Digits_model.ipynb

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    "colab": {
      "name": "Digits_model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPPSWYGsbTQSuh4yzMhgAgV",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandradithya8/Machine_learning-Projects/blob/master/Digits_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "BbVggV8muJQl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import load_digits"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-B6IzDuivfzz",
        "colab_type": "code",
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      },
      "source": [
        "digits=load_digits()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
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        },
        "outputId": "1d269b86-514e-490b-a59d-275277087409"
      },
      "source": [
        "dir(digits)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'images', 'target', 'target_names']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
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        "outputId": "33dc9313-2e20-49b4-98fd-7f225e0ed639"
      },
      "source": [
        "digits.target_names"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
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          "height": 218
        },
        "outputId": "2e4416de-1e58-4b77-994f-652e9bacf9f8"
      },
      "source": [
        "df=pd.DataFrame(digits.data)\n",
        "df['target']=digits.target\n",
        "df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
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              "[5 rows x 65 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adJkHze1wC_k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "97704fe3-dae5-4e07-e205-7bbdf24ec019"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.gray\n",
        "for i in range(0,5):\n",
        "  plt.matshow(digits.images[i])"
      ],
      "execution_count": 21,
      "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": {
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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_mhh7wSw0Ch",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "ac1b0aa1-b34d-4747-86e8-7b3016fb9a63"
      },
      "source": [
        "x=df.drop('target',axis='columns')\n",
        "x.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0    1    2     3     4     5    6   ...   57   58    59    60    61   62   63\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  ...  0.0  6.0  13.0  10.0   0.0  0.0  0.0\n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  ...  0.0  0.0  11.0  16.0  10.0  0.0  0.0\n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  ...  0.0  0.0   3.0  11.0  16.0  9.0  0.0\n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  ...  0.0  7.0  13.0  13.0   9.0  0.0  0.0\n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  ...  0.0  0.0   2.0  16.0   4.0  0.0  0.0\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wj4vMGOhxETw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y=df['target']"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVHGR2wAxuo0",
        "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(x,y,train_size=0.8)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7I_TGZqTyEv0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "6fd26e2f-50fe-4613-c2c9-b91d4287bd6f"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model=SVC(gamma=0.001)\n",
        "model.fit(x_train,y_train)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvKjZRmDyTTO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1b252966-32ee-45c9-d91d-808f38a4165d"
      },
      "source": [
        "model.score(x_test,y_test)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9861111111111112"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    }
  ]
}