Kidney_Stone_Prediction_Team_Pyton_MIS_515_Kaggle_HW_2.ipynb

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        "<a href=\"https://colab.research.google.com/github/tizzce/MIS515/blob/main/Kidney_Stone_Prediction_Team_Pyton_MIS_515_Kaggle_HW_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "#Kidney Stone Prediction - Binary Classification"
      ],
      "metadata": {
        "id": "xyc9ZjJTHd7E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import library**"
      ],
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        "id": "tB-kjcnXE8Ax"
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      "source": [
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score, precision_recall_fscore_support\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create dataframe**"
      ],
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        "id": "uNbU1ckdIF-N"
      }
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      "cell_type": "code",
      "source": [
        "# Read and display the csv file\n",
        "df = pd.read_csv(\"kidney-stone.csv\")\n",
        "display(df.head(5))"
      ],
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              "   id  gravity    ph  osmo  cond  urea  calc  target\n",
              "0   0    1.013  6.19   443  14.8   124  1.45       0\n",
              "1   1    1.025  5.40   703  23.6   394  4.18       0\n",
              "2   2    1.009  6.13   371  24.5   159  9.04       0\n",
              "3   3    1.021  4.91   442  20.8   398  6.63       1\n",
              "4   4    1.021  5.53   874  17.8   385  2.21       1"
            ],
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        "**Exploratory Data Analysis (EDA)**"
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      "cell_type": "code",
      "source": [
        "# Checking for NAN values\n",
        "nan_values = df.isna().sum()\n",
        "nan_values"
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        "id": "76Uir839JZq8",
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          "data": {
            "text/plain": [
              "id         0\n",
              "gravity    0\n",
              "ph         0\n",
              "osmo       0\n",
              "cond       0\n",
              "urea       0\n",
              "calc       0\n",
              "target     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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      "source": [
        "# Checking for outliers using box plots for each feature\n",
        "features = df.columns.drop(['id', 'target'])  # Exclude 'id' and 'target' from the analysis\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "\n",
        "for i, feature in enumerate(features, 1):\n",
        "    plt.subplot(2, 3, i)\n",
        "    sns.boxplot(x=df[feature])\n",
        "    plt.title(feature)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
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          "height": 758
        },
        "id": "Du4eQMnDvurj",
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing outliers using IQR method\n",
        "Q1 = df[features].quantile(0.25)\n",
        "Q3 = df[features].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Defining the criteria to filter out the outliers\n",
        "outlier_criteria = ((df[features] < (Q1 - 1.5 * IQR)) | (df[features] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
        "\n",
        "# Removing the outliers\n",
        "df_cleaned = df[~outlier_criteria]\n",
        "\n",
        "# Number of rows before and after removing outliers\n",
        "rows_before = len(df)\n",
        "rows_after = len(df_cleaned)\n",
        "\n",
        "rows_before, rows_after\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0MejQh-0Iw4",
        "outputId": "da210d13-dc5a-4652-9f83-f293caf988b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(414, 399)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for Multicollinearity\n",
        "sns.heatmap(df_cleaned[features].corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
        "plt.title(\"Correlation Matrix of Independent Variables\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "uephqQ1C5lET",
        "outputId": "757fa640-9cfa-4bc1-a93a-7ec8f0b6cad0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation Matrix of Independent Variables')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The heat map indicated that some independent variables have moderate to high correlations with each other. For instance:\n",
        "gravity and osmo show a correlation of 0.70.\n",
        "osmo and urea have a correlation of 0.81.\n",
        "\n",
        "But there are only 6 features, we decided to keep all of them."
      ],
      "metadata": {
        "id": "JoCkyO84YxFC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build a Logistic Regression model**"
      ],
      "metadata": {
        "id": "mv2ewchA7OMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target (y)\n",
        "X = df_cleaned[features]\n",
        "\n",
        "y = df_cleaned['target']"
      ],
      "metadata": {
        "id": "JHY6wfOh7OqC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "D9XVCeCP7ecs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression Model\n",
        "logisticRegr = LogisticRegression(solver='liblinear', max_iter=1000)\n",
        "logisticRegr.fit(X_train, y_train)\n",
        "\n",
        "# Predictions on the test set\n",
        "y_pred = logisticRegr.predict(X_test)"
      ],
      "metadata": {
        "id": "JYx__4hy7inN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluation Metrics**"
      ],
      "metadata": {
        "id": "Go7pTAdnPCXf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing 5-fold cross-validation\n",
        "k_folds = 5\n",
        "scores = cross_val_score(logisticRegr, X, y, cv=k_folds, scoring='accuracy')\n",
        "\n",
        "# Calculate the average score\n",
        "average_score = scores.mean()\n",
        "\n",
        "print(\"Accuracy scores:\", scores)\n",
        "print(\"Average score:\", average_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VDpThMPQe53",
        "outputId": "b3587186-5633-4a0a-d964-db59f46bfd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy scores: [0.675      0.725      0.7375     0.8125     0.69620253]\n",
            "Average score: 0.729240506329114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy varies across folds, it might indicate that the model is sensitive to the specific composition of the training data."
      ],
      "metadata": {
        "id": "RDIAzTRAYawP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation Metrics\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)"
      ],
      "metadata": {
        "id": "2vuDQnZMPFv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting Sensitivity, Specificity, and F1 Score from the classification report\n",
        "precision, recall, f1_score, support = precision_recall_fscore_support(y_test, y_pred)\n",
        "\n",
        "# Calculating the ROC-AUC score\n",
        "roc_auc = roc_auc_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "hiqwmj1DQyQs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "print(\"Support:\", support)\n",
        "print(\"ROC_AUC Score:\", roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Wsn6kXzQ4Oj",
        "outputId": "9de5a62c-2e70-4d06-8f76-fc368c7b14bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: [0.8  0.88]\n",
            "Recall: [0.93617021 0.66666667]\n",
            "F1 Score: [0.8627451  0.75862069]\n",
            "Support: [47 33]\n",
            "ROC_AUC Score: 0.801418439716312\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "print(\"Classification Report:\")\n",
        "print(classification_rep)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJDDrxNfQ8n0",
        "outputId": "0d56ca0f-441c-4413-d493-b8e24ce7fafe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.94      0.86        47\n",
            "           1       0.88      0.67      0.76        33\n",
            "\n",
            "    accuracy                           0.82        80\n",
            "   macro avg       0.84      0.80      0.81        80\n",
            "weighted avg       0.83      0.82      0.82        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**"
      ],
      "metadata": {
        "id": "j_YPv8hURJoQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Oranges', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "MRpla_Z9RL_4",
        "outputId": "86410c34-13c1-4c8f-e54e-0466af1e406b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Improving the Model**"
      ],
      "metadata": {
        "id": "1Ltal29XEeFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the probability scores\n",
        "probabilities = logisticRegr.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Adjust the threshold\n",
        "new_threshold = 0.31\n",
        "new_predictions = (probabilities > new_threshold).astype(int)\n",
        "\n",
        "\n",
        "# Evaluate the model\n",
        "print(classification_report(y_test, new_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DdHJNU9dBfjc",
        "outputId": "f26ac65e-9db5-4bfa-f15b-922d9f835fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.66      0.78        47\n",
            "           1       0.66      0.94      0.78        33\n",
            "\n",
            "    accuracy                           0.78        80\n",
            "   macro avg       0.80      0.80      0.78        80\n",
            "weighted avg       0.82      0.78      0.78        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_conf_matrix = confusion_matrix(y_test, new_predictions)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(new_conf_matrix, annot=True, fmt='d', cmap='Greens', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Adjusted Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "MLuWKI9FDJKK",
        "outputId": "5e66c592-1dd2-43de-a013-7278184b5476"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}