Case_Study_12.ipynb

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        "<a href=\"https://colab.research.google.com/github/0hnezahn/SMOTE-Comparison/blob/main/Case_Study_12.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "%%capture\n",
        "!pip install smote-variants\n",
        "!pip install imbalanced_databases\n",
        "!pip install -U imbalanced-learn\n",
        "\n",
        "# Please Note that the PkMaker_Data.csv has to be imported before"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "# Author\n",
        "Benjamin M.\\\n",
        "University of Rostock.\\\n",
        "EmailRemoved"
      ]
    },
    {
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      "source": [
        "# Abstract\n",
        "With the help of machine learning, we are able to solve classification problems with a high degree of certainty. However, this becomes difficult as soon as we want to solve such problems of imbalanced datasets. To increase the accuracy of the classification problems with respect to imbalanced datasets, we can use the oversampling algorithms Borderline-SMOTE and CURE SMOTE. This case study will analyze three models consisting of the random forest algorithm or the k-nearest neighbor algorithm. Using these algorithms to predict the class and evaluate the outcome with the help of the 5x5 fold stratified cross validation method. Helping to achieve a significant increase in accuracy with respect to the minority class.\n",
        "\n",
        "This report is intended not only to show how to handle imbalanced datasets in respect to Borderline-SMOTE and CURE SMOTE, but also explore the surroundings of it and give beginners an introduction to machine learning.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Keywords\n",
        "Borderline-SMOTE, CURE SMOTE, Imbalanced datasets, Machine Learning"
      ]
    },
    {
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        "id": "ea3XcsLtqPH-"
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      "source": [
        "# Introduction\n",
        "With the expansion of machine learning, coupled with the arrival of the big data era, class imbalance is becoming a more prominent issue when we want to predict the minority classes of such data sets, the minority-class being the underrepresented part of the dataset and the majority-class being overrepresented. Particularly where there is a large disproportion between the majority and minority class. For it is the rareness of the minority class that is important, often carrying valuable knowledge (Krawczyk, 2016). For example, in bioinformatics, determining if a person has cancer.\n",
        "\n",
        "However, working with an imbalanced dataset poses a classification predictive modelling problem, where the distribution of examples across the classes are not equal. This is an issue as many machine learning algorithms used for classification are created under the assumption that all classes in the dataset are equal in quantity (Brownlee, 2019a). And so it would be inappropriate to train algorithms where a dataset has a severe class imbalance, as the minority class would be underrepresented. Creating bias towards the majority class.\n",
        "\n",
        "Nowadays a plethora of methods can be used, but we will focus on the data-level methods to approach the problem of learning from imbalanced datasets. The data-level methods modify the quantity of classes to be equal, or remove difficult samples. An algorithm that tackles this problem is known as the Synthetic Minority Oversampling Technique (SMOTE). Allowing minority class data to be scaled to an equal quantity as the majority class. Therefore removing bias.\n",
        "We will follow these steps:\n",
        "1. Explaining the SMOTE algorithms\n",
        "2. Understanding the datasets\n",
        "3. Creating the models\n",
        "4. Evaluating the results\n",
        "5. Summary\n",
        "\n",
        "In the following we will discuss the functionality of SMOTE. In particular, we will focus on Borderline-SMOTE, Borderline-SMOTE2 and CURE SMOTE, we will go into the construction and mathematics of these algorithms, and we will use example graphs to illustrate them. Finally, we will look at the drawbacks of these algorithms.\n",
        "\n",
        "Secondly, we will analyze the datasets superficially to see if they need to be revised or reworked. The datasets we are working with are mammography dataset, credit card fraud dataset and the ParkourMaker dataset. This is necessary because the accuracy of the algorithms later depends strongly on their quality.\n",
        "\n",
        "In the third section we will implement the SMOTE algorithms. We will use SMOTE, Borderline-SMOTE, Borderline-SMOTE2 and CURE SMOTE. For this we use the standard parameters and the smote_variants library. We will also explain the k-nearest neighbor and random forest classification algorithms and the 5x5 fold stratified cross validation method on a basic level. For k-nearest neighbors we will be using k=30 and for random forest classification a max_depth=2. We will go through the following scenarios with testing:\n",
        "1. K-nearest neighbor algorithm trained with mammography dataset.\n",
        "2. Random forest algorithm trained with credit card fraud dataset.\n",
        "3. K-nearest neighbor trained with ParkourMaker dataset.\n",
        "A comparison of before and after SMOTE application will be shown with the help of scatterplots.\n",
        "\n",
        "After that we will be using three parameters to measure the performance of the three models: the F1-score, average precision score and the balanced accuracy to evaluate the models. Explaining what we have found, how it is interpreted through the published literature, and what problems or mistakes have occurred. Also mentioning what approaches could be taken to solve these issues.\n",
        "\n",
        "Finally, we will summarize the results and draw conclusions if Borderline-SMOTE and CURE SMOTE can be used for the improvement of classifying imbalanced datasets."
      ]
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      "source": [
        "# The Smote Algorithms\n",
        "Imbalanced data has been a widely discussed topic over the last two decades. With one of the first methods of learning from imbalanced data relating to convergence rates of backpropagation-trained neural networks (Krawczyk, 2016).\n",
        "\n",
        "Today there are a number of methods available for oversampling an imbalanced dataset, but the most common technique is known as the Synthetic Minority Oversampling Technique (SMOTE) (Chawla, 2010). The algorithm creates synthetic minority class samples by linear interpolation of the minority class. Drawing random samples from a line segment joined by two close-enough minority class data points. This simple but effective method alleviates overfitting caused by random oversampling, and its ease of implementation is what makes SMOTE such a popular choice (Bej, 2020).\n"
      ]
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      "source": [
        "### Construction of the Smote Algorithm\n",
        "Let $min_i, min_j \\ \\forall i, j \\in [1,n]$ be minority class data points from an imbalanced dataset C with n minority class samples.\n",
        "\n",
        "**Step 1**:\\\n",
        "Choosing randomly: $min_i$, $min_j$ with $min_i \\neq min_j$ and $min_j$ as  $min_i$’s nearest minority class neighbors.\n",
        "\n",
        "**Step 2**:\\\n",
        "Creating a straight line between $min_i$ and $min_j$ with interval $[min_i, min_j]$ assuming that in respect to the x-axis $min_i \\leq min_j$.\n",
        "\n",
        "**Step 3**:\\\n",
        "Producing new samples $min_{new}$ with $u \\in [0,1]$ as follows:\n",
        ">$min_{new} = min_i + u(min_j − min_i)$.\n",
        "\n",
        "**Step 4**:\\\n",
        "Repeating **Step 1** and generating a population of synthetic samples from the minority class (Bej, 2020).\n"
      ]
    },
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      "source": [
        "### SMOTE Example\n",
        "**Graph 1** - Image of SMOTE after application on minority class dataset.\\\n",
        "![Smote_Algorithm.PNG](data:image/png;base64,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)\\\n",
        "**Graph 1** contains data point samples represented as points from an imbalanced dataset after applying SMOTE. The axis describes features A and B. \n",
        "\n",
        "Black points represent majority class samples and red points represent minority class samples. The white points with a red outline are synthetically generated samples. This graph shows that SMOTE clusters a lot of the minority samples, enabling algorithms to better predict the minority class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tubuaKuKjlDW"
      },
      "source": [
        "### Disadvantages of SMOTE\n",
        "As SMOTE generates synthetic samples for each minority sample, this can lead to overgeneralization. SMOTE does not take neighboring examples from other classes into account, and can therefore increase the overlapping of classes, creating unnecessary noise (Paul, 2018).\n",
        "\n",
        "Furthermore, the decision boundary that describes the area where two classes of  data points meet is often a very important, but difficult part for the classification process. (He and Garcia, 2009). To strengthen the decision boundary we will introduce a new algorithm, Borderline-SMOTE.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmSLMm8AjuX7"
      },
      "source": [
        "## Borderline SMOTE Algorithm\n",
        "Minority class data points which are plotted especially close to the edges of our majority classes data points are termed as ‘borderline’ samples. The main objective of Borderline-SMOTE is to identify only these borderline minority samples, and then further use them for oversampling by linear interpolation (Shelke et al., 2017). This is useful because some ‘borderline’ samples can be particularly confusing as they may not have clear adjacent data to be compared against (Bej, 2020).\n",
        "\n",
        "Borderline-SMOTE2 offers a similar, but slightly different approach. This algorithm does not only take into account the minority samples for the application of SMOTE, but also applies SMOTE to the borderline samples of the majority class. It uses the same algorithm as Borderline-SMOTE, except for the creation of synthetic samples with the help of interpolation between minority and majority samples. Choosing the parameter\n",
        "$u\\in[0,0.5]$ in order to keep the newly created samples closer to the minority class (Han et al., 2005, Bej, 2020).\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOJMidHhj9BU"
      },
      "source": [
        "### Construction of the Borderline Smote Algorithm\n",
        "Let $min_i \\forall i \\in N$ be minority class data points from an imbalanced dataset C.\\\n",
        "Let $max_i \\forall i \\in N$ be majority class data points from an imbalanced dataset C.\n",
        "\n",
        "**Step 1**:\\\n",
        "Identify borderline points by counting $min_i$’s n-nearest neighbors and determine the cardinality of $max_i$ points.\n",
        "This can be done as shown here:\n",
        ">Let $N_{max_i}^n(min_i)$ be the set of the n nearest majority point neighbors of $min_i$ \n",
        "and $\\begin{align*} m(min_i) = |N^n_{max_i}(min_i)| \\end{align*}$\n",
        ">>*Case 1* ($m(min_i) = n$):\\\n",
        "$min_i$ has only majority point neighbors \n",
        "and will not be included because its a noise point.\n",
        "\n",
        ">>*Case 2* ($\\frac{n}{2} \\leq m(min_i) < n$):\\\n",
        "$min_i$ has more than 50% majority point neighbors and will be a borderline point.\n",
        "\n",
        ">>*Case 3* ($\\frac{n}{2} > m(min_i)$):\\\n",
        "$min_i$ has more minority point neighbors and isn't a borderline point.\n",
        "\n",
        "**Step 2**:\\\n",
        "Data samples which are classified as borderline points have the SMOTE algorithm applied to them, strengthening their decision boundary.\n",
        "\n",
        "As previously mentioned, Borderline-SMOTE2 is constructed almost identically as the Borderline-SMOTE algorithm. The additional usage of borderline samples from the majority class being the exception.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNmV76t0LE6K"
      },
      "source": [
        "### Borderline SMOTE Example\n",
        "**Graph 2** - Image of Borderline-SMOTE after appilcation on minority class dataset.\n",
        "\n",
        "![B_Smote_Algorithm.PNG](data:image/png;base64,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)\n",
        "\n",
        "**Graph 2** shows imbalanced dataset samples after the resampling algorithm Borderline-SMOTE was applied. There are 4 types of data points, each representing one sample of the dataset. The axis describes features A and B.\n",
        " \n",
        "Black points represent majority class samples and white points with a thick red outline represent the minority class. The red points are borderline points meant to be oversampled and white circles with a thin red border are newly created samples. \n",
        " \n",
        "This graph shows that the decision boundary between the samples has been strengthened by these newly created synthetic borderline samples.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFWdQ1kYEB9a"
      },
      "source": [
        "### Disadvantages of Borderline-SMOTE\n",
        "On the other hand, Borderline-SMOTE still fails to completely reflect the distribution of the original data. Artificial samples generated by the outlying minor class samples may lead to problems such as repeatability, or noisy and unclear boundaries between the positive and negative classes (Ma and Fan, 2017). The Borderline algorithm can struggle spotting all borderline points. Or by oversampling solely borderline data points it can create overfitting of data.\n",
        "\n",
        "But there is an algorithm that can solve these types of problems too. The CURE SMOTE algorithm is not sensitive to outliers and can recognize abnormal data points. This is important today where big data is more relevant than ever. Offering an efficient solution to oversampling large scale imbalanced data.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QYeTopix_fa"
      },
      "source": [
        "## CURE SMOTE Algorithm\n",
        "Based on the CURE hierarchical clustering algorithm, the CURE SMOTE algorithm obtains a better classification result for imbalanced datasets, using K-means to find clusters of minority class samples to apply SMOTE to. Experiments on imbalanced data have shown that CURE SMOTE enhances the results of classification, compared to the results of applying: random sampling, Borderline-SMOTE, safe-level SMOTE. C-SMOTE and K-means-SMOTE (Ma and Fan, 2017).\n",
        "\n",
        "The main idea of CURE SMOTE is to cluster the minority class samples, remove noise and outliers and then apply the known SMOTE algorithm on the  representative points of each cluster. The construction of the CURE SMOTE algorithm is shown below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J68kppw-H4jr"
      },
      "source": [
        "### The CURE algorithm\n",
        "The CURE SMOTE algorithm requires the CURE algorithm beforehand. Therefor we are going to take a closer look on how the CURE algorithm works and then move on to the construction of CURE SMOTE\n",
        "\n",
        "Using the CURE SMOTE algorithm requires knowledge about the CURE algorithm beforehand. We will first look at how the CURE algorithm functions before moving to the construction of CURE SMOTE. \n",
        "\n",
        "The ‘clustering for representatives’ algorithm (CURE) uses a predefined amount of representative points to represent the clusters. Nearest clusters are merged together until a specific threshold amount of clusters is reached (Leskovec et al., 2016).\n",
        "\n",
        "There are different ways to implement the CURE algorithm. One slightly different approach is described by Rajaraman where he explains a solution for the application of CURE on large datasets. They state that applying the hierarchical clustering algorithm on a randomly selected sample of the data is done beforehand. With remaining data sorted afterwards, in dependence on their distance to the representative points for each cluster. This is helpful when dealing with large quantities of data, which are not able to be loaded into the main memory (Leskovec et al., 2016).\n",
        "\n",
        "However, for this case study we will look at how Ma and Fan implement the CURE algorithm. Their approach is almost identical, differing by using all minority class samples for the cluster building (Ma and Fan, 2017). \n",
        "\n",
        "**Construction of the CURE algorithm**\n",
        "\n",
        "**Step 1**.\\\n",
        "Let $X$ be a normalised dataset.\n",
        "Initialy each point $p$ in $X$ is considered a cluster. Each cluster has a a set of points $U$, a set of representive points $U_r$ and a central point $U_c$.\n",
        "\n",
        "The distance between two clusters U,V is defined as: \n",
        ">$min(U,V) = min_{p\\in U, q\\in V}dist(p,q)$\\\n",
        "Meaning it is the minimum distance between two representative points.\n",
        "\n",
        "**Step 2**.\\\n",
        "Decide on a clustering number $c$ and a represenative number $r$. When the number of cluster-centres reaches $c$ the algorithm terminates. If a cluster has more than r representatives, then new representetive points have to be chosen.\n",
        "\n",
        "**Step 3**.\\\n",
        "Merge the two clusters with the smallest $min(U,V)$. Update $U_r$ and $U_c$ as follows:\n",
        ">$U_c = \\frac{|U|*U_c + |V|*V_c}{|U|+|V|}$, calculating the new centerpoint.\\\n",
        "$U_r \\leftarrow \\{p + \\alpha*(U_c - p) | p \\in U_r\\}$, move all representative points $(100*\\alpha)$% closer to the centerpoint.\n",
        "\n",
        "It is suggested to choose $\\alpha$ = 0.5.\n",
        "If the representative points after a merge are higher than defined, new representative points are selected. This happens by choosing one random representative point and choosing the next one as far away as its former, within its cluster.\n",
        "\n",
        "**Step 3** is repeated until the clustering number c is reached. The class with the slowest growth speed will be removed (Ma and Fan, 2017)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jXqHrsO5YkXj"
      },
      "source": [
        "### Construction of the CURE SMOTE Algorithm\n",
        "The algorithm now oversamples with the help of SMOTE every generated cluster. It does that by interpolating between a point of the cluster and the representative points of said cluster.\n",
        "\n",
        "**Step 4.**\\\n",
        "Let $X^n_{cluster}$ be the n-th cluster with $n \\in [1,n]$\\\n",
        "Let $U^n_r$ be a representative point from the n-th cluster\\\n",
        "Generating new samples by chosing random points from $X^n_{cluster}$:\n",
        ">$X^n_{new} = \\bigcup_{x\\in X^n_{cluster}} x + u(U_r^n-x)$\n",
        "\n",
        "**Step 5.**\\\n",
        "Combining the newly generated samples with $X_{os}$ being the oversampled minority class:\n",
        ">$X_{os} = \\bigcup_{n\\in [1,n]} (X^n_{new} \\cup X^n_{cluster})$ \n",
        "\n",
        "In the clustering process, noisy points have to be removed. When clustering is complete, small clusters will also be removed.\n",
        "\n",
        "Interpolation of our sample points after clustering effectively prevents generalization and preserves the original distribution of the data set. By using the interpolation formula, samples are only generated between representative samples and the samples in the original minor class. Avoiding influence from boundary points. \n",
        "\n",
        "This implementation of clustering with merge operations eliminates the noise points at the end of the process. Simplifying the process as there is no need to eliminate farthest generated artificial samples after the SMOTE algorithm.\n",
        "\n",
        "In addition, all termination criteria, such as: reaching pre-set number of clusters, the number of representative points or the distance threshold are reducing the instability of the proposed algorithm (Ma and Fan, 2017). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOJxomGxuFX8"
      },
      "source": [
        "### CURE Example\n",
        "**Graph 3** - Plot of a dataset after applying the CURE algorithm.\\\n",
        "![C_SMOTE_algorithm.PNG](data:image/png;base64,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)\\\n",
        "**Graph 3** shows an imbalanced dataset after the application of the CURE algorithm. There are 4 types of data points, each representing one sample of the dataset. The axis describes features A and B.\n",
        "\n",
        "Black and red points represent the majority and minority classes respectively. White points with a thin red border are outliers removed from CURE, and orange points describe the representatives of that cluster. As the orange line shows, these have been shifted towards the yellow center point. \n",
        "\n",
        "The brown line surrounding everything is the borderline of the cluster, including all samples that count towards the cluster found by CURE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDWhSqouTIlq"
      },
      "source": [
        "### CURE SMOTE Example\n",
        "**Graph 4** - Plot of a dataset after applying the CURE SMOTE algorithm.\\\n",
        "![C4_Smote.PNG](data:image/png;base64,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)\\\n",
        "**Graph 4** shows an imbalanced dataset after the application of SMOTE onto an existing clustered CURE plot. There are 4 types of data points, each representing one sample of the dataset. The axis describes features A and B.\n",
        " \n",
        "Black and red points represent the majority and minority classes respectively. White points with a thin red border are now the synthetic minority class data points, and orange points describe the representatives of that cluster. It is now visible how the SMOTE algorithm creates new synthetic samples between the representative points and randomly selected data points. \n",
        " \n",
        "Also seeing that CURE SMOTE has created many additional data points surrounding the yellow center and the synthetic representative data points. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5uOon3AahCd"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIuhdE_i5958"
      },
      "source": [
        "# Import needed Libraries\n",
        "import smote_variants as sv\n",
        "from imblearn.datasets import fetch_datasets\n",
        "import imbalanced_databases as imbd\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import tabulate\n",
        "from IPython.display import HTML, display\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import f1_score, average_precision_score, balanced_accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from collections import Counter\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8W88k3isy1O"
      },
      "source": [
        "# A look at datasets\n",
        "With the theory behind and the different SMOTE algorithm variants covered, we will now focus on the datasets that we will analyze. This is a mandatory step in the process of understanding the datasets, which will help us in preprocessing the data for the application of SMOTE.\n",
        "\n",
        "This case study will look at three different imbalanced datasets. The mammography dataset, credit card fraud dataset and a private ParkourMaker dataset. By using multiple datasets we can diversify how we apply the SMOTE algorithm, resulting in a more accurate and versatile analysis. \n",
        "\n",
        "Firstly we will understand what named datasets followed by looking at the samples inside each dataset, visualising this with tables. For the unique, private ParkourMaker dataset we will also be using a plot to better understand it. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hY8_346i9h2"
      },
      "source": [
        "## 1. Mammography Dataset\n",
        "The mammography dataset is a dataset which contains data on radiological scans detecting breast cancer. Specifically the presence of clusters of microcalcifications that appear bright on a mammogram.\n",
        "\n",
        "The mammography dataset is a popular choice for imbalanced classification due to its severe class imbalance. 98% of the classes are labeled as microcalcifications (‘having no cancer’) and the remaining 2% are samples having cancer (Brownlee, 2020a and Woods et al., 1993).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "bfl4II4Xi24C",
        "outputId": "4fedc2b9-e658-4f4a-e114-01c3a8d2cd0d"
      },
      "source": [
        "# Loading Dataset\n",
        "fetch_mam = fetch_datasets()['mammography']\n",
        "\n",
        "# Creating the pandas dataframe and naming columns 'V1' to 'V6'\n",
        "df_mam = pd.DataFrame(np.c_[fetch_mam['data'], fetch_mam['target']], columns=['V1','V2','V3','V4','V5','V6','Class'])\n",
        "\n",
        "# Changing -1 to 0 in dataframe\n",
        "df_mam['Class'] = df_mam['Class'].replace(-1, 0)\n",
        "\n",
        "# Show the first 5 entires of the dataset\n",
        "print(\"Table 1 - First 5 samples of the mammography dataset\")\n",
        "df_mam.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table 1 - First 5 samples of the mammography dataset\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>Class</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.102987</td>\n",
              "      <td>-0.394994</td>\n",
              "      <td>-0.140816</td>\n",
              "      <td>0.979703</td>\n",
              "      <td>-0.377866</td>\n",
              "      <td>1.013566</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         V1        V2        V3        V4        V5        V6  Class\n",
              "0  0.230020  5.072578 -0.276061  0.832444 -0.377866  0.480322    0.0\n",
              "1  0.155491 -0.169390  0.670652 -0.859553 -0.377866 -0.945723    0.0\n",
              "2 -0.784415 -0.443654  5.674705 -0.859553 -0.377866 -0.945723    0.0\n",
              "3  0.546088  0.131415 -0.456387 -0.859553 -0.377866 -0.945723    0.0\n",
              "4 -0.102987 -0.394994 -0.140816  0.979703 -0.377866  1.013566    0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeSr2vNxkdL0"
      },
      "source": [
        "For **Table 1** the values have already been scaled down. The features of this dataset are explained as follows.\\\n",
        "The features of this dataset are explained as follows. \n",
        "*   V1 - Area of object (in pixels).\n",
        "*   V2 - Average gray level of the object.\n",
        "*   V3 - Gradient strength of the object’s perimeter pixels.\n",
        "*   V4 - Root mean square noise fluctuation in the object.\n",
        "*   V5 - Contrast, average gray level of the object minus the average of a two-pixel wide border surrounding the object.\n",
        "*   V6 - A low order moment based on shape descriptor.\n",
        "*   Class - shows if a person has breastcancer: \n",
        ">(1) has cancer\\\n",
        ">(0) has no cancer\n",
        "\n",
        "(Brownlee, 2021 and Kevin Woods et al., 1993)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSSTyGrZ7xae",
        "outputId": "401f65a1-ad7d-46a7-ec4b-b46f09fed3aa"
      },
      "source": [
        "# Showing different information about the dataset\n",
        "print(\"Amount of samples:                 \", len(df_mam))\n",
        "print(\"Amount of marjority class samples: \", len(df_mam[df_mam['Class']==0]))\n",
        "print(\"Amount of minority class samples:  \", len(df_mam[df_mam['Class']==1]))\n",
        "print(\"Percentage of minorty classes:     \", round(100*(len(df_mam[df_mam['Class']==1])/len(df_mam)), 3),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Amount of samples:                  11183\n",
            "Amount of marjority class samples:  10923\n",
            "Amount of minority class samples:   260\n",
            "Percentage of minorty classes:      2.325 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1ZMArf88Wkr"
      },
      "source": [
        "As the output above shows we have a total of 11183 samples, divided as 10923 majority class samples and 260 minority class samples. Resulting in a minority class distribution of 2.325% and therefore a severe class imbalance as stated above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITgp-oSSt2wp"
      },
      "source": [
        "## 2. Credit card fraud dataset\n",
        "The credit card fraud dataset contains transactions made by Mastercards in September 2013 by European cardholders. It represents 2 days of transactions and its focus is to predict fraudulent transactions (Kaggle, 2018)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "yYgNsNY6iFRz",
        "outputId": "1c2670d1-2794-4668-b65b-20a3c0993033"
      },
      "source": [
        "df_cred = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv') \n",
        "\n",
        "print(\"Table 2 - First 5 samples of the credit card fraud dataset\")\n",
        "\n",
        "# Dropping 90% of the dataset randomly, otherwise it would be way too big to use\n",
        "df_cred = df_cred.drop(df_cred.sample(frac=.9).index)\n",
        "\n",
        "# Show the first 5 entires of a few features from the dataset\n",
        "df_cred[['Time','V1','V2','V27','V28','Amount','Class']].head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table 2 - First 5 samples of the credit card fraud dataset\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>23.0</td>\n",
              "      <td>1.059387</td>\n",
              "      <td>-0.175319</td>\n",
              "      <td>0.081461</td>\n",
              "      <td>0.024220</td>\n",
              "      <td>12.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>26.0</td>\n",
              "      <td>-0.535388</td>\n",
              "      <td>0.865268</td>\n",
              "      <td>-0.078306</td>\n",
              "      <td>0.025427</td>\n",
              "      <td>1.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Time        V1        V2       V27       V28  Amount  Class\n",
              "1    0.0  1.191857  0.266151 -0.008983  0.014724    2.69      0\n",
              "2    1.0 -1.358354 -1.340163 -0.055353 -0.059752  378.66      0\n",
              "3    1.0 -0.966272 -0.185226  0.062723  0.061458  123.50      0\n",
              "29  23.0  1.059387 -0.175319  0.081461  0.024220   12.99      0\n",
              "35  26.0 -0.535388  0.865268 -0.078306  0.025427    1.77      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uaz2OtmTwMEs"
      },
      "source": [
        "**Table 2** shows that the dataset contains 30 features. 28 of those are principal components obtained with PCA (Principal Component Analysis) regarding the issue of personal information. The 2 unchanged features are ‘time’ and ‘amount’.\n",
        "\n",
        "Time represents the time which has passed since the first transaction. Amount represents the amount of money in the transaction. The class states whether a transaction was fraudulent (1) or not (0).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmbwzN7bxJd6"
      },
      "source": [
        "# Removing the time feature since it is not relevant.\n",
        "df_cred = df_cred.drop(columns=\"Time\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAqOJcSbxoGC",
        "outputId": "5231104a-d60a-466f-92ea-ce24167d7195"
      },
      "source": [
        "# Showing different information about the dataset\n",
        "print(\"Amount of samples:                    \", len(df_cred))\n",
        "print(\"Amount of minority class samples:     \", len(df_cred[df_cred['Class']==0]))\n",
        "print(\"Amount of majority class samples:     \", len(df_cred[df_cred['Class']==1]))\n",
        "# Showing distribution between classes\n",
        "print(\"Percentage of minorty classes:        \", round(100*(len(df_cred[df_cred['Class']==1])/len(df_cred)), 3),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Amount of samples:                     28481\n",
            "Amount of minority class samples:      28435\n",
            "Amount of majority class samples:      46\n",
            "Percentage of minorty classes:         0.162 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdVDlz-_zlRT"
      },
      "source": [
        "As the outputs above show, we have a total of 28481 samples, divided as 28446 majority class samples and 54 minority class samples. That's a minority class distribution of 0.123% and therefore a severe class imbalance as stated above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpHQ6jYG2_RC"
      },
      "source": [
        "## 3. Parkour-Maker Dataset\n",
        "The ParkourMaker dataset is a collection of data from the video game Minecraft. Specifically, player world data, collected from a small server called Project Omega. These Minecraft worlds inside their server are known as ‘parkour courses’ (Mojang, 2009). \n",
        "\n",
        "Players are able to edit and upload their own courses to the server library, where others can search for their course, then play and rate it. The best courses receive the special status of \"Featured Course\". The purpose of this dataset is to predict if a course is feature worthy or not.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6qUCQ7nEOG3"
      },
      "source": [
        "%%capture\n",
        "# Setting up DataFrame Pk-Data\n",
        "df_pk = pd.read_csv('./sample_data/PkMaker_Data.csv')\n",
        "indexc = list(range(0,21461))\n",
        "\n",
        "# Adding an index to the dataframe\n",
        "df_pk['index'] = indexc\n",
        "df_pk.set_index(['index'])               \n",
        "\n",
        "# Selecting only those courses that are released since others have -1 or null values for most columns\n",
        "df_pk = df_pk[df_pk['isReleased']==1]\n",
        "\n",
        "# Removing samples with >20000 average fails (noise points)\n",
        "df_pk = df_pk[df_pk['AverageFails']<20000]\n",
        "\n",
        "# Removing unneccessary features\n",
        "drop_features = ['ID', 'BestTime', 'AverageTime', 'OriginalSize','CachedPlayerName','UUID','FileID','Name','Generator','Environment'\n",
        "                  ,'StartLocation','DisplayItem','isPremium'\n",
        "                  ,'isVerifying','SuccessfulPlays','v1_8','v1_9','v1_13'\n",
        "                  ,'OpenDoors','SpecialBlocks','AlwaysSprint','NoSprint'\n",
        "                  ,'NoJump','NoSneak','DamageEnabled','DamageNoDamage'\n",
        "                  ,'ResetDamage','ResetWater','ResetLava','DisableSpectator'\n",
        "                  ,'DisableSavestate', 'isReleased', 'isVerified', 'TimeReleased','index']\n",
        "\n",
        "df_pk = df_pk.drop(drop_features, axis=1)\n",
        "\n",
        "# Removing samples that got less then 5 total ratings (noise points)\n",
        "df_pk = df_pk[df_pk['TotalRatings']>5]\n",
        "\n",
        "# checking for invalid entries\n",
        "df_pk.info()\n",
        "\n",
        "# -> everything fine, no changes needed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "5jjEs52H57FE",
        "outputId": "3308f6b9-f939-411c-a17f-471a3e2e48b6"
      },
      "source": [
        "# Show the first 5 entires ofthe now remaining dataset\n",
        "print(\"Table 3 - First 5 samples of the parkour maker dataset\")\n",
        "df_pk.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table 3 - First 5 samples of the parkour maker dataset\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UniquePlays</th>\n",
              "      <th>SuccessRate</th>\n",
              "      <th>AverageFails</th>\n",
              "      <th>TotalRatings</th>\n",
              "      <th>AverageRating</th>\n",
              "      <th>isFeatured</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>577</td>\n",
              "      <td>0.9428</td>\n",
              "      <td>1.0352</td>\n",
              "      <td>120</td>\n",
              "      <td>3.7830</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>495</td>\n",
              "      <td>0.9556</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>98</td>\n",
              "      <td>2.8776</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>258</td>\n",
              "      <td>0.9341</td>\n",
              "      <td>0.0095</td>\n",
              "      <td>54</td>\n",
              "      <td>3.3890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>145</td>\n",
              "      <td>0.5931</td>\n",
              "      <td>1.2791</td>\n",
              "      <td>32</td>\n",
              "      <td>2.9687</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>98</td>\n",
              "      <td>0.3571</td>\n",
              "      <td>9.5427</td>\n",
              "      <td>12</td>\n",
              "      <td>3.9167</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   UniquePlays  SuccessRate  ...  AverageRating  isFeatured\n",
              "0          577       0.9428  ...         3.7830           0\n",
              "1          495       0.9556  ...         2.8776           0\n",
              "3          258       0.9341  ...         3.3890           0\n",
              "5          145       0.5931  ...         2.9687           0\n",
              "9           98       0.3571  ...         3.9167           0\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkyrwiz-SvHN"
      },
      "source": [
        "We are now left with the most important features of that dataset.\n",
        "\n",
        "*   UniquePlays - unique players which played the course\n",
        "*   SuccessRate - average percentage of players passing a course\n",
        "*   AverageFails - average percentage of fails a player has in a course\n",
        "*   TotalRatings - unique number of ratings a course recieved\n",
        "*   AverageRating - average rating from 0-5, where 0 is worst, 5 best\n",
        "*   isFeauted - shows if a course is featured\n",
        ">(1) is featured\\\n",
        ">(0) is not featured\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXFmXE_wNE6q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "1b260785-2c33-432c-9ec4-161d36621183"
      },
      "source": [
        "# describing the values we are interested in\n",
        "print(\"Table 4 - Furhter description of selected features from parkour maker dataset\")\n",
        "df_pk[['UniquePlays', 'SuccessRate', 'TotalRatings', 'AverageRating', 'isFeatured']].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table 4 - Furhter description of selected features from parkour maker dataset\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UniquePlays</th>\n",
              "      <th>SuccessRate</th>\n",
              "      <th>TotalRatings</th>\n",
              "      <th>AverageRating</th>\n",
              "      <th>isFeatured</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9703.000000</td>\n",
              "      <td>9703.000000</td>\n",
              "      <td>9703.000000</td>\n",
              "      <td>9703.000000</td>\n",
              "      <td>9703.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>136.120375</td>\n",
              "      <td>0.530823</td>\n",
              "      <td>20.039885</td>\n",
              "      <td>3.630211</td>\n",
              "      <td>0.015253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>82.225905</td>\n",
              "      <td>0.263242</td>\n",
              "      <td>14.177438</td>\n",
              "      <td>0.724725</td>\n",
              "      <td>0.122564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.011800</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>91.000000</td>\n",
              "      <td>0.307200</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>3.208250</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>122.000000</td>\n",
              "      <td>0.517500</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>3.714300</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>4.142800</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3467.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>609.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       UniquePlays  SuccessRate  TotalRatings  AverageRating   isFeatured\n",
              "count  9703.000000  9703.000000   9703.000000    9703.000000  9703.000000\n",
              "mean    136.120375     0.530823     20.039885       3.630211     0.015253\n",
              "std      82.225905     0.263242     14.177438       0.724725     0.122564\n",
              "min      14.000000     0.011800      6.000000       0.250000     0.000000\n",
              "25%      91.000000     0.307200     11.000000       3.208250     0.000000\n",
              "50%     122.000000     0.517500     17.000000       3.714300     0.000000\n",
              "75%     161.000000     0.750000     26.000000       4.142800     0.000000\n",
              "max    3467.000000     1.000000    609.000000       5.000000     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcrp3Z6eVk_O"
      },
      "source": [
        "**Table 4** includes 9703 samples. With the average unique plays for a course being 136 plays, the average completion rate is 53% and the average course rating is 3.6 out of 5.0. But most importantly, the dataset is heavily imbalanced with only 1.5% of all courses featured."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmV-GjA5L1Ms"
      },
      "source": [
        "### Example Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "yPSuDO3nV7XF",
        "outputId": "f65d80e9-7686-4c66-e9ba-d6d8041f3aa8"
      },
      "source": [
        "# Drawing a plot of the pk dataset\n",
        "print(\"Plot 1 - Relation between average rating and success rate per person\")\n",
        "plt.figure(figsize=(15,6))\n",
        "plt.title(\"Relation between average rating and success rate\")\n",
        "sns.scatterplot(data=df_pk, x=\"SuccessRate\", y=\"AverageRating\", hue=\"isFeatured\")\n",
        "plt.xlabel(\"success rate per person\")\n",
        "plt.ylabel(\"average rating\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Plot 1 - Relation between average rating and success rate per person\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'average rating')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCNuxk5oYaD0"
      },
      "source": [
        "**Plot 1** shows the relation between the orange feature points distributed and the relation between average rating and success rate. On average featured courses have a very good rating of 4.5 to 5.0. But are also harder in difficulty, shown by the general 0.1 - 0.3 success rate per person of featured maps. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVdEnoYUaNoS"
      },
      "source": [
        "# Oversampling with SMOTE\n",
        "Now that we understand the basics of the SMOTE algorithms and datasets, they can be implemented."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V8Q6k-GAy9Im"
      },
      "source": [
        "## Splitting the datasets\n",
        "First, each of the datasets will be divided into the features X, and the class y which will be predicted. Moreover, the new datasets will then be put into arrays for ease of access in the future."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BFS3dRf3Nb_"
      },
      "source": [
        "# Defining X as feature-array and y as class-array\n",
        "X_mam, y_mam = np.array(df_mam.drop('Class', axis=1)), np.array(df_mam['Class'])\n",
        "X_cred, y_cred = np.array(df_cred.drop('Class', axis=1)), np.array(df_cred['Class'])\n",
        "X_pk, y_pk = np.array(df_pk.drop('isFeatured', axis=1)), np.array(df_pk['isFeatured'])\n",
        "\n",
        "# Saving the datasets into arrays\n",
        "X_data_arr, y_data_arr = [X_mam, X_cred ,X_pk], [y_mam, y_cred, y_pk]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdYy3xd31bXx"
      },
      "source": [
        "## Scaling data down\n",
        "Now the data is scaled down in order to avoid bias towards large values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LogSevut9HoB"
      },
      "source": [
        "# Initialising the scaler\n",
        "scaler = StandardScaler()\n",
        "X_scaled_data = []\n",
        "\n",
        "# Using the standard scaler to scale down large values\n",
        "for i in range(3):\n",
        "  scaler.fit(X_data_arr[i])\n",
        "  scaledData = scaler.transform(X_data_arr[i])\n",
        "  X_scaled_data.append(scaledData)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmM-GOpj1mC3"
      },
      "source": [
        "## Initialising the SMOTE algorithms\n",
        "The previously imported SMOTE algorithms are now initialised from smote_variants."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhkgSoQV4geo"
      },
      "source": [
        "%%capture\n",
        "# Initializing the borderline1, borderline2 and CURE SMOTE algorithms\n",
        "smotesampler = sv.SMOTE()\n",
        "borderline1sampler = sv.Borderline_SMOTE1()\n",
        "borderline2sampler = sv.Borderline_SMOTE2()\n",
        "curesampler = sv.CURE_SMOTE()\n",
        "sampler_list = [smotesampler, borderline1sampler, borderline2sampler, curesampler]\n",
        "\n",
        "# Creating list with names of the datasets\n",
        "name_arr = [\"Normal Dataset\", \"Smote Dataset\", \"Borderline1 Dataset\", \"Borderline2 Dataset\", \"CURE SMOTE\"]\n",
        "length = len(name_arr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhN30Z1I5dr_"
      },
      "source": [
        "## The Stratified Cross Validation\n",
        "To estimate the results of the algorithms, the stratified cross validation technique will be implemented. This is an effective method of evaluating machine learning models with limited data as it resamples already existing data into equally sized groups (Brownlee, 2018). Iterating through all groups and choosing one to be the test data, and the rest train data.\n",
        "\n",
        "By using cross validation it creates a more versatile approach. By using many different combinations of train and test groups, the end result will be more robust, and less random. However, there is one major disadvantage: the extra computational time (Schneider, 1997). This method takes k-times longer since models have to be trained k times.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRqzXr7cqj54"
      },
      "source": [
        "### Setting up Stratified Cross Validation\n",
        "For this process we will choose n=5 splits with the datasets being shuffled beforehand with a random_state of 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mE8adMsybENm"
      },
      "source": [
        "# Setting up the stratified cross validation algorithm\n",
        "kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNPb7R_I7OQb"
      },
      "source": [
        "## K-nearest neighbors algorithm trained with mammography dataset\n",
        "The k-nearest neighbor algorithm is a simple, yet effective technique as it doesn’t require any training and is fairly easy to understand. This algorithm can be used for both classification and regression problems, but it will only be used for classification here.\n",
        "\n",
        "When a prediction is made, the algorithm checks the k-nearest neighbors based on their euclidean distance to the data point being checked. After discovering all k-nearest neighbors, the algorithm then classifies the data point to the average class of these neighbors (Brownlee, 2019b).\n",
        "\n",
        "Unfortunately, the k-nearest neighbor algorithm also comes with its own disadvantages. It is sensitive to scaling and to irrelevant features. On top of being dependent on the quality of data and being ineffective with high dimensionality (Soni, 2020).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fF7XP_f-9J7"
      },
      "source": [
        "### Initialising the knn classifier\n",
        "The knn classifier is now initialised with n = 30. In addition, the scaled mammography data will be loaded and then a score list created for later obtained algorithm evaluation scores.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB71lzTK9HhS"
      },
      "source": [
        "# Setting up the classifier for k-nearest neighbors\n",
        "classifier_kn = KNeighborsClassifier(n_neighbors=30)\n",
        "\n",
        "X_mam_scaled = X_scaled_data[0]\n",
        "\n",
        "# Initialising an array to save values to\n",
        "score_list_mam = [[],[],[]]\n",
        "oversample_save = [[0,0],[0,0],[0,0],[0,0],[0,0]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KMBMl1lNRRm"
      },
      "source": [
        "### Applying the SMOTE variants on the mammography dataset \n",
        "Now the data will be split as described above. In doing so training of the knn classifier and prediction using the test data can begin.\n",
        "\n",
        "It is especially important to note that the oversampling process happens after the splitting. Otherwise we would test the algorithm with synthetic samples. This is not good since it could happen that copies of the same point end up in both training and test sets, allowing the algorithm to cheat (S. Wagner, 2013).\n",
        "\n",
        "The accuracy of this, is evaluated with the f1-score, the average precision score and the balanced accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFVxcroQNJ8i"
      },
      "source": [
        "# Repeating the 5 fold cross validation five times\n",
        "for i in range(5):\n",
        "\n",
        "  print(\"Now starting: \", name_arr[i])\n",
        "  f1scoretotal, AVPscoretotal, BAscoretotal = 0, 0, 0\n",
        "\n",
        "  # Splitting up the datasets into training data and test data\n",
        "  for train_index, test_index in kf.split(X_mam, y_mam):\n",
        "\n",
        "    X_train, X_test = X_mam[train_index], X_mam[test_index]\n",
        "    y_train, y_test = y_mam[train_index], y_mam[test_index]\n",
        "\n",
        "    # Oversampling the training data only\n",
        "    if i is not 0:\n",
        "      X_train, y_train = sampler_list[i-1].sample(X_train, y_train)\n",
        "    \n",
        "    # saving one training sample for later comparison\n",
        "    oversample_save[i][0] = X_train\n",
        "    oversample_save[i][1] = y_train\n",
        "\n",
        "    # Training the kn algorithm with the training data\n",
        "    classifier_kn.fit(X_train, y_train)\n",
        "\n",
        "    # Trying to predict the values from the test data\n",
        "    y_pred = classifier_kn.predict(X_test)\n",
        "\n",
        "    # Calculating f1score, average precision and balanced accuracy\n",
        "    f1score = f1_score(y_pred=y_pred,y_true=y_test)\n",
        "    f1scoretotal = f1score + f1scoretotal\n",
        "\n",
        "    AVPscore = average_precision_score(y_score=y_pred,y_true=y_test)\n",
        "    AVPscoretotal = AVPscore + AVPscoretotal\n",
        "\n",
        "    BAscore = balanced_accuracy_score(y_pred=y_pred,y_true=y_test)\n",
        "    BAscoretotal = BAscore + BAscoretotal\n",
        "  \n",
        "  # Saving the calculated values as averages in an array for later access\n",
        "  score_list_mam[0].append(round((f1scoretotal/5),3))\n",
        "  score_list_mam[1].append(round((AVPscoretotal/5),3))\n",
        "  score_list_mam[2].append(round((BAscoretotal/5),3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bn5v49IxQ_Dk"
      },
      "source": [
        "### Example graphs before and after applying SMOTE on mammography dataset\n",
        "Plots are always good for getting an idea of how the data got changed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZzGwX_xYSCS"
      },
      "source": [
        "def generateScatterPlot(ax, f_mam, X, y, title, xlabelName, ylabelName):\n",
        "  counter = Counter(y)\n",
        "\n",
        "  # scatter plot of examples by class label\n",
        "  for label, _ in counter.items():\n",
        "    row_ix = np.where(y == label)[0]\n",
        "    ax.scatter(X[row_ix, 0], X[row_ix, 1], label=str(label))\n",
        "    ax.set_title(title)\n",
        "    ax.set_xlabel(xlabelName)\n",
        "    ax.set_ylabel(ylabelName)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "THOlnI4nTWzb",
        "outputId": "4ab05ce3-77dd-476c-a461-67df4667f9d9"
      },
      "source": [
        "# Setting up subplots so the plots are next to eachother\n",
        "f_mam, ax_mam = plt.subplots(1,5,figsize=(20,4))\n",
        "\n",
        "# Iterating through all 5 training datasets and generating the plots accordingly\n",
        "for i in range(5):\n",
        "  X_mam_samp = oversample_save[i][0]\n",
        "  y_mam_samp = oversample_save[i][1]\n",
        "  label_cap = ''.join(['V1\\n Plot 2.',str(i+1)])\n",
        "  generateScatterPlot(ax_mam[i], f_mam, X_mam_samp, y_mam_samp, name_arr[i], label_cap, 'V2')\n",
        "\n",
        "f_mam.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_1fwILkjg6w"
      },
      "source": [
        "All of the above plots describe the features V1 and V2\n",
        "\n",
        "**Plot 2.1** describes the unchanged dataset.  It shows that a small minority cluster was formed in $x\\in[0,5]$ and $y\\in[0,2]$.\n",
        "\n",
        "**Plot 2.2** describes the dataset after applying SMOTE, showing that the minority-cluster has expanded towards the x-axis with $x$ values up to 10. The minority class has also grown on the y-axis to $y = 4.5$\n",
        "\n",
        "**Plot 2.3** and **Plot 2.4** show the dataset after applying Borderline-SMOTE and Borderline-SMOTE2. Particularly around the area $x=5$ the minority class has been strengthened.\n",
        "\n",
        "**Plot 2.5** shows the data after applying CURE SMOTE. A really dense minority-cluster was formed around $x\\in[0,3]$ and $y\\in[0,1]$, meaning the center has been strengthened."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xR5MQ11e7rCk"
      },
      "source": [
        "## Random forest algorithm trained with credit card fraud dataset\n",
        "The random forest algorithm is an easy to use option for classification and regression problems. It is robust to outliers, efficient on big data and effective on linear data.\n",
        "\n",
        "The algorithm consists of several, highly varied decision trees. Each decision inside a tree is based on the greedy method, which always decides on the best possible choice of that iteration (Trehan, 2020).\n",
        "\n",
        "An oversimplified random forest algorithm can be explained as data that is predicted going through all trees. Each tree classifies the data, and then the average of this classification is calculated. Based on this, the classification is made (Brownlee, 2020b).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slaG69Wcr9iG"
      },
      "source": [
        "### Initialising the rf classifier\n",
        "When setting up the rf classifier, notice that the max_depth=2. This is necessary due to an otherwise very high training time. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFmgAw3jr-6m"
      },
      "source": [
        "# Setting up the classifier for the random forest algorithm\n",
        "classifier_rf = RandomForestClassifier(max_depth=2, random_state=1)\n",
        "\n",
        "X_cred_scaled = X_scaled_data[1]\n",
        "\n",
        "# Initialising an array to save values to\n",
        "score_list_cred = [[],[],[]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwRD4hcC7yNC"
      },
      "source": [
        "### Applying the SMOTE variants on the credit card fraud dataset \n",
        "We are doing the exact same approach as explained above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU24XE4j_xZg"
      },
      "source": [
        "# Repeating the 5 fold cross validation five times\n",
        "for i in range(5):\n",
        "\n",
        "  print(\"Now starting: \", name_arr[i])\n",
        "  f1scoretotal, AVPscoretotal, BAscoretotal = 0, 0, 0\n",
        "\n",
        "  # Splitting up the datasets into training data and test data\n",
        "  for train_index, test_index in kf.split(X_cred_scaled, y_cred):\n",
        "\n",
        "    X_train_2, X_test_2 = X_cred_scaled[train_index], X_cred_scaled[test_index]\n",
        "    y_train_2, y_test_2 = y_cred[train_index], y_cred[test_index]\n",
        "\n",
        "    # Oversampling the training data only\n",
        "    if i is not 0:\n",
        "      X_train_2, y_train_2 = sampler_list[i-1].sample(X_train_2, y_train_2)\n",
        "\n",
        "    # saving one training sample for later comparison\n",
        "    oversample_save[i][0] = X_train_2\n",
        "    oversample_save[i][1] = y_train_2\n",
        "\n",
        "    # Training the rf algorithm with the oversampled training data \n",
        "    classifier_rf.fit(X_train_2, y_train_2)\n",
        "\n",
        "    # Trying to predict the values from the test data\n",
        "    y_pred_2 = classifier_rf.predict(X_test_2)\n",
        "\n",
        "    # Calculating f1score, average precision and balanced accuracy\n",
        "    f1score = f1_score(y_test_2,y_pred_2)\n",
        "    f1scoretotal = f1score + f1scoretotal\n",
        "\n",
        "    AVPscore = average_precision_score(y_test_2,y_pred_2)\n",
        "    AVPscoretotal = AVPscore + AVPscoretotal\n",
        "\n",
        "    BAscore = balanced_accuracy_score(y_test_2,y_pred_2)\n",
        "    BAscoretotal = BAscore + BAscoretotal\n",
        "\n",
        "  # Saving the calculated values as averages in an array for later access\n",
        "  score_list_cred[0].append(round((f1scoretotal/5),3))\n",
        "  score_list_cred[1].append(round((AVPscoretotal/5),3))\n",
        "  score_list_cred[2].append(round((BAscoretotal/5),3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "An1MU9xytDSW"
      },
      "source": [
        "### Example graphs before and after applying SMOTE on credit card fraud dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "LhJWfVKoj1uh",
        "outputId": "cc8192d6-9453-4de3-bb1a-13d77dcf0d0f"
      },
      "source": [
        "# Setting up subplots so the plots are next to eachother\n",
        "f_cred, ax_cred = plt.subplots(1,5,figsize=(20,4))\n",
        "\n",
        "# Iterating through all 4 SMOTE algorithms and generating the plots accordingly\n",
        "for i in range(5):\n",
        "  X_cred_samp = oversample_save[i][0]\n",
        "  y_cred_samp = oversample_save[i][1]\n",
        "  label_cap = ''.join(['V1\\n Plot 3.',str(i+1)])\n",
        "  generateScatterPlot(ax_cred[i], f_cred, X_cred_samp, y_cred_samp, name_arr[i], label_cap, 'V2')\n",
        "\n",
        "f_cred.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyHMr-GGt9s9"
      },
      "source": [
        "The five plots displayed above showcase the original credit card fraud dataset and the datasets with SMOTE applied on them. The x-axis displays the V1 values and the y-axis displays the V2 values.\n",
        "\n",
        "**Plot 3.1** is our original dataset, seeing that there is a somewhat minority class cluster in the top right corner.\n",
        "\n",
        "**Plots 3.2 - 3.5** showcase an almost evolutionary process. Becoming more precise with the creation of minority class clusters. \n",
        "\n",
        "At the beginning of this evolution, **Plot 3.2** has only a single, wide minority cluster pointing in one direction. Whereas by evolution, **Plot 3.5** has many small minority clusters pointing into several directions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qhu6uFtB76Jm"
      },
      "source": [
        "## K-nearest neighbor trained with ParkourMaker dataset\n",
        "The parkour dataset will be evaluated with the k-nearest neighbor algorithm. An identical procedure as the other aforementioned datasets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dx2TJ4u3qyH"
      },
      "source": [
        "### Setting up knn classifier\n",
        "Setting up a new knn classifier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRAz134z3yYD"
      },
      "source": [
        "# Setting up the classifier for the random forest algorithm\n",
        "# classifier_rf_pk = RandomForestClassifier(max_depth=2, random_state=1)\n",
        "classifier_kn_pk = KNeighborsClassifier(n_neighbors=30)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxEmsXbxNLxn"
      },
      "source": [
        "### Applying the SMOTE variants on the ParkourMaker dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKWn4CDYQDie"
      },
      "source": [
        "X_pk_scaled = X_scaled_data[2]\n",
        "\n",
        "# Initialising an array to save values to\n",
        "score_list_pk = [[],[],[]]\n",
        "\n",
        "for i in range(5):\n",
        "\n",
        "  print(\"Now starting: \", name_arr[i])\n",
        "  f1scoretotal, AVPscoretotal, BAscoretotal = 0, 0, 0\n",
        "\n",
        "\n",
        "  # Splitting up the datasets into training data and test data\n",
        "  for train_index, test_index in kf.split(X_pk_scaled, y_pk):\n",
        "\n",
        "    X_train_3, X_test_3 = X_pk_scaled[train_index], X_pk_scaled[test_index]\n",
        "    y_train_3, y_test_3 = y_pk[train_index], y_pk[test_index]\n",
        "\n",
        "    # Oversampling the training data only\n",
        "    if i is not 0:\n",
        "      X_train_3, y_train_3 = sampler_list[i-1].sample(X_train_3, y_train_3)\n",
        "\n",
        "    # saving one training sample for later comparison\n",
        "    oversample_save[i][0] = X_train_3\n",
        "    oversample_save[i][1] = y_train_3\n",
        "\n",
        "    # Training the rf algorithm with the oversampled training data \n",
        "    classifier_kn_pk.fit(X_train_3, y_train_3)\n",
        "\n",
        "    # Trying to predict the values from the test data\n",
        "    y_pred_3 = classifier_kn_pk.predict(X_test_3)\n",
        "\n",
        "    # Calculating f1score, average precision and balanced accuracy\n",
        "    f1score = f1_score(y_test_3,y_pred_3)\n",
        "    f1scoretotal = f1score + f1scoretotal\n",
        "\n",
        "    AVPscore = average_precision_score(y_test_3,y_pred_3)\n",
        "    AVPscoretotal = AVPscore + AVPscoretotal\n",
        "\n",
        "    BAscore = balanced_accuracy_score(y_test_3,y_pred_3)\n",
        "    BAscoretotal = BAscore + BAscoretotal\n",
        "\n",
        "  # Saving the calculated values as averages in an array for later access\n",
        "  score_list_pk[0].append(round((f1scoretotal/5),3))\n",
        "  score_list_pk[1].append(round((AVPscoretotal/5),3))\n",
        "  score_list_pk[2].append(round((BAscoretotal/5),3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMSyuT1B3ar8"
      },
      "source": [
        "### Example graphs before and after applying SMOTE on ParkourMaker dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "fyUuw9Xdka-3",
        "outputId": "cc5fcf6a-24be-459c-ab85-3742f9ea9072"
      },
      "source": [
        "# Setting up subplots so the plots are next to eachother\n",
        "f_pk, ax_pk = plt.subplots(1,5,figsize=(20,4))\n",
        "\n",
        "# Iterating through all 4 SMOTE algorithms and generating the plots accordingly\n",
        "for i in range(5):\n",
        "  X_pk_samp = oversample_save[i][0]\n",
        "  y_pk_samp = oversample_save[i][1]\n",
        "  label_cap = ''.join(['UniquePlays\\n Plot 4.',str(i+1)])\n",
        "  generateScatterPlot(ax_pk[i], f_pk, X_pk_samp, y_pk_samp, name_arr[i], label_cap, 'SuccessRate')\n",
        "\n",
        "f_pk.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmQCM_Tb5odU"
      },
      "source": [
        "**Plots 4.1 - 4.5** above describe the results of applying the oversampling variants. The x-axis shows unique plays and the y-axis the success rate.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcwbyaD868Ty"
      },
      "source": [
        "# Evaluating and analysing the results\n",
        "The following part will take a close look at the results of our analysis. We will be checking if the application of SMOTE onto imbalanced datasets is a valid solution for better classification, and will also try to evaluate errors and mistakes. These errors and mistakes will then be drawn into a conclusion. To evaluate this, the F1-score, average precision (AP) and balanced accuracy (BA) will be used. \n",
        "\n",
        "The F1 score is defined as a weighted average of the precision and recall. It reaches its best value at 1 and its worst value at 0 (sklearn).\n",
        "\\\n",
        "F1 is calculated as follows:\n",
        ">$F1 = 2 * \\frac{(precision * recall)}{(precision + recall)}$\n",
        "\n",
        "AP summarizes the precision-recall curve. It gets calculated as the weighted mean of precisions achieved at each threshold. The increase in recall from the previous threshold is used as the weight (sklearn). It is calculated as follows:\n",
        ">$AP = \\sum_n(R_n-R_{n-1})*P_n$\\\n",
        ">$R_n$: precision at the nth threshold\\\n",
        ">$P_n$: recall at nth treshold\n",
        "\n",
        "The balanced accuracy is defined as the average of recall obtained on each class (sklearn).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CN4DwLHafMj5"
      },
      "source": [
        "def generateBarPlot(ax, mam, cred, pk, title, i):\n",
        "  # data to plot\n",
        "  groups = 5\n",
        "\n",
        "  # create plot\n",
        "  index = np.arange(groups)\n",
        "  bar_width = 0.15\n",
        "  opacity = 1\n",
        "\n",
        "  obj1 = ax.bar(index, mam, bar_width,alpha=opacity,color='b',label='mam')\n",
        "  obj2 = ax.bar(index + bar_width, cred, bar_width,alpha=opacity,color='g',label='cred')\n",
        "  obj3 = ax.bar(index + 2*bar_width, pk, bar_width,alpha=opacity,color='r',label='pk')\n",
        "\n",
        "  stringlabel = label_cap = ''.join(['Datasets\\nPlot 5.',str(i+1)])\n",
        "\n",
        "  ax.set_xlabel(stringlabel)\n",
        "  ax.set_ylabel('Scores in %')\n",
        "  ax.set_title(title)\n",
        "  ax.set_xticklabels(['', 'Normal', 'Smote', 'Bord1', 'Bord2', 'Cure'])\n",
        "  ax.legend()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gT03I6-97Hyt"
      },
      "source": [
        "# Generating the plots for the different scores to compare them\n",
        "# f, ax = plt.subplots(1,3,figsize=(14,6))\n",
        "# generateBarPlot(ax[0], score_list_mam[0], score_list_cred[0], score_list_pk[0], 'F1 Scores', 0)\n",
        "# generateBarPlot(ax[1], score_list_mam[1], score_list_cred[1], score_list_pk[1], 'AP Scores', 1)\n",
        "# generateBarPlot(ax[2], score_list_mam[2], score_list_cred[2], score_list_pk[2], 'BA Scores', 2)\n",
        "# f.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUie61tBenEu"
      },
      "source": [
        "*Despite using preventative measures, setting a value for random_seed, after restarting the runtime of Google Collab results will vary slightly. With the restart creating a different environment for the random_seed values. Unable to find a working solution online, the code has been commented out and replaced with a picture of the current results.*\n",
        "\n",
        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)\n",
        "\n",
        "Each model in this report was evaluated using the 5x5 fold cross validation method. Accordingly, this analysis is robust with respect to randomness. The above plots are describing the results of no SMOTE application, as well as the application of SMOTE, Borderline-SMOTE, Borderline-SMOTE2 and CURE SMOTE. **Plot 5.1** shows F1 scores, **Plot 5.2** describes the AP scores and **Plot 5.3** shows the BA scores.\n",
        " \n",
        "Our analysis starts with the mammography dataset. We trained the k-nearest neighbor algorithm using our 5 different datasets and evaluated it using the 5x5 fold stratified cross validation method. We can see that the F1 scores as well as the AP scores have all fallen below the original value of 0.55, with Borderline-SMOTE coming the closest to the original value. Yet this is not necessarily a bad thing, with our BA scores increasing from 70% to 90%, with only Borderline-SMOTE2 falling below the original value from before. Concluding that the accuracy of our majority class predictions has decreased, but that of the minority class has increased significantly. Therefore when deciding on one algorithm for the mammography dataset, it is not necessary to choose one specific SMOTE algorithm. Due to the small variance of the F1-score, AP and BA values.\n",
        " \n",
        "However, we can observe a change in the credit card fraud dataset. Specifically after the application of the algorithm Borderline-SMOTE and CURE SMOTE we were able to achieve above average values after training with the random forest algorithm. The CURE SMOTE algorithm achieved an increase of almost 0.2 in all three scores, as well as the Borderline-SMOTE algorithm increasing by 0.05 in all three scores. Compared to the other models, this is an impressive improvement and can conclude that the Borderline-SMOTE and CURE SMOTE datasets, in combination with the random forest algorithm have harmonized particularly well. \n",
        " \n",
        "The CURE SMOTE algorithm has a 0.8 F1-score and 0.7 AP score, a lot higher than that of Borderline-SMOTE. With the real danger being Borderline-SMOTE2, which should also be fully avoided due to achieving worse results than even the normal dataset when looking at F1-score, AP and BA values. Therefore, it can be said that the CURE SMOTE should be chosen when deciding on an oversampling algorithm.\n",
        "\n",
        "\n",
        "The last dataset we looked at is the ParkourMaker dataset. Here we use the k-nearest neighbor algorithm for training. We can quickly see that F1 score, AP and BA values have improved slightly compared to the normal dataset. The BA score has the highest improvement with the SMOTE algorithm. But this is only a minimal difference compared to Borderline-SMOTE, Borderline-SMOTE2 and CURE SMOTE.\n",
        " \n",
        "The analysis of Han et al. reinforces the statements made here regarding Borderline SMOTE, stating that \"Experiments indicate that our methods behave better, which validates the efficiency of our methods\" (Han et al., 2005). The paper by Ma and Fan also indicates that CURE SMOTE showcases significant improvement. \"The training set produced from the proposed CURE SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm.\" (Ma and Fan, 2017).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkuchmJM8Gpc"
      },
      "source": [
        "# Summary\n",
        "In conclusion, this case study proves that oversampling with Borderline-SMOTE and CURE SMOTE offers a good solution when it comes to detecting the minority class in a severe imbalanced dataset. Although this can mostly be achieved by the cost of losing predictive value towards the majority class. \n",
        " \n",
        "This case study also proves that of the used SMOTE algorithms in this case study, CURE SMOTE is the most effective. Harmonizing with the credit card fraud dataset. Although the Borderline-SMOTE algorithms also work well with the datasets and more effectively compared to the SMOTE algorithm, it does not compare to CURE SMOTE.\n",
        " \n",
        "The cause for this could stem from using the random forest algorithm, which is said to handle high dimensional data well. However, the Borderline-SMOTE2 algorithm underperforms in every category and therefore would not be suggested for use here.\n",
        " \n",
        "We can also conclude that the application of one of the SMOTE variants is heavily dependent on the quality and type of datasets. If a dataset has two clusters dividing minority and majority classes, then the use of Borderline-SMOTE may be already enough. If the dataset has minority and majority mixed classes, then CURE SMOTE is a better approach.\n",
        " \n",
        "However, there are still many uncovered factors in this case study. Missed opportunities that might improve the predictive strength of these models that can be found in data preparation. The decision behind being better optimised. Or looking even further into more complex problems, such as why outliers were not removed from the train data after the application of CURE SMOTE. Which creates the potential for this algorithm to underperform.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tifMR4R_wSr"
      },
      "source": [
        "# References\n",
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        "**University of Rostock**. [online] Available at: [Accessed: 12 December 2020].\n",
        ">\n",
        "Brownlee, J. (2018) *A Gentle Introduction to k-fold Cross-Validation*.\n",
        "**Machine Learning Mastery**. [online] Available at: https://machinelearningmastery.com/k-fold-cross-validation/ [Accessed: 2 January 2021].\n",
        ">\n",
        "Brownlee, J. (2019a) *A Gentle Introduction to Imbalanced Classification*. **Machine Learning Mastery**. [online] Available at: https://machinelearningmastery.com/what-is-imbalanced-classification/ [Accessed: 2 January 2021].\n",
        " >\n",
        "Brownlee, J. (2019b) *Develop K-Nearest Neighbors in Python From Scratch*.\n",
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        ">\n",
        "Brownlee, J. (2020a) *Imbalanced Classification Model to Detect Mammography Microcalcifications*. **Machine Learning Mastery**. [online] Available at: https://machinelearningmastery.com/imbalanced-classification-model-to-detect-microcalcifications/ [Accessed: 30 December 2020].\n",
        ">\n",
        "Brownlee, J. (2020b) *How to Develop a Random Forest Ensemble in Python*. **Machine Learning Mastery**. [online] Available at: https://machinelearningmastery.com/random-forest-ensemble-in-python/ [Accessed: 2 January 2021].\n",
        ">\n",
        "Chawla N. (2005) *Data Mining for Imbalanced Datasets: An Overview*. **Knowledge Discovery Handbook**. pp.853-867 [online] Available at: https://www.academia.edu/29405695/DATA_MINING_FOR_IMBALANCED_DATASETS_AN_OVERVIEW [Accessed: 21 December 2020].\n",
        ">\n",
        "Han, H. et al. (2005) *Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning*. **Tsinghua University**. pp.878-887 [online] Available at: https://sci2s.ugr.es/keel/keel-dataset/pdfs/2005-Han-LNCS.pdf [Accessed: 12 December 2020].\n",
        ">\n",
        "He, H. and Garcia, E. (2009) *Learning from Imbalanced Data*. **IEEE**. Vol 21(9) pp.1263-1284. [online] Available at: https://ieeexplore.ieee.org/abstract/document/5128907 [Accessed: 28 December 2020].\n",
        ">\n",
        "Kaggle (2018) Credit Card Fraud Detection. [online] Available at: https://www.kaggle.com/mlg-ulb/creditcardfraud [Accessed: 3 January 2021].\n",
        ">\n",
        "Krawczyk, B. (2016) *Learning From Imbalanced Data: Open Challenges And Future Directions*. **Springer**. [online] Available at: https://doi.org/10.1007/s13748-016-0094-0 [Accessed: 18 December 2020].\n",
        ">\n",
        "Leskovec et al. (2016) Lecture 62 — *The CURE Algorithm (Advanced) | Stanford University*. **Artificial Intelligence - All In One**. [online] Available at: https://www.youtube.com/watch?v=JrOJspZ1CUw&ab_channel=ArtificialIntelligence-AllinOne [Accessed: 20 December 2021].\n",
        ">\n",
        "Ma, L. and Fan, S. (2017) *CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests*. **BMC Bioinformatics**. [online] Available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1578-z  \n",
        "[Accessed: 3 January 2021].\n",
        ">\n",
        "Mojang (2009) *Minecraft* [Video game]. Microsoft.\n",
        ">\n",
        "Paul, S. (2018) *Diving Deep with Imbalanced Data*.\n",
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        ">\n",
        "Schneider, J. (1997) *Cross Validation*. **Carnegie Mellon University**. [online] Available at: https://www.cs.cmu.edu/~schneide/tut5/node42.html#:~:text=The%20disadvantage%20of%20this%20method,training%20set%20k%20different%20times [Accessed: 5 January 2021].\n",
        ">\n",
        "Shelke, M. et al. (2017) *A Review on Imbalanced Data Handling Using Undersampling and Oversampling Technique*. **International Journal of Recent Trends in Engineering and Research**. Vol 3(4) pp.444-449. [online] Available: https://www.ijrter.com/papers/volume-3/issue-4/a-review-on-imbalanced-data-handling-using-undersampling-and-oversampling-technique.pdf [Accessed: 20 December 2020]\n",
        ">\n",
        "sklearn (no date) *average_precision_score*. [online] Available at: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score [Accessed: 8 January 2021].\n",
        ">\n",
        "sklearn (no date) *balanced_accuracy*. [online] Available at:\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score [Accessed: 8 January 2021].\n",
        " >\n",
        "sklearn (no date) *sklearn.metrics.f1_score*. [online] Available at: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score [Accessed: 8 January 2021].\n",
        " >\n",
        "Soni, A. (2020) *Advantages and Disadvantages of KNN*. [online] Available at: https://medium.com/@anuuz.soni/advantages-and-disadvantages-of-knn-ee06599b9336 [Accessed: 4 January 2021].\n",
        ">\n",
        "Trehan, D. (2020) *Why Choose Random Forest and Not Decision Trees*. **Towards AI**. [online] Available at: https://towardsai.net/p/machine-learning/why-choose-random-forest-and-not-decision-trees [Accessed: 4 January 2021].\n",
        ">\n",
        "Wagner, S. (2013) *Testing Classification on Oversampled Imbalance Data*. **Stack Exchange**. [online] Available at: https://stats.stackexchange.com/questions/60180/testing-classification-on-oversampled-imbalance-data [Accessed: 5 January 2021].\n",
        ">\n",
        "Woods, K. et al. (1993) *Comparative Evaluation of Pattern Recognition Techniques For Detection of Microcalcifications in Mammography*. **World Scientific**. Vol 7(6) pp. 1417-1436. [online] Available at: https://www.worldscientific.com/doi/abs/10.1142/S0218001493000698 [Accessed: 30 December 2020]."
      ]
    }
  ]
}