{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# CSC 3105 Project"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cda961ffb493d00c"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:53:58.605705507Z",
     "start_time": "2024-02-27T07:53:58.539206652Z"
    }
   },
   "id": "bcd6cbaa5df10ce8",
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load and Clean the Data\n",
    "\n",
    "This code block performs the following operations:\n",
    "\n",
    "1. Imports necessary libraries for data handling and cleaning.\n",
    "2. Defines a function `load_data` to load the data from a given directory into a pandas dataframe.\n",
    "3. Defines a function `clean_data` to clean the loaded data. The cleaning process includes:\n",
    "   - Handling missing values by dropping them.\n",
    "   - Removing duplicate rows.\n",
    "   - Converting the 'NLOS' column to integer data type.\n",
    "   - Normalizing the 'Measured range (time of flight)' column.\n",
    "   - Creating new features 'FP_SUM' and 'SNR'.\n",
    "   - One-hot encoding categorical features.\n",
    "   - Performing feature extraction on 'CIR' columns.\n",
    "   - Dropping the original 'CIR' columns.\n",
    "   - Checking for columns with only one unique value and dropping them.\n",
    "4. Checks if a pickle file with the cleaned data exists. If it does, it loads the data from the file. If it doesn't, it loads and cleans the data using the defined functions.\n",
    "5. Prints the first few rows of the cleaned data and its column headers."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "73fe8802e95a784f"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NLOS     RANGE  FP_IDX  FP_AMP1  FP_AMP2  FP_AMP3  STDEV_NOISE  CIR_PWR  \\\n",
      "0     1  0.220557   749.0   4889.0  13876.0  10464.0        240.0   9048.0   \n",
      "1     1  0.162027   741.0   2474.0   2002.0   1593.0         68.0   6514.0   \n",
      "2     1  0.156674   744.0   1934.0   2615.0   4114.0         52.0   2880.0   \n",
      "3     1  0.045325   748.0  16031.0  17712.0  10420.0         64.0  12855.0   \n",
      "4     0  0.041399   743.0  20070.0  19886.0  15727.0         76.0  11607.0   \n",
      "\n",
      "   MAX_NOISE  RXPACC  FRAME_LEN  PREAM_LEN   FP_SUM         SNR    CIR_MEAN  \\\n",
      "0     3668.0  1024.0          2          0  29229.0   37.700000  768.607283   \n",
      "1     1031.0  1024.0          2          0   6069.0   95.794118  416.879921   \n",
      "2      796.0  1024.0          0          0   8663.0   55.384615  378.266732   \n",
      "3     1529.0   323.0          2          0  44163.0  200.859375  333.926181   \n",
      "4     2022.0   296.0          0          0  55683.0  152.723684  391.251969   \n",
      "\n",
      "       CIR_STD   CIR_SKEW    CIR_KURT  \n",
      "0  1122.978435  10.293579  125.637500  \n",
      "1   903.090169   9.653334  114.460327  \n",
      "2   592.272425   6.944884   60.312664  \n",
      "3  1304.198732  11.892028  150.305512  \n",
      "4  1305.050753  11.689941  151.846488  \n",
      "Index(['NLOS', 'RANGE', 'FP_IDX', 'FP_AMP1', 'FP_AMP2', 'FP_AMP3',\n",
      "       'STDEV_NOISE', 'CIR_PWR', 'MAX_NOISE', 'RXPACC', 'FRAME_LEN',\n",
      "       'PREAM_LEN', 'FP_SUM', 'SNR', 'CIR_MEAN', 'CIR_STD', 'CIR_SKEW',\n",
      "       'CIR_KURT'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pickle\n",
    "\n",
    "def load_data(dataset_dir):\n",
    "    # Load the data\n",
    "    file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames]\n",
    "    data = pd.concat((pd.read_csv(file_path) for file_path in file_paths))\n",
    "    print(f\"Original data shape: {data.shape}\")\n",
    "    return data\n",
    "\n",
    "def clean_data(data):\n",
    "    # Calculate total number of missing values in the data\n",
    "    # This is important to understand the quality of the data\n",
    "    total_missing = data.isnull().sum().sum()\n",
    "    print(f\"Total number of missing values: {total_missing}\")\n",
    "\n",
    "    # Data has no missing values\n",
    "    data = data.dropna()\n",
    "\n",
    "    # Data has no duplicate rows\n",
    "    data = data.drop_duplicates()\n",
    "\n",
    "    # Convert 'NLOS' column to integer data type (0 for LOS, 1 for NLOS)\n",
    "    # This is necessary for further analysis as some algorithms can only handle numeric data\n",
    "    data['NLOS'] = data['NLOS'].astype(int)\n",
    "\n",
    "    # Normalize 'Measured range (time of flight)' column using MinMaxScaler\n",
    "    # Normalization ensures that all features have the same scale\n",
    "    scaler = MinMaxScaler()\n",
    "    data['RANGE'] = scaler.fit_transform(data[['RANGE']])\n",
    "\n",
    "    # Create new feature 'FP_SUM' by adding 'FP_AMP1', 'FP_AMP2', and 'FP_AMP3'\n",
    "    # This can potentially enhance the model's performance by introducing new meaningful information\n",
    "    data['FP_SUM'] = data['FP_AMP1'] + data['FP_AMP2'] + data['FP_AMP3']\n",
    "\n",
    "    # Create new feature 'SNR' by dividing 'CIR_PWR' by 'STDEV_NOISE'\n",
    "    # This can potentially enhance the model's performance by introducing new meaningful information\n",
    "    data['SNR'] = data['CIR_PWR'] / data['STDEV_NOISE']\n",
    "\n",
    "    # One-hot encode categorical features\n",
    "    # This is necessary as many machine learning algorithms cannot handle categorical data directly\n",
    "    categorical_features = ['CH', 'FRAME_LEN', 'PREAM_LEN', 'BITRATE', 'PRFR']\n",
    "    encoder = LabelEncoder()\n",
    "    for feature in categorical_features:\n",
    "        data[feature] = encoder.fit_transform(data[feature])\n",
    "\n",
    "    # List of CIR columns\n",
    "    cir_columns = [f\"CIR{i}\" for i in range(1016)]\n",
    "\n",
    "    # Feature extraction on 'CIR' columns\n",
    "    # This can potentially enhance the model's performance by introducing new meaningful information\n",
    "    data['CIR_MEAN'] = data[cir_columns].mean(axis=1)\n",
    "    data['CIR_STD'] = data[cir_columns].std(axis=1)\n",
    "    data['CIR_SKEW'] = data[cir_columns].apply(stats.skew, axis=1)\n",
    "    data['CIR_KURT'] = data[cir_columns].apply(stats.kurtosis, axis=1)\n",
    "\n",
    "    # Drop the original 'CIR' columns\n",
    "    # This is done to reduce the dimensionality of the data after extracting the necessary information\n",
    "    data = data.drop(columns=cir_columns)\n",
    "\n",
    "    # List of columns to check for unique values\n",
    "    columns_to_check = ['CH', 'PREAM_LEN', 'BITRATE', 'PRFR']\n",
    "\n",
    "    # Iterate over the columns\n",
    "    for column in columns_to_check:\n",
    "        # If the column has only one unique value, drop it\n",
    "        # Columns with only one unique value do not contribute to the model's performance\n",
    "        if data[column].nunique() == 1:\n",
    "            data = data.drop(column, axis=1)\n",
    "\n",
    "    # Print the shape of the cleaned data\n",
    "    print(f\"Cleaned data shape: {data.shape}\")\n",
    "    \n",
    "    # Return the cleaned data\n",
    "    return data\n",
    "\n",
    "# Check if the file exists\n",
    "if os.path.exists('data.pkl'):\n",
    "    # If the file exists, load it\n",
    "    with open('data.pkl', 'rb') as f:\n",
    "        data = pickle.load(f)\n",
    "else:\n",
    "    # If the file doesn't exist, load and clean the data\n",
    "    data = load_data(DATASET_DIR)\n",
    "    data = clean_data(data)\n",
    "    with open('data.pkl', 'wb') as f:\n",
    "        pickle.dump(data, f)\n",
    "\n",
    "print(data.head())\n",
    "\n",
    "# Print Headers\n",
    "print(data.columns)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:53:59.485922432Z",
     "start_time": "2024-02-27T07:53:58.561389045Z"
    }
   },
   "id": "e01fe23e950f89a",
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "The selected code is performing data standardization, which is a common preprocessing step in many machine learning workflows. \n",
    "\n",
    "The purpose of standardization is to transform the data such that it has a mean of 0 and a standard deviation of 1. This is done to ensure that all features have the same scale, which is a requirement for many machine learning algorithms.\n",
    "\n",
    "The mathematical formulas used in this process are as follows:\n",
    "\n",
    "1. Calculate the mean (μ) of the data:\n",
    "\n",
    "$$\n",
    "\\mu = \\frac{1}{n} \\sum_{i=1}^{n} x_i\n",
    "$$\n",
    "Where:\n",
    "- $n$ is the number of observations in the data\n",
    "- $x_i$ is the value of the $i$-th observation\n",
    "- $\\sum$ denotes the summation over all observations\n",
    "\n",
    "2. Standardize the data by subtracting the mean from each observation and dividing by the standard deviation:\n",
    "\n",
    "$$\n",
    "\\text{Data}_i = \\frac{x_i - \\mu}{\\sigma}\n",
    "$$\n",
    "Where:\n",
    "- $\\text{Data}_i$ is the standardized value of the $i$-th observation\n",
    "- $\\sigma$ is the standard deviation of the data\n",
    "- $x_i$ is the value of the $i$-th observation\n",
    "- $\\mu$ is the mean of the data\n",
    "\n",
    "The `StandardScaler` class from the `sklearn.preprocessing` module is used to perform this standardization. The `fit_transform` method is used to calculate the mean and standard deviation of the data and then perform the standardization.\n",
    "\n",
    "**Note:** By setting the explained variance to 0.95, we are saying that we want to choose the smallest number of principal components such that 95% of the variance in the original data is retained. This means that the transformed data will retain 95% of the information of the original data, while potentially having fewer dimensions.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2c13064e20601717"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of principle components after PCA is 10\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardize the data\n",
    "numerical_cols = data.select_dtypes(include=[np.number]).columns\n",
    "scaler = StandardScaler()\n",
    "data[numerical_cols] = scaler.fit_transform(data[numerical_cols])\n",
    "\n",
    "# Initialize PCA with the desired explained variance\n",
    "pca = PCA(0.95)\n",
    "\n",
    "# Fit PCA to your data\n",
    "pca.fit(data)\n",
    "\n",
    "# Get the number of components\n",
    "num_components = pca.n_components_\n",
    "\n",
    "print(f\"The number of principle components after PCA is {num_components}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:53:59.786081548Z",
     "start_time": "2024-02-27T07:53:59.426356025Z"
    }
   },
   "id": "7f9bec73a42f7bca",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Perform Dimensionality Reduction with PCA\n",
    "\n",
    "We can use the `transform` method of the `PCA` object to project the original data onto the principal components. This will give us the transformed data with the desired number of components."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dc9f8c0e194dd07d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original number of components: 18\n",
      "Number of components after PCA: 10\n",
      "PCA has successfully reduced the number of components.\n"
     ]
    }
   ],
   "source": [
    "# Project original data to PC with the highest eigenvalue\n",
    "data_pca = pca.transform(data)\n",
    "\n",
    "# Create a dataframe with the principal components\n",
    "data_pca_df = pd.DataFrame(data_pca, columns=[f\"PC{i}\" for i in range(1, num_components + 1)])\n",
    "\n",
    "# Print the number of components in the original and PCA transformed data\n",
    "print(f\"Original number of components: {data.shape[1]}\")\n",
    "print(f\"Number of components after PCA: {num_components}\")\n",
    "\n",
    "# Compare the number of components in the original and PCA transformed data\n",
    "if data.shape[1] > num_components:\n",
    "    print(\"PCA has successfully reduced the number of components.\")\n",
    "elif data.shape[1] < num_components:\n",
    "    print(\"Unexpectedly, PCA has increased the number of components.\")\n",
    "else:\n",
    "    print(\"The number of components remains unchanged after PCA.\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:53:59.879678825Z",
     "start_time": "2024-02-27T07:53:59.583880502Z"
    }
   },
   "id": "96c62c50f8734a01",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Mining / Machine Learning\n",
    "\n",
    "### I. Supervised Learning\n",
    "- **Decision**: Supervised learning is used due to the labeled dataset.\n",
    "- **Algorithm**: Random Forest Classifier is preferred for its performance in classification tasks.\n",
    "\n",
    "### II. Training/Test Split Ratio\n",
    "- **Decision**: 70:30 split is chosen for training/test dataset.\n",
    "- **Reasoning**: This split ensures sufficient data for training and testing.\n",
    "\n",
    "### III. Performance Metrics\n",
    "- **Classification Accuracy**: Measures the proportion of correctly classified instances.\n",
    "- **Confusion Matrix**: Provides a summary of predicted and actual classes.\n",
    "- **Classification Report**: Provides detailed metrics such as precision, recall, F1-score, and support for each class.\n",
    "\n",
    "The Random Forest Classifier is trained on the training set and evaluated on the test set using accuracy and classification report metrics.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "47d5cb383ce1f7ba"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Split the data into training and testing sets\n",
    "\n",
    "The next step is to split the data into training and testing sets. This is a common practice in machine learning, where the training set is used to train the model, and the testing set is used to evaluate its performance.\n",
    "\n",
    "We will use the `train_test_split` function from the `sklearn.model_selection` module to split the data into training and testing sets. We will use 70% of the data for training and 30% for testing, which is a common split ratio."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "576a6a92fc7fdbfd"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and test sets\n",
    "# X_train, X_test, y_train, y_test = train_test_split(data_pca_df, data['NLOS'], test_size=0.3, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_pca_df, data['NLOS'], test_size=0.3, random_state=42)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:53:59.913309014Z",
     "start_time": "2024-02-27T07:53:59.614411810Z"
    }
   },
   "id": "7db852fafd187d5a",
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train a Random Forest Classifier\n",
    "\n",
    "The next step is to train a machine learning model on the training data. We will use the `RandomForestClassifier` class from the `sklearn.ensemble` module to train a random forest classifier.\n",
    "\n",
    "The random forest classifier is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.\n",
    "\n",
    "We will use the `fit` method of the `RandomForestClassifier` object to train the model on the training data."
   ],
   "metadata": {
    "collapsed": false
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   "id": "5753cc6db18bac73"
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  {
   "cell_type": "code",
   "outputs": [
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     "data": {
      "text/plain": "RandomForestClassifier(random_state=42)",
      "text/html": "<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Initialize the classifier\n",
    "classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:11.696451985Z",
     "start_time": "2024-02-27T07:53:59.667445211Z"
    }
   },
   "id": "b3617711d95450fb",
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Evaluate the Model\n",
    "\n",
    "To evaluate the performance of the trained model on the testing data, we will use the `predict` method of the `RandomForestClassifier` object to make predictions on the testing data. We will then use the `accuracy_score` and `classification_report` functions from the `sklearn.metrics` module to calculate the accuracy and generate a classification report.\n",
    "\n",
    "- **Accuracy:** The accuracy score function calculates the proportion of correctly classified instances.\n",
    "\n",
    "- **Precision:** The ratio of correctly predicted positive observations to the total predicted positive observations. It is calculated as:\n",
    "\n",
    "  $$\n",
    "  \\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}\n",
    "  $$\n",
    "\n",
    "- **Recall:** The ratio of correctly predicted positive observations to all observations in the actual class. It is calculated as:\n",
    "\n",
    "  $$\n",
    "  \\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}\n",
    "  $$\n",
    "\n",
    "- **F1 Score:** The weighted average of precision and recall. It is calculated as:\n",
    "\n",
    "  $$\n",
    "  \\text{F1 Score} = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}\n",
    "  $$\n",
    "\n",
    "- **Support:** The number of actual occurrences of the class in the dataset.\n",
    "\n",
    "The classification report provides a summary of the precision, recall, F1-score, and support for each class in the testing data, giving insight into how well the model is performing for each class.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b63c56956f2f9620"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9985714285714286\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       1.00      1.00      1.00      6311\n",
      "         1.0       1.00      1.00      1.00      6289\n",
      "\n",
      "    accuracy                           1.00     12600\n",
      "   macro avg       1.00      1.00      1.00     12600\n",
      "weighted avg       1.00      1.00      1.00     12600\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "classification_rep = classification_report(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Classification Report:\\n{classification_rep}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:11.787433239Z",
     "start_time": "2024-02-27T07:54:11.680887370Z"
    }
   },
   "id": "1255f5a45a95e482",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Visualize a Decision Tree from the Random Forest\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3017b33cfb9a37df"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x1000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Select one tree from the forest\n",
    "estimator = classifier.estimators_[0]\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plot_tree(estimator,\n",
    "          filled=True,\n",
    "          rounded=True,\n",
    "          class_names=['NLOS', 'LOS'],\n",
    "          feature_names=data_pca_df.columns)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:32.330435816Z",
     "start_time": "2024-02-27T07:54:11.778016953Z"
    }
   },
   "id": "dbdf7b8e9d47e5d7",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Support Vector Machine (SVM)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e1cb5279cf81744e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(kernel='linear', random_state=42)",
      "text/html": "<style>#sk-container-id-2 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-2 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n  padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</pre></div> </div></div></div></div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Support Vector Machine\n",
    "svm = SVC(kernel='linear', random_state=42)\n",
    "svm.fit(X_train, y_train)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:32.407671945Z",
     "start_time": "2024-02-27T07:54:31.974415291Z"
    }
   },
   "id": "d1c8a47edbdb693",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM: 0.9997619047619047\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Predict the labels for the test set with each model\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy of each model\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "\n",
    "# Print the accuracy of each model\n",
    "print(f\"Accuracy of SVM: {accuracy_svm}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:32.409931611Z",
     "start_time": "2024-02-27T07:54:32.158347952Z"
    }
   },
   "id": "8525ab3b6f6c694e",
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Logistic Regression"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a949d75ee11cb49f"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression(random_state=42)",
      "text/html": "<style>#sk-container-id-3 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-3 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-3 pre {\n  padding: 0;\n}\n\n#sk-container-id-3 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-3 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-3 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-3 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-3 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-3 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-3 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-3 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-3 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-3 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-3 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-3 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-3 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-3 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-3 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-3 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n#sk-container-id-3 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-3 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-3 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-3 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-3 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-3 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-3 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-3 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-3 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-3 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "log_reg.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:32.421765664Z",
     "start_time": "2024-02-27T07:54:32.186043850Z"
    }
   },
   "id": "c2c7f3004b9a19c3",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression: 0.9996031746031746\n"
     ]
    }
   ],
   "source": [
    "y_pred_log_reg = log_reg.predict(X_test)\n",
    "accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)\n",
    "print(f\"Accuracy of Logistic Regression: {accuracy_log_reg}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:32.424886386Z",
     "start_time": "2024-02-27T07:54:32.224845170Z"
    }
   },
   "id": "6881654084e029bf",
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Gradient Boosting Classifier"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3f53342001156de3"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "GradientBoostingClassifier(random_state=42)",
      "text/html": "<style>#sk-container-id-4 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-4 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-4 pre {\n  padding: 0;\n}\n\n#sk-container-id-4 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-4 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-4 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-4 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-4 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-4 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-4 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-4 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-4 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-4 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-4 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-4 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-4 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-4 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-4 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-4 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n#sk-container-id-4 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-4 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-4 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-4 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-4 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-4 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-4 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-4 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-4 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-4 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=42)</pre></div> </div></div></div></div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Gradient Boosting Classifier\n",
    "gbc = GradientBoostingClassifier(random_state=42)\n",
    "gbc.fit(X_train, y_train)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:49.300486780Z",
     "start_time": "2024-02-27T07:54:32.265902474Z"
    }
   },
   "id": "b22ad2aa8c5bfadb",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Gradient Boosting Classifier: 0.9983333333333333\n"
     ]
    }
   ],
   "source": [
    "y_pred_gbc = gbc.predict(X_test)\n",
    "accuracy_gbc = accuracy_score(y_test, y_pred_gbc)\n",
    "print(f\"Accuracy of Gradient Boosting Classifier: {accuracy_gbc}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:49.343528955Z",
     "start_time": "2024-02-27T07:54:49.301787938Z"
    }
   },
   "id": "d115411c12fd5566",
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "# K-Nearest Neighbors (KNN, K=3)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a71818e358518b6e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier(n_neighbors=3)",
      "text/html": "<style>#sk-container-id-5 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-5 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-5 pre {\n  padding: 0;\n}\n\n#sk-container-id-5 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-5 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-5 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-5 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-5 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-5 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-5 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-5 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-5 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-5 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-5 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-5 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-5 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-5 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n#sk-container-id-5 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-5 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-5 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-5 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-5 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-5 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-5 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-5 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# K-Nearest Neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, y_train)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:49.405926742Z",
     "start_time": "2024-02-27T07:54:49.334318750Z"
    }
   },
   "id": "7ec3197c11a1a20c",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-Nearest Neighbors: 0.998968253968254\n"
     ]
    }
   ],
   "source": [
    "y_pred_knn = knn.predict(X_test)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(f\"Accuracy of K-Nearest Neighbors: {accuracy_knn}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:50.551737212Z",
     "start_time": "2024-02-27T07:54:49.378285438Z"
    }
   },
   "id": "cf4df4ef7bbfd74",
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Same Operations on the Original Data (no PCA)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "82f439852d40c2f6"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       1.00      1.00      1.00      6311\n",
      "         1.0       1.00      1.00      1.00      6289\n",
      "\n",
      "    accuracy                           1.00     12600\n",
      "   macro avg       1.00      1.00      1.00     12600\n",
      "weighted avg       1.00      1.00      1.00     12600\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, data['NLOS'], test_size=0.3, random_state=42)\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "classification_rep = classification_report(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Classification Report:\\n{classification_rep}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:54.153859839Z",
     "start_time": "2024-02-27T07:54:50.554816057Z"
    }
   },
   "id": "2ecab619275a2be2",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x1000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Select one tree from the forest\n",
    "estimator = classifier.estimators_[0]\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plot_tree(estimator,\n",
    "          filled=True,\n",
    "          rounded=True,\n",
    "          class_names=['NLOS', 'LOS'],\n",
    "          feature_names=data.columns)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:55.025165702Z",
     "start_time": "2024-02-27T07:54:54.186387679Z"
    }
   },
   "id": "4af3dd9c3af35441",
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Support Vector Machine (SVM) with Original Data"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dfdf7c17c38e2055"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Support Vector Machine\n",
    "svm = SVC(kernel='linear', random_state=42)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels for the test set with each model\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy of each model\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "\n",
    "# Print the accuracy of each model\n",
    "print(f\"Accuracy of SVM: {accuracy_svm}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:55.339085534Z",
     "start_time": "2024-02-27T07:54:55.023757867Z"
    }
   },
   "id": "6e799a70ec79ac07",
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Logistic Regression with Original Data"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "abf999505a5312f8"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "y_pred_log_reg = log_reg.predict(X_test)\n",
    "accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)\n",
    "\n",
    "print(f\"Accuracy of Logistic Regression: {accuracy_log_reg}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:54:55.435704902Z",
     "start_time": "2024-02-27T07:54:55.262776828Z"
    }
   },
   "id": "124110fa530178c3",
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Gradient Boosting Classifier with Original Data"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e36bfa0897872cb8"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Gradient Boosting Classifier: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting Classifier\n",
    "gbc = GradientBoostingClassifier(random_state=42)\n",
    "gbc.fit(X_train, y_train)\n",
    "\n",
    "y_pred_gbc = gbc.predict(X_test)\n",
    "accuracy_gbc = accuracy_score(y_test, y_pred_gbc)\n",
    "print(f\"Accuracy of Gradient Boosting Classifier: {accuracy_gbc}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:55:02.912787536Z",
     "start_time": "2024-02-27T07:54:55.308879759Z"
    }
   },
   "id": "85c1f9e875283304",
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "source": [
    "# K-Nearest Neighbors (KNN, K=3) with Original Data"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4746ea209959854"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-Nearest Neighbors: 1.0\n"
     ]
    }
   ],
   "source": [
    "# K-Nearest Neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "y_pred_knn = knn.predict(X_test)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(f\"Accuracy of K-Nearest Neighbors: {accuracy_knn}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:55:03.677475878Z",
     "start_time": "2024-02-27T07:55:02.915528422Z"
    }
   },
   "id": "ac547de0e8701e27",
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-27 15:55:04.018321: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-02-27 15:55:04.022536: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-02-27 15:55:04.069157: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-02-27 15:55:04.069201: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-02-27 15:55:04.070363: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-02-27 15:55:04.076877: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-02-27 15:55:04.078285: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-27 15:55:05.087936: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "2024-02-27 15:55:05.658069: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-27 15:55:05.658590: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "230/230 [==============================] - 1s 1ms/step - loss: -19.1327 - accuracy: 0.3398\n",
      "Epoch 2/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -409.4561 - accuracy: 0.3653\n",
      "Epoch 3/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1941.1949 - accuracy: 0.3702\n",
      "Epoch 4/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -5258.8105 - accuracy: 0.3714\n",
      "Epoch 5/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -10845.6953 - accuracy: 0.3721\n",
      "Epoch 6/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -19072.3613 - accuracy: 0.3722\n",
      "Epoch 7/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -30220.3184 - accuracy: 0.3723\n",
      "Epoch 8/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -44540.8750 - accuracy: 0.3719\n",
      "Epoch 9/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -62233.3164 - accuracy: 0.3722\n",
      "Epoch 10/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -83499.8438 - accuracy: 0.3720\n",
      "Epoch 11/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -108420.3906 - accuracy: 0.3718\n",
      "Epoch 12/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -137176.7969 - accuracy: 0.3720\n",
      "Epoch 13/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -169837.4375 - accuracy: 0.3718\n",
      "Epoch 14/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -206517.2188 - accuracy: 0.3719\n",
      "Epoch 15/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -247315.0469 - accuracy: 0.3714\n",
      "Epoch 16/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -292301.0312 - accuracy: 0.3714\n",
      "Epoch 17/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -341584.6562 - accuracy: 0.3717\n",
      "Epoch 18/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -395302.3125 - accuracy: 0.3714\n",
      "Epoch 19/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -453492.8125 - accuracy: 0.3716\n",
      "Epoch 20/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -516281.2500 - accuracy: 0.3711\n",
      "Epoch 21/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -583805.7500 - accuracy: 0.3712\n",
      "Epoch 22/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -656109.0625 - accuracy: 0.3713\n",
      "Epoch 23/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -733464.9375 - accuracy: 0.3708\n",
      "Epoch 24/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -815842.0625 - accuracy: 0.3711\n",
      "Epoch 25/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -903343.5000 - accuracy: 0.3709\n",
      "Epoch 26/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -996038.6250 - accuracy: 0.3711\n",
      "Epoch 27/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1093968.7500 - accuracy: 0.3707\n",
      "Epoch 28/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1197387.5000 - accuracy: 0.3706\n",
      "Epoch 29/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1306449.6250 - accuracy: 0.3705\n",
      "Epoch 30/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1421196.8750 - accuracy: 0.3705\n",
      "Epoch 31/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1541566.5000 - accuracy: 0.3706\n",
      "Epoch 32/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1667701.8750 - accuracy: 0.3699\n",
      "Epoch 33/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1799910.8750 - accuracy: 0.3695\n",
      "Epoch 34/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -1938218.2500 - accuracy: 0.3696\n",
      "Epoch 35/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -2082461.6250 - accuracy: 0.3689\n",
      "Epoch 36/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -2232775.0000 - accuracy: 0.3684\n",
      "Epoch 37/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -2389588.0000 - accuracy: 0.3675\n",
      "Epoch 38/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -2553076.0000 - accuracy: 0.3673\n",
      "Epoch 39/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -2723191.5000 - accuracy: 0.3673\n",
      "Epoch 40/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -2899923.5000 - accuracy: 0.3679\n",
      "Epoch 41/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -3083593.2500 - accuracy: 0.3671\n",
      "Epoch 42/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -3274249.5000 - accuracy: 0.3673\n",
      "Epoch 43/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -3471893.5000 - accuracy: 0.3675\n",
      "Epoch 44/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -3676461.7500 - accuracy: 0.3677\n",
      "Epoch 45/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -3888376.2500 - accuracy: 0.3669\n",
      "Epoch 46/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -4107541.2500 - accuracy: 0.3672\n",
      "Epoch 47/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -4334098.5000 - accuracy: 0.3668\n",
      "Epoch 48/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -4568260.0000 - accuracy: 0.3668\n",
      "Epoch 49/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -4810140.0000 - accuracy: 0.3660\n",
      "Epoch 50/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -5059728.0000 - accuracy: 0.3658\n",
      "Epoch 51/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -5317130.0000 - accuracy: 0.3654\n",
      "Epoch 52/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -5582602.0000 - accuracy: 0.3646\n",
      "Epoch 53/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -5856069.0000 - accuracy: 0.3643\n",
      "Epoch 54/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -6137739.5000 - accuracy: 0.3643\n",
      "Epoch 55/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -6427532.5000 - accuracy: 0.3641\n",
      "Epoch 56/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -6725599.0000 - accuracy: 0.3639\n",
      "Epoch 57/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -7032113.5000 - accuracy: 0.3639\n",
      "Epoch 58/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -7347491.5000 - accuracy: 0.3641\n",
      "Epoch 59/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -7671550.5000 - accuracy: 0.3632\n",
      "Epoch 60/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -8004591.5000 - accuracy: 0.3628\n",
      "Epoch 61/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -8346428.5000 - accuracy: 0.3631\n",
      "Epoch 62/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -8697095.0000 - accuracy: 0.3629\n",
      "Epoch 63/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -9056447.0000 - accuracy: 0.3622\n",
      "Epoch 64/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -9425149.0000 - accuracy: 0.3623\n",
      "Epoch 65/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -9803305.0000 - accuracy: 0.3618\n",
      "Epoch 66/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -10191131.0000 - accuracy: 0.3615\n",
      "Epoch 67/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -10588455.0000 - accuracy: 0.3611\n",
      "Epoch 68/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -10995289.0000 - accuracy: 0.3607\n",
      "Epoch 69/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -11411787.0000 - accuracy: 0.3606\n",
      "Epoch 70/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -11838013.0000 - accuracy: 0.3600\n",
      "Epoch 71/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -12273911.0000 - accuracy: 0.3598\n",
      "Epoch 72/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -12720122.0000 - accuracy: 0.3593\n",
      "Epoch 73/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -13176404.0000 - accuracy: 0.3591\n",
      "Epoch 74/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -13643101.0000 - accuracy: 0.3589\n",
      "Epoch 75/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -14119996.0000 - accuracy: 0.3586\n",
      "Epoch 76/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -14607277.0000 - accuracy: 0.3581\n",
      "Epoch 77/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -15105317.0000 - accuracy: 0.3581\n",
      "Epoch 78/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -15614050.0000 - accuracy: 0.3577\n",
      "Epoch 79/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -16133103.0000 - accuracy: 0.3579\n",
      "Epoch 80/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -16662942.0000 - accuracy: 0.3576\n",
      "Epoch 81/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -17203682.0000 - accuracy: 0.3570\n",
      "Epoch 82/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -17755866.0000 - accuracy: 0.3568\n",
      "Epoch 83/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -18319566.0000 - accuracy: 0.3561\n",
      "Epoch 84/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -18894216.0000 - accuracy: 0.3557\n",
      "Epoch 85/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -19480140.0000 - accuracy: 0.3554\n",
      "Epoch 86/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -20077216.0000 - accuracy: 0.3546\n",
      "Epoch 87/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -20685576.0000 - accuracy: 0.3543\n",
      "Epoch 88/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -21305556.0000 - accuracy: 0.3539\n",
      "Epoch 89/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -21937652.0000 - accuracy: 0.3536\n",
      "Epoch 90/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -22581768.0000 - accuracy: 0.3532\n",
      "Epoch 91/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -23238590.0000 - accuracy: 0.3526\n",
      "Epoch 92/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -23907316.0000 - accuracy: 0.3526\n",
      "Epoch 93/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -24586888.0000 - accuracy: 0.3522\n",
      "Epoch 94/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -25279108.0000 - accuracy: 0.3513\n",
      "Epoch 95/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -25984422.0000 - accuracy: 0.3510\n",
      "Epoch 96/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -26702450.0000 - accuracy: 0.3499\n",
      "Epoch 97/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -27432426.0000 - accuracy: 0.3495\n",
      "Epoch 98/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -28175068.0000 - accuracy: 0.3489\n",
      "Epoch 99/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -28930770.0000 - accuracy: 0.3485\n",
      "Epoch 100/100\n",
      "230/230 [==============================] - 0s 1ms/step - loss: -29699246.0000 - accuracy: 0.3478\n",
      "394/394 [==============================] - 0s 891us/step\n",
      "Accuracy: 0.35023809523809524\n"
     ]
    }
   ],
   "source": [
    "# Neural Network\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Define the model\n",
    "model = Sequential()\n",
    "model.add(Dense(32, input_dim=X_train.shape[1], activation='relu'))  # Input layer\n",
    "model.add(Dense(32, activation='relu'))  # Hidden layer\n",
    "model.add(Dense(1, activation='sigmoid'))  # Output layer\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=128)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred_prob = model.predict(X_test)\n",
    "y_pred = (y_pred_prob > 0.5).astype(\"int32\")\n",
    "\n",
    "# Calculate the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T07:55:34.882295559Z",
     "start_time": "2024-02-27T07:55:03.680846600Z"
    }
   },
   "id": "7b2464a3243d2114",
   "execution_count": 23
  }
 ],
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