Machine Learning With Python/Titanic Survival Prediction Analysis (Day 5).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harshaljanjani/statsForML/blob/main/FreeCodeCamp_MachineLearningInPython/LinearRegressionContinued(TitanicSurvivalPrediction)_Day5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "7tx9Sq9oiKtF"
      },
      "source": [
        "# **Tensorflow Introduction**\n",
        "\n",
        "### 1) Introduction To Tensors\n",
        "### 2) Core Learning Algorithms - Introduction\n",
        "### 3) Core Learning Algorithms - Working With Data\n",
        "### 4) Core Learning Algorithms - Training And Testing Data ✅\n",
        "### 5) Core Learning Algorithms - The Training Process / Linear Regression ✅"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4N7XbNDVY8P3",
        "outputId": "071e21f6-441e-4b2c-e4ad-dc9dc8caf875"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<module 'tensorflow._api.v2.version' from '/usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/version/__init__.py'>\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf  # import the tensorflow module\n",
        "print(tf.version)  # make sure the version is 2.x (update - Google Colab only allows 2.x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0YrD-hRqD-W",
        "outputId": "d07ee768-5dc5-41c7-e6cc-013d5db52231"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(2, shape=(), dtype=int32)\n",
            "(4, 5)\n"
          ]
        }
      ],
      "source": [
        "# Creating a 2D tensor\n",
        "matrix = [[1,2,3,4,5],\n",
        "          [6,7,8,9,10],\n",
        "          [11,12,13,14,15],\n",
        "          [16,17,18,19,20]]\n",
        "\n",
        "tensor = tf.Variable(matrix, dtype=tf.int32) \n",
        "print(tf.rank(tensor))\n",
        "print(tensor.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "t4Hp-dYmR2jf",
        "outputId": "e23ad538-440f-4e9c-f200-c707c5e50b06"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.0, 6.0, 0.0, 20.0)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = [1, 2, 2.5, 3, 4]\n",
        "y = [1, 4, 7, 9, 15]\n",
        "plt.plot(x, y, 'ro')\n",
        "plt.axis([0, 6, 0, 20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Kv5eKLP_UYZi",
        "outputId": "10b37f32-cd02-4c4f-8c7f-fa0ca5c6526f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(x, y, 'ro')\n",
        "plt.axis([0, 6, 0, 20])\n",
        "plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "dZ8Rbs2xtNqj"
      },
      "outputs": [],
      "source": [
        "tensor1 = tf.ones([1,2,3])  # tf.ones() creates a shape [1,2,3] tensor full of ones\n",
        "tensor2 = tf.reshape(tensor1, [2,3,1])  # reshape existing data to shape [2,3,1]\n",
        "tensor3 = tf.reshape(tensor2, [3, -1])  # -1 tells the tensor to calculate the size of the dimension in that place\n",
        "                                        # this will reshape the tensor to [3,2]\n",
        "                                                                             \n",
        "# The number of elements in the reshaped tensor MUST match the number in the original"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wd85uGI7qyfC",
        "outputId": "5ad96458-a77d-4944-d443-e1db9d321a8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(3, shape=(), dtype=int32)\n",
            "tf.Tensor([1 2 3 4 5], shape=(5,), dtype=int32)\n",
            "tf.Tensor([ 1  6 11 16], shape=(4,), dtype=int32)\n",
            "tf.Tensor(\n",
            "[[ 6  7  8  9 10]\n",
            " [16 17 18 19 20]], shape=(2, 5), dtype=int32)\n",
            "tf.Tensor([ 6 11], shape=(2,), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "# Now lets select some different rows and columns from our tensor\n",
        "\n",
        "three = tensor[0,2]  # selects the 3rd element from the 1st row\n",
        "print(three)  # -> 3\n",
        "\n",
        "row1 = tensor[0]  # selects the first row\n",
        "print(row1)\n",
        "\n",
        "column1 = tensor[:, 0]  # selects the first column\n",
        "print(column1)\n",
        "\n",
        "row_2_and_4 = tensor[1::2]  # selects second and fourth row\n",
        "print(row_2_and_4)\n",
        "\n",
        "column_1_in_row_2_and_3 = tensor[1:3, 0]\n",
        "print(column_1_in_row_2_and_3)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTx6_l7YZyt3",
        "outputId": "b24f7bbf-041e-4a2f-d74a-2b5c6c31bcc4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "<module 'tensorflow._api.v2.version' from '/usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/version/__init__.py'>\n",
            "tf.Tensor(\n",
            "[[[[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]]\n",
            "\n",
            "\n",
            " [[[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]]\n",
            "\n",
            "\n",
            " [[[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]]\n",
            "\n",
            "\n",
            " [[[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]]\n",
            "\n",
            "\n",
            " [[[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]]\n",
            "\n",
            "  [[0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
            "   [0. 0. 0. 0. 0.]\n",
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          ]
        }
      ],
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "print(tf.version)\n",
        "\n",
        "t = tf.zeros([5,5,5,5])\n",
        "print(t)\n",
        "t = tf.reshape(t,[25,25])\n",
        "print(t)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOc4EifEFhZK"
      },
      "source": [
        "# **Titanic Survival Prediction By Linear Regression**\n",
        "\n",
        "**Dataset:** [Tensorflow Datasets - Titanic Dataset](https://www.tensorflow.org/datasets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4xpuxwKHOGL",
        "outputId": "212c480b-1078-47c0-b7db-9234bd92f290"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.0.2)\n",
            "Collecting scikit-learn\n",
            "  Downloading scikit_learn-1.2.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.7.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\n",
            "Installing collected packages: scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "Successfully installed scikit-learn-1.2.1\n"
          ]
        }
      ],
      "source": [
        "pip install -U scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "qcII_xj9Ntyo"
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "from six.moves import urllib\n",
        "import tensorflow as tf\n",
        "import tensorflow.compat.v2.feature_column as fc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpllWsKIOGOy",
        "outputId": "0e14f0af-6304-43e0-f5e0-f22a04b52f99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
            "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
            "1         1  female  38.0                   1      0  71.2833  First        C   \n",
            "2         1  female  26.0                   0      0   7.9250  Third  unknown   \n",
            "3         1  female  35.0                   1      0  53.1000  First        C   \n",
            "4         0    male  28.0                   0      0   8.4583  Third  unknown   \n",
            "\n",
            "   embark_town alone  \n",
            "0  Southampton     n  \n",
            "1    Cherbourg     n  \n",
            "2  Southampton     y  \n",
            "3  Southampton     n  \n",
            "4   Queenstown     y  \n",
            "\n",
            " 0      0\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      0\n",
            "      ..\n",
            "622    0\n",
            "623    0\n",
            "624    1\n",
            "625    0\n",
            "626    0\n",
            "Name: survived, Length: 627, dtype: int64\n",
            "\n",
            " sex                          male\n",
            "age                          22.0\n",
            "n_siblings_spouses              1\n",
            "parch                           0\n",
            "fare                         7.25\n",
            "class                       Third\n",
            "deck                      unknown\n",
            "embark_town           Southampton\n",
            "alone                           n\n",
            "Name: 0, dtype: object \n",
            " 0\n",
            "\n",
            " 0      22.0\n",
            "1      38.0\n",
            "2      26.0\n",
            "3      35.0\n",
            "4      28.0\n",
            "       ... \n",
            "622    28.0\n",
            "623    25.0\n",
            "624    19.0\n",
            "625    28.0\n",
            "626    32.0\n",
            "Name: age, Length: 627, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Load dataset.\n",
        "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data\n",
        "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data\n",
        "print(dftrain.head())\n",
        "#pop labels, to seperate from features to be used to train the model\n",
        "y_train = dftrain.pop('survived') #'survived' column in the training dataset\n",
        "y_eval = dfeval.pop('survived') #'survived' column in the testing dataset\n",
        "#print popped labels/entries\n",
        "print(\"\\n\",y_train)\n",
        "print(\"\\n\",dftrain.loc[0], \"\\n\", y_train[0]) #to print particular row\n",
        "print(\"\\n\",dftrain[\"age\"]) #to reference particular column"
      ]
    },
    {
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      "execution_count": 12,
      "metadata": {
        "colab": {
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        },
        "id": "mKsXeWinQiVR",
        "outputId": "38506f51-71b1-4649-a703-41b5a8a53d3a"
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              "      sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
              "0    male  22.0                   1      0   7.2500  Third  unknown   \n",
              "1  female  38.0                   1      0  71.2833  First        C   \n",
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              "1    Cherbourg     n  \n",
              "2  Southampton     y  \n",
              "3  Southampton     n  \n",
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              "      <th>parch</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.631308</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.379585</td>\n",
              "      <td>34.385399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.511818</td>\n",
              "      <td>1.151090</td>\n",
              "      <td>0.792999</td>\n",
              "      <td>54.597730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.045800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.387500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5daa0264-63e3-46cf-bff0-c1357db526e2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://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-5daa0264-63e3-46cf-bff0-c1357db526e2 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-5daa0264-63e3-46cf-bff0-c1357db526e2');\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",
              "  "
            ],
            "text/plain": [
              "              age  n_siblings_spouses       parch        fare\n",
              "count  627.000000          627.000000  627.000000  627.000000\n",
              "mean    29.631308            0.545455    0.379585   34.385399\n",
              "std     12.511818            1.151090    0.792999   54.597730\n",
              "min      0.750000            0.000000    0.000000    0.000000\n",
              "25%     23.000000            0.000000    0.000000    7.895800\n",
              "50%     28.000000            0.000000    0.000000   15.045800\n",
              "75%     35.000000            1.000000    0.000000   31.387500\n",
              "max     80.000000            8.000000    5.000000  512.329200"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dftrain.describe() #statistical analysis of dataframe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECj0ztv4LFGw"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZASKhYmLFqC",
        "outputId": "d3d9ca47-9f49-48a0-8a5d-956fb582828d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(627, 9)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dftrain.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viye_EBALXJb"
      },
      "source": [
        "# **Data Visualization And Correlation Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "JHgaKPh5L7Fw",
        "outputId": "da8b9f53-5547-47c0-aa06-7e68e1958b2d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f2286190>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Number Of People\")\n",
        "plt.title(\"Age Distribution Across The Titanic Histogram\")\n",
        "dftrain.age.hist(bins=20) #Histogram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1lOKIe6JNgB6",
        "outputId": "11e5d9e6-39cd-4a60-d170-8e36a874aca3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f206bc70>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dftrain.sex.value_counts().plot(kind='barh') #Horizontal Bar Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "10ywxDDIOEK_",
        "outputId": "a055434b-3786-4358-d10e-0d0f3f5459eb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95f2058280>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dftrain['class'].value_counts().plot(kind = 'barh')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "OQ-T2E2-OOa6",
        "outputId": "19e1c741-1698-42a6-c56c-c5a8e80e4791"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, '% Survival')"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pd.concat([dftrain,y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% Survival')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnjIti6uPv8Q"
      },
      "source": [
        "**After analyzing this information, the following information is inferred:**\n",
        "- Most passengers are in their **20's** or **30's**.\n",
        "- Most passengers are **Male**.\n",
        "- Most passengers are in **\"Third\"** class.\n",
        "- **Females** have a much higher chance of survival."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39Ucn5D6Zsuo"
      },
      "source": [
        "# **Day 5 - 25/01/2023**\n",
        "## **Training And Testing Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5OlIFBEo6EO",
        "outputId": "8b78bbd2-ffce-4105-d5fa-cfc25ef14522"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(264, 9) (627, 9)\n"
          ]
        }
      ],
      "source": [
        "print(dfeval.shape, dftrain.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adgSyfvkpcD4",
        "outputId": "30a6d61a-ccaf-4cfe-8487-c826fa245c17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['male' 'female']\n",
            "[1 0 3 4 2 5 8]\n",
            "[0 1 2 5 3 4]\n",
            "['Third' 'First' 'Second']\n",
            "['unknown' 'C' 'G' 'A' 'B' 'D' 'F' 'E']\n",
            "['Southampton' 'Cherbourg' 'Queenstown' 'unknown']\n",
            "['n' 'y']\n",
            "[VocabularyListCategoricalColumn(key='sex', vocabulary_list=('male', 'female'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='n_siblings_spouses', vocabulary_list=(1, 0, 3, 4, 2, 5, 8), dtype=tf.int64, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='parch', vocabulary_list=(0, 1, 2, 5, 3, 4), dtype=tf.int64, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='class', vocabulary_list=('Third', 'First', 'Second'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='deck', vocabulary_list=('unknown', 'C', 'G', 'A', 'B', 'D', 'F', 'E'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='embark_town', vocabulary_list=('Southampton', 'Cherbourg', 'Queenstown', 'unknown'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='alone', vocabulary_list=('n', 'y'), dtype=tf.string, default_value=-1, num_oov_buckets=0), NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='fare', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]\n"
          ]
        }
      ],
      "source": [
        "#Difference between Categorical And Numeric Data (Categorical Data -> Common, all non-numeric data)\n",
        "#Task: Convert categorical data into numeric data\n",
        "#How is this achieved? Encode the data into integer values (Example: Male - 1, Female - 0), since model only needs to know whether values are different or not\n",
        "CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\n",
        "                       'embark_town', 'alone']\n",
        "NUMERIC_COLUMNS = ['age', 'fare']\n",
        "\n",
        "#Feature Columns -> What we need to feed to our linear estimator/model to make predictions\n",
        "#FOR CATEGORICAL COLUMNS:\n",
        "feature_columns = []\n",
        "for feature_name in CATEGORICAL_COLUMNS: #loop through each feature_name\n",
        "  vocabulary = dftrain[feature_name].unique() #gets a list of all unique data values from the feature column \n",
        "  print(vocabulary)\n",
        "  feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name,vocabulary))\n",
        "  #Creates a NumPy data column with the header as the feature_name and the data values in the column as only the unique values/vocabulary of the feature-column\n",
        "#Needed by Linear Model (All columns and all unique entries of that column, also needs to know whether it's a categorical column or a numeric column)\n",
        "\n",
        "#FOR NUMERIC COLUMNS:\n",
        "for feature_name in NUMERIC_COLUMNS:\n",
        "  #Numeric -> There could be an infinite amount of unique values in the data column, hence only data-type is given\n",
        "  feature_columns.append(tf.feature_column.numeric_column(feature_name,dtype=tf.float32))\n",
        "print(feature_columns) #Integer encoding's not necessarily in order"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23kCBJEb34B5"
      },
      "source": [
        "## **Training Data: Loading Data In Batches And Related Terminology**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "CCCu1guv4Dxh"
      },
      "outputs": [],
      "source": [
        "#Load data in batches, 32 entries fed at once to the model\n",
        "#Epochs: The number of times the model is going to see the same data. Data is fed again and again, but in different orders/forms. Helps it pick up on patterns\n",
        "#Overfitting: When we feed the model too much data (Bias-Variance Tradeoff) -> Start with lower epochs, and go higher\n",
        "#Input function: The way that we define how our data is going to be broken into epochs, and into batches to feed to our model. Encodes it into 'tf.data.Dataset' object\n",
        "\n",
        "#INPUT FUNCTION (Source - Tensorflow Documentation)\n",
        "#Features -> data_df (Pandas Dataframe), #Labels -> label_df(y_train/y_eval)\n",
        "#shuffle -> Are we going to shuffle/mix-up the data before passing it to the model?\n",
        "\n",
        "def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n",
        "  def input_function(): #inner function, this will be returned by the outer function\n",
        "    ds = tf.data.Dataset.from_tensor_slices((dict(data_df),label_df)) #create tf.data.Dataset object with data and its label\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(1000) #randomize order of data\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs) #split dataset into batches of 'batch_size' = 32, and repeat it for 'num_epochs' = 10\n",
        "    return ds #return a batch of the dataset\n",
        "  return input_function #return a function object for use\n",
        "\n",
        "train_input_fn = make_input_fn(dftrain, y_train) # here we will call the input_function that was returned to us to get a dataset object we can feed to the model\n",
        "eval_input_fn = make_input_fn(dfeval,y_eval,num_epochs=1,shuffle=False) #Don't need to shuffle the data, because we are not training it. Epoch = 1, since we are only testing it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ah0MO-s-8Lzb"
      },
      "source": [
        "## **Creating And Training The Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cX8ZE8bd8WpO",
        "outputId": "7961bee1-8cea-4cc8-8149-5054beffaa77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "74.62121248245239\n",
            "{'accuracy': 0.7462121, 'accuracy_baseline': 0.625, 'auc': 0.8334558, 'auc_precision_recall': 0.79405004, 'average_loss': 0.47339806, 'label/mean': 0.375, 'loss': 0.4656391, 'precision': 0.6666667, 'prediction/mean': 0.38401347, 'recall': 0.64646465, 'global_step': 200}\n"
          ]
        }
      ],
      "source": [
        "#Linear Estimator\n",
        "linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns) #Estimator -> Core Learning Algorithms Use Estimators, which are basic implementations of algorithms in Tensorflow\n",
        "#Training The Model\n",
        "linear_est.train(train_input_fn) #.train(input_function)\n",
        "result = linear_est.evaluate(eval_input_fn) #get model metrics/stats by testing on testing data\n",
        "\n",
        "clear_output() #clears console output\n",
        "print(result['accuracy'] * 100) #result -> Dictionary about statistics and performence metrics about how well our model performed\n",
        "\n",
        "#Accuracy on testing dataset on 25/01/2023 -> 73.86363744735718 %\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jt7aNi6E-Z6z"
      },
      "source": [
        "## **Using The Model For Predictive Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZFLRVtj-3ZD",
        "outputId": "5dee1fd2-97c1-41a6-ab6d-b410216900fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'logits': array([-2.4203742], dtype=float32), 'logistic': array([0.0816322], dtype=float32), 'probabilities': array([0.9183677 , 0.08163219], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([-0.65740454], dtype=float32), 'logistic': array([0.3413229], dtype=float32), 'probabilities': array([0.65867704, 0.34132287], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([0.9771104], dtype=float32), 'logistic': array([0.7265345], dtype=float32), 'probabilities': array([0.27346548, 0.7265344 ], dtype=float32), 'class_ids': array([1]), 'classes': array([b'1'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': 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dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([0.24885383], dtype=float32), 'logistic': array([0.56189436], dtype=float32), 'probabilities': array([0.43810564, 0.56189436], dtype=float32), 'class_ids': array([1]), 'classes': array([b'1'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([-1.4235235], dtype=float32), 'logistic': array([0.1941098], dtype=float32), 'probabilities': array([0.80589026, 0.19410981], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([-0.3416861], dtype=float32), 'logistic': array([0.41539994], dtype=float32), 'probabilities': array([0.58460003, 0.41539994], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}]\n",
            "{'logits': array([-2.4203742], dtype=float32), 'logistic': array([0.0816322], dtype=float32), 'probabilities': array([0.9183677 , 0.08163219], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}\n",
            "sex                          male\n",
            "age                          35.0\n",
            "n_siblings_spouses              0\n",
            "parch                           0\n",
            "fare                         8.05\n",
            "class                       Third\n",
            "deck                      unknown\n",
            "embark_town           Southampton\n",
            "alone                           y\n",
            "Name: 0, dtype: object\n",
            "8.163218945264816\n",
            "91.83676838874817\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "#Tensorflow models are built to make predictions on a lot of parameters at once, not a single piece of data (large batches of data)\n",
        "#Model Accuracy: Compare predictive results given by the model, with the actual results for every single passenger\n",
        "#Predict\n",
        "result = list(linear_est.predict(eval_input_fn)) #input function is needed to make a prediction\n",
        "print(result) #List of dictionaries that are making predictions for every single data entry in the testing dataset\n",
        "print(result[0]) #A single prediction\n",
        "#For presenting each prediction for each data entry -> Loop through result\n",
        "#Prediction of two classes -> Number between 0 and 1 (either they survived, or did not survive), we are interested in the 'probability' parameter of the logistics array\n",
        "#Survival Percentage: Probability * 100\n",
        "\n",
        "#0 -> Didn't survive, 1 -> Did survive \n",
        "#Display the record of the first person (person 0):\n",
        "print(dfeval.loc[0])\n",
        "#Predictive Analytics Of Whether The Person 0 Has A Chance Of Surviving Or Not:\n",
        "print(result[0]['probabilities'][1] * 100) #Print probability of survival \n",
        "print(result[0]['probabilities'][0] * 100) #Print probability of not surviving\n",
        "#Compare with actual result (did they really survive or not?)\n",
        "print(y_eval[0])"
      ]
    }
  ],
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