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      "name": "python"
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      "cell_type": "markdown",
      "source": [
        "Import packages"
      ],
      "metadata": {
        "id": "Jn5AhozlBRXF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "082i0jfjBSeg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Task 1 - Import train and test set of success data**"
      ],
      "metadata": {
        "id": "_3qnTaXoBdqv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = pd.read_csv(\"https://sxbin.gay/u/Joryn/PythonAi%20-%201/train_data_success.csv\")\n",
        "test_data = pd.read_csv(\"https://sxbin.gay/u/Joryn/PythonAi%20-%201/test_data_success.csv\")"
      ],
      "metadata": {
        "id": "BJcAIvtFBhOh"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "train_data.head()"
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              "   Unnamed: 0   age   interest  success  age_groups\n",
              "0         224  35.0  69.922636        1           3\n",
              "1          38  13.0  18.069521        0           1\n",
              "2         200   9.0  18.603253        0           1\n",
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              "                                                     [key], {});\n",
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              "   Unnamed: 0   age   interest  success  age_groups\n",
              "0          54  26.0  46.679500        1           2\n",
              "1         153  30.0  30.941048        0           2\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-cb957783-8ac7-4e6f-b72c-939b6f115457 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb957783-8ac7-4e6f-b72c-939b6f115457');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Divide the columns to features and label."
      ],
      "metadata": {
        "id": "sITzgGd9CGjI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "success_features_train = train_data.iloc[:,[1,2]]\n",
        "success_label_train =train_data['success']\n",
        "success_features_test = test_data.iloc[:,[1,2]]\n",
        "success_label_test = test_data['success']"
      ],
      "metadata": {
        "id": "nmhCGhqiCMoV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Task 2 - KNN classifier**"
      ],
      "metadata": {
        "id": "VysWyOJrDMSg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the packages."
      ],
      "metadata": {
        "id": "ZBkViITSw0qM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "4hgrxl1gDNAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit the classifier, set the parameters."
      ],
      "metadata": {
        "id": "liGdEzxGw2ww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neigh = KNeighborsClassifier(n_neighbors=5,metric=\"euclidean\")\n",
        "#fit the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClcwHEZHDPXw",
        "outputId": "2100a657-7b39-4549-939d-6a7abdf02c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions for test set."
      ],
      "metadata": {
        "id": "7cfe-dUIxDvN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#make prediction"
      ],
      "metadata": {
        "id": "UcUPsAYYDUNQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJEN5bcfDZbg",
        "outputId": "ca1615f2-18e5-43d3-eed2-30c2271f0988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show the confusion matrix."
      ],
      "metadata": {
        "id": "G022eIyLxHqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "is77adN6DbUx",
        "outputId": "e36329e1-4010-4522-d36f-00fa38bba48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[33  7]\n",
            " [ 3 47]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate the learned measures."
      ],
      "metadata": {
        "id": "M4BXYICBxKVh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision: \")\n",
        "print(\"Recall: \")\n",
        "print(\"Accuracy: \" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oq3BiOGKDe1P",
        "outputId": "d6be638e-d952-4fae-b09f-48a645285ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  0.8703703703703703\n",
            "Recall:  0.94\n",
            "Accuracy:  0.8888888888888888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC curve and AUC score"
      ],
      "metadata": {
        "id": "S1w5vHGKxNDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#make the roc curve"
      ],
      "metadata": {
        "id": "evJgSPz7DkoQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.plot(fpr,tpr,linewidth=2.0)\n",
        "#plot the random classifier line\n",
        "x = np.linspace(0,1,100)\n",
        "y = x\n",
        "plt.plot(x, y, label = \"random classifier\")\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlim([0,1])\n",
        "plt.ylim([0,1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "YRPv51gYDulQ",
        "outputId": "4a8527a7-6859-4cfb-de47-8db73a2c7eba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#auc score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvdrSU6aDwrQ",
        "outputId": "fd1dde4a-403d-41d7-9bca-cb079473de13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.92475"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Task 3 - Decision tree**\n",
        "\n"
      ],
      "metadata": {
        "id": "bkl4wca0D9G_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the package"
      ],
      "metadata": {
        "id": "6xjuqxFWyY0C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree"
      ],
      "metadata": {
        "id": "Mtl9GJ19EgTA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set the classifier"
      ],
      "metadata": {
        "id": "FFeu8-ndyaXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create the classifier"
      ],
      "metadata": {
        "id": "0RZCZmTGEzLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train it on the training data"
      ],
      "metadata": {
        "id": "fWdjwHVEycMu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#fit the modell"
      ],
      "metadata": {
        "id": "I9MFbj4vE164"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion matrix and accuracy measures"
      ],
      "metadata": {
        "id": "ca8LMhWAyfij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#conffusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubsY4RGeHsf6",
        "outputId": "5404f222-24a0-4311-c3b7-f6031e9dee88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[32  8]\n",
            " [ 2 48]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision: \")\n",
        "print(\"Recall: \")\n",
        "print(\"Accuracy: \" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hey_W113HtmI",
        "outputId": "1800252c-0346-43e5-90cc-20a900a50c86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  0.8571428571428571\n",
            "Recall:  0.96\n",
            "Accuracy:  0.8888888888888888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC curve and AUC score"
      ],
      "metadata": {
        "id": "6_lNqT526Kp3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#make the roc curve"
      ],
      "metadata": {
        "id": "mvL15NSlH_0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.plot(fpr_tree,tpr_tree,linewidth=2.0)\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlim([0,1])\n",
        "plt.ylim([0,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "5On_KWEPILj4",
        "outputId": "21dffbef-e099-4a32-ba51-67b5605c6b9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#auc score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42L2Iqx3ISLA",
        "outputId": "05ca75cb-e24f-4f0d-9c1c-2b34cc0991f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.94775"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the tree"
      ],
      "metadata": {
        "id": "JpCK7YX56OBJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(tree_clf) #tree_clf is the name of the modell we made, change if neccessary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "IegxJyStE6rw",
        "outputId": "4aa9e5b3-99d0-44b1-8c9b-0de09f870d6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.4583333333333333, 0.875, 'X[1] <= 44.062\\ngini = 0.489\\nsamples = 207\\nvalue = [88, 119]'),\n",
              " Text(0.25, 0.625, 'X[0] <= 34.5\\ngini = 0.271\\nsamples = 99\\nvalue = [83, 16]'),\n",
              " Text(0.16666666666666666, 0.375, 'X[1] <= 40.361\\ngini = 0.107\\nsamples = 88\\nvalue = [83, 5]'),\n",
              " Text(0.08333333333333333, 0.125, 'gini = 0.049\\nsamples = 80\\nvalue = [78, 2]'),\n",
              " Text(0.25, 0.125, 'gini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.3333333333333333, 0.375, 'gini = 0.0\\nsamples = 11\\nvalue = [0, 11]'),\n",
              " Text(0.6666666666666666, 0.625, 'X[1] <= 48.217\\ngini = 0.088\\nsamples = 108\\nvalue = [5, 103]'),\n",
              " Text(0.5, 0.375, 'X[0] <= 34.5\\ngini = 0.426\\nsamples = 13\\nvalue = [4, 9]'),\n",
              " Text(0.4166666666666667, 0.125, 'gini = 0.494\\nsamples = 9\\nvalue = [4, 5]'),\n",
              " Text(0.5833333333333334, 0.125, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
              " Text(0.8333333333333334, 0.375, 'X[1] <= 56.947\\ngini = 0.021\\nsamples = 95\\nvalue = [1, 94]'),\n",
              " Text(0.75, 0.125, 'gini = 0.124\\nsamples = 15\\nvalue = [1, 14]'),\n",
              " Text(0.9166666666666666, 0.125, 'gini = 0.0\\nsamples = 80\\nvalue = [0, 80]')]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz\n",
        "dot_data = tree.export_graphviz(tree_clf)\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph.render(\"success\")\n",
        "dot_data = tree.export_graphviz(tree_clf, out_file=None, feature_names=[\"age\", \"interest\"], class_names=[\"successful\", \"unsuccessful\"],  filled=True, rounded=True, special_characters=True)\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "HALNNy5aFBSI",
        "outputId": "e94f3874-b7fb-47fe-e37b-e3826b3efa35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7fb7a9ba6b90>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"977pt\" height=\"433pt\"\n viewBox=\"0.00 0.00 976.50 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 429)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-429 972.5,-429 972.5,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#cbe5f8\" stroke=\"#000000\" d=\"M531.5,-425C531.5,-425 403.5,-425 403.5,-425 397.5,-425 391.5,-419 391.5,-413 391.5,-413 391.5,-354 391.5,-354 391.5,-348 397.5,-342 403.5,-342 403.5,-342 531.5,-342 531.5,-342 537.5,-342 543.5,-348 543.5,-354 543.5,-354 543.5,-413 543.5,-413 543.5,-419 537.5,-425 531.5,-425\"/>\n<text text-anchor=\"start\" x=\"413.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">interest ≤ 44.062</text>\n<text text-anchor=\"start\" x=\"429.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.489</text>\n<text text-anchor=\"start\" x=\"419.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 207</text>\n<text text-anchor=\"start\" x=\"413.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [88, 119]</text>\n<text text-anchor=\"start\" x=\"399.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = unsuccessful</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#ea995f\" stroke=\"#000000\" d=\"M438,-306C438,-306 327,-306 327,-306 321,-306 315,-300 315,-294 315,-294 315,-235 315,-235 315,-229 321,-223 327,-223 327,-223 438,-223 438,-223 444,-223 450,-229 450,-235 450,-235 450,-294 450,-294 450,-300 444,-306 438,-306\"/>\n<text text-anchor=\"start\" x=\"348\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">age ≤ 34.5</text>\n<text text-anchor=\"start\" x=\"344.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.271</text>\n<text text-anchor=\"start\" x=\"338.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 99</text>\n<text text-anchor=\"start\" x=\"332.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [83, 16]</text>\n<text text-anchor=\"start\" x=\"323\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = successful</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M437.7711,-341.8796C431.4667,-333.0534 424.749,-323.6485 418.2476,-314.5466\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"421.0163,-312.4011 412.3558,-306.2981 415.3201,-316.4698 421.0163,-312.4011\"/>\n<text text-anchor=\"middle\" x=\"408.2458\" y=\"-327.2579\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#43a2e6\" stroke=\"#000000\" d=\"M616.5,-306C616.5,-306 488.5,-306 488.5,-306 482.5,-306 476.5,-300 476.5,-294 476.5,-294 476.5,-235 476.5,-235 476.5,-229 482.5,-223 488.5,-223 488.5,-223 616.5,-223 616.5,-223 622.5,-223 628.5,-229 628.5,-235 628.5,-235 628.5,-294 628.5,-294 628.5,-300 622.5,-306 616.5,-306\"/>\n<text text-anchor=\"start\" x=\"498.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">interest ≤ 48.217</text>\n<text text-anchor=\"start\" x=\"514.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.088</text>\n<text text-anchor=\"start\" x=\"504.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 108</text>\n<text text-anchor=\"start\" x=\"502.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 103]</text>\n<text text-anchor=\"start\" x=\"484.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = unsuccessful</text>\n</g>\n<!-- 0&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>0&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M497.2289,-341.8796C503.5333,-333.0534 510.251,-323.6485 516.7524,-314.5466\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"519.6799,-316.4698 522.6442,-306.2981 513.9837,-312.4011 519.6799,-316.4698\"/>\n<text text-anchor=\"middle\" x=\"526.7542\" y=\"-327.2579\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#e78945\" stroke=\"#000000\" d=\"M276,-187C276,-187 165,-187 165,-187 159,-187 153,-181 153,-175 153,-175 153,-116 153,-116 153,-110 159,-104 165,-104 165,-104 276,-104 276,-104 282,-104 288,-110 288,-116 288,-116 288,-175 288,-175 288,-181 282,-187 276,-187\"/>\n<text text-anchor=\"start\" x=\"166.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">interest ≤ 40.361</text>\n<text text-anchor=\"start\" x=\"182.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.107</text>\n<text text-anchor=\"start\" x=\"176.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 88</text>\n<text text-anchor=\"start\" x=\"175\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [83, 5]</text>\n<text text-anchor=\"start\" x=\"161\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = successful</text>\n</g>\n<!-- 1&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M325.8403,-222.8796C312.7684,-213.2774 298.7641,-202.9903 285.3727,-193.1534\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"287.2591,-190.1963 277.1277,-187.0969 283.115,-195.8378 287.2591,-190.1963\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<path fill=\"#399de5\" stroke=\"#000000\" d=\"M446.5,-179.5C446.5,-179.5 318.5,-179.5 318.5,-179.5 312.5,-179.5 306.5,-173.5 306.5,-167.5 306.5,-167.5 306.5,-123.5 306.5,-123.5 306.5,-117.5 312.5,-111.5 318.5,-111.5 318.5,-111.5 446.5,-111.5 446.5,-111.5 452.5,-111.5 458.5,-117.5 458.5,-123.5 458.5,-123.5 458.5,-167.5 458.5,-167.5 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id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#e6843e\" stroke=\"#000000\" d=\"M123,-68C123,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,0 12,0 12,0 123,0 123,0 129,0 135,-6 135,-12 135,-12 135,-56 135,-56 135,-62 129,-68 123,-68\"/>\n<text text-anchor=\"start\" x=\"29.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.049</text>\n<text text-anchor=\"start\" x=\"23.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 80</text>\n<text text-anchor=\"start\" x=\"22\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [78, 2]</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = successful</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M163.5284,-103.9815C150.1604,-94.2394 135.9396,-83.8759 122.6528,-74.193\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"124.6595,-71.3247 114.5165,-68.2637 120.5368,-76.9819 124.6595,-71.3247\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#f5cdb0\" stroke=\"#000000\" d=\"M276,-68C276,-68 165,-68 165,-68 159,-68 153,-62 153,-56 153,-56 153,-12 153,-12 153,-6 159,0 165,0 165,0 276,0 276,0 282,0 288,-6 288,-12 288,-12 288,-56 288,-56 288,-62 282,-68 276,-68\"/>\n<text text-anchor=\"start\" x=\"182.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.469</text>\n<text text-anchor=\"start\" x=\"181\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n<text text-anchor=\"start\" x=\"179\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 3]</text>\n<text text-anchor=\"start\" x=\"161\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = successful</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M220.5,-103.9815C220.5,-95.618 220.5,-86.7965 220.5,-78.3409\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"224.0001,-78.2636 220.5,-68.2637 217.0001,-78.2637 224.0001,-78.2636\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#91c9f1\" stroke=\"#000000\" d=\"M616.5,-187C616.5,-187 488.5,-187 488.5,-187 482.5,-187 476.5,-181 476.5,-175 476.5,-175 476.5,-116 476.5,-116 476.5,-110 482.5,-104 488.5,-104 488.5,-104 616.5,-104 616.5,-104 622.5,-104 628.5,-110 628.5,-116 628.5,-116 628.5,-175 628.5,-175 628.5,-181 622.5,-187 616.5,-187\"/>\n<text text-anchor=\"start\" x=\"518\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">age ≤ 34.5</text>\n<text text-anchor=\"start\" x=\"514.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.426</text>\n<text text-anchor=\"start\" x=\"508.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 13</text>\n<text text-anchor=\"start\" x=\"511\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [4, 9]</text>\n<text text-anchor=\"start\" x=\"484.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = unsuccessful</text>\n</g>\n<!-- 6&#45;&gt;7 -->\n<g id=\"edge7\" class=\"edge\">\n<title>6&#45;&gt;7</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M552.5,-222.8796C552.5,-214.6838 552.5,-205.9891 552.5,-197.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"556.0001,-197.298 552.5,-187.2981 549.0001,-197.2981 556.0001,-197.298\"/>\n</g>\n<!-- 10 -->\n<g id=\"node11\" class=\"node\">\n<title>10</title>\n<path fill=\"#3b9ee5\" stroke=\"#000000\" d=\"M786.5,-187C786.5,-187 658.5,-187 658.5,-187 652.5,-187 646.5,-181 646.5,-175 646.5,-175 646.5,-116 646.5,-116 646.5,-110 652.5,-104 658.5,-104 658.5,-104 786.5,-104 786.5,-104 792.5,-104 798.5,-110 798.5,-116 798.5,-116 798.5,-175 798.5,-175 798.5,-181 792.5,-187 786.5,-187\"/>\n<text text-anchor=\"start\" x=\"668.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">interest ≤ 56.947</text>\n<text text-anchor=\"start\" x=\"684.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.021</text>\n<text text-anchor=\"start\" x=\"678.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 95</text>\n<text text-anchor=\"start\" x=\"677\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 94]</text>\n<text text-anchor=\"start\" x=\"654.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = unsuccessful</text>\n</g>\n<!-- 6&#45;&gt;10 -->\n<g id=\"edge10\" class=\"edge\">\n<title>6&#45;&gt;10</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M611.9578,-222.8796C625.8046,-213.1868 640.6484,-202.7961 654.8212,-192.8752\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"656.8906,-195.6989 663.0758,-187.0969 652.8764,-189.9643 656.8906,-195.6989\"/>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<path fill=\"#d7ebfa\" stroke=\"#000000\" d=\"M446.5,-68C446.5,-68 318.5,-68 318.5,-68 312.5,-68 306.5,-62 306.5,-56 306.5,-56 306.5,-12 306.5,-12 306.5,-6 312.5,0 318.5,0 318.5,0 446.5,0 446.5,0 452.5,0 458.5,-6 458.5,-12 458.5,-12 458.5,-56 458.5,-56 458.5,-62 452.5,-68 446.5,-68\"/>\n<text text-anchor=\"start\" x=\"344.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.494</text>\n<text text-anchor=\"start\" 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488.5,0 616.5,0 616.5,0 622.5,0 628.5,-6 628.5,-12 628.5,-12 628.5,-56 628.5,-56 628.5,-62 622.5,-68 616.5,-68\"/>\n<text text-anchor=\"start\" x=\"523\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"513\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4</text>\n<text text-anchor=\"start\" x=\"511\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 4]</text>\n<text text-anchor=\"start\" x=\"484.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = unsuccessful</text>\n</g>\n<!-- 7&#45;&gt;9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>7&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M552.5,-103.9815C552.5,-95.618 552.5,-86.7965 552.5,-78.3409\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"556.0001,-78.2636 552.5,-68.2637 549.0001,-78.2637 556.0001,-78.2636\"/>\n</g>\n<!-- 11 -->\n<g id=\"node12\" class=\"node\">\n<title>11</title>\n<path fill=\"#47a4e7\" stroke=\"#000000\" d=\"M786.5,-68C786.5,-68 658.5,-68 658.5,-68 652.5,-68 646.5,-62 646.5,-56 646.5,-56 646.5,-12 646.5,-12 646.5,-6 652.5,0 658.5,0 658.5,0 786.5,0 786.5,0 792.5,0 798.5,-6 798.5,-12 798.5,-12 798.5,-56 798.5,-56 798.5,-62 792.5,-68 786.5,-68\"/>\n<text text-anchor=\"start\" x=\"684.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.124</text>\n<text text-anchor=\"start\" x=\"678.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 15</text>\n<text text-anchor=\"start\" x=\"677\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 14]</text>\n<text text-anchor=\"start\" x=\"654.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 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80</text>\n<text text-anchor=\"start\" x=\"847\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 80]</text>\n<text text-anchor=\"start\" x=\"824.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = unsuccessful</text>\n</g>\n<!-- 10&#45;&gt;12 -->\n<g id=\"edge12\" class=\"edge\">\n<title>10&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M785.8017,-103.9815C800.8742,-94.0957 816.9222,-83.5701 831.8716,-73.7651\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"834.0885,-76.4968 840.5309,-68.0856 830.2494,-70.6434 834.0885,-76.4968\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Task 4 - SVM**"
      ],
      "metadata": {
        "id": "20RqM-ZFXMrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm"
      ],
      "metadata": {
        "id": "3BFTPNfgXMMR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize the data."
      ],
      "metadata": {
        "id": "PbHMFBsR7PN8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(success_features_train.iloc[:, 0], success_features_train.iloc[:, 1], c=success_label_train, cmap='winter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jLFyLCn2YpI8",
        "outputId": "39c81d8d-d279-4068-9315-91fcbd5492f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb7aac30fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(success_features_test.iloc[:, 0], success_features_test.iloc[:, 1], c=success_label_test, cmap='winter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Z-Gq0wveY_CG",
        "outputId": "57076a00-6f61-43bd-9426-f2514ba3b493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb7a9a9ee50>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train, make predictions show confusion matrix, calculate accuracy measures."
      ],
      "metadata": {
        "id": "DcNBkd997Rak"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model, fit, prediction and confusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tqmh40FWIaVA",
        "outputId": "ecfc3d48-62dc-475f-d8e2-f82700d8740e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[32  8]\n",
            " [ 8 42]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision: \")\n",
        "print(\"Recall: \")\n",
        "print(\"Accuracy: \" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzlM2OONZJIj",
        "outputId": "02690baa-f551-4632-b3c2-e5b1b70ba965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  0.84\n",
            "Recall:  0.84\n",
            "Accuracy:  0.8222222222222222\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC curve and AUC score"
      ],
      "metadata": {
        "id": "sudMSWqv7YeA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#make the roc curve"
      ],
      "metadata": {
        "id": "qch236WXaaCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.plot(fpr_svm,tpr_svm,linewidth=2.0)\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlim([0,1])\n",
        "plt.ylim([0,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "T4TkxMjAd7Db",
        "outputId": "5c5d03d0-d0be-4dd3-b867-09f76d6ad6d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#auc score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiY9HXfPeWX6",
        "outputId": "0860bf05-a26d-4f2c-e6b2-64996ba6210e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9259999999999999"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Task 5 - Summary**"
      ],
      "metadata": {
        "id": "8qYoTT3deg3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"AUC kNN:\", metrics.roc_auc_score(success_label_test,knn_prob[:,1], average='macro', sample_weight=None))\n",
        "print(\"AUC decision tree:\", metrics.roc_auc_score(success_label_test,tree_prob[:,1], average='macro', sample_weight=None))\n",
        "print(\"AUC SVM:\", metrics.roc_auc_score(success_label_test,svm_prob[:,1], average='macro', sample_weight=None))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhcApi3BeeFp",
        "outputId": "2a77ee17-fb2b-4ca7-8963-ba4c68df9d60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC kNN: 0.92475\n",
            "AUC decision tree: 0.94775\n",
            "AUC SVM: 0.9259999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Task 6 - if you still have some time - Multiclass classification**"
      ],
      "metadata": {
        "id": "1fkIotd8RQb_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mobile_data = pd.read_csv(\"https://sxbin.gay/u/Joryn/PythonAi%20-%201/train_mobile.csv\")"
      ],
      "metadata": {
        "id": "QUqdOJ7dRWiY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data head and number of NaN-s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23hjdcdSRjKG",
        "outputId": "bd10c001-2574-44a0-e0e9-a86b02c9fd6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2000\n",
            "battery_power    0\n",
            "blue             0\n",
            "clock_speed      0\n",
            "dual_sim         0\n",
            "fc               0\n",
            "four_g           0\n",
            "int_memory       0\n",
            "m_dep            0\n",
            "mobile_wt        0\n",
            "n_cores          0\n",
            "pc               0\n",
            "px_height        0\n",
            "px_width         0\n",
            "ram              0\n",
            "sc_h             0\n",
            "sc_w             0\n",
            "talk_time        0\n",
            "three_g          0\n",
            "touch_screen     0\n",
            "wifi             0\n",
            "price_range      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCE3zbh4RnnK",
        "outputId": "6d61d83d-49e7-46c8-faeb-010708728561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',\n",
              "       'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',\n",
              "       'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',\n",
              "       'touch_screen', 'wifi', 'price_range'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count the number of price ranges"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YrfUvKTr2pt",
        "outputId": "7b2336ce-0e82-44e7-daa9-a5283d333f64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    500\n",
            "2    500\n",
            "3    500\n",
            "0    500\n",
            "Name: price_range, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create feature and label space"
      ],
      "metadata": {
        "id": "5RPKdiMKRqU2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split train and test data (use sklearn.selection)"
      ],
      "metadata": {
        "id": "w2_ctojvk41s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#predict with decision tree classifier (also create the model)"
      ],
      "metadata": {
        "id": "PYIFCp24mlCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_yysjiZm1Nw",
        "outputId": "93abb0fb-e80d-4c45-a7d5-92421cab10b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[87  7  0  0]\n",
            " [ 7 96  2  0]\n",
            " [ 0 21 62 13]\n",
            " [ 0  0 11 94]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy score (use sklearn built-in accuracy function)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLN5fQnwoNvn",
        "outputId": "ffbdc268-f378-49f9-c14f-252e6fc5cbae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8475\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#make and test the trees for different depths"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3TnyAq4ne2w",
        "outputId": "d6187076-7e08-421c-efff-fde83dabe727"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 depth tree: 0.4975\n",
            "2 depth tree: 0.785\n",
            "3 depth tree: 0.79\n",
            "4 depth tree: 0.8275\n",
            "5 depth tree: 0.8225\n",
            "6 depth tree: 0.835\n",
            "7 depth tree: 0.84\n",
            "8 depth tree: 0.8425\n",
            "9 depth tree: 0.84\n",
            "10 depth tree: 0.825\n",
            "11 depth tree: 0.815\n",
            "12 depth tree: 0.8275\n",
            "13 depth tree: 0.835\n",
            "14 depth tree: 0.825\n",
            "15 depth tree: 0.815\n",
            "16 depth tree: 0.8325\n",
            "17 depth tree: 0.815\n",
            "18 depth tree: 0.8175\n",
            "19 depth tree: 0.8225\n",
            "20 depth tree: 0.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z0YpkldrcG_t"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}