Fraud_detection__analysis.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "PVTD4ZsN6xPA"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy  as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from google.colab import drive\n",
        "from sklearn.linear_model import LogisticRegression \n",
        "from sklearn.model_selection import train_test_split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNMjymy17z7Y",
        "outputId": "a6330eec-4da1-47cd-df6f-7fad32f5f396"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive/')\n",
        "os.chdir('/content/drive/MyDrive/Credit_Fraud_Detection/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "X0Yv5yaE7-gc"
      },
      "outputs": [],
      "source": [
        "# Reading Dataset  Basic data visualisation\n",
        "df = pd.read_csv('creditcard.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "uQiHGDiv-JVC"
      },
      "outputs": [],
      "source": [
        "X= df.drop('Class',axis=1)\n",
        "y= df['Class']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "On9ohh4A62zx",
        "outputId": "9c66d02c-db9d-41c5-9f66-93448229c04f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Cn14QmxnCkPg",
        "outputId": "3d832f22-6547-4d09-a090-763e2976a1f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(X[y == 0]['V1'],X[y == 0]['V2'],label=\"Class #0\", alpha=0.5, linewidth=0.15)\n",
        "plt.scatter(X[y == 1]['V1'],X[y == 1]['V2'],label=\"Class #1\", alpha=0.5, linewidth=0.15)\n",
        "plt.xlabel('V1')\n",
        "plt.ylabel('V2')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(df['Class'].unique(),[df[df['Class']==0].count()[0],df[df['Class']==1].count()[0]],\n",
        "        width = 0.4)\n",
        "\n",
        "plt.title(\"Value Count For Fraud vs Non fraus Cases\")\n",
        "plt.xlim(0,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "DV110Cc36viI",
        "outputId": "8120ad03-edb5-4236-d85e-15d02260ef67"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying SMOTE\n",
        "In this exercise, I am going to re-balance our data using the Synthetic Minority Over-sampling Technique (SMOTE). Unlike ROS, SMOTE does not create exact copies of observations, but creates new, synthetic, samples that are quite similar to the existing observations in the minority class. SMOTE is therefore slightly more sophisticated than just copying observations, so let's apply SMOTE to our credit card data. The dataset df is available and the packages you need for SMOTE are imported. In the following exercise, you'll visualize the result and compare it to the original data, such that you can see the effect of applying SMOTE very cle"
      ],
      "metadata": {
        "id": "NzbzViimAsnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Define the resampling method\n",
        "method = SMOTE()\n",
        "\n",
        "# Create the resampled feature set\n",
        "X_resampled, y_resampled = method.fit_resample(X,y)\n",
        "\n",
        "# Plot the resampled data\n",
        "plt.scatter(X_resampled[y_resampled == 0]['V1'],X_resampled[y_resampled == 0]['V2'],label=\"Class #0\", alpha=0.5, linewidth=0.15)\n",
        "plt.scatter(X_resampled[y_resampled == 1]['V1'],X_resampled[y_resampled == 1]['V2'],label=\"Class #1\", alpha=0.5, linewidth=0.15)\n",
        "plt.xlabel('V1')\n",
        "plt.ylabel('V2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "UA3HQ_Z09MIQ",
        "outputId": "150f0940-4ba8-4f32-bd92-b82736a97c84"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzxWSFTx_xLj",
        "outputId": "4a943386-5a5e-4aa6-e603-9e8c0d27e32e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_resampled[y_resampled==1].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzrVtzba_20M",
        "outputId": "f95ecbd0-0019-4571-d094-eb115340bab9"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "284315"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the last exercise, you saw that using SMOTE suddenly gives us more observations of the minority class. Let's compare those results to our original data, to get a good feeling for what has actually happened. Let's have a look at the value counts again of our old and new data, and let's plot the two scatter plots of the data side by side. You'll use the pre-defined function compare_plot() for that that, which takes the following arguments: X, y, X_resampled, y_resampled, method=''. The function plots your original data in a scatter plot, along with the resampled side by side."
      ],
      "metadata": {
        "id": "81mIIhyhBCJm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(y_resampled.unique(),[y_resampled[y_resampled==0].count(),y_resampled[y_resampled==1].count()],\n",
        "        width = 0.4)\n",
        "\n",
        "plt.title(\"Value Count For Fraud vs Non fraus Cases\")\n",
        "plt.xlim(0,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "RWrG2-_-_mpH",
        "outputId": "043d62f3-0a46-4908-f7e9-19c59389692a"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10, 5))\n",
        "plt.subplot(1, 2, 1) # row 1, col 2 index 1\n",
        "\n",
        "plt.bar(df['Class'].unique(),[df[df['Class']==0].count()[0],df[df['Class']==1].count()[0]],\n",
        "        width = 0.4)\n",
        "\n",
        "plt.title(\"Before SMOT\")\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('cases count')\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 2) # index 2\n",
        "#plt.figure(figsize = (10, 5))\n",
        "plt.bar(y_resampled.unique(),[y_resampled[y_resampled==0].count(),y_resampled[y_resampled==1].count()],\n",
        "        width = 0.4,color='orange')\n",
        "#plt.figure(figsize = (10, 5))\n",
        "plt.title(\"After SMOT\")\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('cases count')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "64fmgr0jCQZW",
        "outputId": "3a3df274-706d-4e55-8614-ea77e8108031"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10, 5))\n",
        "plt.subplot(1, 2, 1) # row 1, col 2 index 1\n",
        "\n",
        "plt.scatter(X[y == 0]['V1'],X[y == 0]['V2'],label=\"Class #0\", alpha=0.5, linewidth=0.15)\n",
        "plt.scatter(X[y == 1]['V1'],X[y == 1]['V2'],label=\"Class #1\", alpha=0.5, linewidth=0.15)\n",
        "\n",
        "plt.title(\"Before SMOT\")\n",
        "plt.xlabel('V1')\n",
        "plt.ylabel('V2')\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 2) # index 2\n",
        "#plt.figure(figsize = (10, 5))\n",
        "plt.scatter(X_resampled[y_resampled == 0]['V1'],X_resampled[y_resampled == 0]['V2'],label=\"Class #0\", alpha=0.5, linewidth=0.15)\n",
        "plt.scatter(X_resampled[y_resampled == 1]['V1'],X_resampled[y_resampled == 1]['V2'],label=\"Class #1\", alpha=0.5, linewidth=0.15)\n",
        "#plt.figure(figsize = (10, 5))\n",
        "plt.title(\"After SMOT\")\n",
        "plt.xlabel('V1')\n",
        "plt.ylabel('V2')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "FYqUTOgjD-9M",
        "outputId": "3cd530b1-1129-41b5-bfbb-a33dd5b61203"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploring the traditional way to catch fraud\n",
        "\n",
        "In this exercise I am going to try finding fraud cases in our credit card dataset the \"old way\". First I'll define threshold values using common statistics, to split fraud and non-fraud. Then, use those thresholds on your features to detect fraud. This is common practice within fraud analytics teams.\n",
        "\n",
        "Statistical thresholds are often determined by looking at the mean values of observations. Let's start this exercise by checking whether feature means differ between fraud and non-fraud cases. Then, you'll use that information to create common sense thresholds. Finally, you'll check how well this performs in fraud detection."
      ],
      "metadata": {
        "id": "-pJbiYN4AYXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Class']==1].describe() # box plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "FPRjZgfpFdAC",
        "outputId": "3f6bee39-790a-49b8-a9d0-df2cabc0d721"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time          V1          V2          V3          V4  \\\n",
              "count     492.000000  492.000000  492.000000  492.000000  492.000000   \n",
              "mean    80746.806911   -4.771948    3.623778   -7.033281    4.542029   \n",
              "std     47835.365138    6.783687    4.291216    7.110937    2.873318   \n",
              "min       406.000000  -30.552380   -8.402154  -31.103685   -1.313275   \n",
              "25%     41241.500000   -6.036063    1.188226   -8.643489    2.373050   \n",
              "50%     75568.500000   -2.342497    2.717869   -5.075257    4.177147   \n",
              "75%    128483.000000   -0.419200    4.971257   -2.276185    6.348729   \n",
              "max    170348.000000    2.132386   22.057729    2.250210   12.114672   \n",
              "\n",
              "               V5          V6          V7          V8          V9  ...  \\\n",
              "count  492.000000  492.000000  492.000000  492.000000  492.000000  ...   \n",
              "mean    -3.151225   -1.397737   -5.568731    0.570636   -2.581123  ...   \n",
              "std      5.372468    1.858124    7.206773    6.797831    2.500896  ...   \n",
              "min    -22.105532   -6.406267  -43.557242  -41.044261  -13.434066  ...   \n",
              "25%     -4.792835   -2.501511   -7.965295   -0.195336   -3.872383  ...   \n",
              "50%     -1.522962   -1.424616   -3.034402    0.621508   -2.208768  ...   \n",
              "75%      0.214562   -0.413216   -0.945954    1.764879   -0.787850  ...   \n",
              "max     11.095089    6.474115    5.802537   20.007208    3.353525  ...   \n",
              "\n",
              "              V21         V22         V23         V24         V25         V26  \\\n",
              "count  492.000000  492.000000  492.000000  492.000000  492.000000  492.000000   \n",
              "mean     0.713588    0.014049   -0.040308   -0.105130    0.041449    0.051648   \n",
              "std      3.869304    1.494602    1.579642    0.515577    0.797205    0.471679   \n",
              "min    -22.797604   -8.887017  -19.254328   -2.028024   -4.781606   -1.152671   \n",
              "25%      0.041787   -0.533764   -0.342175   -0.436809   -0.314348   -0.259416   \n",
              "50%      0.592146    0.048434   -0.073135   -0.060795    0.088371    0.004321   \n",
              "75%      1.244611    0.617474    0.308378    0.285328    0.456515    0.396733   \n",
              "max     27.202839    8.361985    5.466230    1.091435    2.208209    2.745261   \n",
              "\n",
              "              V27         V28       Amount  Class  \n",
              "count  492.000000  492.000000   492.000000  492.0  \n",
              "mean     0.170575    0.075667   122.211321    1.0  \n",
              "std      1.376766    0.547291   256.683288    0.0  \n",
              "min     -7.263482   -1.869290     0.000000    1.0  \n",
              "25%     -0.020025   -0.108868     1.000000    1.0  \n",
              "50%      0.394926    0.146344     9.250000    1.0  \n",
              "75%      0.826029    0.381152   105.890000    1.0  \n",
              "max      3.052358    1.779364  2125.870000    1.0  \n",
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              "      <th>mean</th>\n",
              "      <td>80746.806911</td>\n",
              "      <td>-4.771948</td>\n",
              "      <td>3.623778</td>\n",
              "      <td>-7.033281</td>\n",
              "      <td>4.542029</td>\n",
              "      <td>-3.151225</td>\n",
              "      <td>-1.397737</td>\n",
              "      <td>-5.568731</td>\n",
              "      <td>0.570636</td>\n",
              "      <td>-2.581123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.713588</td>\n",
              "      <td>0.014049</td>\n",
              "      <td>-0.040308</td>\n",
              "      <td>-0.105130</td>\n",
              "      <td>0.041449</td>\n",
              "      <td>0.051648</td>\n",
              "      <td>0.170575</td>\n",
              "      <td>0.075667</td>\n",
              "      <td>122.211321</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47835.365138</td>\n",
              "      <td>6.783687</td>\n",
              "      <td>4.291216</td>\n",
              "      <td>7.110937</td>\n",
              "      <td>2.873318</td>\n",
              "      <td>5.372468</td>\n",
              "      <td>1.858124</td>\n",
              "      <td>7.206773</td>\n",
              "      <td>6.797831</td>\n",
              "      <td>2.500896</td>\n",
              "      <td>...</td>\n",
              "      <td>3.869304</td>\n",
              "      <td>1.494602</td>\n",
              "      <td>1.579642</td>\n",
              "      <td>0.515577</td>\n",
              "      <td>0.797205</td>\n",
              "      <td>0.471679</td>\n",
              "      <td>1.376766</td>\n",
              "      <td>0.547291</td>\n",
              "      <td>256.683288</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>min</th>\n",
              "      <td>406.000000</td>\n",
              "      <td>-30.552380</td>\n",
              "      <td>-8.402154</td>\n",
              "      <td>-31.103685</td>\n",
              "      <td>-1.313275</td>\n",
              "      <td>-22.105532</td>\n",
              "      <td>-6.406267</td>\n",
              "      <td>-43.557242</td>\n",
              "      <td>-41.044261</td>\n",
              "      <td>-13.434066</td>\n",
              "      <td>...</td>\n",
              "      <td>-22.797604</td>\n",
              "      <td>-8.887017</td>\n",
              "      <td>-19.254328</td>\n",
              "      <td>-2.028024</td>\n",
              "      <td>-4.781606</td>\n",
              "      <td>-1.152671</td>\n",
              "      <td>-7.263482</td>\n",
              "      <td>-1.869290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41241.500000</td>\n",
              "      <td>-6.036063</td>\n",
              "      <td>1.188226</td>\n",
              "      <td>-8.643489</td>\n",
              "      <td>2.373050</td>\n",
              "      <td>-4.792835</td>\n",
              "      <td>-2.501511</td>\n",
              "      <td>-7.965295</td>\n",
              "      <td>-0.195336</td>\n",
              "      <td>-3.872383</td>\n",
              "      <td>...</td>\n",
              "      <td>0.041787</td>\n",
              "      <td>-0.533764</td>\n",
              "      <td>-0.342175</td>\n",
              "      <td>-0.436809</td>\n",
              "      <td>-0.314348</td>\n",
              "      <td>-0.259416</td>\n",
              "      <td>-0.020025</td>\n",
              "      <td>-0.108868</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75568.500000</td>\n",
              "      <td>-2.342497</td>\n",
              "      <td>2.717869</td>\n",
              "      <td>-5.075257</td>\n",
              "      <td>4.177147</td>\n",
              "      <td>-1.522962</td>\n",
              "      <td>-1.424616</td>\n",
              "      <td>-3.034402</td>\n",
              "      <td>0.621508</td>\n",
              "      <td>-2.208768</td>\n",
              "      <td>...</td>\n",
              "      <td>0.592146</td>\n",
              "      <td>0.048434</td>\n",
              "      <td>-0.073135</td>\n",
              "      <td>-0.060795</td>\n",
              "      <td>0.088371</td>\n",
              "      <td>0.004321</td>\n",
              "      <td>0.394926</td>\n",
              "      <td>0.146344</td>\n",
              "      <td>9.250000</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>75%</th>\n",
              "      <td>128483.000000</td>\n",
              "      <td>-0.419200</td>\n",
              "      <td>4.971257</td>\n",
              "      <td>-2.276185</td>\n",
              "      <td>6.348729</td>\n",
              "      <td>0.214562</td>\n",
              "      <td>-0.413216</td>\n",
              "      <td>-0.945954</td>\n",
              "      <td>1.764879</td>\n",
              "      <td>-0.787850</td>\n",
              "      <td>...</td>\n",
              "      <td>1.244611</td>\n",
              "      <td>0.617474</td>\n",
              "      <td>0.308378</td>\n",
              "      <td>0.285328</td>\n",
              "      <td>0.456515</td>\n",
              "      <td>0.396733</td>\n",
              "      <td>0.826029</td>\n",
              "      <td>0.381152</td>\n",
              "      <td>105.890000</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>max</th>\n",
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              "      <td>2.132386</td>\n",
              "      <td>22.057729</td>\n",
              "      <td>2.250210</td>\n",
              "      <td>12.114672</td>\n",
              "      <td>11.095089</td>\n",
              "      <td>6.474115</td>\n",
              "      <td>5.802537</td>\n",
              "      <td>20.007208</td>\n",
              "      <td>3.353525</td>\n",
              "      <td>...</td>\n",
              "      <td>27.202839</td>\n",
              "      <td>8.361985</td>\n",
              "      <td>5.466230</td>\n",
              "      <td>1.091435</td>\n",
              "      <td>2.208209</td>\n",
              "      <td>2.745261</td>\n",
              "      <td>3.052358</td>\n",
              "      <td>1.779364</td>\n",
              "      <td>2125.870000</td>\n",
              "      <td>1.0</td>\n",
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              "<p>8 rows × 31 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Class']==0].describe() # box plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "7jUWvPe1GhXZ",
        "outputId": "55bec08d-e31d-4cb8-d086-c7a5de51a66a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time             V1             V2             V3  \\\n",
              "count  284315.000000  284315.000000  284315.000000  284315.000000   \n",
              "mean    94838.202258       0.008258      -0.006271       0.012171   \n",
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              "min         0.000000     -56.407510     -72.715728     -48.325589   \n",
              "25%     54230.000000      -0.917544      -0.599473      -0.884541   \n",
              "50%     84711.000000       0.020023       0.064070       0.182158   \n",
              "75%    139333.000000       1.316218       0.800446       1.028372   \n",
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              "                  V4             V5             V6             V7  \\\n",
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              "25%        -0.850077      -0.689398      -0.766847      -0.551442   \n",
              "50%        -0.022405      -0.053457      -0.273123       0.041138   \n",
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              "max        16.875344      34.801666      73.301626     120.589494   \n",
              "\n",
              "                  V8             V9  ...            V21            V22  \\\n",
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              "mean       -0.000987       0.004467  ...      -0.001235      -0.000024   \n",
              "std         1.161283       1.089372  ...       0.716743       0.723668   \n",
              "min       -73.216718      -6.290730  ...     -34.830382     -10.933144   \n",
              "25%        -0.208633      -0.640412  ...      -0.228509      -0.542403   \n",
              "50%         0.022041      -0.049964  ...      -0.029821       0.006736   \n",
              "75%         0.326200       0.598230  ...       0.185626       0.528407   \n",
              "max        18.709255      15.594995  ...      22.614889      10.503090   \n",
              "\n",
              "                 V23            V24            V25            V26  \\\n",
              "count  284315.000000  284315.000000  284315.000000  284315.000000   \n",
              "mean        0.000070       0.000182      -0.000072      -0.000089   \n",
              "std         0.621541       0.605776       0.520673       0.482241   \n",
              "min       -44.807735      -2.836627     -10.295397      -2.604551   \n",
              "25%        -0.161702      -0.354425      -0.317145      -0.327074   \n",
              "50%        -0.011147       0.041082       0.016417      -0.052227   \n",
              "75%         0.147522       0.439869       0.350594       0.240671   \n",
              "max        22.528412       4.584549       7.519589       3.517346   \n",
              "\n",
              "                 V27            V28         Amount     Class  \n",
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              "mean       -0.000295      -0.000131      88.291022       0.0  \n",
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              "min       -22.565679     -15.430084       0.000000       0.0  \n",
              "25%        -0.070852      -0.052950       5.650000       0.0  \n",
              "50%         0.001230       0.011199      22.000000       0.0  \n",
              "75%         0.090573       0.077962      77.050000       0.0  \n",
              "max        31.612198      33.847808   25691.160000       0.0  \n",
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              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94838.202258</td>\n",
              "      <td>0.008258</td>\n",
              "      <td>-0.006271</td>\n",
              "      <td>0.012171</td>\n",
              "      <td>-0.007860</td>\n",
              "      <td>0.005453</td>\n",
              "      <td>0.002419</td>\n",
              "      <td>0.009637</td>\n",
              "      <td>-0.000987</td>\n",
              "      <td>0.004467</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001235</td>\n",
              "      <td>-0.000024</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.000182</td>\n",
              "      <td>-0.000072</td>\n",
              "      <td>-0.000089</td>\n",
              "      <td>-0.000295</td>\n",
              "      <td>-0.000131</td>\n",
              "      <td>88.291022</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47484.015786</td>\n",
              "      <td>1.929814</td>\n",
              "      <td>1.636146</td>\n",
              "      <td>1.459429</td>\n",
              "      <td>1.399333</td>\n",
              "      <td>1.356952</td>\n",
              "      <td>1.329913</td>\n",
              "      <td>1.178812</td>\n",
              "      <td>1.161283</td>\n",
              "      <td>1.089372</td>\n",
              "      <td>...</td>\n",
              "      <td>0.716743</td>\n",
              "      <td>0.723668</td>\n",
              "      <td>0.621541</td>\n",
              "      <td>0.605776</td>\n",
              "      <td>0.520673</td>\n",
              "      <td>0.482241</td>\n",
              "      <td>0.399847</td>\n",
              "      <td>0.329570</td>\n",
              "      <td>250.105092</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-56.407510</td>\n",
              "      <td>-72.715728</td>\n",
              "      <td>-48.325589</td>\n",
              "      <td>-5.683171</td>\n",
              "      <td>-113.743307</td>\n",
              "      <td>-26.160506</td>\n",
              "      <td>-31.764946</td>\n",
              "      <td>-73.216718</td>\n",
              "      <td>-6.290730</td>\n",
              "      <td>...</td>\n",
              "      <td>-34.830382</td>\n",
              "      <td>-10.933144</td>\n",
              "      <td>-44.807735</td>\n",
              "      <td>-2.836627</td>\n",
              "      <td>-10.295397</td>\n",
              "      <td>-2.604551</td>\n",
              "      <td>-22.565679</td>\n",
              "      <td>-15.430084</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54230.000000</td>\n",
              "      <td>-0.917544</td>\n",
              "      <td>-0.599473</td>\n",
              "      <td>-0.884541</td>\n",
              "      <td>-0.850077</td>\n",
              "      <td>-0.689398</td>\n",
              "      <td>-0.766847</td>\n",
              "      <td>-0.551442</td>\n",
              "      <td>-0.208633</td>\n",
              "      <td>-0.640412</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.228509</td>\n",
              "      <td>-0.542403</td>\n",
              "      <td>-0.161702</td>\n",
              "      <td>-0.354425</td>\n",
              "      <td>-0.317145</td>\n",
              "      <td>-0.327074</td>\n",
              "      <td>-0.070852</td>\n",
              "      <td>-0.052950</td>\n",
              "      <td>5.650000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84711.000000</td>\n",
              "      <td>0.020023</td>\n",
              "      <td>0.064070</td>\n",
              "      <td>0.182158</td>\n",
              "      <td>-0.022405</td>\n",
              "      <td>-0.053457</td>\n",
              "      <td>-0.273123</td>\n",
              "      <td>0.041138</td>\n",
              "      <td>0.022041</td>\n",
              "      <td>-0.049964</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.029821</td>\n",
              "      <td>0.006736</td>\n",
              "      <td>-0.011147</td>\n",
              "      <td>0.041082</td>\n",
              "      <td>0.016417</td>\n",
              "      <td>-0.052227</td>\n",
              "      <td>0.001230</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139333.000000</td>\n",
              "      <td>1.316218</td>\n",
              "      <td>0.800446</td>\n",
              "      <td>1.028372</td>\n",
              "      <td>0.737624</td>\n",
              "      <td>0.612181</td>\n",
              "      <td>0.399619</td>\n",
              "      <td>0.571019</td>\n",
              "      <td>0.326200</td>\n",
              "      <td>0.598230</td>\n",
              "      <td>...</td>\n",
              "      <td>0.185626</td>\n",
              "      <td>0.528407</td>\n",
              "      <td>0.147522</td>\n",
              "      <td>0.439869</td>\n",
              "      <td>0.350594</td>\n",
              "      <td>0.240671</td>\n",
              "      <td>0.090573</td>\n",
              "      <td>0.077962</td>\n",
              "      <td>77.050000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930</td>\n",
              "      <td>18.902453</td>\n",
              "      <td>9.382558</td>\n",
              "      <td>16.875344</td>\n",
              "      <td>34.801666</td>\n",
              "      <td>73.301626</td>\n",
              "      <td>120.589494</td>\n",
              "      <td>18.709255</td>\n",
              "      <td>15.594995</td>\n",
              "      <td>...</td>\n",
              "      <td>22.614889</td>\n",
              "      <td>10.503090</td>\n",
              "      <td>22.528412</td>\n",
              "      <td>4.584549</td>\n",
              "      <td>7.519589</td>\n",
              "      <td>3.517346</td>\n",
              "      <td>31.612198</td>\n",
              "      <td>33.847808</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Under-Sampling\n",
        "\n",
        "In this phase of the project we will implement \"Random Under Sampling\" which basically consists of removing data in order to have a more balanced dataset and thus avoiding our models to overfitting.\n",
        "\n",
        "## Steps:\n",
        "1. The first thing we have to do is determine how imbalanced is our class (use \n",
        "\"value_counts()\" on the class column to determine the amount for each label)\n",
        "Once we determine how many instances are considered fraud transactions (Fraud = \"1\") , we should bring the non-fraud transactions to the same amount as fraud transactions (assuming we want a 50/50 ratio), this will be equivalent to 492 cases of fraud and 492 cases of non-fraud transactions.\n",
        "\n",
        "2. After implementing this technique, we have a sub-sample of our dataframe with a 50/50 ratio with regards to our classes.\n",
        "\n",
        "3. Then the next step we will implement is to shuffle the data to see if our models can maintain a certain accuracy everytime we run this script.\n",
        "\n",
        "Note :- I won't be using undersampled data for the purpuse of training as it is loss of infrmation."
      ],
      "metadata": {
        "id": "VD6jP-sEWMxP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.sample(frac=1)\n",
        "\n",
        "# amount of fraud classes 492 rows.\n",
        "fraud_df = df.loc[df['Class'] == 1]\n",
        "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
        "\n",
        "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
        "\n",
        "# Shuffle dataframe rows\n",
        "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
        "\n",
        "new_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9CTgpUhcYC4R",
        "outputId": "dbe6e392-17ba-42c0-d085-7623419f3e5d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
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              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "121         77.0 -0.427191  0.745708  1.761811 -0.165130  0.058298 -0.213413   \n",
              "248296  153875.0 -0.613696  3.698772 -5.534941  5.620486  1.649263 -2.335145   \n",
              "239        160.0  1.171439  0.474974  0.011761  1.264303  0.116234 -0.865986   \n",
              "239501  150139.0 -6.682832 -2.714268 -5.774530  1.449792 -0.661836 -1.148650   \n",
              "143336   85285.0 -6.713407  3.921104 -9.746678  5.148263 -5.151563 -2.099389   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "121     0.647323  0.073464 -0.291864  ... -0.201681 -0.432070  0.013164   \n",
              "248296 -0.907188  0.706362 -3.747646  ...  0.319261 -0.471379 -0.075890   \n",
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              "239501  0.849686  0.433427 -1.315646  ...  0.220526  1.187013  0.335821   \n",
              "143336 -5.937767  3.578780 -4.684952  ...  0.954272 -0.451086  0.127214   \n",
              "\n",
              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "121     0.161606 -0.401310  0.047423  0.102549 -0.116571    9.12      0  \n",
              "248296 -0.667909 -0.642848  0.070600  0.488410  0.292345    0.00      1  \n",
              "239     0.426278  0.744938 -0.274728  0.008472  0.015492   20.00      0  \n",
              "239501  0.215683  0.803110  0.044033 -0.054988  0.082337  237.26      1  \n",
              "143336 -0.339450  0.394096  1.075295  1.649906 -0.394905  252.92      1  \n",
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              "[5 rows x 31 columns]"
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              "      <td>0.011761</td>\n",
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              "      <td>-0.276375</td>\n",
              "      <td>-0.471302</td>\n",
              "      <td>...</td>\n",
              "      <td>0.070051</td>\n",
              "      <td>0.278843</td>\n",
              "      <td>-0.097491</td>\n",
              "      <td>0.426278</td>\n",
              "      <td>0.744938</td>\n",
              "      <td>-0.274728</td>\n",
              "      <td>0.008472</td>\n",
              "      <td>0.015492</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239501</th>\n",
              "      <td>150139.0</td>\n",
              "      <td>-6.682832</td>\n",
              "      <td>-2.714268</td>\n",
              "      <td>-5.774530</td>\n",
              "      <td>1.449792</td>\n",
              "      <td>-0.661836</td>\n",
              "      <td>-1.148650</td>\n",
              "      <td>0.849686</td>\n",
              "      <td>0.433427</td>\n",
              "      <td>-1.315646</td>\n",
              "      <td>...</td>\n",
              "      <td>0.220526</td>\n",
              "      <td>1.187013</td>\n",
              "      <td>0.335821</td>\n",
              "      <td>0.215683</td>\n",
              "      <td>0.803110</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>-0.054988</td>\n",
              "      <td>0.082337</td>\n",
              "      <td>237.26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143336</th>\n",
              "      <td>85285.0</td>\n",
              "      <td>-6.713407</td>\n",
              "      <td>3.921104</td>\n",
              "      <td>-9.746678</td>\n",
              "      <td>5.148263</td>\n",
              "      <td>-5.151563</td>\n",
              "      <td>-2.099389</td>\n",
              "      <td>-5.937767</td>\n",
              "      <td>3.578780</td>\n",
              "      <td>-4.684952</td>\n",
              "      <td>...</td>\n",
              "      <td>0.954272</td>\n",
              "      <td>-0.451086</td>\n",
              "      <td>0.127214</td>\n",
              "      <td>-0.339450</td>\n",
              "      <td>0.394096</td>\n",
              "      <td>1.075295</td>\n",
              "      <td>1.649906</td>\n",
              "      <td>-0.394905</td>\n",
              "      <td>252.92</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a48205db-3cd5-4637-bac8-3007a5cbacda')\"\n",
              "              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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a48205db-3cd5-4637-bac8-3007a5cbacda 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-a48205db-3cd5-4637-bac8-3007a5cbacda');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Distribution of the Classes in the subsample dataset')\n",
        "print(new_df['Class'].value_counts()/len(new_df))\n",
        "\n",
        "\n",
        "\n",
        "sns.countplot('Class', data=new_df, palette=colors)\n",
        "plt.title('Equally Distributed Classes', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8iUBV22rZZV7",
        "outputId": "4b4e7553-8c68-4994-fb04-23afec6a9396"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution of the Classes in the subsample dataset\n",
            "0    0.5\n",
            "1    0.5\n",
            "Name: Class, dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the mean for each group\n",
        "df.groupby('Class').mean()\n",
        "\n",
        "# Implement a rule for stating which cases are flagged as fraud\n",
        "df['flag_as_fraud'] = np.where(np.logical_and(df['V1']< -3, df['V3']< -5), 1, 0)\n",
        "\n",
        "# Create a crosstab of flagged fraud cases versus the actual fraud cases\n",
        "print(pd.crosstab(df.Class, df.flag_as_fraud, rownames=['Actual Fraud'], colnames=['Flagged Fraud']))"
      ],
      "metadata": {
        "id": "Ed7AkLJgAZ3u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cormat = new_df.corr()\n",
        "mat= round(cormat,2)\n",
        "posively_correlated_columns = mat[mat['Class']>0].index\n",
        "negatively_correlated_columns = mat[mat['Class']<0].index\n",
        "No_correlated_columns = mat[mat['Class']==0].index"
      ],
      "metadata": {
        "id": "zIqFrlHMPPOc"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Negative Correlations**: V17, V14, V12 and V10 are negatively correlated. Notice how the lower these values are, the more likely the end result will be a fraud transaction.\n",
        "\n",
        "**Positive Correlations**: V2, V4, V11, and V19 are positively correlated. Notice how the higher these values are, the more likely the end result will be a fraud transaction."
      ],
      "metadata": {
        "id": "3QumVsxRaVMq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
        "\n",
        "colors = [\"#0101DF\", \"#DF0102\"]\n",
        "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
        "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, palette=colors, ax=axes[0])\n",
        "axes[0].set_title('V17 vs Class Negative Correlation')\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, palette=colors, ax=axes[1])\n",
        "axes[1].set_title('V14 vs Class Negative Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, palette=colors, ax=axes[2])\n",
        "axes[2].set_title('V12 vs Class Negative Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, palette=colors, ax=axes[3])\n",
        "axes[3].set_title('V10 vs Class Negative Correlation')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "o5m0SpY7TRnI",
        "outputId": "fa7e189b-ab7c-4232-e6c7-5869d2de2557"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
        "\n",
        "colors = [\"#0101DF\", \"#DF0102\"]\n",
        "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
        "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[0])\n",
        "axes[0].set_title('V2 vs Class Positive Correlation')\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[1])\n",
        "axes[1].set_title('V4 vs Class Positive Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[2])\n",
        "axes[2].set_title('V11 vs Class Positive Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[3])\n",
        "axes[3].set_title('V19 vs Class positive Correlation')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KeZ7GxLzatKe",
        "outputId": "03c5e412-a785-4beb-ca72-1a449c057bac"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df[new_df['Class']==1].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "7w9owmS0cDeU",
        "outputId": "77688b4b-ce13-4459-acca-bab57543726e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time          V1          V2          V3          V4  \\\n",
              "count     492.000000  492.000000  492.000000  492.000000  492.000000   \n",
              "mean    80746.806911   -4.771948    3.623778   -7.033281    4.542029   \n",
              "std     47835.365138    6.783687    4.291216    7.110937    2.873318   \n",
              "min       406.000000  -30.552380   -8.402154  -31.103685   -1.313275   \n",
              "25%     41241.500000   -6.036063    1.188226   -8.643489    2.373050   \n",
              "50%     75568.500000   -2.342497    2.717869   -5.075257    4.177147   \n",
              "75%    128483.000000   -0.419200    4.971257   -2.276185    6.348729   \n",
              "max    170348.000000    2.132386   22.057729    2.250210   12.114672   \n",
              "\n",
              "               V5          V6          V7          V8          V9  ...  \\\n",
              "count  492.000000  492.000000  492.000000  492.000000  492.000000  ...   \n",
              "mean    -3.151225   -1.397737   -5.568731    0.570636   -2.581123  ...   \n",
              "std      5.372468    1.858124    7.206773    6.797831    2.500896  ...   \n",
              "min    -22.105532   -6.406267  -43.557242  -41.044261  -13.434066  ...   \n",
              "25%     -4.792835   -2.501511   -7.965295   -0.195336   -3.872383  ...   \n",
              "50%     -1.522962   -1.424616   -3.034402    0.621508   -2.208768  ...   \n",
              "75%      0.214562   -0.413216   -0.945954    1.764879   -0.787850  ...   \n",
              "max     11.095089    6.474115    5.802537   20.007208    3.353525  ...   \n",
              "\n",
              "              V21         V22         V23         V24         V25         V26  \\\n",
              "count  492.000000  492.000000  492.000000  492.000000  492.000000  492.000000   \n",
              "mean     0.713588    0.014049   -0.040308   -0.105130    0.041449    0.051648   \n",
              "std      3.869304    1.494602    1.579642    0.515577    0.797205    0.471679   \n",
              "min    -22.797604   -8.887017  -19.254328   -2.028024   -4.781606   -1.152671   \n",
              "25%      0.041787   -0.533764   -0.342175   -0.436809   -0.314348   -0.259416   \n",
              "50%      0.592146    0.048434   -0.073135   -0.060795    0.088371    0.004321   \n",
              "75%      1.244611    0.617474    0.308378    0.285328    0.456515    0.396733   \n",
              "max     27.202839    8.361985    5.466230    1.091435    2.208209    2.745261   \n",
              "\n",
              "              V27         V28       Amount  Class  \n",
              "count  492.000000  492.000000   492.000000  492.0  \n",
              "mean     0.170575    0.075667   122.211321    1.0  \n",
              "std      1.376766    0.547291   256.683288    0.0  \n",
              "min     -7.263482   -1.869290     0.000000    1.0  \n",
              "25%     -0.020025   -0.108868     1.000000    1.0  \n",
              "50%      0.394926    0.146344     9.250000    1.0  \n",
              "75%      0.826029    0.381152   105.890000    1.0  \n",
              "max      3.052358    1.779364  2125.870000    1.0  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>80746.806911</td>\n",
              "      <td>-4.771948</td>\n",
              "      <td>3.623778</td>\n",
              "      <td>-7.033281</td>\n",
              "      <td>4.542029</td>\n",
              "      <td>-3.151225</td>\n",
              "      <td>-1.397737</td>\n",
              "      <td>-5.568731</td>\n",
              "      <td>0.570636</td>\n",
              "      <td>-2.581123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.713588</td>\n",
              "      <td>0.014049</td>\n",
              "      <td>-0.040308</td>\n",
              "      <td>-0.105130</td>\n",
              "      <td>0.041449</td>\n",
              "      <td>0.051648</td>\n",
              "      <td>0.170575</td>\n",
              "      <td>0.075667</td>\n",
              "      <td>122.211321</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47835.365138</td>\n",
              "      <td>6.783687</td>\n",
              "      <td>4.291216</td>\n",
              "      <td>7.110937</td>\n",
              "      <td>2.873318</td>\n",
              "      <td>5.372468</td>\n",
              "      <td>1.858124</td>\n",
              "      <td>7.206773</td>\n",
              "      <td>6.797831</td>\n",
              "      <td>2.500896</td>\n",
              "      <td>...</td>\n",
              "      <td>3.869304</td>\n",
              "      <td>1.494602</td>\n",
              "      <td>1.579642</td>\n",
              "      <td>0.515577</td>\n",
              "      <td>0.797205</td>\n",
              "      <td>0.471679</td>\n",
              "      <td>1.376766</td>\n",
              "      <td>0.547291</td>\n",
              "      <td>256.683288</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>406.000000</td>\n",
              "      <td>-30.552380</td>\n",
              "      <td>-8.402154</td>\n",
              "      <td>-31.103685</td>\n",
              "      <td>-1.313275</td>\n",
              "      <td>-22.105532</td>\n",
              "      <td>-6.406267</td>\n",
              "      <td>-43.557242</td>\n",
              "      <td>-41.044261</td>\n",
              "      <td>-13.434066</td>\n",
              "      <td>...</td>\n",
              "      <td>-22.797604</td>\n",
              "      <td>-8.887017</td>\n",
              "      <td>-19.254328</td>\n",
              "      <td>-2.028024</td>\n",
              "      <td>-4.781606</td>\n",
              "      <td>-1.152671</td>\n",
              "      <td>-7.263482</td>\n",
              "      <td>-1.869290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41241.500000</td>\n",
              "      <td>-6.036063</td>\n",
              "      <td>1.188226</td>\n",
              "      <td>-8.643489</td>\n",
              "      <td>2.373050</td>\n",
              "      <td>-4.792835</td>\n",
              "      <td>-2.501511</td>\n",
              "      <td>-7.965295</td>\n",
              "      <td>-0.195336</td>\n",
              "      <td>-3.872383</td>\n",
              "      <td>...</td>\n",
              "      <td>0.041787</td>\n",
              "      <td>-0.533764</td>\n",
              "      <td>-0.342175</td>\n",
              "      <td>-0.436809</td>\n",
              "      <td>-0.314348</td>\n",
              "      <td>-0.259416</td>\n",
              "      <td>-0.020025</td>\n",
              "      <td>-0.108868</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75568.500000</td>\n",
              "      <td>-2.342497</td>\n",
              "      <td>2.717869</td>\n",
              "      <td>-5.075257</td>\n",
              "      <td>4.177147</td>\n",
              "      <td>-1.522962</td>\n",
              "      <td>-1.424616</td>\n",
              "      <td>-3.034402</td>\n",
              "      <td>0.621508</td>\n",
              "      <td>-2.208768</td>\n",
              "      <td>...</td>\n",
              "      <td>0.592146</td>\n",
              "      <td>0.048434</td>\n",
              "      <td>-0.073135</td>\n",
              "      <td>-0.060795</td>\n",
              "      <td>0.088371</td>\n",
              "      <td>0.004321</td>\n",
              "      <td>0.394926</td>\n",
              "      <td>0.146344</td>\n",
              "      <td>9.250000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>128483.000000</td>\n",
              "      <td>-0.419200</td>\n",
              "      <td>4.971257</td>\n",
              "      <td>-2.276185</td>\n",
              "      <td>6.348729</td>\n",
              "      <td>0.214562</td>\n",
              "      <td>-0.413216</td>\n",
              "      <td>-0.945954</td>\n",
              "      <td>1.764879</td>\n",
              "      <td>-0.787850</td>\n",
              "      <td>...</td>\n",
              "      <td>1.244611</td>\n",
              "      <td>0.617474</td>\n",
              "      <td>0.308378</td>\n",
              "      <td>0.285328</td>\n",
              "      <td>0.456515</td>\n",
              "      <td>0.396733</td>\n",
              "      <td>0.826029</td>\n",
              "      <td>0.381152</td>\n",
              "      <td>105.890000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>170348.000000</td>\n",
              "      <td>2.132386</td>\n",
              "      <td>22.057729</td>\n",
              "      <td>2.250210</td>\n",
              "      <td>12.114672</td>\n",
              "      <td>11.095089</td>\n",
              "      <td>6.474115</td>\n",
              "      <td>5.802537</td>\n",
              "      <td>20.007208</td>\n",
              "      <td>3.353525</td>\n",
              "      <td>...</td>\n",
              "      <td>27.202839</td>\n",
              "      <td>8.361985</td>\n",
              "      <td>5.466230</td>\n",
              "      <td>1.091435</td>\n",
              "      <td>2.208209</td>\n",
              "      <td>2.745261</td>\n",
              "      <td>3.052358</td>\n",
              "      <td>1.779364</td>\n",
              "      <td>2125.870000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2eac4cd0-6689-49c4-b722-964acee2e7f7')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Implement a rule for stating which cases are flagged as fraud\n",
        "df['flag_as_fraud'] = np.where(np.logical_and(df['V1']< -0.419200\t, df['V3']< -2.276185), 1, 0)\n",
        "\n",
        "# Create a crosstab of flagged fraud cases versus the actual fraud cases\n",
        "print(pd.crosstab(df.Class, df.flag_as_fraud, rownames=['Actual Fraud'], colnames=['Flagged Fraud']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb1RgttzbvD1",
        "outputId": "b665cfb7-c407-419f-b104-b5f166718b15"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Flagged Fraud       0     1\n",
            "Actual Fraud               \n",
            "0              277716  6599\n",
            "1                 192   300\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DjHMTSACc-Xx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## **Using ML classification to catch fraud**\n",
        "In this exercise you'll see what happens when you use a simple machine learning model on our credit card data instead.\n",
        "\n",
        "Do you think you can beat those results? Remember, you've predicted 22 out of 50 fraud cases, and had 16 false positives.\n",
        "\n",
        "So with that in mind, let's implement a Logistic Regression model. If you have taken the class on supervised learning in Python, you should be familiar with this model. If not, you might want to refresh that at this point. But don't worry, you'll be guided through the structure of the machine learning model.\n",
        "\n",
        "The X and y variables are available in your workspace."
      ],
      "metadata": {
        "id": "2_rnIRXUc_jw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20, random_state=0)\n",
        "\n",
        "# Fit a logistic regression model to our data\n",
        "model = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Obtain model predictions\n",
        "predicted = model.predict(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "GGMnV3WFdCnJ"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "confusion_matrix = metrics.confusion_matrix(y_test, predicted)\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "rip6FmPYe1nZ",
        "outputId": "62d69b0e-cc85-4612-8daa-53870f82d4bc"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "target_names = ['class 0', 'class 1'] \n",
        "print(classification_report(y_test, predicted, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfW-jyHfe6Mt",
        "outputId": "4c1c8783-4468-4768-8f75-a2a004c54440"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     class 0       1.00      1.00      1.00     56861\n",
            "     class 1       0.86      0.56      0.68       101\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.93      0.78      0.84     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20, random_state=0)\n",
        "\n",
        "# Fit a logistic regression model to our data\n",
        "model = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
        "model.fit(X_resampled,y_resampled)\n",
        "\n",
        "# Obtain model predictions\n",
        "predicted = model.predict(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "_gvfZ8rajnb2"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix = metrics.confusion_matrix(y_test, predicted)\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "XuLz_v79kKBc",
        "outputId": "c9bfbb1d-555e-4e3c-afd2-01ee8a76c05e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "target_names = ['class 0', 'class 1'] \n",
        "print(classification_report(y_test, predicted, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ybe2aWv4kAgz",
        "outputId": "4df9d0a2-0922-40f1-bd5e-1bee5ac132b8"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     class 0       1.00      0.98      0.99     56861\n",
            "     class 1       0.07      0.89      0.14       101\n",
            "\n",
            "    accuracy                           0.98     56962\n",
            "   macro avg       0.54      0.94      0.56     56962\n",
            "weighted avg       1.00      0.98      0.99     56962\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}