DECISION_TREE_on_Slump_dataset.ipynb

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      "source": [
        "#**DECISION TREE REGRESSION MODEL** on Slump dataset With ScikitLearn"
      ],
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        "id": "EdRInz84957r"
      }
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        "<a href=\"https://colab.research.google.com/github/dphi-official/Micro-Courses/blob/master/Supervised_Algorithms_Regression/Exercises/Decision_Tree_Exercise.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": "markdown",
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      "source": [
        "## Tasks\n",
        "1. Load the data from https://raw.githubusercontent.com/dphi-official/Datasets/master/slump_data.csv\n",
        "2. Separate it into input and target variables\n",
        "3. Split the data into train and test sets\n",
        "4. Create and train a Decision Tree Regressor\n",
        "5. Calculate the MSE score\n",
        "6. Calculate the R2 score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Load the data from https://raw.githubusercontent.com/dphi-official/Datasets/master/slump_data.csv"
      ],
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        "id": "L9EAQnJW9U3U"
      }
    },
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      },
      "source": [
        "full_data=pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/slump_data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "full_data.shape"
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      "execution_count": 4,
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          "data": {
            "text/plain": [
              "(103, 8)"
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          "metadata": {},
          "execution_count": 4
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      "cell_type": "code",
      "source": [
        "full_data.head()"
      ],
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      "execution_count": 5,
      "outputs": [
        {
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          "data": {
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              "   Cement   Slag  Fly ash  Water    SP  Coarse Aggr.  Fine Aggr.  SLUMP(cm)\n",
              "0   273.0   82.0    105.0  210.0   9.0         904.0       680.0       23.0\n",
              "1   163.0  149.0    191.0  180.0  12.0         843.0       746.0        0.0\n",
              "2   162.0  148.0    191.0  179.0  16.0         840.0       743.0        1.0\n",
              "3   162.0  148.0    190.0  179.0  19.0         838.0       741.0        3.0\n",
              "4   154.0  112.0    144.0  220.0  10.0         923.0       658.0       20.0"
            ],
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              "      <th></th>\n",
              "      <th>Cement</th>\n",
              "      <th>Slag</th>\n",
              "      <th>Fly ash</th>\n",
              "      <th>Water</th>\n",
              "      <th>SP</th>\n",
              "      <th>Coarse Aggr.</th>\n",
              "      <th>Fine Aggr.</th>\n",
              "      <th>SLUMP(cm)</th>\n",
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              "      <th>0</th>\n",
              "      <td>273.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>9.0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>149.0</td>\n",
              "      <td>191.0</td>\n",
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              "      <td>148.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>838.0</td>\n",
              "      <td>741.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>154.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>923.0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "full_data",
              "summary": "{\n  \"name\": \"full_data\",\n  \"rows\": 103,\n  \"fields\": [\n    {\n      \"column\": \"Cement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.87723002688581,\n        \"min\": 137.0,\n        \"max\": 374.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          165.0,\n          273.0,\n          314.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Slag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.461363389966145,\n        \"min\": 0.0,\n        \"max\": 193.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          40.9,\n          153.4,\n          82.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fly ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.41807955724846,\n        \"min\": 0.0,\n        \"max\": 260.0,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          105.0,\n          178.0,\n          111.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Water\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.208157999282548,\n        \"min\": 160.0,\n        \"max\": 240.0,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          174.0,\n          210.0,\n          193.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8075297845861376,\n        \"min\": 4.4,\n        \"max\": 19.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          7.7,\n          4.7,\n          6.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coarse Aggr.\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88.39139303597541,\n        \"min\": 708.0,\n        \"max\": 1049.9,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          879.0,\n          883.0,\n          881.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fine Aggr.\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.342117108078135,\n        \"min\": 640.6,\n        \"max\": 902.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          815.0,\n          749.0,\n          799.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SLUMP(cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.750844374695014,\n        \"min\": 0.0,\n        \"max\": 29.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          2.0,\n          21.25,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Separate it into input and target variables\n"
      ],
      "metadata": {
        "id": "aJl5ciUH9Yjq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=full_data.drop('SLUMP(cm)', axis =1)\n",
        "y=full_data['SLUMP(cm)']"
      ],
      "metadata": {
        "id": "Mg5UnpfbYjLb"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Split the data into train and test sets\n"
      ],
      "metadata": {
        "id": "D5ookvdF9oit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.20, random_state=101)"
      ],
      "metadata": {
        "id": "sagRtUdkYtw1"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Create and train a Decision Tree Regressor\n"
      ],
      "metadata": {
        "id": "jf0IeRq39s4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor()\n",
        "dtr.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "d1oLD2ydZJIj",
        "outputId": "c5015b29-444c-4b06-b194-dc8fcde9d89e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Calculate the MSE score\n"
      ],
      "metadata": {
        "id": "1OKmZ-by9yJs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "y_pred = dtr.predict(x_test)\n",
        "mse = metrics.mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squrared Error :\", mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kBDRmhsFZspy",
        "outputId": "7c21e9e7-7f5c-4d13-bd43-1dcbdc88ce09"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squrared Error : 67.80654761904762\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. Calculate the R2 score"
      ],
      "metadata": {
        "id": "cL64uGW391FD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2=r2_score(y_test, y_pred)\n",
        "print(\"R2 score :\", r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QenH1BLVa3kl",
        "outputId": "6c37f4ff-2a02-4e74-c33e-4f3fd3d828c2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score : -0.11452951234398223\n"
          ]
        }
      ]
    }
  ]
}