Logistic_regression2.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Logistic_regression2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNbgRIv6aLc+cGSVoh43+qS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandradithya8/Machine-Learning/blob/master/Logistic_regression2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZODiUB6sNZn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_digits # sklearn has some predefined datasets\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8pEf64KtgYW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "digits=load_digits()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rovgrkd_tzo-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f2b9e9d3-4b5b-4ee7-c50a-16e350ec24a2"
      },
      "source": [
        "dir(digits)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'images', 'target', 'target_names']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4XwZUZQt1u2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "9823e785-e6dc-43d9-aeb1-07c1d060a8fd"
      },
      "source": [
        "digits.data[0]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
              "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
              "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
              "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
              "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcAgo6t30vhw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "e62e0402-f143-4efc-9cce-fcc2132ae990"
      },
      "source": [
        "plt.gray\n",
        "plt.matshow(digits.images[0])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7888316400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAECCAYAAADXWsr9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMKklEQVR4nO3d+4tc9RnH8c/HTeJ6SU1rrIqRmpYaCEpNTG1FkTZBiVXSQkuNoKXSklJaUZSKFov1HxD7QxHESwWj4i1QbL1RIyKkahLjLYnFiGKCukq8xFCTrHn6w5yUNGzds/F8vzuZ5/2CIbOzs/M8k+Qz3zOz55zHESEAg+2gyW4AQHkEHUiAoAMJEHQgAYIOJEDQgQT6Iui2F9t+1fZrtq8uXOs22yO2Xy5ZZ696x9teaXu97VdsX1a43rDtZ22/0NS7vmS9puaQ7edtP1S6VlPvDdsv2V5ne3XhWjNs3297o+0Ntk8vWGtO85z2XD62fXknDx4Rk3qRNCRpk6SvS5om6QVJcwvWO0vSfEkvV3p+x0qa31yfLulfhZ+fJR3eXJ8q6RlJ3y38HK+QdJekhyr9nb4haWalWndI+mVzfZqkGZXqDkl6R9LXuni8fljRT5P0WkS8HhE7Jd0j6YelikXEU5K2lnr8Meq9HRFrm+vbJG2QdFzBehERnzRfTm0uxfaKsj1L0nmSbilVY7LYPkK9heFWSYqInRHxYaXyiyRtiog3u3iwfgj6cZLe2uvrzSoYhMlk+wRJ89RbZUvWGbK9TtKIpMcjomS9GyVdJWl3wRr7CkmP2V5je1nBOrMlvSfp9uatyS22DytYb29LJd3d1YP1Q9BTsH24pAckXR4RH5esFRGfRcQpkmZJOs32SSXq2D5f0khErCnx+J/jzIiYL+lcSb+xfVahOlPUe5t3U0TMk7RdUtHPkCTJ9jRJSyTd19Vj9kPQt0g6fq+vZzW3DQzbU9UL+fKIeLBW3WYzc6WkxYVKnCFpie031HvLtdD2nYVq/VdEbGn+HJG0Qr23fyVslrR5ry2i+9ULfmnnSlobEe929YD9EPTnJH3T9uzmlWyppL9Ock+dsW313uNtiIgbKtQ7yvaM5vohks6WtLFErYi4JiJmRcQJ6v27PRERF5WotYftw2xP33Nd0jmSivwGJSLekfSW7TnNTYskrS9Rax8XqsPNdqm3aTKpImLU9m8lPareJ423RcQrperZvlvS9yTNtL1Z0nURcWupeuqtehdLeql53yxJv4+Ivxeqd6ykO2wPqfdCfm9EVPm1VyVHS1rRe/3UFEl3RcQjBetdKml5swi9LumSgrX2vHidLelXnT5u81E+gAHWD5vuAAoj6EACBB1IgKADCRB0IIG+Cnrh3RknrRb1qDfZ9foq6JJq/mVW/YejHvUms16/BR1AAUV2mJnmg2NYEz/IZ5d2aKoO7ryfrmuNzpz4cxv9dLumDO/fgU/HHDPxo2q3bR3V9K/s346PW7bPmPDP7N62XQdN37/nN7x514R/Zufuf2vaQYfsV73YNTrhn6n5f/OL1PtU27Uzdnjf24vsAjusw/QdLyrx0H3h/R8XO8nImH535T1V6/1hTbHTAYzpxCverlpv9J3OjhXpO8/EP8a8nU13IAGCDiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJtAp6zZFJALo3btCbkwz+Wb1T0M6VdKHtuaUbA9CdNit61ZFJALrXJuhpRiYBg6qzg1qaA+WXSdKwDu3qYQF0oM2K3mpkUkTcHBELImJBzcP5AIyvTdAHemQSkMG4m+61RyYB6F6r9+jNnLBSs8IAFMaecUACBB1IgKADCRB0IAGCDiRA0IEECDqQAEEHEigyqWXQ1Z6csnT6B1Xr3Tjjk6r1/rb20ar1Tv3jr6vWm3nzqqr1xsKKDiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJEHQgAYIOJEDQgQTajGS6zfaI7ZdrNASge21W9L9IWly4DwAFjRv0iHhK0tYKvQAohPfoQALMXgMS6GxFZ/Ya0L/YdAcSaPPrtbslrZI0x/Zm278o3xaALrUZsnhhjUYAlMOmO5AAQQcSIOhAAgQdSICgAwkQdCABgg4kQNCBBAZi9trowlOr1ls6fV3VeucuXlq13hEvbqxa76dPL6pab+u8z6rWm1m12thY0YEECDqQAEEHEiDoQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpBAm5NDHm97pe31tl+xfVmNxgB0p82+7qOSroyItbanS1pj+/GIWF+4NwAdaTN77e2IWNtc3yZpg6TjSjcGoDsTeo9u+wRJ8yQ9U6IZAGW0PkzV9uGSHpB0eUR8PMb3mb0G9KlWK7rtqeqFfHlEPDjWfZi9BvSvNp+6W9KtkjZExA3lWwLQtTYr+hmSLpa00Pa65vKDwn0B6FCb2WtPS3KFXgAUwp5xQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSGIjZa58eWfdpXDtyctV6uyvPQqvtuZe+MdktDDxWdCABgg4kQNCBBAg6kABBBxIg6EACBB1IgKADCRB0IAGCDiTQ5iyww7aftf1CM3vt+hqNAehOm53Ed0haGBGfNOd3f9r2wxHxz8K9AehIm7PAhqRPmi+nNpco2RSAbrWd1DJke52kEUmPRwSz14ADSKugR8RnEXGKpFmSTrN90r73sb3M9mrbq3dpR9d9AvgCJvSpe0R8KGmlpMVjfI/Za0CfavOp+1G2ZzTXD5F0tqTBPhMCMGDafOp+rKQ7bA+p98Jwb0Q8VLYtAF1q86n7i5LmVegFQCHsGQckQNCBBAg6kABBBxIg6EACBB1IgKADCRB0IIHBmL325bqvV8tXnV613ol6tmq92qYcsbNqvdGPplWt1w9Y0YEECDqQAEEHEiDoQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpBA66A3Qxyet82JIYEDzERW9MskbSjVCIBy2o5kmiXpPEm3lG0HQAltV/QbJV0laXfBXgAU0mZSy/mSRiJizTj3Y/Ya0KfarOhnSFpi+w1J90haaPvOfe/E7DWgf40b9Ii4JiJmRcQJkpZKeiIiLireGYDO8Ht0IIEJnUoqIp6U9GSRTgAUw4oOJEDQgQQIOpAAQQcSIOhAAgQdSICgAwkQdCCBgZi9NvxB3YPqvn3ypqr1PqpaTZpyzNFV610w93OPl+rcvQ+fWbVeP2BFBxIg6EACBB1IgKADCRB0IAGCDiRA0IEECDqQAEEHEiDoQAKtdoFtTvW8TdJnkkYjYkHJpgB0ayL7un8/It4v1gmAYth0BxJoG/SQ9JjtNbaXlWwIQPfabrqfGRFbbH9V0uO2N0bEU3vfoXkBWCZJwzq04zYBfBGtVvSI2NL8OSJphaTTxrgPs9eAPtVmmuphtqfvuS7pHEkvl24MQHfabLofLWmF7T33vysiHinaFYBOjRv0iHhd0rcq9AKgEH69BiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJEHQggYGYvfalV+tOJ7tu1kNV6/1s2RVV60390XtV69U2+5pVk91CdazoQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSIOhAAgQdSKBV0G3PsH2/7Y22N9g+vXRjALrTdl/3P0l6JCJ+YnuaxIQG4EAybtBtHyHpLEk/l6SI2ClpZ9m2AHSpzab7bEnvSbrd9vO2b2kGOfwP28tsr7a9epd2dN4ogP3XJuhTJM2XdFNEzJO0XdLV+96JkUxA/2oT9M2SNkfEM83X96sXfAAHiHGDHhHvSHrL9pzmpkWS1hftCkCn2n7qfqmk5c0n7q9LuqRcSwC61iroEbFO0oLCvQAohD3jgAQIOpAAQQcSIOhAAgQdSICgAwkQdCABgg4kMBCz13a/uLFqvQtuurJqvWuvvLtqvRs3Lapa77lThqrWy4gVHUiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSIOhAAgQdSGDcoNueY3vdXpePbV9eozkA3Rh3F9iIeFXSKZJke0jSFkkrCvcFoEMT3XRfJGlTRLxZohkAZUw06Esl1T3CAsAX1jrozTndl0i67/98n9lrQJ+ayIp+rqS1EfHuWN9k9hrQvyYS9AvFZjtwQGoV9GZM8tmSHizbDoAS2o5k2i7pyMK9ACiEPeOABAg6kABBBxIg6EACBB1IgKADCRB0IAGCDiRA0IEEHBHdP6j9nqT9OWZ9pqT3O26nH2pRj3q16n0tIo7a98YiQd9ftldHxIJBq0U96k12PTbdgQQIOpBAvwX95gGtRT3qTWq9vnqPDqCMflvRARRA0IEECDqQAEEHEiDoQAL/AV9ErgcL6cKUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaYOIGNZ1Azo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e2b70f3-d569-4a0e-c869-93f7e081a3b8"
      },
      "source": [
        "plt.gray\n",
        "for i in range(5):\n",
        "  plt.matshow(digits.images[i])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A73oXzrm1YPP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "05b284d6-29aa-4397-d272-9ec09fc9d979"
      },
      "source": [
        "digits.target[0:5]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuZjKAMY1mjn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(digits.data,digits.target,test_size=0.2)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVv7hDFv2UJE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "118db320-3d3d-4ab7-953a-aae79692ba89"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "reg=LogisticRegression()\n",
        "reg.fit(x_train,y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmTu2KJz28eD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "52312762-a793-4137-de75-e864c6c47936"
      },
      "source": [
        "reg.score(x_test,y_test)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiya8e212_Yt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "64a98a8a-30c4-4b97-c876-bebf4ea689aa"
      },
      "source": [
        "reg.predict(digits.data[0:5])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdim1Y6j4NDQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2c5d1301-e528-48ae-d412-fd3d40e0dade"
      },
      "source": [
        "# CONFUSION MATRIX is used to show where our model misses out the accuracy.\n",
        "from sklearn.metrics import confusion_matrix\n",
        "y_predicted=reg.predict(x_test)\n",
        "cm=confusion_matrix(y_test,y_predicted)\n",
        "cm\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[32,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 35,  0,  0,  0,  0,  0,  0,  3,  0],\n",
              "       [ 0,  0, 32,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 37,  0,  1,  0,  1,  1,  0],\n",
              "       [ 0,  0,  0,  0, 39,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  1,  0, 34,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0, 35,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0, 35,  0,  0],\n",
              "       [ 0,  1,  0,  1,  0,  1,  0,  0, 38,  0],\n",
              "       [ 0,  0,  0,  1,  0,  0,  0,  0,  1, 31]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_f9P-R55upi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "868c5c7b-d92f-490a-a437-a159e3ef17c1"
      },
      "source": [
        "import seaborn as sn\n",
        "#plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm,annot=True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}