0_MNIST_ImageClassification.ipynb

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      "source": [
        "This data is take from [kaggle](https://www.kaggle.com/datasets/hojjatk/mnist-dataset).  This notebook will go through multi-class classification methods for classifiying the images showing digits 3, 5, and 8."
      ],
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        "id": "HL7fQha5-yhZ"
      }
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      "cell_type": "code",
      "source": [
        "# Import Required Libraries for Notebook\n",
        "from sklearn.datasets import fetch_openml\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import LinearSVC"
      ],
      "metadata": {
        "id": "SCbqcrwjnPqV"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "mnist = fetch_openml(\"mnist_784\")"
      ],
      "metadata": {
        "id": "X4zLrckXqcUU"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keys = list(mnist.keys())\n",
        "print(keys)"
      ],
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        "id": "_bqTfWtYq1i9",
        "outputId": "25afb98a-f348-428b-ea13-1eaacae194e4"
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          "text": [
            "['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "g-P1IAlRnDt9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([mnist[keys[0]],mnist[keys[1]]],axis=1)\n",
        "df"
      ],
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        "outputId": "1f65618c-1588-4317-ee03-a1014c50e32b"
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      "execution_count": 4,
      "outputs": [
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              "      --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-e0c34547-f305-4772-9f46-2fb6a9ae3605 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_df863bc5-e6de-4038-8fd9-717dae7cb3bd\">\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('df')\"\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_df863bc5-e6de-4038-8fd9-717dae7cb3bd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us visualize one observation (row) of the data set.  There are 784 features corresponding to pixel1,...,pixel784."
      ],
      "metadata": {
        "id": "nmGdWkz-tnP6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0 #select any row; i = 0 -> row 0\n",
        "obs = df.iloc[i,:-1].to_numpy()\n",
        "obs_image = obs.astype(np.uint8).reshape(28, 28)\n",
        "plt.imshow(obs_image, plt.cm.binary)\n",
        "plt.show()\n",
        "print(f'\\n*** Image is classified as a {df.loc[i,\"class\"]} ***')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "ejM4p6UUtx9s",
        "outputId": "955c3d6d-513a-4c76-d345-665b623bc3cc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "*** Image is classified as a 5 ***\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are only interested in a 3-digit classification between digits 3, 5, and 8.  Therefore we will perform filtering to extract these relevant datapoints.  Furthermore, as we can see the class (y variable) returns string input.  Let us change this to integers."
      ],
      "metadata": {
        "id": "LeWvvA9QrvY9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3gw7IPdHq1L",
        "outputId": "9fead9d5-530a-4805-b088-b295771c418b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([dtype('int64'),\n",
              "       CategoricalDtype(categories=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], ordered=False, categories_dtype=object)],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['class'] = df['class'].apply(lambda x: int(x))\n",
        "bool_musk = df['class'].isin([3,5,8])\n",
        "df = df.loc[bool_musk].reset_index(drop=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QaoBYGDjrTue",
        "outputId": "bee62832-0a36-4729-e5a4-13f973729196"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
              "0           0       0       0       0       0       0       0       0       0   \n",
              "1           0       0       0       0       0       0       0       0       0   \n",
              "2           0       0       0       0       0       0       0       0       0   \n",
              "3           0       0       0       0       0       0       0       0       0   \n",
              "4           0       0       0       0       0       0       0       0       0   \n",
              "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "20274       0       0       0       0       0       0       0       0       0   \n",
              "20275       0       0       0       0       0       0       0       0       0   \n",
              "20276       0       0       0       0       0       0       0       0       0   \n",
              "20277       0       0       0       0       0       0       0       0       0   \n",
              "20278       0       0       0       0       0       0       0       0       0   \n",
              "\n",
              "       pixel10  ...  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
              "0            0  ...         0         0         0         0         0   \n",
              "1            0  ...         0         0         0         0         0   \n",
              "2            0  ...         0         0         0         0         0   \n",
              "3            0  ...         0         0         0         0         0   \n",
              "4            0  ...         0         0         0         0         0   \n",
              "...        ...  ...       ...       ...       ...       ...       ...   \n",
              "20274        0  ...         0         0         0         0         0   \n",
              "20275        0  ...         0         0         0         0         0   \n",
              "20276        0  ...         0         0         0         0         0   \n",
              "20277        0  ...         0         0         0         0         0   \n",
              "20278        0  ...         0         0         0         0         0   \n",
              "\n",
              "       pixel781  pixel782  pixel783  pixel784  class  \n",
              "0             0         0         0         0      5  \n",
              "1             0         0         0         0      3  \n",
              "2             0         0         0         0      3  \n",
              "3             0         0         0         0      5  \n",
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              "        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('df')\"\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_f97f9692-1b3b-44ac-b388-d088b6d65e21 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note, the y values are given by the digit and the x values are given by the array of 0s and 1s.  Now we split the data into training and testing sets; we will use an 80%-20% split between training and testing respectively."
      ],
      "metadata": {
        "id": "oTHlOUOnwxCA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split data into training and testing\n",
        "cutoff = int(df.shape[0]*(0.8))\n",
        "X, y = df.iloc[:,:-1], df['class']\n",
        "X_train, X_test = X[:cutoff], X[cutoff:]\n",
        "y_train, y_test = y[:cutoff], y[cutoff:]\n",
        "\n",
        "#randomize the split\n",
        "n = X_train.shape[0]\n",
        "rand = np.random.permutation(n)\n",
        "X_train = X_train.iloc[list(rand),:].reset_index(drop = True)\n",
        "y_train = y_train[rand].reset_index(drop = True)"
      ],
      "metadata": {
        "id": "2T3ykC7ztCxq"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "assert X_train.shape[0] + X_test.shape[0] == df.shape[0], 'ERROR'\n",
        "assert X_train.shape[1] == X_test.shape[1] == df.shape[1] - 1, 'ERROR' #class column == y\n",
        "assert y_train.shape[0] + y_test.shape[0] == df.shape[0], 'ERROR'\n",
        "\n",
        "#formats\n",
        "print(f'X_train: {X_train.shape},\\nX_test: {X_test.shape},\\ny_train: {y_train.shape},\\ny_test: {y_test.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ejjuX4KzC2f",
        "outputId": "8b34ddf4-8e5f-424d-dee7-a2476899621a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train: (16223, 784),\n",
            "X_test: (4056, 784),\n",
            "y_train: (16223,),\n",
            "y_test: (4056,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification Methods"
      ],
      "metadata": {
        "id": "AgECQ68xoamO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we need to scale the features."
      ],
      "metadata": {
        "id": "pTtF2f0lod9s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "bPTWGe3qohp7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Store confusion matrix and results in the following dictionary:"
      ],
      "metadata": {
        "id": "2Xee5tL6ySd2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = {'Method':[],'Accuracy':[],'Report':[]}"
      ],
      "metadata": {
        "id": "4NfxmGeayVsl"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "kh8n5_yEs6Wj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will be applying Logistic Regression to classify the data.  Since this is\n",
        "a multi-class classification problem between digits 3, 5, and 8, I will be using One-vs-Rest.  Therefore I will identify 3 vs (5 and 8), 5 vs (3 and 8), and 8 vs (3 and 5)."
      ],
      "metadata": {
        "id": "lg6U1mdB1dkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# OneVsRestClassifier with LogisticRegressionCV\n",
        "log_reg_cv = OneVsRestClassifier(\n",
        "  LogisticRegressionCV(\n",
        "                      max_iter=1000,      # Maximum iterations\n",
        "                      cv=5,               # Cross-validation folds\n",
        "                      n_jobs=-1           # Use all CPU cores for parallel processing\n",
        "  )\n",
        ")\n",
        "\n",
        "log_reg_cv.fit(X_train_scaled, y_train) # Train the model\n",
        "y_pred1 = log_reg_cv.predict(X_test_scaled) # Predictions\n",
        "accuracy = accuracy_score(y_test, y_pred1) # Evaluation\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print('Classification Report:\\n', classification_report(y_test, y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQHMZ-TcYOZN",
        "outputId": "327fd5f1-6d18-40f9-8a0b-5118ee525e34"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9374\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           3       0.93      0.95      0.94      1426\n",
            "           5       0.94      0.92      0.93      1257\n",
            "           8       0.94      0.95      0.94      1373\n",
            "\n",
            "    accuracy                           0.94      4056\n",
            "   macro avg       0.94      0.94      0.94      4056\n",
            "weighted avg       0.94      0.94      0.94      4056\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clearly, Logistic Regression OVR does very well!  See the confusion matrix below.  We see that there is minimal bias and the images are misclassified evenly with a nice diagonal of correct classification strongly outweighing the misclassifications."
      ],
      "metadata": {
        "id": "vZ-gePo6amOh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred1)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix - Logistic Regression OVR\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "z2lLHbYiYoQx",
        "outputId": "05de3c14-bc0e-4473-f93a-962870bde46c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Append data to results dictionary."
      ],
      "metadata": {
        "id": "nkLgN9iq0_Ju"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results['Method'].append('Logistic Regression OVR')\n",
        "results['Accuracy'].append(accuracy)\n",
        "results['Report'].append(classification_report(y_test, y_pred1))"
      ],
      "metadata": {
        "id": "Uw8rLZXj0K65"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multinomial Regression"
      ],
      "metadata": {
        "id": "e-e8lnegRtQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Multinomial Logistic Regression with Cross-Validation\n",
        "log_reg_cv = LogisticRegressionCV(\n",
        "#   multi_class='multinomial',  # multinomial logistic regression is the default\n",
        "    max_iter=1000,              # Maximum iterations\n",
        "    cv=5,                       # Cross-validation folds\n",
        "    n_jobs=-1                   # Use all CPU cores for parallel processing\n",
        ")\n",
        "\n",
        "log_reg_cv.fit(X_train_scaled, y_train) # Train the model\n",
        "y_pred2 = log_reg_cv.predict(X_test_scaled) # Predictions\n",
        "accuracy = accuracy_score(y_test, y_pred2) # Evaluation"
      ],
      "metadata": {
        "id": "ZU0D5CkkRwoG"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print('Classification Report:\\n', classification_report(y_test, y_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xnRF2d_Vbduv",
        "outputId": "7aa5831e-c3f8-469c-d845-6095d152080f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9384\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           3       0.94      0.95      0.94      1426\n",
            "           5       0.94      0.92      0.93      1257\n",
            "           8       0.94      0.95      0.94      1373\n",
            "\n",
            "    accuracy                           0.94      4056\n",
            "   macro avg       0.94      0.94      0.94      4056\n",
            "weighted avg       0.94      0.94      0.94      4056\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred2)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Greens')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix - Multinomial Regression\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "jfu11MaNbiIG",
        "outputId": "0091fba3-2b33-4389-f17d-9f45025863ae"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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VK1eiQoUKiI+Ph4GBgdjm5s2bEAQB1apVE5OX3OXq1aviWJPc83/1HtfV1S10zI+jo6Pa65s3bwJ4mai8eqzvv/8eGRkZ4rkGBQXhn3/+QaVKldCoUSNMnz5dLQlwdHSEv78/vv/+e5QrVw5eXl5YsWKFxmt19+7dQn9W5D3PXNr4WUHvN077fQOmpqaws7PDP//8U6z3FfUXRmGzWgRBeONjvDpgz9DQEBERETh8+DB+//137N27F1u2bMEnn3yC/fv3a21mzducSy6VSoUePXpg/fr1uH379msf3DV37lxMmTIFX3zxBWbNmgVLS0solUqMHj26WDMP8v61WRTnz58Xf6FcunRJHHvxOg0bNlT7oTxt2rQiP5TMzc0NTk5OGD16NKKjowutGAEv74mCrrdUsz+08T0vyv7e5DiJiYlo0aIFTE1NMXPmTDg5OcHAwADnzp3DxIkT33h2ipGRkdrzYTw8PFC/fn188803CA4OBvAyWVYoFPjzzz8LjN3Y2PiNjg3kv19zz2P+/PmFTmvPPV6fPn3QrFkz7NixA/v378f8+fPx3XffYfv27Wjfvj0AYOHChRg4cCB27dqF/fv3Y9SoUQgMDMTJkydRsWLFN447L23fN/T+YULyhjp16oQ1a9YgMjIS7u7ur21rb2+PnJwc3Lx5U/zrAHg56CsxMVGcMaMNFhYWBQ40e/WvEQBQKpVo3bo1WrdujUWLFmHu3LmYPHkyDh8+XODDt3LjvH79er5t165dQ7ly5WBkZPT2J1GAfv364ccff4RSqSxwIHCuX375Ba1atcIPP/ygtj4xMRHlypUTX2uzmpCamopBgwbBxcUFTZo0QVBQELp37y7O5CnMxo0b1R76VqVKlWIdt2/fvpg9ezacnZ1f+ywVCwuLAsveBd0Tr5Ki6pL3Psp7zpmZmYiOji70wW/acOTIETx58gTbt29XG8QcHR2t1ePUqVMHn332GVavXo1x48ahcuXKcHJygiAIcHR0RPXq1Qt9b+71+ffff9UGU7948QJ37txBnTp1NB7fyckJwMs/nopyPW1tbTFixAiMGDECcXFxqF+/PubMmSMmJABQu3Zt1K5dG99++604kD0kJASzZ88u9DwK+1mR9zyJcrHL5g1NmDABRkZGGDJkCGJjY/Ntv3XrFpYuXQrgZZcDgHwzYRYtWgQAWn12hJOTE5KSknDx4kVx3aNHj/KNak9ISMj33txfaq9ORc5la2uLevXqYf369WpJzz///IP9+/eL5ymFVq1aYdasWVi+fHm+B4LlpaOjk+8vqm3btuG///5TW5ebOGnjCaQTJ07EvXv3sH79eixatAgODg7w8fEp9Drm8vDwgKenp7gUNyEZMmQIpk2bhoULF762nZOTE65du6Y29fbChQsaZ1QB2r1OuTw9PaGvr4/g4GC179UPP/yApKQkSZ+lkvtXeN7jZmZmYuXKlVo/1oQJE5CVlSX+P+/Rowd0dHQwY8aMfPeoIAh48uQJAKBBgwYoW7Ys1q5dixcvXohtNm7cWOTuC1dXVzg5OWHBggUFdgXm3gvZ2dn5ul6srKxgZ2cn3r/JyclqcQAvkxOlUvnae7xDhw44ffo0IiMjxXWpqalYs2YNHBwc4OLiUqRzoQ8HKyRvyMnJCWFhYfj000/h7Oys9qTWEydOYNu2beKc/Lp168LHxwdr1qwRS8anT5/G+vXr0a1bt0KnlL4Jb29vTJw4Ed27d8eoUaOQlpaGVatWoXr16mqDOmfOnImIiAh07NgR9vb2iIuLw8qVK1GxYkW1QWivmj9/Ptq3bw93d3cMHjwYz58/x7Jly2BmZlbk7oY3oVQq8e2332ps16lTJ8ycORODBg1CkyZNcOnSJWzcuDHfL3snJyeYm5sjJCQEJiYmMDIygpubW76+eE0OHTqElStXYtq0aeI05HXr1qFly5aYMmUKgoKCirW/4rC3ty/SNf/iiy+waNEieHl5YfDgwYiLi0NISAg++ugjjYOLXV1dAQCTJ0+Gt7c39PT00Llz57eqhJUvXx4BAQGYMWMG2rVrhy5duuD69etYuXIlGjZsKI7nkEKTJk1gYWEBHx8fjBo1CgqFAhs2bJCkW8DFxQUdOnTA999/jylTpsDJyQmzZ89GQEAA7ty5g27dusHExATR0dHYsWMHhg0bhnHjxkFfXx/Tp0/HyJEj8cknn6BPnz64c+cOQkND4eTkVKSqlVKpxPfff4/27dvjo48+wqBBg1ChQgX8999/OHz4MExNTfHbb7/h2bNnqFixInr16oW6devC2NgYBw4cwJkzZ8RE99ChQ/Dz80Pv3r1RvXp1vHjxAhs2bICOjg569uxZaAyTJk3Cpk2b0L59e4waNQqWlpZYv349oqOj8euvv/KprpSfHFN7SpMbN24IQ4cOFRwcHAR9fX3BxMRE8PDwEJYtW6Y2hTArK0uYMWOG4OjoKOjp6QmVKlUSAgIC1NoIwv+mdr7q1ambhU37FQRB2L9/v1CrVi1BX19fqFGjhvDzzz/nm/Z78OBBoWvXroKdnZ2gr68v2NnZCX379hVu3LiR7xivTo09cOCA4OHhIRgaGgqmpqZC586dhStXrqi1yT3eq9OKC5qGWZC8034LU9i037Fjxwq2traCoaGh4OHhIURGRhY49XXXrl2Ci4uLoKurq3aeLVq0ED766KMCj5l3P8nJyYK9vb1Qv359temZgvByqqtSqRQiIyNfew7FUdi9kVdB034FQRB+/vlnoUqVKoK+vr5Qr149Yd++fUWa9isIgjBr1iyhQoUKglKpVPveFTbt99Vj507rPHz4sNr65cuXCzVr1hT09PQEa2trYfjw4cLTp0/V2hT2vSjsWgBQm8Ja0P12/PhxoXHjxoKhoaFgZ2cnTJgwQZwynDfG4kz7Lex+OXLkSL5r+uuvvwpNmzYVjIyMBCMjI6FmzZqCr6+vcP36dbX3BgcHC/b29oJKpRIaNWokHD9+XHB1dRXatWsntsm9toVNyT1//rzQo0cPoWzZsoJKpRLs7e2FPn36CAcPHhQE4eUU4/Hjxwt169YVTExMBCMjI6Fu3brCypUrxX3cvn1b+OKLLwQnJyfBwMBAsLS0FFq1aiUcOHBA7Viv3g+CIAi3bt0SevXqJZibmwsGBgZCo0aNhD179qi1KewcCvv5Q6WXQhA4YoiIqKTLyclB+fLl0aNHD6xdu1bucIi0jjUzIqISJj09PV830k8//YSEhIQCPwaAqDRghYSIqIQ5cuQIxowZg969e6Ns2bI4d+4cfvjhBzg7O+Ps2bOyfsAjkVQ4qJWIqIRxcHBApUqVEBwcjISEBFhaWmLAgAGYN28ekxEqtVghISIiItlxDAkRERHJjgkJERERyY4JCREREcmuVA5qVbTRzoc9UenxfO8NuUOgEkQAh87R/xjqSPMZXHlp6/eSEP5AK/spiVghISIiItmVygoJERFRiSLBJ2eXNkxIiIiIpMb+CI2YkBAREUmNFRKNmLMRERGR7FghISIikhoLJBoxISEiIpIau2w0YpcNERERyY4VEiIiIqnxz3+NmJAQERFJjV02GjFnIyIiItmxQkJERCQ1Fkg0YkJCREQkNSUzEk3YZUNERESyY4WEiIhIaiyQaMSEhIiISGqcZaMRExIiIiKpMR/RiGNIiIiISHaskBAREUmNs2w0YkJCREQkNeYjGrHLhoiIiGTHCgkREZHUOMtGIyYkREREUuMYEo3YZUNERESyY4WEiIhIaiyQaMSEhIiISGocQ6IRu2yIiIhIdqyQEBERSY0FEo2YkBAREUmNs2w0YkJCREQkNeYjGnEMCREREcmOFRIiIiKpcZaNRkxIiIiIpMb+CI14iYiIiEh2rJAQERFJjV02GjEhISIikhrzEY3YZUNERESyY0JCREQkNYVCO0sxRUREoHPnzrCzs4NCocDOnTvFbVlZWZg4cSJq164NIyMj2NnZYcCAAXj48KHaPhISEtC/f3+YmprC3NwcgwcPRkpKilqbixcvolmzZjAwMEClSpUQFBRU7FiZkBAREUlNqaWlmFJTU1G3bl2sWLEi37a0tDScO3cOU6ZMwblz57B9+3Zcv34dXbp0UWvXv39/XL58GeHh4dizZw8iIiIwbNgwcXtycjLatm0Le3t7nD17FvPnz8f06dOxZs2aYsWqEARBKP4plmyKNhXlDoFKmOd7b8gdApUgAkrdjz16C4Y6RpIfQ/GVi1b2I4RcefMYFArs2LED3bp1K7TNmTNn0KhRI9y9exeVK1fG1atX4eLigjNnzqBBgwYAgL1796JDhw548OAB7OzssGrVKkyePBkxMTHQ19cHAEyaNAk7d+7EtWvXihwfKyRERERS01KXTUZGBpKTk9WWjIwMrYWZlJQEhUIBc3NzAEBkZCTMzc3FZAQAPD09oVQqcerUKbFN8+bNxWQEALy8vHD9+nU8ffq0yMdmQkJERCQ1hXaWwMBAmJmZqS2BgYFaCTE9PR0TJ05E3759YWpqCgCIiYmBlZWVWjtdXV1YWloiJiZGbGNtba3WJvd1bpui4LRfIiIiqWnp034DAgLg7++vtk6lUr31frOystCnTx8IgoBVq1a99f7eBBMSIiKi94RKpdJKApJXbjJy9+5dHDp0SKyOAICNjQ3i4uLU2r948QIJCQmwsbER28TGxqq1yX2d26Yo2GVDREQkNZmm/WqSm4zcvHkTBw4cQNmyZdW2u7u7IzExEWfPnhXXHTp0CDk5OXBzcxPbREREICsrS2wTHh6OGjVqwMLCosixMCEhIiKSmpbGkBRXSkoKoqKiEBUVBQCIjo5GVFQU7t27h6ysLPTq1Qt///03Nm7ciOzsbMTExCAmJgaZmZkAAGdnZ7Rr1w5Dhw7F6dOncfz4cfj5+cHb2xt2dnYAgH79+kFfXx+DBw/G5cuXsWXLFixdujRf15LGS8Rpv/Qh4LRfyovTfimvdzLtd1RtrexHCL5UrPZHjhxBq1at8q338fHB9OnT4ejoWOD7Dh8+jJYtWwJ4+WA0Pz8//Pbbb1AqlejZsyeCg4NhbGwstr948SJ8fX1x5swZlCtXDiNHjsTEiROLFSsTEvogMCGhvJiQUF7vIiFRfl1HK/vJWXpRK/spiTiolYiISGIKftqvRhxDQkRERLJjhYSIiEhiLJBoxoSEiIhIYkpmJBqxy4aIiIhkxwoJERGRxDioVTMmJERERBJjQqIZu2xKsGa13bB75jr8t/lvCOEP0LWJl9r2aZ/74+oPR5Cy+wYStv+D8O82oVHNj9XaRG+IhBD+QG2Z+KmvuF2lp8K68Ytwcc0BZO29gx3Tv38n50bS2Lp5K3p164MmDZuiScOm+LzvAByLOCZunzltNjp6dUajjxujpUcrfO07GtG3o2WMmN6lH9euQz2X+ggKnA8ASEpMwrzZ36Frh+5w+9gd7T7pgO/mBOHZs2cyR1r6KBQKrSylGSskJZiRQRlcuH0FP+7bUmCicOPBbfgt/xa3H92DocoAY3oOxf55G1HVpykeJyWI7aaEzsfaP8LE18+ep4hf6+go8TwjHcE7fkTPZh2kPSGSnJW1Nb4eMxKV7StDAPDbzt/wtd8YbPl1M6pWc4LLR87o2Lk9bGxtkZyUhFUrQvDVkBH4I3wPdHR05A6fJPTPpcv4ZeuvqF6jmrguPj4e8fHx8B8/GlWcquDRw0eYPWMu4uPjsWDJfBmjpQ8RE5ISbO+Zw9h75nCh2zcd3qn22j9kBoa074s6VZxx6Pxxcf2ztBTEPo0vcB9p6c8xIvgbAIBHrQYwNzItsB29H1q2aqH2euRoP2zdvA0XL15E1WpO6NWnp7itQgU7+I3yRe/un+Lhfw9RqXKldx0uvSNpqWn4ZsJkTJ0xBWtX/++Pm6rVqmLh0gXi60qVK8Hva19MnvgtXrx4AV1d/orQllJe3NAKWbtsHj9+jKCgIHTv3h3u7u5wd3dH9+7dMX/+fMTHF/wLlAqmp6uHYR36IzElCRduXVHbNsnbF49/vYRzq/ZiXO+voKPkX8IfguzsbPz5x148f/4cdevmf2x1Wtpz7NqxGxUqVijWR4TT+2fu7Hlo1qIpGjdx09g2JSUFxsZGTEa0jF02msl2x505cwZeXl4oU6YMPD09Ub16dQBAbGwsgoODMW/ePOzbtw8NGjSQK8T3Qke31tg8eSXKqAzxKCEObSb2w5Pkp+L24J0/4tzNf5DwLBFNPnJF4BeTYGtphbGrZ8oYNUnp5o2b+LyvDzIzM1GmjCEWBy+EU1UncfuWTVuxeMESPH/+HA6ODlj9/Sro6evJGDFJae8f+3DtyjVs3LpBY9unT59i7aq16NG7xzuIjEidbAnJyJEj0bt3b4SEhOTL+gRBwFdffYWRI0ciMjLytfvJyMhARkaG+socAVCW7kwy1+ELJ1DvKy+UM7PE0Pb9sPXbVXAb1RnxiU8AAIt/XSu2vRR9FZlZWVg9eh4CfpyHzKxMucImCTk4OGDr9s1ISUlB+L4DmPLNVPyw/nsxKenQqT0au7vh8ePHWL/uJ4z3n4j1G9dBpVLJHDlpW8yjGAQFzkfI9ys1fn9TUlIw8quvUcWpCr7y/fIdRfjhKO3VDW2QrcvmwoULGDNmTIHfJIVCgTFjxiAqKkrjfgIDA2FmZqa2IPrDGSGelv4ctx7ewamr5zBk0Ti8yMnG4HbehbY/de089HT14GDNT0QurfT09VDZvjJcPnLB1/6jUL1GdWzcsEncbmJiAnsHe7g2cMXCxQsQHR2NQwcOyRgxSeXK5atIeJKAvr36w7V2Q7jWboizZ85i08+b4Vq7IbKzswEAqampGDHMD0ZGZbBo2ULo6bFipm0KLf0rzWSrkNjY2OD06dOoWbNmgdtPnz4Na2trjfsJCAiAv7+/2jqz7s5aifF9pFQooNIr/C+hek4fITs7G3H/X0Gh0i9HEJBVSDVMgAAIQGZm1juOit4FN/dG+GXXVrV1UydPh6OjAwYNGQgdHR2kpKRgxFBf6OnrY8mKxayUkWxkS0jGjRuHYcOG4ezZs2jdurWYfMTGxuLgwYNYu3YtFixYoGEvgEqlyv8fqJR01xgZlEHVCg7ia0ebSqjr5IKE5EQ8efYUk/uNwu7IcDx6EotyZpbw7eKDCuVssC1iDwCgsXN9uNX8GIcvnMCztFS4u7hi8VfT8PPB7UhMSRL361y5GvT19GBpYg4TQ2PUdXIBgHyDY6nkW7ooGE2be8DG1hZpqan4Y8+f+Pv031i1diUe3H+AfX/ug7uHOywsLBAbG4sfv3/ZVdO0eVO5QycJGBkZoWq1qmrrDA0NYWZuhqrVqiIlJQXDh4xAeno65nw3G6kpqUhNSQUAWFhacCq4FrHLRjPZEhJfX1+UK1cOixcvxsqVK8XSoY6ODlxdXREaGoo+ffrIFV6J0KB6XRxZuE18vXj4dABA6P6t+GpJAGpWqgqfNr1RztQCT549xZnrF9BsTE9cuXsDAJCRlQnvVl0xfYA/VHoqRMfcw+Lta7Eoz7gSAPhjzk9wsPnflM+okP0AAEUbduu8bxISEvDtpCmIj38MYxNjVK9eDavWroR7k8aIi4vDubPn8fOGMCQnJaNsubJwda2Pn8JCUbaspdyhkwyuXrmGSxf/AQB0btdVbdvv4XtQoYKdHGGVSsxHNFMIgiDIHURWVhYeP34MAChXrtxb91/yFym96vneG3KHQCWIANl/7FEJYqhjJPkxzL7RPOW6KJLmntLKfkqiEjHRXE9PD7a2tnKHQUREJAklSyQalYiEhIiIqDTjGBLNmJAQERFJjAmJZvy0XyIiIpIdKyREREQSY4FEMyYkREREEmOXjWbssiEiIiLZsUJCREQkMVZINGNCQkREJDEmJJqxy4aIiIhkxwoJERGRxFgh0YwJCRERkcSYj2jGLhsiIiKSHSskREREEmOXjWZMSIiIiCTGhEQzJiREREQSUzIh0YhjSIiIiEh2rJAQERFJjAUSzZiQEBERSYxjSDRjlw0RERHJjhUSIiIiiSnACokmTEiIiIgkxi4bzdhlQ0RERLJjhYSIiEhirJBoxoSEiIhIYsxHNGOXDREREcmOFRIiIiKJsctGM1ZIiIiIJKZQKLSyFFdERAQ6d+4MOzs7KBQK7Ny5U227IAiYOnUqbG1tYWhoCE9PT9y8eVOtTUJCAvr37w9TU1OYm5tj8ODBSElJUWtz8eJFNGvWDAYGBqhUqRKCgoKKHSsTEiIiIonJlZCkpqaibt26WLFiRYHbg4KCEBwcjJCQEJw6dQpGRkbw8vJCenq62KZ///64fPkywsPDsWfPHkRERGDYsGHi9uTkZLRt2xb29vY4e/Ys5s+fj+nTp2PNmjXFu0aCIAjFPsMSTtGmotwhUAnzfO8NuUOgEkRAqfuxR2/BUMdI8mNUX9ROK/u54b/3jd+rUCiwY8cOdOvWDcDL6oidnR3Gjh2LcePGAQCSkpJgbW2N0NBQeHt74+rVq3BxccGZM2fQoEEDAMDevXvRoUMHPHjwAHZ2dli1ahUmT56MmJgY6OvrAwAmTZqEnTt34tq1a0WOjxUSIiIiiSkU2lm0KTo6GjExMfD09BTXmZmZwc3NDZGRkQCAyMhImJubi8kIAHh6ekKpVOLUqVNim+bNm4vJCAB4eXnh+vXrePr0aZHj4aBWIiIiiWlrUGtGRgYyMjLU1qlUKqhUqmLvKyYmBgBgbW2ttt7a2lrcFhMTAysrK7Xturq6sLS0VGvj6OiYbx+52ywsLIoUDyskRERE74nAwECYmZmpLYGBgXKHpRWskBAREUlMWxWSgIAA+Pv7q617k+oIANjY2AAAYmNjYWtrK66PjY1FvXr1xDZxcXFq73vx4gUSEhLE99vY2CA2NlatTe7r3DZFwQoJERGRxLQ1y0alUsHU1FRtedOExNHRETY2Njh48KC4Ljk5GadOnYK7uzsAwN3dHYmJiTh79qzY5tChQ8jJyYGbm5vYJiIiAllZWWKb8PBw1KhRo8jdNQATEiIiolIrJSUFUVFRiIqKAvByIGtUVBTu3bsHhUKB0aNHY/bs2di9ezcuXbqEAQMGwM7OTpyJ4+zsjHbt2mHo0KE4ffo0jh8/Dj8/P3h7e8POzg4A0K9fP+jr62Pw4MG4fPkytmzZgqVLl+ar5GjCLhsiIiKJyfWg1r///hutWrUSX+cmCT4+PggNDcWECROQmpqKYcOGITExEU2bNsXevXthYGAgvmfjxo3w8/ND69atoVQq0bNnTwQHB4vbzczMsH//fvj6+sLV1RXlypXD1KlT1Z5VUhR8Dgl9EPgcEsqLzyGhvN7Fc0hqLe+klf3847dHK/spidhlQ0RERLJjlw0REZHE+OF6mjEhISIikhgTEs2YkBAREUmM+YhmHENCREREsmOFhIiISGLsstGMCQkREZHUmJBoxC4bIiIikh0rJERERBJjl41mTEiIiIgkxnxEM3bZEBERkexYISEiIpIYu2w0Y0JCREQkMSYkmrHLhoiIiGTHCgkREZHEWCHRjAkJERGRxJiPaMaEhIiISGKskGjGMSREREQku1JZIUn986rcIVAJYzbBQ+4QqARJDDomdwj0gWGFRLNSmZAQERGVJExINGOXDREREcmOFRIiIiKJsUKiGRMSIiIiiTEf0YxdNkRERCQ7VkiIiIgkxi4bzZiQEBERSYwJiWbssiEiIiLZsUJCREQkMVZINGNCQkREJDHmI5oxISEiIpIYKySacQwJERERyY4VEiIiIqmxQqIRExIiIiKJsctGM3bZEBERkexYISEiIpKYkgUSjZiQEBERSYxdNpqxy4aIiIhkxwoJERGRxJSskGjEhISIiEhi7LLRjAkJERGRxDg+QjNeIyIiIpIdKyREREQS4xgSzZiQEBERSYxjSDRjlw0RERHJjhUSIiIiibHLRjNWSIiIiCSmUCi0shRHdnY2pkyZAkdHRxgaGsLJyQmzZs2CIAhiG0EQMHXqVNja2sLQ0BCenp64efOm2n4SEhLQv39/mJqawtzcHIMHD0ZKSopWrkteTEiIiIhKoe+++w6rVq3C8uXLcfXqVXz33XcICgrCsmXLxDZBQUEIDg5GSEgITp06BSMjI3h5eSE9PV1s079/f1y+fBnh4eHYs2cPIiIiMGzYMK3Hyy4bIiIiicnx1/+JEyfQtWtXdOzYEQDg4OCATZs24fTp0wBeVkeWLFmCb7/9Fl27dgUA/PTTT7C2tsbOnTvh7e2Nq1evYu/evThz5gwaNGgAAFi2bBk6dOiABQsWwM7OTmvxskJCREQkMaVCoZWlOJo0aYKDBw/ixo0bAIALFy7g2LFjaN++PQAgOjoaMTEx8PT0FN9jZmYGNzc3REZGAgAiIyNhbm4uJiMA4OnpCaVSiVOnTr3tZVHDCgkREdF7IiMjAxkZGWrrVCoVVCpVvraTJk1CcnIyatasCR0dHWRnZ2POnDno378/ACAmJgYAYG1trfY+a2trcVtMTAysrKzUtuvq6sLS0lJsoy2skBAREUlMW4NaAwMDYWZmprYEBgYWeMytW7di48aNCAsLw7lz57B+/XosWLAA69evf8dnXzSskBAREUlMW9N+AwIC4O/vr7auoOoIAIwfPx6TJk2Ct7c3AKB27dq4e/cuAgMD4ePjAxsbGwBAbGwsbG1txffFxsaiXr16AAAbGxvExcWp7ffFixdISEgQ368trJAQERFJTKGlRaVSwdTUVG0pLCFJS0uDUqn+a15HRwc5OTkAAEdHR9jY2ODgwYPi9uTkZJw6dQru7u4AAHd3dyQmJuLs2bNim0OHDiEnJwdubm5vd1FewQoJERFRKdS5c2fMmTMHlStXxkcffYTz589j0aJF+OKLLwC87EYaPXo0Zs+ejWrVqsHR0RFTpkyBnZ0dunXrBgBwdnZGu3btMHToUISEhCArKwt+fn7w9vbW6gwbgAkJERGR5OR4UuuyZcswZcoUjBgxAnFxcbCzs8OXX36JqVOnim0mTJiA1NRUDBs2DImJiWjatCn27t0LAwMDsc3GjRvh5+eH1q1bQ6lUomfPnggODtZ6vAoh7yPbSom0F8/kDoFKGIuJzeUOgUqQxKBjcodAJYihjpHkx+i/d4RW9rOx3Uqt7Kck4hgSIiIikh27bIiIiCRW3M+h+RAxISEiIpIYP+1XM3bZEBERkexYISEiIpIY6yOaFSkh2b17d5F32KVLlzcOhoiIqDRil41mRUpIch+QoolCoUB2dvbbxENEREQfoCIlJLmPmSUiIqLiY4VEM44hISIikhin/Wr2RglJamoqjh49inv37iEzM1Nt26hRo7QSGBERUWnBColmxU5Izp8/jw4dOiAtLQ2pqamwtLTE48ePUaZMGVhZWTEhISIiomIr9nNIxowZg86dO+Pp06cwNDTEyZMncffuXbi6umLBggVSxEhERPReU2hpKc2KnZBERUVh7NixUCqV0NHRQUZGBipVqoSgoCB88803UsRIRET0XlMqFFpZSrNiJyR6enpQKl++zcrKCvfu3QMAmJmZ4f79+9qNjoiIiD4IxR5D8vHHH+PMmTOoVq0aWrRogalTp+Lx48fYsGEDatWqJUWMRERE77XSXt3QhmJXSObOnQtbW1sAwJw5c2BhYYHhw4cjPj4ea9as0XqARERE7zuFQqGVpTQrdoWkQYMG4tdWVlbYu3evVgMiIiKiDw8fjEZERCSxYndHfICKnZA4Ojq+tmx0+/bttwqIiidkxWqsXrlWbZ2Doz127PkVD/97iI5tC/6ww6BF89DGy/NdhEha1LRKffi3HICPK7rAzqw8eq8bg93/HBG3d639CYa598LHFZ1R1sgcDRd+iosPb6jtY//wtWhRtYHaurUnfoHfr3MAAJ837IzvvWcWePyK0z5BfMpT7Z4UvTM/rl2H4MXL0O/zvpgQMB4AMGvabJw6eRrxcfEoU8YQdevVxddjR8GxiqPM0ZYupb27RRuKnZCMHj1a7XVWVhbOnz+PvXv3Yvz48dqKi4rBqWoVhHy/Unyto/vy22ptY43wI+pdar9u24Gf1m2AR9Mm7zRG0g4jfUNcfHgDoad3YdugRQVuPx4dhV8uhCOkz9RC9/ND5K+YsW+V+DotM138etv5/dh/7YRa+++9Z0Clq2Iy8h7759Jl/LL1V1SvUU1tvfNHzujQuT1sbG2RnJSEkBWrMXyIL34P/w06OjoyRUsfomInJF9//XWB61esWIG///77rQOi4tPR0UW58uUKWK+Tb/3hg4fRpp0nyhiVeVfhkRbtu3Yc+64dL3R72NnfAQD2Frav3U9aVjpinz0pcFv6iwykP8sQX5czskDLqo3w5dYZbxAxlQRpqWn4ZsJkTJ0xBWtXf6+2rVefnuLXFSrYwXfUCPTp7o2H/z1EpcqV3nWopRZn2WimtW6t9u3b49dff9XW7qgY7t27hzYt26GTV1d8M+FbPHoYU2C7K5ev4vq1G+jWo+s7jpBKGu/6HfDfzEM4N24bZnUYCUM9g0LbftagE9Ky0rH9woF3GCFp09zZ89CsRVM0buL22nbP055j147dqFCxAmxsbN5RdB8GPhhNM60Nav3ll19gaWmprd1REdWqUwsz50yHvYM9Hsc/xupVa/HFgCH4ZdcWGBkZqbXd+esuOFZxRL2P68oULZUEW87/iXtPH+FhUjxq21XDnI5fo3p5e3y6flyB7Qc26oYt5/5E+ouMArdTybb3j324duUaNm7dUGibLZu2YsmCpXj+/DkcHB0Q8v1K6OnrvcMoSz+OIdHsjR6MlvfCCoKAmJgYxMfHY+XKla95Z/Hdv38f06ZNw48//lhom4yMDGRkqP+gzNbJhEql0mosJVXTZh7i19VrVEPtOrXQoU0n7N8bju49u4nb0tPT8ecfezH0qyEyREklyQ8nt4tfX475FzHJj7Fv+BpUKVsRt588UGvrZl8HzjZVMGjTt+86TNKCmEcxCAqcj5DvV772Z2KHTu3R2L0xHj+Ox0/rNmCC/0SEblz3wfwcpZKh2AlJ165d1RISpVKJ8uXLo2XLlqhZs6ZWg0tISMD69etfm5AEBgZixgz1vu1vpkzC5Kkf5ufqmJiaoLK9Pe7fU//FcmD/QaQ/T0enLh1lioxKqtP3LgEAnMpVypeQDHLrjqj/ruH8g6tyhEZv6crlq0h4koC+vfqL67Kzs3Hu73PYErYVp6NOQkdHByYmJjAxMYG9Q2XUqVMHzdxb4NCBw2jfsZ2M0ZcuylL/0Xhvr9gJyfTp07V28N27d792e1GmEAcEBMDf319tXbZO5lvF9T5LS03Dg/sP0LFLB7X1O7fvQotWzWFpaSFTZFRS1bWrAQB4lPxYbb2RviF61W2DKX8skyMs0gI390b4ZddWtXVTJ0+Ho6MDBg0ZWOAsGgECIACZmR/uz1EpsMtGs2InJDo6Onj06BGsrKzU1j958gRWVlbIzs4u8r66desGhUIBQRAKbaPpm6hSqfKVFdNePCtyDO+7RfOXoHnLZrCzs0VcXDxCVqyGUkeJdh28xDb37t7Hub/PY9mqpTJGStpgpG8Ip3L/m/ngYFkBdeyq42laMu4nxsDC0BSVLGxgZ/ry/2d1KwcAQOyzJ4h99gRVylbEpx+3x95rx5CQmojadtUxv8tYRNw6i38e3VQ7Vu96XtDV0RFn7tD7x8jICFWrVVVbZ2hoCDNzM1StVhUP7j/Avj/3w92jMSwsLBAbG4d137/sqmnWvKlMUdOHqtgJSWHJQ0ZGBvT19Yu1L1tbW6xcuRJduxY86yMqKgqurq7FDfGDEhsbi4Dxk5GUmAQLSwvUq18XP4WFqlVCdu3YDWtrK7h7NJYxUtIG10ouCB/xv2mb87u+HIj605ndGLp5GjrVaqH2ULONn38HAJi1LwSz969GZnYWPqnuhpHN+8FI3xAPEmOx49JBBIarTwUFgIFu3bDz0iEkpadIfFYkF32VCufOnsfGDWFITkpG2XJlUd+1PtaHrYNlWU5S0KbSPkNGGxTC68oTeQQHBwMAxowZg1mzZsHY2Fjclp2djYiICNy5cwfnz58v8sG7dOmCevXqYebMgp8KeeHCBXz88cfIyckp8j6BD6tCQkVjMbG53CFQCZIYdEzuEKgEMdQx0tzoLX0TOVkr+5nrPkcr+ymJilwhWbx4MYCXFZKQkBC1vkd9fX04ODggJCSkWAcfP348UlNTC91etWpVHD58uFj7JCIiovdPkROS6OhoAECrVq2wfft2WFi8/eDIZs2avXa7kZERWrRo8dbHISIikhMHtWpW7DEkrFgQEREVD8eQaFbsR8f37NkT3333Xb71QUFB6N27t1aCIiIiog9LsROSiIgIdOjQId/69u3bIyIiQitBERERlSYKKLWylGbF7rJJSUkpcHqvnp4ekpOTtRIUERFRacIuG82KnW7Vrl0bW7Zsybd+8+bNcHFx0UpQREREpYlCodDKUpoVu0IyZcoU9OjRA7du3cInn3wCADh48CDCwsLwyy+/aD1AIiIiKv2KnZB07twZO3fuxNy5c/HLL7/A0NAQdevWxaFDh2BpySf7ERERvUrBD9fTqNgJCQB07NgRHTu+/NTY5ORkbNq0CePGjcPZs2eL9Vk2REREHwKOIdHsjYfsRkREwMfHB3Z2dli4cCE++eQTnDx5UpuxERER0QeiWBWSmJgYhIaG4ocffkBycjL69OmDjIwM7Ny5kwNaiYiIClHaB6RqQ5ErJJ07d0aNGjVw8eJFLFmyBA8fPsSyZcukjI2IiKhUUGrpX2lW5ArJn3/+iVGjRmH48OGoVq2alDERERHRB6bI6daxY8fw7NkzuLq6ws3NDcuXL8fjx4+ljI2IiKhU4HNINCtyQtK4cWOsXbsWjx49wpdffonNmzfDzs4OOTk5CA8Px7Nnz6SMk4iI6L3FhESzYndIGRkZ4YsvvsCxY8dw6dIljB07FvPmzYOVlRW6dOkiRYxERET0Bv777z989tlnKFu2LAwNDVG7dm38/fff4nZBEDB16lTY2trC0NAQnp6euHnzpto+EhIS0L9/f5iamsLc3ByDBw9GSkqK1mN9qxEyNWrUQFBQEB48eIBNmzZpKyYiIqJSRTsfrVe8CsnTp0/h4eEBPT09/Pnnn7hy5QoWLlwICwsLsU1QUBCCg4MREhKCU6dOwcjICF5eXkhPTxfb9O/fH5cvX0Z4eDj27NmDiIgIDBs2TGvXJpdCEARB63uVWdoLdh+ROouJzeUOgUqQxKBjcodAJYihjpHkx1gQFaSV/YyrN6HIbSdNmoTjx4/jr7/+KnC7IAiws7PD2LFjMW7cOABAUlISrK2tERoaCm9vb1y9ehUuLi44c+YMGjRoAADYu3cvOnTogAcPHsDOzu7tT+r/le45RERERCWAUqHQylIcu3fvRoMGDdC7d29YWVnh448/xtq1a8Xt0dHRiImJgaenp7jOzMwMbm5uiIyMBABERkbC3NxcTEYAwNPTE0qlEqdOnXrLq6KOCQkREdF7IiMjA8nJyWpLRkZGgW1v376NVatWoVq1ati3bx+GDx+OUaNGYf369QBePuwUAKytrdXeZ21tLW6LiYmBlZWV2nZdXV1YWlqKbbSFCQkREZHEFFr6FxgYCDMzM7UlMDCwwGPm5OSgfv36mDt3Lj7++GMMGzYMQ4cORUhIyDs++6JhQkJERCQxpUKplSUgIABJSUlqS0BAQIHHtLW1zfexLs7Ozrh37x4AwMbGBgAQGxur1iY2NlbcZmNjg7i4OLXtL168QEJCgthGW5iQEBERvSdUKhVMTU3VFpVKVWBbDw8PXL9+XW3djRs3YG9vDwBwdHSEjY0NDh48KG5PTk7GqVOn4O7uDgBwd3dHYmIizp49K7Y5dOgQcnJy4ObmptVzK9aH6xEREVHxyfFQszFjxqBJkyaYO3cu+vTpg9OnT2PNmjVYs2aNGNPo0aMxe/ZsVKtWDY6OjpgyZQrs7OzQrVs3AC8rKu3atRO7erKysuDn5wdvb2+tzrABmJAQERFJTlHMZ4hoQ8OGDbFjxw4EBARg5syZcHR0xJIlS9C/f3+xzYQJE5Camophw4YhMTERTZs2xd69e2FgYCC22bhxI/z8/NC6dWsolUr07NkTwcHBWo+XzyGhDwKfQ0J58TkklNe7eA7JsktLtLKfkbVHa2U/JRErJERERBIr7jNEPkRMSIiIiCQmR5fN+4azbIiIiEh2rJAQERFJjF02mjEhISIikphCwQ4JTZiQEBERSYxjSDRjykZERESyY4WEiIhIYhxDohkTEiIiIonJ8ej49w27bIiIiEh2rJAQERFJTMlBrRoxISEiIpIYu2w0Y5cNERERyY4VEiIiIonxwWiaMSEhIiKSGMeQaMaUjYiIiGTHCgkREZHEOKhVMyYkREREEuNn2WjGhISIiEhirJBoxjEkREREJDtWSIiIiCTGWTaaMSEhIiKSGJ9DohmvEBEREcmOFRIiIiKJcZaNZkxIiIiIJMZZNpqxy4aIiIhkxwoJERGRxNhloxkTEiIiIomxy0YzdtkQERGR7FghISIikhgfjKZZqUxIlAoduUOgEiYx6JjcIVAJUqZ3LblDoBJE2B4t+THYZaNZqUxIiIiIShIFR0hoxCtEREREsmOFhIiISGLsstGMCQkREZHE+BwSzdhlQ0RERLJjhYSIiEhiSnbZaMSEhIiISGLsstGMXTZEREQkO1ZIiIiIJMZZNpoxISEiIpIYH4ymGa8QERERyY4VEiIiIomxy0YzJiREREQS46f9asaEhIiISGKskGjGMSREREQkOyYkREREElNo6d/bmDdvHhQKBUaPHi2uS09Ph6+vL8qWLQtjY2P07NkTsbGxau+7d+8eOnbsiDJlysDKygrjx4/Hixcv3iqWgjAhISIikphCodDK8qbOnDmD1atXo06dOmrrx4wZg99++w3btm3D0aNH8fDhQ/To0UPcnp2djY4dOyIzMxMnTpzA+vXrERoaiqlTp75xLIVhQkJERFSKpaSkoH///li7di0sLCzE9UlJSfjhhx+waNEifPLJJ3B1dcW6detw4sQJnDx5EgCwf/9+XLlyBT///DPq1auH9u3bY9asWVixYgUyMzO1GicTEiIiIokpoNTKkpGRgeTkZLUlIyPjtcf29fVFx44d4enpqbb+7NmzyMrKUltfs2ZNVK5cGZGRkQCAyMhI1K5dG9bW1mIbLy8vJCcn4/Lly1q8QkxIiIiIJKdUKLSyBAYGwszMTG0JDAws9LibN2/GuXPnCmwTExMDfX19mJubq623trZGTEyM2CZvMpK7PXebNnHaLxER0XsiICAA/v7+autUKlWBbe/fv4+vv/4a4eHhMDAweBfhvRVWSIiIiCSmrVk2KpUKpqamakthCcnZs2cRFxeH+vXrQ1dXF7q6ujh69CiCg4Ohq6sLa2trZGZmIjExUe19sbGxsLGxAQDY2Njkm3WT+zq3jbYwISEiIpKYHLNsWrdujUuXLiEqKkpcGjRogP79+4tf6+np4eDBg+J7rl+/jnv37sHd3R0A4O7ujkuXLiEuLk5sEx4eDlNTU7i4uGjn4vw/dtkQERGVQiYmJqhVq5baOiMjI5QtW1ZcP3jwYPj7+8PS0hKmpqYYOXIk3N3d0bhxYwBA27Zt4eLigs8//xxBQUGIiYnBt99+C19f30IrM2+KCQkREZHE3vahZlJZvHgxlEolevbsiYyMDHh5eWHlypXidh0dHezZswfDhw+Hu7s7jIyM4OPjg5kzZ2o9FoUgCILW9yqz9Ow0uUOgEkZAqbvN6S2U6V1LcyP6YAjboyU/xvHYQ1rZj4f1J1rZT0nECgkREZHElByyqRGvEBEREcmOFRIiIiKJvc3n0HwomJAQERFJrKQOai1J2GVDREREsmOFhIiISGLsstGMCQkREZHE2GWjGbtsiIiISHaskBAREUmMFRLNmJAQERFJjWNINGKXDREREcmOFRIiIiKJsctGMyYkREREEuO0X82YkBAREUmMFRLNOIaEiIiIZMcKCRERkcRYIdGMCQkREZHEOIZEM3bZEBERkexYISEiIpIYu2w0Y0JCREQkMSYkmrHLhoiIiGTHCgkREZHEOKhVMyYkREREEmOXjWbssiEiIiLZsUJCREQkMXbZaMaEhIiISGLsstGMCQkREZHEmJBoxjEkREREJDtWSN5zWzdvxdbNv+Dhfw8BAE5Vq+DL4cPQtHlTsc2FqAtYtnQFLl28BB2lDmrUrI5Va1fCwMBArrBJIls3b8O2zdvw8L9HAF7eD8OGD0PT5h4AgPv37mPR/CWIOncemZlZaNK0CSZNnoCy5crKGTa9oWYujTC+6zC4OtWCnaU1us0bhl2nwwEAujq6mN1vLDrUb4kq1pWRlPYMBy4ex6QN3+HR0zhxHx9X+QjffT4JDavWQXZONn6N3Av/0NlITU8T2zSoWgfzPpsAV6faEAQBp29ewIQN83DxztV3fs7vK44h0YwVkveclbU1vh4zEpu2bUTYto1o5NYIX/uNwb83bwF4mYyMGOYH9yaNsXHzzwjb+jO8+3lDqeS3vjSytrbCqDGjELZtI8K2/YyGbg0x+v/vh+dpzzF8qC8UCmDNutUI3fgjsrKyMMp3NHJycuQOnd6AkcoQF+5che/aqfm2lVEZon6VWpi1bTnqj+uMHkFfoYZdFewOWCu2sbWwwoFpP+PfR3fgNrE72s0aiI8qV0PoyAX/O4ZBGeydEop7jx/CbWJ3NJ3cG8/SU7Fvynro6vBv2qJSaOlfaaYQBEGQOwhtS89O09yoFGvWuAXGjB+NHj274zPvAWjcxA1+o3zlDktWAkrdbV5kzRu3xJjxo2FtYw2/L0ci4uQRGBsbAwCePXuG5o1bYtXalWjcxE3mSN+dMr1ryR2C1gnbo9UqJAVpULUOzgTtQuVhHrj/+CGGtumLWX39YTu4EXJ/FdSqXAOXluxF1REtcSvmLlydauPv+btRaWgTPHjyqMA27zthe7Tkx7iR9I9W9lPdrPTdu7n4Z3Ipkp2djT//2Ivnz5+jbt06ePIkAZcuXoKlpSUG9PNBq2at8cWAwTh39rzcodI7kJ2djb1/7MPz589Rp24dZGVmQqFQQF9fX2yjUqmgVCpx/hzviQ+BWRkT5OTkIDE1GQCg0tNH5otM5P279HlmOgCgqXMDAMD1/27jcXICBnv2gZ6uHgz0VRjs2QdX7t/EnbgH7/4k3lOskGjGhKQUuHnjJhq7NkHDem6YM2MOFgcvhFNVJ/z34OUPi5AVq9GjVw+sXL0Czi7OGPbFl7h75/3/q4YKdvPGTbi7eqBRvcaYPWMOFgUvhFPVKqhdtw4MDQ2xZOFSPH/+HM/TnmNR0GJkZ2fjcfxjucMmian09PHd5xOx6dhuPHueAgA4dOkEbMzLY1zXYdDT1YO5kSnmfT4RwMvuHABISU9Fy6l98Vnzbni+6SpSNl5Gu3ot0H72IGTnZMt2Pu8bhUKhlaU0kz0hef78OY4dO4YrV67k25aeno6ffvrpte/PyMhAcnKy2pKRkSFVuCWSg4MDtm7fjJ83/4Ten/bGlG+m4ta/t8RxAb369ES3Hl3h7FIT4yeNg4OjA3Zu3yVz1CQVBwcHbNm+CRs2r0efT3tj6jdTcevf27C0tEDQ4u8QceQvNGnQFE3dmuPZs2dwdqnJMUWlnK6OLraOWwGFQoHhq6eI66/cvwmfZeMwtssQpG26gpgfTyM69j5insYjR3j588NAX4UfRnyH49fOonFAD3hM7oV/7t/A75N/gIG+Sq5TolJI1p9CN27cgLOzM5o3b47atWujRYsWePTokbg9KSkJgwYNeu0+AgMDYWZmprbMn7fgte8pbfT09VDZvjJcPnLB1/6jUL1GdWzcsAnlypcHAFRxqqLW3rGKI2IexcgRKr0Dee+HUf4jUb1GdYRtCAMANPFwx559u3Ho2AEcPn4Ic76bjbjYeFSoWEHmqEkqL5OR5bAvXwFtpn8uVkdybfprN2wHN0KFIe4o61Mf07csQXlTS9yOuQ8A6NesKxysKmLQ8vH4+9+LOHUjCv0Wfw1Hq0ro2rCNHKf0nlJoaSm9ZE1IJk6ciFq1aiEuLg7Xr1+HiYkJPDw8cO/evSLvIyAgAElJSWrL+EnjJIy65MsRBGRlZaJCBTuUtyqPO3fuqG2/e+cubO1s5QmO3rkcIQeZWVlq6ywsLGBqaoLTJ08jISEBLT9pIVN0JKXcZKSarQM8p3+GhJTEQtvGJT1GanoaPvXohPSsDIRf+AvAy9k6OUKO2jiTnJyXr1lZKzp22Wgm65ytEydO4MCBAyhXrhzKlSuH3377DSNGjECzZs1w+PBhGBkZadyHSqWCSqVeNvyQZtksXRSMps09YGNri7TUVPyx50/8ffpvrFq7EgqFAgO/8MGq5SGoUaM6atSsgd27fsOd6DtYuGS+3KGTBIIXLYNH8ybi/fDnnr34+/RZrFy7AgCwc/suVHFyhIWFBS5GXURQ4AJ8NqA/HBwd5A2c3oiRQRlUtbEXXztaVUJdB2ckpCTh0dM4/DJ+JepX+Qid5g6BjlIJa/NyAICElCRkvXiZpPq2H4AT188i5Xka2tRtivk+AZi0IQhJac8AAOEX/sL8AQFYMWwmlv2+HkqlEpO6D8eLnGwc/ify3Z80lVqyJiTPnz+Hru7/QlAoFFi1ahX8/PzQokULhIWFyRjd+yEhIQHfTpqC+PjHMDYxRvXq1bBq7Uq4N2kMAPhsQH9kZGRg/ncLkZSUhBo1qiPk+1WoVLmSzJGTFF7eD1PxOM/9sHLtCvF+uHvnLpYtXo6kpCTYVbDDkC8H4zOf/jJHTW+qgVNtHJm1WXy9+IuX40NCD/2C6VuWoGujl10qFxb9ofa+llO8cfTyKQBAo2p1McN7NIwNyuDaf7fxZchk/Hx0h9j2+n+30TlwCKb1GYXIeduRk5OD89GX0W6WD2Kexkt9iqVGaZ8how2yPoekUaNGGDlyJD7//PN82/z8/LBx40YkJycjO7t4I7k/pAoJFc2H/BwSyq80PoeE3ty7eA5J9LMbWtmPo0l1reynJJK1A7B79+7YtGlTgduWL1+Ovn37ohQ+t42IiD4wHEOiGZ/USh8EVkgoL1ZIKK93USG5k3JTK/txMK6mlf2URPwgAiIiIolxDIlmTEiIiIgkxoREM04iJyIiItmxQkJERCSx0j4gVRtYISEiIpKYHJ/2GxgYiIYNG8LExARWVlbo1q0brl+/rtYmPT0dvr6+KFu2LIyNjdGzZ0/Exsaqtbl37x46duyIMmXKwMrKCuPHj8eLFy/e+pq8igkJERFRKXT06FH4+vri5MmTCA8PR1ZWFtq2bYvU1FSxzZgxY/Dbb79h27ZtOHr0KB4+fIgePXqI27Ozs9GxY0dkZmbixIkTWL9+PUJDQzF16lStx8tpv/RB4LRfyovTfimvdzHt97+0O1rZT4UyDm/83vj4eFhZWeHo0aNo3rw5kpKSUL58eYSFhaFXr14AgGvXrsHZ2RmRkZFo3Lgx/vzzT3Tq1AkPHz6EtbU1ACAkJAQTJ05EfHw89PX1tXFaAFghISIikpy2umwyMjKQnJystmRkZBQphqSkJACApaUlAODs2bPIysqCp6en2KZmzZqoXLkyIiNffk5RZGQkateuLSYjAODl5YXk5GRcvnxZW5cHABMSIiKi90ZgYCDMzMzUlsDAQI3vy8nJwejRo+Hh4YFatV5WCGNiYqCvrw9zc3O1ttbW1oiJiRHb5E1GcrfnbtMmzrIhIiKSnHZm2QQEBMDf319t3aufeF8QX19f/PPPPzh27JhW4pACExIiIiKJaWvSr0qlKlICkpefnx/27NmDiIgIVKxYUVxvY2ODzMxMJCYmqlVJYmNjYWNjI7Y5ffq02v5yZ+HkttEWdtkQERFJTI4P1xMEAX5+ftixYwcOHToER0dHte2urq7Q09PDwYMHxXXXr1/HvXv34O7uDgBwd3fHpUuXEBcXJ7YJDw+HqakpXFxc3uKK5McKCRERUSnk6+uLsLAw7Nq1CyYmJuKYDzMzMxgaGsLMzAyDBw+Gv78/LC0tYWpqipEjR8Ld3R2NGzcGALRt2xYuLi74/PPPERQUhJiYGHz77bfw9fUtdqVGE077pQ8Cp/1SXpz2S3m9i2m/Mc8faGU/NoYVNTf6f4VVVNatW4eBAwcCePlgtLFjx2LTpk3IyMiAl5cXVq5cqdYdc/fuXQwfPhxHjhyBkZERfHx8MG/ePOjqaremwYSEPghMSCgvJiSU17tISGK1lJBYFyMhed9wDAkRERHJjmNIiIiIJMcP19OECQkREZHE+Gm/mrHLhoiIiGTHhISIiIhkxy4bIiIiiSk4hkQjVkiIiIhIdqyQEBERSYwVEs1YISEiIiLZsUJCREQkMU771YwVEiIiIpIdExIiIiKSHbtsiIiIJMZBrZqxQkJERESyY4WEiIhIcqyQaMKEhIiISGJMRzRjlw0RERHJjhUSIiIiifE5JJoxISEiIpIcExJN2GVDREREsmOFhIiISGKsj2jGhISIiEhyTEk0YUJCREQkMQ5q1YxjSIiIiEh2TEiIiIhIduyyISIikhg/XE8zVkiIiIhIdqyQEBERSY4VEk2YkBAREUmM6Yhm7LIhIiIi2bFCQkREJDE+h0QzJiRERESSY0KiCbtsiIiISHaskBAREUmM9RHNmJAQERFJjimJJkxIiIiIJMZBrZpxDAkRERHJjgkJERERyY5dNkRERBLjh+tpxgoJERERyU4hCIIgdxCkfRkZGQgMDERAQABUKpXc4VAJwHuC8uL9QCUNE5JSKjk5GWZmZkhKSoKpqanc4VAJwHuC8uL9QCUNu2yIiIhIdkxIiIiISHZMSIiIiEh2TEhKKZVKhWnTpnGwGol4T1BevB+opOGgViIiIpIdKyREREQkOyYkREREJDsmJERERCQ7JiREREQkOyYkpdSKFSvg4OAAAwMDuLm54fTp03KHRDKJiIhA586dYWdnB4VCgZ07d8odEskoMDAQDRs2hImJCaysrNCtWzdcv35d7rCImJCURlu2bIG/vz+mTZuGc+fOoW7duvDy8kJcXJzcoZEMUlNTUbduXaxYsULuUKgEOHr0KHx9fXHy5EmEh4cjKysLbdu2RWpqqtyh0QeO035LITc3NzRs2BDLly8HAOTk5KBSpUoYOXIkJk2aJHN0JCeFQoEdO3agW7ducodCJUR8fDysrKxw9OhRNG/eXO5w6APGCkkpk5mZibNnz8LT01Ncp1Qq4enpicjISBkjI6KSKCkpCQBgaWkpcyT0oWNCUso8fvwY2dnZsLa2VltvbW2NmJgYmaIiopIoJycHo0ePhoeHB2rVqiV3OPSB05U7ACIikoevry/++ecfHDt2TO5QiJiQlDblypWDjo4OYmNj1dbHxsbCxsZGpqiIqKTx8/PDnj17EBERgYoVK8odDhG7bEobfX19uLq64uDBg+K6nJwcHDx4EO7u7jJGRkQlgSAI8PPzw44dO3Do0CE4OjrKHRIRAFZISiV/f3/4+PigQYMGaNSoEZYsWYLU1FQMGjRI7tBIBikpKfj333/F19HR0YiKioKlpSUqV64sY2QkB19fX4SFhWHXrl0wMTERx5aZmZnB0NBQ5ujoQ8Zpv6XU8uXLMX/+fMTExKBevXoIDg6Gm5ub3GGRDI4cOYJWrVrlW+/j44PQ0NB3HxDJSqFQFLh+3bp1GDhw4LsNhigPJiREREQkO44hISIiItkxISEiIiLZMSEhIiIi2TEhISIiItkxISEiIiLZMSEhIiIi2TEhISIiItkxISEqhQYOHIhu3bqJr1u2bInRo0e/8ziOHDkChUKBxMTEd35sInq/MCEheocGDhwIhUIBhUIBfX19VK1aFTNnzsSLFy8kPe727dsxa9asIrVlEkFEcuBn2RC9Y+3atcO6deuQkZGBP/74A76+vtDT00NAQIBau8zMTOjr62vlmJaWllrZDxGRVFghIXrHVCoVbGxsYG9vj+HDh8PT0xO7d+8Wu1nmzJkDOzs71KhRAwBw//599OnTB+bm5rC0tETXrl1x584dcX/Z2dnw9/eHubk5ypYtiwkTJuDVT4R4tcsmIyMDEydORKVKlaBSqVC1alX88MMPuHPnjvi5NxYWFlAoFOLnm+Tk5CAwMBCOjo4wNDRE3bp18csvv6gd548//kD16tVhaGiIVq1aqcVJRPQ6TEiIZGZoaIjMzEwAwMGDB3H9+nWEh4djz549yMrKgpeXF0xMTPDXX3/h+PHjMDY2Rrt27cT3LFy4EKGhofjxxx9x7NgxJCQkYMeOHa895oABA7Bp0yYEBwfj6tWrWL16NYyNjVGpUiX8+uuvAIDr16/j0aNHWLp0KQAgMDAQP/30E0JCQnD58mWMGTMGn332GY4ePQrgZeLUo0cPdO7cGVFRURgyZAgmTZok1WUjotJGIKJ3xsfHR+jatasgCIKQk5MjhIeHCyqVShg3bpzg4+MjWFtbCxkZGWL7DRs2CDVq1BBycnLEdRkZGYKhoaGwb98+QRAEwdbWVggKChK3Z2VlCRUrVhSPIwiC0KJFC+Hrr78WBEEQrl+/LgAQwsPDC4zx8OHDAgDh6dOn4rr09HShTJkywokTJ9TaDh48WOjbt68gCIIQEBAguLi4qG2fOHFivn0RERWEY0iI3rE9e/bA2NgYWVlZyMnJQb9+/TB9+nT4+vqidu3aauNGLly4gH///RcmJiZq+0hPT8etW7eQlJSER48ewc3NTdymq6uLBg0a5Ou2yRUVFQUdHR20aNGiyDH/+++/SEtLQ5s2bdTWZ2Zm4uOPPwYAXL16VS0OAHB3dy/yMYjow8aEhOgda9WqFVatWgV9fX3Y2dlBV/d//w2NjIzU2qakpMDV1RUbN27Mt5/y5cu/0fENDQ2L/Z6UlBQAwO+//44KFSqobVOpVG8UBxFRXkxIiN4xIyMjVK1atUht69evjy1btsDKygqmpqYFtrG1tcWpU6fQvHlzAMCLFy9w9uxZ1K9fv8D2tWvXRk5ODo4ePQpPT89823MrNNnZ2eI6FxcXqFQq3Lt3r9DKirOzM3bv3q227uTJk5pPkogIHNRKVKL1798f5cqVQ9euXfHXX38hOjoaR44cwahRo/DgwQMAwNdff4158+Zh586duHbtGkaMGPHaZ4g4ODjAx8cHX3zxBXbu3Cnuc+vWrQAAe3t7KBQK7NmzB/Hx8UhJSYGJiQnGjRuHMWPGYP369bh16xbOnTuHZcuWYf369QCAr776Cjdv3sT48eNx/fp1hIWFITQ0VOpLRESlBBMSohKsTJkyiIiIQOXKldGjRw84Oztj8ODBSE9PFysmY8eOxeeffw4fHx+4u7vDxMQE3bt3f+1+V61ahV69emHEiBGoWbMmhg4ditTUVABAhQoVMGPGDEyaNAnW1tbw8/MDAMyaNQtTpkxBYGAgnJ2d0a5dO/z+++9wdHQEAFSuXBm//vordu7cibp16yIkJARz586V8OoQUWmiEAob+UZERET0jrBCQkRERLJjQkJERESyY0JCREREsmNCQkRERLJjQkJERESyY0JCREREsmNCQkRERLJjQkJERESyY0JCREREsmNCQkRERLJjQkJERESyY0JCREREsvs/N2Bsjyx17ocAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multinomial Regression slightly outperforms Logistic Regression OVR (but I would claim that this outperformance is insignificant).  We can observe that Multinomial Regression yields approximately the same results as Logistic Regression OVR.  "
      ],
      "metadata": {
        "id": "mlww0A9ZcC6P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results['Method'].append('Multinomial Regression')\n",
        "results['Accuracy'].append(accuracy)\n",
        "results['Report'].append(classification_report(y_test, y_pred2))"
      ],
      "metadata": {
        "id": "YvlyubjZ3Lhy"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Naive Bayes"
      ],
      "metadata": {
        "id": "P36OPURlRw3M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Naive Bayes, there is no hyperparameters to tune; we want to select the best performing model between Gaussian and Bernoulli."
      ],
      "metadata": {
        "id": "U7dsEIO_nU21"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gaussian Assumption:**"
      ],
      "metadata": {
        "id": "kbPu72BCouNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nb_gaussian = GaussianNB() # Initialize Naive Bayes Classifier (GaussianNB)\n",
        "nb_gaussian.fit(X_train_scaled, y_train) # Train the model\n",
        "y_pred3 = nb_gaussian.predict(X_test_scaled) # Predictions\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred3) # Evaluation\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print('Classification Report:\\n', classification_report(y_test, y_pred3))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rc7ErJxNWJ-o",
        "outputId": "3e63729d-82ad-4171-dde4-0f07bccb89fa"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.4778\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           3       0.89      0.34      0.49      1426\n",
            "           5       0.73      0.10      0.17      1257\n",
            "           8       0.40      0.97      0.56      1373\n",
            "\n",
            "    accuracy                           0.48      4056\n",
            "   macro avg       0.67      0.47      0.41      4056\n",
            "weighted avg       0.67      0.48      0.42      4056\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "These results are very poor compared to Logistic Regression OVR and Multinomial Regression.  Let us look at the confusion matrix."
      ],
      "metadata": {
        "id": "NJKH3eaJo0EG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred3)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Reds')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix - Naive Bayes (Gaussian)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "nH828UQMpB-A",
        "outputId": "7f7ed994-bb92-498c-b0fd-1653631134c1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The digit 8 which corresponds to the hard label 2 was very poorly classified.  The results were horrible.  Let us try and change the assumption away from normal (which clearly is a poor assumption)."
      ],
      "metadata": {
        "id": "jx4uFGRxpN9P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results['Method'].append('Naive Bayes (Gaussian)')\n",
        "results['Accuracy'].append(accuracy)\n",
        "results['Report'].append(classification_report(y_test, y_pred3))"
      ],
      "metadata": {
        "id": "bLvM3Ssb3Xzm"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Bernoulli Assumption:**"
      ],
      "metadata": {
        "id": "IWUhK8MxoyK0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nb_bernoulli = BernoulliNB() # Initialize Bernoulli Naive Bayes Classifier\n",
        "nb_bernoulli.fit(X_train_scaled, y_train) # Train the model\n",
        "y_pred4 = nb_bernoulli.predict(X_test_scaled) # Predictions\n",
        "accuracy = accuracy_score(y_test, y_pred4)\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print('Classification Report:\\n', classification_report(y_test, y_pred4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YV4ImKAcRzCR",
        "outputId": "e39afc43-f8ad-45e9-8880-cc306a498c6c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8733\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           3       0.82      0.90      0.86      1426\n",
            "           5       0.91      0.82      0.86      1257\n",
            "           8       0.90      0.89      0.89      1373\n",
            "\n",
            "    accuracy                           0.87      4056\n",
            "   macro avg       0.88      0.87      0.87      4056\n",
            "weighted avg       0.88      0.87      0.87      4056\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred4)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Purples')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix - Naive Bayes (Bernoulli)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "k7QG6Pyqpwj0",
        "outputId": "c84b25f5-615e-47de-cfe1-1d45146654f2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The reason we may be seeing a better performance in Naive Bayes with the Bernoulli distribution opposed to Naive Bayes with the Gaussian distribution on the MNIST dataset is related to the nature of the data.\n",
        "\n",
        "The Bernoulli Naive Bayes model assumes that each feature (pixel in this case) is binary, i.e., it either indicates the presence or absence of a certain feature. This assumption fits well with MNIST data, where each pixel is either \"on\" or \"off\" (a value greater than 0) in the context of image processing.\n",
        "\n",
        "On the other hand, the Gaussian Naive Bayes assumes that the features are continuous and follow a normal distribution. While this might work well for continuous data, MNIST data consists of pixel values that are discrete integers in the range from 0 to 255. As a result, Gaussian Naive Bayes might not capture the distribution of pixel values as effectively as Bernoulli Naive Bayes.\n",
        "\n",
        "These results are a lot better and rival Logistic Regression OVR and Multinomial Regression.  I would still claim that Logistic Regression OVR and Multinomial Regression outperform Naive Bayes.  However, the computational costs of running Naive Bayes are far better, i.e., this method is far more efficient."
      ],
      "metadata": {
        "id": "aBwnS31g5htk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results['Method'].append('Naive Bayes (Bernoulli)')\n",
        "results['Accuracy'].append(accuracy)\n",
        "results['Report'].append(classification_report(y_test, y_pred4))"
      ],
      "metadata": {
        "id": "ddQq_YBP38Wq"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Linear Discriminant Analysis"
      ],
      "metadata": {
        "id": "dH3y3BI8RzZm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case, LDA does not really require hyperparameter tuning for solver and class priors so we will just fit the default model."
      ],
      "metadata": {
        "id": "eEV8vcLpxRgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lda = LinearDiscriminantAnalysis() # Initialize LDA (Linear Discriminant Analysis)\n",
        "lda.fit(X_train_scaled, y_train) # Train the model\n",
        "y_pred5 = lda.predict(X_test_scaled) # Predictions\n",
        "accuracy = accuracy_score(y_test, y_pred5) # Evaluation\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print('Classification Report:\\n', classification_report(y_test, y_pred5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQ_SmQR8R1x8",
        "outputId": "3e27da86-0a7c-426f-c498-f4ebc1e91f12"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9250\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           3       0.94      0.92      0.93      1426\n",
            "           5       0.90      0.93      0.91      1257\n",
            "           8       0.93      0.92      0.93      1373\n",
            "\n",
            "    accuracy                           0.93      4056\n",
            "   macro avg       0.92      0.93      0.92      4056\n",
            "weighted avg       0.93      0.93      0.93      4056\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred5)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='binary')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix - Linear Discriminant Analysis\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kxe5Pi3Wxd-W",
        "outputId": "1270487e-e414-4b57-8f7d-7e39ca178266"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results['Method'].append('Linear Discriminant Analysis')\n",
        "results['Accuracy'].append(accuracy)\n",
        "results['Report'].append(classification_report(y_test, y_pred5))"
      ],
      "metadata": {
        "id": "Xzfw0xbQ4DQB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Linear SVM: One-vs-Rest"
      ],
      "metadata": {
        "id": "FADmjvxxR3rb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# OneVsRestClassifier with LinearSVC\n",
        "svm_cv = OneVsRestClassifier(\n",
        "    LinearSVC(\n",
        "        max_iter=1000,      # Maximum iterations\n",
        "        random_state=42,    # For reproducibility\n",
        "        dual=False          # Use the primal form of the problem (faster for n_features > n_samples)\n",
        "    )\n",
        ")\n",
        "\n",
        "svm_cv.fit(X_train_scaled, y_train)  # Train the model\n",
        "y_pred6 = svm_cv.predict(X_test_scaled)  # Predictions\n",
        "accuracy = accuracy_score(y_test, y_pred6)  # Evaluation\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print('Classification Report:\\n', classification_report(y_test, y_pred6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBS_9IXdR4nY",
        "outputId": "81518254-68a5-43e2-d3b7-6aa8aeb40d8c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9339\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           3       0.94      0.94      0.94      1426\n",
            "           5       0.93      0.91      0.92      1257\n",
            "           8       0.93      0.94      0.94      1373\n",
            "\n",
            "    accuracy                           0.93      4056\n",
            "   macro avg       0.93      0.93      0.93      4056\n",
            "weighted avg       0.93      0.93      0.93      4056\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred6)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Oranges')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix - Linear SVM OVR\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "uPgkfZTpzDv0",
        "outputId": "d40ecf42-a13e-4cef-bb82-d190b62a99aa"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "These results are also very strong.  In general, I would claim that every method with the exception of Naive Bayes when assuming Gaussian Distribution are all strong methods and have their own pros and cons."
      ],
      "metadata": {
        "id": "KD54-AvIzqu2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results['Method'].append('Linear SVM OVR')\n",
        "results['Accuracy'].append(accuracy)\n",
        "results['Report'].append(classification_report(y_test, y_pred6))"
      ],
      "metadata": {
        "id": "RXDP9eIB4Thc"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Comparison Across Methods"
      ],
      "metadata": {
        "id": "WkB965Uq4ZBX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing the accuracy across methods."
      ],
      "metadata": {
        "id": "8aRTCXAM7kgN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_accuracy = pd.DataFrame({'Method': results['Method'],\n",
        "                          'Accuracy': results['Accuracy']\n",
        "})\n",
        "\n",
        "# df_accuracy = df_accuracy.sort_values(by='Accuracy', ascending = False)\n",
        "df_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "6cwbhqe07k6e",
        "outputId": "887487e6-900c-48db-9374-9bd74c8c47e5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Method  Accuracy\n",
              "0       Logistic Regression OVR  0.937377\n",
              "1        Multinomial Regression  0.938363\n",
              "2        Naive Bayes (Gaussian)  0.477811\n",
              "3       Naive Bayes (Bernoulli)  0.873274\n",
              "4  Linear Discriminant Analysis  0.925049\n",
              "5                Linear SVM OVR  0.933925"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bafcf194-0e5d-4f73-9c81-8287298a1775\" 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>Method</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression OVR</td>\n",
              "      <td>0.937377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Multinomial Regression</td>\n",
              "      <td>0.938363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Naive Bayes (Gaussian)</td>\n",
              "      <td>0.477811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Naive Bayes (Bernoulli)</td>\n",
              "      <td>0.873274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.925049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Linear SVM OVR</td>\n",
              "      <td>0.933925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-bafcf194-0e5d-4f73-9c81-8287298a1775 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bafcf194-0e5d-4f73-9c81-8287298a1775');\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",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    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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              "      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",
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              "    (() => {\n",
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              "        document.querySelector('#df-b3c9fd67-9c2d-4306-8990-56ec6a84c7b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_a6900c0a-0eb6-4e24-9f68-7411f4ce295a\">\n",
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              "        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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        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('df_accuracy')\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a6900c0a-0eb6-4e24-9f68-7411f4ce295a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_accuracy');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_accuracy",
              "summary": "{\n  \"name\": \"df_accuracy\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Method\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Logistic Regression OVR\",\n          \"Multinomial Regression\",\n          \"Linear SVM OVR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.182839545179537,\n        \"min\": 0.47781065088757396,\n        \"max\": 0.9383629191321499,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9373767258382643,\n          0.9383629191321499,\n          0.9339250493096647\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "colors = sns.color_palette(\"deep\", len(results['Method']))\n",
        "bars = plt.bar(df_accuracy['Method'], df_accuracy['Accuracy'], color=colors)\n",
        "\n",
        "for bar, accuracy in zip(bars, df_accuracy['Accuracy']): # Add accuracy values on top of each bar\n",
        "    plt.text(\n",
        "        bar.get_x() + bar.get_width()/2,\n",
        "        bar.get_height() + 0.01,\n",
        "        f'{accuracy:.4f}',\n",
        "        ha='center',\n",
        "        fontweight='bold'\n",
        "    )\n",
        "\n",
        "plt.yticks(np.arange(0, 1.1, 0.1)) # Add more y-axis ticks\n",
        "plt.ylim(0, 1.05)\n",
        "plt.ylabel('Accuracy Score')\n",
        "plt.title('Classification Accuracy by Method')\n",
        "\n",
        "plt.tight_layout() # Adjust layout"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "QcDCpvau94LG",
        "outputId": "61a70cdf-a320-41fd-ca4b-cfafe563da85"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The confusion matrix's across all methods are given below to compare."
      ],
      "metadata": {
        "id": "aKHTFjSr7fwS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(3, 2, figsize=(20, 20)) # Create a 3x2 grid of subplots\n",
        "axes = axes.flatten() # Flatten the axes for easier iteration\n",
        "\n",
        "colors = ['Blues', 'Greens', 'Reds', 'Purples', 'binary', 'Oranges']\n",
        "y_preds = [y_pred1, y_pred2, y_pred3, y_pred4, y_pred5, y_pred6]\n",
        "cms = [confusion_matrix(y_test, y_preds[i]) for i in range(len(y_preds))]\n",
        "titles = results['Method']\n",
        "\n",
        "for i, ax in enumerate(axes[:len(cms)]):\n",
        "    sns.heatmap(cms[i], annot=True, fmt='d', cmap=colors[i], ax=ax)\n",
        "    ax.set_xlabel(\"Predicted\")\n",
        "    ax.set_ylabel(\"Actual\")\n",
        "    ax.set_title(titles[i])\n",
        "\n",
        "plt.tight_layout() # Adjust layout so the subplots don't overlap\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fND1Vefh4a97",
        "outputId": "2679f63c-a384-432d-de6d-e81bcfd2f903"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 12 Axes>"
            ],
            "image/png": 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
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*End of Notebook.*"
      ],
      "metadata": {
        "id": "81jHln9UBAKw"
      }
    }
  ]
}