titanic.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOw73frEMm+M9NIHx0umhQy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hashil7/Titanic-Survival-Prediction/blob/main/titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import necessary libraries"
      ],
      "metadata": {
        "id": "mrFytNFi6Vow"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vK8YA81iEvTv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Read CSV file and display information"
      ],
      "metadata": {
        "id": "KM7rR4yU6Umw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"titanic.csv\")\n",
        "print(\"Dataframe information\")\n",
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unTAorWa66Lz",
        "outputId": "a7994dbf-1416-4e97-aafa-9dcbb97bb25a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe information\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Display first 10 rows of Data"
      ],
      "metadata": {
        "id": "ERhoY8LB7dMB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFirst 10 rows of Data\")\n",
        "pd.set_option('display.width', None)\n",
        "print(df.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFF5rro27hmk",
        "outputId": "ae36f240-fafc-4b18-8145-ccd29074232b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First 10 rows of Data\n",
            "   Survived   Age  SibSp  Parch     Fare  Sex_female  Sex_male  Pclass_1  Pclass_2  Pclass_3\n",
            "0         0  22.0      1      0   7.2500           0         1         0         0         1\n",
            "1         1  38.0      1      0  71.2833           1         0         1         0         0\n",
            "2         1  26.0      0      0   7.9250           1         0         0         0         1\n",
            "3         1  35.0      1      0  53.1000           1         0         1         0         0\n",
            "4         0  35.0      0      0   8.0500           0         1         0         0         1\n",
            "5         0  29.7      0      0   8.4583           0         1         0         0         1\n",
            "6         0  54.0      0      0  51.8625           0         1         1         0         0\n",
            "7         0   2.0      3      1  21.0750           0         1         0         0         1\n",
            "8         1  27.0      0      2  11.1333           1         0         0         0         1\n",
            "9         1  14.0      1      0  30.0708           1         0         0         1         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Display first 5 rows of the data having the given columns only.\n",
        "‘PassengerID’, ‘Name’, ‘Age’, ‘Sex’\n"
      ],
      "metadata": {
        "id": "-9pJdpL97rQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selected_cols  = ['PassengerId','Name','Age','Sex']\n",
        "print('\\nFirst 5 rows of selected columns')\n",
        "print(df[selected_cols].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVs_djwx7xEn",
        "outputId": "eec2276d-7fa6-462d-eac7-896b39a318b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First 5 rows of selected columns\n",
            "   PassengerId                                               Name   Age     Sex\n",
            "0            1                            Braund, Mr. Owen Harris  22.0    male\n",
            "1            2  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0  female\n",
            "2            3                             Heikkinen, Miss. Laina  26.0  female\n",
            "3            4       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0  female\n",
            "4            5                           Allen, Mr. William Henry  35.0    male\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the count of survived passengers."
      ],
      "metadata": {
        "id": "ce4xm6sw82Fm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(data = df, x='Survived')\n",
        "plt.title('Count of Survived Passengers')\n",
        "plt.xlabel('People')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(ticks=[0, 1], labels=['Not Survived', 'Survived'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0WyjoMLq87n9",
        "outputId": "b9b0dee8-c52b-464c-bd1d-7068a5003c6a"
      },
      "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": [
        "Plot Histogram of Age Column"
      ],
      "metadata": {
        "id": "XDcMba4y9CnM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.histplot(data=df,x='Age',bins = 20,kde=True)\n",
        "plt.title('Histogrm of Age')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency (of people)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WlhL7OYW9GqE",
        "outputId": "7b0048cf-1a27-407b-f139-873ab2762379"
      },
      "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": [
        "Drop the following unnecessary columns.\n",
        "‘PassengerID’,’Name’, ‘Ticket’, ‘Cabin’, ‘Embarked’"
      ],
      "metadata": {
        "id": "KeuWLhzm9K1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "un_cols = ['PassengerId','Name','Ticket','Cabin','Embarked']\n",
        "df.drop(columns=un_cols, axis=1,inplace=True)\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8l-s79_9adR",
        "outputId": "1faa9e1c-430c-4493-d4ef-594c27f0a402"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare\n",
            "0         0       3    male  22.0      1      0   7.2500\n",
            "1         1       1  female  38.0      1      0  71.2833\n",
            "2         1       3  female  26.0      0      0   7.9250\n",
            "3         1       1  female  35.0      1      0  53.1000\n",
            "4         0       3    male  35.0      0      0   8.0500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Replace NaN values in 'Age' column with mean value of age."
      ],
      "metadata": {
        "id": "5vKNiZ629l-4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Count number of NaN values"
      ],
      "metadata": {
        "id": "qMigJdqo9_cO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nan_count = df['Age'].isna().sum()\n",
        "print('NaN count: ',nan_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLHJ9c3B90tL",
        "outputId": "58aab51a-ed6e-450a-d969-95ffcb715f7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NaN count:  177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate mean age"
      ],
      "metadata": {
        "id": "J99_QOca-H5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_age  =round(df['Age'].mean(),2)\n",
        "print('Mean age: ',mean_age)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmvAx-5b-Kbp",
        "outputId": "7a3da50f-0cc9-4506-ee19-6fa9c1d64ebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean age:  29.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Replace NaN values with mean value"
      ],
      "metadata": {
        "id": "eURFY9d1-Ogd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].fillna(mean_age,inplace=True)"
      ],
      "metadata": {
        "id": "LxTabjWd-S1I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert entries in'Sex' and 'Pclass' to numerical values, drop the first attribute and concatenate the results"
      ],
      "metadata": {
        "id": "4nNKRlQr_L-z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['Sex', 'Pclass'], drop_first=True)\n",
        "df=df.astype(int)\n",
        "print(df.head())"
      ],
      "metadata": {
        "id": "bkKJx58FBHjs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50d063e5-71db-44cb-d0af-d5d76a298834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Survived  Age  SibSp  Parch  Fare  Sex_male  Pclass_2  Pclass_3\n",
            "0         0   22      1      0     7         1         0         1\n",
            "1         1   38      1      0    71         0         0         0\n",
            "2         1   26      0      0     7         0         0         1\n",
            "3         1   35      1      0    53         0         0         0\n",
            "4         0   35      0      0     8         1         0         1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rename Columns"
      ],
      "metadata": {
        "id": "o2UWz8CM5wCq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'Sex_male':'sex'},inplace=True)\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRNrdVtT500k",
        "outputId": "f6f9f0ca-dfa1-4ad8-a8f0-0040d2d1f6e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Survived  Age  SibSp  Parch  Fare  sex  Pclass_2  Pclass_3\n",
            "0         0   22      1      0     7    1         0         1\n",
            "1         1   38      1      0    71    0         0         0\n",
            "2         1   26      0      0     7    0         0         1\n",
            "3         1   35      1      0    53    0         0         0\n",
            "4         0   35      0      0     8    1         0         1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make a ratio of 30% and 70% for test and train dataset."
      ],
      "metadata": {
        "id": "vfEjym1C58ua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop the last column (output) to get input 'X'\n",
        "X=df.drop(columns=['Survived'])\n",
        "y=df['Survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)"
      ],
      "metadata": {
        "id": "fYimERMO5-Nh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply Logistic Regression and Neural Networks Classifier"
      ],
      "metadata": {
        "id": "omGEGgCV6U-N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_model = LogisticRegression()\n",
        "logistic_model.fit(X_train,y_train)\n",
        "y_pred_logistic = logistic_model.predict(X_test)\n",
        "nn_classifier = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=42)\n",
        "nn_classifier.fit(X_train, y_train)\n",
        "y_pred_nn = nn_classifier.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxV1q3kf63Iz",
        "outputId": "108afc46-cbe3-4023-9c00-181c3eddee82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot Confusion Matrices"
      ],
      "metadata": {
        "id": "q62K8V6A7Fz1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm_logistic = confusion_matrix(y_test, y_pred_logistic)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.imshow(cm_logistic, cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix for Logistic Regression')\n",
        "plt.colorbar()\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.xticks([0, 1], ['Not Survived', 'Survived'])\n",
        "plt.yticks([0, 1], ['Not Survived', 'Survived'])\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j, i, format(cm_logistic[i, j], 'd'), ha=\"center\", va=\"center\", color=\"white\" if cm_logistic[i, j] > np.max(cm_logistic) / 2 else \"black\")\n",
        "plt.show()\n",
        "\n",
        "cm_nn = confusion_matrix(y_test, y_pred_nn)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.imshow(cm_nn, cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix for Neural Network Classifier')\n",
        "plt.colorbar()\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.xticks([0, 1], ['Not Survived', 'Survived'])\n",
        "plt.yticks([0, 1], ['Not Survived', 'Survived'])\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j, i, format(cm_nn[i, j], 'd'), ha=\"center\", va=\"center\", color=\"white\" if cm_nn[i, j] > np.max(cm_nn) / 2 else \"black\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WHy0Ij5f7MCS",
        "outputId": "4827e430-4475-4b49-eabe-d3565c3f582c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find Precision, Recall, F1score, and Accuracy."
      ],
      "metadata": {
        "id": "OzMYH7SU7mKb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "precision_logistic = precision_score(y_test, y_pred_logistic)\n",
        "recall_logistic = recall_score(y_test, y_pred_logistic)\n",
        "f1_logistic = f1_score(y_test, y_pred_logistic)\n",
        "accuracy_logistic = accuracy_score(y_test, y_pred_logistic)\n",
        "\n",
        "print(\"Logistic Regression Metrics:\")\n",
        "print(\"Precision:\", precision_logistic)\n",
        "print(\"Recall:\", recall_logistic)\n",
        "print(\"F1-score:\", f1_logistic)\n",
        "print(\"Accuracy:\", accuracy_logistic)\n",
        "print(\"\\n\")\n",
        "precision_nn = precision_score(y_test, y_pred_nn)\n",
        "recall_nn = recall_score(y_test, y_pred_nn)\n",
        "f1_nn = f1_score(y_test, y_pred_nn)\n",
        "accuracy_nn = accuracy_score(y_test, y_pred_nn)\n",
        "\n",
        "print(\"Neural Network Classifier Metrics:\")\n",
        "print(\"Precision:\", precision_nn)\n",
        "print(\"Recall:\", recall_nn)\n",
        "print(\"F1-score:\", f1_nn)\n",
        "print(\"Accuracy:\", accuracy_nn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpVQAfYq7oqg",
        "outputId": "85b28e4e-5d79-46f8-a8af-52758ac8787a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Metrics:\n",
            "Precision: 0.8210526315789474\n",
            "Recall: 0.7027027027027027\n",
            "F1-score: 0.7572815533980582\n",
            "Accuracy: 0.8134328358208955\n",
            "\n",
            "\n",
            "Neural Network Classifier Metrics:\n",
            "Precision: 0.8351648351648352\n",
            "Recall: 0.6846846846846847\n",
            "F1-score: 0.7524752475247525\n",
            "Accuracy: 0.8134328358208955\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IznOJkQy7tym"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}