Classification.ipynb

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        {
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          "text": [
            "Hello World\n"
          ]
        }
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      "source": [
        "print('Hello World')"
      ]
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      "cell_type": "code",
      "source": [
        "# Location: https://mitu.co.in/dataset\n",
        "# Download: ingredients.csv     Salary_data.csv"
      ],
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        "id": "0zGh-Pd4ipLE"
      },
      "execution_count": null,
      "outputs": []
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        "import pandas as pd"
      ],
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      "source": [
        "data = pd.read_csv('ingredients.csv')\n",
        "data"
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            "text/plain": [
              "    sweetness  crunchiness ingredients      class\n",
              "0           1            1      cheese    protein\n",
              "1           3            1        fish    protein\n",
              "2           2            2      shrimp    protein\n",
              "3           1            4       bacon    protein\n",
              "4           3            5        nuts    protein\n",
              "5           1            9     lettuce  vegetable\n",
              "6           2            8    cucumber  vegetable\n",
              "7           3            7  green bean  vegetable\n",
              "8           5           10      carrot  vegetable\n",
              "9           3           10      celery  vegetable\n",
              "10         10            9       apple      fruit\n",
              "11         10            7        pear      fruit\n",
              "12          9            5       grape      fruit\n",
              "13          9            2      orange      fruit\n",
              "14         10            2      banana      fruit"
            ],
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              "      <th>sweetness</th>\n",
              "      <th>crunchiness</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>cheese</td>\n",
              "      <td>protein</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
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              "      <td>fish</td>\n",
              "      <td>protein</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
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              "      <td>shrimp</td>\n",
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              "      <th>3</th>\n",
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              "      <td>bacon</td>\n",
              "      <td>protein</td>\n",
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              "      <th>4</th>\n",
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              "      <td>nuts</td>\n",
              "      <td>protein</td>\n",
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              "      <th>5</th>\n",
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              "      <td>9</td>\n",
              "      <td>lettuce</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>cucumber</td>\n",
              "      <td>vegetable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>green bean</td>\n",
              "      <td>vegetable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>carrot</td>\n",
              "      <td>vegetable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>celery</td>\n",
              "      <td>vegetable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>apple</td>\n",
              "      <td>fruit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>pear</td>\n",
              "      <td>fruit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>grape</td>\n",
              "      <td>fruit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>orange</td>\n",
              "      <td>fruit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>banana</td>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"sweetness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          3,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crunchiness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ingredients\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"celery\",\n          \"pear\",\n          \"cheese\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"protein\",\n          \"vegetable\",\n          \"fruit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 8
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    {
      "cell_type": "code",
      "source": [
        "# input data\n",
        "x  = data[['sweetness','crunchiness']]\n",
        "\n",
        "# output data\n",
        "y = data['class']"
      ],
      "metadata": {
        "id": "ptt3MkXKnkOW"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# import the class\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# create the object\n",
        "tree = DecisionTreeClassifier(criterion='entropy')\n",
        "\n",
        "# train the algorithm\n",
        "tree.fit(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "04ZaJlvCos7H",
        "outputId": "bd14acbb-e121-4520-ea91-9edca47aed9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "UhAFBSVKqVEv",
        "outputId": "1a641392-3e1f-4ba6-93e5-8639a7757558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
              "      <script>\n",
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "tomato = pd.DataFrame({'sweetness':[9],'crunchiness':[6]})"
      ],
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        "id": "Vbcl12miq4Qm"
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      "execution_count": null,
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        "tomato"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sweetness  crunchiness\n",
              "0          9            6"
            ],
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              "type": "dataframe",
              "variable_name": "tomato",
              "summary": "{\n  \"name\": \"tomato\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"sweetness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 9,\n        \"max\": 9,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crunchiness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree.predict(tomato)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJmwbrEcrZy3",
        "outputId": "61518299-43b7-462f-ee04-80b82b4c34e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['fruit'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import plot_tree\n",
        "plot_tree(tree, fontsize=6, feature_names=x.columns);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "P8BjtW0VslUs",
        "outputId": "943d2796-9ece-4425-b56c-c3c9180c1639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vuESsadYt4rx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}