{ "cells": [ { "cell_type": "markdown", "source": [ "# CSC 3105 Project" ], "metadata": { "collapsed": false }, "id": "cda961ffb493d00c" }, { "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": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'\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 if 'uwb_dataset_part7.csv' not in file]\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" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:26.196695Z", "start_time": "2024-03-05T07:24:26.192532Z" } }, "id": "883affb0ec11a93f", "execution_count": 63 }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "def clean_data(data):\n", " print(\"Starting data cleaning process...\")\n", "\n", " # Calculate total number of missing values in 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", " print(\"Missing values dropped.\")\n", "\n", " # Data has no duplicate rows\n", " data = data.drop_duplicates()\n", " print(\"Duplicate rows dropped.\")\n", "\n", " # Convert 'NLOS' column to integer data type (0 for LOS, 1 for NLOS)\n", " data['NLOS'] = data['NLOS'].astype(int)\n", " print(\"'NLOS' column converted to integer data type.\")\n", "\n", " # Normalize 'Measured range (time of flight)' column using MinMaxScaler\n", " scaler = MinMaxScaler()\n", " data['RANGE'] = scaler.fit_transform(data[['RANGE']])\n", " print(\"'RANGE' column normalized.\")\n", "\n", " # Create new feature 'FP_SUM' by adding 'FP_AMP1', 'FP_AMP2', and 'FP_AMP3'\n", " data['FP_SUM'] = data['FP_AMP1'] + data['FP_AMP2'] + data['FP_AMP3']\n", " print(\"New feature 'FP_SUM' created.\")\n", "\n", " # Create new feature 'SNR' by dividing 'CIR_PWR' by 'STDEV_NOISE'\n", " data['SNR'] = data['CIR_PWR'] / data['STDEV_NOISE']\n", " print(\"New feature 'SNR' created.\")\n", "\n", " # One-hot encode categorical features\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", " print(\"Categorical features one-hot encoded.\")\n", "\n", " # Extract the 'CIR' columns\n", " cir_columns = [f\"CIR{i}\" for i in range(1016)]\n", " cir_data = data[cir_columns]\n", " print(\"'CIR' columns extracted.\")\n", "\n", " # Standardize the 'CIR' columns\n", " # scaler = StandardScaler()\n", " # cir_data = scaler.fit_transform(cir_data)\n", " # print(\"'CIR' columns standardized.\")\n", "\n", " # Perform PCA on the 'CIR' columns\n", " pca = PCA(n_components=0.95)\n", " cir_pca = pca.fit_transform(cir_data)\n", " print(\"PCA performed on 'CIR' columns.\")\n", "\n", " # Create a DataFrame with the principal components\n", " cir_pca_df = pd.DataFrame(cir_pca, columns=[f\"PC{i}\" for i in range(1, pca.n_components_ + 1)])\n", " print(\"DataFrame with principal components created.\")\n", "\n", " # Drop the original 'CIR' columns from the data\n", " data = data.drop(columns=cir_columns)\n", " print(\"Original 'CIR' columns dropped.\")\n", "\n", " # Add the principal components to the original data\n", " # Reset the index of both dataframes\n", " data = data.reset_index(drop=True)\n", " cir_pca_df = cir_pca_df.reset_index(drop=True)\n", " print(\"Indexes of both dataframes reset.\")\n", "\n", " # Concatenate the dataframes\n", " data = pd.concat([data, cir_pca_df], axis=1)\n", " print(\"Dataframes concatenated.\")\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", " if data[column].nunique() == 1:\n", " data = data.drop(column, axis=1)\n", " print(f\"Column '{column}' dropped due to having only one unique value.\")\n", "\n", " # Standardize the numerical columns (excluding 'NLOS')\n", " numerical_cols = data.select_dtypes(include=[np.number]).columns\n", " numerical_cols = numerical_cols.drop('NLOS')\n", " scaler = StandardScaler()\n", " data[numerical_cols] = scaler.fit_transform(data[numerical_cols])\n", " print(\"Numerical columns standardized.\")\n", "\n", " # Print the shape of the cleaned data\n", " print(f\"Cleaned data shape: {data.shape}\")\n", "\n", " print(\"Data cleaning process completed.\")\n", " # Return the cleaned data\n", " return data" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:26.318313Z", "start_time": "2024-03-05T07:24:26.303085Z" } }, "id": "6da110e119fcb241", "execution_count": 64 }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading data from pickle file...\n", "Data loaded successfully.\n", "First few rows of the data:\n", " NLOS RANGE FP_IDX FP_AMP1 FP_AMP2 FP_AMP3 STDEV_NOISE \\\n", "0 1 1.001638 0.740080 -0.603247 0.390535 0.133354 5.746885 \n", "1 1 0.305466 -1.035565 -1.051540 -1.515938 -1.527039 -0.144167 \n", "2 1 0.241792 -0.369698 -1.151779 -1.417515 -1.055181 -0.692172 \n", "3 1 -1.082633 0.518125 1.465026 1.006438 0.125118 -0.281168 \n", "4 0 -1.129327 -0.591654 2.214779 1.355492 1.118434 0.129835 \n", "\n", " CIR_PWR MAX_NOISE RXPACC ... PC30 PC31 PC32 PC33 \\\n", "0 -0.156818 4.049066 1.335213 ... 0.733633 0.692211 -0.176075 -0.658748 \n", "1 -0.671859 -0.490098 1.335213 ... -0.140433 -0.469075 0.039640 0.254559 \n", "2 -1.410478 -0.894612 1.335213 ... -0.864426 0.116302 -0.968280 0.202511 \n", "3 0.616964 0.367127 -0.951544 ... 0.238275 0.366957 0.401399 -0.037006 \n", "4 0.363305 1.215746 -1.039621 ... -0.118822 0.192743 -1.609193 -1.537710 \n", "\n", " PC34 PC35 PC36 PC37 PC38 PC39 \n", "0 -0.696846 -0.037977 0.940675 -0.231988 -0.225130 -0.140194 \n", "1 0.496315 1.228358 0.716242 -0.863852 -0.255854 0.251938 \n", "2 -0.886121 0.050960 0.994024 0.509840 -0.041708 0.673058 \n", "3 0.374036 -0.255314 0.112644 0.312776 0.009381 0.282380 \n", "4 -0.075938 0.157918 -0.475661 0.533037 -0.052929 -0.264907 \n", "\n", "[5 rows x 53 columns]\n", "Column headers:\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', 'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6',\n", " 'PC7', 'PC8', 'PC9', 'PC10', 'PC11', 'PC12', 'PC13', 'PC14', 'PC15',\n", " 'PC16', 'PC17', 'PC18', 'PC19', 'PC20', 'PC21', 'PC22', 'PC23', 'PC24',\n", " 'PC25', 'PC26', 'PC27', 'PC28', 'PC29', 'PC30', 'PC31', 'PC32', 'PC33',\n", " 'PC34', 'PC35', 'PC36', 'PC37', 'PC38', 'PC39'],\n", " dtype='object')\n" ] } ], "source": [ "import pickle\n", "\n", "# File='data_original.pkl'\n", "File='data.pkl'\n", "\n", "# Check if the file exists\n", "if os.path.exists(File):\n", " # If the file exists, load it\n", " print(\"Loading data from pickle file...\")\n", " with open(File, 'rb') as f:\n", " data = pickle.load(f)\n", " print(\"Data loaded successfully.\")\n", "else:\n", " # If the file doesn't exist, load and clean the data\n", " print(\"Pickle file not found. Loading and cleaning data...\")\n", " data = load_data(DATASET_DIR)\n", " data = clean_data(data)\n", " print(\"Data loaded and cleaned successfully.\")\n", " print(\"Saving cleaned data to pickle file...\")\n", " with open(File, 'wb') as f:\n", " pickle.dump(data, f)\n", " print(\"Cleaned data saved to pickle file successfully.\")\n", "\n", "print(\"First few rows of the data:\")\n", "print(data.head())\n", "\n", "# Print Headers\n", "print(\"Column headers:\")\n", "print(data.columns)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:26.430549Z", "start_time": "2024-03-05T07:24:26.409873Z" } }, "id": "e01fe23e950f89a", "execution_count": 65 }, { "cell_type": "code", "outputs": [], "source": [ "MODEL_DIR = './models'\n", "\n", "def train_and_save_model(classifier, X_train, y_train, file_name):\n", " if not os.path.exists(MODEL_DIR):\n", " os.makedirs(MODEL_DIR)\n", " \n", " file_path = os.path.join(MODEL_DIR, file_name)\n", " \n", " # Check if the file exists\n", " if not os.path.exists(file_path):\n", " print(f\"Training the model and saving it to {file_path}\")\n", " # Train the classifier\n", " classifier.fit(X_train, y_train)\n", "\n", " # Save the trained model as a pickle string.\n", " saved_model = pickle.dumps(classifier)\n", "\n", " # Save the pickled model to a file\n", " with open(file_path, 'wb') as file:\n", " file.write(saved_model)\n", "\n", " # Load the pickled model from the file\n", " with open(file_path, 'rb') as file:\n", " loaded_model = pickle.load(file)\n", " \n", " return loaded_model" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:26.502245Z", "start_time": "2024-03-05T07:24:26.495786Z" } }, "id": "c33f69f01c3106dd", "execution_count": 66 }, { "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": "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": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting data cleaning process...\n", "Total number of missing values: 0\n", "Missing values dropped.\n", "Duplicate rows dropped.\n", "'NLOS' column converted to integer data type.\n", "'RANGE' column normalized.\n", "New feature 'FP_SUM' created.\n", "New feature 'SNR' created.\n", "Categorical features one-hot encoded.\n", "'CIR' columns extracted.\n", "PCA performed on 'CIR' columns.\n", "DataFrame with principal components created.\n", "Original 'CIR' columns dropped.\n", "Indexes of both dataframes reset.\n", "Dataframes concatenated.\n", "Column 'CH' dropped due to having only one unique value.\n", "Column 'BITRATE' dropped due to having only one unique value.\n", "Column 'PRFR' dropped due to having only one unique value.\n", "Numerical columns standardized.\n", "Cleaned data shape: (6000, 53)\n", "Data cleaning process completed.\n", "9300 0\n", "7228 1\n", "19929 1\n", "2209 0\n", "20016 1\n", " ..\n", "16850 0\n", "6265 0\n", "11284 1\n", "860 0\n", "15795 1\n", "Name: NLOS, Length: 25200, dtype: int64\n", "Y Test\n", "0 1\n", "1 0\n", "2 0\n", "3 1\n", "4 0\n", " ..\n", "5995 0\n", "5996 0\n", "5997 1\n", "5998 1\n", "5999 0\n", "Name: NLOS, Length: 6000, dtype: int64\n" ] } ], "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, data['NLOS'], test_size=0.3, random_state=42)\n", "\n", "# Load uwb_dataset_part7.csv\n", "uwb_dataset_part7 = pd.read_csv('./UWB-LOS-NLOS-Data-Set/dataset/uwb_dataset_part7.csv')\n", "\n", "# Clean the data\n", "uwb_dataset_part7 = clean_data(uwb_dataset_part7)\n", "\n", "# Split the data into features and labels\n", "X_test = uwb_dataset_part7\n", "y_test = uwb_dataset_part7['NLOS']\n", "\n", "print(f\"{y_train}\")\n", "print(\"Y Test\")\n", "print(f\"{y_test}\")\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:30.849832Z", "start_time": "2024-03-05T07:24:26.565993Z" } }, "id": "7db852fafd187d5a", "execution_count": 67 }, { "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 }, "id": "5753cc6db18bac73" }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "# Initialize the classifier\n", "classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n", "\n", "loaded_model = train_and_save_model(classifier, X_train, y_train, 'random_forest_classifier.pkl')\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:30.869114Z", "start_time": "2024-03-05T07:24:30.852044Z" } }, "id": "b3617711d95450fb", "execution_count": 68 }, { "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: 1.0\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 2950\n", " 1 1.00 1.00 1.00 3050\n", "\n", " accuracy 1.00 6000\n", " macro avg 1.00 1.00 1.00 6000\n", "weighted avg 1.00 1.00 1.00 6000\n", "\n", "Cross Validation Score: [1. 1. 1. 1. 1.]\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Make predictions on the test set using the loaded model\n", "y_pred = loaded_model.predict(X_test)\n", "\n", "# Evaluate the loaded model\n", "accuracy = accuracy_score(y_test, y_pred)\n", "classification_rep = classification_report(y_test, y_pred)\n", "cross_val_score = cross_val_score(loaded_model, X_test, y_test, cv=5)\n", "\n", "print(f\"Accuracy: {accuracy}\")\n", "print(f\"Classification Report:\\n{classification_rep}\")\n", "print(f\"Cross Validation Score: {cross_val_score}\")\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:41.367835Z", "start_time": "2024-03-05T07:24:30.870248Z" } }, "id": "1255f5a45a95e482", "execution_count": 69 }, { "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": "
", "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 = loaded_model.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": "2024-03-05T07:24:50.807826Z", "start_time": "2024-03-05T07:24:41.369755Z" } }, "id": "dbdf7b8e9d47e5d7", "execution_count": 70 }, { "cell_type": "markdown", "source": [ "# Support Vector Machine (SVM)" ], "metadata": { "collapsed": false }, "id": "e1cb5279cf81744e" }, { "cell_type": "code", "outputs": [], "source": [ "# import os\n", "# from sklearn.svm import SVC\n", "# import pickle\n", "# \n", "# svm = SVC(kernel='linear', random_state=42)\n", "# loaded_model = train_and_save_model(svm, X_train, y_train, 'svm_classifier.pkl')\n", "# \n", "# # Predict the labels for the test set with each model\n", "# y_pred_svm = loaded_model.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": "2024-03-05T07:24:50.811896Z", "start_time": "2024-03-05T07:24:50.809138Z" } }, "id": "273bb5cbada1abde", "execution_count": 71 }, { "cell_type": "markdown", "source": [ "# Logistic Regression" ], "metadata": { "collapsed": false }, "id": "a949d75ee11cb49f" }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "# Logistic Regression\n", "log_reg = LogisticRegression(random_state=42)\n", "\n", "# Use the train_and_save_model function to train and save the model\n", "loaded_model = train_and_save_model(log_reg, X_train, y_train, 'logistic_regression_model.pkl')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:50.817356Z", "start_time": "2024-03-05T07:24:50.813014Z" } }, "id": "c2c7f3004b9a19c3", "execution_count": 72 }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Logistic Regression: 1.0\n" ] } ], "source": [ "y_pred_log_reg = loaded_model.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": "2024-03-05T07:24:50.826793Z", "start_time": "2024-03-05T07:24:50.818404Z" } }, "id": "6881654084e029bf", "execution_count": 73 }, { "cell_type": "markdown", "source": [ "# Gradient Boosting Classifier" ], "metadata": { "collapsed": false }, "id": "3f53342001156de3" }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "# Gradient Boosting Classifier\n", "gbc = GradientBoostingClassifier(random_state=42)\n", "\n", "# Use the train_and_save_model function to train and save the model\n", "loaded_model = train_and_save_model(gbc, X_train, y_train, 'gradient_boosting_classifier.pkl')\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:50.844605Z", "start_time": "2024-03-05T07:24:50.829130Z" } }, "id": "b22ad2aa8c5bfadb", "execution_count": 74 }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Gradient Boosting Classifier: 1.0\n" ] } ], "source": [ "y_pred_gbc = loaded_model.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": "2024-03-05T07:24:50.876394Z", "start_time": "2024-03-05T07:24:50.846853Z" } }, "id": "d115411c12fd5566", "execution_count": 75 }, { "cell_type": "markdown", "source": [ "# K-Nearest Neighbors (KNN, K=3)" ], "metadata": { "collapsed": false }, "id": "a71818e358518b6e" }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "# K-Nearest Neighbors\n", "knn = KNeighborsClassifier(n_neighbors=3)\n", "loaded_model = train_and_save_model(knn, X_train, y_train, 'knn_classifier.pkl')\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:50.893326Z", "start_time": "2024-03-05T07:24:50.880795Z" } }, "id": "7ec3197c11a1a20c", "execution_count": 76 }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of K-Nearest Neighbors: 0.8571666666666666\n" ] } ], "source": [ "y_pred_knn = loaded_model.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": "2024-03-05T07:24:51.375896Z", "start_time": "2024-03-05T07:24:50.895618Z" } }, "id": "cf4df4ef7bbfd74", "execution_count": 77 }, { "cell_type": "markdown", "source": [ "# Naive Bayes" ], "metadata": { "collapsed": false }, "id": "5b9b66f92968957c" }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "# Naive Bayes\n", "nb = GaussianNB()\n", "loaded_model = train_and_save_model(nb, X_train, y_train, 'naive_bayes_classifier.pkl')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:51.380047Z", "start_time": "2024-03-05T07:24:51.377192Z" } }, "id": "3d984228fb1d3026", "execution_count": 78 }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Naive Bayes: 1.0\n" ] } ], "source": [ "y_pred_nb = loaded_model.predict(X_test)\n", "accuracy_nb = accuracy_score(y_test, y_pred_nb)\n", "print(f\"Accuracy of Naive Bayes: {accuracy_nb}\")" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:51.392265Z", "start_time": "2024-03-05T07:24:51.380963Z" } }, "id": "98cd350871bc3201", "execution_count": 79 }, { "cell_type": "markdown", "source": [ "# K-Means Clustering" ], "metadata": { "collapsed": false }, "id": "92c8498137a5e32e" }, { "cell_type": "code", "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "\n", "# K-Means Clustering\n", "kmeans = KMeans(n_clusters=2, random_state=42)\n", "loaded_model = train_and_save_model(kmeans, X_train, y_train, 'kmeans_clustering.pkl')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:51.396495Z", "start_time": "2024-03-05T07:24:51.393365Z" } }, "id": "305a796294814705", "execution_count": 80 }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of K-Means Clustering: 0.21533333333333332\n" ] } ], "source": [ "y_pred_kmeans = loaded_model.predict(X_test)\n", "accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n", "print(f\"Accuracy of K-Means Clustering: {accuracy_kmeans}\")" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:24:51.405466Z", "start_time": "2024-03-05T07:24:51.397507Z" } }, "id": "494bb537046bf5a7", "execution_count": 81 }, { "cell_type": "code", "outputs": [ { "data": { "text/plain": "[]" }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (12, 9) # The default value of the figsize parameter is [6.4, 4.8]\n", "\n", "numClusters = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "SSE = []\n", "Error = []\n", "for k in numClusters:\n", " k_means = KMeans(n_clusters=k)\n", " k_means.fit(data)\n", " SSE.append(k_means.inertia_) # Sum of squared distances of samples to their closest cluster center\n", " Error.append(k_means.score(data))\n", "\n", "plt.xlabel('Number of Clusters')\n", "plt.ylabel('SSE')\n", "plt.plot(numClusters, SSE)\n", "plt.plot(numClusters, Error)\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:26:44.080119Z", "start_time": "2024-03-05T07:26:41.126971Z" } }, "id": "62401c8d1a4d61cc", "execution_count": 84 }, { "cell_type": "markdown", "source": [ "# Neural Network" ], "metadata": { "collapsed": false }, "id": "862a9b7ee430a667" }, { "cell_type": "code", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/100\n", "788/788 [==============================] - 2s 2ms/step - loss: 0.1703 - accuracy: 0.9292 - val_loss: 0.0127 - val_accuracy: 0.9975\n", "Epoch 2/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 0.0036 - accuracy: 0.9996 - val_loss: 0.0023 - val_accuracy: 0.9997\n", "Epoch 3/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 5.8895e-04 - accuracy: 1.0000 - val_loss: 8.2601e-04 - val_accuracy: 1.0000\n", "Epoch 4/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0775e-04 - accuracy: 1.0000 - val_loss: 4.3751e-04 - val_accuracy: 1.0000\n", "Epoch 5/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 9.8557e-05 - accuracy: 1.0000 - val_loss: 2.9728e-04 - val_accuracy: 1.0000\n", "Epoch 6/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 5.2590e-05 - accuracy: 1.0000 - val_loss: 1.7790e-04 - val_accuracy: 1.0000\n", "Epoch 7/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.0072e-05 - accuracy: 1.0000 - val_loss: 1.2043e-04 - val_accuracy: 1.0000\n", "Epoch 8/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.7747e-05 - accuracy: 1.0000 - val_loss: 8.1871e-05 - val_accuracy: 1.0000\n", "Epoch 9/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.0651e-05 - accuracy: 1.0000 - val_loss: 5.8266e-05 - val_accuracy: 1.0000\n", "Epoch 10/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 6.5309e-06 - accuracy: 1.0000 - val_loss: 4.4799e-05 - val_accuracy: 1.0000\n", "Epoch 11/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 4.0631e-06 - accuracy: 1.0000 - val_loss: 2.8984e-05 - val_accuracy: 1.0000\n", "Epoch 12/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.5416e-06 - accuracy: 1.0000 - val_loss: 2.1283e-05 - val_accuracy: 1.0000\n", "Epoch 13/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.6039e-06 - accuracy: 1.0000 - val_loss: 1.3697e-05 - val_accuracy: 1.0000\n", "Epoch 14/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.0182e-06 - accuracy: 1.0000 - val_loss: 9.2790e-06 - val_accuracy: 1.0000\n", "Epoch 15/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 6.4964e-07 - accuracy: 1.0000 - val_loss: 6.6996e-06 - val_accuracy: 1.0000\n", "Epoch 16/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 4.1389e-07 - accuracy: 1.0000 - val_loss: 4.3619e-06 - val_accuracy: 1.0000\n", "Epoch 17/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.6578e-07 - accuracy: 1.0000 - val_loss: 3.4618e-06 - val_accuracy: 1.0000\n", "Epoch 18/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.7154e-07 - accuracy: 1.0000 - val_loss: 2.6461e-06 - val_accuracy: 1.0000\n", "Epoch 19/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.1124e-07 - accuracy: 1.0000 - val_loss: 1.4007e-06 - val_accuracy: 1.0000\n", "Epoch 20/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 7.2114e-08 - accuracy: 1.0000 - val_loss: 1.0115e-06 - val_accuracy: 1.0000\n", "Epoch 21/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 4.7193e-08 - accuracy: 1.0000 - val_loss: 6.9804e-07 - val_accuracy: 1.0000\n", "Epoch 22/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.0973e-08 - accuracy: 1.0000 - val_loss: 5.7510e-07 - val_accuracy: 1.0000\n", "Epoch 23/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0449e-08 - accuracy: 1.0000 - val_loss: 4.3252e-07 - val_accuracy: 1.0000\n", "Epoch 24/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.3679e-08 - accuracy: 1.0000 - val_loss: 2.6879e-07 - val_accuracy: 1.0000\n", "Epoch 25/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 9.1879e-09 - accuracy: 1.0000 - val_loss: 1.9581e-07 - val_accuracy: 1.0000\n", "Epoch 26/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 6.2814e-09 - accuracy: 1.0000 - val_loss: 1.5977e-07 - val_accuracy: 1.0000\n", "Epoch 27/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 4.3297e-09 - accuracy: 1.0000 - val_loss: 1.0318e-07 - val_accuracy: 1.0000\n", "Epoch 28/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.0639e-09 - accuracy: 1.0000 - val_loss: 8.7341e-08 - val_accuracy: 1.0000\n", "Epoch 29/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.1897e-09 - accuracy: 1.0000 - val_loss: 6.2719e-08 - val_accuracy: 1.0000\n", "Epoch 30/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.6129e-09 - accuracy: 1.0000 - val_loss: 5.3118e-08 - val_accuracy: 1.0000\n", "Epoch 31/100\n", "788/788 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[==============================] - 1s 1ms/step - loss: 4.2043e-10 - accuracy: 1.0000 - val_loss: 3.6905e-08 - val_accuracy: 1.0000\n", "Epoch 38/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.8516e-10 - accuracy: 1.0000 - val_loss: 3.0117e-08 - val_accuracy: 1.0000\n", "Epoch 39/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.4610e-10 - accuracy: 1.0000 - val_loss: 3.2207e-08 - val_accuracy: 1.0000\n", "Epoch 40/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.2314e-10 - accuracy: 1.0000 - val_loss: 3.4116e-08 - val_accuracy: 1.0000\n", "Epoch 41/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 3.0628e-10 - accuracy: 1.0000 - val_loss: 3.2566e-08 - val_accuracy: 1.0000\n", "Epoch 42/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.9390e-10 - accuracy: 1.0000 - val_loss: 3.2721e-08 - val_accuracy: 1.0000\n", "Epoch 43/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.8092e-10 - accuracy: 1.0000 - val_loss: 3.6162e-08 - val_accuracy: 1.0000\n", "Epoch 44/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.7450e-10 - accuracy: 1.0000 - val_loss: 3.8284e-08 - val_accuracy: 1.0000\n", "Epoch 45/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.6675e-10 - accuracy: 1.0000 - val_loss: 3.9886e-08 - val_accuracy: 1.0000\n", "Epoch 46/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.5744e-10 - accuracy: 1.0000 - val_loss: 4.0116e-08 - val_accuracy: 1.0000\n", "Epoch 47/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.5245e-10 - accuracy: 1.0000 - val_loss: 4.3621e-08 - val_accuracy: 1.0000\n", "Epoch 48/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.5421e-10 - accuracy: 1.0000 - val_loss: 4.6794e-08 - val_accuracy: 1.0000\n", "Epoch 49/100\n", "788/788 [==============================] - 1s 2ms/step - loss: 2.4478e-10 - accuracy: 1.0000 - val_loss: 4.3457e-08 - val_accuracy: 1.0000\n", "Epoch 50/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.3983e-10 - accuracy: 1.0000 - val_loss: 4.6219e-08 - val_accuracy: 1.0000\n", "Epoch 51/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.3543e-10 - accuracy: 1.0000 - val_loss: 4.8556e-08 - val_accuracy: 1.0000\n", "Epoch 52/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.3478e-10 - accuracy: 1.0000 - val_loss: 4.5758e-08 - val_accuracy: 1.0000\n", "Epoch 53/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.2756e-10 - accuracy: 1.0000 - val_loss: 4.8360e-08 - val_accuracy: 1.0000\n", "Epoch 54/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.2836e-10 - accuracy: 1.0000 - val_loss: 5.0342e-08 - val_accuracy: 1.0000\n", "Epoch 55/100\n", "788/788 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[==============================] - 1s 1ms/step - loss: 2.0521e-10 - accuracy: 1.0000 - val_loss: 6.5688e-08 - val_accuracy: 1.0000\n", "Epoch 80/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.1040e-10 - accuracy: 1.0000 - val_loss: 6.9101e-08 - val_accuracy: 1.0000\n", "Epoch 81/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0181e-10 - accuracy: 1.0000 - val_loss: 7.1497e-08 - val_accuracy: 1.0000\n", "Epoch 82/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0235e-10 - accuracy: 1.0000 - val_loss: 7.3293e-08 - val_accuracy: 1.0000\n", "Epoch 83/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0383e-10 - accuracy: 1.0000 - val_loss: 6.9947e-08 - val_accuracy: 1.0000\n", "Epoch 84/100\n", "788/788 [==============================] - 1s 2ms/step - loss: 2.0454e-10 - accuracy: 1.0000 - val_loss: 7.1956e-08 - val_accuracy: 1.0000\n", "Epoch 85/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0575e-10 - accuracy: 1.0000 - val_loss: 7.2903e-08 - val_accuracy: 1.0000\n", "Epoch 86/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0584e-10 - accuracy: 1.0000 - val_loss: 7.3170e-08 - val_accuracy: 1.0000\n", "Epoch 87/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0633e-10 - accuracy: 1.0000 - val_loss: 7.4333e-08 - val_accuracy: 1.0000\n", "Epoch 88/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0662e-10 - accuracy: 1.0000 - val_loss: 7.2096e-08 - val_accuracy: 1.0000\n", "Epoch 89/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0269e-10 - accuracy: 1.0000 - val_loss: 7.2314e-08 - val_accuracy: 1.0000\n", "Epoch 90/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0685e-10 - accuracy: 1.0000 - val_loss: 7.5150e-08 - val_accuracy: 1.0000\n", "Epoch 91/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0479e-10 - accuracy: 1.0000 - val_loss: 7.9010e-08 - val_accuracy: 1.0000\n", "Epoch 92/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0614e-10 - accuracy: 1.0000 - val_loss: 8.0345e-08 - val_accuracy: 1.0000\n", "Epoch 93/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0079e-10 - accuracy: 1.0000 - val_loss: 7.6788e-08 - val_accuracy: 1.0000\n", "Epoch 94/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.9963e-10 - accuracy: 1.0000 - val_loss: 7.8770e-08 - val_accuracy: 1.0000\n", "Epoch 95/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0452e-10 - accuracy: 1.0000 - val_loss: 8.2567e-08 - val_accuracy: 1.0000\n", "Epoch 96/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0664e-10 - accuracy: 1.0000 - val_loss: 8.3165e-08 - val_accuracy: 1.0000\n", "Epoch 97/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0247e-10 - accuracy: 1.0000 - val_loss: 8.2748e-08 - val_accuracy: 1.0000\n", "Epoch 98/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 1.9708e-10 - accuracy: 1.0000 - val_loss: 8.2104e-08 - val_accuracy: 1.0000\n", "Epoch 99/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0020e-10 - accuracy: 1.0000 - val_loss: 8.4163e-08 - val_accuracy: 1.0000\n", "Epoch 100/100\n", "788/788 [==============================] - 1s 1ms/step - loss: 2.0093e-10 - accuracy: 1.0000 - val_loss: 8.7059e-08 - val_accuracy: 1.0000\n", "188/188 [==============================] - 0s 788us/step\n", "Accuracy: 1.0\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=32, validation_data=(X_test, y_test))\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": "2024-03-05T07:26:41.121992Z", "start_time": "2024-03-05T07:24:53.434856Z" } }, "id": "7b2464a3243d2114", "execution_count": 83 }, { "cell_type": "code", "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-03-05T07:26:41.125212Z", "start_time": "2024-03-05T07:26:41.123136Z" } }, "id": "70c9c18ba130fb6f", "execution_count": 83 } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }