Book_Chapter3.ipynb

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        "<a href=\"https://colab.research.google.com/github/Mateusz-best-creator/Learning_ML/blob/main/Book_Chapter3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
            "  warn(\n"
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              "  'pixel679',\n",
              "  'pixel680',\n",
              "  'pixel681',\n",
              "  'pixel682',\n",
              "  'pixel683',\n",
              "  'pixel684',\n",
              "  'pixel685',\n",
              "  'pixel686',\n",
              "  'pixel687',\n",
              "  'pixel688',\n",
              "  'pixel689',\n",
              "  'pixel690',\n",
              "  'pixel691',\n",
              "  'pixel692',\n",
              "  'pixel693',\n",
              "  'pixel694',\n",
              "  'pixel695',\n",
              "  'pixel696',\n",
              "  'pixel697',\n",
              "  'pixel698',\n",
              "  'pixel699',\n",
              "  'pixel700',\n",
              "  'pixel701',\n",
              "  'pixel702',\n",
              "  'pixel703',\n",
              "  'pixel704',\n",
              "  'pixel705',\n",
              "  'pixel706',\n",
              "  'pixel707',\n",
              "  'pixel708',\n",
              "  'pixel709',\n",
              "  'pixel710',\n",
              "  'pixel711',\n",
              "  'pixel712',\n",
              "  'pixel713',\n",
              "  'pixel714',\n",
              "  'pixel715',\n",
              "  'pixel716',\n",
              "  'pixel717',\n",
              "  'pixel718',\n",
              "  'pixel719',\n",
              "  'pixel720',\n",
              "  'pixel721',\n",
              "  'pixel722',\n",
              "  'pixel723',\n",
              "  'pixel724',\n",
              "  'pixel725',\n",
              "  'pixel726',\n",
              "  'pixel727',\n",
              "  'pixel728',\n",
              "  'pixel729',\n",
              "  'pixel730',\n",
              "  'pixel731',\n",
              "  'pixel732',\n",
              "  'pixel733',\n",
              "  'pixel734',\n",
              "  'pixel735',\n",
              "  'pixel736',\n",
              "  'pixel737',\n",
              "  'pixel738',\n",
              "  'pixel739',\n",
              "  'pixel740',\n",
              "  'pixel741',\n",
              "  'pixel742',\n",
              "  'pixel743',\n",
              "  'pixel744',\n",
              "  'pixel745',\n",
              "  'pixel746',\n",
              "  'pixel747',\n",
              "  'pixel748',\n",
              "  'pixel749',\n",
              "  'pixel750',\n",
              "  'pixel751',\n",
              "  'pixel752',\n",
              "  'pixel753',\n",
              "  'pixel754',\n",
              "  'pixel755',\n",
              "  'pixel756',\n",
              "  'pixel757',\n",
              "  'pixel758',\n",
              "  'pixel759',\n",
              "  'pixel760',\n",
              "  'pixel761',\n",
              "  'pixel762',\n",
              "  'pixel763',\n",
              "  'pixel764',\n",
              "  'pixel765',\n",
              "  'pixel766',\n",
              "  'pixel767',\n",
              "  'pixel768',\n",
              "  'pixel769',\n",
              "  'pixel770',\n",
              "  'pixel771',\n",
              "  'pixel772',\n",
              "  'pixel773',\n",
              "  'pixel774',\n",
              "  'pixel775',\n",
              "  'pixel776',\n",
              "  'pixel777',\n",
              "  'pixel778',\n",
              "  'pixel779',\n",
              "  'pixel780',\n",
              "  'pixel781',\n",
              "  'pixel782',\n",
              "  'pixel783',\n",
              "  'pixel784'],\n",
              " 'target_names': ['class'],\n",
              " 'DESCR': \"**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \\n**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \\n**Please cite**:  \\n\\nThe MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \\n\\nIt is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \\n\\nWith some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \\n\\nThe MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\\n\\nDownloaded from openml.org.\",\n",
              " 'details': {'id': '554',\n",
              "  'name': 'mnist_784',\n",
              "  'version': '1',\n",
              "  'description_version': '2',\n",
              "  'format': 'ARFF',\n",
              "  'creator': ['Yann LeCun', 'Corinna Cortes', 'Christopher J.C. Burges'],\n",
              "  'upload_date': '2014-09-29T03:28:38',\n",
              "  'language': 'English',\n",
              "  'licence': 'Public',\n",
              "  'url': 'https://api.openml.org/data/v1/download/52667/mnist_784.arff',\n",
              "  'parquet_url': 'https://openml1.win.tue.nl/datasets/0000/0554/dataset_554.pq',\n",
              "  'file_id': '52667',\n",
              "  'default_target_attribute': 'class',\n",
              "  'tag': ['AzurePilot',\n",
              "   'OpenML-CC18',\n",
              "   'OpenML100',\n",
              "   'study_1',\n",
              "   'study_123',\n",
              "   'study_41',\n",
              "   'study_99',\n",
              "   'vision'],\n",
              "  'visibility': 'public',\n",
              "  'minio_url': 'https://openml1.win.tue.nl/datasets/0000/0554/dataset_554.pq',\n",
              "  'status': 'active',\n",
              "  'processing_date': '2020-11-20 20:12:09',\n",
              "  'md5_checksum': '0298d579eb1b86163de7723944c7e495'},\n",
              " 'url': 'https://www.openml.org/d/554'}"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "\n",
        "mnist = fetch_openml(\"mnist_784\", as_frame=False)\n",
        "mnist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W6IRFriTaIvK"
      },
      "outputs": [],
      "source": [
        "X, y = mnist.data, mnist.target\n",
        "X.shape, y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FNCekOm5Ztpc"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_digit(image_data, title=None):\n",
        "  image = image_data.reshape(28, 28)\n",
        "  plt.imshow(image, cmap=\"binary\")\n",
        "  if title:\n",
        "    plt.title(title) # index same as title\n",
        "  plt.axis(\"off\")\n",
        "\n",
        "some_digit = X[0]\n",
        "plot_digit(some_digit, y[0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gvTCpSTgZ-6i"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqTueN9X7rZE"
      },
      "source": [
        "## Training Binary Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJCsluUpalul"
      },
      "outputs": [],
      "source": [
        "y_train_5 = (y_train == '5')\n",
        "y_test_5 = (y_test == '5')\n",
        "y_train_5[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WrEAzBgL6qIA"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier()\n",
        "sgd_clf.fit(X_train, y_train_5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8lFMLwN96qLL"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val_score(estimator=sgd_clf, X=X_train, y=y_train_5, scoring=\"accuracy\", cv=3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrH7cB_86qOU"
      },
      "source": [
        "Implement cross-val-score => page 108"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHYP9gSD73AA"
      },
      "source": [
        "## Confusion Matrices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OG0FTEJi7wQl"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "y_train_predictions = cross_val_predict(estimator=sgd_clf, X=X_train, y=y_train_5, cv=3, verbose=2)\n",
        "y_train_predictions[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "55MI5lhj8lbs"
      },
      "outputs": [],
      "source": [
        "y_train_predictions, y_train_5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gXKqT5Ud7wTy"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "sgd_cf = confusion_matrix(y_train_5, y_train_predictions)\n",
        "sgd_cf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GmAf4hXx7weK"
      },
      "outputs": [],
      "source": [
        "sns.heatmap(sgd_cf, annot=True, fmt=\".0f\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHUUT5Hr7whU"
      },
      "source": [
        "## Precision and Recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gSr00lGy9Gwy"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "def calculate_metrics(y_train, y_pred):\n",
        "  precision = precision_score(y_train, y_pred)\n",
        "  recall = recall_score(y_train, y_pred)\n",
        "  f1_score = 2 / ((1 / precision) + (1 / recall))\n",
        "  return {\n",
        "      \"precision\": precision,\n",
        "      \"recall\": recall,\n",
        "      \"f1\": f1_score\n",
        "  }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ltmUBJij9Gz0"
      },
      "outputs": [],
      "source": [
        "calculate_metrics(y_train_5, y_train_predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M69gqiwr9G2x"
      },
      "source": [
        "## Precision and Recall TradeOff"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TnTgKeN5-FOu"
      },
      "outputs": [],
      "source": [
        "y_scores = sgd_clf.decision_function([some_digit])\n",
        "y_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O-az_up0-FRQ"
      },
      "outputs": [],
      "source": [
        "threshold = 0\n",
        "y_some_digit_pred = (y_scores > threshold)\n",
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "75PU3UDz-FUC"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "y_scores = cross_val_predict(sgd_clf,\n",
        "                             X_train,\n",
        "                             y_train_5,\n",
        "                             cv=3,\n",
        "                             method=\"decision_function\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nJW5OdDg_LVy"
      },
      "outputs": [],
      "source": [
        "y_scores.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xlYDNDZc9G5a"
      },
      "outputs": [],
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "precision, recall, thresholds = precision_recall_curve(y_true=y_train_5, probas_pred=y_scores)\n",
        "figure(figsize=(20, 6), dpi=80)\n",
        "plt.plot(thresholds, precision[:-1], label=\"precision\")\n",
        "plt.plot(thresholds, recall[:-1], label=\"recall\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.title(\"Precision Recall TradeOff\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFTkEUps7wka"
      },
      "outputs": [],
      "source": [
        "plt.plot(recall, precision, linewidth=2, label=\"Precision/Recall curve\")\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6zW0LjXXAMww"
      },
      "outputs": [],
      "source": [
        "idx_for_90_precent_precision = (precision >= 90).argmax()\n",
        "threshold_for_90 = thresholds[idx_for_90_precent_precision]\n",
        "y_train_pred_90 = (y_scores > threshold_for_90)\n",
        "calculate_metrics(y_train_5, y_train_pred_90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvD6Pd4EAhjz"
      },
      "source": [
        "## ROC Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6qvzHuuuAjUZ"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\n",
        "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve\")\n",
        "plt.plot([0,1],[0,1])\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1N-0KfwmAhxn"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "roc_auc_score(y_train_5, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VN70l7hOHI4p"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_clf = RandomForestClassifier()\n",
        "y_probas_forest = cross_val_predict(rf_clf,\n",
        "                                    X_train,\n",
        "                                    y_train_5,\n",
        "                                    cv=3,\n",
        "                                    method=\"predict_proba\")\n",
        "y_scores_forest = y_probas_forest[:, 1]\n",
        "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(y_true=y_train_5, probas_pred=y_scores_forest)\n",
        "\n",
        "plt.plot(recall, precision, linewidth=2, label=\"SGD\")\n",
        "plt.plot(recall_rf, precision_rf, linewidth=2, label=\"RandomForest\")\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kMCWoQ6mHj6F"
      },
      "outputs": [],
      "source": [
        "y_pred_forest = (y_scores_forest > 0.5)\n",
        "calculate_metrics(y_train_5, y_pred_forest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcOeYeHHI_fK"
      },
      "source": [
        "## MultiClass Classification\n",
        "\n",
        "To classifiy multiple classes:\n",
        "\n",
        "1. Train 10 binary classifiers, one for each digit (1, non 1 detector etc...), then select the class with the highest score. This is called `One vs Rest` approach: **OvsR**.\n",
        "\n",
        "2. We can also train binary classifier for every pair of digits: one to distinguihs 0s and 1s, one for 1s and 2s etc... This will require training N * (N - 1) classifiers! For ten classes it means 45 classifiers. This is called `OvsO` one versus one approach."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHXHhQsJJO9Q",
        "outputId": "d18873d3-9bc4-4c9f-9565-10a6f288c376"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['5'], dtype=object)"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svc_clf = SVC()\n",
        "svc_clf.fit(X_train[:2000], y_train[:2000])\n",
        "svc_clf.predict([X_train[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOzjI4EhJPEo",
        "outputId": "9bc4482d-765f-4faa-a865-531c0ba0d54f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 3.79297828,  0.72949369,  6.06184129,  8.29800527, -0.29383983,\n",
              "         9.30157597,  1.74723215,  2.77365456,  7.20601456,  4.82245092]])"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svc_clf.decision_function([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNB_vHYiJPHk",
        "outputId": "36c87484-660b-4af6-8e8d-1a97c1c095ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['5'], dtype='<U1')"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "\n",
        "ovr_clf = OneVsRestClassifier(estimator=SVC())\n",
        "ovr_clf.fit(X_train[:2000], y_train[:2000])\n",
        "ovr_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gY4s-qU-JPKg",
        "outputId": "8df7c743-0661-465a-f1f3-f8902625b26a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(ovr_clf.estimators_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szQoDe6tMDUA",
        "outputId": "d96a277b-cf29-415c-8c48-19b8bb2c90f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CV] END .................................................... total time=   4.0s\n",
            "[CV] END .................................................... total time=   3.8s\n",
            "[CV] END .................................................... total time=   2.8s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([0.83133433, 0.86196549, 0.86271568])"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier()\n",
        "cross_val_score(sgd_clf, X_train[:4000], y_train[:4000], cv=3, scoring=\"accuracy\", verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5ob3W1pMYxY",
        "outputId": "fe8bcef9-b08a-4460-b93a-3e00f2a89c65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CV] END .................................................... total time=   2.7s\n",
            "[CV] END .................................................... total time=   3.3s\n",
            "[CV] END .................................................... total time=   3.3s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([0.85007496, 0.88372093, 0.88522131])"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "cross_val_score(sgd_clf, X_train_scaled[:4000], y_train[:4000], cv=3, scoring=\"accuracy\", verbose=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sANzeBZaJPNT"
      },
      "source": [
        "## Error Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "C0bm1WISL_2O",
        "outputId": "88a66b2a-b2bf-4324-c58f-6f1b5bce7ac6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "y_train_pred = cross_val_predict(sgd_clf,\n",
        "                                 X_train_scaled[:4000],\n",
        "                                 y_train[:4000],\n",
        "                                 cv=2,\n",
        "                                 verbose=3)\n",
        "ConfusionMatrixDisplay.from_predictions(y_train[:4000], y_train_pred[:4000])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "k7DSsRumL_5M",
        "outputId": "7210c62c-6201-497e-8d0f-307de54b7a0f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "ConfusionMatrixDisplay.from_predictions(y_train[:4000], y_train_pred[:4000], normalize=\"true\")\n",
        "plt.title(\"Normalized by row\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHJKv_0JL_8G"
      },
      "source": [
        "## MultiLabel Classification\n",
        "\n",
        "When we have multiple classes for each instance we are dealing with **multi label** classification."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_vQabsCOWet",
        "outputId": "d8a4616f-7c1b-4050-e57e-65bf626b5a14"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([False, False, False, False,  True]),\n",
              " array([ True, False, False,  True,  True]),\n",
              " array([[False,  True],\n",
              "        [False, False],\n",
              "        [False, False],\n",
              "        [False,  True],\n",
              "        [ True,  True]]))"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import numpy as np\n",
        "\n",
        "y_train_large = (y_train >= '7')\n",
        "y_train_odd = (y_train.astype(\"int8\") % 2 == 1)\n",
        "y_multi_label = np.c_[y_train_large, y_train_odd]\n",
        "y_train_large[:5], y_train_odd[:5], y_multi_label[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VN9LcZqOWhZ",
        "outputId": "61b05fae-2d3c-4078-8e1e-38f7d7a9cd33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[False,  True]])"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_train, y_multi_label)\n",
        "knn_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6pk6FyWL_-x"
      },
      "source": [
        "## MultiOutput Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9kREfPHGQffk"
      },
      "outputs": [],
      "source": [
        "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
        "X_train_mod = X_train + noise\n",
        "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
        "X_test_mod = X_test + noise\n",
        "\n",
        "y_train_mod = X_train\n",
        "y_test_mod = X_test\n",
        "\n",
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_train_mod, y_train_mod)\n",
        "clean_digit = knn_clf.predict([X_test_mod[0]])\n",
        "plot_digit(X_test_mod[0])\n",
        "plot_digit(clean_digit)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTGZ7nUXQfiO"
      },
      "source": [
        "# Exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 1"
      ],
      "metadata": {
        "id": "1Atf0YVkjqHl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KjEe_mtGQfkx",
        "outputId": "1b405a07-5ed7-484e-803f-757ce434de1f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70000, 784), (70000,))"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "X, y = mnist.data, mnist.target\n",
        "X.shape, y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezeTU3m2qRZB",
        "outputId": "aee641f7-95be-4a98-cac2-f457e2b6484e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 784), (10000, 784), (60000,), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "scaler = StandardScaler()\n",
        "knn_clf = KNeighborsClassifier()\n",
        "X_train, X_test, y_train, y_test =  X[:60000], X[60000:], y[:60000], y[60000:]\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "cXzSgve-Qfn4",
        "outputId": "69f20397-3e18-46e9-d837-db1db859aed3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "knn_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eT0rcpfBrB3T",
        "outputId": "cc8ecacd-1f8f-4798-81d1-9b33f46c2eb5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9688"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "test_preds = knn_clf.predict(X_test)\n",
        "accuracy_score(y_test, test_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "ide9hPTkraYj",
        "outputId": "b80cfc2e-c6e3-4018-a1f2-11b0b3e57d1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 12 candidates, totalling 24 fits\n",
            "[CV] END kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=uniform; total time=  23.9s\n",
            "[CV] END kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=uniform; total time=  43.0s\n",
            "[CV] END kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=distance; total time=  30.9s\n",
            "[CV] END kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=distance; total time=  32.7s\n",
            "[CV] END kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=uniform; total time=  35.5s\n",
            "[CV] END kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=uniform; total time=  26.1s\n",
            "[CV] END kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=distance; total time=  23.4s\n",
            "[CV] END kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=distance; total time=  21.8s\n",
            "[CV] END kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=uniform; total time=  23.3s\n",
            "[CV] END kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=uniform; total time=  23.2s\n",
            "[CV] END kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=distance; total time=  21.8s\n",
            "[CV] END kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=distance; total time=  23.2s\n",
            "[CV] END kneighborsclassifier__n_neighbors=8, kneighborsclassifier__weights=uniform; total time=  24.6s\n",
            "[CV] END kneighborsclassifier__n_neighbors=8, kneighborsclassifier__weights=uniform; total time=  22.1s\n",
            "[CV] END kneighborsclassifier__n_neighbors=8, kneighborsclassifier__weights=distance; total time=  22.5s\n",
            "[CV] END kneighborsclassifier__n_neighbors=8, kneighborsclassifier__weights=distance; total time=  23.0s\n",
            "[CV] END kneighborsclassifier__n_neighbors=15, kneighborsclassifier__weights=uniform; total time=  23.4s\n",
            "[CV] END kneighborsclassifier__n_neighbors=15, kneighborsclassifier__weights=uniform; total time=  23.2s\n",
            "[CV] END kneighborsclassifier__n_neighbors=15, kneighborsclassifier__weights=distance; total time=  22.7s\n",
            "[CV] END kneighborsclassifier__n_neighbors=15, kneighborsclassifier__weights=distance; total time=  21.5s\n",
            "[CV] END kneighborsclassifier__n_neighbors=20, kneighborsclassifier__weights=uniform; total time=  23.9s\n",
            "[CV] END kneighborsclassifier__n_neighbors=20, kneighborsclassifier__weights=uniform; total time=  23.6s\n",
            "[CV] END kneighborsclassifier__n_neighbors=20, kneighborsclassifier__weights=distance; total time=  21.5s\n",
            "[CV] END kneighborsclassifier__n_neighbors=20, kneighborsclassifier__weights=distance; total time=  22.8s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2,\n",
              "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                                       ('kneighborsclassifier',\n",
              "                                        KNeighborsClassifier())]),\n",
              "             param_grid={'kneighborsclassifier__n_neighbors': [3, 4, 5, 8, 15,\n",
              "                                                               20],\n",
              "                         'kneighborsclassifier__weights': ['uniform',\n",
              "                                                           'distance']},\n",
              "             scoring='accuracy', verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2,\n",
              "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;kneighborsclassifier&#x27;,\n",
              "                                        KNeighborsClassifier())]),\n",
              "             param_grid={&#x27;kneighborsclassifier__n_neighbors&#x27;: [3, 4, 5, 8, 15,\n",
              "                                                               20],\n",
              "                         &#x27;kneighborsclassifier__weights&#x27;: [&#x27;uniform&#x27;,\n",
              "                                                           &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=2,\n",
              "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;kneighborsclassifier&#x27;,\n",
              "                                        KNeighborsClassifier())]),\n",
              "             param_grid={&#x27;kneighborsclassifier__n_neighbors&#x27;: [3, 4, 5, 8, 15,\n",
              "                                                               20],\n",
              "                         &#x27;kneighborsclassifier__weights&#x27;: [&#x27;uniform&#x27;,\n",
              "                                                           &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;kneighborsclassifier&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid_param = {\n",
        "    \"kneighborsclassifier__weights\": [\"uniform\", \"distance\"],\n",
        "    \"kneighborsclassifier__n_neighbors\": [3,4, 5, 8, 15, 20]\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(estimator=knn_clf,\n",
        "                           param_grid=grid_param,\n",
        "                           scoring=\"accuracy\",\n",
        "                           verbose=2,\n",
        "                           cv=2)\n",
        "grid_search.fit(X_train[:40000], y_train[:40000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4L8-ri-ts5W",
        "outputId": "ae8eeb7b-1cd7-4bee-aea2-45f1558e1235"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kneighborsclassifier__n_neighbors': 4,\n",
              " 'kneighborsclassifier__weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "grid_search.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCQviVYUtA38",
        "outputId": "e052be92-98df-4f0a-dcbd-f6a2e4c2ed5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9439"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "test_preds_best = grid_search.best_estimator_.predict(X_test)\n",
        "accuracy_score(y_test, test_preds_best)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 2"
      ],
      "metadata": {
        "id": "gExuU30d0Ou9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.ndimage import shift\n",
        "\n",
        "def rotate_image(original_image, pixel_shift=1):\n",
        "  rotated_images = []\n",
        "  image = original_image.reshape(28,28)\n",
        "  print(f\"Shape = {image.shape}\")\n",
        "  for i in range(4):\n",
        "    img = None\n",
        "    if i == 0:\n",
        "      img = shift(image, (0, -pixel_shift))\n",
        "    elif i == 1:\n",
        "      img = shift(image, (-pixel_shift, 0))\n",
        "    elif i == 2:\n",
        "      img = shift(image, (0, pixel_shift))\n",
        "    else:\n",
        "      img = shift(image, (pixel_shift, 0))\n",
        "    rotated_images.append(img)\n",
        "  return rotated_images"
      ],
      "metadata": {
        "id": "0ZbsoiMN0P_2"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(X[0].reshape(28, 28));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "gEx_xow70QDd",
        "outputId": "97f2be90-4e7e-486a-f8cd-529ab14230ab"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shifted_images = rotate_image(X[0], 3)\n",
        "plt.imshow(shifted_images[0].reshape(28, 28))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "fiM6Pij90QHI",
        "outputId": "6a344c58-4e55-42e3-ae38-33ae68997186"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape = (28, 28)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7e15178d9570>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def display_images(img_left, img_top, img_right, img_bottom):\n",
        "    fig, axs = plt.subplots(2, 2, figsize=(6, 6))\n",
        "\n",
        "    axs[0, 0].imshow(img_left)\n",
        "    axs[0, 0].set_title('Left')\n",
        "    axs[0, 0].axis('off')\n",
        "\n",
        "    axs[0, 1].imshow(img_top)\n",
        "    axs[0, 1].set_title('Top')\n",
        "    axs[0, 1].axis('off')\n",
        "\n",
        "    axs[1, 0].imshow(img_right)\n",
        "    axs[1, 0].set_title('Right')\n",
        "    axs[1, 0].axis('off')\n",
        "\n",
        "    axs[1, 1].imshow(img_bottom)\n",
        "    axs[1, 1].set_title('Bottom')\n",
        "    axs[1, 1].axis(\"off\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "display_images(shifted_images[0], shifted_images[1], shifted_images[2], shifted_images[3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "9Ft-AAIQ2_aY",
        "outputId": "7e1b1d4f-eb3f-4f1a-a7bf-d05bb8ea48b4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 4 Axes>"
            ],
            "image/png": 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QbACAbCg2AEA2FBsAIBs2D2/BGvYdlsy8emynZGbIsEXJTJmtwmVcu3LP9LPuf7pZngXtWXX/nZKZ1cN3SGaOv/ihUs/7Zvd7S+VayqRl6U3AT/wkvVW4x4wnSz1v2wZbhdsKJzYAQDYUGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsWNDXwiojhiQz8yeUW4Z3w+duSmb26/ReqXs1h3eL9cnM3JUD0jdqWNYM08CWqXqH3snMyuldk5kzBzyWzJzYra7UTC1p/JJ9k5lnrhuWzPS8+4VkpsdblurlyIkNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYUGwAgGxb0lVQ9oF8y8+ppfZKZycf/PJk5ZusVpWZqSRfVjUhmHrtmZDKz7U0WYpGn98ak/xt5b+LKZOainR9IZg7qvKbUTC2prn5tMrPfzEnJzK7/8mIy02NV+j3SkEyQKyc2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZEOxAQCyodgAANlQbACAbGS/oK+6/07JzOrhOyQzx1/8UDLzze73lpqpJU1all6a98RP0ovFesx4MpnZtsHyPdqvRUel/5w4f+hdLTDJ+6atqk1mrnnsoGSmUl8p9bxdL1mYzAysm5fM1Jd6Gnw0JzYAQDYUGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBstMkFfdU79C6VWzm9azJz5oDHkpkTu9WVel5LGb9k31K5Z64blsz0vPuFZKbHWxbrwaYadGZ6ieXhZw5vgUnKGxTpmcuyWI+2wokNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYUGwAgG826oO+9MSPSmYkrk5mLdn6g1PMO6rymVK6l1NWvTWb2mzkpmdn1X14s9bweq9KL9RpK3QkA8uDEBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkI1mXdC36Kh0T5o/9K7mfGTStFW1ycw1jx2UzFTqK8nMrpcsTGYG1s1LZuqTCQDgwzixAQCyodgAANlQbACAbCg2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZEOxAQCy0aybhwed+WQyc/iZw5vzkc1iUKTnLsPGYABoXU5sAIBsKDYAQDYUGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYUGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYUGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYqRVEUrT0EAEBzcGIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbik07t//++8f+++//sX/vkCFDmncgANgEik2GZsyYEZVKpfFHdXV17LjjjnHqqafGkiVLWnyepUuXxuTJk+PZZ59t8WcDm9dfv28qlUpst912MXr06HjwwQc/1j1/8IMfxH333bfR9ccffzwmT54cq1at2rShyVp1aw/A5nPxxRfHgAEDYt26dTF37tyYMWNG/Nd//Ve88MIL0alTp4iIePjhhzf7HEuXLo0pU6ZE//79Y9iwYZv9eUDL++B9UxRF1NXVxYwZM+LQQw+NX/7yl3H44Yf/Xff6wQ9+EGPHjo2jjjqqyfXHH388pkyZEqeeemp07969+YYnK4pNxg455JAYMWJEREScccYZ0bNnz7jsssti5syZcdxxx0VExFZbbdWaIwKZ+Mv3TUTE6aefHttvv33ccccdf3exgU3hr6LakX/6p3+KiIhXX3218dqHfY/N4sWL48gjj4yuXbvGdtttFxMnToxZs2ZFpVKJRx99dKP7/v73v4/Ro0dHly5dYscdd4wf/ehHjb/26KOPxmc+85mIiDjttNMaj6pnzJjR7F8f0HZ07949OnfuHNXV//Pn5zVr1sSkSZOib9++UVNTE7vssktcccUVURRFY6ZSqcSaNWvipptuanxfnHrqqTF58uQ4//zzIyJiwIABjb+2aNGiiIjYsGFDfO9734va2tqoqamJ/v37x0UXXRTvvvtuk7n69+8fhx9+eDz66KMxYsSI6Ny5cwwdOrTx3XbvvffG0KFDo1OnTjF8+PD47W9/u3n/QdHsnNi0Ix+8ALbddtuPzKxZsyY+//nPx7Jly+Jb3/pW9O7dO26//faYM2fOh+b/9Kc/xcEHHxxf+tKX4rjjjou77747Lrjgghg6dGgccsghMXjw4Lj44ovju9/9bnz9619vLFef/exnm/3rA1rP6tWrY8WKFVEURSxfvjyuvfbaePvtt+Pkk0+OiIiiKOLII4+MOXPmxOmnnx7Dhg2LWbNmxfnnnx9LliyJq666KiIibrnlljjjjDNir732iq9//esREVFbWxtdu3aN+fPnxx133BFXXXVV9OzZMyIievXqFRHvn0rfdNNNMXbs2Jg0aVLMmzcvLr300vjDH/4Qv/jFL5rM+sorr8RJJ50U3/jGN+Lkk0+OK664Io444oj46U9/GhdddFGcddZZERFx6aWXxnHHHRcvvfRSdOjgHGCLUZCdG2+8sYiI4pFHHineeOON4rXXXivuvvvuolevXkVNTU3x2muvNWZHjRpVjBo1qvHn//Zv/1ZERHHfffc1Xlu7dm2x6667FhFRzJkzp8nvjYji5ptvbrz27rvvFr179y6OOeaYxmtPPfVUERHFjTfeuFm+XqD1fPC++esfNTU1xYwZMxpz9913XxERxSWXXNLk948dO7aoVCrFK6+80nita9euxSmnnLLRsy6//PIiIoqFCxc2uf7ss88WEVGcccYZTa6fd955RUQUv/rVrxqv9evXr4iI4vHHH2+8NmvWrCIiis6dOxeLFy9uvP7v//7vG733aPtU0IwdcMAB0atXr+jbt2+MHTs2unbtGjNnzoxPfepTH/l7Hnroodhxxx3jyCOPbLzWqVOn+NrXvvah+a233rrxT2QR73/Pzl577RULFixovi8EaPOmTZsWs2fPjtmzZ8ett94ao0ePjjPOOCPuvffeiIh44IEHoqqqKiZMmNDk902aNCmKovjYn6D64N4REeeee+5G946I+I//+I8m13fbbbfYZ599Gn++9957R0TE5z//+dhpp502uu59tmXxV1EZmzZtWgwaNChWr14d06dPj1//+tdRU1PzN3/P4sWLo7a2NiqVSpPrO++884fmP/WpT22U3XbbbeP555/ftOGBLcpee+3V5JuHTzzxxNhzzz1j/Pjxcfjhh8fixYujT58+0a1btya/b/DgwRHx/rvn41q8eHF06NBho/dU7969o3v37hvd+y/LS0TENttsExERffv2/dDrf/rTnz72bLQ8JzYZ22uvveKAAw6IY445JmbOnBlDhgyJk046Kd5+++1me0ZVVdWHXi/+4psBgfanQ4cOMXr06Fi2bFm8/PLLLfLMv/5D1kf5qPeW91keFJt2oqqqKi699NJYunRpTJ069SNz/fr1i1dffXWj/5BfeeWVj/3ssi8bIC8bNmyIiIi33347+vXrF0uXLo233nqrSebFF1+MiPffPR/4qHfGR13v169fNDQ0bFSg6urqYtWqVU3uTf4Um3Zk//33j7322iuuvvrqWLdu3YdmxowZE0uWLImZM2c2Xlu3bl3ccMMNH/u5Xbt2jYiwLRTakfXr18fDDz8cW221VQwePDgOPfTQqK+v3+gPVldddVVUKpU45JBDGq917dr1Q98XH/UuOfTQQyMi4uqrr25y/corr4yIiMMOO2wTvxq2JL7Hpp05//zz49hjj40ZM2bEN7/5zY1+/Rvf+EZMnTo1TjzxxPjWt74VO+ywQ9x2222Nm4o/zulLbW1tdO/ePX76059Gt27domvXrrH33nvHgAEDNvnrAdqGBx98sPH0Zfny5XH77bfHyy+/HN/+9rfjE5/4RBxxxBExevTo+M53vhOLFi2KPfbYIx5++OG4//7745xzzona2trGew0fPjweeeSRuPLKK6NPnz4xYMCA2HvvvWP48OEREfGd73wnTjjhhOjYsWMcccQRsccee8Qpp5wS119/faxatSpGjRoVTz75ZNx0001x1FFHxejRo1vlnwmtpHU/lMXm8MHHL5966qmNfq2+vr6ora0tamtriw0bNmz0ce+iKIoFCxYUhx12WNG5c+eiV69exaRJk4p77rmniIhi7ty5jblRo0YVu++++0bPOOWUU4p+/fo1uXb//fcXu+22W1FdXe2j35CRD/u4d6dOnYphw4YV1113XdHQ0NCYfeutt4qJEycWffr0KTp27FgMHDiwuPzyy5tkiqIoXnzxxWK//fYrOnfuXEREk49+f+973yt23HHHokOHDk0++r1+/fpiypQpxYABA4qOHTsWffv2LS688MJi3bp1Te7dr1+/4rDDDtvo64iIYty4cU2uLVy4sIiI4vLLL9/Ef0q0pEpR+K4o0q6++uqYOHFivP7667Hjjju29jgA8KEUGzaydu3a6Ny5c+PP161bF3vuuWfU19fH/PnzW3EyAPjbfI8NG/nSl74UO+20UwwbNixWr14dt956a7z44otx2223tfZoAPA3KTZsZMyYMfGzn/0sbrvttqivr4/ddtstfv7zn8fxxx/f2qMBwN/kr6IAgGzYYwMAZEOxAQCyodgAANlQbACAbJT+VNSBHY7dnHMAbdjshrtae4QmvI+g/Uq9j5zYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAb1a09APmoVKf/darq1bMFJvkfL53XP5mp79KQzPSrXZ7MdDmrksz88cqtkplnRtyZzERErKhfk8zsfdekZGbnc+eWeh7AlsCJDQCQDcUGAMiGYgMAZEOxAQCyodgAANlQbACAbCg2AEA2FBsAIBsW9G3BqgYPTGaKmo7JzNJR3ZOZtSPTy+B6bJPO/GaPcsvn2poH3+mWzFw29eBkZt7Q25OZhevXlprph3UHJjN9flOUuhdALpzYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsmFBXxtUv/8/lspdOWNaMjOo41abOk721hf1ycx3rz01malek16Gt89d45OZbks2JDMRETUr0ov8ujw9r9S9AHLhxAYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYUGwAgG4oNAJANC/raoJqXlpbK/fe6vsnMoI51mzpOi5u0bGSp3IK3eyYzM2rvTmZWN6QX623/48dLzdSS0lND21KpTv8vp6pX+r/r5vLSef2TmfouDaXu1a92eTLT5axKMvPHK9NLVZ8ZcWcys6J+TTKz912Tkpmdz52bzLQ1TmwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZsKCvDdqw7I+lctdedmwy8/2D00uaqp7fOpl57qxrS82UcsmKTyczrxzQpdS96lctS2ZO2uesZGbRhPSzBsRzZUaCNqNq8MBkpqjpmMwsHdW91PPWjky/a3psk878Zo/08rm26MF3uiUzl009OJmZN/T2ZGbh+rXJzA/rDkxm+vwmzzWfTmwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZqBRFUWpDz4Ed0svgaHuqen4ymal/c2Uys/D29GK93+03PZnZ6wdnJzPbTXs8maFlzW64q7VHaKK9v4/q9//HZOaaGdOSmUEdt2qOcbK2vqgvlfvsj85JZqrXNM9CvG5LNiQzNSvSS/yKp19ojnFaXOp95MQGAMiGYgMAZEOxAQCyodgAANlQbACAbCg2AEA2FBsAIBuKDQCQDcUGAMhGdWsPwOZVv+LNZrnP+j83z4bS3b/8+2Tmjeuqyt2sodxGUMhNzUtLk5n/Xtc3mRnUsa45xmlxk5aNTGYWvN0zmZlRe3cys7qh3Lbg7X/ctjamN8+O4y2TExsAIBuKDQCQDcUGAMiGYgMAZEOxAQCyodgAANlQbACAbCg2AEA2LOijlMEXzE9mThv6hWTmxn7/mcyMOnZcqZm63Tm3VA5ys2HZH5OZay87Npn5/sFrkpmq57cuNdNzZ11bKpdyyYpPJzOvHNAlmalftSyZOWmfs5KZRROSkYiIGBDPlQuy2TmxAQCyodgAANlQbACAbCg2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZMOCPkqpX7U6mXnzzMHJzP+buTaZ+fYlN5ea6cLjjk5mit9uk8z0/f4T6YcVRZmRoM3ocWP63+tev/xkMlP/5spSz9t9yFeTmd/tNz2ZmXn9qGRmu1WPl5oppfJEeqnegBKvB9oWJzYAQDYUGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsWNBHs2l47g/JzAlTzk9mbvvXK0o979mRJRb5jUxHdu86PpkZeMOyZGbDgkXph0EbUr/izWa71/o/b9Us99n9y79PZt64rip9o4b6ZpiGLZETGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYqRVEUZYIHdjh2c88CERFRfG5Yqdwnfvh6MnPHP8zaxGnet+ucM5KZXaasTmbqX17QHOO0uNkNd7X2CE14H7U9Vd23SWZ6PFBJZm7s95/JzKhzxyUz3e6cm8ywZUq9j5zYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsmFBH1usqu23S2aWHr9zMjPvgmuSmQ4l/gzw5YUHJTOr930zmWmLLOijOXTYY3AyM3XmDcnM795L/7d/4fNHJzPFb9NLBft+/4lk5v2blfpfKc3Agj4AoN1QbACAbCg2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZEOxAQCyYUEf7d7/fj29gKtLZatk5p3ivWTm8LPPST/rF/OSmZZmQR8tZeVX90lmbvvXK5KZAdWdmmOc2P3m8aVyA29YlsxsWLBoE6chwoI+AKAdUWwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZqG7tAeCvNew7rFTu1WPTm0WHDFuUzJTZKlzGtSv3TD/r/qeb5VmQqx7T05vAx780Lpn5xA9fT2bu+IdZyczvvjI1mYmI2LXvGcnMLlPSZwn1Ly8o9Tw+mhMbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANizoo9lURgxJZuZPSC/Du+FzN5V63n6d3iuVaw7vFuuTmbkrB6Rv1LCsGaaB9q3yf59NZt4Zu10y85njz05m5l1wTZmR4sXRP0tmvtz/oGRm9b6lHsff4MQGAMiGYgMAZEOxAQCyodgAANlQbACAbCg2AEA2FBsAIBuKDQCQDQv6iOoB/ZKZV0/rk8xMPv7nycwxW68oNVNLuqhuRDLz2DUjk5ltb3qiOcYBmkF93fJkZvsfpzPr/teGUs/rUimxfLT//0lmDj/6nPSzfjGvzEjtlhMbACAbig0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANizo24JV998pmVk9fIdk5viLH0pmvtn93lIztaRJy9JL8574SXr5Xo8ZTyYz2zZYvgdtRcO+w5KZV4/tlMwMGbYomSmzeK+sa1fumX7e/U832/PaKyc2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZEOxAQCyodgAANlQbACAbFjQ18Kqd+idzKyc3rXUvc4c8Fgyc2K3ulL3ainjl+ybzDxz3bBS9+p59wvJTI+3LNaDtqIyYkgyM39CeiHeDZ+7KZnZr9N7pWZqLu8W65OZuSsHpG/UsKwZpmnfnNgAANlQbACAbCg2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZEOxAQCyYUFfSe+NGZHOTFyZzFy08wPJzEGd15SaqSXV1a9NZvabOSmZ2fVfXkxmeqwqt1SvoVQK2BTVA/qVyr16Wp9kZvLxP09mjtl6RanntZSL6tLv/oiIx64Zmcxse5OFoS3BiQ0AkA3FBgDIhmIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbFvSVtOiodAecP/SuFpjkf0xbVZvMXPPYQclMpb6SzOx6ycJkZmDdvGSmPpkAmkN1/52SmdXDd0hmjr/4oVLP+2b3e0vlWsqkZemFeU/8JL18r8eMJ0s9b9sGy/faCic2AEA2FBsAIBuKDQCQDcUGAMiGYgMAZEOxAQCyodgAANlQbACAbCg2AEA2bB4uadCZ6e2Th585vAUm+fsMinJbM1NsDIaWUb1D72Rm5fSuycyZAx5LZk7sVldqppY0fsm+ycwz1w1LZnre/UIy0+Mt24Jz5MQGAMiGYgMAZEOxAQCyodgAANlQbACAbCg2AEA2FBsAIBuKDQCQDQv6AJrBe2NGpDMTVyYzF+38QDJzUOc1pWZqSXX1a5OZ/WZOSmZ2/ZcXk5keq9KL9RqSCXLlxAYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYUGwAgG4oNAJANC/oAmsGio9J/Tpw/9K4WmOR901bVJjPXPHZQMlOpr5R63q6XLExmBtbNS2bqSz0NPpoTGwAgG4oNAJANxQYAyIZiAwBkQ7EBALKh2AAA2VBsAIBsKDYAQDYqRVEUrT0EAEBzcGIDAGRDsQEAsqHYAADZUGwAgGwoNgBANhQbACAbig0AkA3FBgDIhmIDAGTj/wP6c2Ppq27TVQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise 3"
      ],
      "metadata": {
        "id": "cD7Ij-FWtIWa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "train_set = pd.read_csv(\"train.csv\")\n",
        "test_set = pd.read_csv(\"test.csv\")\n",
        "train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mtMgiFtTjtOw",
        "outputId": "cd28a635-d753-40be-91de-6bbe1b37f23d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61415789-2127-4bcf-bab5-b444d4c0dcc7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_set",
              "summary": "{\n  \"name\": \"train_set\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_set[\"Ticket\"].unique()), len(train_set[\"Name\"].unique()), len(train_set[\"Cabin\"].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnrxDgS3opNP",
        "outputId": "51742199-9e1f-4205-9508-97ca22133ff4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(681, 891, 148)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.set_index(\"PassengerId\", inplace=True)\n",
        "test_set.set_index(\"PassengerId\", inplace=True)\n",
        "train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "vw4UUVa-pU_s",
        "outputId": "085f8c85-d150-4566-ac72-38b14c4a5525"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Survived  Pclass  \\\n",
              "PassengerId                     \n",
              "1                   0       3   \n",
              "2                   1       1   \n",
              "3                   1       3   \n",
              "4                   1       1   \n",
              "5                   0       3   \n",
              "\n",
              "                                                          Name     Sex   Age  \\\n",
              "PassengerId                                                                    \n",
              "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
              "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
              "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
              "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
              "5                                     Allen, Mr. William Henry    male  35.0   \n",
              "\n",
              "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
              "PassengerId                                                          \n",
              "1                1      0         A/5 21171   7.2500   NaN        S  \n",
              "2                1      0          PC 17599  71.2833   C85        C  \n",
              "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "4                1      0            113803  53.1000  C123        S  \n",
              "5                0      0            373450   8.0500   NaN        S  "
            ],
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              "      background-color: #3B4455;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4413631b-36ed-4ed4-a1f6-c20836655b49');\n",
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              "          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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3910f43f-6737-4aae-b7e5-abbdbdd5ea88 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_set",
              "summary": "{\n  \"name\": \"train_set\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features like ticket and name are not relevant for our classification\n",
        "train_set.drop(\"Name\", axis=1, inplace=True)\n",
        "train_set.drop(\"Ticket\", axis=1, inplace=True)\n",
        "test_set.drop(\"Name\", axis=1, inplace=True)\n",
        "test_set.drop(\"Ticket\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "BwlMBleEo0B5"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Rp6h15Q3pzP3",
        "outputId": "9670a552-ace2-4616-cdd9-0b2fdc6d823b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin  \\\n",
              "PassengerId                                                                \n",
              "1                   0       3    male  22.0      1      0   7.2500   NaN   \n",
              "2                   1       1  female  38.0      1      0  71.2833   C85   \n",
              "3                   1       3  female  26.0      0      0   7.9250   NaN   \n",
              "4                   1       1  female  35.0      1      0  53.1000  C123   \n",
              "5                   0       3    male  35.0      0      0   8.0500   NaN   \n",
              "\n",
              "            Embarked  \n",
              "PassengerId           \n",
              "1                  S  \n",
              "2                  C  \n",
              "3                  S  \n",
              "4                  S  \n",
              "5                  S  "
            ],
            "text/html": [
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              "  <div id=\"df-4134e128-1561-434d-8caa-0301da8b2654\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th>Survived</th>\n",
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              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
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              "      <th>2</th>\n",
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              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
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              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
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              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>8.0500</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4134e128-1561-434d-8caa-0301da8b2654 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-4134e128-1561-434d-8caa-0301da8b2654');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d658e43e-a8ba-4575-abb4-7e834b052323\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d658e43e-a8ba-4575-abb4-7e834b052323')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d658e43e-a8ba-4575-abb4-7e834b052323 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_set",
              "summary": "{\n  \"name\": \"train_set\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "train_set.hist(bins=50, figsize=(10, 8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "4tyhHmzFrwxW",
        "outputId": "6de4154b-0b21-4b35-f94e-28dcb9f31132"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(train_set.drop(\"Survived\", axis=1), train_set[\"Survived\"], test_size=0.15)\n",
        "X_train.shape, X_valid.shape, y_train.shape, y_valid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUrD_6ZrjtRg",
        "outputId": "28fbe660-1992-44c3-a20e-253bc18cec93"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((757, 8), (134, 8), (757,), (134,))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, FunctionTransformer, StandardScaler\n",
        "import numpy as np\n",
        "\n",
        "# Define a small constant to add to the data to avoid log(0)\n",
        "epsilon = 1e-10\n",
        "def log_transform(x):\n",
        "    return np.log(x + epsilon)\n",
        "\n",
        "# Write all data pipelines\n",
        "cat_pipeline = make_pipeline(SimpleImputer(strategy=\"most_frequent\"),\n",
        "                             OneHotEncoder())\n",
        "cat_ordinal_pipeline = make_pipeline(SimpleImputer(strategy=\"most_frequent\"),\n",
        "                                     OrdinalEncoder())\n",
        "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
        "                             StandardScaler())\n",
        "log_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
        "                             FunctionTransformer(func=log_transform, inverse_func=np.exp))"
      ],
      "metadata": {
        "id": "MjcIVWnijtUQ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "preprocessing = ColumnTransformer([\n",
        "    (\"log\", log_pipeline, [\"Age\", \"Fare\"]),\n",
        "    (\"num\", num_pipeline, [\"SibSp\", \"Parch\"]),\n",
        "    (\"cat_ordinal\", cat_ordinal_pipeline, [\"Cabin\"]),\n",
        "    (\"cat\", cat_pipeline, [\"Sex\", \"Embarked\"])\n",
        "], remainder=\"passthrough\")\n",
        "\n",
        "X_train_ready = preprocessing.fit_transform(X_train)\n",
        "X_valid_ready = preprocessing.fit_transform(X_valid)\n",
        "pd.DataFrame(X_valid_ready)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "t2pPV2oajtWe",
        "outputId": "82573bc9-6a39-4b75-988a-4ba1af1a7b60"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3    4    5    6    7    8    9    10\n",
              "0    2.890372  2.085672 -0.578555 -0.509444  0.0  0.0  1.0  0.0  0.0  1.0  3.0\n",
              "1    2.833213  2.351375 -0.578555 -0.509444  0.0  1.0  0.0  0.0  0.0  1.0  2.0\n",
              "2    3.401197  2.251292 -0.578555 -0.509444  0.0  0.0  1.0  0.0  0.0  1.0  3.0\n",
              "3    3.135494  2.564949 -0.578555 -0.509444  0.0  0.0  1.0  0.0  0.0  1.0  2.0\n",
              "4    3.583519  3.184284  0.355498  0.425700  0.0  0.0  1.0  0.0  0.0  1.0  3.0\n",
              "..        ...       ...       ...       ...  ...  ...  ...  ...  ...  ...  ...\n",
              "129  3.401197  3.184284  0.355498  0.425700  0.0  1.0  0.0  0.0  0.0  1.0  3.0\n",
              "130  3.637586  4.382027 -0.578555 -0.509444  4.0  1.0  0.0  0.0  0.0  1.0  1.0\n",
              "131  3.367296  2.047693 -0.578555 -0.509444  0.0  0.0  1.0  0.0  1.0  0.0  3.0\n",
              "132  3.091042  1.981001 -0.578555 -0.509444  0.0  0.0  1.0  0.0  0.0  1.0  3.0\n",
              "133  3.258097  4.034166 -0.578555 -0.509444  0.0  0.0  1.0  0.0  0.0  1.0  3.0\n",
              "\n",
              "[134 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-062fa4c1-8aa8-4033-b985-c94a81298430\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.890372</td>\n",
              "      <td>2.085672</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.833213</td>\n",
              "      <td>2.351375</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.401197</td>\n",
              "      <td>2.251292</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.135494</td>\n",
              "      <td>2.564949</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.583519</td>\n",
              "      <td>3.184284</td>\n",
              "      <td>0.355498</td>\n",
              "      <td>0.425700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>3.401197</td>\n",
              "      <td>3.184284</td>\n",
              "      <td>0.355498</td>\n",
              "      <td>0.425700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>3.637586</td>\n",
              "      <td>4.382027</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>3.367296</td>\n",
              "      <td>2.047693</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>3.091042</td>\n",
              "      <td>1.981001</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>3.258097</td>\n",
              "      <td>4.034166</td>\n",
              "      <td>-0.578555</td>\n",
              "      <td>-0.509444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>134 rows × 11 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier()\n",
        "model.fit(X_train_ready, y_train)\n",
        "model_predictions = model.predict(X_valid_ready)"
      ],
      "metadata": {
        "id": "ImPIhNQ2tA89"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_valid[\"Cabin\"].isna().sum(), X_train[\"Cabin\"].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2PMFx6cufUJ",
        "outputId": "40a2d663-8767-4159-fc85-4292efa5ba49"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(114, 573)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_valid, model_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xg_fyNePtYtI",
        "outputId": "54d10377-a051-496d-a616-64164d0f020e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8731343283582089"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "1QBejw6Nv7RI",
        "outputId": "8af40fc0-fc96-4f90-8058-a329004bc83b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Pclass     Sex   Age  SibSp  Parch      Fare Cabin Embarked\n",
              "PassengerId                                                             \n",
              "892               3    male  34.5      0      0    7.8292   NaN        Q\n",
              "893               3  female  47.0      1      0    7.0000   NaN        S\n",
              "894               2    male  62.0      0      0    9.6875   NaN        Q\n",
              "895               3    male  27.0      0      0    8.6625   NaN        S\n",
              "896               3  female  22.0      1      1   12.2875   NaN        S\n",
              "...             ...     ...   ...    ...    ...       ...   ...      ...\n",
              "1305              3    male   NaN      0      0    8.0500   NaN        S\n",
              "1306              1  female  39.0      0      0  108.9000  C105        C\n",
              "1307              3    male  38.5      0      0    7.2500   NaN        S\n",
              "1308              3    male   NaN      0      0    8.0500   NaN        S\n",
              "1309              3    male   NaN      1      1   22.3583   NaN        C\n",
              "\n",
              "[418 rows x 8 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a73e5167-f3e2-4259-ac3b-dbb909e33809')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-a73e5167-f3e2-4259-ac3b-dbb909e33809 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-a73e5167-f3e2-4259-ac3b-dbb909e33809');\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",
              "\n",
              "\n",
              "<div id=\"df-80e9b327-23a7-4a71-8cb9-914aaeb05088\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-80e9b327-23a7-4a71-8cb9-914aaeb05088')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-80e9b327-23a7-4a71-8cb9-914aaeb05088 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_df131bfb-2a57-4855-9f63-90997dde0833\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_set')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_df131bfb-2a57-4855-9f63-90997dde0833 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_set');\n",
              "      }\n",
              "      })();\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_set",
              "summary": "{\n  \"name\": \"test_set\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_ready = preprocessing.fit_transform(test_set)"
      ],
      "metadata": {
        "id": "iEdPr3RWvhGp"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = model.predict(X_test_ready)\n",
        "test_predictions[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1f6dxSot8Y4",
        "outputId": "60e9ef56-358b-4cf7-f35c-09e55d0b382f"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMG6KlsuRFBJoK3nIngX0eD",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}