* we applied feature scaling techniques to normalize our data to bring all features on the same scale and make it easier to process by ML algorithms.\n",
        "* Further, we applied Machine Learning Algorithms to determine whether a customer would be interested in Vehicle Insurance.For the logistic regression we got an accuracy of 78% and for the XGBClassifier we got the aacuracy of 79% whereas, Randomforest  and Decision tree are giving us almost similar result of accuracy and ROC_AUC which is 91% and 92% respectively.So, we selected our *best model* as the model with an accuracy score of 91% i.e Random Forest model and Decision Tree this model preform better."
      ],
      "metadata": {
        "id": "NYK6KTthZOQL"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}