SMOTE.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LP5aFpJagFm0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "colors= ['royalblue', 'red','deeppink', 'maroon', 'mediumorchid', 'tan', 'forestgreen', 'olive', 'goldenrod', 'lightcyan', 'navy']\n",
        "vectorizer= np.vectorize(lambda x: colors[x % len(colors)])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_classification\n",
        "X, y= make_classification(n_classes= 2, class_sep= 0.5, weights= [0.05, 0.95], n_informative= 2, n_redundant= 0, flip_y= 0, n_features=2, n_clusters_per_class=1 ,n_samples= 1000, random_state= 10)"
      ],
      "metadata": {
        "id": "_NbExid7ih1V"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.gca().set_title('data')\n",
        "plt.scatter(X[:,0], X[:,1],c= vectorizer(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "3vJIlwDMmqwm",
        "outputId": "73a3eb21-ac9e-400e-bd12-43e71031ad8f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7a875aaf6f20>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size= 0.3, random_state= 0)"
      ],
      "metadata": {
        "id": "n8Mp_-CssRDg"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.bincount(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UQckwGYvRsW",
        "outputId": "dba2fa18-38ef-482d-c1f4-3fc85228ec5c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 31, 669])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "clf= LogisticRegression(random_state= 0)\n",
        "clf.fit(X_train,y_train)\n",
        "y_pred= clf.predict(X_test)"
      ],
      "metadata": {
        "id": "wActuBphvcD5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "print('Confusion Matrix:')\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print('Classification Report:')\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffwWf9u-v-ff",
        "outputId": "ad3802c3-ef7e-4a06-fc55-aa5b52a755db"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[  4  15]\n",
            " [  0 281]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.21      0.35        19\n",
            "           1       0.95      1.00      0.97       281\n",
            "\n",
            "    accuracy                           0.95       300\n",
            "   macro avg       0.97      0.61      0.66       300\n",
            "weighted avg       0.95      0.95      0.93       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm= SMOTE(random_state= 42)\n",
        "X_res, y_res= sm.fit_resample(X_train,y_train)"
      ],
      "metadata": {
        "id": "VguaYzX4wUEh"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.bincount(y_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iz7fK1sKx6D5",
        "outputId": "bdfc533b-9697-4a5d-ec7a-0c8110b44964"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([669, 669])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "clf1= LogisticRegression(random_state= 0)\n",
        "clf1.fit(X_res,y_res)\n",
        "y_pred= clf1.predict(X_test)\n",
        "print('Confusion Matrix:')\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print('Classification Report:')\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uV3nJIIhyRr6",
        "outputId": "3022b241-a5e6-4b9a-bfb0-d1108c8991cf"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 16   3]\n",
            " [ 40 241]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.29      0.84      0.43        19\n",
            "           1       0.99      0.86      0.92       281\n",
            "\n",
            "    accuracy                           0.86       300\n",
            "   macro avg       0.64      0.85      0.67       300\n",
            "weighted avg       0.94      0.86      0.89       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Wy201m3Yy-Yk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}