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aeyage/cpc251_project_part1_cancer6.ipynb

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Cervical Cancer Risk Classification
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cpc251_project_part1_cancer6.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/aeyage/0d8863e31f3e97b70e497072ce90658c/cpc251_project_part1_cancer6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"#### Project Description\n",
"The goal of this project is to train machine learning models for cervical cancer classification based on patient's risk factors. This is to help improve diagnosis of cervical cancer in patients as machine learning can be useful to provide better decision-making in healthcare."
],
"metadata": {
"id": "41BmZz2HoEXB"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "s-t3qmGdkl1D"
},
"source": [
"#### Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l7Wegg4Ikl1I"
},
"outputs": [],
"source": [
"%config Completer.use_jedi=False\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"pd.set_option(\"display.max_columns\", None)\n",
"import seaborn as sns\n",
"from sklearn.feature_selection import chi2, SelectKBest\n",
"from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, recall_score, precision_score, f1_score, classification_report\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier, plot_tree"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vpFXJ-jDkl1M"
},
"source": [
"#### Load the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "w6rpxbjZkl1O"
},
"outputs": [],
"source": [
"# The dataset uses \"?\" to represent missing values\n",
"# Replace them with NaN\n",
"df = pd.read_csv(\"risk_factors.csv\", na_values=\"?\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pZheinGPR2BS"
},
"source": [
"#### Understanding the data\n",
"\n",
"The given dataset has 858 entries and 36 columns, 34 of which are input features and the other 4 are target variables. For this classification problem, we chose \"Biopsy\" as our target variable.\n",
"\n",
"By observing the summary of the dataset, there were many null values, most notably feature 26 and 27. These two features were dropped from the dataset as there were not enough samples for imputation and this would affect the performance of our models during training.\n",
"For missing values in the other features, we imputed them by taking the median value of the given feature.\n",
"\n",
"A histogram of the target class \"Biopsy\" was also plotted to observe the distribution of target classes. We observed that the dataset was unbalanced, heavily favoring the '0' class."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MvpsPjL7kl1Q",
"outputId": "126b22a8-ce01-4cdb-b76a-0c8cf3dd2026"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 858 entries, 0 to 857\n",
"Data columns (total 36 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Age 858 non-null int64 \n",
" 1 Number of sexual partners 832 non-null float64\n",
" 2 First sexual intercourse 851 non-null float64\n",
" 3 Num of pregnancies 802 non-null float64\n",
" 4 Smokes 845 non-null float64\n",
" 5 Smokes (years) 845 non-null float64\n",
" 6 Smokes (packs/year) 845 non-null float64\n",
" 7 Hormonal Contraceptives 750 non-null float64\n",
" 8 Hormonal Contraceptives (years) 750 non-null float64\n",
" 9 IUD 741 non-null float64\n",
" 10 IUD (years) 741 non-null float64\n",
" 11 STDs 753 non-null float64\n",
" 12 STDs (number) 753 non-null float64\n",
" 13 STDs:condylomatosis 753 non-null float64\n",
" 14 STDs:cervical condylomatosis 753 non-null float64\n",
" 15 STDs:vaginal condylomatosis 753 non-null float64\n",
" 16 STDs:vulvo-perineal condylomatosis 753 non-null float64\n",
" 17 STDs:syphilis 753 non-null float64\n",
" 18 STDs:pelvic inflammatory disease 753 non-null float64\n",
" 19 STDs:genital herpes 753 non-null float64\n",
" 20 STDs:molluscum contagiosum 753 non-null float64\n",
" 21 STDs:AIDS 753 non-null float64\n",
" 22 STDs:HIV 753 non-null float64\n",
" 23 STDs:Hepatitis B 753 non-null float64\n",
" 24 STDs:HPV 753 non-null float64\n",
" 25 STDs: Number of diagnosis 858 non-null int64 \n",
" 26 STDs: Time since first diagnosis 71 non-null float64\n",
" 27 STDs: Time since last diagnosis 71 non-null float64\n",
" 28 Dx:Cancer 858 non-null int64 \n",
" 29 Dx:CIN 858 non-null int64 \n",
" 30 Dx:HPV 858 non-null int64 \n",
" 31 Dx 858 non-null int64 \n",
" 32 Hinselmann 858 non-null int64 \n",
" 33 Schiller 858 non-null int64 \n",
" 34 Citology 858 non-null int64 \n",
" 35 Biopsy 858 non-null int64 \n",
"dtypes: float64(26), int64(10)\n",
"memory usage: 241.4 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3-eq_XGpkl1V",
"outputId": "fd8d4e5e-eb58-45e0-ad6d-157839308597"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Age [18, 15, 34, 52, 46, 42, 51, 26, 45, 44, 27, 4...\n",
"Number of sexual partners [4.0, 1.0, 5.0, 3.0, 2.0, 6.0, nan, 7.0, 15.0,...\n",
"First sexual intercourse [15.0, 14.0, nan, 16.0, 21.0, 23.0, 17.0, 26.0...\n",
"Num of pregnancies [1.0, 4.0, 2.0, 6.0, 3.0, 5.0, nan, 8.0, 7.0, ...\n",
"Smokes [0.0, 1.0, nan]\n",
"Smokes (years) [0.0, 37.0, 34.0, 1.266972909, 3.0, 12.0, nan,...\n",
"Smokes (packs/year) [0.0, 37.0, 3.4, 2.8, 0.04, 0.5132021277, 2.4,...\n",
"Hormonal Contraceptives [0.0, 1.0, nan]\n",
"Hormonal Contraceptives (years) [0.0, 3.0, 15.0, 2.0, 8.0, 10.0, 5.0, 0.25, 7....\n",
"IUD [0.0, 1.0, nan]\n",
"IUD (years) [0.0, 7.0, nan, 5.0, 8.0, 6.0, 1.0, 0.58, 2.0,...\n",
"STDs [0.0, 1.0, nan]\n",
"STDs (number) [0.0, 2.0, 1.0, nan, 3.0, 4.0]\n",
"STDs:condylomatosis [0.0, 1.0, nan]\n",
"STDs:cervical condylomatosis [0.0, nan]\n",
"STDs:vaginal condylomatosis [0.0, nan, 1.0]\n",
"STDs:vulvo-perineal condylomatosis [0.0, 1.0, nan]\n",
"STDs:syphilis [0.0, 1.0, nan]\n",
"STDs:pelvic inflammatory disease [0.0, nan, 1.0]\n",
"STDs:genital herpes [0.0, nan, 1.0]\n",
"STDs:molluscum contagiosum [0.0, nan, 1.0]\n",
"STDs:AIDS [0.0, nan]\n",
"STDs:HIV [0.0, 1.0, nan]\n",
"STDs:Hepatitis B [0.0, nan, 1.0]\n",
"STDs:HPV [0.0, nan, 1.0]\n",
"STDs: Number of diagnosis [0, 1, 3, 2]\n",
"STDs: Time since first diagnosis [nan, 21.0, 2.0, 15.0, 19.0, 3.0, 12.0, 1.0, 1...\n",
"STDs: Time since last diagnosis [nan, 21.0, 2.0, 15.0, 19.0, 3.0, 12.0, 1.0, 1...\n",
"Dx:Cancer [0, 1]\n",
"Dx:CIN [0, 1]\n",
"Dx:HPV [0, 1]\n",
"Dx [0, 1]\n",
"Hinselmann [0, 1]\n",
"Schiller [0, 1]\n",
"Citology [0, 1]\n",
"Biopsy [0, 1]\n",
"dtype: object"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"# Get the unique values in each column\n",
"pd.Series({column : df[column].unique() for column in df})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "beUxvx8bkl1b",
"outputId": "139eceb7-03f8-4e2a-a57c-bd231974901f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(858, 34)"
]
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"# Drop feature 26 and 27 because they have too many nulls\n",
"df = df.drop(df.columns[26:28], axis=1)\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EIqOhLFwkl1g"
},
"outputs": [],
"source": [
"# Impute null values in features with its median\n",
"df = df.fillna(df.median())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0FfGpU3Wkl1i"
},
"outputs": [],
"source": [
"# Take Biopsy as the target variable\n",
"X = df.iloc[:,:-4].values\n",
"y = df.iloc[:,-1].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"id": "71vCyeQ_bA6p",
"outputId": "0275d9c4-5c67-404b-838d-a0d5168c01f1"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Distribution of classes in Biopsy')"
]
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# Plot histogram to show distribution of classes in Biopsy\n",
"ax = sns.countplot(x=df[\"Biopsy\"])\n",
"ax.bar_label(ax.containers[0])\n",
"ax.set_title(\"Distribution of classes in Biopsy\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v08PXmtUsYfy"
},
"source": [
"As shown in the figure above, there is a class imbalance in the \"Biopsy\" target, heavily skewed towards the '0' class. As a result, only 6.41% of the patients reported positive for \"Biopsy\" and this bias would affect the decisions made when constructing the models."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HcS4KPAEkl1k"
},
"source": [
"#### Split the dataset\n",
"Split the dataset into training, validation and test sets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mOZWUpq6kl1m"
},
"outputs": [],
"source": [
"# Stratify the split to ensure that each class is represented\n",
"# Do an 8:2 split for training and test set\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,\n",
" test_size=0.2, random_state=42)\n",
"\n",
"# Use training set to split 8:2 for validation set\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, stratify=y_train,\n",
" test_size=0.2, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_BJfxB08kl1p"
},
"source": [
"#### Data preprocessing\n",
"Perform data preprocessing such as normalization, standardization, label encoding etc.\n",
"______________________________________________________________________________________\n",
"Description:\n",
"\n",
"For data preprocessing, we performed feature scaling to ensure that the features were all on the same scale as this could affect the performance of the model during training such as slower converge during gradient descent. The feature scaling method chosen was standardisation to scale the features to have mean, $\\mu=0$ and standard deviation, $\\sigma=1$. The `StandardScaler` class was used to perform the standardisation.\n",
"\n",
"The scaler was first fitted with the input features of the training set to calculate and standard deviation for later scaling. Then, the same scaler was used to transform the input features of the training, validaton, and testing sets to ensure consistent scaling and reliable comparisons.\n",
"\n",
"Label encoding was not required as the features for our dataset were numerical and there were no categorical data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "prECFULFkl1q"
},
"outputs": [],
"source": [
"# Perform standardisation\n",
"scaler = StandardScaler()\n",
"scaler.fit(X_train)\n",
"# Transform the input features with the fitted scaler\n",
"X_train = scaler.transform(X_train)\n",
"X_test = scaler.transform(X_test)\n",
"X_valid = scaler.transform(X_valid)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u7q1fiJNkl1r"
},
"source": [
"#### Feature Selection\n",
"Perform feature selection to select the relevant features.\n",
"______________________________________________________________________________________\n",
"Description:\n",
"\n",
"Chi-squared feature selection was performed to select the relevant features. The `chi2` and `SelectKBest` classes from `sklearn.feature_selection` module were used. For our project, we set $k=10$ to only extract the top 10 features based on chi-squared scores. The `SelectKBest` object was fitted with the $X$ and $y$ values of the dataset and a list of features and their scores were shown.\n",
"\n",
"We validated the selected features by comparing with findings from previous studies on the subject such as by [Wu and Zhou](https://doi.org/10.1109/ACCESS.2017.2763984)[1] and [Ijaz et al.](https://doi.org/10.3390/s20102809)[2].\n",
"These findings suggest that the highest risk factors for cervical cancers were Hormonal Contraceptives(years), Smoke(years), STDs: Number of diagnosis, Dx: Cancer, DX: HPV, and Dx. Several of these factors were chosen by our chi-squared feature selection, thus we proceeded with the features selected.\n",
"\n",
"Then, the features for training, validation and testing set were transformed using the same feature selector to only include the top 10 scoring features in our model evaluation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iXn_m65Wkl1s",
"outputId": "93f5b78d-c229-4a7e-cb84-96d640fe85f2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Hormonal Contraceptives (years), chi2_score = 47.5038\n",
"Smokes (years) , chi2_score = 44.0655\n",
"Dx:HPV , chi2_score = 21.7479\n",
"Dx:Cancer , chi2_score = 21.7479\n",
"Dx , chi2_score = 20.7164\n",
"STDs (number) , chi2_score = 16.5007\n",
"STDs:genital herpes , chi2_score = 14.6000\n",
"STDs:HIV , chi2_score = 13.6986\n",
"Dx:CIN , chi2_score = 10.8740\n",
"STDs , chi2_score = 10.1502\n"
]
}
],
"source": [
"# Perform Chi-squared feature selection\n",
"feature_selector = SelectKBest(chi2, k=10)\n",
"feature_selector.fit(X, y)\n",
"\n",
"# Get the Chi-squared scores of selected features\n",
"chi2_scores = feature_selector.scores_[feature_selector.get_support()]\n",
"\n",
"# Mask feature names according to selected features.\n",
"selected_features = feature_selector.get_feature_names_out(df.columns[:-4])\n",
"\n",
"# Sort the feature in descending order\n",
"sorted_indices = np.argsort(chi2_scores)[::-1]\n",
"sorted_features = [selected_features[i] for i in sorted_indices]\n",
"sorted_scores = chi2_scores[sorted_indices]\n",
"\n",
"# Print the list of features and scores\n",
"max_feature_width = max(len(feature) for feature in sorted_features)\n",
"for feature, score in zip(sorted_features, sorted_scores):\n",
" print(f\"{feature.ljust(max_feature_width)}, chi2_score = {score:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Q0pZ6nFMkl1u"
},
"outputs": [],
"source": [
"# Transform the data to have only selected features\n",
"X_train = feature_selector.transform(X_train)\n",
"X_test = feature_selector.transform(X_test)\n",
"X_valid = feature_selector.transform(X_valid)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xn2EK7WBkl1v"
},
"source": [
"#### Data modeling\n",
"Build the machine learning models. You must build atleast two (2) predictive models. One of the predictive models must be either Decision Tree or Support Vector Machine.\n",
"______________________________________________________________________________________\n",
"Description:\n",
"\n",
"Building the models is an iterative process. First, the models were trained using the training set. Then, the performance of the trained model was evaluated using the validation set to fine-tune the parameters. Finally, the model with the best parameters was used to evaluate the final performance of the model by comparing with the testing set.\n",
"\n",
"\n",
"Due to the imbalance of classes in the data, several considerations were made when evaluating the performance of the models during training.For our problem, accuracy was not a suitable evaluation as it can produce misleading results by naively predicting all classes as '0'. Therefore, we chose the macro averaged F1-score as it provides a better assessment of the model's performance in imbalanced datasets by using the harmonic mean between precision and recall.\n",
"\n",
"\n",
"The first model trained is k-nearest neighbors(KNN). The main parameter affecting the model performance here is the $k$ value, number of nearest neighbors. To find the optimal value for $k$, we plotted a graph of F1-score against different values of $k$. The result of the plot showed a baseline model to work with. Then, we tried finding the optimal parameters for the classifier by using `GridSearchCV`. A dictionary of parameters to be experimented with was defined, namely the number of nearest neighbors, $k$, type of weight function and the type of metric used for calculating the distance between data points. `GridSearchCV` was fitted with the training data to find optimal parameters, and used to test on the validation set for evaluation. The parameters of the KNN model were also experimented by trial and error to see if a better set of parameters could be found.\n",
"\n",
"The second model trained is decision tree(DT). There are many parameters that can be tuned to affect the splitting of the decision tree. For our project we only experimented with the `min_samples_split` and `max_depth` for simplicity as we found that tuning two or more parameters changed the shape of the decision tree drastically and difficult to interprete. Furthermore, `class_weights` was set to `balanced` to give more importance to minority class in the imbalanced dataset and the decision criterion used was gini impurity as it performed better than entropy for our problem. Initially, the optimal parameters were computed using `GridSearch` but did not perform as well so we opted to tune the parameters by trial and error. The trained DT model's performance was measured by evaluating on the validation set. The DT was also visualised during training by using `plot_tree`.\n",
"\n",
"\n",
"The third model trained is logistic regression with gradient descent. The parameters used were the loss function, learning rate, and class weights. The loss function chosen was log loss because the problem is for binary classification. `eta0` is the initial learning rate, and by setting `learning_rate` to `constant`, we ensure that the learning rate is constant during each iteration of gradient descent. Again, `class_weight` was set to `balanced` to emphasise the minority class in the dataset. The performance of the model during training was evaluated using the validation set, and was fine-tuned by trying different `eta0` and `max_iter` values through trial and error.\n",
"\n",
"A summary of the best parameters for each model is shown in the table below.\n",
"\n",
"| Model | Parameters | | | |\n",
"|-------|---------------------------------------------------------------------------------------------------------------|---|---|---|\n",
"| KNN | n_neighbors=3, weights=\"distance\", metric=\"euclidean | | | |\n",
"| Decision Tree | min_samples_split=2, max_depth=4, class_weight=\"balanced\", random_state=42 | | | |\n",
"| Logistic Regression | loss=\"log_loss\", eta0=0.001, max_iter=1000, class_weight=\"balanced\", learning_rate=\"constant\", random_state=42 | | | |"
]
},
{
"cell_type": "markdown",
"source": [
"##### K-nearest neighbors (KNN)"
],
"metadata": {
"id": "KwlmGIsWh5lq"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "cfDDC14mkl1w",
"outputId": "9a974853-a336-402d-8c0a-79f976f4ab0b"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# KNN\n",
"\n",
"# Plot f1 score against k\n",
"score = []\n",
"for i in range(1, 31):\n",
" knn = KNeighborsClassifier(n_neighbors=i).fit(X_train, y_train)\n",
" yhat = knn.predict(X_test)\n",
" score.append(f1_score(y_test, yhat, average=\"macro\"))\n",
"\n",
"plt.plot(range(1,31), score)\n",
"plt.title(\"F1-score against k\")\n",
"plt.xlabel(\"Number of neighbors, k\")\n",
"plt.ylabel(\"F1-score\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PkVOOu72kl1x",
"outputId": "88fe9571-00a5-437e-b5b3-3789ec7ffed8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fitting 5 folds for each of 180 candidates, totalling 900 fits\n",
"{'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'uniform'}\n",
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.99 0.96 129\n",
" 1 0.00 0.00 0.00 9\n",
"\n",
" accuracy 0.93 138\n",
" macro avg 0.47 0.50 0.48 138\n",
"weighted avg 0.87 0.93 0.90 138\n",
"\n"
]
}
],
"source": [
"# Find optimal parameters for KNN using GridSearchCV\n",
"\n",
"# Define a base KNN model\n",
"knn = KNeighborsClassifier()\n",
"\n",
"# Define the parameters to experiment\n",
"search_params = {\n",
" \"n_neighbors\": range(1,31),\n",
" \"weights\": [\"uniform\", \"distance\"],\n",
" \"metric\": [\"euclidean\", \"manhattan\", \"minkowski\"]\n",
"}\n",
"\n",
"# Fit the grid search\n",
"grid_search = GridSearchCV(estimator=knn, param_grid=search_params,\n",
" scoring=\"f1\", verbose=1, cv=5)\n",
"grid_search.fit(X_train, y_train)\n",
"\n",
"# Print the optimal parameters\n",
"print(grid_search.best_params_)\n",
"\n",
"# Train KNN model with optimal parameters found by GridSearchCV\n",
"knn_grid_search = KNeighborsClassifier(**grid_search.best_params_)\n",
"knn_grid_search.fit(X_train, y_train)\n",
"\n",
"# Test on validation set\n",
"y_pred = knn_grid_search.predict(X_valid)\n",
"print(classification_report(y_valid, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N5gO6XStgUx3"
},
"source": [
"We tuned the parameters for the KNN model through trial and error as the performance with the best parameters by `GridSearchCV` was not up to our expectations.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "waQoQ7M8Y60S",
"outputId": "cfb77117-7bdf-41e7-a995-057f9dc30f65"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.94 1.00 0.97 129\n",
" 1 1.00 0.11 0.20 9\n",
"\n",
" accuracy 0.94 138\n",
" macro avg 0.97 0.56 0.58 138\n",
"weighted avg 0.95 0.94 0.92 138\n",
"\n"
]
}
],
"source": [
"# Find the best parameters for KNN by trial and error\n",
"knn = KNeighborsClassifier(n_neighbors=3, weights=\"distance\", metric=\"euclidean\")\n",
"\n",
"# Fit the training set\n",
"knn.fit(X_train, y_train)\n",
"\n",
"# Evaluated tuned parameters with validation set\n",
"y_pred = knn.predict(X_valid)\n",
"print(classification_report(y_valid, y_pred))"
]
},
{
"cell_type": "markdown",
"source": [
"##### Decision Tree"
],
"metadata": {
"id": "YIRJiUIbh_Rq"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 998
},
"id": "OWR7OC-STZVz",
"outputId": "ceeb60da-a6df-425c-e3b7-0334cd4186ef"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
"{'max_depth': 3, 'min_samples_split': 2}\n",
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.95 0.95 129\n",
" 1 0.30 0.33 0.32 9\n",
"\n",
" accuracy 0.91 138\n",
" macro avg 0.63 0.64 0.63 138\n",
"weighted avg 0.91 0.91 0.91 138\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1600x1000 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# Find optimal parameters for decision tree using GridSearchCV\n",
"\n",
"# Define a base DecisionTree model\n",
"dt_grid_search = DecisionTreeClassifier(class_weight=\"balanced\", random_state=42)\n",
"\n",
"# Define the parameters to experiment\n",
"search_params = {\n",
" \"min_samples_split\": [2, 3, 4],\n",
" \"max_depth\": [3, 4, 5],\n",
"}\n",
"\n",
"# Fit the grid search\n",
"grid_search = GridSearchCV(estimator=dt_grid_search, param_grid=search_params,\n",
" scoring=\"f1\", verbose=1, cv=5)\n",
"grid_search.fit(X_train, y_train)\n",
"\n",
"# Print the optimal parameters\n",
"print(grid_search.best_params_)\n",
"\n",
"# Train the decision tree with the optimal parameters found by GridSearch\n",
"dt_grid_search = DecisionTreeClassifier(**grid_search.best_params_,\n",
" class_weight=\"balanced\", random_state=42)\n",
"dt_grid_search.fit(X_train, y_train)\n",
"\n",
"# Test on validation set\n",
"y_pred = grid_search.predict(X_valid)\n",
"print(classification_report(y_valid, y_pred))\n",
"\n",
"# Visualise the decision tree\n",
"plt.figure(figsize=(16, 10))\n",
"plot_tree(dt_grid_search)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"source": [
"We tuned the parameters for the Decision Tree model through trial and error as the performance with the best parameters by `GridSearchCV` was not up to our expectations.\n"
],
"metadata": {
"id": "nv80LyihjWbI"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 963
},
"id": "kyWPA7dKkl10",
"outputId": "2cfc415e-e204-473b-fa95-a72470a02214"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.97 0.95 0.96 129\n",
" 1 0.42 0.56 0.48 9\n",
"\n",
" accuracy 0.92 138\n",
" macro avg 0.69 0.75 0.72 138\n",
"weighted avg 0.93 0.92 0.93 138\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1600x1000 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# Find the best parameters for DecisionTree by trial and error\n",
"dt = DecisionTreeClassifier(min_samples_split=2, max_depth=4,\n",
" class_weight=\"balanced\", random_state=42)\n",
"\n",
"# Fit the training set\n",
"dt.fit(X_train, y_train)\n",
"\n",
"# Evaluate tuned parameters with validation set\n",
"y_pred = dt.predict(X_valid)\n",
"print(classification_report(y_valid, y_pred))\n",
"\n",
"# Visualise the decision tree\n",
"plt.figure(figsize=(16, 10))\n",
"plot_tree(dt)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"source": [
"##### Logistic Regression"
],
"metadata": {
"id": "NM-9psFyiDvI"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LL5V6Z4xXDfE",
"outputId": "5afb0e5d-8605-4285-d102-b6b57dd78f6c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.93 0.94 129\n",
" 1 0.25 0.33 0.29 9\n",
"\n",
" accuracy 0.89 138\n",
" macro avg 0.60 0.63 0.61 138\n",
"weighted avg 0.91 0.89 0.90 138\n",
"\n"
]
}
],
"source": [
"# Logistic regression using gradient descent\n",
"\n",
"# Tune the learning rate by trial and error\n",
"lr = SGDClassifier(loss=\"log_loss\", eta0=0.001, max_iter=1000,\n",
" class_weight=\"balanced\", learning_rate=\"constant\",\n",
" random_state=42)\n",
"\n",
"# Fit the training set\n",
"lr.fit(X_train, y_train)\n",
"\n",
"# Evaluate tuned parameters with validation set\n",
"y_pred = lr.predict(X_valid)\n",
"print(classification_report(y_valid, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fK-8R2tSkl14"
},
"source": [
"#### Evaluate the models\n",
"Perform a comparison between the predictive models. <br>\n",
"Report the accuracy, recall, precision and F1-score measures as well as the confusion matrix if it is a classification problem. <br>\n",
"Report the R2 score, mean squared error and mean absolute error if it is a regression problem.\n",
"______________________________________________________________________________________\n",
"Description:\n",
"\n",
"\n",
"Each trained model was used to predict the testing set to evaluate the final performance of the models for comparison. We report the accuracy, precision, recall, F1-score and confusion matrix for evaluation. The precision, recall and F1-score were reported as macro averages to apply penalty when the model fails to predict the minority class, which was suitable for our unbalanced dataset. The results are reported in the table below.\n",
"\n",
"\n",
"| Model | Accuracy | Precision | Recall | F1-score |\n",
"|-------|----------|-----------|--------|----------|\n",
"| Decision Tree | 0.83 | 0.54 | 0.57 | 0.54 |\n",
"| KNN | 0.92 | 0.47 | 0.49 | 0.48 |\n",
"| Logistic Regression | 0.83 | 0.55 | 0.61 | 0.56 |\n",
"\n",
"From the results, we concluded that the most suitable model was the logistic regression model as it performed the best in terms of F1-score compared to DT and KNN. Although KNN reported the highest accuracy at 0.92, this was not representative of our problem use case and it performed worse in precision, recall and F1-score.\n",
"\n",
"However, even the best model was only able to achieve 0.56 F1-score, which can be explained by the lack of representation of '1' class in the data. Other methods such as oversampling may be considered to address the class imbalance but lie outside the scope of this project.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KWZg4YcWT9va"
},
"outputs": [],
"source": [
"# Helper function to print evaluation metrics\n",
"def show_metrics(y, yhat, clf):\n",
" print(f\"Accuracy: {accuracy_score(y, yhat):.4f}\")\n",
" print(f\"Precision = {precision_score(y, yhat, average='macro'):.4f}\")\n",
" print(f\"Recall = {recall_score(y, yhat, average='macro'):.4f}\")\n",
" print(f\"F1-score = {f1_score(y, yhat, average='macro'):.4f}\")\n",
"\n",
" cm = confusion_matrix(y, yhat, labels=clf.classes_)\n",
" disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
" display_labels=clf.classes_)\n",
" disp.plot()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 559
},
"id": "WEdudcNJkl15",
"outputId": "2b99e632-6f3b-4ed1-83df-b7d1900c8782"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Decision tree model\n",
"-------------------\n",
"Accuracy: 0.8314\n",
"Precision = 0.5355\n",
"Recall = 0.5711\n",
"F1-score = 0.5388\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# Predict the test set with DT model\n",
"y_pred = dt.predict(X_test)\n",
"\n",
"print(\"Decision tree model\\n-------------------\")\n",
"show_metrics(y_test, y_pred, dt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 553
},
"id": "UHFdjuBqkl15",
"outputId": "47d1abff-f8f9-4bf2-caab-91a7f8a0adc7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"K-nearest neighbors model\n",
"-------------------------\n",
"Accuracy: 0.9244\n",
"Precision = 0.4676\n",
"Recall = 0.4938\n",
"F1-score = 0.4804\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# Predict the test set with KNN model\n",
"y_pred = knn.predict(X_test)\n",
"\n",
"print(\"K-nearest neighbors model\\n-------------------------\")\n",
"show_metrics(y_test, y_pred, knn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 553
},
"id": "z2luMxxXX2ki",
"outputId": "adbc4108-23d8-4687-9293-509377ba1caa"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Logistic regression model\n",
"-------------------------\n",
"Accuracy: 0.8256\n",
"Precision = 0.5499\n",
"Recall = 0.6104\n",
"F1-score = 0.5562\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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tWxQTE+NJ9JKUmpqqkJAQbd261afvI9kDAOCDxMREr3VacnJyfL7HyfVfzrY2TFFRkVq1auV1vFGjRoqNjT3t+jFnQxsfAGAL/mrjFxQUyOl0evYHw2vcqewBALZgSCYXwjnB6XR6beeS7E+u/3K2tWHi4+N1+PBhr+PHjx9XSUnJadePORuSPQDAFhr60buzSUpKUnx8vNat+2l9hvLycm3dulUpKSmSpJSUFJWWlmrHjh2ec9avXy+3263k5GSfvo82PgAA9aCiokJ5eXmez/n5+dq1a5diY2PVtm1bTZgwQY8//rg6deqkpKQkZWVlKSEhwbMwXNeuXTVo0CCNGTNGixYtUm1trTIyMjRq1CifZuJLJHsAgE009KN327dvV//+/T2fMzMzJUnp6elasmSJJk2apMrKSo0dO1alpaW6+uqrtWbNGkVE/LSy4rJly5SRkaEbbrhBISEhGjFihObOnetz7CR7AIAtNHSy79evnwzDOONxh8Oh7OxsZWdnn/Gc2NhYLV++3KfvPR3G7AEAsDgqewCALbDELQAAFmcYDhkmEraZawONNj4AABZHZQ8AsAU7r2dPsgcA2IKdx+xp4wMAYHFU9gAAW7DzBD2SPQDAFuzcxifZAwBswc6VPWP2AABYHJU9AMAWDJNt/GCu7En2AABbMCSdZV2aOl0frGjjAwBgcVT2AABbcMshB2/QAwDAupiNDwAALIvKHgBgC27DIQcv1QEAwLoMw+Rs/CCejk8bHwAAi6OyBwDYgp0n6JHsAQC2QLIHAMDi7DxBjzF7AAAsjsoeAGALdp6NT7IHANjCiWRvZszej8E0MNr4AABYHJU9AMAWmI0PAIDFGTK3Jn0Qd/Fp4wMAYHVU9gAAW6CNDwCA1dm4j0+yBwDYg8nKXkFc2TNmDwCAxVHZAwBsgTfoAQBgcXaeoEcbHwAAi6OyBwDYg+EwN8kuiCt7kj0AwBbsPGZPGx8AAIujsgcA2AMv1QEAwNrsPBu/Tsn+3XffrfMNb7nllnMOBgAA+F+dkv2wYcPqdDOHwyGXy2UmHgAA6k8Qt+LNqNMEPbfbXaeNRA8AOF+dbOOb2XzhcrmUlZWlpKQkRUZG6qKLLtJjjz0m4z+m9RuGoWnTpql169aKjIxUamqq9u7d6++fbm42flVVlb/iAACgfhl+2Hzw5JNPauHChZo/f7727NmjJ598UrNmzdK8efM858yaNUtz587VokWLtHXrVjVt2lQDBw70e371Odm7XC499thjuvDCC9WsWTN98803kqSsrCy9/PLLfg0OAIBgtXnzZg0dOlQ333yz2rdvr1tvvVUDBgzQp59+KulEVT9nzhw9/PDDGjp0qC677DItXbpUhYWFWrlypV9j8TnZz5gxQ0uWLNGsWbMUFhbm2X/ppZfqpZde8mtwAAD4j8MPm1ReXu61VVdXn/bbrrrqKq1bt05ff/21JGn37t36+OOPNXjwYElSfn6+ioqKlJqa6rkmOjpaycnJ2rJli19/uc/JfunSpXrhhReUlpam0NBQz/4ePXroq6++8mtwAAD4jZ/a+ImJiYqOjvZsOTk5p/26KVOmaNSoUerSpYsaN26sXr16acKECUpLS5MkFRUVSZLi4uK8rouLi/Mc8xefn7M/ePCgOnbseMp+t9ut2tpavwQFAMD5qqCgQE6n0/M5PDz8tOe9+eabWrZsmZYvX65LLrlEu3bt0oQJE5SQkKD09PSGClfSOST7bt26adOmTWrXrp3X/r/85S/q1auX3wIDAMCv/PQGPafT6ZXsz+TBBx/0VPeS1L17d3333XfKyclRenq64uPjJUnFxcVq3bq157ri4mL17NnTRKCn8jnZT5s2Tenp6Tp48KDcbrfeeecd5ebmaunSpVq9erVfgwMAwG8aeNW7o0ePKiTEe7Q8NDRUbrdbkpSUlKT4+HitW7fOk9zLy8u1detW3XPPPece52n4nOyHDh2qVatWKTs7W02bNtW0adN0+eWXa9WqVbrxxhv9GhwAAMFqyJAhmjFjhtq2batLLrlEn332mZ599lndddddkk68iG7ChAl6/PHH1alTJyUlJSkrK0sJCQl1fpldXZ3Tu/GvueYarV271q+BAABQnxp6idt58+YpKytL9957rw4fPqyEhAT97ne/07Rp0zznTJo0SZWVlRo7dqxKS0t19dVXa82aNYqIiDj3QE/jnBfC2b59u/bs2SPpxDh+7969/RYUAAB+18Cr3kVFRWnOnDmaM2fOGc9xOBzKzs5Wdna2icB+mc/J/sCBA7rtttv0j3/8QzExMZKk0tJSXXXVVXr99dfVpk0bf8cIAABM8Pk5+7vvvlu1tbXas2ePSkpKVFJSoj179sjtduvuu++ujxgBADDv5AQ9M1uQ8rmy37BhgzZv3qzOnTt79nXu3Fnz5s3TNddc49fgAADwF4dxYjNzfbDyOdknJiae9uU5LpdLCQkJfgkKAAC/a+Ax+/OJz238p556SuPHj9f27ds9+7Zv3677779fTz/9tF+DAwAA5tWpsm/evLkcjp/GKiorK5WcnKxGjU5cfvz4cTVq1Eh33XWX358NBADALxr4pTrnkzol+7M9NgAAQFCwcRu/Tsm+oV/YDwAA/OecX6ojSVVVVaqpqfHaV5fFAQAAaHA2rux9nqBXWVmpjIwMtWrVSk2bNlXz5s29NgAAzkt+Ws8+GPmc7CdNmqT169dr4cKFCg8P10svvaTp06crISFBS5curY8YAQCACT638VetWqWlS5eqX79+uvPOO3XNNdeoY8eOateunZYtW6a0tLT6iBMAAHNsPBvf58q+pKREHTp0kHRifL6kpESSdPXVV2vjxo3+jQ4AAD85+QY9M1uw8jnZd+jQQfn5+ZKkLl266M0335R0ouI/uTAOAAA4f/ic7O+8807t3r1bkjRlyhQtWLBAERERmjhxoh588EG/BwgAgF/YeIKez2P2EydO9PxzamqqvvrqK+3YsUMdO3bUZZdd5tfgAACAeaaes5ekdu3aqV27dv6IBQCAeuOQyVXv/BZJw6tTsp87d26db3jfffedczAAAMD/6pTsZ8+eXaebORyOgCT74d37qJGjcYN/L9AQjOovAx0CUG/cxqlLptcbGz96V6dkf3L2PQAAQYvX5QIAAKsyPUEPAICgYOPKnmQPALAFs2/Bs9Ub9AAAQHChsgcA2ION2/jnVNlv2rRJt99+u1JSUnTw4EFJ0muvvaaPP/7Yr8EBAOA3Nn5drs/J/u2339bAgQMVGRmpzz77TNXV1ZKksrIyzZw50+8BAgAAc3xO9o8//rgWLVqkF198UY0b//Qim759+2rnzp1+DQ4AAH+x8xK3Po/Z5+bm6tprrz1lf3R0tEpLS/0REwAA/mfjN+j5XNnHx8crLy/vlP0ff/yxOnTo4JegAADwO8bs627MmDG6//77tXXrVjkcDhUWFmrZsmV64IEHdM8999RHjAAAwASf2/hTpkyR2+3WDTfcoKNHj+raa69VeHi4HnjgAY0fP74+YgQAwDQ7v1TH52TvcDj00EMP6cEHH1ReXp4qKirUrVs3NWvWrD7iAwDAP2z8nP05v1QnLCxM3bp182csAACgHvic7Pv37y+H48wzEtevX28qIAAA6oXZx+fsVNn37NnT63Ntba127dqlzz//XOnp6f6KCwAA/6KNX3ezZ88+7f5HH31UFRUVpgMCAAD+5bdV726//Xa98sor/rodAAD+ZePn7P226t2WLVsUERHhr9sBAOBXPHrng+HDh3t9NgxDhw4d0vbt25WVleW3wAAAgH/4nOyjo6O9PoeEhKhz587Kzs7WgAED/BYYAADwD5+Svcvl0p133qnu3burefPm9RUTAAD+Z+PZ+D5N0AsNDdWAAQNY3Q4AEHTsvMStz7PxL730Un3zzTf1EQsAAKgHPif7xx9/XA888IBWr16tQ4cOqby83GsDAOC8ZcPH7iQfkn12drYqKyt10003affu3brlllvUpk0bNW/eXM2bN1dMTAzj+ACA81cAnrM/ePCgbr/9drVo0UKRkZHq3r27tm/f/lNIhqFp06apdevWioyMVGpqqvbu3WviR55enSfoTZ8+Xb///e/14Ycf+j0IAACs5scff1Tfvn3Vv39//e1vf9MFF1ygvXv3ehXGs2bN0ty5c/Xqq68qKSlJWVlZGjhwoL788ku/vrumzsneME78lea6667z25cDANBQ/PVSnZ8PWYeHhys8PPyU85988kklJiZq8eLFnn1JSUmefzYMQ3PmzNHDDz+soUOHSpKWLl2quLg4rVy5UqNGjTr3YH/GpzH7s612BwDAec1PbfzExERFR0d7tpycnNN+3bvvvqs+ffroN7/5jVq1aqVevXrpxRdf9BzPz89XUVGRUlNTPfuio6OVnJysLVu2+PWn+/Sc/cUXX/yLCb+kpMRUQAAAnM8KCgrkdDo9n09X1UvSN998o4ULFyozM1N//OMftW3bNt13330KCwtTenq6ioqKJElxcXFe18XFxXmO+YtPyX769OmnvEEPAIBg4K82vtPp9Er2Z+J2u9WnTx/NnDlTktSrVy99/vnnWrRoUYMvCe9Tsh81apRatWpVX7EAAFB/GvgNeq1bt1a3bt289nXt2lVvv/22JCk+Pl6SVFxcrNatW3vOKS4uVs+ePU0Eeqo6j9kzXg8AQN317dtXubm5Xvu+/vprtWvXTtKJyXrx8fFat26d53h5ebm2bt2qlJQUv8bi82x8AACCUgNX9hMnTtRVV12lmTNnauTIkfr000/1wgsv6IUXXpB0ooieMGGCHn/8cXXq1Mnz6F1CQoKGDRtmItBT1TnZu91uv34xAAANqaHXs7/iiiu0YsUKTZ06VdnZ2UpKStKcOXOUlpbmOWfSpEmqrKzU2LFjVVpaqquvvlpr1qzx6zP20jkscQsAQFAKwKp3v/71r/XrX//6jMcdDoeys7OVnZ1tIrBf5vO78QEAQHChsgcA2ION17Mn2QMAbKGhx+zPJ7TxAQCwOCp7AIA90MYHAMDaaOMDAADLorIHANgDbXwAACzOxsmeNj4AABZHZQ8AsAXHvzcz1wcrkj0AwB5s3MYn2QMAbIFH7wAAgGVR2QMA7IE2PgAANhDECdsM2vgAAFgclT0AwBbsPEGPZA8AsAcbj9nTxgcAwOKo7AEAtkAbHwAAq6ONDwAArIrKHgBgC7TxAQCwOhu38Un2AAB7sHGyZ8weAACLo7IHANgCY/YAAFgdbXwAAGBVVPYAAFtwGIYcxrmX52auDTSSPQDAHmjjAwAAq6KyBwDYArPxAQCwOtr4AADAqqjsAQC2QBsfAACrs3Ebn2QPALAFO1f2jNkDAGBxVPYAAHugjQ8AgPUFcyveDNr4AABYHJU9AMAeDOPEZub6IEVlDwCwhZOz8c1s5+qJJ56Qw+HQhAkTPPuqqqo0btw4tWjRQs2aNdOIESNUXFxs/oeeBskeAIB6tG3bNj3//PO67LLLvPZPnDhRq1at0ltvvaUNGzaosLBQw4cPr5cYSPYAAHsw/LBJKi8v99qqq6vP+JUVFRVKS0vTiy++qObNm3v2l5WV6eWXX9azzz6r66+/Xr1799bixYu1efNmffLJJ/7+5SR7AIA9ONzmN0lKTExUdHS0Z8vJyTnjd44bN04333yzUlNTvfbv2LFDtbW1Xvu7dOmitm3basuWLX7/7UzQAwDABwUFBXI6nZ7P4eHhpz3v9ddf186dO7Vt27ZTjhUVFSksLEwxMTFe++Pi4lRUVOTXeCWSPerg1U27FNem5pT9q15rpQXT2jd8QEA9G5lRrNF/LNKKF1tq0SMXBjoc+IufXqrjdDq9kv3pFBQU6P7779fatWsVERFh4kv9g2SPX3Tf0EsUEvLT/0Padz6mnP+Tq03vxQYwKqB+XNzjqG6+vUTffBH4f0HDvxry3fg7duzQ4cOHdfnll3v2uVwubdy4UfPnz9ff//531dTUqLS01Ku6Ly4uVnx8/LkHeQYBHbPfuHGjhgwZooSEBDkcDq1cuTKQ4eAMykoa68cfwjzbldeXqvDbcP2/rVGBDg3wq4gmLk2e/53mPNhGR8pCAx0O/O3kc/Zmtjq64YYb9M9//lO7du3ybH369FFaWprnnxs3bqx169Z5rsnNzdX+/fuVkpLi958e0Mq+srJSPXr00F133VVvjxvAvxo1duv6Yf/SOy/HS3IEOhzArzJmHtSn65z6bFOUbru/fp53hj1ERUXp0ksv9drXtGlTtWjRwrN/9OjRyszMVGxsrJxOp8aPH6+UlBT96le/8ns8AU32gwcP1uDBg+t8fnV1tdcjDuXl5fURFs4iZcCPauY8rrV/aRnoUAC/um7oj+rY/ZjG39Qp0KGgnpxvS9zOnj1bISEhGjFihKqrqzVw4EA999xz/v2SfwuqMfucnBxNnz490GHY2qCR32vbhhiVHA4LdCiA31yQUKN7sgs1dVQH1VbzRLJlBXjVu48++sjrc0REhBYsWKAFCxaYu3EdBNWf6qlTp6qsrMyzFRQUBDokW2l1YbV69i3XmjcuCHQogF91vOyYml9wXAv+/rXe379b7+/frR5XVWro6B/0/v7dXhNUgWAUVJV9eHj4GZ9nRP0bcOv3KvtXY326PibQoQB+tWtTM43tf7HXvj/MLlBBXoTeXHCB3G7mp1jB+dbGb0hBlewROA6HoRt/84PWvt1Sbhf/4oO1HKsM1Xe5kV77qo6G6MiPp+5HEGPVO+Dsel1drrgLa/R/32JiHgAEm4BW9hUVFcrLy/N8zs/P165duxQbG6u2bdsGMDL83M5N0RqUdGWgwwAazKRbOwY6BPgZbfwA2b59u/r37+/5nJmZKUlKT0/XkiVLAhQVAMCSAjwbP5ACmuz79esnI4jHQAAACAZM0AMA2AJtfAAArM5tnNjMXB+kSPYAAHuw8Zg9j94BAGBxVPYAAFtwyOSYvd8iaXgkewCAPfAGPQAAYFVU9gAAW+DROwAArI7Z+AAAwKqo7AEAtuAwDDlMTLIzc22gkewBAPbg/vdm5vogRRsfAACLo7IHANgCbXwAAKzOxrPxSfYAAHvgDXoAAMCqqOwBALbAG/QAALA62vgAAMCqqOwBALbgcJ/YzFwfrEj2AAB7oI0PAACsisoeAGAPvFQHAABrs/PrcmnjAwBgcVT2AAB7sPEEPZI9AMAeDJlbkz54cz3JHgBgD4zZAwAAy6KyBwDYgyGTY/Z+i6TBkewBAPZg4wl6tPEBALA4KnsAgD24JTlMXh+kSPYAAFtgNj4AALAsKnsAgD0wQQ8AAIs7mezNbD7IycnRFVdcoaioKLVq1UrDhg1Tbm6u1zlVVVUaN26cWrRooWbNmmnEiBEqLi7256+WRLIHAKBebNiwQePGjdMnn3yitWvXqra2VgMGDFBlZaXnnIkTJ2rVqlV66623tGHDBhUWFmr48OF+j4U2PgDAHhq4jb9mzRqvz0uWLFGrVq20Y8cOXXvttSorK9PLL7+s5cuX6/rrr5ckLV68WF27dtUnn3yiX/3qV+ce689Q2QMA7MHth01SeXm511ZdXV2nry8rK5MkxcbGSpJ27Nih2tpapaames7p0qWL2rZtqy1btpj7rT9DsgcA2MLJR+/MbJKUmJio6Ohoz5aTk/OL3+12uzVhwgT17dtXl156qSSpqKhIYWFhiomJ8To3Li5ORUVFfv3ttPEBAPBBQUGBnE6n53N4ePgvXjNu3Dh9/vnn+vjjj+sztDMi2QMA7MFPY/ZOp9Mr2f+SjIwMrV69Whs3blSbNm08++Pj41VTU6PS0lKv6r64uFjx8fHnHudp0MYHANiD2zC/+cAwDGVkZGjFihVav369kpKSvI737t1bjRs31rp16zz7cnNztX//fqWkpPjlJ59EZQ8AQD0YN26cli9frr/+9a+KioryjMNHR0crMjJS0dHRGj16tDIzMxUbGyun06nx48crJSXFrzPxJZI9AMAuGvjRu4ULF0qS+vXr57V/8eLFuuOOOyRJs2fPVkhIiEaMGKHq6moNHDhQzz333LnHeAYkewCATZhM9vK9jf9LIiIitGDBAi1YsOBcg6oTxuwBALA4KnsAgD3YeCEckj0AwB7chnxtxZ96fXCijQ8AgMVR2QMA7MFwn9jMXB+kSPYAAHtgzB4AAItjzB4AAFgVlT0AwB5o4wMAYHGGTCZ7v0XS4GjjAwBgcVT2AAB7oI0PAIDFud2STDwr7w7e5+xp4wMAYHFU9gAAe6CNDwCAxdk42dPGBwDA4qjsAQD2YOPX5ZLsAQC2YBhuGSZWrjNzbaCR7AEA9mAY5qpzxuwBAMD5isoeAGAPhskx+yCu7En2AAB7cLslh4lx9yAes6eNDwCAxVHZAwDsgTY+AADWZrjdMky08YP50Tva+AAAWByVPQDAHmjjAwBgcW5Dctgz2dPGBwDA4qjsAQD2YBiSzDxnH7yVPckeAGALhtuQYaKNb5DsAQA4zxlumavsefQOAACcp6jsAQC2QBsfAACrs3EbP6iT/cm/ZR03agMcCVB/DP58w8KO68Sf74aomo+r1tQ7dU7GGoyCOtkfOXJEkrSpZkWAIwEAmHHkyBFFR0fXy73DwsIUHx+vj4veN32v+Ph4hYWF+SGqhuUwgngQwu12q7CwUFFRUXI4HIEOxxbKy8uVmJiogoICOZ3OQIcD+BV/vhueYRg6cuSIEhISFBJSf3PGq6qqVFNTY/o+YWFhioiI8ENEDSuoK/uQkBC1adMm0GHYktPp5F+GsCz+fDes+qro/1NERERQJml/4dE7AAAsjmQPAIDFkezhk/DwcD3yyCMKDw8PdCiA3/HnG1YV1BP0AADAL6OyBwDA4kj2AABYHMkeAACLI9kDAGBxJHvU2YIFC9S+fXtFREQoOTlZn376aaBDAvxi48aNGjJkiBISEuRwOLRy5cpAhwT4FckedfLGG28oMzNTjzzyiHbu3KkePXpo4MCBOnz4cKBDA0yrrKxUjx49tGDBgkCHAtQLHr1DnSQnJ+uKK67Q/PnzJZ1YlyAxMVHjx4/XlClTAhwd4D8Oh0MrVqzQsGHDAh0K4DdU9vhFNTU12rFjh1JTUz37QkJClJqaqi1btgQwMgBAXZDs8Yt++OEHuVwuxcXFee2Pi4tTUVFRgKICANQVyR4AAIsj2eMXtWzZUqGhoSouLvbaX1xcrPj4+ABFBQCoK5I9flFYWJh69+6tdevWefa53W6tW7dOKSkpAYwMAFAXjQIdAIJDZmam0tPT1adPH1155ZWaM2eOKisrdeeddwY6NMC0iooK5eXleT7n5+dr165dio2NVdu2bQMYGeAfPHqHOps/f76eeuopFRUVqWfPnpo7d66Sk5MDHRZg2kcffaT+/fufsj89PV1Llixp+IAAPyPZAwBgcYzZAwBgcSR7AAAsjmQPAIDFkewBALA4kj0AABZHsgcAwOJI9gAAWBzJHgAAiyPZAybdcccdGjZsmOdzv379NGHChAaP46OPPpLD4VBpaekZz3E4HFq5cmWd7/noo4+qZ8+epuL69ttv5XA4tGvXLlP3AXDuSPawpDvuuEMOh0MOh0NhYWHq2LGjsrOzdfz48Xr/7nfeeUePPfZYnc6tS4IGALNYCAeWNWjQIC1evFjV1dV6//33NW7cODVu3FhTp0495dyamhqFhYX55XtjY2P9ch8A8Bcqe1hWeHi44uPj1a5dO91zzz1KTU3Vu+++K+mn1vuMGTOUkJCgzp07S5IKCgo0cuRIxcTEKDY2VkOHDtW3337ruafL5VJmZqZiYmLUokULTZo0ST9fXuLnbfzq6mpNnjxZiYmJCg8PV8eOHfXyyy/r22+/9Sy+0rx5czkcDt1xxx2STiwhnJOTo6SkJEVGRqpHjx76y1/+4vU977//vi6++GJFRkaqf//+XnHW1eTJk3XxxRerSZMm6tChg7KyslRbW3vKec8//7wSExPVpEkTjRw5UmVlZV7HX3rpJXXt2lURERHq0qWLnnvuOZ9jAVB/SPawjcjISNXU1Hg+r1u3Trm5uVq7dq1Wr16t2tpaDRw4UFFRUdq0aZP+8Y9/qFmzZho0aJDnumeeeUZLlizRK6+8oo8//lglJSVasWLFWb/3t7/9rf785z9r7ty52rNnj55//nk1a9ZMiYmJevvttyVJubm5OnTokP70pz9JknJycrR06VItWrRIX3zxhSZOnKjbb79dGzZskHTiLyXDhw/XkCFDtGvXLt19992aMmWKz/+dREVFacmSJfryyy/1pz/9SS+++KJmz57tdU5eXp7efPNNrVq1SmvWrNFnn32me++913N82bJlmjZtmmbMmKE9e/Zo5syZysrK0quvvupzPADqiQFYUHp6ujF06FDDMAzD7XYba9euNcLDw40HHnjAczwuLs6orq72XPPaa68ZnTt3Ntxut2dfdXW1ERkZafz97383DMMwWrdubcyaNctzvLa21mjTpo3nuwzDMK677jrj/vvvNwzDMHJzcw1Jxtq1a08b54cffmhIMn788UfPvqqqKqNJkybG5s2bvc4dPXq0cdtttxmGYRhTp041unXr5nV88uTJp9zr5yQZK1asOOPxp556yujdu7fn8yOPPGKEhoYaBw4c8Oz729/+ZoSEhBiHDh0yDMMwLrroImP58uVe93nssceMlJQUwzAMIz8/35BkfPbZZ2f8XgD1izF7WNbq1avVrFkz1dbWyu1263/+53/06KOPeo53797da5x+9+7dysvLU1RUlNd9qqqqtG/fPpWVlenQoUNKTk72HGvUqJH69OlzSiv/pF27dik0NFTXXXddnePOy8vT0aNHdeONN3rtr6mpUa9evSRJe/bs8YpDklJSUur8HSe98cYbmjt3rvbt26eKigodP35cTqfT65y2bdvqwgsv9Poet9ut3NxcRUVFad++fRo9erTGjBnjOef48eOKjo72OR4A9YNkD8vq37+/Fi5cqLCwMCUkJKhRI+8/7k2bNvX6XFFRod69e2vZsmWn3OuCCy44pxgiIyN9vqaiokKS9N5773klWenEPAR/2bJli9LS0jR9+nQNHDhQ0dHRev311/XMM8/4HOuLL754yl8+QkND/RYrAHNI9rCspk2bqmPHjnU+//LLL9cbb7yhVq1anVLdntS6dWtt3bpV1157raQTFeyOHTt0+eWXn/b87t27y+12a8OGDUpNTT3l+MnOgsvl8uzr1q2bwsPDtX///jN2BLp27eqZbHjSJ5988ss/8j9s3rxZ7dq100MPPeTZ9913351y3v79+1VYWKiEhATP94SEhKhz586Ki4tTQkKCvvnmG6Wlpfn0/QAaDhP0gH9LS0tTy5YtNXToUG3atEn5+fn66KOPdN999+nAgQOSpPvvv19PPPGEVq5cqa+++kr33nvvWZ+Rb9++vdLT03XXXXdp5cqVnnu++eabkqR27drJ4XBo9erV+v7771VRUaGoqCg98MADmjhxol599VXt27dPO3fu1Lx58zyT3n7/+99r7969evDBB5Wbm6vly5dryZIlPv3eTp06af/+/Xr99de1b98+zZ0797STDSMiIpSenq7du3dr06ZNuu+++zRy5EjFx8dLkqZPn66cnBzNnTtXX3/9tf75z39q8eLFevbZZ32KB0D9IdkD/9akSRNt3LhRbdu21fDhw9W1a1eNHj1aVVVVnkr/D3/4g/73f/9X6enpSklJUVRUlP7rv/7rrPdduHChbr31Vt17773q0qWLxowZo8rKSknShRdeqOnTp2vKlCmKi4tTRkaGJOmxxx5TVlaWcnJy1LVrVw0aNEjvvfeekpKSJJ0YR3/77be1cuVK9ejRQ4sWLdLMmTN9+r233HKLJk6cqIyMDPXs2VObN29WVlbWKed17NhRw4cP10033aQBAwbosssu83q07u6779ZLL72kxYsXq3v37rruuuu0ZMkST6wAAs9hnGlmEQAAsAQqewAALI5kDwCAxZHsAQCwOJI9AAAWR7IHAMDiSPYAAFgcyR4AAIsj2QMAYHEkewAALI5kDwCAxZHsAQCwuP8Puids9C1ESL8AAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"source": [
"# Predict the test set with logistic regression model\n",
"y_pred = lr.predict(X_test)\n",
"\n",
"print(\"Logistic regression model\\n-------------------------\")\n",
"show_metrics(y_test, y_pred, lr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sUvJX4ax2cwv"
},
"source": [
"#### **References**\n",
"[1] W. Wu and H. Zhou, “Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches,” IEEE Access, vol. 5, pp. 25189–25195, 2017, doi: 10.1109/ACCESS.2017.2763984.\n",
"\n",
"[2] M. F. Ijaz, M. Attique, and Y. Son, “Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods,” Sensors (Basel), vol. 20, no. 10, p. 2809, May 2020, doi: 10.3390/s20102809.\n"
]
}
],
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"_BJfxB08kl1p"
],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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