logistic_regression_mnist.ipynb

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        "# Logistic Regression\n",
        "You should build a machine learning pipeline using a logistic regression model. In particular, you should do the following:\n",
        "- Load the `mnist` dataset using [Pandas](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html). You can find this dataset in the datasets folder.\n",
        "- Split the dataset into training and test sets using [Scikit-Learn](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). \n",
        "- Train and test a logistic regression model using [Scikit-Learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\n",
        "- Check the documentation to identify the most important hyperparameters, attributes, and methods of the model. Use them in practice."
      ]
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      "source": [
        "## Importing Libraries\n",
        "## Loading the dataset\n",
        "## Spliting the dataset into training and test sets\n",
        "## Training the logistic regression Model\n",
        "## Testing the logistic regression model"
      ]
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    {
      "cell_type": "code",
      "source": [
        "## Importing Libraries\n",
        "import pandas as pd\n",
        "import sklearn.model_selection\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
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        "id": "7jchuML_yr_t"
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      "source": [
        "## Loading the dataset\n",
        "df = pd.read_csv('mnist.csv') \n",
        "df = df.set_index(\"id\")\n",
        "df.head()"
      ],
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      "source": [
        "## Spliting the dataset into training and test sets\n",
        "\n",
        "x = df.drop([\"class\"],axis=1)\n",
        "y = df[\"class\"]\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.3, random_state=0)\n",
        "\n",
        "print(\"df:\", df.shape)\n",
        "print(\"X_train:\", x_train.shape)\n",
        "print(\"X_test:\", x_test.shape)\n",
        "print(\"y_train:\", y_train.shape)\n",
        "print(\"y_test:\", y_test.shape)\n"
      ],
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      "id": "dEtefK3YzGaH",
      "execution_count": 12,
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          "text": [
            "df: (4000, 785)\n",
            "X_train: (2800, 784)\n",
            "X_test: (1200, 784)\n",
            "y_train: (2800,)\n",
            "y_test: (1200,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Training the logistic regression Model\n",
        "clf = LogisticRegression(max_iter = 30)\n",
        "fitted_model = clf.fit(x_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRX5TGuBL6HI",
        "outputId": "967e846a-11d0-44f0-cd9f-ffb6167b5d1e"
      },
      "id": "uRX5TGuBL6HI",
      "execution_count": 19,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training Accuracy : \", clf.score(x_train, y_train))\n",
        "print(\"Testing Accuracy  : \",  clf.score(x_test, y_test))\n"
      ],
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      "id": "eThFa5x71Jug",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy :  0.9778571428571429\n",
            "Testing Accuracy  :  0.8683333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict\n",
        "y_pred = clf.predict(x_test)\n",
        "y_pred\n",
        "\n",
        "\n"
      ],
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